ethicalabs/Research-Intent-Collab huggingface dataset:
Integrated Framework of Fragment-Based Method and Generative Model for Lead Drug Molecules Discovery

Generative models have proven valuable in generating novel lead molecules with drug-like properties. However, beyond generating drug-like molecules, the generative model should also be able to create drug molecules with structural properties and pharmacophores to modulate a specific disease. The molecular generation process should also address the multi-objective optimization challenge of producing molecules with the desired efficacy and minimal side effects. This may entail the generation of a diverse pool of molecules with the desired structural properties and pharmacophore, which would offer diverse options and paths to developing potential new drug candidates by prioritizing molecules that balance the desired properties that can cater to the needs of different individuals. Achieving this requires a generative model learning a large dataset of molecular instances with the desired chemical/structural properties. However, large sets of drug molecules are not readily available for many diseases as there are few known drug molecular instances for treating any disease. To address this challenge, this paper presents an in silico molecular generative framework aided by fragment-based molecules’ synthesis for generating a pool of lead molecular instances possessing structural properties and pharmacophores to treat a disease of interest. The operation of the framework is explored using Hypertension as the disease of interest and beta-blocker as the reference hypertension drug to be generated. We generated over 123 beta-blocker-like molecules and further virtual-screened them for drug-likeness, docking probability, scaffold diversity, electrostatic complementarity, and synthesis accessibility to arrive at the final lead beta-blocker-like molecules.
Cross-Language Translation Evaluation: A Comparative Analysis of the Quality of English-Chinese Interpretation by AI Models and Strategies for Improvement

Machine translation, as one of the important functions of artificial intelligence, plays an increasingly important role in cross-linguistic communication. The paper is meant to evaluate the performance of domestic up-and-coming AI mod-els in English-Chinese mutual translation tasks in order to analyze their func-tions and effects comparatively and explore strategies to improve the quality of AI translation in the process of both 2 languages. Based on the literature review in the first section, we put forward the hypothesis that there are certain defi-ciencies in AI translation tasks, and that different types of errors will be pro-duced depending on the target language. We will use both quantitative and qual-itative analysis methods, combined with example analysis and the application of the BLEU model, to evaluate the AI translation output in terms of several di-mensions. We also point out the limitations of the study and discuss the main problems of AI translation and propose targeted enhancement strategies in the conclusion part.
Responsible modelling: Unit testing for infectious disease epidemiology

Abstract                 Infectious disease epidemiology is increasingly reliant on large-scale computation and inference. Models have guided health policy for epidemics including COVID-19 and Ebola and endemic diseases such as malaria and tuberculosis. Yet a single coding bug may bias results, leading to incorrect conclusions and wrong actions that could cause avoidable harm. We are ethically obliged to ensure our code is as free of error as possible. Unit testing is a coding method to avoid such bugs, but unit testing is rarely used in epidemiology. We demonstrate through simple examples how unit testing can handle the particular quirks of infectious disease models.
Covariate Balancing Methods for Randomized Controlled Trials Are Not Adversarially Robust

The first step towards investigating the effectiveness of a treatment via a randomized trial is to split the population into control and treatment groups then compare the average response of the treatment group receiving the treatment to the control group receiving the placebo.   In order to ensure that the difference between the two groups is caused only by the treatment, it is crucial that the control and the treatment groups have similar statistics. Indeed, the validity and reliability of a trial are determined by the similarity of two groups' statistics. Covariate balancing methods increase the similarity between the distributions of the two groups' covariates. However, often in practice, there are not enough samples to accurately estimate the groups' covariate distributions. In this paper, we empirically show that covariate balancing with the Standardized Means Difference (SMD) covariate balancing measure, as well as Pocock's sequential treatment assignment method, are susceptible to worst-case treatment assignments. Worst-case treatment assignments are those admitted by the covariate balance measure, but result in highest possible ATE estimation errors. We developed an adversarial attack to find adversarial treatment assignment for any given trial. Then, we provide an index to measure how close the given trial is to the worst-case. To this end, we provide an optimization-based algorithm, namely Adversarial Treatment ASsignment in TREatment Effect Trials (ATASTREET), to find the adversarial treatment assignments.
Azure DevOps CI/CD Pipeline Automation:  Architecting Enterprise-Grade DevSecOps for Zero-Downtime Continuous Delivery

