ethicalabs/Research-Intent-Collab huggingface dataset:
A Study on Empowering Leadership, Safety Culture, Safety Behavior and Organizational Trust for ESG Management in the Distribution Industry

ESG management, which considers non-financial elements of companies, is the current emphasis of corporate management. To sustain development in the shifting business landscape, the distribution industry is exploring digital transformation(DX) initiatives like AI and robotics. However, to ensure environmental, social, and governance(ESG) safety, industry-specific precautions must be taken. This study focused on the ESG safety management in the distribution industry. It aimed to identify the mechanisms of empowering leadership, safety culture, safety behavior, and organizational trust. The study also empirically confirmed that safety culture acts as a mediator between empowering leadership and safety behavior. Additionally, it found that organizational trust plays a moderating role between safety culture and safety behavior. In order to achieve this objective, a survey was performed online with a sample size of 400 workers in the distribution industry. Out of the 400 replies, 384 were considered reliable for analysis after eliminating 16 dishonest responses. The data was then analyzed using SPSS 25.0 Process Macro and AMOS 22.0 to check the link between the main variables and test the hypotheses. The study revealed that empowering leadership had a large and beneficial impact on safety behavior and safety culture. Furthermore, research has demonstrated that the implementation of empowered leadership has a positive impact on safety culture, hence strengthening and initiating safety-related actions among individuals. Furthermore, it was discovered that organizational trust acts as a moderating mediator between empowering leadership and safety behavior, specifically through safety culture. The study confirmed that as the level of organizational trust increases, the positive impact of empowering leadership on safety behavior is enhanced through the mediating role of safety culture. The given findings provide theoretical and practical recommendations for implementing ESG safety compliance response systems and safety processes in the distribution industry.
The Impact of New Media (Weibo) and Traditional Media (CCTV News) on News Dissemination in China : Master's Thesis / Wang Meilun; BSU, Faculty of Philosophy and Social Sciences, Department of Social Communication; Academic supervisor: Michael A. Guliuk

Цели данного исследования: разработка теоретических и методологических основ интеграции традиционных медиа с новыми медиа.
Predicting concentration levels of air pollutants by transfer learning and recurrent neural network

Air pollution (AP) poses a great threat to human health, and people are paying more attention than ever to its prediction. Accurate prediction of AP helps people to plan for their outdoor activities and aids protecting human health. In this paper, long-short term memory (LSTM) recurrent neural networks (RNNs) have been used to predict the future concentration of air pollutants (APS) in Macau. Additionally, meteorological data and data on the concentration of APS have been utilized. Moreover, in Macau, some air quality monitoring stations (AQMSs) have less observed data in quantity, and, at the same time, some AQMSs recorded less observed data of certain types of APS. Therefore, the transfer learning and pre-trained neural networks have been employed to assist AQMSs with less observed data to build a neural network with high prediction accuracy. The experimental sample covers a period longer than 12-year and includes daily measurements from several APS as well as other more classical meteorological values. Records from five stations, four out of them are AQMSs and the remaining one is an automatic weather station, have been prepared from the aforesaid period and eventually underwent to computational intelligence techniques to build and extract a prediction knowledge-based system. As shown by experimentation, LSTM RNNs initialized with transfer learning methods have higher prediction accuracy; it incurred shorter training time than randomly initialized recurrent neural networks.
Revisiting the Parallel Tempering Algorithm: High-Performance Computing and Applications in Operations Research

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The Sloan Digital Sky Survey Quasar Catalog: twelfth data release

We present the Data Release 12 Quasar catalog (DR12Q) from the Baryon Oscillation Spectroscopic Survey (BOSS) of the SDSS-III. This catalog includes all SDSS-III/BOSS objects that were spectroscopically targeted as quasar candidates during the full survey and that are confirmed as quasars via visual inspection of the spectra, have luminosities Mi[z=2]<-20.5 (in a LCDM cosmology with H_0 = 70 km/s/Mpc, Omega_M = 0.3, and Omega _L=0.7), and either display at least one emission line with a full width at half maximum (FWHM)larger than 500 km/s or, if not, have interesting/complex absorption features. The catalog also includes previously known quasars (mostly from SDSS-I and II) that were reobserved by BOSS. The catalog contains 297,301 quasars detected over 9,376 square degrees with robust identification and redshift measured by a combination of principal component eigenspectra. The number of quasars with z>2.15 is about an order of magnitude greater than the number of z>2.15 quasars known prior to BOSS. Redshifts and FWHMs are provided for the strongest emission lines (CIV, CIII], MgII). The catalog identifies 29,580 broad absorption line quasars and lists their characteristics. For each object, the catalog presents five-band (u, g, r, i, z) CCD-based photometry together with some information on the optical morphology and the selection criteria. When available, the catalog also provides information on the optical variability of quasars using SDSS and PTF multi-epoch photometry. The catalog also contains X-ray, ultraviolet, near-infrared, and radio emission properties of the quasars, when available, from other large-area surveys. The calibrated digital spectra, covering the wavelength region 3,600-10,500A at a spectral resolution in the range 1,300<R<2,500, can be retrieved from the SDSS Catalog Archive Server.
Polygonal Virtual Element Spatial Discretisation Methods for the Neutron Diffusion Equation With Applications in Nuclear Reactor Physics

