bluuebunny/crossref_metadata_2025_split huggingface dataset:
Characteristics of breeding and performance parameters of the different local types of moscovy duck (cairina moschata) reared in south Benin, West Africa
Authors: Libanio Daouda , Behingan Boris Milognon , Kinkpe Lionel , Mankpondji Frederic , Oseni Saidou , Chrysostome Christophe Archille Armand 
Published: June 2024

Abstract

Muscovy duck breeding is a critical component of food security and livelihoods in southern Benin, yet knowledge of breeding practices remains limited. This study characterizes Muscovy duck breeding systems across three agroecological zones using a participatory survey of 420 breeders. The survey revealed a male dominance (77.78%) among breeders, with the Fon ethnic group constituting the majority (85.86%). A consistent male bias was observed across zones in ducks exceeding 6 weeks of age. Free-grazing emerged as the dominant breeding system in Zone 5 (100%), while Zone 6 displayed a more diverse approach (free-grazing: 60%, semi-grazing: 30%, confinement: 10%). Zone 8 primarily utilized semi-grazing (56.25%), followed by free-grazing (31.25%) and confinement (12.5%). Housing also varied across zones, with simple shelters dominating in Zones 5 (100%) and 8 (97.92%), while Zone 6 relied more on duck houses (52.5%). Egg laying performance increased significantly with each breeding cycle, with the third cycle yielding the highest number of eggs (15.37). Hatching rate exhibited a different pattern, peaking during the second cycle (95.42%) and showing statistically significant variations across cycles (P < 0.05). Plumage color did not influence reproductive performance. Interestingly, plumage color was associated with duckling survival, with black ducklings exhibiting the lowest mortality risk (P < 0.05). Diseases were reported as the leading cause of mortality. Future research on morphometrics, whole genome sequencing, and immune function variations, particularly in black ducks, alongside MC1R-MITF gene interactions, can improve our understanding of duck breeding in Benin and optimize production and duckling health.
The causal relationship between Eubacterium and constipation: a Mendelian randomization study
Authors: Fan Dingwen , Zhang Liangliang , Liu Hongchang , Zhu Dan 
Published: June 2024

Abstract

Background: Constipation may be related to an imbalance in flora imbalance. In this study, univariate and multivariate Mendelian randomization (MR) studies were used to further understand the causal relationship between different members of genus Eubacteriumand constipation.
Methods: The constipation and genus Eubacterium datasets were obtained from the Integrative Epidemiology Unit (IEU) Open Genome-Wide Association Study (GWAS) database. The causal effects between constipation and genus Eubacterium were analyzed using MR-Egger, Weighted median, Simple mode, Weighted mode and Inverse variance weighted (IVW). In univariate MR analysis (UVMR), genus Eubacillus was the exposure factor, constipation was the outcome. In addition, the reliability of UVMR analysis was assessed by sensitivity analyses (heterogeneity tests, horizontal pleiotropy tests and leave-one-out (LOO) analysis). Finally, the MVMR analysis was performed.
Results: In the UVMR analysis, genus Eubacterium rectale group id.14374 (p=0.043, OR=0.805) and genus Eubacterium brachy group id.11296 (p=0.036, OR=0.904) were all protective factors on constipation. The results of sensitivity analysis showed that UVMR analysis was reliable. In the MVMR analysis, genus Eubacterium rectalegroup was still a significant protective factor (p=0.007, OR=0.80), while genus Eubacterium brachy group was not a significant protective factor for constipation (p=0.151, OR=0.94) .
Conclusion: The results of study supported that genus Eubacterium rectale group had a significant causal relationship with constipation, and the increase of genus Eubacterium rectale group could reduce the risk of constipation.
The Joint Impact of Depression Status and TyG Index on Overweight Incidence: A cross-sectional Study based on CHARLS in 2015
Authors: He Feiying , Fu Zhaolin , Xu Xiangchun , Yu Hongwen , Liu Hualin , Chen Yanwu , Tang Ping , Chen Yuan Yao 
Published: June 2024

