bluuebunny/crossref_metadata_2025 huggingface dataset:
Repeatability of the Vibroarthrogram in the Temporomandibular Joints
Authors: Łysiak Adam , Marciniak Tomasz , Bączkowicz Dawid
Published: December 2022

Current research concerning the repeatability of the joint’s sounds examination in the temporomandibular joints (TMJ) is inconclusive; thus, the aim of this study was to investigate the repeatability of the specific features of the vibroarthrogram (VAG) in the TMJ using accelerometers. The joint sounds of both TMJs were measured with VAG accelerometers in two groups, study and control, each consisting of 47 participants (n = 94). Two VAG recording sessions consisted of 10 jaw open/close cycles guided by a metronome. The intraclass correlation coefficient (ICC) was calculated for seven VAG signal features. Additionally, a k-nearest-neighbors (KNN) classifier was defined and compared with a state-of-the-art method (joint vibration analysis (JVA) decision tree). ICC indicated excellent (for the integral below 300 Hz feature), good (total integral, integral above 300 Hz, and median frequency features), moderate (integral below to integral above 300 Hz ratio feature) and poor (peak amplitude feature) reliability. The accuracy scores for the KNN classifier (up to 0.81) were higher than those for the JVA decision tree (up to 0.60). The results of this study could open up a new field of research focused on the features of the vibroarthrogram in the context of the TMJ, further improving the diagnosing process.
Development of a Visual Perception System on a Dual-Arm Mobile Robot for Human-Robot Interaction
Authors: Weng Wei-Ting , Huang Han-Pang , Zhao Yu-Lin , Lin Chun-Yeon
Published: December 2022

This paper presents the development of a visual-perception system on a dual-arm mobile robot for human-robot interaction. This visual system integrates three subsystems. Hand gesture recognition is utilized to trigger human-robot interaction. Engagement and intention of the participants are detected and quantified through a cognitive system. Visual servoing uses YOLO to identify the object to be tracked and hybrid, model-based tracking to follow the object’s geometry. The proposed visual-perception system is implemented in the developed dual-arm mobile robot, and experiments are conducted to validate the proposed method’s effects on human-robot interaction applications.
Development of Impurity-Detection System for Tracked Rice Combine Harvester Based on DEM and Mask R-CNN
Authors: Guan Zhuohuai , Li Haitong , Chen Xu , Mu Senlin , Jiang Tao , Zhang Min , Wu Chongyou
Published: December 2022

Impurity rate is one of the key performance indicators of the rice combine harvester and is also the main basis for parameter regulation. At present, the tracked rice combine harvester impurity rates cannot be monitored in real time. Due to the lack of parameter regulation basis, the harvest working parameters are set according to the operator’s experience and not adjusted during the operation, which leads to the harvest quality fluctuating greatly in a complex environment. In this paper, an impurity-detection system, including a grain-sampling device and machine vision system, was developed. Sampling device structure and impurity extraction algorithm were studied to enhance the impurity identification accuracy. To reduce the effect of impurity occlusion on visual recognition, an infusion-type sampling device was designed. The sampling device light source form was determined based on the brightness histogram analysis of a captured image under different light irradiations. The effect of sampling device structures on impurity visualization, grain distribution, and mass flow rate was investigated by the discrete element method (DEM). The impurity recognition algorithm was proposed based on Mask R-CNN, which mainly includes an impurity feature extraction network, an ROI generation network, and a target segmentation network. The test set experiment showed that the precision rate, recall rate, average precision, and comprehensive evaluation indicator of the impurity recognition model were 92.49%, 88.63%, 81.47%, and 90.52%, respectively. The conversion between impurity pixel number and its actual mass was realized according to the pixel density calibration test and impurity rate correction factor. The bench test result showed that the designed system has a good detection accuracy of 91.15~97.26% for the five varieties. The result relative error was in a range of 5.71~11.72% between the impurity-detection system and manual method in field conditions. The impurity-detection system could be applied to tracked rice combine harvesters to provide a reference for the adjustment of operating parameters.
Biometric-Based Key Generation and User Authentication Using Acoustic Characteristics of the Outer Ear and a Network of Correlation Neurons
Authors: Sulavko Alexey
Published: December 2022

