Miracle-dz/UniFAR-FTUs huggingface dataset:
ECONOMIC ANALYSIS OF WORKPLACE INJURIES Kenneth Noble Fortson A DISSERTATION PRESENTED TO THE FACULTY OF PRINCETON UNIVERSITY IN CANDIDACY FOR THE DEGREE OF DOCTOR OF PHILOSOPHY RECOMMENDED FOR ACCEPTANCE BY THE DEPARTMENT OF ECONOMICS

This dissertation comprises three chapters, each of which explores a different topic related to work injuries and the social insurance designed for such injuries, workers’ compensation. The first chapter examines workers’ compensation in conjunction with an alternative means of compensating injured workers: the tort system. This chapter uses data from Texas—the only state not mandating workers’ compensation—to determine how benefits paid under workers’ compensation compare to awards injured workers would receive in the tort system. Average payments are substantially higher in the tort system, but an examination of the entire distribution suggests that only workers who have abnormally low levels of risk aversion would prefer the distribution of awards paid out in the tort system to the distribution of workers’ compensation benefits. Chapter 2 considers the consumption smoothing benefits of workers’ compensation. Using data from the Panel Study of Income Dynamics, I examine how benefit generosity affects the drop in household food consumption following a work injury. The results suggest that in the absence of workers’ compensation, consumption would fall by a substantially larger amount. However, further analysis suggests that current benefit levels are higher than the theoretically optimal level. The final chapter uses data on workers’ compensation claims from Texas together with Current Population Survey worker schedule data to examine how the workplace injury rate varies throughout the day. The injury hazard is substantially higher at night than during regular daytime work hours. As explanations for this result, I consider several factors that are correlated with night work but not inherent features of it. These include compositional changes in industry and age, as well as shift duration changes that would create artificial
Searching for exoplanets by HEPS II. detecting earth-like planets in habitable zone around planet hosts within 30 pc

HEPS (Habitable ExoPlanet Survey) is a planning astrometry satellite that aims to find Earth-like planets in the solar neighbourhood. In this paper, we selected 140 planet harboring stars within 30 pc of the solar system to be potential targets for HEPS. We calculate the detection probability of the planet in habitable zone (HZ) for each system using the simulated data of astrometry measurements. For those host stars without planets in HZ, we inject an additional planet of 10 M⊕ in their HZs and check the stability of the systems. Considering five observation modes of different sampling cadence and total observation time, we obtain a table containing the total detection probability of the planets in HZs for all of the 140 selected systems. This paper provides a potential ranked list of target stars for HEPS, or other astrometric mission to detect Earth-like planets in the future. We also calculate an empirical fitted expression of the detection probability as a function of both sampling cadence and total observation time. We conclude a quantitative method to estimate the detection probability for certain planet hosts and observation modes via the empirical expression. We show the minimum requirements of both sampling cadence and observation time for Proxima Centauri, HD 189733 and HD 102365, if the detection probability of habitable-zone planets of these three systems needs to be 90%.
Raman-Enhanced Spectroscopy (RESpect) Probe for Childhood Non-Hodgkin Lymphoma

Raman-enhanced spectroscopy (RESpect) probe, which enhances Raman spectroscopy technology through a portable fiber-optic device, characterizes tissues and cells by identifying molecular chemical composition showing distinct differences/similarities for potential tumor markers or diagnosis. In a feasibility study with the ultimate objective to translate the technology to the clinic, a panel of pediatric non-Hodgkin lymphoma tissues and non-malignant specimens had RS analyses compared between standard Raman spectroscopy microscope instrument and RESpect probe. Cryopreserved tissues were mounted on front-coated aluminum mirror slides and analyzed by standard Raman spectroscopy and RESpect probe. Principal Component Analysis revealed similarities between non-Hodgkin lymphoma subtypes but not follicular hyperplasia. Standard Raman spectroscopy and RESpect probe fingerprint comparisons demonstrated comparable primary peaks. Raman spectroscopic fingerprints and peaks of pediatric non-Hodgkin lymphoma subtypes and follicular hyperplasia provided novel avenues to pursue diagnostic approaches and identify potential new therapeutic targets. The information could inform new insights into molecular cellular pathogenesis. Translating Raman spectroscopy technology by using the RESpect probe as a potential point-of-care screening instrument has the potential to change the paradigm of screening for cancer as an initial step to determine when a definitive tissue biopsy would be necessary.
Integrating openstreetmap data and sentinel-2 Imagery for classifying and monitoring informal settlements

