bluuebunny/crossref_metadata_2025 huggingface dataset:
Two-Dimensional Wavenumber Analysis Implemented in Ultrasonic Vector Doppler Method with Focused Transmit Beams
Authors: Hasegawa Hideyuki , Omura Masaaki , Nagaoka Ryo , Saito Kozue
Published: December 2022

The multi-angle Doppler method was introduced for the estimation of velocity vectors by measuring axial velocities from multiple directions. We have recently reported that the autocorrelation-based velocity vector estimation could be ameliorated significantly by estimating the wavenumbers in two dimensions. Since two-dimensional wavenumber estimation requires a snapshot of an ultrasonic field, the method was first implemented in plane wave imaging. Although plane wave imaging is predominantly useful for examining blood flows at an extremely high temporal resolution, it was reported that the contrast in a B-mode image obtained with a few plane wave emissions was lower than that obtained with focused beams. In this study, the two-dimensional wavenumber analysis was first implemented in a framework with focused transmit beams. The simulations showed that the proposed method achieved an accuracy in velocity estimation comparable to that of the method with plane wave imaging. Furthermore, the performances of the methods implemented in focused beam and plane wave imaging were compared by measuring human common carotid arteries in vivo. Image contrasts were analyzed in normal and clutter-filtered B-mode images. The method with focused beam imaging achieved a better contrast in normal B-mode imaging, and similar velocity magnitudes and angles were obtained by both the methods with focused beam and plane wave imaging. In contrast, the method with plane wave imaging gave a better contrast in a clutter-filtered B-mode image and smaller variances in velocity magnitudes than those with focused beams.
Investigating User Proficiency of Motor Imagery for EEG-Based BCI System to Control Simulated Wheelchair
Authors: Saichoo Theerat , Boonbrahm Poonpong , Punsawad Yunyong
Published: December 2022

The research on the electroencephalography (EEG)-based brain–computer interface (BCI) is widely utilized for wheelchair control. The ability of the user is one factor of BCI efficiency. Therefore, we focused on BCI tasks and protocols to yield high efficiency from the robust EEG features of individual users. This study proposes a task-based brain activity to gain the power of the alpha band, which included eyes closed for alpha response at the occipital area, attention to an upward arrow for alpha response at the frontal area, and an imagined left/right motor for alpha event-related desynchronization at the left/right motor cortex. An EPOC X neuroheadset was used to acquire the EEG signals. We also proposed user proficiency in motor imagery sessions with limb movement paradigms by recommending motor imagination tasks. Using the proposed system, we verified the feature extraction algorithms and command translation. Twelve volunteers participated in the experiment, and the conventional paradigm of motor imagery was used to compare the efficiencies. With utilized user proficiency in motor imagery, an average accuracy of 83.7% across the left and right commands was achieved. The recommended MI paradigm via user proficiency achieved an approximately 4% higher accuracy than the conventional MI paradigm. Moreover, the real-time control results of a simulated wheelchair revealed a high efficiency based on the time condition. The time results for the same task as the joystick-based control were still approximately three times longer. We suggest that user proficiency be used to recommend an individual MI paradigm for beginners. Furthermore, the proposed BCI system can be used for electric wheelchair control by people with severe disabilities.
Deep Learning in Medical Hyperspectral Images: A Review
Authors: Cui Rong , Yu He , Xu Tingfa , Xing Xiaoxue , Cao Xiaorui , Yan Kang , Chen Jiexi
Published: December 2022

With the continuous progress of development, deep learning has made good progress in the analysis and recognition of images, which has also triggered some researchers to explore the area of combining deep learning with hyperspectral medical images and achieve some progress. This paper introduces the principles and techniques of hyperspectral imaging systems, summarizes the common medical hyperspectral imaging systems, and summarizes the progress of some emerging spectral imaging systems through analyzing the literature. In particular, this article introduces the more frequently used medical hyperspectral images and the pre-processing techniques of the spectra, and in other sections, it discusses the main developments of medical hyperspectral combined with deep learning for disease diagnosis. On the basis of the previous review, tne limited factors in the study on the application of deep learning to hyperspectral medical images are outlined, promising research directions are summarized, and the future research prospects are provided for subsequent scholars.
Vehicle Stability Analysis under Extreme Operating Conditions Based on LQR Control
Authors: Wu Liping , Zhou Ran , Bao Junshan , Yang Guang , Sun Feng , Xu Fangchao , Jin Junjie , Zhang Qi , Jiang Weikang , Zhang Xiaoyou
Published: December 2022

