ethicalabs/Research-Intent-Collab huggingface dataset:
Effect of Land Use Change on Wood Species Diversity, Carbon Stock and Implication for Sustainable Conservation of Biodiversity in Ethiopia

Land use-cover change (LULCC) is one of responsible with the anthropogenic activities that contribute the threat of biodiversity and the largest source of greenhouse gas emissions 12-20% in tropics and world that influence the human wellbeing and disturb functionality of ecosystem. The purpose of this review paper is to assess the LULCC causes, trends on forest land, consequences on plant species diversity and carbon stock and implications for sustainable landscape management in Ethiopia .Population growth, agricultural expansion, settlement, institutional factors, and weak policy enforcement and under value ecosystem were the main derivers of LULCC. At the regional, national and regional scales, these changes have profound influence plant species diversity and carbon stock potential for alterations of normal ecosystem function, particularly loss of plant biodiversity at genetic and species levels and rise of CO2 in atmosphere .the result of all these have direct impacts on livelihoods of local communities and sustainable development. Plant species diversity is reduced when land changed from a relatively undisturbed state to more intensive use. like farming, livestock grazing, selective tree harvesting, etc. while carbon stock also loss due to unsustainable agricultural land practice, conversion of native forest to agricultural land and less managed of degraded land.to revere this synergic conservation strategy is suggested like implement sustainable forest management and agricultural practice like agro-forestry.
Mind the Gap: The Ethics Void Created by the Rise of Citizen Science in Health and Biomedical Research

The target article by Wiggins and Wilbanks (2019) reports on the history and typology of the models of citizen science emerging in health and biomedical research with the rapid dispersion and repur...
Beyond Screen Time: Stress, Loneliness, Emotional Competence and Problematic Internet Use in Adolescence

Background: Problematic Internet use (PIU) among adolescents has emerged as a significant public health concern, associated with the types of online activities and underlying psychological processes rather than screen time alone. Methods: This cross-sectional study included 750 adolescents (46.4% female) with a mean age of 15.39 years (SD = 1.76; range = 13–19) recruited from 7th and 8th grade primary school students and secondary school students in Split-Dalmatia County (Croatia). The study investigated the associations between specific online activities, psychological variables, and PIU using hierarchical regression and multiple mediation analyses. Results: Results revealed that time spent online remains the most strongly associated with PIU. While streaming and online shopping showed significant associations with problematic use, reading and browsing for information did not. Stress and loneliness were identified as variables associated with higher that significantly statistically mediate the relationships between online engagement and PIU, whereas emotional competence functioned as a protective factor, particularly in relation to social media use. These findings support theoretical models that conceptualize PIU as a maladaptive coping strategy for psychological distress. Conclusions: Based on these findings, prevention efforts should move beyond simple screen-time reduction and focus on strengthening adolescents’ emotional competence, stress management, and self-regulatory skills to promote healthier and more adaptive patterns of digital engagement.
Directional Differentiability of the Metric Projection in Bochner Spaces

In this paper, we consider the directional differentiability of metric projection and its properties in uniformly convex and uniformly smooth Bochner space Lp(S; X), in which (S, A, mu) is a positive measure space and X is a uniformly convex and uniformly smooth Banach space. Let (arbitrary) A in A with measure of A greater than 0 and define a subspace Lp(A; X) of Lp(S; X), which is considered as a closed and convex subset of Lp(S; X). We first study the properties of the normalized duality mapping in Lp(S; X) and in Lp(A; X). For any c in Lp(A; X) and r > 0, we define a closed ball BA(c; r) in Lp(A; X) and a cylinder CA(c; r) in Lp(S; X) with base BA(c; r). Then, we investigate some optimal properties of the corresponding metric projections P(Lp(A;X)), P(BA(c;r)) and P(CA(c;r)) that include the inverse images, the directional differentiability and the precise solutions of their directional derivatives.
The Ladder of Gaps Physics A: Stability Ladder and Statistical Mechanics Phase Transitions– An Axiomatic Derivation from Information Conservation to Energy Barriers

This paper constructs a four-tier axiomatic system of the stability ladder toreveal the underlying logic of phase transitions in statistical mechanics. Startingfrom information conservation (L0) and short-range interactions (L1), we rigorously prove, using DLR measure theory and large deviation principles in the thermodynamic limit, that the coexistence of macroscopically distinguishable states insystems of spatial dimension not less than two necessarily requires a strictly positiveenergy barrier in the free energy landscape (L2). In one-dimensional systems, information protection is instead realized via topological boundary conditions. Takingthe two-dimensional Ising model as a paradigm, we reconstruct the ontological basisof the existence of a phase transition in Onsager’s exact solution from the axioms,and extend the framework to general lattice systems and quantum many-bodymodels. The derivation does not rely on mean-field approximations or specific calculations of the renormalization group, thereby providing a necessary explanationfor the universality of phase transitions based on the logic of information conservation. In critical dynamics and quantum phase transitions, the barrier conceptis generalized to a dynamical barrier or quantum gap, demonstrating the broaderapplicability of the stability ladder.
Using Artificial Intelligence in Philosophy Teaching and Ethical Problems

