bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of Saline Irrigation Water Applications on Evapotranspiration Partitioning and Crop Coefficient of Tomato Grown in Mediterranean-Type Greenhouses
Authors: Aslan Gulcin Ece , Bastug Ruhi , Karaca Cihan , Kurunc Ahmet , Buyuktas Dursun , Navarro Alejandra
Published: August 2024

Accurate information regarding crop evapotranspiration (ETc) and its components is essential for proper water management. Salinity has become increasingly serious in many parts of the world, affecting the use of saline water in irrigation for various biochemical processes, hence the importance of understanding the effects of soil and water salinity on ETc. This study aimed to determine the transpiration (Tr) and soil evaporation (Es) components that constitute ETc in tomatoes irrigated by drip irrigation and to investigate the effects of irrigation water salinity levels on ETc and its components, as well as on the crop coefficient (Kc) used to determine ETc. In addition, the reference evapotranspiration (ETo) values determined by the measurements obtained with the lysimeter system were compared with the ETo values calculated using the Almeria (ETo_Alm), FAO Radiation (ETo_FAO-Rad), and Hargreaves (ETo_Harg) models. During the spring growing period of 2022, tomatoes were cultivated under four different irrigation water salinity levels: S0 = 0.7 (control), S1 = 2.5 (low), S2 = 5.0 (medium), and S3 = 7.5 (high) dS m−1. The Tr values in S0, S1, and S2 treatments were measured by the sap flow method. Radiation-based ETo prediction models had strong relationships with lysimeter-measured ETo values (R2 > 0.977 and RMSE < 0.53 mm). The results indicated that increasing salinity levels led to a decrease in both Tr and ETc while causing an increase in Es. In the S0, S1, and S2 treatments, Es accounted for 16.3, 45.0, and 36.2%, respectively. Actual Kc values also decreased with increasing salinity. The results indicate that different actual Kc values can be used when calculating evapotranspiration in decision-making processes in greenhouse tomato production in Antalya and when irrigation water with different salinity levels is used. Additionally, actual ETc responses of tomatoes to salinity can be used to manage saline water in irrigation scheduling.
Discrete Element Modelling and Simulation Parameter Calibration for the Growing Media of Seedling Nursery Blocks
Authors: Li Rangling , Jiang Wei , Wang Wei , Fan Jiayu , Gao Yankun , Wang Hongying
Published: August 2024

Using the discrete element method to simulate the interaction between growing media and machinery is an effective method to design seedling machinery and improve the precision of facility horticultural operations. In order to further improve the accuracy of the study on the interaction between seedling block-forming machines and growing media, the growing media used in the production of seedling nursery blocks was taken as the research object, and the Plackett–Burman screening test and Box–Behnken test were conducted based on the discrete element method using the EEPA model to conduct the calibration of discrete element parameters of the growing media. Optimization was conducted with an actual repose angle as the target value, and the optimal combination is as follows: the interparticle collision-recovery coefficient is 0.5066, the collision-recovery coefficient between particles and the geometric model is 0.714, the interparticle dynamic-friction coefficient is 0.381, and the tangential stiffness factor is 0.375. Finally, the soil uniaxial closed compression test was conducted with optimized calibration parameters. The relative error between the maximum axial load on the punch and the measured value in the simulation process was 4.23%, which verified the accuracy and reliability of parameter calibration of the growing media and provided support for the simulation of growing media and optimization of seedling nursery block-forming machine.
Effects of Biological Nano-Selenium on Yield, Grain Quality, Aroma, and Selenium Content of Aromatic Rice
Authors: Gu Qichang , Luo Haowen , Lin Li , Zhang Qianqian , Yi Wentao , Liu Zhifan , Yu Xianghai , Zuo Changjian , Qi Jianying , Tang Xiangru
Published: August 2024

Selenium (Se) is one of the human essential elements and the input of Se for its biofortification is common in rice production to meet the demand for Se in the population. Biological nano-selenium (nano-Se) is a new type of nanoscale microbial synthetic material. However, the effects of biological nano-Se on aromatic rice performance metrics, such as yield formation, grain quality parameters, and the biosynthesis of 2-acetyl-1-pyrroline (2-AP, the key component of aromatic rice aroma), have rarely been reported. Therefore, this study conducted a field experiment with two cropping seasons and two aromatic rice genotypes to explore the effects of the foliar application of biological nano-Se on aromatic rice performance metrics. The results showed that the foliar application of biological nano-Se at 3–4 days before panicle differentiation or the heading stage increased the grain yield of aromatic rice. Dry matter accumulation and the leaf area index increased under Nano-Se application. Furthermore, the foliar application of Nano-Se at 3–4 days before panicle differentiation significantly enhanced the activity of peroxidase, superoxide dismutase, catalase and reduced malondialdehyde content. The foliar application of Nano-Se at the grain-filling stage also increased 2-AP content. In addition, nano-Se application substantially increased the grain Se content in aromatic rice.
Optimization of Support Vector Machine with Biological Heuristic Algorithms for Estimation of Daily Reference Evapotranspiration Using Limited Meteorological Data in China
Authors: Guo Hongtao , Wu Liance , Wang Xianlong , Xing Xuguang , Zhang Jing , Qing Shunhao , Zhao Xinbo
Published: August 2024

