bluuebunny/crossref_metadata_2025 huggingface dataset:
A New Approach to a Fracturing Sweet Spot Evaluation Method Based on Combined Weight Coefficient Method—A Case Study in the BZ Oilfield, China
Authors: Jiang Mao , Peng Chengyong , Wu Jianshu , Wang Zongyong , Liu Yi , Zhao Bingjin , Zhang Yan
Published: August 2024

The Sha3 member of the BZ Oilfield in Bohai Sea represents a typical ultra-low permeability thin interbedding reservoir. Hydraulic fracturing is an effective method for increasing oil and gas production. However, the complex geological conditions make it difficult to screen out the optimal frac spot. Consequently, the design of hydraulic fracturing exhibits certain limitations. A novel method for evaluating sweet spots is proposed, based on a refined geological reservoir model that considers the varying degrees of influence that geological and engineering factors have on fracturing effectiveness. This method utilizes grey correlation and hierarchical analysis to quantify and characterize the weight coefficients of each factor, ultimately leading to a combined weight coefficient approach. The results indicate that permeability and fracturing scale are the primary factors that impact the post-frac production of the BZ oilfield, with their respective combined weights being 0.33 and 0.25. The average weight for geological factors is 0.18, while for engineering factors it is 0.15. This suggests that geological factors have a greater influence on production than engineering factors. Furthermore, the correlation coefficient between the dual sweet spot index and the production is 0.96, indicating a strong positive correlation between the two variables. After comparing the predicted and actual production of each well, it was determined that the anastomosis rate is 80%. This finding holds significant guiding significance for selecting the optimal fracturing spot.
A Review on the High-Efficiency Detection and Precision Positioning Technology Application of Agricultural Robots
Authors: Wang Ruyi , Chen Linhong , Huang Zhike , Zhang Wei , Wu Shenglin
Published: August 2024

The advancement of agricultural technology has increasingly positioned robotic detection and localization techniques at the forefront, ensuring critical support for agricultural development through their accuracy and reliability. This paper provides an in-depth analysis of various methods used in detection and localization, including UWB, deep learning, SLAM, and multi-sensor fusion. In the domain of detection, the application of deep algorithms in assessing crop maturity and pest analysis is discussed. For localization, the accuracy of different methods in target positioning is examined. Additionally, the integration of convolutional neural networks and multi-sensor fusion with deep algorithms in agriculture is reviewed. The current methodologies effectively mitigate environmental interference, significantly enhancing the accuracy and reliability of agricultural robots. This study offers directional insights into the development of robotic detection and localization in agriculture, clarifying the future trajectory of this field and promoting the advancement of related technologies.
A New Air-Assisted Flare Tip Design for Managing Gas Flare Emissions (CFD Analysis)
Authors: Maaroof Ahmed A. , Smith Joseph D. , Zangana Mohammed H. S.
Published: August 2024

Recently, flares have been considered as a major source of air pollution from the petroleum refining industry. The United Nations has instigated an international effort related to the management of flare emissions to reduce the global warming impact related to flaring. Eliminating or removing the need for gas flares is difficult because these devices are generally used as safety devices to allow the combustion of flammable gases in a controlled fashion which supports safe operation. However, reducing flaring is generally possible using well-designed, efficiently operated flare equipment. In general, flare performance can be enhanced following the API-521 methodology and using assist-media including air and steam to achieve smokeless operation. This present work will discuss flare emissions in the petroleum refining industry and a method to manage flare emissions. Moreover, this work will discuss flare combustion efficiency (CE) and destruction and removal efficiency (DRE) in terms of efficient flare operation. This work uses actual operating flare data, published previously, which will be used in this work together with the CFD Code C3d. This code, developed at the USDOE Sandia National Laboratory, is based on a standard LES methodology to conduct transient flare analysis and is used to simulate flare operation to estimate flame shape and emissions produced. In this work, a new air-assisted flare tip design which uses the Coanda effect to improve flare operation was analyzed. This new flare design reduces the emission rate and demonstrates the design’s effectiveness. The analysis considers a flare 39″ high and 6″ diameter in the center of a 4m x 4m x 4m domain. Boundary conditions assume no cross wind and an ambient temperature of 300 K. The initial condition is a hydrostatic pressure profile across the computational domain. In the air-assist simulation, stoichiometric ratio will be a variable, and therefore, more than one case was considered.
Zai Pits as a Climate-Smart Agriculture Technique in Southern Kenya: Maize Success Is Influenced More by Manure Than Depth
Authors: Bowers Matthew J. , Kasaine Simon , Schulte Bruce A.
Published: August 2024