In this paper the application of the virtual element method (VEM) to the multigroup, neutron diffusion equation will be presented. The VEM is a recently developed Bubnov-Galerkin spatial discretisation method based largely on the mimetic finite difference method (MFD) that preserves the properties of the underlying vector operators. It can discretise elliptic partial differential equations (PDEs) on arbitrary polygonal/polyhedral meshes with arbitrary order and regularity. Deterministic, geometry conforming methods, used to solve multigroup, neutron diffusion, nuclear reactor physics problems have historically used the finite element method (FEM). However, FEM requires high-quality meshes with few highly distorted elements (elements with a poor aspect ratio) or it may may experience convergence problems. The process of creating high quality meshes, even with automated mesh generation algorithms, such as the advancing front and Delaunay methods, is often very time consuming. For these reasons VEM is being studied in this paper as a possible alternative to FEM in the numerical solution of neutron diffusion problems in nuclear reactor physics. A C5G7 UOX pincell problem is presented to demonstrate the application of VEM to mutligroup diffusion problem. The method of manufactured solutions (MMS) is used to determine the order of convergence.
Patient reported quality of life (QOL) and survival outcomes: Analysis of ECOG-ACRIN E3805 chemohormonal androgen ablation randomized trial (CHAARTED) in prostate cancer (PCa).

5014  Background: Chemohormonal therapy with androgen deprivation therapy and docetaxel (ADT+D) improves overall survival (OS) and quality of life (QOL) at 12 months compared to ADT alone in metastatic hormone sensitive prostate cancer (mHSPC). However, the prognostic relationship between QOL, disease characteristics, and OS has not been described in this population. Methods: In this post-hoc exploratory analysis of the CHAARTED trial, the QOL instrument Functional Assessment of Cancer Therapy-Prostate (FACT-P) was completed by patients treated with ADT and ADT+D. Log-rank test and Cox proportional hazards models were used to test the association between QOL and OS by clinical and disease characteristics. Results: 790 men were included in the study (ADT+D, n = 397; ADT, n = 393). Baseline higher QOL by FACT-P (n = 790) was associated with better OS in univariate regression (HR 0.70 [0.55,0.90], p = 0.005), however the association was not significant in multivariate analysis (HR 0.80 [0.62-1.04], p = 0.09). There was a trend towards improved survival for patients with the lowest baseline QOL (lowest quartile) treated with ADT+D compared to ADT (HR 0.75 [0.53, 1.05], p = 0.09). In contrast, patients with the highest baseline QOL (highest quartile, 63% high volume disease) had similar survival regardless of treatment arm (HR 0.92 [0.63-1.36], p=0.69). Higher 3-month QOL by FACT-P (n = 654) was associated with survival in the multivariate analysis independently of treatment arm (HR 0.76 [0.58, 1.0], p = 0.05). Patients with the highest 3-month QOL had comparable survival regardless of treatment arm (HR 1.11 [0.73, 1.67], p = 0.63), while patients with the lowest 3-month QOL experienced a survival benefit with ADT+D compared to ADT (HR 0.69 [0.48, 0.99], p = 0.047). Conclusions: In this analysis, 3-month QOL by FACT-P was significantly associated with survival for patients with mHSPC. Patients with the poorest QOL ("most symptomatic”) appeared to experience survival benefit from ADT+D regardless of disease volume. Conversely, patients reporting the highest QOL (“least symptomatic”) did not seem to benefit from ADT+D despite a predominance of patients with high volume disease in that cohort. These findings suggest that consideration of QOL may enhance decision making and patient selection when considering chemohormonal treatment for mHSPC. [Table: see text]
Unveiling workplace safety and health empowerment: Unraveling the key elements influencing occupational injuries