Precise estimation of daily reference crop evapotranspiration (ET0) is critical for water resource management and agricultural irrigation optimization worldwide. In China, diverse climatic zones pose challenges for accurate ET0 prediction. Here, we evaluate the performance of a support vector machine (SVM) and its hybrid models, PSO-SVM and WOA-SVM, utilizing meteorological data spanning 1960–2020. Our study aims to identify a high-precision, low-input ET0 estimation tool. The findings indicate that the hybrid models, particularly WOA-SVM, demonstrated superior accuracy with R2 values ranging from 0.973 to 0.999 and RMSE values between 0.123 and 0.863 mm/d, outperforming the standalone SVM model with R2 values of 0.955 to 0.989 and RMSE values of 0.168 to 0.982 mm/d. The standalone SVM model showed relatively lower accuracy with R2 values of 0.822 to 0.887 and RMSE values of 0.381 to 1.951 mm/d. Notably, the WOA-SVM model, with R2 values of 0.990 to 0.992 and RMSE values of 0.092 to 0.160 mm/d, emerged as the top performer, showcasing the benefits of the whale optimization algorithm in enhancing SVM’s predictive capabilities. The PSO-SVM model also presented improved performance, especially in the temperate continental zone (TCZ), subtropical monsoon region (SMZ), and temperate monsoon zone (TMZ), when using limited meteorological data as the input. The study concludes that the WOA-SVM model is a promising tool for high-precision daily ET0 estimation with fewer meteorological parameters across the different climatic zones of China.
Effect of Agro-Industrial by Products Derived from Volatile Fatty Acids on Ruminant Feed In Vitro Digestibility
Authors: Parchami Milad , Rustas Bengt-Ove , Taherzadeh Mohammad J. , Mahboubi Amir
Published: August 2024

The growing demand for sustainable ruminant feed alternatives has motivated the application of bioconversion approaches for the valorization of agro-food byproducts (AFB) into feed additives and supplements. The present study thoroughly investigated substituting volatile fatty acids (VFAs) obtained from acidogenic fermentation (AF) of AFB as an energy source in ruminant feed. Rumen in vitro digestibility assays were conducted utilizing the gas production method, wherein the VFAs obtained from AF of apple pomace and potato protein liquor was substituted with partial silage and concentrate energy at levels of 10%, 20%, and 30%. The results indicate that substituting 20% of the concentrate’s energy with VFA mixture significantly reduced methane production and had no adverse effect on the production and accumulation of VFAs in the simulated rumen media. Conversely, replacing 10% of the silage energy with VFAs led to a decrease in methane production and further enhanced the production of VFAs. Readily digestible VFAs in ruminant feed have the potential to enhance energy availability and sustainability in ruminant farming practices, aligning with the principles of circular economy and waste valorization.
Cross-Sectional Study Assessing Management Practices and Udder Health in California Sheep Flocks and Seroprevalence of Small Ruminant Lentivirus
Authors: Digianantonio Rose , Pires Alda F. A. , Busch Roselle
Published: August 2024

(1) Background: Information is lacking on small ruminant lentivirus (SRLV) status, prevalence, risk factors, and control measures for mastitis in California ewes. The goal of this survey was to outline characteristics of the sheep industry in California related to udder health and mastitis management. (2) Methods: An online survey consisting of 48 questions was completed by respondents between April 2022 and February 2023. Descriptive analysis and chi-squared tests were conducted to evaluate associations between variables. A multiple correspondence analysis (MCA) of general management practices, udder health management, and flock demographics was performed to assess clustering. A subset of respondents (20) participated in SRLV serology testing. (3) Results: Seventy-one completed surveys were submitted. The MCA showed two clusters. Larger flock sizes, the use of breeding ewes for meat or wool production or contract grazing, and extensive management practices were more closely related to >5% udder abnormalities per lactation and ≥5% orphan lambs. The flock-level seroprevalence of SRLV was 75% (15/20), and ewe-level seroprevalence was 14.1% (183/1106). (4) Conclusions: The results of this study highlight areas that need further research, such as exploring differences in mastitis and SRLV incidences among management systems, the efficacy of mastitis treatments, and education on critical timepoints for mastitis diagnosis and control.
Applying Iterative Student Feedback across Flipped Classroom and Flexible Teaching Approaches: Impact on Veterinary Students’ Learning Experience
Authors: Singh Arti A. , Shapter Frances M. , Bernard Anne , Whitworth Deanne J. , Holt Marnie G. , Waller Philip S. , Bond Stephanie L.
Published: August 2024

