bluuebunny/crossref_metadata_2025 huggingface dataset:
Design and Self-Calibration Method of a Rope-Driven Cleaning Robot for Complex Glass Curtain Walls
Authors: Wang Jingtian , Li Yuao , Zhang Minglu , Liu Zonghou , Bai Yiyang , Zhao Zhengyang , Su Xiuping , Li Manhong
Published: July 2024

Rope-driven robots are increasingly used to safely and efficiently clean complex glass curtain walls. However, continuous global cleaning is difficult for most robots because of their poor performance in overcoming obstacles and adapting to curved surfaces, and an inconvenient winch calibration on complex surfaces further burdens such work. This paper presents a 3-DOF rope-driven robot and a winch self-calibration method for efficient cleaning. The robot, by integrating a 5-rope parallel configuration, a self-adaptive cleaning body, and a self-compensating driving winch, is designed to perform continuous, compliant, and accurate spatial motion on curved walls with obstacles. By deducing the kinematic model, the constraint relationship related to orderly arranged winch positions, maneuverable body positions, and accessible rope lengths is established during the robot tracking 3D trajectory. Combining the established relationship, a series of regulations are formulated for easily acquiring body positions and rope lengths, and then a self-calibration method is proposed by accurately calculating winch positions without using professional instruments. Experimental results show that the robot can perform global and precise movement on complex glass surfaces. Applying the proposed method, the maximum winch calibration error is 6.6 mm, and the maximum body tracking error is controlled within 9.6 mm.
The Asymmetric Tail Risk Spillover from the International Soybean Market to China’s Soybean Industry Chain
Authors: Zhang Shaobin , Shi Baofeng
Published: July 2024

China is the largest soybean importer and consumer in the world. Soybean oil is the most-consumed vegetable oil in China, while soybean meal is the most important protein feed raw material in China, which affects the costs of animal husbandry. Volatility in the international soybean market would generate risk spillovers to China’s soybean industrial chain. This paper analyzed the channel of risk spillover from the international soybean market to China’s soybean industry chain and the asymmetry of the risk spillover. The degree of risk spillover from the international soybean market to the Chinese soybean industry chain was measured by the Copula–CoVaR model. The moderating role of inventory and demand in asymmetric risk spillovers was analyzed by quantile regression. We draw the following conclusions: First, the international soybean market impacts China’s soybean industry chain through soybeans rather than soybean meal and oil. The price fluctuation of China soybean market is obviously lower than that of the international soybean market. Second, there are apparent asymmetric risk spillovers from the international soybean market to China’s soybean industry chain, especially the soybean meal market. Third, increasing the Chinese soybean inventory and growing demand could effectively prevent the downside risk spillover from international markets to China’s soybean market. This also explains the asymmetry of risk spillovers. The research enriches the research perspective on food security, and the analysis of risk spillover mechanisms provides a scientific basis for relevant companies to develop risk-management strategies.
The Biostimulant Potential of Clove Essential Oil for Treating Soybean Seeds
Authors: Costa Joao Paulo , Nasser Vinícius Guimarães , Macedo Willian Rodrigues , Santos Mario Ferreira Conceição , Silva Geraldo Humberto
Published: July 2024

Increasing soybean productivity can be achieved by treating seeds with biostimulants. To this end, an investigation was conducted into the potential of a formulation prepared with clove es-sential oil (CEO) diluted in soybean oil for seed treatment. Soybean seeds were treated with CEO concentrations between 0.5 to 3.0 mL/L, and subjected to germination, vigor, and sanity analyses. The CEO at 1.6 mL/L exhibited favorable outcomes regarding germination, root length, and re-duced fungal infection. In this way, a two-crop field experiment evaluated soybean seeds treated with CEO at 1.6 mL/L. Soybean seeds treated with CEO in the field in 2021/2022 were not different from the controls. However, in 2019/2020, there was a higher percentage of emergence, nodulation, and production of 749 kg/ha more than in the industrial treatment. These results highlight the potential use of CEO as a biostimulant.
Biochar Co-Compost: A Promising Soil Amendment to Restrain Greenhouse Gases and Improve Rice Productivity and Soil Fertility
Authors: Umair Hassan Muhammad , Huang Guoqin , Munir Rizwan , Khan Tahir Abbas , Noor Mehmood Ali
Published: July 2024

