bluuebunny/crossref_metadata_2025 huggingface dataset:
Lithium-Rich Pegmatite Detection Integrating High-Resolution and Hyperspectral Satellite Data in Zhawulong Area, Western Sichuan, China
Authors: Ding Wenqing , Ding Lin , Li Qingting , Li Jinxiang , Zhang Liyun
Published: August 2023

Lithium (Li) has grown to be a strategic key metal due to the enormous demand for the development of new energy industries over the world. As one of the most significant sources of Li resources, pegmatite-type Li deposits hold a large share of the mining market. In recent years, several large and super-large spodumene (Spd)-rich pegmatite deposits have been discovered successively in the Hoh-Xil–Songpan-Garzê (HXSG) orogenic belt of the northern Tibetan Plateau, indicative of the great Li prospecting potential of this belt. Hyperspectral remote sensing (HRS), as a rapidly developing exploration technology, is especially sensitive to the identification of alteration minerals, and has made important breakthroughs in porphyry copper deposit exploration. However, due to the small width of the pegmatite dykes and the lack of typical alteration zones, the ability of HRS in the exploration of Li-rich pegmatite deposits remains to be explored. In this study, Li-rich pegmatite anomalies were directly extracted from ZY1-02D hyperspectral imagery in the Zhawulong (ZWL) area of western Sichuan, China, using target detection techniques including Adaptive Cosine Estimator (ACE), Constrained Energy Minimization (CEM), Spectral Angle Mapper (SAM), and SAM with BandMax (SAMBM). Further, the Li-rich anomalies were superimposed with the distribution of pegmatite dykes delineated based on GF-2 high-resolution imagery. Our final results accurately identified the known range of Spd pegmatite dykes and further predicted two new exploration target areas. The approaches used in this study could be easily extended to other potential mineralization areas to discover new rare metal pegmatite deposits on the Tibetan Plateau.
YOLO-DCTI: Small Object Detection in Remote Sensing Base on Contextual Transformer Enhancement
Authors: Min Lingtong , Fan Ziman , Lv Qinyi , Reda Mohamed , Shen Linghao , Wang Binglu
Published: August 2023

Object detection for remote sensing is a fundamental task in image processing of remote sensing; as one of the core components, small or tiny object detection plays an important role. Despite the considerable advancements achieved in small object detection with the integration of CNN and transformer networks, there remains untapped potential for enhancing the extraction and utilization of information associated with small objects. Particularly within transformer structures, this potential arises from the disregard of the complex and the intertwined interplay between spatial context information and channel information during the global modeling of pixel-level information within small objects. As a result, valuable information is prone to being obfuscated and annihilated. To mitigate this limitation, we propose an innovative framework, YOLO-DCTI, that capitalizes on the Contextual Transformer (CoT) framework for the detection of small or tiny objects. Specifically, within CoT, we seamlessly incorporate global residuals and local fusion mechanisms throughout the entire input-to-output pipeline. This integration facilitates a profound investigation into the network’s intrinsic representations at deeper levels and fosters the fusion of spatial contextual attributes with channel characteristics. Moreover, we propose an improved decoupled contextual transformer detection head structure, denoted as DCTI, to effectively resolve the feature conflicts that ensue from the concurrent classification and regression tasks. The experimental results on the Dota, VISDrone, and NWPU VHR-10 datasets show that, on the powerful real-time detection network YOLOv7, the speed and accuracy of tiny targets are better balanced.
Joint Power and Bandwidth Allocation with RCS Fluctuation Characteristic for Space Target Tracking
Authors: Yang Qingwei , Jiang Libing , Zheng Shuyu , Zhao Yingjian , Wang Zhuang
Published: August 2023

Reasonable allocation of space-based radar resources is a crucial aspect of improving the accuracy of space multi-target tracking and enhancing spatial awareness. The conventional resource allocation algorithm fails to exploit the high dynamic radar cross-section (RCS) characteristics, resulting in poor tracking robustness, tracking divergence, or even loss of tracking. However, the RCS of space targets fluctuates considerably in actual tracking scenarios, which cannot be disregarded for space target tracking tasks. To address this issue, we propose an adaptive allocation method that considers the dynamic RCS fluctuation characteristic for space-based radar tracking assignments. The proposed method exploits the predictable orbital information of space target to calculate the real-time observation angle of radar, and then obtains the multi-target dynamic RCS through the target RCS dataset. By combining the obtained RCS sequence, radar power, and bandwidth, an optimal model for radar tracking accuracy is established based on the multi-target posterior Cramér–Rao lower bound (PCRLB) to evaluate the tracking performance. By resolving the aforementioned multivariance optimization problem, we eventually acquire the results of power and bandwidth pre-allocation for tracking multiple space targets. Simulation results validate that, compared with the traditional methods, the proposed joint dynamic RCS power and bandwidth allocation (JRPBA) method can achieve superior tracking accuracy and minimize instances of missed tracking.
Siam-EMNet: A Siamese EfficientNet–MANet Network for Building Change Detection in Very High Resolution Images
Authors: Huang Liang , Tian Qiuyuan , Tang Bo-Hui , Le Weipeng , Wang Min , Ma Xianguang
Published: August 2023

