ethicalabs/Research-Intent-Collab huggingface dataset:
Support Vector Machine and Deep Learning in Medical Applications

Deep learning is a new field in machine learning that focuses mainly on deep neural networks. In this thesis, we focus on machine learning and deep learning, present support vector machine and neural networks, and then we study some medical research in which these methods have been applied. The main deep learning methods in this thesis are convolutional neural networks and recurrent neural networks. After presenting the medical applications we also check some other applications of deep learning. Then we orient to some tools that can be used to implement deep learning algorithms and see how a recurrent neural network could be implemented by using TensorFlow.
Do universities have moral duties with regard to a human right to health? In defense of some proposals by UAEM 1 (Universities Allied for Essential Medicines)

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Analysis of Factors Causing High Workplace Accidents and Efforts to Improve the Implementation of Occupational Health and Safety (OHS) Systems in Modern Industry

This study aims to analyze the factors causing high workplace accidents and to improve the implementation of Occupational Health and Safety (OHS) systems in modern industry in West Java. The study employs a mixed-method approach with a descriptive analytical design through questionnaires administered to 40 workers and interviews with 6 key informants. The research process includes risk identification using HIRARC, root cause analysis through Fishbone and 5 Why methods, and evaluation of OHS system implementation. The study was conducted over a three-month period. The results indicate that unsafe behavior, lack of training, and weak supervision are the main causes of workplace accidents. The implications suggest the need to strengthen safety culture and enhance the integration of OHS systems
Ultrafast Electron Dynamics in the Topological Insulator Bi2Se3 Studied by Time-Resolved Photoemission Spectroscopy

We characterize the topological insulator Bi$_2$Se$_3$ using time- and angle- resolved photoemission spectroscopy. By employing two-photon photoemission, a complete picture of the unoccupied electronic structure from the Fermi level up to the vacuum level is obtained. We demonstrate that the unoccupied states host a second, Dirac surface state which can be resonantly excited by 1.5 eV photons. We then study the ultrafast relaxation processes following optical excitation. We find that they culminate in a persistent non-equilibrium population of the first Dirac surface state, which is maintained by a meta-stable population of the bulk conduction band. Finally, we perform a temperature-dependent study of the electron-phonon scattering processes in the conduction band, and find the unexpected result that their rates decrease with increasing sample temperature. We develop a model of phonon emission and absorption from a population of electrons, and show that this counter-intuitive trend is the natural consequence of fundamental electron-phonon scattering processes. This analysis serves as an important reminder that the decay rates extracted by time-resolved photoemission are not in general equal to single electron scattering rates, but include contributions from filling and emptying processes from a continuum of states.
Climate Change Adaptation in Sustainable Agriculture: Implications for Food Security and Sustainability

Climate change poses significant pressures on the sustainability of global agricultural systems and food security stability. This study aims to analyze the development of global literature on climate change adaptation in the agricultural sector and identify conceptual gaps and future research directions. The study employs a Systematic Literature Review (SLR) approach combined with bibliometric analysis of reputable international publications. A total of 39 articles were selected for final analysis. The results indicate that agricultural adaptation research has grown rapidly over the past two decades, with a primary focus on Climate-Smart Agriculture (CSA), ecosystem-based resource management, production system diversification, and the integration of digital technologies such as precision agriculture and artificial intelligence. However, the literature remains fragmented and technically oriented, with limited integration between technological innovation, farmers’ adaptive capacity, institutional governance, and the dimensions of food security and social equity. This study proposes an integrative conceptual framework that links technological innovation, ecosystem sustainability, socioeconomic capacity, and public policy in strengthening the resilience of the global food system. These findings provide theoretical contributions and policy implications for accelerating sustainable agricultural transformation amid the dynamics of climate change.
Interpretable neural networks prioritize cancer driver genes from genome-wide dependency landscapes

