ethicalabs/Research-Intent-Collab huggingface dataset:
Non-violent conflict resolution and peace building in South Africa

No Abstract.African Safety Promotion Vol. 3(1) 2005: pp. 40-51
Behavior of Quillen (co)homology with respect to adjunctions

This paper aims to answer the following question: Given an adjunction between two categories, how is Quillen (co)homology in one category related to that in the other? We identify the induced comparison diagram, giving necessary and sufficient conditions for it to arise, and describe the various comparison maps. Examples are given. Along the way, we clarify some categorical assumptions underlying Quillen (co)homology: cocomplete categories with a set of small projective generators provide a convenient setup.
Projected Changes in Türkiye's Wind Energy Potential Using Next-Generation Climate Models and Scenarios

Renewable energy is a cornerstone in reducing greenhouse gas emissions and, accordingly, mitigating changes in the global climate system. Wind energy is becoming more common among all renewable energy sources used for electricity generation in terms of generation capacity, rapid growth and technological maturity. The share of wind energy in T&amp;#252;rkiye's total electricity production, whose installed capacity has been increasing in recent years, has nearly tripled in the last decade. However, given that wind energy potential varies with wind speed, even small changes in future wind patterns and characteristics can strongly affect future wind power generation dependent on projections. For this purpose, in this study, T&amp;#252;rkiye's mid-future (2031-2060) wind energy potential is examined under optimistic (SSP2-4.5) and pessimistic (SSP3-7.0) scenarios. In the study, 0.25&amp;#176; x 0.25&amp;#176; spatial resolution CMIP6 models from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) dataset were used. The results point to regional differences in Turkey's mid-future (2031-2060) wind energy potential.Acknowledgement: This research was supported by DaVinci Energy Investments and Consulting Industry and Trade Inc.
The Role Of Using Hris (Human Resource Information System) Technology In Human Resource Management At Pt Pegadaian Medan North Sumatera

This study aims to find how the influence of compensation, discipline, and HRIS Development on HR Management at PT Pegadaian Medan Branch, the research method used is quantitative with a Multiple Regression Analysis approach. The number of samples taken was 52 respondents calculated using the Slovin formula. The problem to be studied is to find the right model of application of Compensation, Discipline, and development of HRIS applications that will affect HR management in general and specifically increase the business productivity of Medan branch pawnshop employees to provide the best, both for internal companies and in the wider community to help the process of pawning goods or gold. The results of this study indicate that a) The results obtained from research conducted on HRIS variables (X1), namely there is an influence between HRIS on HR Management at PT Pegadaian Medan Petisah. This means that one of the factors of HR management is influenced by the use of HRIS by employees. Employees who can operate IT properly will get optimal performance results, b) The results obtained from the Work Discipline variable influence HR Management. Where the greater the discipline received by employees both internal and external, the greater the management of HR at PT. Pegadaian, c) The results obtained prove that compensation influences HR Management. This means that the amount of compensation obtained for their work provides enthusiasm that gives birth to maximum HR management.
[Theories of behavior change through preventive and health promotion interventions in occupational therapy].

Community occupational therapy practice challenges therapists in their health educator role and incites them to implement preventive strategies with their clients. Working in the community also provides an interesting context for the implementation of strategies targeting health promotion at the community level.This article describes some of the theories that are used in the public health and health promotion fields to explain health-related behaviour change. It also highlights their potential for community practice in occupational therapy. The theories presented in this paper are the health belief model, social cognitive theory, theory of reasoned action and theory of planned behavior. They are among the most widely used for health-related behaviour analysis and intervention.Since these theories emphasize a set of factors that influence health behaviours, reviewing these theories could contribute to enhance the effectiveness of educational interventions with regards to clients'adherence to their prevention and health promotion recommendations.
Linking Analysis to Atmospheric PFAS: An Integrated Framework for Exposure Assessment, Health Risks, and Future Management Strategies