In semi-arid southern Kenya, climate change is putting rainfed agriculture at risk with major implications for food security. The zai pit, a Climate-Smart Agriculture (CSA) technique, has proven to enhance yields in arid regions, but its labor demands have limited adoption rates. This study assessed how the zai pit depth and manure application within zai pits influenced maize (Zea mays) success (i.e., growth, development, and productivity). Three zai pit treatments were prepared at the Wildlife Works Research Center in southeastern Kenya: (1) deep (50 cm) with manure; (2) shallow (25 cm) with manure; and (3) deep (50 cm) without manure, and all were compared to a non-zai pit control (surface planting). Maize growth/development (e.g., height, stage, roots) and productivity (e.g., yield) measurements were taken over two growing periods. For most measures, shallow zai pits performed equally as well as deep zai pits, with both performing better than the control. Zai pits without manure performed significantly worse than zai pits with manure, oftentimes not differing from the control. Results suggest that maize success is influenced more by manure than the depth of the pit. Kenyan farmers are encouraged to dig shallower, manure-enriched zai pits to enhance food security in response to climate change.
Claim Prediction and Premium Pricing for Telematics Auto Insurance Data Using Poisson Regression with Lasso Regularisation
Authors: Usman Farha , Chan Jennifer S. K. , Makov Udi E. , Wang Yang , Dong Alice X. D.
Published: August 2024

We leverage telematics data on driving behavior variables to assess driver risk and predict future insurance claims in a case study utilising a representative telematics sample. In the study, we aim to categorise drivers according to their driving habits and establish premiums that accurately reflect their driving risk. To accomplish our goal, we employ the two-stage Poisson model, the Poisson mixture model, and the Zero-Inflated Poisson model to analyse the telematics data. These models are further enhanced by incorporating regularisation techniques such as lasso, adaptive lasso, elastic net, and adaptive elastic net. Our empirical findings demonstrate that the Poisson mixture model with the adaptive lasso regularisation outperforms other models. Based on predicted claim frequencies and drivers’ risk groups, we introduce a novel usage-based experience rating premium pricing method. This method enables more frequent premium updates based on recent driving behaviour, providing instant rewards and incentivising responsible driving practices. Consequently, it helps to alleviate cross-subsidization among risky drivers and improves the accuracy of loss reserving for auto insurance companies.
Automatic Aircraft Identification with High Precision from SAR Images Considering Multiscale Problems and Channel Information Enhancement
Authors: Wang Jing , Liu Guohan , Liu Jiaxing , Dong Wenjie , Song Wanying
Published: August 2024

The SAR system possesses the ability to carry out all-day and all-weather imaging, which is highly valuable in the application of aircraft identification. However, aircraft identification from SAR images still faces great challenges due to speckle noise interference, multiscale problems, and complex background interference. To solve these problems, an efficient bidirectional path multiscale fusion and attention network (EBMA-Net) is proposed in this paper. It employs bidirectional connectivity to fuse the features of aircraft with different scales to perform the accurate detection of aircraft even when the background is highly complex. In the presented EBMA-Net, a module called efficient multiscale channel attention fusion (EMCA) and three parallel squeeze efficient channel attention (SECA) modules are proposed. In the EMCA module, the bidirectional paths are created by stacking upper and lower fusion modules, which effectively integrate shallow detailed features and deep semantic information. So, the detection performance of aircraft at different scales is improved. In the SECA module, the dependency relationship between feature channels is explicitly modeled, which can automatically learn the importance of different channels, prioritize key features, so as to improve the precision and robustness of aircraft identification. In the experiment, the public dataset of aircraft identification (i.e., SAR-AIRcraft-1.0, which is generated from the GF-3 satellite) from high-resolution SAR systems is used, and several other excellent target-detection networks are used for performance comparison, namely, YOLOv5s, YOLOv7, MGCAN, and EBPA2N. According to the results, the average aircraft detection accuracy of EBMA-Net is 91.31%, which is 4.5% higher than YOLOv7; and the false alarm rate is decreased by 5%. Its accuracy in the identification of aircraft can reach 95.6%, which is about 3.7% higher than YOLOv7. Therefore, the EBMA-Net obviously outperforms the other networks for aircraft detection and identification. The proposed EBMA-Net, which can capture the detailed information and better restrain the background interference, could also be used to perform the detection and identification of dense targets with different scales and background from SAR images.
Aerial Hybrid Adjustment of LiDAR Point Clouds, Frame Images, and Linear Pushbroom Images
Authors: Jonassen Vetle O. , Kjørsvik Narve S. , Blankenberg Leif Erik , Gjevestad Jon Glenn Omholt
Published: August 2024