Abstract                 Identifying cancer driver genes and their therapeutic impact remains a core challenge in computational cancer biology. We introduce xNNDriver and xAEDriver, two interpretable neural network frameworks that connect cancer mutations with genome-wide DepMap gene dependencies, pathway activity, and drug-response patterns. xNNDriver is a supervised pathway-guided model that evaluates whether a gene’s mutation status is encoded in the genome-wide dependency landscape; we interpret model fitness as a driver potential score, which quantifies the strength of this mutation-dependency signal and prioritizes genes with broad functional footprints. Across 3,008 candidate genes, xNNDriver recovers major established drivers and highlights literature-supported candidates, while pathway analyses reveal biologically coherent programs related to metabolism, growth factor signaling, and immune regulation. To capture combinatorial functional states, xAEDriver uses an unsupervised autoencoder to learn Driver Variant Representations (DVRs), latent binary features guided by the frequency distribution of known driver mutations. DVRs capture cell-line-specific dependency patterns and expression patterns and are associated with drug sensitivity and pathway activity. Together, these interpretable deep learning models demonstrate that gene dependency landscapes encode rich, interpretable signals of oncogenic function and provide a hypothesis-generating framework for prioritizing drivers, pathways, and therapeutic vulnerabilities for further experimental validation.                                    Author summary                   Cancer is often driven by genetic changes that give tumor cells a growth advantage, but finding which changes matter and how they affect cell behavior remains difficult. In this study, we used large-scale gene-editing data from cancer cell lines to ask whether the pattern of genes a cell depends on can reveal information about its cancer-driving alterations. We developed interpretable neural-network models that connect mutation patterns, cell survival dependencies, and biological pathways. One model prioritizes genes whose mutation status leaves a clear functional signature across the cell. A second model summarizes broader, combined driver-like states in each cell line. These summaries were linked to known cancer biology, tissue type, and differences in drug response, suggesting that functional dependency data contain useful clues about cancer mechanisms. By connecting mutation patterns with functional dependencies and pathway activity, our approach helps identify candidate cancer drivers and interpretable drug-response patterns that can guide future experimental studies. Making the models interpretable allows researchers to move from large screening datasets toward biological explanations of cancer-driving processes.
The Impact of Nurses Clinical Decisions on Patient Safety and Quality of Care

Patient safety (PS) and quality of care are central to healthcare delivery, with clinical decisions playing a pivotal role in influencing outcomes. Objective: To assess the impact of clinical decisions on PS culture and the quality of care in tertiary care hospitals of Khyber Pakhtunkhwa, Pakistan, and to identify key predictors influencing these outcomes. Methods: A cross-sectional analytical study was conducted from January to April 2025 in three tertiary care hospitals of KPK. A total of 384 participants (192 healthcare providers and 192 patients) were enrolled. PS culture was assessed using the Hospital Survey on Patient Safety Culture (HSOPSC), and patient satisfaction was evaluated using the PSQ-18. An audit of 450 clinical decisions was also conducted. Descriptive statistics and inferential statistics were employed for data analysis using SPSS version 26.0. Results: Experienced staff (&gt;5 years) reported significantly higher PS scores (3.96 ± 0.49, p&lt;0.001). ICU/Emergency Departments had higher safety perceptions compared to medical and surgical units (p=0.041). Teamwork climate (4.15 ± 0.47) and supervisor expectations (3.96 ± 0.59) were rated highest among safety dimensions. Guideline non-adherence was significantly associated with adverse events (12.3% vs. 4.5%, p=0.018). Teamwork score (OR: 1.78) and experience (OR: 2.33) emerged as strong predictors of high safety culture. Conclusions: It was concluded that clinical decisions significantly affect PS and quality of care. Enhancing teamwork and experience-based leadership can strengthen safety culture, reduce adverse events, and improve patient satisfaction.
Calibration, validation, and verification including uncertainty of a physically motivated internal state variable plasticity and damage model

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Integration of Reinforcement Learning in a Virtual Robotic Surgical Simulation

Background.                     The revolutions in AI hold tremendous capacity to augment human achievements in surgery, but robust integration of deep learning algorithms with high-fidelity surgical simulation remains a challenge. We present a novel application of reinforcement learning (RL) for automating surgical maneuvers in a graphical simulation.                                                           Methods.                     In the Unity3D game engine, the Machine Learning-Agents package was integrated with the NVIDIA FleX particle simulator for developing autonomously behaving RL-trained scissors. Proximal Policy Optimization (PPO) was used to reward movements and desired behavior such as movement along desired trajectory and optimized cutting maneuvers along the deformable tissue-like object. Constant and proportional reward functions were tested, and TensorFlow analytics was used to informed hyperparameter tuning and evaluate performance.                                                           Results.                     RL-trained scissors reliably manipulated the rendered tissue that was simulated with soft-tissue properties. A desirable trajectory of the autonomously behaving scissors was achieved along 1 axis. Proportional rewards performed better compared to constant rewards. Cumulative reward and PPO metrics did not consistently improve across RL-trained scissors in the setting for movement across 2 axes (horizontal and depth).                                                           Conclusion.                     Game engines hold promising potential for the design and implementation of RL-based solutions to simulated surgical subtasks. Task completion was sufficiently achieved in one-dimensional movement in simulations with and without tissue-rendering. Further work is needed to optimize network architecture and parameter tuning for increasing complexity.
Artificial intelligence in the diagnosis and prognosis of upper tract urothelial carcinoma: A comprehensive review of current evidence, methodological standards, and future directions.

