bluuebunny/crossref_metadata_2025 huggingface dataset:
Sensing Mechanism and Real-Time Bridge Displacement Monitoring for a Laboratory Truss Bridge Using Hybrid Data Fusion
Authors: Zeng Kun , Zeng Sheng , Huang Hai , Qiu Tong , Shen Shihui , Wang Hui , Feng Songkai , Zhang Cheng
Published: July 2023

Remote and real-time displacement measurements are crucial for a successful bridge health monitoring program. Researchers have attempted to monitor the deformation of bridges using remote sensing techniques such as an accelerometer when a static reference frame is not available. However, errors accumulate throughout the double-integration process, significantly reducing the reliability and accuracy of the displacement measurements. To obtain accurate reference-free bridge displacement measurements, this paper aims to develop a real-time computing algorithm based on hybrid sensor data fusion and implement the algorithm via smart sensing technology. By combining the accelerometer and strain gauge measurements in real time, the proposed algorithm can overcome the limitations of the existing methods (such as integration errors, sensor drifts, and environmental disturbances) and provide real-time pseud-static and dynamic displacement measurements of bridges under loads. A wireless sensor, SmartRock, containing multiple sensing units (i.e., triaxial accelerometer and strain gauges) and a Micro Controlling Unit (MCU) were utilized for remote data acquisition and signal processing. A remote sensing system (with SmartRocks, an antenna, an industrial computer, a Wi-Fi hotspot, etc.) was deployed, and a laboratory truss bridge experiment was conducted to demonstrate the implementation of the algorithm. The results show that the proposed algorithm can estimate a bridge displacement with sufficient accuracy, and the remote system is capable of the real-time monitoring of bridge deformations compared to using only one type of sensor. This research represents a significant advancement in the field of bridge displacement monitoring, offering a reliable and reference-free approach for remote and real-time measurements.
Vision-Aided Hyperspectral Full-Waveform LiDAR System to Improve Detection Efficiency
Authors: Wu Hao , Lin Chao , Li Chengliang , Zhang Jialun , Gaoqu Youyang , Wang Shuo , Wang Long , Xue Hao , Sun Wenqiang , Zheng Yuquan
Published: July 2023

The hyperspectral full-waveform LiDAR (HSL) system based on the supercontinuum laser can obtain spatial and spectral information of the target synchronously and outperform traditional LiDAR or imaging spectrometers in target classification and other applications. However, low detection efficiency caused by the detection of useless background points (ULBG) hinders its practical applications, especially when the target is small compared with the large field of view (FOV) of the HSL system. A novel vision-aided hyperspectral full-waveform LiDAR system (V-HSL) was proposed to solve the problem and improve detection efficiency. First, we established the framework and developed preliminary algorithms for the V-HSL system. Next, we experimentally compared the performance of the V-HSL system with the HSL system. The results revealed that the proposed V-HSL system could reduce the detection of ULBG points and improve detection efficiency with enhanced detection performance. The V-HSL system is a promising development direction, and the study results will help researchers and engineers develop and optimize their design of the HSL system and ensure high detection efficiency of spatial and spectral information of the target.
Triple Collocation of Ground-, Satellite- and Land Surface Model-Based Surface Soil Moisture Products in Oklahoma Part II: New Multi-Sensor Soil Moisture (MSSM) Product
Authors: Hong Zhen , Moreno Hernan A. , Alvarez Laura V. , Li Zhi , Hong Yang
Published: July 2023

This study develops a triple-collocation (TC) based, multi-source shallow-soil moisture product for Oklahoma. The method uses a least squared weights (LSW) optimization to find the set of parameters that result in the lowest root mean squared error (RMSE) with respect to the “unknown truth”. Soil moisture information from multiple sources and resolutions, including the Soil Moisture Active Passive SMAP L3_SM_P_E (9 km, daily), the physically-based, land surface model (LSM) estimates from NLDAS_NOAH0125_H (1/8°, hourly), and the Oklahoma Mesonet ground sensor network (9 km interpolated from point, 30 min) is merged into a 9 km spatial and daily temporal resolution product across the state of Oklahoma from April 2015 to July 2019. This multi-sensor surface soil moisture (MSSM) product is assessed in terms of a state-wide benchmark and previously tested, in situ-based soil moisture product and SMAP L4. Results show that: (1) independent source products have differential values according to the regional conditions they represent, including land cover type, soils, irrigation, or climate regime; (2) beyond serving as validation sets, in situ measurements are of significant value for improving the accuracy of multi-sensor soil moisture datasets through TC; and (3) state-wide RMSE values obtained with MSSM are similar to the typical measurement error found on in situ ground measurements which provides some degree of confidence on the new product. MSSM is an improvement over currently available products in Oklahoma due to its minimized uncertainty, easiness of production, and continuous temporal and geographic coverage. Nevertheless, to exploit its utility, further tests of this methodology are needed in different climates, land cover types, geographic regions, and for other independent products and spatiotemporal resolutions.
A Novel Method for Building Contour Extraction Based on CSAR Images
Authors: Zhao Jia , An Daoxiang , Chen Leping
Published: July 2023

