text:
I switched my plan to a visualization of individual tweets on top of something like Nasa World Wind, but as I began to investigate it, I found it very useless. A visualization of an individual tweet isn't nearly as cool as a visualization of many tweets over time, which brought me to my final idea (with the help of Tim)...
I think I'm going to do a visualization of twitter geographical data on top of google maps (or google earth). This way I'm aggregating the data before showing it to the user, which should be much easier to understand. My plan is as follows:
- Divide the map up into a grid. For starters I will just do this on a single major city.
- At different zoom levels the data will be regrouped. This way when a user is looking at the entire city it should show them a set of grids and as they zoom, the grids will become more detailed.
- Provide (in tooltips or based on click events) details about the data in the grid they're selecting.
- Provide some kind of coloring of each grid so that a user can tell where the most tweets are coming from.
- Weight the tweets based on how old they are (e.g. a tweet from yesterday doesn't influence the coloring as much as a tweet from today).
This should provide an aggregation of data that shows where the most geo-coded tweets come from. The plan is to architect this in a way that if it works well that I could expand my search to include a larger area.


Main Content
What are Functional Materials?
what are FM_1.jpg
Functional Materials play an ever-increasing role in our modern society. They form the basis for a wide range of technologies. Computation, communication, storage and displaying of information would not work without them, making them essential for the entire IT sector. The same is true for the generation of and the storage of energy, for mobility and for sensing.
The term functional materials covers different material classes ranging from semiconductors over polymers and molecular crystals to nanoparticles. It is their special electrical, optical and magnetic properties that make functional materials so important.
If you have any further questions please contact:


text:
History Clipping in Event-driven Distributed Systems
The problem of managing event histories is at the core of distributed computing algorithms used for distributed run-time monitoring and distributed data replication using logs. As a distributed program executes, individual histories of process execution are diffused through the systems by a technique known as gossiping. In this way, each process has a view of the actions of other processes in the system and can monitor their behavior for correctness or security. Unfortunately, the size of the histories grows without bound. Bernstein originally proposed clipping the history of gossip messages when the history becomes known to every process in the system. However, when used in distributed monitoring, even though every process may know about an event history, it is not a sufficient condition to clip that part of event history, as it might still be useful to monitor future event occurrences. What is needed is a way to clip the histories only when they have both been disseminated through the system and the monitoring process no longer needs them. In this paper, we propose the concept of clipping histories in event-driven systems for the purposes of monitoring. The core of this work is the observation that event-driven systems transition through many semi-stable states, then periodically return to stable, recurrent states as each action is completed. We propose an algorithm to clip histories only when the system has returned to a stable, recurrent state and the monitor no longer has use for the collected histories. The effectiveness of the algorithm is demonstrated by the reduction of history size in monitoring an event-driven maneuver drawn from an automated highway system.
M. Chittajallu and B. M. McMillin, "History Clipping in Event-driven Distributed Systems," ISCA 13th International Conference on Parallel and Distributed Computing Systems, ISCA, Aug 2000.
Keywords and Phrases
Distributed; Events; History Maintenance; Monitoring
Article - Conference proceedings
© 2000 ISCA, All rights reserved.
This document is currently not available here.

source: openbmb/Ultra-FineWeb


text:
Vehicles destined for demolition and recycling activities contain important quantities of electric wiring (the current average is of about 25 kg per vehicle); these quantities will increase with the implementation of further electric and electronic devices in modern vehicles and with the arrival of hybrid and electric power systems. The performance of the automotive wiring is high and it is expected to exceed 50-55% in the metal/insulation ratio. Stokkermill has created a series of technical measures and innovations that allow the processing of automotive wiring and the recovery of the contained copper. The Stokkermill CL system allows all types of automotive cable to be processed without removing glue and adhesives present within them, thank to an automatic system that neutralizes adhesive components and components contaminated with oil and grease. In the lines with the pre-grinder, the particular design of the rotor allows to process the wiring without the need of removing the ferrous parts within it (connections, plugs, anchoring systems, etc.), which are removed and separated from the recycling line without the intervention of the operator. These devices make the recycling of car cables highly profitable as they greatly reduce the need for manual intervention in the preparation of the material to be treated. The final screening systems allow to obtain high quality of output copper without the contamination of plastic and other metals.
Equipmet for wire harness recycling :
Wire granulator :
Copper shredder single-shaft shredder :
Industrial shredder twin-shaft shredder :
Aluminum shredder single-shaft hydraulic shredder :
Cable Stripper :
Video of wire harness recycling plant M150 :

source: m-a-p/FineFineWeb


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Show menu
Multiple comparison graphs
Next selectMultiple comparison graphs
Multiple comparison graphs allow to visualize differences between subgroups of a variable, or the influence of a qualitative (discrete) factor on another (continuous) variable.
The graph can be composed from different elements: Bars, Horizontal lines, Markers and or Connecting lines for mean or median, with choice of different error bars for mean (95% CI, 1 SEM, 1 SD, 2 SD, 3 SD, range) or median (95% CI, 25-75 percentiles, 10-90 percentiles, 5-95 percentiles, 2.5-97.5 percentiles, 1-99 percentiles, range), Box-and-whisker plot or Notched box-and-whisker plot, Violin plot, and/or Dot plot (display all data).
Required input
The following need to be entered in the dialog box: for Data select a continuous variable, and for Factor codes a qualitative factor. The qualitative factor may either be character or numeric codes. These codes are used to break-up the data into several subgroups.
When you want to use a continuous variable as the qualitative, discrete factor, you can convert the continuous data by using the Create groups tools.
Dialog box for multiple comparison graphs
Several elements can be selected to compose the graph, and some of these can be combined.
The box-and-whisker plot in combination with dot plot has the advantage of displaying a statistical summary of the data (range, quartiles and median), without concealing the data.
If the data require a logarithmic transformation, select the Logarithmic transformation option.
Chart with bars representing the mean age of patients in a multicenter study.
'Error bars' represent 95% confidence intervals.
Bar diagram with error bars
Chart with all data points for systolic blood pressure.
'Error bars' represent 95% confidence intervals.
A line connects the mean of the two treatment groups (coded 0 and 1).
Dot plot with error bars
Chart with the evolution of sperm motility during 12 years.
The means for every year are connected by a line.
Error bars represent 95% confidence intervals for the mean.
Line graph with error bars
See also


5e SRD >Creatures >
Eonic Savant
Medium humanoid (human), neutral
Armor Class 13 (16 with mage armor)
Hit Points 115 (16d8 + 51)
Speed 30 ft.
9 (-1) 16 (+3) 16 (+3) 20 (+5) 11 (+0) 15 (+2)
Saving Throws Wis +3
Skills Arcana +8, History +8, Perception +3, Persuasion +5
Condition Immunities frightened
Senses passive Perception 13
Languages Common, Eonic, Giant, Sylvan
Challenge 7 (2,900 XP)
Special Traits
 - Amend Time. As a bonus action, the savant alters the strands of time surrounding a creature it can see within 30 feet. If the target is friendly, the target has advantage on its next weapon attack roll. If the target is hostile, the target must succeed on a DC 15 Charisma saving throw or have disadvantage on its next weapon attack roll.
 - Magic Resistance. The savant has advantage on saving throws against spells and other magical effects.
 - Past and Future, Present. A savant is always accompanied by its past and future selves. They occupy the savant's same space seconds before and after the savant and can't be targeted separately. They provide the savant with the following benefits. The savant can concentrate on two spells simultaneously. If it casts a third spell that requires concentration, the savant loses concentration on the oldest spell. If the savant is concentrating on two spells and loses concentration because of taking damage, it loses concentration on the oldest spell. When a creature targets the savant with an attack, roll a d6. On a 1 through 4, the attack hits one of the savant's future or past selves, halving the damage the savant takes. On a 5 or 6, the attack hits the savant, and it takes damage as normal. The savant is still the target of the attack and is subject to effects that trigger when a creature is hit or damaged.
 - Spellcasting. The savant is a 9th-level spellcaster. Its spellcasting ability is Intelligence (spell save DC 15, +8 to hit with spell attacks). The savant has the following wizard spells prepared:
 - Multiattack. The eonic savant makes three melee attacks.
 - Time Warping Staff. Melee Weapon Attack: +6 to hit, reach 5 ft., one target. Hit: 6 (1d6 + 3) bludgeoning damage plus 7 (2d6) force damage.
A jagged blue scar of magical energy forms in the air, and a blue-robed figure steps out a moment later.
As it moves, echoes of it appear to move a split second before and after it. The creature focuses its attention and the echoes solidify around it, one wielding the creature's staff, the other a spell.
The eonic savant is an eonic drifter who learned to fully harness the power of its many future and past selves.
Still Adrift in Time. Through their mastery of time, they were able to spare their bodies many of the same ravages of time experienced by their brethren. In spite of this mastery, they are no closer to finding the cure or the correct time stream for their dying ancient civilization.
Calm and Assured. Unlike their kin, eonic savants hold the energy to jump through time within their bodies, freeing them from a reliance on their people's crystal belts and the paranoia its loss brings. Savants devote themselves to studying the rivers of time and helping their kin, who are barely surviving time's flood. They are certain with enough study and enough of their kin by their sides, they can discover the way to a cure and the way home.
Section 15: Copyright Notice


