bluuebunny/crossref_metadata_2025 huggingface dataset:
High-Order Neural-Network-Based Multi-Model Nonlinear Adaptive Decoupling Control for Microclimate Environment of Plant Factory
Authors: Wang Yonggang , Chen Ziqi , Jiang Yingchun , Liu Tan
Published: October 2023

Plant factory is an important field of practice in smart agriculture which uses highly sophisticated equipment for precision regulation of the environment to ensure crop growth and development efficiently. Environmental factors, such as temperature and humidity, significantly impact crop production in a plant factory. Given the inherent complexities of dynamic models associated with plant factory environments, including strong coupling, strong nonlinearity and multi-disturbances, a nonlinear adaptive decoupling control approach utilizing a high-order neural network is proposed which consists of a linear decoupling controller, a nonlinear decoupling controller and a switching function. In this paper, the parameters of the controller depend on the generalized minimum variance control rate, and an adaptive algorithm is presented to deal with uncertainties in the system. In addition, a high-order neural network is utilized to estimate the unmolded nonlinear terms, consequently mitigating the impact of nonlinearity on the system. The simulation results show that the mean error and standard error of the traditional controller for temperature control are 0.3615 and 0.8425, respectively. In contrast, the proposed control strategy has made significant improvements in both indicators, with results of 0.1655 and 0.6665, respectively. For humidity control, the mean error and standard error of the traditional controller are 0.1475 and 0.441, respectively. In comparison, the proposed control strategy has greatly improved on both indicators, with results of 0.0221 and 0.1541, respectively. The above results indicate that even under complex conditions, the proposed control strategy is capable of enabling the system to quickly track set values and enhance control performance. Overall, precise temperature and humidity control in plant factories and smart agriculture can enhance production efficiency, product quality and resource utilization.
A Blind Source Separation Method Based on Bounded Component Analysis Optimized by the Improved Beetle Antennae Search
Authors: Tang Mingyang , Wu Yafeng
Published: October 2023

Currently, the widely used blind source separation algorithm is typically associated with issues such as a sluggish rate of convergence and unstable accuracy, and it is mostly suitable for the separation of independent source signals. Nevertheless, source signals are not always independent of one another in practical applications. This paper suggests a blind source separation algorithm based on the bounded component analysis of the enhanced Beetle Antennae Search algorithm (BAS). Firstly, the restrictive assumptions of the bounded component analysis method are more relaxed and do not require the signal sources to be independent of each other, broadening the applicability of this blind source separation algorithm. Second, the objective function of bounded component analysis is optimized using the improved Beetle Antennae Search optimization algorithm. A step decay factor is introduced to ensure that the beetle does not miss the optimal point when approaching the target, improving the optimization accuracy. At the same time, since only one beetle is required, the optimization speed is also improved. Finally, simulation experiments show that the algorithm can effectively separate independent and dependent source signals and can be applied to blind source separation of images. Compared to traditional blind source separation algorithms, it has stronger universality and has faster convergence speed and higher accuracy compared to the original independent component analysis algorithm.
A Smart, Textile-Driven, Soft Exosuit for Spinal Assistance
Authors: Zhu Kefan , Phan Phuoc Thien , Sharma Bibhu , Davies James , Thai Mai Thanh , Hoang Trung Thien , Nguyen Chi Cong , Ji Adrienne , Nicotra Emanuele , La Hung Manh , Vo-Doan Tat Thang , Phan Hoang-Phuong , Lovell Nigel H. , Do Thanh Nho
Published: October 2023

Work-related musculoskeletal disorders (WMSDs) are often caused by repetitive lifting, making them a significant concern in occupational health. Although wearable assist devices have become the norm for mitigating the risk of back pain, most spinal assist devices still possess a partially rigid structure that impacts the user’s comfort and flexibility. This paper addresses this issue by presenting a smart textile-actuated spine assistance robotic exosuit (SARE), which can conform to the back seamlessly without impeding the user’s movement and is incredibly lightweight. To detect strain on the spine and to control the smart textile automatically, a soft knitting sensor that utilizes fluid pressure as a sensing element is used. Based on the soft knitting hydraulic sensor, the robotic exosuit can also feature the ability of monitoring and rectifying human posture. The SARE is validated experimentally with human subjects (N = 4). Through wearing the SARE in stoop lifting, the peak electromyography (EMG) signals of the lumbar erector spinae are reduced by 22.8% ± 12 for lifting 5 kg weights and 27.1% ± 14 in empty-handed conditions. Moreover, the integrated EMG decreased by 34.7% ± 11.8 for lifting 5 kg weights and 36% ± 13.3 in empty-handed conditions. In summary, the artificial muscle wearable device represents an anatomical solution to reduce the risk of muscle strain, metabolic energy cost and back pain associated with repetitive lifting tasks.
Classifying Tremor Dominant and Postural Instability and Gait Difficulty Subtypes of Parkinson’s Disease from Full-Body Kinematics
Authors: Gong N. Jabin , Clifford Gari D. , Esper Christine D. , Factor Stewart A. , McKay J. Lucas , Kwon Hyeokhyen
Published: October 2023

