bluuebunny/crossref_metadata_2025 huggingface dataset:
Parameter Tuning of a Vapor Cycle System for a Surveillance Aircraft
Authors: Drego Adelia Darlene , Andersson Daniel , Staack Ingo
Published: January 2024

Surveillance aircraft perform long-duration missions (>eight hours) that include detection and identification of objects on the ground, the water, or in the air. They have surveillance systems that require large amounts of cooling power (typically 10 s of kW) for long durations. For aircraft application, vapor cycle systems (VCS) are emerging as a more efficient alternative to conventional cooling systems. In this study, a two-part method was applied to a cooling system with a VCS that can be installed on a surveillance aircraft. The first part focused on a parameter tuning study set-up and demonstrated how after identifying the operating conditions, constraints, and requirements, the only cooling system parameter available for tuning was the VCS compressor speed. The second part focused on a modelling and solving strategy for the cooling system and showed how the capacity of an aircraft cooling system was impacted by tuning the VCS compressor speed (Hz) for a surveillance system heat flow rate from 10 kW to 70 kW. The results from this study can be used to design a control strategy for the compressor. In a broader perspective, the two-part method and the results analysis presented can serve as a preliminary method for aircraft VCS control optimization studies.
End of the Cage Age? A Study on the Impacts of the Transition from Cages on the EU Laying Hen Sector
Authors: Majewski Edward , Potori Norbert , Sulewski Piotr , Wąs Adam , Mórawska Martyna , Gębska Monika , Malak-Rawlikowska Agata , Grontkowska Anna , Szili Viktor , Erdős Adél
Published: January 2024

This paper’s main objective is to assess the impacts of the ban on cages for housing laying hens, planned by the European Commission to raise animal welfare standards beyond the level set in the current legislation. The farm-level economic assessments of the ban were carried out in three stages: farm surveys and expert consultations, farm-level analyses, and aggregation to the EU-27 egg production sector. Four scenarios were constructed. All financial estimates were conducted with fixed prices from the year 2021 for which the reference scenario was built. Alternative hen-housing systems were barn (Voliera), free range, and organic. Until now, more than 50% of laying hens in the EU have already been transferred to alternative systems. The remaining part is subject to the transition. The basic assumptions included a reduction in yields due to the required lower densities and specifics of the production systems. A factor strongly differentiating the scenarios is likelihood of exists form the sector, as declared in the survey by many farmers, mainly those reaching retirement age without successors and keeping relatively small flocks of hens. The introduction of the ban will cause a decrease in egg production, varying between the scenarios. Substantial investments will be required within the range of 2–3.2 billion EUR, depending on the scenario.
Impacts of Geographical Indications on Agricultural Growth and Farmers’ Income in Rural China
Authors: Yin Xiaoyu , Li Jia , Wu Jingyi , Cao Ruihan , Xin Siqian , Liu Jianxu
Published: January 2024

Geographical indications (GIs) mitigate information asymmetry in agri-food transactions by providing consumers with origin and quality information. This paper explores the impact of GIs on rural development in China by examining agricultural output and farmers’ income. Utilizing a large county-level dataset and comprehensive official GI information, this study estimates the impact of GIs on agricultural output and rural income using panel-fixed-effects models. The results reveal that GIs significantly boost agricultural added value and rural per capita disposable income. A series of methods, including difference-in-differences, propensity score matching with difference-in-differences, and double machine learning combined with difference-in-differences using random forests verify the robustness of the results. Moreover, by categorizing GIs based on product types, the analysis reveals heterogeneous effects of different GI categories on agricultural growth and income gains for farmers. The research findings in this paper offer valuable insights to inform policymaking aimed at advancing rural development, raising farmers’ incomes, and promoting sustainable agri-food supply chains.
Is It Possible to Measure the Quality of Sugarcane in Real-Time during Harvesting Using Onboard NIR Spectroscopy?
Authors: Corrêdo Lucas de Paula , Molin José Paulo , Canal Filho Ricardo
Published: January 2024

In-field quality prediction in agricultural products is mainly based on near-infrared spectroscopy (NIR). However, initiatives applied to sugarcane quality are only observed under laboratory-controlled conditions. This study proposed a framework for NIR spectroscopy sensing to measure sugarcane quality during a real harvest operation. A platform was built to support the system composed of the NIR sensor and external lighting on the elevator of a sugarcane harvester. Real-time data were acquired in commercial fields. Georeferenced samples were collected for calibration, validation, and adjustment of the multivariate models by partial least squares (PLS) regression. In addition, subsamples of defibrated cane were NIR-acquired for the development of calibration transfer models by piecewise direct standardization (PDS). The method allowed the adjustment of the spectra collected in real time to predict the quality properties of soluble solids content (Brix), apparent sucrose in juice (Pol), fiber, cane Pol, and total recoverable sugar (TRS). The results of the relative mean square error of prediction (RRMSEP) were from 1.80 to 2.14%, and the ratio of interquartile performance (RPIQ) was from 1.79 to 2.46. The PLS-PDS models were applied to data acquired in real-time, allowing estimation of quality properties and identification of the existence of spatial variability in quality. The results showed that it is possible to monitor the spatial variability of quality properties in sugarcane in the field. Future studies with a broader range of quality attribute values and the evaluation of different configurations for sensing devices, calibration methods, and data processing are needed. The findings of this research will enable a valuable spatial information layer for the sugarcane industry, whether for agronomic decision-making, industrial operational planning, or financial management between sugar mills and suppliers.
Environmental Impact Assessment of Rice–Wheat Rotation Considering Annual Nitrogen Application Rate
Authors: Yang Yulin , Liu Xiaohu , Chen Yinglong , Xu Qiang , Dai Qigen , Wei Huanhe , Xu Ke , Zhang Hongcheng
Published: January 2024

