bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effects of arbuscular mycorrhizal fungi on root foraging and competitive ability depend on soil phosphorus distribution: Evidence from two pairs of invasive and native plants
Authors: Yao Na-Na , Wang Xiao-Jing , Deng Hao-Hui , Fan Sheng-Qi , Zhang Hai-Jie , Peng Shao-Lin , Chen Bao-Ming 
Published: January 2024

Abstract
Besides uptake of nutrients by roots, plants can acquire nutrients through arbuscular mycorrhizal fungi (AMF). AMF play a crucial role in plant growth and competition. However, few studies have investigated the effects of AMF on root-foraging and competition between invasive and native species in response to heterogeneous nutrients. Two pairs of invasive and native plants of the Asteraceae family were selected to create a common garden experiment involving three factors (heterogeneous vs. homogeneous phosphorus (P), with vs. without AMF, and monoculture vs. mixture). The results showed that AMF significantly reduced the foraging scale of the invasive species, Bidens pilosa,and decreased the precision of the invasive species, Praxelis clematidea, and the native species, Emilia sonchifolia. In monoculture, AMF significantly decreased the total biomass of the two invasive species under heterogeneous P rather than homogeneous P, which was confirmed by N and P uptake. In mixture, heterogeneity significantly decreased the tolerance competitive ability of B. pilosabut increased that of P. clematidea. In the homogeneous P, AMF significantly decreased the suppression ability of B. pilosa, while in the heterogeneous P, AMF decreased that of P. clematidea. Heterogeneous P with AMF increased the suppression ability of B. pilosa but decreased that of P. clematidea. The interactive effects of AMF and soil P distribution on root foraging and nutrient uptake and competition differ among the four species and show invasive-native pair differences. These findings provide valuable insights into the interactive effects and highlight the context dependency of these interactions.
Improving Semen Quality of rams fed with ration containing protected maggot oil
Authors: Septiana Tabita Thertini , Khotijah Lilis , Wiryawan I Komang Gede 
Published: January 2024

Abstract
Black soldier fly maggot oil has emerged as a potential alternative energy source for livestock because of its high-fat content, which contains lauric, myristic, oleic, and palmitic acids that are known to improve livestock reproduction. A study was conducted to evaluate the effect of feeding protected maggot oil at different levels on the macroscopic and microscopic ram sperm quality. The study used 15 local rams with an age of approximately 10–12 months. The study used a randomized group design with treatments in the form of concentrates containing different levels of protected maggot oil: P0(0 protected maggot oil (control)), P1(4% protected maggot oil), and P2(8% protected maggot oil). The variables measured were nutrient consumption, blood cholesterol levels, scrotal circumference, and sperm quality. The data obtained were analysed using ANOVA. The results showed that there were no significant differences in the consumption of dry matter, crude protein, crude fiber, scrotal circumference, and semen macroscopic quality. The 4% and 8% protected maggot oil treatments produced significantly (p < 0.05) higher consumption of lauric, myristic, and oleic acids, blood cholesterol levels, and sperm motility than the control, but there were no significant differences between P1 and P2. It can be concluded that protected maggot oil up to 8% can increase the availability of lauric, myristic, and oleic fatty acids, and improve microscopic sperm quality.
ESRNN: Effective Residual Self-Attention Recurrent Neural Network with Soft Threading Function for Sound Event Location
Authors: Zhang Bin , He Jiawen , Liu Peishun , Wang Liang , Zhou Hao , Wang Xuening , Tang Ruichun 
Published: January 2024