Agriculture is a major source of greenhouse gas (GHG) emissions. Biochar has been recommended as a potential strategy to mitigate GHG emissions and improve soil fertility and crop productivity. However, few studies have investigated the potential of biochar co-compost (BCC) in relation to soil properties, rice productivity, and GHG emissions. Therefore, we examined the potential of BC, compost (CP), and BCC in terms of environmental and agronomic benefits. The study comprised four different treatments: control, biochar, compost, and biochar co-compost. The application of all of the treatments increased the soil pH; however, BC and BCC remained the top performers. The addition of BC and BBC also limited the ammonium nitrogen (NH4+-N) availability and increased soil organic carbon (SOC), which limited the GHG emissions. Biochar co-compost resulted in fewer carbon dioxide (CO2) emissions, while BC resulted in fewer methane (CH4) emissions, which was comparable with BCC. Moreover, BC caused a marked reduction in nitrous oxide (N2O) emissions that was comparable to BCC. This reduction was attributed to increased soil pH, nosZ, and nirK abundance and a reduction in ammonia-oxidizing archaea (AOA) and ammonia-oxidizing bacteria (AOB) abundance. The application of different amendments, particularly BCC, favored rice growth and productivity by increasing nutrient availability, soil carbon, and enzymatic activities. Lastly, BCC and BC also increased the abundance and diversity of soil bacteria, which favored plant growth and caused a reduction in GHG emissions. Our results suggest that BCC could be an important practice to recycle organic sources while optimizing climate change and crop productivity.
A Deep-Learning-Based Model for the Detection of Diseased Tomato Leaves
Authors: Abdullah Akram , Amran Gehad Abdullah , Tahmid S. M. Ahanaf , Alabrah Amerah , AL-Bakhrani Ali A. , Ali Abdulaziz
Published: July 2024

This study introduces a You Only Look Once (YOLO) model for detecting diseases in tomato leaves, utilizing YOLOV8s as the underlying framework. The tomato leaf images, both healthy and diseased, were obtained from the Plant Village dataset. These images were then enhanced, implemented, and trained using YOLOV8s using the Ultralytics Hub. The Ultralytics Hub provides an optimal setting for training YOLOV8 and YOLOV5 models. The YAML file was carefully programmed to identify sick leaves. The results of the detection demonstrate the resilience and efficiency of the YOLOV8s model in accurately recognizing unhealthy tomato leaves, surpassing the performance of both the YOLOV5 and Faster R-CNN models. The results indicate that YOLOV8s attained the highest mean average precision (mAP) of 92.5%, surpassing YOLOV5’s 89.1% and Faster R-CNN’s 77.5%. In addition, the YOLOV8s model is considerably smaller and demonstrates a significantly faster inference speed. The YOLOV8s model has a significantly superior frame rate, reaching 121.5 FPS, in contrast to YOLOV5’s 102.7 FPS and Faster R-CNN’s 11 FPS. This illustrates the lack of real-time detection capability in Faster R-CNN, whereas YOLOV5 is comparatively less efficient than YOLOV8s in meeting these needs. Overall, the results demonstrate that the YOLOV8s model is more efficient than the other models examined in this study for object detection.
Comparative Transcriptomic Analysis Reveals Transcriptional Differences in the Response of Quinoa to Salt and Alkali Stress Responses
Authors: Bao Qinghan , Wu Yang , Wang Yang , Zhang Yongping
Published: July 2024

Soil salinization is a global agro-ecological problem and a major factor impeding agricultural development. Planting salt-tolerant plants to improve saline soils offers both ecological and economic benefits. Currently, there are few studies addressing the combined effects of salt and alkali stress. Quinoa is known for its salinity tolerance. However, research has predominantly focused on the effects of salinity stress on quinoa’s morphology and physiology, with its molecular mechanisms remaining unclear. To better understand quinoa’s response mechanisms to salinity and alkali stress, we employed RNA-seq technology to analyze transcriptomes under these conditions. We identified 1833 differentially expressed genes (DEGs) under salt stress and 2233 DEGs under alkali stress. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotations revealed that quinoa responds to salt and alkali stress through similar mechanisms. Both stresses promoted sucrose synthesis, starch synthesis and catabolism, which increased the osmotic potential of quinoa leaves. Additionally, there was a regulation of the down-regulated expression of the abscisic acid receptor PYR/PYL and the up-regulated expression of the serine/threonine protein kinase (PP2C) gene in the ABA signaling pathway. Contrasting with salt tolerance, the mechanism specific to quinoa’s alkalinity tolerance involves the up-regulation of the citric acid cycle via an active γ-aminobutyric acid (GABA) branch, enhancing quinoa’s energy metabolism. In summary, our transcriptome analysis revealed key regulatory mechanisms in quinoa’s response to saline and alkaline stress. This study deepens the understanding of quinoa’s stress response mechanisms and provides theoretical references for the biological improvement of salinized soils.
Gibberellins Regulate Expression of Cyclins to Control Leaf Width in Rice
Authors: Zou Ruifeng , Guo Xiaoyuan , Shan Siyao , Wang Quan
Published: July 2024

Leaves are the nutritive organs of rice. Leaf shape influences rice photosynthesis, subsequently impacting yield. Gibberellins, GAs, are important hormones, but the way in which GAs regulate leaf width is largely unknown. This study focuses on the d18 mutant with broader leaves due to defective GA biosynthesis. Statistical analysis indicates broader leaves in the d18 mutant compared to the wild-type group. An examination of leaf cell morphology shows a higher count of secondary vascular bundles in d18 than in the wild-type group. RNA-seq analysis demonstrates significantly higher expression of CYCB (CYCLIN B) and H4 (HISTONE H4) in d18 compared to wild type. In summary, the leaf width of d18 may due to a higher activity of cell division at leaf margin.
Effectiveness of Hydrated Sodium Calcium Aluminosilicates and Discarded Date Pits as Dietary Adsorbents for Aflatoxin B1 in Enhancing Broiler Chicken Productive Performance, Hepatic Function, and Intestinal Health
Authors: Abudabos Ala E. , Aljumaah Riyadh S. , Alabdullatif Abdulaziz A. , Al Sulaiman Ali R. , Hakmi Zafar , Alharthi Abdulrahman S.
Published: July 2024