Under extreme working conditions such as high-speed driving on roads with a large road surface unevenness coefficient, turning on a road with a low road surface adhesion coefficient, and emergency acceleration and braking, a vehicle’s stability deteriorates sharply and reduces ride comfort. There is extensive existing research on vehicle active suspension control, trajectory tracking, and control methods. However, most of these studies focus on conventional operating conditions, while vehicle stability analysis under extreme operating conditions is much less studied. In order to improve the stability of the whole vehicle under extreme operating conditions, this paper investigates the stability of a vehicle under extreme operating conditions based on linear quadratic regulator (LQR) control. First, a seven degrees of freedom (7-DOF) dynamics model of the whole vehicle is established based on the use of electromagnetic active suspension, and then an LQR controller of the electromagnetic active suspension is designed. A joint simulation platform incorporating MATLAB and CarSim was built, and the CarSim model is verified by real vehicle tests. Finally, the stability of the vehicle under four different ultimate operating conditions was analyzed. The simulation results show that the root mean square (RMS) values of body droop acceleration and pitch angle acceleration are improved by 57.48% and 28.81%, respectively, under high-speed driving conditions on Class C roads. Under the double-shift condition with a low adhesion coefficient, the RMS values of body droop acceleration, pitch acceleration, and roll angle acceleration are improved by 58.25%, 55.41%, and 31.39%, respectively. These results indicate that electromagnetic active suspension can significantly improve vehicle stability and reduce driving risk under extreme working conditions when combined with an LQR controller.
Recent Developments in Wireless Soil Moisture Sensing to Support Scientific Research and Agricultural Management
Authors: Bogena Heye Reemt , Weuthen Ansgar , Huisman Johan Alexander
Published: December 2022

In recent years, wireless sensor network (WSN) technology has emerged as an important technique for wireless sensing of soil moisture from the field to the catchment scale. This review paper presents the current status of wireless sensor network (WSN) technology for distributed, near real-time sensing of soil moisture to investigate seasonal and event dynamics of soil moisture patterns. It is also discussed how WSN measurements of soil measurements contribute to the validation and downscaling of satellite data and non-invasive geophysical instruments as well as the validation of distributed hydrological models. Finally, future perspectives for WSN measurements of soil moisture are highlighted, which includes the improved integration of real-time WSN measurements with other information sources using the latest wireless communication techniques and cyberinfrastructures.
Improving Tracking of Trajectories through Tracking Rate Regulation: Application to UAVs
Authors: Diaz-del-Rio Fernando , Sanchez-Cuevas Pablo , Iñigo-Blasco Pablo , Sevillano-Ramos J. L.
Published: December 2022

The tracking problem (that is, how to follow a previously memorized path) is one of the most important problems in mobile robots. Several methods can be formulated depending on the way the robot state is related to the path. “Trajectory tracking” is the most common method, with the controller aiming to move the robot toward a moving target point, like in a real-time servosystem. In the case of complex systems or systems under perturbations or unmodeled effects, such as UAVs (Unmanned Aerial Vehicles), other tracking methods can offer additional benefits. In this paper, methods that consider the dynamics of the path’s descriptor parameter (which can be called “error adaptive tracking”) are contrasted with trajectory tracking. A formal description of tracking methods is first presented, showing that two types of error adaptive tracking can be used with the same controller in any system. Then, it is shown that the selection of an appropriate tracking rate improves error convergence and robustness for a UAV system, which is illustrated by simulation experiments. It is concluded that error adaptive tracking methods outperform trajectory tracking ones, producing a faster and more robust convergence tracking, while preserving, if required, the same tracking rate when convergence is achieved.
Modeling Driver Behavior in Road Traffic Simulation
Authors: Mecheva Teodora , Furnadzhiev Radoslav , Kakanakov Nikolay
Published: December 2022

Driver behavior models are an important part of road traffic simulation modeling. They encompass characteristics such as mood, fatigue, and response to distracting conditions. The relationships between external factors and the way drivers perform tasks can also be represented in models. This article proposes a methodology for establishing parameters of driver behavior models. The methodology is based on road traffic data and determines the car-following model and routing algorithm and their parameters that best describe driving habits. Sequential and parallel implementation of the methodology through the urban mobility simulator SUMO and Python are proposed. Four car-following models and three routing algorithms and their parameters are investigated. The results of the performed simulations prove the applicability of the methodology. Based on more than 7000 simulations performed, it is concluded that in future experiments of the traffic in Plovdiv it is appropriate to use a Contraction Hierarchies routing algorithm with the default routing step and the Krauss car-following model with the default configuration parameters.
Efficient Low-Frequency SSVEP Detection with Wearable EEG Using Normalized Canonical Correlation Analysis
Authors: Kartsch Victor Javier , Kumaravel Velu Prabhakar , Benatti Simone , Vallortigara Giorgio , Benini Luca , Farella Elisabetta , Buiatti Marco
Published: December 2022

Recent studies show that the integrity of core perceptual and cognitive functions may be tested in a short time with Steady-State Visual Evoked Potentials (SSVEP) with low stimulation frequencies, between 1 and 10 Hz. Wearable EEG systems provide unique opportunities to test these brain functions on diverse populations in out-of-the-lab conditions. However, they also pose significant challenges as the number of EEG channels is typically limited, and the recording conditions might induce high noise levels, particularly for low frequencies. Here we tested the performance of Normalized Canonical Correlation Analysis (NCCA), a frequency-normalized version of CCA, to quantify SSVEP from wearable EEG data with stimulation frequencies ranging from 1 to 10 Hz. We validated NCCA on data collected with an 8-channel wearable wireless EEG system based on BioWolf, a compact, ultra-light, ultra-low-power recording platform. The results show that NCCA correctly and rapidly detects SSVEP at the stimulation frequency within a few cycles of stimulation, even at the lowest frequency (4 s recordings are sufficient for a stimulation frequency of 1 Hz), outperforming a state-of-the-art normalized power spectral measure. Importantly, no preliminary artifact correction or channel selection was required. Potential applications of these results to research and clinical studies are discussed.
Failure Prediction and Replacement Strategies for Smart Electricity Meters Based on Field Failure Observation
Authors: Dong Xianguang , Jing Zhen , Dai Yanjie , Wang Pingxin , Chen Zhen
Published: December 2022

