bluuebunny/crossref_metadata_2025 huggingface dataset:
Factors influencing sleep disorders in perimenopausal women: a systematic review and meta-analysis
Authors: Zeng Weisi , Xu Jialan , Yang Ying , Lv Meiling , Chu Xin
Published: February 2025

Background

To determine the influencing factors of sleep disorders in perimenopausal women by Meta-analysis.

Methods

A comprehensive literature search was conducted by PubMed, Embase, CINAHL, and Web of Science(from inception to December 1,2023). Two researchers independently performed literature screening, quality evaluation and data extraction, and Stata16.0 software were used for Meta-analysis.

Results

A total of 12 studies involving 11,928 perimenopausal women with sleep disorders were included. The results of Meta-analysis showed that depression(OR = 2.73, 95%CI 1.65 ~ 4.52), hot flashes (OR = 2.70, 95%CI 1.81 ~ 4.02), chronic disease (OR = 1.39, 95%CI 1.24 ~ 1.56) and psychotropic drug use(OR = 3.19, 95%CI 1.31 ~ 7.77) were risk factors for sleep disorders in perimenopausal women (p < 0.05).

Conclusion

Sleep disorder is one of the most common symptoms in perimenopausal women, and its influencing factors should be paid attention to. Healthcare managers can further improve and standardize the prevention and management of sleep disorders in perimenopausal women according to the influencing factors, accurately identify high-risk groups, implement intervention measures, and reduce the severity and incidence of sleep disorders in perimenopausal women.
The role of glial cells in neuralgia: a bibliometric exploration
Authors: He Ting , Wang DongDong , Wu Linman , Jin Liuyin
Published: February 2025

Neuropathic pain is a chronic pathological pain caused by nervous system damage, characterized by complex mechanisms and limited treatment efficacy. Glial cells play a pivotal role in the initiation and maintenance of neuropathic pain. This study employs bibliometric analysis to explore trends and emerging hotspots in research on the relationship between glial cells and neuropathic pain. Based on literature data from the Web of Science Core Collection spanning 2003 to 2022, the study identifies key contributors in the field, including leading countries such as China, the United States, and Japan, as well as influential institutions and journals, such as the University of California system and Pain. Keyword analysis highlights research hotspots focusing on glial cell activation, spinal cord injury, satellite glial cells, oxidative stress, and neuroinflammation. The findings suggest that these themes may shape future directions in the field. This study provides researchers with a comprehensive overview of trends and hotspot analysis, offering valuable insights for further investigation into the role of glial cells in neuropathic pain.
Individualized prediction of stroke-associated pneumonia for patients with acute ischemic stroke
Authors: Zhang Lulu , Wang Qi , Li Yidan , Fang Qi , Tang Xiang
Published: February 2025

Background

Stroke-associated pneumonia (SAP) remains a neglected area despite its high morbidity and mortality. We aimed to establish an easy-to-use model for predicting SAP.

Methods

Two hundred seventy-five acute ischemic stroke (AIS) patients were enrolled, and 73 (26.55%) patients were diagnosed with SAP. T-test, Chi-square test and Fisher’s exact test were used to investigate the associations of patient characteristics with pneumonia and its severity, and multivariable logistic regression models were used to construct a prediction scale.

Results

Three variables with the most significant associations, including age, NGT placement, and right cerebral hemisphere lesions combined with gender, were used to construct a stroke-associated pneumonia prediction scale with high accuracy (AUC = 0.93). Youden index of our SAP prediction model was 0.77. The sensitivity and specificity of our SAP prediction model were 0.89 and 0.88, respectively.

Conclusion

We identified the best predictive model for SAP in AIS patients. Our study aimed to be as clinically relevant as possible, focusing on features that are routinely available. The contribution of selected variables is visually displayed through SHapley Additive exPlanations (SHAP). Our model can help to distinguish AIS patients of high-risk, provide specific management, reduce healthcare costs and prevent life-threatening complications and even death.
Association between the expression of specific microRNAs and prostate cancer progression- a systematic review and meta-analysis
Authors: Alrehaili Jihad Awadallah
Published: February 2025

Background

This systematic review and metanalysis aimed to summarize the evidence supporting the significance of particular MiRNAs in PrCa progression and to thoroughly examine the body of prior research.

Methods

In accordance with the PRISMA guidelines, this review was conducted using a specifically design data extraction protocol and searching several online databases for relevant articles. The protocol was registered in the PROSPERO database (CRD42023428460).

Results

8 studies were ultimately included in this review. MiRNAs significantly reduced PrCa proliferation, with an odds ratio (OR) of 0.28 (95% CI: 0.21–0.39) and a risk ratio (RR) of 0.51 (95% CI: 0.43–0.61), though moderate heterogeneity was observed (I² = 57%). For two studies investigating MiRNAs as biomarkers for predicting metastasis, the pooled OR was 0.60 (95% CI: 0.47–0.76) and the RR was 0.72 (95% CI: 0.62–0.84), both demonstrating significant predictive value with low heterogeneity (I² = 0%).

