ethicalabs/Research-Intent-Collab huggingface dataset:
Toward the precision breast cancer survival prediction utilizing combined whole genome-wide expression and somatic mutation analysis

Breast cancer is the most common type of invasive cancer in woman. It accounts for approximately 18% of all cancer deaths worldwide. It is well known that somatic mutation plays an essential role in cancer development. Hence, we propose that a prognostic prediction model that integrates somatic mutations with gene expression can improve survival prediction for cancer patients and also be able to reveal the genetic mutations associated with survival.Differential expression analysis was used to identify breast cancer related genes. Genetic algorithm (GA) and univariate Cox regression analysis were applied to filter out survival related genes. DAVID was used for enrichment analysis on somatic mutated gene set. The performance of survival predictors were assessed by Cox regression model and concordance index(C-index).We investigated the genome-wide gene expression profile and somatic mutations of 1091 breast invasive carcinoma cases from The Cancer Genome Atlas (TCGA). We identified 118 genes with high hazard ratios as breast cancer survival risk gene candidates (log rank p <  0.0001 and c-index = 0.636). Multiple breast cancer survival related genes were found in this gene set, including FOXR2, FOXD1, MTNR1B and SDC1. Further genetic algorithm (GA) revealed an optimal gene set consisted of 88 genes with higher c-index (log rank p <  0.0001 and c-index = 0.656). We validated this gene set on an independent breast cancer data set and achieved a similar performance (log rank p <  0.0001 and c-index = 0.614). Moreover, we revealed 25 functional annotations, 15 gene ontology terms and 14 pathways that were significantly enriched in the genes that showed distinct mutation patterns in the different survival risk groups. These functional gene sets were used as new features for the survival prediction model. In particular, our results suggested that the Fanconi anemia pathway had an important role in breast cancer prognosis.Our study indicated that the expression levels of the gene signatures remain the effective indicators for breast cancer survival prediction. Combining the gene expression information with other types of features derived from somatic mutations can further improve the performance of survival prediction. The pathways that were associated with survival risk suggested by our study can be further investigated for improving cancer patient survival.
Fifteen Diplomats on a Powder Keg : Africa and the United Nations Security Council

Nordic Africa Institute's Projects on the UN Security Council and the Elected Ten
Making Large Language Models Better Reasoners with Alignment

Reasoning is a cognitive process of using evidence to reach a sound conclusion. The reasoning capability is essential for large language models (LLMs) to serve as the brain of the artificial general intelligence agent. Recent studies reveal that fine-tuning LLMs on data with the chain of thought (COT) reasoning process can significantly enhance their reasoning capabilities. However, we find that the fine-tuned LLMs suffer from an \textit{Assessment Misalignment} problem, i.e., they frequently assign higher scores to subpar COTs, leading to potential limitations in their reasoning abilities. To address this problem, we introduce an \textit{Alignment Fine-Tuning (AFT)} paradigm, which involves three steps: 1) fine-tuning LLMs with COT training data; 2) generating multiple COT responses for each question, and categorizing them into positive and negative ones based on whether they achieve the correct answer; 3) calibrating the scores of positive and negative responses given by LLMs with a novel constraint alignment loss. Specifically, the constraint alignment loss has two objectives: a) Alignment, which guarantees that positive scores surpass negative scores to encourage answers with high-quality COTs; b) Constraint, which keeps the negative scores confined to a reasonable range to prevent the model degradation. Beyond just the binary positive and negative feedback, the constraint alignment loss can be seamlessly adapted to the ranking situations when ranking feedback is accessible. Furthermore, we also delve deeply into recent ranking-based alignment methods, such as DPO, RRHF, and PRO, and discover that the constraint, which has been overlooked by these approaches, is also crucial for their performance. Extensive experiments on four reasoning benchmarks with both binary and ranking feedback demonstrate the effectiveness of AFT.
Reinforcement Learning with Human Feedback (RLHF): Shaping the Future of AI Alignment Roadmap 2025-2035

We present a comprehensive survey and roadmap for Reinforcement Learning with Human Feedback (RLHF) and closely related methods (DPO, RRHF, Constitutional AI). We unify practical training recipes (SFT → reward modeling → PPO-style optimization), discuss failure modes (reward hacking, KL instabilities), and evaluate data-efficiency trade-offs using reproducible synthetic experiments. We include a managerial roadmap (2025-2035) for scaling human and AI feedback, and cite key works with DOIs.
On the ratification of the Rome statute of the International Criminal Court by Ukraine

