ethicalabs/Research-Intent-Collab huggingface dataset:
Central Bank Digital Currency (CBDC) – Barriers to Its Introduction

Theoretical background: The beginning of the third decade of the 21st century brought a dynamic development of new technologies, intensifying the ongoing process of digitalization of the global economy. Among these changes, one of the undoubtedly noticeable areas that are developed both on theoretical and practical grounds is the central bank digital currency (CBDC). It is a new – different from cash – currency issued by the monetary authorities, considered as a response to the development of private virtual currencies, which is aimed at the same time to improve the circulation of money. An increasing number of central banks are considering issuing their own CBDC. According to the BIS studies, in 2021 nine out of ten central banks actively research implementation of a digital currency. This number is constantly growing. More and more monetary authorities are at an advanced stage of CBDC work, 26% of central banks already running pilot tests (compared to only 8% in 2018). Purpose of the article: The main aim of the article is to identify barriers to the central bank digital currency introduction as well the case study analysis of the current involvement of selected central banks in the process of its implementation. Research methods: The main research method used in the conducted empirical research is the case study analysis of selected central banks that have undertaken works on the CBDC implementation. The research verifies the main hypothesis that despite growing involvement of central banks in the work for the issue of CBDC, its barriers and limitations are large enough to inhibit practical implementation. Main findings: The results indicated that such barriers as limited validity period, lack of anonymity and full control of a central bank over its use may significantly limit further monetary authorities’ work on its implementation.
A comparative metagenomic analysis of antibiotic resistance in the fecal microbiome of pigs raised with and without antibiotics

Abstract                                    Purpose                   The use of antibiotics in swine production has raised concerns about its impact on animal health, microbial ecology, and the emergence of antibiotic resistance. The gut microbiome, a critical component of animal health, is susceptible to disruptions caused by antibiotic use. The fecal microbiome can provide a snapshot of changes in the gut microbiome and provide information on the spread of antibiotic resistance from pig feces to the environment.                                                     Methods                   We investigated the differences between the fecal microbiome of pigs raised with (AB+) and without (AB-) antibiotics to assess how antibiotic exposure influenced the microbial community, antibiotic resistance genes (ARGs), and mobile genetic elements (MGEs).                                                     Results                                        Our findings revealed that the use of antibiotics combined with study-specific conditions alters the fecal microbiome. Antibiotic usage in pigs explains 20% of the variation in the diversity of bacterial species detected between AB+ and AB- pigs. Similarly, variations in ARGs were observed in the two groups. The RND efflux pump gene family was abundant in both groups. The OXA                     β                     -lactamase gene and associated plasmids were more prevalent in the AB- group. We also identified integrons linked to chloramphenicol resistance and transposon-associated integrative elements that can facilitate the spread of antibiotic resistance genes within and between bacterial species.                                                                        Conclusion                   These findings highlight that antibiotics influence both the microbial community and the antibiotic resistance gene repertoire, regardless of study-specific or environmental conditions. This emphasises the potential for fecal matter to contribute to the spread of antibiotic resistance in the environment, underscoring the need for more research to fully understand these dynamics in agricultural settings.
TRANSPARENCY, ACCOUNTABILITY, AND CORRUPTION RISK ASSESSMENT: JOINT ADMISSIONS AND MATRICULATION BOARD (JAMB)

The Joint Admissions and Matriculation Board (JAMB), established in 1978, regulates admissions into Nigeria’s tertiary institutions through the Unified Tertiary Matriculation Examination (UTME). Since 2016, the agency has implemented reforms aimed at modernising its processes and strengthening accountability. While notable progress has been achieved, persistent challenges continue to undermine transparency, inclusivity, and public trust. This report assesses JAMB’s performance in promoting transparency, accountability, and mitigating corruption risks. It finds that reforms such as centralised e-payments, biometric verification, and the Central Admissions Processing System (CAPS) have improved financial management and reduced fraud. JAMB’s compliance with Freedom of Information (FOI) requests, coupled with expanded use of digital platforms, has further enhanced public communication and oversight. However, weaknesses remain. Limited disclosure of expenditure details and procurement outcomes constrains accountability. Historical corruption scandals and ongoing legal cases highlight governance vulnerabilities, while disparities in access to examination centres for rural and disabled candidates expose gaps in inclusivity. Technical disruptions during examinations also point to infrastructural deficiencies. The report recommends that JAMB enhance transparency by publishing detailed expenditure data and audit reports, while sustaining anti-corruption measures and supporting investigations. Expanding examination centres in underserved regions and improving disability accommodations are critical for equitable access. Investing in resilient infrastructure and engaging proactively in legislative matters is crucial to enhancing credibility and aligning JAMB more effectively with Nigeria’s broader educational objectives.
AI Economics: Annotation Economics — Crowdsourcing vs Expert Labeling in Enterprise AI