It is helpful to have a replacement strategy by predicting the number of failures of in-service electricity meters. This paper presents a failure number prediction method for smart electricity meters based on on-site fault data. The prediction model was constructed by combining Weibull distribution with odds ratios, then the distribution parameters, failure prediction number, and confidence intervals of prediction number were calculated. A strategy of meter replacement and reserve were developed according to the prediction results. To avoid the uncertainty of prediction results due to the small amount of field data information, a Bayesian failure number prediction method was developed. The research results have value for making operation plans and reserve strategies for electricity meters.
Finding a Landing Site in an Urban Area: A Multi-Resolution Probabilistic Approach
Authors: Pinkovich Barak , Matalon Boaz , Rivlin Ehud , Rotstein Hector
Published: December 2022

This paper considers the problem of finding a landing spot for a drone in a dense urban environment. The conflicting requirements of fast exploration and high resolution are solved using a multi-resolution approach, by which visual information is collected by the drone at decreasing altitudes so that the spatial resolution of the acquired images increases monotonically. A probability distribution is used to capture the uncertainty of the decision process for each terrain patch. The distributions are updated as information from different altitudes is collected. When the confidence level for one of the patches becomes larger than a prespecified threshold, suitability for landing is declared. One of the main building blocks of the approach is a semantic segmentation algorithm that attaches probabilities to each pixel of a single view. The decision algorithm combines these probabilities with a priori data and previous measurements to obtain the best estimates. Feasibility is illustrated by presenting several examples generated by a realistic closed-loop simulator.
Operational and Analytical Modal Analysis of a Bridge Using Low-Cost Wireless Arduino-Based Accelerometers
Authors: Komarizadehasl Seyedmilad , Huguenet Pierre , Lozano Fidel , Lozano-Galant Jose Antonio , Turmo Jose
Published: December 2022

Arduino-based accelerometers are receiving wide attention from researchers to make long-term Structural Health Monitoring (SHM) feasible for structures with a low SHM budget. The current low-cost solutions found in the literature share some of the following drawbacks: (1) high noise density, (2) lack of wireless synchronization, (3) lack of automatic data acquisition and data management, and (4) lack of dedicated field tests aiming to compare mode shapes from Operational Modal Analysis (OMA) with those of a digital model. To solve these problems, a recently built short-span footbridge in Barcelona is instrumented using four Low-cost Adaptable Reliable Accelerometers (LARA). In this study, the automatization of the data acquisition and management of these low-cost solutions is studied for the first time in the literature. In addition, a digital model of the bridge under study is generated in SAP2000 using the available drawings and reported characteristics of its materials. The OMA of the bridge is calculated using Frequency Domain Decomposition (FDD) and Covariance Stochastic Subspace Identification (SSI-cov) methods. Using the Modal Assurance Criterion (MAC), the mode shapes of OMA are compared with those of the digital model. Finally, the acquired eigenfrequencies of the bridge obtained with a high-precision commercial sensor (HI-INC) showed a good agreement with those obtained with LARA.
Transcutaneous Functional Electrical Stimulation Controlled by a System of Sensors for the Lower Limbs: A Systematic Review
Authors: Chaikho Layal , Clark Elizabeth , Raison Maxime
Published: December 2022

In the field of transcutaneous functional electrical stimulation (FES), open-loop and closed-loop control strategies have been developed to restore functions of the lower limbs: walking, standing up, maintaining posture, and cycling. These strategies require sensors that provide feedback information on muscle activity or biomechanics of movement. Since muscle response induced by transcutaneous FES is nonlinear, time-varying, and dependent on muscle fatigue evolution, the choice of sensor type and control strategy becomes critical. The main objective of this review is to provide state-of-the-art, emerging, current, and previous solutions in terms of control strategies. Focus is given on transcutaneous FES systems for the lower limbs. Using Compendex and Inspec databases, a total of 135 review and conference articles were included in this review. Recent studies mainly use inertial sensors, although the use of electromyograms for lower limbs has become more frequent. Currently, several researchers are opting for nonlinear controllers to overcome the nonlinear and time-varying effects of FES. More development is needed in the field of systems using inertial sensors for nonlinear control. Further studies are needed to validate nonlinear control systems in patients with neuromuscular disorders.
Influence of Parameter Uncertainty to Stator Current Reconstruction Using Modified Luenberger Observer for Current Sensor Fault-Tolerant Induction Motor Drive
Authors: Adamczyk Michal , Orlowska-Kowalska Teresa
Published: December 2022