text:
Query Optimizer for MariaDB Xpand
How the Xpand Query Optimizer is Different
At the core of Xpand Query Optimizer is the ability to execute one query with maximum parallelism and many simultaneous queries with maximum concurrency. This is achieved via a distributed query planner and compiler and a distributed shared-nothing execution engine.
What is a Query Optimizer?
SQL is a declarative language, i.e. a language that describes what is to be computed but not how. The job of the query optimizer is to determine how to do this computation, which ends up being critical to the performance of the entire system. For example, you might say in SQL that you want to join 3 tables and compute an aggregate operation. This leaves the following questions for the Query Optimizer:
In what order should the tables be joined? This can be the difference between your query executing in 1ms or 10 minutes. Imagine if a predicate on one of the tables causes it to return no rows – starting the read from that table is likely optimal and fast.
Which indexes should be used? Not using a proper index on a join constraint could be catastrophic, causing broadcast messages and full reads of the second table for each row of the first.
Should we provide a non-blocking sort/aggregate? Should we do the sort/aggregate in stages, i.e. first on separate nodes and then re-aggregate/re-sort later?
The set of query plans that these permutations create is known as the Search Space. The job of the Query Optimizer is to explore the Search Space and determine which plan uses the least amount of database resources. Typically, this is done by assigning costs to each plan, then choosing the cheapest one.
The Xpand Query Optimizer is also known as Sierra.
sql> -- Query sql> SELECT Bar.a, Sum(Bar.b) FROM Foo, Bar WHERE Foo.pk = Bar.pk GROUP BY Bar.a; sql> -- Foo schema sql> CREATE TABLE 'Foo' ( 'pk' INT(11) NOT NULL auto_increment, 'a' INT(11), 'b' INT(11), 'c' INT(11), PRIMARY KEY ('pk') /*$ DISTRIBUTE=1 */, KEY 'idx_ab' ('a', 'b') /*$ DISTRIBUTE=2 */ ) auto_increment=50002; sql> -- Bar schema sql> CREATE TABLE 'Bar' ( 'pk' INT(11) NOT NULL auto_increment, 'a' INT(11), 'b' INT(11), 'c' INT(11), PRIMARY KEY ('pk') /*$ DISTRIBUTE=1 */, KEY 'idx_ab' ('a', 'b') /*$ DISTRIBUTE=2 */ ) auto_increment=25001;
SQL Representation – What
Sierra Output – How
(display ((2. "a") (0. "expr0")) (aggregate ((2. "a") ("expr0" sum (2. "b"))) (filter (inner_join (table_scan (1 : Foo ("pk" "a" "b" "c"))) (table_scan (2 : Bar ("pk" "a" "b" "c"))) (func eq (ref (1. "pk")) (ref (2. "pk"))))))
(display ((2. "a") (0. "expr0")) (stream_aggregate ((2. "a") ("expr0" sum (2. "b"))) (msjoin (stream_merge (index_scan (2 : Bar.idx_ab ("b" "pk" "a"))) (index_scan (1 : Foo.idx_PRIMARY ("pk")) range equal (ref (2. "pk"))))))
First, some explanations for these plans. We'll go from most indented to least indented. These descriptions should be read top to bottom for equally indented statements.
This SQL statement is performing the following:
Fooand join to
Evaluate the join constraint.
Compute the aggregate.
Display the output to the user.
The Query Optimizer is doing the following:
Bar, preserving the sort order via stream_
Evaluate the join constraint while reading
Fooand still preserving sort order via msjoin this time.
Compute the aggregate in a non-blocking way with stream_
aggregate. This can be done because we preserved order.
How did the Query Optimizer find this plan? How did it make sure it even worked? Lets find out:
The Xpand Query Optimizer
Sierra is modeled off of the Cascades Query optimization framework, which was chosen primarily because it provides the following:
Extensible via a rule based mechanism
General separation of logical vs. physical operators and properties
Modern query optimizers are often split into two parts, the Model and Search Engine.
The Model lists the equivalence transformations (rules), which are used by the search engine to expand the search space.
The Search Engine defines the interfaces between the search engine and the model, and provides the code to expand the search space and to search for the optimal plan. This is implemented by the stack of tasks waiting to be computed. More on this below.
Improvements in Xpand 6.1
Xpand 6.1's query optimizer includes improvements to planning and compilation time for the following types of queries:
Joins with 20 or more tables
IN(..)lists with hundreds of entries
Improvements to query execution times (due to better plan choices) for complex joins to aggregates, unions and expressions
Improvements to query execution times due to improvements to aggregate costing
Reduction in frequency of replanning/recompilation of cached plans
Enhancements to the query optimization cost model that result in better query plan choices
Streaming aggregation operations are enabled by default
Xpand 6.1 also adds support for the following:
Logical vs. Physical Model
In the Query Optimizer, the Logical model describes what is to be computed, and the Physical model describes how it is to be computed. In diagram 1 above, the SQL representation shows logically what to do and the Sierra output shows physically how to do it.
Operators and Expressions
An expression consists of: an Operator (required), Arguments (possibly none), and Inputs (possibly none). Arguments describe particular characteristics of the operator. There are both logical and physical operators and every logical operator maps to 1 or more physical operators. In the example the logical operator:
(table_scan (1 : Foo ("pk" "a" "b" "c")))
maps to the physical expression:
(index_scan (1 : Foo ("pk") (range equal (ref (2. "pk")))
These operators have arguments which describe their Namespace, Object Id, and Columns. Here, the table_
Physical properties are related to intermediate results, or sub-plans. They describe things like how the data is ordered and how the data is partitioned. It is important to note that expressions (either logical or physical) and groups (see below) do not have physical properties. However, every physical expression has two descriptions related to physical properties:
What Physical properties can and can't I provide to my parent?
What Physical properties do I require of my input?
Here are some considerations Sierra takes while optimizing our query:
Physical Property Flow
The invalid plan fails because the stream_
Groups correspond to intermediate tables, or equivalently subplans of the query. Groups are logical and contain the following:
All the logically equivalent expressions that describe that intermediate table.
All the physical implementations of those logical expressions.
Winners: A physical expression that had the best cost given a set of physical properties.
Logical properties: Which columns it is required to produce as well as statistics about some of those columns.
Groups are the fundamental data structure in Sierra. The inputs to operators are always groups (indicated by group #'s), and every expression corresponds to some group.
The Group for "Read Table Bar"
In the process of optimization, Sierra will keep track the intermediate tables that could be used in computing the final result table. Each of these corresponds to a group, and the set of all groups for a plan defines the memo. In Sierra, the memo is designed to represent all logical query trees and physical plans in the search space for a given initial query. The memo is a set of groups, with one group designated as the final (or top) group. This is the group which corresponds to the table of results from the evaluation of the initial query. Sierra has no explicit representation of all possible query trees, in part because there are simply too many. Instead, this memo represents a compact version of all possible query trees.
Rules (The Model)
The model's rule set can be thought of as defining the logical and physical search space of the optimizer. The memo is expanded to encompass the full logical and physical search space through the application of rules. The application of rules is a multi-step process of finding a binding in the memo, evaluating a rule condition (if the rule has one) and (if the condition is satisfied) firing the rule, which puts new expressions in the memo. When the optimizer is done applying rules, the memo structure will have been expanded to where it conceptually represents the entire search space. Here is an example of the swap_
Swap Join Order Rule
(display ((1. "pk") (2. "pk") (inner_join (index_scan (1:Foo ("pk"))) (index_scan (2:Bar ("pk")) (range_equal ref (1. "pk"))))
(display ((1. "pk") (2. "pk") (inner_join (index_scan (2:Bar ("pk")) (index_scan (1:Foo ("pk"))) (range_equal ref (2. "pk"))))
Tasks (The Search Engine)
Sierra's search engine is a series of tasks that are waiting to be computed. At any point in time during optimization, there are tasks waiting on a stack to be


text:
The Inside Analysis Tech Lab showcases the design, production and use of enterprise applications that solve real-world challenges. Each Tech Lab project features a Data Scientist who works with an innovative software vendor to create a solution that leverages data, big and small, in order to deliver meaningful results.
The process typically spans three months, and begins with a brainstorming session, then works through a series of steps to identify, capture, prepare, and ultimately integrate appropriate data sets. The solution can incorporate any number of enterprise technologies, and use any type of data. The key is to demonstrate how each step along the way is accomplished.
Like a story, every successful project has a beginning, middle and end. The Tech Lab demonstrates this through a series of Webcasts and blogs that tell the tale of the solution:
1) Initial Webcast defines the business challenge, objectives, tools, methods and overall strategy
2) Second Webcast, a month later, explains in detail key aspects of how the project is progressing
3) Final Webcast shows the solution, offers insights about lessons learned and ideas for the future
Additionally, the Data Scientist and team will write a series of at least five blogs throughout the three-month period, offering perspectives on tactics employed, hurdles encountered, solutions designed and other related topics. These articles will run every other week throughout the campaign.
Finally, a five-page white paper will summarize key components of the project, explaining how specific challenges were overcome, and providing advice on how organizations can incorporate various strategies and tactics to achieve enterprise success.


text:
Yesterday, on our walk down the side lane, I was excited to see a goat in the distance but, as we got closer, the single goat turned into a flock of sheep.
I stared at them, for ages, trying to decide if they were sheep or goats, or even both. It was the floppy ears that had me confused. Upon Googling, I found that sheep and goats can interbreed, but it's rare for they have a differing number of chromosomes, presenting survival difficulties.
Below, this black sheep has a woolly coat and, by the longer bits on the shoulder, looks like it has not long shed. I learned that the top lip determines a sheep from a goat. Since this has a split top lip, it's a sheep.
The division in the top lip is better seen in this next tan-coloured one. Their eating habits differ – a sheep grazes, as these do, but a goat eats bushes and twigs, stuff off the ground.
But look at the coat on that little spotted on at the back – not particularly woolly. And then there are the tails. A sheep's tail will hang down. A goat's sticks up. In the end, I had to admit all of them were sheep – despite the gangly legs and floppy ears. Still, it's great to have some new animals within easy reach of the Nikon D3000.
Thanks for looking. Stay safe.


