ethicalabs/Research-Intent-Collab huggingface dataset:
An Implementation of ECHONET Lite Web API Gateway for DER Management

To realize decarbonized society, it is important to manage Distributed Energy Resources (DERs) such as photovoltaic power generators (PVs), wind power generators and storage batteries considering balance between energy supply and demand. In this paper, we proposes a gateway software for DER management. The gateway software converts ECHONET Lite Web API and ECHONET Lite™, therefore, it can connect cloud-based DER management services and DERs distributed everywhere. The gateway software is evaluated with a simple service and an emulator of storage battery. The evaluation result shows the gateway software connects the service and the battery correctly.
The Right to Self–determination and Individual rights in the Era of Decolonization in Sub-Saharan Africa: The Case of UNESCO.

Yes
Monolithically Integrated Saturable Absorbers for All-Optical Nonlinear Activation in Photonic Neural Networks

Integrated photonics offers a promising route toward low-latency and energy-efficient neural computing. However, the inherently linear nature of most photonic devices makes the realization of compact and efficient optical nonlinear activation functions a key obstacle for scalable photonic neural networks. Moreover, large-scale architectures require monolithically integrated nonlinearities for dense cascading without the losses and complexity associated with hybrid or heterogeneous integration. Here, we experimentally demonstrate monolithically integrated all-optical nonlinear activation units based on quantum-well saturable absorbers (SAs) on a generic InP platform, co-integrated with passive waveguides and electro-optic components. Three nonlinear activation units are realized: a standalone SA, a Mach-Zehnder-interferometer-integrated SA, and a microring-resonator-enhanced SA. By exploiting saturable absorption, resonance, and interference, these devices provide programmable nonlinear transfer functions, including softplus-, sigmoid-, ReLU-, and RBF-like responses. Experimentally measured transfer functions are incorporated into convolutional neural networks for CIFAR-10 and Fashion-MNIST classifications, achieving 89% and 92% accuracies, respectively.
Value-at-Risk- and Expectile-based Systemic Risk Measures and Second-order Asymptotics: With Applications to Diversification

Systemic risk measures play a crucial role in analyzing individual losses conditional on extreme system-wide disasters. In this paper, we provide a unified asymptotic treatment for systemic risk measures. First, we classify them into two families of Value-at-Risk- (VaR-) and expectile-based systemic risk measures. While VaR-based risk measures have been extensively studied, in the latter family, we propose two new systemic risk measures named the Individual Conditional Expectile (ICE) and the Systemic Individual Conditional Expectile (SICE), as alternatives to Marginal Expected Shortfall (MES) and Systemic Expected Shortfall (SES). Second, to characterize general mutually dependent and heavy-tailed risks, we consider a multivariate loss system following a multivariate Sarmanov distribution with common marginal distributions exhibiting second-order regular variation. Third, within this setting, we provide second-order asymptotic results for both families of systemic risk measures. These results extend standard first-order asymptotics and allow for more accurate tail approximations. Through numerical and analytical examples, we demonstrate the superiority of second-order asymptotics in accurately assessing systemic risk. We further conduct a comprehensive comparison between expectile-based and VaR-based systemic risk measures. The results indicate that expectile-based measures often yield higher asymptotic accuracy than VaR-based ones, emphasizing the former's potential advantages in reporting extreme events and tail risk. As a financial application, we use the asymptotic treatment to discuss the diversification benefits associated with various risk measures. Finally, we extend and obtain the second-order asymptotic formulas for generalized-quantile-based systemic risk measures with power functions.
Object Detection with Multimodal Large Vision-Language Models: An In-depth Review

The fusion of language and vision in large vision-language models (LVLMs) has revolutionized deep learning-based object detection by enhancing adaptability, contextual reasoning, and generalization beyond traditional architectures. This in-depth review presents a structured exploration of the state-of-the-art in LVLMs, systematically organized through a three-step research review process. First, we discuss the functioning of vision language models (VLMs) for object detection, describing how these models harness natural language processing (NLP) and computer vision (CV) techniques to revolutionize object detection and localization. We then explain the architectural innovations, training paradigms, and output flexibility of recent LVLMs for object detection, highlighting how they achieve advanced contextual understanding for object detection. The review thoroughly examines the approaches used in integration of visual and textual information, demonstrating the progress made in object detection using VLMs that facilitate more sophisticated object detection and localization strategies. This review presents comprehensive visualizations demonstrating LVLMs' effectiveness in diverse scenarios including localization and segmentation, and then compares their real-time performance, adaptability, and complexity to traditional deep learning systems. Based on the review, its is expected that LVLMs will soon meet or surpass the performance of conventional methods in object detection. The review also identifies a few major limitations of the current LVLM modes, proposes solutions to address those challenges, and presents a clear roadmap for the future advancement in this field. We conclude, based on this study, that the recent advancement in LVLMs have made and will continue to make a transformative impact on object detection and robotic applications in the future.
Computational electrochemistry of Pillar[5]quinone cathode material for lithium-ion batteries

