ethicalabs/Research-Intent-Collab huggingface dataset:
Digital finance and the mobile money “social” enterprise : a socio-legal critique of M-Pesa in Kenya

Financial technology or fintech initiatives are gaining increasing global attention as instruments for financial inclusion and economic and social development. Among such initiatives, mobile-phone-enabled money transfer systems, or “mobile money”, have been particularly acclaimed for facilitating access to financial services and creating opportunities for the so-called “unbanked poor”. One of the first and most-discussed mobile money projects to date is M-Pesa in Kenya, a digital payment system which is now used by over 70 per cent of the Kenyan population across a variety of sectors including finance, commerce, education, health, and social welfare. M-Pesa is premised on a narrative of social entrepreneurship and has increasingly embraced the idea of philanthrocapitalism, promoting the logic that digital financial inclusion can simultaneously address social problems and produce profit. This paper brings together socio-legal enquiry and international political economy analysis to illustrate the institutional arrangements underpinning the development of M-Pesa and examine some of the projects built on its infrastructure. It argues that social entrepreneurship promotes a logic of opportunity rather than a politics of redistribution, favouring mobile money providers and the institutions involved in the mobile money social business over improving the lives of the intended beneficiaries, namely the unbanked poor.
Modification of a force field for molecular dynamics simulations of silicon etching by chlorine atoms

A modified classical molecular dynamics (MD) force field is presented for the interaction between silicon (Si) and chlorine (Cl). The original version of the force field is shown to significantly overestimate the probability of Si etching by thermal Cl atoms. However, the modified force field corrects this problem and results in generally good agreement with experimental data. Further, it is shown that while the modification of the force field improves the prediction of Si spontaneous etching with Cl atoms, it does not degrade predictions of atomic-layer etching of Si with Cl2 molecules.
Decolonization in a Digital Age: Cryptocurrencies and Indigenous Self-Determination in Canada

Abstract             Indigenous scholars and leaders have long been interested in reducing the role of the Canadian state in their political, economic, and social lives. This paper explores the extent to which digital currencies, such as Bitcoin or MazaCoin, might be used to facilitate Indigenous self-determination, political autonomy, and economic prosperity. Based on our review of the literature, we argue that cryptocurrencies demonstrate some potential for advancing these goals but that there are a number of potential roadblocks as well. Future research should investigate how Indigenous communities might use digital currencies and other related technologies to further their political, economic, and social goals.
Benchmarking EEG-based Cross-dataset Driver Drowsiness Recognition with Deep Transfer Learning

It usually takes a long time to collect data for calibration when using electroencephalography (EEG) for driver drowsiness monitoring. Cross-dataset recognition is desirable since it can significantly save the calibration time when an existing dataset is used. However, the recognition accuracy is affected by the distribution drift problem caused by different experimental environments when building different datasets. In order to solve the problem, we propose a deep transfer learning model named Entropy-Driven Joint Adaptation Network (EDJAN), which can learn useful information from source and target domains simultaneously. An entropy-driven loss function is used to promote clustering of target-domain representations and an individual-level domain adaptation technique is proposed to alleviate the distribution discrepancy problem of test subjects. We use two public driving datasets SEEG-VIG and SADT to test the model on the cross-dataset setting. The proposed model achieved an accuracy of 83.3% when SADT is used as source domain and SEED-VIG is used as target domain and 76.7% accuracy on the reverse setting, which is higher than the other SOTA methods. The results are further analyzed with both global and local interpretation methods. Our work illuminates a promising direction of using EEG for calibration-free driver drowsiness recognition.
The engagement of auditors in the reporting of corporate social responsibility disclosure

In this research, we aim to examine how large auditing firms and audit/non‐audit fees affect corporate social responsibility (CSR) disclosure. We show that the big four auditing firms and the audit and non‐audit fees paid by audited firms encourage CSR reporting. Overall, our findings suggest that big auditing firms play a relevant role in CSR disclosure, which may help to mitigate informative asymmetries between managers and stakeholders. Furthermore, audit and non‐audit fees paid by audited companies promote voluntary non‐financial information disclosure. These findings should be of interest to policymakers given the relevant role that CSR disclosure may play in the decision‐making processes of all stakeholders.
Selecting Artificially-Generated Sentences for Fine-Tuning Neural Machine Translation

Neural Machine Translation (NMT) models tend to achieve best performance when larger sets of parallel sentences are provided for training. For this reason, augmenting the training set with artificially-generated sentence pairs can boost performance.   Nonetheless, the performance can also be improved with a small number of sentences if they are in the same domain as the test set. Accordingly, we want to explore the use of artificially-generated sentences along with data-selection algorithms to improve German-to-English NMT models trained solely with authentic data.   In this work, we show how artificially-generated sentences can be more beneficial than authentic pairs, and demonstrate their advantages when used in combination with data-selection algorithms.
Data-driven Reynolds-averaged turbulence modeling with generalizable non-linear correction and uncertainty quantification using Bayesian deep learning

