bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of Artificially Modified Microbial Communities on the Root Growth and Development of Tall Fescue in Nutrient-Poor Rubble Soil
Authors: Luo Zhengyu , Han Hongrui , Yao Hui , Yan Guoru , Bai Jinxin , Shi Lihao , Pei Xiangjun , Li Jingji , Li Qiang
Published: November 2024

The granite rubble soil produced through excavation during construction is nutrient-poor and has a simplified microbial community, making it difficult for plants to grow and increasing the challenges of ecological restoration. Recent studies have demonstrated that microbial inoculants significantly promote plant growth and are considered a potential factor influencing root development. Microorganisms influence root development either directly or indirectly, forming beneficial symbiotic relationships with plant roots. However, the mechanisms by which microorganisms affect root development and root anatomy, as well as the dynamics of soil microbial communities following the artificial application of microbial inoculants, remain unclear. This experiment utilized granite rubble soil from construction excavation in a pot trial, implementing five different treatment methods. After the fast-growing grass species tall fescue (Festuca arundinacea) was planted, four growth-promoting microbial inoculants—Bacillus subtilis (K), Bacillus amyloliquefaciens (JD), Aspergillus niger (H), and Trichoderma harzianum (HC)—were applied to the soil in the pots. These treatments were compared with a control group (CK) that received no microbial inoculant. At 120 days of plant growth, the composition of the soil microbial community, biomass, root structure, and root anatomy were measured for each treatment group. This analysis aimed to explore the effects of different microbial treatments on the microbial communities and root development of Festuca arundinacea root soil. The study found that the addition of microbial inoculants reduced the number of microbial operational taxonomic units (OTUs) of bacteria and fungi in the soil, affecting both the marker species and their abundance at the phylum level. Additionally, microbial inoculants promoted the development of the tall fescue root structure, increasing metrics such as the total root length, root surface area, root volume, and root-to-shoot ratio per plant. Redundancy analysis (RDA) revealed that the area ratios of various components in the root anatomy of tall fescue’s primary roots, such as the root cortex area, stele area, and the number of lateral roots, were influenced by Proteobacteria. Mortierellomycota was found to affect the root epidermis area.
Improving Thermo-Sealing of Poly(3-hydroxybutyrate-co-3-hydroxyvalerate) by Blending with Polycaprolactone
Authors: Moll Eva , Chiralt Amparo
Published: November 2024

Poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) is a biodegradable biopolymer from the PHAs family that has potential to replace conventional plastics and reduce plastic pollution. However, PHBV has thermo-sealability issues, making it challenging to use for bags. Blending it with polycaprolactone (PCL) could address this but may alter the barrier properties of the films, affecting their effectiveness as food packaging material. This study examined the properties and heat-sealing capacity of PHBV/PCL blend films (ratios: 60/40, 50/50, and 40/60), obtained by melt blending and compression moulding. Both polymers are immiscible and were in separated phases; the continuous phase was PHBV in the 60/40 blend and PCL in the 40/60 blend, while the 50/50 sample exhibited interpenetrating bicontinuous phases of both polymers. The permeability to water vapour, oxygen, and D-limonene increased as the PCL content rose, especially when it formed the continuous phase in the matrix. The elastic modulus and resistance to break decreased, while extensibility increased, more markedly when PCL was the continuous phase. However, the continuity of PCL phase provided the films with better thermal adhesion and seal strength. The 50/50 blend showed the best balance between heat sealability and barrier properties, making it the most suitable for food packaging in sealed bags.
Enhancing the Stability and Anticancer Activity of Escherichia coli Asparaginase Through Nanoparticle Immobilization: A Biotechnological Perspective on Nano Chitosan
Authors: Alharthi Fahad , Althagafi Hussam A. , Jafri Ibrahim , Oyouni Atif Abdulwahab A. , Althaqafi Mohammed M. , Al-Hazmi Nawal E. , Al Hijab Layla Yousif Abdullah , Naguib Deyala M.
Published: November 2024

There is a shortage in the experimental research directly comparing the effectiveness of different nanoparticles in boosting asparaginase (ASNase) activity. This study assessed the impact of various nanoparticles on enhancing ASNase activity, stability, and anticancer effects through immobilization. Escherichia coli ASNase was immobilized on different nanoparticles, and its efficiency was measured. The research included analyzing the enzyme’s secondary structure, stability, activity at different temperatures, kinetic parameters, shelf life, and activity in blood serum. The anticancer efficacy was determined by measuring the IC50. The study also investigated the anticancer mechanisms by examining the enzyme’s toxicity on cancer cells, focusing on apoptosis indicators like nuclear intensity, membrane permeability, mitochondrial membrane permeability, and cytochrome c release. Among the tested nanoparticles, nano chitosan yielded the best improvements. ASNase immobilized on nano chitosan reached 90% immobilization efficiency fastest among the studied nanoparticles, achieving this within 72 h, whereas other nanoparticles took 120 h. Immobilization modified ASNase’s secondary structure by increasing alpha helices and reducing random coils, with nanochitosan and magnetic iron oxide showing the most pronounced effects. Immobilized ASNase exhibited enhanced activity, stability across temperature (widest with nanochitosan, 25–65 °C), and a broader optimal pH range compared to the free enzyme, with a Km of 1.227 mM and a Vmax of 454.54 U/mg protein. Notably, the nano-chitosan-immobilized ASNase retained over 85% of its activity after 9 months of storage and maintained high activity in blood serum. This improved stability and activity translated into the highest anticancer activity (Lowest IC50) and was more effective than doxorubicin in disrupting cancer cell structures.
Analyzing Thermal Degradation Effects on Devulcanized GTR-Based NR/SBR/NBR Rubber Compounds Reinforced with SiO2 Particles
Authors: Colom Xavier , Farrés Laia , Mujal Ramon , Wang Shifeng , Cañavate Javier
Published: November 2024