Trustworthy AI applications such as biometric authentication must be implemented in a secure manner so that a malefactor is not able to take advantage of the knowledge and use it to make decisions. The goal of the present work is to increase the reliability of biometric-based key generation, which is used for remote authentication with the protection of biometric templates. Ear canal echograms were used as biometric images. Multilayer convolutional neural networks that belong to the autoencoder type were used to extract features from the echograms. A new class of neurons (correlation neurons) that analyzes correlations between features instead of feature values is proposed. A neuro-extractor model was developed to associate a feature vector with a cryptographic key or user password. An open data set of ear canal echograms to test the performance of the proposed model was used. The following indicators were achieved: EER = 0.0238 (FRR = 0.093, FAR < 0.001), with a key length of 8192 bits. The proposed model is superior to known analogues in terms of key length and probability of erroneous decisions. The ear canal parameters are hidden from direct observation and photography. This fact creates additional difficulties for the synthesis of adversarial examples.
Bounded Model Checking for Metric Temporal Logic Properties of Timed Automata with Digital Clocks
Authors: Zbrzezny Agnieszka M. , Zbrzezny Andrzej
Published: December 2022

Metric temporal logic (MTL) is a popular real-time extension of linear temporal logic (LTL). This paper presents a new simple SAT-based bounded model-checking (SAT-BMC) method for MTL interpreted over discrete infinite timed models generated by discrete timed automata with digital clocks. We show a new translation of the existential part of MTL to the existential part of linear temporal logic with a new set of atomic propositions and present the details of the new translation. We compare the new method’s advantages to the old method based on a translation of the hard reset LTL (HLTL). Our method does not need new clocks or new transitions. It uses only one path and requires a smaller number of propositional variables and clauses than the HLTL-based method. We also implemented the new method, and as a case study, we applied the technique to analyze several systems. We support the theoretical description with the experimental results demonstrating the method’s efficiency.
Adaptive Bidirectional Gray-Scale Center of Gravity Extraction Algorithm of Laser Stripes
Authors: Zhang Miaomiao , Li Zhengnan , Zhang Fuquan , Ma Lidong
Published: December 2022

Aiming at the realization of fast and high-precision detection of the workpiece, an adaptive bidirectional gray-scale center of gravity extraction algorithm for laser stripes is proposed in this paper. The algorithm is processed in the following steps. Firstly, the initial image processing area is set according to the floating field of the camera’s light stripe, followed by setting the adaptive image processing area according to the actual position of the light stripe. Secondly, the center of light stripe is obtained by using the method of combining the upper contour with the barycenter of the bidirectional gray-scale. The obtained center of the light stripe is optimized by reducing the deviation from adjacent center points. Finally, the slope and intercept are used to complete the breakpoint. The experimental results show that the algorithm has the advantages of high speed and precision and has specific adaptability to the laser stripes of the complex environment. Compared with other conventional algorithms, it greatly improves and can be used in industrial detection.
Deep Deterministic Policy Gradient-Based Autonomous Driving for Mobile Robots in Sparse Reward Environments
Authors: Park Minjae , Lee Seok Young , Hong Jin Seok , Kwon Nam Kyu
Published: December 2022

In this paper, we propose a deep deterministic policy gradient (DDPG)-based path-planning method for mobile robots by applying the hindsight experience replay (HER) technique to overcome the performance degradation resulting from sparse reward problems occurring in autonomous driving mobile robots. The mobile robot in our analysis was a robot operating system-based TurtleBot3, and the experimental environment was a virtual simulation based on Gazebo. A fully connected neural network was used as the DDPG network based on the actor–critic architecture. Noise was added to the actor network. The robot recognized an unknown environment by measuring distances using a laser sensor and determined the optimized policy to reach its destination. The HER technique improved the learning performance by generating three new episodes with normal experience from a failed episode. The proposed method demonstrated that the HER technique could help mitigate the sparse reward problem; this was further corroborated by the successful autonomous driving results obtained after applying the proposed method to two reward systems, as well as actual experimental results.
Compensation for Electrode Detachment in Electrical Impedance Tomography with Wearable Textile Electrodes
Authors: Hu Chang-Lin , Lin Zong-Yan , Hu Shu-Yun , Cheng I-Cheng , Huang Chih-Hsien , Li Yu-Hao , Li Chien-Ju , Lin Chii-Wann
Published: December 2022

