bluuebunny/crossref_metadata_2025 huggingface dataset:
Financial Literacy: Identification of the Challenges, Needs, and Difficulties among Adults Living in Rural Areas
Authors: Czech Katarzyna , Ochnio Luiza , Wielechowski Michał , Zabolotnyy Serhiy
Published: September 2024

Financial literacy plays a crucial role in individuals’ decision-making processes. The paper aims to thoroughly identify the financial literacy needs and challenges of adults with low financial literacy living in rural areas. The paper presents the results of a focus group study conducted among a target group (35 people) and interviews with financial experts (14 people) in the first quarter of 2023. The study allows us to identify common areas of financial knowledge crucial for adults with low financial literacy living in rural areas. The study was conducted in seven European countries as part of the Erasmus+ project “Learning by Experiencing Escape Rooms: Financial Literacy for Adults (FLER)”. Key findings from the study revealed that participants exhibited low awareness of financial fraud and expressed a need to protect personal information when using digital platforms. There was a clear demand for improved knowledge in certain areas, such as budgeting, emergency funds, balancing risk and reward, and planning for retirement or long-term savings. Participants consistently viewed gamification as a valuable tool for enhancing their understanding of financial topics. The contribution of our study is that we narrow the research gap on the needs and challenges related to financial literacy among rural residents. The results will help pinpoint specific areas of financial knowledge and competencies that are particularly important for adults with low financial literacy. Moreover, these insights are crucial for developing educational content integrated into a virtual escape room to improve financial literacy through engaging, scenario-based learning.
Evaluation of Nitrogen Fertilizer Supply and Soil Nitrate Thresholds for High Yields of Foxtail Millet
Authors: Lu Yiwei , Zhao Yu , Xia Xueyan , Liu Meng , Wei Zhimin , Wang Jingxin , Liu Jianjun , Cui Jihan , Li Shunguo
Published: September 2024

Foxtail millet is an important cereal crop in the North China Plain. However, excessive nitrogen fertilizer application over the years has led to declining yield and soil quality. This study investigated nutrient management strategies for foxtail millet based on crop yield levels and soil nutrient availability. In a field where targeted fertilization was conducted over six seasons, nitrogen fertilization effects and the dynamics of soil-available nitrogen were monitored continuously for two consecutive years (2022–2023) across five different foxtail millet varieties with varying yield levels. The study aimed to determine the optimal nitrogen application rate for achieving a high yield of foxtail millet, the minimum soil nitrate threshold required to maintain soil fertility, and the effective nitrogen application rate range for sustaining soil-available nitrate levels. Results showed that fertilization significantly affected dry matter weight during flowering, while variety affected dry matter weight at maturity. The average nitrogen application rate for achieving high yield across all five millet varieties was 141.3 kg·ha−1. Specifically, the average nitrogen application rate of nitrogen-efficient varieties achieving high yield (5607.32–5637.19 kg·ha−1) was 151.5 kg·ha−1, while the average nitrogen application rate of nitrogen-inefficient varieties achieving high yield (4749.77–4847.74 kg·ha−1) was 134.5 kg·ha−1. Soil NH4+-N and NO3−-N content increased when nitrogen application rate exceeded 360 kg·ha−1, posing environmental risks. To achieve high yield, soil nitrate levels would be maintained at an average of 17.23 mg·kg−1 (before sowing) and 9.75 mg·kg−1 (at maturity). A relationship between soil nitrate and nitrogen application rate was established: y = 867.5 − 50z (where y represents the optimal nitrogen application rate for high yield (kg·ha−1), and z represents soil NO3−-N content in the 0–20 cm layer before sowing, ranging from 10.0 to 17.35 mg·kg−1), which provided a practical method for nitrogen fertilization to achieve high yield of foxtail millet. In this study, the fertilization strategy was optimized according to soil nutrient level and yield targets, and the nitrogen application rate was controlled within 360 kg·ha−1 based on the soil nitrate nitrogen content, which will be instructive for reducing fertilizer use, maximizing fertilizer efficiency, and increasing yield.
Soil Moisture Detection and Linear Deceleration Control Strategy Enhancing Trenching Depth Precision and Stability for Rapeseed Sowing
Authors: Xu Peiru , Kou Jianchuan , Wang Minghang , Tu Tianyu , Chen Xiaoling , Luo Jie , Hu Jianfeng , Lei Xiaolong
Published: September 2024