The large number of tires produced annually demands new recycling methods. A key challenge associated with recycling elastomers is their crosslinking structure that prevents fusion. It is possible to reverse crosslinking through a process called devulcanization. Devulcanized elastomers can be blended with fresh rubber and revulcanized for reuse. This paper examines samples made from natural rubber (NR), styrene–butadiene rubber (SBR), and nitrile butadiene rubber (NBR), blended with varying proportions of devulcanized ground tire rubber (dGTR) and newly revulcanized rubber. SiO2, commonly present in tire formulations, is also added. Samples of these materials, with 0, 10, 20, and 40 phr of dGTR are subjected to accelerated degradation for 0, 30, 60, 120, and 240 h. The effects of this treatment, the influence of SiO2, and the presence of a silane-based devulcanization agent (TESPT) that promotes the interaction between the rubber and silica, are analyzed at the microstructural level (FTIR, TGA, SEM) and through mechanical properties testing. The microstructural results of the spectroscopy and thermal analysis show that interactions between dGTR, silica, and silane compounds form aggregates that impact the material properties and degradation of the tires. Mechanically, when the sample contained up to 20 phr of dGTR, the compound presented a more brittle behavior, due to the crosslinking induced by the degradation.
Effect of Density of Acrylic Acid Ester on Sulfonate-Modified Polycarboxylate Superplasticizers on Cementitious Systems
Authors: Xie Yuxiang , Zhang Zixuan , Chen Yujie , Ren Xu , Liu Yuan , Tao Jia , Liu Runxia , Li Min , Li Ziwei
Published: November 2024

To tackle high viscosity in fresh concretes, especially high-performance concrete, the research of polycarboxylate superplasticizers (PCEs) is relevant. By designing the molecular structure of PCEs, problems such as pumping difficulties in high viscosity of high-performance concrete can be solved. Therefore, in this paper, a suite of novel viscosity reducing PCEs containing sulfonic acid groups and different acrylate densities were synthesized on the basis of inventive molecular structure design, and characterized to determine the predicted structure. The maximum adsorption, the best fluidity, and the Minimum zeta potential value can be seen for PCEs with a small number of ester groups (PCE-MA0.5) due to the combination of the rigidity of its backbone and the density of the adsorption groups. Moreover, the investigation of working mechanism showed the introduction of ester groups can significantly reduce viscosity, but also reduces the adsorption capacity. This research aims to propose a feasible method for synthesizing PCE with superior processability and viscosity reduction capability in cement and concrete.
Preliminary In Vitro Assessment of Whey Protein Isolate Hydrogel with Cannabidiol as a Potential Hydrophobic Oral Drug Delivery System for Colorectal Cancer Therapy
Authors: Baines Daniel K. , Wright Karen , Douglas Timothy E. L.
Published: November 2024

Colorectal cancer (CRC) is the second global cause of cancer morbidity. Often, potent CRC drugs fail to reach the market, due to the molecule having low solubility levels. Therefore, there is a need to develop a viable, targeted delivery system for hydrophobic drugs. Whey protein isolate (WPI), in the form of hydrogels, has demonstrated loadability with hydrophobic molecules. Hydrophobic cannabidiol (CBD) has demonstrated potential in inhibiting and suppressing CRC tumour growth. Therefore, in this study, WPI hydrogels were assessed as a novel oral hydrophobic drug delivery vehicle, using CBD as a model drug. The hydrogels were analysed in conditions consistent with the alimentary tract. The investigation was performed at pH 2 (stomach), pH 7 (small intestines) and pH 9 (large intestines) and using the enzymes pepsin (stomach) and protease (small and large intestines) to simulate the digestive environment. Polymer swelling assays demonstrated that the swelling potential of the hydrogels was strongly dependent on pH. At pH 2, hydrogels decreased in mass, losing around 10% of their initial mass, while hydrogels in a pH 9 environment increased in mass by approximately 50%. However, the enzymatic degradation of the hydrogels at pH 2 (pepsin, stomach), pH 7 (protease, small intestines) and pH 9 (protease, large intestines) was more pronounced in the neutral–alkaline pH range. Pepsin at pH 2 had no significant effect on the hydrogels. In contrast, protease at pH 9 significantly degraded the hydrogels, resulting in a mass loss of 30–40% from the initial mass. The results suggesting a higher rate of degradation in the intestines rather than in the stomach. Furthermore, CBD release, analysed with U.V. spectroscopy, demonstrated a higher release rate in pH conditions associated with the intestines (pH 7 and pH 9) rather than the stomach (pH 2), suggesting a higher rate of CBD release in regions of the digestive tract affected by CRC. Significantly, the hydrogels significantly reduced the viability of HT29 CRC cells. This study demonstrates the potential of the utilisation of WPI hydrogels as an oral hydrophobic drug delivery system.
Clinical and Psychological Disturbances of Myasthenia Gravis: How Anxiety, Sleep Quality, and Thymectomy Interact
Authors: García-Sanchoyerto Maddalen , Amayra Imanol , Ruiz de Lazcano Aitana , Rodríguez Alicia Aurora , Anguiano Samuel , Salgueiro Monika
Published: November 2024

