ethicalabs/Research-Intent-Collab huggingface dataset:
A smart shop-floor monitoring system through Internet of Things and wireless sensor networks

With the advent of the fourth industrial revolution manufacturing systems are transformed into digital ecosystems. In this transformation, the Internet of Things (IoT) and other emerging technologies pose a major role. Small and medium enterprises (SMEs) in manufacturing, often employ outdated equipment which does not have connectivity capabilities. Therefore, smart sensor systems are required to connect these resources into the digital world. To address this issue, this master thesis presents a monitoring framework and the development of a data acquisition device for machine-tools. The system is designed for installation into the electrical cabinet of the machine-tools and transmits the data related to their operation to a Cloud server via a wireless sensor topology. The data transmission is performed in two levels i.e. locally in the shop-floor using a star wireless sensor network topology coordinated by a microcomputer gateway, and from the microcomputer to Cloud using Internet protocols. The collected data provide information about the status of the resources, along with a set of performance indicators to support decision making. The developed system follows the IoT paradigm in terms of connecting the physical with the cyber world and offering integration capabilities with existing industrial systems. The gateway level supports connectivity with industrial networks through the OPC-UA standard, while the higher level supports integration with industrial software tools via Web-Services. The operation of the system was evaluated in a 3-axis machine-tool under actual machining operations. Moreover, the capabilities for knowledge reuse were validated through the case-based reasoning methodology in the subject of the energy consumption estimation.
A descriptive model featuring expansive clinical microbiology approaches and antimicrobial stewardship to control antimicrobial resistance in low- and middle-income countries

The upsurge and escalation of antimicrobial resistance (AMR) poses a major worry on the global scene. Effective antimicrobial stewardship (AMS) programs are critical to combat this challenge, and applied knowledge in clinical microbiology serves as the cornerstone for the successful implementation. Despite technological advances, many low- and middle-income countries (LMICs) still adopt traditional phenotypic techniques in clinical microbiology laboratories. An expansive implementation of clinical microbiology approaches can bridge this apparent gap. Good laboratory assessment, hospital epidemiology, and infection prevention and control, which are the sub-divisions of the expansive clinical microbiology approaches, can be deployed to implement AMS appropriately in LMICs. In this paper, we describe a model featuring a functional AMS equipped with expansive clinical microbiology approaches to control AMR in LMICs. &amp;nbsp;
Sensor Deployment for Air Pollution Monitoring Using Public Transportation System

Air pollution monitoring is a very popular research topic and many monitoring systems have been developed. In this paper, we formulate the Bus Sensor Deployment Problem (BSDP) to select the bus routes on which sensors are deployed, and we use Chemical Reaction Optimization (CRO) to solve BSDP. CRO is a recently proposed metaheuristic designed to solve a wide range of optimization problems. Using the real world data, namely Hong Kong Island bus route data, we perform a series of simulations and the results show that CRO is capable of solving this optimization problem efficiently.
Evaluating the human right to water and sanitation: the use of participatory diagnosis tools at a local level

Participatory analysis frameworks can help realize the Human Right to Water and Sanitation (HRtWS) by helping stakeholders self-assess how equitable is access to water and sanitation services, achieving a better understanding of the rights to water and sanitation and their principles, and raising awareness. This article presents the experiences of Castelló (Spain), Lima (Peru) and Barcelona (Spain) in using the “Equitable Access Score-card” to analyze the equity of access to safe drinking water and sanitation in these three cities. The aim is to compare and discuss the three different ways in which the diagnosis tool was applied. Results show that, although all three approaches served as a first-assessment and overview of the HRtWS by sector stakeholders at local level, developing an effective inter-stakeholder collaboration was pivotal for these participatory diagnosis tools to have significant impact.
Covariant Density Functional Theory in Nuclear Physics and Astrophysics

How does subatomic matter organize itself? Neutron stars are cosmic laboratories uniquely poised to answer this fundamental question that lies at the heart of nuclear science. Newly commissioned rare isotope facilities, telescopes operating across the entire electromagnetic spectrum, and ever more sensitive gravitational wave detectors will probe the properties of neutron-rich matter with unprecedented precision over an enormous range of densities. Yet, a coordinated effort between observation, experiment, and theoretical research is of paramount importance for realizing the full potential of these investments. Theoretical nuclear physics provides valuable insights into the properties of neutron-rich matter in regimes that are not presently accessible to experiment or observation. In particular, nuclear density functional theory is likely the only tractable framework that can bridge the entire nuclear landscape by connecting finite nuclei to neutron stars. This compelling connection is the main scope of the present review.
Principal component analysis for second-order stationary vector time series