Rice–wheat rotation is a widely adopted multiple-cropping system in the Yangtze River Basin, China. Nitrogen (N) fertilizer is a key factor in regulating crop yield; however, only a few studies have considered the impact of annual N application on the yield, environmental impacts, and economic profits of rice–wheat rotation systems. In this study, a field experiment was conducted in the Jiangsu Province from 2020 to 2022. The rice and wheat seasons included six and five N fertilizer application rates, respectively (Rice: 0, 180, 240, 300, 360, and 420 kg N ha−1; Wheat: 0, 180, 240, 300, and 360 kg N ha−1), combined to form a total of 30 treatments. Life-cycle assessment was used to evaluate the environmental impacts of rice–wheat rotation under different N application treatments, using area, yield, and economic profit as functional units. Ten environmental impact categories were selected, including global warming. The results showed that grain yield did not consistently increase with an increase in N application, and the annual yield was the highest when 300 and 240 kg N ha−1 (R300W240 treatment) was applied in the rice and wheat seasons, respectively. The area-based weighting index of the R300W240 treatment ranked 20th among the 30 treatments, while the yield- and profit-based weighting indices were the lowest among the 30 treatments, decreasing by 14.9% and 28.7%, respectively, compared to the other treatments. The R300W240 treatment was the optimal annual N application strategy for rice–wheat rotation. Among the 10 environmental impacts considered, urea production contributed significantly to over eight environmental impacts, whereas the pollutant losses caused by its application contributed significantly to six environmental impacts. These findings reveal the dependence of the rice–wheat rotation system on the unsustainable use of N fertilizer and indicate that N fertilizer management practices should be further optimized to improve the environmental sustainability of grain production in the future.
ZmGRAS46 Negatively Regulates Flowering Time in Arabidopsis thaliana
Authors: Zhang Honglin , Jiang Zhenzhong , Jiao Peng , Zhao Yang , Gao Bai , Liu Siyan , Guan Shuyan , Ma Yiyong
Published: January 2024

Flowering is an essential process in plant development, and there are six major flowering pathways: the photoperiodic pathway, gibberellin pathway, vernalization pathway, age pathway, autonomous pathway, and temperature pathway. In this study, we screened the transcriptome sequencing of early flowering mutants from the laboratory for the significantly differentially expressed ZmGRAS46, which belongs to the DELLA subfamily of the GRAS family. DELLA is involved in the gibberellin pathway to regulate plant flowering. However, it is not clear whether ZmGRAS46 is involved in the gibberellin pathway which regulates plant flowering; therefore, in this experiment, we investigated the regulatory role of this gene in Arabidopsis flowering by overexpressing ZmGRAS46. It was found that overexpression of ZmGRAS46 in Arabidopsis promotes the formation of rosette leaves and flower buds and delays flowering time in Arabidopsis, and experiments have shown that ZmGRAS46 represses the expression of FLOWERING LOCUS T (FT), SUPPRESSOR OF CONSTANS1 (SOC1), CONSTANS (CO), and LEAFY (LFY). Our results indicated the possibility that ZmGRAS46 represses flowering through the CO-FT-SOC1-mediated photoperiodic flowering pathway. The delayed flowering phenotype of overexpressing ZmGRAS46 Arabidopsis could be rescued by applying GA3. The experimental results indicate that ZmGRAS46 depends on the GA3 pathway to regulate flowering in Arabidopsis.
Molecular Basis and Engineering Strategies for Transcription Factor-Mediated Reproductive-Stage Heat Tolerance in Crop Plants
Authors: Sharma Niharika , Sharma Lakshay , Onkarappa Dhanyakumar , Yogendra Kalenahalli , Bose Jayakumar , Sharma Rita A.
Published: January 2024

Heat stress (HS) is a major threat to crop productivity and is expected to be more frequent and severe due to climate change challenges. The predicted increase in global temperature requires us to understand the dimensions of HS experienced by plants, particularly during reproductive stages, as crop productivity is majorly dependent on the success of plant reproduction. The impact of HS on crop productivity is relatively less-studied than the other abiotic stresses, such as drought and salinity. Plants have evolved diverse mechanisms to perceive, transduce, respond, and adapt to HS at the molecular, biochemical, and physiological levels. Unraveling these complex mechanisms underlying plant HS response and tolerance would facilitate designing well-informed and effective strategies to engineer HS tolerance in crop plants. In this review, we concisely discuss the molecular impact of HS on plant reproductive processes and yield, with major emphasis on transcription factors. Moreover, we offer vital strategies (encompassing omics studies, genetic engineering and more prominently gene editing techniques) that can be used to engineer transcription factors for enhancing heat tolerance. Further, we highlight critical shortcomings and knowledge gaps in HS tolerance research that should guide future research investigations. Judicious studies and a combination of these strategies could speed up the much-needed development of HS-resilient crop cultivars.
Secure Internet Financial Transactions: A Framework Integrating Multi-Factor Authentication and Machine Learning
Authors: Aburbeian AlsharifHasan Mohamad , Fernández-Veiga Manuel
Published: January 2024

