bluuebunny/crossref_metadata_2025_split huggingface dataset:
Tunes that move us: the impact of music-induced emotions on prosocial decision-making
Authors: Wu Hongwei , Wang Danni , Zhou Linshu 
Published: January 2025

IntroductionThe significance of music might be attributed to its role in social bonding, a function that has likely influenced the evolution of human musicality. Although there is substantial evidence for the relationship between prosocial songs and prosocial behavior, it remains unclear whether music alone, independent of lyrics, can influence prosocial behaviors. This study investigates whether music and the emotions it induces can influence prosocial decision-making, utilizing the classical two-dimensional model of emotion (mood and arousal).MethodsIn Experiment 1,42 undergraduate students listened to happy music (positive, high arousal), sad music (negative, low arousal), and white noise while reading stories describing helping scenarios and then assessed their willingness to help. Experiments 2 and 3 further explore mood and arousal effects by manipulating the mode (major vs. minor) and tempo (fast vs. slow) of the music.ResultsExperiment 1’s results indicated that sad music increases willingness to help more than happy music or white noise, suggesting that music-induced emotions influence prosocial behavior through immediate prosocial emotions like empathy. Experiments 2 and 3 demonstrated that only mood, influenced by the music mode, affects prosocial decision-making, while tempo-induced arousal does not. Additionally, Theory of Mind and memory strength do not mediate these effects.DiscussionThese findings reveal the role of pure music listening and specific emotional dimensions on prosocial decision-making, providing evidence to support the music-social bonding hypothesis.
Predictive modeling of burnout dimensions based on basic socio-economic determinants in health service managers and support personnel in a resource-limited health center
Authors: Castro-Tamayo Grey , Hernandez-Tapia Mario , Lozada-Martinez Ivan David , Portnoy Ivan , Manosalva-Sandoval Jessica , Parodi-Camaño Tobías 
Published: January 2025

BackgroundBurnout is a prevalent condition in the healthcare sector, and although it has been extensively studied among healthcare professionals, less is known about its impact on non-professional workers, particularly in low-resource settings. This study aimed to test a preliminary predictive model based on basic socioeconomic and sociodemographic determinants to predict symptoms of burnout among support personnel and health services managers in a resource-limited health center.MethodsA prospective cross-sectional study was conducted. Using simple random sampling, symptoms of burnout were surveyed among health service managers and support personnel using the Maslach Burnout Inventory (MBI). Statistical analyses included correlation tests and predictive models using random forest models to identify significant associations and cast predictions.ResultsA total of 76 participants were included. Of these, 34.21% exhibited high levels of emotional exhaustion (EE), 42.11% showed elevated depersonalization (DP), and 7.89% reported low personal accomplishment (PA). Significant negative correlations were observed between household income and the EE and DP dimensions. The predictive models demonstrated acceptable performance in identifying socioeconomic factors associated with burnout, with prediction errors ranging from 7.68% to 20.31%.ConclusionsBurnout is common among support personnel and health services managers in resource-limited settings, particularly among those with lower incomes. The findings underscore the importance of implementing policies that address both working conditions and economic well-being to mitigate the risk of burnout. More robust predictive models could serve as a valuable tool for early identification and prevention of burnout in this type of setting.
Correlation between night sweats and season fluctuation in China
Authors: Chen Cai , Li Meng , Peng Fulai , Lv Danyang , Ding Xiao , Tian Chongxuan , Ren Weilian , Meng Xiangwei , Sun Tiefeng , Wang Yi , Du Haitao , Wu Fengxia , Li Wei , Wang Ping 
Published: January 2025

