ethicalabs/Research-Intent-Collab huggingface dataset:
ARTIFICIAL INTELLIGENCE-DRIVEN OPTIMIZATION OF TRANSPORT OPERATIONS FOR REDUCING LOGISTICS COSTS AND CARBON FOOTPRINT

Purpose. The purpose of this paper is to investigate ways to enhance the efficiency of transport and logistics systems in the context of global digital transformation and economic decarbonization. The study focuses on the optimization of transport routes using artificial intelligence technologies as a tool for reducing logistics costs and the carbon footprint of enterprises. Methodology. The research is based on a comprehensive methodological framework that integrates systemic, analytical, logical-structural, and comparative approaches. The study employs mathematical modeling, machine learning algorithms, big data analytics, and multi-criteria optimization techniques. To evaluate the model’s performance, a digital twin of the transport network was developed, allowing the simulation of various transportation scenarios in real time. The optimization model combines three key parameters-logistics costs, CO₂ emissions, and delivery time-using adjustable weighting coefficients according to enterprise-specific priorities. Findings. The results of simulation modeling demonstrate that the implementation of AI-driven routing technologies enables a reduction in fuel consumption by 18.7%, a decrease in average delivery time by 12.3%, a reduction in CO₂ emissions by 20.1%, and an overall decrease in logistics costs by 22.4%. These outcomes are consistent with global trends in the digital transformation of logistics and confirm the effectiveness of intelligent transport systems in achieving sustainable development objectives. The application of machine learning, genetic algorithms, and particle swarm optimization provided superior stability of solutions and adaptability to changing operational conditions. Practical implications. The developed model can be applied by enterprises of different sizes to increase competitiveness, reduce operational costs, and meet climate targets. The findings may also support the design of public policies aimed at promoting sustainable transport, digitalization, and decarbonization across the economy. Value / originality. The study contributes to the advancement of green logistics and the concept of sustainable digital supply chains by integrating intelligent optimization algorithms into transport management. It presents a conceptual and methodological framework for AI-based optimization of transport operations, which bridges economic efficiency and environmental sustainability. Future research should focus on developing industry standards for AI integration in logistics systems, creating hybrid optimization algorithms, and exploring the socio-economic impacts of digital transformation in the transport sector.
Genome Instability-Related miRNAs Predict Survival, Immune Landscape, and Immunotherapy Responses in Gastric Cancer

Background. Increasing evidence suggests that microRNAs (miRNAs) are involved in genome instability (GI) and drive the occurrence of tumors. However, the role of GI-related miRNAs in gastric cancer (GC) remains largely unknown. Herein, we developed a novel GI-related miRNA signature (GIMiSig) and further investigated its role in prognosis, the immune landscape, and immunotherapy responses in GC patients. Methods. An analysis of somatic mutation data on 434 gastric cancer cases from The Cancer Genome Atlas (TCGA) database was performed, thereby generating genome stability (GS) and GI groups. By detecting differentially expressed miRNAs between the GS and GI groups that were associated with overall survival, 8 miRNAs were identified and used to construct the GIMiSig. Results. The GIMiSig showed high accuracy in detecting GC patients. Using GIMiSig to stratify the patients into the high- and low-risk subgroups to predict survival outperformed the use of regular clinical features such as age, gender, or disease stage. Patients with low risk had a more favorable survival time than those with high risk. More importantly, the high-risk patients were associated with decreased UBQLN4 expression, higher accumulation of immune cells, lower Titin (TTN) mutation frequency, worse immunotherapy efficacy, and cancer-associated pathways. Conversely, the low-risk patients were characterized by UBQLN4 overexpression, lower fraction of immune cells, higher TTN mutation frequency, better response to immunotherapy, and GI-related pathways. Conclusion. In summary, we constructed a novel GIMiSig that could stratify GC patients into distinct risk groups that have different survival outcomes and immunotherapy efficacy. The results may provide new clues for improving GC outcomes.
Round Table Discussion (Moderator : R. Nalewajski). A reproduction of the tape recorded panel discussion at the Two day International Symposium organized under the auspices of the FWO-Flanders Scientific Network "Quantum Chemistry : Fundamental and Applied Aspects of Density Functional Theory", May 14-15, 1998, VUB, Brussels