Background: Myasthenia gravis (MG) is an autoimmune neuromuscular disease characterized by fatigue and muscle weakness. Previous research has noted a high incidence of psychological problems, particularly anxiety and depression, as well as alterations in sleep quality among patients with MG. The aim of this study was to assess depression, anxiety, sleep problems, and fatigue in individuals with MG and to analyze the relationships among these variables, as well as to determine the influence of corticosteroid use and thymectomy. Methods: The sample consisted of 72 participants, 42 belonging to the clinical group with MG and 30 healthy participants to the control group. The instruments used were Hospital Anxiety and Depression Scale (HADS), Pittsburgh Sleep Quality Index (PSQI), and Fatigue Severity Scale (FSS). Results: The results showed that MG patients exhibited higher levels of fatigue, anxiety, depression, and poorer sleep quality compared to the control group (p < 0.05). Significant differences in sleep quality and anxiety levels were observed between thymectomized and non-thymectomized patients. Additionally, a positive correlation was found between anxiety symptoms and sleep quality. Conclusions: The study demonstrated the presence of anxious and depressive symptoms in patients with MG, as well as poorer sleep quality and increased fatigue, suggesting that MG affects not only neuromuscular function but also the psychological well-being of patients.
Importance of the CuAAC Reaction in the Synthesis of Platinum Complexes with 1,4-Disubstituted-1H-1,2,3-triazoles: A Review of Their Anticancer Activity
Authors: Hernández-Romero Delia , Rosete-Luna Sharon , Méndez-Bolaina Enrique , Herrera-Cogco Elena de la C. , Amador-Gómez Luis P. , Soto-Contreras Anell , Rivera-Villanueva José M. , Colorado-Peralta Raúl
Published: November 2024

Despite multiple advances in treatment and prevention, cancer remains one of the leading causes of death worldwide. Chemotherapy remains the most effective method for cancer treatment. However, commercial chemotherapeutic drugs have limited efficacy, severe side effects, and acquired resistance. Therefore, the scientific community has devoted a great effort to designing new, more effective, and cheaper drugs. In this sense, copper-catalyzed azide-alkyne cycloaddition reactions (CuAAC) provide 1,4-disubstituted 1H-1,2,3-triazoles in high yields without forming by-products. This reaction allows the easy, efficient, functional, ordered, rapid, selective, and specific joining of small molecules, giving rise to more complex molecules. The CuACC reaction simplifies the synthesis processes, accelerating the discovery of new chemotherapeutic agents by allowing the joining of commercial platinum drugs, slightly altering their structure, or creating new molecules with improved properties. This work shows the importance of CuAAC reactions in the search for new metallodrugs with possible anticancer activity.
Faith and Reality: Marx’s Understanding of an Ontological Argument in Reference to Kant
Authors: Feng Chuantao , Li Jianmei
Published: November 2024

In the Appendix to his dissertation, with respect to Kant, Marx provided an in-depth analysis of the ontological proof of the existence of God. Here, we explore this analysis in detail. Firstly, we argue that “faith” (Glaube) is the foundation of Marx’s interpretation of the ontological proof of God and its difference from that of Kant. On one hand, Marx’s understanding of the ontological argument can be called the “Realization of Belief” (RB). The object of faith is, for the believer, endowed with some kind of real power; that is to say, the object is real for the believer who has faith in it. This line of argument differs from the Kantian Hypostatization of Idea (HI), which attempts to prove the transcendental God as an a priori concept that implies itself as an empirical being or a posteriori phenomenon. On the other hand, “faith” was also the foundation upon which Marx based his interpretation of Kant. Subsequently, in the context of Marx’s dissertation, we clarify the connotations of “reality”, “belief”, and “faith”. “Reality” refers to objects exerting a real force that works on those with faith in their imagination while not necessarily entailing that the imagined object of belief is an empirical one. “Belief” refers either to an opinion (doxa) based on “faith” or to an idea without necessity in the sense of David Hume’s philosophy. As for “faith”, Marx uses this term in the Protestant sense, meaning obedience to the object one believes in, where obedience refers to the absence of self-righteousness. In a state of “faith”, the faithful one possesses “the assurance of things hoped for, the conviction of things not seen” (Heb. 11:1). Finally, on the basis of these two considerations, we reflect on the existing academic interpretations of the theme of this article and highlight some differences between these interpretations and the present article. We show that existing interpretations of Marx’s summary either conflate the two theories of the HI and RB or miss the importance of “faith” in Marx’s arguments. We conclude that Marx, at the time of his dissertation, interpreted the ontological argument by way of the RB, which was based on the concept of “faith”, and that his critical understanding of Kant’s refutation of the ontological proof was founded on the same interpretation.
The Phenomenon of Religious Goods Consumption Among Young Koreans
Authors: Lee Min Hyoung
Published: November 2024

