bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impact of Reactor Architecture on Thermal Management and Hydrogen Discharge from LaNi 5 -H 2 Metal Hydride Beds Engineered with Phase Change Material
Authors: Chibani Atef , Merouani Slimane , Boucetta Chahrazed , Boukhari Ali , Keddouda Abdelhak , Badji Riad , Gouga Samir , Lamiri Leila 
Published: October 2024

Abstract

The performance of solid-state hydrogen storage devices is critically influenced by the design of the heat exchanger. This study explores various reactor designs, incorporating different configurations of phase change material (PCM) within the reactor walls to optimize thermal management. A CFD-based model, implemented in ANSYS Fluent, was utilized to simulate heat and mass transport during hydrogen discharge from a LaNi5-H2 metal hydride vessel. The simulations focused on parameters such as temperature distribution, melt fraction, enthalpy, and heat exchange efficiency, including key metrics like heat transfer area and Nusselt number. Among the configurations tested, the design with PCM placed laterally alongside the metal hydride exhibited superior performance, leading to faster heat dissipation and more efficient hydrogen discharge. This optimal design enabled complete hydrogen desorption within approximately 2 hours, compared to 3 to 4 hours for other structures, making it suitable for industrial-scale applications. Moreover, the PCM not only contributed to effective heat transfer but also played a crucial role in regulating the thermal dynamics of the system, ensuring a more controlled and efficient hydrogen release process.
The role of MEGF10 in myoblast fusion and hypertrophic response to overload of skeletal muscle
Authors: Richardson Louise , Hughes Ruth , Johnson Colin A , Egginton Stuart , Peckham Michelle 
Published: October 2024

Abstract

Biallelic mutations in multiple EGF domain protein 10 (MEGF10) gene cause EMARDD (early myopathy, areflexia, respiratory distress and dysphagia) in humans, a severe recessive myopathy, associated with reduced numbers of PAX7 positive satellite cells. To better understand the role of MEGF10 in satellite cells, we overexpressed human MEGF10 in mouse H-2kb-tsA58 myoblasts and found that it inhibited fusion. Addition of purified extracellular domains of human MEGF10, with (ECD) or without (EGF) the N-terminal EMI domain to H-2kb-tsA58 myoblasts, showed that the ECD was more effective at reducing myoblast adhesion and fusion by day 7 of differentiation, yet promoted adhesion of myoblasts to non-adhesive surfaces, highlighting the importance of the EMI domain in these behaviours. We additionally tested the role of Megf10 in vivo using transgenic mice with reduced (Megf10+/−) or no (Megf10−/−) Megf10. We found that the extensor digitorum longus muscle had fewer Pax7 positive satellite cell nuclei and was less able to undergo hypertrophy in response to muscle overload concomitant with a lower level of satellite cell activation. Taken together, our data suggest that MEGF10 may promote satellite cell adhesion and survival and prevent premature fusion helping to explain its role in EMARDD.
A Robust Deep Learning System for Motor Bearing Fault Detection: Leveraging Multiple Learning Strategies and a Novel Double Loss Function
Authors: Tran Khoa , Pham Lam , Nguyen Vy-Rin , Nguyen Ho-Si-Hung 
Published: October 2024

Abstract

Motor bearing fault detection (MBFD) is vital for ensuring the reliability and efficiency of industrial machinery. Identifying faults early can prevent system breakdowns, reduce maintenance costs, and minimize downtime. This paper presents an advanced MBFD system using deep learning, integrating multiple training approaches: supervised, semi-supervised, and unsupervised learning to improve fault classification accuracy. A novel double-loss function further enhances the model’s performance by refining feature extraction from vibration signals. Our approach is rigorously tested on well-known datasets: the American Society for Mechanical Failure Prevention Technology (MFPT), Case Western Reserve University Bearing Data Center (CWRU), and Paderborn University's Condition Monitoring of Bearing Damage in Electromechanical Drive Systems (PU). Results indicate that the proposed deep learning method outperforms traditional machine learning models, achieving high accuracy across all datasets. These findings underline the potential for applying deep learning in MBFD, providing a robust solution for predictive maintenance in industrial settings and supporting proactive management of machinery health.
Small rodent disturbance impact on Arctic graminoid forage quality.
Authors: Celis Gerardo , Bråthen Kari Anne , Ehrich Dorothee , Paine Oliver , Sponheimer Matt , Heskel Mary , Soininen Eeva , Ungar Peter 
Published: October 2024