No single teaching strategy supports all learning styles in veterinary science students. To facilitate more convenient and flexible teaching, learning, and revision, an innovative online digital learning platform—VetCloud—was developed to provide access to modularized programme content across courses to promote active, integrated learning. This study aimed to understand student perceptions regarding the enhancement of the student learning experience in a foundational course in gastrointestinal anatomy and physiology at The University of Queensland across two learning cycles, via applying iterative student feedback in transitioning a flipped classroom approach using VetCloud for the delivery of lecture content in 2022 to a flexible learning approach in 2023. By 2023, the use of VetCloud in the flexible learning approach improved students’ work/study/life balance, reduced their stress levels, and enabled a more efficient use of their time when studying, compared to the flipped classroom approach in 2022. Surveying student perceptions was integral to maximizing their learning experience. Data clearly demonstrates that students will mix-and-match how they interact with available options provided via flexible delivery on an individualized basis. This teaching method offers veterinary educators an innovative and efficient approach to veterinary student education in anatomy and physiology while enhancing student well-being.
Immunolocalization of Two Neurotrophins, NGF and BDNF, in the Pancreas of the South American Sea Lion Otaria flavescens and Bottlenose Dolphin Tursiops truncatus
Authors: Gatta Claudia , Avallone Luigi , Costagliola Anna , Scocco Paola , D’Angelo Livia , de Girolamo Paolo , De Felice Elena
Published: August 2024

In this study, we have investigated the immunolocalization of NGF (Nerve Growth Factor) and BDNF (Brain-Derived Neurotrophic Factor) in the pancreas of two species of marine mammals: Tursiops truncatus (common bottlenose dolphin), belonging to the order of the Artiodactyla, and Otaria flavescens (South American sea lion), belonging to the order of the Carnivora. Our results demonstrated a significant presence of NGF and BDNF in the pancreas of both species with a wide distribution pattern observed in the exocrine and endocrine components. We identified some differences that can be attributed to the different feeding habits of the two species, which possess a different morphological organization of the digestive system. Altogether, these preliminary observations open new perspectives on the function of neurotrophins and the adaptive mechanisms of marine mammals in the aquatic environment, suggesting potential parallels between the physiology of marine and terrestrial mammals.
Regulation of Antibiotic Resistance Genes on Agricultural Land Is Dependent on Both Choice of Organic Amendment and Prevalence of Predatory Bacteria
Authors: Rosberg Anna Karin , Silva Maria João , Skøtt Feidenhans’l Cecilie , Cytryn Eddie , Jurkevitch Edouard , Lood Rolf
Published: August 2024

Antibiotic resistance genes (ARGs) are widespread in the environment, and soils, specifically, are hotspots for microorganisms with inherent antibiotic resistance. Manure and sludge used as fertilizers in agricultural production have been shown to contain vast amounts of ARGs, and due to continued applications, ARGs accumulate in agricultural soils. Some soils, however, harbor a resilience capacity that could depend on specific soil properties, as well as the presence of predatory bacteria that are able to hydrolyse living bacteria, including bacteria of clinical importance. The objectives of this study were to (i) investigate if the antibiotic resistance profile of the soil microbiota could be differently affected by the addition of cow manure, chicken manure, and sludge, and (ii) investigate if the amendments had an effect on the presence of predatory bacteria. The three organic amendments were mixed separately with a field soil, divided into pots, and incubated in a greenhouse for 28 days. Droplet digital PCR (ddPCR) was used to quantify three ARGs, two predatory bacteria, and total number of bacteria. In this study, we demonstrated that the choice of organic amendment significantly affected the antibiotic resistance profile of soil, and promoted the growth of predatory bacteria, while the total number of bacteria was unaffected.
Loading Dose of Ceftazidime Needs to Be Increased in Critically Ill Patients: A Retrospective Study to Evaluate Recommended Loading Dose with Pharmacokinetic Modelling
Authors: Launay Manon , Ollier Edouard , Kably Benjamin , Le Louedec Félicien , Thiery Guillaume , Lanoiselée Julien , Perinel-Ragey Sophie
Published: August 2024

To rapidly achieve ceftazidime target concentrations, a 2 g loading dose (LD) is recommended before continuous infusion, but its adequacy in critically ill patients, given their unique pharmacokinetics, needs investigation. This study included patients from six ICUs in Saint-Etienne and Paris, France, who received continuous ceftazidime infusion with plasma concentration measurements. Using MONOLIX and R, a pharmacokinetic (PK) model was developed, and the literature on ICU patient PK models was reviewed. Simulations calculated the LD needed to reach a 60 mg/L target concentration and assessed ceftazidime exposure for various regimens. Among 86 patients with 223 samples, ceftazidime PK was best described by a one-compartment model with glomerular filtration rate explaining clearance variability. Typical clearance and volume of distribution were 4.45 L/h and 88 L, respectively. The literature median volume of distribution was 37.2 L. Simulations indicated that an LD higher than 2 g was needed to achieve 60 mg/L in 80% of patients, with a median LD of 4.9 g. Our model showed a 4 g LD followed by 6 g/day infusion reached effective concentrations within 1 h, while a 2 g LD caused an 18 h delay in achieving target steady state.
Global Variation in Escherichia coli mcr-1 Genes and Plasmids from Animal and Human Genomes Following Colistin Usage Restrictions in Livestock
Authors: Garcias Biel , Flores Mayra Alejandra , Fernández Mercedes , Monteith William , Pascoe Ben , Sheppard Samuel K. , Martín Marga , Cortey Martí , Darwich Laila
Published: August 2024

