ethicalabs/Research-Intent-Collab huggingface dataset:
Photovoltaic-ferroelectric materials for the realization of all-optical devices

Following how the electrical transistor revolutionized the field of electronics,the realization of an optical transistor in which the flow of light is controlled optically should open the long-sought era of optical computing and new data processing possibilities. However, such function requires photons to influence each other, an effect which is unnatural in free space. Here it is shown that a ferroelectric and photovoltaic crystal gated optically at the onset of its bandgap energy can act as a photonic transistor. The light-induced charge generation and distribution processes alter the internal electric field and therefore impact the optical transmission with a memory effect and pronounced nonlinearity. The latter results in an optical computing possibility, which does not need to operate coherently. These findings advance efficient room temperature optical transistors, memristors, modulators and all-optical logic circuits.
Parallelizing simulated annealing algorithms based on high-performance computer

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In Situ TEM Electrochemistry Platforms for Li-Ion Battery Materials

Abstract not Available.
Association of Palliative Care Use and Setting With Health-care Utilization and Quality of Care at the End of Life Among Patients With Advanced Lung Cancer

Palliative care is associated with improved survival and quality of life among patients with lung cancer; however, its influence on health-care utilization and quality of care is unclear.Is palliative care, and the setting in which it occurs, associated with health-care resource utilization and quality of care among patients with advanced lung cancer?This was a retrospective cohort study of 23,142 patients with stage IIIB/IV lung cancer in the Veterans Affairs HealthCare System between 2007 and 2013. Exposures included the receipt of specialist-delivered palliative care, and the setting of the initial palliative care encounter (inpatient or outpatient) received after cancer diagnosis. Primary outcomes included rates of ED visits, along with rates of hospitalization and odds of ICU admission within the last 30 days of life. Secondary outcomes included any health-care utilization (ED, hospital, or ICU) related to chemotherapy toxicity. We used propensity score methods to perform Poisson and logistic regression modeling.Among the 23,142 patients, 57% received palliative care, and 36% of initial palliative care encounters were outpatient. Compared with no palliative care, initial palliative care encounter in the outpatient setting was associated with reduced rates of ED visits (adjusted incidence rate ratio [aIRR], 0.86; 95% CI, 0.77-0.96) and hospitalizations in the last 30 days of life (aIRR, 0.64; 95% CI, 0.59-0.70). Initial palliative care encounters in both inpatient (adjusted OR [aOR], 0.63; 95% CI, 0.53-0.75) and outpatient (aOR, 0.42; 95% CI, 0.35-0.52) settings were associated with reduced odds of ICU admission in the last 30 days of life. Palliative care was also associated with reduced health-care utilization related to chemotherapy toxicity (aOR, 0.88; 95% CI, 0.82-0.95).Palliative care (particularly in outpatient settings) is associated with reduced health-care utilization at the end of life and may improve the quality of care among patients with advanced lung cancer. These findings support the role of palliative care as an important component of comprehensive cancer care and highlight the potential benefits of outpatient palliative care services.
The implementation of a community-based aerobic walking program for mild to moderate knee osteoarthritis (OA): a knowledge translation (KT) randomized controlled trial (RCT): Part I: The Uptake of the Ottawa Panel clinical practice guidelines (CPGs)

The implementation of evidence based clinical practice guidelines on self-management interventions to patients with chronic diseases is a complex process. A multifaceted strategy may offer an effective knowledge translation (KT) intervention to promote knowledge uptake and improve adherence in an effective walking program based on the Ottawa Panel Evidence Based Clinical Practice Guidelines among individuals with moderate osteoarthritis (OA).A single-blind, randomized control trial was conducted. Patients with mild to moderate (OA) of the knee (n=222) were randomized to one of three KT groups: 1) Walking and Behavioural intervention (WB) (18 males, 57 females) which included the supervised community-based aerobic walking program combined with a behavioural intervention and an educational pamphlet on the benefits of walking for OA; 2) Walking intervention (W) (24 males, 57 females) wherein participants only received the supervised community-based aerobic walking program intervention and the educational pamphlet; 3) Self-directed control (C) (32 males, 52 females) wherein participants only received the educational pamphlet. One-way analyses of variance were used to test for differences in quality of life, adherence, confidence, and clinical outcomes among the study groups at each 3 month assessment during the 12-month intervention period and 6-month follow-up period.Short-term program adherence was greater in WB compared to C (p 0.05) was observed for long-term adherence (6 to 12 months), and total adherence between the three groups. The three knowledge translation strategies demonstrated equivalent long-term results for the implementation of a walking program for older individuals with moderate OA. Lower dropout rates as well as higher retention rates were observed for WB at 12 and 18 months.The additional knowledge translation behavioural component facilitated the implementation of clinical practice guidelines on walking over a short-term period. More studies are needed to improve the long-term walking adherence or longer guidelines uptake on walking among participants with OA. Particular attention should be taken into account related to patient's characteristic and preference. OA can be managed through the implementation of a walking program based on clinical practice guidelines in existing community-based walking clubs as well as at home with the minimal support of an exercise therapist or a trained volunteer.Current Controlled Trials IRSCTNO9193542.
African Decolonization and Self-Determination

