ethicalabs/Research-Intent-Collab huggingface dataset:
Statistical Mechanics of Biomembrane Phase Transition. II. The First Order Transition with Pseudo-Critical Properties

The modified dimer model representing the cooperative nature of the conformation changes of the hydrocarbon chains is analyzed on the basis of the transfer matrix formalism established rigorously in the previous paper. The free energy is evaluated in the limit of infinite dimer lattices by means of the trial function of the free fermion type. The power series expansion of the free energy with respect to ρ, the disorder parameter, has been calculated up to the ρ 4 term without resorting to any further approximation. The modified dimer model is extended so as to include the dilation of the dimer lattice. This extended model with certain parameter values exhibits the first order phase transition. Finiteness of the hydrocarbon chains is also taken into consideration in our formalism. The finiteness may bring the transition point to the vicinity of the spinodal. In consequence, the pseudo-critical phenomena appear in a very narrow temperature region around the transition point. The quasi-critical behavior obse...
Human-AI Interaction: Research and Innovation in Real-Time Speech Generation, Interpretation, and Translation

The 6th International Conference, AI-HCI 2025, held as part of the 27th HCI International Conference, HCII 2025, Gothenburg, Sweden, June 22–27, 2025.
The Use of International Force to Prevent or Halt Atrocities: From Humanitarian Intervention to the Responsibility to Protect

Abstract                This article examines the history of the use of international force for preventing atrocities and human rights abuses. It analyses the concept of humanitarian intervention in the context of the historical origins of sovereignty and the reasons behind the shift to the use of the term responsibility to protect (R2P). It evaluates the progress of R2P from its unanimous endorsement in 2005 to its implementation in Libya in 2011. This article also discusses the role of United Nations Security Council (UNSC) in implementing R2P and the General Assembly in refining the concept and building political understanding and support for the norm.
Fourier Learning Machines: Nonharmonic Fourier-Based Neural Networks for Scientific Machine Learning

We introduce the Fourier Learning Machine (FLM), a neural network (NN) architecture designed to represent a multidimensional nonharmonic Fourier series. The FLM uses a simple feedforward structure with cosine activation functions to learn the frequencies, amplitudes, and phase shifts of the series as trainable parameters. This design allows the model to create a problem-specific spectral basis adaptable to both periodic and nonperiodic functions. Unlike previous Fourier-inspired NN models, the FLM is the first architecture able to represent a multidimensional Fourier series with a complete set of basis functions in separable form, doing so by using a standard Multilayer Perceptron-like architecture. A one-to-one correspondence between the Fourier coefficients and amplitudes and phase-shifts is demonstrated, allowing for the translation between a full, separable basis form and the cosine phase-shifted one. Additionally, we evaluate the performance of FLMs on several scientific computing problems, including benchmark Partial Differential Equations (PDEs) and a family of Optimal Control Problems (OCPs). Computational experiments show that the performance of FLMs is comparable, and often superior, to that of established architectures like SIREN and vanilla feedforward NNs.
3D PRINTING: A REVIEW ON THE TRANSFORMATION OF ADDITIVE MANUFACTURING

3D printing and nanotechnology have been two of the most important tools in the development of personalized medical treatments. More recently, their alliance has developed in an effort to create new, flexible, multidisciplinary, and/or medical and drug-wise products. Therefore, a comprehensive review of scientific studies, including 3D printing and nanomaterials on the development of new pharmaceutical methods and medical applications for the treatment and prevention of diseases, is presented here with the help of secondary research from most recent articles. 3D printing, also known as additive manufacturing, has held the power of building a new class of active nanocomposites. With the ability to print a layer of complex 3D objects by layer, additional production of nanomaterials can be used in new ways to significantly control architectural structures of all sizes. The high efficiency of embedded nanomaterials can further extend the power of nanocomposites to structures such as gradients in thermal conductivity, converted photonic emissions, and increased energy and reduced weight. According to the survey done by annual industry, around 50% of the market of 3d printing in the industrial sectors is credited to created prototypes by means of photopolymers. While, Formlabs, Stratasys, HP, Desktop Metal, Ultimaker, Carbon, EOS, Nanoscribe and Markforged are among the top additive manufacturers. This work is hereby an effort to focus on different techniques, merits and demerits, applications, recent advances, relation with nanotechnology along with future aspects.
Retarded electric and magnetic fields of a moving charge: Feynman's derivation of Liénard-Wiechert potentials revisited