Circular synthetic aperture radar (CSAR) can obtain more complete scattering characteristics by observing the target with different azimuth angles. Therefore, extracting the complete structure of the target from CSAR images is of great significance for accurate interpretation. At present, the artificial target extraction based on CSAR images mostly uses anisotropic scattering features. For special targets such as buildings, as the walls and the ground form dihedral corner structures, there are also obvious strong scattering features such as double-scattering lines in SAR images. Therefore, combining the strong scattering features of buildings at specific aspects with anisotropic scattering characteristics at different aspects can obtain better extraction results, and how to extract these features accurately and efficiently is the key point. Based on this, this paper proposes a novel method for building contour extraction based on CSAR images. For strong scattering features, a fast fuzzy C-means (FCM) clustering algorithm was used to extract them. For anisotropic scattering features, aspect entropy was used to characterize the anisotropy degree, and K-means clustering was combined to extract. Finally, a more accurate result is obtained by merging the two feature extraction results. In order to verify the effectiveness and practicability of the proposed method, a lot of measured data acquired by the self-developed airborne L-band and Ku-band CSAR systems were processed. The experiments show that, compared with state-of-the-art algorithms, the proposed method can obtain more accurate results in less time.
Evaluation of Post-Tunneling Aging Buildings Using the InSAR Nonuniform Settlement Index
Authors: Liu Yuzhou , Cao Wenxi , Shi Zhongqi , Yue Qingrui , Chen Tiandong , Tian Lu , Zhong Rumian , Liu Yuke
Published: July 2023

Tunneling work, including the construction of municipal tunnels and metro lines, may disturb the structural health of aging buildings in densely built urban areas. Deformation monitoring and risk assessments of aging buildings are crucial to mitigate incidents and prevent losses of people’s lives and properties. Time-series InSAR reveals spatio-temporal information about observed targets by extracting persistent scatterers of the structures, which can achieve the wide-range monitoring of buildings and infrastructure. However, solely relying on InSAR-derived general parameters (deformation rates and time series of specific points) cannot objectively assess the safety conditions of buildings. To address this issue, this study proposes an InSAR Nonuniform Settlement Index. First, the point targets of buildings are extracted through time-series InSAR processing. Then, using the points as inputs, the Nonuniform Settlement Index calculates the 3D settlement plane and the inclination angle of the plane corresponding to each building. In this way, the proposed Nonuniform Settlement Index acts as a subsequent analysis method of time-series InSAR to characterize the safety statuses of buildings. In our study, 147 scenes of COSMO-SkyMed images from 2013 to 2022 were used to inverse the nine-year deformation evolution of the tested area. After time-series InSAR processing and index analysis based on the above SAR datasets, cross-validation was implemented with static-level and manual investigation data. The approach was to use one aging, collapsed building affected by tunneling work, as well as the eight adjacent aging buildings. The results showed high consistency with the in situ data, which proves the efficiency of the proposed approach.
A Multi-Scale Object Detector Based on Coordinate and Global Information Aggregation for UAV Aerial Images
Authors: Zhou Liming , Liu Zhehao , Zhao Hang , Hou Yan-e , Liu Yang , Zuo Xianyu , Dang Lanxue
Published: July 2023

Unmanned aerial vehicle (UAV) image object detection has great application value in the military and civilian fields. However, the objects in the captured images from UAVs have problems of large-scale variation, complex backgrounds, and a large proportion of small objects. To resolve these problems, a multi-scale object detector based on coordinate and global information aggregation is proposed, named CGMDet. Firstly, a Coordinate and Global Information Aggregation Module (CGAM) is designed by aggregating local, coordinate, and global information, which can obtain features with richer context information. Secondly, a Feature Fusion Module (FFM) is proposed, which can better fuse features by learning the importance of different scale features and improve the representation ability of multi-scale features by reusing feature maps to help models better detect multi-scale objects. Moreover, more location information of low-level feature maps is integrated to improve the detection results of small targets. Furthermore, we modified the bounding box regression loss of the model to make the model more accurately regress the bounding box and faster convergence. Finally, we tested the CGMDet on VisDrone and UAVDT datasets. The proposed CGMDet improves mAP0.5 by 1.9% on the VisDrone dataset and 3.0% on the UAVDT dataset.
A Self-Attention Integrated Learning Model for Landing Gear Performance Prediction
Authors: Lin Lin , Tong Changsheng , Guo Feng , Fu Song , Lv Yancheng , He Wenhui
Published: July 2023

The landing gear structure suffers from large loads during aircraft takeoff and landing, and an accurate prediction of landing gear performance is beneficial to ensure flight safety. Nevertheless, the landing gear performance prediction method based on machine learning has a strong reliance on the dataset, in which the feature dimension and data distribution will have a great impact on the prediction accuracy. To address these issues, a novel MCA-MLPSA is developed. First, an MCA (multiple correlation analysis) method is proposed to select key features. Second, a heterogeneous multilearner integration framework is proposed, which makes use of different base learners. Third, an MLPSA (multilayer perceptron with self-attention) model is proposed to adaptively capture the data distribution and adjust the weights of each base learner. Finally, the excellent prediction performance of the proposed MCA-MLPSA is validated by a series of experiments on the landing gear data.
A Comprehensive Characterization of the TI-LGAD Technology
Authors: Senger Matias , Macchiolo Anna , Kilminster Ben , Paternoster Giovanni , Centis Vignali Matteo , Borghi Giacomo
Published: July 2023