BackgroundNight sweats are a condition in which an individual sweats excessively during sleep without awareness, and stops when they wake up. Prolonged episodes of night sweats might result in the depletion of trace elements and nutrients, affecting the growth and development of children.PurposeTo investigate the relationship between sweat nights and season.MethodThe Internet search index for night sweats in Zhengzhou, China was obtained from the Baidu index during 2011–2022. Meteorological factors, including ambient temperature, humidity, pressure, precipitation and average wind speed in Zhengzhou were obtained from the website https://en.tutiempo.net/climate. A time series decomposition model was used to study the relationship between night sweats and seasonality. Continuous wavelet transform and cross wavelet transform were utilized to explore the relationship between night sweats and meteorological factors.ResultA typical periodic pattern is evident in the seasonal trend. Specifically, following a peak in January each year, there is a rapid decline followed by a secondary peak, after which a trough occurs. The search index of night sweats increased rapidly in the first stage, slowed down in the second stage, and showed negative growth from 2011 to 2014. The correlation coefficients between the search index for night sweats and atmospheric pressure as well as average temperature, are 0.25 and-0.26, respectively.ConclusionNight sweat was connected with season. Specifically, the number of night sweat search index increased in the cold season and declined in the summer season. Night sweat was negatively connected with temperature but favorably correlated with air pressure.
Detecting anomalies in smart wearables for hypertension: a deep learning mechanism
Authors: Kishor Kumar Reddy C. , Kaza Vijaya Sindhoori , Madana Mohana R. , Alhameed Mohammed , Jeribi Fathe , Alam Shadab , Shuaib Mohammed 
Published: January 2025

IntroductionThe growing demand for real-time, affordable, and accessible healthcare has underscored the need for advanced technologies that can provide timely health monitoring. One such area is predicting arterial blood pressure (BP) using non-invasive methods, which is crucial for managing cardiovascular diseases. This research aims to address the limitations of current healthcare systems, particularly in remote areas, by leveraging deep learning techniques in Smart Health Monitoring (SHM).MethodsThis paper introduces a novel neural network architecture, ResNet-LSTM, to predict BP from physiological signals such as electrocardiogram (ECG) and photoplethysmogram (PPG). The combination of ResNet’s feature extraction capabilities and LSTM’s sequential data processing offers improved prediction accuracy. Comprehensive error analysis was conducted, and the model was validated using Leave-One-Out (LOO) cross-validation and an additional dataset.ResultsThe ResNet-LSTM model showed superior performance, particularly with PPG data, achieving a mean absolute error (MAE) of 6.2 mmHg and a root mean square error (RMSE) of 8.9 mmHg for BP prediction. Despite the higher computational cost (~4,375 FLOPs), the improved accuracy and generalization across datasets demonstrate the model’s robustness and suitability for continuous BP monitoring.DiscussionThe results confirm the potential of integrating ResNet-LSTM into SHM for accurate and non-invasive BP prediction. This approach also highlights the need for accurate anomaly detection in continuous monitoring systems, especially for wearable devices. Future work will focus on enhancing cloud-based infrastructures for real-time analysis and refining anomaly detection models to improve patient outcomes.
Protecting digital assets using an ontology based cyber situational awareness system
Authors: Almoabady Tariq Ammar , Alblawi Yasser Mohammad , Albalawi Ahmad Emad , Aborokbah Majed M. , Manimurugan S. , Aljuhani Ahmed , Aldawood Hussain , Karthikeyan P. 
Published: January 2025

IntroductionCyber situational awareness is critical for detecting and mitigating cybersecurity threats in real-time. This study introduces a comprehensive methodology that integrates the Isolation Forest and autoencoder algorithms, Structured Threat Information Expression (STIX) implementation, and ontology development to enhance cybersecurity threat detection and intelligence. The Isolation Forest algorithm excels in anomaly detection in high-dimensional datasets, while autoencoders provide nonlinear detection capabilities and adaptive feature learning. Together, they form a robust framework for proactive anomaly detection.MethodsThe proposed methodology leverages the Isolation Forest for efficient anomaly identification and autoencoders for feature learning and nonlinear anomaly detection. Threat information was standardized using the STIX framework, facilitating structured and dynamic assessment of threat intelligence. Ontology development was employed to represent knowledge systematically and enable semantic correlation of threats. Feature mapping enriched datasets with contextual threat information.ResultsThe proposed dual-algorithm framework demonstrated superior performance, achieving 95% accuracy, a 99% F1 score, and a 94.60% recall rate. These results outperformed the benchmarks, highlighting the model’s effectiveness in proactive anomaly detection and cyber situational awareness enhancement.DiscussionThe integration of STIX and ontology development within the proposed methodology significantly enhanced threat information standardization and semantic analysis. The dual-algorithm approach provided improved detection capabilities compared to traditional methods, underscoring its potential for scalable and effective cybersecurity applications. Future research could explore further optimization and real-world deployments to refine and validate the approach.
Evaluating accuracy and reproducibility of large language model performance on critical care assessments in pharmacy education
Authors: Yang Huibo , Hu Mengxuan , Most Amoreena , Hawkins W. Anthony , Murray Brian , Smith Susan E. , Li Sheng , Sikora Andrea 
Published: January 2025