Abstract

Arctic rodents influence tundra plant communities by altering species diversity, structure, and nutrient dynamics. These dynamics are intensified during rodent population peaks. Plants are known to induce defenses in response to rodent herbivory. However, changes in plant tissue digestibility may also play a role in deterring rodents or impacting their survival. This study presents a first look at the impacts of rodent herbivory on crude protein (CP) and acid detergent fiber (ADF) of three of the most common graminoid species (Calamagrostis sp., Carex nigra and Deschampsia cespitosa) in the tundra meadows of the Varanger Peninsula, Norway. We selected 32 experimental plots representing both rodent-disturbed and adjacent, undisturbed control graminoid patches. During a rodent population peak, the disturbed plots had higher ADF (28.5%) values than less disturbed ones (26.6%), controlling for plant species. We also found differences between species, with Carex nigra having the lowest fiber content (24.3%, ADF) and highest protein content (18.2% CP) – making it the most palatable species. These results show that rodent activity can potentially alter plant food quality, suggesting that increased fiber content may be a defensive adaptation against herbivory.
Association between Social Interaction and Depressive Symptoms among Middle-aged and Elderly Adults in China: Evidence from the China Health and Retirement Longitudinal Study (CHARLS)
Authors: MPH Huang Zhipeng. , Zuxun Lu , Wang Peigang , Wang Chao 
Published: October 2024

Abstract

Objective
To explore the effect of social interaction on depressive symptoms in middle-aged and elderly adults in China.
Methods
Data from the 2020 China Health and Retirement Longitudinal Study (CHARLS) were analyzed. Participants were divided into social interaction and non-social interaction groups. Depressive symptoms were assessed using the Center for Epidemiologic Studies Depression Scale (CESD-10). Propensity score matching (PSM) was employed to balance confounding factors, and the average treatment effect (ATT) of social interaction on depressive symptoms was estimated. Binary logistic regression analyzed influencing factors.
Results
A total of 14,741 subjects (76.26%) were included; 9,869 (66.91%) participated in social interaction, and 5,593 (37.94%) had depressive symptoms. After PSM, social interaction significantly negatively affected depressive symptoms (ATT=-0.04, P < 0.05). Binary logistic regression revealed that those with social interaction had a lower risk of depressive symptoms (OR = 0.87, P < 0.05), particularly males (OR = 0.53, P < 0.05). However, older age (60–69: OR = 1.20; 70–79: OR = 1.24), poorer self-rated health (general: OR = 2.20; poor: OR = 4.48; very poor: OR = 7.70), lower satisfaction (general: OR = 1.67; dissatisfaction: OR = 8.10), and infrequent meetings with children (every six months: OR = 1.20; less than half a year: OR = 1.27) were associated with a higher risk of depressive symptoms (P < 0.05).
Conclusion
Middle-aged and elderly Chinese adults have a high risk of depressive symptoms, and social interaction significantly reduces this risk. Promoting social interaction and mental health initiatives can improve the health of middle-aged and elderly individuals.
Observation of string breaking on a (2+1)D Rydberg quantum simulator
Authors: Bylinskii Alexei , Gonzalez-Cuadra Daniel , Hamdan Majd , Zache Torsten , Braverman Boris , Kornjaca Milan , Lukin Alexander , Cantu Sergio , Liu Fangli , Wang Shengtao , Keesling Alexander , Lukin Mikhail , Zoller Peter 
Published: October 2024