Round Table Discussion (Moderator : R. Nalewajski). A reproduction of the tape recorded panel discussion at the Two day International Symposium organized under the auspices of the FWO-Flanders Scientific Network "Quantum Chemistry : Fundamental and Applied Aspects of Density Functional Theory", May 14-15, 1998, VUB, Brussels, in "Density Functional Theory : a Bridge between Chemistry and Physics", VUB Press, Brussels, Belgium, 300 pp., 1999, ISBN 90 5487 235 7.
China's “Responsible Protection” Concept: Reinterpreting the Responsibility to Protect (R2P) and Military Intervention for Humanitarian Purposes

This paper assesses the extent to which the recently formulated Chinese concept of “Responsible Protection” (RP) offers a valuable contribution to the normative debate over R2P's third pillar following the controversy over military intervention in Libya. While RP draws heavily on previous proposals including the 2001 ICISS report and Brazil's “Responsibility while Protecting” (RwP), by amalgamating and repackaging these earlier ideas in a more restrictive form the initiative represents a new and distinctive interpretation of R2P. However, some aspects of RP are framed too narrowly to provide workable guidelines for determining the permissibility of military intervention for humanitarian purposes, and should be clarified and refined. Nevertheless, the Chinese proposal remains significant because it offers important insights into Beijing's current stance on R2P. More broadly, China's RP and Brazil's RwP initiatives illustrate the growing willingness of rising, non-Western powers to assert their own normative preferences on sovereignty, intervention, and global governance.
Africa needs to prioritize One Health approaches that focus on the environment, animal health and human health

Urbanization, armed conflict, and deforestation in African countries have increased the risk of zoonotic infections, which requires a One Health approach focused on the environment, animal health and human health.
New methods to determine the importance measures of initiating and enabling events in fault tree analysis

Abstract   Components' importance measures play a very important role in system reliability analysis. They are used to identify the weakest parts of the system for design improvement, failure diagnosis and maintenance. This paper deals with the problem of determining the importance measures of basic events in case of unreliability analysis of binary coherent and non-coherent fault trees. This type of analysis is typical of catastrophic top events, characterised by unacceptable consequences. Since the unreliability of systems with repairable components cannot be exactly calculated via fault tree, the Expected Number of Failures – which is obtained by integrating the unconditional failure frequency – is considered as it represents a good upper bound. In these cases it is important to classify events as initiators or enablers since their roles in the system are different, their sequence of occurrence is different and consequently they must be treated differently. New equations based on system failure frequency are described in this paper for determining the exact importance measures of initiating and enabling events. Simple examples are provided to clarify the application of the proposed calculation methods. Compared with the exact methods available in the literature, those proposed in this paper are easier to apply by hand and are simpler to implement in a fault tree analyser.
Deepfakes, Free Speech, and the Right to Truth: A Comparative Legal Study on Regulating Synthetic Media in the USA, UK, and India.