Abstract   Multi-carbonyl macrocyclic compounds have recently attracted much attention due to their high performance relative to some short chain carbonyl compounds as the cathode active constituents for lithium-ion batteries (LIBs). However, little is known about the evolution mechanism of their electrochemical properties during charging and discharging processes. In this paper, the application of density functional calculations at the M06-2X/6-31G(d,p) level of theory is presented to study systematically the electrochemical properties of pillar[5]quinone (P5Q) as a cathode active material for LIBs. The optimized structures of P5Q accepting different number of electrons and binding different number of lithium atoms are obtained, respectively. The geometry structure, thermodynamics property, electronic structural property, solvent effect and redox potential are discussed in detail. The uneven-distribution of extra electrons in several P5Qn− anions can minimize the repulsive interactions as far as possible. The macrocyclic skeletons in P5QLin structures are distorted to different extents by the binding interactions between Li atoms and P5Q. More than eight intercalated lithium atoms into per P5Q molecule are confirmed in this work, indicating a high utilization ratio of carbonyl groups of P5Q as a cathode material. Compared with pillar[4]quinone and pillar[6]quinone, P5Q is predicted to have better cycling performance due to its higher structural stability.
Central bank digital currency: What is it and why now?

A central bank digital currency ( CBDC) is electronic in nature and one which amounts to the currency of the country implementing it. The value of a CBDC is the same as the physical form, i.e. a £10 CBDC would equate to a £10 bank note. It is not classed as a cryptocurrency, which is a form of digital currency, however, the Bank of England has said that it may use the distributed ledger technology ( DLT) which is utilised for cryptocurrency. Central banks are researching into the benefits and implications of a CBDC, such as making payments more resilient and faster, and adapting to the digital economy. Cynically, it appears that central banks are concerned by the potential of cryptocurrencies becoming mainstream and making central banks redundant. This article will explore the following: what is a CBDC, why is it being explored now and the future of money and payment services.
Development of an Information-Based Dashboard: Automation of Barangay Information Profiling System (BIPS) for Decision Support towards e-Governance

The need to address societal issues of every community is a salient aspect that demands attention from the people in authority. These are important responsibilities of every barangay and its official in the Philippines. Profiling each household in the community using information and communication technology could achieve good governance through E-government as its core. Once profile data is aggregated, essential information could provide statistics in labor and employment, family income and expenditures, demography by (population) and (age), water and sanitation, type of housing and education. The focus is based on the profiling of Zone 42 and adding other facets as mentioned above was initiated, with the idea that educational institution around the barangay can help towards the areas included. This paper intends to aid barangay official in budget allocation and decision making in their respective governed area with the use of Barangay Information Profiling System (BIPS). Building an Information-Based Dashboard was initiated last 2016 and assessed by IT expert, was given readiness for beta launch to its target users. The functionality criteria were given a mean score of 4.47, which means that the respondents agreed that the system sequence of operation is easy to understand, and the result of their queries is correct and accurate. The system testing had a favorable result with a mean of 4.50 which means that the system passed the standard of completing, processing of a request, response time and the usage of computer resources for all of its function.
Accelerated Particle Swarm Optimization and Support Vector Machine for Business Optimization and Applications

Business optimization is becoming increasingly important because all business activities aim to maximize the profit and performance of products and services, under limited resources and appropriate constraints. Recent developments in support vector machine and metaheuristics show many advantages of these techniques. In particular, particle swarm optimization is now widely used in solving tough optimization problems. In this paper, we use a combination of a recently developed Accelerated PSO and a nonlinear support vector machine to form a framework for solving business optimization problems. We first apply the proposed APSO-SVM to production optimization, and then use it for income prediction and project scheduling. We also carry out some parametric studies and discuss the advantages of the proposed metaheuristic SVM.
A New Paradigm of Customary International Criminal Law: The UN War Crimes Commission of 1943–1948 and its Associated Courts and Tribunals

This article focuses on the United Nations War Crimes Commission’s significant contribution to the development of customary international criminal law defined by the development of international legal standards and proceedings to combat impunity and promote justice. It draws on the Commission’s official history and its increasingly open archives in order to provide an overview of the UNWCC and its work, its members and its legacy for the contemporary era of international criminal law. The article firstly places the Commission in its historical context through the events and agreements that led to its creation and provided the legal character of the UNWCC. The defining characteristics of the Commission are afterwards described: the nations involved, the committee structure it formed and the sub-commission located in the Far East. Lastly, the accomplishments of the Commission are emphasised and criticisms of its work are presented. The article concludes with a discussion on the legacy of the Commission’s work and a possible future research agenda.
Additional file 1: of Eliciting meta consent for future secondary research use of health data using a smartphone application - a proof of concept study in the Danish population

