ethicalabs/Research-Intent-Collab huggingface dataset:
A Law and Economics Analysis of Trade Secrets: Optimal Scope of Law, Misappropriation and Alternative Damages Regimes

This thesis is primarily based on three core chapters, focused on the fundamental issues of trade secrets law. The goal of this thesis is to come up with policy recommendations to improve legal structure governing trade secrets. The focal points of this research are the following. What is the optimal scope of trade secrets law? How does it depend on the market characteristics such as degree of product differentiation between competing products? What factors need to be considered to balance the contradicting objectives of promoting innovation and knowledge diffusion? The second strand of this research focuses on the desirability of lost profits or unjust enrichment damage regimes in case of misappropriation of a trade secret. A comparison between these regimes is made and simple policy implications are extracted from the analysis. The last part of this research is an empirical analysis of a possible relationship between trade secrets sharing and misappropriation instances faced by firms.
A Novel Non-sinusoidal Compensation of Transformer Based on Core Loss and Magnetic Saturation Model

This paper deals with non-sinusoidal compensation of transformer. Due to core loss and magnetic saturation in iron core of transformer, the transported signal in power system has greatly distorted, which has caused unexpected loss and lower capacitance of the system. By comparing and analyzing physical characteristics of transformer, a new kind of model of transformer based on core loss and magnetic saturation is presented. Then, a method of non-sinusoidal compensation for the model has been introduced. The model has been verified by simulation in steady and transient states. Moreover, the simulated results of non-sine compensation of transformer shed light on the feasibility and practicability of the method.
QuadR-Tree Indexing Selection Engine for Tuning Spatial Database System using Mobile Geographical Information System Technology

The latest Mobile GIS technology is probable to manage the spatial components of daily business project, in the corporate database, and to apply proper geographic analysis efficiently in a desktop-focused application. This technology uses wireless internet for transferring process of spatial data from server to Mobile GIS or vice versa. However, the problem in wireless Internet is system bottlenecks that can slow down in the process of transferring data. One of the reasons why system bottlenecks could happen in this situation is because the spatial data is too large. The database design therefore is the most important part that must be considered. Database systems are now expected to be self-managing, self healing, and always-up. Another parts which also contribute in this problem are network, application server and web server (Kwan & Shi, 2002).
A systematic mapping study of educational technologies based on educational data mining and learning analytics

The traditional flipped classroom is defined based on the video instructions of flipped class at home. In addition, students have time in class to work with their teacher on key learning activities. With introduction of Flipped Classroom model and rapid development of data sciences for taking the advantages of the learners' data to optimize learning process there has been an increasing consideration of exploiting Educational Data Mining and Learning Analysis. Large amount of data taken from performed actions of students and teachers in FC has made data-driven learning and self-paced learning possible to perform different algorithms of Learning Analytics and Educational Data Mining. The goal in this paper is to devise a systematic mapping study, in order to explore and analyze existing researches about implementations of learning strategies and learning algorithms. We selected 122 papers and assorted according to field of interest, video field of interests and technology used in FC.
Predicting Phase Transitions in Chemically Functionalized Nanopore Systems: An Ising-Ensemble Statistical Mechanics Approach

The controlled manipulation of materials at the nanoscale often hinges on a precise understanding and prediction of phase behavior within confined geometries. This paper explores the intricate phase transitions occurring in chemically functionalized nanopore systems, which are crucial for applications ranging from separation technologies to energy storage. We propose an Ising-ensemble statistical mechanics approach to model the adsorption and desorption phenomena, focusing on how chemical modifications of the nanopore surface fundamentally alter the intermolecular interactions and thereby influence critical phase transition characteristics. Our methodology conceptualizes the nanopore as a lattice of adsorption sites, where guests can occupy sites or remain in the bulk, and interactions are described via an effective spin Hamiltonian. Functionalization is incorporated through site-specific interaction energies and modified coupling constants between adjacent sites, reflecting tailored surface chemistry. Through this framework, we aim to predict changes in critical temperature, pressure, and the nature of phase transitions (e.g., capillary condensation, layering transitions) as a function of functionalization parameters. The model provides insights into the design principles for nanoporous materials with tunable phase behavior, offering a theoretical foundation for engineering advanced materials with enhanced selectivity and performance. This work emphasizes the critical role of statistical mechanics in bridging microscopic chemical details with macroscopic thermodynamic properties in confined nanostructures.
A Statistical Mechanical Study of Current Spikes Due to Phase Transitions at Electrode-Electrolyte Interfaces