Securing online financial transactions has become a critical concern in an era where financial services are becoming more and more digital. The transition to digital platforms for conducting daily transactions exposed customers to possible risks from cybercriminals. This study proposed a framework that combines multi-factor authentication and machine learning to increase the safety of online financial transactions. Our methodology is based on using two layers of security. The first layer incorporates two factors to authenticate users. The second layer utilizes a machine learning component, which is triggered when the system detects a potential fraud. This machine learning layer employs facial recognition as a decisive authentication factor for further protection. To build the machine learning model, four supervised classifiers were tested: logistic regression, decision trees, random forest, and naive Bayes. The results showed that the accuracy of each classifier was 97.938%, 97.881%, 96.717%, and 92.354%, respectively. This study’s superiority is due to its methodology, which integrates machine learning as an embedded layer in a multi-factor authentication framework to address usability, efficacy, and the dynamic nature of various e-commerce platform features. With the evolving financial landscape, a continuous exploration of authentication factors and datasets to enhance and adapt security measures will be considered in future work.
Farmers’ Perceptions on Implementing Automatic Milking Systems in Large USA Dairies: Decision-Making Process, Management Practices, Labor, and Herd Performance
Authors: Lage Camila Flavia de Assis , Marques Thaisa Campos , Bruno Daniela R. , Endres Marcia I. , Ferreira Fernanda , Pires Ana Paula Alves , Leão Karen , de Lima Fabio Soares
Published: January 2024

Automatic Milking System (AMS) installations are increasing in the USA despite the higher investment cost than conventional systems. Surveys on AMSs conducted outside the USA focused on small–medium herds, specific regions, or aspects of AMS milking. This study described farmers’ perceptions about the decision-making process of adopting an AMS in the USA’s large dairies (≥7 AMS boxes) regarding changes in technology, housing, management practices, labor, herd performance, and health. After being contacted, 27 of 55 farmers from large AMS herds completed the survey. The main reasons for adopting an AMS were labor costs, cows’ welfare, and herd performance. Most farms constructed new barns, used a free-flow traffic system, and changed their feed management. Increases in water and energy use were perceived by 42% and 62% of farmers, respectively. Farmers estimated decreases in labor costs of over 21%, and AMS employees worked 40–60 h/week. Milk production increases were reported by 58%, with 32% observing higher milk fat and protein content. Easier sick cow detection, better mastitis management, and improvements in pregnancy rates were reported. Thus, farmers transitioning to AMSs perceived altered resource utilization, labor cost savings, and improvements in employee quality of life, animal welfare, and farm management. While 54% of respondents would recommend an AMS to other farms, 38% suggested considering additional aspects prior to adoption.
Cultivar and Harvest Time of Almonds Affect Their Antioxidant and Nutritional Profile through Gut Microbiota Modifications
Authors: Delgado-Osorio Adriana , Navajas-Porras Beatriz , Pérez-Burillo Sergio , Hinojosa-Nogueira Daniel , Toledano-Marín Ángela , Pastoriza de la Cueva Silvia , Paliy Oleg , Rufián-Henares José Ángel
Published: January 2024

Almonds are a rich source of beneficial compounds for human health. In this work, we assessed the influence of almond cultivars and harvest time on their morphological (length, width and thickness) and nutritional (ash, moisture, proteins) profiles. We also evaluated the impact of an in vitro digestion and fermentation process on almonds’ antioxidant and phenolic content, as well as their support of gut microbiota community and functionality, including the production of short-chain fatty acids (SCFAs), lactic and succinic acids. The length, width, and thickness of almonds varied significantly among cultivars, with the latter two parameters also exhibiting significant changes over time. Moisture content decreased with maturity, while protein and ash increased significantly. Total antioxidant capacity released by almonds after digestion and fermentation had different trends depending on the antioxidant capacity method used. The fermentation step contributed more to the antioxidant capacity than the digestion step. Both cultivar and harvest time exerted a significant influence on the concentration of certain phenolic compounds, although the total content remained unaffected. Similarly, fecal microbiota modulation depended on the cultivar and maturity stage, with the Guara cultivar and late maturity showing the largest effects. Cultivar type also exerted a significant impact on the concentration of SCFAs, with the Guara cultivar displaying the highest total SCFAs concentration. Thus, we conclude that cultivar and harvest time are key factors in shaping the morphological and nutritional composition of almonds. In addition, taking into account all the results obtained, the Guara variety has the best nutritional profile.
Ultrastructural, Antioxidant, and Metabolic Responses of Male Genetically Improved Farmed Tilapia (GIFT, Oreochromis niloticus) to Acute Hypoxia Stress
Authors: Tao Yifan , Hua Jixiang , Lu Siqi , Wang Qingchun , Li Yan , Jiang Bingjie , Dong Yalun , Qiang Jun , Xu Pao
Published: January 2024