Pixelated low-gain avalanche diodes (LGADs) can provide both precision spatial and temporal measurements for charged particle detection; however, electrical termination between the pixels yields a no-gain region, such that the active area or fill factor is not sufficient for small pixel sizes. Trench-isolated LGADs (TI-LGADs) are a strong candidate for solving the fill-factor problem, as the p-stop termination structure is replaced by isolated trenches etched in the silicon itself. In the TI-LGAD process, the p-stop termination structure, typical of LGADs, is replaced by isolating trenches etched in the silicon itself. This modification substantially reduces the size of the no-gain region, thus enabling the implementation of small pixels with an adequate fill factor value. In this article, a systematic characterization of the TI-RD50 production, the first of its kind entirely dedicated to the TI-LGAD technology, is presented. Designs are ranked according to their measured inter-pixel distance, and the time resolution is compared against the regular LGAD technology.
Surgical Instrument Signaling Gesture Recognition Using Surface Electromyography Signals
Authors: Freitas Melissa La Banca , Mendes José Jair Alves , Dias Thiago Simões , Siqueira Hugo Valadares , Stevan Sergio Luiz
Published: July 2023

Surgical Instrument Signaling (SIS) is compounded by specific hand gestures used by the communication between the surgeon and surgical instrumentator. With SIS, the surgeon executes signals representing determined instruments in order to avoid error and communication failures. This work presented the feasibility of an SIS gesture recognition system using surface electromyographic (sEMG) signals acquired from the Myo armband, aiming to build a processing routine that aids telesurgery or robotic surgery applications. Unlike other works that use up to 10 gestures to represent and classify SIS gestures, a database with 14 selected gestures for SIS was recorded from 10 volunteers, with 30 repetitions per user. Segmentation, feature extraction, feature selection, and classification were performed, and several parameters were evaluated. These steps were performed by taking into account a wearable application, for which the complexity of pattern recognition algorithms is crucial. The system was tested offline and verified as to its contribution for all databases and each volunteer individually. An automatic segmentation algorithm was applied to identify the muscle activation; thus, 13 feature sets and 6 classifiers were tested. Moreover, 2 ensemble techniques aided in separating the sEMG signals into the 14 SIS gestures. Accuracy of 76% was obtained for the Support Vector Machine classifier for all databases and 88% for analyzing the volunteers individually. The system was demonstrated to be suitable for SIS gesture recognition using sEMG signals for wearable applications.
AI-Assisted Ultra-High-Sensitivity/Resolution Active-Coupled CSRR-Based Sensor with Embedded Selectivity
Authors: Abdolrazzaghi Mohammad , Kazemi Nazli , Nayyeri Vahid , Martin Ferran
Published: July 2023

This research explores the application of an artificial intelligence (AI)-assisted approach to enhance the selectivity of microwave sensors used for liquid mixture sensing. We utilized a planar microwave sensor comprising two coupled rectangular complementary split-ring resonators operating at 2.45 GHz to establish a highly sensitive capacitive region. The sensor’s quality factor was markedly improved from 70 to approximately 2700 through the incorporation of a regenerative amplifier to compensate for losses. A deep neural network (DNN) technique is employed to characterize mixtures of methanol, ethanol, and water, using the frequency, amplitude, and quality factor as inputs. However, the DNN approach is found to be effective solely for binary mixtures, with a maximum concentration error of 4.3%. To improve selectivity for ternary mixtures, we employed a more sophisticated machine learning algorithm, the convolutional neural network (CNN), using the entire transmission response as the 1-D input. This resulted in a significant improvement in selectivity, limiting the maximum percentage error to just 0.7% (≈6-fold accuracy enhancement).
A Time-Division Multiplexing Multi-Channel Micro-Electrochemical Workstation with Carbon-Based Material Electrodes for Online L-Trosine Detection
Authors: Bao Qiwen , Li Gang , Yang Zhengchun , Wei Jun , Cheng Wenbo , Qu Zilian , Lin Ling
Published: July 2023

In the background of the rapid development of artificial intelligence, big data, IoT, 5G/6G, and other technologies, electrochemical sensors pose higher requirements for high-throughput detection. In this study, we developed a workstation with up to 10 channels, which supports both parallel signal stimulation and online electrochemical analysis functions. The platform was wired to a highly integrated Bluetooth chip used for wireless data transmission and can be visualized on a smartphone. We used this electrochemical test platform with carbon–graphene oxide/screen-printed carbon electrodes (CB-GO/SPCE) for the online analysis of L-tyrosine (Tyr), and the electrochemical performance and stability of the electrodes were examined by differential pulse voltammetry (DPV). The CB-GO-based screen-printed array electrodes with a multichannel electrochemical platform for Tyr detection showed a low detection limit (20 μM), good interference immunity, and 10-day stability in the range of 20–200 μM. This convenient electrochemical analytical device enables high-throughput detection and has good economic benefits that can contribute to the improvement of the accuracy of electrochemical analysis and the popularization of electrochemical detection methods in a wide range of fields.
Effective Components of Behavioural Interventions Aiming to Reduce Injury within the Workplace: A Systematic Review
Authors: Bowdler Mairi , Steijn Wouter Martinus Petrus , van der Beek Dolf
Published: July 2023

