ethicalabs/Research-Intent-Collab huggingface dataset:
Decade of Natural Language Processing in Chronic Pain: A Systematic Review

In recent years, the intersection of Natural Language Processing (NLP) and public health has opened innovative pathways for investigating various domains, including chronic pain in textual datasets. Despite the promise of NLP in chronic pain, the literature is dispersed across various disciplines, and there is a need to consolidate existing knowledge, identify knowledge gaps in the literature, and inform future research directions in this emerging field. This review aims to investigate the state of the research on NLP-based interventions designed for chronic pain research. A search strategy was formulated and executed across PubMed, Web of Science, IEEE Xplore, Scopus, and ACL Anthology to find studies published in English between 2014 and 2024. After screening 132 papers, 26 studies were included in the final review. Key findings from this review underscore the significant potential of NLP techniques to address pressing challenges in chronic pain research. The past 10 years in this field have showcased the utilization of advanced methods (transformers like RoBERTa and BERT) achieving high-performance metrics (e.g., F1&gt;0.8) in classification tasks, while unsupervised approaches like Latent Dirichlet Allocation (LDA) and k-means clustering have proven effective for exploratory analyses. Results also reveal persistent challenges such as limited dataset diversity, inadequate sample sizes, and insufficient representation of underrepresented populations. Future research studies should explore multimodal data validation systems, context-aware mechanistic modeling, and the development of standardized evaluation metrics to enhance reproducibility and equity in chronic pain research.
Accountability for the human right to health through treaty monitoring: Human rights treaty bodies and the influence of concluding observations

Employing novel coding methods to evaluate human rights monitoring, this article examines the influence of United Nations (UN) treaty bodies on national implementation of the human right to health. The advancement of the right to health in the UN human rights system has shifted over the past 20 years from the development of norms under international law to the implementation of those norms through national policy. Facilitating accountability for this rights-based policy implementation under the right to health, the UN Committee on Economic, Social and Cultural Rights (CESCR) monitors state implementation by reviewing periodic reports from state parties, engaging in formal sessions of ‘constructive dialogue’ with state representatives, and issuing concluding observations for state response. These concluding observations recognise the positive steps taken by states and highlight the principal areas of CESCR concern, providing recommendations for implementing human rights and detailing issues to be addressed in the next state report. Through analytic coding of the normative indicators of the right to health in both state reports and concluding observations, this article provides an empirical basis to understand the policy effects of the CESCR monitoring process on state implementation of the right to health.
An intelligent neural network programming system (NNPS)

A neural network programming system based on parallel neural information processing has been presented. With the neural network programming system built upon a 100M local computer network, the system has thus provided users high speed, general purpose and large scale neural network application development platforms.
Integrating proteomics and explainable artificial intelligence: a comprehensive analysis of protein biomarkers for endometrial cancer diagnosis and prognosis

Endometrial cancer, which is the most common gynaecological cancer in women after breast, colorectal and lung cancer, can be diagnosed at an early stage. The first aim of this study is to classify age, tumor grade, myometrial invasion and tumor size, which play an important role in the diagnosis and prognosis of endometrial cancer, with machine learning methods combined with explainable artificial intelligence. 20 endometrial cancer patients proteomic data obtained from tumor biopsies taken from different regions of EC tissue were used. The data obtained were then classified according to age, tumor size, tumor grade and myometrial invasion. Then, by using three different machine learning methods, explainable artificial intelligence was applied to the model that best classifies these groups and possible protein biomarkers that can be used in endometrial prognosis were evaluated. The optimal model for age classification was XGBoost with AUC (98.8%), for tumor grade classification was XGBoost with AUC (98.6%), for myometrial invasion classification was LightGBM with AUC (95.1%), and finally for tumor size classification was XGBoost with AUC (94.8%). By combining the optimal models and the SHAP approach, possible protein biomarkers and their expressions were obtained for classification. Finally, EWRS1 protein was found to be common in three groups (age, myometrial invasion, tumor size). This article’s findings indicate that models have been developed that can accurately classify factors including age, tumor grade, and myometrial invasion all of which are critical for determining the prognosis of endometrial cancer as well as potential protein biomarkers associated with these factors. Furthermore, we were able to provide an analysis of how the quantities of the proteins suggested as biomarkers varied throughout the classes by combining the SHAP values with these ideal models.
Technology Enhanced Learning and National Education Policy 2020: Analytical Review of E-Content Platforms in Indian Education

