ethicalabs/Research-Intent-Collab huggingface dataset:
Una Propuesta De Expansiin Del Sistema De Soluciin De Controversias De La Organizaciin Mundial Del Comercio Como Contrapeso a La Tendencia Creciente De Los Tratados De Comercio Preferencial (A Proposal to Exand the Dispute Settlement Understanding of the World Trade Organization as a Counterweight to the Growing Trend of Preferential Trade Agreements)

Spanish: Este articulo analiza el conflicto entre las normas de la Organizacion Mundial del Comercio y las de los Tratados de Comercio Preferencial realizando una prediccion y, con base en esta, presentando una propuesta. Este conflicto, que ocurre porque la proliferacion de estos tratados puede tornar irrelevantes muchas de las normas de la Organizacion Mundial del Comercio, posiblemente se decantara en favor de los Tratados de Comercio Preferencial sin que mucho pueda hacerse por evitar esta tendencia. En efecto, ante el estancamiento de las rondas multilaterales, estos tratados regularan en el futuro casi la totalidad de los temas sustanciales de vanguardia en el comercio internacional. Basado en esa prediccion, y con los objetivos tanto de evitar que el rol preponderante de los Tratados de Comercio Preferencial minimice la importancia de la Organizacion Mundial del Comercio como de aprovechar el exito de esta organizacion en el arreglo de diferencias, este articulo propone que el sistema de solucion de controversias multilateral no solo tenga jurisdiccion sobre las disputas actuales sino que tambien abarque las diferencias que surjan con motivo de cualquier Tratado de Comercio Preferencial.English: This paper studies the conflict between the legal rules of the World Trade Organization and the provisions of Preferential Trade Agreements making a prediction and, based on it, putting forward a proposal. This conflict, which arises because the increase in number and scope of Preferential Trade Agreements may make irrelevants many of the legal rules of the World Trade Organization, would likely tilt in favor of Preferential Trade Agreements. Indeed, and because of the stagnation of multilateral rounds, Preferential Trade Agreemetns would regulate in the future almost all important topics related to international trade. Based on this prediction, and with the goals of avoiding not only that the growing role of Preferential Trade Agreements minimize the importance of the World Trade Organization but also of taking advantage of the success of this organization in the settlement of disputes, this paper proposes that the Dispute Settlement Body does not only have jurisdiction over the current controversies but that also cover the differences arising out the interpretation or application of any Preferential Trade Agreement.
Graph Neural Network Knowledge Graph Recommendation Model Integrating Deep Domain Information and Important Domain Information

Abstract           The importance between source and target nodes is often overlooked in higher-order information, which can introduce excessive noise in complex network scenarios, thereby affecting recommendation performance. To address this issue, we propose a graph neural network-based knowledge graph recommendation model that integrates deep domain information and important domain information, aiming to enhance recommendation accuracy and diversity. Initially, we pre-train the knowledge graph using graph embedding techniques to obtain structural information. Subsequently, we employ a graph convolutional network to delve deeper into the semantic information of the knowledge graph, capturing rich structural and semantic details from both depth and importance perspectives. Finally, we compute the interaction probability between users and items using the inner product of the enhanced vectors and user vectors, facilitating recommendations. Experiments on the Last-FM, Book-Crossing, and MovieLens-20M datasets yield AUC and F1 scores of 83.2% and 75.4%, 74.9% and 66.8%, 97.9% and 93.1%, respectively. Additionally, Recall@50 scores are 34.5%, 11.2%, and 35.0%. Our model outperforms RippleNet, KGCN, LKGR, and other models, indicating that integrating knowledge graph recommendation models with meta-graph neighborhoods effectively improves recommendation performance.
African Court on Human and Peoples’ Rights

This chapter provides a concise overview of the African Court on Human and Peoples’ Rights. It includes a discussion of its legal framework, organization, procedures, landmark cases and mechanisms for enforcing its decisions. The chapter considers the position of the African Court vis-a-vis other African Union institutions and the different contributions of the African Court to enrich the corpus of African human rights law.
The EU GDPR and secondary use of health and genetic data for research support purposes

