bluuebunny/crossref_metadata_2025 huggingface dataset:
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

Background

Burnout 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.

Methods

A 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.

Results

A 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%.

Conclusions

Burnout 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

Background

Night 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.

Purpose

To investigate the relationship between sweat nights and season.

Method

The 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.

Result

A 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.

Conclusion

Night 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

Introduction

The 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).

Methods

This 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.

Results

The 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.

Discussion

The 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

Introduction

Cyber 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.

Methods

The 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.

Results

The 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.

Discussion

The 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

Background

Large 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.

Methods

In 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.

Results

ChatGPT-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%).

Conclusion

ChatGPT-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.
Non-linear research on artificial intelligence empowering green economic efficiency under integrated governance framework
Authors: Song Zhichun , Deng Yao
Published: January 2025

Artificial intelligence (AI) plays a pivotal role in the development of the green economy. This paper examines the impact of artificial intelligence (AI) on green economic efficiency (GEE) using panel data from 30 provinces in China spanning 2011–2020. A multiple linear regression model, alongside various endogeneity and robustness tests, is applied to ensure reliable findings. The empirical results indicate that AI significantly enhances GEE. However, the marginal effect of AI on GEE is influenced by different governance approaches. In terms of policy governance, excessive market-based environmental regulation (MER) diminishes the marginal impact of AI, while stronger administrative-command environmental regulations (CER) and informal environmental regulations (IER) amplify it. Regarding technological governance, substantive green technological innovations (SUG) reduce AI's marginal effect, whereas symbolic green technological innovations (SYG) may increase it. Notably, the threshold effect of SUG surpasses that of SYG. In legal governance, both administrative and judicial intellectual property protections reduce the marginal effect of AI, though administrative protection (AIP) exhibits a more significant threshold effect than judicial protection (JIP). These findings offer practical insights for optimizing governance strategies to maximize AI's role in promoting GEE. These insights highlight the need for balanced governance to maximize AI's role in sustainable development. Policymakers should tailor regulations and encourage regional collaboration to harness AI's spatial spillover effects. Enterprises can leverage AI-driven innovations to align growth with ecological goals, fostering coordinated green development.
Bridging silos through governance innovations: the role of the EU cities mission
Authors: Buylova Alexandra , Nasiritousi Naghmeh , Bergman Jennifer , Sanderink Lisa , Wickenberg Björn , Casiano Flores Cesar , McCormick Kes
Published: January 2025

Cities and local governments are increasingly under pressure to accelerate transformative change in energy and climate transitions. To help cities in their climate actions, the European Commission (EC) has established the EU Cities Mission, which aims for climate neutrality by 2030 for participating cities. The literature argues that one of the main obstacles to accelerating decarbonization lies in organizational divisions and other forms of structural silos. One of the possible ways to address these challenges and accelerate transformation is through governance innovations. The EU Cities Mission is a governance innovation that aims to incentivize and support climate and energy transitions in cities. In this paper, we critically assess the EU Cities Mission’s framework and implementation plan in terms of its potential and possible gaps in addressing different types of silos. To do so, we develop an analytical framework based on academic literature that outlines types of silos and strategies for addressing them. Our results show that key EU Cities Mission documents include several strategies to bridge silos, but that some silos are less frequently addressed. This is particularly the case for silos that rely on political leadership. The paper concludes by drawing out the implications of our findings for the scholarly literature and practice.
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

Introduction

The 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.

Methods

ULySS software was employed to obtain atmospheric parameters by fitting full spectra with model spectra. Wilson–Devinney code was used to analyze the light curves.

Results

The 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.

Discussion

The 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

Background

In 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.

Method

English 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.

Results

From 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).