Abstract                   Chapter 3 centers on decolonization and national liberation movements in Africa, focusing on decolonization of the Sudan, the Biafran secession crisis, and the competing self-determination claims of the African National Congress and the apartheid government of South Africa. Each raised profound questions about the nature of decolonization, the legitimacy of secession and armed struggle, and the meaning of postcolonial sovereignty. Even as anticolonial leaders deployed self-determination claims against colonial administrators, “minority” groups within them made similar demands for political, economic, or cultural sovereignty, whether for democratic rights within postcolonial states (as in South Africa) or, more explosively, as secessionist movements demanding independence from them (as in South Sudan and Biafra).
Self-Supervised Representation Learning for Ultrasound Video

Recent advances in deep learning have achieved promising performance for medical image analysis, while in most cases ground-truth annotations from human experts are necessary to train the deep model. In practice, such annotations are expensive to collect and can be scarce for medical imaging applications. Therefore, there is significant interest in learning representations from unlabelled raw data. In this paper, we propose a self-supervised learning approach to learn meaningful and transferable representations from medical imaging video without any type of human annotation. We assume that in order to learn such a representation, the model should identify anatomical structures from the unlabelled data. Therefore we force the model to address anatomy-aware tasks with free supervision from the data itself. Specifically, the model is designed to correct the order of a reshuffled video clip and at the same time predict the geometric transformation applied to the video clip. Experiments on fetal ultrasound video show that the proposed approach can effectively learn meaningful and strong representations, which transfer well to downstream tasks like standard plane detection and saliency prediction.
Fermi Large Area Telescope Second Source Catalog

We present the second catalog of high-energy gamma-ray sources detected by the Large Area Telescope (LAT), the primary science instrument on the Fermi Gamma-ray Space Telescope (Fermi), derived from data taken during the first 24 months of the science phase of the mission, which began on 2008 August 4. Source detection is based on the average flux over the 24-month period. The Second Fermi-LAT catalog (2FGL) includes source location regions, defined in terms of elliptical fits to the 95% confidence regions and spectral fits in terms of power-law, exponentially cutoff power-law, or log-normal forms. Also included are flux measurements in 5 energy bands and light curves on monthly intervals for each source. Twelve sources in the catalog are modeled as spatially extended. We provide a detailed comparison of the results from this catalog with those from the first Fermi-LAT catalog (1FGL). Although the diffuse Galactic and isotropic models used in the 2FGL analysis are improved compared to the 1FGL catalog, we attach caution flags to 162 of the sources to indicate possible confusion with residual imperfections in the diffuse model. The 2FGL catalog contains 1873 sources detected and characterized in the 100 MeV to 100 GeV range of which we consider 127 as being firmly identified and 1170 as being reliably associated with counterparts of known or likely gamma-ray-producing source classes.
Leveraging Serverless Computing: An In-Depth Review of Azure Functions and ASP.NET Core Integration

Serverless computing has revolutionized the way developers build and deploy applications by abstracting the underlying infrastructure management. Azure Functions, a leading serverless computing service by Microsoft, provides a scalable and cost-effective solution for running event-driven code. When integrated with ASP.NET Core, Azure Functions enables the creation of robust, scalable, and high-performance web applications. This review paper explores the architecture, benefits, and use cases of Azure Functions and ASP.NET Core integration. We discuss various methods and best practices for leveraging these technologies and analyze their performance through real-world examples. The findings highlight the transformative potential of serverless computing in modern web development.
Genetic Privacy: The Potential for Genetic Discrimination in Insurance

The threat of modern genetics has been perceived as coming, rather dramatically, from genetic engineering, but the less flashy field of medical genetic testing poses significant and immediate issues. This article discusses the potential for breach of confidentiality or invasion of privacy through the acquisition of information, the disclosure of information, and the potential for prejudicial use of that information by third parties. The author concludes that New Zealand's ethical and legal aspects of human genetics needed a review at the time of writing, recommending an advisory group to be set up to monitor developments in human genetics, facilitate discussion with all relevant persons, groups and bodies, and report on issues arising from new developments in human genetics that can be expected to have wider ethical, social, economic, and legal consequences. However, the author does not find it necessary to enact genetic-specific legislation.
An interpretation of E_n-homology as functor homology