The research aimed to evaluate how effective hydrated sodium calcium aluminosilicates (HSCASs) and discarded date pits (DDPs) are as dietary adsorbents for aflatoxin B1 (AFB1) in enhancing the performance and health of broiler chickens aged 16 to 30 days. A total of 240 Ross 308 straight-run broilers were randomly allocated into four dietary groups, each with 10 replicates: a control diet, a control diet with 1000 ppb AFB1, an AFB1-contaminated diet with 0.5% HSCAS, and an AFB1-contaminated diet with 4% DDP. Incorporating HSCASs or DDPs into the AFB1-contaminated diet resulted in significant improvements across various parameters, involving increased body weight, improved feed conversion ratio, higher dressing percentage, decreased relative weights of kidney and spleen, elevated serum levels of total protein, globulin, and glucose, reduced serum alanine aminotransferase activity, and heightened hepatic protein concentration and glutathione peroxidase activity, along with diminished hepatic malondialdehyde content and glutamic oxaloacetic transaminase activity. Moreover, both supplements led to increased ileal villus height and surface area, enhanced apparent nitrogen-corrected metabolizable energy digestibility, and decreased AFB1 residues in the liver and kidney. Moreover, the dietary inclusion of DDPs significantly decreased relative liver weight, raised serum albumin concentration, lowered serum alkaline phosphatase activity, enhanced hepatic total antioxidant capacity level, and augmented ileal villus width. Conversely, the dietary addition of HSCASs significantly heightened apparent crude protein digestibility. In conclusion, the inclusion of HSCASs and DDPs in AFB1-contaminated diets can mitigate the toxic effects of AFB1 on broiler chickens, with DDPs exhibiting additional advantages in optimizing liver function and gut morphology.
Small for Gestational Age Calves: Part I—Concept and Definition, Contributing Prenatal Factors and Neonatal Body Morphometrics in Holstein Friesian Calves
Authors: Meesters Maya , Van Eetvelde Mieke , Verdru Karel , Govaere Jan , Opsomer Geert
Published: July 2024

Low birth weight (BW) calves exhibit higher mortality rates, reduced body weights at parturition, lower first-lactation milk yields, and longer parturition to first insemination intervals. In human medicine, small for gestational age (SGA) births are associated with increased perinatal morbidity and long-term metabolic risks. This study aimed to define SGA in Holstein Friesian (HF) calves, evaluate their body measurements and proportions, and identify its prenatal risk factors. Four linear regression models were built with weight as a function for gestation length for bull and heifer calves born from nulli- or multiparous dams. Calves with a BW below the 10th percentile were classified as SGA. Differences in body measurements were analyzed using ANOVA, and logistic regression models identified prenatal risk factors to be born SGA. Gestation length, calf sex, and dam parity were crucial variables in defining SGA. SGA calves had significantly smaller body measurements (p < 0.001) and larger body proportions (p < 0.001) compared to average and large calves. For nulliparous dams, a higher 2nd trimester temperature–humidity index (p = 0.032) and older age at parturition (>26 months, p = 0.026) significantly increased the birth of SGA calves. For multiparous dams, both low (<5800 kg, p = 0.049) and high (6700–8600 kg, p = 0.027) milk yields during gestation lead to more SGA births, although very high-yielding dams (>8600 kg) did not birth more SGA calves. This study establishes SGA in HF calves, suggests SGA calves are asymmetrical with evidence of “brain sparing”, and highlights the impact of prenatal factors on calf size at birth. Further research is needed to determine the long-term effects of being born SGA on growth, reproductive performance, and productivity.
The Effect of Vitamins on the Immune Systems of Pigs
Authors: Mainardi Edda , Corino Carlo , Rossi Raffaella
Published: July 2024

In modern pig farming, there are many environmental, physiological or social stresses that weaken the immune response and increase susceptibility to disease. Nutritional management has a significant impact on the efficiency of the immune system in pigs. Among the various nutrients, vitamins have been shown to have specific effects on immune system activity. However, the needs of modern genetic types are not met by the dietary recommendations for vitamins in pig diets. The present study therefore summarises the data on dietary integration with supranutritional doses of vitamins in gestating and lactating sows and post-weaning piglets in terms of the immune response. The present data highlight that high doses of dietary vitamins are an effective way to improve the immune system, antioxidant status and gut health. Further studies are needed to deepen the understanding of the role of dietary supplementation with vitamins in pigs on immune system and gut functionality.
One-Year-Old Precocious Chinese Mitten Crab Identification Algorithm Based on Task Alignment
Authors: Gu Hao , Gan Dongmei , Chen Ming , Feng Guofu
Published: July 2024