Enterprise software delivery demands continuous integration and continuous deployment pipelines that enforce quality, security, and compliance at every stage. Azure Pipelines, combined with SonarQube static application security testing, BlackDuck software composition analysis, and mature Git branching strategies, enables organizations to achieve daily zero-downtime deployments across portfolios of fifteen or more .NET services while maintaining one-hundred-percent quality gate compliance. This practitioner-focused account demonstrates how pipeline-as-code via YAML, artifact lifecycle management, and gated environment promotion transforms release cadences from biweekly batch deliveries into continuous deployment cycles. Version control maturity—progressing from legacy systems and Team Foundation Server to modern Git workflows—provides a bridging perspective that serves teams at all DevOps maturity levels. Security integrations embedded throughout the pipeline preempt vulnerabilities before production, making these capabilities especially critical in regulated environments where compliance automation is non-negotiable. Cloud migration initiatives requiring Infrastructure-as-Code readiness and containerization groundwork, including Docker Compose orchestration for data workloads, benefit directly from these established patterns. The practical significance extends beyond speed: compressed release cycles, parallel branch merges without conflicts, and scalable pipeline architectures collectively reduce organizational risk while accelerating value delivery.
THE CIVIL LIABILITY IMPLICATIONS OF TRADE SECRET MISAPPROPRIATION UNDER THE LEGAL FRAMEWORK OF AFGHANISTAN

In today's world, where knowledge and technology are key elements in economic growth and development, trade secrets, as part of the intangible assets of businesses and commercial companies, play a significant role in enhancing competitiveness and securing their market position. The importance of protecting these secrets becomes even more critical when considering that unauthorized disclosure or misuse can cause irreparable harm to their owners. As economic interactions become more complex and commercial relations expand, the phenomenon of trade secret violations has emerged as a serious legal challenge, leading to an increase in private law disputes. The legal system of Afghanistan, recognizing the importance of the issue, has made efforts to establish effective legal frameworks to protect trade secrets. Among these efforts, civil liability serves as a key legal tool, playing an essential role in compensating the injured parties and creating deterrence.  This study, employing a descriptive-analytical approach and using library-based research methods, aims to conduct a legal analysis of the effects of civil liability resulting from the violation of trade secrets, with a detailed examination of the role and function of civil liability in Afghanistan’s legal system. The findings of this research indicate that the right to compensation, the right to use legal actions, the right to employ precautionary measures, and the right to file a lawsuit are the most important effects of civil liability. Although Afghanistan’s legal system has taken steps towards the protection of trade secrets, it still requires further strengthening, revision, and greater alignment with global developments in this field. By offering practical recommendations, this study seeks to contribute to enhancing legal understanding, improving existing legal mechanisms, and creating more effective frameworks for the protection of trade secret owners’ rights.
Patient reported outcomes of recovery and quality of life post hepatitis C treatment

Background: Hepatitis C Virus (HCV) is a global health burden, with most infections found among people who inject drugs (PWID). While Direct Acting Antivirals (DAA) treatment now offers a speedy (8-12 weeks) and effective (95%) HCV cure, patient reported long term outcomes require greater understanding. Most research to date is based on short follow-up periods (&lt;12 months) and lack focus on DAA treatment. We report on patients’ perspectives on their quality of life up to 24 months after treatment with DDAs.Methods: We conducted a qualitative study with 30 patients who had been cured of HCV, with a mean follow-up time of 18 months. Most interviewees were male, older (36-55 years), and were treated in urban community-based needle and syringe exchanges in, Tayside, Scotland. All interviews were semi-structured, conducted over the phone, and analysed thematically.Results: Most interviewees reported positive recovery outcomes including improved physical and mental health, increased social contacts, motivation to stay drug free, and engage with social and healthcare services. One third of interviewees, however, reported poor quality of life post recovery from HCV. Some lost contact with their drug using social networks and became isolated. Poor mental health and lack of contact with services were commonly reported. Many sought help from services however found this challenging to navigate.Conclusion: Whilst DAA treatment of HCV is successful in the short-term, long-term recovery from substance use undoubtedly compromises these gains. Greater effort is required to address the long-term issues of social isolation and lack of services that strengthen ‘social’ recovery in the community. These could comprise access to local recovery groups and gateway services to improve life chances such as better housing, further education, and where appropriate employment opportunities.
Evaluating Vision-Language Models as Evaluators in Path Planning