text:
(The text below was scanned in so I hope the OCR software got everything right)
Why do warthogs drop to their knees to feed?
This is more common in winter when most of their food is underground and needs to be dug up. The disc at the end of the nose and the tushes (tusks) are used. The digging is more easily accomplished while the warthogs are on their knees, as more leverage and resultant digging efficiency are obtained, especially in harder soils.
Warthogs are the only 'pigs' in Africa to exhibit this phenomenon, and the reason for it may lie in the fact that they are the only ones that live and mostly graze in open savannah. The other species, like bushpig, live in the forest and generally have enough food in the form of fallen fruits, and other goods in the winter. Rooting at this time takes up less of their day, and the litter and soil are generally softer than on the sunbaked open areas where warthogs live. In winter warthogs rely almost entirely on rooting to find adequate and sufficient food, and spend most of their time doing this. This puts a strain on the animal, which is alleviated by dropping to the knees and resting while rooting, and possibly creates greater leverage with which to dig in hard soils. Thus the knees have developed heavy callouses. Warthogs, having to work relatively harder to obtain food than their forest relatives, spend a lot of time on their knees for comfort and convenience, if not out of necessity. One could then ask why they do not have much shorter front legs to facilitate this strategy, but such a feature would make them vulnerable to attack and less agile in escape.
Why do bats hang upside down?
Essentially because their legs are too weak to hold their body weight. This has occurred as a result of adaptations to fill their particular niche as mammals. Bats are the only mammals that fly. As in birds, weight-saving mechanisms evolve to make flight possible. Most birds have hollow bones but have minimal reduction (in most species) of the legs as they need them for perching and walking about in search of food. Bats have followed a different route altogether. Rather than having feathers (which are modified scales that hint at birds' reptilian ancestors),
they have developed extensive but light, membranous wings, have kept the solid bones, but have reduced on the one thing that was not of prime importance - their legs! Because most bats are feeders of aerial insects which they catch on the wing, they can afford this, and so have adapted their wings (forelimbs) to have claws (hooks). Because birds have occupied most of the niches for flying, bats compete by hunting at night (when most birds are inactive) and use their claws to hang rather than perch. The legs in insect-eating varieties are, therefore, rudimentary and not capable of holding the animal's weight (cumbersome shuffling being the only movement of these limbs). Fruit-eating bats still need some locomotive ability of the legs to aid in clambering around for fruit - but they are nowhere near as agile as birds.
Why do oxpeckers sit on mammals?
Why do comb ducks (knob-billed ducks) have knob bills?
Why do hamerkops have such big nests?
These are just some of the questions answered in a new book I bought last night called "Beat about the bush: Animals and birds
Author: Trevor Carnaby
I bought it at Wordsworth for R277.00

source: HuggingFaceFW/fineweb-edu


LenItsuki LenItsuki - 1 year ago 121
C++ Question
read binary data by C++
I try to read binary data by C++ program below. But it can not display values. The data is saved as 8bit unsigned char. Let me know how to solve it.
#include <iostream>
#include <fstream>
using namespace std;
int main(int argc,char *argv[])
{
if(argc!=2)
{
cout << "argument error" << endl;
return 1;
}
ifstream file (argv[1], ios::in|ios::binary);
//ifstream fin( outfile, ios::in | ios::binary );
if (!file)
{
cout << "Can not open file";
return 1;
}
unsigned char d;
while(!file.eof())
{
file.read( ( char * ) &d, sizeof( unsigned char ) );
cout << d << endl;
}
file.close();
return 0;
}
Answer Source
First of all don't do while (!file.eof()).
Then for your problem: It is that you output a character. That means the stream will attempt to print it as a character, which will not be correct for binary data.
If you want to print the values you read, you need to convert it to integers. Something like
std::cout << std::hex << std::setw(2) << std::setfill('0') <<
 << static_cast<unsigned int>(d);
The above should print out the values as 2-digit hexadecimal numbers. The important bit is the static_cast.
Recommended from our users: Dynamic Network Monitoring from WhatsUp Gold from IPSwitch. Free Download


text:
Maintenance work is essential to keep the motorway network operational. However works may increase the risk for the travelling public as well as placing workers in potentially hazardous situations. For many years the safety performance of works has been monitored on a regular basis and this project aims to provide data on the current situation. This data can be used to help in decisions with respect to future guidance on maintenance operations.
The work required has five main objectives:
- To establish the accident rate and severity split at major road works sites on motorways, and compare this with the rate in the absence of works;
- To compare accidents occurring at sites with and without speed cameras;
- To examine the location of accidents in relation to features of the traffic management scheme;
- To estimate accident rates at road works in a form suitable for use in QUADRO;
- To establish accident risk factors for use in the risk assessments.


text:
Elicitation of Diagnosis Strategy During Scanning Chest X-Rays from Eye Tracking Stimulated Retrospections
Authors: Hirotaka Aoki, Koji Morishita, Marie Takahashi, Rea Machida, Kousuke Hirata, Atsushi Kudoh, Tsuyoshi Shirai
Abstract: In the present paper, we apply a debriefing technique aided by eye movement recordings to elicitation of clinical reasoning processes during chest x-rays diagnosis. It can be expected that this technique allows us to obtain elaborated information regarding hidden cognitive processes compared to conventional verbal protocol approach. Two experiments where medical tasks of 20 medical doctors (10 majoring in radiology/ respiratology and 10 majoring in surgery/acute medicine) on diagnosis of chest x-rays images were recorded with a video camera and an eye tracking system were performed. In the first experiment, each one of 5 chest x-rays having four patterns of cancer, pneumonia, normal and others were shown. A participant was asked to make his/her diagnosis decisions about whether each of chest x-rays. Immediately afterwards, a debriefing where each eye tracking recording was used as a cue to verbalize the participant's implicit diagnosis processes was conducted. In the second experiment, a comparative diagnosis on the current patient's status was carried out. Five pairs of x-rays images were shown to each participant. In each pair, one was a current image and the other one was an image taken a year ago. The participant examined the current patient's status by identifying small changes with time. The debriefing stimulated by eye movement recordings was performed just after the task. Based on the verbal protocols form the debriefing session, each participant's reasoning processes were traced. The results indicated that there were mainly four effective reasoning strategies as followings: Postponement of lung field interpretation, avoidance of preconception by applying redundant scanning rule, critical area revisited, and complying with one's mental check-list at any moment. At the same time, one problematic strategy was also identified that can be named as "single lesion focusing strategy." Based on all results as well as implications obtained, we discuss insights relating to effective medical reasoning processes as well as validity of verbal protocols/comments on eye mark recordings.
Keywords: Expertise analysis, clinical reasoning process, chest x-rays, debriefing using eye movement recording
Cite this paper:


text:
I like a good binge-watching session as much as anyone. Not long ago, I blew through all 96 episodes of Entourage in about two weeks! It seems I'm in good company. According to a recent survey, 70 percent of Americans are binge watchers.
With technology including streaming services and on-demand content transforming the way we consume media, it's important to ask: What effects does all this binge viewing have on the soundness of our sleep?
Binge watching and sleep quality
A new study tackles just that question. Scientists from University of Michigan and Belgium's University of Leuven investigated the prevalence of binge watching, and how these extended viewing sessions impact sleep. The study included 423 young adults ages 18-25. Researchers analyzed their regular TV-watching habits and binge-watching habits—the latter defined as "watching multiple, consecutive episodes of the same TV-show in one sitting"—along with assessments of their sleep quality, fatigue, and insomnia.
They found strong links between binge watching and sleep problems:
- Slightly more than 80 percent of participants considered themselves binge watchers—and among those, slightly more than 20 percent had binge watched at least a few times a week over the past month
- Among people identified as poor sleepers, about 1 in 3 experienced poor sleep quality linked to binge watching.
- Binge watching was linked to insomnia symptoms, fatigue, and poor sleep quality.
Researchers found differences between regular TV watching and binge viewing. Regular TV watching wasn't associated with poor sleep quality—but binge watching was.
How does binge watching disrupt sleep?
There may be a number of factors involved. Researchers in this study found sleep disturbances from binge watching were a result of mental stimulation that came from extended viewing in the evenings, a form of stimulation known as "pre-sleep arousal." Being exposed to the content of the programming—storylines, action, imagery—stimulates brain activity and alertness. And the duration of a binge-watch session creates enough pre-sleep arousal to interfere with our ability to fall asleep. Watching back-to-back episodes of your current favorite show may feel like a relaxing escape at the end of the day, but it's actually getting your brain fired up, not helping it wind down.
Binge watching is a relatively new phenomenon, and scientific findings about its relationship with mental and physical health are just beginning to arrive. Recent research links binge watching to feelings of loneliness, as well as depression and anxiety. A new study out of the United Kingdom shows that nearly a third of UK adults and teenagers think binge-watching has caused them to miss out on sleep or feel tired.
There's a longer history of research into the effects of TV watching—and plenty of evidence suggesting that too much of it is bad for sleep, as well as mental and physical well being. Watching more than two hours of TV on a daily basis is linked to several common sleep problems:
- trouble falling asleep
- waking during the night
- waking early in the morning and being unable to fall back to sleep
The lure of television can push bedtimes later and result in greater sleep debt, according to research. Sleep debt is the difference between the amount of sleep you need and the amount of sleep you actually get.


text:
Hydrothermal activity is significant in regulating the dynamics of trace elements in the ocean. Biogeochemical models suggest that hydrothermal iron might play an important role in the iron-depleted Southern Ocean by enhancing the biological pump. However, the ability of this mechanism to affect large-scale biogeochemistry and the pathways by which hydrothermal iron reach the surface layer have not been observationally constrained. Here we present the first observational evidence of upwelled hydrothermally influenced deep waters stimulating massive phytoplankton blooms in the Southern Ocean. Captured by profiling floats, two blooms were observed in the vicinity of the Antarctic Circumpolar Current, downstream of active hydrothermal vents along the Southwest Indian Ridge. These hotspots of biological activity are supported by mixing of hydrothermally sourced iron stimulated by flowtopography interactions. Such findings reveal the important role of hydrothermal vents on surface biogeochemistry, potentially fueling local hotspot sinks for atmospheric CO2 by enhancing the biological pump.
EPIC Alfred Wegener Institut