BackgroundLarge language models (LLMs) have demonstrated impressive performance on medical licensing and diagnosis-related exams. However, comparative evaluations to optimize LLM performance and ability in the domain of comprehensive medication management (CMM) are lacking. The purpose of this evaluation was to test various LLMs performance optimization strategies and performance on critical care pharmacotherapy questions used in the assessment of Doctor of Pharmacy students.MethodsIn a comparative analysis using 219 multiple-choice pharmacotherapy questions, five LLMs (GPT-3.5, GPT-4, Claude 2, Llama2-7b and 2-13b) were evaluated. Each LLM was queried five times to evaluate the primary outcome of accuracy (i.e., correctness). Secondary outcomes included variance, the impact of prompt engineering techniques (e.g., chain-of-thought, CoT) and training of a customized GPT on performance, and comparison to third year doctor of pharmacy students on knowledge recall vs. knowledge application questions. Accuracy and variance were compared with student’s t-test to compare performance under different model settings.ResultsChatGPT-4 exhibited the highest accuracy (71.6%), while Llama2-13b had the lowest variance (0.070). All LLMs performed more accurately on knowledge recall vs. knowledge application questions (e.g., ChatGPT-4: 87% vs. 67%). When applied to ChatGPT-4, few-shot CoT across five runs improved accuracy (77.4% vs. 71.5%) with no effect on variance. Self-consistency and the custom-trained GPT demonstrated similar accuracy to ChatGPT-4 with few-shot CoT. Overall pharmacy student accuracy was 81%, compared to an optimal overall LLM accuracy of 73%. Comparing question types, six of the LLMs demonstrated equivalent or higher accuracy than pharmacy students on knowledge recall questions (e.g., self-consistency vs. students: 93% vs. 84%), but pharmacy students achieved higher accuracy than all LLMs on knowledge application questions (e.g., self-consistency vs. students: 68% vs. 80%).ConclusionChatGPT-4 was the most accurate LLM on critical care pharmacy questions and few-shot CoT improved accuracy the most. Average student accuracy was similar to LLMs overall, and higher on knowledge application questions. These findings support the need for future assessment of customized training for the type of output needed. Reliance on LLMs is only supported with recall-based questions.
The first photometric investigation of two short-period Algol-type eclipsing binaries: IW UMa and V367 Gem
Authors: Tian Xiao-Man , Jiang Lin-Qiao , Wang Zhi-Hua , Wang Jing-Jing 
Published: January 2025

IntroductionThe first multi-color photometric observations and low-resolution spectra of the Algol-type short-period eclipsing binary systems IWUMa and V367 Gem are presented here. The stellar atmospheric parameters of the primary stars were derived through spectral analysis. Ground-based and TESS-based light curves were analyzed.MethodsULySS software was employed to obtain atmospheric parameters by fitting full spectra with model spectra. Wilson–Devinney code was used to analyze the light curves.ResultsThe photometric solutions suggest that IW UMa and V367 Gem are both typical Algol systems in a semidetached configuration, with secondary stars filling their Roche lobes. The mass ratios of IW UMa and V367 Gem are 0.145 (3) and 0.478 (15), respectively. A third light was detected in the light curve analysis of V367 Gem with a luminosity contribution of L3/Ltotal=44.38%. Using O−C analysis, we found that the period of IW UMa is likely increasing over the long term, superposed with cyclic variation. The period of V367 Gem is decreasing at a rate of dP/dt=−7.40(± 0.43)×10−8d⋅yr−1 on a long time scale.DiscussionThe period decrease of V367 Gem may be due to the mass and angular momentum loss from the system. Additionally, a probable cyclic variation has been identified for the period of V367 Gem, which may be caused by the third body.
Research progress on the relationship between fine motor skills and academic ability in children: a systematic review and meta-analysis
Authors: Li Yucen , Wu Xin , Ye Danni , Zuo Jinye , Liu Liu 
Published: January 2025