We prove that E_n-homology of non-unital commutative algebras can be described as functor homology when one considers functors from a certain category of planar trees with n levels. For different n these homology theories are connected by natural maps, ranging from Hochschild homology and its higher order versions to Gamma homology.
Bringing in health technology assessment and cost‐effectiveness considerations at an early stage of drug development

This paper reviews the issues involved in undertaking HTA studies early in the development of new cancer therapies, and discusses the data and methods for estimating the cost‐effectiveness of new diagnostics and treatments. The value for patients of new cancer therapies is based on access to the treatment and optimal use. Realising potential value depends on successful completion of a series of steps, from the initial economic evaluations based on clinical trial data, to the reimbursement decisions based on the evaluations and the implementation of these decisions in clinical practice. Considerable resources have been devoted to the study of the cost‐effectiveness of new cancer drugs as a basis for decisions about payment and use. Such resources could be used much more effectively if industry and HTA agencies were to collaborate at an early stage in the development process. The traditional clinical trial approach of using progression‐free survival and cross‐overs has serious shortcomings, producing data that cannot be used to determine outcomes and, so, cost‐effectiveness. A new standard is needed; both regulatory and HTA authorities should be involved in its development.
Group Privacy for Personalized Federated Learning

Federated learning (FL) is a type of collaborative machine learning where participating peers/clients process their data locally, sharing only updates to the collaborative model. This enables to build privacy-aware distributed machine learning models, among others. The goal is the optimization of a statistical model's parameters by minimizing a cost function of a collection of datasets which are stored locally by a set of clients. This process exposes the clients to two issues: leakage of private information and lack of personalization of the model. On the other hand, with the recent advancements in various techniques to analyze data, there is a surge of concern for the privacy violation of the participating clients. To mitigate this, differential privacy and its variants serve as a standard for providing formal privacy guarantees. Often the clients represent very heterogeneous communities and hold data which are very diverse. Therefore, aligned with the recent focus of the FL community to build a framework of personalized models for the users representing their diversity, it is also of utmost importance to protect the clients' sensitive and personal information against potential threats. To address this goal we consider $d$-privacy, also known as metric privacy, which is a variant of local differential privacy, using a a metric-based obfuscation technique that preserves the topological distribution of the original data. To cope with the issue of protecting the privacy of the clients and allowing for personalized model training to enhance the fairness and utility of the system, we propose a method to provide group privacy guarantees exploiting some key properties of $d$-privacy which enables personalized models under the framework of FL. We provide theoretical justifications to the applicability and experimental validation on real datasets to illustrate the working of our method.
AI-Augmented API Gateways: Intelligent Traffic Management, Threat Detection, and Adaptive Policy Enforcement

The rapid proliferation of microservices, cloud-native architectures, and distributed digital platforms has significantly increased the complexity of managing Application Programming Interface (API) ecosystems. Traditional API gateways, while effective for routing, authentication, and rate limiting, are increasingly inadequate in addressing dynamic traffic patterns, evolving cyber threats, and the need for real-time decision-making. This paper explores the concept of AI-augmented API gateways, which integrate Artificial Intelligence (AI) and Machine Learning (ML) techniques to enhance traffic management, threat detection, and adaptive policy enforcement. The article presents a generalized, vendor-neutral architecture for AI-enabled API gateways, highlighting how predictive analytics, anomaly detection models, and reinforcement learning can optimize API performance and security. It examines intelligent traffic routing strategies that dynamically adapt to workload fluctuations, user behavior, and service-level objectives. Additionally, the study investigates AI-driven threat detection mechanisms capable of identifying zero-day attacks, API abuse patterns, and bot-driven anomalies in real time. A key contribution of this work is the introduction of adaptive policy enforcement frameworks, where policies evolve autonomously based on contextual insights, risk scoring, and historical data patterns. The paper also discusses challenges such as model drift, data privacy, explainability, and integration complexity within enterprise environments. Practical use cases across finance, healthcare, and large-scale digital platforms are analyzed to demonstrate real-world applicability. By combining AI capabilities with API gateway functionalities, organizations can achieve enhanced scalability, resilience, and security in modern digital infrastructures. This paper concludes that AI-augmented API gateways represent a critical evolution in API management, enabling intelligent, self-optimizing, and secure communication layers for next-generation applications.
Integrating Governance, AI Accountability, and Systemic Resilience: A Framework for Critical Infrastructure Protection in Nigeria