For years, the connection between safety behaviours and injury and illness in high-risk industries has been recognised, but the effectiveness of this link has been somewhat overlooked. Since there is still a significant amount of injury within high-risk workplaces, this systematic review aims to examine the effectiveness of behavioural interventions to decrease fatal and non-fatal injuries within high-risk industries. Scopus and Google Scholar were used to find relevant systematic reviews and meta-analyses on this topic. In total, 19 articles met the inclusion criteria. Of these articles, 11 suggested that their reviewed interventions revealed some evidence of being effective in reducing injury/accident rates. Additionally, seven of the papers found that the interventions affected certain determinants, such as safety knowledge, health and safety behaviours, attitudes, efficacy, and beliefs. One of the papers found no effect at all. It must be noted that a significant amount of the articles (n = 10) reported methodological quality or quantity issues, implying that the results should be approached with caution. Nonetheless, it was found that certain components, such as multi-faceted interventions tailored to the target group, contribute to either reducing injury/accident rates or improving the specific aforementioned determinants. There is a need for additional safety interventions in high-risk industries that are based on methodologically sound structural elements and theoretical frameworks. Existing approaches, such as Intervention Mapping, can assist safety professionals in achieving this goal.
Reflections on Collective Healing at the Community of El Juego
Authors: Gioacchino Gioel
Published: July 2023

El Juego is a multicultural community of 30 people that exists as a permanent laboratory of conflict resolution and healing. Two and a half years ago, during the pandemic and after four years of living as nomads, we bought together one hundred and forty hectares in San Rafael, Antioquia, to live in close connection with nature and promote reforestation by creating a natural reserve. In this article, we share some of our reflections and experiences of collective healing. We do so through an exercise in autoethnography carried out by two members of our community following the death of Camelia, one of our horses. We then pull out themes that appeared in the autoethnographic pieces, sharing our reflections on developing a more intimate relationship with nature. In the discussion, we draw on the literature on anarchism and Indigenous ways of knowing and enrich it with the lived experience of the community.
Speed Matters in Nordic Hamstring Exercise: Higher Peak Knee Flexor Force during Fast Stretch-Shortening Variant Compared to Standard Slow Eccentric Execution in Elite Athletes
Authors: Augustsson Jesper , Alt Tobias , Andersson Håkan
Published: July 2023

Hamstring strain injuries are prevalent in many sports. Research has demonstrated that the Nordic hamstring exercise (NHE), a knee-dominant exercise addressing the posterior chain muscles, can aid in reducing the risk of hamstring injuries in athletes. However, most research on hamstring injury prevention has focused on performing the eccentric version of the NHE (NHEECC). In contrast, in sports, it is quite frequent for athletes to use an eccentric–concentric version of the NHE. Additionally, eccentric NHE is typically performed using a slow, controlled tempo. The effect of a fast stretch-shortening cycle NHE (NHESSC) compared to standard slow NHEECC on peak knee flexor force has not been investigated. The aim of the study was therefore to investigate fast NHESSC vs. standard slow NHEECC. Our hypothesis posited that peak knee flexor force would be greater for fast NHESSC compared with standard slow NHEECC. The study involved 22 elite athletes (actively competing in both national and international events) consisting of female (n = 10) and male (n = 7) track and field athletes and male football players (n = 5), aged 17–31 years. The participants performed maximum trials of slow NHEECC and fast NHESSC repetitions in which measurement of bilateral peak knee flexor force was conducted at the ankle with the use of a load cell. During the NHEs, a linear encoder was used to measure both the position where the peak knee flexor force was recorded and the average eccentric velocity. SSC contributed to an enhanced NHE performance, where bilateral absolute peak knee flexor force was 13% higher for fast NHESSC vs. standard slow NHEECC (822 vs. 726 N, p < 0.01, ES = 0.54). Participants achieved a 32% greater forward distance at the breakpoint stage during NHEECC compared to the coupling phase for NHESSC (54 vs. 41 cm, p < 0.001, ES = 1.37). Eccentric average velocity was more than three times higher for NHESSC compared with NHEECC (0.38 vs. 0.12 m/s, p < 0.001, ES = 3.25). The key findings of this study were that SSC contributed to an enhanced NHE performance, where absolute peak knee flexor force was 13% greater for fast NHESSC compared to standard slow NHEECC. The fast NHESSC could therefore be an interesting alternative to the standard slow NHEECC execution, as it may offer potential advantages for sprint performance, as well as hamstring injury prevention and rehabilitation.
Profiling the Physical Performance of Young Boxers with Unsupervised Machine Learning: A Cross-Sectional Study
Authors: Merlo Rodrigo , Rodríguez-Chávez Ángel , Gómez-Castañeda Pedro E. , Rojas-Jaramillo Andrés , Petro Jorge L. , Kreider Richard B. , Bonilla Diego A.
Published: July 2023