text:
What are some differences between IRR and PI? What would Lisa Cross say about IRR?
Why would an organization choose to lease an asset instead of buying it? Refer to the text and article by D.O. Burgess.© BrainMass Inc. brainmass.com June 3, 2020, 6:05 pm ad1c9bdddf
Please see response attached (also presented below), as well as one relevant and supporting document. I hope this helps and take care.
1. What are some differences between IRR and PI?
The following information is an excerpt from the attached article concerning the differences between IRR and PI and how they are used in choosing between two options (i.e., between two projects, between lease or buy, etc.). It also discusses the pitfalls and provides excellent pointers when to use and when not to use these indicators. I make references to specific pages in the article periodically for you to consider and make reference to.
The internal rate of return
? The basic rationale behind the internal rate of return (IRR) is that it tries to find a single number that summarizes the merits of a project. That number does not depend on the interest rate that prevails in the capital market. That is why it is called internal - the number is internal or intrinsic to the project and depends only on the project's cash flows.
? The IRR is the discount rate that would make the NPV of the project equal to zero. For example, if a project has cash flows (-C0, C1, C2, C3) the IRR will be the solution for r in the equation (1) (see attached article p. 3).
? The basic investment rule based on the IRR is "accept the project if the IRR is greater than the discount rate; reject it if the IRR is less than the discount rate."
? The first problem with the basic IRR rule is that it will give an incorrect decision if the NPV function cuts the r axis from below rather than above. In that case, if the discount rate is below the IRR, the NPV will be negative at the discount rate and the project should not be undertaken. Conversely, if the discount rate is above the IRR the NPV will be positive at the discount rate and the project should be undertaken.
? The implicit assumption in the simple IRR rule is that the negative cash flows occur immediately to be followed by positive cash flows. In that case, the NPV will be decreasing in r so the NPV curve will cut the r axis from above. If a project has positive cash flows followed by negative ones, however, the NPV will be increasing in r and the NPV curve will cut the r axis from below.
? In the case where the negative cash flows occur first, the project is a substitute for lending so the project should have a return greater than the discount rate before it should be undertaken. Conversely, when the positive cash flows occur first, to be followed by negative cash flows, the project is a substitute for borrowing. The project therefore should only be undertaken if the IRR is less than the discount rate.
? The second problem with the IRR rule is that the IRR may not be unique. The NPV function may cross the r axis a number of times. In fact, we can see that equation (1) that is to be solved for r is a cubic equation. If there are T periods of future cash flows from the project, the equation to be solved will be a T-th degree polynomial. In general, a polynomial of degree T will have T roots (values of r where it equals zero).
? Fortunately, we can discard roots that are not in the [0,1] interval as not being sensible rates of return. Furthermore, as we argued above, the NPV of projects that have a negative cash flow followed by all positive ones or, vice versa, a positive cash flow followed by all negative ones, will have a monotonic NPV as a function of r and therefore just one IRR.
? Even so, there are many important types of projects that will have multiple IRR. In particular, projects where the cash flow changes sign two or more times will have this characteristic. Examples of such projects are most surface mining investments where rehabilitation expenditures are required once the mineral resource has been exploited.
? When there are multiple IRR it is less clear where the discount rate should fall to make the project worthwhile. If we graph the NPV as a function of r, however, all discount rates that would result in a positive NPV would make the project worthwhile. For example in the following case:
Figure 1: NPV for a project with several changes in cash flow sign the project would have a positive net present value for discount rates below r1or between r2 and r3 and hence should be undertaken in these ...
This solution identifies and discusses some of the main differences between IRR and PI, and what Lisa Cross might say about IRR. Then, reasons why an organization might choose to lease an asset instead of buying it are explored. Supplemented with a highly relevant article about alternative investment rules.


text:
Question 1: What do you mean by a mall?
Answer: A mall is a type of market found in the urban areas. It is an enclosed space for shopping. It is usually a large building with many floors which have shops, restaurants, cinema theatres etc. These most often sell branded goods.
Question 2: Write briefly about weekly markets.
Answer: Weekly markets are the markets found at a place during one or two days of the week. They are not daily markets. They sell a range of products from fruits and vegetables to clothes to utensils. They have high competition.
Question 3: Describe briefly the concept of 'markets everywhere'.
Answer: These days it is not necessary for the consumers to go to the market like weekly markets or neighbourhood shops etc (which have a specific location and time of functioning) to buy things. They can either order goods on phone or use the internet and buy things online. The medical representatives in clinics are also actually selling their goods. Hence we can say that markets are found everywhere and they have different styles of functioning.
Question 1: Define the term 'chain of markets'.
Answer: Chain of markets refers to the series of markets where products pass from one market to another. These markets are connected like a link in a chain.
Question 2: Define the term retailer.
Answer: The trader who finally sells to the consumer is called the retailer.
Question 3: What do you mean by wholesale?
Answer: This refers to buying and selling in bulk. Products like fruits, vegetables and flowers have wholesale markets.
Question 4: Why are the goods in a weekly market cheap?
Answer: The products in the weekly market are cheap because since these shops are not permanent, they save on expenses such as rent, electricity, and fees to the government. Since these are family-run, they also save the expenses of wages to workers.
Question 1: How are the buyers and sellers of a small shop different from that of a mall?
Answer: The difference in the buyers and sellers of a small shop and a mall is as under:
Question 2: Write a note on markets for indirect goods.
Answer: A market exists also for goods that are not directly used by the consumer. These goods are the raw materials or other inputs used in the process of manufacture. For example the fertilizers used by the farmers are purchased by him from a shop in the city. The shopkeeper in turn buys them from factories. The products used in manufacturing and assembling a car like the engine, axles, petrol tanks etc are also bought and sold in different markets. Hence a chain of markets exists for indirect goods also.
Question 3: Describe the chain of markets.
Answer: Chain of markets refers to the series of exchange of goods that takes place from the moment the goods are produced to the time they reach the end user i.e. the final consumer. Goods are produced in factories, farms and homes. But we do not buy directly from factories or farms. Even the producers will not be willing to sell small quantities like one dress or one kilogram of wheat to the consumers. Hence traders come into picture. Traders are the people in between the producer and the final consumer.
The wholesale trader is the first to buy goods from factories or farms in bulk and stores them in the godown. For example, he may buy 50 to 100 kilograms of rice. He then sells it to other traders. There could be any number of traders involved in the chain. Between these traders, buying and selling takes place. It is because of these traders that goods reach faraway places. The trader who finally sells the product to the consumer is called the retailer. These retailers are the traders in the weekly markets or neighbourhood shops or malls.
Question 4: What are the features of a weekly market?
Answer: Weekly markets have the following features:
Cheaper Rates: Many items are available in the weekly markets at cheaper rates. This is because since these shops are not permanent, they save on expenses such as rent, electricity and fees to the government.
Family run: The shop owners store the items they sell at home. Mostly, they are helped by the family members and hence they do not employ outside people. Hence they save the money spent as wages to workers.
High Competition: Since these markets have a large number of shops selling the same item, there is high competition among them. Hence if a seller charges a higher amount for an item, buyers will buy from another seller who either charges a lower rate or allows the buyer to bargain to some extent.
Copyright © excellup 2014


text:
A body-of-revolution finite-difference time-domain (BOR-FDTD) method was developed and employed to rigorously analyze axisymmetric transformation optics (TO) lenses. The novelty of the proposed BOR-FDTD technique is that analytical expressions were derived and presented to introduce obliquely incident plane waves into the total-field/ scattered-field formulation, allowing for accurate simulation of BOR objects in layered media illuminated by obliquely incident waves. The accuracy of the proposed method was verified by comparing numerical results with analytical solutions. The developed code was further utilized to study the imaging properties of a cylindrical TO Luneburg lens on a substrate, demonstrating the desired focusing of light onto a flat plane.
|Original language||English (US)|
|Number of pages||3|
|State||Published - Jan 1 2013|
All Science Journal Classification (ASJC) codes
- Atomic and Molecular Physics, and Optics


text:
Fieldwork opportunities for occupational therapy students studying outside Canada
If you are an occupational therapy student studying outside of Canada and interested in a fieldwork placement in Canada, please note that you must:
- choose only one city/catchment area of Canada to inquire about a potential placement.
- arrange to have your home university fieldwork coordinator contact the Canadian university fieldwork coordinator for the catchment area in which you would like to have the placement*. – make this contact at least six months prior to the beginning of the placement.
- gather more information about the method of application, associated costs, student requirements, and placement availability from that particular Canadian university fieldwork coordinator.**
- get the contact information you need: Canadian university catchment areas and fieldwork coordinators
*Please note that students interested in fieldwork opportunities are not allowed to directly contact any Canadian fieldwork facilities or universities asking about placements. This is a strict rule throughout Canada for both Canadian and international students.
**The University of British Columbia is not involved with arranging placements for international students in British Columbia and the Yukon Territory. International students seeking placements in British Columbia or the Yukon must have their fieldwork coordinators contact the placement sites directly.
- Placements for international students within Toronto and surrounding Greater Toronto area are unlikely as these placements are in high demand by Canadian OT students and Canadian students are given priority over international students.
- Placement availability during the Canadian summer (Jul-Aug) is unlikely due to high demand and low capacity.