BackgroundIn recent years, an increasing number of scholars have begun to focus on the relationship between children's motor development and school activities, with the relationship between children's fine motor skills and academic achievement being a particularly researched area. However, due to different research perspectives among scholars, the results in this field have been somewhat controversial. Therefore, this study aims to delve deeper into the relationship between children's fine motor skills and their various academic abilities through systematic review and meta-analysis.MethodEnglish databases (PubMed, Web of Science, Embase) and Chinese databases (CNKI, Wei Pu) were searched, and a quantitative meta-analysis was conducted using STATA software, along with a systematic descriptive analysis of the included literature.ResultsFrom the 1,147 documents retrieved, 11 studies were ultimately included. All meta-analysis results are significant, and there is a medium correlation between fine motor skills and reading ability, a larger correlation is observed with mathematical ability. In the subgroup analysis of each fine motor skill component and academic ability, except for the fine motor coordination, which shows only a small correlation with reading ability, the variables in the other subgroups all exhibit a medium degree of correlation. Notably, the correlation between visual-motor integration and mathematical ability is the strongest in subgroup (r = 0.47).ConclusionThe meta-analysis provides evidence supporting a positive and statistically significant correlation between preschool children's fine motor skills and learning outcomes. However, the scope of academic abilities examined in this domain is predominantly confined to mathematics and reading. Moreover, existing research largely focuses on surface-level correlational analyses, necessitating deeper exploration into the underlying mechanisms.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier (CRD42023415498).
Gas-producing potential of intestinal microbiota representatives in patients with post-infarction cardiosclerosis
Authors: Chervinets Y. V. , Stepanova E. V. , Chervinets V. M. , Belyaev V. S. 
Published: January 2025

Relevance. Analyzing the spectrum of gaseous signaling molecules (GSM) produced and consumed by the intestinal microbiota in patients with post-infarction cardiosclerosis (PICS) is essential for understanding their potential role in the pathogenesis of cardiovascular complications and for identifying strategies to address these conditions. Purpose. To investigate the prevalence and abundance of specific intestinal microbiota representatives isolated from patients with post-infarction cardiosclerosis (PICS) and to examine the species diversity and concentrations of microbial gaseous signaling molecules in this population.Materials and methods. This case-control study involved the analysis of stool samples from 35 healthy individuals (14 men and 21 women) aged 45–60 years (mean age: 51.8 ± 4.8 [49; 56] years) and 34 patients with PICS (19 men and 15 women) aged 40–84 years (mean age: 64.5 ± 8.1 [53; 72] years). Gaseous signaling molecules (CO, CH₄, NO, H₂S) were quantified using gas chromatography.Results. Patients with post-infarction cardiosclerosis exhibited a functional microbial imbalance marked by disrupted production of gaseous signaling molecules (GSM) compared to healthy individuals. In the PICS group, Lactobacillus spp. were observed to exclusively absorb NO, whereas in healthy individuals, Lactobacilli produced NO at a statistically significant higher mean concentration of 5.283 µg/mL (p < 0.001). Additionally, Staphylococcus aureus in the PICS group produced CO at levels 880 times higher than those observed in healthy individuals, a difference that was also statistically significant (p < 0.001).Conclusion. Restoring the functional activity of the intestinal normal microbiota in patients with PICS is essential. Through the production of gaseous signaling molecules (primarily NO and CO), the normal microbiota can support neuromodulatory, cardiomodulatory, immunomodulatory, and other beneficial functions that are critical for the rehabilitation process.
بقاء الحكم بعد زوال علته الأصلية أو دونها- دراسة صرفية تطبيقية.
Authors: أحمد عبدالمؤمن محمود 
Published: January 2025