Conclusion

The results emphasize the potential of miRNAs as biomarkers for predicting PrCa metastasis and demonstrate that miRNAs have a discernible effect on PrCa proliferation. However, to improve our comprehension of MiRNA’s function in this condition, additional studies are required to address the limitations and investigate how MiRNA acts in many areas of PrCa.

Systematic review registration

https://www.crd.york.ac.uk/prospero/, identifier CRD42023428460.
Effects of irrigation and organic fertilizer on pumpkin yield, quality, and water-fertilizer use efficiency in arid northwest China
Authors: Yin Meng , Zhang Jinxia , Du Liangliang , Ding Lin , Zhong Tao , Tian Pengliang , Yang Runheng
Published: February 2025

Due to the increasing water scarcity and the need for sustainable agricultural practices in arid regions, optimizing water and fertilizer management is crucial for enhancing crop productivity and resource efficiency. Field experiments in 2022 and 2023 in northwestern China’s arid region explored the impacts of irrigation volume, organic fertilizer use, and their coupling on pumpkin yield, quality, and water-fertilizer efficiency. The study included ten treatments with a completely randomized two-factor design, comprising three irrigation quotas, three organic fertilizer application rates and a control group (CK). The results showed that the organic fertilizer application significantly enhanced soil moisture content, which peaked at a depth of 50 cm. Irrigation quota and organic fertilizer application had a highly significant impact on pumpkin vine length and stem diameter (P < 0.01), with a significant interaction between the two factors (P < 0.05). The rate of dry matter accumulation in pumpkin peaked at 60 ~ 80 days after sowing, with a trend of F2 > F3 > F1 in dry matter accumulation at identical irrigation quota. The effects of irrigation volume, organic fertilizer application and water-fertilizer coupling on pumpkin yield, irrigation water use efficiency (IWUE), partial fertilizer productivity (PFP) and pumpkin quality were statistically highly significant (P < 0.01). Specifically, increasing the irrigation volume from W1 to W3 increased the yield by 17.36%. However, pumpkin yield initially increased and then decreased in response to increasing organic fertilizer application. IWUE increased and then decreased with the increase of organic fertilizer application, while PFP increased with the increase of irrigation volume. Regression analysis revealed that the optimal range for irrigation quota to ensure pumpkin quality was 430 ~ 506 m3·ha-1, and that for organic fertilizer application was 5,373 ~ 6,570 kg·ha-1. When only quality indicators were considered, the W2F2 treatment performed well. However, from the comprehensive evaluation of pumpkin yield, quality, and water and fertilizer use efficiency using the TOPSIS method, the W3F2 treatment was identified as the most suitable among the water- fertilizer coupling management modes considered in this study for pumpkin cultivation in the arid northwestern China.
An initial exploration of core collection construction and DNA fingerprinting in Elymus sibiricus L. using SNP markers
Authors: Li Xinrui , Song Daping , Li Mingfeng , Li Daxu , You Minghong , Peng Yan , Yan Jiajun , Bai Shiqie
Published: February 2025

Elymus sibiricus L., an excellent forage and ecological restoration grass, plays a key role in grassland ecological construction and the sustainable development of animal husbandry. In China, the wild germplasm resources of E. sibiricus are abundant, and they are shaped by similar and contrasting climatic conditions to form distinct populations, which enrich the genetic diversity of E. sibiricus. To more comprehensively aggregate E. sibiricus germplasm resources at a lower cost and to more accurately utilize its genetic variation, this study conducted a preliminary exploration of core germplasm collections and fingerprinting of E. sibiricus using single nucleotide polymorphism (SNP) markers. By combining multiple evaluation measures with weighted processing, we successfully identified 36 materials from 90 wild E. sibiricus samples to serve as a core collection. Genetic diversity assessments, allele evaluations, and principal component analyses of the 36 core germplasm samples all indicate that these 36 samples accurately and comprehensively represent the genetic diversity of all 90 E. sibiricus germplasm accessions. Additionally, we identified 290 SNP loci from among the high-quality SNP loci generated by whole-genome sequencing of the 90 E. sibiricus samples as candidate markers. Of these, 52 SNP loci were selected as core markers for DNA fingerprinting of E. sibiricus. Using kompetitive allele-specific PCR (KASP) technology, we also performed population origin identification for 60 wild E. sibiricus germplasm accessions based on these core markers. The core SNP markers screened in this study were able to accurately distinguish between E. sibiricus germplasms from the Qinghai–Tibet Plateau and those from elsewhere. This study not only provides a reference for the continued collection and identification of E. sibiricus germplasm resources but also offers a scientific basis for their conservation and utilization.
Social employee well-being challenges of academics in the hybrid work environment
Authors: Marozva Rudo Rachel , Pelser Anna-Marie
Published: February 2025