Tilapia tolerate hypoxia; thus, they are an excellent model for the study of hypoxic adaptation. In this study, we determined the effect of acute hypoxia stress on the antioxidant capacity, metabolism, and gill/liver ultrastructure of male genetically improved farmed tilapia (GIFT, Oreochromis niloticus). Fish were kept under control (dissolved oxygen (DO): 6.5 mg/L) or hypoxic (DO: 1.0 mg/L) conditions for 72 h. After 2 h of hypoxia stress, antioxidant enzyme activities in the heart and gills decreased, while the malondialdehyde (MDA) content increased. In contrast, in the liver, antioxidant enzyme activities increased, and the MDA content decreased. From 4 to 24 h of hypoxia stress, the antioxidant enzyme activity increased in the heart but not in the liver and gills. Cytochrome oxidase activity was increased in the heart after 4 to 8 h of hypoxia stress, while that in the gills decreased during the later stages of hypoxia stress. Hypoxia stress resulted in increased Na+-K+-ATP activity in the heart, as well as hepatic vacuolization and gill lamella elongation. Under hypoxic conditions, male GIFT exhibit dynamic and complementary regulation of antioxidant systems and metabolism in the liver, gills, and heart, with coordinated responses to mitigate hypoxia-induced damage.
Aggregation of Risk Management and Non-Parametric Models to Rank Failure Modes of Radio Frequency Identification Systems
Authors: Chnina Khaoula , Daneshvar Sahand
Published: January 2024

The failure mode causes and effects analysis (FMCEA) is a commonly used reliability approach. It identifies, predicts, and analyzes potential failure modes affecting the proper function of equipment or the process under study, along with their roots and consequences. FMCEA aims to evaluate and assess the risks resulting from their occurrence, intending to suggest corresponding repair, adjustment, and precautionary measures to be planned during the conception, instruction, or implementation stages. However, the FMCEA has been criticized in the literature for its many inherent shortcomings related to risk assessment and prioritization. Therefore, this study presents an enhanced FMCEA method to address the deficiencies of the traditional risk priority number (RPN) and improve the reliability of risk assessments and corrective actions. A data envelopment analysis (DEA), as a non-parametric method, is used to evaluate the efficiency of these failures by considering their fixing time and cost and deciding on their final priority ranks. Sub-failure modes and their interrelationships are also taken into account. The radio frequency identification (RFID) system was chosen as an example due to its core role in Industry 4.0 and the Internet of Things (IoT) to demonstrate the effectiveness and usefulness of the proposed method. A total of 67 failures related to both hardware and software parts, including the environmental impacts of this technology, have been disclosed. The results of the conventional and the suggested FMCEA methods are found to be considerably different, with ten failure modes classified as being the most efficient.
Comparative Study for Sentiment Analysis of Financial Tweets with Deep Learning Methods
Authors: Memiş Erkut , Akarkamçı (Kaya) Hilal , Yeniad Mustafa , Rahebi Javad , Lopez-Guede Jose Manuel
Published: January 2024

Nowadays, Twitter is one of the most popular social networking services. People post messages called “tweets”, which may contain photos, videos, links and text. With the vast amount of interaction on Twitter, due to its popularity, analyzing Twitter data is of increasing importance. Tweets related to finance can be important indicators for decision makers if analyzed and interpreted in relation to stock market. Financial tweets containing keywords from the BIST100 index were collected and the tweets were tagged as “POSITIVE”, “NEGATIVE” and “NEUTRAL”. Binary and multi-class datasets were created. Word embedding and pre-trained word embedding were used for tweet representation. As classifiers, Neural Network, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU) and GRU-CNN models were used in this study. The best results for binary and multi-class datasets were observed with pre-trained word embedding with the CNN model (83.02%, 72.73%). When word embedding was employed, the Neural Network model had the best results on the multi-class dataset (63.85%) and GRU-CNN had the best results on the binary dataset (80.56%).
Coal–Rock Data Recognition Method Based on Spectral Dimension Transform and CBAM-VIT
Authors: Yang Jianjian , Zhang Yuzeng , Wang Kaifan , Tong Yibo , Liu Jinteng , Wang Guoyong
Published: January 2024

Coal–gangue sorting is a vital component of intelligent mine construction. As intelligent manufacturing continued to advance, data-driven coal–gangue recognition emerged as a prominent research topic. However, conventional data-driven methods for coal–gangue recognition heavily rely on expert-extracted features. The process of feature extraction is labor-intensive and significantly impacts the final outcome. Deep learning (DL) offers an effective approach to automatically extract features from raw data. Among the various DL techniques, convolutional neural networks (CNNs) have proven to be particularly effective. In this paper, we propose an intelligent method for recognizing coal–rock by fusing multiple preprocessing techniques applied to near-infrared spectra and employing dual attention. Initially, a signal-to-RGB image conversion method is applied to fuse three types of preprocessing data, namely first-order differential, second-order differential, and standard normal transform, into an RGB image representation. Subsequently, we propose a neural network model (CBAM-VIT) that integrates the convolutional block attention mechanism (CBAM) and Vision Transformer (VIT). When evaluated on the coal–rock dataset, this model achieves an accuracy of 98.5%, surpassing the performance of VIT (95.3%), VGG-16 (89%), and AlexNet (82%). The comparative results clearly demonstrate that the proposed coal–gangue recognition method yields significant improvements in classification outcomes.
A Deep Bidirectional LSTM Model Enhanced by Transfer-Learning-Based Feature Extraction for Dynamic Human Activity Recognition
Authors: Hassan Najmul , Miah Abu Saleh Musa , Shin Jungpil
Published: January 2024

