ethicalabs/Research-Intent-Collab huggingface dataset:
Comprehensive Security Framework for Serverless Computing: Integrating DevSecOps Practices in Aws Lambda and Azure Functions

Serverless computing has fundamentally transformed application architecture by abstracting infrastructure management, yet this paradigm shift introduces distinctive security challenges that conventional tools struggle to address. The ephemeral nature of serverless functions creates visibility gaps that leave organizations vulnerable to configuration drift, runtime attacks, and compliance violations. This article presents a comprehensive security framework that addresses these challenges through systematic integration of protection mechanisms across the entire serverless lifecycle. The framework encompasses static code analysis tailored for function-based architectures, automated configuration scanning that adapts to rapid deployment cycles, and agentless runtime monitoring that maintains performance efficiency. By embedding Policy as Code within CI/CD pipelines and implementing lightweight instrumentation techniques, organizations can achieve continuous compliance with standards such as PCI-DSS and CIS benchmarks while maintaining developer agility. The proposed model demonstrates how security controls can be seamlessly integrated without impeding deployment velocity, utilizing automated compliance checks and real-time threat detection to minimize both misconfiguration risks and runtime vulnerabilities. This holistic approach enables organizations to leverage the benefits of serverless computing while maintaining robust security postures across their AWS Lambda and Azure Functions deployments.
Weak lensing: Dark Matter, Dark Energy and Dark Gravity

In this non-specialist review I look at how weak lensing can provide information on the dark sector of the Universe. The review concentrates on what can be learned about Dark Matter, Dark Energy and Dark Gravity, and why. On Dark Matter, results on the confrontation of theoretical profiles with observation are reviewed, and measurements of neutrino masses discussed. On Dark Energy, the interest is whether this could be Einstein's cosmological constant, and prospects for high-precision studies of the equation of state are considered. On Dark Gravity, we consider the exciting prospects for future weak lensing surveys to distinguish General Relativity from extra-dimensional or other gravity theories.
Ecological and genetic effects of introduced species on their native competitors

Species introductions to new habitats can cause a decline in the population size of competing native species and consequently also in their genetic diversity. We are interested in why these adverse effects are weak in some cases whereas in others the native species declines to the point of extinction. While the introduction rate and the growth rate of the introduced species in the new environment clearly have a positive relationship with invasion success and impact, the influence of competition is poorly understood. Here, we investigate how the intensity of interspecific competition influences the persistence time of a native species in the face of repeated and ongoing introductions of the nonnative species. We analyze two stochastic models: a model for the population dynamics of both species and a model that additionally includes the population genetics of the native species at a locus involved in its adaptation to a changing environment. Counterintuitively, both models predict that the persistence time of the native species is lowest for an intermediate intensity of competition. This phenomenon results from the opposing effects of competition at different stages of the invasion process: With increasing competition intensity more introduction events are needed until a new species can establish, but increasing competition also speeds up the exclusion of the native species by an established nonnative competitor. By comparing the ecological and the eco-genetic model, we detect and quantify a synergistic feedback between ecological and genetic effects.
S-520 Sounding Rocket Experiments of Materials Science under Microgravity

資料番号: SA1004948000
Governance and Anti-Corruption Measures in Nigeria: Strategies for Enhancing Transparency, Accountability and Public Trust

This study examines governance and anti-corruption measures in Nigeria: strategies for enhancing transparency, accountability and public trust. The study performed a comprehensive analysis of institutional frameworks, previous anti-corruption strategies and regional variations. Utilizing Ordinary Least Squares (OLS) regression analysis, the research investigates the relationship between the strength of institutional frameworks for anti-corruption and levels of transparency and accountability, the impact of previous anti-corruption strategies on reducing corrupt practices within Nigerian governmental institutions, and the significance of regional differences in anti-corruption measures on public trust levels towards governmental institutions. The findings reveal a significant positive relationship between robust institutional frameworks and transparency/accountability, indicating that stronger frameworks correlate with higher governance integrity. The study also found a significant negative impact of the implementation of anti-corruption strategies on corrupt practices, suggesting that effective strategies can reduce corruption. However, no significant relationship was found between regional anti-corruption measures and public trust, indicating that varied regional impacts do not uniformly influence public perceptions of trust in governance. The study's recommendations include enhancing institutional capacities, refining anti-corruption strategies based on effective measures, and fostering inter-regional collaboration to share best practices. These measures aim to strengthen transparency, accountability, and public trust in Nigerian governance, contributing to sustainable development and societal well-being. This research underscores the importance of tailored anti-corruption efforts and robust institutional frameworks in addressing governance challenges in Nigeria, offering insights for policy formulation and strategic interventions in combating corruption.
Reaction extent or advancement of the reaction: A new general definition