R statistics/rgrass7
From GRASS-Wiki
< R statistics
Revision as of 15:35, 7 January 2020 by Neteler (talk | contribs) (+library(sf) and use_sf())
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This page refers to the usage of R within a GRASS GIS 7 session and the use of GRASS GIS 7 within an R session (see also R_statistics/spgrass6)
Terminology
Using R in conjunction with GRASS GIS can have two meanings:
 - Using R within GRASS GIS session, i.e. you start R (or RStudio) from the GRASS GIS command line. You may like this variant if you are primarily a GRASS GIS user.
 - Using GRASS GIS within a R session, i.e. you connect to a GRASS GIS location/mapset from within R (or RStudio). You may like this variant if you are primarily a R user.
References: see "Overview" in R_statistics
Installation
See R_statistics/Installation
R within GRASS
Using R within GRASS GIS session, i.e. you start R (or RStudio) from the GRASS GIS command line.
Startup
 - First start a GRASS GIS session. Then, at the GRASS command line start R (for a 'rstudio' session, see below)
In this example we will use the North Carolina sample dataset.
Reset the region settings to the defaults
GRASS 7.0.1svn (nc_spm_08_grass7):~ > g.region -d
Launch R from the GRASS prompt
GRASS 7.0.1svn (nc_spm_08_grass7):~ > R
Load the rgrass7 library:
library(rgrass7)
If you plan to follow the example using the North Carolina sample dataset, load the rgdal library:
library(rgdal)
Get the GRASS environment (mapset, region, map projection, etc.); you can display the metadata for your location by printing G:
G <- gmeta()
gisdbase /home/neteler/grassdata
location nc_spm_08_grass7
mapset user1
rows 620
columns 1630
north 320000
south 10000
west 120000
east 935000
nsres 500
ewres 500
projection +proj=lcc +lat_1=36.16666666666666 +lat_2=34.33333333333334
+lat_0=33.75 +lon_0=-79 +x_0=609601.22 +y_0=0 +no_defs +a=_PHONE_
+rf=298.257222101 +towgs84=0.000,0.000,0.000 +to_meter=1
Listing of existing maps
List available vector maps:
execGRASS("g.list", parameters = list(type = "vector"))
List selected vector maps (wildcard):
execGRASS("g.list", parameters = list(type = "vector", pattern = "precip*"))
Save selected vector maps into R vector:
my_vmaps <- execGRASS("g.list", parameters = list(type = "vector", pattern = "precip*"))
attributes(my_vmaps)
attributes(my_vmaps)$resOut
List available raster maps:
execGRASS("g.list", parameters = list(type = "raster"))
List selected raster maps (wildcard):
execGRASS("g.list", parameters = list(type = "raster", pattern = "lsat7_2002*"))
Reading in data from GRASS
Example 1: raster maps
Read in two GRASS raster maps (the maps "geology_30m" and "elevation" from the North Carolina sample data location) into the R current session as a new object "ncdata" (containing then two SpatialGridDataFrames as well as metadata):
# the cat parameter indicates which raster values to be returned as factors
# - geology_30m is a categorical map (i.e., it comes with classes)
# - elevation is a continuous surface
ncdata <- readRAST(c("geology_30m", "elevation"), cat=c(TRUE, FALSE))
(A warning may appear since in the "geology_30m" map two labels are not unique - as found in the original data.)
We can verify the new R object:
str(ncdata)
Formal class 'SpatialGridDataFrame' [package "sp"] with 4 slots
 ..@ data :'data.frame': 16616 obs. of 2 variables:
and also check the data structure in more detail:
str(ncdata@data)
'data.frame': 16616 obs. of 2 variables:
 $ geology_30m: Factor w/ 12 levels "CZfg_217","CZlg_262",..: NA NA NA NA NA NA NA NA NA NA ...
 $ elevation : num NA NA NA NA NA NA NA NA NA NA ...
The metadata are now accessed and available, but are not (yet) used to structure the sp class objects, in this case a SpatialGridDataFrame object filled with data from two North Carolina layers. Here is a plot of the elevation data:
image(ncdata, "elevation", col = terrain.colors(20))
Add a title to the plot:
title("North Carolina elevation")
Ncdata.png
In addition, we can show what is going on inside the objects read into R:
str(G)
List of 26
 $ DEBUG : chr "0"
 $ LOCATION_NAME: chr "nc_spm_08_grass7"
 $ GISDBASE : chr "/home/veroandreo/grassdata"
 $ MAPSET : chr "PERMANENT"
 $ GUI : chr "wxpython"
 $ projection : chr "99"
 $ zone : chr "0"
 $ n : num 228500
 $ s : num 215000
 $ w : num 630000
 $ e : num 645000
 $ t : num 1
 $ b : num 0
 $ nsres : num 27.5
 $ nsres3 : num 10
 $ ewres : num 37.5
 $ ewres3 : num 10
 $ tbres : num 1
 $ rows : int 491
 $ rows3 : int 1350
 $ cols : int 400
 $ cols3 : int 1500
 $ depths : int 1
 $ cells : chr "196400"
 $ cells3 : chr "_PHONE_"
 $ proj4 : chr "+proj=lcc +lat_1=36.16666666666666 +lat_2=34.33333333333334 +lat_0=33.75 +lon_0=-79 +x_0=609601.22 +y_0=0 +no_defs +a=_PHONE_ +"| __truncated__
 - attr(*, "class")= chr "gmeta"
summary(ncdata)
Object of class SpatialGridDataFrame
Coordinates:
 min max
[1,] 630000 645000
[2,] 215000 228500
Is projected: TRUE
proj4string :
[+proj=lcc +lat_1=36.16666666666666 +lat_2=34.33333333333334
+lat_0=33.75 +lon_0=-79 +x_0=609601.22 +y_0=0 +no_defs +a=_PHONE_
+rf=298.257222101 +towgs84=0.000,0.000,0.000 +to_meter=1]
Grid attributes:
 cellcentre.offset cellsize cells.dim
1 630018.8 37.50000 400
2 215013.7 27.49491 491
Data attributes:
 geology_30m elevation
 CZfg_217:70381 Min. : 55.92
 CZig_270:66861 1st Qu.: 94.78
 CZbg_405:24561 Median :108.88
 CZlg_262:19287 Mean :110.38
 CZam_862: 6017 3rd Qu.:126.78
 CZbg_910: 4351 Max. :156.25
 (Other) : 4942
Example 2: raster maps
Here an example for a single raster map transfer from GRASS GIS to R:
library(rgrass7)
execGRASS("g.region", raster = "elevation", flags = "p")
ncdata <- readRAST("elevation", cat=FALSE)
summary(ncdata)
spplot(ncdata, col = terrain.colors(20))
Example 3: vector maps
Here an example for a single vector map transfer from GRASS GIS to R:
# needed prere


text:
No matter what goal you want to achieve, you can measure your progress as a percentage of the goal. For example, if you have a sales goal for the month, you could measure your sales thus far as a percentage of the sales goal. Similarly, if you have a goal for running a certain number of miles per week or raising a certain amount of money, you can also measure your progress as a percentage. Using a percentage helps to put your progress in perspective. For example, if your goal is running 10 miles a week, eight miles means you're almost there. If your goal is 80 miles a week, however, eight miles is just getting started.
Identify your goal or target. For example, if you want to run 30 miles in a week, 30 miles would be your goal. Alternatively, if you want to raise $1,000, $1,000 would be your goal.
Measure your progress toward your goal. For example, if you ran 5 miles the first day and then 4 miles the next day, you would have 9 miles toward your goal. Alternatively, if you got a $200 donation from one donor and a $240 donation from another, you would have $440 raised toward your goal.
Divide your progress toward your goal by your goal. In the first example, divide 9 by 30 get 0.3. In the second example, divide $440 by $1,000 to get 0.44.
Multiply the result by 100 to convert to a percent. Finishing the first example, multiply 0.3 by 100 to get 30 percent. Completing the second example, multiply 0.44 by 100 to get 44 percent.
About the Author
Mark Kennan is a writer based in the Kansas City area, specializing in personal finance and business topics. He has been writing since 2009 and has been published by "Quicken," "TurboTax," and "The Motley Fool."


text:
Alevi ritual practice is a significant means in (re)producing a specific Alevi habitus and cultural identity. It is well known that the
Alevi ritual practice is a significant means in (re)producing a specific Alevi habitus and cultural identity. It is well known that the cem ritual and the emblematic semah were formative in the so-called Alevi revival. Representations of such performances became core symbols of 'Aleviness'. Despite this, Alevis often complain of a lack of 'authenticity' of the contemporary Alevi ritual. And yet, contemporary rituals and ceremonies still preserve rites and ritual elements constitutive for the Alevi tradition. By drawing on a large body of audio-visual material collected from 2003 to 2010, this lecture will present examples from a systematic analysis of the repertoire and structures of transnational ritual practice, focussing on its aesthetics.
holds an M.A. (Islamic Studies, Cultural Anthropology) and a PhD, both from the University of Heidelberg, where he was appointed 'Privatdozent' with a venia legendi for 'Islamwissenschaft' in 2016. Since October 2017, he has been a senior researcher at the Orient-Institut Istanbul, responsible for the research area 'History of Religions'. He conducted fieldwork in Iran, Turkey, Armenia, and Germany among Zoroastrians, Alevis, Yezidis, and Twelver Shiites, both as a member of the Collaborative Research Centre 'Dynamics of Ritual' (University of Heidelberg) and as a research group leader at the Department for the Study of Religions (University of Bayreuth).
Susam Sokak No:16 Kat:3 Daire: 7 Cihangir - İstanbul


# sierpinksy-triangle.py from tkinter import * def init(data): data.level = 1 def drawSierpinskyTriangle(canvas, x, y, size, level): # (x,y) is the lower-left corner of the triangle # size is the length of a side if (level == 0): canvas.create_polygon(x, y, x+size, y, x+size/2, y-size*(3**0.5)/2, fill="black") else: drawSierpinskyTriangle(canvas, x, y, size/2, level-1) drawSierpinskyTriangle(canvas, x+size/2, y, size/2, level-1) drawSierpinskyTriangle(canvas, x+size/4, y-size*(3**0.5)/4, size/2, level-1) def keyPressed(event, data): if (event.keysym in ["Up", "Right"]): data.level += 1 elif ((event.keysym in ["Down", "Left"]) and (data.level > 0)): data.level -= 1 def redrawAll(canvas, data): drawSierpinskyTriangle(canvas, 25, 450, 450, data.level) canvas.create_text(250, 25, text = "Level %d Sierpinsky Triangle" % (data.level), font = "Arial 26 bold") canvas.create_text(250, 50, text = "Use arrows to change level", font = "Arial 10") def mousePressed(event, data): pass def timerFired(data): pass #################################### # use the run function as-is #################################### def run(width=300, height=300): def redrawAllWrapper(canvas, data): canvas.delete(ALL) canvas.create_rectangle(0, 0, data.width, data.height, fill='white', width=0) redrawAll(canvas, data) canvas.update() def mousePressedWrapper(event, canvas, data): mousePressed(event, data) redrawAllWrapper(canvas, data) def keyPressedWrapper(event, canvas, data): keyPressed(event, data) redrawAllWrapper(canvas, data) def timerFiredWrapper(canvas, data): timerFired(data) redrawAllWrapper(canvas, data) # pause, then call timerFired again canvas.after(data.timerDelay, timerFiredWrapper, canvas, data) # Set up data and call init class Struct(object): pass data = Struct() data.width = width data.height = height data.timerDelay = 100 # milliseconds init(data) # create the root and the canvas root = Tk() canvas = Canvas(root, width=data.width, height=data.height) canvas.pack() # set up events root.bind("", lambda event: mousePressedWrapper(event, canvas, data)) root.bind("", lambda event: keyPressedWrapper(event, canvas, data)) timerFiredWrapper(canvas, data) # and launch the app root.mainloop() # blocks until window is closed print("bye!") run(500, 500)


text:
You can use the 2D shapes in Yenka Mathematics to construct your own nets, which can then be folded into 3D shapes.
When you move one 2D shape - like this square - close to another one, the shapes' edges will snap together. You'll see a square block between them when this happens. Yenka automatically forms a hinge where the shapes join.
Repeat this with several shapes, and you'll get a net. One square is darker than the others: this is the base of the net. You can fold the net up by dragging the corners of any other square, and check if it makes the shape you want.
Once you've folded the net completely, and made a 3D shape, you can double-click on that shape and click "Flatten" in the panel that appears, to flatten out the net.