Abstract

Background
Depressive status and triglyceride-glucose index (TyG) have both been reported to be associated with the development of overweight. However, there is a paucity of literature exploring the joint effect of TyG changes and depressive status on the occurrence of overweight. The purpose of this study was to investigate the relationship between depression status and TyG index with the incidence of hypertension, and to analyze the combined effect of depression status and TyG index on the incidence of overweight.
Methods
We conducted a prospective cohort study using data from the China Health and Retirement Longitudinal Study (CHARLS), a nationwide population-based study. Occurrence of overweight was determined among eligible respondents to CHARLS in 2015 on the basis of 10-item scores on the Center for Epidemiologic Studies Depression Scale (CES-D-10). logistic model was used to calculate the relationship between depressive status and TyG index and the incidence of overweight. Logistic regression model was used for subgroup analysis of smoking variables. In addition, after propensity score matching, the logistic model was re-applied to calculate the relationship between depression status and TyG index and the incidence of overweight. Finally, the multiplicative interaction between depression status and TyG index was calculated.
Results
In the results, the risk of overweight in those with depression was 79% of that in those without depression (OR = 0.79, 95% CI: (0.66,0.95)),The risk of overweight with a TyG index > 5 was 302% of that with a TyG index of 4–5 (OR = 3.02, 95% CI: (2.45,3.73))。There was a significant interaction between depression status and TyG index, except for the presence of a depressive state plus a TyG index of 4–5 this combination (OR = 0.69, 95% CI: (0.55,0.86)) had a negative combined effect on the development of overweight, the other factors showed a positive combined effect on overweight, and the combined effect of depression and TyG index > 5 (OR = 3.6, 95%CI: (2.27,5.73)) was the largest.
Conclusion
Our research findings further confirm that the TyG index and depression have a significant impact on the occurrence of overweight. To reduce the high medical expenses caused by obesity and overweight, We need to pay more attention to psychological status and insulin resistance status.
Special attribute-based cross-modal interactive fusion network for RGBT tracking
Authors: Shao Xiaoqiang , Li Hao , Lyu Zhiyue , Ma Bo , HAN Zehui , Liu Mingqian 
Published: June 2024

Abstract

Due to the strong robustness of RGBT object tracking, which is less susceptible to the effects of illumination and occlusion, it has been widely used in the fields of video surveillance and automated driving. In this paper, an effective tracking network is constructed by fully interacting with both modal information using challenging attributes in infrared and visible images. The network consists of three parts: the special attribute fusion(SAF) module, the common attribute fusion(CAF) module, and the cross-modal interaction(CMI) module. The SAF module enables the network to extract unique challeng attribute information from two modalities, fully leveraging the advantages of different modal information. The CAF module extracts features from attributes matched in both modalities, and adaptively aggregates them, assigning corresponding weights to each challenging attribute to enhance the tracker's adaptability. The CMI module facilitates modal interaction between the infrared and visible image modalities, integrating common modal information with the specific modal information of each, thereby enhancing the network's robustness. The proposed network is tested on GTOT, RGBT234, and LasHer datasets, respectively. The results show that our tracker outperforms other trackers, proving the superiority of our method.
Energy Consumption-Coverage Optimization for Node Deployment: Using Hybridized FOA-BAO Algorithm in Underwater Wireless Sensor Networks (UWSNs)
Authors: Marco Kadoke , Goyal Sonia , Kaur Ranjit 
Published: June 2024

Abstract

Coverage and energy consumption are critical issues in underwater wireless sensor networks due to the unique challenges posed by the aquatic environment. This study introduces a hybrid fruit fly-bat optimization algorithm (FOA-BOA) for sensor node deployment in underwater wireless sensor networks (UWSNs). This technique leverages both fixed and mobile sensor nodes to enhance network connectivity and coverage. By developing a novel fitness function that incorporates coverage quality, connection cost, and network lifetime, the algorithm optimizes sensor node placement using modified bat and fruit fly optimization models. Probability detection and a hybrid FOA-BOA-based multipoint search are utilized to determine the optimal deployment configuration. Simulation results, compared with three existing algorithms, show that the proposed method significantly improves network coverage, reduces energy consumption, and extends network lifetime. These findings underscore the potential of this hybrid optimization technique to enhance UWSN performance in environmental monitoring applications.
Machine Learning Algorithms Optimize Prediction Accuracy in Submersible Ballast Tank High-pressure Air Blowing Processes
Authors: HE Xi-Guang , HUANG Bin , LOU Jing-jun , CHEN Jia-bao , PENG Li-kun 
Published: June 2024