Characterizing motor subtypes of Parkinson’s disease (PD) is an important aspect of clinical care that is useful for prognosis and medical management. Although all PD cases involve the loss of dopaminergic neurons in the brain, individual cases may present with different combinations of motor signs, which may indicate differences in underlying pathology and potential response to treatment. However, the conventional method for distinguishing PD motor subtypes involves resource-intensive physical examination by a movement disorders specialist. Moreover, the standardized rating scales for PD rely on subjective observation, which requires specialized training and unavoidable inter-rater variability. In this work, we propose a system that uses machine learning models to automatically and objectively identify some PD motor subtypes, specifically Tremor-Dominant (TD) and Postural Instability and Gait Difficulty (PIGD), from 3D kinematic data recorded during walking tasks for patients with PD (MDS-UPDRS-III Score, 34.7 ± 10.5, average disease duration 7.5 ± 4.5 years). This study demonstrates a machine learning model utilizing kinematic data that identifies PD motor subtypes with a 79.6% F1 score (N = 55 patients with parkinsonism). This significantly outperformed a comparison model using classification based on gait features (19.8% F1 score). Variants of our model trained to individual patients achieved a 95.4% F1 score. This analysis revealed that both temporal, spectral, and statistical features from lower body movements are helpful in distinguishing motor subtypes. Automatically assessing PD motor subtypes simply from walking may reduce the time and resources required from specialists, thereby improving patient care for PD treatments. Furthermore, this system can provide objective assessments to track the changes in PD motor subtypes over time to implement and modify appropriate treatment plans for individual patients as needed.
HOMLC-Hyperparameter Optimization for Multi-Label Classification of Intrusion Detection Data for Internet of Things Network
Authors: Sharma Ankita , Rani Shalli , Sah Dipak Kumar , Khan Zahid , Boulila Wadii
Published: October 2023

The comparison of low-rank-based learning models for multi-label categorization of attacks for intrusion detection datasets is presented in this work. In particular, we investigate the performance of three low-rank-based machine learning (LR-SVM) and deep learning models (LR-CNN), (LR-CNN-MLP) for classifying intrusion detection data: Low Rank Representation (LRR) and Non-negative Low Rank Representation (NLR). We also look into how these models’ performance is affected by hyperparameter tweaking by using Guassian Bayes Optimization. The tests has been run on merging two intrusion detection datasets that are available to the public such as BoT-IoT and UNSW- NB15 and assess the models’ performance in terms of key evaluation criteria, including precision, recall, F1 score, and accuracy. Nevertheless, all three models perform noticeably better after hyperparameter modification. The selection of low-rank-based learning models and the significance of the hyperparameter tuning log for multi-label classification of intrusion detection data have been discussed in this work. A hybrid security dataset is used with low rank factorization in addition to SVM, CNN and CNN-MLP. The desired multilabel results have been obtained by considering binary and multi-class attack classification as well. Low rank CNN-MLP achieved suitable results in multilabel classification of attacks. Also, a Gaussian-based Bayesian optimization algorithm is used with CNN-MLP for hyperparametric tuning and the desired results have been achieved using c and γ for SVM and α and β for CNN and CNN-MLP on a hybrid dataset. The results show the label UDP is shared among analysis, DoS and shellcode. The accuracy of classifying UDP among three classes is 98.54%.
Cardiac Electromechanical Activity in Healthy Cats and Cats with Cardiomyopathies
Authors: Brložnik Maja , Lunka Ema , Avbelj Viktor , Nemec Svete Alenka , Domanjko Petrič Aleksandra
Published: October 2023

Optimal heart function depends on perfect synchronization between electrical and mechanical activity. In this pilot study, we aimed to investigate the electromechanical activity of the heart in healthy cats and cats with cardiomyopathy with phonocardiography (PCG) synchronized to an electrocardiography (ECG) pilot device. We included 29 cats (12 healthy cats and 17 cats diagnosed with cardiomyopathy) and performed a clinical examination, PCG synchronized with ECG and echocardiography. We measured the following durations with the pilot PCG device synchronized with ECG: QRS (ventricular depolarization), QT interval (electrical systole), QS1 interval (electromechanical activation time (EMAT)), S1S2 (mechanical systole), QS2 interval (electrical and mechanical systole) and electromechanical window (end of T wave to the beginning of S2). The measured parameters did not differ between healthy cats and cats with cardiomyopathy; however, in cats with cardiomyopathy, EMAT/RR, QS2/RR and S1S2/RR were significantly longer than in healthy cats. This suggests that the hypertrophied myocardium takes longer to generate sufficient pressure to close the mitral valve and that electrical systole, i.e., depolarization and repolarization, and mechanical systoles are longer in cats with cardiomyopathy. The PCG synchronized with the ECG pilot device proved to be a valuable tool for evaluating the electromechanical activity of the feline heart.
Hybrid Approach to Colony-Forming Unit Counting Problem Using Multi-Loss U-Net Reformulation
Authors: Jumutc Vilen , Suponenkovs Artjoms , Bondarenko Andrey , Bļizņuks Dmitrijs , Lihachev Alexey
Published: October 2023