Abstract                   Under European Union law, the processing of health and genetic data, which are special categories of personal data under the General Data Protection Regulation (GDPR), is prohibited under Article 9(1) GDPR unless at least one of the 10 exemptions under Article 9(2) is fulfilled. A common interpretation among legal commentators and policymakers is that where an entity discloses (ie shares or otherwise makes available) health and genetic data under its control to various external scientific research organizations and projects for secondary use purposes, the Article 9(2)(j) scientific research exemption applies to that data disclosure. We challenge this common interpretation of the GDPR. Our aim is to provide more clarity around the ancillary aspect in secondary use, allowing for a more coherent, consistent, and efficient interpretation of the interplay between secondary use research and its closely related activities, such as the disclosure of data for such research. We argue there should be more nuance in interpreting the underlying processing activities: where the purpose of the requesting data user is scientific research, the purpose for the data-holding entity’s data disclosure to them ought not generally be construed as a research purpose itself, such that reliance on Article 9(2)(j) could obtain. Instead, there may be greater legal suitability in relying on another, hitherto under-explored exemption under Article 9(2)(g), that is, processing is necessary for reasons of substantial public interest, namely for research support purposes, and where such research can be considered as bona fide and in the public interest.
Transforming Supply Chain Operations through AI and Machine Learning: Optimizing Demand Forecasting, Inventory Management, and Logistics Efficiency

This paper examines the application of Artificial Intelligence (AI) and Machine Learning (ML) technologies in optimizing demand forecasting, inventory management, and logistics within online retail supply chains. Through a comprehensive dataset of a UK-based retailer (541,909 records of transactional data) the paper explores the possibility of AI/ML algorithms to optimize the effectiveness and accuracy of these important supply chain factors. The study presents a new two-stage Customer-to-Inventory (C2I) framework based on DBSCAN to separate customers and tests a variety of forecasting models, such as Long Short-Term Memory (LSTM) networks, Prophet and such classical forecasting models as Linear Regression and Random Forest. The findings indicate that Linear Regression model is more effective than the other two in demand forecasting with RMSE of 23,120 which is still much lower in terms of prediction error than Prophet (31,613) and Random Forest (23,880). Moreover, demand forecasting and inventory management algorithms based on AI result in a 15% optimization in stocks levels, which minimizes stockouts and overstocking. It is also noted in the study that customer segmentation via DBSCAN identifies the high-value customers who are contributing 60% of total sales, which can be used as actionable information to use in targeted marketing and tailored promotions. This study provides a viable blueprint of the deployment of models of e-commerce companies, the findings of which can be used to increase the efficiency of operations, minimize expenses, and increase customer satisfaction.
Impact Of Social Media Usage On Mental Health Of Adolescents – A Research Of Students Across India

The increasing use of social media has brought about significant changes in our society, with both positive and negative implications. Since the onset of the COVID-19 pandemic, the impact of social media on adolescents has been a prominent topic of discussion, particularly as this stage marks a critical period in their development into independent adults, accompanied by significant brain changes. This study examines the effects of social media on the mental health of 53 high school students, consisting of 33 females and 20 males, predominantly within the age range of 13 to 19 years. The findings contribute to ongoing discussions on the nuanced effects of social media on mental health.
Neural Network Potentials for Enabling Advanced Small-Molecule Drug Discovery and Generative Design

Despite a Cambrian explosion in therapeutic modalities, small-molecule drugs remain a prominent and advantageous medical intervention. The universe of synthesizable, drug-like small molecules is astronomical. Given this scale, efficiently narrowing in on therapeutic candidates that are potent, selective, and tolerable cannot occur by happenstance. Over the past several decades, computational tools have become commonplace among pharma companies seeking to discover new small-molecule drugs. For example, molecular mechanics force fields are used to power molecular dynamics simulations—an effective approach for virtually screening and optimizing candidate molecules. In parallel, data-driven methods such as machine learning have supercharged the field’s ability to design potentially bioactive compounds. Despite these advances, established computational methods still suffer from issues relating to throughput, accuracy, generalizability, or combinations thereof. We argue that a merger of these technologies is inevitable and desirable, allowing the strengths of each to address the weaknesses of the other. This fusion—in the form of neural network potentials (NNPs)—is an exciting frontier for small-molecule discovery and design. Ostensibly, NNPs enable a swift, accurate, and generalizable solution for researchers developing the next generation of small-molecule drugs.
Perceived Level of Interest in Health Research from Patients Who Use Social Networking Sites: A Secondary Data Analysis of HINTS Data (Preprint)

