bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of Spatial–Temporal Variability of PM2.5 Concentrations Using Optical Satellite Images and Geographic Information System
Authors: Heriza Dewinta , Wu Chih-Da , Syariz Muhammad Aldila , Lin Chao-Hung
Published: April 2023

Particulate matter less than 2.5 microns in diameter (PM2.5) is an air pollutant that has become a major environmental concern for governments around the world. Management and control require air quality monitoring and prediction. However, previous studies did not fully utilize the spectral information in multispectral satellite images and land use data in geographic datasets. To alleviate these problems, this study proposes the extraction of land use information not only from geographic inventory but also from satellite images with a machine learning-based classification. In this manner, near up-to-date land use data and spectral information from satellite images can be utilized, and the integration of geographic and remote sensing datasets boosts the accuracy of PM2.5 concentration modeling. In the experiments, Landsat-8 imagery with a 30-m spatial resolution was used, and cloud-free image generation was performed prior to the land cover classification. The proposed method, which uses predictors from geographic and multispectral satellite datasets in modeling, was compared with an approach which utilizes geographic and remote sensing datasets, respectively. Quantitative assessments showed that the proposed method and the developed model, with a performance of RMSE = 3.06 µg/m3 and R2 = 0.85 comparatively outperform the models with a performance of RMSE = 3.14 µg/m3 and R2 = 0.68 for remote sensing datasets and a performance of RMSE = 3.47 µg/m3 and R2 = 0.79 for geographic datasets.
Effective Surface Roughness Impact in Polarimetric GNSS-R Soil Moisture Retrievals
Authors: Munoz-Martin Joan Francesc , Rodriguez-Alvarez Nereida , Bosch-Lluis Xavier , Oudrhiri Kamal
Published: April 2023

Single-pass soil moisture retrieval has been a key objective of Global Navigation Satellite System-Reflectometry (GNSS-R) for the last decade. Achieving this goal will allow small satellites with GNSS-R payloads to perform such retrievals at high temporal resolutions. Properly modeling the soil surface roughness is key to providing high-quality soil moisture estimations. In the present work, the Physical Optics and Geometric Optics models of the Kirchhoff Approximation are implemented to the coherent and incoherent components of the reflectometry measurements collected by the SMAP radar receiver (SMAP-Reflectometry or SMAP-R). Two surface roughness products are retrieved and compared for a single-polarization approach, critical for single-polarization GNSS-R instruments that target soil moisture retrievals. Then, a polarization decoupling model is implemented for a dual-polarization retrieval approach, where the ratio between two orthogonal polarizations is evaluated to estimate soil moisture. Differences between linear and circular polarization ratios are evaluated using this decoupling parameter, and the theoretical soil moisture error with varying decoupling parameters is analyzed. Our results show a 1-sigma soil moisture error of 0.08 cm3/cm3 for the dual-polarization case for a fixed polarization decoupling value used for the whole Earth, and a 2-sigma error of 0.08 cm3/cm3 when the measured reflectivity and the VOD are used to estimate the polarization decoupling parameter.
Differential Evolution Optimization of Microwave Focused Hyperthermia Phased Array Excitation for Targeted Breast Cancer Heating
Authors: Lyu Cheng , Li Wenxing , Yang Bin
Published: April 2023

Microwave hyperthermia using the phased array applicator is a non-invasive treatment modality for breast cancer. Hyperthermia treatment planning (HTP) is critical to accurately treating breast cancer and avoiding damage to the patient’s healthy tissue. A global optimization algorithm, differential evolution (DE) algorithm, has been applied to optimize HTP for breast cancer and its ability to improve the treatment effect was proved by electromagnetic (EM) and thermal simulation results. DE algorithm is compared to time reversal (TR) technology, particle swarm optimization (PSO) algorithm, and genetic algorithm (GA) in HTP for breast cancer in terms of convergence rate and treatment results, such as treatment indicators and temperature parameters. The current approaches in breast cancer microwave hyperthermia still have the problem of hotspots in healthy tissue. DE enhances focused microwave energy absorption into the tumor and reduces the relative energy of healthy tissue during hyperthermia treatment. By comparing the treatment results of each objective function used in DE, the DE algorithm with hotspot to target quotient (HTQ) as the objective function has outstanding performance in HTP for breast cancer, which can increase the focused microwave energy of the tumor and decrease the damage to healthy tissue.
A Dragon Fruit Picking Detection Method Based on YOLOv7 and PSP-Ellipse
Authors: Zhou Jialiang , Zhang Yueyue , Wang Jinpeng
Published: April 2023