The concept of reaction extent (the progress of a reaction, advancement of the reaction, conversion, etc.) was introduced around 100 years ago. Most of the literature provides a definition for the exceptional case of a single reaction step or gives an implicit definition that cannot be made explicit. There are views that the reaction extent somehow has to tend to 1 when the reaction goes to completion as time tends to infinity. However, there is no agreement on which function should tend to 1. Starting from the standard definition by IUPAC and following the classical works by De Donder, Aris, and Croce we extend the classic definition of the reaction extent for an arbitrary number of species and reaction steps. The new general, explicit definition is also valid for non-mass action kinetics. We also studied the mathematical properties (evolution equation, continuity, monotony, differentiability, etc.) of the defined quantity, connecting them to the formalism of modern reaction kinetics. Our approach tries to adhere to the customs of chemists and be mathematically correct simultaneously. To make the exposition easy to understand, we use simple chemical examples and many figures, throughout. We also show how to apply this concept to exotic reactions: reactions with more than one stationary state, oscillatory reactions, and reactions showing chaotic behavior. The main advantage of the new definition of reaction extent is that by knowing the kinetic model of a reacting system one can now calculate not only the time evolution of the concentration of each reacting species but also the number of occurrences of the individual reaction events.
Benchmark Health Index: A Systematic Framework for Benchmarking the Benchmarks of LLMs

Large Language Models (LLMs) are advancing rapidly, yet the benchmarks used to measure this progress are becoming increasingly unreliable. Score inflation and selective reporting have eroded the authority of standard benchmarks, leaving the community uncertain about which evaluation results remain trustworthy. We introduce the Benchmark Health Index (BHI), a pure data-driven framework for auditing evaluation sets along three orthogonal and complementary axes: (1) Capability Discrimination, measuring how sharply a benchmark separates model performance beyond noise; (2) Anti-Saturation, estimating remaining headroom before ceiling effects erode resolution and thus the benchmark's expected longevity; and (3) Impact, quantifying influence across academic and industrial ecosystems via adoption breadth and practice-shaping power. By distilling 106 validated benchmarks from the technical reports of 91 representative models in 2025, we systematically characterize the evaluation landscape. BHI is the first framework to quantify benchmark health at a macro level, providing a principled basis for benchmark selection and enabling dynamic lifecycle management for next-generation evaluation protocols.
Optimization of PC micro-drilling using a continuous CO2 laser: an experimental and theoretical comparative study

AbstractLaser drilling on polymers has many applications in various industries, such as sensors, aerospace, medical devices, and microelectronics. In this research, a CO2laser machine was employed for micro-drilling PC samples. Design–Expert analysis was applied to understand the laser drilling process better. Based on a Box–Behnken design (BBD) of the experimental software, 17 experiments were designed to examine the laser parameters’ influence on the micro-drilling process. The impact of parameters, such as power (P), exposure time (t), and focal plane position (FPP), on the depth, entry diameter, and heat-affected zone (HAZ) was investigated using analysis of variance (ANOVA). Quadratic regression models were applied to model different hole factors throughout the process. The experiments were optimized using the developed objective model as a function to attain the best hole. The outcomes revealed that a full hole with a 351-μm diameter and 102-μm HAZ was obtained at 0 FPP, with a laser power of 4 W, and at 0.15 s. To conduct virtual tests alongside the experimental study, simulation of the drilling mechanism’s temperature distribution was achieved via the COMSOL Multiphysics program. The simulation’s refined accuracy was able to predict the hole’s geometry and presented outcomes that favorably corresponded with the experimental results. A numerical optimization technique was used to generate an ideal hole by minimizing or maximizing the objective function, achieving full holes of 350-μm diameter and 90-μm HAZ, obtained at 0 FPP, with 3.6 W, and at 0.1 s.
The Kinematic Morphology-Density Relation from the SAMI Pilot Survey

We present the kinematic morphology-density relation in three galaxy clusters, Abell 85, 168 and 2399, using data from the SAMI Pilot Survey. We classify the early-type galaxies in our sample as fast or slow rotators (FRs/SRs) according to a measured proxy for their projected specific stellar angular momentum. We find each cluster contains both fast and slow rotators with and average fraction of SRs in the sample of $f_{SR}=0.15\pm$0.04. We investigate this fraction within each cluster as a function of local projected galaxy density. For Abell 85 we find that $f_{SR}$ increases at high local density but for Abell 168 and 2399 this trend is not seen. We find SRs not just at the centres of our clusters but also on the outskirts and hypothesise that these SRs may have formed in group environments eventually accreted to the larger cluster.
A Fast Discrete Event Simulation Model for Queueing Network Systems