One of the most noteworthy consumer cultures among young Koreans today is the growing interest in and consumption of religious “goods”. Interestingly, the consumption of religious goods has been observed to foster interest in religion itself among the younger generation. This phenomenon is undoubtedly significant when viewed in the context of the social climate in Korea, where there is a notable decline in religious interest with age. It can be argued that religious goods possess a deeper significance than that of mere objects of consumption. The objective of this study is to examine the phenomenon of religious goods consumption and to discuss the religiosity that it reflects in the context of the 21st century. Initially, this study examines the significance of religious goods and defines the act of consuming religious goods as a religious ritual. The mediated nature of religious goods is explored in greater depth to ascertain how they are influencing the religiousness of contemporary individuals. By connecting theories of the sociology of religion with the materiality of religions, this study reveals that the consumption of religious goods is a religious activity that extends beyond mere consumption. It also discusses the potential for the kinds of cultural and religious phenomena it could cause to develop in the future.
Food Waste to Food Security: Transition from Bioresources to Sustainability
Authors: Sarangi Prakash Kumar , Pal Priti , Singh Akhilesh Kumar , Sahoo Uttam Kumar , Prus Piotr
Published: November 2024

The transition from food waste to food security is a critical component of sustainability efforts. This approach focuses on repurposing organic waste products generated throughout the food supply chain into valuable resources. Food waste, encompassing everything from agricultural residues to post-consumer waste, represents a significant untapped potential that can be harnessed to enhance food security. By implementing strategies such as composting, bioconversion, and innovative recycling technologies, biowastes can be transformed into fertilizers, animal feed, and even new food products, thus closing the loop in the food system and aiding sustainable solutions for waste valorization. This transition not only addresses environmental concerns by reducing landfill waste and greenhouse gas emissions but also contributes to economic sustainability by creating new opportunities within the food production and waste management sectors. Ultimately, transforming food waste into a resource aligns with the broader goals of a circular economy, ensuring a sustainable, resilient, and food-secure future.
A Case Study on the Impact of Boundary Layer Turbulence on Convective Clouds in the Eastern Margin of the Tibetan Plateau
Authors: Wang Ting , Li Maoshan , Jiang Yonghao , Liu Yuchen , Gong Ming , Wang Shaoyang , Sun Peng , Ma Yaoming , Sun Fanglin
Published: November 2024

In this study, we utilized ECMWF Reanalysis of the Global Climate at Atmospheric Resolution 5 (ERA5) data, FengYun-4B satellite (FY-4B) data, a Wind3D 6000 Three-Dimensional Scanning Laser Wind Radar, and raindrop spectrum data to analyze the circulation background, convective cloud changes, boundary layer wind field variations, and precipitation drop size spectrum characteristics of a severe convective rainfall process that occurred on 3 April 2024 in the eastern margin of the Tibetan Plateau. The findings indicated the following: (1) The rain belt of this precipitation event showed a southwest–northeast trend. During the vigorous development of convection, the rainfall intensity and total precipitation at the station increased, with a wider raindrop spectrum, and the raindrop spectrum of this precipitation process was unimodal. (2) On 3 April, the interaction between the eastward movement of the plateau trough at 500 hPa and the upper-level jet stream at 200 hPa in the eastern Tibetan Plateau and the Sichuan Basin area, along with the necessary conditions for precipitation, such as energy and moisture, led to severe convective rainfall. (3) This intense convective precipitation process was caused by the vigorous convective clouds that developed in the eastern part of the Tibetan Plateau. As these clouds developed and moved eastward out of the plateau, they precipitated with increased turbulence intensity at the station, leading to the generation of intense convective activities at the site. (4) One hour before the precipitation, there were significant increases in horizontal wind speed, vertical air velocity, and turbulence intensity within the boundary layer, and there were also significant changes in the horizontal wind direction. The results obtained can provide important theoretical references for the prediction of severe convective rainfall and the performance of numerical simulations thereon.
Deep Learning for Weed Detection and Segmentation in Agricultural Crops Using Images Captured by an Unmanned Aerial Vehicle
Authors: Silva Josef Augusto Oberdan Souza , Siqueira Vilson Soares de , Mesquita Marcio , Vale Luís Sérgio Rodrigues , Marques Thiago do Nascimento Borges , Silva Jhon Lennon Bezerra da , Silva Marcos Vinícius da , Lacerda Lorena Nunes , Oliveira-Júnior José Francisco de , Lima João Luís Mendes Pedroso de , Oliveira Henrique Fonseca Elias de
Published: November 2024