The authors continue their statistical mechanical study of voltammogram spikes. They present a theory from which voltammogram spikes corresponding to first-order phase transitions can be obtained. Moreover the theory can be applied to experiment and show that macroscopic properties of a voltammogram spike determine the strength of microscopic interactions in a lattice gas that one may use to model the associated deposition process.
Inclusion and accessibility in STEM education: Navigating the duty to accommodate and disability rights

The duty to accommodate is a fundamental legal concept embedded in Canadian human rights law. The concept itself makes a contribution to advancing the goals of human rights law by attempting to extend the right to equality by protecting people from discrimination. In post-secondary institutions, pursuant to human rights legislation, the duty to accommodate requires that educators and administrators should attempt to accommodate students with disabilities short of undue hardship. Despite these legal requirements, students with disabilities are often underrepresented in STEM (science, technology, mathematics and engineering) disciplines because they face multiple barriers to accessing reasonable accommodation within the classroom and laboratory environments in Canadian universities (Sukhai and Mohler, 2017, Sukhai et al, 2014).
Responsible science communication in Africa: rethinking drivers of policy, Afrocentricity and public engagement

The EU-funded RETHINK Project has demonstrated the critical need for transformational pathways in how science communicators navigate the increasingly challenging landscape of the field, in an era of growing public distrust, the expansion of online ‘mis-information’ digital platforms, and the resulting disconnection between science communicators and the general public. This Commentary seeks to locate, contextualise, and interrogate the good practice outcomes and recommendations of the RETHINK Project within the African regional scenario, and within the contexts, challenges and opportunities that exist therein. To achieve this, the author argues, African science communicators must actively pursue a radical and explicitly transformational agenda of intellectual Afrocentricity, the decolonisation of their practices and programmes, and address the multiple gaps inherent across the policy, practice, research, resources, and capacity-building divides on the continent. The prospects for the delivery of this agenda are further elaborated in a transformative and re-defined — ‘SMART’ Framework for Science Communication &amp; Public Engagement in Africa.
Harnessing Decentralized Finance (DeFi) protocols for institutional asset securitization in cross-jurisdictional banking ecosystems

The emergence of Decentralized Finance (DeFi) has introduced a paradigm shift in global financial intermediation, challenging traditional banking systems with transparent, algorithm-driven, and blockchain-based financial services. As institutional investors and banks navigate increasingly complex cross-jurisdictional regulatory environments, DeFi presents an opportunity to reimagine asset securitization through decentralized, programmable frameworks. This paper explores the integration of DeFi protocols into institutional asset securitization, focusing on how smart contracts, tokenization, and decentralized liquidity pools can streamline processes, enhance transparency, and reduce reliance on intermediaries across disparate legal and regulatory jurisdictions. At a broader level, the study outlines the limitations of conventional securitization—such as opacity, time lags, and fragmentation—especially in multinational banking ecosystems. It then narrows in on how DeFi tools like automated market makers (AMMs), decentralized exchanges (DEXs), and overcollateralized lending protocols can be adapted to structure, issue, and trade tokenized asset-backed securities (ABS). Particular attention is paid to the challenges of legal enforceability, compliance, and interoperability between blockchain platforms and regulatory frameworks. Case scenarios and pilot initiatives are analyzed to demonstrate the feasibility of decentralized securitization in cross-border finance, including synthetic credit instruments and on-chain risk analytics. The paper further examines how oracles and compliance layers (e.g., KYC/AML-integrated smart contracts) can reconcile DeFi's permissionless nature with institutional governance standards. The findings support a hybrid finance future, where regulated entities harness DeFi infrastructure for secure, compliant, and efficient asset securitization. Policy recommendations are offered to foster collaboration between regulators, financial institutions, and protocol developers in building trust-minimized, scalable, and cross-jurisdictionally aligned financial ecosystems.
Leveraging Deep Learning and Xception Architecture for High-Accuracy MRI Classification in Alzheimer Diagnosis

Exploring the application of deep learning technologies in the field of medical diagnostics, Magnetic Resonance Imaging (MRI) provides a unique perspective for observing and diagnosing complex neurodegenerative diseases such as Alzheimer Disease (AD). With advancements in deep learning, particularly in Convolutional Neural Networks (CNNs) and the Xception network architecture, we are now able to analyze and classify vast amounts of MRI data with unprecedented accuracy. The progress of this technology not only enhances our understanding of brain structural changes but also opens up new avenues for monitoring disease progression through non-invasive means and potentially allows for precise diagnosis in the early stages of the disease.   This study aims to classify MRI images using deep learning models to identify different stages of Alzheimer Disease through a series of innovative data processing and model construction steps. Our experimental results show that the deep learning framework based on the Xception model achieved a 99.6% accuracy rate in the multi-class MRI image classification task, demonstrating its potential application value in assistive diagnosis. Future research will focus on expanding the dataset, improving model interpretability, and clinical validation to further promote the application of deep learning technology in the medical field, with the hope of bringing earlier diagnosis and more personalized treatment plans to Alzheimer Disease patients.
FOREGROUND PREDICTIONS FOR THE COSMIC MICROWAVE BACKGROUND POWER SPECTRUM FROM MEASUREMENTS OF FAINT INVERTED RADIO SOURCES AT 5 GHz