Dragon fruit is one of the most popular fruits in China and Southeast Asia. It, however, is mainly picked manually, imposing high labor intensity on farmers. The hard branches and complex postures of dragon fruit make it difficult to achieve automated picking. For picking dragon fruits with diverse postures, this paper proposes a new dragon fruit detection method, not only to identify and locate the dragon fruit, but also to detect the endpoints that are at the head and root of the dragon fruit, which can provide more visual information for the dragon fruit picking robot. First, YOLOv7 is used to locate and classify the dragon fruit. Then, we propose a PSP-Ellipse method to further detect the endpoints of the dragon fruit, including dragon fruit segmentation via PSPNet, endpoints positioning via an ellipse fitting algorithm and endpoints classification via ResNet. To test the proposed method, some experiments are conducted. In dragon fruit detection, the precision, recall and average precision of YOLOv7 are 0.844, 0.924 and 0.932, respectively. YOLOv7 also performs better compared with some other models. In dragon fruit segmentation, the segmentation performance of PSPNet on dragon fruit is better than some other commonly used semantic segmentation models, with the segmentation precision, recall and mean intersection over union being 0.959, 0.943 and 0.906, respectively. In endpoints detection, the distance error and angle error of endpoints positioning based on ellipse fitting are 39.8 pixels and 4.3°, and the classification accuracy of endpoints based on ResNet is 0.92. The proposed PSP-Ellipse method makes a great improvement compared with two kinds of keypoint regression method based on ResNet and UNet. Orchard picking experiments verified that the method proposed in this paper is effective. The detection method proposed in this paper not only promotes the progress of the automatic picking of dragon fruit, but it also provides a reference for other fruit detection.
Rapid and On-Scene Chemical Identification of Intact Explosives with Portable Near-Infrared Spectroscopy and Multivariate Data Analysis
Authors: van Damme Irene M. , Mestres-Fitó Pol , Ramaker Henk-Jan , Hulsbergen Annemieke W. C. , van der Heijden Antoine E. D. M. , Kranenburg Ruben F. , van Asten Arian C.
Published: April 2023

There is an ongoing forensic and security need for rapid, on-scene, easy-to-use, non-invasive chemical identification of intact energetic materials at pre-explosion crime scenes. Recent technological advances in instrument miniaturization, wireless transfer and cloud storage of digital data, and multivariate data analysis have created new and very promising options for the use of near-infrared (NIR) spectroscopy in forensic science. This study shows that in addition to drugs of abuse, portable NIR spectroscopy with multivariate data analysis also offers excellent opportunities to identify intact energetic materials and mixtures. NIR is able to characterize a broad range of chemicals of interest in forensic explosive investigations, covering both organic and inorganic compounds. NIR characterization of actual forensic casework samples convincingly shows that this technique can handle the chemical diversity encountered in forensic explosive investigations. The detailed chemical information contained in the 1350–2550 nm NIR reflectance spectrum allows for correct compound identification within a given class of energetic materials, including nitro-aromatics, nitro-amines, nitrate esters, and peroxides. In addition, the detailed characterization of mixtures of energetic materials, such as plastic formulations containing PETN (pentaerythritol tetranitrate) and RDX (trinitro triazinane), is feasible. The results presented illustrate that the NIR spectra of energetic compounds and mixtures are sufficiently selective to prevent false-positive results for a broad range of food-related products, household chemicals, raw materials used for the production of home-made explosives, drugs of abuse, and products that are sometimes used to create hoax improvised explosive devices. However, for frequently encountered pyrotechnic mixtures, such as black powder, flash powder, and smokeless powder, and some basic inorganic raw materials, the application of NIR spectroscopy remains challenging. Another challenge is presented by casework samples of contaminated, aged, and degraded energetic materials or poor-quality HMEs (home-made explosives), for which the spectral signature deviates significantly from the reference spectra, potentially leading to false-negative outcomes.
A GHz Silicon-Based Width Extensional Mode MEMS Resonator with Q over 10,000
Authors: Liu Wenli , Lu Yujie , Chen Zeji , Jia Qianqian , Zhao Junyuan , Niu Bo , Wang Wei , Hao Yalu , Zhu Yinfang , Yang Jinling , Yang Fuhua
Published: April 2023

This work presents a silicon-based capacitively transduced width extensional mode (WEM) MEMS rectangular plate resonator with quality factor (Q) of over 10,000 at a frequency of greater than 1 GHz. The Q value, determined by various loss mechanisms, was analyzed and quantified via numerical calculation and simulation. The energy loss of high order WEMs is dominated by anchor loss and phonon-phonon interaction dissipation (PPID). High-order resonators possess high effective stiffness, resulting in large motional impedance. To suppress anchor loss and reduce motional impedance, a novel combined tether was designed and comprehensively optimized. The resonators were batch fabricated based on a reliable and simple silicon-on-insulator (SOI)-based fabrication process. The combined tether experimentally contributes to low anchor loss and motional impedance. Especially in the 4th WEM, the resonator with a resonance frequency of 1.1 GHz and a Q of 10,920 was demonstrated, corresponding to the promising f × Q product of 1.2 × 1013. By using combined tether, the motional impedance decreases by 33% and 20% in 3rd and 4th modes, respectively. The WEM resonator proposed in this work has potential application for high-frequency wireless communication systems.
Recognition and Counting of Apples in a Dynamic State Using a 3D Camera and Deep Learning Algorithms for Robotic Harvesting Systems
Authors: Abeyrathna R. M. Rasika D. , Nakaguchi Victor Massaki , Minn Arkar , Ahamed Tofael
Published: April 2023