17.00 - 17.05 - Assessing a Busy System - Advanced SQL Engine - Teradata Database
Teradata Vantage™ - Database Administration
Product
Advanced SQL Engine
Teradata Database
Release Number
17.00
17.05
Published
June 2020
Language
English (United States)
Last Update
2021-01-22
dita:mapPath
rgu1556127906220.ditamap
dita:ditavalPath
lze1555437562152.ditaval
CPU Saturation
Systems that run frequently at or near capacity are often the subject of assessments in which attempts are made to determine the extent of resource exhaustion.
When CPU is the binding factor and the scarce resource on a system, it is useful to determine the highest level of saturation of the system, that is, the point to which you can drive the system before it becomes so overloaded that it appears to be hanging.
Once a system reaches this level of saturation, the system should still be able to work itself out, but that may require extra time to do so.
Without appropriate performance monitoring, users may start, once the system appears to be hanging, to:
 - Abort jobs that could cause rollbacks, or
 - Submit duplicate queries that create more work on an already-exhausted system.
While ResUsage data provides the bulk of the information needed to know that a system is at 100% busy with respect to CPU or CPU+I/O Wait, other information may also be needed in order to examine the extent of CPU saturation and system congestion. In other words, one can know that the system is running at capacity, but not the extent of the overload.
When the system becomes so busy that logons become slow or hung, performance monitoring is not able to determine whether the system is actually hung or simply overloaded without using other tools.
Suggested Monitoring Techniques
Since the goal is to be able to drive the system as hard as possible without overloading it, some techniques for assessing the level of busy can be used when CPU usage is high:
 1. Check AWT utilization. If the number is constantly at or near maximum, then
 2. Check the message flow control. If there are tasks apparently in flow control, then
 3. Check run queue ResUsageSawt MailBoxDepth data. If the run queue is growing longer and longer, the system is too busy and will slow down dramatically.
While it is not unusual to see a busy system with high AWT counts, the presence of flow control means that some tasks are currently being blocked from sending more work to busy AMPs.
Finding a Saturated Resource
Use Resource Check Tools, located in the /usr/pde/bin directory, to check for saturated resources.
IF … THEN …
mboxchk is not already running a background task run mboxchk to check current response time.
the mboxchk log shows a slow response or timeout run syscheck to get a report showing attributes that falls below the specified danger level.
no attribute is reported at the WARN level check disk and AMP CPU usage.
For information about mboxchk and other PDE resource check tools, such as nodecheck and syscheck, see man pages or pdehelp.
High I/O Wait
Teradata recommends configurations with CPU to I/O bandwidth ratios according to typical database workload demands in order to avoid CPU starvation. If these guidelines are not followed, or customer workload is unusually heavy with respect to I/O, it is possible that CPU starvation may still occur, as reflected by high I/O WAIT on the system.
If ResUsage data shows that I/O WAIT is rising while CPU busy is falling, it is an indication that the system is not able to use the available CPU because I/O has become the limiter.
If the onset of high I/O wait is sudden:
 1. Determine if the high I/O wait is due to disk I/O, waiting for AMPs from other nodes (because of skewing or coexistence imbalance), low system demand, or BYNET I/O.
 CPU+WIO less than 90% may suggest low system demand without a true I/O bottleneck. Look at node efficiency to determine if the I/O wait is due to node waiting.
 2. Look at the actual disk I/O wait queue using sar –d, and examine:
 - avwait
 Average time in milliseconds that transfer requests wait idly on queue for response (in the FibreChannel driver queue or disk queue).
 - avserv
 Average time to be serviced (includes seek, rotational latency and data transfer times).
 For more information on the sar utility, see the Linux operating system documentation.


text:
|Product #: GR-10125|
PRESCHOOL HEALTH AND SAFETY MATTERS
Early childhood educators are responsible for the health and safety of the children they care for. From preventing harm from known safety risks to promoting children's medical, nutritional, oral, and mental health, educators and program directors act as children's daily protection and support. This easy-to-use manual provides educators with the information they need to ensure that the children in their care are safe, while they learn the best practices to stay healthy for life. With suggestions, ideas, activities, and ways to involve families at home, Preschool Health and Safety Matters covers topics such as hygiene, injury prevention, mental health, diversity, nutrition, and safety.
Submit a review


text:
Can Descregut Òrbites Improbables
Revolving around the territory
Style Can Descregut Òrbites Improbables 2022
Characteristics Can Descregut Òrbites Improbables
We can't control meteorological phenomena, life cycles and harvests. We are at the mercy of nature. But what might seem negative at first, Arantxa de Cara and Marc Milà, who run Can Descregut, have managed to turn and make the best of. The beauty of wine is that it is a living process and a good winemaker adapts the winemaking process to the grapes that are harvested each year. So every bottle is unique and original. But although each vintage is different in itself, it must be said that they have also managed to bring their own style of wine that we can recognise and enjoy year after year. Located in Vilobí del Penedès, in the heart of the Penedès, they work 30 hectares of vineyards spread over 4 estates with different soils, altitudes and orientations and, in each of them, they are committed to local varieties, organic practices and minimal intervention. This is how they achieve a range of original wines that reflect their passion for their work and their territory.
Under this new philosophy that claims a natural approach to production, Can Descregut Òrbites Improbables is born, the first orange wine made by Can Descregut that is a masterful coupage of the native varieties Xarel·lo, Xarel·lo Rojo and Muscat of Alexandria. Sourced from vineyards with an average age of 35 years old planted on calcareous soils with clay elements, the estate gets a lot of cool wind from the sea, which produces grapes with a good level of freshness. They are cultivated with organic certification and biodynamic practices and, once the grapes reach optimum ripeness, they are harvested and selected by hand in the vineyard. Then, in the winery, the grapes are destemmed, crushed and vatted directly into stainless steel tanks where spontaneous fermentation takes place with native yeasts and in contact with the skins for 20 days. The wine then ages in 500-litre amphorae for 8 months and is bottled without clarifying, stabilising or adding sulphur.
With a label showing a wine amphora orbiting a planet, Can Descregut Òrbites Improbables is energetic, fresh and mineral. An orange wine that joins with the forces of nature and shows us that although life is full of unforeseen events, we can always make the best of it.
Real reviews from customers who have purchased this product
Ratings Can Descregut Òrbites Improbables
Tasting notes Can Descregut Òrbites Improbables
Can Descregut Òrbites Improbables 2021 is a fragrant orange wine with aromas of orange fruit, notes of white flowers and pleasant citrus, mineral and herbal hints. On the palate it is fresh, energetic and slightly tannic. A wine with very good concentration, marked acidity and a deliciously saline finish.


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How Long Is the Flight From Atlanta to Miami?
If you are planning a trip from Atlanta to Miami, it is essential to know how long the flight will take. The duration of a flight between these two cities can vary depending on several factors. In this article, we will explore the average flight time, as well as answer some common questions related to this popular route.
The average flight time from Atlanta to Miami is approximately 1 hour and 50 minutes. However, it is important to note that this is just an estimate, and the actual duration may differ due to various reasons. These factors include weather conditions, air traffic, and the specific airline you choose.
Now, let's address some common questions related to this flight route:
1. How far is Atlanta from Miami?
Atlanta is approximately 650 miles northwest of Miami.
2. What is the quickest flight time from Atlanta to Miami?
The quickest flight time recorded on this route is around 1 hour and 40 minutes.
3. Are there any non-stop flights from Atlanta to Miami?
Yes, several airlines offer non-stop flights between Atlanta and Miami.
4. Which airlines operate flights on this route?
Airlines such as Delta Air Lines, American Airlines, and Southwest Airlines operate flights from Atlanta to Miami.
5. How often do these flights operate?
Flights between Atlanta and Miami operate multiple times a day, providing travelers with flexible options.
6. Are there any overnight flights available?
Yes, some airlines offer overnight flights for those who prefer to travel during nighttime.
7. Can I find cheap flights from Atlanta to Miami?
It is possible to find affordable flights if you book in advance or look for promotional offers.
8. Is it necessary to have a passport to fly between these cities?
No, a passport is not required for domestic flights within the United States.
9. How early should I arrive at the airport for my flight?
It is recommended to arrive at least 1.5 to 2 hours before your scheduled departure time.
10. Is in-flight entertainment provided on these flights?
Most airlines offer in-flight entertainment options, including movies, TV shows, and music, to keep passengers entertained during the flight.
11. Can I bring my own food on the flight?
Yes, you are allowed to bring your own snacks or meals on the flight, but it is advisable to check with the airline's specific regulations regarding outside food.
12. Are Wi-Fi services available on the flight?
Some airlines provide Wi-Fi services on this route, but it is always better to check with the airline beforehand.
13. Is it possible to upgrade to a higher class?
Yes, many airlines offer the option to upgrade to a higher class, such as business or first class, for an additional fee.
In conclusion, the average flight time from Atlanta to Miami is around 1 hour and 50 minutes. However, this can vary due to factors like weather conditions and air traffic. Several airlines operate on this route, and flights are available multiple times a day. By being aware of these details and considering the common questions mentioned above, you can better plan your trip from Atlanta to Miami.