The identification and monitoring of informal settlements in urban areas is an important step in developing and implementing pro-poor urban policies. Understanding when, where and who lives inside informal settlements is critical to efforts to improve their resilience. This study aims at integrating OSM data and sentinel-2 imagery for classifying and monitoring the growth of informal settlements methods to map informal areas in Kampala (Uganda) and Dar es Salaam (Tanzania) and to monitor their growth in Kampala. Three building feature characteristics of size, shape and Distance to nearest Neighbour were derived and used to cluster and classify informal areas using Hotspot Cluster analysis and ML approach on OSM buildings data. The resultant informal regions in Kampala were used with Sentinel-2 image tiles to investigate the spatiotemporal changes in informal areas using Convolutional Neural Networks (CNNs). Results from Optimized Hot Spot Analysis and Random Forest Classification show that Informal regions can be mapped based on building outline characteristics. An accuracy of 90.3% was achieved when an optimally trained CNN was executed on a test set of 2019 satellite image tiles. Predictions of informality from new datasets for the years 2016 and 2017 provided promising results on combining different open source geospatial datasets to identify, classify and monitor informal settlements.
The effect of branding on consumer choice through blind and non-blind taste tests

Brand names have an effect on consumers’ evaluation of a product. Most studies deal with a single measure of this brand effect such as choice, preference or perceptions about the product. Few have investigated how branding affects consumers’ choice process when sensory-based factors are involved, especially after tasting the product. This study examines the role of branding on consumer behavior through blind and non-blind taste tests of wine. Using a conceptual value model and a probabilistic choice model the author analyzes consumers’ choice process between different wines. Results indicate that there is a differential brand effect on the saliency of sensory-based attributes in this choice process. More reputable brands have a positive effect on the saliency of some of these attributes.
Investigation on a quantum communication phenomenon between subatomic properties of substances by quantum eraser pattern quantification

Abstract. The proposed idea is concerned with the investigation of the probability of subatomic quantum communication between chemical substances by use of “interferometric quantum eraser” pattern analysis, which would be the first step toward the further comprehensive study of similar natural communications between the medications and diseased organic tissues. The hypothesis is based on the idea that subatomic particles, such as photons, attempt to access or gain complementary information causing their “wave–particle duality” shifting, which is used to observe their time-sequenced activities. Such information would probably be provided by subatomic quantum communication action demonstrated between the substances in a chemical reaction. Similarly, the medications may have curing effects on a diseased organic tissue after such “natural” communication by which complementary information is transferred from the subatomic properties of a chemical substance to the diseased organic tissue at the same level for the treatment.
Biology-Inspired Microphysiological Systems to Advance Patient Benefit and Animal Welfare in Drug Development

The first microfluidic microphysiological systems (MPS) entered the academic scene more than 15 years ago and were considered an enabling technology to human in vitro (patho)biology and, therefore, to provide alternative approaches to laboratory animals in pharmaceutical drug development and academic research. Currently, the field generates more than a thousand scientific publications per year. Despite the MPS hype in academia and by platform providers, which say this technology is about to reshape the entire in vitro culture landscape in basic and applied research, MPS approaches neither have been widely adopted by the pharmaceutical industry yet nor have they reached regulated drug authorization processes. Here, 46 leading international experts from all stakeholder groups - academia, MPS supplier industry, pharmaceutical and consumer products industries, and leading regulatory agencies - analyzed challenges and hurdles along the MPS-based assay life cycle in the second workshop of its kind in June 2019. The main findings were that the level of qualification of MPS-based assays for a given context of use and communication gaps between stakeholders are the major challenges slowing industrial adoption by end users, which in turn is causing a regulatory acceptance dilemma. This report elaborates on these findings and proposes solutions by providing recommendations and a roadmap towards regulatory acceptance of MPS-based models, which will benefit patients and further reduce laboratory animal use in drug development. Finally, the potential of MPS-based human disease models to feed back into laboratory animal replacement in basic life science research is discussed.
Molecular Mechanisms Linking Oxidative Stress and Diabetes Mellitus

Type 2 diabetes mellitus (T2DM) is the most prevalent metabolic disorder characterized by chronic hyperglycemia and an inadequate response to circulatory insulin by peripheral tissues resulting in insulin resistance. Insulin resistance has a complex pathophysiology, and it is contributed to by multiple factors including oxidative stress. Oxidative stress refers to an imbalance between free radical production and the antioxidant system leading to a reduction of peripheral insulin sensitivity and contributing to the development of T2DM via several molecular mechanisms. In this review, we present the molecular mechanisms by which the oxidative milieu contributes to the pathophysiology of insulin resistance and diabetes mellitus.
Parametric Properties in Chaotic Neural Networks