Recognition and 3D positional estimation of apples during harvesting from a robotic platform in a moving vehicle are still challenging. Fruit clusters, branches, foliage, low resolution, and different illuminations are unavoidable and cause errors in different environmental conditions. Therefore, this research aimed to develop a recognition system based on training datasets from an augmented, complex apple orchard. The recognition system was evaluated using deep learning algorithms established from a convolutional neural network (CNN). The dynamic accuracy of the modern artificial neural networks involving 3D coordinates for deploying robotic arms at different forward-moving speeds from an experimental vehicle was investigated to compare the recognition and tracking localization accuracy. In this study, a Realsense D455 RGB-D camera was selected to acquire 3D coordinates of each detected and counted apple attached to artificial trees placed in the field to propose a specially designed structure for ease of robotic harvesting. A 3D camera, YOLO (You Only Look Once), YOLOv4, YOLOv5, YOLOv7, and EfficienDet state-of-the-art models were utilized for object detection. The Deep SORT algorithm was employed for tracking and counting detected apples using perpendicular, 15°, and 30° orientations. The 3D coordinates were obtained for each tracked apple when the on-board camera in the vehicle passed the reference line and was set in the middle of the image frame. To optimize harvesting at three different speeds (0.052 ms−1, 0.069 ms−1, and 0.098 ms−1), the accuracy of 3D coordinates was compared for three forward-moving speeds and three camera angles (15°, 30°, and 90°). The mean average precision (mAP@0.5) values of YOLOv4, YOLOv5, YOLOv7, and EfficientDet were 0.84, 0.86, 0.905, and 0.775, respectively. The lowest root mean square error (RMSE) was 1.54 cm for the apples detected by EfficientDet at a 15° orientation and a speed of 0.098 ms−1. In terms of counting apples, YOLOv5 and YOLOv7 showed a higher number of detections in outdoor dynamic conditions, achieving a counting accuracy of 86.6%. We concluded that the EfficientDet deep learning algorithm at a 15° orientation in 3D coordinates can be employed for further robotic arm development while harvesting apples in a specially designed orchard.
Electrospun Polyvinylidene Fluoride Piezoelectric Fiber Glass/Carbon Hybrid Self-Sensing Composites for Structural Health Monitoring
Authors: Cheng Wei-Han , Wu Ping-Lun , Huang Hsin-Haou
Published: April 2023

In this study, a polyvinylidene fluoride (PVDF)/graphene nanoplatelet (GNP) micro-nanocomposite membrane was fabricated through electrospinning technology and was employed in the fabrication of a fiber-reinforced polymer composite laminate. Some glass fibers were replaced with carbon fibers to serve as electrodes in the sensing layer, and the PVDF/GNP micro-nanocomposite membrane was embedded in the laminate to confer multifunctional piezoelectric self-sensing ability. The self-sensing composite laminate has both favorable mechanical properties and sensing ability. The effects of different concentrations of modified multiwalled carbon nanotubes (CNTs) and GNPs on the morphology of PVDF fibers and the β-phase content of the membrane were investigated. PVDF fibers containing 0.05% GNPs were the most stable and had the highest relative β-phase content; these fibers were embedded in glass fiber fabric to prepare the piezoelectric self-sensing composite laminate. To test the laminate’s practical application, four-point bending and low-velocity impact tests were performed. The results revealed that when damage occurred during bending, the piezoelectric response changed, confirming that the piezoelectric self-sensing composite laminate has preliminary sensing performance. The low-velocity impact experiment revealed the effect of impact energy on sensing performance.
High Pulsed Voltage Alkaline Electrolysis for Water Splitting
Authors: Albornoz Matías , Rivera Marco , Wheeler Patrick , Ramírez Roberto
Published: April 2023

Pulsed electrolysis has become a promising research topic in recent decades due to advances in solid-state semiconductor devices. These technologies have enabled the design and construction of simpler, more efficient, and less costly high-voltage and high-frequency power converters. In this paper, we study high-voltage pulsed electrolysis considering variations in both power converter parameters and cell configuration. Experimental results are obtained for frequency variations ranging from 10 Hz to 1 MHz, voltage changes from 2 V to 500 V, and electrode separations from 0.1 to 2 mm. The results demonstrate that pulsed plasmolysis is a promising method for decomposing water for hydrogen production.
Development of a Novel Spherical Light-Based Positioning Sensor in Solar Tracking
Authors: Gora Oğuz , Akkan Taner
Published: April 2023

Tracking of the sun, which increases the efficiency of solar energy production systems, has shown considerable development in recent years. This development has been achieved by custom-positioned light sensors, image cameras, sensorless chronological systems and intelligent controller supported systems or by synergetic use of these systems. This study contributes to this research area with a novel spherical-based sensor which measures spherical light source emittance and localizes the light source. This sensor was built by using miniature light sensors placed on a spherical shaped three-dimensional printed body with data acquisition electronic circuitry. Besides the developed sensor data acquisition embedded software, preprocessing and filtering processes were conducted on these measured data. In the study, the outputs of Moving Average, Savitzky-Golay, and Median filters were used for the localization of the light source. The center of gravity for each filter used was determined as a point, and the location of the light source was determined. The spherical sensor system obtained by this study is applicable for various solar tracking methods. The approach of the study also shows that this measurement system is applicable for obtaining the position of local light sources such as the ones placed on mobile or cooperative robots.
Passive Backscatter Communication Scheme for OFDM-IM with Dynamic Carrier Activation
Authors: Li Shibao , Lu Rui
Published: April 2023

