ethicalabs/Research-Intent-Collab huggingface dataset:
Transforming evidence-based clinical guidelines into implementable clinical decision support services: the CAREPATH study for multimorbidity management

IntroductionThe CAREPATH Project aims to develop a patient-centered integrated care platform tailored to older adults with multimorbidity, including mild cognitive impairment (MCI) or mild dementia. Our goal is to empower multidisciplinary care teams to craft personalized holistic care plans while adhering to evidence-based guidelines. This necessitates the creation of clear specifications for clinical decision support (CDS) services, consolidating guidance from multiple evidence-based clinical guidelines. Thus, a co-creation approach involving both clinical and technical experts is essential.MethodsThis paper outlines a robust methodology for generating implementable specifications for CDS services to automate clinical guidelines. We have established a co-creation framework to facilitate collaborative exploration of clinical guidelines between clinical experts and software engineers. We have proposed an open, repeatable, and traceable method for translating evidence-based guideline narratives into implementable specifications of CDS services. Our approach, based on international standards such as CDS-Hooks and HL7 FHIR, enhances interoperability and potential adoption of CDS services across diverse healthcare systems.ResultsThis methodology has been followed to create implementable specifications for 65 CDS services, automating CAREPATH consensus guideline consolidating guidance from 25 selected evidence-based guidelines. A total of 296 CDS rules have been formally defined, with input parameters defined as clinical concepts bound to FHIR resources and international code systems. Outputs include 346 well-defined CDS Cards, offering clear guidance for care plan activities and goal suggestions. These specifications have led to the implementation of 65 CDS services integrated into the CAREPATH Adaptive Integrated Care Platform.DiscussionOur methodology offers a systematic, replicable process for generating CDS specifications, ensuring consistency and reliability across implementation. By fostering collaboration between clinical expertise and technical proficiency, we enhance the quality and relevance of generated specifications. Clear traceability enables stakeholders to track the development process and ensure adherence to guideline recommendations.
Millions of Mothers Lack Health Insurance Coverage in the United States

Some 5.9 million American mothers caring for young or school-aged children lack health insurance. Although nearly nine in ten uninsured mothers are members of working families, most lack access to affordable coverage through their job or a spouse's job. Most are ineligible for publicly subsidized coverage unless their incomes are far below the poverty line. The millions of uninsured mothers are at high risk of going without needed preventive and primary care. If they become seriously ill, their families can face the prospect of a financial crisis. The nation has made significant progress in extending health care coverage to children in low-income families through Medicaid and the State Children's Health Insurance Program (SCHIP), but no comparable effort has been made to insure the mothers of these children. A few states have started to address the problem by transforming their SCHIPs into family-based programs that also cover low-income parents. Bipartisan legislation under consideration, known as FamilyCare, would encourage this trend by providing more federal funding to states that could be used to extend health insurance to the parents of children already covered by publicly funded programs.
‚Rechtsstaatlichkeit in Europa: Unabhängigkeit der Justiz und wirksame Rechtsmittel / Rule of Law in Europe: Judicial independence and effective remedies / État de droit en Europe: indépendance de la justice et recours effectifs

editorial reviewed
Phonetics and Phonology as the Two Aspects of one Science of Human Speech Sounding

ABSTRACT The article enunciates the theory of the unity of phonetics and phonology as the two equally functional aspects of one science, i.e. the science of human speech sounding. The author gives the reasons in favor of such unity and offers a discussion on the problem of the traditional division of this science into two levels – phonetics and phonology, with the apportionment inside phonetics of two sublevels – the proper phonetic sublevel and autonomously phonological (pre-phonological) sublevel. The article also discusses a thesis, belonging to traditional phonology, that phonetics ends and phonology begins where the substance starts to be considered from the functional point of view. The author thinks that the study of the material side of sounds is always conducted by a linguist with some functional, linguistic (psycholinguistic) purpose. The equal rights of the discussed aspects and their equal importance for the language functioning mechanism depend on two-in-one phonological action of a sound unit – constitutional (forming) one and distinctive one. In the spirit of classical (traditional) phonology only that sound quality was always considered to be functional (phonological), which could differentiate the morphemes of a language. Nevertheless, in the author’s opinion, in order such a differentiation could exist, there should be not less but even more fundamental condition fulfilled: there should be such sound quality created, which is acceptable for a certain linguistic system (a language, a dialect, a subdialect). Phonology can’t deal with incorrect speech. Just as following grammar rules leads to a grammatically correct utterance, so phonology being functional phonetics should be oriented to speech, which is correct in a certain language system. Constitution of a phonological unit in the structure of a morph (or word) as a certain language system unit with all its qualitative and quantitative features means nothing else but distinction.
Stacked Deep Learning Ensembles for Binary Image Classification: Benchmarking with the ASSIRA Dataset