Retarded electromagnetic potentials are derived from Maxwell's equations and the Lorenz condition. The difference found between these potentials and the conventional Liénard-Wiechert ones is explained by neglect, for the latter, of the motion-dependence of the effective charge density. The corresponding retarded fields of a point-like charge in arbitary motion are compared with those given by the formulae of Heaviside, Feynman, Jefimenko and other authors. The fields of an accelerated charge given by the Feynman are the same as those derived from the Liénard-Wiechert potentials but not those given by the Jefimenko formulae. A mathematical error concerning partial space and time derivatives in the derivation of the Jefimenko equations is pointed out.
The Lateral Trigger Probability function for the Ultra-High Energy Cosmic Ray showers detected by the Pierre Auger Observatory

In this paper we introduce the concept of Lateral Trigger Probability (LTP) function, i.e., the probability for an Extensive Air Shower (EAS) to trigger an individual detector of a ground based array as a function of distance to the shower axis, taking into account energy, mass and direction of the primary cosmic ray. We apply this concept to the surface array of the Pierre Auger Observatory consisting of a 1.5 km spaced grid of about 1600 water Cherenkov stations. Using Monte Carlo simulations of ultra-high energy showers the LTP functions are derived for energies in the range between 10(17) and 10(19) eV and zenith angles up to 65 degrees. A parametrization combining a step function with an exponential is found to reproduce them very well in the considered range of energies and zenith angles. The LTP functions can also be obtained from data using events simultaneously observed by the fluorescence and the surface detector of the Pierre Auger Observatory (hybrid events). We validate the Monte Carlo results showing how LTP functions from data are in good agreement with simulations.
Linking Patient Safety Culture, Job Satisfaction, And Nursing Care Quality: Evidence From Private Hospitals

Patient safety and nursing care quality are two crucial aspects of healthcare delivery. Todays, ensuring a robust patient safety culture is fundamental to achieving high standards in nursing care. This present study aims to investigate the relationship between patient safety culture and nursing care quality. In addition, job satisfaction assessed as mediating variable. Using convenience sampling technique, a total of 236 nurse participated in the survey during February until April 2024. The respondents are nurses who works in private hospital, located in Tangerang. SPSS 26 is employed as a statistical tool to assess path analysis. The result showed that patient safety culture has a positive influence on nursing care quality. Patient safety culture also shares positive effect on nurse’s job satisfaction. In addition, based on the sable test result, job satisfaction significantly mediates the connection between patent safety culture and nursing care quality. These findings suggest that enhancing patient safety culture not only improves the quality of nursing care but also boosts nurses’ job satisfaction. Therefore, interventions aimed at strengthening patient safety culture can provide dual benefits in improving healthcare outcomes.
Understanding and Improving Continuous Experimentation: From A/B Testing to Continuous Software Optimization.

Controlled experiments (i.e. A/B tests) are used by many companies with user-intensive products to improve their software with user data. Some companies adopt an experiment-driven approach to software development with continuous experimentation (CE). With CE, every user-affecting software change is evaluated in an experiment and specialized roles seek out opportunities to experiment with functionality. The goal of the thesis is to describe current practice and support CE in industry. The main contributions are threefold. First, a review of the CE literature on: infrastructure and processes, the problem-solution pairs applied in industry practice, and the benefits and challenges of the practice. Second, a multi-case study with 12 companies to analyze how experimentation is used and why some companies fail to fully realize the benefits of CE. A theory for Factors Affecting Continuous Experimentation (FACE) is constructed to realize this goal. Finally, a toolkit called Constraint Oriented Multi-variate Bandit Optimization (COMBO) is developed for supporting automated experimentation with many variables simultaneously, live in a production environment.The research in the thesis is conducted under the design science paradigm using empirical research methods, with simulation experiments of tool proposals and a multi-case study on company usage of CE. Other research methods include systematic literature review and theory building.From FACE we derive three factors that explain CE utility: (1) investments in data infrastructure, (2) user problem complexity, and (3) incentive structures for experimentation. Guidelines are provided on how to strive towards state-of-the-art CE based on company factors. All three factors are relevant for companies wanting to use CE, in particular, for those companies wanting to apply algorithms such as those in COMBO to support personalization of software to users' context in a process of continuous optimization.
Cosmic microwave background statistics for a direction-dependent primordial power spectrum

Published in Phys. Rev. D; 8 pages; 1 table; Table 1 corrected; references added
Public attitudes towards personal health data sharing in long-term epidemiological research: a Citizen Science approach in the KORA study