The security and systemic resilience of critical infrastructure (CI) in developing economies, such as Nigeria, require executive-level governance akin to the NIS 2 Directive's mandates for management body oversight and direct liability (European Parliament &amp; Council of the European Union, 2022). Nigeria's CI faces annual losses exceeding $1 billion from cyber incidents, underscoring the urgency for integrated frameworks (Central Bank of Nigeria, 2024). This governance mandate is necessary because current risk models are often inadequate for accurately assessing the probability and consequence of sophisticated hybrid attacks (NIST, 2008). This paper establishes a synthesized governance framework, leveraging Agency Theory to diagnose internal accountability failures within Nigerian CI organisations (Burch et al., 2024). The resulting model structurally integrates strategies for confronting two paramount systemic threats: complex vulnerabilities within the Artificial Intelligence (AI) supply chain (DHS, 2024; IBM, 2024) and the multiplying effects of the Cyber Climate Nexus (Guy Carpenter, 2025; UNDP, 2024). The prescribed five step model provides a practical and auditable blueprint for strengthening corporate accountability, institutionalising formal risk acceptance procedures, and transitioning from fragmented compliance to proactive, integrated operational resilience (CISA, 2024; Parlov et al., 2025).
'Is Transparency Enough? An Examination of the Effect of the Extractive Industry Transparency Initiative (EITI) on Accountability, Corruption and Trust in Zambia

The Extractive Industry Transparency Initiative (EITI) is the leading global transparency standard for the extractive industry. It aims to improve governance standards in the extractive industry by providing a public platform for information sharing and multi-stakeholder dialogue. However, the success of the initiative has been brought into question by numerous scholars. This paper aims to shed new light on this work by presenting a unique analytical framework. The framework hypothesises that improved transparency, through the EITI, can lead to improved extractive industry governance: increased accountability, reduced corruption and increased trust. However, this improvement of governance can only take place when combined with three scope conditions: 1) transparency condition, 2) publicity condition, and 3) accountability condition. The paper applies this framework to the single case study of Zambia, and finds that the EITI has failed to meaningfully improve these three governance outcomes in the extractive industry in Zambia. The paper argues that the reason for this is that none of the three necessary scope conditions are sufficiently present. The paper advocates for policymakers to support the growth of these three conditions in contexts of poor extractive industry governance, to ensure transparency standards have meaningful impact.
GeoFWI: A large velocity model dataset for benchmarking full waveform inversion using deep learning

The is the resource of GeoFWI  model and the corresponding benchmark code.
Public space and the public that frequents it: dilemmas of the human rights to water and sanitation

The human rights to water and sanitation were officially recognized by the United Nations in 2010. Despite this, these rights have not been explicitly recognized as basic rights in Brazilian legislation and their interpretation in law comes from the concept of universal access. However, little is discussed regarding the universalization of this access in spaces beyond the household, particularly in public spaces, serving people in socio-spatial vulnerability and who need urban furniture such as drinking fountains and toilets, either because they work or live there. It is in public spaces that homeless people, street vendors, women and girls, people with disabilities and transgender people are neglected, given the lack of access to water and public toilets. Thus, this work has sought to understand the possibilities of interpreting public sanitation policy in Brazil from the definitions of “universal access” and “household” using content analysis when reading these documents. It was concluded that the Law excludes public spaces, neglecting the aforementioned groups.
Pore Characterization of Organic-Rich Shales through Application of Topological Data Analysis and Persistent Homology

This paper discusses pore characterization of organic-rich shales using 3D high-resolution imaging and a topological data analysis (TDA) framework. In particular, our focus is on persistent homolog...
Incorporating biodiversity conservation and recreational wildlife values into smart growth land use planning

Smart growth land use planning seeks to balance the infrastructure needs of a growing human population and protection for the environment. Unfortunately, the data required to adequately incorporate biodiversity objectives into land use plans is often not available to planners. One problem is that there are few documented methods that detail how biodiversity data held by resource management agencies can be converted to a format useful for inclusion into smart growth plans. Here we demonstrate an approach that allows for state, provincial, or federal resource management agencies to disseminate data on (1) biodiversity conservation, and (2) conservation of wildlife of recreational value for incorporation into local land use plans. Our approach uses modeled threats and species richness data to identify high priority conservation areas and areas more suitable for future development. This approach provides a transparent mechanism to facilitate inclusion of biodiversity objectives into smart growth planning.