Based on Lindley&apos;s recursive equations for G/G/1 systems, this paper proposes a Fast Discrete Event Simulation (FDES) model for queueing networks. Equations for multiplexer and de-multiplexer elements are presented, which allows to simulate not only tandem but queueing networks with an arbitrary topology. Time savings obtained with FDES could speed up the analyses of large-scale queueing network systems. Experimental results show that FDES modeling can be two orders of magnitude faster than their counterparts based on the event-scheduling for practical cases.
Optimal Piecewise-Linear Approximation Algorithms for Complex Dependencies

Starting from five cited references the authors sketch methods to express the dependence of an output on a vector of several input parameters. The underlying idea is to partition the space of the input parameters into domains, in each of which a function that is complicated in the whole space is approximated by a linear function. If the authors had been more thorough and rigorous in their description, the usefulness of this paper would have increased substantially.
Artistic Experience in Creating Visual Works through the AI Art Generator Midjourney

The rapid advancement of Generative AI and Text-to-Image Generators has introduced a paradigm shift in art production, challenging traditional notions of creativity and authorship. This study aims to examine the “artistic experience” of creators in the process of producing visual works using the Midjourney platform, specifically within the context of urban photography (portrait, street, and landscape). Employing a qualitative descriptive method, this research utilizes Terry Barrett’s theory of photography criticism as a methodological bridge to translate visual elements such as Subject, Form, Medium, and Style into precise linguistic prompts. The findings reveal that while Midjourney can successfully reconstruct the “image logic” of a scene based on text, it introduces a significant “aesthetic deviation.” Comparative analysis shows that AI- generated images tend to be hyper-realistic, displaying “more mature” color tones and a cinematic polish that often removes the raw, authentic grit found in human-captured urban photography. Furthermore, the study identifies a “framing effect,” where direct comparison paradoxically heightens the appreciation for human creativity. The research concludes that the integration of AI into the creative process does not eliminate the artist but fundamentally shifts their role from a technical operator of visual machinery to a linguistic architect. In the urban creative ecosystem, this implies a new form of literacy where the ability to articulate visual concepts textually becomes as crucial as the ability to capture light, democratizing artistic creation for urban society while raising new questions about the ontology of authenticity.
MediONN: an integrated photonic chip optical neural network for deep learning-based semantic segmentation in early detection of pancreatic cancer

Pancreatic cancer remains one of the deadliest cancers due to the lack of effective early detection tools. While deep neural networks (DNNs) have shown promise in tumor segmentation, electronic accelerators suffer from power inefficiency and latency. To address this, we propose MediONN—a photonic neural network system implemented on an integrated chip, optimized for 3D medical image segmentation. MediONN integrates a 4×4 photonic neural processor within a hierarchical 3D optical computation framework. To improve training convergence, we introduce a segmentation-specific Gaussian weight initialization strategy, along with 3D optical convolutional layers for volumetric feature extraction. Unlike prior photonic systems focused on classification, MediONN is the first to demonstrate optical neural networks (ONNs) directly applied to 3D segmentation. On the NIH pancreas CT dataset, MediONN achieves a Dice similarity coefficient (DSC) of 0.5215 (2D) and 0.5302 (3D), with peak DSCs of 0.5919 (2D) and 0.8788 (3D). Comprehensive evaluation metrics confirm MediONN’s segmentation accuracy is comparable to electronic counterparts, while offering significant gains in computational speed and energy efficiency. These results highlight the scalability and biomedical potential of integrated photonic ONNs.
The Relevance of a Multidisciplinary Interpretation of Selected Aspects Related to Women's Sexual and Reproductive Health Rights in Africa

This article is based on the author’s dissertation submitted in partial compliance with the requirements for the degree LLM (Multidisciplinary Human Rights) 2010, Centre for Human Rights, University of Pretoria. (http://hdl.handle.net/2263/25302) ; An earlier draft of this article was presented at a conference on Multi- and Inter-disciplinary Human Rights in Africa which took place on 2 and 3 June 2011 at the Centre for Human Rights, Pretoria. ; Generally, women's rights and interest are steadily being recognised and given the attention they deserve at the global, regional and national levels. Despite this recognition of women's rights at these levels, and in several human rights instruments, it should be noted that the protection and enforcement of women's sexual and reproductive health rights, particularly, has not been given sufficient attention in the priority list of some governments and the legislation they have enacted. Unfortunately, this legislative vacuum intensifies the traditional recognition and the value of women in some communities only for their childbearing ability. This, the absence of their childbearing ability automatically extinguishes such value and limits women's enjoyment of their sexual and reproductive health rights. This is the more so, because conventional African traditions, such as patriarchal customary practices do not empower women to make decisions especially on issues that relates to their own lives, families, the lives of their children, or communities. This is so because these practices are deep-rooted concepts of culture and tradition in many sub-Saharan African societies designed by men. ; http://www.ldd.org.za/ ; am2014
Development and Evaluation of a Natural Language Processing Annotation Tool to Facilitate Phenotyping of Cognitive Status in Electronic Health Records (Preprint)