In airborne surveying, light detection and ranging (LiDAR) strip adjustment and image bundle adjustment are customarily performed as separate processes. The bundle adjustment is usually conducted from frame images, while using linear pushbroom (LP) images in the bundle adjustment has been historically challenging due to the limited number of observations available to estimate the exterior image orientations. However, data from these three sensors conceptually provide information to estimate the same trajectory corrections, which is favorable for solving the problems of image depth estimation or the planimetric correction of LiDAR point clouds. Thus, our purpose with the presented study is to jointly estimate corrections to the trajectory and interior sensor states in a scalable hybrid adjustment between 3D LiDAR point clouds, 2D frame images, and 1D LP images. Trajectory preprocessing is performed before the low-frequency corrections are estimated for certain time steps in the following adjustment using cubic spline interpolation. Furthermore, the voxelization of the LiDAR data is used to robustly and efficiently form LiDAR observations and hybrid observations between the image tie-points and the LiDAR point cloud to be used in the adjustment. The method is successfully demonstrated with an experiment, showing the joint adjustment of data from the three different sensors using the same trajectory correction model with spline interpolation of the trajectory corrections. The results show that the choice of the trajectory segmentation time step is not critical. Furthermore, photogrammetric sub-pixel planimetric accuracy is achieved, and height accuracy on the order of mm is achieved for the LiDAR point cloud. This is the first time these three types of sensors with fundamentally different acquisition techniques have been integrated. The suggested methodology presents a joint adjustment of all sensor observations and lays the foundation for including additional sensors for kinematic mapping in the future.
Ship Lock Extraction from High-Resolution Remote Sensing Images Based on Fuzzy Theory and Prior Knowledge
Authors: Chen Bingsun , Bao Yi , Song Yanjiao , Li Ziyang , Wang Zhe , Wang Xi , Ma Runsheng , Meng Lingkui , Zhang Wen , Li Linyi
Published: August 2024

As crucial water conservancy projects, ship locks play a key role in flood control, shipping, water resource allocation, and promoting regional economic development, making them an indispensable part of the modern water transportation system. Utilizing satellite remote sensing for lock extraction can significantly reduce manual workload and costs, assist in the daily dynamic maintenance of lock hubs, and provide more comprehensive data support for the construction and management of water transport infrastructure. In this context, this paper proposes a new method for ship lock object extraction. Leveraging fuzzy theory and prior knowledge of locks, the extraction of lock objects is achieved from Gaofen-1 (GF-1) high-resolution remote sensing images. The experimental results demonstrate that the proposed algorithm can effectively extract small lock objects in remote sensing images, achieving an average extraction accuracy of 80.9% in the study area.
Multi-View Feature Fusion and Rich Information Refinement Network for Semantic Segmentation of Remote Sensing Images
Authors: Liu Jiang , Cheng Shuli , Du Anyu
Published: August 2024

Semantic segmentation is currently a hot topic in remote sensing image processing. There are extensive applications in land planning and surveying. Many current studies combine Convolutional Neural Networks (CNNs), which extract local information, with Transformers, which capture global information, to obtain richer information. However, the fused feature information is not sufficiently enriched and it often lacks detailed refinement. To address this issue, we propose a novel method called the Multi-View Feature Fusion and Rich Information Refinement Network (MFRNet). Our model is equipped with the Multi-View Feature Fusion Block (MAFF) to merge various types of information, including local, non-local, channel, and positional information. Within MAFF, we introduce two innovative methods. The Sliding Heterogeneous Multi-Head Attention (SHMA) extracts local, non-local, and positional information using a sliding window, while the Multi-Scale Hierarchical Compressed Channel Attention (MSCA) leverages bar-shaped pooling kernels and stepwise compression to obtain reliable channel information. Additionally, we introduce the Efficient Feature Refinement Module (EFRM), which enhances segmentation accuracy by interacting the results of the Long-Range Information Perception Branch and the Local Semantic Information Perception Branch. We evaluate our model on the ISPRS Vaihingen and Potsdam datasets. We conducted extensive comparison experiments with state-of-the-art models and verified that MFRNet outperforms other models.
GaitKeeper: An AI-Enabled Mobile Technology to Standardize and Measure Gait Speed
Authors: Davey Naomi , Harte Gillian , Boran Aidan , Mc Elwaine Paul , Kennelly Seán P.
Published: August 2024

Gait speed is increasingly recognized as an important health indicator. However, gait analysis in clinical settings often encounters inconsistencies due to methodological variability and resource constraints. To address these challenges, GaitKeeper uses artificial intelligence (AI) and augmented reality (AR) to standardize gait speed assessments. In laboratory conditions, GaitKeeper demonstrates close alignment with the Vicon system and, in clinical environments, it strongly correlates with the Gaitrite system. The integration of a cloud-based processing platform and robust data security positions GaitKeeper as an accurate, cost-effective, and user-friendly tool for gait assessment in diverse clinical settings.
Dual-Mode Embedded Impulse-Radio Ultra-Wideband Radar System for Biomedical Applications
Authors: Hung Wei-Ping , Chang Chia-Hung
Published: August 2024

This paper presents a real-time and non-contact dual-mode embedded impulse-radio (IR) ultra-wideband (UWB) radar system designed for microwave imaging and vital sign applications. The system is fully customized and composed of three main components, an RF front-end transmission block, an analog signal processing (ASP) block, and a digital processing block, which are integrated in an embedded system. The ASP block enables dual-path receiving for image construction and vital sign detection, while the digital part deals with the inverse scattering and direct current (DC) offset issues. The self-calibration technique is also incorporated into the algorithm to adjust the DC level of each antenna for DC offset compensation. The experimental results demonstrate that the IR-UWB radar, based on the proposed algorithm, successfully detected the 2D image profile of the object as confirmed by numerical derivation. In addition, the radar can wirelessly monitor vital sign behavior such as respiration and heartbeat information.
Research on an Autonomous Localization Method for Trains Based on Pulse Observation in a Tunnel Environment
Authors: Shi Jianqiang , Zhang Youpeng , Chen Guangwu , Si Yongbo
Published: August 2024

