ethicalabs/Research-Intent-Collab huggingface dataset:
Doppler Peaks in the Angular Power Spectrum of the Cosmic Microwave Background: A Fingerprint of Topological Defects

The Doppler peaks (Sacharov peaks) in the angular power spectrum of the cosmic microwave background anisotropies, are mainly due to coherent oscillations in the baryon radiation plasma before recombination. Here we present a calculation of the Doppler peaks for perturbations induced by global textures and cold dark matter. We find that the height of the first Doppler peak is smaller than in standard cold dark matter models, and that its position is shifted to $\ell\sim 350$. We believe that our analysis can be easily extended to other types of global topological defects and general global scalar fields.
Failure Investigation of Products and Components in Metal Forming Industry: Root Cause Analysis and Process-Based Approach

Fatigue fracture, occurring in selected industrial manufactured products and components during production, testing or service conditions, is a typical failure mode. Primarily, the main qualitative and phenomenological aspects and features of failure modes are presented for industrial components (track-type chain blocks and steel rebars), based on case history approach, and correlated with the prevailed operating loading conditions. These characteristics are mainly recorded for the identification of the emergent failure mechanism(s). The second part of the paper includes the presentation of selected methodological techniques which are briefly summarized and discussed. The aim is to provide a wider perspective of failure prevention, using the process and systems approach principles, which broaden the scope of the organization capability to address and minimize the risks and capitalize the opportunities, assisting to quality and business improvement.
The Controllability Precautionary Principle: Justification of a Climate Policy Goal Under Uncertainty

How can we reasonably justify a climate policy goal if we accept that only possible consequences from climate change are known? Precautionary principles seem to offer promising guidelines for reasoning in such epistemic situations. This chapter presents two versions of the precautionary principle (PP) and defends one of them as morally justifiable. However, it argues that current versions of the PP do not allow discrimination between relevant climate change policies. Therefore, the chapter develops a further version of the PP, the Controllability Precautionary Principle (CPP), and defends its moral plausibility. The CPP incorporates the following idea: in a situation when the possible outcomes of the available actions cannot be ranked with regard to their value, the choice between available options for action should rest on the comparison of how well decision makers can control the processes of the implementation of the available strategies.
Root Cause Failure Analysis as a Tool for Investigating Operational Failures: A Case Study

Abstract                Production liner cementation in deepwater wells is critical to contain formation pressures and provide zonal isolation. This is especially challenging in the Gulf of Mexico because of depleted zones, small drilling margins, and HPHT reservoirs. Failure to achieve zonal isolation with the primary cement job leads to added cost and time for remedial operations. A recent primary cement job failure in the Viosca Knoll Block, located in the Gulf of Mexico, prompted the use of an established tool, Root Cause Failure Analysis (RCFA), in a novel way to identify the causes of the incident and avoid repeating the same mistakes in future wells.                A team of operations and engineering personnel was assembled to investigate the cause of the primary cement job failure that resulted in nonproductive time and cost of approximately 7 days and $3 MM, respectively. RCFAs have typically been performed on equipment failures; however, this RCFA was conducted by the operator on a non-equipment related failure.                The RCFA team initially collected data available from both the planning and operation phases of the job in order to identify which elements of the cementing process contributed to the cement job failure. After identifying the root cause, the team communicated learnings to office and rig-based personnel, developed a new tool for real time monitoring of primary cementation, and adjusted plans for upcoming primary cement jobs. The findings from the RCFA were applied to a well on another asset owned by the operator, with which the production liner was successfully rotated throughout cementation for the first time.                The RCFA identified that the cement job design was not a contributing factor; instead, the root causes were operational and started during circulation of drilling fluid prior to cementing operations. The learnings from the RCFA were transferred to both office and rig-based personnel, influencing engineering and operations plans for subsequent wells. Furthermore, the investigation identified previously unknown issues and showed that the use of the RCFA process was an effective approach that can be standardized and scaled to determine root causes of future operational failures.
Representation Learning Dynamics of Self-Supervised Models