India’s National Education Policy 2020 emphasizes on how technology is essential to democratize access and quality learning through digital platforms. In order to operationalize the technological vision of NEP 2020, Ministry of Education and NCERT were assigned with the responsibility of creating flagship e content platforms like DIKSHA, SWAYAM, e Pathshala etc. for online and blended pedagogies. DIKSHA, which went live in 2017 through the Sunbird platform, now has over 200 million students and 7 million teachers covered from the states and unions, with multilingual module supports for the school curricula. During July 2017, the government’s free MOOC portal, SWAYAM, launched which hosts 11,772 courses in excess of 12.5 million enrollments and provides self-paced certificate at secondary to the postgraduate levels. E-Pathshala, a collaboration between MoE and NCERT introduced in November 2015, offers access to more than 504 e-textbooks and 3,886 other e-resources, such as flipbooks, audio and video, through web and mobile apps, that have received over 5 million downloads. Using a composite analytical framework including content analysis, SWOT analysis and synthesis of utilization metrics derived from Government reports and platform analytics, the study analyzes the design of each platform, content diversity, user engagement and curricular alignment. Findings point to successful adoption and a high degree of user satisfaction where 97% of students use self assessment tools and teachers are widely using the tool for planning lesson. Nevertheless, chronic problems in broadband infrastructure, localization, and teacher digital literacy hamper equitable access and pedagogy innovation in distant areas. Primary findings contribute to policy imperatives such as increased rural connectivity fund, e pedagogy capacity building for educators and iterative feedback loops to motivate the improvement of the platform offerings. By integrating empirical knowledge with the NEP 2020 objectives, such a review presents evidence-based ideas for strengthening the technology enhanced learning ecosystem and reaching comprehensive, quality education across the whole country.
Towards Time Series Reasoning with LLMs

Multi-modal large language models (MLLMs) have enabled numerous advances in understanding and reasoning in domains like vision, but we have not yet seen this broad success for time-series. Although prior works on time-series MLLMs have shown promising performance in time-series forecasting, very few works show how an LLM could be used for time-series reasoning in natural language. We propose a novel multi-modal time-series LLM approach that learns generalizable information across various domains with powerful zero-shot performance. First, we train a lightweight time-series encoder on top of an LLM to directly extract time-series information. Then, we fine-tune our model with chain-of-thought augmented time-series tasks to encourage the model to generate reasoning paths. We show that our model learns a latent representation that reflects specific time-series features (e.g. slope, frequency), as well as outperforming GPT-4o on a set of zero-shot reasoning tasks on a variety of domains.
Addressing Climate Change Resilience in Pavements: Major Vulnerability Issues and Adaptation Measures

Climate change is the one of the greatest challenges of our time, and it poses a threat to the surrounding built and natural environments. This review paper addresses climate change resilience in pavements by considering major vulnerability issues and adaptation measures. First, a review on foundational information of climate change related to transportation infrastructure is provided to bring all transportation professionals and practitioners to the same knowledge base on climate change terminology. Such information includes sources of climate information, climate scenarios, downscaling climate data, and uncertainty in climate projection information. Relevant climate stressors to pavements are discussed in some depth, including the most significant ones, which are increases in temperature and precipitation intensity. Thus, the proposed different engineering-informed adaptation measures relevant to the climate stressors of interest were evidence-based with reference to published peer-reviewed articles and case studies. Such adaptation solutions are related to monitoring pavement key performance parameters and pavement adaptations in structural design, robust materials and mix design, along with adaptation in maintenance, regulation, and construction. Efforts to adapt pavement systems to climate change are ongoing. In addition to such research works, this study concludes that impacts of adaptation measures on pavement and environment should be incorporated in the decision-making process in planning and design. This makes it important to integrate practical adaptation strategies in design and construction standards and guides, and implement awareness and education of climate change adaptation among engineers and practitioners.
Evaluating nurse-to-patient ratio legislation to improve patient safety and care quality: A mixed-methods policy study

Insufficient nurse staffing is a global issue, especially in lower-resource settings. High workloads are linked to increased infection rates, readmissions, and adverse clinical events. Jordanian hospitals face chronic understaffing, nurse migration, and limited regulatory oversight.This study aimed to assess the impact of implementing standardized nurse-to-patient ratios on patient outcomes and care quality in Jordanian hospitals, and to evaluate the feasibility of national legislation based on international models.A mixed-methods design was employed. Quantitative analysis included administrative data from four acute care hospitals between 2021 and 2024, focusing on patient safety indicators such as infection rates, readmissions, and hospital stays. Semi-structured interviews were used to gather qualitative data from 22 administrative and clinical stakeholders. American Nurses Association values and the Centers for Disease Control and Prevention's Policy Analysis Framework were used in the study to direct evaluation.Units with staffing ratios of five or fewer patients per nurse had significantly better outcomes, including 55 % lower infection rates and nearly half the number of readmissions compared to poorly staffed units. Interview participants highlighted systemic barriers to safe staffing, including limited budgets, migration-driven shortages, and rigid hierarchical cultures. However, there was broad consensus in support of regulated staffing standards adapted to patient acuity and care setting.The study suggests that standardizing nurse-to-patient ratios in Jordan can significantly enhance patient safety and care quality, but requires phased legislation, dynamic acuity adjustments, and workforce development investment for successful implementation.
Radio Detection of Ultra High-Energy Cosmic Ray Air Showers