We extend the principal component analysis (PCA) to second-order stationary vector time series in the sense that we seek for a contemporaneous linear transformation for a $p$-variate time series such that the transformed series is segmented into several lower-dimensional subseries, and those subseries are uncorrelated with each other both contemporaneously and serially. Therefore those lower-dimensional series can be analysed separately as far as the linear dynamic structure is concerned. Technically it boils down to an eigenanalysis for a positive definite matrix. When $p$ is large, an additional step is required to perform a permutation in terms of either maximum cross-correlations or FDR based on multiple tests. The asymptotic theory is established for both fixed $p$ and diverging $p$ when the sample size $n$ tends to infinity. Numerical experiments with both simulated and real data sets indicate that the proposed method is an effective initial step in analysing multiple time series data, which leads to substantial dimension reduction in modelling and forecasting high-dimensional linear dynamical structures. Unlike PCA for independent data, there is no guarantee that the required linear transformation exists. When it does not, the proposed method provides an approximate segmentation which leads to the advantages in, for example, forecasting for future values. The method can also be adapted to segment multiple volatility processes.
The propagation of a cultural or biological trait by neutral genetic drift in a subdivided population

We study fixation probabilities and times as a consequence of neutral genetic drift in subdivided populations, motivated by a model of the cultural evolutionary process of language change that is described by the same mathematics as the biological process. We focus on the growth of fixation times with the number of subpopulations, and variation of fixation probabilities and times with initial distributions of mutants. A general formula for the fixation probability for arbitrary initial condition is derived by extending a duality relation between forwards- and backwards-time properties of the model from a panmictic to a subdivided population. From this we obtain new formulae, formally exact in the limit of extremely weak migration, for the mean fixation time from an arbitrary initial condition for Wright's island model, presenting two cases as examples. For more general models of population subdivision, formulae are introduced for an arbitrary number of mutants that are randomly located, and a single mutant whose position is known. These formulae contain parameters that typically have to be obtained numerically, a procedure we follow for two contrasting clustered models. These data suggest that variation of fixation time with the initial condition is slight, but depends strongly on the nature of subdivision. In particular, we demonstrate conditions under which the fixation time remains finite even in the limit of an infinite number of demes. In many cases - except this last where fixation in a finite time is seen - the time to fixation is shown to be in precise agreement with predictions from formulae for the asymptotic effective population size.
<strong>КУДА ИДЁТ УКРАИНА:</strong> <strong>СКВОЗЬ СТЕКЛО СМУТНО</strong> The article offers an analysis of the two main trends in Ukrainian politics that offer different approaches in addressing the problems faced by Ukrainian society. One tendency includes proponents of the approach that relies on government institutions and formal bureaucratic practices. The other tendency represents the radically inclined militant activist who insist on immediate and radical policies that will eliminate corruption and treason in the country. The standoff between these two tendencies prolongs the war in Ukraine and is harmful for the country. It makes difficult the resolution of the conflict that destroys the country, prevents the rebuilding of its economy and improving the life of Ukrainian citizens. The article also analyzes the political practice of president Zelensky who largely pursues the approach advocated by the radicals.

This is a political analysis of the war in Ukraine
Self-supervised Structured Object Representation Learning

Self-supervised learning (SSL) has emerged as a powerful technique for learning visual representations. While recent SSL approaches achieve strong results in global image understanding, they are limited in capturing the structured representation in scenes. In this work, we propose a self-supervised approach that progressively builds structured visual representations by combining semantic grouping, instance level separation, and hierarchical structuring. Our approach, based on a novel ProtoScale module, captures visual elements across multiple spatial scales. Unlike common strategies like DINO that rely on random cropping and global embeddings, we preserve full scene context across augmented views to improve performance in dense prediction tasks. We validate our method on downstream object detection tasks using a combined subset of multiple datasets (COCO and UA-DETRAC). Experimental results show that our method learns object centric representations that enhance supervised object detection and outperform the state-of-the-art methods, even when trained with limited annotated data and fewer fine-tuning epochs.
Breast Cancer Survival Prediction Modeling Based on Genomic Data: An Improved Prognosis-Driven Deep Learning Approach

Breast cancer has a wide range of possible outcomes due to its complexity and heterogeneity. The process of manually detecting breast cancer is laborious, intricate, and inaccurate. It is essential for individualized treatment planning to have a reliable prognosis of patient survival. Increased focus in recent years has been placed on genomics-based techniques be-because of their potential to better predict outcomes. In this study, we propose a novel framework for breast cancer survival prediction using optimized deep learning models. We begin by preprocessing and integrating multi-omic data, including gene expression profiles, somatic mutations, and clinical features, obtained from a large cohort of breast cancer patients. In our proposed research, deep learning models were trained to detect the survival case of breast cancer and were optimized using Stochastic Gradient Descent Optimizer which was used for the initial population generation and modification for the selected dataset and divided into 80% for the training set and 20% for the testing set. Long Short-Term Memory, Variational Autoencoders, and Graph Convolutional Networks architectures optimized by Stochastic Gradient Descent Optimizer are used for training and validation of the breast cancer dataset and get the best accuracy of 98.7% for the optimized Long Short-Term Memory model. Our results demonstrate that the proposed genomics-based predictive modeling approach achieves high performance in breast cancer survival prediction compared to conventional methods.
The Development of the Law of Armed Conflict by International Criminal Tribunals

