ethicalabs/Research-Intent-Collab huggingface dataset:
Gender, Peace and Security : Examining the assumed benefits of involving men and work on transforming masculinities in the implementation of United Nations Security Council resolutions 1325 and 1820

My aim of this this thesis is to examine the assumed benefits of involving men and work on transforming masculinities when implementing the WPS (Women Peace and Security) agenda focusing on UNSCR (United Nations Security Council Resolutions) 1325 and 1820, in peacebuilding efforts and peacekeeping missions. The empirical findings derives from four semi- structured interviews with informants involved in implementing a masculinity perspective in UNSCR 1325, or informants that had conducted research of this area. A document analysis was also conducted to complement with the interviews and to dig deeper into how a focus on masculinities could be implemented. The document analysis consisted of training material from the Swedish Police force training on UNSCR 1325 for Swedish and international personnel going to UN Peacekeeping missions. The data was analysed with the theories radical feminism and R.W Connell’s masculinity theory. The thesis result indicate that the assumed benefits are multiple. The key reason to include men and to work on transforming masculinities could be that it would mean to take a holistic gender perspective, and to focus on the gendered drivers of conflict as well as the impacts of conflict.
EvoSort: a genetic-algorithm-based adaptive parallel sorting framework for large-scale high performance computing

We present EvoSort, a general-purpose adaptive parallel parallel sorting framework accessible at the Python level. EvoSort employs a Genetic Algorithm (GA) to automatically discover and refine critical parameters, including insertion sort thresholds and algorithm selection (e.g., versus LSD radix sort). By adapting continuously to input data and system architecture, EvoSort provides a drop-in replacement for standard Python routines like NumPy and Pandas. Experiments up to10 billion elements across nine data distributions and two hardware platforms demonstrate that EvoSort consistently outperforms competing methods. Results show speedups of up to 225x, exemplifying a powerful auto-tuning solution for large-scale data processing.
Collaborative Filtering with Data Classification : A Combined Approach to Hotel Recommendation Systems

Recommendation systems aim to help users make decisions more efficiently. The most widely used method in recommendation systems is collaborative filtering, of which, a critical step is to analyze a user's preferences and make recommendations of products or services based on similarity analysis with other users' ratings. However, collaborative filtering is less usable for recommendation facing the "cold start" problem, i.e. few comments being given to products or services. To tackle this problem, we propose an improved method that combines collaborative filtering and data classification. We use hotel recommendation data to test the proposed method. The accuracy of the recommendation is determined by the rankings. Evaluations regarding the accuracies of Top-3 and Top-10 recommendation lists using the 10-fold cross-validation method and ROC curves are conducted. The results show that the Top-3 hotel recommendation list proposed by the combined method has the superiority of the recommendation performance than the Top-10 list under the cold start condition in most of the times.
Application of a Doehlert experimental design to the optimization of microbial degradation of crude oil in sea water by continuous culture

Crude oil was degraded by a mixed bacterial community grown in continuous culture on sea water. The fermentation process included an emulsification step prior to the introduction of the substrate in the reactor, with external cell recycling by a tangential-flow filtration system. Optimization of the fermentation technique was achieved by using the surface response methodology (Doehlert experimental design). Besides reducing the number of experiments, this approach allowed optimal experimental conditions to be chosen, for the particular goal: percent degradation of crude oil (80%), biomass (7.6 g·l-1) and degradation rate (0.73 g·l-1·h-1). This biodegradation process could be used as a tool to fight against pollutions by petroleum products.
Improved genetic algorithm with two-level multipoint approximation for complex frame structural optimization