Dynamic human activity recognition (HAR) is a domain of study that is currently receiving considerable attention within the fields of computer vision and pattern recognition. The growing need for artificial-intelligence (AI)-driven systems to evaluate human behaviour and bolster security underscores the timeliness of this research. Despite the strides made by numerous researchers in developing dynamic HAR frameworks utilizing diverse pre-trained architectures for feature extraction and classification, persisting challenges include suboptimal performance accuracy and the computational intricacies inherent in existing systems. These challenges arise due to the vast video-based datasets and the inherent similarity in the data. To address these challenges, we propose an innovative, dynamic HAR technique employing a deep-learning-based, deep bidirectional long short-term memory (Deep BiLSTM) model facilitated by a pre-trained transfer-learning-based feature-extraction approach. Our approach begins with the utilization of Convolutional Neural Network (CNN) models, specifically MobileNetV2, for extracting deep-level features from video frames. Subsequently, these features are fed into an optimized deep bidirectional long short-term memory (Deep BiLSTM) network to discern dependencies and process data, enabling optimal predictions. During the testing phase, an iterative fine-tuning procedure is introduced to update the high parameters of the trained model, ensuring adaptability to varying scenarios. The proposed model’s efficacy was rigorously evaluated using three benchmark datasets, namely UCF11, UCF Sport, and JHMDB, achieving notable accuracies of 99.20%, 93.3%, and 76.30%, respectively. This high-performance accuracy substantiates the superiority of our proposed model, signaling a promising advancement in the domain of activity recognition.
Comparison of Tensile and Creep Properties of SAC305 and SACX0807 at Room Temperature with DIC Application
Authors: Paska Zbynek , Halama Radim , Dymacek Petr , Govindaraj Bhuvanesh , Rojicek Jaroslav
Published: January 2024

The contribution presents the verification of the methodology of accelerated creep tests from the point of view of obtaining more information about the stress–strain behaviour of the investigated materials using the Digital Image Correlation method. Creep tests are performed on SAC305 and SACX0807 lead-free solders and are supplemented by numerical modelling using the finite element method, considering the viscoplastic model based on the theory of Perzyna, Chaboche, and Norton. The stress–strain behaviour of both solders appears to be very similar at applied strain rates of 0.0002–0.0026%/s and applied creep stresses of 15–28 MPa. Initially, the viscoplastic model is calibrated using an analytical approach. Then, the finite element model updating approach is used to optimise the material parameters based on the simultaneous simulations of creep and tensile tests. As a result, the total objective function value is reduced almost five times due to optimisation. The proposed type of accelerated test with an hourglass specimen proves to be suitable for calibrating the considered class of viscoplastic models. The main benefit is that a single specimen is required to obtain creep curves on various stress levels.
The Dynamics of Soil Mesofauna Communities in a Tropical Urban Coastal Wetland: Responses to Spatiotemporal Fluctuations in Phreatic Level and Salinity
Authors: Ortiz-Ramírez Gloria , Hernández Elix , Pinto-Pacheco Solimar , Cuevas Elvira
Published: January 2024

Coastal wetlands, vital for ecological diversity, have been significantly altered by anthropogenic activities, particularly in the Caribbean. These changes have created a complex mosaic of habitats and physicochemical conditions, further stressed by climate variability and sea-level rise. This study, conducted in Las Cucharillas Natural Reserve, a tropical urban coastal wetland in Puerto Rico, aimed to determine the effects of spatiotemporal variations in phreatic levels and salinity on soil mesofauna assemblages, crucial bio-indicators of environmental change. In 2020 and 2021, soil samples were collected from five diverse habitat types during different hydroperiods. Each sample was taken under four randomly selected plant types and processed using lighted Tullgren–Berlese extractors. Phreatic level and salinity were also measured. A total of 43 families were quantified, underscoring distinct habitat differences, similarities, and overall ecosystem diversity. Moderate correlations between phreatic levels, salinity, and mesofauna richness and abundance were determined. Peak richness and abundance were quantified at shallow (−0.03 to −0.07 m) and slightly moderate (−0.12 to −0.17 m) phreatic levels where oligohaline salinity (>0.5 to 5.0 ppt) prevails. The study highlights the adaptability of mesofauna to environmental shifts and their potential as biosensors for effective coastal wetland management amid climatic and anthropogenic pressures.
Convection-Permitting Future Climate Simulations for Bulgaria under the RCP8.5 Scenario
Authors: Valcheva Rilka , Popov Ivan , Gerganov Nikola
Published: January 2024