Sowing depth significantly affects the germination of rapeseed, and different soil moisture conditions require corresponding sowing depths. However, most current trenching devices do not account for soil moisture content, and commonly used hydraulic or constant-force trenching equipment also exhibits deficiencies in stability and consistency. To address these challenges, this study developed an automatic depth adjustment control system based on soil moisture content. A soil moisture detection device and an innovative sliding mechanism that maintained the soil moisture sensor in a relatively stationary position relative to the soil during seeder movement were introduced. An automatic sowing depth adjustment device was designed to modulate the sowing depth. A control strategy that incorporated the Kalman filtering algorithm and linear deceleration equations was conducted. At an observation noise covariance matrix (Q/R) of 0.001, a deceleration range of 40 mm and a minimum speed of 10, the control system exhibited minimal overshoot (approximately 4%) and steady-state error (approximately 3.2 mm). It effectively adjusted the trenching depth while operating at speeds ranging from 2 to 3.6 km/h, successfully adapting to variations in soil topography. The system performance tests revealed that the control system adjustment time (ts) was 534 ms and the steady-state error remained within 1 mm. Under three different soil moisture content conditions, the sowing depth qualification rate and stability coefficients consistently surpassed 90% and 80%, respectively. This research offers a sowing depth adjustment control system based on soil moisture content, contributing to more precise depth regulation for rapeseed sowing.
Leveraging Digital Technologies for Carbon Footprint Tracking in Perennial Cultivations: A Case Study of Walnut Orchard Establishment in Central Greece
Authors: Lampridi Maria , Kateris Dimitrios , Myresiotis Charalampos , Berruto Remigio , Fragos Vassilios , Kotsopoulos Thomas , Bochtis Dionysis
Published: September 2024

The present paper aims to quantify the carbon emissions associated with the establishment of 15 walnut orchards (“Juglans californica”) in the greater area of Magnisia, Greece, with the use of a carbon footprint tool interconnected to a Farm Management Information System. The data collection spanned the first five years following the planting of the trees, providing a comprehensive view of the emissions during this critical establishment phase. Over the five-year period examined (February 2019–December 2023), the results revealed net carbon emissions amounting to 13.71 tn CO2 eq ha−1, with the calculated emissions showing an increasing trend from the first year through the fifth year. Scope 1 (7.38 tn CO2 eq ha−1) and Scope 2 (3.71 tn CO2 eq ha−1) emissions emerged as the most significant, while irrigation (drip irrigation) and fertilizing practices were identified as the highest contributors to emissions. This study highlights the significance of using integrated digital tools for monitoring the performance of cultivations rather than standalone tools that are currently widely available. Integrated tools that incorporate various applications simplify data collection, encourage accurate record-keeping, and facilitate certification processes. By automating data entry and calculations, these tools reduce human error during agricultural carbon management and save time; thus, the integration of digital monitoring tools is vital in improving data accuracy, streamlining certification processes, and promoting eco-friendly practices, crucial for the evolving carbon market.
Correction: Liu et al. Effects of Film Mulching on Soil Microbial Diversity and Community Structure in the Maize Root Zone under Drip Irrigation in Northwest China. Agronomy 2024, 14, 1139
Authors: Liu Mengjie , Zheng Jiliang , Li Quansheng , Liang Fei , Mu Xiaoguo , Pei Dongjie , Jia Hongtao , Wang Zhenhua
Published: September 2024

In the published article [...]
Evaluating Procedure-Linked Risk Determinants in Trichinella spp. Inspection under a Quality Management System in Southern Spain
Authors: Villegas Pérez José , Navas González Francisco Javier , Serrano Salud , García Viejo Fernando , Buffoni Leandro
Published: September 2024

Trichinellosis is a major foodborne zoonotic disease responsible for 41 human cases, according to the European Union One Health Zoonoses Report. In southern Spain, a quality management system (QMS) was applied to satellite laboratories (SLs) that conduct meat inspections of Trichinella spp. ensuring excellence practices. This study aimed to determine how eventual deviations from standard procedures may influence risk levels using Canonical Discriminant Analysis (CDA). Data were collected during slaughterhouses and game handling establishments’ official audits in 18 SLs located in the provinces of Cordoba and Seville during a 6-year period. Technical requirement deviations regarding technique and trial information, such as performing tests or calculations incorrectly or not following technical procedures, significantly increased risk level differences. Imminent risk levels were detected if the above-mentioned deviations arose. Quality assurance compromising deviations were responsible for 1150 times risk level differences, suggesting finding such may be critical for risk determination. A lack of significant influence of records and documents compromising deviations (incomplete forms or missing-erroneous or illegible data) was found. These results strengthen Trichinella spp. control strategies by pinpointing crucial aspects within QMS that require improvement, particularly in addressing deviations related to technique, trial information, and quality assurance procedures to mitigate associated risks effectively.
Positive Selection of Mitochondrial cytochrome b Gene in the Marine Bivalve Keenocardium buelowi (Bivalvia, Cardiidae)
Authors: Choi Hyeongwoo , Gwon Yeongjin , An Yun Keun , Eyun Seong-il
Published: September 2024

The mitochondrial genome provides valuable data for phylogenetic analysis and evolutionary research. In this study, we sequenced, assembled, and annotated the mitochondrial genome of Keenocardium buelowi using the Illumina platform. The genome spanned 16,967 bp and included 13 protein-coding genes (PCGs), two ribosomal RNAs, and 22 transfer RNAs. All PCGs utilized standard ATN start codons and TAN stop codons. The phylogenetic tree based on maximum likelihood and Bayesian inference analyses revealed Clinocardiinae as the sister group to Trachycardiinae, with the estimated divergence time being 44.5 million years ago (MYA) between K. buelowi and Vasticardium flavum. Notably, the cytochrome b gene (cob) exhibited a positive selection signal. Our findings provide valuable insights into the evolutionary history and molecular phylogeny of K. buelowi.
Problems with Congestive Heart Failure and Lameness That Have Increased in Grain-Fed Steers and Heifers
Authors: Grandin Temple
Published: September 2024