Abstract

Lattice gauge theories (LGTs) describe a broad range of phenomena in condensed matter and par11 ticle physics. A prominent example is confinement, responsible for bounding quarks inside hadrons such as protons or neutrons [1]. When quark-antiquark pairs are separated, the energy stored in the string of gluon fields connecting them grows linearly with their distance, until there is enough energy to create new pairs from the vacuum and break the string. While such phenomena are ubiquitous in LGTs, simulating the resulting dynamics is a challenging task [2]. Here, we report the observation of string breaking in synthetic quantum matter using a programmable quantum simulator based on neutral atom arrays [3–5]. We show that a (2+1)D LGT with dynamical matter can be efficiently implemented when the atoms are placed on a Kagome geometry [6], with a local U(1) symmetry emerging from the Rydberg blockade [7], while long-range Rydberg interactions naturally give rise to a linear confining potential for a pair of charges, allowing us to tune both their masses as well as the string tension. We experimentally map out the corresponding phase diagram by adiabatically preparing the ground state of the atom array in the presence of defects, and observe substructure of the confined phase, distinguishing regions dominated by fluctuating strings or by broken string con24 figurations. Finally, by harnessing local control over the atomic detuning, we quench string states and observe string breaking dynamics exhibiting a many-body resonance phenomenon. Our work paves a way to explore phenomena in high-energy physics using programmable quantum simulators.
Metagenomics analysis of viruse populations from feces of diarrheal geese in Sichuan Province
Authors: Chen Guo , Ma Rongzhi , Zhang Huanrong 
Published: October 2024

Abstract

In recent years, novel viruses have brought serious economic losses to the goose industry. In this study, we performed viral metagenomics analysis on fecal samples from sick geese with clinical symptoms of diarrhea in a goose farm in Sichuan, China. The results showed that six viruses belonging to four virus families were identified in the fecal samples, including goose calicivirus, a member of the proposed genus sanovirus in the family Caliciviridae; two novel species in Picornaviridae, goose megrivirus and goose picornavirus; goose astrovirus type 1, a lesser-reported member of goose astrovirus; and partial segments of Rotavirus F and Rotavirus G. All of these viruses are associated with avian diarrhea and may be the pathogens causing diarrhea in geese on this farm. This research enhances the diversity of viruses that infect geese and serves as a valuable reference for the prevention and control of goose diseases.
FNDC1 Competitively Binds Gβ2 to Suppress the β-Catenin Destruction Complex and Enhance Wnt Signaling Pathway Activation
Authors: Jiang Tao , Chen Hao , Lin Fangyu , Liu Jialin , Lin Xiaoyan , Gao Wenyu 
Published: October 2024

Abstract

Background: Elevated FNDC1 expression in gastric cancer (GC) cells activates the Wnt/β-catenin pathway, contributing to their malignant traits. However, the exact mechanism governing this process remains unclear.
Methods: Bioinformatics analyses were used to identify the expression of FDNC1 in pan-cancer, its correlation with clinical outcomes, and biological functions in GC. Co-immunoprecipitation assays and western blot elucidate the interaction between FDNC1 and Gβ2/Gγ2 and the expression of Axin1, GSK3β, APC, and Dvl. The specific binding sites of FNDC1 and Gβ2 were explored by co-immunoprecipitation.
Results: FNDC1 was highly expressed in GC tissue, and its expression level is positively correlated with poor prognosis of GC. Functional enrichment analysis shows that FNDC1-related genes may participate in regulating the Wnt signaling pathway. In vitro assays prove that FNDC1 competitively binds to the Gβ2 protein. This binding caused Dvl to separate from Gβγ. Subsequently, Dvl was released and recruited Axin1, facilitating the degradation of Axin1 and activating the Wnt/β-catenin signaling pathway. We further identified the WD5 amino acid segment (residues 224-254) as the specific binding region of FNDC1 on Gβ2.
Discussion: This study reveals a novel mechanism where FNDC1 competitively binds Gβ2, inhibiting β-catenin degradation and enhancing the Wnt/β-catenin signaling pathway.
Comparative Study of Linear &amp; Non-linear Gravity Models in Bianchi Type-III Space-Time
Authors: MISHRA RAVI KANT , Sharma Rahul 
Published: October 2024

Abstract

In this study, we explore the dynamics of Bianchi type-III space-time within the framework of gravity, focusing on both linear and non-linear forms of function. We analyze the behavior of cosmological parameters by assuming the deceleration parameter (DP) as a simple linear function of the Hubble parameter. Key cosmological parameters such as the scale factor, Hubble parameter, DP, spatial volume, shear scalar, expansion scalar, energy density, pressure, and the equation of state (EoS) parameter are expressed in terms of the redshift parameter. Their dynamic behaviors are graphically presented for both linear and non-linear forms of gravity. Our results align with recent cosmological observations, with the non-linear form of exhibiting a stronger tendency toward accelerated cosmic expansion compared to the linear model. The EoS parameter indicates a quintessence phase, driving the universe's accelerated expansion, as recently investigated by Varshney et al. (2023). Additionally, we examine the violation of the strong energy conditions, a crucial aspect in modified gravity theories. The model parameter and the current value of the Hubble parameter are estimated using the Hubble data set and Pantheon+ SHOES data set, further validating our theoretical model.
Evaluation of Knowledge, Attitude, and Practice of Dental Professionals Towards Green Dentistry in Karachi City- a Cross-Sectional Survey
Authors: Billoo Muhammad Sadiq , Khan Shahrukh Ali , Kazmi Syed Murtaza Raza , Jabeen Syeda Farah , Khalid Taimur 
Published: October 2024