In recent decades, climate change has become a critical global issue with far-reaching consequences for regional climates and ecosystems. While regional climate models provide valuable information, there is a growing need for high-resolution simulations to assess local impacts. This paper addresses this gap by presenting the first simulation of a 3 km convection-permitting (CP) scenario simulation for Bulgaria. The main aim of this study is to assess different precipitation indices and their future changes for Bulgaria under the Representative Concentration Pathway 8.5 (RCP8.5) scenario following the Coordinated Regional Climate Downscaling Experiment Flagship Pilot Study protocol. The simulations are evaluated against high-resolution observations. We downscale Coupled Model Intercomparison Project 5 Global Climate Model (CMIP5 GCM) data for historical (1995–2004) and future (2089–2098) periods using a regional climate model (RCM) at 15 km grid spacing and parametrized convection. We use these fields as initial and boundary conditions for convection-permitting kilometer-scale simulations. The 15 km grid spacing driving model is used as a reference to assess the added value of the kilometer-scale simulation. Additionally, the 3 km seasonal mean and projected 2 m temperature and the winter snow water equivalent are presented. The results show that the kilometer-scale simulation shows better performance of wet-hour intensity in all seasons, wet-hour frequency in the spring, fall, and winter, and extreme precipitation (99.9th percentile of all precipitation events, p99.9) in the winter and fall. The kilometer-scale simulation improves the projected precipitation distribution and modifies the signal of the precipitation frequency, intensity, and heavy precipitation change over some areas. A positive projected change in the wet-hour intensity is expected in all seasons (13.86% in spring, MAM, 17.48% in summer, JJA, 1.97% in fall, SON, and 17.43% in winter, DJF) and in the heavy precipitation in the spring (13.14%) and winter (31.19%) in the kilometer-scale experiment. The projected increase in mean winter precipitation is accompanied by a significant decrease in mean winter snowfall over lowlands (50−70%). The convection-permitting Regional Climate Model, version 4.7.1 (RegCM4.7.1) suggests an increase in winter snowfall over the highest parts of the country, but a significant increase in the 2 m temperatures there. The results of this study are encouraging and may be of interest to the community of climate scientists and users of climate data for making reliable estimates of the local impacts of future climate change.
A Health Assessment Method for Lithium-Ion Batteries Based on Evidence Reasoning Rules with Dynamic Reference Values
Authors: Yang Zijiang , Zhao Xiaofeng , Zhang Hongquan
Published: January 2024

The health assessment of lithium-ion batteries holds great research significance in various areas such as battery management systems, battery usage and maintenance, and battery economic evaluation. However, because environmental perturbations are not taken into account during the assessment, the accuracy and reliability of the assessment are limited. Thus, a health assessment model for lithium-ion batteries based on evidence reasoning rules with dynamic reference value (ER-DRV) is proposed in this paper. Firstly, considering that the data are subject to changes, dynamic reference values, real-time weights, and real-time reliability were utilized in the model to ensure the effectiveness and accuracy of the assessment. Moreover, an enhanced optimization method based on the whale optimization algorithm (WOA) was developed to improve the accuracy of the assessment model. In addition, the robustness of the ER-DRV model was studied with perturbation analysis methods. Finally, the proposed method was validated on two open lithium-ion battery datasets. The experimental results show that the health assessment method proposed in this article not only has higher accuracy and transparent reasoning process but also has strong robustness and good generalization ability.
Intra-Day and Inter-Day Reliability and Usefulness of Performance, Kinetic and Kinematic Variables during Drop Jumping in Hurling Players
Authors: Atkins Luke , Coyle Colin , Moody Jeremy , Ramirez-Campillo Rodrigo , Byrne Paul J.
Published: January 2024

The aim of this study was to estimate the intra-day and inter-day reliability and usefulness of performance (Jump height (JH), ground contact time (GCT) and reactive strength index (RSI)), kinetic (force, power, eccentric rate of force development [E-RFD] and leg stiffness [LS]) and kinematic (velocity) variables during drop jumping (DJ) in hurling players. Seventeen (n = 17; mean ± SD; age = 23.35 ± 5.78 years, height = 178.35 ± 6.30 cm, body mass = 78.62 ± 8.06 kg) male club-level hurling players completed two maximal DJs from 0.20, 0.30, 0.40, 0.50 and 0.60 m drop heights on three testing days separated by 5–9 days of rest. Reliability was assessed using the coefficient of variation percentage (CV% ≤ 15%) and intraclass correlation coefficient (ICC > 0.70). For intra-day reliability, GCT (0.40 m, 0.50 m and 0.60 m), peak force (absolute and relative) (0.40 m and 0.50 m) and leg stiffness (0.40 m and 0.50 m) were found to be unreliable (ICC = 0.32–0.68 and CV% = 3.67–11.83%) from those specific drop heights. All other variables were found to be reliable (ICC = 0.72–0.98 and CV% = 1.07–14.02%) intra-day. All variables were found to be reliable (ICC = 0.72–0.96 and CV% = 2.57–14.68%) inter-day except for relative peak force and absolute and relative eccentric RFD (0.30 m and 0.40 m) (ICC = 0.68–0.90 and CV% = 7.76–16.47%). Practitioners have multiple reliable DJ performance, kinetic and kinematic variables for performance testing and training purposes.
ES-SCLC Patients with PD-L1+ CTCs and High Percentages of CD8+PD-1+T Cells in Circulation Benefit from Front-Line Immunotherapy Treatment
Authors: Xagara Anastasia , Roumeliotou Argyro , Kokkalis Alexandros , Tsapakidis Konstantinos , Papakonstantinou Dimitris , Papadopoulos Vassilis , Samaras Ioannis , Chantzara Evagelia , Kallergi Galatea , Kotsakis Athanasios
Published: January 2024