The article deals with conceptual issues related to legal, political and geopolitical aspects of Ukraine’s ratification of the Rome Statute of the International Criminal Court (ICC) in the context of the armed aggression of the Russian Federation.  The author analyses the reasons for the delay in the ratification process, including legal reservations of the Constitutional Court of Ukraine, political circumstances and the international context. The author examines the consequences of ratification for the legal system of Ukraine, including changes in criminal and criminal procedure legislation, as well as the peculiarities of cooperation with the ICC.  The role of the Rome Statute in ensuring justice for war crimes, crimes against humanity and crimes of aggression committed during the war of the Russian Federation against Ukraine is considered. Attention is focused on the principle of complementarity, which determines the priority of national jurisdiction over international jurisdiction. It is noted that due to this principle, Ukraine has the opportunity to investigate crimes as a matter of priority, which avoids excessive interference of international judicial institutions in the internal affairs of the country.  The article also emphasises that the ratification of the Rome Statute carries potential risks, including the possibility of prosecution of Ukrainian military personnel in case of doubts about their compliance with international humanitarian law. The author analyses the political challenges associated with ratification, including possible attempts by aggressor states to use the ICC mechanisms to exert political pressure on Ukraine. The article highlights Ukraine’s international obligations to the European Union in the light of European integration, in particular, compliance with the provisions of the EU-Ukraine Association Agreement.  At the same time, the benefits gained by Ukraine after joining the Rome Statute are assessed, including the expansion of international cooperation, strengthening of Ukraine’s role in global law enforcement and strengthening of its position in the field of international justice. However, the author emphasises the need for further improvement of national legislation in order to effectively implement the provisions of the Rome Statute.  The author concludes that Ukraine’s ratification of the Rome Statute is an important step towards strengthening the international legal order and combating impunity for the most serious crimes, but it requires a careful approach to the adaptation of national legislation and law enforcement mechanisms.
R2P – A Problem of Inconsistency in Mass Atrocity Response in the United Nations Security Council : A Comparative Case Study of Libya, Cote d’Ivoire, and Myanmar

The “Responsibility to Protect” (R2P) doctrine was created with the purpose of providing an implementation mechanism for the international community to halt and prevent mass atrocity conflicts, however, it is not a legally binding framework, and requires the UNSC’s engagement for its successful implementation. Whilst R2P is a rhetorically compelling international norm, it falls apart in practice. The lack of consistency in its implementation and the UNSC’s inaction to various cases of mass atrocity, which are, in principle, applicable to the doctrine, has sparked controversy. This thesis examines conflicts in which R2P has been utilized and one conflict of similar dimensions in which the principle wasn’t applied, discussing the factors that could explain the question ‘why has the application of R2P been inconsistent at halting atrocity conflicts?’. By juxtaposing two theoretical lenses, Realism and English School, and applying a comparative analysis to these three cases, the thesis establishes that state behavior is driven by preserving power and resources, when it converges with upholding international norms and values. Thus, the thesis concludes that members of the Security Council will support R2P implementation and uphold shared norms and values, only when it serves their national interests.
Graph Neural Networks for Complex Relational Modeling and Knowledge Discovery

The paradigm of machine learning has traditionally relied on the assumption that data instances are independent and identically distributed. However, this foundational assumption is fundamentally flawed when applied to complex systems where interconnectedness is the defining characteristic. Graph Neural Networks have emerged as a revolutionary framework designed to process and learn from data represented in graph structures, thereby capturing the intricate relational dependencies that conventional models overlook. This comprehensive academic paper explores the theoretical underpinnings, methodological advancements, and empirical applications of Graph Neural Networks in the context of complex relational modeling and knowledge discovery. By systematically examining the transition from early structural heuristics to contemporary deep learning architectures tailored for graph topologies, this study elucidates the mechanisms through which nodes, edges, and global graph attributes are embedded into continuous vector spaces. Furthermore, the paper investigates the critical challenges inherent in scaling these models, addressing phenomena such as oversmoothing, bottleneck issues, and the computational complexity associated with dense information aggregation across vast networks. Through detailed analysis of message passing paradigms and structural encoding techniques, this research demonstrates the capacity of graph-based models to uncover hidden patterns, predict latent relationships, and facilitate unprecedented levels of knowledge discovery across diverse domains including computational biology, social network analysis, and complex recommendation systems. The findings synthesize current theoretical frameworks and propose comprehensive pathways for future research in structural representation learning.
Evaluating the effectiveness of digital scenario-based English teaching at the university level using the artificial intelligence generated content