Colony-Forming Unit (CFU) counting is a complex problem without a universal solution in biomedical and food safety domains. A multitude of sophisticated heuristics and segmentation-driven approaches have been proposed by researchers. However, U-Net remains the most frequently cited and used deep learning method in these domains. The latter approach provides a segmentation output map and requires an additional counting procedure to calculate unique segmented regions and detect microbial colonies. However, due to pixel-based targets, it tends to generate irrelevant artifacts or errant pixels, leading to inaccurate and mixed post-processing results. In response to these challenges, this paper proposes a novel hybrid counting approach, incorporating a multi-loss U-Net reformulation and a post-processing Petri dish localization algorithm. Firstly, a unique innovation lies in the multi-loss U-Net reformulation. An additional loss term is introduced in the bottleneck U-Net layer, focusing on the delivery of an auxiliary signal that indicates where to look for distinct CFUs. Secondly, the novel localization algorithm automatically incorporates an agar plate and its bezel into the CFU counting techniques. Finally, the proposition is further enhanced by the integration of a fully automated solution, which comprises a specially designed uniform Petri dish illumination system and a counting web application. The latter application directly receives images from the camera, processes them, and sends the segmentation results to the user. This feature provides an opportunity to correct the CFU counts, offering a feedback loop that contributes to the continued development of the deep learning model. Through extensive experimentation, the authors of this paper have found that all probed multi-loss U-Net architectures incorporated into the proposed hybrid approach consistently outperformed their single-loss counterparts, as well as other comparable models such as self-normalized density maps and YOLOv6, by at least 1% to 3% in mean absolute and symmetric mean absolute percentage errors. Further significant improvements were also reported through the means of the novel localization algorithm. This reaffirms the effectiveness of the proposed hybrid solution in addressing contemporary challenges of precise in vitro CFU counting.
FGCN: Image-Fused Point Cloud Semantic Segmentation with Fusion Graph Convolutional Network
Authors: Zhang Kun , Chen Rui , Peng Zidong , Zhu Yawei , Wang Xiaohong
Published: October 2023

In interpreting a scene for numerous applications, including autonomous driving and robotic navigation, semantic segmentation is crucial. Compared to single-modal data, multi-modal data allow us to extract a richer set of features, which is the benefit of improving segmentation accuracy and effect. We propose a point cloud semantic segmentation method, and a fusion graph convolutional network (FGCN) which extracts the semantic information of each point involved in the two-modal data of images and point clouds. The two-channel k-nearest neighbors (KNN) module of the FGCN was created to address the issue of the feature extraction’s poor efficiency by utilizing picture data. Notably, the FGCN utilizes the spatial attention mechanism to better distinguish more important features and fuses multi-scale features to enhance the generalization capability of the network and increase the accuracy of the semantic segmentation. In the experiment, a self-made semantic segmentation KITTI (SSKIT) dataset was made for the fusion effect. The mean intersection over union (MIoU) of the SSKIT can reach 88.06%. As well as the public datasets, the S3DIS showed that our method can enhance data features and outperform other methods: the MIoU of the S3DIS can reach up to 78.55%. The segmentation accuracy is significantly improved compared with the existing methods, which verifies the effectiveness of the improved algorithms.
MEMS-Switched Triangular and U-Shaped Band-Stop Resonators for K-Band Operation
Authors: Marcelli Romolo , Sardi Giovanni Maria , Proietti Emanuela , Capoccia Giovanni , Iannacci Jacopo , Tagliapietra Girolamo , Giacomozzi Flavio
Published: October 2023

Triangular resonators re-shaped into Sierpinski geometry and U-shaped resonators were designed, linking them with single-pole-double-through (SPDT) RF MEMS switches to provide frequency tuning for potential applications in the K-Band. Prototypes of band-stop narrowband filters working around 20 GHz and 26 GHz, interesting for RADAR and satellite communications, were studied in a coplanar waveguide (CPW) configuration, and the tuning was obtained by switching between two paths of the devices loaded with different resonators. As a result, dual-band operation or fine-tuning could be obtained depending on the choice of the resonator, acting as a building block. The studied filters belong to the more general group of devices inspired by a metamaterial design.
Sphygmomanometer Dynamic Pressure Measurement Using a Condenser Microphone
Authors: Tomazini Žan , Geršak Gregor , Beguš Samo
Published: October 2023

