ethicalabs/Research-Intent-Collab huggingface dataset:
Health insurance coverage and access to care in China

Abstract                 Objective                 The study examined the relationship between health insurance coverage and access to needed healthcare including preventive, primary, and tertiary care among Chinese adult population.                                Data and methods                 Data for this study came from the 2018 China Health and Retirement Longitudinal Study (CHARLS), a population-based probability sample survey. Key measures included insurance coverage (high-, moderate-, low- and no-insurance), access to care (physical examination, physician visit, office visit, inpatient care, and satisfaction with care), and personal sociodemographics. Multiple-factor generalized linear mixed model was applied to estimate the odds ratio (OR) and the 95% confidence interval (CI) of HI coverage for the four indicators of access to care, after controlling for individual characteristics and aggregation among different villages.                                Results                 The majority of Chinese adults had some health insurance with only 3.15% uninsured. However, most had low-coverage insurance (64.82%), followed by moderate-coverage insurance (16.70%), and high-coverage insurance (15.33%). Health insurance was significantly and positively associated with access to needed healthcare (preventive, primary, and tertiary). There was also a significant gradient association between extent of insurance coverage and access to care.                                Conclusion                 Not only health insurance mattered in enhancing access to care but that there was a significant gradient association between extent of insurance coverage and access to care with higher coverage relating to better access.
Dissemination and implementation science activities across the Clinical Translational Science Award (CTSA) Consortium: Report from a survey of CTSA leaders

AbstractIntroduction:Dissemination and implementation (D&amp;I) science is not a formal element of the Clinical Translational Science Award (CTSA) Program, and D&amp;I science activities across the CTSA Consortium are largely unknown.Methods:The CTSA Dissemination, Implementation, and Knowledge Translation Working Group surveyed CTSA leaders to explore D&amp;I science-related activities, barriers, and needed supports, then conducted univariate and qualitative analyses of the data.Results:Out of 67 CTSA leaders, 55.2% responded. CTSAs reported directly funding D&amp;I programs (54.1%), training (51.4%), and projects (59.5%). Indirect support (e.g., promoted by CTSA without direct funding) for D&amp;I activities was higher – programs (70.3%), training (64.9%), and projects (54.1%). Top barriers included funding (39.4%), limited D&amp;I science faculty (30.3%), and lack of D&amp;I science understanding (27.3%). Respondents (63.4%) noted the importance of D&amp;I training and recommended coordination of D&amp;I activities across CTSAs hubs (33.3%).Conclusion:These findings should guide CTSA leadership in efforts to raise awareness and advance the role of D&amp;I science in improving population health.
Modulation of high‐energy cosmic rays in the heliosphere

We have used sidereal diurnal variations observed by neutron monitors and surface and underground muon telescopes to examine the north‐south anisotropy and radial density gradient of high‐energy galactic cosmic rays between 1957 and 1985. We have derived the average amplitude and rigidity spectrum of the anisotropy for this period and estimate the radial gradient to be best represented as a function of rigidity P by 1.02 × (P/10)−0.5% AU−1 for rigidities less than ≈450 GV. The temporal variation of both the north‐south anisotropy and the radial density gradient show a solar cycle variation. Possible dependence of the anisotropy on the solar magnetic polarity state has been examined under the assumptions of a static and a nonstatic rigidity spectrum, with conflicting results. We show that the analysis is affected by a further sidereal variation and that the responsible anisotropy is related to the asymmetric heliospheric modulation of a galactic anisotropy.
Comparing the Error Rate of the Shor and Steane Quantum Error Correction Codes to No Error Correction in a Simulated Quantum Computer