Per- and polyfluoroalkyl substances (PFASs) are highly chemically stable synthetic compounds. They are widely used in industrial and commercial sectors due to their ability to repel water and oil, thermal stability, and surfactant properties. However, this stability results in environmental persistence and bioaccumulation, posing significant health risks as PFASs eventually find their way into environmental media. Key PFAS compounds, including PerFluoroOctanoic Acid (PFOA), PerFluoroOctane Sulfonic acid (PFOS), and PerFluoroHexane Sulfonic acid (PFHxS), have been linked to hepatotoxicity, immunotoxicity, neurotoxicity, and endocrine disruption. In response to the health threats these substances pose, global regulatory measures, such as the Stockholm Convention restrictions and national drinking water standards, have been implemented to reduce PFAS exposure. Despite these efforts, a lack of universally accepted definitions or comprehensive inventories of PFAS compounds hampers the effective management of these substances. As definitions differ across regulatory bodies, research and policy integration have become complicated. PFASs are broadly categorized as either perfluoroalkyl acids (PFAAs), precursors, or other fluorinated substances; however, PFASs encompass over 5000 distinct compounds, many of which are poorly characterized. PFAS contamination arises from direct industrial emissions and indirect environmental formation, these substances have been detected in water, soil, and even air samples from all over the globe, including from remote regions like Antarctica. Analytical methods, such as primarily liquid and gas chromatography coupled with tandem mass spectrometry, have advanced PFAS detection. However, standardized monitoring protocols remain inadequate. Future management requires unified definitions, expanded monitoring efforts, and standardized methodologies to address the persistent environmental and health impacts of PFAS. This review underscores the need for improved regulatory frameworks and further research.
Latency-Sensitive Function Placement among Heterogeneous Nodes in Serverless Computing

Function as a Service (FaaS) is highly beneficial to smart city infrastructure due to its flexibility, efficiency, and adaptability, specifically for integration in the digital landscape. FaaS has serverless setup, which means that an organization no longer has to worry about specific infrastructure management tasks; the developers can focus on how to deploy and create code efficiently. Since FaaS aligns well with the IoT, it easily integrates with IoT devices, thereby making it possible to perform event-based actions and real-time computations. In our research, we offer an exclusive likelihood-based model of adaptive machine learning for identifying the right place of function. We employ the XGBoost regressor to estimate the execution time for each function and utilize the decision tree regressor to predict network latency. By encompassing factors like network delay, arrival computation, and emphasis on resources, the machine learning model eases the selection process of a placement. In replication, we use Docker containers, focusing on serverless node type, serverless node variety, function location, deadlines, and edge-cloud topology. Thus, the primary objectives are to address deadlines and enhance the use of any resource, and from this, we can see that effective utilization of resources leads to enhanced deadline compliance.
Optimization of critical quality attributes in continuous twin-screw wet granulation via design space validated with pilot scale experimental data

In this study, the influence of key process variables (screw speed, throughput and liquid to solid (L/S) ratio) of a continuous twin screw wet granulation (TSWG) was investigated using a central composite face-centered (CCF) experimental design method. Regression models were developed to predict the process responses (motor torque, granule residence time), granule properties (size distribution, volume average diameter, yield, relative width, flowability) and tablet properties (tensile strength). The effects of the three key process variables were analyzed via contour and interaction plots. The experimental results have demonstrated that all the process responses, granule properties and tablet properties are influenced by changing the screw speed, throughput and L/S ratio. The TSWG process was optimized to produce granules with specific volume average diameter of 150μm and the yield of 95% based on the developed regression models. A design space (DS) was built based on volume average granule diameter between 90 and 200μm and the granule yield larger than 75% with a failure probability analysis using Monte Carlo simulations. Validation experiments successfully validated the robustness and accuracy of the DS generated using the CCF experimental design in optimizing a continuous TSWG process.
Key factors in developing effective digital health promotion tools for cancer prevention and health behavior change in adolescence through a multi-country survey

Abstract                 Objectives Primary cancer prevention through behavior change in adolescence, a crucial period for shaping lifelong health habits, presents a major public health challenge across Europe. Addressing this, the SUNRISE project aims to tackle the challenge of primary cancer prevention in adolescents by developing and implementing an innovative, digitally-enhanced life-skills program tailored to diverse socio-economic, cultural, and environmental backgrounds by incorporating various Digital Health Promotion (DHP) tools to foster sustainable health behavior change in adolescents. Methods A survey was conducted on 505 stakeholders (students, parents, and educators), from seven European countries to assess a set of key features for effective DHP tools for their importance on a five-point likert scale. Results Our findings revealed that nine of the proposed DHP tools’ features were identified as important across all the stakeholder groups, while significant differences in the importance of certain features across different stakeholder groups and countries were identified. Students, as primary users, demonstrated distinct preferences, which often diverged from educators and parents, suggesting that stakeholders hold distinct priorities driven by their roles and contextual backgrounds. Additionally, country-level variations were notable; for example, Swiss participants rated the proposed features, in general, as of lower importance than the Spanish respondents. Conclusions These insights emphasize the necessity of developing adaptable and context-sensitive DHP tools that reflect the diverse needs and preferences of adolescents across Europe. The large-scale implementation and evaluation of this program will provide valuable data for shaping future digital health interventions aimed at cancer prevention in youth.
Generative Fractional Diffusion Models