Grain-fed steers and heifers have increasing problems with both lameness and congestive heart failure. Congestive heart failure used to be limited to cattle raised at high altitudes. It is now occurring at much lower elevations. An inspection of hearts at the slaughter plant indicated that some groups of grain-fed steers and heifers had abnormally swollen hearts in 34% of the animals. Congestive heart failure may also increase death losses in the late stages of the feeding program. Lameness has also increased to 8% of grain-fed steers and heifers arriving at U.S. slaughter plants. Twenty years ago, observations by the author indicated that lameness in fed cattle arriving at the slaughter plant was almost zero. There has also been an increasing occurrence of abnormal hoof structure and poor leg conformation. Genetic selection for rapid growth and a large ribeye may be associated with the increase in both of these problems. Lameness and heart problems severely compromise animal welfare.
Bioavailability of Supplemented Free Oleanolic Acid and Cyclodextrin–Oleanolic Acid in Growing Pigs, and Effects on Growth Performance, Nutrient Digestibility and Plasma Metabolites
Authors: Lachica Manuel , Borrás-Linares Isabel , Borges Thays Helena , Nieto Rosa , Seiquer Isabel , García-Contreras Consolación , Lara Luis , Arráez-Román David , Segura-Carretero Antonio , Pinilla José María , Quintela José Carlos , Fernández-Fígares Ignacio
Published: September 2024

Oleanolic acid (OLA) has beneficial health effects in animals, but in vivo efficacy in monogastric animals is questioned due to its low bioavailability. To gain further insight on the nutritional effects of OLA it was administered as part of a diet. We investigated digestibility and plasma OLA in pigs and the associated influence on growth, organs, digestibility of nutrients and plasma biochemical profile. Twenty-four crossbred barrows (23.7 ± 1.0 kg BW) were assigned one of three treatments: Control (basal diet without OLA), OLA-1 (basal diet with 260 mg/free OLA) and OLA-2 (basal diet with 260 mg/kg cyclodextrin-OLA). Diets included chromium oxide to estimate digestibility. Blood samples were collected on day 14 for OLA analysis and feces on days 22–24 for determining digestibility. Pigs were slaughtered on day 31 (39.9 ± 2.43 kg BW) and their blood collected for analysis. Growth and organ weights were not affected (p > 0.05). OLA-1 decreased apparent total tract digestibility (ATTD) of energy (p < 0.05). OLA-2 increased ATTD of dry and organic matter compared with Control pigs (p < 0.05). OLA-1 increased plasma calcium and alkaline phosphatase (p < 0.05). Ileal digestibility of OLA was not affected (0.88), although OLA ATTD increased in OLA-1 compared to Control pigs (0.75 vs. 0.82; p < 0.05). OLA-1 and OLA-2 increased plasma OLA compared to Control pigs (p < 0.05 and p = 0.083). In conclusion, although the OLA was digested and absorbed, plasma concentration was low (4.29 µg/L), and pig growth, organs and plasma parameters were not affected.
Establishing Joint Orientation Angles of the Limbs in Korean Raccoon Dogs (Nyctereutes procyonoides koreensis) Using Computed Tomographic Imaging
Authors: Ko Seongju , Ahn Sangjin , Kwak Ho-Hyun , Woo Heung-Myong , Kim Junhyung
Published: September 2024

Studies are being conducted on the anatomical structures of various wild animals. Despite the ecological importance of the Korean raccoon dog (Nyctereutes procyonoides koreensis), limited research has been conducted on its anatomical structure. This study is the first to establish a reference range for joint orientation angles in the limbs of the Korean raccoon dog. Joint orientation angles are an unexplored concept not only in Korean raccoon dogs but also in other wildlife. However, they are important in the examination of the skeletal anatomy of humans and companion animals, such as dogs and cats. Because this type of measurement is still emerging in wildlife research, we applied the methodology used in the domestic dog (Canis lupus familiaris). Angles were measured between the mechanical or anatomical axis and the joint orientation lines in the thoracic and pelvic limbs of Korean raccoon dogs. No significant differences were observed between the sexes or between the left and right sides. These findings are consistent with those observed in domestic dogs. Based on this study, a reference range of joint orientation angles could be established for Korean raccoon dogs.
Molecular Simulation of CH4 Adsorption Characteristics under the Coupling of Different Temperature and Water Content
Authors: Gao Yabin , Hou Gaojie , Cao Jing , Zhang Shaoqi , Li Ziwen
Published: September 2024