Abstract

Sound event location is a critical aspect of two-dimensional direction-of-arrival (2D-DOA) estimation, predicting azimuth and elevation angles in 3D Cartesian coordinates for active sound events using multi-label regression. Challenges with conventional methods like the multi-signal classification (MUSIC) algorithm and baseline convolution recurrent neural network (BCRNN) include decreased precision and high computational demands, particularly in low signal-to-noise ratio (SNR) environments (SNR\textless-5 dB). Our work introduces an innovative solution, the effective residual self-attention recurrent neural network (ESRNN). ESRNN addresses distortion problems in low SNR conditions caused by the MUSIC algorithm, also enhancing 2D-DOA prediction accuracy in various SNR-reverberation scenarios. We propose two filter structures, ESRNN-L and ESRNN-G, tailored for SNRs above 0 dB and below -5 dB, respectively. Evaluating on TAU Spatial Sound Events 2019 datasets with synthetic SNRs from -10 dB to 30 dB, our experiments demonstrate ESRNN-L achieves a 21 \(%\) lower 2D-DOA error than BCRNN at SNRs below -5 dB. Additionally, ESRNN-G exhibits a 15$%$ lower error with a 10$%$ parameter reduction when SNRs exceed 0 dB. When compared with other principal attention methods through ablation study, it also showcases the model's efficiency and robustness.
Optimization of Machine Tool Settings for Spirac Hypoid Gears by Controlling Symmetry of Contact Paths
Authors: Xuan Jiamin , Li Haitao , Zhang Wei 
Published: January 2024

Abstract
A new method is developed for computing the machine tool settings for Spirac hypoid gears by controlling the symmetry of contact paths on the concave and convex tooth surfaces of the gear. By modifying the angular setting of the head cutter when cutting the pinion, the direction angles of the two contact paths are equated to estimate their symmetry. The relation between the direction angles is formulated precisely, the influence of the angular setting on the contact paths is investigated, and the equations for obtaining the values of the machine tool settings are derived. The proposed method is applied to a numerical example of a Spirac hypoid gear pair, and the results reveal that the contact paths on the concave and convex tooth surfaces are approximately symmetrical and the transmission errors of both sides are comparable.
Performance Evaluation of DG Interconnected Radial Distribution System
Authors: Chauhan Gaurav , Bangia Sakshi 
Published: January 2024

Abstract
The distribution system has become very complex entity due to increase in power system utilities, so it is very difficult task to improve the voltage profile and to maintain the quality of power of such complex system. Distribution generation (DG) can play major role to enhance the power quality of the system and also improves the reliability of the system. DGs act as an active distribution system and form the link between high voltage transmission line and low voltage utility. This helps in reducing the active power loss of the system. This paper presents optimizing techniques for DG model in terms of operating point, size and location to minimize the active power loss of overall system. For the proposed objective functions the variation in power loss can be observed with respect to DG current injection. The load characteristics have been taken into consideration to develop the proposed techniques and the system has been treated as constant current model. The proposed system has been simulated and verified by MATLAB software for RDS (radial distribution system).
The Hospital Emigration to Another Region in the Light of the Environmental, Social and Governance Model in Italy During the Period 2004-2021
Authors: Resta Emanuela , Resta Onofrio , Costantiello Alberto , Leogrande Angelo 
Published: January 2024

Abstract
The following article presents an analysis of the impact of the Environmental, Social and Governance-ESG determinants on Hospital Emigration to Another Region-HEAR in the Italian regions in the period 2004-2021. The data are analysed using Panel Data with Random Effects, Panel Data with Fixed Effects, Pooled Ordinary Least Squares-OLS, Weighted Least Squares-WLS, and Dynamic Panel at 1 Stage. Results show that HEAR is negatively associated to E, positively to S and negatively associated to the G within the ESG model. The data were subjected to clustering with a k-Means algorithm optimized with the Silhouette coefficient. The optimal clustering with k=2 is compared to the sub-optimal cluster with k=3. The results suggest a negative relationship between the resident population and hospital emigration at regional level. Finally, a prediction is proposed with machine learning algorithms classified based on statistical performance. The results show that the Artificial Neural Network-ANN algorithm is the best predictor. The ANN predictions are critically analyzed in light of health economic policy directions.
JEL CODE: I11, I12, I13, I14, I15, I18
Cross-scale phase relationship of the Ca II K index with solar wind parameters: a Space Climate focus
Authors: Reda Raffaele , Giovannelli Luca , Alberti Tommaso 
Published: January 2024

