ethicalabs/Research-Intent-Collab huggingface dataset:
Chromatin and epigenetic regulation in malignant tumors: A comprehensive review

Abstract                   Epigenetics and chromatin biology have been two distinct research fields for many years. There is a close relationship between epigenetics and chromatin biochemistry, and the dynamic changes of chromatin serve as an important basis for epigenetic regulation. The relationship between epigenetics and chromatin is reflected in the dynamic changes and structural adjustments of chromatin. In this article, we focus on previously underappreciated chromatin remodeling proteins. This review summarizes the biological significance of chromatin remodeling complexes in malignancies, including the physiological role of chromatin remodeling complexes in normal cells and the dysregulation of chromatin remodeling complexes in malignant tumors with genetic, epigenetic, and gene expression alterations. It is important to explore the impact of chromatin remodeling complexes on cancer therapy, as predictors of drug response, targets of drug response, and contributors to therapeutic resistance.
Issues arising from benchmarking single-cell RNA sequencing imputation methods

On June 25th, 2018, Huang et al. published a computational method SAVER on Nature Methods for imputing dropout gene expression levels in single cell RNA sequencing (scRNA-seq) data. Huang et al. performed a set of comprehensive benchmarking analyses, including comparison with the data from RNA fluorescence in situ hybridization, to demonstrate that SAVER outperformed two existing scRNA-seq imputation methods, scImpute and MAGIC. However, their computational analyses were based on semi-synthetic data that the authors had generated following the Poisson-Gamma model used in the SAVER method. We have therefore re-examined Huang et al.'s study. We find that the semi-synthetic data have very different properties from those of real scRNA-seq data and that the cell clusters used for benchmarking are inconsistent with the cell types labeled by biologists. We show that a reanalysis based on real scRNA-seq data and grounded on biological knowledge of cell types leads to different results and conclusions from those of Huang et al.
Artificial Intelligence and Machine Learning in the Diagnosis and Prognosis of Diseases Through Breath Analysis: A Scoping Review

Breath analysis is a non-invasive diagnostic method that offers insights into both physiological and pathological conditions. Exhaled breath contains volatile organic compounds, which act as biomarkers for disease detection, allowing for the monitoring of treatments and the tailoring of medicine to individuals. Recent advancements in chemical sensing, mass spectrometry, and spectroscopy have improved the ability to identify these biomarkers; however, traditional statistical approaches often struggle to handle the complexities of breath data. Artificial intelligence (AI) and machine learning (ML) have revolutionized breath analysis by uncovering intricate patterns among volatile breath markers, enhancing diagnostic precision, and facilitating real-time disease identification. Despite significant progress, challenges remain, including issues with data standardization, model interpretability, and the necessity for extensive and varied datasets. This study reviews the applications of ML in analyzing breath volatile organic compounds, highlighting methodological shortcomings and obstacles to clinical validation. A thorough literature review was performed using the PubMed and Scopus databases, which included studies that focused specifically on the role of machine learning in disease diagnosis and incidence prediction via breath analysis. Among the 524 articles reviewed, 97 satisfied the specified inclusion criteria. The selected studies applied ML techniques, fell within the scope of this review, and emphasize the potential of ML models for non-invasive diagnostics. The findings indicate that traditional ML methods dominate, while ensemble methods are on the rise, and deep learning (DL) techniques (especially CNNs and LSTMs) are increasingly used for classifying respiratory diseases. Techniques for feature selection (such as PCA and ML-based methods) were frequently implemented, though challenges related to explainability and data standardization persist. Future studies should focus on enhancing model transparency and developing methods to further integrate AI into the clinical setting to facilitate early disease detection and advance precision medicine.
Scalable Cold-Start Optimization in Serverless Computing: Leveraging Function Fusion With PanOpticon Simulator

Serverless computing has transformed cloud computing with its inherent scalability, pay-as-you-go pricing model, and low-latency execution. However, cold-start delays arising from the on-demand initialization of execution environments such as containers or virtual machines remain a persistent bottleneck, particularly for latency-sensitive applications. This paper addresses this challenge by proposing a novel function fusion approach to minimize cold-start latency in serverless environments. The proposed strategy leverages the PanOpticon simulator, a comprehensive platform for deploying and evaluating Function-as-a-Service (FaaS) applications. The proposed approach reduces redundant initialization overhead and improves resource utilization by dynamically merging functions within a workflow. Unlike conventional frameworks, the proposed method integrates function fusion directly into the simulation and evaluation pipeline, enabling more scalable and performance-aware optimizations. Extensive simulation-based evaluations demonstrate that the fusion strategy significantly reduces cold-start delays while enhancing system throughput and responsiveness across diverse workloads. The results confirm that function fusion not only minimizes latency but also maximizes efficiency, making it a robust solution for real-time, scalable serverless applications. This work contributes a practical framework for cold-start mitigation and lays the groundwork for future research in optimizing FaaS-based cloud architectures.
Natural Language Processing for Electronic Health Record Optimization in Android Applications