The rapid development of artificial intelligence has sparked the emergence of deepfakes, hyper-realistic synthetic media created using deep learning algorithms that can convincingly imitate human appearance and voice. While these tools offer creative and innovative possibilities, they also raise unprecedented legal, ethical, and social challenges. Deepfakes have been exploited for various malicious purposes, including non-consensual pornography, identity theft, political misinformation, financial fraud, and undermining public trust in media and democratic institutions. This paper provides a comparative legal analysis of how three major countries, the United States, the United Kingdom, and India, are addressing the regulatory challenges posed by deepfake technology. In the U.S., a fragmented legal system exists, with some federal legislation such as the No Fakes Act and the Defiance Act, along with diverse state laws targeting electoral manipulation and sexual privacy. The UK’s Online Safety Act 2023 is a significant step forward, criminalising the distribution of non-consensual explicit deepfake content. However, broader misuse remains covered by traditional laws like defamation, data protection, and harassment statutes. India, without a specific deepfake legislation, depends on provisions within the Information Technology Act, 2000, and the Bharatiya Nyaya Sanhita, 2023, which inadequately address the complex threats posed by synthetic media. The paper further explores the central normative tension between the right to freedom of expression and the right to truth and protection from harm. It critically evaluates whether existing free speech doctrines are equipped to handle the unique challenges of synthetic falsification, concluding that while deepfakes pose novel technological risks, they do not create fundamentally new constitutional dilemmas. Rather, the regulation of deepfakes requires a strong extension of existing legal doctrines, with appropriate safeguards for satire, parody, and artistic freedom. Finally, the paper emphasises the growing threat that deepfakes pose to democratic discourse, particularly through their capacity to degrade informational trust, fuel the Liar’s dividend and destabilise the evidentiary value of digital content. It calls for the development of harmonised legal standards, AI detection mechanisms, and ethical guidelines to safeguard individual dignity, ensure media authenticity, and strengthen democratic resilience in the digital age.
Effect of early palliative care (PC) on quality of life (QOL), aggressive care at the end-of-life (EOL), and survival in stage IV NSCLC patients: Results of a phase III randomized trial.

7509 Background: Patients withmetastatic NSCLC face significant physical and psychological symptoms and receive aggressive care at the EOL. The goal of this study was to examine the impact of early...
Trace Element Geochemistry and Sr‐Nd Isotopic Characteristics of Scheelite from the Gejiu Sn‐Cu‐(W) Ore District: Constraints on Tungsten Sources and Evolution of W‐bearing Hydrothermal Fluids

Abstract                                        The Gejiu tin‐copper‐(tungsten) (Sn‐Cu‐(W)) polymetallic district is located in the southwest of the W‐Sn metallogenic belt in the western Youjiang Basin, Yunnan, Southwest China. Abundant W minerals have been identified in the region via exploration. However, metallogenic sources and evolution of W remain unclear, and the existing metallogenic model has to be updated to guide further ore prospecting. Elemental and Sr‐Nd isotopic data for scheelites assist in the determination of sources and evolution of the W‐mineralizing fluids and metals in the district. Based on field geological survey, the scheelites in the Gejiu district can be categorized into three types: altered granite (Type I), quartz vein (Type II) from the Laochang deposit, and skarn (Type III) from the Kafang deposit. Types I and II scheelites have low molybdenum (Mo) and strontium (Sr) contents, and Type II scheelite has lower Sr contents than Type I as well as higher Mo and Sr contents than Type III scheelites. Varying Mo contents across the scheelite types suggests that the oxygen fugacity varied during ore accumulation. Type I and Type II scheelites exhibit similar rare earth elements (REE) patterns; Type III scheelite contains lower REE content, particularly HREE, compared with the other scheelites. All scheelites exhibit negative Eu anomalies in the chondrite‐normalized REE patterns. As the W‐mineralization and two‐mica granite share close spatial and temporal relationships, the negative Eu anomalies were likely inherited from the two‐mica granite. Type I and Type II scheelites display varied (                     87                     Sr/                     86                     Sr)                     82 Ma                     (0.7090–0.7141) and                     ε                     Nd                     (82 Ma) (from –9.9 to –5.4) values, similar to those of granite. However, Type III scheelite exhibits lower (                     87                     Sr/                     86                     Sr)                     82 Ma                     (0.7083–0.7087) and lower                     ε                     Nd                     (82 Ma) (from –10.5 to –6.9) values than the two‐mica granite. This indicates that the two‐mica granite alone did not provide the ore‐forming fluids and metals and that the Type III scheelite ore‐forming fluids likely involved external fluids that were probably derived from carbonate rocks. The implication is that highly differentiated two‐mica granites were the source of primary W‐bearing metals and fluids, which is consistent with earlier research on the origin of Sn ore‐forming materials.
Post-seismic supply chain risk management: A system dynamics disruption analysis approach for inventory and logistics planning

zbMATH Open Web Interface contents unavailable due to conflicting licenses.
The Effect of Occupational Health and Safety (OHS) Knowledge and Organizational Commitment on Employees’ Ability to Minimize Workplace Accidents at Hospital X, Surabaya