The complex nature of the hybrid work environment impacts on the overall well-being of employees. There is limited research on how the hybrid work environment has impacted the social dimension of employee well-being, particularly among academics in higher education institutions. The aim of the study was to explore and understand social well-being challenges that academics face as a result of the hybrid work environment. The study adopted a cross-sectional qualitative research approach and used semi-structured interviews using an interview guide to collect data. The sample included 23 academics who are representatives of the three campuses of North-West University. Thematic analysis was used to analyse the data. Three themes emerged from the analyses of data: social connections, sense of belonging and work relationships. The social connections theme had four sub-themes: social isolation, informal communication breakdown, limited networking and personality conflict. Sense of belonging theme entailed four sub-themes: lack of resources, work-life imbalance, onboarding challenges and non-inclusive work environment. Relationships theme had three sub-themes: lack of trust, reduced social support and delayed feedback. Experiences of the research participants are noted to comprehend the negative impact of the hybrid work environment on social employee well-being. The findings suggest that social well-being challenges that academics face are heightened by the nature of the industry, personality traits and the context of the study which is Africa. This requires HEIs to explore the internal and external environment when managing social well-being challenges that academics face as a result of the hybrid work environment.
Self-selected vs. prescribed aerobic exercise intensity: impacts on pleasure in women with obesity
Authors: Da Silva Carlos Eduardo Rosa , Santana Wilian Jesus , Almeida Gustavo , Verame Adriano , Doro Antonio Roberto , Barbosa Eduardo , Lima Leonardo , Dias Helton Magalhães , Zanetti Marcelo Callegari , Junior Aylton Figueira
Published: February 2025

Introduction

Self-selection of aerobic training intensity is an approach that allows practitioners to develop autonomy (choice of pace), improve physical fitness, and contribute to better affective responses and training adherence. However, it is still unclear whether self-selection of training intensity in group settings is comparable to individual training conditions. The aim of the present study was to compare the effect of three aerobic training protocols on psychophysiological responses in physically inactive adult obese women.

Methods

The sample consisted of 90 women with a mean age of 48.3 ± 5.5 years and BMI of 31.2 ± 4.8 kg/m2, who participated in three treadmill aerobic training protocols: 1- Control group (IPI) with individually prescribed intensity (64 to 76% of HRmax), 2- Small group (SGS) with self-selected intensity, and 3- Individual with self-selected intensity (ISS). Heart rate (HR), total session volume (VL), affective valence was determined by feeling scale (FS), enjoyment was determined by Physical Activity Enjoyment Scale (PACES), intention to repeat the exercise session (INT), and Basic Psychological Needs Satisfaction Scale (BPNFS) were evaluated. Statistical analyses were conducted using two-way ANOVA for HR and FS, and one-way ANOVA for VL, PACES, INT, and BPNFS with Tukey post hoc test and significance criteria (p < 0.05).

Results

The results showed that HR and VL were similar (p > 0.05) between the protocols IPI, SGS and ISS, while FS, PACES, and INT were significantly higher in SGS and ISS than IPI protocols (p < 0.05). BPNFS also presented better results for the SGS and ISS protocols (p < 0.05).

Discussion

These findings suggest that the intensity and training volume were similar among different protocols. Affective responses were more positive perceived in SGS and ISS protocols.

Conclusion

We conclude that intensity self-selection is a safe, effective, and comparable strategy to prescribed intensity at moderate levels, providing a more enjoyable experience that may contribute to greater adherence to aerobic training.
Pastoral livestock farming constraints and adaptation strategies in response to institutional reforms in the Sudano-Sahelian zone of West Africa
Authors: Ilboudo Basnewindé , Zaré Alhassane , Traoré Innocent Charles Emmanuel , Bondé Loyapin , Guuroh Reginald Tang , Ouédraogo Oumarou
Published: February 2025

Introduction

Livestock farming is one of the main socio-economic activities in West Africa, predominantly extensive and reliant on natural rangelands. In Burkina Faso, the pastoral zone creation process began in 1980 to sedentarize livestock farmers, reduce social conflict, and strengthen livestock production. Therefore, these areas were subjected to institutional reforms with direct consequences on pastoralism. This study aimed to analyze the effects of land tenure and protected area governance reforms on the pastoral production system by identifying concomitantly related constraints and adaptation strategies adopted by local communities.

Methods

Data were collected through analysis of policy documents, and surveys from 280 key informants and household heads related to the pastoral zone of Yallé using individual interviews and focus group discussions. The main information collected included pastoral landscape and the current state of the livestock production system. Data were also taken on pastoral constraints related to land reforms, protected area governance, and alternative adaptation strategies developed to address constraints.

Results and discussion

The changes highlighted are the challenge to the pastoral zone and the appropriation of pastureland by other users inducing range shrinkage, inaccessibility to water resources, and farmer-herders conflicts. In response, some livestock farmers drive their herds out of the country, while others illegally graze in protected areas. They also combine socio-professional conversion to support their livelihoods, with atypical transhumance in the rainy season and seek herd feed satisfaction. New forage harvesting techniques, supported by training, and the integration of agriculture into livestock farming are opportunities to promote agropastoralism, more sustainable. Policymakers should ensure that livestock development actions are legally secure and that institutional reforms promote common resource use in pastoral areas. Also, livestock farmers are encouraged to create community structures for coordinated resource management and conflict resolution. Further, State institutions must supervise investor establishment in pastoral zones while preventing private property rights over the land.
Nutrients regulation of skin cells from canines and cats via Wnt/β-catenin signaling pathway
Authors: Sun Xue , Ma Yan , Gao Yi , Li Jiaxi , Li Yunliang , Lv Lei
Published: February 2025