Despite their promise to perform complex reasoning, large language models (LLMs) have been shown to have limited effectiveness in end-to-end planning. This has inspired an intriguing question: if these models cannot plan well, can they still contribute to the planning framework as a helpful plan evaluator? In this work, we generalize this question to consider LLMs augmented with visual understanding, i.e., Vision-Language Models (VLMs). We introduce PathEval, a novel benchmark evaluating VLMs as plan evaluators in complex path-planning scenarios. Succeeding in the benchmark requires a VLM to be able to abstract traits of optimal paths from the scenario description, demonstrate precise low-level perception on each path, and integrate this information to decide the better path. Our analysis of state-of-the-art VLMs reveals that these models face significant challenges on the benchmark. We observe that the VLMs can precisely abstract given scenarios to identify the desired traits and exhibit mixed performance in integrating the provided information. Yet, their vision component presents a critical bottleneck, with models struggling to perceive low-level details about a path. Our experimental results show that this issue cannot be trivially addressed via end-to-end fine-tuning; rather, task-specific discriminative adaptation of these vision encoders is needed for these VLMs to become effective path evaluators.
Depression, anxiety and suicidality among Chinese mental health professionals immediately after China's dynamic zero-COVID policy: A network perspective

Using network analysis, the interactions between mental health problems at the symptom level can be explored in depth. This study examined the network structure of depressive and anxiety symptoms and suicidality among mental health professionals after the end of China's Dynamic Zero-COVID Policy.A total of 10,647 mental health professionals were recruited nationwide from January to February 2023. Depression and anxiety were assessed using the 9-item Patient Health Questionnaire (PHQ-9) and 7-item Generalized Anxiety Disorder Scale (GAD-7), respectively, while suicidality was defined by a 'yes' response to any of the standard questions regarding suicidal ideation (SI), suicide plan (SP) and suicide attempt (SA). Expected Influence (EI) and Bridge Expected Influence (bEI) were used as centrality indices in the symptom network to characterize the structure of the symptoms.The prevalence of depression, anxiety, and suicidality were 45.99 %, 28.40 %, and 7.71 %, respectively. The network analysis identified GAD5 ("Restlessness") as the most central symptom, followed by PHQ4 ("Fatigue") and GAD7 ("Feeling afraid"). Additionally, PHQ6 ("Guilt"), GAD5 ("Restlessness"), and PHQ8 ("Motor disturbance") were bridge nodes linking depressive and anxiety symptoms with suicidality. The flow network indicated that the strongest connections of S ("Suicidality") was with PHQ6 ("Guilt"), GAD7 ("Feeling afraid"), and PHQ2 ("Sad mood").Depression, anxiety, and suicidality among mental health professionals were highly prevalent after China's Dynamic Zero-COVID Policy ended. Effective measures should target central and bridge symptoms identified in this network model to address the mental health problems in those at-risk.
CMIP6 multi-model projections of the groundwater response to climate change during the 21st century

Climate change will impact every component of the climate system and water cycle. It does not spare groundwater which account for approximately one third of the human fresh water withdrawals. The combined effect of climate change and groundwater pumping could lead to water scarcity and food insecurity in some regions. Therefore, it is essential to study the groundwater response to climate change to improve the development of adaptation and mitigation plans in water management.Here, we analyze the response of groundwater recharge to climate change using an ensemble of simulations runs with 22 fully coupled ocean-atmosphere-land models participating to the CMIP6 exercise. They are run from 1850 to 2100 and follow four of the latest IPCC scenarios of greenhouse gas future evolution. This analysis is supplemented with the assessment of the climate-driven response of groundwater level given by the CNRM global climate models (which are part of the CMIP6 exercise). These models represent the hydrogeological processes involving groundwater, including the two-way water exchanges with rivers and the unsaturated soil, the lateral groundwater fluxes, and the interactions with the atmosphere. Results show that on global average, groundwater recharge is expected to increase with climate change. The changes in groundwater recharge follow those of precipitation and, to a lesser extent, evapotranspiration and thus follow the same regional patterns. As these CMIP6 models do not represent human groundwater withdrawals, the projected changes in recharge are somewhat optimistic and could be out of step in regions with strong groundwater pumping. To address this limitation, results are put in perspective with projections of water withdrawals following the CMIP6 experiments. This analysis shows the combined effects of climate change and groundwater pumping on groundwater and help to understand the evolution of the future large scale water resource.
The patriarchy dressed in feminist clothes : A discourse analysis of the United Nations Security Council’s gendering of the concept Civilians