Abstract                In this paper, an improved structural topology and sizing optimization method is developed for the fast and efficient engineering design of complex frame structures where beam elements are mainly used in the structures. Discrete and continuous variables are included that the elimination or existence of beam elements are treated as discrete variables (0,1), and the continuous sizing variables of beam cross sections are considered to be continuous variables. To solve the mixed variable problem, the paper introduces a two-level multipoint approximation strategy (TMA). The first-level approximate problem is established by using the branched multipoint approximate function, which includes both two types of variables. Genetic algorithm (GA) is used to determine the absence or presence of beam members. The second-level approximate problem that only involving retained continuous size variables is made on this basis, which uses Taylor expansion and dual methods to solve the inner layer continuous optimization problem. Meanwhile, a strategy of adding a new complementary design point is adopted to expend the search scopes and improve the precision. Temporal deletion techniques are used to temporarily remove redundant constraints and local vibration modes processing techniques are used for continuum topology optimization under frequency constraints. Several representative examples are investigated to validate the effectiveness of the improved method.
WITHDRAWN: Evaluation of occupational stress management for improving performance and productivity at workplaces by monitoring the health, well-being of workers

Abstract   Competence lack, inadequate social support at work leads to the inability of workers since they are suffering from occupational stress. This will cause distress, burnout or psychosomatic difficulties, decreases in quality of life and service provision. Some of them may connect to work in an individual's personal life, both as managers, recognize stressors in their department, and respond on a departmental basis or individually. Many workers say that their employee utilization monitoring is not sufficient until computer counting involves. In addition, the systems are associated with higher stress, health hazards, and work unhappiness among supervised personnel. Monitoring these problems can increase employee awareness of personal productivity, providing performance information more promptly and frequently. Interventions are based on an examination of the variables that impact the performance of health workers. The article for employee stress management and health monitoring using information technology (SMHM-IT) gives better working conditions, motivation, retention, etc. Evaluation of occupational risks is a framework introduced to manage health and safety implications associated with preventative measures for improving and protecting the highest physical, social, or emotional working skills. Statistical data analysis is introduced to compare a medical specialty which includes analysis of employee's details. Results are compared with assessments shows that architecture offers successful in-time accessibility of performance 98.12% is achieved.
Measurement properties of personality traits: Social desirability, dimensionality, and assessment of correlates

Personality traits can be measured with reasonable dependability and are related to important real-life outcomes. On the other hand, there remain several important questions in relation to personality measurement. In this symposium, we investigate three interrelated issues of interpretation and measurement properties of personality traits. The first two presentations are about social desirability: Konstabel addresses the question of why do people consider a trait desirable and which factors influence these judgements; Lonnqvist shows how failure in an application situation may accentuate socially desirable responding in subsequent testing. Booth and Johnson investigate the degree to which Multidimensional Personality Questionnaire scales cover different trait-standings and whether this degree can be improved by modifying the questionnaire. Finally, Mottus introduces a novel method of testing the specificity of trait-outcome relationships.
Integrating Social Science into Managing Herbicide-Resistant Weeds and Associated Environmental Impacts

Controlling herbicide resistance (HR) and its associated environmental risks is impossible without integrating social and economic science with biophysical and technology aspects. Herbicide resistance is a dynamically complex and ill-structured problem involving coupled natural–human systems that defy management approaches based on simple scientific and technology applications. The existence of mobile herbicide resistance and/or herbicide tolerance traits add complexity because susceptibility to the herbicide is a resource open to all farmers, impacting the weed population. Weed scientists have extensively researched the biophysical aspects and grower perceptions of HR. They also recognize that the “tragedy of the commons” can appear when herbicide resistance is mobile across farms. However, the human structures and processes, especially private and public institutions that influence individual and group decisions about HR, have received little analysis. To start filling that gap, we discuss an integrative management approach to sustainable weed control that addresses the social complexity of farm heterogeneity. For example, the need for a private or public collective mechanism becomes apparent to address common-pool resource (CPR) aspects when one farmer's weed control actions influence their neighbors' situations. In such conditions, sole reliance on education, technical assistance, and other incentives aimed at changing individual grower behavior likely will fail to stem the advance of HR. Social science theories can be used to enrich the understanding of human interaction with the biophysical environment and identify key actors and social change processes influencing those interactions in the case of HR. The short-run economic advantages of herbicides such as glyphosate work against social change to address HR, including the development of collective actions when mobile HR conditions exist. We discuss seven design principles that can improve the efficacy and cost of such collective approaches and draw insights from CPR approaches outside of HR.
The Missing Shield: Examining the Absence of Anti-Retaliation Laws in India's Whistleblower Protection Framework