As well as very high resolution (VHR) remote sensing technology and deep learning, methods for detecting changes in buildings have made great progress. Despite this, there are still some problems with the incomplete detection of change regions and rough edges. To this end, a change detection network for building VHR remote sensing images based on Siamese EfficientNet B4-MANet (Siam-EMNet) is proposed. First, a bi-branches pretrained EfficientNet B4 encoder structure is constructed to enhance the performance of feature extraction and the rich shallow and deep information is obtained; then, the semantic information of the building is input into the MANet decoder integrated by the dual attention mechanism through the skip connection. The position-wise attention block (PAB) and multi-scale fusion attention block (MFAB) capture spatial relationships between pixels in the global view and channel relationships between layers. The integration of dual attention mechanisms ensures that the building contour is fully detected. The proposed method was evaluated on the LEVIR-CD dataset, and its precision, recall, accuracy, and F1-score were 92.00%, 88.51%, 95.71%, and 90.21%, respectively, which represented the best overall performance compared to the BIT, CDNet, DSIFN, L-Unet, P2V-CD, and SNUNet methods. Verification of the efficacy of the suggested approach was then conducted.
An Accurate and Efficient Supervoxel Re-Segmentation Approach for Large-Scale Point Clouds Using Plane Constraints
Authors: Lai Baokang , Yuan Yingtao , Zhang Yueqiang , Hu Biao , Yu Qifeng
Published: August 2023

The accurate and efficient segmentation of large-scale urban point clouds is crucial for many higher-level tasks, such as boundary line extraction, point cloud registration, and deformation measurement. In this paper, we propose a novel supervoxel segmentation approach to address the problem of under-segmentation in local regions of point clouds at various resolutions. Our approach introduces distance constraints from boundary points to supervoxel planes in the merging stage to enhance boundary segmentation accuracy between non-coplanar supervoxels. Additionally, supervoxels with roughness above a threshold are re-segmented using random sample consensus (RANSAC) to address multi-planar coupling within local areas of the point clouds. We tested the proposed method on two publicly available large-scale point cloud datasets. The results show that the new method outperforms two classical methods in terms of boundary recall, under-segmentation error, and average entropy in urban scenes.
ICESat-2 for Coastal MSS Determination—Evaluation in the Norwegian Coastal Zone
Authors: Tomić Matea , Baltazar Andersen Ole
Published: August 2023

Radar satellite altimeters enable the determination of the mean sea surface to centimeter accuracy, which can be degraded in coastal areas because of the lack of valid altimetry observations due to land contamination and the altimeter footprint size. In 2018, the National Aeronautics and Space Administration launched ICESat-2, a laser altimetry mission equipped with the Advanced Topographic Laser Altimeter System, providing measurements every 0.7 m in the along-track direction. Taking into account the complexity of the Norwegian coastline, this study aims to evaluate coastal observations from ICESat-2 in order to use it to update the existing mean sea surface for Norway, NMBU18. We, therefore, determined the mean sea surface using only ICESat-2 observations and compared it with mean sea level observations from 23 permanent tide gauges along the entire coast and 21 temporary tide gauges in Norway’s largest and deepest fjord, Sognefjorden. We also included two global mean sea surface models and NMBU18 for comparison. The results have shown that ICESat-2 is indeed able to provide more valid observations in the coastal zone, which can be used to improve the mean sea surface model, especially along the coast.
Biomedical Sensors for Functional Mapping: Techniques, Methods, Experimental and Medical Applications
Authors: Mastropietro Alfonso , Rivolta Massimo Walter , Scano Alessandro
Published: August 2023

The rapid advancement of biomedical sensor technology has revolutionized the field of functional mapping in medicine, offering novel and powerful tools for diagnosis, clinical assessment, and rehabilitation [...]
Microbial Fuel Cell Biosensor with Capillary Carbon Source Delivery for Real-Time Toxicity Detection
Authors: Adekunle Ademola , Bambace Stefano , Tanguay-Rioux Fabrice , Tartakovsky Boris
Published: August 2023

A microbial fuel cell (MFC) biosensor with an anode as a sensing element is often unreliable at low or significantly fluctuating organic matter concentrations. To remove this limitation, this work demonstrates capillary action-aided carbon source delivery to an anode-sensing MFC biosensor for use in carbon-depleted environments, e.g., potable water. First, different carbon source delivery configurations using several thread types, silk, nylon, cotton, and polyester, are evaluated. Silk thread was determined to be the most suitable material for passive delivery of a 40 g L−1 acetate solution. This carbon source delivery system was then incorporated into the design of an MFC biosensor for real-time detection of toxicity spikes in tap water, providing an organic matter concentration of 56 ± 15 mg L−1. The biosensor was subsequently able to detect spikes of toxicants such as chlorine, formaldehyde, mercury, and cyanobacterial microcystins. The 16S sequencing results demonstrated the proliferation of Desulfatirhabdium (10.7% of the total population), Pelobacter (10.3%), and Geobacter (10.2%) genera. Overall, this work shows that the proposed approach can be used to achieve real-time toxicant detection by MFC biosensors in carbon-depleted environments.
An Instrumented Mouthguard for Real-Time Measurement of Head Kinematics under a Large Range of Sport Specific Accelerations
Authors: Jones Chris M. , Austin Kieran , Augustus Simon N. , Nicholas Kieran Jai , Yu Xiancheng , Baker Claire , Chan Emily Yik Kwan , Loosemore Mike , Ghajari Mazdak
Published: August 2023

