ethicalabs/Research-Intent-Collab huggingface dataset:
Corporate Social Responsibility and Its Reporting

Introduction: Along with the developments of countries and regarding the human rights, over the normal responsibility of corporates; i. e. profitability, the responsibility of corporates against the society and the environment has been taken into consideration. Shareholders are not the only beneficiaries of the companies anymore. Now, a lot of companies all over the world are aware of their responsibilities, and they are attempting to play their own role in this respect. Social reporting responsibilities is an instrument for evaluating the corporates social responsibility, and via this, the companies are able to pronounce their own activities to the whole society for the enhancement of the quality of lives for all people of the society and the interested institutions. Literature Review: In this research, it is tried to state the reasons, motivations, and the interests in inclining to the implementation and reporting of the corporate social responsibility by using the existent theories in this field. Moreover, it is attempted to set forth some deficiencies of the traditional reporting in financial accounting, present the resulted advancements in social and environmental reporting, and at last, suggest the appropriate framework for developing methods in social responsibilities reporting. Conclusion: According to the conducted researches in this field, and regarding the investigating of some models, it seems that the necessary obligations and incentives should be compiled and presented for reporting the social responsibilities in performing this type of reporting.
A Lightweight Deep Learning Framework for Galaxy Morphology Classification

Abstract                   With the construction of large telescopes and the explosive growth of observed galaxy data, we are facing the problem to improve the data processing efficiency while ensuring the accuracy of galaxy morphology classification. Therefore, this work designed a lightweight deep learning framework, EfficientNet-G3, for galaxy morphology classification. The proposed framework is based on EfficientNet which integrates the Efficient Neural Architecture Search algorithm. Its performance is assessed with the data set from the Galaxy Zoo Challenge Project on Kaggle. Compared with several typical neural networks and deep learning frameworks in galaxy morphology classification, the proposed EfficientNet-G3 model improved the classification accuracy from 95.8% to 96.63% with F1-Score values of 97.1%. Typically, this model uses the least number of parameters, which is about one tenth that of DenseNet161 and one fifth that of ResNet-26, but its accuracy is about one percent higher than them. The proposed EfficientNet-G3 can act as an important reference for fast morphological classification for massive galaxy data in terms of efficiency and accuracy.
Exploring the Impact of Artificial Intelligence Generated Content (AIGC) on Game Design and User Experience

With the rapid development of artificial intelligence-generated content (AIGC) technology, the gaming industry is undergoing major changes. This study explores the application of AIGC in game design and analyzes its impact on art, music, text, interaction and user experience. The innovation of this study is the integration of AIGC to enhance the creativity, quality, and interactivity of game design while proposing solutions to current industry challenges. This study uses methods such as literature review, case analysis, and user surveys to evaluate the effectiveness of AIGC by comparing traditional and AIGC-enhanced game design methods. Follow user-centered design principles and analyze data from case studies, user questionnaires, and designer feedback to identify key issues in user experience. The results show that AIGC significantly increases user engagement by creating personalized, immersive experiences and provides new development opportunities for the gaming industry. In summary, AIGC technology has demonstrated the potential to revolutionize game design by solving creative and technical challenges, paving the way for future innovation and prosperity in the gaming industry.
Humanitarian Intervention and the Responsibility to Protect: Redefining a Role for “Kind-hearted Gunmen”

In the aftermath of the complex humanitarian crises of the 1990s, the concept of the Responsibility to Protect (R2P) emerged powerfully on the international stage. The report of the International C...
Regenerative Medicine for the Kidney: Renotropic Factors, Renal Stem/Progenitor Cells, and Stem Cell Therapy

The kidney has the capacity for regeneration and repair after a variety of insults. Over the past few decades, factors that promote repair of the injured kidney have been extensively investigated. By using kidney injury animal models, the role of intrinsic and extrinsic growth factors, transcription factors, and extracellular matrix in this process has been examined. The identification of renal stem cells in the adult kidney as well as in the embryonic kidney is an active area of research. Cell populations expressing putative stem cell markers or possessing stem cell properties have been found in the tubules, interstitium, and glomeruli of the normal kidney. Cell therapies with bone marrow-derived hematopoietic stem cells, mesenchymal stem cells, endothelial progenitor cells, and amniotic fluid-derived stem cells have been highly effective for the treatment of acute or chronic renal failure in animals. Embryonic stem cells and induced pluripotent stem cells are also utilized for the construction of artificial kidneys or renal components. In this review, we highlight the advances in regenerative medicine for the kidney from the perspective of renotropic factors, renal stem/progenitor cells, and stem cell therapies and discuss the issues to be solved to realize regenerative therapy for kidney diseases in humans.
Supplementary Table S1 from Prediction of Stage, Grade, and Survival in Bladder Cancer Using Genome-wide Expression Data: A Validation Study