China’s rail transit system is developing rapidly, but achieving seamless high-precision localization of trains throughout the entire route in closed environments such as tunnels and culverts still faces significant challenges. Traditional localization technologies cannot meet current demands, and the present paper proposes an autonomous localization method for trains based on pulse observation in a tunnel environment. First, the Letts criterion is used to eliminate abnormal gyro data, the CEEMDAN method is employed for signal decomposition, and the decomposed signals are classified using the continuous mean square error and norm method. Noise reduction is performed using forward linear filtering and dynamic threshold filtering, respectively, maximizing the retention of its effective signal components. A SINS/OD integrated localization model is established, and an observation equation is constructed based on velocity matching, resulting in an 18-dimensional complex state space model. Finally, the EM algorithm is used to address Non-Line-Of-Sight and multipath effect errors. The optimized model is then applied in the Kalman filter to better adapt to the system’s observation conditions. By dynamically adjusting the noise covariance, the localization system can continue to maintain continuous high-precision position information output in a tunnel environment.
Efficient Model Updating of a Prefabricated Tall Building by a DNN Method
Authors: Liu Chunqing , Zhang Fengliang , Ni Yanchun , Ai Botao , Zhu Siyan , Zhao Zezhou , Fu Shengjie
Published: August 2024

The significance of model updating methods is becoming increasingly evident as the demand for greater precision in numerical models rises. In recent years, with the advancement of deep learning technology, model updating methods based on various deep learning algorithms have begun to emerge. These methods tend to be complicated in terms of methodological architectures and mathematical processes. This paper introduces an innovative model updating approach using a deep learning model: the deep neural network (DNN). This approach diverges from conventional methods by streamlining the process, directly utilizing the results of modal analysis and numerical model simulations as deep learning input, bypassing any additional complex mathematical calculations. Moreover, with a minimalist neural network architecture, a model updating method has been developed that achieves both accuracy and efficiency. This distinctive application of DNN has seldom been applied previously to model updating. Furthermore, this research investigates the impact of prefabricated partition walls on the overall stiffness of buildings, a field that has received limited attention in the previous studies. The main finding was that the deep neural network method achieved a Modal Assurance Criterion (MAC) value exceeding 0.99 for model updating in the minimally disturbed 1st and 2nd order modes when compared to actual measurements. Additionally, it was discovered that prefabricated partitions exhibited a stiffness ratio of about 0.2–0.3 compared to shear walls of the same material and thickness, emphasizing their role in structural behavior.
Mechanical Strain, Temperature, and Misalignment Effects on Data Communication between Piezoceramic Ultrasonic Transducers
Authors: Camerini Isabel Giron , de Souza Luis Paulo Brasil , Gouvea Paula Medeiros Proença , Braga Arthur Martins Barbosa
Published: August 2024

Acoustic waves can be used for wireless telemetry as an alternative to situations where electrical or optical penetrators are unsuitable. However, the response of the ultrasonic transducer can be greatly affected by temperature variations, mechanical deformations, misalignment between transducers, and multiple layers in the propagation zone. Therefore, this work sought to quantify such influences on communication between ultrasonic transducers. The experimental measurements were performed at the frequency where power transfer is maximized. Moreover, there were four experimental models, each with its own performed setup. The ultrasonic transducers are attached to both sides of a 6 mm thick stainless-steel plate for configuring just one barrier. Multiple layers of transducers are attached to the outer side of two plates immersed in an acoustic fluid with a 100 mm thick barrier. In both cases, the S21 parameter was used to quantify the influence of the physical barrier because it correlates with the power flow between ports that return after a given excitation. The results showed that when a maximum deformation of 1250 μm/m was applied, the amplitude of the S21 parameter varied around +0.7 dB. Furthermore, increasing the temperature from 30 to 100 °C slightly affected the S21 (+0.8 dB), but the signal decayed quickly for temperatures beyond 100 °C. Additionally, the ultrasonic communication with a multiple layer was found to occur under misalignment with an intersection area of up to 40%. None of the factors evaluated resulted in insufficient power transfer, except for a large misalignment between the transducers. Such results indicate that this type of communication can be a robust alternative, with a minimum alignment of 40% between transducers and electrical penetrators.
A Novel Shunt Zigzag Double-Tap Low-Harmonic Multi-Pulse Rectifier Based on a Three-Stage Power Electronic Phase-Shifting Transformer
Authors: Mu Xiuqing , Chen Xiaoqiang , Liu Qianxiao , Wang Ying , Bai Tun , Ge Leijiao , Ma Xiping
Published: August 2024