Conclusion

The 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 Registration

https://www.crd.york.ac.uk/PROSPERO/, identifier (CRD42023415498).
Mental preparation of karateka for sports competition in kata
Authors: Piepiora Paweł Adam , Jurczyk Julia Barbara , Vveinhardt Jolita
Published: January 2025

Mental preparation for sports competition in karate is significant, as it is deeply embedded in the philosophical and ethical values that underpin this combat method. In practice, the mental preparation of karateka varies depending on the type of competition, for example preparation for kata (forms) and kumite (fights). Thus, this perspective offers a concise account of the authors' viewpoint on the leading mental skills required of kata competitors. It is argued that self-esteem, inner speech, imagination, visualisation, values and personality play a significant role in the development of confidence. In addressing stress, attention was directed towards the role of arousal, coping strategies, Jacobson's progressive relaxation, Schultz's autogenic training, biofeedback, schemas and scripts. Effective management of anxiety relies on the utilisation of mindfulness and desensitisation techniques. In maintaining attention, effective attention management, attention styles, the ability to filter out distractions, mantras and affirmations are significant. Conversely, practicing tasks that induce a Stroop effect can improve executive function skills. The above elements of mental preparation for kata competitors are universally applicable to all competitors in this field, yet they are not the sole elements that may be employed. Given the individual predispositions of kata competitors, other mental training techniques may also be applicable.
Protective effects of 18β-glycyrrhetinic acid on Pasteurella multocida–induced vascular inflammatory injury in mice
Authors: Lu Qirong , Wang Luyao , Jiang Xueping , Han Wantong , Guo Pu , Liu Yu , Fu Shulin , Xiong Jianglin , Wu Zhongyuan , Qiu Yinsheng
Published: January 2025

Pasteurella multocida (Pm) is a widespread zoonotic pathogen with the ability to infect wild animals, livestock, and humans. Pm infection can cause haemorrhagic pneumonia, indicating that the pathogenesis involves serious vascular injury and inflammation. 18β-Glycyrrhetinic acid (GA) has cardiovascular protective and anti-inflammatory effects, but its effect on vascular injury caused by Pm infection is not clear. This study focused on the protective effects of GA on Pm-induced vascular inflammatory injury in mice. The results showed that GA intervention significantly improved the survival rate and the changes in haematological and biochemical parameters caused by Pm infection in mice. Haematoxylin and eosin staining revealed that GA delayed the progression of vascular injury, including abnormalities in elastic fibres, local rupture of the vascular intima, and inflammatory cell infiltration in response to Pm infection. The immunohistochemical results showed that after the GA intervention, the vascular inflammatory response in Pm-infected mice was alleviated. These protective effects may be related to the reduced expression of poly (ADP-ribose) polymerase-1, high mobility group box 1, interleukin-1β, and interleukin-18 in vascular tissue by GA. These findings suggest that GA inhibits the activation of inflammation to protect vascular injury in vivo. Hence, GA exhibits therapeutic potential in the treatment of vascular injury.
Application of Deep Learning to Identify Flutter Flight Testing Signals Parameters and Analysis of Real F-18 Flutter Flight Test Data
Authors: Abou-Kebeh Sami , Gil-Pita Roberto , Rosa-Zurera Manuel
Published: January 2025

Aircraft envelope expansion during the installation of new underwing stores presents significant challenges, particularly due to the aeroelastic flutter phenomenon. Accurate modeling of aeroelastic behavior often necessitates flight testing, which poses risks due to the potential catastrophic consequences of reaching the flutter point. Traditional methods, like frequency sweeps, are effective but require prolonged exposure to flutter conditions, making them less suitable for transonic flight validations. This paper introduces a robust deep learning approach to process sine dwell signals from aeroelastic flutter flight tests, characterized by short data lengths (less than 5 s) and low frequencies (less than 10 Hz). We explore the preliminary viability of different deep learning networks and compare their performances to existing methods such as the PRESTO algorithm and Laplace Wavelet Matching Pursuit estimation. Deep learning algorithms demonstrate substantial accuracy and robustness, providing reliable parameter identification for flutter analysis while significantly reducing the time spent near flutter conditions. Although the results with the networks trained show less accuracy than the PRESTO algorithm, they are more accurate than the Laplace Wavelet estimation, and the results are promising enough to justify extended investigation on this area. This approach is validated using both synthetic data and real F-18 flight test signals, which highlights its potential for real-time analysis and broader applicability in aeroelastic testing.
Vibration Mitigation in the Transport of Fruit Boxes Using 3D-Printed Devices
Authors: Sanchez-Cachinero Pedro , Aguilar-Porro Cristina , Sola-Guirado Rafael R.
Published: January 2025