The past few years have witnessed a renewed blossoming of data-driven turbulence models. Quantification of the concomitant modeling uncertainty, however, has mostly been omitted, and the generalization performance of the data-driven models is still facing great challenges when predicting complex flows with different flow physics not seen during training. A robust data-driven Reynolds-averaged turbulence model with uncertainty quantification and non-linear correction is proposed in this work with the Bayesian deep neural network. In this model, the Reynolds stress tensor is decomposed into linear and non-linear parts. The linear part is taken as the usual linear eddy viscosity model while the non-linear counterpart is learned by a Bayesian deep neural network. Independent tensor bases of invariants and tensors constituted by mean strain rate tensor and rotation rate tensor are embedded into the neural network to effectively consider key turbulence features in different flows. The proposed model is well validated through numerical simulations of four canonical flows that significantly deviate in geometrical configurations and/or Reynolds numbers from those in the training data. With the non-linear corrections of embedded invariants and tensors representing key features of turbulence, the proposed model not only improves the predictive capabilities of Reynolds-averaged turbulence models on the same mesh but also has better generalization performance when simulating complex turbulent flows with large scale separation. In addition, this model allows us to quantitatively demonstrate the confidence interval of the predicted flow quantities that are originated from the model itself.
Natural Language Processing in Software Engineering: A Systematic Literature Review

Context: Software engineering (SE) artifacts and documents, such as requirements specifications, user stories, test cases, and concepts of operations (ConOps), are typically written in natural language, making their manipulation challenging. Natural Language Processing (NLP) is a viable solution for managing these tasks. Objective: To conduct a systematic literature review to explore the current use of NLP in SE artifacts and tasks, supplemented by a tertiary study focusing on the emerging role of Large Language Models (LLMs) in software engineering research. Method: We searched digital libraries for relevant papers and applied inclusion and exclusion criteria to filter the primary studies. We then analyzed NLP techniques applied to SE documents and examined their usage in this context. Our research methodology followed Kitchenham and Charters' guidelines. Additionally, we conducted a tertiary study to synthesize findings from existing systematic literature reviews and surveys specifically addressing LLMs in software engineering. Results: We selected 60 primary studies to identify the most common methods for NLP pipelines, feature extraction, language models, and machine learning algorithms used in SE. We also assessed the purposes of these methods, their benefits for SE, their difficulty, and their contribution to SE advancement. The tertiary study revealed a rapid proliferation of LLM-focused research, with comprehensive reviews documenting exponential growth in publications and widespread adoption across diverse SE tasks. Conclusion: Requirements are the most frequently addressed artifacts using NLP techniques, with preprocessing and part-of-speech (POS) tagging being widely used. There is a notable increase in the use of large language models for various SE tasks, such as requirements elicitation, source code generation, bug fixing, and software testing. The tertiary study confirms that LLMs represent a pivotal shift in the research landscape, warranting dedicated investigation to understand their transformative impact on NLP applications in software engineering.
Prostate Cancer Survival Prediction: Integrating Clinical Insights, Genomics and Machine Learning for Precision Care

Prostate cancer remains a significant global health challenge among men, necessitating robust survival prediction models to enhance clinical decision-making and improve patient outcomes. While traditional prognostic tools—such as Gleason scores, PSA levels and TNM staging—offer valuable insights, they have limitations in precision, scalability and adaptability across diverse patient populations.
An Essay on Fighting with One Arm Tied Behind One's Back, or: The Responsibility to Protect, General Principles and the Future of Humanitarian Intervention

Article published in the Michigan State International Law Review.
FACTORS AFFECTING THE BEHAVIORAL INTENTION TO USE CENTRAL BANK DIGITAL CURRENCY: EXPERIMENTAL RESEARCH IN VIETNAM

This study examines the factors influencing the intention to use Central Bank Digital Currency (CBDC) in Vietnam. Through analysis of 404 survey responses from Vietnamese citizens using Partial Least Squares Structural Equation Modeling (PLS-SEM), the results demonstrate that perceived usefulness, perceived ease of use, perceived innovativeness, and subjective norms all positively influence the intention to use CBDC. However, contrary to expectations, perceived security does not have a significant impact. Additionally, perceived usefulness plays a mediating role in the relationship between perceived ease of use, subjective norms, and usage intention. Based on these findings, the authors propose recommendations for government regulatory agencies regarding CBDC implementation to promote broader public acceptance when CBDC is officially launched in Vietnam.
Rule of Law and Judicial Independence in Albania