In modern systems with induction motors (IM), in addition to precision, it is also very important to ensure the highest possible reliability and safety. To ensure the above, information about the stator current value is required. If the current sensor (CS) fails, a redundant sensor or an algorithmic solution can be used. The Luenberger observer (LO) can be used to estimate the lost stator current without increasing the cost of the drive system. However, this solution is based on the mathematical model of IM, which is sensitive to its parameters. Therefore, this paper presents a modified LO (MLO) and investigates the effect of a coefficient in the error gain matrix on improving robustness to changes in the IM parameters. As shown by extensive studies, the proposed solution has significantly reduced the influence of the IM parameters on the accuracy of the stator current estimation, which has not been previously reported in the known literature.
Liquid Core Detection and Strand Condition Monitoring in a Continuous Caster Using Optical Fiber
Authors: Neelakandan Deva Prasaad , Alla Dinesh Reddy , Huang Jie , O’Malley Ronald J.
Published: December 2022

Real-time monitoring of the liquid core position during the continuous casting of steel has been demonstrated using low-cost distributed optical-fiber-based strain sensors. These sensors were installed on the containment roll support structures in the segments of a production continuous caster to detect the position of the solid–liquid interface and monitor the strand condition during the continuous casting. Distributed Fiber Bragg Grating sensors (FBGs) were used in this work to monitor strain at six roll positions in the caster. The sensor performance was first validated by comparing optical strain measurements with conventional strain gauge measurements in the lab. Next, optical strain measurements were performed on an isolated caster segment in a segment maintenance facility using hydraulic jacks to simulate the presence of a liquid core under the roll. Finally, the sensors were evaluated during caster operation. The sensors successfully detected the load increase associated with the presence of a liquid core under each instrumented roll location. Incidents of bulging and roll eccentricity were also detected using frequency analysis of the optical strain signal. The liquid core position measurements were compared using predictions from computer models (digital twins) in use at the production site. The measurements were in good agreement with the model predictions, with a few exceptions. Under certain transient caster operating conditions, such as spraying practice changes and SEN exchanges, the model predictions deviated slightly from the liquid core position determined from strain measurements.
Design of CB-PDMS Flexible Sensing for Monitoring of Bridge Cracks
Authors: Huang Yifeng , Chen Yugen , Deng Fangming , Wang Xiaoming
Published: December 2022

This paper proposes a flexible sensor for detecting cracks on bridges. Strain and deflection sensing modules are integrated on the film that is made of composite conductive materials. By optimizing the preparation ratio and internal structure, the strain detection accuracy and sensitivity of the sensor have been improved. The bridge crack detection accuracy reached 91%, which is higher than current sensors. Experimental results show that the composite material containing 2.23 wt% carbon black (CB) mixed hybrid filler has good linearity, higher accuracy than sensors in use, excellent stretchability (>155%), high gauge factor (GF ~ 43.3), and excellent durability over 2000 stretching-releasing cycles under 10 N. The designed flexible sensor demonstrates the practicality and effectiveness of bridge crack detection and provides a feasible solution for accurate bridge health monitoring in the future.
Scintillation Response of Nd-Doped LaMgAl11O19 Single Crystals Emitting NIR Photons for High-Dose Monitoring
Authors: Nakauchi Daisuke , Kato Takumi , Kawaguchi Noriaki , Yanagida Takayuki
Published: December 2022

The Nd-doped LaMgAl11O19 single crystals were synthesized by the floating zone method, and the photoluminescence and scintillation properties were evaluated. Under X-ray irradiation, several sharp emission peaks due to the 4f–4f transitions of Nd3+ were observed at 900, 1060, and 1340 nm in the near-infrared range, and the decay curves show the typical decay time for Nd3+. The samples show good afterglow properties comparable with practical X-ray scintillators. The 1% and 3% Nd-doped LaMgAl11O19 samples show a good linearity in the dynamic range from 6–60,000 mGy/h.
Agreement between Azure Kinect and Marker-Based Motion Analysis during Functional Movements: A Feasibility Study
Authors: Jo Sungbae , Song Sunmi , Kim Junesun , Song Changho
Published: December 2022

(1) Background: The present study investigated the agreement between the Azure Kinect and marker-based motion analysis during functional movements. (2) Methods: Twelve healthy adults participated in this study and performed a total of six different tasks including front view squat, side view squat, forward reach, lateral reach, front view lunge, and side view lunge. Movement data were collected using an Azure Kinect and 12 infrared cameras while the participants performed the movements. The comparability between marker-based motion analysis and Azure Kinect was visualized using Bland–Altman plots and scatter plots. (3) Results: During the front view of squat motions, hip and knee joint angles showed moderate and high level of concurrent validity, respectively. The side view of squat motions showed moderate to good in the visible hip joint angles, whereas hidden hip joint angle showed poor concurrent validity. The knee joint angles showed variation between excellent and moderate concurrent validity depending on the visibility. The forward reach motions showed moderate concurrent validity for both shoulder angles, whereas the lateral reach motions showed excellent concurrent validity. During the front view of lunge motions, both the hip and knee joint angles showed moderate concurrent validity. The side view of lunge motions showed variations in concurrent validity, while the right hip joint angle showed good concurrent validity; the left hip joint showed poor concurrent validity. (4) Conclusions: The overall agreement between the Azure Kinect and marker-based motion analysis system was moderate to good when the body segments were visible to the Azure Kinect, yet the accuracy of tracking hidden body parts is still a concern.
Prediction and Optimization Algorithm for Intersection Point of Spatial Multi-Lines Based on Photogrammetry
Authors: Zhao Chengli , Xiao Hao , Zhao Zhangyan , Wang Guoxian
Published: December 2022

