bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimizing the Deployment of Ground Tracking Stations for Low Earth Orbit Satellite Constellations Based on Evolutionary Algorithms
Authors: Kralfallah Mansour , Wu Falin , Tahir Afnan , Oubara Amel , Sui Xiaohong
Published: February 2024

Low Earth orbit (LEO) satellite constellations have emerged as an effective alternative for the provision of high-accuracy positioning, navigation and timing (PNT) solutions which are based on high-precision orbit and clock information. Determining an orbit with high precision is dependent on the number and distribution of ground tracking stations. Therefore, it is important to investigate methodologies that can ensure the adequate observing coverage of LEO navigation constellations. In this study, an evolutionary algorithm is applied to optimize the number and deployment of ground stations for tracking LEO constellations. According to the distribution area, two schemes of study are analyzed: (a) global deployment—the ground stations are deployed throughout the globe; (b) regional deployment—a selected region is used for deployment. For global deployment, the optimization objectives are focused on the ground station and observing rate for k-heavy observing coverage (HC), while the sole objective for the regional deployment scheme is the satellite position dilution of precision (SPDOP). It is shown that a deployment of 95 ground stations is optimal for achieving 3-HC with an observing rate of 97.37% and 4-HC with an observing rate of 92.01%. For regional distribution, 15, 20 and 25 ground stations are used for three optimal configurations of SPDOP at 2.058, 1.399 and 1.330, respectively. The results are significantly enhanced using intersatellite links for SPDOP evaluation, from 2.058, 1.399 and 1.330 to 0.439, 0.422 and 0.409, with 15, 20 and 25 ground stations, respectively.
Temporal and Spatial Variation Analysis of Groundwater Stocks in Xinjiang Based on GRACE Data
Authors: Duan Li , Chen Xi , Bu Lingjie , Chen Chaoliang , Song Shiran
Published: February 2024

Situated in China’s arid and semi-arid zones, the Xinjiang region heavily relies on groundwater for its freshwater supply. This study utilizes data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission, covering the years 2003 to 2021, to quantitatively evaluate the temporal and spatial changes in groundwater storage anomalies (GWSA) in the Xinjiang region. Furthermore, we incorporate the HydroSHEDS dataset to examine the spatial variations in groundwater storage anomalies across watersheds of varying scales. Based on our findings, the GWSA decreased during the study period at a mean rate of −0.381 mm/month, marked by a consistent trend and notable interannual variability. In addition, significant regional disparities are observed; while groundwater storage in the southeastern watersheds is on an upward trend, a general decline is noted in the northern and central regions. The most pronounced depletion is detected in the northwest, especially in the Ili River basin and along the western slopes of the Tianshan Mountains. These changes are intricately linked to anthropogenic factors, including population growth and escalating water demands. In response, the study advocates for the development and enforcement of more rigorous and scientifically informed groundwater management strategies to promote sustainable water use in Xinjiang.
Sound Speed Inversion Based on Multi-Source Ocean Remote Sensing Observations and Machine Learning
Authors: Feng Xiao , Tian Tian , Zhou Mingzhang , Sun Haixin , Li Dingzhao , Tian Feng , Lin Rongbin
Published: February 2024

Ocean sound speed is important for underwater acoustic applications, such as communications, navigation and localization, where the assumption of uniformly distributed sound speed profiles (SSPs) is generally used and greatly degrades the performance of underwater acoustic systems. The acquisition of SSPs is necessary for the corrections of the sound ray propagation paths. However, the inversion of SSPs is challenging due to the intricate relations of interrelated physical ocean elements and suffers from the high costs of calculations and hardware deployments. This paper proposes a novel sound speed inversion method based on multi-source ocean remote sensing observations and machine learning, which adapts to large-scale sea regions. Firstly, the datasets of SSPs are generated utilizing the Argo thermohaline profiles and the empirical formulas of the sound speed. Then, the SSPs are analyzed utilizing the empirical orthogonal functions (EOFs) to reduce the dimensions of the feature space as well as the computational load. Considering the nonlinear regression relations of SSPs and the observed datasets, a general framework for sound speed inversion is formulated, which combines the designed machine learning models with the reduced-dimensional feature representations, multi-source ocean remote sensing observations and water temperature data. After being well trained, the proposed machine learning models realize the accurate inversion of the targeted ocean region by inputting the real-time ocean environmental data. The experiments verify the advantages of the proposed method in terms of the accuracy and effectiveness compared with conventional methods.
Electrochemical Sensors Based on Self-Assembling Peptide/Carbon Nanotube Nanocomposites for Sensitive Detection of Bisphenol A
Authors: Zhang Yuhang , Shao Tingting , Zhang Hangyu
Published: February 2024