870                                                           Background:                     Upper tract urothelial carcinoma (UTUC) is a rare malignancy (5–10% of urothelial cancers) with challenging diagnosis and heterogeneous outcomes. Artificial intelligence (AI)—including radiomics, machine learning (ML), deep learning (DL), and digital pathology—has emerged as a tool to improve detection, risk stratification, and treatment planning.                     Methods:                     A systematic literature review (PubMed, Scopus, Embase) to August 2025 identified original human studies using AI for UTUC diagnosis or prognosis. Extracted data included sample size, AI methodology, validation type, and key performance metrics.                     Results:                     Twenty-one studies met inclusion criteria (2018–2025): CT urography radiomics (n = 12), prognostic perirenal fat/peritumoral texture analysis (n = 2), AI-assisted urine cytology (n = 2), DL-based digital pathology (n = 2), and multimodal models integrating imaging and clinical/molecular data (n = 3). Radiomics achieved AUCs of 0.80–0.94 for grading/staging; multimodal approaches improved predictive accuracy (ΔAUC ≈ +0.06). AI-assisted cytology reached sensitivity &gt; 85% for recurrence detection, and digital pathology predicted lymph-node status with AUC up to 0.85. Most studies were retrospective and single-center; only 14% reported external validation.                     Conclusions:                     AI shows strong promise for UTUC diagnosis and prognostication, particularly via CT radiomics and multimodal integration. However, clinical adoption requires multicenter prospective validation, standardized imaging/pathology workflows, regulatory approval, and cost-effectiveness evaluation.
Strengthening the health system to improve the quality of care and patient safety

No abstract available
A Drive towards Thermodynamic Efficiency for Dissipative Structures in Chemical Reaction Networks

Dissipative accounts of structure formation show that the self-organisation of complex structures is thermodynamically favoured, whenever these structures dissipate free energy that could not be accessed otherwise. These structures therefore open transition channels for the state of the universe to move from a frustrated, metastable state to another metastable state of higher entropy. However, these accounts apply as well to relatively simple, dissipative systems, such as convection cells, hurricanes, candle flames, lightning strikes, or mechanical cracks, as they do to complex biological systems. Conversely, interesting computational properties—that characterize complex biological systems, such as efficient, predictive representations of environmental dynamics—can be linked to the thermodynamic efficiency of underlying physical processes. However, the potential mechanisms that underwrite the selection of dissipative structures with thermodynamically efficient subprocesses is not completely understood. We address these mechanisms by explaining how bifurcation-based, work-harvesting processes—required to sustain complex dissipative structures—might be driven towards thermodynamic efficiency. We first demonstrate a simple mechanism that leads to self-selection of efficient dissipative structures in a stochastic chemical reaction network, when the dissipated driving chemical potential difference is decreased. We then discuss how such a drive can emerge naturally in a hierarchy of self-similar dissipative structures, each feeding on the dissipative structures of a previous level, when moving away from the initial, driving disequilibrium.
Study of Muons in Ultra-High-Energy Cosmic-Ray Air Showers with the Telescope Array Experiment

One of the uncertainties in the interpretation of ultra-high-energy cosmic-ray (UHECR) data comes from the high-energy hadronic interaction models used for air shower Monte-Carlo (MC) simulations. A long-standing problem of the so-called “muon excess”, the discrepancy of number of muons predicted by simulations and observed in the data, is believed to be caused by the incompleteness of modern hadronic interaction models, all of which are known to use the extrapolated values of the parameters of hadronic interactions, such as cross sections and multiplicities. The present work is dedicated to the study of muon densities in UHE extensive air showers from the Telescope Array experiment surface detector data. In the 7-year-data from the Telescope Array experiment, we find that the number of particles observed for signals with an expected muon purity of ∼65% at a lateral distance of 2000 m from the shower core is 1.72 ± 0.10(stat.) ± 0.37(syst.) times larger than the MC prediction value using the QGSJETII-03 model for the proton-induced showers. A similar effect is also seen in comparison with other hadronic models such as QGSJETII-04, which shows a 1.67 ± 0.10 ± 0.36 excess. We also studied the dependence of these excesses on lateral distances and found a slower decrease of the lateral distribution of muons in the data as compared to the MC, causing larger discrepancy at the larger lateral distances.
Policy gradient fuzzy reinforcement learning

This work presents a new approach for tuning conclusions of fuzzy rules based on reinforcement learning. Unlike the most of existing fuzzy reinforcement learning algorithms, which are based on value function, while our approach called policy gradient fuzzy reinforcement learning (PGFRL) bases on gradient estimate. In PGFRL, the algorithm GPOMDP is employed to estimate the performance gradient with respect to the parameters of fuzzy rules. In our work we prove the convergence of fuzzy rules' parameters to a local optimum given necessary conditions. The experiment results show the effectiveness of PGFRL.
The Role of <i>Bacillus</i>-Based Biological Control Agents in Integrated Pest Management Systems: Plant Diseases