BACKGROUND                         There is a need to address the factors associated with underrepresentation of socioeconomically disadvantaged groups in research participation. The growth of social networking sites over the past decade provides an opportunity to engage and educate patients from underrepresented populations about health information and research.                                                                       OBJECTIVE                         To use the National Cancer Institute’s Health Information National Trends Survey (HINTS) to determine if there is an association between social networking site use and interest in patient engagement in research, and to identify sociodemographic disparities between social networking site use and interest in patient engagement in research.                                                                       METHODS                         Data from the 2013 administration of HINTS were analyzed. Descriptive statistics were generated for all items, and bivariate analyses were conducted between sociodemographic variables and interest in participating in patient engaged research. Multivariate logistic regression modeling was used to examine the effects of each independent variable on respondent interest in patient-engaged research.                                                                       RESULTS                         There was a statistically significant association between social networking site use for reading/sharing a medical topic (P&lt; .001) and being interested in patient engagement in research, after adjusting for relevant covariates (OR=3.17; 95% CI: 2.04, 4.90). Respondents who had some college education (OR=3.13; 95% CI: 1.56, 6.27) or were college graduates (OR=3.98; 95% CI: 2.19, 7.24) had higher odds of interest in patient engagement in research, as compared to respondents with less than a high school education (P=.002). Among respondents who indicated using social networking sites for medical topics, males (P=.006) showed increased interest in patient engagement in research, as compared to females (OR=1.56; 95% CI: 1.13, 2.17). Interest in patient engagement in research did not differ significantly between different races/ethnicities, irrespective of their social networking site use (P&lt;.001).                                                                       CONCLUSIONS                         The relationship found between social networking site use and increased interest in patient engagement in research gives researchers an avenue to overcome barriers that have limited participation among different groups. Our study found no significant difference in this association among race/ethnicity, suggesting that social networking could be a tool to address the underrepresentation of certain groups regarding participation in research.
Ascertainment of Veterans With Metastatic Prostate Cancer in Electronic Health Records: Demonstrating the Case for Natural Language Processing

PURPOSE Prostate cancer (PCa) is among the leading causes of cancer deaths. While localized PCa has a 5-year survival rate approaching 100%, this rate drops to 31% for metastatic prostate cancer (mPCa). Thus, timely identification of mPCa is a crucial step toward measuring and improving access to innovations that reduce PCa mortality. Yet, methods to identify patients diagnosed with mPCa remain elusive. Cancer registries provide detailed data at diagnosis but are not updated throughout treatment. This study reports on the development and validation of a natural language processing (NLP) algorithm deployed on oncology, urology, and radiology clinical notes to identify patients with a diagnosis or history of mPCa in the Department of Veterans Affairs. PATIENTS AND METHODS Using a broad set of diagnosis and histology codes, the Veterans Affairs Corporate Data Warehouse was queried to identify all Veterans with PCa. An NLP algorithm was developed to identify patients with any history or progression of mPCa. The NLP algorithm was prototyped and developed iteratively using patient notes, grouped into development, training, and validation subsets. RESULTS A total of 1,144,610 Veterans were diagnosed with PCa between January 2000 and October 2020, among which 76,082 (6.6%) were identified by NLP as having mPCa at some point during their care. The NLP system performed with a specificity of 0.979 and sensitivity of 0.919. CONCLUSION Clinical documentation of mPCa is highly reliable. NLP can be leveraged to improve PCa data. When compared to other methods, NLP identified a significantly greater number of patients. NLP can be used to augment cancer registry data, facilitate research inquiries, and identify patients who may benefit from innovations in mPCa treatment.
Public engagement with Raimona National Park: a science communication perspective of wildlife conservation

Public engagement plays a crucial role in wildlife conservation by bridging scientific and community-driven communication. However, conservation communication often faces conflicts and challenges in stakeholder engagement. This study examines the role of public engagement in establishing Raimona National Park (RNP), focusing on interactions between scientific communication from conservationists and culturally informed communication from local communities. By analyzing survey responses from 340 participants and participatory observations, this research explores how structured public engagement contributes to conflict resolution and long-term park management. The study also highlights the role of NGOs, governance structures, and behavioral change in shaping conservation outcomes in the Raimona landscape.
A machine learning approach to improve scalability and robustness of variational quantum circuits

Quantum computing is an emerging new field that aims to leverage the power of a “quantum computer” to solve problems which are currently considered to NP-Hard or NP-Complete. The key idea is to encode inputs as quantum states and device a system where the measured outcomes correspond to a solution of the given problem. While a fault-tolerant quantum computer is still a theoretical possibility, we are able to evaluate the potential of quantum algorithms by running them on a class of devices called Noisy Intermediate Scale Quantum (NISQ) computers. ☐ The advantage of having access to NISQ computers is that they allow for immediate verification of the speedup provided by a proposed quantum algorithm. However, there are significant downsides to the current generation of these devices. Most notable of them are limited gate depth, high sensitivity to noise, ability to scale to only a few number of qubits and a tendency to get stuck in “barren plateaus”. In this research proposal, we introduce and define some key problems encountered in the simulation of quantum algorithms on NISQ devices. We then propose mitigating solutions inspired from machine learning methods and show the ecacy of our methods by simulating different types of variational algorithms on NISQ devices.
Profiling the geoscience community: exploring patterns of science communication and public engagement