The increasing cases exposing fraud, corruption, and other forms of misconduct are highlighting a need for a person who plays crucial role in promoting transparency, accountability and ethical conduct within corporate organizations by reporting such cases. And such individuals are called as whistleblowers. For sound corporate governance whistle blowers are important and hence those are to be protected. In India, the legal framework governing whistle blower protection are Whistle Blowers Protection Act, 2014, the Companies Act, 2013, and various regulatory mechanisms established by the Securities and Exchange Board of India (SEBI). These frameworks mainly gives protection of whistleblowers and punishment of person disclosing the identity of whistle blower and, hence it does not provide punishment against individuals, who retaliate whistleblowers through acts such as harassment, demotion, transfer, discrimination, or wrongful termination. This paper seeks to critically assess particularly emphasizing on the absence of direct penalties for retaliators under Indian law. And also analyses the limitations of the current legal framework and compares it with international legal frameworks of various jurisdictions such as the United States, the United Kingdom, and the European Union, where stringent anti-retaliation measures and enforcement mechanisms exist. It concludes by recommending legislative and policy reforms for strengthening the whistle blower protection and enhance the effectiveness of corporate governance in India.
Investigating the variance increase of readout error mitigation through classical bit-flip correction on IBM and Rigetti quantum computers

11 pages, 5 figures, Proceedings of the 38th International Symposium on Lattice Field Theory, 26th-30th July 2021, Zoom/Gather@Massachusetts Institute of Technology
CYP450-mediated drug-drug interactions: towards prediction and informed dose recommendation using physiologically-based pharmacokinetic (PBPK) modeling

Une des principales sources de la variabilité de réponse aux médicaments est la variabilité pharmacocinétique pour laquelle les interactions médicamenteuses constituent une des explications les plus fréquentes. Ce travail de thèse consiste en grand partie en la conception et la réalisation de deux études cliniques dans lesquelles les interactions entre les médicaments antiplaquettaires clopidogrel, prasugrel, ticagrelor et le ritonavir, un médicament couramment prescrit chez des patients infectés par le VIH, ont été évaluées. Pour la première fois, le technique de modélisation pharmacocinétique basée sur la physiologie, appelée PBPK, a été utilisé afin de prédire l'ampleur de ces interactions. Il s'agit d'une méthode innovante et prometteuse de prédiction du devenir du médicament dans le corps humain sur la base des données in vitro et in vivo du médicament ainsi que des caractéristiques physiopathologiques du sujet virtuel. Une adaptation de dose permettra de réduire les conséquences cliniques de ces interactions.
Protection of agricultural land in realizing sustainable food security

Abstract                Protection of agricultural land in realizing sustainable food security Merauke as one of the regencies that has large agricultural land often experiences crop failure due to climate change which can no longer be predicted. If there is a long dry season, agricultural land will become dry because Merauke has agricultural land that relies on rain, and vice versa, if the rainfall is high, it will flood so that agricultural land is damaged. The government’s efforts to realize food security are also threatened along with the widespread conversion of food agricultural land to non-agricultural food which is increasing every year. The research used in this research is normative legal research and empirical legal research The PLPPB Law regulates the protection of agricultural land to ensure national food availability and anticipate changing times and land use needs by various sectors. Efforts to overcome the problem of conversion of food agricultural land are by providing protection and empowerment as well as providing assistance to farmers such as providing seeds, providing free fertilizer, and tools that can be used to expedite the processing of agricultural land and also harvest equipment. Providing cooperatives to accommodate harvests at prices in line with market prices.
Future Educational Technology with Big Data and Learning Analytics