The LOPES experiment (LOFAR Prototype Station) has been built at the KASCADE-Grande experiment in order to test the LOFAR (LOw Frequency ARray) technology and demonstrate its capability for radio measurements in extensive air showers (EAS). We could for the first time demonstrate by this detector and the coincidence to KASCADE the origin of the radio signal from a geo-synchrotron effect and the coherent nature of the measured signals. After these results we developed the next generation of antennas easy to calibrate, with precise detection of polarization, low power electronics, and trigger with capability of self-triggering. Furthermore the background situation was measured and analyzed at the site of the southern and northern Auger observatory and KASCADE
Ultra High Energy Cosmic Ray and UHE Neutrino-Z Showering in Dark Halos

17 pages,10 figures, Vulcano Conference Multichannel Astrophysics,May 2001 Minor Typo Corrections and Conference Discussion
Is computer vision still AI?

Recent general AI conferences show a decline in both the number and the quality of vision papers, but there is tremendous growth in, and specialization of, computer vision conferences. Hence, one might conclude that computer vision is parting or has parted company with AI. This article proposes that the divorce of computer vision and AI suggested here is actually an open marriage: Although computer vision is developing through its own research agenda, there are many shared areas of interest, and many of the key goals, assumptions, and characteristics of computer vision are also clearly found in AI.
Governing AI-Enabled Health Data Across Borders: Comparative Privacy and Security Frameworks Under GDPR, HIPAA, CCPA, LGPD, PIPEDA, and the Australian Privacy Act

The rise of digital healthcare systems with the exponential growth of cross-border data exchange has transformed the processes of patient information collection, processing, and protection [1]. With the growing use of electronic health records, telemedicine, and AI-driven diagnostics, the security of sensitive health data has become a worldwide concern [9]. To explore the similarities, structural differences, and implications of privacy rules and regulations in healthcare data protection, this study reviews six prominent privacy rules and regulations: the European Union's GDPR, the HIPAA and CCPA of the United States, the LGPD of Brazil, the PIPEDA of Canada, and the Privacy Act of Australia [1].    The comparative legal and operational analysis conducted within the framework of this research  determines the availability of alignment opportunities, as well as compliance challenges in ensuring the  consistent provision of patient privacy across jurisdictions. The results show that the world is moving  towards the harmonization of policies, accountability, and transparency; however, in practice,  fragmentation of enforcement and interoperability [7] is evident to an extent. This study concludes with  practical suggestions for healthcare organizations, including the implementation of harmonized privacy  governance frameworks, data protection impact analysis, and PrivacyOps automation to find the right  balance between compliance, innovation, and patient trust [10] in a developing global healthcare  ecosystem.  The healthcare sector has entered the digital age, where patient records are considered essential clinical  assets and regulatory obligations [2]. The growth of electronic health records (EHRs), remote patient  monitoring systems, and AI-driven diagnostics has grown exponentially in terms of the volume, speed,  and usefulness of health data being processed [14]. This development has resulted in a pressing need for  privacy standards that not only ensure the privacy of patients but also allow safe international data-driven  innovation [11].
A Study Of Legal Rights And Protections For Minor Victims In The Criminal Justice System: With Special Emphasis On Cases Involving Domestic Violence

Focusing on both legislative frameworks and the practical implementation issues, this study investigates the legal entitlements and protections for minor victims of domestic violence in India. It investigates important constitutional and statutory provisions, including the Domestic Violence Act, the Juvenile Justice (Care and Protection of Children) Act, and the Protection of Children from Sexual Offences (POCSO). The work also investigates the historical evolution of legal protections for children, stressing the changing function of India’s courts and legal reforms. Particular focus is on the legal scene in Punjab, where regional issues, including cultural norms and lack of knowledge, are addressed and the efficacy of current enforcement tools is evaluated. The study also addresses implementation gaps such as delayed justice, insufficient psychological support, and the absence of a child-friendly procedure infrastructure. Comparative viewpoints from international frameworks, including the UN Convention on the Rights of the Child (UNCRC), are combined to indicate best practices for enhancing legal reactions. In the end, the article offers legal reform ideas, institutional capacity-building, and policy changes to guarantee complete protection for minor domestic violence victims.
SWEDEN IN THE UNITED NATIONS SECURITY COUNCIL : Influence and history in high-table diplomacy