Background: Head impacts in sports can produce brain injuries. The accurate quantification of head kinematics through instrumented mouthguards (iMG) can help identify underlying brain motion during injurious impacts. The aim of the current study is to assess the validity of an iMG across a large range of linear and rotational accelerations to allow for on-field head impact monitoring. Methods: Drop tests of an instrumented helmeted anthropometric testing device (ATD) were performed across a range of impact magnitudes and locations, with iMG measures collected concurrently. ATD and iMG kinematics were also fed forward to high-fidelity brain models to predict maximal principal strain. Results: The impacts produced a wide range of head kinematics (16–171 g, 1330–10,164 rad/s2 and 11.3–41.5 rad/s) and durations (6–18 ms), representing impacts in rugby and boxing. Comparison of the peak values across ATD and iMG indicated high levels of agreement, with a total concordance correlation coefficient of 0.97 for peak impact kinematics and 0.97 for predicted brain strain. We also found good agreement between iMG and ATD measured time-series kinematic data, with the highest normalized root mean squared error for rotational velocity (5.47 ± 2.61%) and the lowest for rotational acceleration (1.24 ± 0.86%). Our results confirm that the iMG can reliably measure laboratory-based head kinematics under a large range of accelerations and is suitable for future on-field validity assessments.
Aging Compensation in a Class-A High-Frequency Amplifier with DC Temperature Measurements
Authors: Altet Josep , Aragones Xavier , Barajas Enrique , Gisbert Xavier , Martínez Sergio , Mateo Diego
Published: August 2023

One of the threats to nanometric CMOS analog circuit reliability is circuit performance degradation due to transistor aging. To extend circuit operating life, the bias of the main devices within the circuit must be adjusted while the aging degradation process affects them by using a monitor circuit that tracks the evolution of the circuit performance. In this paper, we propose the use of DC temperature measurements in the proximity of the circuit to perform the monitoring of circuit performance degradation and as an observable variable to adjust the bias of the main devices to restore the degraded performance to the original values. To this end, we present experimental results obtained from nine samples of a standard CMOS integrated circuit containing a high-frequency class-A power amplifier and a differential temperature sensor. After accelerated aging, the gain of the amplifier is degraded up to 50%. We propose two different procedures to perform DC temperature measurements that allow tracking of the amplifier gain degradation due to aging and, by uniquely observing temperature readings, automatically set a new bias for the amplifier devices that restores the original amplifier gain. Whereas one of the procedures is able to restore the gain up to a certain limit, the second allows full gain restoration.
Estimation of Wideband Multi-Component Phasors Considering Signal Damping
Authors: Zhao Dongfang , Li Shisong , Wang Fuping , Zhao Wei , Huang Songling
Published: August 2023

Harmonic and interharmonic content in power system signals is increasing with the development of renewable energy generation and power electronic devices. These multiple signal components can seriously degrade power quality, trip thermal generators, cause oscillations, and threaten system stability, especially the interharmonic tones with positive damping factors. The first step to mitigate these adverse effects is to accurately and quickly monitor signal features, including frequency, damping factor, amplitude, and phase. This paper proposes a concise and robust index to identify the number of modes present in the signal using the singular values of the Hankel matrix and discusses the scope of its application by testing the influence of various factors. Next, the simplified matrix pencil theory is employed to estimate the signal component frequency and damping factor. Then their estimates are considered in the modified least-squares algorithm to extract the wideband multi-component phasors accurately. Finally, this paper designs a series of scenarios considering varying signal frequency, damping factor, amplitude, and phase to test the proposed algorithm thoroughly. The results verify that the proposed method can achieve a maximum total vector error of less than 1.5%, which is more accurate than existing phasor estimators in various signal environments. The high accuracy of the proposed method is because it considers both the estimation of the frequency number and the effect of signal damping.
FlyDetector—Automated Monitoring Platform for the Visual–Motor Coordination of Honeybees in a Dynamic Obstacle Scene Using Digital Paradigm
Authors: Huang Yuanyuan , Lu Guyue , Zhao Wei , Zhang Xinyao , Jiang Jiawen , Xing Qiang
Published: August 2023