Millions of workers globally experience work-related injuries, leading to fatalities, injuries, job loss, and wider societal implications. This study utilizes both the U.S. database and the General Social Survey (GSS) to delve into the factors linked to occupational injuries.In this study, 30 questions from the GSS between 2002 and 2014 were used. The sample included 5,914 workers from various sectors, distributed between both female and male, and were of 18 years and above. The analysis encompasses several dimensions: demographics, job-related characteristics, administrative factors, and health and safety measures. The study particularly focuses on assessing the prevalence of three common work-related injuries: nonfatal injuries, back pain and arm pain. Logistic regression models were constructed to gauge the effects of identified factors.Significant insights emerge from the analysis. Factors such as age, gender, race, working hours, overtime work, trust in management, coworker support, and workflow smoothness were identified as having notable impacts on work-related injuries. For each model, the study quantifies these impacts through odds ratio and relative effects. For example, in the nonfatal injuries model, the relative effects of age showed that workers aged 65 years or older experience a 2.56% decrease in the frequency of nonfatal injuries compared to younger workers. Furthermore, in the back pain model, the estimated odds of having back pain in males are 0.90 lower than that in females. Moreover, in the arm pain model, workers who have sufficient time to complete their tasks have estimated odds of experiencing arm pain that are 0.85 lower than those who don't have adequate time, etc. Conclusions: Understanding the intricate interplay of various factors influencing workers' safety and health is vital for addressing occupational injuries. By addressing these factors, there's potential for reducing work-related injuries, enhancing work environment, and minimizing costs.Organizations can use the study's findings for reducing work related injuries. Flexible work arrangements, tailored training programs, and ergonomic improvements might highlight factors such as age, gender, and working hours. Enhancing communication, fostering a positive work culture, and prioritizing safety and health measures could reduce risks associated with trusting management and coworker support. Cost-benefit analysis and continuous monitoring ensure these interventions' effectiveness, reduce back and hand pain incidents, minimize cost, and create safer workplaces, thereby boosting the overall well-being of workers and organizational success.
Generating Synthetic Satellite Imagery With Deep-Learning Text-to-Image Models -- Technical Challenges and Implications for Monitoring and Verification

Novel deep-learning (DL) architectures have reached a level where they can generate digital media, including photorealistic images, that are difficult to distinguish from real data. These technologies have already been used to generate training data for Machine Learning (ML) models, and large text-to-image models like DALL-E 2, Imagen, and Stable Diffusion are achieving remarkable results in realistic high-resolution image generation. Given these developments, issues of data authentication in monitoring and verification deserve a careful and systematic analysis: How realistic are synthetic images? How easily can they be generated? How useful are they for ML researchers, and what is their potential for Open Science? In this work, we use novel DL models to explore how synthetic satellite images can be created using conditioning mechanisms. We investigate the challenges of synthetic satellite image generation and evaluate the results based on authenticity and state-of-the-art metrics. Furthermore, we investigate how synthetic data can alleviate the lack of data in the context of ML methods for remote-sensing. Finally we discuss implications of synthetic satellite imagery in the context of monitoring and verification.
Bayesian Deep Learning for Exoplanet Atmospheric Retrieval

Over the past decade, the study of extrasolar planets has evolved rapidly from plain detection and identification to comprehensive categorization and characterization of exoplanet systems and their atmospheres. Atmospheric retrieval, the inverse modeling technique used to determine an exoplanetary atmosphere's temperature structure and composition from an observed spectrum, is both time-consuming and compute-intensive, requiring complex algorithms that compare thousands to millions of atmospheric models to the observational data to find the most probable values and associated uncertainties for each model parameter. For rocky, terrestrial planets, the retrieved atmospheric composition can give insight into the surface fluxes of gaseous species necessary to maintain the stability of that atmosphere, which may in turn provide insight into the geological and/or biological processes active on the planet. These atmospheres contain many molecules, some of them biosignatures, spectral fingerprints indicative of biological activity, which will become observable with the next generation of telescopes. Runtimes of traditional retrieval models scale with the number of model parameters, so as more molecular species are considered, runtimes can become prohibitively long. Recent advances in machine learning (ML) and computer vision offer new ways to reduce the time to perform a retrieval by orders of magnitude, given a sufficient data set to train with. Here we present an ML-based retrieval framework called Intelligent exoplaNet Atmospheric RetrievAl (INARA) that consists of a Bayesian deep learning model for retrieval and a data set of 3,000,000 synthetic rocky exoplanetary spectra generated using the NASA Planetary Spectrum Generator. Our work represents the first ML retrieval model for rocky, terrestrial exoplanets and the first synthetic data set of terrestrial spectra generated at this scale.
Test Paper 5

Pre-labeled paper.
Hierarchical Federated Learning with Privacy

Federated learning (FL), where data remains at the federated clients, and where only gradient updates are shared with a central aggregator, was assumed to be private. Recent work demonstrates that adversaries with gradient-level access can mount successful inference and reconstruction attacks. In such settings, differentially private (DP) learning is known to provide resilience. However, approaches used in the status quo (\ie central and local DP) introduce disparate utility vs. privacy trade-offs. In this work, we take the first step towards mitigating such trade-offs through {\em hierarchical FL (HFL)}. We demonstrate that by the introduction of a new intermediary level where calibrated DP noise can be added, better privacy vs. utility trade-offs can be obtained; we term this {\em hierarchical DP (HDP)}. Our experiments with 3 different datasets (commonly used as benchmarks for FL) suggest that HDP produces models as accurate as those obtained using central DP, where noise is added at a central aggregator. Such an approach also provides comparable benefit against inference adversaries as in the local DP case, where noise is added at the federated clients.
Beyond airspace safety: a feminist perspective on drone privacy regulation