In this study, a cationic amphiphilic self-assembling peptide (SAP) Z23 was designed, and a simple bisphenol a (BPA) sensor, based on SAP Z23/multiwalled carbon nanotubes (Z23/MWCNTs) composite, was successfully fabricated on the surface of a glassy carbon electrode (GCE). The composite material was formed by π-π stacking interaction between the aromatic group on the hydrophobic side of Z23 and the side-wall of MWCNTs, with the charged hydrophilic group of Z23 exposed. During the electrocatalytic process of BPA, a synergistic effect was observed between Z23 and MWCNTs. The current response of the sensor based on composite material was 3.24 times that of the MWCNTs-modified electrode, which was much higher than that of the peptide-based electrode. Differential pulse voltammetry (DPV) was used to optimize the experimental conditions affecting the analytical performance of the modified electrode. Under optimal conditions, the linear range of the sensor was from 10 nM to 100 μM by amperometric measurement with sensitivity and limit of detection (LOD) at 6.569 μAμM−1cm−2 and 1.28 nM (S/N = 3), respectively. Consequently, the sensor has excellent electrochemical performance and is easy to fabricate, making it a good prospect in the field of electrochemical detection in the future.
A Combined Controller for Closed-Loop Control Systems Affected by Electromagnetic Interference
Authors: Baskys Algirdas
Published: February 2024

In this paper, a new approach for the solution of the electromagnetic interference impact problem in closed-loop control systems with proportional-integral-derivative (PID) controllers is proposed. The approach is based on the application of a combined controller that consists of three controllers: PID, PI and I, when only one of them is operating at a time, and which one is operating determines the control error value. The proposed combined controller allows for achieving better resistance of the control system to the noise signals produced by electromagnetic interference compared to PID and PID with low-pass filters in derivative term controllers without deteriorating the dynamic performance of the control system. The operation of the controller has been analysed using simulation and experiments with plants, the dynamics of which are described by first-order plus dead-time transfer functions.
A Novel LIBS Sensor for Sample Examinations on a Crime Scene
Authors: Lazic Violeta , Andreoli Fabrizio , Almaviva Salvatore , Pistilli Marco , Menicucci Ivano , Ulrich Christian , Schnürer Frank , Chirico Roberto
Published: February 2024

In this work, we present a compact LIBS sensor developed for characterization of samples on a crime scene following requirements of law enforcement agencies involved in the project. The sensor operates both in a tabletop mode, for aside measurements of swabbed materials or taken fragments, and in handheld mode where the sensor head is pointed directly on targets at the scene. The sensor head is connected via an umbilical to an instrument box that could be battery-powered and contains also a color camera for sample visualization, illumination LEDs, and pointing system for placing the target in focus. Here we describe the sensor’s architecture and functionalities, the optimization of the acquisition parameters, and the results of some LIBS measurements. On nano-plotted traces at silica wafer and in optimized conditions, for most of the elements the detection limits, in term of the absolute element masses, were found to be below 10 picograms. We also show results obtained on some representative materials, like fingerprints, swabbed soil and gunshot residue, varnishes on metal, and coated plastics. The last, solid samples were used to evaluate the depth profiling capabilities of the instrument, where the recognition of all four car paint layers was achieved.
Modelling and Thermographic Measurements of LED Optical Power
Authors: Strąkowska Maria , Urbaś Sebastian , Felczak Mariusz , Torzyk Błażej , Shatarah Iyad S. M. , Kasikowski Rafał , Tabaka Przemysław , Więcek Bogusław
Published: February 2024

This paper presents a simple engineering method for evaluating the optical power emitted by light-emitting diodes (LEDs) using infrared thermography. The method is based on the simultaneous measurement of the electrical power and temperature of an LED and a heat source (resistor) that are enclosed in the same plastic packaging under the same cooling conditions. This ensures the calculation of the optical power emitted by the LED regardless of the value of the heat transfer coefficient. The obtained result was confirmed by comparing it with the standard direct measurement method using an integrated sphere. The values of the estimated optical power using the proposed method and the integrated sphere equipped with a spectrometer were consistent with each other. The tested LED exhibited a high optical energy efficiency, reaching approximately η ≈ 30%. In addition, an uncertainty analysis of the obtained results was performed. Compact modelling based on a thermal resistor network (Rth) and a 3D-FEM analysis were performed to confirm the experimental results.
Vision System for a Forestry Navigation Machine
Authors: Pereira Tiago , Gameiro Tiago , Pedro José , Viegas Carlos , Ferreira N. M. Fonseca
Published: February 2024