Self-Supervised Learning (SSL) is an important paradigm for learning representations from unlabelled data, and SSL with neural networks has been highly successful in practice. However current theoretical analysis of SSL is mostly restricted to generalisation error bounds. In contrast, learning dynamics often provide a precise characterisation of the behaviour of neural networks based models but, so far, are mainly known in supervised settings. In this paper, we study the learning dynamics of SSL models, specifically representations obtained by minimising contrastive and non-contrastive losses. We show that a naive extension of the dymanics of multivariate regression to SSL leads to learning trivial scalar representations that demonstrates dimension collapse in SSL. Consequently, we formulate SSL objectives with orthogonality constraints on the weights, and derive the exact (network width independent) learning dynamics of the SSL models trained using gradient descent on the Grassmannian manifold. We also argue that the infinite width approximation of SSL models significantly deviate from the neural tangent kernel approximations of supervised models. We numerically illustrate the validity of our theoretical findings, and discuss how the presented results provide a framework for further theoretical analysis of contrastive and non-contrastive SSL.
Application of Mobile Technology-Based Learning Analytics in Educational Assessment

With the rapid development of mobile technology, its application in the educational sector, particularly in learning analytics, has garnered increasing attention. The widespread adoption of modern mobile devices has introduced new opportunities for educational assessment. However, challenges remain in effectively utilizing these technologies for educational assessment. Current research predominantly focuses on data collection and analysis, yet the methods employed are limited in scope and application. This study aims to address these gaps by exploring data collection methods and teaching assessment approaches based on mobile technology. The objective is to contribute new perspectives and methodologies that modernize educational assessment. Through an in-depth analysis of the application of mobile technology in educational assessment, this study seeks to provide educators with scientifically grounded assessment tools and strategies, enabling more precise teaching management and personalized education.
Neutron Capture and Neutron Halos

The connection between the neutron halo observed in light neutron rich nuclei and the neutron radiative capture process is outlined. We show how nuclear structure information such as spectroscopic factors and external components of the radial wave function of loosely bound states can be derived from the neutron capture cross section. The link between the direct radiative capture and the Coulomb dissociation process is elucidated.
Structural Group Sparse Representation for Image Compressive Sensing Recovery

Compressive Sensing (CS) theory shows that a signal can be decoded from many fewer measurements than suggested by the Nyquist sampling theory, when the signal is sparse in some domain. Most of conventional CS recovery approaches, however, exploited a set of fixed bases (e.g. DCT, wavelet, contourlet and gradient domain) for the entirety of a signal, which are irrespective of the nonstationarity of natural signals and cannot achieve high enough degree of sparsity, thus resulting in poor rate-distortion performance. In this paper, we propose a new framework for image compressive sensing recovery via structural group sparse representation (SGSR) modeling, which enforces image sparsity and self-similarity simultaneously under a unified framework in an adaptive group domain, thus greatly confining the CS solution space. In addition, an efficient iterative shrinkage/thresholding algorithm based technique is developed to solve the above optimization problem. Experimental results demonstrate that the novel CS recovery strategy achieves significant performance improvements over the current state-of-the-art schemes and exhibits nice convergence.
Harnessing Metaheuristics for Superior Intrusion Detection: Deep Learning on Benchmark Datasets

While intrusion detection systems (IDS) are crucial to safeguarding network environments from evolving cyber threats, traditional methods often struggle to optimize detection capabilities and balance computational efficiency. To enhance the performance of IDS, this study integrates metaheuristic optimization techniques with deep learning algorithms. Combining various metaheuristics, including Genetic Algorithms, Particle Swarm Optimization, and Harris Hawks Optimization, with deep learning models, such as Graph Neural Networks, Recurrent Neural Networks, and Convolutional Neural Networks, is a systematic approach. Using benchmark datasets CICIDS-2017 and KDD, our research shows that metaheuristic optimization improves model accuracy, reduces false positives, and improves overall robustness. Based on performance metrics, Capsule Networks with metaheuristic optimization achieve superior results, with accuracy rates of 89% and 87%, respectively, on the CICIDS-2017 and KDD datasets. In addition to advancing the effectiveness of intrusion detection, this study provides a robust framework for future explorations and practical applications in the field of cybersecurity.
Quality of palliative and end-of-life care: a qualitative study of experts’ recommendations to improve indicators in Quebec (Canada)