Vision plays a crucial role in the ability of compound-eyed insects to perceive the characteristics of their surroundings. Compound-eyed insects (such as the honeybee) can change the optical flow input of the visual system by autonomously controlling their behavior, and this is referred to as visual–motor coordination (VMC). To analyze an insect’s VMC mechanism in dynamic scenes, we developed a platform for studying insects that actively shape the optic flow of visual stimuli by adapting their flight behavior. Image-processing technology was applied to detect the posture and direction of insects’ movement, and automatic control technology provided dynamic scene stimulation and automatic acquisition of perceptual insect behavior. In addition, a virtual mapping technique was used to reconstruct the visual cues of insects for VMC analysis in a dynamic obstacle scene. A simulation experiment at different target speeds of 1–12 m/s was performed to verify the applicability and accuracy of the platform. Our findings showed that the maximum detection speed was 8 m/s, and triggers were 95% accurate. The outdoor experiments showed that flight speed in the longitudinal axis of honeybees was more stable when facing dynamic barriers than static barriers after analyzing the change in geometric optic flow. Finally, several experiments showed that the platform can automatically and efficiently monitor honeybees’ perception behavior, and can be applied to study most insects and their VMC.
Development and Characterization of Hybrid, Temperature Sensing and Heating Yarns with Color Change
Authors: Junge Theresa , Brendgen Rike , Grassmann Carsten , Weide Thomas , Schwarz-Pfeiffer Anne
Published: August 2023

A person’s body temperature is an important indicator of their health status. A deviation of that temperature by just 2 °C already has or can lead to serious consequences, such as fever or hypothermia. Hence, the development of a temperature-sensing and heatable yarn is an important step toward enabling and improving the monitoring and regulation of a person’s body temperature. This technology offers benefits to several industries, such as health care and sports. This paper focuses on the characterization and development of a hybrid yarn, which can measure and visualize temperature changes through a thermoresistive and thermochromic effect. Moreover, the yarn is able to serve as a flexible heating element by connecting to a power source. The structure of the yarn is designed in three layers. Each layer and component ensures the functionality and flexibility of the yarn and additional compatibility with further processing steps. A flexible stainless steel core was used as the heat-sensitive and heat-conducting material. The layer of polyester wrapped around the stainless steel yarn improves the wearing comfort and serves as substrate material for the thermochromic coating. The resulting hybrid yarn has a reproducible sensory function and changes its resistance by 0.15 Ω between 20 and 60 °C for a length of 30 cm. In addition, the yarn has a uniform and reproducible heating power, so that temperature steps can be achieved at a defined length by selecting certain voltages. The thermochromic color change is clearly visible between 28 and 29 °C. Due to its textile structure, the hybrid sensory and actuating yarn can easily be incorporated into a woven fabric or into a textile by means of joining technology sewing.
A Salient Object Detection Method Based on Boundary Enhancement
Authors: Wen Falin , Wang Qinghui , Zou Ruirui , Wang Ying , Liu Fenglin , Chen Yang , Yu Linghao , Du Shaoyi , Yuan Chengzhi
Published: August 2023

Visual saliency refers to the human’s ability to quickly focus on important parts of their visual field, which is a crucial aspect of image processing, particularly in fields like medical imaging and robotics. Understanding and simulating this mechanism is crucial for solving complex visual problems. In this paper, we propose a salient object detection method based on boundary enhancement, which is applicable to both 2D and 3D sensors data. To address the problem of large-scale variation of salient objects, our method introduces a multi-level feature aggregation module that enhances the expressive ability of fixed-resolution features by utilizing adjacent features to complement each other. Additionally, we propose a multi-scale information extraction module to capture local contextual information at different scales for back-propagated level-by-level features, which allows for better measurement of the composition of the feature map after back-fusion. To tackle the low confidence issue of boundary pixels, we also introduce a boundary extraction module to extract the boundary information of salient regions. This information is then fused with salient target information to further refine the saliency prediction results. During the training process, our method uses a mixed loss function to constrain the model training from two levels: pixels and images. The experimental results demonstrate that our salient target detection method based on boundary enhancement shows good detection effects on targets of different scales, multi-targets, linear targets, and targets in complex scenes. We compare our method with the best method in four conventional datasets and achieve an average improvement of 6.2% on the mean absolute error (MAE) indicators. Overall, our approach shows promise for improving the accuracy and efficiency of salient object detection in a variety of settings, including those involving 2D/3D semantic analysis and reconstruction/inpainting of image/video/point cloud data.
Architecture for a Mobile Robotic Camera Positioning System for Photogrammetric Data Acquisition in Hydroelectric Tunnels
Authors: Keizer Ryan , Dubay Rickey , Waugh Lloyd , Bradley Cody
Published: August 2023

The structural condition of hydroelectric tunnels is important to the overall performance, safety, and longevity of generating stations. Significant effort is required to inspect, monitor, and maintain these tunnels. Photogrammetry is an effective method of collecting highly accurate visual and spatial data. However, it also presents the complex challenge of positioning a camera at thousands of difficult-to-reach locations throughout the large and varying-diameter tunnels. A semi-automated robotic camera positioning system was developed to enhance the collection of images within hydroelectric tunnels for photogrammetric inspections. A continuous spiral image network was developed to optimize the collection speed within the bounds of photography and capture-in-motion constraints. The positioning system and image network optimization reduce the time and effort required while providing the ability to adapt to different and varying tunnel diameters. To demonstrate, over 28,000 images were captured at a ground sampling distance of 0.4 mm in the 822 m long concrete-lined section of the Grand Falls Generating Station intake tunnel.
High-Speed Tracking with Mutual Assistance of Feature Filters and Detectors
Authors: Matsuo Akira , Yamakawa Yuji
Published: August 2023