14 pages, 9 figures, includes ApJ proof corrections
Genetic testing and employer‐sponsored wellness programs: An overview of current vendors, products, and practices

AbstractBackgroundEmployer‐sponsored corporate wellness programs have spread despite limited evidence of effectiveness in improving health or reducing costs. Some programs have offered genetic testing as a benefit to employees, but little is known about this practice.MethodsIn December 2019, we conducted a systematic Google search to identify vendors offering corporate wellness programs involving genetics. We performed qualitative content analysis of publicly available information about the vendors’ products and practices disclosed on their websites.ResultsFifteen vendors were identified. Details regarding genetic testing offered within wellness programs were difficult to decipher from vendors’ websites, including which specific products were included. No evidence was provided to support vendor claimed improvements in employer costs, employee health, and job performance. Only half offered health and genetic counseling services. Most vendors were ambiguous regarding data sharing. Disclaimer language was included in vendors’ stated risks and limitations, ostensibly to avoid oversight and liability.ConclusionWe found a lack of transparency among corporate wellness program vendors, underscoring challenges that stakeholders encounter when trying to assess (a) how such programs are using genetics, (b) the potential benefits of such applications, and (c) the adequacy of protections to ensure scientific evidence support any health claims and genetic nondiscrimination.
Accident Sequence Analysis of Fault Tree with Event Sequence Dependency

Conventional fault trees composed of AND and OR logic gates do not consider event sequence dependency. This paper presents a simple evaluation method for fault trees containing AND and OR gates plus priority AND gates, which restrict the occurrence order of basic events. Basic logical operations of priority AND gates with conventional logic gates are given. Due to these simple operations, not only the minimal cut set derivation procedure, but also the inclusion-exclusion method can be applied in the same way as coherent fault trees. An illustrative example of a standby system shows the merits and characteristics of the proposed method.
Enhancing Accessibility: Exploring the Impact of AI in Assistive Technologies for Disabled Persons

As per the World Health Organization, approximately 15% of the global population experiences some form of disability. The integration of Assistive Technology with Artificial Intelligence of Things devices has witnessed significant advancements. This paper, through research, aims to identify various assistive models utilized in diverse studies focusing on the application of Artificial Intelligence. Starting with past research studies in this area and emphasizing the manifold and noteworthy roles of AI in assistive technologies, the paper delves into the prospective applications of AI in the future of assistive technologies.
Of Custom, Treaties, Scholars and the Gavel: The Influence of the International Criminal Tribunals on the ICRC Customary Law Study

This article investigates the influence of the international criminal tribunals on the conclusions reached by ICRC Customary Study. To engage in such an analysis requires an understanding of the interplay of treaties and customary international law and of the status of the jurisprudence of international tribunals as a source of international law. In relation to the latter, it is noted that the impact of any individual case is strongly linked to the quality of the reasoning it contains. Having thus set the scene, this article then goes on to appraise the ICRC Study's approach to the international criminal tribunals founding documents and case-law. It criticises the Study for not setting out the precise normative status it places on the ICTY, ICTR and Special Court for Sierra Leone's Statutes, and for failing to take a sufficiently nuanced view on the Rome Statute, in particular owing to Article 10 of that Treaty. The piece then moves on to look at some instances where case-law is relied upon by the Study without sufficient discussion of its merits, and where it has placed too much reliance on controversial aspects of the Rome Statute. It concludes by noting the problematic relationship between some of the rules and their commentary, but, despite the criticisms made, also agreeing with many of the conclusions the Study reaches.
Investigation of Root Causes Analysis, Preventive Maintenance Delays &amp; Breakdowns in Diesel Generators Using IOT Integrated Hardware &amp; FEA Method

This study investigates the root cause of preventive maintenance in diesel generators by integrating IoT-based monitoring and Finite Element Analysis (FEA). Using an ESP8266 controller with temperature sensors, vibration sensors, and an accelerometer, real-time data is collected to analyse temperature variations, vibration occurrences, and potential misalignment effects. Additionally, FEA is performed in ANSYS for different shaft rotational speeds, evaluating frequencies with modal analysis. A 3D model is developed in CATIA to simulate modal and thermal responses, aiding in identifying breakdown causes and optimizing generator performance for enhanced reliability and lifespan.
Federated Learning Under Temporal Drift -- Mitigating Catastrophic Forgetting via Experience Replay