The adsorption characteristics of CH4 have an important influence on gas content prediction, gas extraction, and hazard prevention. Therefore, we explored the mechanism of CH4 adsorption under the action of water and temperature to grasp the influence of water and temperature on the adsorption characteristics of CH4. In this paper, a giant, regular-system Monte Carlo method is used to simulate the CH4 adsorption behavior at the molecular level under different temperatures, water contents, and the coupling of both. The results indicate that an empirical formula for the coupling effect of temperature and water content on CH4 adsorption was obtained. The impact of different effects on CH4 adsorption is as follows: coupling effect > single temperature effect > single water content effect. The optimal combination is at a temperature of 363 K and a water content of 8.31%. Compared with the CH4 adsorption capacity without water at room temperature, the CH4 adsorption capacity is reduced by 68.04% under the coupling effect of the optimal combination. Temperature has a negative effect on the adsorption of CH4, and temperature changes the adsorption capacity by changing the average molecular kinetic energy of CH4. The reason why the increase in H2O reduces the adsorption capacity of CH4 is that the interaction between H2O and the oxygen-containing functional groups of coal is stronger than that of CH4. As the water content increases, the adsorption heat decreases, thereby inhibiting the adsorption of CH4. In addition, H2O has a smaller molecular dynamics radius as compared to CH4; the larger the free volume and surface area in the pore structure, the more adsorption pores it occupies, resulting in a more significant reduction in the adsorption of CH4.
A Compact All-Band Spacecraft Antenna with Stable Gain for Multi-Band GNSS Applications
Authors: Kiris Orcun
Published: September 2024

This study presents a compact and stable gain spacecraft antenna that operates in all Global Navigation Satellite System (GNSS) bands from 1.164 GHz to 1.610 GHz. The proposed antenna structure based on the single-feed crossed bowtie antenna concept consists of four triangular patches excited with a 90° phase difference in between to generate right-hand circular polarization (RHCP), without needing complex feed networks. The radiator part of the antenna is covered by a radome and is also supported by a cylindrical dielectric cavity frame (DCF) to weaken the diffracted waves propagating along the ground plane while increasing vibration resistance. The fabricated antenna provides a return loss better than 10 dB with lower than 3 dB axial ratio and a stable gain around 7.2 ± 0.3 dBic over the entire GNSS bands, as well as a more compact and lightweight structural performance. It is also verified that the structural integrity and functional performance of the fabricated antenna remain consistent despite exposure to an equivalent vibration level in the launch process. The presented all-band spacecraft GNSS antenna is an innovative implementation with space industry insight for multi-band space applications that have application-specific limitations and provides consistent performance, as well as operational safety with the antenna design simplicity.
Study on the Impact of Air Pressure on the Laser-Induced Breakdown Spectroscopy of Intumescent Fireproof Coatings
Authors: Wang Jun , Jian Honglin , Wang Shouhe , Zhang Fengzhen , Wang Xilin
Published: September 2024

Intumescent fireproof coatings protect steel structures and cables by forming a thick, fire-resistant layer under high temperatures. These coatings can deteriorate over time, impacting their fire resistance. Current testing methods are largely lab-based, lacking in-service evaluation platforms. Laser-Induced Breakdown Spectroscopy (LIBS) is emerging as a promising in situ detection technology but is influenced by low air pressure in high-altitude areas. This study investigates how air pressure affects LIBS signals in intumescent coatings on galvanized steel. Using pressures between 35 and 101 kPa, a linear model was developed to correlate laser pulses to ablation depth for characterizing coating thickness. Results show that spectral intensity decreases with lower air pressure. However, a strong linear relationship persists between laser pulses and ablation depth, with a fitting accuracy above 0.9. The coating thickness is identified by the number of laser pulses required to detect the Zn spectral line from the underlying galvanized steel. As air pressure decreases, the ablation depth increases. The study effectively models and corrects for air pressure effects on LIBS data, enabling its application for field detection of fireproof coatings. This advancement enhances the reliability of LIBS technology in assessing the fire performance of these materials, providing a reference for their in situ evaluation and ensuring better fire safety standards for building steel structures and cables.
Multi-Stress Accelerated Degradation Testing Reliability Assessment of LED Lamp Beads Considering Generalized Coupling
Authors: Dong Yinglong , Zhou Zhen , Liu Kaixin , Yang Xu
Published: September 2024

Due to the complex operating environment of LED lamp beads (hereinafter referred to as LED), the test method that only considers the action of multiple stresses alone ignores the mutual influence between stresses, making it difficult to accurately obtain the life indicators of LEDs, resulting in a large deviation in the theoretical life results. In this regard, this paper proposes a multi-stress accelerated degradation evaluation method considering generalized coupling and conducts an accelerated degradation test (ADT) to evaluate the life of LEDs. We identified three stress sources and designed five new high-gradient ADTs. Through experimental data, we found that the three stress sources are strongly coupled on this LED. Then, a generalized coupling maximum likelihood estimation method (MLE) for the entire sample was constructed, and the particle swarm algorithm was used to solve the parameters. Finally, the life of this LED was evaluated based on the experimental data. The results show that the life of the LED considering multiple-stress coupling is within 6.5% of the historical life scatter point, which is more in line with the actual working environment.
Signaling Pathways in Clear Cell Renal Cell Carcinoma and Candidate Drugs Unveiled through Transcriptomic Network Analysis of Hub Genes
Authors: Suratos Khyle S. , Orda Marco A. , Tsai Po-Wei , Tayo Lemmuel L.
Published: September 2024