Artificial Intelligence (AI) has changed how processes are developed, and decisions are made in the agricultural area replacing manual and repetitive processes with automated and more efficient ones. This study presents the application of deep learning techniques to detect and segment weeds in agricultural crops by applying models with different architectures in the analysis of images captured by an Unmanned Aerial Vehicle (UAV). This study contributes to the computer vision field by comparing the performance of the You Only Look Once (YOLOv8n, YOLOv8s, YOLOv8m, and YOLOv8l), Mask R-CNN (with framework Detectron2), and U-Net models, making public the dataset with aerial images of soybeans and beans. The models were trained using a dataset consisting of 3021 images, randomly divided into test, validation, and training sets, which were annotated, resized, and increased using the Roboflow application interface. Evaluation metrics were used, which included training efficiency (mAP50 and mAP50-90), precision, accuracy, and recall in the model’s evaluation and comparison. The YOLOv8s variant achieved higher performance with an mAP50 of 97%, precision of 99.7%, and recall of 99% when compared to the other models. The data from this manuscript show that deep learning models can generate efficient results for automatic weed detection when trained with a well-labeled and large set. Furthermore, this study demonstrated the great potential of using advanced object segmentation algorithms in detecting weeds in soybean and bean crops.
Enhancing Binary Convolutional Neural Networks for Hyperspectral Image Classification
Authors: Tang Xuebin , Zhang Ke , Zhou Xiaolei , Zeng Lingbin , Huang Shan
Published: November 2024

Hyperspectral remote sensing technology is swiftly evolving, prioritizing affordability, enhanced portability, seamless integration, sophisticated intelligence, and immediate processing capabilities. The leading model for classifying hyperspectral images, which relies on convolutional neural networks (CNNs), has proven to be highly effective when run on advanced computing platforms. Nonetheless, the high degree of parameterization inherent in CNN models necessitates considerable computational and storage resources, posing challenges to their deployment in processors with limited capacity like drones and satellites. This paper focuses on advancing lightweight models for hyperspectral image classification and introduces EBCNN, a novel binary convolutional neural network. EBCNN is designed to effectively regulate backpropagation gradients and minimize gradient discrepancies to optimize BNN performance. EBCNN incorporates an adaptive gradient scaling module that utilizes a multi-scale pyramid squeeze attention (PSA) mechanism during the training phase, which can adjust training gradients flexibly and efficiently. Additionally, to address suboptimal training issues, EBCNN employs a dynamic curriculum learning strategy underpinned by a confidence-aware loss function, Superloss, enabling progressive binarization and enhancing its classification effectiveness. Extensive experimental evaluations conducted on five esteemed public datasets confirm the effectiveness of EBCNN. These analyses highlight a significant enhancement in the classification accuracy of hyperspectral images, achieved without incurring additional memory or computational overheads during the inference process.
High-Precision and High-Reliability Positioning, Navigation, and Timing: Opportunities and Challenges
Authors: Zhang Zhetao , Xiao Guorui , Nie Zhixi , Ferreira Vagner , Casula Giuseppe
Published: November 2024

The research scope of the papers published in this Special Issue mainly focuses on high-precision and high-reliability positioning, navigation, and timing (PNT) with Global Navigation Satellite System (GNSS) or multi-source sensors, resilient PNT with GNSSs or multi-source sensors in challenging environments, integrated PNT with GNSSs and multi-sensor systems, applications of PNT with GNSSs or multi-source sensors, etc.
MugenNet: A Novel Combined Convolution Neural Network and Transformer Network with Application in Colonic Polyp Image Segmentation
Authors: Peng Chen , Qian Zhiqin , Wang Kunyu , Zhang Lanzhu , Luo Qi , Bi Zhuming , Zhang Wenjun
Published: November 2024

Accurate polyp image segmentation is of great significance, because it can help in the detection of polyps. Convolutional neural network (CNN) is a common automatic segmentation method, but its main disadvantage is the long training time. Transformer is another method that can be adapted to the automatic segmentation method by employing a self-attention mechanism, which essentially assigns different importance weights to each piece of information, thus achieving high computational efficiency during segmentation. However, a potential drawback with Transformer is the risk of information loss. The study reported in this paper employed the well-known hybridization principle to propose a method to combine CNN and Transformer to retain the strengths of both. Specifically, this study applied this method to the early detection of colonic polyps and to implement a model called MugenNet for colonic polyp image segmentation. We conducted a comprehensive experiment to compare MugenNet with other CNN models on five publicly available datasets. An ablation experiment on MugenNet was conducted as well. The experimental results showed that MugenNet can achieve a mean Dice of 0.714 on the ETIS dataset, which is the optimal performance on this dataset compared to other models, with an inference speed of 56 FPS. The overall outcome of this study is a method to optimally combine two methods of machine learning which are complementary to each other.
A Quantitative Method to Guide the Integration of Textile Inductive Electrodes in Automotive Applications for Respiratory Monitoring
Authors: Duverger James Elber , Bellemin Victor , Forcier Patricia , Decaens Justine , Gagnon Ghyslain , Saidi Alireza
Published: November 2024