The transport of freshly post-harvested fruit to its collection point is mainly achieved using trailers over uneven terrain, which generates impacts and vibrations that negatively affect the quality of the fruit. Although some solutions to mitigate these effects have been proposed in previous studies, none of them are applied directly to the source of the problem, i.e., the transport boxes. In this context, metamaterial sheets inspired by the design of quasi-zero stiffness isolators (QZSs) open up the possibility of exploring ways of vibration isolation thanks to their associated nonlinear characteristics. In this work, ABS sheets with different internal geometries were manufactured and compared as possible bottoms of transport boxes. Vibration reduction not only protects the physical integrity of the fruit, avoiding visible damage such as bumps or bruises, but also preserves its chemical properties, such as texture and freshness, which directly impacts its shelf life and presentation for sale. The design variables analyzed for these geometries included the number of ribs, their thickness and their angle of inclination. In these specimens, their behavior to impact-type forces and their experimental dynamic behavior were studied using an electromagnetic shaker against a sinusoidal signal and against the uniaxial vibration recorded at the base of a trailer in a real rural route. The results showed that the specimens with a rib angle of 30° and a thickness of 0.4 mm showed the best impact performance and a higher amplification of vibration transmissibility in the steady state. In the presence of the signal recorded on the route, transmissibility reduction percentages between 13% and 19% were obtained in the principal acceleration impact.
Effects of Different Intertillage Practices on Soil Biochemical Properties and Soybean Yield in Soybean Fields
Authors: Zhang Mingcong , Zhou Yanhong , Li Chenglin , Yuan Cheng , Shan Mingfen , Fan Yuxin , Yu Zhongxia , Ren Linfeng , Cui Liu , Wang Chen
Published: January 2025

Heilongjiang Province, China, is a major soybean-producing area where low temperatures in early spring and poor soil moisture retention are the main limiting factors leading to low soybean yields. To improve land productivity, this study was conducted in Heilongjiang Province, China, from 2020–2021 via a field plot trial method, where four different intertillage treatments were set up in 2020: conventional intertillage (T1), early intertillage (T2), conventional subsoiling (T3), and early subsoiling (T4). In 2021, the effects of intertillage on soil biochemical characteristics and soybean yield formation were systematically analyzed under the T5 treatment (subsoiling at the stage of full development from the fourth to the fifth compound leaf) and the T6 treatment (soil culture at the stage of full development from the fourth to the fifth compound leaf). The results of the experiment revealed that deep loosening in advance improved the chemical properties of the soil. Compared with those of the T1 treatment, the contents of the organic carbon, total nitrogen, and available phosphorus and potassium in the early subsoiling treatments (T4, T5, and T6) were significantly increased. Intertillage time and depth had a strong influence on the soil microbial characteristics. Early medium intertillage could significantly increase the species abundance of bacteria and fungi in the soil, whereas subsoiling could increase the relative abundance ratio and uniformity of the dominant species. Compared with that in the T1 treatment, the yield of soybean in the T4 treatment was significantly greater, with yield increases ranging from 18.71% to 19.36%. In summary, the intertillage measures of one deep loosening and one medium soil cultivation at the stage of full development from the fourth to the fifth compound leaf and one large soil cultivation at the stage of full development from the sixth to the seventh compound leaf can be adopted on the basis of early deep loosening 4–5 d after soybean sowing to achieve high soybean yields. The results provide a theoretical basis for increasing the yield and efficiency of soybean fields under corn–soybean rotation, as well as innovation and development of cultivation systems
Analysis and Verification of a Slope Steering Model of TRVs in Hilly and Mountainous Environments
Authors: Duan Luojia , Kang Kaibo , Chen Shiying , Du Zixing , Zhang Longhai , Liu Zhijie , Yang Fuzeng , Wang Zheng
Published: January 2025