In the recent years, big data and learning analytics have been emerging as fast-growing research fields. The application of these emerging research areas is gradually addressing the contemporary challenges of school and university education. Tracing out the information regarding students' misconceptions and dropping-out probabilities from the courses at the right instant of time, development of detectors of a range of educational importance and achieving the highest level of quality in the higher education are becoming more challenging. Moreover, providing well timed and the best suitable solutions to the students at-risk are even more strenuous. In this concept paper, we aim to address these contemporary challenges of school and the university education and their probable solutions by utilizing our research experiences of automated assessment, immediate feedback, learning analytics and the IT technologies. Solving such problems by knowing the history of students' activities, submissions, and the performances data is possible. The identification of students' misconceptions during the learning process, examining behavioral patterns and significant trends by efficiently aggregating and correlating the massive data, improving the state-of-the-art skills in creative thinking and innovation, and detecting the drop-outs on-time are highlighted in this article. We are aiming at extracting such knowledge so that adaptive and personalized learning will become a part of the current education system. Not only the available algorithm of supervised learning methods such as support vector machine, neural network, decision trees, discriminant analysis, and nearest neighborhood method but also new engineering and distillation of relevant data features can be carried out to solve these educational challenges.
Changes in health insurance coverage and health care access as teens with disabilities transition to adulthood

Pre-ACA, less than 6% of children with disabilities were uninsured, compared with more than 42% of young adults with disabilities ages 19-25. Individuals with disabilities face greater barriers to health care access upon transition into adulthood.We examined whether the ACA dependent coverage provision and Medicaid expansion improved the transition from pediatric to adult healthcare systems by reducing the gap in insurance coverage and access to care between teens and young adults with disabilities.Using cross-sectional data from the 2006-2009 and 2011-2015 National Health Interview Survey (n = 10,136), we estimate the change in insurance coverage and access to care from pre-to post-ACA time periods for teens (ages 13-18) and young adults (ages 19-25) with disabilities in a difference-in-differences model. We completed the same analyses for these groups in Medicaid expansion and non-expansion states.Both teens and young adults with disabilities made significant gains in insurance coverage (4.55 and 8.96% point gains respectively, p < .001) and access to care (4.01 and 3.14% points decline in delayed care due to cost, p < .05) under the dependent coverage provision and Medicaid expansion.Medicaid expansion had a greater impact on both insurance coverage and on access to care than did the dependent coverage provision. The benefits of these changes flowed primarily to young adults with disabilities, reducing the gaps in insurance coverage between teens and young adults, and expanding access to care for both groups, providing a more seamless transition from pediatric to adult health care systems, post-ACA.
Anxiety-depression comorbidity and suicide attempts in high-risk adolescents : Aggravating effect of lower parental interest in mental health

본 연구의 목적은 청소년 불안-우울 공존 양상을 파악하고, 이에 따른 자살시도 위험에서 부모의 낮은 정신건강 관심정도의 가중효과를 검증하는 것이다. 이를 위해 한국청소년정책연구원이 2012년 수행한 ’아동·청소년 정신건강 증진 지원방안 연구Ⅱ’의 횡단데이터를 활용하였으며 연구대상은 초등학교 4학년부터 고등학교 3학년 총 1,942명이다. 주요 연구결과는 다음과 같다. 첫째, 불안-우울 공존 양상을 파악하기 위해 잠재프로파일분석을 실시한 결과, 불안과 우울의 공병률은 일정한 양상을 보이며 저수준(59.2%, n=1,210), 중수준(33.5%, n=686), 그리고 고수준 집단(7.5%, n=146)으로 구분되었다. 또한 저수준 집단에 비해 중수준 집단의 자살시도 위험은 2.19배, 고수준의 경우 7.82배 증가하였다. 둘째, 부모의 정신건강 관심정도가 낮을수록 자살시도 위험비는 최대 2.42배 까지 증가하는 양상을 보였다. 셋째, 불안-우울 공존 양상과 부모의 정신건강 관심정도에 따른 자살시도 위험비를 검증한 결과, 이론적인 최저 위험집단(저수준 집단×부모의 정신건강 관심정도 매우 높음)에 비해 타집단은 최소 2.53배에서 최대 21.51배까지 자살시도 위험이 높은 것으로 검증되었다. 이와 같은 연구 결과를 바탕으로 청소년 불안-우울 공존 양상 및 부모의 정신건강 관심정도에 따른 자살시도 위험을 완화하기 위한 임상적, 정책적 제언을 하였다.
LEVERAGING ECONOMIC INCENTIVES FOR SUSTAINABLE AGRICULTURE TO ACHIEVE FOOD SECURITY BY 2047