Antimicrobial resistance (AMR) is a significant global health threat, with multidrug-resistant (MDR) bacterial clones becoming a major concern. Polymyxins, especially colistin, have reemerged as last-resort treatments for MDR Gram-negative infections. However, colistin use in livestock has spread mobile colistin resistance (mcr) genes, notably mcr-1, impacting human health. In consequence, its livestock use was banned in 2017, originating a natural experiment to study bacterial adaptation. The aim of this work was to analyse the changes in the mcr-1 genetic background after colistin restriction across the world. This study analyses 3163 Escherichia coli genomes with the mcr-1 gene from human and livestock hosts, mainly from Asia (n = 2621) and Europe (n = 359). Genetic characterisation identifies IncI2 (40.4%), IncX4 (26.7%), and multidrug-resistant IncHI2 (18.8%) as the most common plasmids carrying mcr-1. There were differences in plasmids between continents, with IncX4 (56.6%) being the most common in Europe, while IncI2 (44.8%) was predominant in Asia. Promoter variants related to reduced fitness costs and ISApl1 showed a distinct pattern of association that appears to be associated with adaptation to colistin restriction, which differed between continents. Thus, after the colistin ban, Europe saw a shift to specialised mcr-1 plasmids as IncX4, while ISApl1 decreased in Asia due to changes in the prevalence of the distinct promoter variants. These analyses illustrate the evolution of mcr-1 adaptation following colistin use restrictions and the need for region-specific strategies against AMR following colistin restrictions.
A Hunt for the Resistance of Haemophilus influnezae to Beta-Lactams
Authors: Denizon Mélanie , Hong Eva , Terrade Aude , Taha Muhamed-Kheir , Deghmane Ala-Eddine
Published: August 2024

Infections due to Haemophilus influnezae require prompt treatment using beta-lactam antibiotics. We used a collection of 81 isolates obtained between 1940 and 2001 from several countries. Whole genome sequencing showed the high heterogeneity of these isolates but allowed us to track the acquisition of beta-lactamase, which was first detected in 1980. Modifications of the ftsI gene encoding the penicillin-binding protein 3, PBP3, also involved in resistance to beta-lactams, appeared in 1991. These modifications (G490E, A502V, R517H, and N526K) were associated with resistance to amoxicillin that was not relieved by a beta-lactamase inhibitor (clavulanic acid), but the isolates retained susceptibility to third-generation cephalosporins (3GC). The modeling of the PBP3 structure suggested that these modifications may reduce the accessibility to the PBP3 active site. Other modifications appeared in 1998 and were associated with resistance to 3GC (S357N, M377I, S385T, and L389F). Modeling of the PBP3 structure suggested that they lie near the S379xN motif of the active site of PBP3. Overall resistance to amoxicillin was detected among 25 isolates (30.8%) of this collection. Resistance to sulfonamides was predicted by a genomic approach from the sequences of the folP gene (encoding the dihydropteroate synthase) due to difficulties in interpreting phenotypic anti-microbial testing and found in 13 isolates (16.0%). Our data suggest a slower spread of resistance to sulfonamides, which may be used for the treatment of H. influnezae infections. Genomic analysis may help in the prediction of antibiotic resistance, inform structure–function analysis, and guide the optimal use of antibiotics.
Serum Concentrations of IGF-1R, ERK2, and EGFR and Their Clinical Significance in Patients with Neuroendocrine Tumors
Authors: Duszkiewicz Roksana , Strzelczyk Janusz , Chełmecka Elżbieta , Strzelczyk Joanna Katarzyna
Published: August 2024

Neuroendocrine tumors are a heterogeneous group of tumors located mainly in the gastrointestinal tract or the respiratory system. We aimed to determine the concentrations of IGF-1R, ERK2, and EGFR using the ELISA method in serum samples from patients with NETs and from a control group. Results were evaluated with the selected demographic, clinicopathological, and biochemical characteristics. The analyses performed on a group of patients (80 in the study group and 62 in the control group) showed that the concentration of EGFR in patients with neuroendocrine tumors was significantly higher (p < 0.001) compared to the control group. Additionally, a significantly higher (p < 0.001) EGFR concentration was found in GEP-NET. Our results indicate that impaired EGFR signaling pathways are important in the context of neuroendocrine tumors. The data presented are a good starting point for further analysis of these proteins.
Post-Isometric Relaxation versus Self-Stretching for Non-Specific Neck Pain in Working-Age Individuals
Authors: Dudonienė Vilma , Mikalajūnė Kornelija , Pažėrienė Aistė , Žlibinaitė Laura
Published: August 2024

The study aimed to investigate the effectiveness of post-isometric relaxation (PIR) compared to self-stretching (SS) in working-age individuals with chronic non-specific neck pain. A parallel-group study was conducted with 50 patients who were randomized to either the self-stretching (SS, n = 25) or post-isometric relaxation (PIR, n = 25) group and received interventions three times a week for four weeks. In addition to these interventions, all patients were prescribed transcutaneous electrical nerve stimulation (TENS). Outcome measures were neck pain, the neck disability index (NDI), hand grip strength (HGS), and cervical spine range of motion (ROM). Data were collected at baseline and after four weeks of outpatient rehabilitation. Repeated measures ANOVA was used to determine within-group differences, and an independent t-test compared between-group differences. There were no significant differences between the groups at baseline in neck pain intensity. Following both interventions, pain intensity and functional disability decreased, cervical spine ROM increased, and HGS improved (p < 0.05). Between-group analysis showed that participants in the PIR group achieved lower pain intensity (p = 0.032, Cohen’s d = 0.81), greater neck extension ROM (p = 0.001, Cohen’s d = 0.96), and lower neck disability index (p = 0.004, Cohen’s d = 0.85) compared to the SS group. In conclusion, both PIR and SS interventions effectively reduced neck pain, increased range of motion, and decreased the neck disability index in working-age individuals. Neither technique showed superiority in increasing neck ROM and HGS. However, PIR was superior to SS in reducing neck pain and NDI, indicating potential benefits from integrating these interventions during treatment sessions.
Voltage Problems on Farms with Agricultural Biogas Plants—A Case Study
Authors: Skibko Zbigniew , Borusiewicz Andrzej , Romaniuk Wacław , Pietruszynska Marta , Milewska Anna , Marczuk Andrzej
Published: August 2024