Abstract                 Background                 In 2021, the National Institute of Public Health (INSPQ) (Quebec, Canada), published an update of the palliative and end-of-life care (PEoLC) indicators. Using these updated indicators, this qualitative study aimed to explore the point of view of PEoLC experts on how to improve access and quality of care as well as policies surrounding end-of-life care.                                Methods                 Semi-directed interviews were conducted with palliative care and policy experts, who were asked to share their interpretations on the updated indicators and their recommendations to improve PEoLC. A thematic analysis method was used.                                Results                 The results highlight two categories of interpretations and recommendations pertaining to: (1) data and indicators and (2) clinical and organizational practice. Participants highlight the lack of reliability and quality of the data and indicators used by political and clinical stakeholders in evaluating PEoLC. To improve data and indicators, they recommend: improving the rigour and quality of collected data, assessing death percentages in all healthcare settings, promoting research on quality of care, comparing data to EOL care directives, assessing use of services in EOL, and creating an observatory on PEoLC. Participants also identified barriers and disparities in accessing PEoLC as well as inconsistency in quality of care. To improve PEoLC, they recommend: early identification of palliative care patients, improving training for all healthcare professionals, optimizing professional practice, integrating interdisciplinary teams, and developing awareness on access disparities.                                Conclusions                 Results show that PEoLC is an important aspect of public health. Recommendations issued are relevant to improve PEoLC in and outside Quebec.
Confronting Antimicrobial Resistance across Humans, Animals, and the Environment: Integrating the One Health Approach

Antimicrobial resistance (AMR) represents one of the most critical threats to global health, food safety, and sustainable development in the 21st century. The rapid emergence and dissemination of resistant microbial strains compromise the effectiveness of antimicrobial agents, rendering common infections increasingly difficult to treat. A key driver of AMR is the widespread, and often indiscriminate, use of antibiotics across multiple sectors—including human healthcare, veterinary medicine, and agriculture. In particular, intensive livestock production systems are recognized as significant contributors to AMR due to the routine use of antimicrobials not only for therapeutic purposes but also for prophylaxis and growth promotion. This extensive usage fosters the selection pressure that promotes the evolution of multidrug-resistant bacteria within animal populations. These resistant pathogens, along with their resistance genes, can be transmitted to humans through direct contact with animals, consumption of contaminated animal products, and via environmental pathways such as water bodies, soil, and air polluted with animal waste. The interconnectedness of humans, animals, and the environment underlines the need for a comprehensive One Health approach to address AMR. This review summarizes the major causes of AMR in humans, livestock, and environmental systems, and explores the complex mechanisms by which resistance develops and spreads. Additionally, current global and regional strategies aimed at mitigating AMR are discussed, including antimicrobial stewardship programs, surveillance networks, regulatory policies, alternatives to antibiotics, and public awareness campaigns. Strengthening intersectoral coordination, enforcing responsible antimicrobial use, and investing in research and innovation are pivotal to curbing this global health crisis. A holistic and sustained effort is essential to preserve the efficacy of existing antimicrobials and to safeguard public and animal health in the long term.
Lightlike simultaneity, comoving observers and distances in general relativity

We state a condition for an observer to be comoving with another observer in general relativity, based on the concept of lightlike simultaneity. Taking into account this condition, we study relative velocities, Doppler effect and light aberration. We obtain that comoving observers observe the same light ray with the same frequency and direction, and so gravitational redshift effect is a particular case of Doppler effect. We also define a distance between an observer and the events that it observes, that coincides with the known affine distance. We show that affine distance is a particular case of radar distance in the Minkowski space-time and generalizes the proper radial distance in the Schwarzschild space-time. Finally, we show that affine distance gives us a new concept of distance in Robertson-Walker space-times, according to Hubble law.
Photonic Neural Networks for Ultra-Fast, Energy-Efficient AI: A Paradigm Shift in Optical Computing and Machine Learning Architectures

Abstract:  Photonic Neural Networks (PNNs) represent a transformative leap in artificial intelligence, offering ultra-fast processing speeds and significant energy efficiency compared to traditional electronic models. This research explores the architecture, implementation, and performance evaluation of PNNs as a paradigm shift in optical computing and machine learning. Using experimental simulations and predictive regression models, this study evaluates the potential of PNNs to outperform electronic neural networks in processing speed, power consumption, and scalability. Key findings indicate a 4–6× improvement in processing throughput and a 70% reduction in energy consumption under standardized machine learning workloads. These insights provide a new direction for AI system development, merging photonics and neuromorphic computing.  Keywords: Photonic Neural Networks, Optical Computing, Machine Learning, Energy Efficiency, AI Acceleration, Neuromorphic Computing, Regression Analysis, Predictive Modeling, Optical Hardware, High-Speed Computing
Local ecological knowledge (LEK) reveals the drivers and barriers for sustainable conservation of protected forest biodiversity