The cultivation of the Chinese mitten crab (Eriocheir sinensis) is an important component of China’s aquaculture industry and also a field of concern worldwide. It focuses on the selection of high-quality, disease-free juvenile crabs. However, the early maturity rate of more than 18.2% and the mortality rate of more than 60% make it difficult to select suitable juveniles for adult culture. The juveniles exhibit subtle distinguishing features, and the methods for differentiating between sexes vary significantly; without training from professional breeders, it is challenging for laypersons to identify and select the appropriate juveniles. Therefore, we propose a task-aligned detection algorithm for identifying one-year-old precocious Chinese mitten crabs, named R-TNET. Initially, the required images were obtained by capturing key frames, and then they were annotated and preprocessed by professionals to build a training dataset. Subsequently, the ResNeXt network was selected as the backbone feature extraction network, with Convolutional Block Attention Modules (CBAMs) and a Deformable Convolution Network (DCN) embedded in its residual blocks to enhance its capability to extract complex features. Adaptive spatial feature fusion (ASFF) was then integrated into the feature fusion network to preserve the detailed features of small targets such as one-year-old precocious Chinese mitten crab juveniles. Finally, based on the detection head proposed by task-aligned one-stage object detection, the parameters of its anchor alignment metric were adjusted to detect, locate, and classify the crab juveniles. The experimental results showed that this method achieves a mean average precision (mAP) of 88.78% and an F1-score of 97.89%. This exceeded the best-performing mainstream object detection algorithm, YOLOv7, by 4.17% in mAP and 1.77% in the F1-score. Ultimately, in practical application scenarios, the algorithm effectively identified one-year-old precocious Chinese mitten crabs, providing technical support for the automated selection of high-quality crab juveniles in the cultivation process, thereby promoting the rapid development of aquaculture and agricultural intelligence in China.
Beware of Strangers: Dogs’ Empathetic Response to Unknown Humans
Authors: Rivera Micael M. , Meyers-Manor Julia E.
Published: July 2024

Empathy is a complex cognitive ability that has been studied in many social animals, including dogs. Previous studies have found that dogs would rescue their distressed owner more quickly than a calm owner and that dogs respond physiologically and behaviorally to the sound of crying strangers. However, no studies have explored the empathetic and emotional contagion capabilities of dogs towards strangers in rescue paradigms. In the present study, a stranger was placed behind a clear door and was told to cry (distress) or hum (neutral). The dogs’ door opening, stress behaviors, tone of approach, and physiological responses were measured. Dogs did not open more frequently or more quickly for the stranger in the distressed condition compared to the neutral condition. Additionally, there was no significant difference between the behavioral or physiological indicators of stress across conditions. It was also found that non-openers were reported by owners to have more fear and, in the empathy test, were more aggressive and fearful in their tone of approach. These results suggest that dogs may be less likely to exhibit empathy-like behaviors to unknown humans in an unfamiliar environment and that owners may be necessary to moderate a dog’s stress to show empathetic behaviors.
Transcriptomic Analysis Reveals the Effects of miR-122 Overexpression in the Liver of Qingyuan Partridge Chickens
Authors: Luo Xiaolu , Zhang Jiahang , Guo Jiancheng , Zhao Wenjuan , Tian Yinan , Xiang Hai , Kang Huimin , Ye Fei , Chen Siyu , Li Hua , Ma Zheng
Published: July 2024

The liver of chickens is essential for maintaining physiological activities and homeostasis. This study aims to investigate the specific function and molecular regulatory mechanism of microRNA-122 (miR-122), which is highly expressed in chicken liver. A lentivirus-mediated overexpression vector of miR-122 was constructed and used to infect 12-day-old female Qingyuan Partridge chickens. Transcriptome sequencing analysis was performed to identify differentially expressed genes in the liver. Overexpression of miR-122 resulted in 776 differentially expressed genes (DEGs). Enrichment analyses, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) revealed associations with lipid metabolism, cellular senescence, cell adhesion molecules, and the MAPK signaling pathway. Eight potential target genes of miR-122 (ARHGAP32, CTSD, LBH, PLEKHB2, SEC14L1, SLC2A1, SLC6A14, and SP8) were identified through miRNA target prediction platforms and literature integration. This study provides novel insights into the molecular regulatory mechanisms of miR-122 in chicken liver, highlighting its role in key biological processes and signaling pathways. These discoveries enhance our understanding of miR-122’s impact on chicken liver function and offer valuable information for improving chicken production performance and health.
Numerical Calculation Method of Key Performance Parameters of Proppant Based on 2D Computer Simulation
Authors: Zhao Yunxiang , Ke Xijun , Kang Yunwei , Li Ke
Published: July 2024