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Media Contact: Fred Strohl (_EMAIL_)|
Communications and External Relations
ORNL sponsoring two summer science camps (1998)
OAK RIDGE, Tenn.,
May 12, 1998
The Department of Energy's (DOE) Oak Ridge National Laboratory (ORNL) will this summer again sponsor two camps for elementary and middle school students.
The one-week camps offered will be Science Explorers, scheduled during the weeks of June 8 and June 15, and SciCops, scheduled the week of July 6.
Science Explorers is designed for rising fifth, sixth and seventh graders, focusing on a variety of science topics approached via field explorations and hands-on activities. Campers learn about insects, habitats, water, life, science, weather, rockets, geology, astronauts and robots. Sessions will be held from 8 a.m. to 3 p.m. at the Freels Bend Cabin in Oak Ridge.
SciCops is for rising seventh through ninth graders, focusing on investigations in science, technology and law enforcement. Campers learn about how science and technology are used in police investigations through the Knox County Sheriff's Department, ORNL and the Center for Science and Technology for Law Enforcement at the Y-12 Plant. Campers work with fingerprinting techniques, radar, evidence analysis, scuba diving, arson investigation, forensic science, video enhancement, DNA analysis and the K-9 team. Sessions are scheduled from 8 a.m. to 3 p.m. at the Knox County Sheriff's Department Training Facility in Knoxville.
Each camp is limited to 25 participants, and the cost is $160.
Applications are available by calling Claretta Sullivan of ORNL's Office of University and Science Education at _PHONE_ or via e-mail at _EMAIL_.
ORNL, one of DOE's multiprogram national research and development facilities, is managed by Lockheed Martin Energy Research Corporation.


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model how to graph each trial number using the attached graph (Teacher Model). I begin by creating a scale along the y-axis that counts by 1 second so that student can see a scale with one second intervals can fit data less than 15 seconds. I ask students: Will all of my data fit on this graph? Students point out that several trials extend above the 15 second mark (including 16.61 sec and 21.00 sec). We collectively decide to make the scale interval 2 seconds instead of 1 second. This is important as students will have to decide which scale will fit their data. For example, one student will have to create a scale interval greater than 2 seconds in order to fit her 40+ second ride times.
After modeling how to graph each trial, I connect the dots and draw an "Average" line so that students can see the trials that were above and below average.
Students complete the same steps on their own and then they move on to answering the questions below.
Here are a few examples of student data and graphs during this time:
As students finish graphing and analyzing their data, I ask them to get ready for presentations by taking the following notes on a notecard: Failure Point, Improvement, Average Ride Time, and Explain why a trial was above or below the average ride time.
I want students to reflect on the process of engineering as well as their findings after analyzing their data. Being able to connect roller coaster prototypes with actual steps that engineers complete is the goal of this project, so it's important to circle back around to our starting point, the Engineering Method.
Here are a few student examples:
At the end of the day, students left their finished roller coasters on their desks. Tomorrow, students will present their roller coasters to our class and to several other classes in our school! Here are a few examples of finished roller coasters!
Many students also added some special little details to make their roller coaster special, such as a roller coaster name, little cones at the of supports, signs, arrows, and flags:


Why are some black women so fat
The real reason men find women with big butts attractive
Evolutionary psychologists have long argued that the physical traits that heterosexual men are attracted to are traits that emphasize female health and fertility. The main argument was that our ancestors developed this attraction because it increased the chances of being successful in the reproductive process.
These pairing preferences, it was believed, were put aside over the generations, but still subconsciously influence men today.
A new study just published in the journal Evolution and Human Behavior shows that men who like women with large buttocks might follow such an evolutionary mating preference.
The researchers hypothesized that the shape of a woman's buttocks is an important indicator of her fertility. It is not the mass of the butt that is decisive, but rather the degree to which it protrudes in comparison to the spine.
They say that women with a straight spine are at a reproductive disadvantage because when they are pregnant they shift their mass forward. This puts a lot of pressure on the muscles and the lower end, so it can cause pain and increase the risk of injury.
Conversely, women with a curved spine have fewer problems shifting the center of their body, across their hips, during pregnancy, which leads to greater stability and reduces the risk of pain. Due to the appearance of the buttocks, this can be an indication of whether the lower end of the spine is bent or not, depending on how curved it is. This led the researchers to believe that men believe such women to be more attractive.
They presented two studies to support this.
In the first study, 102 college students were shown images that looked the same but had bum curves ranging from 26 to 61 degrees.
Lumbar curvature: a previously undiscovered standard of attractiveness
They found that the lumbar curve reached the optimal level at 45.5 degrees, so the models here were rated as more attractive.
The ratings for attractiveness decreased again when this level was exceeded. A spine that is bent too much can result in completely different health conditions, which is why it makes sense that the preference for the spine curve only exists to a certain extent.
The second study looked at whether men really choose based on their spine position or whether men just like big butts in general.
Lumbar curvature: a previously undiscovered standard of attractiveness
To explore this thesis, the scientists demonstrated a series of changes in female figure to 202 male college students, all of whom had the same butt size, but for different reasons. For example, the female figures with larger buttocks had larger gluteal muscles, more fatty tissue or a larger lumbar curvature.
The size of the buttocks was the same everywhere, in all variants, only the position was changed, depending on what the reason was. As has been suggested, men preferred women with curved loins, not women with larger glutes.
The data are only partially reliable because the study only considered college students from the United States who had to evaluate their attraction to digital, abstract body shapes, not actual women.
In the real world, attraction is a complex, multi-factor evaluation that goes beyond the mere evaluation of a single part of the body. The investigation does not clarify how much the curvature of the spine contributes to the attractiveness of a woman in relation to other physical or psychological characteristics.
Still, the research shows that men are attracted to curvy women, and that one of those curves that men subconsciously select is the back end of a woman.


)
would be better for the future--we hope that Moses will be able to experiment with navigation algorithms in the future.
This method returns a ray (not normalized) pointing from the given point to the nearest object. If there are no objects in the map, I'm not quite sure what this should do. Note that the ray that is returned does not point to the center of the closest object. It points to the closest occupied cell at the maximum tree-depth (maximum resolution).
Requirements for Predicate Mining
The main things we need here are:
 - an index listing all the cells containing EntityID E in their EntityStructure
 - an efficient method to find all entities that are within radius r of a certain point
Note that it's not important to exactly find all entities in the circle of radius r. Approximating the circle with squares fairly crudely is just fine.
Notes on opencog\spatial\
Class Entity holds a position and bounding box of an object in a virtual world.
Class SuperEntity holds a list of overlapping Entity 's. The edges of the resulting SuperEntity object consist only of the outside perimeter of the overlapped Entity 's. Methods rebuild() and splitEdges() are used to re-compute the outside edges of the combined Entity's outside edges. An exception will be thrown if attempting to create a SuperEntity with Entity's that don't overlap.
The outside perimeter of combined objects may be used for 2D navigation in a virtual world. See image of overlapping Entity's forming the SuperEntity (SuperEntity.png).
API for 3D Maps
Requirements for TangentBug
Dan will fill this in... later...
Requirements for Predicate Mining
As for 2D, the main things we need here are:
 - an index listing all the cells containing EntityID E in their EntityStructure (this should be organized by node depth, so that queries can be constrained to a given "resolution"/granularity).