Understanding the key drivers and barriers to sustainable conservation of protected and unprotected forests, heavily utilised by humans and wildlife, is essential in sustainable forest management and law enforcement. This study aimed to: i) evaluate fringe community perceptions towards forest biodiversity sustainability across villages; ii) establish the key drivers and barriers of biodiversity conservation, and iii) develop an integrated conservation framework for the Gwaai Forest in Zimbabwe. Data were collected from 143 respondents using questionnaires, key informant interviews and direct observations. Locals, simultaneously, preferred and exerted negative pressure on clusterleaf Terminalia sericea and African teak Baikea plurijuga for carving, roofing and furniture making increasing the distance travelled to collect forest products. Human population increase, poaching, veld fires and overexploitation were the key barriers whereas tangible and intangible benefits for local people and improved management systems were the key drivers of sustainable conservation. Gwaai Forest access and utilisation is heavily regulated by the forest authorities whose policies are not human centred but rather aimed at maintaining the resilience and stability of the forest. There is a need to explore suitable alternative livelihoods such as farming of drought resistant small grains less dependent on the Gwaai Forest ecosystem services to reduce pressure on tree species and other natural resources. An in-depth understanding of the ecological, economic, and social factors that influence forest resilience is necessary for planning and managing Gwaai Forest operations efficiently.
Systematic review and synthesis without meta-analysis (SWiM) reveals lack of clinical studies and weak preclinical evidence for interaction between glucose regulating drugs and environmental contaminant exposure

Environmental chemical exposure is associated with T2D incidence and may underly some of the large observed variation in therapeutic responses. Clinical and applicable preclinical studies could help reveal whether environmental chemicals interact with the action of drugs used for glucose management. We systematically searched for studies testing the interaction between environmental contaminants and T2D drug action on glucose regulation outcomes. We found no clinical studies that examined chemical exposure interaction with T2D drug action. Nine of 458 papers were eligible, all of which were preclinical. Four contained in vivo studies, four contained in vitro work and one contained both approaches. Bisphenols were the main focus (n = 4). Inhaled particulates (PM2.5), polychlorinated biphenyls (PCBs), cadmium, arsenic, and per-and-poly-fluorinated-alkalated substances (PFAS) were each examined once. Metformin and rosiglitazone were the most frequently examined drugs (n = 4). Exendin-4 was investigated twice and glibenclamide once. Lack of study design comparability precluded meta-analysis. Instead, we calculated effect sizes and differences in outcome values (mean ± 95 % CI) for synthesis without meta-analysis (SWiM). Five studies reported impairment of drug action. Our analysis shows support for this conclusion was only present in two papers. Small sample sizes, short duration exposures, unrealistic chemical levels, lack of full factorial analysis and absence of testing in suitable T2D models restrict the applicability of the current, limited preclinical evidence for translation to clinical practice. The potential for chemical exposure to impact T2D medication effects on glucose control needs to be addressed with dedicated clinical studies in patients with T2D.
Design of a compact all-optical digital-to-analog converter based on photonic crystals using neural networks

This paper presents the design and optimization of a compact 2-bit all-optical Digital-to-Analog Converter (DAC) based on photonic crystals (PCs), utilizing an Artificial Neural Network (ANN) for enhanced performance. The proposed structure features a square lattice photonic crystal composed of silicon rods in an air background, designed to operate within the photonic bandgap for the TM mode at a wavelength of 1.55 μm. The dimensions of defect rods in the photonic crystal structure are optimized using a feed forward ANN, trained with simulated data to maximize the output power of the DAC. The ANN model demonstrates high precision in predicting the optimal dimensions, achieving a significant reduction in design size and complexity. The designed DAC performance is validated through simulations using two software, and the results are consistent with predictions, confirming the validity of the design. The proposed 2-bit DAC exhibits superior performance with minimal footprint of 116 µm2, making it highly suitable for integration into all-optical digital circuits and next-generation optical fiber technologies.
EVALUATION OF THE ACTIVITIES OF THE COMMISSION ON MINOR’S AFFAIRS AND PROTECTION OF THEIR RIGHTS DEALING WITH TROUBLED TEENAGERS (BASED ON MATERIALS OF NIZHNEKAMSK MUNICIPAL DISTRICT)

Evaluation of the activities of the commission on minor’s affairs and protection of their rights dealing with troubled teenagers is very important problem is very relevant in connection with the increase of teenage crime. In article present the evaluation of the activities of the commission on minor’s affairs and protection of their rights dealing with troubled teenagers. A particular example of the conducted qualitative and quantitative analysis of activities and on this basis to identify problems.  Little attention is paid to the prevention of juvenile crime and this is the main reason for increasing the number of these offenses. Identified problems has allowed to formulate a number of recommendations. The prevention of juvenile delinquency is one of the leading directions throughout the prevention of crime. The effective prevention of them is essential to protect the younger generation’s moral health.DOI: http://dx.doi.org/10.12731/2218-7405-2013-7-62