Mexico City is the location with the largest number of boxers in Mexico; in fact, it is the first city in the country to open a Technological Baccalaureate in Education and Sports Promotion with a pugilism orientation. This cross-sectional study aimed to determine the physical–functional profile of applicants for admission to the baccalaureate in sports. A total of 227 young athletes (44F; 183M; 15.65 (1.79) years; 63.66 (14.98) kg; >3 years of boxing experience) participated in this study. Body mass (BM), maximal isometric handgrip (HG) strength, the height of the countermovement jump (CMJ), the velocity of straight boxing punches (PV), and the rear hand punch impact force (PIF) were measured. The young boxers were profiled using unsupervised machine learning algorithms, and the probability of superiority (ρ) was calculated as the effect size of the differences. K-Medoids clustering resulted in two sex-independent significantly different groups: Profile 1 (n = 118) and Profile 2 (n = 109). Except for BM, Profile 2 was statistically higher (p < 0.001) with a clear distinction in terms of superiority on PIF (ρ = 0.118), the PIF-to-BM ratio (ρ = 0.017), the PIF-to-HG ratio (ρ = 0.079) and the PIF-to-BM+HG ratio (ρ = 0.008). In general, strength levels explained most of the data variation; therefore, it is reasonable to recommend the implementation of tests aimed at assessing the levels of isometric and applied strength in boxing gestures. The identification of these physical–functional profiles might help to differentiate training programs during sports specialization of young boxing athletes.
Systemic Capacity in Food System Governance in the Solomon Islands: “It’s More than Just Training”
Authors: Mauli Senoveva , Maelaua Josephine , Reeve Erica , Thow Anne Marie , Johnson Ellen , Farrell Penny , Patay Dori
Published: July 2023

Food security and malnutrition are leading health and development issues in the Pacific Island region. The food system scholarship points to the need for capacity building across multiple levels of governance to improve food system outcomes in the Pacific Island Countries and Territories. This paper aims to identify the strengths and weaknesses of systemic capacity in the food system governance of the Solomon Islands and identify opportunities for capacity building. A theoretically informed, empirical policy analysis was undertaken, informed by qualitative semistructured key informant interviews. Challenges related to capacity included slow information flows, inadequate human resourcing, and skill gaps at all levels of government. Opportunities for capacity building span workload, personal, performance, supervisory, role, systems, and structural capacities. These include the improvement of coordination between food system actors through the establishment of a multisectoral food system platform or agency, and increasing the involvement of vulnerable populations in policy planning and decision making. The current food system governance of the Solomon Islands shows important strengths in systemic capacity across multiple capacity types at national, provincial, and community levels. Our analysis provides insights for future capacity building efforts that build on these strengths to improve social, environmental, and economic outcomes.
Optimizing Perishable Product Supply Chain Network Using Hybrid Metaheuristic Algorithms
Authors: Pan Lihong , Shan Miyuan , Li Linfeng
Published: July 2023

This paper focuses on optimizing the long- and short-term planning of the perishable product supply chain network (PPSCN). It addresses the integration of strategic location, tactical inventory, and operational routing decisions. Additionally, it takes into consideration the specific characteristics of perishable products, including their shelf life, inventory management, and transportation damages. The main objective is to minimize the overall supply chain cost. To achieve this, a nonlinear mixed integer programming model is developed for the multi-echelon, multi-product, and multi-period location-inventory-routing problem (LIRP) in the PPSCN. Two hybrid metaheuristic algorithms, namely genetic algorithm (GA) and multiple population genetic algorithm (MPGA), are hybridized with variable neighborhood search (VNS) and proposed to solve this NP-hard problem. Moreover, a novel coding method is devised to represent the complex structure of the LIRP problem. The input parameters are tuned using the Taguchi experimental design method, considering the sensitivity of meta-heuristic algorithms to these parameters. Through experiments of various scales, the hybrid MPGA with VNS indicates superior performance, as evidenced by the experimental results. Sensitivity analysis is conducted to examine the influence of key model parameters on the optimal objective, providing valuable management implications. The results clearly validate the efficacy of the proposed model and solution method as a reliable tool for optimizing the design problem of the PPSCN.
Investigating the Effects of Automated Vehicles on Large Urban Road Networks: Some Evidence from Rome
Authors: Crisalli Umberto , Gemma Andrea , Petrelli Marco
Published: July 2023

This paper explores the possibility of applying simulation models formalized in the macrosimulation approach to predict the effects from the presence of automated vehicles in our cities. It is based on the use of a robust equilibrium assignment model allowing us to obtain multiclass traffic flows, including automated vehicles (AVs) and conventional ones (CVs) on large real-sized road networks. This modelling framework has been successfully applied to the road network of the metropolitan area of Rome, allowing us to assess the effects of AVs in future traffic at increasing penetration rates and the effects of possible transport policies involving AVs.
Numerical Simulation and Application of an Oxygen-Enriched Side-Blown Smelting Furnace for the Treatment of Electroplating Sludge
Authors: Yang Biwei , Liu Wei , Jiao Fen , Zhang Lin , Qin Wenqing , Jiang Shanqin
Published: July 2023