Abstract

Introduction: Environmental degradation from pollution, global warming, and industrialization, is alarming with dentistry significantly contributing through mercury and plastic toxicity from improper waste handling
Aim: To evaluate the knowledge, attitude, and practice of Dental professionals towards “Green Dentistry” working in the public and private sectors of Karachi city.
Design: A cross-sectional survey
Setting: Karachi, Pakistan (Online)
Materials and Methods: An online questionnaire was conducted among dentists of Karachi, Pakistan, from January to June 2023 collecting participant’s demographic data and assessing knowledge, attitude, and practice of eco-friendly dentistry.
Results: With a 36% response rate, most respondents (48.54%) were General Dental Practitioners (GDPs) (58.48%). Dental consultants (DC) exhibited higher awareness of green dentistry (29%) with 50% of DCs favoring digital radiography as environment friendly (p=<0.05). Green dentistry practices were more prevalent among DCs with 42.8% (p=<0.05) eliminating amalgam usage, while more GDPs (72.2%, p=<0.05) adopted re-usable lab coats and patient drapes.
Discussion and Conclusion: Awareness of “Green Dentistry” is generally lacking among participants. Both GDPs and DCs are inclined towards adopting environment-friendly dental practices. Education on energy conservation and materials is essential to reduce waste in the dental industry.
WITHDRAWN: Design and Simulation of a Microstrip 16x16 Butler Matrix for NR-IFF Radar Application
Authors: Fallah Mohsen , Nekounam Majid , Armaki Seyyed Hossein Mohseni 
Published: October 2024

Abstract

In this paper, a new microstrip design for the 16x16 Butler matrix is presented. It is well-known that as the number of input/output ports of the Butler matrix increases, so does the number of internal crossovers and thus the complexity of the whole structure increases impractically. Therefore, most reports have been limited to the design and realization of 4x4 and 8x8 Butler matrices. However, in this paper and through the relocation of input/output ports and also by using a two-layer substrate, a new 16x16 Butler matrix is designed to be used in a Non-Rotating IFF radar for the commutation of the active sector of its cylindrical antenna array.
Population Pharmacokinetics of Brexpiprazole in Japanese Healthy Subjects and Patients with Schizophrenia
Authors: Higashi Koushi , Sasaki Tomohiro , Aoki Kazuo , Sekine Daisuke , Maeda Kenji , Shiomi Yuki , Kawai Yosuke 
Published: October 2024

Abstract

Brexpiprazole, widely approved for the treatment of schizophrenia, is an atypical antipsychotic that modulates serotonin–dopamine activity. To better understand the pharmacokinetics (PK) of brexpiprazole in Japanese patients, a population PK model was constructed and used to estimate steady state PK profiles and parameters as well as dopamine D2/D3 receptor occupancy profiles after repeated oral administrations of brexpiprazole at 1 and 2 mg/day. Nonlinear mixed effects modelling was used to analyse data from a total of 398 healthy subjects and patients with schizophrenia who received brexpiprazole in three Japanese clinical trials. The PK of brexpiprazole were well described by a two-compartment disposition model with transit absorption compartments. Estimated glomerular filtration rate, age and cytochrome P450 2D6 phenotype were identified as significant covariates on CL/F only. The model predicted that, at a dose of 2 mg/day, trough plasma concentration (90% prediction interval) of brexpiprazole is 77.1 (22.4–173) ng/mL and that dopamine D2/D3 receptor occupancy is > 80% over one day for most patients at steady state. This suggests the recommended maintenance dose of 2 mg/day of brexpiprazole leads to clinically useful dopamine D2/D3 receptor occupancy at steady state in Japanese patients.
Multi-stage Crop Planting Strategy Optimization Model Based on PSO Algorithm
Authors: Wang Jiaying , Wang Yiquan 
Published: October 2024