This article presents the development of a vision system designed to enhance the autonomous navigation capabilities of robots in complex forest environments. Leveraging RGBD and thermic cameras, specifically the Intel RealSense 435i and FLIR ADK, the system integrates diverse visual sensors with advanced image processing algorithms. This integration enables robots to make real-time decisions, recognize obstacles, and dynamically adjust their trajectories during operation. The article focuses on the architectural aspects of the system, emphasizing the role of sensors and the formulation of algorithms crucial for ensuring safety during robot navigation in challenging forest terrains. Additionally, the article discusses the training of two datasets specifically tailored to forest environments, aiming to evaluate their impact on autonomous navigation. Tests conducted in real forest conditions affirm the effectiveness of the developed vision system. The results underscore the system’s pivotal contribution to the autonomous navigation of robots in forest environments.
Methodology for Calculating the Damaged Surface and Its Relationship with Power Loss in Photovoltaic Modules by Electroluminescence Inspection for Corrective Maintenance
Authors: Saborido-Barba Nieves , García-López Carmen , Clavijo-Blanco José Antonio , Jiménez-Castañeda Rafael , Álvarez-Tey Germán
Published: February 2024

Photovoltaic panels are exposed to various external factors that can cause damage, with the formation of cracks in the photovoltaic cells being one of the most recurrent issues affecting their production capacity. Electroluminescence (EL) tests are employed to detect these cracks. In this study, a methodology developed according to the IEC TS 60904-13 standard is presented, allowing for the calculation of the percentage of type C cracks in a PV panel and subsequently estimating the associated power loss. To validate the methodology, it was applied to a polycrystalline silicon module subjected to incremental damage through multiple impacts on its rear surface. After each impact, electroluminescence images and I-V curves were obtained and used to verify power loss estimates. More accurate estimates were achieved by assessing cracks at the PV cell level rather than by substring or considering the entire module. In this context, cell-level analysis becomes indispensable, as the most damaged cell significantly influences the performance of the photovoltaic model. Subsequently, the developed methodology was applied to evaluate the conditions of four photovoltaic panels that had been in operation, exemplifying its application in maintenance tasks. The results assisted in decision making regarding whether to replace or continue using the panels.
Impact-Aware Foot Motion Reconstruction and Ramp/Stair Detection Using One Foot-Mounted Inertial Measurement Unit
Authors: Wang Yisen , Fehr Katherine H. , Adamczyk Peter G.
Published: February 2024

Motion reconstruction using wearable sensors enables broad opportunities for gait analysis outside laboratory environments. Inertial Measurement Unit (IMU)-based foot trajectory reconstruction is an essential component of estimating the foot motion and user position required for any related biomechanics metrics. However, limitations remain in the reconstruction quality due to well-known sensor noise and drift issues, and in some cases, limited sensor bandwidth and range. In this work, to reduce drift in the height direction and handle the impulsive velocity error at heel strike, we enhanced the integration reconstruction with a novel kinematic model that partitions integration velocity errors into estimates of acceleration bias and heel strike vertical velocity error. Using this model, we achieve reduced height drift in reconstruction and simultaneously accomplish reliable terrain determination among level ground, ramps, and stairs. The reconstruction performance of the proposed method is compared against the widely used Error State Kalman Filter-based Pedestrian Dead Reckoning and integration-based foot-IMU motion reconstruction method with 15 trials from six subjects, including one prosthesis user. The mean height errors per stride are 0.03±0.08 cm on level ground, 0.95±0.37 cm on ramps, and 1.27±1.22 cm on stairs. The proposed method can determine the terrain types accurately by thresholding on the model output and demonstrates great reconstruction improvement in level-ground walking and moderate improvement on ramps and stairs.
A Machine-Learning Strategy to Detect Mura Defects in a Low-Contrast Image by Piecewise Gamma Correction
Authors: Lin Zo-Han , Lai Qi-Yuan , Li Hung-Yuan
Published: February 2024

A detection and classification machine-learning model to inspect Thin Film Transistor Liquid Crystal Display (TFT-LCD) Mura is proposed in this study. To improve the capability of the machine-learning model to inspect panels’ low-contrast grayscale images, piecewise gamma correction and a Selective Search algorithm are applied to detect and optimize the feature regions based on the Semiconductor Equipment and Materials International Mura (SEMU) specifications. In this process, matching the segment proportions to gamma values of piecewise gamma is a task that involves derivative-free optimization which is trained by adaptive particle swarm optimization. The detection accuracy rate (DAR) is approximately 93.75%. An enhanced convolutional neural network model is then applied to classify the Mura type through using the Taguchi experimental design method that identifies the optimal combination of the convolution kernel and the maximum pooling kernel sizes. A remarkable defect classification accuracy rate (CAR) of approximately 96.67% is ultimately achieved. The entire defect detection and classification process can be completed in about 3 milliseconds.
Evaluating a 3D Ultrasound Imaging Resolution of Single Transmitter/Receiver with Coding Mask by Extracting Phase Information
Authors: Syaryadhi Mohammad , Nakazawa Eiko , Tagawa Norio , Yang Ming
Published: February 2024