Object detection and tracking in camera images is a fundamental technology for computer vision and is used in various applications. In particular, object tracking using high-speed cameras is expected to be applied to real-time control in robotics. Therefore, it is required to increase tracking speed and detection accuracy. Currently, however, it is difficult to achieve both of those things simultaneously. In this paper, we propose a tracking method that combines multiple methods: correlation filter-based object tracking, deep learning-based object detection, and motion detection with background subtraction. The algorithms work in parallel and assist each other’s processing to improve the overall performance of the system. We named it the “Mutual Assist tracker of feature Filters and Detectors (MAFiD method)”. This method aims to achieve both high-speed tracking of moving objects and high detection accuracy. Experiments were conducted to verify the detection performance and processing speed by tracking a transparent capsule moving at high speed. The results show that the tracking speed was 618 frames per second (FPS) and the accuracy was 86% for Intersection over Union (IoU). The detection latency was 3.48 ms. These experimental scores are higher than those of conventional methods, indicating that the MAFiD method achieved fast object tracking while maintaining high detection performance. This proposal will contribute to the improvement of object-tracking technology.
Enhanced Slime Mould Optimization with Deep-Learning-Based Resource Allocation in UAV-Enabled Wireless Networks
Authors: Alkanhel Reem , Rafiq Ahsan , Mokrov Evgeny , Khakimov Abdukodir , Muthanna Mohammed Saleh Ali , Muthanna Ammar
Published: August 2023

Unmanned aerial vehicle (UAV) networks offer a wide range of applications in an overload situation, broadcasting and advertising, public safety, disaster management, etc. Providing robust communication services to mobile users (MUs) is a challenging task because of the dynamic characteristics of MUs. Resource allocation, including subchannels, transmit power, and serving users, is a critical transmission problem; further, it is also crucial to improve the coverage and energy efficacy of UAV-assisted transmission networks. This paper presents an Enhanced Slime Mould Optimization with Deep-Learning-based Resource Allocation Approach (ESMOML-RAA) in UAV-enabled wireless networks. The presented ESMOML-RAA technique aims to efficiently accomplish computationally and energy-effective decisions. In addition, the ESMOML-RAA technique considers a UAV as a learning agent with the formation of a resource assignment decision as an action and designs a reward function with the intention of the minimization of the weighted resource consumption. For resource allocation, the presented ESMOML-RAA technique employs a highly parallelized long short-term memory (HP-LSTM) model with an ESMO algorithm as a hyperparameter optimizer. Using the ESMO algorithm helps properly tune the hyperparameters related to the HP-LSTM model. The performance validation of the ESMOML-RAA technique is tested using a series of simulations. This comparison study reports the enhanced performance of the ESMOML-RAA technique over other ML models.
Efficient Photoacoustic Image Synthesis with Deep Learning
Authors: Rix Tom , Dreher Kris K. , Nölke Jan-Hinrich , Schellenberg Melanie , Tizabi Minu D. , Seitel Alexander , Maier-Hein Lena
Published: August 2023

Photoacoustic imaging potentially allows for the real-time visualization of functional human tissue parameters such as oxygenation but is subject to a challenging underlying quantification problem. While in silico studies have revealed the great potential of deep learning (DL) methodology in solving this problem, the inherent lack of an efficient gold standard method for model training and validation remains a grand challenge. This work investigates whether DL can be leveraged to accurately and efficiently simulate photon propagation in biological tissue, enabling photoacoustic image synthesis. Our approach is based on estimating the initial pressure distribution of the photoacoustic waves from the underlying optical properties using a back-propagatable neural network trained on synthetic data. In proof-of-concept studies, we validated the performance of two complementary neural network architectures, namely a conventional U-Net-like model and a Fourier Neural Operator (FNO) network. Our in silico validation on multispectral human forearm images shows that DL methods can speed up image generation by a factor of 100 when compared to Monte Carlo simulations with 5×108 photons. While the FNO is slightly more accurate than the U-Net, when compared to Monte Carlo simulations performed with a reduced number of photons (5×106), both neural network architectures achieve equivalent accuracy. In contrast to Monte Carlo simulations, the proposed DL models can be used as inherently differentiable surrogate models in the photoacoustic image synthesis pipeline, allowing for back-propagation of the synthesis error and gradient-based optimization over the entire pipeline. Due to their efficiency, they have the potential to enable large-scale training data generation that can expedite the clinical application of photoacoustic imaging.
Exploring Digital Twin-Based Fault Monitoring: Challenges and Opportunities
Authors: Bofill Jherson , Abisado Mideth , Villaverde Jocelyn , Sampedro Gabriel Avelino
Published: August 2023