There is a worldwide need to improve blood pressure (BP) measurement error in order to correctly diagnose hypertension. Cardiovascular diseases cause 17.9 million deaths annually and are a substantial monetary strain on healthcare. The current measurement uncertainty of 3 mmHg should be improved upon. Dynamic pressure measurement standards are lacking or non-existing. In this study we propose a novel method of measuring air pressure inside the sphygmomanometer tubing during BP measurement using a condenser microphone. We designed, built, and tested a system that uses a radiofrequency (RF) modulation method to convert changes in capacitance of a condenser microphone into pressure signals. We tested the RF microphone with a low-frequency (LF) sound source, BP simulator and using a piezoresistive pressure sensor as a reference. Necessary tests were conducted to assess the uncertainty budget of the system. The RF microphone prototype has a working frequency range from 0.5 Hz to 280 Hz in the pressure range from 0 to 300 mmHg. The total expanded uncertainty (k = 2, p = 95.5%) of the RF microphone was 4.32 mmHg. The proposed method could establish traceability of BP measuring devices to acoustic standards described in IEC 61094-2 and could also be used in forming dynamic BP standards.
3D Ultrasonic Brain Imaging with Deep Learning Based on Fully Convolutional Networks
Authors: Ren Jiahao , Wang Xiaocen , Liu Chang , Sun He , Tong Junkai , Lin Min , Li Jian , Liang Lin , Yin Feng , Xie Mengying , Liu Yang
Published: October 2023

Compared to magnetic resonance imaging (MRI) and X-ray computed tomography (CT), ultrasound imaging is safer, faster, and more widely applicable. However, the use of conventional ultrasound in transcranial brain imaging for adults is predominantly hindered by the high acoustic impedance contrast between the skull and soft tissue. This study introduces a 3D AI algorithm, Brain Imaging Full Convolution Network (BIFCN), combining waveform modeling and deep learning for precise brain ultrasound reconstruction. We constructed a network comprising one input layer, four convolution layers, and one pooling layer to train our algorithm. In the simulation experiment, the Pearson correlation coefficient between the reconstructed and true images was exceptionally high. In the laboratory, the results showed a slightly lower but still impressive coincidence degree for 3D reconstruction, with pure water serving as the initial model and no prior information required. The 3D network can be trained in 8 h, and 10 samples can be reconstructed in just 12.67 s. The proposed 3D BIFCN algorithm provides a highly accurate and efficient solution for mapping wavefield frequency domain data to 3D brain models, enabling fast and precise brain tissue imaging. Moreover, the frequency shift phenomenon of blood may become a hallmark of BIFCN learning, offering valuable quantitative information for whole-brain blood imaging.
Non-Invasive Blood Pressure Sensing via Machine Learning
Authors: Attivissimo Filippo , D’Alessandro Vito Ivano , De Palma Luisa , Lanzolla Anna Maria Lucia , Di Nisio Attilio
Published: October 2023

In this paper, a machine learning (ML) approach to estimate blood pressure (BP) using photoplethysmography (PPG) is presented. The final aim of this paper was to develop ML methods for estimating blood pressure (BP) in a non-invasive way that is suitable in a telemedicine health-care monitoring context. The training of regression models useful for estimating systolic blood pressure (SBP) and diastolic blood pressure (DBP) was conducted using new extracted features from PPG signals processed using the Maximal Overlap Discrete Wavelet Transform (MODWT). As a matter of fact, the interest was on the use of the most significant features obtained by the Minimum Redundancy Maximum Relevance (MRMR) selection algorithm to train eXtreme Gradient Boost (XGBoost) and Neural Network (NN) models. This aim was satisfactorily achieved by also comparing it with works in the literature; in fact, it was found that XGBoost models are more accurate than NN models in both systolic and diastolic blood pressure measurements, obtaining a Root Mean Square Error (RMSE) for SBP and DBP, respectively, of 5.67 mmHg and 3.95 mmHg. For SBP measurement, this result is an improvement compared to that reported in the literature. Furthermore, the trained XGBoost regression model fulfills the requirements of the Association for the Advancement of Medical Instrumentation (AAMI) as well as grade A of the British Hypertension Society (BHS) standard.
Emergency Floor Plan Digitization Using Machine Learning
Authors: Hassaan Mohab , Ott Philip Alexander , Dugstad Ann-Kristin , Torres Miguel A. Vega , Borrmann André
Published: October 2023

An increasing number of special-use and high-rise buildings have presented challenges for efficient evacuations, particularly in fire emergencies. At the same time, however, the use of autonomous vehicles within indoor environments has received only limited attention for emergency scenarios. To address these issues, we developed a method that classifies emergency symbols and determines their location on emergency floor plans. The method incorporates color filtering, clustering and object detection techniques to extract walls, which were used in combination to generate clean, digitized plans. By integrating the geometric and semantic data digitized with our method, existing building information modeling (BIM) based evacuation tools can be enhanced, improving their capabilities for path planning and decision making. We collected a dataset of 403 German emergency floor plans and created a synthetic dataset comprising 5000 plans. Both datasets were used to train two distinct faster region-based convolutional neural networks (Faster R-CNNs). The models were evaluated and compared using 83 floor plan images. The results show that the synthetic model outperformed the standard model for rare symbols, correctly identifying symbol classes that were not detected by the standard model. The presented framework offers a valuable tool for digitizing emergency floor plans and enhancing digital evacuation applications.
Printed Eddy Current Testing Sensors: Toward Structural Health Monitoring Applications
Authors: Brun Eliott , Cottinet Pierre-Jean , Pelletier Arnaud , Ducharne Benjamin
Published: October 2023