This paper aims to demonstrate the development of an Android application designed to improve healthcare data management using Natural Language Processing (NLP) technologies. It develops an Android application that enables healthcare professionals to access patient records anytime, enhancing decision-making. The system uses Optical Character Recognition (OCR) to extract patient data from documents, simplifying data entry for diagnoses and treatments. It also incorporates Quick Response (QR) code generation for accurate data retrieval. By leveraging NLP, the mobile application will enhance efficiency, accessibility, and accuracy in healthcare, particularly benefiting developing countries where digital health records are uncommon..
“Access Denied” – Interpreting the Digital Divide by Examining the Right of Prisoners to Access the Internet in the Case Law of the European Court of Human Rights

Abstract                   The present paper aims to investigate prisoners’ rights to use and access the Internet, focusing on the jurisprudence of the European Court of Human Rights (ECtHR). The study’s principal objective is to assess the fundamental rights aspects of Internet access, particularly within the context of relevant EU legislation, and to interpret how the digital divide impacts prisoners. The study primarily relies on a thorough review of pertinent literature and legal materials, and it incorporates case studies from Estonia, Lithuania and Türkiye to contextualize the literature findings within specific legal jurisdictions. The research findings suggest that prisoners’ Internet access should be evaluated in accordance with fundamental rights, including Article 10 of the European Convention on Human Rights. Regarding the digital divide, the study concludes that limiting prisoners’ access to the Internet could exacerbate the already existing societal gap, potentially hindering their reintegration into society. The particular focus on the above three countries stems from the fact that as per the precedent of the ECtHR, only four countries are involved in cases concerning restriction of access to the Internet (Estonia, Lithuania, Türkiye and Russia); however, Russia ceased to be a party of the European Convention on Human Rights, so analyzing the future of the issue of Internet acces there is highly limited. Our research contributes significantly to the literature on the digital divide, particularly in terms of its legal implications. The study’s comprehensive approach, which integrates both theoretical and practical aspects, is beneficial for domestic legal professionals and researchers engaged in interdisciplinary investigations of EU law and fundamental rights.
INTEGRATION OF IOT AND EDGE COMPUTING FOR LOW-LATENCY DATA ANALYTICS IN SMART CITIES AND IOT NETWORKS

This quantitative, cross-sectional, case-based study investigates how the integration of Internet of Things (IoT) infrastructures with edge computing architectures enhances low-latency data analytics and improves the quality of smart city services. Although smart city ecosystems continue to expand globally, a persistent challenge lies in their reliance on cloud-centric analytics models, which often struggle to satisfy stringent latency, reliability, and responsiveness requirements associated with time-sensitive public services such as traffic control, emergency response, environmental monitoring, and utility management. The central problem addressed in this study is that traditional cloud-dependent analytics pipelines frequently introduce processing delays and network congestion, thereby constraining the ability of municipalities and smart service operators to deliver real-time, high-quality services. This study therefore aims to provide empirical evidence, grounded in real IoT edge deployment scenarios across cloud and enterprise environments, regarding how integration quality and underlying infrastructure conditions shape latency outcomes and perceived service performance. Data were collected using a structured questionnaire administered to professionals directly involved in smart city, IoT systems design, and network infrastructure projects. Out of 250 distributed surveys, 200 valid responses were obtained, yielding an 80 percent usable response rate suitable for inferential analyses. Key constructs—including IoT Edge Integration Quality, Network Infrastructure Quality, System Reliability, Low Latency Analytics Performance, Smart City Service Quality, Adoption or Optimization Intention, and Perceived Integration Challenges—were measured using multi-item Likert scales. Analytical procedures included descriptive statistics, reliability and validity testing, Pearson correlations, and multiple regression modeling to examine predictive relationships among the variables. The regression models accounted for 56 percent of the variance in low-latency analytics performance and 61 percent of the variance in smart city service quality. IoT edge integration quality (β = 0.39), system reliability (β = 0.26), and network infrastructure quality (β = 0.17) emerged as significant predictors of low-latency analytics, underscoring the combined importance of architectural coherence, dependable system behavior, and communication efficiency. Furthermore, low-latency analytics performance demonstrated a strong positive effect on smart city service quality (β = 0.37), highlighting latency reduction as a key mechanism for improving user experience, operational responsiveness, and service effectiveness.
Indigenous Peoples’ Land Rights and Peacemaking