Multicarrier backscattering has been proposed to improve the communication rate, but the complex circuit structure of multicarrier backscattering devices requires more power consumption, resulting in devices far away from the radio frequency (RF) source without enough power to maintain communication, which greatly reduces the limited communication range in backscattering. To solve this problem, this paper introduces carrier index modulation (IM) into orthogonal frequency division multiplexing (OFDM) backscattering and proposes a dynamic subcarrier activated OFDM-IM uplink communication scheme suitable for passive backscattering devices. When the existing power collection level of the backscatter device is detected, only a subset of carrier modulation is activated using part of the circuit modules to reduce the power threshold required for device activation. The activated subcarriers are mapped by a block-wise combined index using the look-up table method, which can not only transmit information using traditional constellation modulation but also carry additional information through the frequency domain carrier index. Monte Carlo experiments show that this scheme can effectively increase the communication distance and improve the spectral efficiency of low-order modulation backscattering when the power of the transmitting source is limited.
A General-Purpose Distributed Analytic Platform Based on Edge Computing and Computational Intelligence Applied on Smart Grids
Authors: Guerrero Juan Ignacio , Martín Antonio , Parejo Antonio , Larios Diego Francisco , Molina Francisco Javier , León Carlos
Published: April 2023

Currently, in many data landscapes, the information is distributed across various sources and presented in diverse formats. This fragmentation can pose a significant challenge to the efficient application of analytical methods. In this sense, distributed data mining is mainly based on clustering or classification techniques, which are easier to implement in distributed environments. However, the solution to some problems is based on the usage of mathematical equations or stochastic models, which are more difficult to implement in distributed environments. Usually, these types of problems need to centralize the required information, and then a modelling technique is applied. In some environments, this centralization may cause an overloading of the communication channels due to massive data transmission and may also cause privacy issues when sending sensitive data. To mitigate this problem, this paper describes a general-purpose distributed analytic platform based on edge computing for distributed networks. Through the distributed analytical engine (DAE), the calculation process of the expressions (that requires data from diverse sources) is decomposed and distributed between the existing nodes, and this allows sending partial results without exchanging the original information. In this way, the master node ultimately obtains the result of the expressions. The proposed solution is examined using three different computational intelligence algorithms, i.e., genetic algorithm, genetic algorithm with evolution control, and particle swarm optimization, to decompose the expression to be calculated and to distribute the calculation tasks between the existing nodes. This engine has been successfully applied in a case study focused on the calculation of key performance indicators of a smart grid, achieving a reduction in the number of communication messages by more than 91% compared to the traditional approach.
Multi-Agent Modeling and Jamming-Aware Routing Protocols for Movable-Jammer-Affected WSNs
Authors: Xu Biao , Lu Minyan , Zhang Hong
Published: April 2023

Wireless sensor networks (WSNs) are widely used in various fields, and the reliability and performance of WSNs are critical for their applications. However, WSNs are vulnerable to jamming attacks, and the impact of movable jammers on WSNs’ reliability and performance remains largely unexplored. This study aims to investigate the impact of movable jammers on WSNs and propose a comprehensive approach for modeling jammer-affected WSNs, comprising four parts. Firstly, agent-based modeling of sensor nodes, base stations, and jammers has been proposed. Secondly, a jamming-aware routing protocol (JRP) has been proposed to enable sensor nodes to weigh depth and jamming values when selecting relay nodes, thereby bypassing areas affected by jamming. The third and fourth parts involve simulation processes and parameter design for simulations. The simulation results show that the mobility of the jammer significantly affects WSNs’ reliability and performance, and JRP effectively bypasses jammed areas and maintains network connectivity. Furthermore, the number and deployment location of jammers has a significant impact on WSNs’ reliability and performance. These findings provide insights into the design of reliable and efficient WSNs under jamming attacks.
A Scalable Distributed Control Algorithm for Bearing-Only Passive UAV Formation Maintenance
Authors: Gao Yuchong , Feng Huiqi , Chen Jiexiang , Li Junhui , Wei Zhiqing
Published: April 2023

Unmanned Aerial Vehicles (UAVs) can cooperate through formations to perform tasks. Wireless communication allows UAVs to exchange information, but for the situations requiring high security, electromagnetic silence is needed to avoid potential threats. The passive UAV formation maintenance strategies can fulfill the requirement of electromagnetic silence at the cost of heavy real-time computing and precise locations of UAVs. To pursue high real-time performance without the localization of UAVs, this paper proposes a scalable distributed control algorithm for bearing-only passive UAV formation maintenance. By minimizing necessary communication, pure angle information is applied to maintain UAV formations through distributed control, without the knowledge of the UAVs’ precise locations. The convergency of the proposed algorithm is proven strictly and the converging radius is derived. Through simulation, the proposed algorithm is proven to be suitable for a general case and demonstrates fast convergence speed, strong anti-interference capability, and high scalability.
Improving Recognition Accuracy of Pesticides in Groundwater by Applying TrAdaBoost Transfer Learning Method
Authors: Chen Donghui , Wang Bingyang , Yang Xiao , Weng Xiaohui , Chang Zhiyong
Published: April 2023