Compared to wheeled vehicles, tracked robotic vehicles have less ground pressure, greater traction adhesion, and stronger climbing and obstacle crossing capabilities, making them suitable for agricultural production in hilly areas. Good steering performance directly relates to the mobility performance and operating efficiency of tracked robotic vehicles. Affected by the ground slope, the ground pressure distribution of the vehicle’s two tracks is uneven, leading to changes in its steering performance. Therefore, analyzing and researching the steering performance of a tracked robotic vehicle under sloped conditions is of great significance. This study establishes a slope steering model for tracked robotic vehicles based on a ground pressure model of the multi-peak varying amplitude cosine distribution and the shearing displacement relationship between the track and the ground, and analyzes the impact of vehicle structural parameters, road surface parameters, and steering parameters on steering performance. To verify the proposed theoretical model, multi-body dynamics software is utilized for simulation modeling and analysis. Turning tests on different slopes are conducted on a “soil–machine–crop” integrated experimental platform. The relative error between the numerical analysis results and the virtual simulation software’s results is less than 12%, and the relative error between the numerical analysis results and the experimental results is less than 10.3%; the good consistency between the theoretical results and the simulation’s results and the experimental results indicates that the model is, indeed, correct and effective. The established steering model can provide a theoretical basis for the design and control of new steering mechanisms for agricultural tracked robotic vehicles.
Selective Breeding for Genetic Improvement of Nile tilapia (Oreochromis niloticus Linnaeus, 1758) in Uganda: Current Status, Challenges, and Future Perspectives
Authors: Abaho Ivan , Kwikiriza Gerald , Atukwatse Faith , Izaara Andrew A. , Ekwangu Joseph , Baguma Sylvester D. , Kubiriba Jerome , Kasozi Nasser
Published: January 2025

Nile tilapia (Oreochromis niloticus) aquaculture continues to significantly contribute to the growth of the aquaculture sector in Uganda. However, its production is beset by erratic and unreliable seed supply. Also, most hatcheries practice inbreeding of broodstock, resulting in inferior seed characterized by low growth rates. As such, a selective breeding program is necessary to readily avail fast-growing seed that respond to farmers’ needs. The present review consolidates available information on developing a Nile tilapia breeding program in Uganda. The article discusses the significance of genetic improvement, drawing lessons from successful Nile tilapia selective breeding programs in other countries. From a literature review, no systematic Nile tilapia selective breeding program was traceable in Uganda. Scanty information on the selective breeding efforts of the species was available, with little evidence of selection for improved performance. Overall, the national capacity for aquaculture research and development, including fish breeding and strain improvement, was weak and poorly funded. The review recommends purposive support for developing a systematic strain improvement breeding program, which will be a source of improved broodstock and seed for hatcheries and farmers, respectively. The program would guide the formulation of standard operating procedures for quality seed production towards ensuring sustainable aquaculture growth in Uganda.
Genetic Diversity and Population Structure of Macrobrachium nipponense Populations in the Saline–Alkaline Regions of China
Authors: Iko Romaric , Gao Zijian , Jiang Sufei , Xiong Yiwei , Zhang Wenyi , Qiao Hui , Jin Shubo , Fu Hongtuo
Published: January 2025

The oriental river prawn Macrobrachium nipponense is of great economic importance in China. However, its culture in the saline–alkaline regions is limited. This study used D-loop region sequences to analyze the genetic diversity and population structure across the saline–alkaline regions of China. A total of 264 individuals were successfully sequenced from nine sites, including Daqing in Heilongjiang Province (HLJ), Songyuan in Jilin Province (JL), Ulanhot in the east of inner Mongolia (NMG), Jingtai in Gansu Province (GS), Dongying in Shandong Province (SD), Dongtai in Jiangsu Province (JS), Nanchang in Jiangxi province (JX), Tianjin in mainland China (TJ), and Yinchuan in Ningxia Province (NX). In addition, 89 haplotypes were defined. The haplotype diversity (h) and nucleotide diversity π showed remarkable genetic diversity in the JS, NX, JL, and TJ sites. It was found that the genetic variation within sites was relatively greater. The genetic distance (D) and the pairwise genetic differentiation index (FST) revealed that SD and GS are closely related. The FST values among the nine sites were significant except for the one between SD and HLJ (p-value > 0.05). The cluster analyses and the phylogenetic tree identified two main groups. There is an association among sites by geographic location, and the JS site shows higher diversity. The results of this study provide basic information for the protection and development of M. nipponense resources in the saline–alkaline regions of China.
SQL Injection Detection Based on Lightweight Multi-Head Self-Attention
Authors: Lo Rui-Teng , Hwang Wen-Jyi , Tai Tsung-Ming
Published: January 2025