High efficiency and safety are critical factors in ensuring the optimal performance and reliability of systems and equipment across various industries. Fault monitoring (FM) techniques play a pivotal role in this regard by continuously monitoring system performance and identifying the presence of faults or abnormalities. However, traditional FM methods face limitations in fully capturing the complex interactions within a system and providing real-time monitoring capabilities. To overcome these challenges, Digital Twin (DT) technology has emerged as a promising solution to enhance existing FM practices. By creating a virtual replica or digital copy of a physical equipment or system, DT offers the potential to revolutionize fault monitoring approaches. This paper aims to explore and discuss the diverse range of predictive methods utilized in DT and their implementations in FM across industries. Furthermore, it will showcase successful implementations of DT in FM across a wide array of industries, including manufacturing, energy, transportation, and healthcare. The utilization of DT in FM enables a comprehensive understanding of system behavior and performance by leveraging real-time data, advanced analytics, and machine learning algorithms. By integrating physical and virtual components, DT facilitates the monitoring and prediction of faults, providing valuable insights into the system’s health and enabling proactive maintenance and decision making.
Mutual Coupling Reduction in Antenna Arrays Using Artificial Intelligence Approach and Inverse Neural Network Surrogates
Authors: Roshani Saeed , Koziel Slawomir , Yahya Salah I. , Chaudhary Muhammad Akmal , Ghadi Yazeed Yasin , Roshani Sobhan , Golunski Lukasz
Published: August 2023

This paper presents a novel approach to reducing undesirable coupling in antenna arrays using custom-designed resonators and inverse surrogate modeling. To illustrate the concept, two standard patch antenna cells with 0.07λ edge-to-edge distance were designed and fabricated to operate at 2.45 GHz. A stepped-impedance resonator was applied between the antennas to suppress their mutual coupling. For the first time, the optimum values of the resonator geometry parameters were obtained using the proposed inverse artificial neural network (ANN) model, constructed from the sampled EM-simulation data of the system, and trained using the particle swarm optimization (PSO) algorithm. The inverse ANN surrogate directly yields the optimum resonator dimensions based on the target values of its S-parameters being the input parameters of the model. The involvement of surrogate modeling also contributes to the acceleration of the design process, as the array does not need to undergo direct EM-driven optimization. The obtained results indicate a remarkable cancellation of the surface currents between two antennas at their operating frequency, which translates into isolation as high as −46.2 dB at 2.45 GHz, corresponding to over 37 dB improvement as compared to the conventional setup.
Distribution of Bioelectrical Impedance Vector Analysis and Phase Angle in Korean Elderly and Sarcopenia
Authors: Jiang Fang-Lin , Tang Saizhao , Eom Seon-Ho , Lee Jae-Young , Chae Ji Heon , Kim Chul-Hyun
Published: August 2023

Purpose: This study aimed to verify whether bioelectrical impedance vector analysis (BIVA) can support the clinical evaluation of sarcopenia in elderly individuals and evaluate the relationships between phase angle (PhA), physical performance, and muscle mass. Methods: The sample comprised 134 free-living elderly individuals of both sexes aged 69–91 years. Anthropometric parameters, grip strength, dual-energy X-ray absorptiometry findings, bioimpedance analysis results, and physical performance were also measured. The impedance vector distributions were evaluated in elderly individuals using BIVA. Results: BIVA revealed significant differences between the sarcopenia and non-sarcopenia groups (both sexes). The sarcopenia group had a significantly lower PhA than the non-sarcopenia group in both sexes (p < 0.05). PhA was significantly correlated with age, appendicular skeletal muscle (ASM), handgrip strength (HGS), and muscle quality in both sexes and significantly correlated with ASM/Height2 and physical performance in males. Conclusion: BIVA can be used as a field assessment method in elderly Koreans with sarcopenia. PhA is a good indicator of muscle strength, muscle quality, and physical performance in males. These methods can help diagnose sarcopenia in elderly individuals with reduced mobility.
Intervention Mapping as a Framework for Developing and Testing an Intervention to Promote Safety at a Rail Infrastructure Maintenance Company
Authors: van der Beek Dolf , Steijn Wouter Martinus Petrus , Groeneweg Jop
Published: August 2023

In this article, the authors apply the intervention mapping (IM) protocol to develop safety leadership training for a rail infrastructure maintenance company. The IM protocol helps to create an evidence-based intervention in a structured way, based on concrete evidence. The application of IM within the occupational safety domain is limited, a research gap that this article bridges with the development and testing of a safety leadership intervention to promote safety behavior among managers. The company was positively and actively engaged in the training program thanks to the IM protocol. The local support group took full advantage of the opportunities to provide input during the development of the training’s various components. Despite this, interpersonal problems within the leadership team itself, such as a lack of psychological safety, were not identified during the needs assessment. These issues had an impact on the overall effectiveness of the training, as they manifested during the training when managers met physically for the first time in several years (due to the coronavirus). Our IM protocol will be adjusted accordingly for future applications, and we hope that sharing our experiences will enable fellow researchers to avoid this problem.
Representative Elementary Volume Estimation and Neural Network-Based Prediction of Change Rates of Dense Non-Aqueous Phase Liquid Saturation and Dense Non-Aqueous Phase Liquid–Water Interfacial Area in Porous Media
Authors: Cheng Zhou , Lu Guoping , Wu Ming , Li Qusheng
Published: August 2023