The basic theory of photogrammetry is mature and widely used in engineering. The environment in engineering is very complex, resulting in the corners or multi-line intersections being blocked and unable to be measured directly. In order to solve this problem, a prediction and optimization algorithm for intersection point of spatial multi-lines based on photogrammetry is proposed. The coordinates of points on space lines are calculated by photogrammetry algorithm. Due to the influence of image point distortion and point selection error, many lines do not strictly intersect at one point. The equations of many space lines are used to fit their initial value of intersection point. The initial intersection point is projected onto each image, and the distances between the projection point and each line on the image plane are used to weight the calculated spatial lines in combination with the information entropy. Then the intersection point coordinates are re-fitted, and the intersection point is repeatedly projected and recalculate until the error is less than the threshold value or reached the set number of iterations. Three different scenarios are selected for experiments. The experimental results show that the proposed algorithm significantly improves the prediction accuracy of the intersection point.
Validation of Novel Ultrasonic Phased Array Borehole Probe by Using Simulation and Measurement
Authors: Prabhakara Prathik , Mielentz Frank , Stolpe Heiko , Behrens Matthias , Lay Vera , Niederleithinger Ernst
Published: December 2022

Low-frequency ultrasonic testing is a well-established non-destructive testing (NDT) method in civil engineering for material characterization and the localization of cracks, reinforcing bars and delamination. A novel ultrasonic borehole probe is developed for in situ quality assurance of sealing structures in radioactive waste repositories using existing research boreholes. The aim is to examine the sealing structures made of salt concrete for any possible cracks and delamination and to localize built-in components. A prototype has been developed using 12 individual horizontal dry point contact (DPC) shear wave transducers separated by equidistant transmitter/receiver arrays. The probe is equipped with a commercially available portable ultrasonic flaw detector used in the NDT civil engineering industry. To increase the sound pressure generated, the number of transducers in the novel probe is increased to 32 transducers. In addition, the timed excitation of each transducer directs a focused beam of sound to a specific angle and distance based on the previously calculated delay time. This narrows the sensitivity of test volume and improves the signal-to-noise ratio of the received signals. In this paper, the newly designed phased array borehole probe is validated by beam computation in the CIVA software and experimental investigations on a half-cylindrical test specimen to investigate the directional characteristics. In combination with geophysical reconstruction methods, it is expected that an optimised radiation pattern of the probe will improve the signal quality and thus increase the reliability of the imaging results. This is an important consideration for the construction of safe sealing structures for the safe disposal of radioactive or toxic waste.
Effect of Ultraviolet Activation on Sub-ppm NO2 Sensing Dynamics of Poly(3-hexylthiophene)-Bearing Graft Copolymers
Authors: Kaluzynski Piotr , Kepska Kinga , Maciuch Monika , Maciak Erwin , Stolarczyk Agnieszka , Procek Marcin , Jarosz Tomasz
Published: December 2022

Nitrogen dioxide (NO2) sensors utilising graft copolymers bearing poly(3-hexylthiophene) chains have been developed and investigated in terms of their operation parameters using different carrier gases (N2 or air) and in either dark conditions or with ultraviolet (UV) irradiation. Interestingly, sensor performance improved upon transition from N2 to air, with the inverse being true for most NO2 sensors. UV irradiation both improved sensor dynamics and stabilised the sensor electrical baseline, allowing sensors based on SilPEG to fulfil the requirements of sensing solutions used in industry (below 10% baseline drift after sensors reach saturation) and making them promising candidates for further development and applications. Based on conducted multi-variate experiments, an initial mechanism underlying the interplay of exposure to oxygen (present in air) and UV irradiation was postulated.
The Addition of High-Load Resistance Exercises to a High-Intensity Functional Training Program Elicits Further Improvements in Body Composition and Strength: A Randomized Trial
Authors: Posnakidis Georgios , Aphamis George , Giannaki Christoforos D. , Mougios Vassilis , Bogdanis Gregory C.
Published: December 2022