In the oxygen-enriched side-blown smelting furnace for the treatment of electroplating sludge, fluent was used to simulate the gas–liquid two-phase flow process. The relationship between the lance diameter, lance inclination, bath depth, and the bath evaluation indicators were studied, and the oxygen lance spacing was optimized. The results show that the high velocity and high gas rate areas were near the oxygen lance, while the stirring dead zones with low velocity appeared in the central and bottom areas of the molten pool. The key parameters were optimized using single-factor analysis and multifactor comprehensive optimization. The results showed that the bath evaluation indicators were all at good levels under the optimal parameter conditions. These were comprehensively obtained as the following: the lance diameter was 25 mm, the lance inclination was 15°, the lance spacing was 1050 mm, and the bath depth was 1500 mm. The industrial test carried out in an environmental protection enterprise in Guangdong achieved satisfactory results. The test shows that the electroplating sludge containing 7.24% Cu can be melted at 1300~1400 °C to obtain matte. Compared with industrial copper slag, the smelting slag has a higher CaO and a lower Fe content.
Influence of Seasonal Water Level Fluctuations on Food Web Structure of a Large Floodplain Lake in China
Authors: Zhang Huan , Wang Yuyu , Xu Jun
Published: July 2023

Seasonal shifts in hydrology are known to alter the abundance and diversity of basal production resources and habitats and hence strongly influence the structure and function of river ecosystems. However, equivalent knowledge of natural lake ecosystems in floodplain regions is lacking. Here, we used stable isotope ratios of carbon and nitrogen to assess available primary production sources and consumer taxa during the dry and wet seasons in a large floodplain lake connected to the Yangtze River. Fish species showed distinct δ13C values between two hydrological periods but only small changes in δ15N values. Most of the fish species had higher estimated trophic levels in the dry season, likely indicating greater carnivory. Results of Bayesian mixing models revealed that benthic algae and benthic organic matter (BOM), combined with C3 vegetation, were the principal food sources supporting the biomass of most fish species during the low-water period, whereas benthic algae and seston were the most important carbon sources during the flood period. Overall, these findings demonstrate that seasonal hydrological changes, such as water-level fluctuations, can affect the trophic structure and ecosystem functioning of floodplain lake food webs in the subtropical zone.
The Definition of Play: A Measurement Scale for Well-Being Based on Human Physiological Mechanisms
Authors: Shimomura Yoshihiro
Published: July 2023

Play is an activity common to all cultures and is thought to be a useful way to improve well-being since it brings about enjoyment. This study aimed to comprehensively define play and develop a method to evaluate what types of play lead to well-being. It defined play as “the activation of one’s reward system through intrinsically motivated decisions and actions of the self, not for the direct purpose of survival”, based on human physiology, including brain science relating to motivation and behaviour. It checked this definition by conducting an online survey and applying a measurement scale to quantify the degree of play. The results revealed that the favourite play activity involved a significantly higher degree of play than the highest-effort habits (defined as the activities that participants most disliked but continued to take part in). There was also a significant difference in intrinsic motivation, reward system activation, and decision-making but no difference in action of the self. This method made it possible to evaluate the intensity of each element of the brain mechanism making up play, and it can, therefore, be used to examine the well-being of groups, organisations, and individuals across domains.
Life Insurance Prediction and Its Sustainability Using Machine Learning Approach
Authors: Shamsuddin Siti Nurasyikin , Ismail Noriszura , Nur-Firyal R.
Published: July 2023

Owning life insurance coverage that is not enough to pay for the expenses is called underinsurance, and it has been found to have a significant influence on the sustainability and financial health of families. However, insurance companies need to have a good profile of potential policyholders. Customer profiling has become one of the essential marketing strategies for any sustainable business, such as the insurance market, to identify potential life insurance purchasers. One well-known method of carrying out customer profiling and segmenting is machine learning. Hence, this study aims to provide a helpful framework for predicting potential life insurance policyholders using a data mining approach with different sampling methods and to lead to a transition to sustainable life insurance industry development. Various samplings, such as the Synthetic Minority Over-sampling Technique, Randomly Under-Sampling, and ensemble (bagging and boosting) techniques, are proposed to handle the imbalanced dataset. The result reveals that the decision tree is the best performer according to ROC and, according to balanced accuracy, F1 score, and GM comparison, Naïve Bayes seems to be the best performer. It is also found that ensemble models do not guarantee high performance in this imbalanced dataset. However, the ensembled and sampling method plays a significant role in overcoming the imbalanced problem.
Sustainable Livelihood Evaluation and Influencing Factors of Rural Households: A Case Study of Beijing Ecological Conservation Areas
Authors: Guo Mengqi , Xie Mingyi , Xu Guangcai
Published: July 2023