Constructing agricultural microbial gasification plants near livestock farms is essential for technical, economic, and environmental reasons. Utilising substrates from these farms allows for producing electricity, heat, and environmentally friendly manure. However, biogas plants often face technical challenges. This study evaluates the power quality of an agricultural biogas plant on a dairy farm. It was found that the plant was connected via a cable with an insufficient conductor cross-section, leading to significant voltage overshoots exceeding 14.6%, which prevented the activation of the second generator. Both generators could operate after replacing the feed-in cable, but considerable fluctuations in the feed-in voltage persisted. Further measurements indicated the need for changes in the digester design. Specifically, replacing the current two mixers with more lower-powered mixers operating alternately was proposed. Sharing these solutions more broadly can help prevent similar issues in future microbial gas plant constructions and optimise electricity production.
Effect of 24-Week FIFA 11+ Referees Program on Quality of Change of Direction Maneuver in Elite Soccer Referees
Authors: Alimoradi Mohammad , Hosseini Elham , Iranmanesh Mojtaba , Monfaredian Omid , Kozinc Žiga
Published: August 2024

Soccer referees are at risk of injury due to the physical demands of their role. This study evaluated the impact of the 24-week FIFA 11+ Referees training program on the quality of change of direction (COD) maneuvers in elite soccer referees. Eighty-one male referees were randomly assigned to either the 11+ referee group (n = 40) or a control group (n = 41). The intervention group replaced their usual warm-up routines with the FIFA 11+ program, while the control group continued their standard training. Both groups were assessed using the cutting movement assessment score (CMAS) before and after the intervention. The results demonstrated significant improvements in CMAS scores for the 11+ referee group at all tested angles (45°, 70°, and 90°), with the most notable improvements observed at 90° angles. These findings indicate that the FIFA 11+ Referees program enhances COD maneuver quality, which is critical for injury prevention. Implementing this program may therefore improve movement quality and reduce injury risks among soccer referees. Further research should explore the program’s long-term efficacy and applicability across different populations and injury factors.
Enhancing Decentralized Decision-Making with Big Data and Blockchain Technology: A Comprehensive Review
Authors: Theodorakopoulos Leonidas , Theodoropoulou Alexandra , Halkiopoulos Constantinos
Published: August 2024

Big data and blockchain technology are coming together to revolutionize how decisions are made in a decentralized way across various industries. This review looks at how these technologies, along with distributed systems, can improve data security, transparency, and real-time processing, making decision-making more efficient and informed. The integration enhances data security with unchangeable records, increases transparency and traceability, and supports real-time data analysis. However, there are challenges to overcome, including scalability, data privacy, interoperability, regulatory compliance, and high costs. By examining case studies such as Estonia’s healthcare system, IBM and Walmart’s Food Trust, and the Brooklyn Microgrid project, we explore the practical applications and benefits of combining big data with blockchain. Despite these hurdles, the review finds that the ongoing advancements and innovative solutions in these technologies offer significant promise. They are set to drive the adoption and effectiveness of decentralized decision-making, ultimately leading to better efficiency and outcomes across multiple sectors.
Extracting Representations from Multi-View Contextual Graphs via Convolutional Neural Networks for Point-of-Interest Recommendation
Authors: Jiang Shaojie , Feng Wen , Ding Xuefeng
Published: August 2024

In recent years, graph-based learning methods have gained significant traction in point-of-interest (POI) recommendation systems due to their strong generalization capabilities. These approaches commonly transform user check-in records into graph-structured data and leverage graph neural networks (GNNs) to model the representations of both POIs and users. Despite their effectiveness, GNNs face inherent limitations in message passing, which can impede the deep extraction of meaningful representations from the graph structure. To mitigate this challenge, we introduce a novel framework, Multi-view Contextual Graphs via Convolutional Neural Networks for Point-of-Interest Recommendation (MCGRec). The MCGRec framework consists of three primary components. Firstly, it employs a personalized PageRank (PPR) sampling technique based on super nodes to transform the graph-structured data into a grid-like feature matrix. This step is crucial as it prepares the data for subsequent processing by convolutional neural networks (CNNs), which are adept at extracting spatial features from grid-like structures. Secondly, a CNN is utilized to extract the representations of POIs from the constructed feature matrix. The usage of CNNs enables the capture of local patterns and hierarchical features within the data, which are essential for accurate POI representation. Lastly, MCGRec incorporates a novel approach for estimating user preferences that integrates both geographical and temporal factors, thereby providing a more comprehensive model of users’ behaviors. To evaluate the effectiveness of our proposed method, we conduct extensive experiments on real-world datasets. Our results demonstrate that MCGRec outperforms state-of-the-art POI recommendation methods, showcasing its superiority in terms of recommendation accuracy.
Kinematic IMU-Based Assessment of Postural Transitions: A Preliminary Application in Clinical Context
Authors: Amici Cinzia , Pollet Joel , Ranica Giorgia , Bussola Roberto , Buraschi Riccardo
Published: August 2024