The current study aimed to examine the effects of adding specific high-load resistance exercises to a high-intensity functional training (HIFT) program on healthy adults’ physical fitness and body composition. Twenty recreationally active volunteers (30 ± 4 y, 12 females, 8 males) were randomly assigned to either a HIFT-control (HIFT-C, n = 10) or HIFT-power (HIFT-P, n = 10) group and trained three times per week for eight weeks. The HIFT-C protocol included four rounds of an 8-exercise circuit (30:15 s work: rest, 2 min rest after the second round). The exercises used were clean-and-press, box jump, TRX chest press, wall ball throws, burpees, repeated 10 m sprints, sumo squat-and-upright row, and abdominal crunches. The HIFT-P-group replaced TRX chest press with bench press and squat-and-upright row with squat, both at an intensity of 80% 1 RM. Before and after the intervention, participants underwent an evaluation of body composition, cardiorespiratory fitness, vertical jump, 1 RM bench press, and the maximum number of abdominal crunches in 1 min. In both groups, cardiorespiratory fitness, squat jump, countermovement jump, bench press 1 RM, and percent body fat improved significantly after the intervention (p < 0.050), while a trend towards significant time x group interaction was found for bench press 1 RM (p = 0.076), indicating a superiority of HIFT-P over HIFT-C. Muscle mass significantly increased by 3.3% in the HIFT-P group, while abdominal muscle endurance improved by 16.2% in the HIFT-C group (p < 0.050). Short-term HIFT resulted in improvements in whole-body cardiorespiratory and neuromuscular fitness and reduction of body fat. The addition of high-load resistance exercises was well tolerated and resulted in increased muscle mass and upper body maximal strength. HIFT-P programs can be suitable for individuals seeking to enhance muscle mass and physical fitness in a short time.
Development of Vacuum Tube Well Dewatering Test Device and Study on Vacuum Degree Transfer Law
Authors: Yu Yongqiang , Tang Jinzhao , Fan Lidan , Guo Cong , Zhang Jiyun , Yang Jie
Published: December 2022

Vacuum tube well dewatering technology has gradually become an effective means to deal with low-permeability soil groundwater. The vacuum degree transfer law is an important element in the study of the mechanism of deep foundation pit vacuum tube well dewatering. To further study the transfer law of vacuum degree in vacuum tube well dewatering, a laboratory test setup for vacuum tube well dewatering was developed, and vacuum tube well dewatering tests were conducted under various in-well vacuum values, tube diameter, depth of tube well, and other factors. The variation law of vacuum degree under various working condition coupling was thoroughly analyzed in this research. The results show that with the increase of vacuum value in the well, the radial vacuum degree and vertical vacuum degree have about 14.9~47.7% and 14.9~32.4% increase, respectively, and transmission attenuation of radial vacuum degree, transmission attenuation of vertical vacuum degree, and the range of the vacuum field all increase to some extent. With the outward transmission of the vacuum degree, the transmission attenuation of the radial vacuum degree decreases rapidly and then tends to be stable, and the transmission attenuation of the vertical vacuum degree decreases gradually. The radial vacuum degree, the transmission attenuation of the radial vacuum degree, and the range of the radial vacuum field are not affected by the buried depth of the tube well. The study’s conclusions can serve as a foundation and point of reference for projects involving the design and implementation of deep foundation pit vacuum tube well dewatering.
Effect of Environmental Information Disclosure on the Financing Efficiency of Enterprises—Evidence from China’s Listed Energy Companies
Authors: Zhou Yingying , Shi Zilin , Lei Fengyi , Sun Wanxuan , Zhang Jiaxuan
Published: December 2022

In recent years, China has made it clear that the whole society should constantly enhance its awareness of ecological and environmental protection and promote the development of public safety and health. For listed energy enterprises, it is the responsibility of the enterprise to disclose the extent of environmental pollution in the development process to the whole society. The environmental information disclosure of energy enterprises not only promotes social and environmental protection but also has an impact on the financing efficiency of the enterprises themselves. After screening all A-share listed energy enterprises in China and eliminating the non-adoptable samples, 182 listed energy enterprises were finally selected as the research samples. Based on the data samples of 182 A-share listed energy industry enterprises in China from 2012 to 2019, this paper empirically tests the relationship between environmental information disclosure and corporate financing efficiency. The study finds that the environmental information disclosure has a significant positive impact on corporate financing efficiency, and the mediating effect model is used to analyze the intermediary role of corporate social responsibility in the impact of corporate environmental information disclosure on financing efficiency. This study expands the economic impact of corporate environmental information disclosure, improves the research framework on corporate financing efficiency, and provides informative suggestions for energy enterprises to improve financing efficiency and long-term development. In addition, the research shows that the environmental information disclosure and corporate social responsibility of listed energy enterprises in China still need to be improved, and the government and regulatory authorities should strengthen relevant policy constraints.
Data-Driven Graph Filter-Based Graph Convolutional Neural Network Approach for Network-Level Multi-Step Traffic Prediction
Authors: Lin Lei , Li Weizi , Zhu Lei
Published: December 2022