Workplace accidents remain a critical Occupational Health and Safety (K3/OSH) issue, particularly in hospitals where employees are exposed to multiple hazards and high work intensity. Strengthening employees’ ability to minimize workplace accidents is essential to protect staff, maintain service continuity, and improve organizational performance. In this context, K3/OSH knowledge and organizational commitment are assumed to be key factors supporting accident-prevention capability. Objective: This study aims to analyze the effects of K3/OSH knowledge and organizational commitment on employees’ ability to minimize workplace accidents at Hospital X Surabaya, both simultaneously and partially. Methods: This study employed a quantitative, cross-sectional design. Data were collected using a structured questionnaire administered to 84 employees selected from a population of 179 using Slovin’s formula (e = 0.10). Respondents included clinical and non-clinical staff (doctors, nurses, midwives, medical record officers, pharmacists, radiology staff, and administrative personnel). Data were analyzed using multiple linear regression with SPSS, assessing the overall model significance (F-test) and the partial effects of each predictor (t-tests). Results: The regression model was statistically significant, indicating that K3/OSH knowledge and commitment jointly predict accident-minimization ability (F = 142.686, p &lt; .001). Partially, K3/OSH knowledge had a significant positive effect on accident-minimization ability (t = 4.423, p &lt; .001), and organizational commitment also had a significant positive effect (t = 3.104, p = .003). These results suggest that employees’ ability to minimize workplace accidents tends to increase when they have stronger OSH knowledge and higher commitment. Implications: Hospitals should implement integrated OSH interventions that combine competency development (e.g., role-specific K3 training, SOP reinforcement) with commitment strengthening (e.g., leadership consistency, safety culture reinforcement, supportive supervision) to enhance accident prevention. Originality: This study provides context-specific evidence from an Indonesian hospital by testing K3/OSH knowledge and commitment simultaneously within one predictive model across diverse occupational roles, highlighting the need for combined knowledge–commitment strategies in accident prevention.
L1 penalization of volumetric dose objectives in optimal control of PDEs

This work is concerned with a class of optimal control problems governed by a partial differential equation that are motivated by an application in radiotherapy treatment planning, where the primary design objective is to minimize the volume where a functional of the state violates a prescribed level, but prescribing these levels in the form of pointwise state constraints can lead to infeasible problems. We therefore propose an alternative approach based on $L^1$ penalization of the violation. We establish well-posedness of the corresponding optimal control problem, derive first-order optimality conditions, and present a semismooth Newton method for the efficient numerical solution of these problems. The performance of this method for a model problem is illustrated and contrasted with the alternative approach based on (regularized) state constraints.
Voices from frontline nurses on care quality and patient safety during COVID-19: An application of the Donabedian model

The COVID-19 pandemic has adversely impacted quality of care and patient safety. This study aimed to describe registered nurses' (RNs) perceptions on the impact of the COVID-19 pandemic on their ability to adhere to patient safety protocols using Donabedian's Health Care Quality model.In October 2020, a survey was conducted among all actively licensed RNs in New Jersey who provided direct patient care during the first peak of COVID-19.Of 3,027 participants, 68% reported that the number of patients assigned impacted their ability to adhere to protocols. RNs identified a variety of organizational structures impacting adherence, including inadequate staffing, staff qualifications, and inadequate resources. Impacted processes included the inability to adhere to patient safety protocols and conduct comprehensive assessments and surveillance, the need for additional time spent on personal protective equipment and isolation policies, and difficulty maintaining isolation integrity; the need to prioritize and cluster care; and guidelines limiting personnel who could enter the room. Nurses attributed both adverse patient and staff outcomes to inadequate staffing and high patient acuity.These findings highlight the need for health care organizations to support frontline nursing staff in adhering to patient safety and infection prevention and control protocols during times of crises. Infection preventionists have substantial contact with bedside nurses and should leverage their collegial relationships to promote patient safety.
Multi Pseudo Q-learning Based Deterministic Policy Gradient for Tracking Control of Autonomous Underwater Vehicles