Data annotation represents one of the most underestimated cost centers in enterprise AI development. This article presents a comprehensive economic framework for annotation decision-making, analyzing the crowdsourcing versus expert labeling dichotomy through total cost of ownership, quality-adjusted returns, and strategic implications. Drawing on case studies from healthcare, autonomous vehicles, financial services, and manufacturing.
Error Mitigation in Quantum Computers through Instruction Scheduling

Quantum systems have potential to demonstrate significant computational advantage, but current quantum devices suffer from the rapid accumulation of error that prevents the storage of quantum information over extended periods. The unintentional coupling of qubits to their environment and each other adds significant noise to computation, and improved methods to combat decoherence are required to boost the performance of quantum algorithms on real machines. While many existing techniques for mitigating error rely on adding extra gates to the circuit, calibrating new gates, or extending a circuit's runtime, this paper's primary contribution leverages the gates already present in a quantum program without extending circuit duration. We exploit circuit slack for single-qubit gates that occur in idle windows, scheduling the gates such that their timing can counteract some errors. Spin-echo corrections that mitigate decoherence on idling qubits act as inspiration for this work. Theoretical models, however, fail to capture all sources of noise in NISQ devices, making practical solutions necessary that better minimize the impact of unpredictable errors in quantum machines. This paper presents TimeStitch: a novel framework that pinpoints the optimum execution schedules for single-qubit gates within quantum circuits. TimeStitch, implemented as a compilation pass, leverages the reversible nature of quantum computation to boost the success of circuits on real quantum machines.
Corporate social responsibility disclosure in emerging countries. Evidence from a content analysis of chinese CSR and annual reports

Despite the increased academic and practitioner interest in Corporate Social Responsibility (CSR) in emerging countries, there has been little research about the definition of what firms actually tell about their sustainability efforts on the CSR and Annual Reports. This paper addresses this gap by analysing, from a quali-quantitative point of view, the CSR or Annual Reports of the largest 74 Chinese enterprises in different industries and along a time period that goes from 1997 to 2013. The article is structured as follows. In the first section, we briefly review the literature about CSR and emerging market companies (EMNCs). After the methodology part, we discuss the results and draw our conclusions, along with a future research agenda.
Applying quantum error-correcting codes for fault-tolerant blind quantum cloud computation

Blind Quantum Computation (BQC) is a delegation computing protocol that allows a client to utilize a remote quantum server to implement desired quantum computations while keeping her inputs, outputs, and algorithms private. However, qubit errors during the quantum computation are important issues that need to be addressed. In this paper, we propose a fault-tolerant blind quantum computation protocol with quantum error-correcting codes to avoid the accumulation and propagation of qubit errors during the computational process. We further propose to apply the concatenated codes in the protocol to improve the error correction performance. More importantly, we quantify the resource consumption of photon pulses by our optimal level concatenation codes. Extensive simulation results show that our scheme not only can improve the preparation efficiency but also reduce quantum resource consumption, which shows a significant improvement to realize a fault-tolerant BQC.
Hierarchical Pre-Training of Vision Encoders with Large Language Models

The field of computer vision has experienced significant advancements through scalable vision encoders and multimodal pre-training frameworks. However, existing approaches often treat vision encoders and large language models (LLMs) as independent modules, limiting the integration of hierarchical visual features. In this work, we propose HIVE (Hierarchical Pre-Training of Vision Encoders), a novel framework that enhances vision-language alignment by introducing hierarchical cross-attention between the vision encoder and LLM. Unlike conventional methods that flatten image embeddings, HIVE enables structured feature fusion across multiple layers, improving gradient flow and representation learning. To optimize this interaction, we introduce a three-stage training strategy that progressively aligns the vision encoder with the LLM, ensuring stable optimization and effective multimodal fusion. Empirical evaluations demonstrate that HIVE achieves superior performance not only in image classification but also on various vision-language tasks, outperforming self-attention-based methods in benchmarks such as MME, GQA, OK-VQA, and ScienceQA. Our results highlight the benefits of hierarchical feature integration, paving the way for more efficient and expressive vision-language models.
Research on Adaptive Noise Mechanism for Differential Privacy Optimization in Federated Learning