Rural households’ livelihood sustainability is the core issue that affects their production and life standards, and it is a necessary evaluation index for rural sustainable development. As a sensitive area of urban–rural integration development in Beijing, the environmental protection measures and industrial structure adjustments in ecological conservation areas (ECAs) have influenced the sustainability and stability of local rural households’ livelihood. First, based on livelihood capital quantification data, this study established an evaluation model of rural households’ livelihood sustainability, which employed the combined weighting approach and the technique for order preference by similarity to ideal solution (TOPSIS). Second, this study investigated the influencing factors of rural households’ livelihood using the methodologies of regression analysis and the mediating effect. The results showed the following: (1) the overall livelihood sustainability of rural households was at a medium level, scoring 0.4436; (2) the sanitation conditions of the community were rated as the critical factor with a positive impact on rural households’ livelihood, which was followed by the frequency of online shopping, family expenditure, and management capability of village cadres; (3) improving one’s rural household income can enhance rural livelihood sustainability indirectly. Finally, effective livelihood strategies were explored and put forward for ECA development.
Invasive Ageratina adenophora (Asteraceae) in Agroecosystems of Kumaun Himalaya, India: A Threat to Plant Diversity and Sustainable Crop Yield
Authors: Negi Bhawna , Khatri Kavita , Bargali Surendra Singh , Bargali Kiran
Published: July 2023

Invasive plant species pose a serious threat to agricultural yield, although how the threat varies with the distance of crops from invasive plants remains unclear. Therefore, utilizing this viewpoint, objectives were formulated to quantify differences in the native vegetation and performance of crops growing near A. adenophora–invaded (treatment plots) and uninvaded ridges (control plots) in the terraced agricultural fields of Kumaun Central Himalaya. Morphological and yield parameters of two crops, viz. rice (Oryza sativa L.) and soybean (Glycine max L.), were assessed systematically by laying parallel transects from the ridge of the field up to 5 m towards the centre of the field in three equal distance intervals of 1 m in the order: 0–1 m, 2–3 m, and 4–5 m. Crofton weed showed 95–100% crown cover throughout invaded ridges/risers of terraced fields, with stem densities of 134–208 ind. m−2 and an average basal area of 0.15 cm2. The total mean density of herbs (other than A. adenophora) in invaded ridges/risers decreased by 38–85% compared to uninvaded ridges, but species richness increased by 31–37%. Soil nutrient availability was high across invaded ridges/risers as well as crop fields. The average irradiance level near uninvaded ridges was 19.6 ± 1.80%, and near invaded ridges, it was 1.8 ± 0.31%. Compared to straw, root, and grain yields obtained at the farthest distance, the yield of rice near uninvaded ridges was reduced by 27%, 19%, and 33%, while near invaded ridges, it was reduced by 37%, 39%, and 43%, respectively; the yield of soybean near uninvaded ridges declined by 62%, 66%, and 42%, while near invaded ridges, it decreased by 59%, 69%, and 47%, respectively. Compared to the values obtained at the farthest distance, the harvest index (HI) of rice near ridges was reduced by 7% and 13%, while the HI of soybean increased by 15 and 10% across uninvaded and invaded field ridges, respectively. The findings indicated that the Crofton weed could suppress field crops and could form a single dominant population in the invaded area, causing a serious threat to the plant community, its diversity, and the yield of the native agroecosystem in the foreseeable future if timely management actions would not be taken.
Challenges Faced by Marginalized Communities in a Post-Disaster Context: A Systematic Review of the Literature
Authors: Mendis Kalindu , Thayaparan Menaha , Kaluarachchi Yamuna , Pathirage Chaminda
Published: July 2023

Many international organizations embrace the ideals of resilience and inclusion in the service of marginalized communities but neglect their inclusion in post-disaster settings. It is imperative to explore the challenges faced by marginalized communities to increase their inclusion in the post-disaster context. Therefore, this paper presents a systematic literature review of the challenges facing marginalized communities in the post-disaster context. The study identified 57 challenges faced by children, women, people with disabilities, and older adults under six categories: social, health, political, infrastructure, economic, and communication. The most common challenges all four marginalized communities face are the development of post-traumatic stress disorder, the struggle to acquire the necessities of life due to unequal distribution, the lack of income-generating opportunities, and sexual and gender-based violence. Most studies focus on women’s challenges, followed by those of children, older adults, and persons with disabilities. The study also explored the challenges faced in terms of intersectionality, experienced by groups of people who fall under more than one marginalized community. Finally, a conceptual framework was developed to improve the inclusion of marginalized communities during the post-disaster context by incorporating the challenges as one of the key components of the framework.
An Application Designed for Guiding the Coordinated Charging of Electric Vehicles
Authors: Lu Dingyi , Lu Yunqian , Zhang Kexin , Zhang Chuyuan , Ma Shao-Chao
Published: July 2023