Bacillus-based biological control agents (BCAs) have great potential in integrated pest management (IPM) systems; however, relatively little work has been published on integration with other IPM management tools. Unfortunately, most research has focused on BCAs as alternatives to synthetic chemical fungicides or bactericides and not as part of an integrated management system. IPM has had many definitions and this review will use the national coalition for IPM definition: “A sustainable approach to managing pests by combining biological, cultural, physical and chemical tools in a way that minimizes economic, health and environmental risks.” This review will examine the integrated use of Bacillus-based BCAs with disease management tools, including resistant cultivars, fungicides or bactericides, or other BCAs. This integration is important because the consistency and degree of disease control by Bacillus-based BCAs is rarely equal to the control afforded by the best fungicides or bactericides. In theory, integration of several tools brings stability to disease management programs. Integration of BCAs with other disease management tools often provides broader crop adaptation and both more efficacious and consistent levels of disease control. This review will also discuss the use of Bacillus-based BCAs in fungicide resistance management. Work with Bacillus thuringiensis and insect pest management is the exception to the relative paucity of reports but will not be the focus of this review.
The GO-DACT is an investigator-initiated, national, multicentric randomized placebo-controlled double-blinded trial, that assesses dactylitis as primary endpoint. Psoriatic arthritis patients naïve to methotrexate and biologic disease modifying anti-rheumatic drugs, with at least one active dactylitis, were assigned to golimumab in combination with methotrexate or placebo in combination with methotrexate, for 24 weeks. Both clinical (dactylitis severity score and the Leeds dactylitis index) and imaging (high resolution magnetic resonance imaging), among others, were assessed as outcomes. The main objective of GO-DACT is to provide evidence to improve the treatment algorithm and care of psoriatic arthritis patients with active dactylitis. In this manuscript we describe the GO-DACT protocol and general concepts of the methodology of this trial.

The GO-DACT is an investigator-initiated, national, multicentric randomized placebo-controlled double-blinded trial, that assesses dactylitis as primary endpoint. Psoriatic arthritis patients naïve to methotrexate and biologic disease modifying anti-rheumatic drugs, with at least one active dactylitis, were assigned to golimumab in combination with methotrexate or placebo in combination with methotrexate, for 24 weeks. Both clinical (dactylitis severity score and the Leeds dactylitis index) and imaging (high resolution magnetic resonance imaging), among others, were assessed as outcomes. The main objective of GO-DACT is to provide evidence to improve the treatment algorithm and care of psoriatic arthritis patients with active dactylitis. In this manuscript we describe the GO-DACT protocol and general concepts of the methodology of this trial.
Application of the SERVQUAL model in the tourism industry across hospitality, travel, and destination management sectors

This study critically examines the application of the SERVQUAL model in evaluating service quality within the travel and tourism sector. While SERVQUAL is widely used in service industries, its relevance and adaptability in varying tourism contexts have not been sufficiently explored. This gap is particularly significant given the influence of cultural variability and evolving service environments. The objective of this study is to assess how the five dimensions of SERVQUAL, namely reliability, assurance, tangibles, empathy, and responsiveness, affect customer satisfaction and loyalty across different segments of tourism, including destination management, hospitality, travel agencies, and tour operations. A qualitative review method was employed to synthesize empirical findings from multiple studies. The results reveal that each tourism sector prioritizes different dimensions depending on the nature of the service environment. For example, adventure tourism emphasizes assurance and reliability, whereas empathy is more valued in resort and cultural tourism settings. The study also identifies emerging challenges such as technological changes and cultural diversity that influence interpersonal service interactions. These findings affirm the continued usefulness of SERVQUAL while highlighting the importance of contextual adaptation and integration with digital models in international tourism research and practice. Keywords: Customer satisfaction; qualitative review; service quality; SERVQUAL; tourism management.
3D printing of Inconel 718 by MEAM (Metal Extrusion Additive Manufacturing)

In this study the production of 3D-printed pieces made from Inconel 718 using Metal Extrusion Additive Manufacturing of highly-filled polymers has been investigated. The printer used was a Prusa I3 MK3 printer modified with a gravity fed-pellets screw extruder. The feedstock was pellets composed of metal powder and polymeric binders. Different types of geometries and sizes were printed during the study with the aim to determine both the limits of the technique and the final mechanical properties of the pieces after the whole process (Fig 1). After printing, debinding, and sintering some of the samples were cut and observed by SEM to determine the presence of remaining porosity inside the pieces. The samples for tensile tests were subjected to a final precipitation heat-treatment to check the mechanical properties. As a general conclusion of the study, better results were obtained in small pieces with a small thickness. As thickness increases porosity and the amount of large defects created during the printing or debinding steps rises. On the other hand, pieces with regular geometries show less warping. This warping comes from the large shrinkage experimented by the samples during sintering. The hardness and yield strength of the tensile samples are within the expected range for this material, but ductility is easily affected by the presence of defects.