Abstract                 Background                 Loss to follow-up in long-term epidemiological studies is well-known and often substantial. Consequently, there is a risk of bias to the results. The motivation to take part in an epidemiological study can change over time, but the ways to minimize loss to follow-up are not well studied. The Citizen Science approach offers researchers to engage in direct discussions with study participants and to integrate their opinions and requirements into cohort management.                                Methods                 Guided group discussions were conducted with study participants from the KORA cohort in the Augsburg Region in Germany, established 40 years ago, as well as a group of independently selected citizens. The aim was to look at the relevant aspects of health studies with a focus on long-term participation. A two-sided questionnaire was developed subsequently in a co-creation process and presented to 500 KORA participants and 2,400 employees of the research facility Helmholtz Munich.                                Results                 The discussions revealed that altruistic motivations, (i.e. supporting research and public health), personal benefits (i.e. a health check-up during a study examination), data protection, and information about research results in layman’s terms were crucial to ensure interest and long-term study participation. The results of the questionnaire confirmed these aspects and showed that exclusively digital information channels may be an obstacle for older and less educated people. Thus, paper-based media such as newsletters are still important.                                Conclusions                 The findings shed light on cohort management and long-term engagement with study participants. A long-term health study needs to benefit public and individual health; the institution needs to be trustworthy; and the results and their impact need to be disseminated in widely understandable terms and by the right means of communication back to the participants.
THE EFFECT OF THE INTERACTION BETWEEN LEARNING STYLE (SURFACE / DEEP) AND CONTENT PRESENTATION MODE (CONDITIONAL / FLEXIBLE) IN AN ADAPTIVE LEARNING ENVIRONMENT ON ACQUIRING COMPUTER MAINTENANCE SKILLS AND DEVELOPING ANALYTICAL THINKING AMONG EDUCATIONAL TECHNOLOGY STUDENTS

The necessary adjustments the objective of this study is to examine how students studying educational technology develop their analytical thinking and computer maintenance skills in an adaptive learning environment by interacting with their learning style (deep/surface) and content presentation mode (conditional/flexible). The necessity to create electronic learning environments that take into account the unique characteristics of each student and modify content in accordance with their cognitive levels and learning styles led to the study. A 2×2 factorial experimental design was used in the study, which involved 56 first-year students from Kafrelsheikh University's Department of Educational Technology, Faculty of Specific Education. When compared to the surface learning style, the results showed statistically significant differences favoring the deep learning style in the development of computer maintenance abilities and analytical thinking. Furthermore, in terms of improving students' overall performance, the flexible material presentation option fared better than the conditional method. Since students with a deep learning style who studied using flexible content attained the highest levels of analytical thinking and practical abilities, the results also showed a strong relationship between learning style and content presentation manner. These findings highlight how crucial it is to use adaptive learning environments in higher education since they offer individualized learning experiences that take into consideration students' learning preferences and foster their independence and drive to study. Additionally, to attain more profound and long-lasting learning outcomes, adaptive and AI-based technologies can be included into contemporary teaching methodologies.
Statistical mechanical theory for nonequilibrium systems. X. Nonequilibrium phase transitions

A general theory for the stability and coexistence of nonequilibrium phases is formulated. An integral formulation of the second entropy is given, the functional maximization of which yields nonlinear hydrodynamics. Rayleigh–Bénard convection is analyzed, and analytic approximations are obtained for the second entropy for conduction and for convection. Despite the simplicity of the model, coexistence is predicted for a Rayleigh number within 5% of the known value.
Methods to improve the translation of evidence-based interventions: A primer on dissemination and implementation science for occupational safety and health researchers and practitioners

A limited focus on dissemination and implementation (D&I) science has hindered the uptake of evidence-based interventions (EBIs) that reduce workplace morbidity and mortality. D&I science methods can be used in the occupational safety and health (OSH) field to advance the adoption, implementation, and sustainment of EBIs for complex workplaces. These approaches should be responsive to contextual factors, including the needs of partners and beneficiaries (such as employers, employees, and intermediaries).By synthesizing seminal literature and texts and leveraging our collective knowledge as D&I science and/or OSH researchers, we developed a D&I science primer for OSH. First, we provide an overview of common D&I terminology and concepts. Second, we describe several key and evolving issues in D&I science: balancing adaptation with intervention fidelity and specifying implementation outcomes and strategies. Next, we review D&I theories, models, and frameworks and offer examples for applying these to OSH research. We also discuss widely used D&I research designs, methods, and measures. Finally, we discuss future directions for D&I science application to OSH and provide resources for further exploration.We compiled a D&I science primer for OSH appropriate for practitioners and evaluators, especially those newer to the field.This article fills a gap in the OSH research by providing an overview of D&I science to enhance understanding of key concepts, issues, models, designs, methods and measures for the translation into practice of effective OSH interventions to advance the safety, health and well-being of workers.
Minority Protection in the European Union: From Economic Rights to the Protection of European Values