The key performance parameters of proppant are mainly the crushing rate and fracture conductivity, which are usually evaluated using physical experimental methods. However, the testing method for fracture conductivity has limitations, such as its long time-consumption, high testing costs, instability, and even the presence of large errors in testing results under the same conditions. The purpose of this paper is to propose a calculation method that can replace physical experiments. Firstly, we analyze the random and deterministic phenomena in the contact relationship between proppant particles from a microscopic perspective. Subsequently, we develop a physical model of the microscopic arrangement of these particles, enabling us to conduct further computer simulations of their microscopic configuration. Secondly, we conduct a microscopic mechanical analysis of the contact between proppant particles and between particles and boundaries and establish a corresponding mathematical model. Then, utilizing the simulation and mechanical analysis results of the proppant, we calculate the crushing rate. Considering the crushing rate of proppant, we improve the Kozeny–Carmen equation to determine the fracture permeability, and subsequently calculate the fracture conductivity. Finally, the calculated results are compared with the experimental results. The results show that the calculated values for the proppant crushing rate and fracture conductivity matched well with experimental data, and that the model’s calculation values were more accurate. As the number of simulations increased, the accuracy of the calculation results became higher. Research shows that the fracture conductivity is influenced by factors such as the particle size, microstructure, and crushing rate. Numerical calculation methods can replace physical experiments and provide theoretical support for engineering applications of hydraulic fracturing proppant materials.
Multilevel Hierarchical Bayesian Modeling of Cross-National Factors in Vehicle Sales
Authors: Sukiennik Monika , Baranowski Jerzy
Published: July 2024

SUVs (sport utility vehicles), as a car segment, have become a foundation within the automotive industry due to their versatility, which is used by a wide range of customers. Recognising the complex interplay between geographical and economic conditions across countries, we delve into cross-national factors that significantly influence SUV sales. This article presents an analysis of the global sales of SUVs (sport utility vehicles) using multilevel hierarchical Bayesian modelling. We identify key predictors of SUV sales, including the effects of fuel prices, income levels and geographical aspects. We prepared four statistical models that differ in their probability distribution or hierarchical internal structure. The last presented model, with Student’s t-distribution and separate distribution for unique alpha parameter values, turned out to be the best one. Our analysis contributes to a deeper understanding of the automotive market dynamics, and it can also assist manufacturers and policymakers in designing effective sales strategies.
Impact of Wood Structure Variability on the Surface Roughness of Chestnut Wood
Authors: Chavenetidou Marina , Kamperidou Vasiliki
Published: July 2024

Wood constitutes a unique and valuable material that has been used from ancient times until nowadays in a wide variety of applications, in which the surface quality of wood often constitutes a critical factor. In this study, the influence of different wood areas and therefore, of different anatomical characteristic areas of chestnut wood (Castanea sativa Mill.) on the surface quality, was thoroughly studied, in terms of surface roughness. Five different chestnut tree trunks were harvested, from which five different disks were obtained corresponding to five different trunk heights. Surface roughness was measured on these disks on the transverse, radial, and tangential planes, on the areas of sapwood and heartwood, measuring the roughness in each point both vertically and in parallel to the wood grain. The results revealed that the examined roughness indexes (Ra, Rz, Rq) follow a parallel path to one another. In the case of all surfaces (transverse, radial, tangential) of the disks examined, when the measurement was implemented perpendicularly to the wood grain, a significantly higher roughness was recorded, compared to the wood grain measurements being implemented in parallel with the wood grain. Significant differences between heartwood and sapwood roughness were not demonstrated, although sapwood often appeared to exhibit a higher surface roughness than heartwood sites. Among the roughness values of the three different surfaces, the highest roughness in the vertical-to-wood-grain measurements was recorded by tangential surfaces, with slightly lower values on the transverse surfaces and the lowest roughness on radial surfaces. Meanwhile, for the measurements in parallel with the wood grain, the transverse surfaces presented significantly higher roughness values compared to the tangential and radial surfaces. Significant roughness differences were not detected among the surfaces at different trunk heights. Although, significant differences in roughness were recorded among different trees, it was observed that all the studied trees align with the identified and described within-tree trends.
Evaluating Commercial Electrical Neuromodulation Devices with Low-Cost Neural Phantoms
Authors: LaRocco John , Eom Taeyoon , Seth Ekansh , Gandhi Vania , Bontempo Anna , Zachariah Eric
Published: July 2024

Non-invasive transcranial electrical stimulation is a category of neuromodulation techniques used for various disorders. Although medically approved devices exist, the variety of consumer electrical stimulation devices is increasing. Because clinical trials and animal tests are costly and risky, using a brain phantom can provide preliminary experimental validation. However, existing brain phantoms are often costly or require excessive preparation time, precluding their use for rapid, real-time optimization of stimulation settings. A limitation of direct electric fields in a phantom is the lack of 3D spatial resolution. Using well-researched modalities such as transcranial direct current stimulation (tDCS) and newer modalities such as amplitude-modulated transcranial pulsed-current stimulation (am-tPCS), a range of materials was tested for use as electrical phantoms. Based on cost, preparation time, and efficiency, ground beef and agar gel with a 10% salt mix were selected. The measured values for the total dosages were 0.55 W-s for am-tPCS and 0.91 W-s for tDCS. Due to a low gain on the recording electrodes, the signal efficiency measured against the power delivered was 4.2% for tDCS and 3.1% for am-tPCS. Issues included electrodes shifting in the soft material and the low sensitivity of the recording electrodes. Despite these issues, the effective combination of the phantom and recording methodologies can enable low costs and the rapid testing, experimentation, and verification of consumer neuromodulation devices in three dimensions. Additionally, the efficiency factors (EFs) between the observed dosage and the delivered dosage could streamline the comparison of experimental configurations. As demonstrated by comparing two types of electrical neuromodulation devices across the 3D space of a phantom, EFs can be used in conjunction with a cost-effective, time-expedient phantom to rapidly iterate and optimize stimulation parameters.
Detecting Selected Instruments in the Sound Signal
Authors: Kostrzewa Daniel , Szwajnoch Paweł , Brzeski Robert , Mrozek Dariusz
Published: July 2024