This chapter discusses how to effectively integrate the use of artificial intelligence into the critical, questioning and reflective thinking skills that are inherent in philosophy. In this context, the role of the teacher in the process of teaching philosophy is discussed. The role of the philosophy teacher in teaching with artificial intelligence is discussed under the headings of being a philosophical guide, a developer of wisdom and morality, a facilitator in discussions, and a developer of critical thinking. In the section of AI-supported philosophy course activities, simulations dealing with philosophical problems, conversation with philosophers in the history of philosophy, ethical dilemma and decision-making game, creative argument development and analysis, philosophical perspective mapping, and current argument conflict are exemplified, and it is evaluated how these activities can be put into practice by supporting students' in-depth, critical and questioning thinking skills.
OPERATIONAL REMOTE SENSING SERVICES IN NORTH EASTERN REGION OF INDIA FOR NATURAL RESOURCES MANAGEMENT, EARLY WARNING FOR DISASTER RISK REDUCTION AND DISSEMINATION OF INFORMATION AND SERVICES

Abstract. North Eastern Region (NER) of India comprising of eight states considered to be most unique and one of the most challenging regions to govern due to its unique physiographic condition, rich biodiversity, disaster prone and diverse socio-economic characteristics. Operational Remote Sensing services increased manifolds in the region with the establishment of North Eastern Space Applications Centre (NESAC) in the year 2000. Since inception, NESAC has been providing remote sensing services in generating inventory, planning and developmental activities, and management of natural resources, disasters and dissemination of information and services through geo-web services for NER. The operational remote sensing services provided by NESAC can be broadly divided into three categories viz. natural resource planning and developmental services, disaster risk reduction and early warning services and information dissemination through geo-portal services.  As a apart of natural resources planning and developmental services NESAC supports the state forest departments in preparing the forest working plans by providing geospatial inputs covering entire NER, identifying the suitable culturable wastelands for cultivation of silkworm food plants, mapping of natural resources such as land use/land cover, wastelands, land degradation etc. on temporal basis.  In the area of disaster risk reduction, NESAC has initiated operational services for early warning and post disaster assessment inputs for flood early warning system (FLEWS) using satellite remote sensing, numerical weather prediction, hydrological modeling etc.; forest fire alert system with actionable attribute information; Japanese Encephalitis Early Warning System (JEWS) based on mosquito vector abundance, pig population and historical disease intensity and agriculture drought monitoring for the region.  The large volumes of geo-spatial databases generated as part of operational services are made available to the administrators and local government bodies for better management, preparing prospective planning, and sustainable use of available resources. The knowledge dissemination is being done through online web portals wherever the internet access is available and as well as offline space based information kiosks, where the internet access is not available or having limited bandwidth availability.  This paper presents a systematic and comprehensive study on the remote sensing services operational in NER of India for natural resources management, disaster risk reduction and dissemination of information and services, in addition to outlining future areas and direction of space applications for the region.
Thermal modeling of buried structures in permafrost regions: Assessing climate change impacts through future projections

La dégradation du pergélisol représente un défi majeur pour la stabilité des infrastructures dans les régions froides, particulièrement dans le contexte des changements climatiques. Cette étude examine l’évolution thermique des températures souterraines sous des remblais situés en zone de pergélisol, en mettant l’accent sur les structures enfouies soumises à un réchauffement futur. Des projections climatiques issues du modèle CanESM5 selon le scénario SSP5-8.5 sont intégrées aux simulations réalisées avec COMSOL Multiphysics. Ces simulations évaluent les effets des cycles de gel-dégel et des variations de température à long terme entre 1980 et 2060. La zone d’étude, High Level en Alberta, située sur le territoire du Traité 8, constitue un site représentatif des conditions de pergélisol. Trois configurations géométriques de remblais ont été évaluées, avec et sans ponceaux. Le modèle a permis d’analyser l’influence de la géométrie du remblai, du matériau de remplissage (remblai sableux typique, basalte concassé et calcaire concassé), de l’emplacement de l’isolation et de la présence de ponceaux sur les températures souterraines et la profondeur de dégel du pergélisol.L’épaississement de la couche active est particulièrement marqué dans les remblais contenant des ponceaux et des matériaux à forte conductivité thermique, bien que l’ajout d’isolant ait permis d’atténuer ces effets dans certaines configurations. Les scénarios avec isolation montrent une efficacité variable pour ralentir la dégradation du pergélisol, influencée par le type de matériau et la géométrie d’installation. Les comparaisons saisonnières et décennales révèlent une augmentation progressive de l’épaisseur de la couche active dans toutes les configurations. Cette étude met en évidence l’influence combinée de la géométrie, des matériaux, de la présence de ponceaux et de l’isolation sur la stabilité thermique à long terme. Les résultats offrent des pistes pour la conception et l’adaptation future des infrastructures dans les régions nordiques touchées par les changements climatiques
Identifying Information Gaps in Electronic Health Records by Using Natural Language Processing: Gynecologic Surgery History Identification (Preprint)