Electrical impedance tomography (EIT) is a radiation-free and noninvasive medical image reconstruction technique in which a current is injected and the reflected voltage is received through electrodes. EIT electrodes require good connection with the skin for data acquisition and image reconstruction. However, detached electrodes are a common occurrence and cause measurement errors in EIT clinical applications. To address these issues, in this study, we proposed a method for detecting faulty electrodes using the differential voltage value of the detached electrode in an EIT system. Additionally, we proposed the voltage-replace and voltage-shift methods to compensate for invalid data from the faulty electrodes. In this study, we present the simulation, experimental, and in vivo chest results of our proposed methods to verify and evaluate the feasibility of this approach.
A Survey on Deep-Learning-Based LiDAR 3D Object Detection for Autonomous Driving
Authors: Alaba Simegnew Yihunie , Ball John E.
Published: December 2022

LiDAR is a commonly used sensor for autonomous driving to make accurate, robust, and fast decision-making when driving. The sensor is used in the perception system, especially object detection, to understand the driving environment. Although 2D object detection has succeeded during the deep-learning era, the lack of depth information limits understanding of the driving environment and object location. Three-dimensional sensors, such as LiDAR, give 3D information about the surrounding environment, which is essential for a 3D perception system. Despite the attention of the computer vision community to 3D object detection due to multiple applications in robotics and autonomous driving, there are challenges, such as scale change, sparsity, uneven distribution of LiDAR data, and occlusions. Different representations of LiDAR data and methods to minimize the effect of the sparsity of LiDAR data have been proposed. This survey presents the LiDAR-based 3D object detection and feature-extraction techniques for LiDAR data. The 3D coordinate systems differ in camera and LiDAR-based datasets and methods. Therefore, the commonly used 3D coordinate systems are summarized. Then, state-of-the-art LiDAR-based 3D object-detection methods are reviewed with a selected comparison among methods.
Contemporary Research and Developments in the Low-Toxic Chelating Reagents for the Extraction of Non-Ferrous and Noble Metals from Poor Polymetallic Ores and Processing Tailings
Authors: Matveeva Tamara N. , Getman Viktoriya V. , Gromova Nadezhda K. , Karkeshkina Anna Yu.
Published: December 2022

An urgent technological, economic and environmental task of mining and metallurgical enterprises is to involve poor, off-balance and hard-to-beneficiate ores in the technological process, as well as accumulated and current waste from mining and metallurgical industries. As the reserves of developed deposits are depleted, technogenic objects may become a priority, and in some cases the only, source of mineral raw materials. Mining wastes represent a large reserve of raw materials for the extraction of non-ferrous and precious metals, and at the same time, they are centers of local or regional environmental pollution. Stale waste re-processing may promote territorial cultivation and reduce the environmental burden. The conventional methods of poor ore and waste treatment do not fully provide for a sufficient separation degree of high metal extraction, and lead to significant valuable ore losses, while the quality of the obtained concentrates often does not meet the requirements for subsequent technological process. In this regard, the development of novel chelating agents with specific functional groups that can selectively adsorb on the mineral surface, change the contrast of chemical surface composition and improve the flotation properties of mineral complexes, is an innovative solution for increasing their flotation selectivity. Furthermore, the synthesis and application of novel flotation reagents may help to replace toxic reagents by ecologically friendly or less-toxic ones.
Spatial-Temporal Evolution of Coupling Coordination between Green Transformation and the Quality of Economic Development
Authors: Zhang Yi , Xu Yao , Kong Hao , Zhou Gang
Published: December 2022

Taking “blue granary” as the research object, this study focuses on the mechanism and evolution of coupling coordination relationship between green transformation and the quality of economic development, to explore the path of sustainable development. Firstly, the theoretical framework of coupling relationship between green transformation and the quality of development is constructed. Secondly, an evaluation index system is established to measure green transformation and the quality of economic development. Thirdly, the entropy approach and coupled coordination degree model are used to calculate the coordination of green transformation and the quality of economic development in different provinces in China from 2009 to 2018. The results show that: (1) Green transformation affects the quality of economic development through resource effect, social effect, and technological effect; the quality of economic development affects green transformation through new growth momentum effect, income distribution effect, scale effect, and opening up effect. (2) Both the quality of economic development and the level of green transformation continue to improve, but the growth rate of green transformation is relatively slow. (3) The overall coupled coordination relationship improves from a barely balanced stage to a favorably balanced stage, but it has not reached the ideal state of superiorly balanced, and there is significant regional heterogeneity. It will help to clarify the difference in coordinated development levels in different regions and provide a reference value for the precise implementation of eco-economic coordinated development.
A Decision Support Methodology to Foster Renewable Energy Communities in the Municipal Urban Plan
Authors: Gerundo Roberto , Marra Alessandra
Published: December 2022