Jan 27, 2020
The Hidden Dangers of Shadow Admins
*****By Rich Peckham, Sr. Solution Architect, Silverfort*****
Shadow Admin accounts are user accounts that have sensitive privileges – not because they are members of a privileged Active Directory (AD) group, but because they were inadvertently assigned permissions that can allow them to take over other privileged accounts, and leverage them to reach their target systems to compromise them.
If a Shadow Admin account is compromised, it can be very risky for the organization. After all, these user accounts can be used to compromise additional accounts and gain administrative privileges.
Yet identifying these accounts and restricting their access is not a trivial task.
A Deeper Look at Shadow Admins
A Shadow Admin is a user who is not a member of AD administrative groups like Domain Admins, Enterprise Admins, Schema Admins, Administrators, etc. Yet this user will have rights to some sort of administrative capabilities on an admin account, that enable this user to gain further administrative capabilities.
These administrative capabilities include:
 - Full Control Rights (user or group)
 - Write All Properties (on a group)
 - Reset Password (on a user)
 - All Extended Rights (on a user)
 - Change Permissions (user or group)
 - Write Member (on a group)
 - Write Owner (user or group)
 - The Actual Owner (user or group)
Additionally, any user who can take over and control a Shadow Admin of any level, would also be considered a shadow admin.
Let me explain with an example:
Let's say Bob is a Domain Admin (a member of the Domain Administrators group). This means that Bob has administrative access to Active Directory.
Figure 1: Bob is a member of the Domain Administrators Group
Alice is not a member of the Domain Administrators Group. However, Alice has the ability to reset Bob's password. Therefore, Alice can reset Bob's password, login as Bob and execute tasks that require Domain Admin privileges on his behalf. This makes Alice a "Shadow Admin".
Figure 2: Alice can reset Bob's password which makes her a "Shadow Admin"
Now, let's say Larry can reset Alice's password. This means that Larry can reset Alice's password, login as Alice, change Bob's password, then login as Bob and execute tasks that require Domain Admin privileges. This makes Larry a level 2 shadow admin.
Figure 3: Larry can reset Alice's password, which makes him a "Shadow Admin" as well
And It doesn't end with Larry. Who can reset Larry's password?
The process continues and an organization could, potentially, have many shadow admins.
Why and How Are Shadow Admins Created?
There are a few reasons why shadow admins exist in our networks:
 1. Human Error or Mismanagement of User Rights: Shadow Admins can be created by inexperienced administrators by mistake or because they did not fully understand the implications of direct privilege assignments. Although there are no malicious intents behind such shadow admin accounts, they can still present risk to the environment by allowing users unauthorized access to sensitive assets.
 2. Created for Temporary Use but Never Removed: Although this is considered a bad practice, in some cases IT administrators may grant accounts temporary privileges that turn the users into shadow admins, with the intention to remove these privileges at a later date. While this can solve immediate problems, these privileges are often left in place, leaving these accounts with unsupervised administrative privileges.
 3. Adversaries May Create Shadow Admins for Hiding Activities and Remaining Stealthy: Once admin privileges have been achieved, attackers can create a shadow admin accounts to allow persistence and to conceal their activities. These shadow admin accounts can then be used to hide malicious activities while that attacker remains stealthy.
Regardless of the reason behind their creation, shadow admins pose risk to the organization as they allow unauthorized individuals to perform activities that they shouldn't perform. If an adversary gains control over a shadow admin account, it can be used as an attack vector.
The fact that these accounts are not monitored or supervised means that not only you don't know you should restrict their access (you can't fix problems you don't know about), but it also means that unauthorized access and changes may go undetected. In some cases, such unauthorized access or changes may be detected too late – after sensitive data was leaked or after critical systems were compromised.
How Can I Determine Who My Shadow Admins Are?
Identifying shadow admins is a very challenging and complex problem. You first have to determine who your administrators are, i.e. all the users who belong the AD Groups that provide them administrative privileges. Some AD groups are obvious, like the Domain Admin group. But some groups are less obvious. We've seen that in many organizations, different administrative groups are created to support different business purposes. In some cases, you can even find nested groups. It's important to map out all the members of these groups. As we map out the group memberships, we have to take into consideration not only the user identities that show up in the member list, but also any user's Primary Group ID configurations.
Understanding the members of our administrative groups in AD is an important first step, but it's not enough for identifying all privileged accounts in the domain. This is because they do not include the shadow admins. To detect the shadow admins, you need to analyze the ACL permissions granted to each account.
Think You Can Analyze the ACLs? Think Again
As explained above, to detect shadow admins you need to analyze the ACL permission of each account in AD, to understand if the account has privileges over any administrative groups or individual admin accounts. This, in itself, is a very difficult, if not impossible, task for any human being.
If you are able to perform this analysis – it will provide you the first level of shadow admins.
But it's not enough – you now need to analyze all the ACLs again to understand who has privileges to change these first level shadow admins. And then again, and again, and again – this process needs to continue until all the levels of your shadow admins are detected.
And what if you found a shadow admin group? That complicates things even more.
The bottom line is that this important analysis can't be a manual task.
How Can Silverfort Help?
Silverfort periodically queries Active Directory to get the different ACLs of all the objects in the domain. It automatically identifies common admin groups. It then analyzes the ACLs in search of shadow admin users and groups that have equivalent privileges to the members of these admin groups – privileges that effectively make them shadow admin accounts/groups. It continues to analyze the ACLs to determine as many times as needed to identify all levels of shadow admin accounts and groups making sure you have full visibility into those potentially dangerous accounts.
This comprehensive list should then be reviewed by AD administrators to determine if the privileges or these shadow admin accounts and groups are legitimate or not, and if they should be restricted or supervised.
Figure 4: Silverfort automatically identifies shadow admin accounts that should be reviewed to determine if their privileges are legitimate or not
In addition, Silverfort continuously monitors and analyzes all the access requests across the domain. It considers shadow admins as high-risk accounts. Silverfort automatically identifies, in real-time, sensitive activities, like an attempt to reset a user password, and either alert on it, or require the user the validate his/her identity with multi-factor authentication (MFA) before allowing the password reset. This can prevent unauthorized changes to user accounts as well as any unauthorized access to sensitive assets in the network.
Figure 5: example of a Shadow Admin policy defined to request MFA verification before allowing the user to reset a password
Rich Peckham, Sr. Solution Architect, Silverfort
Rich is a Solution Architect at Silverfort. He has more than 20 years of experience with Active Directory and Cybersecurity. Prior to joining Silverfort, Rich was a Senior Infrastructure Systems Engineer with Facebook for 2 years. Prior to Facebook, Rich was with Microsoft for over 10 years as a Senior Premier Field Engineer for Active Directory and then as a Senior Service Engineer specializing in Active Directory Cybersecurity. Rich is also a Microsoft Certified Master in Active Directory on Windows Server 2008 and Windows Server 2008 R2 and a Microsoft Certified Solutions Master in Windows Server 2012.
If you would like to learn more about this – contact us to schedule a demo


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|Exploring Indiana's Historic Sites,|
Markers & Museums
South East Edition
County - Clark
Township Silver Creek
Area - 3.97 sq mi (10.28 km2)
Elevation - 486 ft
Population (2010) - 6,128
ZIP code - 47172
Area code(s) - 812 and 930
Located on US Route 31 and Interstate 64 just north of its intersection of Interstate 265, Sellersburg has access to I-64 at Exits 7 and 9 and I-265 at Exit 4 via Charlestown Road.
Laid out by Moses Sellers as Sellersburgh in 1846, however the name became Sellersburg over time. One of the many "railroad towns" that sprang up along rail lines in the middle of the Nineteenth Century, Sellersburg was platted on the route of the Jeffersonville, Madison & Indianapolis Railroad, which passed on the east side of the town. In addition to platting the town, Sellers also ran the first general store in the village. The building still exists on the north side of New Albany Street.
Moses Wilkerson Sellers (August 22, 1796 - Died June 1, 1868)
The son of Robert Sellers and Sarah Sellers, Moses was native to Burke, North Carolina. The family moved to Kentucky around 1800, and then migrated into the Indiana Territory around 1810. Sellers acquired the land that became Sellersburg, which he platted in 1846. He and Margaret Littell married and would have four children. Moses kept the first store in town and served as the first postmaster when the post office was established in 1854.
Moses and his wife are interred in the Sellersburg cemetery.
Indiana State Road 60
Wilkerson Park and Mosley Park in Sellersburg, Speed Park and Silver Creek Township Park
For more information about dining, lodging and shopping in Sellersburg, contact:
Town of Sellersburg
316 E Utica St
Sellersburg, IN 47172


Legacy : One Family, a Cup of Tea and the Company that Took On the World
Harding, Thomas
Format: Hardback
Publisher: Cornerstone (William Heinemann Ltd)
Published: August 29, 2019
ISBN: 9781785150883
592 pages
A panoramic new history of modern Britain, as told through the story of one extraordinary family, and one groundbreaking company.
In the early 1800s, Lehmann Gluckstein and his family escaped the pogroms of Eastern Europe and made their way to Whitechapel in London's East End. There, starting with nothing, they worked tirelessly to pull themselves out of poverty, creating a small tobacco factory that grew to become the largest catering company in the world: J. Lyons.
For over a century, Lyons was on every high street, in every home, in every coffee and teacup. It was an ascent from rags to riches in the face of many obstacles: poverty, hatred and anti-Semitism stood between this poor immigrant family and the British Dream. Legacy charts the rise and fall of one of the most influential dynasties in British history through the lives of five astonishing generations.


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Positive Psychology & UT's Undergraduate Experience
UT Instructor and ClassroomUndergraduate Student Success through a Strengths-Mindset Lens
Higher education is in transition as technology, finances, changing demographics of our country, and enrollment challenges are at the forefront of our competitive landscape. As such, to remain competitive, we must move beyond merely recruiting and retaining student scholars through traditional measures. Generation Z wants morethey are questioning the value of higher education.
UT can set itself apart by creating the conditions in which each undergraduate student scholar feels empowered to thrive. We propose developing a positive psychology alternative to most student success programs that often operate from a deficit-oriented perspective with little focus on college student scholars' unique strengths that they bring to post-secondary education. Empirical evidence supporting positive psychology in higher education continues to emerge as researchers recognize the relation between a person's wellbeing and personal and professional goals and dreams. Consequently, we believe strengthening our curricular and co-curricular experiences through a strength-based perspective will be particularly useful to prepare UT's undergraduate student scholars to change the world, not just change with it.
Working closely with the campus community, we propose three preliminary strategic pathways to transform the UT undergraduate experience through a personalized strength and talent-based initiative
After reviewing popular positive psychology theories such as Growth Mindset, Hope, Grit, and Self-Determination, each with its own sets of opportunities and limitations, we decided to develop a conceptual framework that best fits the Volunteer spirit. A spirit that values courage, creativity, flexibility, and hard work. As such, UT's positive psychology framework is defined in three ways.
We believe that our student scholars can learn at high levels. We share this message often and consistently.
High Expectations
We challenge every student scholar intellectually. We believe expectations are attainable.
Every Story Matters
We will provide the structures necessary for every student scholar to reach their potential.
Preliminary Strategic Pathways
1. Person. Maximize each studentinnate talents and build the relationships necessary to:
 1. Meet their academic, career, and leadership potential
 2. Increase self-awareness and learn ways to enact new knowledge of self
 3. Understandappreciate, and draw upon the talents of others
2. InfrastructureEvaluate and reimagine academic and social support infrastructure to facilitate student success, high-resiliency, and self-efficacy.
3. CommunityReimagine the curricular and co-curricular efforts that elevate transformational, strengths-based, active-learning deliberately woven throughout the UT undergraduate experience across time and diverse experiences.
Preliminary Metrics for Success
 1. Students strengths awareness will positively impact fall and spring GPA and academic persistence through students' engagement, heightened awareness, and academic self-efficacy.
 2. Increased knowledge of self and appreciation of others' perspectives will increase students' ability to identify and solve problems and communicate with others more effectively.
 3. Increased engagement in Vol Success teams, student organizations, leadership experiences, and co-curricular activities (such as co-op experiences, internships, undergraduate research)
 4. Student scholars will respond to academic belongingness survey questions at the following rates:
 1. I feel a part of the UT community-70% agree or strongly agree
 2. I believe that I will excel academically at UT—70% agree or strongly agree
 3. I would rate my current overall stress level since enrolling at UT this fall is—70% average or less than average stress.
Strengths-Mindset Consultants & Advisory Council
Partnership with Teaching & Learning Innovation
To support the university's Strengths-Mindset initiative, Student Success is partnering with Teaching & Learning Innovation to launch two faculty-specific programs.
TLI's Course Redesign Institute
A virtual three-week event during the May 2021 mini term, the Course Redesign Institute will bring together faculty from across UT to share ideas from their own pedagogies and participate in a focused track to redesign one of their courses.
TLI's Certificate Curriculum
The six evidence-based certificates were developed by TLI staff and piloted by select UT faculty and staff and will allow participants to pursue beneficial, in-depth self-paced learning experiences and professional development.
What Is Deficit Thinking? An Analysis of Conceptualizations of Deficit Thinking and Implications for Scholarly Research
Impact of Positive Psychology
Towards a Positive University
Challenging the Deficit Mindset Handout
In the News