Reliable measurements in structural health monitoring mean for the instrumentation to be set in perfect reproducible conditions. The solution described in this study consists of printing the sensors directly on the parts to be controlled. This method solves the reproducibility issue, limits human error, and can be used in confined or hazardous environments. This work was limited to eddy current testing, but the settings and conclusions are transposable to any non-destructive testing methods (ultrasounds, etc.). The first salve of tests was run to establish the best dielectric and conductive ink combination. The Dupont ink combination gave the best performances. Then, the dispenser- and the screen-printing methods were carried out to print flat spiral coils on flexible substrates. The resulting sensors were compared to flex-printed circuit boards (PCB-flex) using copper for the electrical circuit. The conductive ink methods were revealed to be just as efficient. The last stage of this work consisted of printing sensors on solid parts. For this, 20-turn spiral coils were printed on 3 mm thick stainless-steel plates. The permanent sensors showed good sensibility in the same range as the portative ones, demonstrating the method’s feasibility.
In Silico Study of Potential Small Molecule TIPE2 Inhibitors for the Treatment of Cancer
Authors: Wilson Jerica , Evangelou Katerina , Chen Youhai H. , Ji Hai-Feng
Published: October 2023

Context: Chronic inflammation has been linked to cancer since the 19th century. Tumor growth is supported by the proangiogenic factors that chronic inflammation requires. Polarized leukocytes initiate these angiogenic and tumorigenic factors. TIPE2, a transport protein, manages the cytoskeletal rearrangement that gives a polarized leukocyte its motility. Inhibition of this protein could lead to a therapeutic option for solid tumor cancers; however, no such inhibitors have been developed so far due to the large cavity size of the TIPE2 protein. Here we have examined possible small molecule inhibitors by combining structure-based and fragment-based drug design approaches. The highest binding ligands were complexed with the protein, and fragment libraries were docked with the complex with the intention of linking the hit compounds and fragments to design a more potent ligand. Three hit compounds were identified by in silico structure-based screening and a linked compound, C2–F14, of excellent binding affinity, was identified by linking fragments to the hit compounds. C2–F14 demonstrates good binding stability in molecular dynamic simulations and great predicted ADME properties. Methods: High throughput molecular docking calculations of mass libraries were performed using AutoDock Vina 1.1.2. Molecular docking of individual ligands was performed using AutoDock Vina with PyRx. Ligand libraries were prepared using OpenBabel, linked ligands were prepared using Avogadro. The protein was prepared using AutoDockTools-1.5.6. Protein-ligand complexes were visualized with PyMOL. Two- and three-dimensional representations of protein–ligand interactions were plotted with BIOVIA Discovery Studio Visualizer. In silico absorption, distribution, metabolism, and excretion (ADME) properties were calculated using SwissADME. Molecular dynamics simulations were conducted with GROMACS.
Surveying ‘Dating Violence’ and Stalking Victimisation among Students at an English University: Findings and Methodological Reflections on Using a US Survey Instrument
Authors: Bull Anna , Bradley Alexander
Published: October 2023

Domestic abuse and stalking in higher education (HE) have been overlooked in research in comparison to sexual harassment and sexual violence. This article reports on survey data from 725 students at an English university using measures of stalking and ‘dating violence’—physical and psychological violence from an intimate partner—from a US survey instrument (the Administrator Researcher Campus Climate Collaborative (ARC3) survey). According to this measure, 26% of respondents had been subjected to ‘dating violence’ and 16% to stalking behaviours. However, these findings need to be contextualised within a critical discussion about the use of the ARC3 survey tool in the English context. The ARC3 questions on ‘dating violence’ focus on physical and ‘psychological violence’; the questions therefore omit further types of domestic abuse under UK definitions. In relation to stalking, US definitions—as captured in the ARC3 survey instrument—define specific behaviours. By contrast, in England and Wales, stalking involves behaviours that engender fear or distress in a pattern of behaviour over time. These differences mean that the ARC3 modules on stalking and ‘dating violence’ would need to be significantly adapted to be suitable for use in England and Wales.
Computationally Efficient Poisson Time-Varying Autoregressive Models through Bayesian Lattice Filters
Authors: Sui Yuelei , Holan Scott H. , Yang Wen-Hsi
Published: October 2023

Estimation of time-varying autoregressive models for count-valued time series can be computationally challenging. In this direction, we propose a time-varying Poisson autoregressive (TV-Pois-AR) model that accounts for the changing intensity of the Poisson process. Our approach can capture the latent dynamics of the time series and therefore make superior forecasts. To speed up the estimation of the TV-AR process, our approach uses the Bayesian Lattice Filter. In addition, the No-U-Turn Sampler (NUTS) is used, instead of a random walk Metropolis–Hastings algorithm, to sample intensity-related parameters without a closed-form full conditional distribution. The effectiveness of our approach is evaluated through model-based and empirical simulation studies. Finally, we demonstrate the utility of the proposed model through an example of COVID-19 spread in New York State and an example of US COVID-19 hospitalization data.
Closed-Loop Supply Chain Network Design with Flexible Capacity under Uncertain Environment
Authors: Liu Chang , Ji Ying , Li Xinqi
Published: October 2023