Clear cell renal cell carcinoma (ccRCC) is a type of kidney cancer. It advances quickly and often metastasizes, making the prognosis for patients challenging. This study used weighted gene co-expression network analysis (WGCNA) to study gene expression data of different stages of ccRCC obtained in the GEO database. The analysis identified three significant highly preserved gene modules across the datasets: GSE53757, GSE22541, GSE66272, and GSE73731. Functional annotation and pathway enrichment analysis using DAVID revealed inflammatory pathways (e.g., NF-kB, Hippo, and HIF-1 pathways) that may drive ccRCC development and progression. The study also introduced the involvement of viral infections associated with the disease in the metabolic reprogramming of ccRCC. A drug repurposing analysis was also conducted to identify potential drug candidates for ccRCC using the upregulated and downregulated hub genes. The top candidates are ziprasidone (dopamine and serotonin receptor antagonist) and fentiazac (cyclooxygenase inhibitor). Other drug candidates were also obtained, such as phosphodiesterase/DNA methyltransferase/ATM kinase inhibitors, acetylcholine antagonists, and NAD precursors. Overall, the study’s findings suggest that identifying several genes and signaling pathways related to ccRCC may uncover new targets, biomarkers, and even drugs that can be repurposed, which can help develop new and effective treatments for the disease.
ML-Based Maintenance and Control Process Analysis, Simulation, and Automation—A Review
Authors: Rojek Izabela , Mikołajewski Dariusz , Dostatni Ewa , Piszcz Adrianna , Galas Krzysztof
Published: September 2024

Automation and digitalization in various industries towards the Industry 4.0/5.0 paradigms are rapidly progressing thanks to the use of sensors, Industrial Internet of Things (IIoT), and advanced fifth generation (5G) and sixth generation (6G) mobile networks supported by simulation and automation of processes using artificial intelligence (AI) and machine learning (ML). Ensuring the continuity of operations under different conditions is becoming a key factor. One of the most frequently requested solutions is currently predictive maintenance, i.e., the simulation and automation of maintenance processes based on ML. This article aims to extract the main trends in the area of ML-based predictive maintenance present in studies and publications, critically evaluate and compare them, and define priorities for their research and development based on our own experience and a literature review. We provide examples of how BCI-controlled predictive maintenance due to brain–computer interfaces (BCIs) play a transformative role in AI-based predictive maintenance, enabling direct human interaction with complex systems.
Evaluation of Hand Muscle Strength Using Manual Dynamometry: A Reliability and Validity Study of the Activ5 Instrument
Authors: Pino-Ortega José , Carvajal-Espinoza Rafael , Becerra-Patiño Boryi A.
Published: September 2024

Manual dynamometry (HHD) allows for the assessment of musculature because its use has been supported as an indicator of health in different population groups. The objective of this study was to examine the reliability and validity of the Activ5 dynamometer for assessing grip strength in a population of adults. A total of 106 individuals with an age of 20.38 ± 1.64, body mass of 71.52 ± 11.32 kg, and height of 1.70 ± 0.11 m were evaluated during two sessions. A cross-sectional agreement study was conducted on Sports Science students from a university community, and 106 individuals were evaluated during two sessions. Statistical analysis of reliability and validity was performed using intraclass correlation coefficients (ICCs), Pearson correlations, and Lin’s coefficient. According to Lin’s coefficient, both instruments measure grip strength for both conditions, either for the right hand or the left hand. The correlation coefficient to determine the linear relationship between both instruments determined that between the Jamar right-hand dynamometer and the right-hand Activ5, a coefficient R2 = 0.580, p = 0.00, was obtained. In contrast, the correlation between the Jamar left-hand dynamometer and the left-hand Activ5 had a coefficient R2 = 0.543, p = 0.001. Both intraclass correlation coefficients and Cronbach’s alpha presented high values, indicating that both instruments have good reproducibility in their measurements. The Activ5 dynamometer cannot be used interchangeably with the Jamar dynamometer; however, the close values reported make it a reliable tool in grip strength assessment. The different characteristics of the Activ5 instrument, such as its ergonomics, weight, portability, wireless connection, dimensions, and applications, make it a promising daily- use tool for assessing, monitoring, and the prescription of physical activity and exercise.
Objective Sliding Mode Control for the Flotation Motor Replacement Process Based on a Stepwise Stochastic Configuration Network
Authors: Tang Yunze , Qiao Jinghui , Xiong Ningkang
Published: September 2024