Abstract                This chapter demonstrates how judicial decisions of international courts and tribunals have shaped international humanitarian law. Judicial decisions, specifically those of the ICTY and ICTR, have facilitated clearer comprehension of the law of non-international armed conflict. The discussion celebrates positive developments such as Tadić (ICTY), which defined “non-international armed conflict,” and Akayesu (ICTR), where “rape” is defined. However, this contribution cautions against judicial activism when interpreting war crimes because the resultant confusion frustrates those who deploy international humanitarian law during armed conflict. The International Criminal Court’s Ntaganda decision illustrates the delicate balance sustaining the relationship between international humanitarian law and international criminal law and is a reminder of the importance of the principle that war crimes vest in prohibitive rules under international humanitarian law. International humanitarian law is the starting point in interpretation, and a reversal of the correct order results in a dilution of international humanitarian law rules.
Mitigating Measurement Errors in Quantum Computers by Exploiting State-Dependent Bias

Quantum computers are susceptible to errors. While quantum computers can be guarded against errors using error correction codes, near-term quantum computers will not have sufficient number of qubits to implement error correction and must perform their computation in the presence of errors. Qubit measurement is typically the most error-prone operation on a quantum computer, with measurement errors ranging from 8% to 30% reported on current machines. This goal of this paper is to mitigate measurement errors by exploiting the state-dependent bias of measurement errors. Experiments on the IBM-Q5 and IBM-Q14 machines show variation in measurement errors depending on the state being measured. For example, measuring an all-zero state on IBM-Q5 has a fidelity of 84%; however, the fidelity drops to 62% while measuring the all-one state. To improve measurement fidelity, we propose Invert-and-Measure, which transforms the system from a vulnerable state to a stronger state and then performs the measurement in the stronger state. We propose two designs for Invert-and-Measure. First, Static Invert-and-Measure (SIM), which executes two instances of the program, one with standard measurements and the other with inverted measurements and combines the results. Second, Adaptive Invert and Measure (AIM), which learns the relative bias of different states using runtime profiling and produces specialized inversions to increase the likelihood of obtaining the correct answer. Our evaluations, using IBM-Q5 and IBM-Q14, show that SIM improves the application reliability by up to 2X, and AIM by up to 3X.
Point-contact spectroscopy of two-band superconductor MgB2

The progress in investigation of two-band superconductor MgB$_2$ by the point-contact spectroscopy (PCS) is given. Results of study of superconducting gap temperature and magnetic field dependence for two-dimensional $σ$ and three-dimensional $π$ band and electron-phonon-interaction spectral function are presented. Correlation between the gap value and the intensity of the high T$_c$ driving force -- $E_{2g}$ boron vibration mode, is provided. PCS data on some nonsuperconducting transition metal diborides are surveyed for comparison.
Tsunami Readiness in Kudat, Sabah: Examining Community Trust, Risk Perception, and Preparedness

One of the emerging critical concerns on disaster risk reduction is tsunami preparedness as its impact is substantial to damaging infrastructure and to cause loss of lives. Kudat located at the tip of Sabah is an area that is prone to a tsunami disaster originating from either the Brunei Slide, Sulu Sea, or from the Marina Trench. Although there have been efforts to increase their awareness and preparedness, there remains a lack of localised studies exploring how community level factors affect disaster readiness. Thus, this study aims to address the gap by examining socio-economic factors, trust in agencies, risk perception, intention to prepare and preparedness of the local community in Kudat. The study employed a quantitative approach through self-administered questionnaires among local leaders and members of the community. Data obtained was then analysed using Statistical Package for Social Sciences (SPSS) to identify their perceptions and patterns. Preliminary findings indicate that there is a positive perception towards the agencies, intention to prepare and have taken necessary steps to prepare for a tsunami disaster. However, financial preparedness is expected to be impeded due to their average household income. The findings suggest that the community can be prepared with the right intervention by the local government and agencies however their financial capacity could affect their recovery. This study offers practical recommendations for policy and capacity building efforts that can be adapted in other tsunami prone areas. The importance of how locally driven strategies is underlined in study to enhance tsunami preparedness and community resilience.
An introduction to systematic reviews and meta-analysis: A workshop report on promoting evidence based medical practice through capacity building in research synthesis