SCLC is an aggressive cancer type with high metastatic potential and bad prognosis. CTCs are a valuable source of tumor cells in blood circulation and are among the major contributors to metastasis. In this study we evaluated the number of CTCs that express PD-L1 in treatment-naïve ES-SCLC patients receiving ICI in a front-line setting. Moreover, we explored the percentages of different immune T-cell subsets in circulation to assess their potential role in predicting responses. A total of 43 patients were enrolled—6 of them with LS-SCLC, and 37 with ES-SCLC disease. In addition, PBMCs from 10 healthy donors were used as a control group. Different T-cell subtypes were examined through multicolor FACS analysis and patients’ CTCs were detected using immunofluorescence staining. SCLC patients had higher percentages of PD-1-expressing CD3+CD4+ and CD3+CD8+ T-cells, as well as elevated PD-1 protein expression compared to healthy individuals. Additionally, in ES-SCLC patients, a positive correlation between CD3+CD8+PD-1+ T-cells and PD-L1+ CTCs was detected. Importantly, patients harboring higher numbers of CD3+CD8+PD-1+ T-cells together with PD-L1+CTCs had a survival advantage when receiving front-line immunotherapy. Thus, this study proposes, for first time possible, immune cell–CTCs interaction, as well as a potential novel clinical biomarker for ICI responses in ES-SCLC patients.
Exploring the Relationship between Gut Microbiome Composition and Blood Indole-3-acetic Acid in Hemodialysis Patients
Authors: Wu Ping-Hsun , Tseng Yu-Fang , Liu Wangta , Chuang Yun-Shiuan , Tai Chi-Jung , Tung Chun-Wei , Lai Kean-Yee , Kuo Mei-Chuan , Chiu Yi-Wen , Hwang Shang-Jyh , Hung Wei-Chun , Lin Yi-Ting
Published: January 2024

Indole-3-acetic acid (IAA), a protein-bound uremic toxin resulting from gut microbiota-driven tryptophan metabolism, increases in hemodialysis (HD) patients. IAA may induce endothelial dysfunction, inflammation, and oxidative stress, elevating cardiovascular and cognitive risk in HD patients. However, research on the microbiome–IAA association is limited. This study aimed to explore the gut microbiome’s relationship with plasma IAA levels in 72 chronic HD patients aged over 18 (August 2016–January 2017). IAA levels were measured using tandem mass spectrometry, and gut microbiome analysis utilized 16s rRNA next-generation sequencing. Linear discriminative analysis effect size and random forest analysis distinguished microbial species linked to IAA levels. Patients with higher IAA levels had reduced microbial diversity. Six microbial species significantly associated with IAA levels were identified; Bacteroides clarus, Bacteroides coprocola, Bacteroides massiliensi, and Alisteps shahii were enriched in low-IAA individuals, while Bacteroides thetaiotaomicron and Fusobacterium varium were enriched in high-IAA individuals. This study sheds light on specific gut microbiota species influencing IAA levels, enhancing our understanding of the intricate interactions between the gut microbiota and IAA metabolism.
The Effect of Spanwise Folding on the Aerodynamic Performance of a Passively Deformed Flapping Wing
Authors: Qi Ming , Ding Menglong , Zhu Wenguo , Li Shu
Published: January 2024

The wings of birds exhibit multi-degree-of-freedom motions during flight. Among them, the flapping folding motion and chordwise passive deformation of the wings are prominent features of large birds in flight, contributing to their exceptional flight capabilities. This article presents a method for the fast and accurate calculation of folding passive torsional flapping wings in the early design stage. The method utilizes the unsteady three-dimensional panel method to solve the aerodynamic force and the linear beam element model to analyze the fluid–structure coupling problem. Performance comparisons of folding flapping wings with different kinematics are conducted, and the effects of various kinematic parameters on folding flapping wings are analyzed. The results indicate that kinematic parameters significantly influence the lift coefficient, thrust coefficient, and propulsion efficiency. Selecting the appropriate kinematic and geometric parameters is crucial for enhancing the efficiency of the folding flapping wing.
Special Issue: Design and Control of a Bio-Inspired Robot
Authors: Zhao Mingguo , Hu Biao
Published: January 2024

Bionics, the interdisciplinary field that draws inspiration from nature to design and develop innovative technologies, has paved the way for the creation of “bio-inspired robots” [...]
Assessment and Training of Perceptual-Motor Function: Performance of College Wrestlers Associated with History of Concussion
Authors: Wilkerson Gary B. , Fleming Lexi R. , Adams Victoria P. , Petty Richard J. , Carlson Lynette M. , Hogg Jennifer A. , Acocello Shellie N.
Published: January 2024

Concussion may affect sport performance capabilities related to the visual perception of environmental events, rapid decision-making, and the generation of effective movement responses. Immersive virtual reality (VR) offers a means to quantify, and potentially enhance, the speed, accuracy, and consistency of responses generated by integrated neural processes. A cohort of 24 NCAA Division I male wrestlers completed VR assessments before and after a 3-week VR training program designed to improve their perceptual-motor performance. Prior to training, the intra-individual variability (IIV) among 40 successive task trials for perceptual latency (i.e., time elapsed between visual stimulus presentation and the initiation of movement response) demonstrated strong discrimination between 10 wrestlers who self-reported a history of concussion from 14 wrestlers who denied ever having sustained a concussion (Area Under Curve ≥ 0.750 for neck, arm, and step movements). Natural log transformation improved the distribution normality of the IIV values for both perceptual latency and response time (i.e., time elapsed between visual stimulus presentation and the completion of movement response). The repeated measures ANOVA results demonstrated statistically significant (p < 0.05) pre- and post-training differences between groups for the IIV in perceptual latency and the IIV in response time for neck, arm, and step movements. Five of the six IIV metrics demonstrated a statistically significant magnitude of change for both groups, with large effect sizes. We conclude that a VR assessment can detect impairments in perceptual-motor performance among college wrestlers with a history of concussion. Although significant post-training group differences were evident, VR training can yield significant performance improvements in both groups.
Measurement Invariance of the Multidimensional Jealousy Scale and Quality of Relationships Inventory (Friend)
Authors: Leite Ãngela , Silva Beatriz , Vilela Beatriz , Rodrigues Inês , Fernandes Joana , Romão Joana , Ribeiro Ana Margarida
Published: January 2024