Kvantdatorers potential har förutspåtts och bevisats sedan länge. Redan 1995 upptäckte Peter Shor en algoritm för att primtalsfaktorisera tal effektivare än vanliga datorer. 30 år senare så har fortfarande ingen lyckats bygga en kvant-dator som kan arbeta med tillräckligt mycket data för att beräkna något som en vanlig dator inte skulle klara av. Innan en praktiskt användbar kvantdator kan byggas så finns många problem kvar att lösa. Ett av dem är problemet med brus i kvantdatorer, alltså fel som sker i beräkningarna. Detta brus kommer från omgivningen, datorn själv och från varje beräkning som görs. Vår forskning undersöker två felkorrigeringsmetoder för kvantdatorer: Shors och Steanes felkorrigeringsmetod. Dessa testades i en simulerad kvantdator genom IBM:s Qiskit-ramverk. Vårt resultat visar att båda felkorrigeringsmetoderna presterar sämre än ingen felkorrigeringsmetod alls.
Identification of Ultra High-Energy Cosmic Rays Primary Particles Initializing Air Showers on a base of ADC traces from Water Cherenkov Detectors

Ultra high-energy cosmic rays (UHECRs) remain still a mystery in the modern astrophysics. Cosmic rays have been detected up to particle energies around 1020 eV and are the highest energy particles in the present Universe. In spite of a reasonable number of events already collected by large experiments around the world, their origin is still largely unknown. One of the most important topics is an identification of the primary particle initializing a development of air showers in the atmosphere. One of widely used techniques of detection are water Cherenkov detectors (WCD) spread on a large area allowing a registration of detector crossing particles.Simulation packages a) CORSIKA (simulated a development of air showers in the atmosphere) and b) Offline (simulated a photo-multipliers response in the WCD) allow a analysis of expected ADC traces in the detector electronics. As up to now neutrino-origin traces are practically not detected, it is crucial to solve a problem: either the neutrino stream is too rare for detector or the detector technique is not oriented enough for neutrino events. The paper analyses three methods which could be used for a support of the standard triggers.
Application of artificial intelligence in the diagnosis of peri-implantitis and assessment of the prognosis of dental implantion. (A systematic review)

Dental implantology plays a central role in modern dentistry, but the long-term success of implant therapy largely depends on the health of peri-implant tissues. Peri-implantitis remains one of the most common causes of implant failure, characterized by soft tissue inflammation and progressive bone resorption. Conventional diagnostic methods, such as clinical examination and radiographic evaluation, are limited by low sensitivity at early stages and high operator dependence, resulting in reduced reproducibility and diagnostic accuracy. In recent years, artificial intelligence (AI) technologies, including machine learning, deep learning, and convolutional neural networks (CNN), have been increasingly applied in the diagnosis and prognosis of peri-implant diseases. Literature analysis demonstrated that AI-based models can detect subtle signs of bone resorption with accuracy comparable to or exceeding that of clinicians, provide standardized diagnostic protocols, and generate personalized treatment outcome predictions. Furthermore, integrating radiological findings with microbiological and immunological data (such as IL-1β and IL-6 levels) enhances risk stratification and allows the identification of patients with higher susceptibility to peri-implant disease progression. Nevertheless, several challenges hinder the routine clinical implementation of AI. Most studies are retrospective, involve small and heterogeneous samples, and use different architectures and performance metrics, limiting comparability and generalizability. Large-scale, multicenter, prospective studies with standardized evaluation protocols are required to validate and establish AI as a reliable tool in clinical implant dentistry.
Snowfall induced by corona discharge

We demonstrated for the first time the condensation and precipitation (or snowfall) induced by a corona discharge inside a cloud chamber. Ionic wind was found to have played a more significant role than ions as extra Cloud Condensation Nuclei (CCN). 2.25 g of net snow enhancement was measured after applying a 30 kV corona discharge for 25 min. In comparison with another newly emerging femtosecond laser filamentation method, the snow precipitation induced by the corona discharge has about 4 orders of magnitude higher wall-plug efficiency under similar conditions.
Use of Sahridayata in Conflict Resolution and Peace Building

This article deals with spiritual philosophy and its implication for conflict resolution and peace building in awakening astha chakras through physical and emotional dimensions of human body. Being empathetic, compassionate, cooperative and feeling equal to be oneness, can be defined as Sahridayata. The concept Sahridayata comes from the word ‘Saha’ means a quality characteristics or state of being and ‘hridaya’ refers to names a person of that faculty. So, etymologically Sahridaya refers to ‘one with a shared heart’ or ‘like hearted’. Similarly, Chakras are the energy centers of human body through which physical, mental and spiritual functions take place.
Judicial Independence and the Rule of Law in Hong Kong