Abstract

The optimization of prediction accuracy in the high-pressure air blowing processes of submersible ballast tanks is crucial for enhancing the safety and efficiency of submersible operations. This study presents a comparative analysis between machine learning algorithms and traditional numerical simulation methods for predicting the dynamics of air blowing in submersible ballast tanks. The investigation focuses on the performance of the Back Propagation Neural Network (BPNN) and the Radial Basis Function Neural Network (RBFNN), employing a series of experimental data to train and validate the models.The results indicate that while traditional numerical simulations exhibit a maximum relative error of 124.805%, the RBFNN, in particular, achieves a remarkable linear correlation coefficient of 1, a mean square error of 5.6758e-23, and a mean absolute percentage error of 7.4888e-11. These findings demonstrate the superior predictive capability of the RBFNN, suggesting that machine learning algorithms can significantly improve the accuracy of predictions concerning the high-pressure air blowing process. The study advocates for the adoption of machine learning techniques in submersible engineering, potentially transforming the reliability and safety of deep-sea exploration endeavors.
Determinants of Academic Performance of Armed Forces Schools: Perceptions of Teachers of Ghana Armed Forces Basic Schools
Authors: Boye Eric Sefa , Agyei Douglas Darko 
Published: June 2024

Abstract

The purpose of the study was to investigate the determinants of academic performance of armed forces basic schools from the perspectives of teachers of Ghana Armed Forces basic schools. A concurrent mixed method approach was used and this allowed the authors to use qualitative and quantitative evidence to define relationships among variables. Specifically, the study employed the use of structured questionnaires and interviews to collect data from sixty-one (61) participants for the study. The outcomes of the study revealed that prescribed school uniforms for teachers, effective supervision/inspections, motivation for teachers, parental concern as well as safety and security of armed forces schools contribute to the academic performance of armed forces schools. Other determinants identified include conduciveness of the school environment, good leadership and discipline among learners, teachers and school administrators. The authors suggested that the study should be extended to other armed forces schools to marry up the findings and also help come out with recommendations to inform theory, policy and practice.
Torque Regression Using Machine Learning Techniques in Automotive ECUs
Authors: Canal Rafael , Bonomo João Paulo Araujo , de Carvalho Rodrigo Santos , Gracioli Giovani 
Published: June 2024

Abstract

With the modernization of the automotive industry, a large amount of data is generated by vehicles during operation. When interpreted analytically, it can result in advances in several areas, such as safety, durability, production efficiency, fuel consumption, and gas emissions. In this work, we focus on engine torque and its regression, to enable the understanding of its behavior and help improve the performance of engines and vehicles, mainly in product development and testing. In addition, it highlights the relationship between torque and misfire for optimizing engine components by showing their impacts, as it is a common failure in combustion engines that impairs engine performance. After a rigorous process of feature selection relevant to torque, we collect data directly from a vehicle's ECU to train and evaluate machine learning algorithms to perform torque regression without relying on synthetic data or public datasets, using misfire detection as one of the inputs to the algorithms. In a car that reaches a torque of 144 Nm, our results reached up to an RMSE of 3.3830 (Nm)², MAE of 2.1620 (Nm), and R² of 0.99. Furthermore, our methodology acquired and processed data in real-time at a cloud server, that detects faults and calculates torque with the vehicle in motion, using less computationally demanding algorithms. These findings not only highlight our ability to predict the engine's torque and its relation with misfires but also our competence in analyzing additional parameters essential to vehicle performance.
Numerical Simulations to Illuminate the Sufficient Conditions of Integral Approximation Theorems
Authors: Hernadi Julan 
Published: June 2024

Abstract

One of the crucial issue in the mathematical reasoning is that many students do not really understand the mathematical rules underlying the conditional statement p → q, whereas most of mathematical theorems are of this pattern. They believe that the falsity of p implies the falsity of q. This is tantamount to presuming that implication is equivalent to the inverse, but that’s not the case. This fact suggests that students have not been able in distinguishing the sufficient and the necessary conditions of a conditional statement. This article is concerned with this problem by investigating the sufficient conditions on the basic quadrature formulas of integral approximation and their implication to the convergence order attainable. In order to instill students’ critical thinking skills, theorems relating to the convergence orders of three basic methods are proven. Furthermore, the cases where the sufficient conditions are fulfilled as well as cases where they are not satisfied are examined by a series of numerical simulation.
Mathematics Subject Classification: 03F03 , 65D30 , 65D32 , 65G50 , 65Y20
Network pharmacology, computational biology integrated surface plasmon resonance technology reveals the mechanism of salidroside in alleviating diabetic amyotrophy
Authors: Wang Hejie , Yousaf Wafa , Haseeb Abdul , Wang Ziyang , Zheng Jiangang 
Published: June 2024