For pets, healthy hair not only represents their beautiful appearance, but also reflects their overall health status. Nutritional imbalance, skin parasites, and stress can all cause a large amount of hair loss in pets, leading to skin-related diseases and posing a threat to their health. So, it is very important to understand and find solutions to alleviate or treat pet hair loss. The Wnt/β-catenin signal plays a core role in the hair regeneration process. Here, we report that keratin hydrolyzed, egg yolk lecithin, and fish collagen peptide promote β-catenin nuclear translocation and significantly enhance the expression of their target genes in canines and cats skin cell lines, indicating that these nutrients are likely to play a beneficial role in promoting hair regeneration in canines and cats.
The Application of Negative Poisson’s Ratio Metamaterials in the Optimization of a Variable Area Wing
Authors: Wang Haifeng , Zhang Chao , Wang Chen , Qiu Jinhao
Published: February 2025

Mechanical metamaterials, especially the cells with a negative Poisson’s ratio (NPR), have received much attention since they offer more deformability potential in morphing wings. This paper proposes a strategy for regulating the deformation of metamaterial cells based on the deformation form of the wing planform. The deformation of the wing shape was achieved through this strategy, with the main control factor of NPR. In light of the strategy, taking bi-directional re-entrant anti-tetrachiral (BRATC) metamaterial cells with NPR as an example, a scheme for BRATC metamaterial cells to regulate NPR is proposed. Driven by the same increase in wingspan (Δspan = 5%), the wing models, which are constructed based on the BRATC metamaterial cells with NPR characteristics at the different chord length increment at wing root (Δchord = 20%, 25%, and 30%), achieved an acceptable object-contour shape error (K = 1.29%, 1.40%, and 2.10%) with corresponding relative area increases (Ar = 15.5%, 18.13%, and 20.75%). Finally, the feasibility of the method is verified by experimentally measuring the deformation of the wing model.
Exploring the Spatial Distribution Characteristics of Urban Soil Heavy Metals in Different Levels of Urbanization
Authors: Sun Jianwei , Chen Mengchan , Xiao Jingrou , Xu Gang , Zhang Haitao , Zhang Ganlin , Yang Fangqin , Zhao Chang , Guo Long
Published: February 2025

With the development of urbanization and industrialization worldwide, soil heavy metal pollution has become a critical and pressing environmental problem in urban areas. Soil heavy metals exhibit complex and varying spatial aggregation and diffusion processes within diverse urban landscapes, especially in different urban areas with varying urbanization levels. However, many existing experimental methods and conventional models overlook the crucial aspects of spatial autocorrelation and heterogeneity between soil heavy metals and influencing factors. This neglect poses significant environmental concerns, as rapid monitoring of soil heavy metals and accurate identification of their determinants become imperative. This study investigated four environmentally sensitive and potentially harmful soil heavy metals, arsenic (As), cadmium (Cd), copper (Cu), and lead (Pb), in two urban areas in China with varying urbanization levels. Enshi (a prefecture-level city) and Wuhan (a provincial capital city) were selected for comparison of the spatially variable relationships between soil heavy metals and their influencing factors. We employed a global stepwise linear regression (STR) model and a local spatial model-geographically weighted regression (GWR) to map the spatial distribution of soil heavy metals based on 121 auxiliary variables, including terrain, geophysical, socioeconomic factors, and remote sensing data. Our results showed that: (1) soil heavy metals exhibited strong spatial aggregation in the prefecture-level city (Enshi) but, nonetheless, have strong spatial heterogeneity in the provincial capital city (Wuhan) due to elevated anthropogenic disturbances; (2) GWR accurately mapped the spatial distributions of As (r = 0.47 and 0.66), Cd (r = 0.74 and 0.53), Cu (r = 0.60 and 0.54), and Pb (r = 0.44 and 0.50) based on auxiliary variables in different cities and also can clearly reveal the spatially variable relationships with main influence factors; (3) human activities were the primary driving factors influencing As and Pb, while natural environment variables were identified as the main potential sources of Cd and Cu. This study demonstrates a methodology to explore spatially variable characteristics of soil heavy metals and their spatial varying relationships with influence factors. The comparative analysis between two cities provides insights that can greatly enhance quantitative source apportionment and support sustainable management strategies for controlling soil heavy metal pollution across varied urban environments.
Plastic Film Residue Reshaped Protist Communities and Induced Soil Nutrient Deficiency Under Field Conditions
Authors: Wang Ge , Sun Qian , Wei Maolu , Xie Miaomiao , Shen Ting , Liu Dongyan
Published: February 2025