To solve the problem of the large size of traditional industrial frequency phase-shift transformers and the harmonic distortion of multi-pulse wave rectifier systems, this paper proposes a three-stage shunt zigzag power electronic phase-shift transformer based on a double-tap multi-pulse wave rectifier, which combines the power factor correction (PFC) converter with the voltage-type SPWM inverter circuit to form a power electronic converter to realize the frequency boost and power factor correction. Through AC–DC–AC conversion, the frequency of the three-phase AC input voltage is increased, the number of core and coil turns in the transformer is reduced to reduce the size of the phase-shifter transformer, a zigzag structure of the phase-shifter transformer is used to solve the unbalanced distribution of current between the diode bridges, and a passive harmonic suppression method on the DC side is used to generate a loop current by using a group of single-phase rectifier bridges to regulate the input line current of the phase-shifter transformer. The phase-shifted voltage is input into two three-phase diode rectifier bridges to rectify and supply power to the load. Simulation and semi-physical test results show that the proposed method reduces the total harmonic distortion (THD) value of the input current of the phase-shifted transformer to 7.17%, and the THD value of the grid-side input current is further reduced to 2.49%, which meets the harmonic standard and realizes the purpose of power factor correction as well as being more suitable for high-power applications.
Detection of Road Risk Sources Based on Multi-Scale Lightweight Networks
Authors: Pang Rong , Ning Jiacheng , Yang Yan , Zhang Peng , Wang Jilong , Liu Jingxiao
Published: August 2024

Timely discovery and disposal of road risk sources constitute the cornerstone of road operation safety. Presently, the detection of road risk sources frequently relies on manual inspections via inspection vehicles, a process that is both inefficient and time-consuming. To tackle this challenge, this paper introduces a novel automated approach for detecting road risk sources, termed the multi-scale lightweight network (MSLN). This method primarily focuses on identifying road surfaces, potholes, and scattered objects. To mitigate the influence of real-world factors such as noise and uneven brightness on test results, pavement images were carefully collected. Initially, the collected images underwent grayscale processing. Subsequently, the median filtering algorithm was employed to filter out noise interference. Furthermore, adaptive histogram equalization techniques were utilized to enhance the visibility of cracks and the road background. Following these preprocessing steps, the MSLN model was deployed for the detection of road risk sources. Addressing the challenges associated with two-stage network models, such as prolonged training and testing times, as well as deployment difficulties, this study adopted the lightweight feature extraction network MobileNetV2. Additionally, transfer learning was incorporated to elevate the model’s training efficiency. Moreover, this paper established a mapping relationship model that transitions from the world coordinate system to the pixel coordinate system. This model enables the calculation of risk source dimensions based on detection outcomes. Experimental results reveal that the MSLN model exhibits a notably faster convergence rate. This enhanced convergence not only boosts training speed but also elevates the precision of risk source detection. Furthermore, the proposed mapping relationship coordinate transformation model proves highly effective in determining the scale of risk sources.
A Method for Real-Time Measurement of the Vertical Vortex at Flood Discharge Outlets Using Ultrasonic Sensors
Authors: Fan Dingfan , Yu Min , Yao Zhixiang , Du Yang , Liu Hang
Published: August 2024

In this study, ultrasonic sensors were used to measure the vertical vortex at flood discharge outlets in real time, and numerical simulations and model experiments were conducted. When a sound signal passes through a vortex, its propagation characteristics will change, which helps to determine the existence of the vortex. Moreover, its characteristic parameters can be obtained through inversion. In this paper, first, the theories of acoustic measurement methods were introduced and their feasibility was verified through a comparison between Particle Image Velocimetry (PIV) measurement and numerical simulation results. Then, the Computational Fluid Dynamics (CFD) method was used to simulate the vertical vortex at the flood discharge outlets of hydraulic structures and the simulation data were restored to the actual size at scale. Finally, acoustic numerical simulations of actual vortex data were conducted, and ultrasonic sensors were used to measure the velocity of a simplified vertical vortex model under laboratory conditions. The research results indicate that the acoustic measurement method proposed in this article is effective in the measurement of the characteristic parameters of vertical vortex with a core radius of 0.03~0.05 m and a maximum tangential velocity of 0.5 m/s, the measurement error of the maximum tangential velocity is within 10%.
Improvement of SAW Resonator Performance by Petal-like Topological Insulator
Authors: Bai Jin , Li Lixia , Chai Chenyang
Published: August 2024