Guiding the coordinated charging of electric vehicles can alleviate the load fluctuation of power systems within a local area caused by uncoordinated charging of electric vehicles and greatly reduce the cost of power system operation. This will become an inevitable development trend of future energy system transformation. In this paper, a new mobile application is built to realize the dynamic adjustment of electric vehicle charging prices according to the change in weather conditions to guide the coordinated charging of electric vehicles. After systematically introducing the structure and data flow process of the application, we simulate the fluctuation of charging prices under various weather conditions using the electricity load data of North China and verify the good performance of the application. We believe that this application can help power systems to achieve low-carbon transformation by adopting a new dynamic time-of-use pricing charging model.
A Visual Knowledge Map Analysis of Cross-Border Agri-Food Supply Chain Research Based on CiteSpace
Authors: Wang Gaofeng , Li Shuai , Zhang Zihao , Hou Yanning , Shin Changhoon
Published: July 2023

How to reshape the cross-border agricultural supply chain is a common concern in the global agricultural field. Using the bibliometrics method, 761 studies in the literature were selected from the core database of WOS (Web of Science) to explore research hotspots and development trends in this field, clarify the historical context, and explore future trends. Using the knowledge map visualization software CiteSpace, an in-depth review and visualization analysis of hot topics in this field were conducted. The results showed that since 2017, research in this field has shown a strong development trend. The United States and the University of Wageningen are the field’s most influential country and research institution, respectively. Sustainability is the journal with the highest number of publications in the field. Research hotspots included cross-border agricultural product safety, cross-border agricultural supply chain systematization, and technology integration, and gradually evolved towards the high-quality development of digital intelligence with informatization, digitization, and big data as the main focus. The sustainability of cross-border agricultural supply chains and the resilience of the global food supply system have become the main lines of research in this field.
Bayesian Estimation for the Difference between Coefficients of Quartile Variation of Delta-Lognormal Distributions: An Application to Rainfall in Thailand
Authors: Yosboonruang Noppadon , Niwitpong Sa-Aat
Published: July 2023

The coefficient of quartile variation is a valuable measure used to assess data dispersion when it deviates from a normal distribution or displays skewness. In this study, we focus specifically on the delta-lognormal distribution. The lognormal distribution is characterized by its asymmetrical nature and comprises exclusively positive values. However, when these values undergo a logarithmic transformation, they conform to a symmetrical (or normal) distribution. Consequently, this research aims to establish confidence intervals for the difference between coefficients of quartile variation within lognormal distributions incorporating zero values. We employ the Bayesian, generalized confidence interval, and fiducial generalized confidence interval methods to construct these intervals, involving data simulation using RStudio software. We evaluate the performance of these methods based on coverage probabilities and average lengths. Our findings indicate that the Bayesian method, employing Jeffreys’ prior, performs well in low variability, while the generalized confidence interval method is more suitable for higher variability. Therefore, we recommend using both approaches to construct confidence intervals for the difference between the coefficients of the quartile variation in lognormal distributions that include zero values. Furthermore, we apply these methods to rainfall data in Thailand to illustrate their alignment with actual and simulated data.
Applications of Symmetric Identities for Apostol–Bernoulli and Apostol–Euler Functions
Authors: He Yuan
Published: July 2023

In this paper, we perform a further investigation on the Apostol–Bernoulli and Apostol–Euler functions introduced by Luo. By using the Fourier expansions of the Apostol–Bernoulli and Apostol–Euler polynomials, we establish some symmetric identities for the Apostol–Bernoulli and Apostol–Euler functions. As applications, some known results, for example, Raabe’s multiplication formula and Hermite’s identity, are deduced as special cases.
Study on Temperature Field Distribution Law and Mechanical Properties of Hydraulic Tunnel-Surrounding Rock under the Action of Large Temperature Differences
Authors: Guan Pengyuan , Li Shuangxi , Jiang Haibo , Xiang Pengfei
Published: July 2023

This study aims to explore the temperature field and mechanical characteristics of hydraulic tunnels during operation in an area with large temperature differences throughout a year. By relying on the measured displacement field change results of a water transmission tunnel in Xinjiang, a finite element calculation model applicable to the hydraulic tunnel-surrounding rock under the action of large temperature differences was established. Then, the FEM model was used to analyze the temperature field distribution and stress field variation of hydraulic tunnel-surrounding rock under the effect of large temperature differences. The results showed that, under the action of large temperature differences, the displacement of the cave top was the largest, followed by the cave bottom, and finally the cave waist. The obtained displacement field data using the calculation model in this study were basically consistent with the on-site measured data, and the displacement field change law was the same, indicating that the established finite element calculation model is reliable. When the ambient temperature was below zero, the freezing front gradually expanded to the depth of the surrounding rock along the tunnel radial direction, while the frozen rock at the intersection of the lining and the surrounding rock melted first when the ambient temperature was above zero. The stress in various parts of the surrounding rock of the hydraulic tunnel increased continuously. To be specific, the growth rate of the tensile stress at the cave bottom, the tensile stress at the cave top, and the compressive stress at the cave waist of the tunnel reached 0.052 MPa/year, 0.053 MPa/year, and 0.096 MPa/year, respectively, in the first year, but the increasing rate subsequently tended to slow down continuously. Therefore, in actual projects, attention should be focused on the effect of large temperature differences on the stress growth of tunnel-surrounding rocks at an early stage, especially the growth of compressive stress at the cave waist. Notably, there are a large number of symmetrical and asymmetric phenomena related to engineering in this study.