The Swedish state has participated as an elected member of the United Nations Security Council three times and is entering their fourth. The UNSC is an old institution that is not equal to what powers the states within it has. The permanent states, the victors from the second world war, have a more prominent role in the council and do not have to be elected every two years as the other states that want to be a part of the council. The cost of for small states like Sweden to be a part of the council can be quite large, but in financial means and as a time-consuming task for the foreign department. Therefore, it is essential to know if the time spent in the council for a state like Sweden amounts to something in the sense of influence over the outcome. This bears the question if Sweden has used and is using its membership in the UNSC in an effective manner. By looking at the official documentation by the foreign department and interviewing former diplomats about Sweden’s role in the UNSC during these periods this thesis analyses the way that Sweden has acted before. Furthermore, the thesis also looks at the first five weeks in the current period of 2017. The results are analyzed through a theoretical framework of what should be methods to achieve influence in the UNSC. As the analysis shows, Sweden has moved from being a more passive participant to a more active and proactive part of the UNSC. This, however, can come at the cost of changing other states view of the Swedish state in international relations.
Pengaruh Human Resource Information System (HRIS) dan Disiplin Kerja Terhadap Kinerja Karyawan pada Divisi Project Deployment Center PT Len Industri (Persero)

Penelitian ini bertujuan untuk mengetahui gambaran dan pengaruh dari human resource information system (HRIS) dan disiplin kerja terhadap kinerja karyawan. Objek penelitian ini adalah karyawan pada divisi Project Deployment Center (PDC) PT Len Industri (PERSERO). Metode yang digunakan adalah deskriptif dan verifikatif dengan pendekatan kuantitatf. Populasi pada penelitian berjumlah 214 orang dan menggunakan teknik non probability sampling yaitu Purposive Sampling dengan pertimbangan tertentu yang diambil berdasarkan pada perhitungan rumus Slovin sebanyak 100 responden. Berdasarkan hasil yang diperoleh bahwa gambaran responden mengenai human resource information system dan kinerja karyawan termasuk kedalam kategori tinggi, disiplin kerja termasuk dalam kategori sangat tinggi. Berdasarkan hasil pengujian determinasi diperoleh bahwa HRIS berpengaruh sebesar 81,4% terhadap kinerja karyawan, disiplin berpengaruh sebesar 68,4% dan hasil pengujian HRIS dan disiplin berpengaruh sebesar 83% terhadap kinerja karyawan. Berdasarkan hasil pengujian hipotesis secara parsial menunjukkan HRIS berpengaruh terhadap kinerja karyawan, disiplin kerja berpengaruh terhadap kinerja karyawan. Berdasarkan hasil pengujian hipotesis secara simultan HRIS dan disiplin kerja berpengaruh terhadap kinerja karyawan.
On the Generalization Properties of Diffusion Models

Accepted at NeurIPS 2023
Differences in land cover – biodiversity relationships complicate the assignment of conservation values in human-used landscapes

Abstract   The anthropogenic conversion of natural landscapes continues to be a severe threat to biodiversity. As humans depend on these natural landscapes for sustainable resource provisioning as well, the conversion likewise affects biodiversity and people’s livelihoods. To identify options for reconciling human needs and biodiversity conservation we studied the relationship of land use and reptile diversity.  In a dryland in southwestern Madagascar, we compared reptile diversity between five land-cover classes and identified trait-dependent effects of land use on reptiles with regard to substrate preference and activity phase. Thresholds of diversity declines were detected at the transition from intact forest towards land-cover types with reduced forest cover, and at the transition to habitats with a woody plant cover below 10–30%. Community equitability increased towards lower vegetation cover. With increasing habitat openness, rare species were lost and a small subset of similarly successful degradation-tolerant species remained. Land cover – biodiversity relationships varied considerably between reptile assemblages that differed in activity phases. Nocturnal reptiles were more strongly affected by habitat conversion than diurnal reptiles. Substrate preference did not determine degradation tolerance.  These findings provide important implications for conservation planning in drylands. For many species converted habitats can still be suitable. However, ignoring the fine-scale habitat requirements of animals will likely lead to imprecise conclusions on the conservation value of converted drylands.
Air Quality Monitoring System Design Based on Wireless Sensor Network Integrated with the Internet of Things

Government and officials set rules to keep the air clean and healthy. To accommodate this, an efficient air quality monitoring system is required. Real-time monitoring is crucial for observing air quality. This allows for immediate action if air quality declines. However, current systems often rely on just one measurement point, risking inaccurate results due to rapid pollutant dispersion. To overcome this problem, researchers propose designing an air quality monitoring system based on a wireless sensor network. Sensor nodes will be installed at various points within the area to be monitored, forming a connected sensor network using the ESP-Now protocol. The data obtained from each node will be sent to the base station, then the data will be transmitted via the Message Queuing Telemetry Transport (MQTT) protocol using the internet network. Thus, this design produces a wireless sensor network that is integrated with the internet of things (IoT). The advantages of the IoT system include ease of data storage and accessibility that can be accessed from anywhere as long as it is connected to the internet and has appropriate authorization.