BACKGROUND                         Electronic health records (EHRs) are a rich source of longitudinal patient data. However, missing information due to clinical care that predated the implementation of EHR system(s) or care that occurred at different medical institutions impedes complete ascertainment of a patient’s medical history.                                                                       OBJECTIVE                         This study aimed to investigate information discrepancies and to quantify information gaps by comparing the gynecological surgical history extracted from an EHR of a single institution by using natural language processing (NLP) techniques with the manually curated surgical history information through chart review of records from multiple independent regional health care institutions.                                                                       METHODS                         To facilitate high-throughput evaluation, we developed a rule-based NLP algorithm to detect gynecological surgery history from the unstructured narrative of the Mayo Clinic EHR. These results were compared to a gold standard cohort of 3870 women with gynecological surgery status adjudicated using the Rochester Epidemiology Project medical records–linkage system. We quantified and characterized the information gaps observed that led to misclassification of the surgical status.                                                                       RESULTS                         The NLP algorithm achieved precision of 0.85, recall of 0.82, and F1-score of 0.83 in the test set (n=265) relative to outcomes abstracted from the Mayo EHR. This performance attenuated when directly compared to the gold standard (precision 0.79, recall 0.76, and F1-score 0.76), with the majority of misclassifications being false negatives in nature. We then applied the algorithm to the remaining patients (n=3340) and identified 2 types of information gaps through error analysis. First, 6% (199/3340) of women in this study had no recorded surgery information or partial information in the EHR. Second, 4.3% (144/3340) of women had inconsistent or inaccurate information within the clinical narrative owing to misinterpreted information, erroneous “copy and paste,” or incorrect information provided by patients. Additionally, the NLP algorithm misclassified the surgery status of 3.6% (121/3340) of women.                                                                       CONCLUSIONS                         Although NLP techniques were able to adequately recreate the gynecologic surgical status from the clinical narrative, missing or inaccurately reported and recorded information resulted in much of the misclassification observed. Therefore, alternative approaches to collect or curate surgical history are needed.
Energy Efficient Data Transfer in Internet of Things Sensor Network using Message Queuing Telemetry Transport

Abstract                Sensors and actuators play a vital role in realizing the Internet of Things (IoT) applications. The data transmission capability from edge devices to servers and vice-versa enables IoT applications to be more realistic and effective. However, one of the major challenges to IoT applications is power management at the edge devices. The power consumption of the edge devices depends on the amount of data transferred through the network in a defined interval. Data size and its applicability to the receiving device are controllable parameters, and consequently, energy efficiency can be improved. This work aims to establish an IoT architecture using a message queuing telemetry transport (MQTT) protocol that effectively handles the data transmission in the network. It also provides a solution for the elimination of data redundancy; hence, the network was effectively utilized, and the energy consumption was reduced in the edge devices. The proposed architecture was demonstrated with a potentiometer as a data generator and MQTT as a message broker. Further, the transmitted data was received at the edge devices and its performance was analyzed. Based on the results, it was verified that the client was able to receive the data without any loss in terms of precision. Data redundancy was avoided by disabling the data transmission whenever the nodeMCU identifies the constant sensor value. This retains the power at the edge devices and data transmission devices.
Dynamical evolution of massive black holes in galactic-scale<i>N</i>-body simulations – introducing the regularized tree code ‘rvine’