This research investigates how members of the geoscience community in Portugal perceive and engage in science communication, identifying distinct patterns and practitioner profiles. Statistical analysis and a clustering algorithm were used to identify communication patterns based on practitioners' communication goals, target audiences, training, and self-efficacy. The results align with expected patterns but provide new empirical evidence of the relationship between communication goals and audience targeting, offering specific data for the geoscience community.
Incivility and disrespect as a workplace hazard: a new framework from occupational health and safety

Disrespectful behaviour of physicians is increasingly recognized as a significant problem in health care, contributing to a dysfunctional culture, decreased staff well-being, and compromised patient safety. Regulatory approaches frequently focus on individual actions or conduct, treating such behaviour as isolated issues rather than hazards in the workplace. Such approaches are long and slow and fail to address the broader systemic impacts of incivility and disrespect. We advocate reframing incivility and disrespectful behaviours as workplace hazards, drawing heavily from the workplace-specific principles of occupational health and safety (OHS). This approach would encompass accurate detection of harmful behaviours; root cause analyses of workplace conditions that perpetuate incivility; and a hierarchy of interventions.
Toxicology and risk assessment in the analysis and management of environmental risk

Abstract                Risk-based decision making increasingly has global dimensions, extending from the international management of chemical risks to the sustainable development of our planet. Environmental risk assessment is firmly based on toxicological sciences with input from other public health disciplines. Increasing understanding of how the human genotype and phenotype affects absorption, distribution, metabolism, and excretion of external agents, including foods, is providing insight into answers to the oldest human question about disease: ‘Why me?’ The risk paradigm components of hazard assessment, dose-response analysis, exposure assessment, and risk characterization, and the toxicological concepts on which they are based, have proven durable in approaching increasingly complex environmental hazards. Newer approaches to managing risk, such as the precautionary principle, and newer challenges ranging from nanotechnology to the health impacts of global climate change, are necessitating more systematic thinking on how best to protect human health and the environment.
TinyverseGP: Towards a Modular Cross-domain Benchmarking Framework for Genetic Programming

Over the years, genetic programming (GP) has evolved, with many proposed variations, especially in how they represent a solution. Being essentially a program synthesis algorithm, it is capable of tackling multiple problem domains. Current benchmarking initiatives are fragmented, as the different representations are not compared with each other and their performance is not measured across the different domains. In this work, we propose a unified framework, dubbed TinyverseGP (inspired by tinyGP), which provides support to multiple representations and problem domains, including symbolic regression, logic synthesis and policy search.
Recurrent-and-Parallel GMDH Algorithms for High-Performance Computing

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Synthetic Biology in Leishmaniasis: Design,simulation and validation of constructed Genetic circuit

Building circuits and studying their behavior in cells is a major goal of systems and synthetic biology. Synthetic biology enables the precise control of cellular states for systems studies, the discovery of novel parts, control strategies, and interactions for the design of robust synthetic systems. To the best of our knowledge,there are no literature reports for the synthetic circuit construction for protozoan parasites. This paper describes the construction of genetic circuit for the targeted enzyme inositol phosphorylceramide synthase belonging to the protozoan parasite Leishmania. To explore the dynamic nature of the circuit designed, simulation was done followed by circuit validation by qualitative and quantitative approaches. The genetic circuit designed for inositol phosphorylceramide synthase shows responsiveness, oscillatory and bistable behavior, together with intrinsic robustness.
A Fast and Refined Cancer Regions Segmentation Framework in Whole-slide Breast Pathological Images

AbstractSupervised learning methods are commonly applied in medical image analysis. However, the success of these approaches is highly dependent on the availability of large manually detailed annotated dataset. Thus an automatic refined segmentation of whole-slide image (WSI) is significant to alleviate the annotation workload of pathologists. But most of the current ways can only output a rough prediction of lesion areas and consume much time in each slide. In this paper, we propose a fast and refined cancer regions segmentation framework v3_DCNN, which first preselects tumor regions using a classification model Inception-v3 and then employs a semantic segmentation model DCNN for refined segmentation. Our framework can generate a dense likelihood heatmap with the 1/8 side of original WSI in 11.5 minutes on the Camelyon16 dataset, which saves more than one hour for each WSI compared with the initial DCNN model. Experimental results show that our approach achieves a higher FROC score 83.5% with the champion’s method of Camelyon16 challenge 80.7%. Based on v3 DCNN model, we further automatically produce heatmap of WSI and extract polygons of lesion regions for doctors, which is very helpful for their pathological diagnosis, detailed annotation and thus contributes to developing a more powerful deep learning model.