We introduce the first continuous-time score-based generative model that leverages fractional diffusion processes for its underlying dynamics. Although diffusion models have excelled at capturing data distributions, they still suffer from various limitations such as slow convergence, mode-collapse on imbalanced data, and lack of diversity. These issues are partially linked to the use of light-tailed Brownian motion (BM) with independent increments. In this paper, we replace BM with an approximation of its non-Markovian counterpart, fractional Brownian motion (fBM), characterized by correlated increments and Hurst index $H \in (0, TRUE)$, where $H=0.5$ recovers the classical BM. To ensure tractable inference and learning, we employ a recently popularized Markov approximation of fBM (MA-fBM) and derive its reverse-time model, resulting in generative fractional diffusion models (GFDM). We characterize the forward dynamics using a continuous reparameterization trick and propose augmented score matching to efficiently learn the score function, which is partly known in closed form, at minimal added cost. The ability to drive our diffusion model via MA-fBM offers flexibility and control. $H \leq 0.5$ enters the regime of rough paths whereas $H&gt;0.5$ regularizes diffusion paths and invokes long-term memory. The Markov approximation allows added control by varying the number of Markov processes linearly combined to approximate fBM. Our evaluations on real image datasets demonstrate that GFDM achieves greater pixel-wise diversity and enhanced image quality, as indicated by a lower FID, offering a promising alternative to traditional diffusion models
THE IMPORTANCE OF RESILIENCE IN ENSURING THE PROTECTION AND SECURITY OF CRITICAL ENERGY INFRASTRUCTURES

In the face of crisis situations caused by natural disasters, wars, terrorist risks and threats, internal disorders, work accidents, technological events, psychological trauma, epidemics and pandemics, etc, state authorities appear to be limited in their prevention, intervention and management, particularly at the level of directly or indirectly affected communities. Contemporary society has been, is and probably will be dominated by conflicts expressed most often through violence or social confrontations, due to multiple causes: dissatisfaction with the authorities, the non-involvement or disregard of the authorities towards the citizen, the political and social unpredictability, the generalized corruption, the daily stress, the lack of daily necessities, the precarious living, the collapse of the authorities and the most serious, the collapse of the moral and spiritual norms, the collapse of the moral and spiritual norms, thus stress, anxiety, civic indifference and violence accompany the daily life of the population and for this reason there is a lack of interest or low interest in the actions of individuals to intervene and recover from unforeseen major events that disrupt life daily of a society. For this reason, resilience becomes a major national security objective because it expresses "the response capacity of the population (many individuals/community) to confront and recover from an unforeseen traumatic event or crisis situation and express ways of action to ensure and maintain an optimal level of security and safety of the population"
<i>Landmarks</i>—Correcting Quantum Computer Errors

In the mid-1990s, researchers proposed methods to preserve the integrity of quantum bits—techniques that may become the key to practical quantum computing on a large scale.
Blockchain-Enhanced Differential Privacy Federated Learning Framework (B-DPFL) for Healthcare IoT Applications

The proliferation of Internet of Medical Things (IoMT) devices has generated massive amounts of sensitive healthcare data, presenting unprecedented opportunities for machine learning applications while raising critical privacy concerns. This paper proposes a novel Block-chain-Enhanced Differential Privacy Federated Learning Framework (B-DPFL) that integrates federated learning, differential privacy mechanisms, and blockchain technology to enable secure, privacy-preserving collaborative learning in healthcare IoT environments. Our framework addresses three fundamental challenges: (1) preserving patient privacy during model training, (2) ensuring data integrity and model transparency, and (3) preventing mali-cious attacks in decentralized learning systems. Extensive experiments on real-world medical datasets demonstrate that B-DPFL achieves superior performance with 94.3% accuracy while providing strong privacy guarantees (ε = 1.0) and maintaining Byzantine fault tolerance. Compared to conventional federated learning approaches, our framework reduces privacy leakage by 78.5% and improves model convergence speed by 34.2%.
A generative diffusion model for amorphous materials

Abstract                                        Generative models show great promise for the inverse design of molecules and inorganic crystals, but remain largely ineffective within more complex structures such as amorphous materials. Here, we present a diffusion model that reliably generates amorphous structures up to 3 orders of magnitude times faster than conventional simulations across processing conditions, compositions, and data sources. Generated structures recovered the short- and medium-range order, sampling diversity, and macroscopic properties of silica glass, as validated by simulations and an information-theoretical strategy. Conditional generation allowed sampling large structures at low cooling rates of 10                     −2                     K/ps to uncover a ductile-to-brittle transition and mesoporous silica structures. Extension to metallic glassy systems accurately reproduced local structures and properties from both computational and experimental datasets, demonstrating how synthetic data can be generated from characterization results. Our methods provide a roadmap for the design and simulation of amorphous materials previously inaccessible to computational methods.
Mineralogy,                     <i>in situ</i>                     trace element geochemistry, and C–O isotopic composition of contaminated allanite-bearing Khaderpet carbonatite: insights regarding magmatic and supergene processes