This paper proposes an adaptive differential privacy mechanism for federated learning that optimizes the trade-off between model performance and privacy protection. The mechanism incorporates a dynamic noise generation algorithm that adjusts noise levels based on training states and gradient information, coupled with an efficient privacy budget allocation strategy. The proposed approach addresses the limitations of existing static noise addition methods by introducing a multi-factor adaptation framework that considers both local training characteristics and global model convergence states. The system architecture implements a dual-layer privacy protection scheme, combining adaptive noise injection at the client level with optimized privacy budget management at the server level. Experimental evaluation on multiple benchmark datasets, including MNIST and CIFAR-10, demonstrates that our approach performs better than existing methods. The results show a 3.5-5.8% improvement in model accuracy while maintaining equivalent privacy guarantees and a 25-30% reduction in communication overhead. Theoretical analysis establishes rigorous bounds on privacy protection and model convergence, providing formal guarantees for the proposed mechanism. The comprehensive evaluation validates the effectiveness of our approach across various operational scenarios and data distributions, making it particularly suitable for real-world applications with heterogeneous privacy requirements.
On Interactive Machine Learning and the Potential of Cognitive Feedback

14 pages, 2 figures, submitted and accepted to the 2nd Workshop on Deep Models and Artificial Intelligence for Defense Applications: Potentials, Theories, Practices, Tools and Risks sponsored by the Association for the Advancement of Artificial Intelligence in cooperation with the Stanford University Computer Science Department
The Value-Adding Potentials of Corporate Social Responsibility Report Audit to Corporate Reporting in Nigerian Banks

The increasing trend in the clamour for a more responsible business has necessitated organizations publishing their social responsibility investments. This nonfinancial form of companies' disclosure of their activities has further evolved concerns about the truth and fairness of the content of the social responsibility reports. This paper explores relevant literature, theories and provides empirical evidences in expressing the importance of social responsibility disclosure and the need to address the concerns about the truth and fairness of its content. Mean scores of responses were used in answering the research questions while binary logistic regression was adopted in the test of hypotheses. Perceptual evidence from stakeholders shows a positive response in favour of CSR report audit. The paper concludes that auditing CSR report will add value to the entire corporate reports in the Nigerian banking industry.Keywords: Audit, Cost-benefit, Corporate Social Responsibility Report, Value-Adding
Foreign-Educated Nurses: Effects on Nurse, Quality of Care, and Patient-Safety-Indicator Outcomes

Approximately 8% of nurses in the United States were educated abroad, and in Florida and California, foreign-educated nurses (FENs) represent large percentages of the nurse labor force. It is important to know whether these nurses are prepared to care for patients who have complex needs; communicate successfully with other nurses, physicians, patients, and families; adapt to high technology in today's acute-care settings; and successfully transition to practice in the United States. It has been noted there is no comparative outcomes research on FENs and U.S.-educated nurses. The purpose of this study was to examine the effects of hospitals' proportions of FENs on nurse outcomes, hospital quality of care, perceptions of work environment, and patient-safety incidences.
Protection of Property Rights of Minors: The Analysis of the Consistency of Law Enforcement Practice

Introduction: the problem of protecting the property rights of minors is currently one of the most urgent in civil and family law. Due to the lack of full capacity and insufficient level of psycho-physiological development, children cannot protect their violated property rights on their own. For this reason, this function is assigned to their legal representatives. At the same time, persons who are obliged to protect the property rights of children do not always act in their interests. In this regard, the authors set a goal: based on the analysis of materials of the judicial practice, to identify the circumstances in which the property interests of minors are violated and the problems of bringing to justice persons who have committed such acts. Results: the features of the judicial consideration of cases on the protection and restoration of violated property rights of minors are investigated, the absence of legislative consolidation of the responsibility of persons who have violated the property rights of children, allowing the judicial authorities to apply a sanction commensurate with the illegal act, is established. Conclusions: based on the analysis of the legislation and materials of the judicial practice, the problems of protecting the property rights and legitimate interests of minor children are formulated. Based on the results of the study, the authors have proposed recommendations for improving the legislation.
Multiobjective Optimization and Multicriteria Analysis of Rail Freight Transportation Logistics with Deterministic Queues