The increasing urgency for evidence based practice, especially in resource limited settings has inspired many initiatives to this effect. In Africa there is limited skill in research synthesis and the production of systematic reviews. The Centre for the Development of Best Practices in Health, together with the South African Cochrane Centre organised a workshop to train Cameroonian researchers on how to initiate and complete systematic reviews. Five facilitators and fifteen participants met over a period of four days. At the end of the workshop the participants expressed high levels of satisfaction and motivation to conduct systematic reviews, but expressed the need for additional support. Facilitators of future systematic review courses should address challenges related to internet access, adult education and realistic expectations from the participants.
Additional file 3: Figure S3. of Histone deacetylase inhibitors potentiate photodynamic therapy in colon cancer cells marked by chromatin-mediated epigenetic regulation of CDKN1A

Effect of NaPB ± HY-PDT on DNA methylation of CDKN1A regulatory regions. Measurements were done in HT-29 cells after a sequential treatment starting with NaPB (1000 μM) for 24 h followed by activation with hypericin (75 nM) for 8 h. Samples treated with drug-free vehicle solvents (&lt;0.1% DMSO) were used as the reference control. The DNA methylation levels of (A) enhancer, (B) promoter, and (C) gene body regions in the CDKN1A gene were analyzed and are expressed for each CpG site as the mean ± SD of three independent experiments each done in triplicates. Methylation values of 0% were set as 1% for graphical visibility (PPTX 44 kb).
HUMAN RIGHT PERSPECTIVE OF DATA PRIVACY, SURVEILLANCE WITH REFERENCE TO MODEL PRISONS AND CORRECTIONAL SERVICES ACT, 2023

Abstract:    Prison reform in India has evolved from a punitive colonial legacy to a more rehabilitative and Human Rights centric approach.  It was rooted in the society that the harsh treatments can correct the criminals but now a day reformation of criminals has focused.  Indian prison system has undergone various phases of transformation which came due to Constitution of India, judicial interventions, social movements and legislative initiatives.  This paper examines the introduction of reformative frameworks like Model Prison Manual, followed by Model Prisons and Correctional Services Act, 2023 with Human Right perspective.This Act replace the old legislation i.e. Prisons Act, 1894 and related laws.  The aim of new Act is to modernize prison governance and move from punitive detention to rehabilitation, reformation and reintegration of prisoners. The Act has been circulated to states and union territories for adoption but till today no state has formally adopted it in full.This Act focuses on the dynamic security assessment and segregation of prisoners, use of technology and scientific interventions in prison administration. This Act also focus on the Grievance redressal, prison boards and individualized prisoner plans, Video conferencing with courts, rehabilitative services and vocational training to the prisoners. The Act also envisages enhanced use of surveillance and monitoring technology, both inside prisons and for inmates on temporary release i.e. Parole.  This Act emphasizing International standards and Constitutional mandates which focus on the need for empathy-driven correctional practices for an inclusive model based on human rights, human dignity, transparency, education and post-release care to the prisoners.      Keywords: Human Rights, Prison Reform, Constitution, Surveillance.
The Relationship between Job Efficacy, Patient Safety and Quality of Patient Care among Nurses Working in Secondary Hospital in Riyadh Second Health Cluster, Saudi Arabia

Background: Nursing profession is one of the most crucially healthcare profession in hospitals, and there are many factors affecting job efficacy of these nurses which may then affect positively or negatively the patients’ safety and quality of care in the healthcare settings. Aim: This study aims to determine the relationship between Job Efficacy, Patient Safety and Quality of Patient Care among nurses working in Secondary Hospital in Riyadh Second Health Cluster, Saudi Arabia. Methodology: A cross-sectional study was conducted on conveniently selected nurses in various hospitals located in Second health cluster in Riyadh, Saudi Arabia to fill questionnaire for six weeks, an online questionnaire was used, and it consist of Hospital Survey on Patient Safety Culture and the Self-Efficacy Scale, all data then was analysed using SPSS program. Results: From total 326 participants with mroe than half (51%) are male and 44% of the sample are 31-40 years old, as well as, about 44% of the sample are BSN graduate, 51% of nurses have 5 years to 10 years, the responses of the sample of the study sample showed a general trend towards approving for Job Efficacy with mean =2.66 ± 0.46, the study sample showed a general trend towards approving for Patient Safety, with mean is 3.25 ± 0.41, and the sample showed a general trend towards approving for Quality of Patient Care among nurses, with mean is 3.71± 0.73. The results revealed a significant difference in job efficacy among participants due to gender (P-value =0.043), age (p-value = 0.027), total years of experience working as a nurse (p-value = 0.017), and there is no significant difference with Job efficacy among participants due to marital Status (p-value = 0.948). Conclusion: Nurses’ job efficacy can affect positively on the patient’s safety and quality of care among patients who are visiting second health cluster.