This thesis analyses key documents from the United Nations Security Council (the Council) meetings during the period of 1999 to 2001. This thesis maps out the shift in the discourses that occurred within the Council, when adopting United Nations Security Council’s resolution (UNSCR) 1325. Moreover, this thesis argues that the nodal point ‘Civilians’ has become gendered by being replaced by the concept of ‘Women’. This thesis argues that UNSC is misrepresenting female agency within the discourses, which has contributed to a gendering of the concept of civilians. Sexual violence, defined as a wartime weapon, has also been part of the construction of stereotypical gender binaries, which has constituted a representation of women as either victims or saviors within the discourses. It becomes evident that the notion of female agency as for example independent, empowered or strong has been neglected. The discourse theory provided by Ernesto Laclau and Chantal Mouffe is applied in order to map out the existing discourses within the Security Council meetings. The aim of this study is to acknowledge the importance of that women have been and still are being excluded from the ontology of war. Furthermore, when the role of women in war is described, it is in relation to constructed stereotypical gender binaries.
A practical guide to amplicon and metagenomic analysis of microbiome data

AbstractAdvances in high-throughput sequencing (HTS) have fostered rapid developments in the field of microbiome research, and massive microbiome datasets are now being generated. However, the diversity of software tools and the complexity of analysis pipelines make it difficult to access this field. Here, we systematically summarize the advantages and limitations of microbiome methods. Then, we recommend specific pipelines for amplicon and metagenomic analyses, and describe commonly-used software and databases, to help researchers select the appropriate tools. Furthermore, we introduce statistical and visualization methods suitable for microbiome analysis, including alpha- and beta-diversity, taxonomic composition, difference comparisons, correlation, networks, machine learning, evolution, source tracing, and common visualization styles to help researchers make informed choices. Finally, a step-by-step reproducible analysis guide is introduced. We hope this review will allow researchers to carry out data analysis more effectively and to quickly select the appropriate tools in order to efficiently mine the biological significance behind the data.
Artificial neural networks in weather forecasting

A method of statistical weather forecasting using databases of meteorological information is presented. The authors propose a special technique for constructing the learning set of images which represent the actual data. Forecasting of forthcoming weather events is based on the recognition of known data images using artificial neural networks and metric methods. Significant probabilities of true forecasting (up to 91%) on different data sets have been achieved. >
Tweet for Behavior Change: Using Social Media for the Dissemination of Public Health Messages

Background: Social media public health campaigns have the advantage of tailored messaging at low cost and large reach, but little is known about what would determine their feasibility as tools for inducing attitude and behavior change.   Objective: The aim of this study was to test the feasibility of designing, implementing, and evaluating a social media-enabled intervention for skin cancer prevention.   Conclusions: Social media-disseminated public health messages reached more than 23% of the Northern Ireland population. A Web-based survey suggested that the campaign might have contributed to improved knowledge and attitudes toward skin cancer among the target population. Findings suggested that shocking and humorous messages generated greatest impressions and engagement, but information-based messages were likely to be shared most. The extent of behavioral change as a result of the campaign remains to be explored, however, the change of attitudes and knowledge is promising. Social media is an inexpensive, effective method for delivering public health messages. However, existing and traditional process evaluation methods may not be suitable for social media.
Discontinuities in the Maximum-Entropy Inference

We revisit the maximum-entropy inference of the state of a finite-level quantum system under linear constraints. The constraints are specified by the expected values of a set of fixed observables. We point out the existence of discontinuities in this inference method. This is a pure quantum phenomenon since the maximum-entropy inference is continuous for mutually commuting observables. The question arises why some sets of observables are distinguished by a discontinuity in an inference method which is still discussed as a universal inference method. In this paper we make an example of a discontinuity and we explain a characterization of the discontinuities in terms of the openness of the (restricted) linear map that assigns expected values to states.
The impact of depression removal on catchment geomorphology, soil erosion and landscape evolution

AbstractThe current generation of landscape evolution models use a digital elevation model for landscape representation. These programs also contain a hydrological model that defines overland flow with the drainage network routed to an outlet. One of the issues with landscape evolution modelling is the hydrological correctness of the digital elevation model used for the simulations. Despite the wide use and increased quality of digital elevation models, data pits and depressions in the elevation data are a common feature and their removal will remain a necessary step for many data sets. This study examines whether a digital elevation model can be hydrologically correct (i.e. all depressions removed so that all water can run downslope) before use in a landscape evolution model and what effect depression removal has on long‐term geomorphology and hydrology. The impact on sediment transport rates is also examined. The study was conducted using a field catchment and a proposed landform for a post‐mining landscape. The results show that there is little difference in catchment geomorphology and hydrology for the non‐depression removed and depression removed data sets. The non‐depression removed and depression removed digital elevation models were also evaluated as input to a landscape evolution model for a 50 000 year simulation period. The results show that after 1000 years there is little difference between the data sets, although sediment transport rates did vary considerably early on in the simulation. Copyright © 2007 John Wiley &amp; Sons, Ltd.