This study evaluates the effectiveness of digital scenario-based English conversation teaching at the university level using Artificial Intelligence Generated Content (AIGC). This study aims to design a digital-scenario-based AIGC teaching model, to evaluate its effectiveness on the learning experience and communication skills of the students, and to identify the pedagogical and technical challenges related to it. Through the mixed-method approach that involves 130 first-year English majors at the Punjab University of Pakistan, the research applied a comparative experiment of 18 weeks (experimental group: AIGC Framework; Control Group: Traditional Methods). The results demonstrated that the AIGC model dynamically generated the pronunciation, language accuracy, and communication flow compared to the scenario-generated, interactive functions, and the personal response in real-time. Additionally, the model increased learning interest, work adaptability, and teacher-student interactions. However, challenges included the quality of incompatible material, limited emotional depth in AI interaction, technical adaptability barriers for less efficient students and risks of more dependence on technology. The study concludes that while AIGC provides transformational ability to learn individual, immersive language, its successful integration requires advanced teacher training, strong material review mechanisms, and analogous support for diverse learners. The recommendations highlight refining cultural relevance, ensuring moral deployment, and discovering multimodal AI integration for future educational innovation.
Quantum Generative Models for Small Molecule Drug Discovery

Existing drug discovery pipelines take 5-10 years and cost billions of dollars. Computational approaches aim to sample from regions of the whole molecular and solid-state compounds called chemical space which could be on the order of 1060 . Deep generative models can model the underlying probability distribution of both the physical structures and property of drugs and relate them nonlinearly. By exploiting patterns in massive datasets, these models can distill salient features that characterize the molecules. Generative Adversarial Networks (GANs) discover drug candidates by generating molecular structures that obey chemical and physical properties and show affinity towards binding with the receptor for a target disease. However, classical GANs cannot explore certain regions of the chemical space and suffer from curse-of-dimensionality. A full quantum GAN may require more than 90 qubits even to generate QM9-like small molecules. We propose a qubit-efficient quantum GAN with a hybrid generator (QGAN-HG) to learn richer representation of molecules via searching exponentially large chemical space with few qubits more efficiently than classical GAN. The QGANHG model is composed of a hybrid quantum generator that supports various number of qubits and quantum circuit layers, and, a classical discriminator. QGAN-HG with only 14.93% retained parameters can learn molecular distribution as efficiently as classical counterpart. The QGAN-HG variation with patched circuits considerably accelerates our standard QGANHG training process and avoids potential gradient vanishing issue of deep neural networks. Code is available on GitHub https://github.com/jundeli/quantum-gan.
Improving Fairness in Criminal Justice Algorithmic Risk Assessments Using Conformal Prediction Sets

We found an interpretive error in the method. We are trying now to develop a better approach
Analysis of Thermal Transport in Zinc Oxide Nanowires Using Molecular-Dynamics Simulations With the ReaxFF Reactive Force-Field

The objective of this paper is to determine the thermal conductivity of Zinc Oxide nanowire by Steady State Non-equilibrium and Transient Non-equilibrium Molecular Dynamics (SS-NEMD and T-NEMD) simulations using the ReaxFF reactive force field [5]. While SS-NEMD uses an equilibrated system and statistical averaging; T-NEMD uses cooling/heating rates in order to calculate the conductivity. The validity of the methods is first verified using Argon as a test case. The thermal conductivity of Argon thus calculated is compared with those presented by Bhowmick and Shenoy [20]. We then study the effects of system size using SS-NEMD method while effects of periodic boundary conditions — 1D, 2D and bulk variation of conductivity with temperature are analyzed using T-NEMD simulations. The results obtained compare favorably with those measured experimentally [12, 13]. Thus the SS-NEMD and T-NEMD methods are alternatives to the traditional Green-Kubo approach. In conjunction with ReaxFF, they are computationally cheaper than the Green-Kubo method and can be used to determine the thermal conductivity of materials involved in surface chemistry reactions such as catalysis and sintering.
An accelerated pipeline for multi-label renal pathology image segmentation at the whole slide image level