Abstract

The solar wind, representing one of the most impacting phenomena in the circum-terrestrial space, constitute one of the several manifestation of the magnetic activity of the Sun. With the aim of shedding light on the scales beyond the rotational period of the Sun (i.e., Space Climate scales), this study investigates the phase relationship of a solar activity physical proxy, the Ca II K index, with solar wind properties measured near the Earth, over the whole space era (last 5 solar cycles). By taking advantage of a powerful tool such as the Hilbert-Huang Transform, we investigate the dependence of their phase coherence on the obtained time scale components. Phase coherence at same time scales is found between all the components, while it is preserved also between adjacent components with time scales $\gtrsim$ 2 yrs. Finally, given the availability of the intrinsic modes of oscillation, we explore how the relationship of Ca II K index with solar wind parameters depends on the time scale considered. According to our results, we hypothesize the presence of a bifurcation in Ca II K index - solar wind speed (dynamic pressure) phase-space, where the time scale seems to act as bifurcation parameter. This concept may be pivotal for unraveling the complex interplay between solar activity and solar wind, bearing implications from the prediction and the interpretation point of view in Space Climate studies.
Aerodynamic Performance Analysis on Various Wheel Configurations of Commercial Vehicle
Authors: Cho SeongJin , LEE SangWook 
Published: January 2024

Abstract
In this study, the aerodynamic characteristics of wheel layouts in commercial vehicles, including dual and tandem axle wheels, were investigated using Computational Fluid Dynamics (CFD) with isolated and in-vehicle systems.
For the dual wheel system, an increase in offset distance reduced the drag by up to 31.2% for the Fackrell A2 wheels thanks to the decrease in the low pressure area behind the wheels. In the case of tandem wheel configuration, drag increased as the axle spread increased. As the axle spread increases, the rear wheels move out of the slipstream of the front wheel, causing a sharp increase in drag, while the front wheels' drag remains constant across all axle spreads. In a realistic commercial vehicle model, the same trend was observed. An increase in offset distance reduced the drag by approximately 4.30, and the increase in axle spread increased the drag by 4.62%.
These results are expected to contribute to the improvement of fuel efficiency and the reduction of greenhouse gas emissions by reducing the aerodynamic drag of commercial vehicles.
A novel high-frequency phase/frequency detector circuit with minimum dead zone for use in phase-locked loops For IoT applications
Authors: ghezelsofla Essmaeel , Ghorbani Alireza , javadifar nader 
Published: January 2024

Abstract
In this paper, a phase/frequency detector circuit is presented by using a transmission gate to minimize the dead zone in phase lock loop circuits. Reduction in power consumption, increase in speed and minimization of the dead zone which are the important parameters that are considered in the design of phase lock loop. Jitter decreasing and frequency range increasing is attained by correction in the operation of PFD block in PLL. The loss of the clock edge and the creation of a dead zone are the basic problems of the most of phase/frequency detectors at high frequencies. The lowest phase difference detectable by this circuit is 3 ps at the frequency of 4 GHz, which has a much better performance compared to other detectors. It should be noted that this circuit is capable of detecting very small phase differences at high frequencies. The occupied area of the designed circuit 39.5 * 44.5 µm2 and the power consumption is 490µ. The proposed circuit is simulated in 0.18µm TSMC technology with 1.8v power supply.
Glutamatergic system regulates feather pecking behaviors in laying hens: the gut-brain axis mechanism
Authors: Yan Xiliang , Wang Chao , Li Yaling , Lin Yating , Wu Yinbao , Wang Yan 
Published: January 2024