Review
Statistical mechanics and phase transitions in clustering

A new approach to clustering based on statistical physics is presented. The problem is formulated as fuzzy clustering and the association probability distribution is obtained by maximizing the entropy at a given average variance. The corresponding Lagrange multiplier is related to the ``temperature'' and motivates a deterministic annealing process where the free energy is minimized at each temperature. Critical temperatures are derived for phase transitions when existing clusters split. It is a hierarchical clustering estimating the most probable cluster parameters at various average variances.
The Hague Declaration and the Acceptance of the Compulsory Jurisdiction of the International Court of Justice: A Missed Opportunity?

“[I]n the last analysis it is the political wills and attitudes of governments that must be changed and -dare one say it?- educated. Political change is not a matter for the jurist, but the education that may lead to it perhaps is.”
International Criminal Law as Global Law: An Assessment of the Hybrid Tribunals

This contribution will focus on the facet of International Criminal Justice where the influence of global law is the most apparent, namely the hybrid or internationalized criminal tribunals. Since many of these tribunals have closed their doors or are in the advanced stage of the proceedings, the time is ripe for a preliminary evaluation. Furthermore, the future necessity and viability of hybrid tribunals will be assessed, both for crimes that fall within and without the jurisdictional regime established by the International Criminal Court (ICC).
Learning analytics in practice

Even though the recent influx of tablets in primary education goes together with the vision that educational technology empowered with learning analytics will revolutionize education, empirical results supporting this claim are scares. Adaptive educational technology Snappet combines extracted and embedded learning analytics daily in classrooms. While students make exercises on the tablet this technology displays real-time data of learner performance in a teacher dashboard (extracted analytics). At the same time, learner performance is used to adaptively adjust exercises to students' progress (embedded analytics). This quasiexperimental study compares the development of students' arithmetic skills over one schoolyear (grade 2 and 4) in a traditional paper based setting to learning with the adaptive educational technology Snappet. The results indicate that students in the Snappet condition make significantly more progress on arithmetic skills in grade 4. Moreover, in this grade students with a high ability level, benefit the most from working with this adaptive educational technology. Overall the development pattern of students with different abilities was more divergent in the AET condition compared to the control condition. These results indicate that adaptive educational technologies combining extracted and embedded learning analytics are indeed creating new education scenarios that contribute to personalized learning in primary education.
ADVANCEMENTS IN REAL-TIME STREAM PROCESSING: A COMPARATIVE STUDY OF APACHE FLINK, SPARK STREAMING, AND KAFKA STREAMS

This article presents a comprehensive comparative analysis of three leading stream processing platforms: Apache Flink, Spark Streaming, and Kafka Streams, examining their architectural approaches, performance characteristics, and operational considerations in real-time data processing scenarios. Through extensive benchmarking and evaluation, we investigated these platforms across multiple dimensions, including processing latency, throughput capacity, resource utilization, and operational complexity. The article reveals that Apache Flink demonstrates superior performance in low-latency scenarios with its true streaming model, while Spark Streaming excels in high-throughput situations with its micro-batch approach and robust ecosystem integration. Kafka Streams emerge as a compelling solution for lightweight stream processing needs, particularly in Kafka-centric architectures. The article also uncovers a significant convergence of features across these platforms, with each adopting strengths from the others while maintaining their distinct architectural advantages. Performance benchmarks indicate that Flink consistently achieves sub-100-millisecond latency for complex operations, Spark Streaming offers unparalleled throughput for large-scale data processing, and Kafka Streams provides the most straightforward operational model. These insights, combined with detailed use case analyses, provide organizations with crucial decision-making criteria for selecting the most appropriate stream processing platform based on their specific requirements, existing infrastructure, and technical expertise.
EVALUASI FAKTOR PENGARUH PENERIMAAN PENGGUNA HUMAN RESOURCE INFORMATION SYSTEM (HRIS) PADA PT. PELAYARAN MULTI JAYA SAMUDERA DENGAN METODE TECHNOLOGY ACCEPTANCE MODEL