Federated Learning struggles under temporal concept drift where client data distributions shift over time. We demonstrate that standard FedAvg suffers catastrophic forgetting under seasonal drift on Fashion-MNIST, with accuracy dropping from 74% to 28%. We propose client-side experience replay, where each client maintains a small buffer of past samples mixed with current data during local training. This simple approach requires no changes to server aggregation. Experiments show that a 50-sample-per-class buffer restores performance to 78-82%, effectively preventing forgetting. Our ablation study reveals a clear memory-accuracy trade-off as buffer size increases.
Analysis of the Effectiveness and Contribution of Customs Tax Land Rights Building and Land Tax Building Maintenance and Improvement of the Indigenous People of South Aceh

This study aims to determine the effectiveness and contribution of Tax Customs acquisition of land and building rights (BPHTB) and Urban and rural land and Building Tax (PBB-P2) to the receipt of local revenue (PAD) from 2018-2022. This research method uses a qualitative method that uses a descriptive approach. The object of this study is the Office of the Financial Management Agency of South Aceh region, the source of data used is secondary and primary data, secondary data obtained from the realization of the effectiveness and contribution of tax revenues acquisition of land and building duty (BPHTB) and land tax and Urban and rural building (PBB-P2),and primary data obtained from interviews. Based on the results of the discussion, the level of effectiveness of BPHTB from 2018-2022 is very effective with a percentage level of 167.45%, while the level of contribution is in the good category with a percentage of 45%. For the level of effectiveness of PBB-P2 from 2018-2022, it is quite effective with a value of 80.36%. While the level of contribution is in the medium category with a percentage value of 23%.
Abstract 17845: Logic-Based Natural Language Processing Identifies VT Recurrence in the Electronic Health Record

Introduction:             With the advent of integrated electronic healthcare record (EHR) systems, it is increasingly important to identify patients who may suffer a clinical endpoint to allocate resources and develop predictive models. For the endpoint of VT recurrence after ablation, outcomes are currently assessed manually by experts, which is time consuming with few automated tools. We set out to develop a robust natural language processing model to parse cardiology and electrophysiology notes to detect VT.                                   Hypothesis:             We hypothesize that natural language processing (NLP) with logic-based algorithms can be used to accurately extract specific information on VT recurrence from complex clinical notes, compared to experts.                                   Methods:             We studied N=639 full-text clinical notes written by board certified cardiologists from N = 125 patients (32.0% female, LVEF 48.9±13.9%, 61±14.0 years). Notes were labeled as “No” or “Yes” for VT recurrence, and split into a development set of N=303 (median word count = 236) from N=75 patients and a test set of N=336 (median words = 297) from N=50 patients. We developed an NLP rule based expert system that uses hierarchical groups of keywords to identify sentences related to VT, then uses negation and quantifier functions to interpret context and assign a label.                                   Results:             The logical flow is shown in figure A. This approach achieved an 93.73% accuracy in the development set (precision = 57.14%, recall = 69.56%), and 90.48% accuracy (precision = 36.84%, recall = 63.63%) in the test set (figure B).                                   Conclusions:             An NLP logic-based approach can identify VT recurrence post ablation from complex clinical notes. NLP can thus be used to accelerate data extraction for clinical purposes or for semi-supervised learning. Future work will determine if logic-based NLP need to be augmented for noisier data, such as the entire EHR system.
VISUAL DESIGN FRAMEWORKS FOR CREATIVE QUALITY MANAGEMENT: INTEGRATING LEAN SIX SIGMA IN CONTEMPORARY DESIGN INDUSTRIES

The contemporary design industry operates at the intersection of creativity, visual communication, and quality-driven production, demanding structured yet flexible frameworks to sustain innovation. This study proposes a visual design framework for creative quality management by integrating Lean Six Sigma principles within contemporary design industries. Moving beyond traditional industrial applications, Lean Six Sigma is reinterpreted as a visual and strategic tool to enhance creative workflows, reduce process inefficiencies, and improve design consistency without constraining artistic expression. The framework emphasizes visual thinking, design iteration, and process transparency, aligning quality management practices with creative decision-making. Through conceptual analysis and illustrative design-process models, the study demonstrates how visual mapping, workflow optimization, and feedback loops can support design quality across disciplines such as graphic design, media production, fashion, and digital arts. The proposed framework bridges the gap between creativity and systematic quality management, offering designers and creative managers a structured approach to maintain artistic integrity while achieving operational excellence. This research contributes to the growing discourse on interdisciplinary design management by positioning Lean Six Sigma as a design-enabled methodology rather than a purely industrial tool, thereby expanding its relevance within visual and creative industries.