This article introduces a novel petal-like SAW topology insulator, which can transmit sound waves with low loss and high flexibility in an ultra-wide frequency band by simultaneously adjusting multiple structural parameters of phononic crystals. Using finite element analysis, it was found that adjusting these parameters can generate a broadband gap of 55.8–65.7 MHz. This structure can also achieve defect immunity and sharp bending in waveguide transmission. When this topology insulator is applied to resonators, compared to traditional designs, the insertion loss is reduced by 22 dB, the on-load quality factor is increased by 227%, the off-load quality factor is increased by 1024.5%, and the quality sensitivity is improved by 3.7 times compared to bare devices.
I Was the Violence Victim, I Am the Perpetrator: Bullying and Cyberbullying Perpetration and Associated Factors among Adolescents
Authors: Jankowiak Barbara , Jaskulska Sylwia , Pérez-Martínez Vanesa , Pyżalski Jacek , Sanz-Barbero Belén , Bowes Nicola , Claire Karen De , Neves Sofia , Topa Joana , Silva Estefânia , Mocanu Veronica , Vives-Cases Carmen
Published: August 2024

Bullying and cyberbullying significantly threaten the development and mental health of both victims and perpetrators. This study aimed to analyze the associations between socioeconomic characteristics, personal experiences of violence, perceived social support from peers, and acceptance of violence and (cyber)bullying perpetration. The study involved 1146 secondary school students, consisting of 698 females and 448 males, aged 13 to 16. Prevalence ratios (PRs) were calculated using Poisson regression with robust variance. The results indicated that 12.32% of girls and 18.97% of boys reported engaging in bullying and/or cyberbullying. The likelihood of perpetration was lower among adolescents who had not experienced physical and/or sexual abuse before age 15, but higher among those in romantic relationships who had been victims of dating violence or had experienced (cyber)bullying victimization. Additionally, perceived social support from classmates was associated with a lower likelihood of becoming a perpetrator, whereas acceptance of violence was positively associated with (cyber)bullying perpetration. Preventing adolescents from becoming perpetrators of bullying and/or cyberbullying requires early intervention to prevent all forms of violence in childhood and adolescence, as well as bolstering personal and environmental resources by providing social support.
‘Compassionate’ Control: Social Work and the Rise of Carceral Feminism in Progressive Era Police Reform
Authors: Murray Bethany Jo , Erwin Jennifer , Leotti Sandra , Allen Elizabeth , Bakko Matthew , Jacobs Leah A. , Hostetter C. Riley , Tomczak Stephen Monroe , Fixler Alexandra
Published: August 2024

The contemporary Defund the Police movement has renewed interest in social work’s role in public safety, leading some to call for increased police–social work collaborations. However, claims regarding the potential virtues and pitfalls of social work–police collaborations are largely ahistorical. To contextualize current debates, a systematic investigation into the evolution of social work and its relationship with law enforcement is necessary, particularly the impact that gender norms have had on this relationship. Drawing from the National Conference on Charities and Corrections proceedings, we examined how gendered underpinnings have shaped social work’s relationship to law enforcement and the understanding of social work’s role in public safety. During the Progressive Era, social workers acted as an intervention to reform police by infusing ‘rehabilitative’, ‘protective’, ‘preventative’, and ‘quarantining’ approaches in law enforcement. What emerges from the archives is a chronicle detailing how using social work as a gendered intervention for police reform during the Progressive Era fell short of addressing the root causes of carceral issues, drawing parallels between the Progressive Era reforms and today’s contemporary reforms.
Investigating Runner’s High: Changes in Mood and Endocannabinoid Concentrations after a 60 min Outdoor Run Considering Sex, Running Frequency, and Age
Authors: Weiermair Theresia , Svehlikova Eva , Boulgaropoulos Beate , Magnes Christoph , Eberl Anita
Published: August 2024

Runner’s high is a euphoric emotional state occurring during and post-physical exercise. Although previous data indicate endocannabinoids’ involvement in animal runner’s high, their role in human runner’s high remains to be established. We investigated runner’s high in healthy humans assessing mood and plasma endocannabinoid concentration changes pre- and post a 60 min outdoor run, considering sex (8 females/8 males), running frequency (4 occasional/12 regular runners) and age (median split 36 years). Mood, AEA, and 2-AG concentrations were significantly increased post-run considering all participants (p < 0.0001, p < 0.0001, p < 0.01, respectively), with 2-AG varying more than AEA concentrations. Concentrations of both endocannabinoids increased pre- to post-run in women (p < 0.01) but the AEA concentration increase was higher in females than in males (p < 0.05). Post-run concentration increase appeared to be more pronounced in occasional than in regular runners for 2-AG but not for AEA. However, regular runners experienced stronger mood increases and better post-run mood than occasional runners. Post-run endocannabinoid concentrations were increased regardless of age. AEA concentrations and their post-run changes were less affected by running frequency and age than those of 2-AG. These findings provide insights into the interplay of physical exercise, physiological/psychological factors and demographics, laying a valuable foundation for future research.
Associations of School-Level Factors and School Sport Facility Parameters with Overweight and Obesity among Children and Adolescents in Pakistan: An Empirical Cross-Sectional Study
Authors: Tanveer Moazzam , Asghar Ejaz , Badicu Georgian , Tanveer Umar , Roy Nadeem , Zeba Asifa , Badri Al-Mhanna Sameer , Batrakoulis Alexios
Published: August 2024