In the framework of reservoir computing a randomly constructed recurrent neural network (RNN) is used to function as a reservoir containing input history and its many random transformations. Output units transform whatever is inside this reservoir into a meaningful output. However, it is diﬃcult to appropriately construct a random reservoir such that the complete model is able to learn and perform a certain task. A model’s performance depends on two things: the parameters of the RNN and the task’s complexity. The used network parameters need to be ﬁne-tuned to the task at hand for achieving the best results. First we propose and investigate several measures of quantifying the diﬃculty of the chosen to-be-trained pattern. The used patters are visual. Hereafter we investigate RNN performance after FORCE learning on tasks of diﬀerent complexities, selected with the apparently most appropriate of these measures, and we investigate how an RNN’s parameters determine its eventual capability to learn. Under investigation are reservoir size, connectivity and weight scaling. All ﬁndings were done through simulation studies, with additional theoretical explanations and references to relevant literature.
Creating artificial moral agents for surveillance robots that can identify care and harm

As artiﬁcial intelligence develops, robots are becoming progressively more intelligent, autonomous and intertwined with societal life. Currently an important question is how we can make these robots apply morality and ethics. One useful application for artiﬁcial moral agents are surveillance robots, which could beneﬁt from a human-like moral judgement. Therefore we investigate the possibility of creating an artiﬁcial moral agent (AMA) that can distinguish between care and harm. Care and harm are looked at from the perspective of the Moral Foundations Theory. We attempt to solve the problem by working within the bottom-up approach to designing artiﬁcial moral agents. First a survey-based approach is used to gather human judgements on diﬀerent moral problems related to Care/harm. Then a multi-layer perceptron is trained to learn the underlying moral function in the survey data. Finally parameter choices for the network are determined that yield the highest performance when classifying new moral problems. Additionally we also take a look at the hidden units of the trained network. Based on the results we discuss the limitations of a survey-based approach, the bottom-up approach and the ethical and philosophical implications.
A novel approach to optimize harvesting losses using modified engine operated reaper.

Government of Pakistan is imposing heavy taxes on importing the farm machinery which reduces the adoption of advance machinery in Pakistan. Agriculture sector is the back bone of Pakistan economy with 22 million hectares cropped area. Agricultural sector contributes 24 % share of gross domestic production (GDP) and provides livelihood for 48 % of labor force of this country (Zaheer, 2013; Roohi, 2007). According to FAO (2004) report, about 67% population of Pakistan is residing in rural areas and their income is attached with agriculture sector. After fulfilling the national demand of food and fiber agriculture sector is also contributing towards foreign export (Raza et al., 2012). Seeds, fertilizer, irrigation and farm mechanization are the basic inputs for any crop production system. Farm mechanization has one of the important roles from these inputs (Roohi, 2007). Rice and wheat crops are commonly harvested with traditional sickle in Pakistan, which is time consuming and laborious operation. Furthermore, labor shortage is another issue. Majority of villager’s labor community is moving towards cities and overseas to get better jobs in industrial sectors (Gill, 1989). At the time of harvesting (peak season), farmers have to pay a large amount of money for harvesting due to labor shortage (Tahir, 2003). Therefore, farmers are inclined towards harvesting machinery. Nowadays front mounted reaper is very common in Pakistan for wheat and rice harvesting, but brassica is harvested manually. Development of low-cost harvesting machines not only overcomes the shortage of labor and the on-time operations but also facilitating timely sowing and the multi cropping rotation in the country. High cost of machinery and small land holding (5-10 acres) are the major constrains for adoption of small reapers and advanced harvesting machinery in Pakistan (Tahir, 2003; Raza et al., 2018). Many researchers have evaluated the cost of mechanical harvesting with manual harvesting (Alizadeh et al., 2007; Chavan et al., 2015; Murumkar et al., 2014; Morad et al., 2007). Chavan et al. (2015) reported that sickle harvesting is more laborious than mechanical harvesting with reaper. Mechanical harvesting needs 50 % less labor than manual harvesting in wheat crop. They also suggested that harvesting losses can be reduced 50% by using mechanical harvester, compared with manual harvesting. Murumkar et al. (2014) evaluated engine operated vertical conveyor reaper. Field Pak. J. Agri. Sci., Vol. 57(2), 319-326 : 2020 ISSN (Print) 0552-9034, ISSN (Online) 2076-0906 DOI: 10.21162/PAKJAS/20.9469 http://www.pakjas. com.pk
Describing and Predicting Early Reviewers for Efficient Product Marketing on E -Commerce Sites

: Now a days, the decision of purchasing an online product is relying on the reviews it get . This paper emphasizes an marketing a product based on its reviews . These reviews are mainly grouped into three types they are premature reviews, matured reviews and late responding reviews. However, this division is done on the basis of time span of a product as it has starting stage, central stage and finishing stage in its sale process. Comparison of these reviews is done on real time E commerce websites for example amazon and yelp. It is known that the impact of premature reviews is high on the customers when compared with matured and late responding reviews. As the product is newly available in the market and based on the reviews the scope of the project in sustaining will be decided. The product is judged as a good product/bad product by depending on its attributes like rating ,number of positive responses, Number of product sold in a specific time and so on.The implementation of this process is done by collecting the sample datasets of reviews on e commerce websites.
Effect of Metformin For Decreasing Proliferative Marker in Women with Endometrial Cancer: A Randomized Double-Blind Placebo-Controlled Trial