We are currently investigating the ultrasound imaging of a sensor that consists of a randomized encoding mask attached to a single lead zirconate titanate (PZT) oscillator for a puncture microscope application. The proposed model was conducted using a finite element method (FEM) simulator. To increase the number of measurements required by a single element system that affects its resolution, the transducer was rotated at different angles. The image was constructed by solving a linear equation of the image model resulting in a poor quality. In a previous work, the phase information was extracted from the echo signal to improve the image quality. This study proposes a strategy by integrating the weighted frequency subbands compound and a super-resolution technique to enhance the resolution in range and lateral direction. The image performance with different methods was also evaluated using the experimental data. The results indicate that better image resolution and speckle suppression were obtained by applying the proposed method.
Application of Chlorophyll Fluorescence Analysis Technique in Studying the Response of Lettuce (Lactuca sativa L.) to Cadmium Stress
Authors: Zhou Lina , Zhou Leijinyu , Wu Hongbo , Jing Tingting , Li Tianhao , Li Jinsheng , Kong Lijuan , Zhu Fengwu
Published: February 2024

To reveal the impact of cadmium stress on the physiological mechanism of lettuce, simultaneous determination and correlation analyses of chlorophyll content and photosynthetic function were conducted using lettuce seedlings as the research subject. The changes in relative chlorophyll content, rapid chlorophyll fluorescence induction kinetics curve, and related chlorophyll fluorescence parameters of lettuce seedling leaves under cadmium stress were detected and analyzed. Furthermore, a model for estimating relative chlorophyll content was established. The results showed that cadmium stress at 1 mg/kg and 5 mg/kg had a promoting effect on the relative chlorophyll content, while cadmium stress at 10 mg/kg and 20 mg/kg had an inhibitory effect on the relative chlorophyll content. Moreover, with the extension of time, the inhibitory effect became more pronounced. Cadmium stress affects both the donor and acceptor sides of photosystem II in lettuce seedling leaves, damaging the electron transfer chain and reducing energy transfer in the photosynthetic system. It also inhibits water photolysis and decreases electron transfer efficiency, leading to a decline in photosynthesis. However, lettuce seedling leaves can mitigate photosystem II damage caused by cadmium stress through increased thermal dissipation. The model established based on the energy captured by a reaction center for electron transfer can effectively estimate the relative chlorophyll content of leaves. This study demonstrates that chlorophyll fluorescence techniques have great potential in elucidating the physiological mechanism of cadmium stress in lettuce, as well as in achieving synchronized determination and correlation analyses of chlorophyll content and photosynthetic function.
Computer-Vision-Based Sensing Technologies for Livestock Body Dimension Measurement: A Survey
Authors: Ma Weihong , Sun Yi , Qi Xiangyu , Xue Xianglong , Chang Kaixuan , Xu Zhankang , Li Mingyu , Wang Rong , Meng Rui , Li Qifeng
Published: February 2024

Livestock’s live body dimensions are a pivotal indicator of economic output. Manual measurement is labor-intensive and time-consuming, often eliciting stress responses in the livestock. With the advancement of computer technology, the techniques for livestock live body dimension measurement have progressed rapidly, yielding significant research achievements. This paper presents a comprehensive review of the recent advancements in livestock live body dimension measurement, emphasizing the crucial role of computer-vision-based sensors. The discussion covers three main aspects: sensing data acquisition, sensing data processing, and sensing data analysis. The common techniques and measurement procedures in, and the current research status of, live body dimension measurement are introduced, along with a comparative analysis of their respective merits and drawbacks. Livestock data acquisition is the initial phase of live body dimension measurement, where sensors are employed as data collection equipment to obtain information conducive to precise measurements. Subsequently, the acquired data undergo processing, leveraging techniques such as 3D vision technology, computer graphics, image processing, and deep learning to calculate the measurements accurately. Lastly, this paper addresses the existing challenges within the domain of livestock live body dimension measurement in the livestock industry, highlighting the potential contributions of computer-vision-based sensors. Moreover, it predicts the potential development trends in the realm of high-throughput live body dimension measurement techniques for livestock.
CUSCO: An Unobtrusive Custom Secure Audio-Visual Recording System for Ambient Assisted Living
Authors: Albert Pierre , Haider Fasih , Luz Saturnino
Published: February 2024

The ubiquity of digital technology has facilitated detailed recording of human behaviour. Ambient technology has been used to capture behaviours in a broad range of applications ranging from healthcare and monitoring to assessment of cooperative work. However, existing systems often face challenges in terms of autonomy, usability, and privacy. This paper presents a portable, easy-to-use and privacy-preserving system for capturing behavioural signals unobtrusively in home or in office settings. The system focuses on the capture of audio, video, and depth imaging. It is based on a device built on a small-factor platform that incorporates ambient sensors which can be integrated with the audio and depth video hardware for multimodal behaviour tracking. The system can be accessed remotely and integrated into a network of sensors. Data are encrypted in real time to ensure safety and privacy. We illustrate uses of the device in two different settings, namely, a healthy-ageing IoT application, where the device is used in conjunction with a range of IoT sensors to monitor an older person’s mental well-being at home, and a healthcare communication quality assessment application, where the device is used to capture a patient–clinician interaction for consultation quality appraisal. CUSCO can automatically detect active speakers, extract acoustic features, record video and depth streams, and recognise emotions and cognitive impairment with promising accuracy.
Lightweight Meter Pointer Recognition Method Based on Improved YOLOv5
Authors: Zhang Chi , Wang Kai , Zhang Jie , Zhou Fan , Zou Le
Published: February 2024