Induction-based breathing sensors in automobiles enable unobtrusive respiratory rate monitoring as an indicator of a driver’s alertness and health. This paper introduces a quantitative method based on signal quality to guide the integration of textile inductive electrodes in automotive applications. A case study with a simplified setup illustrated the ability of the method to successfully provide basic design rules about where and how to integrate the electrodes on seat belts and seat backs to gather good quality respiratory signals in an automobile. The best signals came from the subject’s waist, then from the chest, then from the upper back, and finally from the lower back. Furthermore, folding the electrodes before their integration on a seat back improves the signal quality for both the upper and lower back. This analysis provided guidelines with three design rules to increase the chance of acquiring good quality signals: (1) use a multi-electrode acquisition approach, (2) place the electrodes in locations that maximize breathing-induced body displacement, and (3) use a mechanical amplifying method such as folding the electrodes in locations with little potential for breathing-induced displacement.
On Construction of Real-Time Monitoring System for Sport Cruiser Motorcycles Using NB-IoT and Multi-Sensors
Authors: Kristiani Endah , Yu Tzu-Hao , Yang Chao-Tung
Published: November 2024

This study leverages IoT technology to develop a real-time monitoring system for large motorcycles. We collaborated with professional mechanics to define the required data types and system architecture, ensuring practicality and efficiency. The system integrates the NB-IoT for efficient remote data transmission and uses MQTT for optimized messaging. It also includes advanced database management and intuitive data visualization for enhancing the user experience. For hardware installation, the system follows strict guidelines to avoid damaging the motorcycle’s original structure, comply with Taiwan’s legal standards, and prevent unauthorized modifications. The implementation of this real-time monitoring system is anticipated to significantly reduce safety risks associated with mechanical failures as it continuously monitors inappropriate driving behaviors and detects mechanical abnormalities in real time. The study indicates that the integration of advanced technologies, such as the NB-IoT and multi-sensor systems, can lead to improved driving safety and operational efficiency. Furthermore, the research suggests that the system’s ability to provide instant notifications and alerts through the platforms’ instant messaging can enhance user responsiveness to potential hazards, thereby contributing to a safer riding experience.
Assessment of the Aging State for Transformer Oil-Barrier Insulation by Raman Spectroscopy and Optimized Support Vector Machine
Authors: Liu Deliang , Lu Biao , Wu Wenping , Zhou Wei , Liu Wansu , Sun Yiye , Wu Shilong , Shi Guolong , Yuan Leiming
Published: November 2024

Accurate assessment of the aging state of transformer oil-barrier insulation is crucial for ensuring the safe and reliable operation of power systems. This study presents the development of indoor accelerated thermal aging experiments to simulate the degradation of oil-immersed barrier insulation within transformers. A series of samples reflecting various aging states was obtained and categorized into six distinct groups. Raman spectroscopy analytical technology was employed to characterize the information indicative of different aging states of the oil-immersed barrier insulation. The raw Raman spectra were processed using asymmetric reweighted penalty least squares to correct baseline shifts, Savitzky–Golay (S-G) smoothing to eliminate fluctuation noise, and principal component analysis (PCA) to reduce data dimensionality by extracting principal components. A support vector machine (SVM) classifier was developed to discriminate between the Raman spectra and category labels. The SVM parameters were optimized using grid search, particle swarm optimization (PSO), and genetic algorithm (GA), yielding the optimal parameters (C and gamma). Notably, the grid search method demonstrated high efficiency in identifying the best combination of SVM parameters (c and g). Comparative analyses with varying numbers of principal components in SVM classifiers revealed that incorporating an optimal subset of PCA features achieved the highest classification accuracy of 94.44% for external validation samples, with only eight samples being misclassified into adjacent categories. This study offers technical support and a theoretical foundation for the effective assessment of the aging state of oil-barrier type insulation in transformers, contributing to the advancement of condition monitoring and maintenance strategies in power systems.
Development of Automated 3D LiDAR System for Dimensional Quality Inspection of Prefabricated Concrete Elements
Authors: Li Shuangping , Zhang Bin , Zheng Junxing , Wang Dong , Liu Zuqiang
Published: November 2024

The dimensional quality inspection of prefabricated concrete (PC) elements is crucial for ensuring overall assembly quality and enhancing on-site construction efficiency. However, current practices remain heavily reliant on manual inspection, which results in high operator dependency and low efficiency. Existing Light Detection and Ranging (LiDAR)-based methods also require skilled professionals for scanning and subsequent point cloud processing, thereby presenting technical challenges. This study developed a 3D LiDAR system for the automatic identification and measurement of the dimensional quality of PC elements. The system consists of (1) a hardware system integrated with camera and LiDAR components to acquire 3D point cloud data and (2) a user-friendly graphical user interface (GUI) software system incorporating a series of algorithms for automated point cloud processing using PyQt5. Field experiments comparing the system’s measurements with manual measurements on prefabricated bridge columns demonstrated that the system’s average measurement error was approximately 5 mm. The developed system can provide a quick, accurate, and automated inspection tool for dimensional quality assessment of PC elements, thereby enhancing on-site construction efficiency.
Innovative Seatbelt-Integrated Metasurface Radar for Enhanced In-Car Healthcare Monitoring
Authors: Asyari Rifa Atul Izza , Simorangkir Roy B. V. B. , Teichmann Daniel
Published: November 2024