We know the importance that the rule of law has for our society, our democracy, and the kind of civilization we want, but we rarely take the time to think about what the components of the rule of law are and how we ensure that the rule of law is maintained. In its most basic form, the rule of law is the principle that no one is above the law. Legal documents, such as constitutions, national legislation, a court system, and international agreements, govern a state’s actions towards its citizens. Governmental authority is legitimately exercised only in accordance with written, publicly disclosed laws adopted and enforced in accordance with established procedural steps that are referred to as due process. The rule of law is a very broad concept. It involves several aspects rooted in democracy. This paper will focus on the independence of the judiciary as part of the rule of law. Judicial independence means that judges are independent from political pressures and influences when they make their decisions, that they should not be pressured by a political party, a private interest, or popular opinion when they are called upon to determine what the law requires. Keeping the judiciary independent of these influences ensures that everyone has a fair chance to make their case in court and that judges will be impartial in making their decisions. Presidents, ministers, and legislators, at times, rush to find convenient solutions to the exigencies of the day. An independent judiciary is uniquely positioned to reflect on the impact of such acts on rights and liberty, and must ensure that those values are not subverted. The need for an independent judiciary in Albania is of paramount importance for Albania’s integration in the European Union.
Protection of the rights of minor patients and public health ballance: challenges of mandatory vaccination / Nepilngadīgo pacientu tiesību aizsardzība un sabiedrības veselības līdzsvars: obligātās vakcinācijas izaicinājumi

The article addresses the legal and ethical challenges of balancing the rights of minor patients, their legal representatives, and public health in Latvia, with a focus on mandatory vaccination. Triggered by a recent case of unvaccinated children contracting diphtheria, including a fatality, this study explores the legal framework surrounding parental decision-making on vaccinations and the state’s authority to enforce public health measures. The findings reveal that while parents have the right to decide on their child’s vaccination, this right is not absolute. The state may limit parental choices if mandatory vaccination is necessary to protect public health and the best interests of the child. The article highlights the need for informed refusal, emphasising comprehensive education for medical personnel and parents about vaccination consequences. It also calls for regulatory amendments to enhance public health protection in a democratic society.    Keywords: child’s best interests; mandatory vaccination; parental responsibility; public health and security.
Humanitarian Intervention, the Responsibility to Protect and jus in bello

AbstractThis article assesses the moral importance of a humanitarian intervener's fidelity to the principles of international humanitarian law or jus in bello (principles of just conduct in war). I begin by outlining the particular principles of jus in bello that an intervener should follow when discharging the responsibility to protect, drawing on Jeff McMahan's recent work. The second section considers more broadly the moral underpinnings of these principles. I claim that consequentialist justifications of these principles cannot fully grasp their moral significance and, in particular, the difference between doing and allowing. Overall, I argue that these principles are (i) more important and (ii) more stringent in the context of humanitarian intervention.
Evolution of accretion disk structure of the black hole X-ray binary MAXI J1820+070 during the rebrightening phase

identifier:oai:t2r2.star.titech.ac.jp:50744069
Variational quantum circuit-based quantum machine learning approach for predicting corrosion inhibition efficiency of pyridine-quinoline compounds

This work used a variational quantum circuit (VQC) in conjunction with a quantitative structure-property relationship (QSPR) model to completely investigate the corrosion inhibition efficiency (CIE) displayed by pyridine-quinoline compounds acting as corrosion inhibitors. Compared to conventional methods like multilayer perceptron neural networks (MLPNN), the VQC model predicts the CIE more accurately. With a coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean absolute deviation (MAD) values of 0.989, 0.027, 0.024, and 0.019, respectively, VQC performs better. The established VQC model predicts the CIE with outstanding predictive accuracy for four newly synthesized pyrimidine derivative compounds: 1-(4-fluorophenyl)- 3-(4-(pyridin-4-ylmethyl)phenyl)urea (P1), 1-phenyl-3-(4-(pyridin-4-ylmethyl)phenyl)urea (P2), 1-(4-methylphenyl)-3-(4-(pyridin-4-ylmethyl)phenyl)urea (P3), and quaternary ammonium salt dimer (P4). It generates remarkably high CIE values of 92.87, 94.05, 94.96, and 96.93 for P1, P2, P3, and P4, respectively. With its ability to streamline the testing and production processes for novel anti-corrosion materials, this innovative approach holds the potential to revolutionize the market.
A critical assessment of the nature, scope and adequacy of the Taliban and Al Qaeda sanctions regime(s) established by the United Nations Security Council