In substation lightning rod meter reading data taking, the classical object detection model is not suitable for deployment in substation monitoring hardware devices due to its large size, large number of parameters, and slow detection speed, while is difficult to balance detection accuracy and real-time requirements with the existing lightweight object detection model. To address this problem, this paper constructs a lightweight object detection algorithm, YOLOv5-Meter Reading Lighting (YOLOv5-MRL), based on the improved YOLOv5 model’s speed while maintaining accuracy. Then, the YOLOv5s are pruned based on the convolutional kernel channel soft pruning algorithm, which greatly reduces the number of parameters in the YOLOv5-MRL model while maintaining a certain accuracy loss. Finally, in order to facilitate the dial reading, the dial external circle fitting method is proposed to calculate the dial reading using the circular angle algorithm. The experimental results on the self-built dataset show that the YOLOv5-MRL object detection model achieves a mean average precision of 96.9%, a detection speed of 5 ms/frame, and a model weight size of 5.5 MB, making it better than other advanced dial reading models.
Influence of Blind Spot Assistance Systems in Heavy Commercial Vehicles on Accident Reconstruction
Authors: König Thomas , Paula Daniel , Quaschner Stefan , Schweiger Hans-Georg
Published: February 2024

Accidents between right-turning commercial vehicles and crossing vulnerable road users (VRUs) in urban environments often lead to serious or fatal injuries and therefore play a significant role in forensic accident analysis. To reduce the risk of accidents, blind spot assistance systems have been installed in commercial vehicles for several years, among other things, to detect VRUs and warn the driver in time. However, since such systems cannot reliably prevent all turning accidents, an investigation by experts must clarify how the accident occurred and to what extent the blind spot assistance system influenced the course of the accident. The occurrence of the acoustic warning message can be defined as an objective reaction prompt for the driver, so that the blind spot assistance system can significantly influence the avoidability assessment. In order to be able to integrate the system into forensic accident analysis, a precise knowledge of how the system works and its limitations is required. For this purpose, tests with different systems and accident constellations were conducted and evaluated. It was found that the type of sensor used for the assistance systems has a great influence on the system’s performance. The lateral distance between the right side of the commercial vehicle and the VRU, as well as obstacles between them, along with the speed difference can have great influence on the reliability of the assistance system. Depending on the concrete time of the system’s warning signal, the accident can be avoided or not by the driver when reacting to this signal.
Quantifying the Impact of Motions on Human Aiming Performance: Evidence from Eye Tracking and Bio-Signals
Authors: Li Yuzhang , Li Xinming , Grant Peter R. , Zheng Bin
Published: February 2024

Working on a moving platform can significantly impede human performance. Previous studies on moving vehicles have often focused on the overall impact on general task performance, whereas our study’s emphasis is on precise hand movements, exploring the interaction between body motion and the escalation of task difficulty. We recruited 28 participants to engage in reciprocal aiming tasks, following Paul Fitts’s setting, under both in-motion and stationary conditions. The task index of difficulty (ID) was manipulated by varying the width of the targets and the distance between the targets. We measured participants’ movement time (MT), performance errors, and monitored their eye movements using an eye-tracking device, heart rate (HR), and respiration rate (RR) during the tasks. The measured parameters were compared across two experimental conditions and three ID levels. Compared to the stationary conditions, the in-motion conditions degraded human aiming performance, resulting in significantly prolonged MT, increased errors, and longer durations of eye fixations and saccades. Furthermore, HR and RR increased under the in-motion conditions. Linear relationships between MT and ID exhibited steeper slopes under the in-motion conditions compared to the stationary conditions. This study builds a foundation for us to explore the control mechanisms of individuals working in dynamic and demanding environments, such as pilots in airplanes and paramedics in ambulances.
Inertial Measuring System to Evaluate Gait Parameters and Dynamic Alignments for Lower-Limb Amputation Subjects
Authors: Han Shao-Li , Cai Meng-Lin , Pan Min-Chun
Published: February 2024