Childhood overweight and obesity are increasingly prevalent in Pakistan, posing significant public health challenges. This study explores the associations of school-level factors and school sports facility parameters with overweight and obesity among children and adolescents in Pakistan. A cross-sectional study across seven random districts in Punjab province, Pakistan, was conducted using a representative multistage random cluster sample. Underweight (BMI < 5th percentile), overweight (85th ≤ BMI < 95th percentile), and obese (95th percentile ≤ BMI) were defined using the US Center for Disease Control (CDC) 2000 criteria. Statistical analyses including the Chi-square test, Pearson correlation coefficient, and linear regression were performed to investigate predictive characteristics. Logistic regression analysis assessed the simultaneous impact of several covariates on dichotomous outcomes, with 95% confidence intervals (CIs) computed and a significance level set at p < 0.05. The study included 4108 Pakistani school children aged 9 to 17 years (mean age = 13.92 years, 59.3% boys) from 62 schools. The prevalence of overweight and obesity was 19.4% and 10.7%, respectively. Findings revealed a concerning lack of physical education activities (60% reported 0 sessions per week), morning exercise (60%), and classroom exercises (66%) among school-aged children. Leadership attitudes toward physical education (β = 0.04, p = 0.006) and students’ satisfaction with the playground (β = 0.05, p = 0.015) showed significant associations with body weight status. Conversely, provision of physical education facilities, effective fund utilization for physical education, meeting school sports facility requirements, and weekend opening of school sports grounds did not significantly impact weight status. Satisfaction with the playground was significantly associated with a lower risk of overweight (OR 0.81, 95% CI 0.69–0.95, p < 0.05), indicating reduced overweight likelihood among students satisfied with school playgrounds. The study underscores significant gaps in promoting physical activity within school environments and highlights the urgent need for interventions to enhance physical education resources and sports facilities, aiming to create healthier school environments and reduce overweight and obesity prevalence among students. Further research and interventions are crucial for promoting lifelong physical activity habits and improving student health and well-being.
The Mediating Role of Total Quality Management between Corporate Social Responsibility and Corporate Environmental Performance
Authors: Saeidi Sayedeh Parastoo , Saeidi Parvaneh , Saeidi Sayyedeh Parisa
Published: August 2024

Manufacturers have been under increasing pressure from various groups to recognize and deal with the environmental effects of their operations, especially in the past twenty years. To manage this challenge, they have adopted two practices: corporate social responsibility (CSR) and total quality management (TQM). However, it is still not clear how these practices are supposed to affect environmental performance in developing countries, particularly the less developed ones. This study aimed to fill this gap by researching whether TQM could mediate between CSR and green management in Iranian manufacturing companies. The Baron and Kenny method was used to achieve the answer. The structural equation modeling method, using AMOS, was additionally used to explore the relationships between the concepts. Out of 849 questionnaires distributed, 328 usable responses were received. The findings supported all hypotheses: CSR is linked to a company’s environmental performance, CSR is connected to TQM, TQM is associated with the company’s environmental performance, and the relationship between CSR and environmental performance is partly mediated by TQM. Practically, the findings of this study provide organizations with actionable guidance to enhance their environmental sustainability practices, resulting in fostering long-term sustainability and gaining a competitive advantage.
Occupant-Detection-Based Individual Control of Four-Way Air Conditioner for Sustainable Building Energy Management
Authors: Ham Joon-Hee , Kim Bum-Soo , Bae In-Woo , Joe Jaewan
Published: August 2024

In this study, individual control of a four-way air conditioner was developed based on the distribution of occupants to prevent unnecessary energy consumption during room-wide control. An occupancy detection algorithm was created in Python using YOLOv5 object recognition technology to identify the occupants’ distribution in space. Recorded video data were used to test the algorithm. A simulation case study for a building energy model was conducted, assuming that this algorithm was applied using surveillance cameras in commercial buildings, such as cafés and restaurants. A grey-box model was established based on measurements in a thermal zone, dividing one space into two zones. The temperature data for the two zones were collected by individually turning on the air conditioner for each zone in turns for a specific period. Manual closure was applied to each supply blade using a tape to provide cooling to the target zone. Finally, through energy simulations, the decreased rates in energy consumption between the proposed individual control and existing room-wide controls were compared. Different scenarios for the occupants’ schedules were considered, and average rates in energy savings of 21–22% were observed, demonstrating the significance of individual control in terms of energy consumption. However, marginal comfort violations were observed, which is inevitable. The developed control method is expected to contribute to sustainable energy management in buildings.
Comparative Analysis of Leak Detection Methods Using Hydraulic Modelling and Sensitivity Analysis in Rural and Urban–Rural Areas
Authors: Bartkowska Izabela , Wysocki Łukasz , Zajkowski Artur , Tuz Piotr
Published: August 2024