Accurately predicting network-level traffic conditions has been identified as a critical need for smart and advanced transportation services. In recent decades, machine learning and artificial intelligence have been widely applied for traffic state, including traffic volume prediction. This paper proposes a novel deep learning model, Graph Convolutional Neural Network with Data-driven Graph Filter (GCNN-DDGF), for network-wide multi-step traffic volume prediction. More specifically, the proposed GCNN-DDGF model can automatically capture hidden spatiotemporal correlations between traffic detectors, and its sequence-to-sequence recurrent neural network architecture is able to further utilize temporal dependency from historical traffic flow data for multi-step prediction. The proposed model was tested in a network-wide hourly traffic volume dataset between 1 January 2018 and 30 June 2019 from 150 sensors in the Los Angeles area. Detailed experimental results illustrate that the proposed model outperforms the other five widely used deep learning and machine learning models in terms of computational efficiency and prediction accuracy. For instance, the GCNN-DDGF model improves MAE, MAPE, and RMSE by 25.33%, 20.45%, and 29.20% compared to the state-of-the-art models, such as Diffusion Convolution Recurrent Neural Network (DCRNN), which is widely accepted as a popular and effective deep learning model.
Participatory Assessment of Potato Production Systems and Cultivar Development in Rwanda
Authors: Muhinyuza Jean Baptiste , Mukamuhirwa Alphonsine , Mutimawurugo Marie Chantal , Mazimpaka Jean Damascène , Muhinyuza Delitha Girumugisha , Rios Rodomiro Octavio Ortiz
Published: December 2022

Potato cultivars grown in Rwanda are very old, low yielding and not amenable to food processing. High yielding and late blight tolerant cultivars for this country should be evaluated at different agro-ecozones prior to releasing them to farmers, who are yet to be integrated into potato breeding. The objectives of this study were to assess farmers’ preferred traits in potato cultivars and to gather knowledge from farmers about potato clones bred in Rwanda. Four respondents per village in 36 villages each for the districts of Musanze, Burera and Nyamagabe participated in the survey, whose questionnaire was about farm size, gender balance, land allocated to potatoes and other main crops, potato “seed” sourcing, potato production constraints and most important potato attributes. Potato was rated as the most important food and cash crop. ‘Kirundo’, ‘Cruza’, ‘Mabondo’ and ‘Victoria’ were the most popular cultivars. Among them, Mabondo’ was the most resistant to the oomycete Phytophthora infestans causing late blight. Potato production in Rwanda is limited by lack of improved cultivars, high temperature, drought, acidic soil, pathogens, insects, weeds, inadequate storage of tubers as planting material, post-harvest technology, low market price of tubers at harvest, lack of access to credit, climate change, and gaps such as inadequate fertilizer and fungicide applications. The most important cultivar attributes were high tuber yield, host plant resistance and high specific gravity or dry matter.
Effects of Planting Density and Nitrogen (N) Application Rate on Light Energy Utilization and Yield of Maize
Authors: Meng Chuntong , Wang Zhaoyue , Cai Ying , Du Fengyi , Chen Jinyang , Xiao Chunhua
Published: December 2022

Planting density and N fertilizer application amount directly affect the planting quality of maize. Therefore, this study analyzed the impact of these two factors on light energy utilization and yield. The field experiment was carried out with Xinyu 57 maize as the experimental variety. An experiment was set up with four different planting densities and three different N fertilizer concentrations. The light use efficiency, productivity, and growth of maize were observed. The effects of planting density and N fertilization on light use efficiency at the heading stage were analyzed in detail. Finally, it was concluded that high-density planting and the proper application of N fertilizer can significantly improve the light energy efficiency and yield of maize. High-density planting has a significant effect on corn light energy utilization. Increasing N fertilizer can improve the photosynthetic characteristics of plants, increase the content of photosynthetic pigments in maize ear leaves, and improve the utilization rate of light energy and yield.
Shuttle Bus Timetable Adjustment in Response to Behind-Schedule Commuter Railway Disturbance
Authors: Feng Yinfei , Cao Zhichao , Zhang Silin
Published: December 2022

Shuttle bus connection is a valid technique to handle unplanned problems and promote sustainable transportation. The study describes tools that facilitate the shuttle bus timetable adjustment responding to a disturbance resulting from behind-schedule trains on a commuter railway. This behind-schedule disturbance is divided in four stages allowing for different delay ranges. The problem and its solution involve different elements, such as shuttle bus route selection, stop location, and timetable adjustment. We propose a nonlinear integer programming model, in which the objective function is based on the waiting, travelling, and walking costs for passengers as well as the operation cost of the route chosen. Vehicle capacity constraints and precise passengers’ waiting times are considered. A genetic algorithm and a simulated annealing algorithm combined with a priori decomposition are used to derive an efficient solution. A case study of a shuttle bus serving the Jinshan Railway in Shanghai, China, is tested to validate that, compared to the no-planning timetable, the total cost of the optimized timetable is reduced by 7.6%, especially including a dramatic reduction in the cost of passenger waiting time by 49.1%.
Geotechnical Characteristics of Fine-Grained Soils Stabilized with Fly Ash, a Review
Authors: Turan Canan , Javadi Akbar A. , Vinai Raffaele , Beig Zali Ramiz
Published: December 2022