The study aims to construct an inertial measuring system for the application of amputee subjects wearing a prosthesis. A new computation scheme to process inertial data by installing seven wireless inertial sensors on the lower limbs was implemented and validated by comparing it with an optical motion capture system. We applied this system to amputees to verify its performance for gait analysis. The gait parameters are evaluated to objectively assess the amputees’ prosthesis-wearing status. The Madgwick algorithm was used in the study to correct the angular velocity deviation using acceleration data and convert it to quaternion. Further, the zero-velocity update method was applied to reconstruct patients’ walking trajectories. The combination of computed walking trajectory with pelvic and lower limb joint motion enables sketching the details of motion via a stickman that helps visualize and animate the walk and gait of a test subject. Five participants with above-knee (n = 2) and below-knee (n = 3) amputations were recruited for gait analysis. Kinematic parameters were evaluated during a walking test to assess joint alignment and overall gait characteristics. Our findings support the feasibility of employing simple algorithms to achieve accurate and precise joint angle estimation and gait parameters based on wireless inertial sensor data.
Improved RRT* Algorithm for Disinfecting Robot Path Planning
Authors: Wang Haotian , Zhou Xiaolong , Li Jianyong , Yang Zhilun , Cao Linlin
Published: February 2024

In this paper, an improved APF-GFARRT* (artificial potential field-guided fuzzy adaptive rapidly exploring random trees) algorithm based on APF (artificial potential field) guided sampling and fuzzy adaptive expansion is proposed to solve the problems of weak orientation and low search success rate when randomly expanding nodes using the RRT (rapidly exploring random trees) algorithm for disinfecting robots in the dense environment of disinfection operation. Considering the inherent randomness of tree growth in the RRT* algorithm, a combination of APF with RRT* is introduced to enhance the purposefulness of the sampling process. In addition, in the context of RRT* facing dense and restricted environments such as narrow passages, adaptive step-size adjustment is implemented using fuzzy control. It accelerates the algorithm’s convergence and improves search efficiency in a specific area. The proposed algorithm is validated and analyzed in a specialized environment designed in MATLAB, and comparisons are made with existing path planning algorithms, including RRT, RRT*, and APF-RRT*. Experimental results show the excellent exploration speed of the improved algorithm, reducing the average initial path search time by about 46.52% compared to the other three algorithms. In addition, the improved algorithm exhibits faster convergence, significantly reducing the average iteration count and the average final path cost by about 10.01%. The algorithm’s enhanced adaptability in unique environments is particularly noteworthy, increasing the chances of successfully finding paths and generating more rational and smoother paths than other algorithms. Experimental results validate the proposed algorithm as a practical and feasible solution for similar problems.
Ultra-Broadband Ultraviolet–Visible Light–Short Wavelength Infrared InGaAs Focal Plane Arrays via n-InP Contact Layer Removal
Authors: Zhang Jiaxin , Wang Wei , Ye Haifeng , Huang Runyu , Liu Chen , Zhao Weilin , Shi Yanli
Published: February 2024

PIN InGaAs short wavelength infrared (SWIR) focal plane array (FPA) detectors have attracted extensive attention due to their high detectivity, high quantum efficiency, room temperature operation, low dark current, and good radiation resistance. Furthermore, InGaAs FPA detectors have wide applications in many fields, such as aviation safety, biomedicine, camouflage recognition, and infrared night vision. Recently, extensive research has been conducted on the extension of the response spectrum from short wavelength infrared (SWIR) to visible light (VIS) through InP substrate removal and reserving the n-InP contact layer. However, there is little research on the absorption of InGaAs detectors in the ultraviolet (UV) band. In this paper, we present an ultra-broadband UV–VIS–SWIR 640 × 512 15 μm InGaAs FPA detector by removing the n-InP contact layer in the active area and reserving the InP contact layer around the pixels for n contact, creating incident light to be directly absorbed by the In0.53Ga0.47As absorption layer. In addition, the optical absorption characteristics of InGaAs infrared detectors with and without an n-InP contact layer are studied theoretically. The test results show that the spectral response is extended to the range of 200–1700 nm. The quantum efficiency is higher than 45% over a broad wavelength range of 300–1650 nm. The operability is up to 99.98%, and the responsivity non-uniformity is 3.28%. The imaging capability of InGaAs FPAs without the n-InP contact layer has also been demonstrated, which proves the feasibility of simultaneous detection for these three bands.
Graph Attention Network and Informer for Multivariate Time Series Anomaly Detection
Authors: Zhao Mengmeng , Peng Haipeng , Li Lixiang , Ren Yeqing
Published: February 2024

Time series anomaly detection is very important to ensure the security of industrial control systems (ICSs). Many algorithms have performed well in anomaly detection. However, the performance of most of these algorithms decreases sharply with the increase in feature dimension. This paper proposes an anomaly detection scheme based on Graph Attention Network (GAT) and Informer. GAT learns sequential characteristics effectively, and Informer performs excellently in long time series prediction. In addition, long-time forecasting loss and short-time forecasting loss are used to detect multivariate time series anomalies. Short-time forecasting is used to predict the next time value, and long-time forecasting is employed to assist the short-time prediction. We conduct a large number of experiments on industrial control system datasets SWaT and WADI. Compared with most advanced methods, we achieve competitive results, especially on higher-dimensional datasets. Moreover, the proposed method can accurately locate anomalies and realize interpretability.
Continuous Purification of Biodiesel with Deep Eutectic Solvent in a Laboratory Karr Column
Authors: Sander Aleksandra , Petračić Ana , Vrsaljko Domagoj , Parlov Vuković Jelena , Hršak Patricija , Jelavić Antonija
Published: February 2024