The use of plastic agricultural mulching films presents a “double-edged sword”: while these films enhance crop yields, they also lead to the accumulation of plastic film residues in the soil, creating new pollutants (microplastics). Our understanding of the “plastisphere”, a niche formed by agricultural film residues in the soil, where unique microbial communities and soil conditions converge remains limited. This is particularly true for protists, which are recognized as key determinants of soil health. Therefore, this study simulated a field experiment to analyze the effects of long-term plastic film residues on the structure of protist microbial communities in the rhizosphere, bulk soil and plastisphere of oilseed rape as well as their effects on soil nutrients. The results revealed that the residual plastic films underwent significant structural and chemical degradations. Protist diversity and co-occurrence network complexity were markedly reduced in plastisphere soils. In addition, soil moisture content, inorganic nitrogen and available phosphorus levels declined, leading to deficiencies in soil nutrients. Functional shifts in consumer protists and phototrophs along with weakened network interactions, have been identified as key drivers of impaired nutrient turnover. Our study underscores the critical role of protist communities in maintaining soil nutrient cycling and highlights the profound adverse effects of plastic film residues on soil ecosystems. These findings provide valuable insights into mitigating plastic residue accumulation to preserve long-term soil fertility and ensure sustainable agricultural productivity.
Leveraging Transformer Models for Enhanced Pharmacovigilance: A Comparative Analysis of ADR Extraction from Biomedical and Social Media Texts
Authors: Elbiach Oumayma , Grissette Hanane , Nfaoui El Habib
Published: February 2025

The extraction of Adverse Drug Reactions from biomedical text is a critical task in the field of healthcare and pharmacovigilance. It serves as a cornerstone for improving patient safety by enabling the early identification and mitigation of potential risks associated with pharmaceutical treatments. This process not only helps in detecting harmful side effects that may not have been evident during clinical trials but also contributes to the broader understanding of drug safety in real-world settings, ultimately guiding regulatory actions and informing clinical practices. In this study, we conducted a comprehensive evaluation of eleven transformer-based models for ADR extraction, focusing on two widely used datasets: CADEC and SMM4H. The task was approached as a sequence labeling problem, where each token in the text is classified as part of an ADR or not. Various transformer architectures, including BioBERT, PubMedBERT, and SpanBERT, were fine-tuned and evaluated on these datasets. BioBERT demonstrated superior performance on the CADEC dataset, achieving an impressive F1 score of 86.13%, indicating its strong capability in recognizing ADRs within patient narratives. On the other hand, SpanBERT emerged as the top performer on the SMM4H dataset, with an F1 score of 84.29%, showcasing its effectiveness in processing the more diverse and challenging social media data. These results highlight the importance of selecting appropriate models based on the specific characteristics such as text formality, domain-specific language, and task complexity to achieve optimal ADR extraction performance.
Conditional Generative Adversarial Networks and Deep Learning Data Augmentation: A Multi-Perspective Data-Driven Survey Across Multiple Application Fields and Classification Architectures
Authors: Ribas Lucas C. , Casaca Wallace , Fares Ricardo T.
Published: February 2025

Effectively training deep learning models relies heavily on large datasets, as insufficient instances can hinder model generalization. A simple yet effective way to address this is by applying modern deep learning augmentation methods, as they synthesize new data matching the input distribution while preserving the semantic content. While these methods produce realistic samples, important issues persist concerning how well they generalize across different classification architectures and their overall impact in accuracy improvement. Furthermore, the relationship between dataset size and model accuracy, as well as the determination of an optimal augmentation level, remains an open question in the field. Aiming to address these challenges, in this paper, we investigate the effectiveness of eight data augmentation methods—StyleGAN3, DCGAN, SAGAN, RandAugment, Random Erasing, AutoAugment, TrivialAugment and AugMix—throughout several classification networks of varying depth: ResNet18, ConvNeXt-Nano, DenseNet121 and InceptionResNetV2. By comparing their performance on diverse datasets from leaf textures, medical imaging and remote sensing, we assess which methods offer superior accuracy and generalization capability in training models with no pre-trained weights. Our findings indicate that deep learning data augmentation is an effective tool for dealing with small datasets, achieving accuracy gains of up to 17%.
Risk Screening of the Non-Native Fish in the Jiulong River Basin of Southeast China
Authors: Feng Shilong , Pan Xindong , Wang Jiaqiao , Liu Wenjuan , Hui Yapeng , Wang Guangzhao , Liu Kai , Li Jun , Xu Haoqi , Lin Lin , Wang Xu , Wu Zhiqiang , Huang Liangmin , Ji Fenfen
Published: February 2025

Non-native fish species introduced into new areas, especially when they develop into large populations, pose a threat to native fauna. Understanding the current status of the fish community and invasion risks of non-native fish are essential for invasive species control and diversity conservation. The community structure of fish and a risk assessment on 10 non-native fish species were systematically assessed in the Jiulong River Basin, China, in January, April, and July of 2024. The species richness, with 105 species, showed a notable decrease compared to the 124 species recorded in 1975, while the number of non-native species has increased from zero to ten. Furthermore, the non-native fish species, Coptodon zillii and Sarotherodon galilaeus, have become dominant species, with IRI values of 4038.43 and 1180.30, respectively. The AS-ISK established risk thresholds for BRA and BRA + CCA as 29.5 and 35.5, respectively, identifying 70% of the non-native fish species as high-risk species, including C. zillii, S. galilaeus, Oreochromis niloticus, Clarias batrachus, Hypostomus plecostomus, and Oreochromis aureus. This study indicates that the fish species richness in the Jiulong River Basin has declined, with C. zillii and S. galilaeus becoming dominant and posing high ecological risks to the native fish community. In addition, targeted fishing during the breeding season should be used to control the population of tilapia and restore fish diversity.
Effects of Dietary Multi-Carbohydrase and Phytase Complex Supplementation on Nutrient Digestibility, Bone Mineralization and Puberty Onset in Gilts
Authors: Chen Fangyuan , Zhou Rui , Zhao Lianpeng , Huang Lingjie , Zhuo Yong , Xu Shengyu , Lin Yan , Che Lianqiang , Feng Bin , Wu De , Fang Zhengfeng
Published: February 2025