The aim of this study is to measure the invariance of the Multidimensional Jealousy Scale (MJS) and of the Quality of Relationships Inventory (Friend) (QRI-F) across gender, age, education, and being in a romantic relationship in a Portuguese sample (N = 662). A confirmatory factor analysis was performed to test the fit of different potential factor structures. The results pointed out that both MJS and QRI-F were most suitable if represented by three first-order factors correlated between them. Results from multi-group analyses suggested there was factorial invariance for these structures across groups, suggesting that the MJS and the QRI-F provide, respectively, an assessment of romantic jealousy and quality of relationship that are equivalent across gender, age, education, and being in a romantic relationship. The study established the strong psychometric properties of its instruments, validating reliability and convergent and discriminant validity, thereby bolstering the research’s overall credibility. Additionally, cognitive jealousy is primarily influenced by heightened conflict values, with education, relationship status, and gender moderating the associations between QRI-F dimensions and MJS behavioral and cognitive jealousy. The research offered in-depth perspectives on jealousy, underscoring its diverse manifestations across demographic variables and illuminating the complexities within the dynamics of friendships.
Geometric Properties of Steel Components with Stability and Fatigue Risks Using 3D-Laser-Scanning
Authors: Li Zheng , Zhang Qiulei , Shi Fei , Wang Jian , Pasternak Hartmut
Published: January 2024

Nowadays, 3D laser scanning technology is extensively employed in laboratory investigations of steel structural components, providing accurate geometric dimensions to reduce uncertainties caused by indeterminate geometry in experimental results. It is often used in conjunction with the Finite Element (FE) Method and analytical solutions, which are more accurate deterministic operators in the research on steel structures. However, establishing a common methodological framework for transferring or mapping 3D-scanned information into finite element models for complex steel structures with stability and fatigue risks remains an ongoing task. In light of this, this study has developed a 3D scanning platform capable of obtaining accurate geometric dimensions for various types of steel components. Different coordinate systems and point cloud mapping algorithms have been established for different types of components to construct actual finite element models with initial imperfections. The feasibility of the self-developed 3D scanning platform and finite element modelling has been validated through three experimental cases: weld details, steel girders, and cylindrical shells. The research findings demonstrate that the captured point cloud can be automatically processed and corrected using the developed algorithm. The scanned data can then be input into the numerical model using various mapping algorithms tailored to the specific geometric properties of the specimens. The differences between the experimental test results and the simulated results obtained from the 3D-scanned finite element models remain within a small range. The self-developed 3D scanning platform and finite element modelling technique effectively capture the actual dimensions of different steel components, enabling the prediction of their stability and fatigue risks through numerical simulations.
Seismic Failure Mechanisms of Concrete Pile Groups in Layered Soft Soil Profiles
Authors: Hökelekli Emin , Bayraktar Alemdar , Şermet Fethi
Published: January 2024

So far, little attention has been paid to the investigation on the seismic failure mechanisms of flexible concrete pile groups embedded in the layered soft soil profiles considering the material non-linearities of soil and concrete piles. The purpose of this study is to investigate seismic failure mechanism models of flexible concrete piles with varied groups in silt layered loose sand profiles under horizontal strong ground motions. Three-dimensional finite element models of the pile–soil interaction systems, which include nonlinearities of soil and concrete piles as well as coupling interactions between the piles and soil, were created for Models I, II, and III of the soil domains, encompassing 1x1, 2x2, and 3x3 flexible pile groups with diameters of 0.80 m and 1.0 m. Model I consists of a homogenous sand layer and a bedrock, Models II and III are composed of a five-layered domain with homogeneous sand and silt soil layers of different thicknesses. The linear elastic perfectly plastic constitutive model with a Mohr–Coulomb failure criterion is considered to represent the behavior of the soil layers, and the Concrete Damage Plasticity (CDP) model is used for the nonlinear behavior of the concrete piles. The interactions between the soil and the pile surfaces are modeled by defining tangential and normal contact behaviors. The models were analyzed for the scaled acceleration records of the 1999 Düzce and Kocaeli earthquakes, considering peak ground accelerations of 0.25 g, 0.50 g, and 0.75 g. The numerical results indicated that failure mechanisms of flexible concrete groups occur near the silt layers, and the silt layers have led to a significant increase in the spread area of the damaged zone and the number of damaged elements.
Advanced Techniques for the Analysis of Proteins and RNAs
Authors: Yang Xiaolong
Published: January 2024

Proteins and RNAs, as fundamental components of cellular machinery, play pivotal roles in the intricate landscape of life [...]