Investigation of the change rate for contaminant parameters is important to characterize dense non-aqueous phase liquid (DNAPL) transport and distribution in groundwater systems. In this study, four experiments of perchloroethylene (PCE) migration are conducted in two-dimensional (2D) sandboxes to characterize change rates of PCE saturation (So) and PCE–water interfacial area (AOW) under different conditions of salinity, surface active agent, and heterogeneity. Associated representative elementary volume (REV) of the change rate of So (So rate) and change rate of AOW (AOW rate) is derived over the long-term transport process through light transmission techniques. REV of So rate (SR-REV) and REV of AOW rate (AR-REV) are estimated based on the relative gradient error (εgi). Regression analysis is applied to investigate the regularity, and a model based on a back-propagation (BP) neural network is built to simulate and predict the frequencies of SR-REV and AR-REV. Experimental results indicated the salinity, surface active agent, and heterogeneity are important factors that affect the So rate, AOW rate, SR-REV, and AR-REV of the PCE plume in porous media. The first moment of the PCE plume along the vertical direction is decreased under conditions of high salinity, surface active agent, and heterogeneity, while these factors have different effects on the second moment of the PCE plume. Compared with the salinity and surface active agent, heterogeneity has the greatest effect on the GTP, the distributions of the So rate and AOW rate along the depth, and dM, dI. For SR-REV, the standard deviation is increased by the salinity, surface active agent, and heterogeneity. Simultaneously, the salinity and heterogeneity lead to lower values of the mean value of SR-REV, while the surface active agent increases the mean value of SR-REV. However, the mean and standard deviation of AR-REV have no apparent difference under different experimental conditions. These findings reveal the complexity of PCE transport and scale effect in the groundwater system, which have important significance in improving our understanding of DNAPL transport regularity and promoting associated prediction.
Evaluation of the Training Session in Elite Paralympic Powerlifting Athletes Based on Biomechanical and Thermal Indicators
Authors: Santos Larissa Christine Vieira , Aidar Felipe J. , Villar Rodrigo , Greco Gianpiero , de Santana Jefferson Lima , Marçal Anderson Carlos , de Almeida-Neto Paulo Francisco , de Araújo Tinoco Cabral Breno Guilherme , Badicu Georgian , Nobari Hadi , de Souza Raphael Frabrício , da Silva Júnior Walderi Monteiro
Published: August 2023

Background: Paralympic powerlifting (PP) is performed on a bench press, aiming to lift as much weight as possible in a single repetition. Purpose: To evaluate thermal asymmetry and dynamic force parameters with 45 and 80% 1 Repetition Maximum (1 RM) in PP athletes. Methods: Twelve elite PP male athletes were evaluated before and after a training session regarding skin temperature (thermography) and dynamic force indicators (Average Propulsive Velocity-MPV, Maximum Velocity-VMax, and Power). The training consisted of five series of five repetitions (5 × 5) with 80% 1 RM. The force indicators and dynamics before and after (45% 1 RM) were evaluated in series “1” and “5” with 80% 1 RM. Results: The temperature did not present asymmetry, and there were differences between the moment before and after. In MPV, Vmax, and Power, with 45% 1 RM, there were differences both in asymmetry and in moments (p < 0.005). With 80% 1 RM, asymmetry was observed, but no differences between moments (p < 0.005). Conclusion: No thermal asymmetry was observed. There were reductions in MVP and VMax at 45 and 80% 1 RM but without significant differences between time points (before and after). However, there was asymmetry in the moments before and after within a safety standard, where Paralympic powerlifting was safe in terms of asymmetries.
Promoting Sustainable Food Practices in Food Service Industry: An Empirical Investigation on Saudi Arabian Restaurants
Authors: Abdou Ahmed Hassan , Hassan Thowayeb H. , Salem Amany E.
Published: August 2023