This paper incorporates flexible facility capacity and government subsidy factors into the consideration of the design of a closed-loop supply chain(CLSC) network based on an uncertain environment. Considering the minimization of economic cost and carbon emission, a multi-objective multi-period multi-product mixed integer linear programming model with fixed and flexible facility capacity is constructed respectively. The robust optimization method is applied to deal with the uncertain environment of demand, recycled product quality, and recycling rate faced by the CLSC, and the robust models under six uncertain sets are constructed respectively. For model solving, the designed algorithm uses the augmented ϵ-constraint method to handle multi-objective problems and introduces a three-stage method on top of the Benders decomposition algorithm to accelerate the efficiency of solving the main problem. Finally, through numerical cases, a CSLC with a flexible supply strategy can manage economic and environmental costs to cope with the negative impacts of an uncertain environment, while this paper verifies the effectiveness of the government subsidy strategy under different conditions and analyzes the potential limitations.
Managing Urban Mobility during Big Events through Living Lab Approach
Authors: Covelli Garrido Cristina Isabel , Giovannini Alessandro , Mangone Annalisa , Silvestri Fulvio
Published: October 2023

Urban transportation systems encounter distinctive challenges during planned major city events characterized by large gatherings that disrupt traffic patterns. The surge in private car usage for attending such events leads to a sudden increase in traffic, unauthorized parking, pollutant emissions, and risks to pedestrian safety in the vicinity of the event venue. This study delves into the challenges and advantages of employing Decision Support Systems (DSSs) to manage urban mobility during special urban events with the goal of reducing car dependency and promoting sustainable transportation options. The proposed methodology for designing and testing the DSS is based on the living lab principles of co-planning, co-implementing, co-monitoring, co-validating, and co-reviewing with engaged stakeholders. Moreover, testing of the DSS measures in real-world cases (i.e., during a football match at the San Siro Stadium and a concert at the Alcatraz music hall in the city of Milan, Italy) highlights the potential of the DSS in reducing the use of individual private cars in favor of shared mobility and micro-mobility solutions. As a result, the living lab has proven to be a valuable tool for interacting with stakeholders from the outset of brainstorming ideas for potential transport policies to their practical implementation, with the goal of bridging the gap between what decision-makers believe should be done, what transport operators can feasibly do, and what users desire and expect to be done. The insights presented in this paper contribute to the debate on leveraging technology to cultivate more efficient, resilient, and livable urban environments.
Influence of Steel and Polypropylene Fibers on the Structural Behavior of Sustainable Reinforced Lightweight Concrete Beams Made from Crushed Clay Bricks
Authors: Elsherbiny Esraa A. , Mortagi Mohamed , Youssf Osama , Abd Elrahman Mohamed , Madawy Mohamed E. El
Published: October 2023

Structural lightweight concrete is preferred over traditional concrete due to its ability to reduce the dead load, minimize the size of load-bearing structural members, and provide more economical solutions for foundation deteriorations. This research sheds light on sustainable lightweight concrete using waste crushed clay bricks (CCB) as a lightweight aggregate. To reduce micro-crack propagation of the developed concrete, two types of fiber were implemented and investigated. Steel fibers (SF) with amounts of 0.5% and 1.0% by volume of concrete, and polypropylene fibers (PPF) with amounts of 0.1% and 0.2% by volume of concrete, were employed. Five reinforced concrete beams were made and tested in order to precisely evaluate the structural behavior of the proposed lightweight CCB concrete. Additionally, ABAQUS software for nonlinear finite element analysis has been utilized to simulate the tested beams and compare the numerical model predictions with the experimental findings. The findings revealed that the addition of SF and PPF exhibited a notable influence on enhancing the mechanical characteristics of lightweight CCB concrete. Adding 0.2% PPF increased the ultimate load and deformation capacity at failure by approximately 16% and 24%, respectively. Furthermore, after 28 days, the addition of 0.5% and 1.0% SF enhanced the compressive strength by around 11.7% and 17.6%, respectively. Moreover, a significant level of consistency between the results obtained from the numerical model and the experimental findings was observed. In general, the use of SF and PPF in CCB concrete successfully produced high-quality lightweight concrete with interesting results for use in reinforced concrete beams.
Fundamental Analysis of the Ages of Children and Road Structures Involved in Traffic Accidents
Authors: Ohnishi Hiroki , Fujiu Makoto , Morisaki Yuma , Takayama Junichi
Published: October 2023

The population of children in Japan has steadily declined, and the percentage of the population aged 14 years or younger is smaller than in other countries. Therefore, it is important to protect children by preventing their involvement in traffic accidents. Recent trends associated with elementary school students show that 90% of accidents occur while walking or riding bicycles. This study aims to clarify the road structures in which traffic accidents involving walking or bicycle-riding children occur. For this purpose, we analyzed the trends based on the age of children and road structures involved using statistical data provided by the Ishikawa Prefectural Police Headquarters. The results showed that the accident rate among students of elementary school age and younger while walking or riding bicycles was mainly high on one-lane roads, roads with no division, and roads with a speed limit of 30 km/h or less. We conclude that to reduce pedestrian and bicycle accidents for students of elementary school age or younger, raising traffic safety awareness among automobile drivers who use community roads is important.
Research on the Compilation of a Composite Index from the Perspective of Public Value—The Case of the Global Health Security Index
Authors: Wang Bing , Lyu Yiwei
Published: October 2023