Accurate and rapid prediction of pesticides in groundwater is important to protect human health. Thus, an electronic nose was used to recognize pesticides in groundwater. However, the e-nose response signals for pesticides are different in groundwater samples from various regions, so a prediction model built on one region’s samples might be ineffective when tested in another. Moreover, the establishment of a new prediction model requires a large number of sample data, which will cost too much resources and time. To resolve this issue, this study introduced the TrAdaBoost transfer learning method to recognize the pesticide in groundwater using the e-nose. The main work was divided into two steps: (1) qualitatively checking the pesticide type and (2) semi-quantitatively predicting the pesticide concentration. The support vector machine integrated with the TrAdaBoost was adopted to complete these two steps, and the recognition rate can be 19.3% and 22.2% higher than that of methods without transfer learning. These results demonstrated the potential of the TrAdaBoost based on support vector machine approaches in recognizing the pesticide in groundwater when there were few samples in the target domain.
Investigation of the Effect of Students’ Nodding on Their Arousal Levels in On-Demand Lectures
Authors: Sumida Kotaro , Ohnishi Ayumi , Terada Tsutomu , Kato Hiroshi , Kuzuoka Hideaki , Kubota Yoshihiko , Suzuki Hideyuki , Tsukamoto Masahiko
Published: April 2023

Due to COVID-19, various lecture styles are being explored. On-demand lectures are attracting increasing attention due to advantages such as being able to watch without restrictions due to location and time. In contrast, on-demand lectures have disadvantages, such as no interaction with the lecturer, so the quality of on-demand lectures should be improved. Our previous study showed that when participants nod without showing their faces in a real-time remote lecture, their heart rate state changes to arousal and nodding can increase arousal. In this paper, we hypothesize that nodding during on-demand lectures increases participants’ arousal levels, and we investigate the relationship between natural and forced nodding and the level of arousal based on heart rate information. Students taking on-demand lectures rarely nod naturally, so we used entrainment to encourage nodding by showing a video of another participant nodding, and by forcing the participants to nod when the other participant nodded in the video. The results showed that only participants who nodded spontaneously changed the value of pNN50, an index of the arousal level, which reflected a state of high arousal after one minute. Thus, participants’ nodding in on-demand lectures can increase their arousal levels; however, the nodding must be spontaneous, not forced.
Stochastic Resonance with Parameter Estimation for Enhancing Unknown Compound Fault Detection of Bearings
Authors: Xu Min , Zheng Chao , Sun Kelei , Xu Li , Qiao Zijian , Lai Zhihui
Published: April 2023

Although stochastic resonance (SR) has been widely used to enhance weak fault signatures in machinery and has obtained remarkable achievements in engineering application, the parameter optimization of the existing SR-based methods requires the quantification indicators dependent on prior knowledge of the defects to be detected; for example, the widely used signal-to-noise ratio easily results in a false SR and decreases the detection performance of SR further. These indicators dependent on prior knowledge would not be suitable for real-world fault diagnosis of machinery where their structure parameters are unknown or are not able to be obtained. Therefore, it is necessary for us to design a type of SR method with parameter estimation, and such a method can estimate these parameters of SR adaptively by virtue of the signals to be processed or detected in place of the prior knowledge of the machinery. In this method, the triggered SR condition in second-order nonlinear systems and the synergic relationship among weak periodic signals, background noise and nonlinear systems can be considered to decide parameter estimation for enhancing unknown weak fault characteristics of machinery. Bearing fault experiments were performed to demonstrate the feasibility of the proposed method. The experimental results indicate that the proposed method is able to enhance weak fault characteristics and diagnose weak compound faults of bearings at an early stage without prior knowledge and any quantification indicators, and it presents the same detection performance as the SR methods based on prior knowledge. Furthermore, the proposed method is more simple and less time-consuming than other SR methods based on prior knowledge where a large number of parameters need to be optimized. Moreover, the proposed method is superior to the fast kurtogram method for early fault detection of bearings.
Effects of Mechanical Stirring and Ultrasound Treatment on the Separation of Graphite Electrode Materials from Copper Foils of Spent LIBs: A Comparative Study
Authors: Ren Xibing , Tong Zheng , Dai Yanshan , Ma Guoying , Lv Zhongze , Bu Xiangning , Bilal Muhammad , Vakylabad Ali Behrad , Hassanzadeh Ahmad
Published: April 2023