While facing environmental challenges and rising concerns around food security and equitable access to nutritious food, sustainable food practices (SFPs) have emerged as a crucial focus area for the restaurant industry. Hence, this study aims to investigate key drivers of promoting SFPs, including customers’ and stakeholders’ sustainable behaviors, governmental laws and regulations around sustainability, the commitment of restaurants to combat climate change, the financial and non-financial outcomes of adopting SFPs, and restaurants’ values and culture toward sustainability. In addition, it explores the power of promoting these practices in driving restaurants’ economic, environmental, and social performance. To achieve these objectives, an online survey was administered to restaurant owners and top managers interested in implementing these practices. Accordingly, eight hypotheses, which explored the direct relationships between this study’s variables, were tested using PLS-SEM with bootstrapping. Based on 221 valid responses, this study revealed that all proposed paths were significant and aligned with each hypothesis. Notably, sustainable behavior exhibited by customers and stakeholders had the greatest influence on promoting SFPs, followed by the values and culture of restaurants related to sustainability and restaurants’ commitment to combatting climate change. Additionally, promoting SFPs was a crucial predictor for enhancing restaurants’ economic, environmental, and social performance, respectively. Upon these findings, restaurant owners and top managers should build a solid framework for promoting SFPs in their establishments by focusing on these factors, thereby improving their overall economic, environmental, and social performance.
Categorization of Loads in Educational Institutions to Effectively Manage Peak Demand and Minimize Energy Cost Using an Intelligent Load Management Technique
Authors: Ramu Priyadharshini , Gangatharan Sivasankar , Rangasamy Sankar , Mihet-Popa Lucian
Published: August 2023

The inclusion of photovoltaics (PV) in electric power supply systems continues to be a significant factor in global interest. However, solar power exhibits intermittent uncertainty and is further unpredictable. Accurate solar generation prediction and efficient utilization are mandatory for power distribution management and demand-side management. Peak demand management and reducing energy costs can be effectively tackled through the implementation of a reliable solar power forecasting system and its efficient utilization. In this regard, the proposed work is related to efficiently managing solar PV power and optimizing power distribution using an enhanced reinforced binary particle swarm optimization (RBPSO) technique. This DSM (demand-side management) strategy involves utilizing a forecast of solar PV generation for the upcoming day and adjusting the consumption schedule of the load to decrease the highest energy demand. The proposed approach improves user comfort by adjusting the non-interruptible and flexible institutional load through clipping and shifting techniques. To evaluate the effectiveness of this approach, its performance is assessed by analyzing the peak demand range and PAR (peak-to-average ratio). It is then compared to the conventional genetic algorithm to determine its effectiveness. Simulation results obtained using MATLAB show that the PAR peak demand before DSM was found to be 1.8602 kW and 378.06 kW, and after DSM, it was reduced to 0.7211 kW and 266.54 kW. This indicates a 29% reduction in Peak demand and performance compared to the conventional genetic algorithm (GA).
Environmental Effects of Sport Horse Production Farms in Argentina
Authors: Vaccaro Mariana M. , Volpedo Alejandra V. , Garcia-Liñeiro Alberto , Fernández-Cirelli Alicia
Published: August 2023

Argentina is one of the countries that exports animals for equestrian sports. This paper analyzes the environmental effects of sport horse production farms in Argentina and proposes actions to minimize the environmental effects of this type of production. Twenty-six sport horse production farms in the province of Buenos Aires were studied. The proximity of the farms to a surface water body, the destination of the stall bedding, management practices and whether they receive veterinary advice were the characteristics analyzed in relation to feed, its composition and water consumption according to the performance of the animals. A nominal qualitative analysis on the impact was carried out considering three impact categories: low, medium and high. The association between the four environmental variables analyzed has shown that only two farms have a low environmental impact, while eighteen farms have a medium impact and five farms have a high impact. The results show that the role of the professional veterinarian is key in minimizing environmental impact and that the management of excretions and stall bedding should be reviewed in order to reduce the impact. This paper presents recommendations associated with water use, feed and manure management.
High School Students’ Use of Information, Media, and Technology Skills and Multidimensional 21st-Century Skills: An Investigation within the Context of Students, Teachers, and Curricula
Authors: Hursen Cigdem , Paşa Derviş , Keser Hafize
Published: August 2023

The aim of this study is to determine high school students’ information, media, and technology skills and their level of use of multidimensional 21st-century skills. A mixed quantitative and qualitative method was preferred in the study conducted in Northern Cyprus. Quantitative data were obtained from 612 senior high school students, and qualitative data were obtained from teachers and three different curricula. Face-to-face interviews were conducted with 10 teachers in order to determine the in-class activities that teachers carried out to provide information, media, and technology skills. The degree to which the Turkish language and literature, English, and mathematics curricula, which are among the basic courses offered in the senior year of high school, have outcomes for gaining information, media, and technology skills was determined through document analysis. The results obtained from the study revealed that the students’ use of information, media, and technology skills was generally moderate. The students’ use of programming skills, which are among the sub-dimensions of information, media, and technology skills as well as their problem-solving skills, were found to be very inadequate. Students’ use of information and data literacy, communication, and collaboration skills, which are among the sub-dimensions of information, media, and technology skills, were found to be adequate. Similarly, it was concluded that the students’ use of multidimensional 21st-century skills was also adequate. The results revealed that the classroom activities implemented by the teachers were insufficient in developing students’ problem-solving skills in particular. Moreover, the results obtained with respect to curricula revealed that the mathematics curriculum had the highest number of outcomes related to information, media, and technology skills. The Turkish language and literature curriculum had the least number of outcomes related to information, media, and technology skills. The results of the research also revealed that the learning outcomes in the curricula were mostly related to communication and collaboration skills. The skill with the lowest number of related outcomes was found to be problem-solving.