This paper presents a novel neural network model for the detection of Structured Query Language (SQL) injection attacks for web applications. The model features high detection accuracy, fast inference speed, and low weight size. The model is based on a novel Natural Language Processing (NLP) technique, where a tokenizer for converting SQL queries into tokens is adopted as a pre-processing stage for detection. Only SQL keywords and symbols are considered as tokens for removing noisy information from input queries. Moreover, semantic labels are assigned to tokens for highlighting malicious intentions. For the exploration of correlation among the tokens, a lightweight multi-head self-attention scheme with a position encoder is employed. Experimental results show that the proposed algorithm has high detection performance for SQL injection. In addition, compared to its lightweight NLP counterparts based on self-attention, the proposed algorithm has the lowest weight size and highest inference speed. It consumes only limited computation and storage overhead for web services. In addition, it can even be deployed in the edge devices with low computation capacity for online detection. The proposed algorithm therefore is an effective low-cost solution for SQL injection detection.
Effects of Adding Livestock and Poultry Manure to Field Strips of Straw on Soil Organic Carbon Components
Authors: Zhao Xinyu , Ji Jiapeng , Wu Jinggui
Published: January 2025

This experiment takes typical chernozem soil as the research object to investigate the effects of adding various livestock and poultry manures during in situ strip composting of corn straw on the decomposition characteristics of the straw and the soil organic carbon content. This study set up a total of four treatments under the condition of following the equal carbon principle: (1) corn straw (T1); (2) corn straw + chicken manure (T2); (3) corn straw + cow dung (T3); (4) corn straw + decomposition agent (T4). The cumulative mass loss rate of straw in the treatment of adding livestock and poultry manure ranged from 51.60% to 54.33%, with a carbon release rate of 75.34% to 76.64%. Correlation analysis revealed a significant positive relationship between SOC, straw mass loss rate, and straw carbon release rate. Furthermore, there was a significant positive correlation between organic carbon components such as DOC, EOC, POC, and MBC with CPMI, while showing a significant negative correlation with the oxidation stability coefficient (KOS). Incorporating corn straw into livestock and poultry manure and returning it to the field in in situ strips effectively enhances the decomposition process of straw, leading to an increase in the organic carbon content of chernozem soil.
Artificial Intelligence (ChatGPT) and Bloom’s Taxonomy in Theoretical Computer Science Education
Authors: Habiballa Hashim , Kotyrba Martin , Volna Eva , Bradac Vladimir , Dusek Martin
Published: January 2025

The study focuses on evaluating the performance of AI-based tools, specifically ChatGPT versions 3.5 and 4.0, in comparison to human students in the field of Theoretical Computer Science Education. The experiment aims to assess the capabilities of both AI and human subjects in solving learning tasks based on Bloom’s Taxonomy. The primary objectives of the study are to determine the educational performance of AI and students, identify areas where students may outperform AI in learning tasks, and evaluate the normalized overall results of educational performance with equal assignment weights. The assessment included testing on various types of Bloom’s Learning Task Taxonomy (LTT) based on specific Learning Objectives (LOs). Hypotheses were formulated for quantitative and qualitative analysis to compare the performance of AI and human subjects. Quantitative analysis revealed engaging results regarding the educational performance evaluation with AI-based tools. While some differences were observed in the performance of AI and students, the normalized overall results with equal assignment weights did not show statistically significant differences. The study highlighted the advantages and disadvantages of both humans and AI bots in solving learning tasks. The study concludes that there are areas where students may have an advantage over AI in tasks requiring understanding, evaluation, and creative thinking. Recommendations are provided for educators on utilizing AI-based tools in education, emphasizing the coexistence of AI possibilities with well-designed assignments. The findings suggest that AI has a valuable role in education, and a thorough analysis of teaching approaches and student evaluation is essential in leveraging artificial intelligence effectively.
Experimental and Numerical Simulation Study of Ultrasonic Vibration Effect on Abrasive Grain Distribution and Movement Behavior in Laser Cladding Melt Pool for Abrasive Layer Fabrication
Authors: Wang Zixuan , Chen Ying , Guan Chuang , Du Jiahui , Yu Tianbiao , Zhao Ji , Zhao Jun
Published: January 2025