Abstract

Background
Feather pecking (FP), characterized by pecking at and pulling out of feathers of conspecifics, is a serious welfare and economic problem in laying hen husbandry. There is increasing evidence pointing pout to a critical role of gut microbiota in regulating host behaviors. However, the biological mechanism underlying the role of gut microbiota in FP is still unclear, evidenced by the lack of an appropriate treatment.
Results
In this work, we applied a multi-omics approach combined with a series of physiology assays to investigate the gut-brain axis of FP behavior. We show that chronic treatment with environmental stress induced severe FP accompanied by reduced production performance and increased anxiety- and depression-related behaviors, in comprison with control-treated laying hens. In addition, the immune system was severely suppressed in FP chickens. It is worth noting that the diversity and composition of gut microbiota were significantly altered, and thus leading to the reduced stability in microbial community. Importantly, a variety of differential metabolites were identified from non-targeted metabolomic analysis, which were mainly associated with arginine and histidine biosynthesis. A significant increase of glutamate levels was also observed in the hippocampus of FP chickens. Moreover, the eukaryotic transcriptome sequencing analysis revealed that the expressions of two glutamate-related receptors, GRIN2A and SLC17A6, were significantly upregulated in the hippocampus. Spearman correlation analysis showed that both genes GRIN2A and SLC17A6 in the hippocampus were significantly positively correlated with arginine levels in the duodenum, and Romboutsia in the duodenum were significantly negatively correlated with arginine.
Conclusions Romboutsia
and other bacteria genera in the intestine may increase the plasma levels of arginine and histidine by increasing the synthesis of arginine and histidine and decreasing the metabolism of arginine, which in turn increases glutamate levels and GRIN2A and SLC17A6 gene expression in the hippocampus, and regulates the glutamatergic system to influence the FP behavior of laying hens.
Heat transfer in material having random thermal conductivity using Monte Carlo simulation and deep neural network
Authors: kumar rakesh 
Published: January 2024

Abstract
Stochastic heat transfer simulations play a pivotal role in capturing real-world uncertainties, where randomness in material properties and boundary conditions are present. Traditional methods, such as Monte Carlo simulation , perturbation methods, and polynomial chaos expansion, have provided valuable insights but face challenges in efficiency and accuracy, particularly in high-dimensional systems. This paper introduces a methodology for one-dimensional heat transfer modeling that incorporates random boundary conditions and treats thermal conductivity as a random process The proposed approach integrates Monte Carlo simulation with Cholesky decomposition to generate a vector of thermal conductivity realizations, capturing the inherent randomness in material properties. Finite Element Method (FEM) simulations based on these realizations yield rich datasets of temperatures at various locations. A deep neural network (DNN) is then trained on this FEM data, enabling not only rapid and accurate temperature predictions but also bidirectional computations—predicting temperatures based on thermal conductivity and inversely estimating thermal conductivity from observed temperatures.
Emergency Department Care Experiences among 2SLGBTQQIA+ Patients: A Mixed Methods Study
Authors: Chard Sidonie , Karabelas-Pittman Sawyer , Martin Kel , Chapple Elliot , Messenger David , Bartels Susan , Walker Melanie 
Published: January 2024

Abstract
Background Equity-deserving groups (EDG), including those who identify as two-spirit, lesbian, gay, bisexual, transgender, queer, questioning, intersex, and/or asexual (2SLGBTQQIA+), are disproportionately treated in the Emergency Department (ED). This study aimed to understand ED care experiences of 2SLGBTQQIA+ individuals compared to those who do not identify as equity-deserving in Kingston, Canada, ultimately aiming to enhance inclusivity and better meet healthcare needs.
Methods Data were collected through a mixed qualitative/quantitative cross-sectional study using a novel electronic survey tool (Spryng.io), which purposely integrates qualitative and quantitative data, while minimising researcher bias. A community-based participatory approach was employed to involve community stakeholders. Participants were recruited from the Kingston Health Sciences Centre's ED, Urgent Care Centre, and at community-based organisations. Quantitative data were analysed using chi-squared tests, while qualitative data underwent thematic analysis. Results were triangulated. Focus group discussions with community partners were then undertaken to contextualise findings.
Results Compared to the control group (n=949), 2SLGBTQQIA+ individuals (n=118) felt their identity had a more negative impact on their care (p<0.0001) and experienced more judgment and disrespect from healthcare providers (HCPs) (p<0.0001). Four themes emerged from triangulation of qualitative and quantitative data: (1) mixed emotions regarding ED care; (2) transgender and non-binary health care considerations; (3) unmet mental health needs; and (4) lack of patient-centred care for 2SLGBTQQIA+ patients.
Conclusions This study highlights the need for 2SLGBTQQIA+-specific competency training for ED HCPs to provide effective, patient-centered care. 2SLGBTQQIA+ individuals often face unmet mental health care needs, requiring tailored mental health care provision in the ED. Intersectionality within the 2SLGBTQQIA+ population underscores the importance of trauma-informed care. Strategies to improve 2SLGBTQQIA+ healthcare include implementing safer spaces, clear feedback mechanisms, referrals to gender-affirming specialists, and privacy in triage. Further research should assess the impact of educational interventions on HCP knowledge and patient experiences in the ED.
Toxicometabolomics-Based Cardiotoxicity Evaluation of Thiazolidinedione Exposure in Human-Derived Cardiomyocytes.
Authors: Sultan Abdullah Al , Rattray Zahra , Rattray Nicholas J. W. 
Published: January 2024