Flotation motors face constant vibration and friction in the working process, resulting in wear and often need to be replaced, reducing production efficiency. In order to ensure the efficient operation of flotation equipment, we designed a manipulator to assist the work. The manipulator model is difficult to be accurate, and the diversity of external information brings great challenges to the precise control of the manipulator. Therefore, an Objective Sliding Mode Control (OSMC) strategy combined with Stepwise Stochastic Configuration Networks (SSCNs) is developed to solve the above problems while reducing the oscillation of the system. The SSCN is distinguished by its innovative node selection mechanism for the hidden layer, which operates in two distinct phases: initially, ‘single time’, where weights and biases are randomly assigned through a uniform distribution, and subsequently, ‘double time’, where these parameters are adjusted based on a normal distribution centered around the values selected in the first phase. This approach enhances the system’s robustness by incorporating a disturbance observer based on SSCN, designed to accurately estimate and compensate for system uncertainties. The simulation model realistically integrates these uncertainties and disturbances, demonstrating the effectiveness of the proposed OSMC in satisfying the stringent control requirements of mechanical arms under uncertain conditions. This advancement offers a promising new method for the precise control of robotic systems in challenging environments.
Differences in Diet Assessment and Body Composition among Young Spanish Elite Footballers: Morning Training vs. Evening Training
Authors: Almendros-Ruiz Antonio , Latorre Jose Antonio , Conde-Pipó Javier , Fernández-Martínez José , Acuña-Castroviejo Darío , Requena Bernardo , Mariscal-Arcas Miguel
Published: September 2024

The training schedule is a factor that influences sports performance optimization. In a sport like soccer, there is often significant disparity in training schedules among different teams within the same club, without considering whether this may affect players’ performance. The aim of this study was to describe differences in nutrient intake and body composition in elite youth soccer players from the Spanish league with different training schedules (morning and evening). A cross-sectional study was conducted to determine differences in anthropometric variables and dietary assessment in a sample of Spanish young soccer players. A total of 41 players participated in this study. After comparing the groups according to their training schedule, no differences were observed in body composition between both groups; the evening-night training group showed higher consumption of lipids and saturated fats. In summary, more experimental studies are needed to determine the effects on various health and performance parameters of different training schedules in young population.
MTC-GAN Bearing Fault Diagnosis for Small Samples and Variable Operating Conditions
Authors: Li Jinghua , Wei Yonghe , Gu Xiaojiao
Published: September 2024

In response to the challenges of bearing fault diagnosis under small sample sizes and variable operating conditions, this paper proposes a novel method based on the two-dimensional analysis of vibration acceleration signals and a Multi-Task Conditional Generative Adversarial Network (MTC-GAN). This method first constructs two-dimensional images of vibration signals by leveraging the physical properties of the bearing acceleration signals and employs Local Binary Patterns (LBP) to extract subtle texture features from these images, thereby generating fault feature signatures with high discriminative power across different operating conditions. Subsequently, MTC-GAN is utilized for data augmentation, and the trained discriminator is used to perform fault classification tasks, improving classification accuracy under conditions with small sample sizes. Experimental results demonstrate that the proposed method achieves excellent fault diagnosis accuracy and robustness under both small sample sizes and varying operating conditions. Compared to traditional methods, this approach exhibits higher efficiency and reliability in handling complex operating conditions and data scarcity.
EDPNet: A Transmission Line Ice-Thickness Recognition End-Side Network Based on Efficient Dynamic Perception
Authors: Jiao Yangyang , Zhang Yu , Dou Yinke , Zhao Liangliang , Liu Qiang
Published: September 2024

Ice-covered transmission lines seriously affect the stable operation of the power system. Deploying a recognition network for measuring the ice thickness on transmission lines within a deicing robot, and controlling the robot to perform resonant deicing, is an effective solution. In order to solve the problem that the existing recognition network is not suitable for an edge device, an ice-thickness recognition network for transmission lines based on efficient dynamic perception (EDPNet) is proposed. Firstly, a lightweight multidimensional recombination convolution (LMRC) is designed to split the ordinary convolution for lightweight design and extract feature information of different scales for reorganization. Then, a lightweight deep fusion module (LDFM) is designed, which combines the attention mechanism with different features to enhance the information interaction between the encoder and decoder. Then, a new dynamic loss function is adopted in the training process to guide the model to perform refined detection of ice-covered boundaries. Finally, we count the ice pixels and calculate the ice thickness. The model is deployed on an OrangePi5 Plus edge computing board. Compared with the baseline model, the maximum ice-thickness detection error is 4.2%, the model parameters are reduced by 86.1%, and the detection speed is increased by 74.6%. Experimental results show that EDPNet can efficiently complete the task of identifying ice-covered transmission lines and has certain engineering application value.
The Influence of Elite Race Walkers’ Year-Long Training on Changes in Total Energy and Energy Cost While Walking at Different Speeds
Authors: Chwała Wiesław , Klimek Andrzej T. , Mirek Wacław , Ambroży Tadeusz , Rydzik Łukasz
Published: September 2024