The ultrasonic vibration laser cladding method is a material-saving and green method to fabricate super abrasive structured grinding wheels. However, the mechanism of the ultrasonic vibration’s effect on the movement behavior of abrasive grains in the laser cladding process has not been clarified. To address this, the impacts of ultrasonic vibration on the abrasive grain distribution and movement behavior were experimentally studied, and the numerical simulation method was introduced to simulate the ultrasonic vibration laser cladding process. A two-dimensional Gaussian cross-section heat source model was developed, and its energy density conformed to a Gaussian distribution in both space and time. The simulations of the temperature and fluid fields of the melt pool were carried out. The CBN abrasive grains in the melt pool were subjected to gravity, the buoyancy force, the drag force of the metal fluid, and the sonophoretic radiation force of the ultrasonic vibration. Based on them, the effects of ultrasonic vibration on the movement behaviors and trajectories of the CBN abrasive grains were analyzed. The influence of the ultrasonic amplitude on the distribution of abrasive grains was studied. The simulation results revealed that the abrasive grains could be uniformly distributed on the surface of the cladding layer during the ultrasonic vibration laser cladding process.
Extraction of Carotenoids from Pumpkin (Cucurbita moschata) and Spinach (Spinacia oleracea) Using Environmentally Friendly Deep Eutectic Solvents (DESs)
Authors: Tanrıver Koray , Bilgin Mehmet , Şahin Sevgili Selin , Toprakçı Yüksel İrem , Kurtulbaş Şahin Ebru
Published: January 2025

The annually wasted amount of food has surpassed 1 billion metric tons. Food waste is considered as an important source for the recovery of bioactive compounds, such as carotenoids. There is a demand for antioxidants, nutraceuticals and natural colorants in various industries and carotenoids are one of the commonly used compounds that fit this description. Pumpkin and spinach waste, whose combined amount is over 2 million metric tons, contains bioactive compounds and these wasted foods could be utilized for the recovery of carotenoids. Carotenoids are hydrophobic molecules; therefore, commercial extraction processes often use highly non-polar solvents, and these are rarely environmentally friendly. The aim of this research was to develop effective extraction processes for carotenoids from pumpkin and spinach using environmentally friendly green chemicals. A series of deep eutectic solvents (DESs) composed with L-menthol and carboxylic aliphatic acids were made for the extraction of carotenoids from pumpkin (Cucurbita moschata) and spinach (Spinacia oleracea) via mechanical mixing–assisted extraction (MMAE) and homogenization-assisted extraction (HAE). Response surface methodology (RSM) and analysis of variance (ANOVA) were used to analyze the data and optimization. The DESs composed from L-menthol and propionic acid had the best effect on the extraction of total carotenoid content (TCC) (represented as β-carotene) from pumpkin and spinach via solutions with 1:2 and 1:4 molar ratios, respectively. The yield of carotenoid extraction is expressed in μg-β-carotene/g of pumpkin or spinach. Under the calculated optimum conditions, the yields are estimated to be 11.528 μg-β-carotene/g-pumpkin for the MMAE method, 8.966 μg-β-carotene/g-pumpkin for the HAE method, 16.924 μg-β-carotene/g-spinach for the MMAE method and 18.870 μg-β-carotene/g-spinach for the HAE method.
Optimizing Thermal Comfort in Urban Squares of Hot-Humid Regions: A Case Study Considering Tree Growth, Species, and Planting Intervals
Authors: Xiao Yixuan , Huang Yong , Pan Xinchen
Published: January 2025