Detecting instruments in a music signal is often used in database indexing, song annotation, and creating applications for musicians and music producers. Therefore, effective methods that automatically solve this issue need to be created. In this paper, the mentioned task is solved using mel-frequency cepstral coefficients (MFCC) and various architectures of artificial neural networks. The authors’ contribution to the development of automatic instrument detection covers the methods used, particularly the neural network architectures and the voting committees created. All these methods were evaluated, and the results are presented and discussed in the paper. The proposed automatic instrument detection methods show that the best classification quality was obtained for an extensive model, which is the so-called committee of voting classifiers.
Nonlinear Multivariable System Identification: A Novel Method Integrating T-S Identification and Multidimensional Membership Functions
Authors: Comina Mayra , Al-Hadithi Basil Mohammed , Jiménez Agustín
Published: July 2024

In this paper, a new multidimensional Takagi–Sugeno (T-S) identification technique is proposed for multivariable nonlinear systems. In this technique, multidimensional membership functions are designed using concepts from solid mechanics. The design of membership functions is carried out in multidimensional space, defining the principal axes from the eigenvectors of the inertia matrix, and it has the characteristic of dividing the space into regions with the same inertia. These regions are analyzed to define the center of gravity for each rule. Illustrative examples of multivariable nonlinear systems, such as a thermal mixing process and a binary distillation column, are selected to evaluate the effectiveness of the proposed method. The proposed method is compared with traditional T-S identification that uses one-dimensional membership functions and shows a reduction in the relative identification error and the algorithm execution time. Additionally, the proposed method prevents rules from being positioned outside the system’s range, thereby avoiding the generation of unnecessary rules.
How Can Artificial Intelligence Identify Knee Osteoarthritis from Radiographic Images with Satisfactory Accuracy?: A Literature Review for 2018–2024
Authors: Touahema Said , Zaimi Imane , Zrira Nabila , Ngote Mohamed Nabil
Published: July 2024

Knee osteoarthritis is a chronic, progressive disease that rapidly progresses to severe stages. Reliable and accurate diagnosis, combined with the implementation of preventive lifestyle modifications before irreversible damage occurs, can effectively protect patients from becoming an inactive population. Artificial intelligence continues to play a pivotal role in computer-aided diagnosis with increasingly convincing accuracy, particularly in identifying the severity of knee osteoarthritis according to the Kellgren–Lawrence (KL) grading scale. The primary objective of this literature review is twofold. Firstly, it aims to provide a systematic analysis of the current literature on the main artificial intelligence models used recently to predict the severity of knee osteoarthritis from radiographic images. Secondly, it constitutes a critical review of the different methodologies employed and the key elements that have improved diagnostic performance. Ultimately, this study demonstrates that the considerable success of artificial intelligence systems will reinforce healthcare professionals’ confidence in the reliability of machine learning algorithms, facilitating more effective and faster treatment for patients afflicted with knee osteoarthritis. In order to achieve these objectives, a qualitative and quantitative analysis was conducted on 60 original research articles published between 1 January 2018 and 15 May 2024.
A Novel Symmetric Fine-Coarse Neural Network for 3D Human Action Recognition Based on Point Cloud Sequences
Authors: Li Chang , Huang Qian , Mao Yingchi , Qian Weiwen , Li Xing
Published: July 2024

Human action recognition has facilitated the development of artificial intelligence devices focusing on human activities and services. This technology has progressed by introducing 3D point clouds derived from depth cameras or radars. However, human behavior is intricate, and the involved point clouds are vast, disordered, and complicated, posing challenges to 3D action recognition. To solve these problems, we propose a Symmetric Fine-coarse Neural Network (SFCNet) that simultaneously analyzes human actions’ appearance and details. Firstly, the point cloud sequences are transformed and voxelized into structured 3D voxel sets. These sets are then augmented with an interval-frequency descriptor to generate 6D features capturing spatiotemporal dynamic information. By evaluating voxel space occupancy using thresholding, we can effectively identify the essential parts. After that, all the voxels with the 6D feature are directed to the global coarse stream, while the voxels within the key parts are routed to the local fine stream. These two streams extract global appearance features and critical body parts by utilizing symmetric PointNet++. Subsequently, attention feature fusion is employed to capture more discriminative motion patterns adaptively. Experiments conducted on public benchmark datasets NTU RGB+D 60 and NTU RGB+D 120 validate SFCNet’s effectiveness and superiority for 3D action recognition.
Considering the Optimization Design of Urban Bus Network Scheduling
Authors: Yu Xi , Cao Huan , Cao Ke , Zou Liang , Zhu Lingxiang
Published: July 2024