Manajemen Sumber Daya Manusia (MSDM) pada sebuah perusahaan tergolong bagus jika tertata rapi dengan sistem yang efektif dan efisien. Salah satunya dengan cara diterapkan Human Resource Information System (HRIS) pada pengoperasian data SDM perusahaan. Sistem baru yang diterapkan akan dianggap berhasil jika dapat diterima dan digunakan dengan baik oleh penggunanya. Oleh karena itu penting untuk dilakukan evaluasi terhadap penerimaan sistem informasi tersebut. Tujuan penelitian ini adalah mengevaluasi faktor yang mungkin dapat mempengaruhi tingkat penerimaan penggunaan website HRIS pada PT. Pelayaran Multi Jaya Samudera dengan menggunakan metode Technology Acceptance Model (TAM). Metode ini mencakup 5 variabel yaitu variabel kemudahan pengguna sistem (PEU), kebermanfaatan sistem (PU), sikap penggunaan sistem (ATU), intensitas penggunaan sistem (BIU), dan penggunaan aktual sistem (ASU). Pengolahan data penelitian menggunakan perangkat lunak SPSS. Dari hasil penelitian pada HRIS PT. Pelayaran Multi Jaya Samudera menunjukkan bahwa terdapat 2 faktor yang dapat mempengaruhi tingkat penerimaan sistem informasi tersebut yaitu variabel kebermanfaatan sistem (PU) dan sikap penggunaan sistem (ATU), namum yang paling dominan adalah pada aspek sikap penggunaan sistem.
Persistent Homology-Guided Frequency Filtering for Image Compression

Feature extraction in noisy image datasets presents many challenges in model reliability. In this paper, we use the discrete Fourier transform in conjunction with persistent homology analysis to extract specific frequencies that correspond with certain topological features of an image. This method allows the image to be compressed and reformed while ensuring that meaningful data can be differentiated. Our experimental results show a level of compression comparable to that of using JPEG using six different metrics. The end goal of persistent homology-guided frequency filtration is its potential to improve performance in binary classification tasks (when augmenting a Convolutional Neural Network) compared to traditional feature extraction and compression methods. These findings highlight a useful end result: enhancing the reliability of image compression under noisy conditions.
A systematic approach and mathematical review to textile production optimization: implementing lean six sigma DMAIC for enhanced product consistency and quality in the field of industrial engineering management

The main aim of this paper is to analyze the application of LSS tools to enhance efficiency and product quality within the region's textile sector. Specifically, the paper's objective is three-fold: to discuss how Lean and Six Sigma may be implemented separately, to discuss how these two improvement methodologies can be integrated, and to develop an integrated framework of Lean Six Sigma (LSS). The current research being conducted includes the use of journal articles and the use of the actual method. This gives an overview of previous frameworks and approaches to Lean and Six Sigma integration and establishes a new LSS Framework using DMAIC methodology. This study also comprises the quantitative and qualitative data collection from the survey and process evaluation in the Ready-Made Garment (RMG) factory. The research presented here outlines a comprehensive LSS strategy that would drastically cut down on defects and increase efficiency in the textile industry. According to the case, using LSS, the rework's Sigma level improved from 2.7 to 3.0, proving fewer defects. This paper introduces a novel  LSS framework tailored for the textile industry, especially Small and Medium-sized Enterprises (SMEs). Building on previous research, it identifies specific challenges faced by the textile sector and recommends adopting digital technologies and environmental considerations, including Green  LSS. The study offers valuable insights for practitioners aiming to implement continuous improvement approaches, particularly in SMEs, by integrating digital tools.
Effect of Time Delay in Velocity Feedback Loop on the Dynamic Behaviors of Magnetic Bearing System