Abstract

Objective
To explore the target and mechanism of Salidroside (SDS) in the treatment of Diabetic amyotrophy (DPN) employing network pharmacology, computational biology, and surface plasmon resonance verification. Method: The target associated with SDS was acquired from the ChEmBL database and DPN-related targets were obtained from the GeneCards database. Relevant targets were imported into the Venny platform to generate a Venn diagram, and their intersections were visualized. The target protein-protein interaction (PPI) network was constructed using STRING, DAVID database, and Cytoscape software, and core targets were screened. After subjecting the targets to GO enrichment and KEGG pathway analysis, a network "target-pathway for SDS in alleviating DPN" was set up. The Schrodinger Maestro 13.5 software was utilized for molecular docking in order to ascertain the binding free energy and binding mode between SDS and target proteins. Molecular dynamics simulations were performed using the Desmond program. Saturation mutation analysis was performed using Schrodinger's Maestro 13.5 software. Finally, SPR technology was used to explore the affinity between SDS and Caspase3 protein.
Results
Network pharmacological analysis showed that there was a total of 61 intersection proteins, among which TNF, APP, Caspase3, PPARG, NQO1, HDAC1, BCL2, SRC, HDAC6, ACE, MAPK3, HSP90AA1, ATM, and REN were potential core targets for SDS to alleviate DPN. The enrichment analysis of GO function and KEGG pathways revealed that the targets primarily participated in diverse biological processes, cellular components alteraions, and molecular functions associated with apoptosis, neurons and transmitters, as well as metabolic pathways involved in lipid and atherosclerosis, apoptosis, and neurodegenerative pathways. Based on the crystal structure of the potential core protein, the complex structure model of the core target-SDS was created using molecular docking (XP mode of flexible docking), and the MMGBS analysis was carried out. Finally, the molecular dynamics simulation was carried out. The Δaffinity of Caspase3 was highest in 206 (TRP→GLY), 206 (TRP→LYS), and 206 (TRP→ALA). The corresponding values were 10.847 kcal/mol, 10.008 kcal/mol, and 9.725 kcal/mol. The SPR results data demonstrated specific binding and kinetic compatibility between the SDS and Caspase3 proteins.
Conclusion
Caspase3 is a potential target for SDS to alleviate DPN which may eventually play a role in alleviating DPN by regulating apoptosis-related pathways and providing a theoretical basis along with clues for the research and development of SDS as anti-alleviating DPN drugs.
Convective drying and quality attributes of osmo-dehydrated banana slices using coconut sugar and sucrose as osmotic agents
Authors: Araújo Cintia da Silva , Macedo Leandro Levate , Vimercati Wallaf Costa , Osório Vanessa Moreira , Saraiva Sérgio Henriques 
Published: June 2024

Abstract

Dried fruits have gained more and more space in the food market. Osmotic dehydration (OD) can be applied as a pre-treatment to convective drying, aiming to produce foods with different characteristics. Therefore, the present study evaluated the OD process of banana slices using coconut sugar and sucrose, as well as its influence on convective drying (CD) and the physicochemical parameters of the product. Osmotic solutions at 40 and 60% were prepared, and OD was conducted at 30 and 50°C. OD and CD kinetic parameters were analyzed. The dried product was characterized by moisture, water activity, shrinkage, texture, color, bioactive and volatile compounds. The higher concentration (60%) and higher temperature (50°C) resulted in higher values of water loss, solid gain, and weight reduction during OD for both sugars. CD time varied between 225 and 345 minutes. OD as pre-treatment reduced drying time by up to 65%. The dried banana had low moisture content and low water activity. The shrinkage was up to 73.44%, associated with the higher concentration treatment and higher temperature during OD. OD reduced product hardness after CD. In general, using coconut sugar resulted in greater changes in color parameters and higher levels of bioactive compounds in dried bananas. Volatile compounds highly related to banana flavor were present after drying. Coconut sugar proved a good alternative for producing osmo-dehydrated dried banana slices.
Phlebotomine Sand Flies (Diptera: Psychodidae) Population of Particular Ecosystem in the Urban and Periurban Area in Morocco
Authors: FOUAD OUANAIMI , BOUSSAA SAMIA , BOUMEZZOUGH ALI 
Published: June 2024