This study introduces a rigorous stacked ensemble framework for binary image classification, specifically targeting the Cats vs. Dogs dataset. The proposed method integrates eight transfer-learning convolutional neural networks (CNNs): Xception, VGG16, VGG19, InceptionV3, InceptionResNetV2, MobileNet, MobileNetV2, and DenseNet121 as base learners, combined via eight different meta-learners. Through extensive experimentation that included hyperparameter optimization, ablation studies, and robustness assessments (under Gaussian noise and image blur), the ensemble demonstrated superior accuracy and reliability. The optimal configuration, which utilized a Support Vector Machine (SVM) meta-learner, achieved an accuracy of 99.28%, surpassing prior benchmarks. Statistical analysis confirmed the significance of this improvement (paired t-test: t = 29.94, p &lt; 0.0001). InceptionResNetV2 and Xception made the largest contributions to ensemble performance, as revealed by ablation studies. The model also maintained robust performance under noise (97.91% accuracy) and blur (98.43% accuracy), attesting to its suitability for practical applications such as veterinary diagnostics and pet identification. This work highlights the effectiveness of deep learning ensembles in binary image classification tasks and suggests future directions in adaptive ensemble weighting and cross-dataset validation.
Negative and Positive Effects of Social Media on Adolescent Mental Health

Social media is a widely utilized and essential aspect of daily life, particularly for teens. Adolescents in today's digital age are growing up in a culture where social media platforms are prevalent and substantially influence various parts of their lives. The amount of social media usage in Indonesia is steadily growing annually. This article examines the negative and positive effects of social media on the mental wellbeing of young people. Several articles have suggested that excessive usage of social media among adolescents can result in sleeplessness, anxiety, and depression. On the other hand, social media can positively affect health and improve relationships. This article highlights the significance of using social media prudently and ethically while effectively managing one's emotions, which can increase benefits. Therefore, minimizing might lead to negative consequences, particularly about adolescents' psychological wellbeing and societal support.
Blue Economy Integration as a Management Strategy for Small-Scale Fisheries: Economic Valuation of Sustainable Coastal Aquaculture through Digital Market Platforms

This study investigates the strategic integration of blue economy principles within Indonesian small-scale fisheries (SSF) through the deployment of digital market platforms. This study examines the integration of blue economy principles. Utilizing a quantitative secondary data analysis combined with the Total Economic Value (TEV) framework and comparative statistical testing from the Ministry of Marine Affairs and Fisheries (KKP) and the Central Bureau of Statistics (BPS) for the 2020-2025 period, the study evaluates production metrics across three primary geographic clusters. The findings demonstrate that IoT-integrated management significantly optimizes operational efficiency, evidenced by a 22.09% reduction in the Feed Conversion Ratio (FCR) and a substantial elevation of the Fishers' Exchange Rate (NTPi) via direct market linkages. Furthermore, digital traceability was found to command a 15-20% price premium in international markets, effectively internalizing sustainability into financial assets. The research concludes that while digitalization serves as a powerful catalyst for coastal prosperity and resource resilience, its success remains contingent upon closing the digital infrastructure gap. These results offer a data-driven framework for policymakers to harmonize technological acceleration with environmental stewardship in the maritime sector.
Additional file 1 of The development and implementation of a guideline-based clinical decision support system to improve empirical antibiotic prescribing

Additional file 1. The resistance viewer in the CDSS for empirical antibiotic therapy.
Setting up research infrastructure for secondary use of routinely collected health care data in Croatia

U radu se raspravlja infrastruktura za sekundarnu uporabu podataka u zdravstvenom sustavu Republike Hrvatske - kakvo je sadašnje stanje i što bi trebalo razviti kako bi bolje bio ispunjen potencijal sekundarne uporabe za razvoj znanstvenoga rada, kliničke medicine i bolje upravljanje sustavom.
Negotiating the new deal of consent for citizen science in health data research

Panel 3 Informed Consent? The Blurred Boundary of Research Subjects
AI-Driven Cybersecurity and Big Data-Enabled MIS Frameworks: Strengthening Supply Chain Integrity, Energy Resilience, and Critical Infrastructure Protection