BACKGROUND                         Electronic Health Records (EHR) with large sample sizes and rich information offer great potential for dementia research but current methods of phenotyping cognitive status are not scalable.                                                                       OBJECTIVE                         To evaluate whether Natural Language Processing (NLP)-powered semi-automated annotation can improve the speed and interrater reliability of chart reviews for phenotyping cognitive status                                                                       METHODS                         In this diagnostic study, we developed and evaluated a semi-automated NLP-powered annotation tool (NAT) to facilitate phenotyping of cognitive status.  Clinical experts adjudicated the cognitive status of 627 patients at Mass General Brigham (MGB) Healthcare using NAT or traditional chart reviews. Patient charts contained EHR data from two datasets: (1) Records from January 1, 2017 to December 31, 2018 for 100 Medicare beneficiaries from the MGB Accountable Care Organization (ACO), and (2) Records from 2-years pre-COVID diagnosis to the date of COVID diagnosis for 527 MGB patients. All EHR data from the relevant period were extracted; diagnosis codes, medications, and laboratory test values were processed and summarized; clinical notes were processed through an NLP pipeline; and a web tool was developed to present an integrated view of all data. Cognitive status was rated as cognitively normal, cognitively impaired, or undetermined. Assessment time and interrater agreement of NAT compared to manual chart reviews for cognitive status phenotyping was evaluated.                                                                       RESULTS                         NAT adjudication provided higher interrater agreement (Cohen k=0.89 vs. k=0.80) and significant speed up (time difference mean [SD]: 1.4 [1.3] minutes, P &lt; 0.001; ratio median [min, max]: 2.2 [0.4, 20]) over manual chart reviews. There was moderate agreement with manual chart reviews (Cohen k=0.67). In the cases that exhibited disagreement with manual chart review, NAT adjudication was able to produce assessments that had broader clinical consensus due to its integrated view of highlighted relevant information and semi-automated NLP features.                                                                       CONCLUSIONS                         NAT adjudication improves the speed and interrater reliability for phenotyping cognitive status compared to manual chart reviews. This study underscores the potential of an NLP-based clinically adjudicated method to build large-scale dementia research cohorts from EHR.
Mixotrophic chemosynthesis in a deep-sea anemone from hydrothermal vents in the Pescadero Basin, Gulf of California

Abstract                                        Background                     Numerous deep-sea invertebrates, at both hydrothermal vents and methane seeps, have formed symbiotic associations with internal chemosynthetic bacteria in order to harness inorganic energy sources typically unavailable to animals. Despite success in nearly all marine habitats and their well-known associations with photosynthetic symbionts, Cnidaria remain one of the only phyla present in the deep-sea without a clearly documented example of dependence on chemosynthetic symbionts.                                                           Results                                            A new chemosynthetic symbiosis between the sea anemone                       Ostiactis pearseae                       and intracellular bacteria was discovered at ~ 3700 m deep hydrothermal vents in the southern Pescadero Basin, Gulf of California. Unlike most sea anemones observed from chemically reduced habitats, this species was observed in and amongst vigorously venting fluids, side-by-side with the chemosynthetic tubeworm                       Oasisia                       aff.                       alvinae                       . Individuals of                       O. pearseae                       displayed carbon, nitrogen, and sulfur tissue isotope values suggestive of a nutritional strategy distinct from the suspension feeding or prey capture conventionally employed by sea anemones. Molecular and microscopic evidence confirmed the presence of intracellular SUP05-related bacteria housed in the tentacle epidermis of                       O. pearseae                       specimens collected from 5 hydrothermally active structures within two vent fields ~ 2 km apart. SUP05 bacteria (Thioglobaceae) dominated the                       O. pearseae                       bacterial community, but were not recovered from other nearby anemones, and were generally rare in the surrounding water. Further, the specific                       Ostiactis                       -associated SUP05 phylotypes were not detected in the environment, indicating a specific association. Two unusual candidate bacterial phyla (the OD1 and BD1-5 groups) appear to associate exclusively with                       O. pearseae                       and may play a role in symbiont sulfur cycling.                                                                                Conclusion                                            The Cnidarian                       Ostiactis pearseae                       maintains a physical and nutritional alliance with chemosynthetic bacteria. The mixotrophic nature of this symbiosis is consistent with what is known about other cnidarians and the SUP05 bacterial group, in that they both form dynamic relationships to succeed in nature. The advantages gained by appropriating metabolic and structural resources from each other presumably contribute to their striking abundance in the Pescadero Basin, at the deepest known hydrothermal vents in the Pacific Ocean.