In this paper, mechanical stirring and ultrasonic treatment are used to separate graphite electrode materials from copper foils in recycling spent lithium-ion batteries (LIBs). Firstly, the effects of ultrasonic power (60–180 W), ultrasonic time (1–8 min), stirring speed (420–2000 rpm), and stirring time (1–8 min) on the abscission rate of active material on copper foil were studied. It was found that the peeling-off ratio of electrode material under ultrasonic treatment was 91.34% compared with stirring treatment (84.22%). The removal of electrode material from copper foil during stirring was mainly through mechanical scrubbing. As a comparison, the generation of the microjets induced by ultrasound, the local high-temperature and high-pressure environment, and the free radicals during ultrasonic treatment are the key factors to further improve electrode material removal efficiency. An integrated ultrasound-mechanical stirrer technique can achieve a high-efficient separation performance (approximately 100% peeling-off ratio) of anode electrode materials from copper foils. The effects of mechanical stirring speed, temperature, and treatment time on the peeling-off ratios of the ultrasound-mechanical stirrer-assisted system were investigated. Finally, the results of XRF (X-ray fluorescence spectrometer), XRD (X-ray diffraction), and SEM-EDS (scanning electron microscopy coupled with energy dispersive X-ray spectroscopy) showed that the as-separated graphite electrode material had high purity and contained almost no copper foil impurities. Numerical simulation analyses briefly showed that the difference between pressure and ultrasonic temperature changes in the boundary between different anode layers (graphite on copper foil in aqueous solution) was the main effective factor in the considerable separation of graphite from copper anode foil under ultrasonic-assisted delamination.
Surface Formations Salinity Survey in an Estuarine Area of Northern Morocco, by Crossing Satellite Imagery, Discriminant Analysis, and Machine Learning
Authors: El Jarjini Youssouf , Morarech Moad , Valles Vincent , Touiouine Abdessamad , Touzani Meryem , Arjdal Youssef , Barry Abdoul Azize , Barbiero Laurent
Published: April 2023

The salinity of estuarine areas in arid or semi-arid environments can reach high values, conditioning the distribution of vegetation and soil surface characteristics. While many studies focused on the prediction of soil salinity as a function of numerous parameters, few attempted to explain the role of salinity and its distribution within the soil profile in the pattern of landscape units. In a wadi estuary in northern Morocco, landscape units derived from satellite imagery and naturalistic environmental analysis are compared with a systematic survey of salinity by means of apparent electrical conductivity (Eca) measurements. The comparison is based on the allocation of measurement points to an area of the estuary from Eca measurements alone, using linear discriminant analysis and four machine learning methods. The results show that between 57 and 66% of the points are well-classified, highlighting that salinity is a major factor in the discrimination of estuary zones. The distribution of salinity is mainly the result of the interaction between capillary rise and flooding by the tides and the wadi. The location of the misclassified points is analysed and discussed, as well as the possible causes of the confusions.
Effect of Natural Phytohormones on Growth, Nutritional Status, and Yield of Mung Bean (Vigna radiata L.) and N Availability in Sandy-Loam Soil of Sub-Tropics
Authors: Parveen Aasma , Aslam Muhammad Mahran , Iqbal Rashid , Ali Muhammad , Kamran Muhammad , Alwahibi Mona S. , Akram Muhammad , Elshikh Mohamed S.
Published: April 2023

Climate changes and poor soil nutrient profiles in sub-tropics are determinant factors to estimate crop productivity. This study aims to evaluate the impact of phytohormones, e.g., indole acetic acid (IAA) and gibberellic acid (GA3), on mung bean yield, seed nutritional profile, and soil N availability in the sub-tropical region of Pakistan. The mung bean plants were treated with three levels (0, 30, and 60 mg L−1) of IAA and GA3 individually and/or in combination using a hydraulic sprayer. The amendments were applied in the flowering stage (approximately 25 days after germination) in a randomized complete block design. The results revealed that the 60 mg L−1 concentration of IAA and GA3 led to significant changes in the growth and yield traits compared to non-treated plants. For example, GA3 positively influenced the biological yield (35.0%), total carbohydrate (7.0%), protein (16.0%), and nitrogen (14.0%) contents in mung bean seeds, compared to the control (CK). Additionally, the combined foliar treatment of IAA and GA3 (IAA2 + GA2) displayed a much stronger influence on yield attributes, such as the number of pods by 66.0%, pods’ weights by 142.0%, and seed yield by 106.5%, compared with the CK. Mung bean plants showed a significant improvement in leaf photosynthetic pigments under a higher level (60 mg L−1) of sole and combined treatments of IAA and GA3. Moreover, except abscisic acid, the endogenous concentration of IAA, GA3, and zeatin was enhanced by 193.0%, 67.0%, and 175.0% after the combined application of IAA and GA3 (IAA2 + GA2) compared to the CK treatment. In addition, soil N availability was increased by 72.8% under the IAA2 treatment and 61.5% under IAA2 + GA2, respectively, compared with the control plot. It was concluded that the combined treatment of IAA and GA3 (IAA2 + GA2) followed by the sole application of GA3 and IAA at a 60 mg L−1 concentration were most effective treatments to improve the morpho-physiology and nutrient profile of mung beans; however, the underlying molecular mechanisms need to be explored further.
Digital Quantitative Study on Fracture Gas Storage Space for a Three-Layer Composite Residual Mining Area
Authors: Feng Guorui , Fan Weichao , Li Zhen , Wang Zhiwei , Zhang Yidie , Yang Yanqun , Yang Xiaohong , Zhang Xiangming , Feng Gan
Published: April 2023