This paper investigates trajectory tracking problem for a class of underactuated autonomous underwater vehicles (AUVs) with unknown dynamics and constrained inputs. Different from existing policy gradient methods which employ single actor-critic but cannot realize satisfactory tracking control accuracy and stable learning, our proposed algorithm can achieve high-level tracking control accuracy of AUVs and stable learning by applying a hybrid actors-critics architecture, where multiple actors and critics are trained to learn a deterministic policy and action-value function, respectively. Specifically, for the critics, the expected absolute Bellman error based updating rule is used to choose the worst critic to be updated in each time step. Subsequently, to calculate the loss function with more accurate target value for the chosen critic, Pseudo Q-learning, which uses sub-greedy policy to replace the greedy policy in Q-learning, is developed for continuous action spaces, and Multi Pseudo Q-learning (MPQ) is proposed to reduce the overestimation of action-value function and to stabilize the learning. As for the actors, deterministic policy gradient is applied to update the weights, and the final learned policy is defined as the average of all actors to avoid large but bad updates. Moreover, the stability analysis of the learning is given qualitatively. The effectiveness and generality of the proposed MPQ-based Deterministic Policy Gradient (MPQ-DPG) algorithm are verified by the application on AUV with two different reference trajectories. And the results demonstrate high-level tracking control accuracy and stable learning of MPQ-DPG. Besides, the results also validate that increasing the number of the actors and critics will further improve the performance.
Capture and Annihilation of Dark Matter in Milky Way Globular Clusters

Paper has been withdrawn due to a critical error in the numerical evaluation of the capture fraction.
AI-Powered Neural Networks for Music Composition and Generation

The application of artificial intelligence (AI) in music composition and generation has gained significant interest in recent years. Neural networks, particularly deep learning techniques, are now being used to create original compositions, remix existing music, and even generate new sounds. This article explores the role of AI-powered neural networks in music generation, focusing on key methods such as recurrent neural networks (RNNs), generative adversarial networks (GANs), and transformers. We discuss the potential of these technologies to revolutionize music creation, as well as their implications for the music industry, musicians, and listeners..
BioPreDyn-bench: benchmark problems for kinetic modelling in systems biology

Dynamic modelling is one of the cornerstones of systems biology. Many research efforts are currently being invested in the development and exploitation of large-scale kinetic models. The associated problems of parameter estimation (model calibration) and optimal experimental design are particularly challenging. The community has already developed many methods and software packages which aim to facilitate these tasks. However, there is a lack of suitable benchmark problems which allow a fair and systematic evaluation and comparison of these contributions. Here we present BioPreDyn-bench, a set of challenging parameter estimation problems which aspire to serve as reference test cases in this area. This set comprises six problems including medium and large-scale kinetic models of the bacterium E. coli, baker's yeast S. cerevisiae, the vinegar fly D. melanogaster, Chinese Hamster Ovary cells, and a generic signal transduction network. The level of description includes metabolism, transcription, signal transduction, and development. For each problem we provide (i) a basic description and formulation, (ii) implementations ready-to-run in several formats, (iii) computational results obtained with specific solvers, (iv) a basic analysis and interpretation. This suite of benchmark problems can be readily used to evaluate and compare parameter estimation methods. Further, it can also be used to build test problems for sensitivity and identifiability analysis, model reduction and optimal experimental design methods. The suite, including codes and documentation, can be freely downloaded from http://www.iim.csic.es/%7egingproc/biopredynbench/.