Abstract
Introduction
Thiazolidinediones (TZDs), represented by pioglitazone and rosiglitazone, are a class of cost-effective oral antidiabetic agents posing a marginal hypoglycaemia risk. Nevertheless, observations of heart failure have hindered the clinical use of both therapies.
Objective
Since the mechanism of TZD-induced heart failure remains largely uncharacterised, this study aimed to explore the as-yet-unidentified mechanisms underpinning TZD cardiotoxicity using a toxicometabolomics approach.
Methods
The present investigation included an untargeted liquid chromatography–mass spectrometry-based toxicometabolomics pipeline, followed by multivariate statistics and pathway analyses to elucidate the mechanism(s)of TZD-induced cardiotoxicity using AC16 human cardiomyocytes as a model, and to identify the prognostic features associated with such effects.
Results
Acute administration of either TZD agent resulted in a significant modulation in carnitine content, reflecting potential disruption of the mitochondrial carnitine shuttle. Furthermore, perturbations were noted in purine metabolism and amino acid fingerprints, strongly conveying aberrations in cardiac energetics associated with TZD usage. The results also highlighted changes in polyamines (spermine and spermidine) and amino acid levels (L-tyrosine and valine), indicating phenotypic alterations in cardiac tissue (hypertrophy), which represents another characteristic of cardiotoxicity and a potential associated mechanism. In addition, this comprehensive study identified two groupings – (i) valine and creatine, and (ii) L-tryptophan and L-methionine – that were significantly enriched in the above-mentioned mechanisms, emerging as potential fingerprint biomarkers for pioglitazone and rosiglitazone cardiotoxicity, respectively.
Conclusion
These findings demonstrate the utility of toxicometabolomics in elaborating on mechanisms of drug toxicity and identifying potential biomarkers, thus encouraging its application in the toxicological sciences.
Development of an Intelligent Virtualization Platform Key Metrics Monitoring System: Collaborative Implementation with Self-Training and Bagging Algorithm
Authors: Wu Ruey-Chyi 
Published: January 2024

Abstract

In recent years, virtualization platforms have not only been used to integrate data from traditional application systems but have also actively collected Internet of Things (IoT) data from various network transmissions. To address the challenges of real-time monitoring for key metrics on virtualization platforms, this study proposes an optimal machine learning training model that combines semi-supervised Self-Training algorithms with supervised ensemble algorithms. In the application of semi-supervised training learning algorithms, this study utilizes a Self-Training learning algorithm to label a large number of unlabeled virtual machine operational states with a small amount of labeled data, laying the foundation for subsequent model construction. Subsequently, an ensemble learning classification algorithm is introduced to further validate and identify learning models suitable for generalization. Empirical evaluations show that the RandomForest algorithm serves as the optimal base estimator for Self-Training, while the Bagging algorithm is the optimal choice for ensemble learning. The synergy of these two achieves an accuracy exceeding 99%, enabling the model to accurately differentiate between various operational states such as normal operation, resource insufficiency, and faults. Finally, the integrated training model is deployed to a dashboard, displaying the real-time operational status of virtual machines through different colored lights. Simultaneously, operational status information is communicated to stakeholders through various media, further improving coordination, decision-making, and resource allocation issues on the virtualization platform. This study provides an efficient and feasible solution for monitoring and managing virtualization platforms.