The vehicle routing problem with queues is a critical challenge in freight railway logistics, where congestion and variable service times impact operational efﬁciency. This study presents a mathematical model to optimize the sequencing of railway ﬂows, considering travel times, loading and unloading processes, and queue management. The problem is formulated as a multi-objective optimization model, aiming to maximize the transported volume while minimizing idle times and empty displacements. Due to the nonlinear nature of the problem, particularly the recursive behavior of queues, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) metaheuristic was used to efﬁciently solve the problem. Although the exact evaluation of objective functions using the mathematical formulation was computationally intensive, it demonstrated that queues can be successfully modeled as an endogenous component of the system. This allowed for a structured analysis of queue propagation across routing decisions without resorting to full-scale simulation. A Pareto front was estimated using the NSGA-II, and a representative solution was selected. The use of the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) in the decision-making stage provided a structured and effective selection process, consistent with the deﬁned priorities.
The complex relationship between transparency, legitimation and accountability : some evidence from the fight against corruption

As an institutional value, transparency has spread widely and become popular. The present essay underlines the many trade-offs linked to interaction of the instruments of transparency and the aims it pursues. Drawing inspiration from the success of transparency as a tool for preventing and fighting corruption, the authors also highlight a significant trade-off between accountability and trust in this sphere, revealing the tensions within the value-principle of transparency, due to its plural and granular nature, and the contingency of outcomes which depend on the context of reference and the awareness with which the instruments are set up and used.
Variability Properties of 4 Million Sources in the $TESS$ Input Catalog Observed with the Kilodegree Extremely Little Telescope Survey

The Kilodegree Extremely Little Telescope (KELT) has been surveying more than $70\%$ of the celestial sphere for nearly a decade. While the primary science goal of the survey is the discovery of transiting, large-radii planets around bright host stars, the survey has collected more than $10^6$ images, with a typical cadence between $10-30$ minutes, for more than $4$ million sources with apparent visual magnitudes in the approximate range $7<V<13$. Here we provide a catalog of 52,741 objects showing significant large-amplitude fluctuations likely caused by stellar variability and 62,229 objects identified with likely stellar rotation periods. The detected variability ranges in $rms$-amplitude from 3 mmag to 2.3 mag, and the detected periods range from $\sim$0.1 days to $\gtrsim$2000 days. We provide variability upper limits for all other $\sim$4 million sources. These upper limits are principally a function of stellar brightness, but we achieve typical 1$σ$ sensitivity on 30-minute timescales down to $\sim5$ mmag at $V\sim 8$, and down to $\sim43$ mmag at $V\sim 13$. We have matched our catalog to the $TESS$ Input catalog and the AAVSO Variable Star Index to precipitate the follow up and classification of each source. The catalog is maintained as a living database on the Filtergraph visualization portal at the URL https://filtergraph.com/kelt$\_$vars
Mental Health, Second Physics, and Digital Phenotyping: A Framework of Non-Invasive Indicators for Psychiatric Monitoring

This technical note explores how the core quantities of Second Physics—correspondencepressure A, bond E, and staying pressure Ψ—can be used as a conceptual and mathematicalframework for understanding mental strain, resilience, and breakdown. It proposes thatexisting digital phenotyping tools (wearables, smartphones, messaging logs) can be mappedonto these variables to provide non-invasive, relational indicators of a person’s structuralsituation: how much they are being pressed to respond (A), what has been structurallyrecorded in the relation (E), and how much response has been sustained over time (Ψ).The note is intended as a bridge between Second Physics and mental-health practice: astructured language for phenomena clinicians already encounter, and a set of hypotheses thatcan be tested, criticised, and refined by empirical research.Relational “syntactic memory” is treated as M ≡ E + δΨ (defined in the Second Physicsformalism), combining structural change and sustained response.This note is non-clinical and non-diagnostic, and it does not propose diagnostic criteria ortreatment protocols.
Artificial intelligence and intellectual property rights with special reference to patent and copyright

Artificial intelligence (AI) has come to stay. The use of AI has helped human society greatly. At the same time, it has posed several concerns or issues. One of the issues concerns intellectual property rights (IPR). There has been a debate about whether IPR should be given to AI. Countries like Australia and South Africa have granted IPR in favor of AI. At the same time, countries such as the United States have not recognized IPR in favor of AI. Many countries firmly believe that human intervention is required to grant an IPR. Be it copyright, patent, etc., human intervention is a condition precedent.On the other hand, the countries in favor of granting IPR to AI believe in AI as a person capable of creating literary work or innovation and are convinced to grant AI with IPR. Before we recognize IPR in favor of AI, we have to grant the person’s status to AI. This paper aims to understand various issues related to identifying or non-recognizing IPR to AI.