Quantifying the fracture gas storage space is the key to improving the coalbed methane (CBM) extraction efficiency in residual mining areas (RMAs). In this paper, a new digital quantitative description method of fractures is proposed when using a digital image correlation (DIC) system to monitor strata displacement, which improves the accuracy of fracture statistical data. The results show that with the evolution of RMA from single to three layers, the rock strata area with displacement greater than 3.5 mm increases radially and the maximum fracture rate of the uppermost RMA increases by 64.26%. The fracture rate increases exponentially from top to bottom in a long-distance composite RMA and distributes parabolically in the horizontal partition. The area with the highest average fracture rate (12.65%) in the close-distance composite RMA is defined as the concentrated growth area. The longitudinal fracture rate of a cross-layer fracture area in the three-layer RMA exceeds 60%. The cross-layer fracture area connecting the composite RMA at the open-cut side is a favorable extraction location for surface drilling. The research results will provide theoretical support for the safe and sustainable exploitation of CBM and residual coal in composite RMA.
Analysis of the Factors Influencing Speed Cushion Effectiveness in the Urban Context: A Case Study Experiment in the City of Bari, Italy
Authors: Berloco Nicola , Coropulis Stefano , Garofalo Giuseppe , Intini Paolo , Ranieri Vittorio
Published: April 2023

The installation of Traffic-Calming Devices (TCDs) is an extremely valuable countermeasure to prevent vulnerable road users from fatalities in urban contexts. Among all the TCDs, Berlin Speed Cushions (BSCs) seem to be one of the most promising because they reduce speeds but do not affect emergency vehicles. However, previous research on BSCs is limited and lacks some important aspects, such as the analysis of speeds at different distances from the cushion or the investigation of the influence of other context variables. In this study, BSCs of different lengths (2.20 m, 2.70 m, and 3.20 m) were deployed in the City of Bari on three roads belonging to the same area. To overcome the limitations of previous research, speeds were recorded using a laser-speed gun before and after the implementation of BSCs, in different conditions, in order to take into account the effect of the following factors: the time of day, day of the week, and average hourly traffic. An ANOVA analysis was performed, with speed as the dependent variable and the above-reported factors and the test road site (proxy variable for the cushion length) as factors, independently repeated for six distance ranges with respect to the cushion. The results reveal that speed evidently decreases immediately before (down to about 13 km/h) and after the cushion (down to about 12 km/h), time of the day is an important factor (speed decrease is much more evident during the morning than the evening), and the length of the cushion has some influence on speed decrease (the speed decrease is lower for the longest cushion).
Analysis of Influencing Factors of Gangue Ball Milling Based on Multifractal Theory
Authors: Zhu Lei , Gu Wenzhe , Qiu Fengqi , Zhang Peng
Published: April 2023

To study the heterogeneity and local heterogeneity of gangue particle size distribution (PSD) under ball milling, gangue from northern Shaanxi coal mine was taken as a research object. The multifractal pattern of PSD and the variation trend of characteristic parameters of gangue under different ball-to-gangue ratios and grinding times were analyzed by introducing multifractal theory and microscopic research methods such as laser particle size analysis and scanning electron microscopy. The results show that the multifractal characteristics of the gangue particle size distribution with different ball-to-gangue ratios and grinding time periods demonstrate obvious changes. When the ball-to-gangue ratio is 3~9, the multifractal parameters D(0), D(1), Δα, and Δf all show linear changes with grinding time. It is demonstrated that due to the phenomenon of particle agglomeration during ball milling, the multifractal characteristics of the particle size distribution of the gangue changes significantly when the ball-to-gangue ratio is 12~15. Furthermore, the results indicate that with the increase in time, D(0), Δα, and Δf show a trend of decreasing first and then increasing, and D(1) and D(1)/D(0) show a trend of first increasing and then decreasing, and both reach their extreme values at 30 min.
Spatial Distribution of Soil Organic Carbon and Total Nitrogen in a Micro-Catchment of Northeast China and Their Influencing Factors
Authors: Tian Jiayu , Yuan Yaru , Zhou Pengchong , Wang Lixin , Chen Zhuoxin , Chen Qiang
Published: April 2023

This study aimed to analyze the spatial distribution of soil organic carbon (SOC) and total nitrogen (STN) in a micro-catchment area comprising farmland and a gully with vegetation restoration (artificial forest and grassland) and their influencing factors. We surveyed a total of 52 topsoil sampling sites to measure the SOC and STN content, as well as topographical factors (elevation, curvature, slope gradient, and aspect), soil properties (bulk density, aggregate distribution, soil texture, and soil moisture), and land use and management. We used traditional statistical and geostatistical methods to analyze the spatial variability of SOC and STN. The results of this study indicate that SOC and STN content varied moderately across the entire micro-catchment area, with greater values in the west (gully head area) than in the east (gully mouth area). Additionally, SOC and STN were significantly positively correlated with soil water content, >2 mm size aggregate fraction, and elevation, but negatively correlated with <0.053 mm size fraction. Land use types also influenced the spatial distribution of SOC and STN contents, with the highest averages of SOC and STN content found at the edge of farmland road and grassland, respectively, and the lowest values in bare land. This study provides a valuable supplement to the understanding of SOC and STN in micro-catchment areas, and the research results also support the evaluation of the impact of gully erosion control on SOC and STN dynamics after vegetation restoration. Overall, vegetation restoration should be adopted for gully erosion control and sustainable agricultural development in the Mollisols region of Northeast China.
Application of Fuzzy Theory to the Investigation of Children’s Preference for Wooden Toy Materials—A Case Study of Rocking Horses
Authors: Liu Yu-Chun , Lee An-Sheng
Published: April 2023