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The Sloan Digital Sky Survey Quasar Catalog: Fourteenth Data Release

We present the Data Release 14 Quasar catalog (DR14Q) from the extended Baryon Oscillation Spectroscopic Survey (eBOSS) of the Sloan Digital Sky Survey IV (SDSS-IV). This catalog includes all SDSS-IV/eBOSS objects that were spectroscopically targeted as quasar candidates and that are confirmed as quasars via a new automated procedure combined with a partial visual inspection of spectra, have luminosities $M_{\rm i} \left[ z=2 \right] < -20.5$ (in a $Λ$CDM cosmology with $H_0 = 70 \ {\rm km \ s^{-1} \ Mpc ^{-1}}$, $Ω_{\rm M} = 0.3$, and $Ω_{\rm Λ} = 0.7$), and either display at least one emission line with a full width at half maximum (FWHM) larger than $500 \ {\rm km \ s^{-1}}$ or, if not, have interesting/complex absorption features. The catalog also includes previously spectroscopically-confirmed quasars from SDSS-I, II and III. The catalog contains 526,356 quasars 144,046 are new discoveries since the beginning of SDSS-IV) detected over 9,376 deg$^2$ (2,044 deg$^2$ having new spectroscopic data available) with robust identification and redshift measured by a combination of principal component eigenspectra. The catalog is estimated to have about 0.5% contamination. The catalog identifies 21,877 broad absorption line quasars and lists their characteristics. For each object, the catalog presents SDSS five-band CCD-based photometry with typical accuracy of 0.03 mag. The catalog also contains X-ray, ultraviolet, near-infrared, and radio emission properties of the quasars, when available, from other large-area surveys.
Contributions to the optimized deployment of connected sensors on the Internet of Things collection networks. (Contributions au déploiement optimisé des capteurs connectés dans les réseaux de collecte de l'Internet des Objets).

IoT collection networks raise many optimization problems; in particular because the sensors have limited capacity in energy, processing and memory. In order to improve the performance of the network, we are interested in a contribution related to the optimization of the 3D indoor deployment of nodes using multi-objective mathematics models relying on hybrid meta-heuristics. Therefore, our main objective is to propose hybridizations and modifications of the optimization algorithms to achieve the appropriate 3D positioning of the nodes in the wireless sensor networks with satisfaction of a set of constraints and objectives that are often antagonistic. We propose to focus our contribution on meta-heuristics hybridized and combined with procedures to reduce dimensionality and to incorporate user preferences. These hybridization schemes are all validated by numerical tests. Then, we proposed simulations that are completed by, and confronted with experiments on real testbeds. ; Les Réseaux de collecte de l’IoT soulèvent de nombreux problèmes d'optimisation, à cause des capacités limitées des capteurs en énergie, en traitement et en mémoire. Dans l'optique d’améliorer la performance du réseau, nous nous intéressons à une contribution liée à l'optimisation du déploiement 3D d’intérieur des noeuds sur les réseaux de capteurs sans fil en utilisant des méta-heuristiques hybrides se basant sur des modèles mathématiques multi-objectif. L’objectif principal est donc de proposer des hybridations et modifications des algorithmes d’optimisation dans le but de réaliser le positionnement 3D adéquat des noeuds dans les réseaux de capteurs sans fil avec satisfaction d’un ensemble de contraintes et objectifs qui sont souvent antagonistes. Nous proposons d'axer notre contribution sur les méta-heuristiques hybrides et combinés avec des procédures de réduction de dimentionalité et d’incorporation de préférences des utilisateurs. Ces schémas d’hybridation sont tous validés par des résultats numériques de test. Ensuite, des simulations ...
Stem Cell Course in the Middle East: Science Diplomacy and International Collaborations During the Arab Spring