Objective: To compare the Ki-67 index of endometrial cancer cells before and after treatment between the metformin and placebo group in women with endometrial cancer (EC). Methods: This study was a randomized, double-blind, placebo-controlled trial conducting in non-diabetic women who diagnosed with endometrioid EC and had a schedule for elective surgical staging at Rajavithi Hospital between August 2018 and June 2019. Tissue specimens were obtained via endometrial curettage at the time of initial diagnosis (pre-treatment) and hysterectomy (post-treatment) to assess the value of the Ki-67 index by immunochemistry. Patients were randomly assigned into 2 groups: metformin and placebo group. Metformin 850 mg or placebo 1 tab were administered once daily for at least 7 days, starting on the first morning after recruitment until one day before surgery. Baseline characteristics (e.g., age, body mass index, co-morbidities) including surgical and pathological characteristics were recorded. The metabolic effect of metformin was also evaluated by a recording of fasting blood sugar, HbA1C and potential adverse events including nausea, vomiting, dizziness, and hypoglycemic symptom. Results: A total of 49 EC patients were included in this study. Twenty-five patients were assigned to the metformin group and 24 patients were assigned to the placebo group. Baseline demographic, surgical, and pathological characteristics between the 2 groups were similar. Metformin significantly changed the Ki-67 index relative to placebo, with a mean decrease of 23.3% (p=0.001) and a mean proportional decrease of 39.1% (p=0.006) before and after treatment. Additionally, no significant differences were detected in metabolic effects and adverse events between the metformin and the placebo groups. Conclusion: Short-term treatment with an oral metformin significantly reduced a proliferative marker Ki-67 index in women with endometrioid EC awaiting surgical staging. This study supports the biological effect of metformin in EC and potential applications in the adjuvant treatment in EC patients.
THERMAL CONTROL SYSTEM FOR THE MAIUS ATOM INTERFEROMETER PAYLOADS ON A VSB-30 SOUNDING ROCKET

The QUANTUS consortium started activities to perform atom interferometry on a sounding rocket in the year 2011. Three sounding rocket missions (MAIUS-1 to MAIUS-3) have been planned to demonstrate BoseEinstein condensation and atom interferometry with rubidium and potassium atoms. Two different payloads MAIUS-A and MAIUS-B have been designed qualified and integrated in the time frame from 2011 until 2020. The MAIUS-A scientific payload is capable of performing single species experiment with rubidium only and was used as a technology demonstrator in the MAIUS-1 mission. The MAIUS-1 mission was conducted in fall 2016. On the 23 of January 2017 the payload MAIUS-A was launched on a VSB-30 sounding rocket. The payload created the first BoseEinstein condensate (BEC) and performed atom interferometry aboard a sounding rocket with rubidium 87 atoms [1]. Its successor MAIUS-B will be able to perform simultaneous experiments with Bose-Einstein Condensates of Rubidium-87 and Potassium-41 atoms. In order to achieve this ambitious scientific goal the experiments use various sensitive instruments imposing strong requirements on the thermal and mechanical design of the scientific payload. This paper gives a summary of the MAIUS-A and MAIUS-B scientific payload design with a focus on the thermal control system. Furthermore, the design of the GSE and the water cooling umbilicals is presented. The paper closes with an outlook on future atom interferometry payloads on sounding rockets and beyond.
Towards a 3-DOF Mobile and Self-Reconfigurable Modular Robot

This paper presents early work on a new selfreconfigurable modular robot capable of independent locomotion. The prototype features three degrees of freedom, creating a spherical joint, and houses four connection surfaces allowing it to form arbitrary cubic lattice structures. The design is built around the recent HiGen connector, allowing for single-sided disconnect and enabling modules to rotate freely in place within a lattice position.
Identifying the News Topics Prevalent in Social Media and provide Ranking using SociRank Framework

To predict interactions between social media and traditional news streams is becoming increasingly relevant for a variety of applications, including: understanding the underlying factors that drive the evolution of data sources, tracking the triggers behind events, and discovering emerging trends. Researchers have developed such interactions by examining volume changes or information diffusions, however, most of them ignore the semantical and topical relationships between news and social media data. Our work is the first attempt to study how news influences social media, or inversely, based on topical knowledge. We introduce a hierarchical Bayesian model that jointly models the news and social media topics and their interactions. We show that our proposed model can capture distinct topics for individual datasets as well as discover the topic influences among multiple datasets. By applying our model to large sets of news and tweets, we demonstrate its significant improvement over baseline methods and explore its power in the discovery of interesting patterns for real world cases .