The aim of the study was to assess the influence of year-long training of race walkers on physiological cost and total energy center of mass (CoM). The assessment performed was based on indicating the differences between the resulting energy cost in a group of elite race walkers walking at technical, threshold, and racing speeds calculated by physiological and biomechanical methods before beginning and after finishing a year-long training cycle. The study involved 12 competitive race walkers who had achieved champion or international champion level. Their aerobic endurance was determined by means of a direct method, applying an incremental exercise test on the treadmill. The gait of the participants was recorded using the 3D Vicon analysis system. Changes in mechanical energy amounted to the value of the total external work of the muscles needed to accelerate and lift the center of mass during a normalized gait cycle. The highest influence on the total external work increase for increasing speeds of gait in both examinations was attributed to the changes in the kinetic energy resulting from the center of mass movement. A statistically significant decrease of the mean value of total external work for racing speed was observed in the second examination (p < 0.001). An approx. 8% decrease (NS) of EE energy cost, standardized by body mass and distance covered, was found between the first and second examinations. The energy cost and total external work were significantly differentiated by the walkers’ gait speeds (p < 0.05–0.001). The energy cost significantly differed from the total external work at p < 0.001.
Diabetic Retinopathy Detection Using Convolutional Neural Networks with Background Removal, and Data Augmentation
Authors: Suedumrong Chaichana , Phongmoo Suriya , Akarajaka Tachanat , Leksakul Komgrit
Published: September 2024

Diabetic retinopathy (DR) is a potentially blinding complication affecting individuals with diabetes, where early diagnosis and treatment are crucial to preventing vision loss. Recent advances in deep learning have shown promise in automating DR diagnosis, offering faster, more reliable, and cost-effective solutions. Our study employed convolutional neural networks (CNNs) to classify the severity of DR using retinal images from the EyePACS dataset, which includes 35,155 images categorized into five classes. Building on previous research that often classified DR into two classes, such as no DR and varying levels of DR, we found that while these studies typically used models like Inception V3, VGGNet, and ResNet, they focused on simplifying the diagnostic process by reducing the number of classes. However, our approach utilized a smaller, more flexible CNN architecture, allowing for a more detailed classification into five stages of DR. We employed various image preprocessing techniques, including grayscale conversion, background removal, and data augmentation, with our findings indicating that background removal significantly enhanced model performance, achieving a validation accuracy of 90.60%. This underscores the importance of sophisticated data preprocessing in medical imaging, and our study contributes to the ongoing development of automated DR diagnosis, potentially easing the burden on healthcare systems and improving patient outcomes.
Modeling and Simulation of the Aging Behavior of a Zinc Die Casting Alloy
Authors: Martinez Page Maria Angeles , Hartmann Stefan
Published: September 2024

While zinc die-casting alloy Zamak is widely used in vehicles and machines, its solidified state has yet to be thoroughly investigated experimentally or mathematically modeled. The material behavior is characterized by temperature and rate sensitivity, aging, and long-term influences under external loads. Thus, we model the thermo-mechanical behavior of Zamak in the solid state for a temperature range from −40 °C to 85 °C, and the aging state up to one year. The finite strain thermo-viscoplasticity model is derived from an extensive experimental campaign. This campaign involved tension, compression, and torsion tests at various temperatures and aging states. Furthermore, the thermo-physical properties of temperature- and aging-dependent heat capacity and heat conductivity are considered. One significant challenge is related to the multiplicative decompositions of the deformation gradient, which affects strain and stress measures relative to different intermediate configurations. The entire model is implemented into an implicit finite element program and validation examples at more complex parts are provided so that the predicability for complex parts is available, which has not been possible so far. Validation experiments using digital image correlation confirm the accuracy of the thermo-mechanically consistent constitutive equations for complex geometrical shapes. Moroever, validation measures are introduced and applied for a complex geometrical shape of a zinc die casting specimen. This provides a measure of the deformation state for complex components under real operating conditions.
Variation in the Quanta-to-Energy Ratio of Photosynthetically Active Radiation under the Cloudless Atmosphere
Authors: Wang Weibo , Cai Shangzhan , Huang Jiang , Ding Rui , Chen Lei
Published: September 2024

The quanta-to-energy ratio plays a crucial role in converting energy units to quantum units in the context of photosynthetically active radiation (PAR). Despite its widespread use, the effects of atmospheric particles and solar zenith angle (SZA) on the quanta-to-energy ratio remain unclear. In this study, both simulation and observation data revealed that the principal wavelength, which can be transformed into the quanta-to-energy ratio using a constant, exhibits a slow initial growth, followed by a rapid increase beyond 60° solar zenith angles and a subsequent dramatic decrease after reaching its maximum value. The measured quanta-to-energy ratio demonstrates a variable range of less than 3% for SZA under 70° in a cloudless atmosphere, with significant changes only occurring at zenith angles above 80°. Simulation data indicate that ozone, wind speed, surface-level pressure, surface air temperature, and relative humidity have negligible effects on the quanta-to-energy ratio. The Ångstrom exponent exerts a minor influence on the quanta-to-energy ratio by affecting diffuse radiation. Visibility, however, is found to have a substantial impact on the quanta-to-energy ratio. As a result, two relationships are established, linking the principal wavelength to visibility and the diffuse fraction of PAR. The principal wavelength serves as an effective measure of solar spectrum variability, remaining unaffected by radiation energy. This implies that atmospheric parameters which do not alter the solar spectrum will not influence the principal wavelength. The strong correlations between the principal wavelength, visibility, and the diffuse fraction of PAR suggest a broader range of applications for the principal wavelength in various research domains, opening up new avenues for exploration and potential contributions to numerous fields.
Do Chinese Residents’ Perceptions of Air Pollution Affect Their Pro-Environmental Behaviors: The Moderating Role of Environmental Knowledge
Authors: Chen Yiru , Lu Chuntian , Zhang Linhe , Huang Yijing
Published: September 2024