At present, the optimization of public transportation networks and vehicle scheduling are carried out independently in stages. However, through analysis, it has been found that scheduling information such as route schedules is an important factor related to passenger route selection. Therefore, in order to further improve the optimization effect, this article proposes an innovative idea of simultaneously optimizing the line network and scheduling. Based on the construction of a real–virtual public transportation network, this article constructs a synchronous optimization model for the line network and scheduling by considering both passenger waiting and on-board time. To achieve the consideration of passengers for different route choices, a shortest path traversal algorithm based on Yen was proposed to analyze the number and weight of the shortest paths between the same OD, and a genetic algorithm was used to solve the model. Finally, the effectiveness of the model was verified through numerical examples, and the results showed that synchronous optimization was superior to phased optimization: the passenger time cost was reduced by 21.5%, the bus operation cost was reduced by 13.7%, and the total bus system cost was reduced by 18.0%.
Study on Fire Smoke Movement Characteristics and Their Impact on Personal Evacuation in Curved Highway Tunnels
Authors: Cui Yuang , Liu Zhiqiang
Published: July 2024

In the existing research on tunnel fires, researchers primarily focus on straight tunnels, neglecting the impact of curved sidewalls in curved tunnels. Based on the theory of smoke diffusion, a series of CFD numerical simulations was conducted using the Fire Dynamics Simulator to investigate the characteristics of smoke distribution in a curved highway tunnel. The results indicated that distinct smoke distribution characteristics were observed when a fire occurred in a curved tunnel compared with those observed in straight tunnels, with significant differences particularly evident for the radius of curvature of the tunnel below 1000 m. By comparing the smoke distribution characteristics from various fire source locations, the most unfavorable fire source locations within a curved tunnel were determined. High-temperature fire smoke bounds between the inner and outer walls of the tunnel, leading to the formation of multiple high-temperature zones in proximity to the fire source, rather than diffusing directly towards the exit in a linear tunnel. Additionally, based on an analysis of temperature, visibility, and CO concentration at characteristic heights, suitable locations for pedestrian crossings within the tunnel were deduced and an evacuation strategy for persons within the core fire area was proposed. The results can provide a reference for personal evacuation strategies in curved highway tunnel fire scenarios and the design of an adit for people passing in such tunnels.
Plant Organic Acids as Natural Inhibitors of Foodborne Pathogens
Authors: Karpiński Tomasz M. , Ożarowski Marcin
Published: July 2024

Background: Foodborne infections affect approximately 600 million people annually. Simultaneously, many plants contain substances like organic acids, which have antimicrobial activity. The aim of this study was to examine the effects of 21 organic acids, naturally occurring in plants, on four foodborne bacteria (Staphylococcus aureus, Listeria monocytogenes, Escherichia coli, and Salmonella enterica Typhimurium) and two fungi (Geotrichum candidum and Penicillium candidum). Methods: The minimal inhibitory concentrations (MIC) of the organic acids against foodborne bacteria and in silico toxicity prediction of acids were investigated. Results: Benzoic and salicylic acids exhibit the best activity against foodborne bacteria (mean MIC < 1 mg/mL). Acetic, chlorogenic, formic, malic, nicotinic, and rosmarinic acids demonstrate slightly weaker activity (mean MICs 1–2 mg/mL). Other acids have moderate or poor activity. The effectiveness of organic acids against foodborne fungi is weaker than against bacteria. Most acids require high concentrations (from 10 to >100 mg/mL) to inhibit fungal growth effectively. The predicted LD50 of organic acids ranges from 48 to 5000 mg/kg. Those potentially safe as food preservatives (MIC < LD50) include ascorbic, chlorogenic, malic, nicotinic, rosmarinic, salicylic, succinic, tannic, and tartaric acids. The studied organic acids are not carcinogenic but many can cause adverse effects such as skin sensitization, eye irritation, and potential nephrotoxicity, hepatotoxicity, or neurotoxicity. Conclusions: Most of the investigated plant-derived organic acids exhibit good antibacterial activity and moderate or poor antifungal effects. Among 21 acids, only 9 appear to be safe as food preservatives (MIC < LD50). The relationship between MIC and LD50 is crucial in determining the suitability of organic acids as food preservatives, ensuring that they are effective against bacteria or fungi at concentrations that are not harmful to humans.
Advances in 2D Material Transfer Systems for van der Waals Heterostructure Assembly
Authors: Somphonsane Ratchanok , Buapan Kanokwan , Ramamoorthy Harihara
Published: July 2024