This study aims to develop a new methodology for assessing postural transitions, such as sit-to-stand movements, and to preliminarily apply it in a clinical setting. These movements provide valuable information about the state of movement effector system components, whether musculoskeletal, nervous, or cognitive, and their evaluation is a key point in the functional assessment in the clinical setting of patients with complex rehabilitative needs. The objective of this study was developed by pursuing three goals: verifying the ability to discriminate between healthy and pathological subjects, defining a set of parameters for movement assessment, and thus designing a preliminary evaluation paradigm for future clinical applications. We investigated the signals from a single IMU sensor applied to subjects (20 healthy and 13 patients) performing five different postural transitions. A set of six kinematic variables that allowed a quantitative assessment of motion was identified, namely total time, smoothness, fluency, velocity, jerk root mean square, and maximum jerk variation. At the end of the study, the adopted methodology and set of parameters were shown to be able to quantitatively assess postural transitions in a clinical context and to be able to distinguish healthy subjects from pathological subjects. This, together with future studies, will provide researchers and clinicians with a valuable resource for evaluating the results of a rehabilitation program, as well as for keeping track of patients’ functional status in follow-up evaluations.
Second-Order Text Matching Algorithm for Agricultural Text
Authors: Sun Xiaoyang , Song Yunsheng , Huang Jianing
Published: August 2024

Text matching promotes the research and application of deep understanding of text information, and it provides the basis for information retrieval, recommendation systems and natural language processing by exploring the similar structures in text data. Owning to the outstanding performance and automatically extract text features for the target, the methods based-pre-training models gradually become the mainstream. However, such models usually suffer from the disadvantages of slow retrieval speed and low running efficiency. On the other hand, previous text matching algorithms have mainly focused on horizontal domain research, and there are relatively few vertical domain algorithms for agricultural text, which need to be further investigated. To address this issue, a second-order text matching algorithm has been developed. This paper first obtains a large amount of text about typical agricultural crops and constructs a database by using web crawlers and querying relevant textbooks, etc. Then BM25 algorithm is used to generate a candidate set and BERT model is used to filter the optimal match based on the candidate set. Experiments have shown that the Precision@1 of this second-order algorithm can reach 88.34% on the dataset constructed in this paper, and the average time to match a piece of text is only 2.02 s. Compared with BERT model and BM25 algorithm, there is an increase of 8.81% and 13.73% in Precision@1 respectively. In terms of the average time required for matching a text, it is 55.2 s faster than BERT model and only 2 s slower than BM25 algorithm. It can improve the efficiency and accuracy of agricultural information retrieval, agricultural decision support, agricultural market analysis, etc., and promote the sustainable development of agriculture.
New Approach for Automated Explanation of Material Phenomena (AA6082) Using Artificial Neural Networks and ChatGPT
Authors: Goričan Tomaž , Terčelj Milan , Peruš Iztok
Published: August 2024

Artificial intelligence methods, especially artificial neural networks (ANNs), have increasingly been utilized for the mathematical description of physical phenomena in (metallic) material processing. Traditional methods often fall short in explaining the complex, real-world data observed in production. While ANN models, typically functioning as “black boxes”, improve production efficiency, a deeper understanding of the phenomena, akin to that provided by explicit mathematical formulas, could enhance this efficiency further. This article proposes a general framework that leverages ANNs (i.e., Conditional Average Estimator—CAE) to explain predicted results alongside their graphical presentation, marking a significant improvement over previous approaches and those relying on expert assessments. Unlike existing Explainable AI (XAI) methods, the proposed framework mimics the standard scientific methodology, utilizing minimal parameters for the mathematical representation of physical phenomena and their derivatives. Additionally, it analyzes the reliability and accuracy of the predictions using well-known statistical metrics, transitioning from deterministic to probabilistic descriptions for better handling of real-world phenomena. The proposed approach addresses both aleatory and epistemic uncertainties inherent in the data. The concept is demonstrated through the hot extrusion of aluminum alloy 6082, where CAE ANN models and predicts key parameters, and ChatGPT explains the results, enabling researchers and/or engineers to better understand the phenomena and outcomes obtained by ANNs.
Back to the Metrics: Exploration of Distance Metrics in Anomaly Detection
Authors: Lin Yujing , Li Xiaoqiang
Published: August 2024