In April 2011, an international advanced course and workshop entitled "Frontiers in Human Pluripotent Stem Cells" and an International Congress on Fertility and Genetics ( http://www.fertigen.com.jo/ConferenceDetails.aspx ) was held in Amman Jordan hosted by the Jordanian Society of Fertility and Genetics under the auspices of the International Cell Research Organization (ICRO), a UNESCO associated NGO. The Congress President Dr. Zaid Kilani, with Dr. Abdel Latif Abu Khadra, President of the Jordanian society for Fertility and Genetics, Dr. Rana Dajani of the Hashemite University of Jordan, and their Organizing Committee proved to be an excellent organizers and dedicated physician-scientists and, focusing on fertility, genetics and stem cells in a wide range of advanced therapeutic applications. Brilliant course participants included trainees, scientists and clinicians from the Greater Middle East. The lectures and practical sessions, presented by internationally acknowledged scientists, included overviews of recent achievements in pluripotent stem cell research, emphasizing the role of both the embryonic (ES) and induced pluripotent stem (iPS) cells. A major emphasis was placed on the clinical achievements in germ cell and umbilical cord stem cell transplantation issues, and on the potential of fast and successful prenatal and pre-implantation molecular genetics diagnostics. The organization of the stem cell course in the Holy Land especially emphasized that issues of "eternal life" and "rejuvenation" are already at hand--at least in the pluripotent stem cell research field. In the lively atmosphere of the course about 60 participants had heated discussions on the possibility and ethics of advanced prenatal diagnostics, and on regulatory issues reflecting the need of separation of clinically effective versus unapproved, unwarranted stem cell treatments. An open discussion of many ethical issues, reflecting profound differences in religion and medical tradition in the different countries, made this course exceptionally interesting for both teachers and trainees.
Revisiting the propensity score's central role: Towards bridging balance and efficiency in the era of causal machine learning

About forty years ago, in a now--seminal contribution, Rosenbaum & Rubin (1983) introduced a critical characterization of the propensity score as a central quantity for drawing causal inferences in observational study settings. In the decades since, much progress has been made across several research fronts in causal inference, notably including the re-weighting and matching paradigms. Focusing on the former and specifically on its intersection with machine learning and semiparametric efficiency theory, we re-examine the role of the propensity score in modern methodological developments. As Rosenbaum & Rubin (1983)'s contribution spurred a focus on the balancing property of the propensity score, we re-examine the degree to which and how this property plays a role in the development of asymptotically efficient estimators of causal effects; moreover, we discuss a connection between the balancing property and efficient estimation in the form of score equations and propose a score test for evaluating whether an estimator achieves balance.
Woman in Conflict Resolution and Peace Building in Ethiopia: Rhetoric or Reality? A Call for Reconsideration

This paper reflects women’s position in peace building process in Ethiopia, and examines whether women’s participation in conflict resolution and peace building process are real or rhetoric. Then it provides mechanisms of empowering women in peace building activities. This exploratory evaluation research provides evidence using both primary and secondary data. The research result shows that women’s role in conflict resolution and peace building activities are highly marginalized due to structural barriers which includes lack of capacity in conflict resolution, victimization and sexual harassment in conflict distance women’s from peace building activities. Furthermore, sociocultural influences, patriarchal norms limit their visibility in conflict resolution and peace building activities in Ethiopia. Lack of genuine representation and absence of peace policy are among the challenges that limits women’s from peace building activities. Therefore, women’s role in conflict resolution and peace building activities are just a rhetoric and taken as a political instrument for government gains than reality in determining women’s living as well as the sustainability of peace in the country especially since 2018 Ethiopia’s political transition. Based up on the finding, the research call the following reconsideration measures, namely: (1) Peace policy formulation that addresses the current marginalized position of women in the country’s peace building process, (2) Promoting and strengthening genuine representation of women in governmental decision making institutions, and (3) measures that raise practical engagement of women in peace process, and (4) addressing sociocultural barriers using civic empowerment works so as to get women capable actor in conflict resolution and peace building activities in the country.
Exploring video diffusion models in echocardiogram generation : a novel approach to data augmentation in cardiac imaging

Ejection fraction (EF) serves as a critical indicator of cardiac function, traditionally assessed through expert clinicians' manual interpretation of echocardiograms. However, the labor-intensive nature of this process, along with inter-observer variability and data scarcity, highlights the need for automated and scalable solutions. This thesis explores the application of video diffusion models to generate synthetic echocardiograms as a means to augment limited datasets, thereby enhancing ejection fraction estimation models. By leveraging synthetic data generation, we aim to address data scarcity, enhance model performance, and validate the effectiveness of synthetic data in echocardiography (echo). The proposed methodology integrates diffusion models with echo video data to create realistic cardiac echocardiograms tailored to each patient. We also develop a data augmentation framework aiming at improving EF estimation. Extensive experiments are conducted to evaluate the contribution of the synthetic data to model performance on EF prediction accuracy, focusing on scenarios with limited labeled data. Our results demonstrate that incorporating diffusion-augmented training data leads to improvements in both the accuracy and robustness of automated EF estimation models. In addition, we investigate and present various strategies for the rapid generation of synthetic echocardiograms through model distillation. Our preliminary findings establish a foundation for future research in real-time echocardiogram synthesis, facilitating applications in clinical training and procedural guidance. Ultimately, this thesis provides a novel approach to controlled synthetic data-driven augmentation, contributing to the broader field of cardiac imaging by enabling more efficient and precise diagnostic tools. This work advances the potential for scalable, Artificial Intelligence (AI)-driven cardiac assessments, offering enhanced accessibility to high-quality care in high-resource and low-resource clinical environments.
Measurement of the Flux of Ultra High Energy Cosmic Rays using very inclined Extensive Air Showers measured at the Pierre Auger Observatory