The Global Health Security Index (GHSI), the first report on the world’s defensive capabilities against major infectious diseases, released in 2019, deviated from the actual performance of countries globally during COVID-19. Principal component analysis is used to deconstruct the multiple dimensions of public value on the GHSI index; reasons are explored for the deviation between the GHSI scoring results of countries worldwide and their performance in the COVID-19 pandemic, and the logical principles of composite index compilation are analyzed. The results show that the dimensions selected for inclusion in the GHSI are relatively isometric, and omissions of important values are the fundamental reason for the deviation. The composite index is the quantification of qualitative values, and public value affects the process of compiling the composite index in at least four respects: dimension selection, specific indicators, weight-setting, and evaluation-scoring. Therefore, public value should become the theoretical basis for compiling a composite index. This study effectively combines qualitative and quantitative research, provides theoretical explanations and practical guidance for further iterative updates of the GHSI and the optimization of world health and security governance tools, and provides a broader research perspective for the development of composite indices.
Chronic Effects of Rotational Inertial Devices on Adolescents’ Physical Capacities in Team Sports: A Systematic Review
Authors: López-Mariscal Samuel , Reina-Gómez Álvaro , Suárez-Arrones Luís , Ortega-Becerra Manuel
Published: October 2023

Inertial training is one of the most popular training methodologies in recent years and one of the objects of study in recent literature. The aim of this systematic review is to evaluate the current literature surrounding the chronic effect of rotational inertial devices on the physical capacities of team sports athletes through jumping performance, sprinting time, and change of direction performance. This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocols. Three databases were screened up to January 2023. Eight studies were included in the final analysis. The results revealed the effectiveness of rotational inertial devices as flywheels or conic pulleys, showing significant improvements (from trivial to large effect size) in jump performances, significant improvements in some tests for change of direction ability and non-consistent results for sprint tests with significant improvements mainly in short distances. In conclusion, inertial training has been shown to be a useful way to improve performance in young athletes in team sports.
Assessing the Psychometric Properties of the Practice and Product Inventory of Supporting Students with ASD (PPI-SSA): A Concise Assessment Tool for Teachers in Inclusive Classrooms
Authors: Yang Lan , Pang Feifan , Sin Kuen-Fung
Published: October 2023

Globally prevalent, Autism Spectrum Disorders (ASDs) have negative consequences on students’ social, intellectual, emotional, and post-school transition results. While assessment tools exist, the majority of them were developed from the perspective of clinical psychology and/or healthcare. Some are lengthy and commercialized, and some have weak factorial validity. A feasible and practical assessment method, from the perspective of educational psychology and assessment, is required to better accurately and effectively assess the social and academic performance of ASD students in inclusive settings. With an emphasis on teachers’ practice and output in helping students with ASD in inclusive education, this study evaluated the Practice and Product Inventory of Supporting Students with ASD (PPI-SSA) psychometric qualities. PPI-SSA was designed to be practical, concise, and especially suited for quantitative research on ASD. The inventory was administered to 411 teachers in Hong Kong, and exploratory and confirmatory factor analyses confirmed the PPI-SSA’s reliability and validity. A follow-up path analysis examined the concurrent validity of the PPI-SSA, revealing significant pathways between teachers’ intentions to implement inclusive education and each of the respective dimensions within the PPI-SSA. Measurement invariance results showed that the PPI-SSA demonstrated configural, metric, scalar, and residual invariance across primary and secondary teachers. The PPI-SSA, as a non-commercial instrument, has meaningful implications. It can facilitate the efficient collection of more empirical data on the social and academic achievements of students with ASD, informing evidence-based improvement of inclusive practices. It can assist in teachers’ practice to identify the diverse social and academic needs of students with ASD to align with their corresponding adjustments of teaching strategies.
A Study on Resource Carrying Capacity and Early Warning of Urban Agglomerations of the Yellow River Basin Based on Sustainable Development Goals, China
Authors: Bu Xiaoyan , Wang Xiaomin , Wang Jiarui , Shi Ge
Published: October 2023