Abstract

This paper presents a multi-stage crop planting strategy optimization model utilizing the Particle Swarm Optimization (PSO) algorithm. The model aims to address key challenges in agriculture, such as fluctuating market demands, climate variability, and rising production costs. Over a seven-year planning horizon (2024–2030), the model dynamically adjusts crop planting strategies by integrating factors like crop yield, market prices, and crop rotation. The proposed framework optimizes both crop selection and area allocation to maximize economic profitability while maintaining sustainable practices. Unlike traditional static models, this approach accounts for uncertainties and adapts to real-time changes in market conditions. The model's ability to adjust planting strategies based on PSO ensures that farmers can optimize yields and reduce waste through flexible decision-making. Experimental results demonstrate the model's effectiveness in dynamically optimizing planting strategies while satisfying constraints such as land availability and crop rotation.
AI-Driven Decision Support Systems for Healthcare Diagnosis
Authors: Egon Axel , broklyn Peter , Gracias Abram 
Published: October 2024

Abstract

Artificial Intelligence (AI) has transformed various industries, with healthcare being one of the primary beneficiaries. AI-Driven Decision Support Systems (AI-DSS) have shown tremendous potential in augmenting healthcare diagnosis, enhancing both accuracy and efficiency. These systems leverage advanced machine learning algorithms, natural language processing, and data analytics to assist healthcare professionals in diagnosing diseases, predicting patient outcomes, and personalizing treatment plans.
AI-DSS in healthcare can analyze large volumes of patient data, including medical history, lab results, and imaging, far beyond the capabilities of human practitioners. By identifying patterns and correlations that might not be immediately apparent, these systems provide diagnostic insights that can improve decision-making. Moreover, AI models, trained on vast datasets, can support early detection of diseases such as cancer, cardiovascular conditions, and neurological disorders, potentially improving patient outcomes by enabling timely interventions.
Despite the benefits, integrating AI-DSS into healthcare also presents challenges. These include ensuring data privacy and security, addressing algorithmic biases, and fostering trust among healthcare providers and patients. Additionally, the role of human oversight remains crucial, as AI should complement rather than replace clinical judgment.
In conclusion, AI-driven decision support systems represent a significant advancement in healthcare diagnosis, offering tools to improve diagnostic precision and patient care. However, careful implementation, ethical considerations, and continued human involvement are essential to maximize their potential and ensure they enhance rather than disrupt the healthcare landscape.
The effectiveness of a mobile mammography units in breast cancer screening
Authors: Kaprin D. A. , Perkhov V. I. , Denisova M. N. 
Published: October 2024

Breast cancer remains the leading cause of death in the female population. Screening studies are conducted in order to detect the disease at an early stage. At the same time, domestic authors emphasize the lack of alternatives to preventive mammography in breast cancer screening and the prospects of using mobile mammographs for early diagnosis of breast cancer and precancerous breast diseases.Aim. To evaluat the results of the use of mobile mammography units for active and early detection of breast cancer.Materials and methods. The research uses methods of content analysis, information and analytical materials of Russian and foreign researchers, and forms of federal statistical observation. Statistical research methods were used to analyze the data obtained.Results. Regression analysis with a very low probability of error (p = 0.001) showed that the larger the total volume of mammographic studies with preventive purposes in the subjects of the Russian Federation, the higher the proportion of cases of active detection of breast cancer, although this relationship is rather weak. Taking into account the obtained value of R2 (0.192), only 19.2% of the increase in the proportion of breast cancer cases detected actively can be explained by an increase in the volume of preventive mammograms. The correlation analysis showed that the volumes of preventive mammograms performed using mobile devices in the subjects of the Russian Federation were not associated with either the proportion of breast cancer cases detected actively or with the proportion of breast cancer cases detected in the early stages. At the same time, it is worth noting that the obtained value of p ≥ 0.5 indicates a statistically insignificant result of evaluating the relationship between variables, the interpretation of which should be performed with caution.Conclusion. Despite the high cost of mobile mammography units, the increase in their number in the subjects of the Russian Federation and the growth in their activities, the contribution of these diagnostic devices to solving the problem of early and active diagnosis of breast cancer remains uncertain. More promising are personalized approaches to the timely detection of cancers, including breast cancer, based on an assessment of the individual risk of their occurrence.