This study adopts a conceptual and analytical approach to examine the intersection of AI-driven cybersecurity, big data analytics, and MIS frameworks. The rapid evolution of cyber threats, combined with the exponential growth of big data. It puts a great challenge on the security, resilience, and efficiency of current supply chains and critical infrastructure systems. Cybersecurity AI-based and integrated with Management Information Systems. It offers a transformative approach to enhancing organizational defense mechanisms. AI enables enterprises to strengthen supply chain integrality, improve energy resilience, and ensure robust protection of critical infrastructures. It explores how AI algorithms, machine learning models, and anomaly detection systems work. It can process large volumes of supply chain and energy sector data to predict vulnerabilities and mitigate cyber risks. The proposed MIS framework integrates cybersecurity protocols, data-driven insights, and adaptive decision-making strategies to create a holistic model that addresses both operational efficiency and resilience against cyberattacks. The findings suggest that AI-enabled MIS frameworks significantly improve organizational preparedness, resilience, and adaptability in the face of evolving cyber threats. Enhanced supply chain integrality reduces disruptions, while energy resilience ensures sustainable operations under stress conditions. This framework provides a strategic blueprint for governments, businesses, and energy stakeholders aiming to align cybersecurity initiatives with sustainable development and digital transformation goals.
Dispute resolution with the participation of a judge, mediation, amicable agreement, dispute resolution in an arbitration court as measures of alternative dispute resolution

The article notes that in accordance with the pilot decisions of the European Court of Human Rights, Ukraine should develop the institution of reconciliation of the parties. An innovative legal institution that was introduced into the system of civil procedural law of Ukraine was the institution of dispute resolution with the participation of a judge. In 2021, the Law of Ukraine “On Mediation” was adopted, which defined the legal principles and procedure for conducting mediation as an out-of-court procedure for resolving a conflict (dispute), the principles of mediation, the status of a mediator, requirements for his training and other issues related to this procedure. This Law was influenced by the Mediation Directive of May 21, 2008.  Common and distinctive features of such alternative dispute resolution measures as dispute resolution with the participation of a judge, mediation, amicable agreement, dispute resolution in an arbitration court are highlighted.  The common features are as follows: the goal is amicable settlement of the dispute; the basis is the consent of the parties; a person’s appeal to the court is not an obstacle to the application of any of the means of amicable settlement of the dispute; at the beginning of the application of the procedure for amicable settlement of the dispute, the person conducting it is obliged to explain to the parties the goal, the procedure for conducting the dispute resolution, the rights and obligations of the parties; participants, as well as entities ensuring the conduct of the dispute resolution with the participation of a judge, mediation, amicable agreement, arbitration court, do not have the right to disclose confidential information; means of amicable settlement of disputes are not applied if they may affect the rights and legitimate interests of third parties who are not participants in the dispute resolution; if a third party “joins the case” and makes independent claims regarding the subject of the dispute – it is prohibited to settle the dispute using these means; the party to the dispute has the right to terminate the application of a peaceful means of dispute settlement; the application of means of peaceful dispute settlement cannot violate the rights and interests of other persons, the interests of the state or public interests.  The distinctive features of dispute settlement with the participation of a judge, mediation, amicable agreement, and dispute settlement in an arbitration court are highlighted.  It is emphasized that, unlike dispute settlement with the participation of a judge, the legislation of Ukraine establishes restrictions on the jurisdiction of cases to arbitration courts (Article 6 of the Law of Ukraine “On Arbitration Courts”), and may also provide for the peculiarities of mediation in certain categories of conflicts (disputes) (Part 2, Clause 1, Article 3 of the Law of Ukraine “On Mediation”).
Failure analysis and reliability improvement of small turbine engine blades

Abstract   A series of analysis and testing procedures for small turbine engines blades are presented. These procedures can be used to (i) verify the critical failure modes, and (ii) propose approaches for improving the performance, reliability and safety of engine blades in service. The procedures include metallographic examination, chemical ingredient analysis, vibration and modal analysis, stress analysis, fatigue life prediction and modal testings with and without coating. For the particular small turbine engine considered, it is demonstrated that the most probable failure mode for blades is fatigue fracture rather than creep fracture.  Possible approaches to enhance the fatigue performance and reliability of engine blades have been proposed and investigated. These include the surface coating, the use of high strength material, and a reduction of the number of stators. Among these approaches, a reduction of the number of stators from 17 to 14 is most beneficial, because it removes vibrational frequencies of the turbine engine from the operating frequencies, thus significantly reducing the vibrational level of engine blades. The vibrational stress in blades can thus be reduced to a level below the fatigue limit.
Large Language Models for Wearable Sensor-Based Human Activity Recognition, Health Monitoring, and Behavioral Modeling: A Survey of Early Trends, Datasets, and Challenges

The proliferation of wearable technology enables the generation of vast amounts of sensor data, offering significant opportunities for advancements in health monitoring, activity recognition, and personalized medicine. However, the complexity and volume of these data present substantial challenges in data modeling and analysis, which have been addressed with approaches spanning time series modeling to deep learning techniques. The latest frontier in this domain is the adoption of large language models (LLMs), such as GPT-4 and Llama, for data analysis, modeling, understanding, and human behavior monitoring through the lens of wearable sensor data. This survey explores the current trends and challenges in applying LLMs for sensor-based human activity recognition and behavior modeling. We discuss the nature of wearable sensor data, the capabilities and limitations of LLMs in modeling them, and their integration with traditional machine learning techniques. We also identify key challenges, including data quality, computational requirements, interpretability, and privacy concerns. By examining case studies and successful applications, we highlight the potential of LLMs in enhancing the analysis and interpretation of wearable sensor data. Finally, we propose future directions for research, emphasizing the need for improved preprocessing techniques, more efficient and scalable models, and interdisciplinary collaboration. This survey aims to provide a comprehensive overview of the intersection between wearable sensor data and LLMs, offering insights into the current state and future prospects of this emerging field.
Destroying a Way of Life: the Forest Rights Act of India and Land Dispossession of Indigenous Peoples\ud