Water scarcity is a significant global challenge, exacerbated by leakages in water distribution networks. This paper addresses the challenge of detecting leakages in rural and urban–rural water supply systems through hydraulic modelling and a sensitivity analysis. Two distinct real-world network models were studied to assess real and simulated leakage scenarios varying in location and magnitude. A distinct leakage detection approach utilizing outflow measurements from hydrants was tested. Additionally, the effectiveness of various statistical measures—such as correlation, angular closeness, Euclidean distance, Manhattan distance, Chebyshev distance, cosine similarity, and Spearman correlation—were evaluated to determine their efficacy in leakage detection. Different methods for identifying leak candidates were explored and compared, either by selecting a single leak candidate based on similarity measures or by identifying a group of candidates to mark leak hotspots. Density-based spatial clustering of applications with noise was used to assess the number of potential leak candidate groups. The study’s findings contribute to the optimization of leak detection strategies in water supply networks, particularly in rural settings, where detection is challenging due to scarce measurement datasets, budget restrictions, and operational constraints.
Comparative Environmental Life Cycle Assessment on Corn Starch Plasticization and Co-Plasticization Processes
Authors: Surendren Aarsha , Hasan Yusra , Mohanty Amar K. , Abbassi Bassim , Misra Manjusri
Published: August 2024

Starch has overtaken the bioplastic market in developing thermoplastic starch-based blends and composite systems owing to its biodegradability and sustainability. Thermoplastic starch (TPS) development is mostly a two-stage process involving plasticizing starch and blending plasticized starch with a polymer. Most of the research focuses on improving the properties of the blend system through different methodologies, including various plasticizers and co-plasticizers. However, limited studies have analyzed the environmental effects of plasticizers or co-plasticizers and their processing. Thus, in this research, the environmental impact of starch plasticization processes performed by co-plasticization (glycerol–urea, glycerol–citric acid, and glycerol–succinic anhydride) and by conventional glycerol-based plasticization is compared through life cycle assessment (LCA). The results showed that glycerol–citric acid- and glycerol–succinic anhydride-based co-plasticization had a comparable environmental impact to traditional glycerol-based plasticization. In contrast, the glycerol–urea-based co-plasticization process exhibited the highest effect on the environment. Furthermore, to reduce the environmental impact, a sensitivity analysis of the plasticization processes was conducted by changing the energy aspect of the processes through quantitative and qualitative approaches. The qualitative approach significantly reduced major impact categories such as global warming, carcinogens, ecotoxicity, and fossil fuel depletion.
Navigating Green Ship Recycling: A Systematic Review and Implications for Circularity and Sustainable Development
Authors: ElMenshawy Omar M. , Ülkü M. Ali , Hsuan Juliana
Published: August 2024

The shipping industry is the cornerstone that facilitates the movement of approximately 90% of international commercial goods. However, environmental challenges, particularly in the ship recycling (SR) industry, have become increasingly evident. Via closed-loop production patterns within an economic system, a circular economy aims to improve resource-use efficiency by focusing on urban and industrial waste to achieve better balance and harmony between the economy, environment, and society. A key element in this process is a well-executed disassembly that enables reuse, remanufacturing, high-value recycling, and implementing other circular strategies. Based on a systematic literature review, this paper delineates the SR process, identifies influential scholarly works on recycling end-of-life ships, discusses factors affecting shipowners’ decision to recycle, and opportunities for sustainability and circularity in SR processes. The results confirm the increasing need for green SR to reduce shipbreaking waste. Also discussed is how greening SR could be integrated into sustainable development goals under proper environmental and safety regulations and an aligned cultural mindset for stakeholders.
A Case Study on the Innovative Development of Digital Supply Chain Finance Based on MYbank in China
Authors: Yu Longjin , Ji Man , Haleem Fazli , Gong Yilong , Shen Yang , Zeng Shaolong
Published: August 2024

Small and medium-sized enterprises (SMEs) play a critical role in promoting the development of China’s real economy and improving national productivity, but their financing still faces challenges. In recent years, supply chain finance (SCF) has become one of the most important solutions to SMEs’ financing difficulties. Promoting the digital and innovative development of SCF can better meet the financing needs of SMEs. This study is based on a case study of Zhejiang MYbank Co., Ltd. (MYbank) in Hangzhou, China, which is a representative institution of digital supply chain finance development in China and committed to realizing the digital innovation development of SCF. Based on MYbank’s financial index data from 2018 to 2022, the implementation effect of MYbank’s digital supply chain finance is quantitatively analyzed from the perspectives of SMEs and MYbank. The main findings are as follows.(1) In the practice of digital supply chain finance, MYbank implements the new concepts of SCF decentralization and full coverage of supply chain links while enhancing the sustainability of SCF. (2) For SMEs, MYbank’s digital supply chain finance development has led to an increase in the financing scale and financing availability of SMEs. (3) The analysis of MYbank’s comprehensive benefits shows that the digital innovation development of SCF effectively increased the overall economic value of the enterprise during the period of 2018–2022. Based on these findings, this study provides implications for commercial banks and other financial institutions to develop digital supply chain finance.