Abstract                                        The Khaderpet carbonatite (15°58′N, 77°33′E) occurs as a small plug-like intrusion (45 m × 60 m) within altered ultramafic volcaniclastic breccia of unknown parentage. Both the carbonatite and its host rock contain crustal xenoliths of granite and quartz monzonite. Although the absence of primary silicate and oxide phases obscures its direct genetic link with the host rocks, the carbonatite preserves clear evidence of magmatic crystallization subsequently overprinted by hydrothermal alteration, crustal assimilation and supergene oxidation. The rock is dominated by calcite, which occurs in three distinct generations. Early Sr-Ba-rich calcite-1 (0.8–2.1 wt.% SrO and 0.4–2.2 wt.% BaO) phenocrysts co-crystallized with rounded fluorite at temperatures above ∼600°C, and are hosted within a Ba-Sr-poor calcite-2 matrix. Mantle-like bulk-rock δ                     13                     C values (–4.21 to –4.62 ‰, VPDB), together with (La/Yb)                     Cn                     (&gt;1–100) and Y/Ho (24–34) ratios in calcite-1 and calcite-2, support a primary magmatic origin. Evidence for crustal assimilation includes REE-Si enrichment in apatite (up to 1.3 wt.% SiO                     2                     ) by a britholite-type substitution, increased allanite abundance near xenolith contacts, Si-rich pyrochlore and interstitial quartz. Coarse calcite-3 veins crosscut the calcite-2 matrix and comprise Mn-Fe-Mg-rich bright calcite-3a cores and nearly pure, dark calcite-3b peripheries. Elevated Mn-Fe-Mg contents and high Y/Ho ratios (up to 64) in calcite-3a reflect rapid crystallization during waning hydrothermal stages. High δ                     18                     O values (+9.17 to +11.54 ‰ VSMOW) indicate low-temperature H                     2                     O-rich, CO                     2                     -poor meteoric fluid alteration. Negative Ce anomalies in apatite (Ce/Ce*: 0.8–0.3) and calcite (Ce/Ce*: 0.8–0.4), most pronounced in calcite-3b (Ce/Ce*: 0.2–0.6), together with apatite trace element compositions, indicate supergene alteration. Textural evidence of supergene alteration includes replacement of pyrochlore-1 by pyrochlore-2, pyrochlore-1 and pyrite by goethite, allanite-(La) by ferriallanite-(Ce), and late precipitation of baryte, REE-fluorocarbonates and vanadinite, indicating involvement of F                     –                     , SO                     4                     2–                     , Pb and V in oxidizing hydrothermal fluids.
Predictive Modeling of Anatomy with Genetic and Clinical Data

We present a semi-parametric generative model for predicting anatomy of a patient in subsequent scans following a single baseline image. Such predictive modeling promises to facilitate novel analyses in both voxel-level studies and longitudinal biomarker evaluation. We capture anatomical change through a combination of population-wide regression and a non-parametric model of the subject's health based on individual genetic and clinical indicators. In contrast to classical correlation and longitudinal analysis, we focus on predicting new observations from a single subject observation. We demonstrate prediction of follow-up anatomical scans in the ADNI cohort, and illustrate a novel analysis approach that compares a patient's scans to the predicted subject-specific healthy anatomical trajectory. The code is available at https://github.com/adalca/voxelorb.
The Applicability of Humanitarian Intervention and the Responsibility to Protect During Armed Conflicts: Russia-Ukraine War in Focus

Purpose: Humanitarian intervention and the responsibility to protect have been at the centre of debate in the UN, among states and civil societies. The failure of the International Community to use military force to intervene in the case of Syria and the ongoing atrocities in Ukraine has weakened the enthusiasm that was kindled by the authorized use of force in Libya. This paper aimed at critically examining the situation in Russia-Ukraine in order to propose a way forward for the protection of civilians in Ukraine through humanitarian intervention.  Methodology: To this end, the paper recalls the responsibility to protect bestowed on States and the International Community, evaluating the positive impacts of humanitarian intervention in Kosovo and Libya. Adopting a purely doctrinal research method, this paper questions why the international community is reluctant in using military intervention in the Russia-Ukraine situation in order to protect civilians, curb human rights violations and maintain peace and security.  Findings: The study found out that the fear to trigger war in the whole of Europe and the fact that Russia is a permanent member of the Security Council with veto power have weakened this enthusiasm to intervene.  Conclusion: The study concludes that there is necessity for humanitarian intervention to protect civilians in Ukraine by NATO and other States.  Recommendation: This study recommends the use of the majority voting system in decisions to apply humanitarian intervention instead of the veto system. The study also recommend that permanent members of the Security Council who are involved in gross violation of human rights like Russia should be suspended and eventually removed as a Permanent member if violations persist.