Air pollution has become an important environmental and public health problem in China; improving air quality has become one of the most important tasks of the Chinese central government in recent years. In all aspects of environmental governance, the public’s pro-environmental behavior is crucial to comprehensively improve air quality. This study used data provided by the 2021 China General Social Survey (CGSS) to explore the relationship between air pollution perception, environmental knowledge, and pro-environmental behavior. The results of the study indicated that: (1) Residents’ subjective perception of air pollution has a significant positive impact on residents’ pro-environmental behaviors. The more serious the air pollution in the surrounding area is, the more likely residents are to adopt more pro-environmental behaviors; (2) Environmental knowledge also has a positive impact on residents’ pro-environmental behaviors and individuals with higher environmental knowledge are more likely to adopt more pro-environment behaviors; and (3) Environmental knowledge plays a positive regulating role in the relationship between air pollution perception and pro-environmental behavior. Individuals with higher environmental knowledge are more willing to engage in more pro-environmental behaviors when they perceive air pollution. To promote the residents’ pro-environmental behaviors, we should further strengthen the education of environmental knowledge and strengthen the disclosure of environmental information. This study provides valuable insights for China to formulate environmental governance policies, enrich the research on pro-environmental behavior to a certain extent, and provide lessons for other developing countries and high-polluting countries to find environmental governance methods.
STFM: Accurate Spatio-Temporal Fusion Model for Weather Forecasting
Authors: Liu Jun , Wu Li , Zhang Tao , Huang Jianqiang , Wang Xiaoying , Tian Fang
Published: September 2024

Meteorological prediction is crucial for various sectors, including agriculture, navigation, daily life, disaster prevention, and scientific research. However, traditional numerical weather prediction (NWP) models are constrained by their high computational resource requirements, while the accuracy of deep learning models remains suboptimal. In response to these challenges, we propose a novel deep learning-based model, the Spatiotemporal Fusion Model (STFM), designed to enhance the accuracy of meteorological predictions. Our model leverages Fifth-Generation ECMWF Reanalysis (ERA5) data and introduces two key components: a spatiotemporal encoder module and a spatiotemporal fusion module. The spatiotemporal encoder integrates the strengths of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), effectively capturing both spatial and temporal dependencies. Meanwhile, the spatiotemporal fusion module employs a dual attention mechanism, decomposing spatial attention into global static attention and channel dynamic attention. This approach ensures comprehensive extraction of spatial features from meteorological data. The combination of these modules significantly improves prediction performance. Experimental results demonstrate that STFM excels in extracting spatiotemporal features from reanalysis data, yielding predictions that closely align with observed values. In comparative studies, STFM outperformed other models, achieving a 7% improvement in ground and high-altitude temperature predictions, a 5% enhancement in the prediction of the u/v components of 10 m wind speed, and an increase in the accuracy of potential height and relative humidity predictions by 3% and 1%, respectively. This enhanced performance highlights STFM’s potential to advance the accuracy and reliability of meteorological forecasting.
Estimating Rainfall Intensity Using an Image-Based Convolutional Neural Network Inversion Technique for Potential Crowdsourcing Applications in Urban Areas
Authors: Shalaby Youssef , Alkhatib Mohammed I. I. , Talei Amin , Chang Tak Kwin , Chow Ming Fai , Pauwels Valentijn R. N.
Published: September 2024

High-quality rainfall data are essential in many water management problems, including stormwater management, water resources management, and more. Due to the high spatial–temporal variations, rainfall measurement could be challenging and costly, especially in urban areas. This could be even more challenging in tropical regions with their typical short-duration and high-intensity rainfall events, as some of the undeveloped or developing countries in those regions lack a dense rain gauge network and have limited resources to use radar and satellite readings. Thus, exploring alternative rainfall estimation methods could be helpful to back up some shortcomings. Recently, a few studies have examined the utilisation of citizen science methods to collect rainfall data as a complement to the existing rain gauge networks. However, these attempts are in the early stages, and limited works have been published on improving the quality of such data. Therefore, this study focuses on image-based rainfall estimation with potential usage in citizen science. For this, a novel convolutional neural network (CNN) model is developed to predict rainfall intensity by processing the images captured by citizens (e.g., by smartphones or security cameras) in an urban area. The developed model is merely a complementary sensing tool (e.g., better spatial coverage) to the existing rain gauge network in an urban area and is not meant to replace it. This study also presents one of the most extensive datasets of rain image data ever published in the literature. The estimated rainfall data by the proposed CNN model of this study using images captured by surveillance cameras and smartphone cameras are compared with observed rainfall by a weather station and exhibit strong R2 values of 0.955 and 0.840, respectively.