The effect of time delay in velocity feedback control loop on the dynamic behaviors of single degree of freedom magnetic bearing system is studied. The stability of trivial equilibrium point is discussed and the critical time delay where Hopf bifurcation may occur is obtained by analyzing distribution of the roots of the associated characteristic equation. Then an approximate analytical method is performed to determine the direction and stability of Hopf bifurcation and analyze the primary resonance of system. Given system parameters, the validity of above analytical method is verified and the effect of time delay on the forced vibration of system is studied by numerical method. It is found that the amplitude of stable period motion increases, some complex dynamical behaviors such as quasi-periodic motion may occur, and the system may get out of control with the increase of time delay. Therefore the effect of time delay should be taken into sufficient consideration in the design of system control loop.
A multicenter randomized clinical trial of one-rod etonogestrel and two-rod levonorgestrel contraceptive implants with nonrandomized copper-IUD controls: methodology and insertion data

Comparative data on etonogestrel and two-rod levonorgestrel contraceptive implants are lacking.A multicenter, open, parallel-group trial with random allocation of implants was performed. For every second implant user, an age-matched woman choosing an intrauterine device (IUD) (TCu380A) was admitted. Methods and data on implant/IUD insertion and 6-week follow-up are reported.A total of 2008 women were randomized to an implant, and 974 women were enrolled in the IUD group. Results from 997 etonogestrel implant users, 997 levonorgestrel implant users and 971 IUD users were analyzed. In the etonogestrel and levonorgestrel groups, respectively, mean insertion durations were 51 (SD 50.2) s and 88 (SD 60.8) s; complication rates at insertion were 0.8% and 0.2%; and at follow-up, 27.2% and 26.7% of women, respectively, had signs or symptoms at the insertion site. At follow-up within 6 weeks after insertion, all implants were in situ, while 2.1% of IUDs were expelled.Performance of etonogestrel and levonorgestrel implants at insertion and within the first 6 weeks is similar. Short-term (6 weeks) continuation rates appear higher for implants than TCu380A.
Zeeman spin-orbit coupling in antiferromagnetic conductors

This article is a brief review of Zeeman spin-orbit coupling, arising in a low-carrier commensurate Néel antiferromagnet subject to magnetic field. The field tends to lift the degeneracy of the electron spectrum. However, a hidden symmetry protects double degeneracy of Bloch eigenstates at special momenta in the Brillouin zone. The effective transverse $g$-factor vanishes at such points, thus acquiring a substantial momentum dependence, which turns a textbook Zeeman term into a spin-orbit coupling. After describing the symmetry underpinnings of the Zeeman spin-orbit coupling, I compare it with its intrinsic counterparts such as Rashba coupling, and then show how Zeeman spin-orbit coupling may survive in the presence of intrinsic spin-orbit coupling. Finally, I outline some of the likely experimental manifestations of Zeeman spin-orbit coupling, and compare it with similar phenomena in other settings such as semiconducting quantum wells.
Separate or joint? Estimation of multiple labels from crowdsourced annotations

Abstract   Artificial intelligence techniques aimed at more naturally simulating human comprehension fit the paradigm of multi-label classification. Generally, an enormous amount of high-quality multi-label data is needed to form a multi-label classifier. The creation of such datasets is usually expensive and time-consuming. A lower cost way to obtain multi-label datasets for use with such comprehension–simulation techniques is to use noisy crowdsourced annotations. We propose incorporating label dependency into the label-generation process to estimate the multiple true labels for each instance given crowdsourced multi-label annotations. Three statistical quality control models based on the work of Dawid and Skene are proposed. The label-dependent DS (D-DS) model simply incorporates dependency relationships among all labels. The label pairwise DS (P-DS) model groups labels into pairs to prevent interference from uncorrelated labels. The Bayesian network label-dependent DS (ND-DS) model compactly represents label dependency using conditional independence properties to overcome the data sparsity problem. Results of two experiments, “affect annotation for lines in story” and “intention annotation for tweets”, show that (1) the ND-DS model most effectively handles the multi-label estimation problem with annotations provided by only about five workers per instance and that (2) the P-DS model is best if there are pairwise comparison relationships among the labels. To sum up, flexibly using label dependency to obtain multi-label datasets is a promising way to reduce the cost of data collection for future applications with minimal degradation in the quality of the results.