Minority protection did not receive attention in the original EC treaty of 1956. The concept of nondiscrimination of laborers and later of EU citizens became the cornerstone for minority protection. Gradually the EU became familiar with the concept of human rights because of judgments of the European Court of Justice (ECJ). This concept has officially been introduced in the 2009 Lisbon Treaty, which includes the rights of persons belonging to minorities and has been elaborated in Article 21 of the EU Charter of Human Rights. If the Charter is not applicable, minorities have to address national legal instances where there is no say for the EU regarding minorities. In this paper, I will demonstrate that the complex European legal system is not easy to understand in terms of protection of minorities. Recently, the ECJ has decided in the CHEZ versus Nikolova case that it can empower lower national courts against measures of systematic discrimination against minorities based on EU equality directives and Article 21 of the Charter. This verdict together with the EU Commission‘s intention to give the Charter a broad practical legal context shown in the working areas of the Fundamental Rights Agency, should ensure national minorities that European institutions pay really attention to their problems.Keywords: minority policy, minority protection, EU Charter of Human Rights, EU equality law, Fundamental Rights Agency
Facilitating efficient knowledge management and discovery in the Agronomic Sciences

The advancement of empirical and information technologies in the recent years have drastically increased the amount of data in fields of Life Sciences, and Agronomic Sciences. To understand the complexity of a given system it is important to link (integrate) diverse datasets. A promising solution towards data integration challenges is offered by the Semantic Web technologies1. The Semantic Web was proposed, to remedy the fragmentation of all potentially useful information on the web. Currently, the bio-medical domain has accepted the Semantic Web technologies as a means to manage (integrate) knowledge. Although we are witnessing an increased usage of ontologies within the Agronomic Sciences, the data in this domain is highly distributed in nature. Utilizing these data resources more effectively and taking advantage of associated cross-disciplinary research opportunities poses a major challenge to both domain experts and information technologists.
STEP CHANGE - Science Transformation in EuroPe through Citizens involvement in HeAlth, coNservation and enerGy rEsearch

Opis vira z dne 15. 12. 2025.
Neural Collision Detection for Multi-arm Laparoscopy Surgical Robots through Learning-from-Simulation

In this thesis, a novel neural collision detection for a multi-arm laparoscopy robotic system was developed, trained, and validated. To this end, first an analytical method was developed to model robotic arms and calculate the minimum distance between them using multi-linear shape representation of the robots. Afterwards, the analytical model was validated with ten experimental configurations. Given acceptable method accuracy, it was implemented in a 3D real-time simulation environment which was used to simulate various configurations of a dual-arm laparoscopic systems. The results of minimum distance between the two robotic arms were validated by direction comparison with the experimental configurations. In the end, the validated simulation was used to generate a substantially scaled dataset, i.e., &gt;100,000 sample dual configurations to be used for neural regression to find the minimum distance between the two arms. The outcomes demonstrated a fair agreement between our analytical, simulation, and learning methods with experimental results, indicating the accuracy of the approach
Comparative analysis of deep learning approaches for forest stand type classification: insights from the new VHRTreeSpecies benchmark dataset

Remotely sensed data are widely used for forest stand type classification; however, traditional classification methods are time consuming and mostly implemented for spatially limited areas and species, restricting the transferability of these models to other locations and diverse species. With the increasing availability of high-resolution satellite imagery, deep learning techniques have become widely used in forest management and species classification research. In this study, we generated a new benchmark dataset, VHRTreeSpecies, designed for forest stand type classification. We used Very High Resolution satellite images and corresponding labels derived from forest stand maps provided by the General Directorate of Forestry, covering 15 dominant tree species from Türkiye’s diverse forest ecosystems. Various deep learning models including CNN-based architectures (ResNet50, ResNet101, VGG16, ResNeXt, InceptionV3, ConvNeXt, EfficientNet), transformer models (DeepViT, Swin Transformer, CaiT, SegFormer-b0-b1-b2), and YOLO models (YOLOv8-l and YOLOv11-l) were utilized to assess the performance of these models for the forest tree species classification. Our results demonstrate the effectiveness of SegFormer, which achieved the highest classification accuracy of 96.25%, outperforming traditional CNN models. Additionally, YOLOv8-l proved to be a highly efficient model for both precision and real-time classification tasks, demonstrating its robustness across different tree species.
Knowledge Discovery in Climate Science using Jess rule Engine

Climate change is a difficult research problem, requiring insight from vast fields such as chemistry, biology, climatology and oceanography. As the ever increasing publishing of information in these fields continue, text mining and knowledge discovery systems are being developed to alleviate the workload of extracting relevant information from literature. This master thesis focuses on knowledge discovery from extracted information, and ranking of proposed hypotheses. A rule-based inference system was implemented i Java using the Jess rule engine. The application consist of a set of general and domain specific logic rules, which operate on domain knowledge inputted to the system, and proposes hypotheses based on this input. The system can also abductively attempt to explain contradictions between expected and inputted knowledge. The system was tested on the domain hypotheses known as the iron hypothesis and the DOC hypothesis. The system was able to infer these hypotheses.