Alongside the development of methods of terrorism, society has developed ways of fighting it. This dissertation addresses modern forms of countering terrorism in the sense of sanctions regime(s) established by the United Nations Security Council. It is therefore primarily intended for people who already have some knowledge in this area and wish to explore this field further. This work is divided into three main sections. While relying largely, but not exclusively, on the Report of the National Commission on Terrorist Attacks Upon the United States and Steve Coll’s Ghost wars, the first section describes the circumstances under which the Taliban and al Qaeda formed and evolved. It also focuses on the motivations of the United Nations Security Council to impose targeted sanctions under Resolution 1267 (1999). Grasping the historical background is necessary for understanding the next section, which examines the core of the sanctions regime, particularly its general idea, structure and processes that allow it to achieve its designated goals. Based on research findings and a wide range of relevant materials, the concluding section assesses the effectiveness of the sanctions regime as well as providing a view towards future challenges and prospects.
Direction dependence of the power spectrum and its effect on the cosmic microwave background radiation

19 pages, 1 figure
Chromatin remodeling and epigenetic regulation in chronic kidney disease

The progression of chronic kidney disease (CKD) stems from persistent maladaptive alterations in renal cellular gene expression, processes governed by dynamic epigenetic regulation. The dynamic regulation of chromatin encompasses structural changes, condensation, altered accessibility, and ATP-dependent remodeling, playing a pivotal role in CKD progression. Dysregulation of these processes is increasingly implicated in key pathogenic events such as fibroblast activation and inflammatory responses. Recent advances in multi-omics approaches have provided crucial insights into the composition, mechanisms, and dysregulation of chromatin states within diseased kidneys. In this review, we summarize the current understanding of chromatin dynamics and remodeling in CKD, emphasizing their role in disease progression and their interplay with other epigenetic layers. We further discuss how these mechanisms contribute to establishing pathogenic transcriptional memory and highlight emerging therapeutic opportunities.
A benchmark analysis of saliency-based explainable deep learning methods for the morphological classification of radio galaxies

7 pages, 4 figures
Health Coverage in a Migration Hub: Comparing Insurance Access Among Immigrants and Ghanaians

Abstract                 As Ghana transitioned into a key destination for international migrants, understanding the health coverage landscape for this growing population is increasingly vital; Particularly in the context of global health crises. Using an explanatory sequential mixed method design, this study investigated disparities in health insurance coverage between 116 immigrants and 116 non-migrants in Ghana. The quantitative result revealed that, only 41.4% of all the respondents were registered with a health insurance scheme in Ghana with a stark gap by migration status: 75% of immigrants had never enrolled, compared to just 7.8% of non-migrants. Among the insured, the National Health Insurance Scheme (NHIS) was the dominant provider (88.5%). Qualitative insights further explain low enrolment among immigrants, citing limited knowledge of NHIS registration procedures, reliance on self-medication, and aversion to Ghana’s paper- and card-based insurance system particularly among digitally oriented migrants accustomed to electronic health platforms. The study identifies these factors as structural and informational barriers that function as disabling conditions within Andersen’s Behavioural Model of Health Services Use. We recommend that Ghana’s National Health Insurance Authority fully transition toward a digital, user-friendly enrolment and verification system and integrate pre-arrival health insurance orientation into visa processing for prospective immigrants. Such reforms could significantly improve equitable access to care in an increasingly mobile society.
Gene-DML: Dual-Pathway Multi-Level Discrimination for Gene Expression Prediction from Histopathology Images

Accurately predicting gene expression from histopathology images offers a scalable and non-invasive approach to molecular profiling, with significant implications for precision medicine and computational pathology. However, existing methods often underutilize the cross-modal representation alignment between histopathology images and gene expression profiles across multiple representational levels, thereby limiting their prediction performance. To address this, we propose Gene-DML, a unified framework that structures latent space through Dual-pathway Multi-Level discrimination to enhance correspondence between morphological and transcriptional modalities. The multi-scale instance-level discrimination pathway aligns hierarchical histopathology representations extracted at local, neighbor, and global levels with gene expression profiles, capturing scale-aware morphological-transcriptional relationships. In parallel, the cross-level instance-group discrimination pathway enforces structural consistency between individual (image/gene) instances and modality-crossed (gene/image, respectively) groups, strengthening the alignment across modalities. By jointly modeling fine-grained and structural-level discrimination, Gene-DML is able to learn robust cross-modal representations, enhancing both predictive accuracy and generalization across diverse biological contexts. Extensive experiments on public spatial transcriptomics datasets demonstrate that Gene-DML achieves state-of-the-art performance in gene expression prediction. The code and processed datasets are available at https://github.com/YXSong000/Gene-DML.