The study was conducted to determine the effects of multi-carbohydrase and phytase complex (MCPC) supplementation in standard and commercial diets on growth performance, nutrient digestibility, bone mineralization, blood biochemical parameters and puberty onset in gilts. A total of 144 healthy gilts (Duroc × (Landrace × York)) were assigned randomly to four treatments (n = 36), with 10 replicate pens (6 pens, each containing 4 gilts, and 4 pens, each containing 3 gilts). The trial consisted of two phases (phase 1: 70–100 kg; phase 2: 100–140 kg). The commercial diet (COM) had 33% higher calcium (Ca) and phosphorus (P) levels than the standard diet (CON) across all phases. The four treatment diets were as follows: CON (phase 1: 0.56 g/kg Ca and 0.49 g/kg P; phase 2: 0.49 g/kg Ca and 0.45 g/kg P), COM (phase 1: 0.75 g/kg Ca and 0.65 g/kg P; phase 2: 0.65 g/kg Ca and 0.60 g/kg P), CON + MCPC and COM + MCPC, where both the CON and COM diets were supplemented with 100 mg/kg of MCPC, respectively. The commercial diet significantly (p < 0.05) increased the total number of follicles and the number of follicles < 4 mm and tended to reduce (p = 0.07) the age at puberty compared to the standard diet. Besides, MCPC supplementation improved the apparent total-tract digestibility (ATTD) of Ca (p < 0.05), P (p < 0.05) and Ash (p = 0.07) in gilts during phase 1, compared to the basal diets without MCPC supplementation. Additionally, MCPC supplementation significantly elevated serum creatinine (CREA, p < 0.05) concentrations and had a tendency to increase serum Ca (p = 0.07) concentrations in gilts. Specifically, COM + MCPC supplementation significantly increased the osteocalcin (OCN) concentration compared with the COM treatment. Moreover, dietary MCPC supplementation significantly improved the bone strength (p < 0.05) compared to the basal diets without MCPC supplementation. In conclusion, dietary MCPC supplementation increased the ATTD of Ca and P in gilts, while also improving bone strength. This improvement not only extends the reproductive lifespan of sows, but it also allows for reduced supplementation levels of Ca and P in the dietary formula.
Eleutheroside B Ameliorates Cardiomyocytes Necroptosis in High-Altitude-Induced Myocardial Injury via Nrf2/HO-1 Signaling Pathway
Authors: Duan Huxinyue , Han Yue , Zhang Hongying , Zhou Tianyue , Wu Chunjie , Wang Zhenxing , He Yacong
Published: February 2025

This study was designed to evaluate the protective effects of eleutheroside B (EB) in high-altitude-induced myocardial injury (HAMI) and to unravel the underlying molecular mechanisms. SD rats were used for in vivo experiments. Following pretreatment with EB, the SD rats were exposed to a hypobaric environment within a hypobaric chamber for 48 h. Electrocardiograms, H&E staining, and serum biochemical indices were measured to evaluate the protective effects of EB on HAMI. Immunofluorescence and Western blotting were utilized to detect the expression of associated proteins. In parallel, a hypobaric hypoxic cell incubator was used to establish an in vitro model of hypobaric hypoxia-induced cell injury. The anti-necroptotic effect and its potential underlying mechanisms were investigated and verified in vitro. Exposure to hypobaric hypoxia led to electrocardiogram disorders, pathological changes in myocardial tissue, increased concentrations of BNP and CK-MB, and elevated levels of oxidative stress indicators and inflammatory factors. Additionally, the expression of necroptosis-related proteins was upregulated. Pretreatment with EB effectively ameliorated myocardial injury caused by hypobaric hypoxia, mitigated oxidative stress and inflammation, and suppressed necroptosis. Furthermore, EB facilitated the translocation of Nrf2 into the nucleus. In conclusion, this study provides evidence suggesting that EB may exert a protective effect against HAMI by inhibiting cardiomyocyte necroptosis via the Nrf2/HO-1 signaling pathway.
A Multi-Branch Adaptive Model with Hybrid Time—Frequency Loss to the Enhanced Joint Moment Prediction of Prosthetic Control and Human Motion Applications
Authors: Xiong Baoping , Lou Jie , Ni Wenshu , Su Zhikang , Huang Shan
Published: February 2025