Deep-learning techniques have been used widely to alleviate the labour-intensive and time-consuming manual annotation required for pixel-level tissue characterization. Our previous study introduced an efficient single dynamic network - Omni-Seg - that achieved multi-class multi-scale pathological segmentation with less computational complexity. However, the patch-wise segmentation paradigm still applies to Omni-Seg, and the pipeline is time-consuming when providing segmentation for Whole Slide Images (WSIs). In this paper, we propose an enhanced version of the Omni-Seg pipeline in order to reduce the repetitive computing processes and utilize a GPU to accelerate the model's prediction for both better model performance and faster speed. Our proposed method's innovative contribution is two-fold: (1) a Docker is released for an end-to-end slide-wise multi-tissue segmentation for WSIs; and (2) the pipeline is deployed on a GPU to accelerate the prediction, achieving better segmentation quality in less time. The proposed accelerated implementation reduced the average processing time (at the testing stage) on a standard needle biopsy WSI from 2.3 hours to 22 minutes, using 35 WSIs from the Kidney Tissue Atlas (KPMP) Datasets. The source code and the Docker have been made publicly available at https://github.com/ddrrnn123/Omni-Seg.
Assessment of Employees' Ergonomic Issues in The Workplace in Terms of Occupational Health and Safety Practices: A Meta-Analysis Study

Purpose: This study is a meta-analysis aimed at evaluating ergonomic issues in workplaces in terms of occupational health and safety practices.  Materials and Methods: This study is of a meta-analytic nature. Data used in the meta-analysis were obtained through searches on databases such as Google Scholar, YÖK Thesis, EBSCO, and Web Of Science without any time restrictions between January and March 2024. A total of 15 research studies were included in the search results. The data obtained were synthesized using narrative synthesis and meta-analysis methods. The total sample size of the studies used in this study is 65,160.  Results: In this meta-analysis study, it was found that the practices implemented for addressing ergonomic issues faced by employees in the workplace were effective (SMD: 0.367, 95% CI: 0.055-0.679; Z = 2.305, p = 0.021, I2 = 97.761%, Q = 625.169). The variance among studies is statistically significant according to the analysis results (p &lt; 0.05). It is believed that challenges related to the structure of work in workplaces lead to various discomforts in employees, and therefore, ergonomic practices can minimize physical strains on employees, contribute to work efficiency, and enhance employee health and safety.
Theorising management accounting practices and service quality: the case of Malaysian health tourism hospital destinations

Private hospitals in Malaysia are now extending their healthcare services to cater for patients travelling from the global market. These organisations are aggressively gearing themselves up to deliver quality services and to demonstrate quality assurance on their services. Hence, service quality strategies and initiatives have become critical for success in hospitals and the way forward. In pursuing quality initiatives, hospitals need to gain access to quality-related information and adopting the appropriate management accounting practices would be an important enabler and facilitator to generate useful management information leading to organisational successes. This study examines the usage of management accounting practices in these hospitals and attempts to develop a management accounting framework that would effectively facilitate the implementation of service quality initiatives pursued. Structured questionnaire was used to gather the perceptions of hospital management on service quality implementation level, usage level of management accounting practices usage and performance level of hospitals. Quantitative methods using MANOVA and structural equation modelling with AMOS were employed for data analysis. The results show that service quality implementation level was not found to be higher in hospitals that have obtained more types of quality achievements as compared to those that have obtained less or no achievements. Hospitals have benefitted from the implementation of quality initiatives related to management, process and analysis. However, such benefit was not found in quality initiatives related to patient care. In terms of the mediating role of management accounting practices, the results show no mediation effect on the impact of patient care on hospital performance. Meanwhile, there was partial mediating effect on the impact of management, process and analysis on hospital performance. Specifically, the mediating effect was found to be stronger from the advanced accounting practices compared to the basic accounting practices. The findings lead to a conclusion that adopting the appropriate management accounting practices would effectively facilitate hospitals in their quality pursuits. Limitations of this study and recommendation for future research are presented.
Water, Sanitation, and Hygiene (WASH) in Ethiopia: An International Human Rights Framework

Human rights are the legal claims that individuals have on society on the basis of being human. It is surprising then that water, a basic necessity to life, has only been explicitly recognized as a human right since 2002 by the Committee on Economic, Social, and Cultural Rights (CESCR) and since 2010 by the United Nations General Assembly. Recognition as a human right makes its fulfillment a duty of the state to its citizens, obligating steps towards implementation accountable to international law. Access to safe drinking water, adequate sanitation, and hygiene (WASH) would alleviate 10% of the global disease burden. The magnitude of its effect on health is seen clearly in Ethiopia where 60% to 80% of communicable diseases are attributable to limited access to WASH. Ethiopia has achieved MDG 7c for access to clean water supply and has made good progress on ending open defecation. However, its progress on handwashing lags far behind despite its cost-effectiveness. This is partly because of the differences in the codification of the rights to water, sanitation, and hygiene individually. This paper will examine the implications of the international law framework for WASH implementation in Ethiopia.