The flux of ultrahigh energy cosmic rays has been determined using very inclined showers detected by the surface detector of the Pierre Auger Observatory. Two methods suitable for very inclined air showers have been implemented and compared. The regime of very inclined showers extends the aperture of the surface detector of the Pierre Auger Observatory by 30%.
The unified theoretical frame of logarithmic alternative model for sparse information recovery and its application in multiple source location problem by TDOA

In sparse information recovery, the core problem is to solve the $l_0$-minimization which is NP-hard. On one hand, in order to recover the original sparse solution, there are a lot of papers designing alternative model for $l_0$-minimization. As one of the most popular choice, the logarithmic alternative model is widely used in many applications. In this paper, we present an unified theoretical analysis of this alternative model designed for $l_0$-minimization. By the theoretical analysis, we prove the equivalence relationship between the alternative model and the original $l_0$-minimization. Furthermore, the main contribution of this paper is to give an unified recovery condition and stable result of this model. By presenting the local optimal condition, this paper also designs an unified algorithm and presents the corresponding convergence result. Finally, we use this new algorithm to solve the multiple source location problem.
Representing Digital Assets using MPEG-21 Digital Item Declaration

Various XML-based approaches aimed at representing compound digital assets have emerged over the last several years. Approaches that are of specific relevance to the digital library community include the Metadata Encoding and Transmission Standard (METS), the IMS Content Packaging XML Binding, and the XML Formatted Data Units (XFDU) developed by CCSDS Panel 2. The MPEG-21 Digital Item Declaration (MPEG-21 DID) is another standard specifying the representation of digital assets in XML that, so far, has received little attention in the digital library community. This article gives a brief insight into the MPEG-21 standardization effort, highlights the major characteristics of the MPEG-21 DID Abstract Model, and describes the MPEG-21 Digital Item Declaration Language (MPEG-21 DIDL), an XML syntax for the representation of digital assets based on the MPEG-21 DID Abstract Model. Also, it briefly demonstrates the potential relevance of MPEG-21 DID to the digital library community by describing its use in the aDORe repository environment at the Research Library of the Los Alamos National Laboratory (LANL) for the representation of digital assets.
Consensus-based antimicrobial resistance and stewardship competencies for UK undergraduate medical students

Background: In the UK there is limited coverage of antimicrobial stewardship across postgraduate curricula and evidence that final year medical students have insufficient and inconsistent antimicrobial stewardship teaching. A national undergraduate curriculum for antimicrobial resistance and stewardship is required to standardise an adequate level of understanding for all future doctors.Objectives: To provide a UK national consensus on competencies for antimicrobial resistance and stewardship for undergraduate medical education.Methods: Using the modified Delphi method over two online survey rounds, an expert panel comprising leads for infection teaching from 25 UK medical schools reviewed competency descriptors for antimicrobial resistance and stewardship education.Results: There was a response rate of 100% with all 28 experts who agreed to take part completing both survey rounds. Following the first-round survey, of the initial 55 descriptors, 43 reached consensus (78%). The second-round survey included the 12 descriptors from the first-round in which agreement had not been reached, four amended descriptors and 12 new descriptors following qualitative feedback from the panel members. Following the second-round survey, a total of 58 consensus-based competency descriptors within 6 overarching domains were identified.Conclusion: The consensus-based competency descriptors defined here can be used to inform standards, design curricula, develop assessment tools and direct UK undergraduate medical education.
Quantum error correction and fault-tolerant quantum computation

Quantum computers need to be protected by quantum error-correcting codes against decoherence. One of the most interesting and useful classes of quantum codes is the class of quantum stabilizer codes. Entanglement-assisted (EA) quantum codes are a class of stabilizer codes that make use of preshared entanglement between the sender and the receiver. We provide several code constructions for entanglement-assisted quantum codes. ? The MacWilliams identity for quantum codes leads to linear programming bounds on the minimum distance. We find new constraints on the simplified stabilizer group and the logical group, which help improve the linear programming bounds on entanglement-assisted quantum codes. The results also can be applied to standard stabilizer codes. ? In the real world, quantum gates are faulty. To implement quantum computation fault-tolerantly, quantum codes with certain properties are needed. We first analyze Knill?s postselection scheme in a two-dimensional architecture. The error performance of this scheme is better than other known concatenated codes. Then we propose several methods to protect syndrome extraction against measurement errors.