Abstract                   Almost forty years of successive peace processes addressing the Bangsamoro armed conflict in the Philippines have led to the creation of a Bangsamoro (‘Moro nation’) autonomous region in Mindanao, the acknowledgement of ‘Bangsamoro internal self-determination’, and the (formal) end of armed hostilities. These very same peace processes, however, have undermined the right to self-determination of those indigenous peoples, or Lumads, whose ancestral domains coincide with areas that are claimed to be part of a historic Bangsamoro homeland. For years, Lumads have sought legal recognition of their land claims, and Lumads now have to contend with a regional government where Bangsamoro goals, aspirations, and way of life hold precedence. This article makes the case that these peace processes have infringed on Lumads’ rights as indigenous peoples by substantially excluding them from peace negotiations and by privileging the Bangsamoro armed groups’ claims in the resulting peace agreements, particularly with regard to the notion of a Bangsamoro ‘ancestral domain’. The exclusionary language of these peace agreements, as well as resulting legislation, prevents the establishment of just, equitable, and inclusive governance in the Bangsamoro, and ultimately prevents the establishment of positive peace. This article argues that as long as there are no sufficient means for the protection and fulfilment of Lumads’ rights in the Bangsamoro autonomous region, particularly with regard to their ancestral domains, there can be no real peace—that is, both negative and positive peace—in this protracted conflict, despite the formal end of hostilities.
Features of military expertise on aggression against the sovereignty and territorial integrity of Ukraine

У тезах розглянуто деякі питання організації і проведення судових експертиз щодо агресії проти суверенітету та територіальної цілісності України. Запропонований алгоритм дій судового військового експерта, показані шляхи організації і підтримання його взаємодії з фахівцями інших експертних спеціальностей.  The abstracts consider some issues of organizing and conducting forensic military examinations on aggression against the sovereignty and territorial integrity of Ukraine. An algorithm of actions of a forensic military expert is proposed, ways of organizing and maintaining his interaction with specialists of other expert specialties are shown
Fragmentation of Public Opinion and Countermeasures in the New Media Environment : Master's Thesis / Xu Dan; BSU, Faculty of Philosophy and Social Sciences, Department of Social Communication; Academic supervisor: Alexander S. Solodukho

Aim of the Study: To investigate the challenges of public opinion formation and governance in the new media environment. Identify strategies to mitigate public opinion defocus, enhance audience engagement, and promote informed discourse, proposing relevant governance measures.
Peace and Conflict Resolution in Islam: A Perspective  Building

The critics of Islam have hypothesized that several teachings of Islam jeopardize the modern nation-state and its law and order. Islam is perceived as a part of the problem rather than a solution. Islam, however, has a rich inheritance of addressing conflicts in the world. The Islamic texts and Muslim heritage approaches to conflict resolution draw on religious values and present a considerable wealth of responses based on positive solutions to the conflict. The terms/concepts like social justice al-adl to the terms like sulh, translated as settlement and mūslāha translated as reconciliation, wasta (patronage-mediation), tāhkim (arbitration), and importantly salam a peace greeting among Muslims form the conceptual framework of the conflict resolution and peacebuilding in Islam. The specific term for non-violence in the Arabic language is not known, however the alternate vocabulary which stresses non-vehemence and peace is al-unf (no force), sabr (patience), akhlāq (virtuous character), etc. Since the conflict begins within a person, one strives to resolve that conflict through aql (intellect) to fight al-hawa (passions and desire) to overcome jahl (ignorance) with ikhlas (purity) then ihsan (beneficence) or putting the good and beautiful into practice is exercised and above all fighting for social justice jihad. This paper is an effort to build the theoretical aspect of Islam and conflict resolution. A descriptive research design has been employed for the present study. The data was collected through primary and secondary sources of information, and the method of data collection adopted was content analysis.
Phylogenetic Convolutional Neural Networks in Metagenomics

Background: Convolutional Neural Networks can be effectively used only when data are endowed with an intrinsic concept of neighbourhood in the input space, as is the case of pixels in images. We introduce here Ph-CNN, a novel deep learning architecture for the classification of metagenomics data based on the Convolutional Neural Networks, with the patristic distance defined on the phylogenetic tree being used as the proximity measure. The patristic distance between variables is used together with a sparsified version of MultiDimensional Scaling to embed the phylogenetic tree in a Euclidean space. Results: Ph-CNN is tested with a domain adaptation approach on synthetic data and on a metagenomics collection of gut microbiota of 38 healthy subjects and 222 Inflammatory Bowel Disease patients, divided in 6 subclasses. Classification performance is promising when compared to classical algorithms like Support Vector Machines and Random Forest and a baseline fully connected neural network, e.g. the Multi-Layer Perceptron. Conclusion: Ph-CNN represents a novel deep learning approach for the classification of metagenomics data. Operatively, the algorithm has been implemented as a custom Keras layer taking care of passing to the following convolutional layer not only the data but also the ranked list of neighbourhood of each sample, thus mimicking the case of image data, transparently to the user. Keywords: Metagenomics; Deep learning; Convolutional Neural Networks; Phylogenetic trees
Role of data governance in food and nutrition security: insights from a multi-method approach to public policy and practices in Chile