Following the trend of transport decarbonization, biodiesel has become a promising alternative fuel option. Its production includes multiple steps, all of which can be time-consuming and energy intensive. Improving any of these steps could bring considerable environmental and economic benefits. The utilization of deep eutectic solvents (DESs) for glycerol extraction from crude biodiesel has predominantly been explored as a batch process. This work provides insight into continuous column extraction. Different waste cooking oils were used to produce biodiesel via transesterification with methanol, and the selective solvent for purification was DES choline chloride–ethylene glycol (1:2.5, mol.). A laboratory Karr column at different pulsation frequencies and DES to biodiesel mass ratio was used for extraction. Plate material (steel and 3D printed PETG) and geometry influence on the efficiency of extraction were investigated. Contact angle measurement was used to measure the surface free energy of steel and PETG and the spreading ability of biodiesel and DESs on both materials. Extraction efficiency was analyzed by several analytical techniques. Higher efficiency was observed with steel plates of a triangular pitch hole arrangement. Increasing the mixing intensity and DES to biodiesel mass ratio further increased the efficiency of extraction.
Assessment of Pedagogical Contributions toward Enhancing Physical Activity within the Secondary School Physical Education Curricula in Southwestern China
Authors: Li Yu , Yu Hongli
Published: February 2024

Students enrolled in secondary schools often fail to engage in moderate to vigorous levels of physical activity (MVLPA) due to inadequacies in their physical education programs. Physical education teachers (PETs) foster student involvement in MVLPA. Consequently, it becomes imperative to identify and scrutinize factors pertaining to PETs that could influence students’ MVLPA within secondary school settings, an area that has been relatively overlooked in China. To address this gap, the present study delved into MVLPA among secondary school students in southwest China with the following objectives: (i) to assess students’ MVLPA alongside PET characteristics and teaching behaviors during physical education classes; (ii) to explore discrepancies in PET behaviors and characteristics; and (iii) to establish connections between students’ MVLPA and PET behaviors and characteristics during physical education sessions. A questionnaire survey was administered to 54 full-time PETs across nine secondary schools in Chengdu, China, aimed at gathering data on their personal and professional attributes. Additionally, a system for observing fitness instruction time was employed to document PET instructional traits, while accelerometers were utilized to track students’ MVLPA. The study objectives were investigated using multiple statistical analyses. The findings indicate that PETs do not meet the recommended 50% MVLPA time allocation during physical education sessions. Noteworthy patterns emerged, revealing that PETs with 1–5 years of teaching experience allocated less time to student observation and more time to classroom management compared to their counterparts with 6–10 years and over 10 years of teaching experience. Moreover, MVLPA time invested in lessons led by male PETs (B = −3.221) was significantly higher than time spent in lessons led by female PETs, which was attributed to PET gender. Furthermore, students under the tutelage of PETs with 6–10 years of teaching experience (B = 3.101) and those with over 10 years of experience (B = 2.989) exhibited significantly higher MVLPAs than under those with 1–5 years of teaching experience. Additionally, PET attitudes such as observation (B = 1.621) and promoting (B = 1.317) behaviors during physical education sessions were positively correlated with students’ MVLPA. A regression analysis revealed that PET characteristics and behaviors explained 21.3% of students’ MVLPA variance. This study offers insights into PETs’ pivotal role in promoting physical activity. It underscores the ramifications for students’ MVLPA in the Chinese educational context.
Artificial Intelligence Adoption by SMEs to Achieve Sustainable Business Performance: Application of Technology–Organization–Environment Framework
Authors: Badghish Saeed , Soomro Yasir Ali
Published: February 2024

The primary purpose of this study was to investigate and present a theoretical model that identifies the most influential factors affecting the adoption of artificial intelligence (AI) by SMEs to achieve sustainable business performance in Saudi Arabia by integrating the Technology–Organization–Environment (TOE) framework. The authors utilized a quantitative method, using a survey instrument for this research. Data for this research were collected from managers working in six different sectors. Subsequently, based on company size, firms were divided into two groups, allowing multi-group analysis of small and medium-sized businesses to explore group differences. Hence, firm size played a moderating role in the conceptualized model. Data analysis was performed on SmartPLS 3, and the results suggest that dimensions of the TOE framework, such as relative advantage, compatibility, sustainable human capital, market and customer demand, and government support, play a significant role in the adoption of AI. Moreover, this study found a significant influence of AI on SMEs’ operational and economic performance. The multi-group analysis (MGA) results reveal significant group differences, with a medium-sized firm strengthening the relationship between relative advantage and AI adoption compared to small-size firms. The findings lead to practical implications for companies on how to increase the adoption of AI to help SMEs embrace their technological challenges in KSA and obtain sustainable business performance to contribute to the economy.
Utilization of Municipal Solid Waste Incineration Bottom Ash in Cement-Bound Mixtures
Authors: Węgliński Szymon , Martysz Gabriel
Published: February 2024