In the fields of prosthetic control and rapid response prediction for human motion, the accurate prediction of joint moments is crucial for understanding and simulating human behavior. However, traditional time series models, especially when trained using small batches and limited data for single-time step predictions, frequently encounter difficulties in managing long-term dependencies. This deficiency significantly compromises their ability to generalize and maintain predictive accuracy over extended periods. To address these challenges, an innovative model called Multi-Branch Adaptive Encoding (MAE) has been introduced. This model features an adaptive weight module that employs a multi-branch input strategy to dynamically allocate weights to different surface electromyography (sEMG) signals and joint angles, thereby optimizing the processing of small sample data. Additionally, a feature extraction encoder, named Simplified Feature Transformer (SFT) has been designed. This encoder substitutes traditional attention mechanisms with a Multilayer Perceptron (MLP) and omits the decoder component to enhance the model’s efficiency and offer significant advantages in small-batch training and long-term prediction capabilities. A Hybrid Time–Frequency Loss (HTFLoss) has also been introduced to complement the MAE model. This approach significantly enhances the model’s ability to handle long-term dependencies. The MAE model and HTFLoss demonstrate an increase in Variance Accounted For (VAF) of 0.08 ± 0.03, a reduction in Root Mean Square Error (RMSE) of 1.77 ± 0.735, and an improvement in the coefficient of determination (R²) of 0.09 ± 0.05, indicating substantial superiority. These enhancements highlight the extensive potential applications of the model in the fields of rehabilitation medicine, and human-machine interaction. The improved predictive accuracy and the ability to manage long-term dependencies make this model particularly valuable in designing advanced prosthetic devices that can better mimic natural limb movements, thereby improving the quality of life for amputees.
Data-Driven Machine-Learning-Based Seismic Response Prediction and Damage Classification for an Unreinforced Masonry Building
Authors: Ravichandran Nagavinothini , Bidorn Butsawan , Mercan Oya , Paneerselvam Balamurugan
Published: February 2025

Unreinforced masonry buildings are highly vulnerable to earthquake damage due to their limited ability to withstand lateral loads, compared to other structures. Therefore, a detailed assessment of the seismic response and resultant damage associated with such buildings becomes necessary. The present study employs machine learning models to effectively predict the seismic response and classify the damage level for a benchmark unreinforced masonry building. In this regard, eight regression-based models, namely, Linear Regression (LR), Stepwise Linear Regression (SLR), Ridge Regression (RR), Support Vector Machine (SVM), Gaussian Process Regression (GPR), Decision Tree (DT), Random Forest (RF), and Neural Networks (NN), were used to predict the building’s responses. Additionally, eight classification-based models, namely, Naïve Bayes (NB), Discriminant Analysis (DA), K-Nearest Neighbours (KNN), Adaptive Boosting (AB), DT, RF, SVM, and NN, were explored for the purpose of categorizing the damage states of the building. The material properties of the masonry and the earthquake intensity were considered as the input parameters. The results from the regression models indicate that the GPR model efficiently predicts the seismic response with larger coefficients of determination and smaller root mean square error values than other models. Among the classification-based models, the RF, AB, and NN models effectively classify the damage states with accuracy levels of 92.9%, 91.1%, and 92.6%, respectively. In conclusion, the overall performance of the non-parametric models, such as GPR, NN, and RF, was found to be better than that of the parametric models.
Comparison of Multi-Tracked Running Gears in Terms of Obstacle Negotiation Capabilities
Authors: Szpaczyńska Daniela , Przybysz Mirosław , Muszyński Tomasz
Published: February 2025

This paper concerns lightweight—up to 800 kg—UGVs (unmanned ground vehicles), where multi-tracked running gears are used to improve the obstacle negotiation performance. A comparison of four different multi-tracked systems and, for reference, classic tracked running gear is presented. Simulations in a multi-body dynamics program were performed, where running gear solutions overcame three typical obstacles and were assessed using a total of five effectiveness and functionality criteria. The simulation models took into account the variable track–ground contact surface. The ground parameters were validated by means of experimental tests for grassy terrain. In the results, the most advantageous solutions in each category are indicated, and design guidelines for increasing the obstacle-overcoming capabilities of multi-tracked UGVs are presented.
The Uncertainty of Tomorrow in Art on the “Eve” of War
Authors: Blazy Rafał , Hrehorowicz-Gaber Hanna , Hrehorowicz-Nowak Alicja
Published: February 2025

Intuitive reasoning is a fundamental aspect of both the creation and interpretation of art. It allows artists to express their deepest emotions and subconscious thoughts, leading to works that can evoke strong reactions in viewers. For those who interpret art, intuition enables a direct and holistic understanding that goes beyond analytical fragmentation, allowing for a more personal and profound engagement with the work of art. Academic research conducted on the works of Polish and Ukrainian students has shown that art in times of unrest reflects deeply hidden emotions. The works of students from Zaporizhzhia collected less than a week before the armed invasion of Ukraine in 2022 indicated national sentiments, fears, and premonitions that were absent from the works of Polish students. The armed conflict with Russia, which has been ongoing since 2014, has strengthened the sense of national identity and patriotism among Ukrainian youth. A comparison of the perspectives of Polish and Ukrainian youth shows differences resulting from direct experience of the conflict and the influence of media, education, and politics on their perception.
Leveraging Deep Learning for Automated Experimental Semivariogram Fitting
Authors: Yu Siyu , Zhao Lifang , Li Shaohua
Published: February 2025