India stands at a crucial crossroads in its agricultural evolution. With its population projected to reach 1.6 billion by 2047, ensuring food security while embracing sustainability is imperative. Agriculture, employing 42% of the workforce and contributing 17.8% to GDP, faces mounting threats from climate change, soil degradation, and water scarcity. The Green Revolution boosted food production but left a legacy of excessive chemical use, groundwater depletion, and declining soil fertility. To meet the projected food grain demand of 402-437 million tonnes by 2047, India must transition to sustainable agriculture without compromising productivity or farmer livelihoods.
The quest for a universal density functional: The accuracy of density functionals across a broad spectrum of databases in chemistry and physics

Kohn-Sham density functional theory is in principle an exact formulation of quantum mechanical electronic structure theory, but in practice we have to rely on approximate exchange-correlation (xc) functionals. The objective of our work has been to design an xc functional with broad accuracy across as wide an expanse of chemistry and physics as possible, leading-as a long-range goal-to a functional with good accuracy for all problems, i.e., a universal functional. To guide our path toward that goal and to measure our progress, we have developed-building on earlier work in our group-a set of databases of reference data for a variety of energetic and structural properties in chemistry and physics. These databases include energies of molecular processes such as atomization, complexation, proton addition, and ionization; they also include molecular geometries and solid-state lattice constants, chemical reaction barrier heights, and cohesive energies and band gaps of solids. For the present paper we gather many of these databases into four comprehensive databases, two with 384 energetic data for chemistry and solid-state physics and another two with 68 structural data for chemistry and solid-state physics, and we test 2 wave function methods and 77 density functionals (12 Minnesota meta functionals and 65 others) in a consistent way across this same broad set of data. We especially highlight the Minnesota density functionals, but the results have broader implications in that one may see the successes and failures of many kinds of density functionals when they are all applied to the same data. Therefore the results provide a status report on the quest for a universal functional.
From CASP13 to the Nobel Prize: DeepMind’s AlphaFold Journey in Revolutionizing Protein Structure Prediction and Beyond

Four years ago, at the 14th Critical Assessment of Structure Prediction (CASP14), John Moult made a historic announcement that the long-standing challenge of Protein Structure Prediction— a problem that had confounded scientists for over five decades—had been "solved" for single protein chains. Supporting this groundbreaking statement was a plot depicting the median Global Distance Test (GDT) across 87 out of 92 domains, where AlphaFold2, developed by DeepMind, achieved an unprecedented score of 92.4. The bar chart not only underscored AlphaFold2’ s remarkable performance—standing out prominently among other methods—but also revealed a level of accuracy that exceeded all prior expectations. In the years since this breakthrough, DeepMind's team has made significant strides. The AlphaFold Database now hosts approximately 214 million structures for various model organisms, covering nearly the entire genome. Research continues to explore multiple facets of protein science, including the prediction of multi-chain protein complex structures and the impact of missense mutations on protein function. The open availability of this extensive database and the suite of AlphaFold2 algorithms has catalysed remarkable advancements in protein biology and bioinformatics. This review will begin by revisiting DeepMind's early efforts in CASP13, detailing the architecture and the remarkable progress that led to their breakthrough of AlphaFold2 in CASP14 (2020). It will then delve into two main areas: (1) AlphaFold’s contributions to the scientific community across various fields over the past four years, and (2) the latest improvements, enhancements, and achievements by DeepMind, including AlphaFold3 and the Nobel Prize in Chemistry.