With increasing researched focus on industrial anomaly detection, numerous methods have emerged in this domain. Notably, memory bank-based approaches coupled with k distance metrics have demonstrated remarkable performance in anomaly detection (AD) and anomaly segmentation (AS). However, upon examination of the back to the feature (BTF) method applied to the MVTec-3D AD dataset, it was observed that while it exhibited exceptional segmentation performance, its detection performance was lacking. To address this discrepancy, this study improves the implementation of BTF, especially the improvement of the anomaly score metric. It hypothesizes that when calculating the anomaly score for each sample, only the k-nearest neighbors within the same cluster should be considered. For ease of algorithm implementation, this assumption is distilled into the proposition that AD and AS tasks necessitate different k values in k distance metrics. Consequently, the paper introduces the BTM method, which utilizes distinct distance metrics for AD and AS tasks. This innovative approach yields superior AD and AS performance (I-AUROC 93.0%, AURPO 96.9%, P-AUROC 99.5%), representing a substantial enhancement over the BTF method (I-AUROC 5.7% ↑, AURPO 0.5% ↑, P-AUROC 0.2% ↑).
Cloud-Enabled Deployment of 5G Core Network with Analytics Features
Authors: Zieba Mateusz , Natkaniec Marek , Borylo Piotr
Published: August 2024

The ongoing evolution of network softwarization is particularly evident in mobile networks. The 5G standard defines core network functions as discrete processes, facilitating seamless virtualization. The next crucial step is to enable cloud-based deployments independent of specific hardware and hypervisors. In this work, we propose a testbed designed for cloud-based 5G network deployment. Our primary objective is to create an environment conducive to experimenting with cloud-based 5G core deployments and facilitating future research in this domain. We rigorously verified the deployment’s correctness, identified key issues, and developed effective solutions to create a robust environment for emerging applications. Additionally, we introduce an innovative extension to a widely used 5G core network implementation by creating a network function that replicates the functionalities of the Network Exposure Function (NEF). This new component facilitates advanced analytics and AI-based optimization, significantly enhancing cloud-based deployments of virtualized 5G networks.
Modeling a Wave Energy Harvester for Supplying Data Buoys
Authors: Lo Schiavo Alessandro , Nicora Filippo , Boragno Corrado
Published: August 2024

An energy harvester scavenging the kinetic energy of fluctuating waves for supplying small sea monitoring buoys is studied and tested. The harvester exploits a magnetic cylinder that rolls on a track due to the pitching motion of the buoy. The electromagnetic coupling between the rolling magnet and pairs of coils placed along the track generates an electromotive force used to supply a DC load through a bridge rectifier. The considered harvester is characterized by low-cost, simplicity, lightness and efficiency. An analytical model of the harvester is presented to investigate its operating conditions and to predict its nonlinear dynamic behavior. The operating mode of the energy harvester named bang-bang is studied in depth as it allows maximizing the extracted power, and analytical equations that characterize the behavior of the harvester in this operating mode are deduced. A prototype of the energy harvester was built and tested in order to identify the model parameters and to validate the theoretical results.
Comparative Evaluation of Temporomandibular Condylar Changes Using Texture Analysis of CT and MRI Images
Authors: Ogawa Celso Massahiro , Flaiban Everton , Ricardo Ana Lúcia Franco , Lopes Diana Lorena Garcia , de Oliveira Lays Assolini Pinheiro , de Almeida Bruna Maciel , de Oliveira Lira Adriana , Orhan Kaan , de Castro Lopes Sérgio Lúcio Pereira , Costa Andre Luiz Ferreira
Published: August 2024

This study aims to compare computed tomography (CT) with magnetic resonance imaging (MRI) of the temporomandibular joint (TMJ) by using texture analysis (TA) to detect condylar bone marrow changes associated with the flattening and erosion of cortical bone. A total of 47 patients from the Dentomaxillofacial Radiology Division at São Paulo State University were evaluated. Images from 250 CT and 250 MRI images were assessed by experienced radiologists employing OnDemand3D software. Texture parameters were extracted with MaZda software (version 4.6), and we focused on regions of interest within the condyles. Statistical analysis revealed significant differences in texture parameters between the affected and control groups. CT images showed higher correlation values in cases of flattening, whereas MRI images demonstrated substantial changes in texture parameters for both flattening and erosion. These findings suggest that the texture analysis of CT and MRI images can effectively detect early and advanced degenerative changes in the TMJ, thus providing valuable insights into the underlying pathophysiology and aiding in early intervention and treatment planning.
Fast-Neutron Radiolysis of Sub- and Supercritical Water at 300–600 °C and 25 MPa: A Monte Carlo Track Chemistry Simulation Study
Authors: Bhuiyan Md Shakhawat Hossen , Meesungnoen Jintana , Sultana Abida , Jay-Gerin Jean-Paul
Published: August 2024