The variogram function is a crucial tool for quantifying spatial correlation and a key component of Kriging interpolation, directly influencing the accuracy of interpolation results. However, traditional variogram fitting methods, such as least squares and weighted polynomial regression, often face limitations when handling complex or non-stationary spatial structures, which can lead to unstable fitting outcomes. To address these challenges, this paper proposes an automatic fitting method for experimental variogram functions based on deep learning. The variogram fitting process is inherently a nonlinear optimization problem, where the goal is to optimize the alignment between experimental and theoretical variogram functions. In this study, we construct a deep learning model designed to capture the nonlinear characteristics of experimental variograms with the objective of minimizing fitting errors and improving both the accuracy and stability of the fitting process. Extensive experimental results demonstrate that the proposed neural network model can effectively learn to fit a variety of experimental variograms and accurately represent different theoretical variogram functions. Compared to traditional methods, this approach consistently achieves superior fitting performance across multiple datasets, validating its effectiveness and robustness under diverse conditions. The stronger generalization capability and improved accuracy of the proposed method significantly enhance the automatic fitting of experimental variogram functions.
Quantum Private Set Intersection Scheme Based on Bell States
Authors: Hou Min , Wu Yue , Zhang Shibin
Published: February 2025

In this paper, we introduce a quantum private set intersection (QPSI) scheme that leverages Bell states as quantum information carriers. Our approach involves encoding private sets into Bell states using unitary operations, enabling the computation of the intersection between two private sets from different users while keeping their individual sets undisclosed to anyone except for the intersection result. In our scheme, a semi-honest third party (TP) distributes the first and second qubits of the Bell states to the two users. Each user encodes their private sets by applying unitary operations on the received qubits according to predefined encoding rules. The modified sequence is encrypted and then sent back to TP, who can compute the set intersection without learning any information about the users’ private inputs. The simulation outcomes on the IBM quantum platform substantiate the viability of our scheme. We analyze the security and privacy aspects of the sets, showing that both external attacks and internal threats do not compromise the security of the private inputs. Furthermore, our scheme exhibits better practicality by utilizing easily implementable Bell states and unitary operations, rather than relying on multiple encoded states for set intersection calculations.
Pseudocapacitive Storage in High-Performance Flexible Batteries and Supercapacitors
Authors: Lu Zhenxiao , Ren Xiaochuan
Published: February 2025

Attention to electrochemical energy storage (EES) devices continues to grow as the demand increases for energy storage systems in the storage and transmission of renewable energy. The expanded market requirement for mobile electronics devices and flexible electronic devices also calls for efficient energy suppliers. EES devices applying pseudocapacitive materials and generated pseudocapacitive storage are gaining increasing focus because they are capable of overcoming the capacity limitations of electrical double-layer capacitors (EDLCs) and offsetting the rate performance of batteries. The pseudocapacitive storage mechanism generally occurs on the surface or near the surface of the electrode materials, which could avoid the slow ion diffusion process. Developing materials with beneficial nanostructures and optimized phases supporting pseudocapacitive storage would efficiently improve the energy density and charging rate for EES devices, such as batteries and flexible supercapacitors. This review offers a detailed assessment of pseudocapacitance, including classification, working mechanisms, analysis methods, promotion routes and advanced applications. The future challenges facing the effective utilization of pseudocapacitive mechanisms in upcoming energy storage devices are also discussed.
Dependency Reduction Techniques for Performance Improvement of Hyperledger Fabric Blockchain
Authors: Kim Ju-Won , Song Jae-Geun , Park In-Hwan , Jo Dong-Hwan , Kim Yong-Jin , Jang Ju-Wook
Published: February 2025

We propose dependency reduction techniques for the performance enhancement of the Hyperledger Fabric blockchain. A dependency hazard may result from the parallelism in Hyperledger Fabric, which executes multiple transactions simultaneously in a single block. Since multiple transactions in a block are executed in parallel for throughput enhancement, dependency problems may arise among transactions involving the same key (If Z = A + D is executed in parallel with A = B + C, a read-after-write hazard for A will occur). To address these issues, our scheme proposes a transaction dependency checking system that integrates a dependency-tree-based management approach to dynamically prioritize transactions based on factors such as the tree level, arrival time, and starvation possibility. Our scheme constructs a dependency tree for transactions in a block to be executed in parallel over multiple execution units. We rearrange the transactions into blocks in such a way that the dependency among the transactions are removed as far as possible. This allows parallel execution of transactions to be performed without collision, enhancing the throughput against the conventional implementation of Hyperledger Fabric. Our illustrative implementation of the proposed scheme in a testbed for trading renewable energy shows a performance improvement as big as 27%, depending on the input mixture of transactions. A key innovation is the introduction of the Starve-Avoid method, which mitigates data starvation by dynamically adjusting the transaction priorities to balance throughput and fairness, ensuring that no transaction experiences indefinite delays. Unlike existing approaches that require structural modifications to the conventional Hyperledger Fabric, the proposed scheme optimizes the performance as an independent module, maintaining compatibility with the conventional Hyperledger Fabric architecture.
Beneficial Soil Bacteria: Many Recipes to Promote Plant Growth and Protection
Authors: Alexandre Ana , Wippel Kathrin
Published: February 2025

This Special Issue includes studies originating from various countries around the globe and covers diverse types and mechanisms of microbial beneficial functions for plant life [...]