The assembly of van der Waals (vdW) heterostructures using 2D material transfer systems has revolutionized the field of materials science, enabling the development of novel electronic and optoelectronic devices and the probing of emergent phenomena. The innovative vertical stacking methods enabled by these 2D material transfer systems are central to constructing complex devices, which are often challenging to achieve with traditional bottom-up nanofabrication techniques. Over the past decade, vdW heterostructures have unlocked numerous applications leading to the development of advanced devices, such as transistors, photodetectors, solar cells, and sensors. However, achieving consistent performance remains challenging due to variations in transfer processes, contamination, and the handling of air-sensitive materials, among other factors. Several of these challenges can be addressed through careful design considerations of transfer systems and through innovative modifications. This mini-review critically examines the current state of transfer systems, focusing on their design, cost-effectiveness, and operational efficiency. Special emphasis is placed on low-cost systems and glovebox integration essential for handling air-sensitive materials. We highlight recent advancements in transfer systems, including the integration of cleanroom environments within gloveboxes and the advent of robotic automation. Finally, we discuss ongoing challenges and the necessity for further innovations to achieve reliable, cleaner, and scalable vdW technologies for future applications.
Application of Microbial-Induced Carbonate Precipitation for Disintegration Control of Granite Residual Soil
Authors: Luo Xiaoyan , Feng Yingqi , Li Chunjun , Liu Weiping
Published: July 2024

Granite residual soil is widely distributed in Southeastern China. Such soils exhibit mechanical characteristics such as loose, rich cracks and easy disintegration, resulting in severe soil erosion disasters under rainfall conditions. Microbial-induced carbonate precipitation (MICP) is a green alternative for soil stabilization. In this study, a new strategy for the disintegration control of granite residual soil using MICP technology is proposed. The effects of the bacterial solution concentration, the cementation solution concentration, and the treatment cycle are investigated through a disintegration test. The optimal treatment parameters for granite residual soil using MICP technology are determined by analyzing the disintegration processes and residual quality indicators of disintegration. The results show that the treated samples have three types of disintegration: complete disintegration, incomplete disintegration, and non-disintegration. The precipitated calcium carbonate (CaCO3) bonds the soil particles and fills the pores. Taking into account the effectiveness and cost and a bacterial solution concentration OD600 = 0.75, five cycles of MICP treatment with a cementation solution concentration of 1.2 mol/L is optimal for the disintegration control of granite residual soil. The cementation-action effects of CaCO3 are verified through scanning electron microscopy (SEM) tests with an energy-dispersive X-ray (EDX) spectroscope. These findings suggest that MICP is a promising candidate to control the disintegration of granite residual soil.
VEPO-S2S: A VEssel Portrait Oriented Trajectory Prediction Model Based on S2S Framework
Authors: Yang Xinyi , Han Zhonghe , Zhang Yuanben , Liu Hu , Liu Siye , Ai Wanzheng , Liu Junyi
Published: July 2024

The prediction of vessel trajectories plays a crucial role in ensuring maritime safety and reducing maritime accidents. Substantial progress has been made in trajectory prediction tasks by adopting sequence modeling methods, containing recurrent neural networks (RNNs) and sequence-to-sequence networks (Seq2Seq). However, (1) most of these studies focus on the application of trajectory information, such as the longitude, latitude, course, and speed, while neglecting the impact of differing vessel features and behavioral preferences on the trajectories. (2) Challenges remain in acquiring these features and preferences, as well as enabling the model to sensibly integrate and efficiently express them. To address the issue, we introduce a novel deep framework VEPO-S2S, consisting of a Multi-level Vessel Trajectory Representation Module (Multi-Rep) and a Feature Fusion and Decoding Module (FFDM). Apart from the trajectory information, we first defined the Multi-level Vessel Characteristics in Multi-Rep, encompassing Shallow-level Attributes (vessel length, width, draft, etc.) and Deep-level Features (Sailing Location Preference, Voyage Time Preference, etc.). Subsequently, Multi-Rep was designed to obtain trajectory information and Multi-level Vessel Characteristics, applying distinct encoders for encoding. Next, the FFDM selected and integrated the above features from Multi-Rep for prediction by employing both a priori and a posteriori mechanisms, a Feature Fusion Component, and an enhanced decoder. This allows the model to efficiently leverage them and enhance overall performance. Finally, we conducted comparative experiments with several baseline models. The experimental results demonstrate that VEPO-S2S is both quantitatively and qualitatively superior to the models.
Enhancing Robot Behavior with EEG, Reinforcement Learning and Beyond: A Review of Techniques in Collaborative Robotics
Authors: Gonzalez-Santocildes Asier , Vazquez Juan-Ignacio , Eguiluz Andoni
Published: July 2024

Collaborative robotics is a major topic in current robotics research, posing new challenges, especially in human–robot interaction. The main aspect in this area of research focuses on understanding the behavior of robots when engaging with humans, where reinforcement learning is a key discipline that allows us to explore sophisticated emerging reactions. This review aims to delve into the relevance of different sensors and techniques, with special attention to EEG (electroencephalography data on brain activity) and its influence on the behavior of robots interacting with humans. In addition, mechanisms available to mitigate potential risks during the experimentation process such as virtual reality are also be addressed. In the final part of the paper, future lines of research combining the areas of collaborative robotics, reinforcement learning, virtual reality, and human factors are explored, as this last aspect is vital to ensuring safe and effective human–robot interactions.