Control of land is a source of contention among indigenous peoples, governments, conservationists and extractive industries.  Forests are crucial to the existence and survival of tribal and pastoralist populations in India. The competition for control of land, coupled with a historical lack of rights of indigenous peoples, has resulted in land dispossession and impoverishment. To address this, the Indian Government passed the Forest Rights Act in 2006. \ud Using empirical evidence from India, I critically examine, from a socio-legal perspective, the challenges of implementing the Forest Rights Act. The Act itself, while pushing the new ‘inclusion’ paradigm within conservation thinking, has been a shock to bureaucratic structures in India such as the Forest Department, which sees its role as fighting the ‘encroachment’ of tribal communities, and whose attitudes towards the community ranges from apathetic to openly hostile. In violation of human rights, state and central governments are ignoring participatory and democratic requirements of the Act, such as informed consent and the right to self-determination, while handing over huge tracts of forest lands to private entities. \ud I also examine the failure of governance, reflected in the implementation of the Act, ranging from infringing on usufruct rights to illegal evictions. This begs the question of whether the new Act increases the vulnerability of mobile indigenous peoples rather than serving as a powerful tool for them to protect and manage their own forests and advocate for their rights.\ud
Enhancing Fundamental Robot-Assisted Surgical Proficiency by Using a Portable Virtual Simulator

Background. The development of a virtual reality (VR) training platform provides an affordable interface. The learning effect of VR and the capability of skill transfer from the VR environment to clinical tasks require more investigation. Methods. Here, 14 medical students performed 2 fundamental surgical tasks—bimanual carrying (BC) and peg transfer (PT)—in actual and virtual environments. Participants in the VR group received VR training, whereas participants in the control group played a 3D game. The learning effect was examined by comparing kinematics between pretraining and posttraining in the da Vinci Surgical System. Differences between VR and playing the 3D game were also examined. Results. Those who were trained with the VR simulator had significantly better performance in both actual PT ( P = .002) and BC ( P &lt; .001) tasks. The time to task completion and the total distance traveled were significantly decreased in both surgical tasks in the VR group compared with the 3D game group. However, playing the 3D game showed no significant enhancement of fundamental surgical skills in the actual PT task. The difference between pretraining and posttraining was significantly larger in the VR group than in the 3D game group in both the time to task completion ( P = .002) and the total distance traveled ( P = .027) for the actual PT task. Participants who played the 3D game seemed to perform even worse in posttraining. Conclusions. Training with the portable VR simulator improved robot-assisted surgical skill proficiency in comparison to playing a 3D game.
Cosmological simulations of mixed ultralight dark matter

Added references
Additional file 2 of Mixotrophic chemosynthesis in a deep-sea anemone from hydrothermal vents in the Pescadero Basin, Gulf of California

Additional file 2: Table S2. 16S rRNA amplicon results. Number of total 16S rRNA amplicon reads, the Shannon Diversity index (H′) and the relative abundance (%) of the SUP05 group (based on 16S rRNA barcode amplification) associated with Ostiactis pearseae, water samples, and other Anthozoa from the Pescadero Basin vents.
Intelligent human resource information system (i-HRIS): a holistic decision support framework for HR excellence.

Nowadays, Human Resource Information System (HRIS) plays a strategic role in the decision making process for effective and efficient Human Resource Management (HRM). For Human Resource (HR) decision making, most of the researchers propose expert systems or knowledge-based systems. Unfortunately, there are some limitations in both of expert system and knowledge-based system. In this paper, we have proposed a framework of Intelligent Human Resource Information System (i-HRIS) applying Intelligent Decision Support System (IDSS) along with Knowledge Discovery in Database (KDD) to improve structured, especially semistructured and unstructured HR decision making process. Moreover, the proposed HR IDSS stores and processes information with a set of Artificial Intelligent (AI) tools such as knowledge-based reasoning, machine learning and others. These AI tools are used to discover useful information or knowledge from past data and experience to support decision making process. We have likewise attempted to investigate IDSS applications for HR problems applying hybrid intelligent techniques such as machine learning and knowledge-based approach for new knowledge extraction and prediction. In summation, the proposed framework consists of input subsystems, decision making subsystems and output subsystems with ten HR application modules.