The Yellow River Basin is an essential ecological barrier in China, but it is relatively underdeveloped. The human–land relationship needs to be coordinated, and the ecological environment is fragile, which seriously restricts the sustainable development of the urban agglomeration in the Yellow River Basin. In this study, a “five-dimensional integrated” comprehensive carrying capacity evaluation model is constructed using the five dimensions of water, land, ecology, monitoring, and early warning to evaluate its resource carrying capacity quantitatively. It constructs an early warning system of the resource carrying capacity based on the quantitative evaluation results and monitors the state of the resource carrying capacity. The results show that (1) seven major urban agglomerations’ populations, grain productions, and land are surplus, and 50.85% of prefecture-level cities have food surpluses regarding human–food relationships. (2) There are shortages in the urban agglomeration’s water resources and a deficit in the water resource carrying capacity. (3) The average ecological carrying capacity index is 0.519, indicating a state of ecological affluence. (4) The comprehensive resource carrying capacity is defined as level-three heavy-load conditions, while 67%, 22%, and 14% of cities have level-one, -two, and -three heavy-load conditions, respectively. This study can aid in the monitoring of the resource carrying status of the Yellow River Basin. These results provide a scientific basis for effectively restraining the utilization and development of natural resources in the Yellow River Basin. It can also provide a research paradigm for the world’s river basins, as well as the sustainable development of man and nature in the world.
Examining How and When Knowledge and Motivation Contribute to Organic Food Purchase Intention among Individuals with Chronic Diseases: Testing a Moderated Mediation Model
Authors: Limbu Yam B. , McKinley Christopher , Ganesan P. , Wang Tianfu , Zhang Junzhou
Published: October 2023

Prior research underscores a need for applying theoretical frameworks to understand the factors influencing diverse populations’ organic food purchase intentions. The objectives of this study are threefold. First, we evaluate the applicability of the information–motivation–behavioral skills model for predicting organic food purchase intention in adults with chronic conditions. Second, we examine the indirect effects of organic food knowledge, attitudes toward organic food purchase, and subjective norms on purchase intention through self-efficacy. Third, we examine whether these indirect effects are moderated by gender and educational attainment. Data were collected from Indian adults with chronic conditions using a self-administered questionnaire. The results show significant indirect effects of organic food knowledge, attitude toward organic food purchase, and subjective norms on organic food purchase intention through self-efficacy. Moreover, the mediating effect of knowledge was moderated by gender and educational attainment, with the effect being stronger for females and among individuals with a lower level of education. Organic food marketers, social marketers, and public health agencies promoting organic food consumption to people with chronic conditions should aim to increase their confidence in comprehending organic food. This study contributes to the literature by assessing the applicability of the information–motivation–behavioral skills model in understanding behavioral intentions toward organic food.
Adsorption of Pyraclostrobin in Water by Bamboo-Derived and Pecan Shell-Derived Biochars
Authors: Yang Kongtan , Wang Xumi , Wu Huanqi , Fang Nan , Liu Yuxue , Zhang Changpeng , Yu Xiaobin , Wang Xiangyun
Published: October 2023

Pyraclostrobin is a potent extensive-spectrum fungicide widely used in agricultural production but poses a substantial threat to aquatic life. Therefore, there is an urgent need to remove pyraclostrobin from the ecological environment. This study reports the adsorption of pyraclostrobin in water using pecan-shell biochar, bamboo biochar, and their deashing products. The kinetics and isotherms indicate that the pseudo-second-order kinetics and Freundlich model are the most suitable for both types of biochar. The thermodynamic results demonstrate that the adsorption process of biochar is spontaneous and exothermic. Combined with characterization and factor analysis experiments, it is revealed that the adsorption of pyraclostrobin on biochar is attributed to various mechanisms, including pore filling, hydrophobic interactions, π-π and p-π interactions, and hydrogen bonding. At the initial concentration of 0.5 mg·L−1, the adsorption rates of pyraclostrobin of the four biochar samples (<0.075 mm) reached 67–80% within 5 min. These findings suggest that both pecan-shell and bamboo biochars are efficient pyraclostrobin adsorbents, with the former showing better outcomes. There is still an adsorption rate of >97% after 5 cycles of adsorption by two types of biochars. Deashing significantly enhances the adsorption efficiency of pecan biochar, but it has an insignificant effect on bamboo biochar. This study will aid in the selection of cost-effective and ecofriendly adsorbents to reduce the environmental risk associated with pyraclostrobin.
Investigation of Pyrolysis/Gasification Process Conditions and Syngas Production with Metal Catalysts Using Waste Bamboo Biomass: Effects and Insights
Authors: Guo Yue , Wang Qingyue
Published: October 2023

The primary objective of this study was to examine the catalytic behaviors exhibited by diverse metal catalysts such as CaO, NiO, and K2CO3 for pyrolysis and gasification application with waste biomass. The investigation involved fine tuning the conditions of pyrolysis/gasification by optimizing the pyrolysis atmosphere, catalyst addition methods, and catalyst quantities. The behaviors were investigated using thermal analysis (TG-DTA), and the production gaseous contents were analyzed via GC-FID. The results showed that Ar gas proved to be well suited for the pyrolysis reaction. The incorporation of catalysts through mixing and impregnation techniques ensured the homogeneous dispersion of catalyst particles within the sample, offering a clear advantage over the two-stage approach. Among the various catalysts explored, K2CO3 demonstrated the most favorable catalytic impact, resulting in an enhancement of char yield from 20.2 to 26.8%, while the tar yield was reduced from 44.3 to 38.6%. Furthermore, the presence of K during gasification reactions was found to foster accelerated reaction rates and an increase in syngas production yield.