The impact of drones on women’s privacy has recently garnered sensational attention in media and popular discussions. Media headlines splash stories about drones spying on sunbathing or naked women and girls, drones being used to stalk women through public spaces, and drones delivering abortion pills to women who might otherwise lack access. Yet despite this popular attention, and the immense literature that has emerged analyzing the privacy implications of drone technology, questions about how the drone might enhance or undermine women’s privacy in particular have not yet been the subject of significant academic analysis. This paper contributes to the growing drone privacy literature by examining how the technology can be especially apt to impact women’s privacy. In particular, various features of the technology allow it to take advantage of the ways in which privacy protection has traditionally been - and in many cases continues to be - gendered. While the analytical focus is on the gendered privacy impacts of drone technology, the article and its conclusions are about more than women's privacy. Examining some of the differential impacts of the technology, and the laws that guide its use, helps to reveal broader inequities that can go unseen when we think about technology without social context. The paper ultimately argues that drone regulators cannot continue to treat the technology as though it is value-neutral - impacting all individuals in the same manner. Going forward, the social context in which drone technology is emerging must inform both drone-specific regulations, and how we approach privacy generally. This paper is framed as a starting point for a further discussion about how this can be done within the Canadian context and elsewhere.
Algorithms for integration of stochastic differential equations using parallel optimized sampling in the Stratonovich calculus

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Avoiding genetic discrimination in insurance: An exploration of the legality and ethics of precautionary measures in anticipation of unfavourable test outcomes

This paper explores the legality and ethics of an individual securing insurance (life, disability or other forms of income protection insurance for which there is individual risk assessment) in anticipation of undergoing genetic testing. It also seeks to examine the situation from the perspective of genetic counsellors and the extent of their obligations in providing information and advice to individuals contemplating genetic testing. These are matters of importance for health care professionals, human research ethics committees as well, of course, as individuals who may be contemplating undertaking genetic testing.
Comparing the use and quality of palliative and end-of-life care between Asian and non-Asian patients with cancer.

e13765                                   Background:             Palliative care (PC) is associated with improved outcomes in patients with cancer. The term Asian-American refers to various cultures and ethnicities and is the fastest growing minority in the US. Literature suggests Asians receive more intense care at end of life. A small study conducted at the same medical center, prior to the growth of PC services, found a higher rate of in-patient hospital and ICU deaths and greater use of mechanical ventilation in Asian patients (AP) with cancer compared with non-Asian patients (AP). Our goal was to quantify PC use and end-of-life quality (EOLQ) metrics in AP versus non-AP populations in a retrospective cohort study of hospitalized cancer patients. We predicted AP would have less PC use and lower EOLQ metrics.             Methods:             We identified deceased patients admitted to Tufts Medical Center’s oncology unit between 1/1/2019-12/1/2022. Demographics, admission statistics, inpatient/ outpatient PC visits, advance care planning (ACP), and EOLQ outcomes, including emergency department (ED) visits, hospitalization, and ICU stay within 30 days of death, were collected from the electronic medical record. Chi square and Fisher's exact tests were used to compare the differences between AP and non-AP regarding PC use and EOLQ metrics.             Results:             Of the 220 patients included in the study, 24% (n = 52) were AP. AP and non-AP did not differ in use of CPR (2% vs 3%, p = 1.0), ventilation (6% vs 9%, p = 0.57), ICU admission (19% vs 21%, p = 0.17), or PC consults (48% vs 53%, p = 0.53) during their last admission; nor in outpatient PC visits (53% vs 60%, p = 0.37) or first inpatient PC visit before last admission (27% vs 36%, p = 0.24). There was no significant difference in EOLQ within 30 days of death between the AP and non-AP group, specifically in ED visits (58% vs 58%, p = 0.94), hospitalization (58% vs 63%, p = 0.51), ICU stay (16% vs 21%, p = 0.37) and hospice enrollment (58% vs 68%, p = 0.71).             Conclusions:             In this retrospective study of hospitalized patients with cancer, AP patients had no significant differences in use of PC services or EOLQ metrics when compared to non-AP patients. These findings differ from our hypothesis and may reflect positive changes that correspond to the expansion of PC services and greater awareness or acceptance of PC. More work is needed to explore how PC might help reduce disparities between AP and non-AP.