The appendix contains a translation of each of the screens in the meta consent application. (DOCX 19 kb)
Trade secret misappropriation, possession of confidential information, and preliminary injunctions

The United States District Court for the Northern District of California, ruling against Silicon Image, Inc., denied a preliminary injunction which sought to preclude Analogix from producing, selling, or offering for sale certain of its semiconductor chips that, Silicon Image alleged, were illegal copies of its own chips. Although there was strong evidence of copying by Analogix, Silicon Image's receipt of competitors' confidential information from its customers belied Silicon Image's claim of reasonable reliance on its own non-disclosure agreements (NDAs) to protect its confidential information.
Palliative Care Unit at Home: Impact on Quality of Life in Cancer Patients at the End of Life in a Rural Environment

Introduction:                     Advanced cancer is accompanied by a substantial burden of symptoms, and palliative care (PC) plays an essential role, especially at the end of life (EOL). In fact, a comprehensive PC through Home Palliative Care Units (HPCU) has been associated with reducing potentially aggressive care at the EOL. We aim to study the impact of HPCU on the quality of assistance of cancer patients at Alcoy Health Department.                                                           Methods:                     A retrospective study was conducted including patients diagnosed with terminal cancer at the Medical Department of Hospital Virgen de los Lirios who died between January 2017 and December 2018. The Multivariate Cox regression model was used to assess the impact of HPCU assistance on the quality of life indicators.                                                           Results:                     388 patients were included. The median age was 71 years; 65% patients were male, and 68% presented with a 0-2 score on the ECOG scale. On the multivariate analysis, a lack of assistance by HPCU was associated with a higher risk of consulting in the emergency department (OR = 1.29, 95% CI: 1.02-1.67), of hospital admissions (OR = 4.72, 95% CI: 2.45-9.09), a higher probability of continuing active treatment (OR = 2.59, 95% CI: 1.44-4.67), and a greater probability of dying in hospital (OR = 6.52, 95% CI: 3.78-11.27).                                                           Conclusions:                     Patients receiving HPCU assistance have a lower number of emergency room visits and hospital admissions, and are more likely to die at home. Taken together, these results support the relevance of HPCU providing a high quality attention of cancer patients.
HyperDehazing: A hyperspectral image dehazing benchmark dataset and a deep learning model for haze removal

Haze contamination severely degrades the quality and accuracy of optical remote sensing (RS) images, including hyperspectral images (HSIs). Currently, there are no paired benchmark datasets containing hazy and haze-free scenes in HSI dehazing, and few studies have analyzed the distributional properties of haze in the spatial and spectral domains. In this paper, we developed a new hazy synthesis strategy and constructed the first hyperspectral dehazing benchmark dataset (HyperDehazing), which contains 2000 pairs synthetic HSIs covering 100 scenes and another 70 real hazy HSIs. By analyzing the distribution characteristics of haze, we further proposed a deep learning model called HyperDehazeNet for haze removal from HSIs. Haze-insensitive longwave information injection, novel attention mechanisms, spectral loss function, and residual learning are used to improve dehazing and scene reconstruction capability. Comprehensive experimental results demonstrate that the HyperDehazing dataset effectively represents complex haze in real scenes with synthetic authenticity and scene diversity, establishing itself as a new benchmark for training and assessment of HSI dehazing methods. Experimental results on the HyperDehazing dataset demonstrate that our proposed HyperDehazeNet effectively removes complex haze from HSIs, with outstanding spectral reconstruction and feature differentiation capabilities. Furthermore, additional experiments conducted on real HSIs as well as the widely used Landsat-8 and Sentinel-2 datasets showcase the exceptional dehazing performance and robust generalization capabilities of HyperDehazeNet. Our method surpasses other state-of-the-art methods with high computational efficiency and a low number of parameters.
Data Sciences Technology for Homeland Security Information Management and Knowledge Discovery

The Department of Homeland Security (DHS) has vast amounts of data available, but its ultimate value cannot be realized without powerful technologies for knowledge discovery to enable better decision making by analysts. Past evidence has shown that terrorist activities leave detectable footprints, but these footprints generally have not been discovered until the opportunity for maximum benefit has passed. The challenge faced by the DHS is to discover the money transfers, border crossings, and other activities in advance of an attack and use that information to identify potential threats and vulnerabilities. The data to be analyzed by DHS comes from many sources ranging from news feeds, to raw sensors, to intelligence reports, and more. The amount of data is staggering; some estimates place the number of entities to be processed at 1015. The uses for the data are varied as well, including entity tracking over space and time, identifying complex and evolving relationships between entities, and identifying organization structure, to name a few. Because they are ideal for representing relationship and linkage information, semantic graphs have emerged as a key technology for fusing and organizing DHS data. A semantic graph organizes relational data by using nodes to represent entities and edgesmore » to connect related entities. Hidden relationships in the data are then uncovered by examining the structure and properties of the semantic graph.« less
223 Strengthening dissemination and implementation capacity at UW–Madison: A landscape survey to inform and support the translational science workforce