In order to protect the natural resources, it is beneficial for the environment when materials that are not valuable, such as waste or recycled materials, are used in engineering. This article presents laboratory studies on the use of mixtures of incineration bottom ash (IBA) from municipal waste incinerators with natural, fine grain and uniform aggregate in cement-bound layers. The mechanical and engineering properties of aggregates were studied, their usefulness was assessed and possible applications are indicated. The chemical composition of the material was found to be consistent with typical IBA from other incineration plants, and leachability studies were carried out, confirming lack of any environmental impact. The authors’ own mixtures were prepared based on optimal water content and maximal dry densities of solid particles, and the compressive strength was calculated after 7 and 28 days of hardening. The results indicate that replacing natural aggregates with IBA permits an increase in the compressive values for the specimens using the same amount of CEM I 42.5R while improving the frost resistance of cement–aggregate mixtures.
The Use of Plant Extracts as Sustainable Corrosion Inhibitors for Cultural Heritage Alloys: A Mini-Review
Authors: González-Parra Jesús Rafael , Di Turo Francesca
Published: February 2024

Corrosion inhibitors have traditionally been utilised to protect copper alloy sculptures from corrosion despite the recognised environmental and human health risks. Knowing the associated toxicity, ongoing extensive research seeks alternative substances for corrosion reduction, giving rise to the emergence of green inhibitors. In this pursuit, plant extract inhibitors have gained attention, particularly in the heritage field. This review discusses recent advancements in synthesising sustainable and eco-friendly products designed to preserve metallic heritage from corrosion. We discuss the key findings on plant extracts used for corrosion control in metal alloys, taking into account the extraction methods to ensure the comprehensive coverage of relevant details. The use of plant extracts as corrosion inhibitors for metallic materials in cultural heritage (CH) is a highly promising and valuable research area. This review highlights recent advancements in the field, providing utility for conservators, conservation scientists, and restorers in identifying the most effective, eco-friendly, and safe compounds for material application.
Towards Greener Campuses: Assessing Pro-Environmental Behaviours in the University of Bahrain Campus
Authors: Alalawi Noor Saleh , Omar Osama
Published: February 2024

The significant contribution of building materials and emissions, accounting for approximately 40%, underscores the crucial role that buildings play in addressing climate change. Considering that buildings and transport are among the main contributors of energy-related emissions, Bahrain’s greenhouse gas emissions (GHGs) increased from 16 (Mt/year) in 1990 to 54 (Mt/year) in 2020, which requires immediate attention. Pro-environmental behaviours play a significant role in reducing overall emissions and mitigating climate change. Therefore, this research aims to clarify the main factors influencing pro-environmental behaviour in the University of Bahrain campus, in order to create a strategic framework encompassing pro-environmental solutions working towards zero emissions. The problem is analysed through a mixed methodology based on a literature review, questionnaire and photographic analysis. The findings highlight the importance of several internal and external factors in relation to pro-environmental behaviour in higher education environments. The results provide valuable insights for stakeholders and decision-makers to implement change through green initiatives at a policy level.
Perceptions of Personal Lighting Devices and Associated Behaviors: Shifting Personal Norms and Behavior for Broader Conservation Actions
Authors: Crump Morgan , Taff Brendan Derrick , Himschoot Elizabeth A. , Newton Jennifer , Beeco Adam , Newman Peter
Published: February 2024

Anthropogenic light impacts both wildlife and human well-being, and national parks are some of the only remaining large swaths of land where natural dark skies remain. Over the past two decades, a significant amount of science has contributed both to our understanding of these impacts and to engineering advances to reduce negative lighting effects. This has resulted in changes to lighting infrastructure in some national parks, and growth in Dark Sky Certification for many protected areas globally. To date, changing infrastructure, such as street and parking lot lighting, to less intrusive hues and intensities or removing lights altogether have been some of the sustainable changes made in these areas. This study advances our understanding of lighting issues by examining national park visitors’ perceptions of personal lighting use (e.g., headlamps). Specifically, this study explores camper and mountaineer perceptions of personal lighting devices and their impact on social and ecological systems in Grand Teton National Park, USA. During peak visitation in the summer of 2023, 17 mountaineer interviews and 16 general camper interviews took place in the park at night. Results indicate that campers and mountaineers are largely unaware of anthropogenic light impacts on wildlife and humans. However, once informed, they are willing to change their behaviors and reduce the use of personal lighting devices and use more wildlife-friendly lighting with amber or red settings (which, to date, are just emerging and available for general consumers by several companies). These results provide insights for developing educational strategies and personal lighting engineering designs that will ultimately lead to more sustainable normative shifts capable of reducing anthropogenic lighting impacts in parks and beyond.