Abstract

The Khettara is an abandoned irrigation system localized in Marrakech region, where no entomological investigations have been made. In the present study, sand flies (Diptera: Psychodidae) were collected with the aim to determine its abundance and species composition in this particular ecosystem. A total of 477 specimens were collected belonging to six species; Phlebotomus (Phlebotomus) papatasi (28.72%) was the predominant species followed by Sergentomyia (Grassomyia) dreyfussi (23.06%), S. (Sergentomyia) fallax (18.87%), S. (S.) minuta (10.69%), P. (Larroussius) longicuspis (9.85%), and P. (Paraphlebotomus) sergenti (8.81%). When, S. (G.) dreyfussi was collected for the first time in the urban area of Marrakech. The results showed important sand fly diversity in Khettara ecosystem compared to sand fly composition in urban area of Marrakech. In addition, of the six species of phlebotomine sand flies caught, three are proven vectors of leishmaniasis in Morocco. Thus, Marrakech region, and especially its Khettara ecosystem, should be regarded as a potential focus for leishmaniasis.
Identifying troublesome behavior in the classroom: Greek teachers’ and parents’ views
Authors: M.Kouimtzi Eleni , Frosi Labrini , Kolias Pavlos 
Published: June 2024

Abstract

According to the systems perspective, the influence of various systems (e.g., family, school, community) on children's behavior at school is highly acknowledged. It is therefore accepted that problem behavior in the classroom originates from social interactions (Upton & Cooper, 1990), providing a conceptual framework where problems are seen as indicative of dysfunction within the system, thus removing blame from the individual child, the teacher or the parents (Dowling, 2003). Addressing the importance of the interactions among students, teachers and parents in this microsystem, the present study aimed at identifying and comparing the types of behaviors that Greek primary and secondary teachers and parents view as problematic in the classroom. A sample of 378 teachers and 69 parents were asked to identify which behaviors are considered troublesome. Exploratory factor analysis revealed 5 categories of behaviors perceived as problematic by parents and teachers, Externalizing behaviors, School Difficulties, Internalizing behaviors, Attention seeking behaviors and Hyperactivity/attention difficulties. Both teachers and parents considered externalizing behaviors to be more troublesome than other types of behavior. Teachers tend to worry less than parents about all types of children’s behaviors, except for school difficulties. Findings emphasize the significance of considering multiple perspectives of the school microsystem in designing interventions to meet the specific needs of school communities and encouraging collaboration among all members of the school system.
Drug Targeting and Nanotherapeutic Advancement in the Treatment of Rheumatoid Arthritis: Recent Progress in Drug Delivery Systems
Authors: Deshmukh Rohitas 
Published: June 2024

Abstract:
Pain and swelling in the joints, increased synovial thickness, and bone and cartilage
degeneration are all symptoms of Rheumatoid Arthritis (RA). Anti-rheumatic medications,
which are used in conventional treatment plans for RA, need high doses, frequent administration,
and long-term use, all of which increase the risk of major adverse effects and low patient compliance.
Drug Delivery Systems (DDS) have been developed for RA treatment in an effort to
avoid these obstacles and improve clinical efficacy. There have been many successful experimental
RA models using these techniques. There has been a notable uptick in the study of RA
nanotherapies as a prospective improvement over conventional systemic therapy. In order to
overcome the limits of traditional treatments, researchers have begun looking into nanotherapeutic
approaches, notably drug-delivery nanosystems. The precise delivery and concentration of
therapeutic drugs in the affected regions are made possible by the passive or active targeting of
systemic administration. Several new DDS for treating RA have been addressed here. Therefore,
nanoscale drug delivery devices increase drug solubility and bioavailability while decreasing the
need for higher doses.