Fly ash is a waste material obtained from burning of coal in thermal power plants. Coal consumption is still very high and is expected to remain above 38% globally. Therefore, large volumes of fly ash are produced every year that need to be managed as waste. Improper disposal of fly ash can lead to surface water and ground water pollution and adversely affect human health and environment. The use of fly ash as an agent to stabilize soil has recently become popular in geotechnical engineering due to its many benefits such as being eco-friendly and cost-effective, and improving the geotechnical characteristics of the soil. This paper presents a review of the geotechnical properties of fly ash-stabilized fine-grained soils. Several features of fly ash, including classification, physical, geotechnical, chemical, and mineralogical properties, health concerns, disposal, availability, and cost are analyzed. The effects of fly ash in improving a wide range of mechanical properties of soils including unconfined compressive strength, shear strength, CBR value, consolidation and/or swelling characteristics, and permeability are reviewed in detail. It is shown that fly ash can be a substitute material for use in soil stabilization, leading to substantial economic and environmental benefits.
Synergistic Activity of Dimethyl Disulfide Mixtures with Two Chemical Compounds against Meloidogyne incognita
Authors: Wang Qing , Ren Lirui , Zhang Daqi , Song Zhaoxin , Fang Wensheng , Li Yuan , Wang Qiuxia , Cao Aocheng , Yan Dongdong
Published: December 2022

The prevention and control of root-knot nematode disease is a worldwide challenge and there are not many varieties of pesticides for nematode control. To meet the huge market demand, the development of new nematicides is urgently needed. The lethal effects of soil fumigant dimethyl disulfide (DMDS) mixed with the chemical compounds copper sulfate (CuSO4) and ammonium bicarbonate (NH4HCO3) on Meloidogyne incognita were tested using the immersion method. The results showed that the LC50 of DMDS, CuSO4, and NH4HCO3 on the second stage juveniles (J2) of M. incognita were 19.28, 187.42, and 213.49 mg/L, respectively. The lethal effect on J2 were enhanced with the combination of DMDS and CuSO4 or NH4HCO3. The compound uses of DMDS (2.5 mg/L) and CuSO4 (46.58 mg/L) or NH4HCO3 (80.25 mg/L) have obvious synergistic effects on the control of M. incognita, with corrected mortalities of 97.09% and 94.00%, respectively. The synergistic effect of fumigant and chemical compounds on M. incognita was investigated to provide a new concept for the control of root-knot nematode disease.
Plantitas/Plantitos Preference Analysis on Succulents Attributes and Its Market Segmentation: Integrating Conjoint Analysis and K-means Clustering for Gardening Marketing Strategy
Authors: Ong Ardvin Kester S. , Prasetyo Yogi Tri , De Leon Lance Albert S. , Ayuwati Irene Dyah , Nadlifatin Reny , Persada Satria Fadil
Published: December 2022

Many people have switched to gardening as their new hobby during the COVID-19 pandemic, including Filipinos. With its increasing popularity, Filipinos called the new hobbyists “plantitas” and “plantitos” instead of the old-fashioned term “plant people”. Among different plants, succulents are one of the most popular for plant lovers as they can thrive with even minimal care, making them suitable to be an indoor/outdoor plant. This study aims to determine the various preferences of plantitas and plantitos based on succulent attributes using a conjoint analysis approach, and to discover the market segments using a k-means clustering approach. The attributes presented in this study are the types of succulents, succulent variegation, price, size of the succulent (in terms of diameter), size of the pot, pot material, and payment method. The conjoint analysis results indicated that the price was the attribute that significantly affected consumer buying behavior, followed by the diameter size of the succulent. On the other hand, the k-means cluster analysis identified three customer segments based on the buying frequency of customers, namely high-value customers, core-value customers, and lower-value customers. A marketing strategy for succulent sellers was proposed based on these segmentations, particularly on how to gain and attract more customers. This study is one of the first studies that analyzed the preferences related to succulent attributes. Finally, the conjoint analysis approach and k-means clustering in this study can be utilized to analyze succulent preferences worldwide.
Five-Dimensional Straw Utilization Model and Its Impact on Carbon Emission Reduction in China
Authors: Sun Ning , Gao Chunyu , Ding Yahui , Bi Yuyun , Seglah Patience Afi , Wang Yajing
Published: December 2022

Enormous quantities of straw in China are burnt in open fields or discarded randomly, leading to a serious waste of biomass resources and environmental pollution. To accelerate sustainable development, straw resources must be used efficiently and reduce carbon emissions. Based on a systematic literature review, this study summarizes China’s latest development in straw utilization. It analyzes the accounting methods, carbon emission reduction effects and potential of straw utilization. The study highlights that straw utilization in China can be categorized into five-dimensional straw utilization models (FDSUM). The cost of collection, storage and transportation, and straw utilization technology are the main factors affecting straw utilization. FDSUM contributes greatly to agricultural carbon reduction. Straw-to-fertilizer has the highest contribution, and straw-to-fuel has the largest carbon emission reduction potential. The carbon emission reduction from straw–to-fuel utilization in 2020 was 63.43 × 109 kg CO2. In addition, China has not developed a standardized carbon accounting method for straw utilization. China needs to prioritize straw-to-fertilizer and straw-to-fuel conversion and develop low-carbon production technologies. This study will serve as a reference to further improve the utilization of straw in China and provide preliminary ideas for establishing a unified national carbon accounting system for straw utilization.