Sustainable food systems and their governance, particularly what’s related to the role of data and information, are essential for ensuring health, heritage, and environmental sustainability. This thesis investigates how an integrated and diversified data governance approach for food and nutrition security (FNS) can effectively address Chile's double burden of malnutrition. In order to assess these mechanisms and their complexity, this research includes (i) a literature review to explore the emerging debates on data governance within the field of FNS, (ii) a policy integration and coherence analysis at the national level through FNS public programs, (ii) a demographic and spatial food insecurity distribution analysis, and (iii) a reflective piece on multimethod approaches to research and knowledge creation. Key findings indicate that current debates call for integrating diverse data, enhancing data quality and access, and developing robust, ethical, and culturally sensitive governance frameworks. While there is some interministerial governance integration, coherence with the National Policy of Food and Nutrition is insufficient. Vulnerable households in Chile face significant challenges in meeting recommended dietary intakes, with socio-demographic and territorial factors influencing dietary diversity. Effective governance of food systems requires a multi-level approach that integrates national policies, identifies groups most at risk of food insecurity, and adopts a multimethod strategy to enable participatory co-creation and broader dissemination of data and knowledge. These strategies are essential for achieving sustainable diets and addressing food security challenges in diverse contexts. Future research should focus on exploring challenges in program execution, considering contextual variations in policy implementation, expanding the scope to include other government bodies and civil society organizations, and analyzing temporal variations in geospatial analysis to identify differences and interactions in critical food groups.
DiM: Distilling Dataset into Generative Model

Dataset distillation reduces the network training cost by synthesizing small and informative datasets from large-scale ones. Despite the success of the recent dataset distillation algorithms, three drawbacks still limit their wider application: i). the synthetic images perform poorly on large architectures; ii). they need to be re-optimized when the distillation ratio changes; iii). the limited diversity restricts the performance when the distillation ratio is large. In this paper, we propose a novel distillation scheme to \textbf{D}istill information of large train sets \textbf{i}nto generative \textbf{M}odels, named DiM. Specifically, DiM learns to use a generative model to store the information of the target dataset. During the distillation phase, we minimize the differences in logits predicted by a models pool between real and generated images. At the deployment stage, the generative model synthesizes various training samples from random noises on the fly. Due to the simple yet effective designs, the trained DiM can be directly applied to different distillation ratios and large architectures without extra cost. We validate the proposed DiM across 4 datasets and achieve state-of-the-art results on all of them. To the best of our knowledge, we are the first to achieve higher accuracy on complex architectures than simple ones, such as 75.1\% with ResNet-18 and 72.6\% with ConvNet-3 on ten images per class of CIFAR-10. Besides, DiM outperforms previous methods with 10\% $\sim$ 22\% when images per class are 1 and 10 on the SVHN dataset.
Steel 3D-Printing : Evaluation of Metal Additive Manufacturing(MAM) capabilities on Automotive Spares

The primary intention behind performing this thesis is to identify possibilities of implementing Metal Additive Manufacturing (MAM) in automotive industries in spare part manufacturing. This project tries to analyse the differences between conventional and contemporary manufacturing techniques. The industrial partner we worked with, Frauenthal Gnotec AB, specializes in traditional manufacturing techniques for making automobile spare parts primarily by stamping. Hence, a large building area is required to store the die and materials. Automobile spare parts are manufactured by demand. The organization has to have the die and material ready to go, forcing it to expand its inventories, workforce, and transportation, causing substantial financial liabilities.  The projects include a wide range of information from the different scientific articles, Journals, and consultations with AM services, Professors, and Technicians. The thesis studied the various available options in MAM and compared its specification with our client's requirements. The project estimates the cost, time for printing the components, thermo-mechanical properties, and structural properties of the component and its feasibility.   The project helped us put our theoretical knowledge about MAM into practice. It was very significant for us to have the opportunity to work with Frauenthal Gnotec AB, one of the leading automobile spare parts manufacturers in Sweden. Examine and evaluate their manufacturing and production strategies, which was very helpful for us in determining the efficacy of our efforts. Our scientific study, based on various simulations, optimizations, mechanical tests, and cost estimates, found MAM to be a promising future technology for the automotive industry.