This study aims to investigate the sensory preferences of children aged 8–12 regarding the material and characteristics of wooden toys. Taking wooden rocking horses as research samples, this study selected three types of wood, respectively Pinus taiwanensis, Fagus sylvatica, and Juglans nigra, which are significantly different in appearance from solid wood commonly used to make wooden toys. The experiment was conducted through on-site observation and questionnaire surveys to record children’s preferences for wood characteristics, such as the characteristic factors of wood texture, color and luster, touch, weight, smell, and overall preference. Descriptive statistics and analysis were performed using SPSS 25V, and the experimental results were analyzed and interpreted using fuzzy trigonometric functions. The findings reveal that Taiwan pine had the highest average worth score among the various evaluation characteristics in terms of wood surface performance preferences. Other types of wood also showed different favorability results in different characteristics. This study also analyzes the correlation and significant differences in favorability evaluation using fuzzy theory. This study provides insights that can be useful for toy designers and children’s teaching aid developers to purchase and match wooden toys effectively while promoting the efficient use of wood resources.
A Cost-Effective Multi-Verse Optimization Algorithm for Efficient Power Generation in a Microgrid
Authors: Lakhina Upasana , Elamvazuthi Irraivan , Badruddin Nasreen , Jangra Ajay , Truong Bao-Huy , Guerrero Joseph M.
Published: April 2023

Renewable energy sources (RESs) are a great source of power generation for microgrids with expeditious urbanization and increase in demand in the energy sector. One of the significant challenges in deploying RESs with microgrids is efficient energy management. Optimizing the power allocation among various available generation units to serve the load is the best way to achieve efficient energy management. This paper proposes a cost-effective multi-verse optimizer algorithm (CMVO) to solve this optimization problem. CMVO focuses on the optimal sharing of generated power in a microgrid between different available sources to reduce the generation cost. The proposed algorithm is analyzed for two different scale microgrids (IEEE 37-node test system and IEEE 141-node test system) using IEEE test feeder standards to assess its performance. The results show that CMVO outperforms multi-verse optimizer (MVO), particle swarm optimization (PSO), artificial hummingbird algorithm (AHA), and genetic algorithm (GA). The simulation results emphasize the cost reduction and execution time improvement in both IEEE test systems compared with other meta-heuristic algorithms.
Development and Validation of an Evaluation Toolkit to Appraise eLearning Courses in Higher Education: A Pilot Study
Authors: Ličen Sabina , Cassar Maria , Filomeno Lucia , Yeratziotis Alexandros , Prosen Mirko
Published: April 2023

The development and evaluation of eLearning approaches is a global trend in higher education today. This study aimed to develop a companion evaluation toolkit consisting of formative and summative assessment scales to evaluate academics’ experiences in designing, delivering, and evaluating eLearning. To test the psychometric properties of the companion evaluation toolkit, an instrument validation study was conducted. Items were created, then tested for content and face validity. A confirmatory factor analysis (n = 185 participants) for the summative assessment scale examined the underlying structure of the scale, while reliability was assessed using the Cronbach’s alpha coefficient. The results show that the model examined is consistent with the 3 factors (33 items) explaining a total of 62% of the variance. The results also show a high level of reliability for both the formative and summative scales that comprise the companion evaluation toolkit. The results of this study can be used and welcomed by both teachers and professionals involved in the development and use of learning management systems or in the design, delivery, and evaluation of the eLearning process.
Political Hierarchy and Long-Term Effects on TFP: Evidence from a Provincial Elevation in China
Authors: Hu Wenjie , Yang Yuguang
Published: April 2023

This paper examines the effects of political hierarchy on firms’ long-term total factor productivity using evidence from the elevation of Chongqing to provincial-level government in China. Using the elevation as an exogenous shock and the Sichuan-Chongqing border as a dividing line, we utilized the spatial regression discontinuity approach to identify the causal link. We found that before Chongqing’s elevation to a provincial-level municipality, the TFP of Chongqing firms was not significantly different from that of Sichuan firms, but after the elevation, the TFP of Chongqing firms near the border was significantly lower than that of Sichuan firms. The mechanism analysis shows that the land transaction price in Chongqing is significantly lower than that in Sichuan Province, which leads to an abundance of low-productivity firms and the “crowding out” of high-productivity firms. We also found that government intervention in Chongqing exacerbates the degree of resource misallocation at the firm level, which together lead to a decline in firm TFP.