(1) Background: Supercritical water-cooled reactors (SCWRs) and their smaller modular variants (SMRs) are part of the ‘Generation IV International Forum’ (GIF) on advanced nuclear energy systems. These reactors operate beyond the critical point of water (tc = 373.95 °C and Pc = 22.06 MPa), which introduces specific technical challenges that need to be addressed. The primary concerns involve the effects of intense radiation fields—including fast neutrons, recoil protons/oxygen ions, and γ rays—on the chemistry of the coolant fluid and the integrity of construction materials. (2) Methods: This study employs Monte Carlo simulations of radiation track chemistry to investigate the yields of radiolytic species in SCWRs/SMRs exposed to 2 MeV neutrons. In our calculations, only the contributions from the first three recoil protons with initial energies of 1.264, 0.465, and 0.171 MeV were considered. Our analysis was conducted at both subcritical (300 and 350 °C) and supercritical temperatures (400–600 °C), maintaining a constant pressure of 25 MPa. (3) Results: Our simulations provide insights into the radiolytic formation of chemical species such as e−aq, H●, H2, ●OH, and H2O2 from ~1 ps to 1 ms. Compared to data from radiation with low linear energy transfer (LET), the G(e−aq) and G(●OH) values obtained for fast neutrons show a similar temporal dependence but with smaller amplitude—a result demonstrating the high LET nature of fast neutrons. A notable outcome of our simulations is the marked increase in G(●OH) and G(H2), coupled with a corresponding reduction in G(H●), observed during the homogeneous chemical stage of radiolysis. This evolution is attributed to the oxidation of water by the H● atom according to the reaction H● + H2O → ●OH + H2. This reaction acts as a significant source of H2, potentially reducing the need to add extra hydrogen to the reactor’s coolant water to suppress the net radiolytic production of oxidizing species. Unlike in subcritical water, our simulations also indicate that G(H2O2) remains very low in low-density SCW throughout the interval from ~1 ps to 1 ms, suggesting that H2O2 is less likely to contribute to oxidative stress under these conditions. (4) Conclusions: The results of this study could significantly impact water-chemistry management in the proposed SCWRs and SCW-SMRs, which is crucial for assessing and mitigating the corrosion risks to reactor materials, especially for long-term operation.
Power Consumption Comparison of GPU Linear Solvers for Cellular Potts Model Simulations
Authors: De Luca Pasquale , Galletti Ardelio , Marcellino Livia
Published: August 2024

Power consumption is a significant challenge in the sustainability of computational science. The growing energy demands of increasingly complex simulations and algorithms lead to substantial resource use, which conflicts with global sustainability goals. This paper investigates the energy efficiency of different parallel implementations of a Cellular Potts model, which models cellular behavior through Hamiltonian energy minimization techniques, leveraging modern GPU architectures. By evaluating alternative solvers, it demonstrates that specific methods can significantly enhance computational efficiency and reduce energy use compared to traditional approaches. The results confirm notable improvements in execution time and energy consumption. In particular, the experiments show a reduction in terms of power of up to 53%, providing a pathway towards more sustainable high-performance computing practices for complex biological simulations.
MédicoBERT: A Medical Language Model for Spanish Natural Language Processing Tasks with a Question-Answering Application Using Hyperparameter Optimization
Authors: Padilla Cuevas Josué , Reyes-Ortiz José A. , Cuevas-Rasgado Alma D. , Mora-Gutiérrez Román A. , Bravo Maricela
Published: August 2024

The increasing volume of medical information available in digital format presents a significant challenge for researchers seeking to extract relevant information. Manually analyzing voluminous data is a time-consuming process that constrains researchers’ productivity. In this context, innovative and intelligent computational approaches to information search, such as large language models (LLMs), offer a promising solution. LLMs understand natural language questions and respond accurately to complex queries, even in the specialized domain of medicine. This paper presents MédicoBERT, a medical language model in Spanish developed by adapting a general domain language model (BERT) to medical terminology and vocabulary related to diseases, treatments, symptoms, and medications. The model was pre-trained with 3 M medical texts containing 1.1 B words. Furthermore, with promising results, MédicoBERT was adapted and evaluated to answer medical questions in Spanish. The question-answering (QA) task was fine-tuned using a Spanish corpus of over 34,000 medical questions and answers. A search was then conducted to identify the optimal hyperparameter configuration using heuristic methods and nonlinear regression models. The evaluation of MédicoBERT was carried out using metrics such as perplexity to measure the adaptation of the language model to the medical vocabulary in Spanish, where it obtained a value of 4.28, and the average F1 metric for the task of answering medical questions, where it obtained a value of 62.35%. The objective of MédicoBERT is to provide support for research in the field of natural language processing (NLP) in Spanish, with a particular emphasis on applications within the medical domain.
A Blockchain-Based Supervision Data Security Sharing Framework
Authors: Yong Jiu , Lei Xiaomei , Huang Zixin , Dang Jianwu , Wang Yangping
Published: August 2024

Ensuring trust, security, and privacy among all participating parties in the process of sharing supervision data is crucial for engineering quality and safety. However, the current centralized architecture platforms that are commonly used for engineering supervision data have problems such as low data sharing and high centralization. A blockchain-based framework for the secure sharing of engineering supervision data is proposed by utilizing the tamper-proof, decentralized, and traceable characteristics of blockchain. The secure storage of supervision data is achieved by combining it with the IPFS (InterPlanetary File System), reducing the storage pressure of on-chain data. Additionally, a fast data retrieval framework is designed based on the storage characteristics of supervision data. Then, CP-ABE (Ciphertext Policy Attribute Based Encryption) is combined with a data storage framework to ensure the privacy, security, and reliability of supervisory data during the sharing process. Finally, smart contracts are designed under the designed framework to ensure the automatic and trustworthy execution of access control processes. The analysis and evaluation results of the security, encryption and decryption, and cost performance of the proposed blockchain framework show that the encryption and decryption time is completed within 0.1 s, the Gas cost is within the normal consumption range, and the time cost of smart contract invocation does not exceed 5 s, demonstrating good availability and reusability of the method proposed in this article.