We present a hybrid code combining the OpenMP-parallel tree code VINE with an algorithmic chain regularization scheme. The new code, called rVINE, aims to significantly improve the accuracy of close encounters of massive bodies with supermassive black holes in galaxy-scale numerical simulations. We demonstrate the capabilities of the code by studying two test problems, the sinking of a single massive black hole to the centre of a gas-free galaxy due to dynamical friction and the hardening of a supermassive black hole binary due to close stellar encounters. We show that results obtained with rVINE compare well with NBODY7 for problems with particle numbers that can be simulated with NBODY7. In particular, in both NBODY7 and rVINE we find a clear N-dependence of the binary hardening rate, a low binary eccentricity and moderate eccentricity evolution, as well as the conversion of the galaxy's inner density profile from a cusp to a a core via the ejection of stars at high velocity. The much larger number of particles that can be handled by rVINE will open up exciting opportunities to model stellar dynamics close to SMBHs much more accurately in a realistic galactic context. This will help to remedy the inherent limitations of commonly used tree solvers to follow the correct dynamical evolution of black holes in galaxy scale simulations.
Reinforced statistical learning of auditory categories

A critical step in language acquisition is learning phoneme categories. While L1 learning has been thought to use unsupervised learning (using the distributional statistics of cues), recent research raises the possibility of supervised learning (using teaching signals). Similarly, L2 learning is studied with supervised learning, but unsupervised may also contribute. We developed the reinforced statistical learning paradigm to examine their interaction. Participants first underwent unsupervised learning, hearing a series of non-linguistic sounds whose statistical structure reflected two categories. In subsequent supervised learning, categories either matched or mismatched. Supervised learning was faster when phases matched, though benefits were limited to specific category configurations. Unsupervised learning did not affect the steepness of categorization along the relevant dimension, but it helped subjects learn to ignore irrelevant dimensions. Unsupervised learning may set the stage for supervised learning, but its role may be to determine which dimensions are important, and not to directly acquire categories.
The African Court on Human and Peoples' Rights: Safeguarding the Interests of African States

AbstractThis article examines the extent to which the decision to establish the African Court on Human and Peoples' Rights was motivated by the desire of African states to safeguard their own interests at the expense of effectively protecting human rights in Africa. Using an examination of the drafting history of the Banjul Charter and the establishment of the African Commission as a background, this article explores the potential implications for the future of human rights protection on the continent as a result of the creation of the African Court on Human and Peoples' Rights and its proposed merger with the African Court of Justice.
Keyword-based AI assistance in the generation of radiology reports: A pilot study

Radiology reporting is a time-intensive process, and artificial intelligence (AI) shows potential for textual processing in radiology reporting. In this study, we proposed a keyword-based AI-assisted radiology reporting paradigm and evaluated its potential for clinical implementation. Using MRI data from 100 patients with intracranial tumors, two radiology residents independently wrote both a routine complete report (routine report) and a keyword report for each patient. Based on the keyword reports and a designed prompt, AI-assisted reports were generated (AI-generated reports). The results demonstrated median reporting time reduction ratios of 27.1% and 28.8% (mean, 28.0%) for the two residents, with no significant difference in quality scores between AI-generated and routine reports (p > 0.50). AI-generated reports showed primary diagnosis accuracies of 68.0% (Resident 1) and 76.0% (Resident 2) (mean, 72.0%). These findings suggest that the keyword-based AI-assisted reporting paradigm exhibits significant potential for clinical translation.
Stem Cell Therapy Enhanced by Nanotechnology and Biotechnology: Emerging Applications in Regenerative Medicine and Disease Treatment

Stem cells are unique cells with the ability to develop into different cell types in the body. They play a crucial role in growth, repair, and regenerative medicine. Stem cell therapy has emerged as a transformative modality in regenerative medicine, leveraging the intrinsic abilities of stem cells for self-renewal, differentiation, immune modulation, and paracrine signalling. It is advantageous over conventional treatments that often manage symptoms rather than address underlying pathology. Despite their promise, clinical translation is hindered by challenges such as poor cell survival, limited engraftment, immune rejection, and uncontrolled differentiation. To overcome these barriers, integration with nanotechnology and biotechnology has provided innovative solutions. Nano delivery systems, including liposomes, gold nanoparticles, mesoporous silica nanoparticles, carbon nanotubes, and magnetic nanoparticles, enhance stem cell viability, targeting precision, and controlled release of therapeutic agents while minimizing systemic toxicity. Parallel advances in biotechnology, such as exosome-based therapeutics, single-cell RNA sequencing, and CRISPR-Cas mediated genome editing, further refine stem cell applications by improving safety, lineage specification, and personalized treatment strategies. Collectively, these innovations strengthen the efficacy, reliability, and scalability of stem cell–based therapies, positioning them as powerful tools against degenerative, inflammatory, and metabolic diseases. The convergence of cellular biology, nanotechnology, and geneediting platforms underscores a decisive step toward safe and effective regenerative medicine capable of addressing previously incurable conditions.