Objectives/Goals:                     The Dissemination and Implementation (D&amp;I) Launchpad at the University of Wisconsin – Madison (UW) supports a growing community of implementation scientists with the goal of strengthening the field. We conducted a survey to identify those doing the work, assess experiences, priorities, and needs to strengthen D&amp;I at UW.                     Methods/Study Population:                     In September 2025, the UW ICTR D&amp;I Launchpad administered a landscape survey via REDCap to UW individuals who had signed up for the D&amp;I listserv, attended a D&amp;I event, or received a D&amp;I grant. The survey was also promoted at events for broader participation. The survey assessed D&amp;I identity, skills, training experiences and needs, barriers, support preferences, and network. Of 851 invited and 13 requests, 229 responded (26.5% response rate). Respondents were 53% faculty, 39% staff, and 8% students/postdocs from over 10 UW schools, colleges, and partners.                     Results/Anticipated Results:                     Most respondents (67%) identified as contributing to D&amp;I science; 33% said “No” or “Not sure,” citing lack of training, awareness, or other identity. Skill levels varied: 41% beginner, 39% intermediate, 12% advanced, and 4% expert. Top support needs for D&amp;I research were funding, training, grant advice, and consultations. 47% reported receiving D&amp;I training, most frequently through UW programs, coursework, and national programs. Requested training topics included implementation frameworks, strategy design, and trial design. 59% reported challenges, including funding, IRB, and lack of collaborators. Barriers to participating in training included time, cost, and relevance. Most individuals expressed interest in future D&amp;I training, events, and growing the D&amp;I community at UW.                     Discussion/Significance of Impact:                     The survey revealed strong interest in D&amp;I across UW, with many contributing to the field but lacking formal training or support. Findings will guide the D&amp;I Launchpad’s efforts to expand training, resources, and the D&amp;I community network.
Antimicrobial Resistance and Stewardship: A Survey of Prescribers’ Attitudes, Concerns and Practices in A Tertiary Care Facility in Lagos, Nigeria

Background: Antimicrobial resistance (AMR) has become a universal phenomenon affecting both resource-rich and resource-limited countries. Antibiotic stewardship (AMS) is an organisational or healthcare-system-wide approach to foster and monitor the judicious use of antimicrobials in order to preserve their effectiveness. The objective of this study was to determine the attitudes and practices of prescribers about AMR and AMS. Methods: This was a cross-sectional study carried out in Lagos University Teaching Hospital (LUTH), using self-administered questionnaires (Google forms) created by the Commonwealth Partnerships for Antimicrobial Stewardships (CwPAMS) and delivered individually by the interviewers to examine attitudes, concerns, practices, and strategies for AMR and AMS. Responses to 18 items that covered the effectiveness of antibiotics, causes of AMR, important aspects of tackling AMR, and WHO categorisation of antibiotics were analysed in a Microsoft Excel spreadsheet using Epi Info™ 7. Results: One hundred seventy-six prescribers responded. Majority (73.3%) were registrars/senior registrars, and 61.0% had been in service for &lt; 6 years. Almost all prescribers’ (96.0%) were concerned about AMR, and the ‘need to promote awareness of AMR’ (95.5%). Sources of information used by prescribers in the management of infections were obtained from ‘previous clinical experience’ (61.4%) and ‘local clinical practice guidelines (52.3%). ‘Medical representatives from the pharmaceutical industry’ (5.1%), ‘social media’ (3.4%), and ‘Medscape’ (0.6%) were the least used. Majority ‘expected pharmacists to query a prescription if they have concerns’ (70.2%), with 66.7% of prescribers reporting being pressured to ‘prescribe antibiotics even when it was not clinically required’. About 65.5% ‘prescribe antibiotics that were not recommended’ as a first line out of fear of complications or patient deterioration, and 58.9% reported that the choice of antimicrobial to prescribe was ‘based on cost rather than the cause of infection’. Majority of prescribers believed that increasing public awareness about AMS will mitigate antibiotic misuse. Conclusion: Most prescribers are concerned about AMR though many have poor prescribing practices due to pressure, fear of complications and cost of antibiotics. Training on rational antimicrobial prescription and public awareness on AMR will reduce the pressure on prescribers for antimicrobials and improve prescribing practices.