This study introduces a novel seatbelt-integrated, non-invasive, beam-focusing metamaterial sensing system characterized by its thinness and flexibility. The system comprises a flexible transmitarray lens and an FMCW radar sensor, enabling the accurate detection and analysis of seatbelt usage and positioning through human tissue. The metasurface design remains effective even when subjected to different bending angles. Our system closely tracks heart rate and respiration, validated against standard reference methods, highlighting its potential for enhancing in-car healthcare monitoring. Experimental results demonstrate the system’s reliability in monitoring physiological signals within dynamic vehicular environments.
A Large-Scale Building Unsupervised Extraction Method Leveraging Airborne LiDAR Point Clouds and Remote Sensing Images Based on a Dual P-Snake Model
Authors: Tian Zeyu , Fang Yong , Fang Xiaohui , Ma Yan , Li Han
Published: November 2024

Automatic large-scale building extraction from the LiDAR point clouds and remote sensing images is a growing focus in the fields of the sensor applications and remote sensing. However, this building extraction task remains highly challenging due to the complexity of building sizes, shapes, and surrounding environments. In addition, the discreteness, sparsity, and irregular distribution of point clouds, lighting, and shadows, as well as occlusions of the images, also seriously affect the accuracy of building extraction. To address the above issues, we propose a new unsupervised building extraction algorithm PBEA (Point and Pixel Building Extraction Algorithm) based on a new dual P-snake model (Dual Point and Pixel Snake Model). The proposed dual P-snake model is an enhanced active boundary model, which uses both point clouds and images simultaneously to obtain the inner and outer boundaries. The proposed dual P-snake model enables interaction and convergence between the inner and outer boundaries to improve the performance of building boundary detection, especially in complex scenes. Using the dual P-snake model and polygonization, this proposed PBEA can accurately extract large-scale buildings. We evaluated our PBEA and dual P-snake model on the ISPRS Vaihingen dataset and the Toronto dataset. The experimental results show that our PBEA achieves an area-based quality evaluation metric of 90.0% on the Vaihingen dataset and achieves the area-based quality evaluation metric of 92.4% on the Toronto dataset. Compared with other methods, our method demonstrates satisfactory performance.
Determining the Level of Threat in Maritime Navigation Based on the Detection of Small Floating Objects with Deep Neural Networks
Authors: Łącki Mirosław
Published: November 2024

The article describes the use of deep neural networks to detect small floating objects located in a vessel’s path. The research aimed to evaluate the performance of deep neural networks by classifying sea surface images and assigning the level of threat resulting from the detection of objects floating on the water, such as fishing nets, plastic debris, or buoys. Such a solution could function as a decision support system capable of detecting and informing the watch officer or helmsman about possible threats and reducing the risk of overlooking them at a critical moment. Several neural network structures were compared to find the most efficient solution, taking into account the speed and efficiency of network training and its performance during testing. Additional time measurements have been made to test the real-time capabilities of the system. The research results confirm that it is possible to create a practical lightweight detection system with convolutional neural networks that calculates safety level in real time.
A Multivariable Probability Density-Based Auto-Reconstruction Bi-LSTM Soft Sensor for Predicting Effluent BOD in Wastewater Treatment Plants
Authors: Li Wenting , Li Yonggang , Li Dong , Zhou Jiayi
Published: November 2024

The precise detection of effluent biological oxygen demand (BOD) is crucial for the stable operation of wastewater treatment plants (WWTPs). However, existing detection methods struggle to meet the evolving drainage standards and management requirements. To address this issue, this paper proposed a multivariable probability density-based auto-reconstruction bidirectional long short-term memory (MPDAR-Bi-LSTM) soft sensor for predicting effluent BOD, enhancing the prediction accuracy and efficiency. Firstly, the selection of appropriate auxiliary variables for soft-sensor modeling is determined through the calculation of k-nearest-neighbor mutual information (KNN-MI) values between the global process variables and effluent BOD. Subsequently, considering the existence of strong interactions among different reaction tanks, a Bi-LSTM neural network prediction model is constructed with historical data. Then, a multivariate probability density-based auto-reconstruction (MPDAR) strategy is developed for adaptive updating of the prediction model, thereby enhancing its robustness. Finally, the effectiveness of the proposed soft sensor is demonstrated through experiments using the dataset from Benchmark Simulation Model No.1 (BSM1). The experimental results indicate that the proposed soft sensor not only outperforms some traditional models in terms of prediction performance but also excels in avoiding ineffective model reconstructions in scenarios involving complex dynamic wastewater treatment conditions.
A Novel Dataset and Detection Method for Unmanned Aerial Vehicles Using an Improved YOLOv9 Algorithm
Authors: Gao Depeng , Tang Jianlin , Li Hongqi , Wang Bingshu , Qiu Jianlin , Chen Shuxi , Mei Xiangxiang
Published: November 2024