فكرة هذا البحث تدور حول العلاقة بين العلّة والحكم، فلا يخفى ما للعلّة من أثر في الحكم، فهي الباعثة عليه والمؤثرة فيه؛ ومن ثَمّ يدور الحكم مع علّته وجودًا وعدمًا، غير أن هناك مسائل صرفية خرجت عن هذا الأصل، بَقِيَ فيها الحكم مع زوال علّته أو دونها، وأوّل من أشار إلى هذا ونَبَّهَ عليه من اللغويين ابن جنّي في كتابه الخصائص 3/157؛ حيث ترجم له بقوله: (باب في بقاء الحكم مع زوال العلة).
والهدف من البحث الوقوف على تلك المسائل التي خالفت هذا الأصل، وبيان مقاصد الصرفيين فيها.
ومشكلة البحث التي حاولت الإجابة عنها: هل بقاء الحكم بعد زوال علّته دليل على فساد العلّة وَوَهْنِها؟ وما مقاصد الصرفيين في بقاء الحكم بعد فَقْد علّته؟
واتّبعت المنهج الوصفي القائم على تتبّع تلك المسائل وتحليلها، ثم بيان مقصد الصرفيين في بقاء الحكم بعد زوال علّته الأصيلة.
وكان من أهم النتائج التي خلُصت إليها: أن زوال الحكم بزوال علّته متوقّفٌ على كون العلة تامّة قطعية مؤثّرة، فينتفي الحكم إذا زالت علّته، وما عدا ذلك فيجوز بقاء الحكم برغم زوال علّته إذا كانت علّته ظنّية. وأن الصرفيين حملوا بقاء الحكم مع زوال علّته الأصيلة على مقاصدَ عامّة خلفت العلّة الأصيلة للحكم، كالأخذ بالاستحسان، وطرد الباب على سنن واحد، والاستخفاف، وأمن اللبس، والحمل على الكثير، وتقوية الشبه بين النظيرين، والطارئ لا يزيل حكم الثابت.
ثم أوصى البحث بدراسة الفكر اللغوي لابن جنّي -ولاسيّما ما تفرّد به- في ضوء الدراسات اللغوية الحديثة.
Modeling of Multi-Frequency Microwave Backscatter and Emission of Land Surface by a Community Land Active Passive Microwave Radiative Transfer Modeling Platform
Authors: Zhao Hong , Zeng Yijian , Hofste Jan G. , Duan Ting , Wen Jun , Su Zhongbo 
Published: January 2025

Utilizing combined active and passive microwave signals at different frequencies provides complementary information of soil and vegetation physical states for ecosystem monitoring. We present here a prototype of a unified multi-frequency backscatter and emission simulator called the Community Land Active Passive Microwave Radiative Transfer Modeling Platform (CLAP). By assessing CLAP’s performance using in situ and satellite multi-frequency measurements of grassland backscatter and emission at the Maqu site, the impacts of different grass properties (i.e., structure, water, and temperature dynamics) and soil conditions (i.e., varied moisture and temperature profiles) are investigated. Results indicate that CLAP with cylinder parameterization for vegetation representation simulates grassland backscatter at X- and C-bands better than the disc parameterization does (e.g., root mean square errors [RMSEs] of 2.1 vs. 3.7 dB) during summer. Dynamic vegetation water content (VWC) partially explains diurnal variations of observed signals at low frequencies, while changes in vegetation temperature predominantly affect high-frequency signals. The study also reveals that grassland optical depth is frequency-dependent, with diurnal variation linked to VWC irrespective of frequency. CLAP using the cylinder parameterization and either the in situ measurements or the process model outputs simulates well the observed C-band backscatter at vertical transmit/vertical receive polarization (e.g., RMSE of 1.9 dB) during winter, while it cannot simulate signal dynamics at other bands during this period. Achieving consistent matches between CLAP modeled and observed signals across all frequencies, particularly for both passive and active signals, remains challenging. The limitations discussed in the study shed light on future directions to enhance CLAP’s modeling capability.
Tracing Economic Transformation: The Impact of Nationalisation and Disinvestment in India (1990-2023)
Authors: Harshitha P 
Published: January 2025

This study looks at the economic development of India over the period of time ranging from 1990 upto 2023 with reference to two broad policies, that is Nationalisation and Disinvestment. Nationalisation policies, the key focus in the years after the independence, was about government ownership of certain sectors such as coal, insurance, and banking in order to promote selfsustenance and fair distribution of resources. However, as time passed by, many PSUs were not functioning at optimum levels and were incurring losses and hence the need for structural changes became inevitable. The 1990s marked the advent of a new economic order with liberalization being the turnaround point along with Disinvestment policies which became the bedrock of reforms aimed at enhancing efficiency, minimising budgetary pressures and increasing the role of the private sector. This paper stresses on the privatisation of Air India and Life Insurance Corporation (LIC) listing as milestones while at the same time mentioning the limiting factors including political interference, disputes about valuation and unemployment.While analysing and discussing the impacts of Nationalisation on social welfare and the effects of Disinvestment in the enhancement of productive efficiency respectively, the study attempts to look at the two policies in their interaction. The Discussion emphasises the need for a hybrid ownership model that combines the features of private enterprises and public tutelage. Such a perspective enhances coverage, meets evolving economic needs, and is integral to sustainable development.