The worsening urban thermal environment has become a critical challenge in many cities. Trees, as vital components of urban green spaces, provide multiple ecosystem services, especially in improving the microclimate. However, limited studies address how morphological changes during tree growth influence their cooling benefits. This study combined the tree growth model with ENVI-met to simulate 27 scenarios in a subtropical urban square, considering three planting intervals, three urban tree species, and three growth stages to evaluate their daytime thermal impacts. The key findings include: (1) Tree size and planting intervals are more important than tree quantity in enhancing thermal comfort. (2) Reducing intervals by 2 m enhances cooling effects but minimally affects PET (physiological equivalent temperature). (3) Increasing DBH (diameter at breast height) significantly improves cooling. For every 10 cm increase in DBH, Michelia alba, Mangifera indica, and Ficus microcarpa L. f. reduced solar radiation by 19.54, 18.09, and 34.50 W/m2, and mean radiant temperature by 0.61 °C, 0.68 °C, and 1.35 °C, respectively, while decreasing PET by 0.23 °C, 0.23 °C, and 0.46 °C. These findings provide empirical evidence and practical recommendations for designing comfortable open spaces in subtropical cities.
Optimizing Microclimate Comfort in Macao: ENVI-Met Simulation of High-Density Urban Layouts Under the Climate in the Lingnan Area
Authors: Zhou Xun , Sun Yichen , Li Tianyu , Zhou Yaming , Tan Tao , Yu Chengzhi , Dai Siyang
Published: January 2025

In the context of increasing urbanization, understanding the influence of urban spatial layouts on microclimatic conditions has become crucial, particularly in high-density reclaimed areas like those in the Macao Peninsula, where optimizing thermal comfort is essential for enhancing residents’ quality of life and promoting sustainable urban development. For this study, we assessed high-density reclaimed plots on the Macao Peninsula for a detailed case study. We conducted on-site measurements along with simulations using ENVI-met software, comparing datasets to ensure model accuracy and reliability. Through careful adjustment of simulation parameters to accurately reflect real-world conditions, we found that building layout and size have a significant effect on the Physiologically Equivalent Temperature (PET). Notably, in the unique climate of the Lingnan area, courtyard layouts demonstrably block external heat and wind, fostering a significantly more comfortable internal microclimate. This design strategy provides not only generous public space for fostering community activities and stronger social bonds, but also creates a visually appealing and cohesive aesthetic. This research seeks to improve resident comfort, optimize urban energy efficiency, and promote sustainable urban development principles. Accordingly, our findings serve as a valuable adaptation reference for urban environmental planning initiatives under the climate in the Lingnan area.
Exploring the Nuclear Chart via Precision Mass Spectrometry with the TITAN MR-TOF MS
Authors: Czihaly Annabelle , Beck Soenke , Bergmann Julian , Brown Callum L. , Brunner Thomas , Dickel Timo , Dilling Jens , Dunling Eleanor , Flowerdew Jake , Fusco Danny , Graham Leigh , Hockenbery Zach , Izzo Chris , Jacobs Andrew , Kootte Brian , Lan Yang , Malbrunot-Ettenauer Stephan , Millán Fernando Maldonado , Mollaebrahimi Ali , Leistenschneider Erich , Lykiardopoulou Eleni Marina , Mukul Ish , Paul Stefan F. , Plaß Wolfgang R. , Reiter Moritz Pascal , Scheidenberger Christoph , Tracy James L. , Kwiatkowski A. A.
Published: January 2025

Isotopes at the limits of nuclear existence are of great interest for their critical role in nuclear astrophysical reactions and their exotic structure. Experimentally, exotic nuclides are challenging to address due to their low production cross-sections, overwhelming amounts of contamination, and lifetimes of typically less than a second. To this end, a Multiple-Reflection Time-of-Flight mass spectrometer at the TITAN-TRIUMF facility was built to determine atomic masses. This device is the preferred tool to work with exotic nuclides due to its ability to resolve the species of interest from contamination and short measurement cycle times, enabling mass measurements of isotopes with millisecond half-lives. With a relative precision of the order 10−7, we demonstrate why the TITAN MR-TOF MS is the tool of choice for precision mass surveys for nuclear structure and astrophysics. The capabilities of the device are showcased in this work, including new mass measurements of short-lived tin isotopes (104–107Sn) approaching the proton dripline as well as 89Zr, 90Y, and 91Y. The last three illustrate how the broadband surveys of MR-TOF MS reach beyond the species of immediate interest.