With the growing popularity of unmanned aerial vehicles (UAVs), their improper use is significantly disrupting society. Individuals and organizations have been continuously researching methods for detecting UAVs. However, most existing detection methods fail to account for the impact of similar flying objects, leading to weak anti-interference capabilities. In other words, when such objects appear in the image, the detector may mistakenly identify them as UAVs. Therefore, this study aims to enhance the anti-interference ability of UAV detectors by constructing an anti-interference dataset comprising 5062 images. In addition to UAVs, this dataset also contains three other types of flying objects that are visually similar to the UAV targets: planes, helicopters, and birds. This dataset can be used in model training to help detectors distinguish UAVs from these nontarget objects and thereby improve their anti-interference capabilities. Furthermore, we propose an anti-interference UAV detection method based on YOLOv9-C in which the dot distance is used as an evaluation index to assign positive and negative samples. This results in an increased number of positive samples, improving detector performance in the case of small targets. The comparison of experimental results shows that the developed method has better anti-interference performance than other algorithms. The detection method and dataset used to test the anti-interference capabilities in this study are expected to assist in the development and validation of related research methods.
Overview of Key Techniques for In Situ Tests of Electromagnetic Radiation Emission Characteristics
Authors: Lu Zhonghao , Chen Yan , Xue Yunxiao
Published: November 2024

With the growing number of electronic devices loaded and increasing influence from electromagnetic interference, large-scale systems or platforms are confronted with increasingly severe electromagnetic compatibility challenges. Due to the vast size of these systems and the multitude of electronic devices they contain, standard laboratory environments are often inadequate for meeting test requirements. This paper reviews the state-of-art in the area of field measurement techniques related to the checking of electromagnetic compatibility, and the key technologies of electromagnetic interference filtering and wide-bandwidth, large-dynamic, and rapidly transient signal extraction in the measurement field are analyzed. The research status of electromagnetic interference suppression, transient and broadband measurement, and environmental interference suppression combined with time-domain fast measurement and other technologies are summarized and analyzed. Based on a comparative analysis of the aforementioned technologies, the future development trends of field measurement technology are also discussed.
Development of Advanced Positioning Techniques of UWB/Wi-Fi RTT Ranging for Personal Mobility Applications
Authors: Perakis Harris , Gikas Vassilis , Retscher Günther
Published: November 2024

“Smart” devices, such as contemporary smartphones and PDAs (Personal Digital Assistance), play a significant role in our daily live, be it for navigation or location-based services (LBSs). In this paper, the use of Ultra-Wide Band (UWB) and Wireless Fidelity (Wi-Fi) based on RTT (Round-Trip Time) measurements is investigated for pedestrian user localization. For this purpose, several scenarios are designed either using real observation or simulated data. In addition, the localization of user groups within a neighborhood based on collaborative navigation (CP) is investigated and analyzed. An analysis of the performance of these techniques for ranging the positioning estimation using different fusion algorithms is assessed. The methodology applied for CP leverages the hybrid nature of the range measurements obtained by UWB and Wi-Fi RTT systems. The proposed approach stands out due to its originality in two main aspects: (1) it focuses on developing and evaluating suitable models for correcting range errors in RF-based TWR (Two-Way Ranging) technologies, and (2) it emphasizes the development of a robust CP engine for groups of pedestrians. The results obtained demonstrate that a performance improvement with respect to position trueness for UWB and Wi-Fi RTT cases of the order of 74% and 54%, respectively, is achieved due to the integration of these techniques. The proposed localization algorithm based on a P2I/P2P (Peer-to-Infrastructure/Peer-to-Peer) configuration provides a potential improvement in position trueness up to 10% for continuous anchor availability, i.e., UWB known nodes or Wi-Fi access points (APs). Its full potential is evident for short-duration events of complete anchor loss (P2P-only), where an improvement of up to 53% in position trueness is achieved. Overall, the performance metrics estimated based on the extensive evaluation campaigns demonstrate the effectiveness of the proposed methodologies.
Developing a New Expected Goals Metric to Quantify Performance in a Virtual Reality Soccer Goalkeeping App Called CleanSheet
Authors: Simpson Matthew , Craig Cathy
Published: November 2024

As virtual reality (VR) sports training apps start to become more mainstream, it is important that human performance is measured from VR gameplay interaction data in a more meaningful way. CleanSheet is a VR training app that is played by over 100,000 users around the world. Many of those players are aspiring goalkeepers who want to use the app as a new way to train and improve their general goalkeeping performance. Whilst the leaderboards display how many shots players saved, these data do not take into account the difficulty of the shot faced. This study presents a regression model developed from a combination of existing expected goals (xG) models, goalkeeper performance metrics, and psychological research to produce a new shot difficulty metric called CSxG. Utilizing user save rate data as the target variable, a model was developed that incorporated three input variables relating to ball flight and in-goal positioning. Our analysis showed that the required rate of closure (RROC), adapted from Tau theory, was the most significant predictor of the proportion of goals conceded. A validation process evaluated the new xG model for CleanSheet by comparing its difficulty predictions against user performance data across players of varying skill levels. CSxG effectively predicted shot difficulty at the extremes but showed less accuracy for mid-range scores (0.4 to 0.8). Additional variables influencing shot difficulty, such as build-up play and goalpost size, were identified for future model enhancements. This research contributes to the advancement of predictive modeling in sports performance analysis, highlighting the potential for improved goalkeeper training and strategy development using VR technology.