Renewable Energy Communities (RECs) represent a possible solution to facilitate the transition to carbon neutrality and reduce energy poverty in urban areas. Although the topic has received little attention from urban planning scholars and practitioners, they can make a significant contribution in the enhancement of RECs. To this end, this article proposes a methodology that allows identifying priority urban areas where the Municipal Urban Plan can incentivize RECs’ establishment. These areas are spatially identified where a minimization of the constraints on RECs’ formation and a maximization of their energy and social benefits are expected. The application of the proposed methodology to an Italian municipal area where the Plan is being drawn up is presented. The obtained results show how priority areas can be found both in the urban center and in rural areas, suggesting that urban planning can encourage different REC configurations, depending on the settlement fabric and land use, as well as the number of buildings to be clustered and potential leaders who can lead the community development process.
Effects of Exogenous Bacterial Agents on Material Transformation and Microbial Community Composition during Composting of Tomato Stalks
Authors: Li Yang , Zhang Guanzhi , Xu Peng , Zhou Shun , Li Yan , Ma Liyuan , Yang Zhenchao , Wu Yongjun
Published: December 2022

Tomato stems can pollute the environment and also cause resource costs. In this study, five combinations of microbial agents were added to tomato stems for aerobic composting to find effective microbial formulations to improve composting performance and product quality through comparative analysis. Six treatments were set up: T1 (Microbial agents A), T2 (0.5% Microbial agents B), T3 (0.5% Microbial agents C), T4 (0.5% Microbial agents D), T5 (0.5% Microbial agents E) and T6 (no addition). The physicochemical parameters of the composting system were measured, and the dynamics of the microbial community during the composting process were studied using high-throughput sequencing technology. The results showed that the T1 treatment had a longer high-temperature period than T6 and the highest cellulose degradation rate (62.0%). The contents of total phosphorus (TP), total potassium (TK) and effective potassium (AK) were 8.11 g·kg−1, 53.98 g·kg−1 and 45.62 g·kg−1, respectively, at the end of composting, representing increases of 270.3%, 56.6% and 25.40% compared to the initial values, significantly higher than the control (p < 0.05). The Chao1 and Shannon indexes of T1 treatment were 73.5% and 41.7% higher in the compost decay stage for bacteria, and 50.2% and 18.1% for fungi, significantly higher than in T6 (p < 0.05) compared to the initial values. During the high-temperature phase, the abundance of Aspergillus increased (4.13% to 44.24%) the abundance of Staphylococcus decreased (58.31% to 8.90%). In terms of numbers and species diversity, bacterial communities were more abundant than fungal communities. Proteobacteria, Firmicutes, Actinomycetes and Bacteroides were the four main bacterial phyla, while Ascomycetes was the absolutely dominant fungal phylum. In conclusion, the addition of microbial agent A can effectively improve the nutrient content of tomato stem compost, promote the maturation of compost, and regulate the microbial community structure and can realize the resource utilization of tomato stems.
Aquatic Ecological Risk Evaluation of Chiral Triazole Fungicide Prothioconazole and Its Metabolite Prothioconazole-Desthio on Lemna minor
Authors: Yang Yaling , Li Xiaomeng , Tang Qiyue , Mei Liangchi , Cao Jian , Huang Haiyun , Zhang Zhaoxian
Published: December 2022

The potential risk posed by the chiral triazole fungicide prothioconazole and its metabolite, prothioconazole-desthio to aquatic ecosystems has attracted attention. At present, the aquatic toxicity of prothioconazole is focused on aquatic animals, and the study of aquatic plants is limited. In this work, the acute toxicity of prothioconazole (PTZ) and its metabolite, prothioconazole-desthio (PTD), to the aquatic plant Lemna minor (L. minor) was evaluated at the enantiomer level. The effects of the prothioconazole and its metabolite enantiomer on the physiological and biochemical indices, including growth rate, photosynthetic pigment content, and antioxidant-defense-enzymes activity, of L. minor were measured to evaluate the potential risk. The results showed that prothioconazole and prothioconazole-desthio possessed obvious stereoselective toxicity to Lemna minor with an LC50 (7 days) of 0.76–5.63 mg/L. The toxicity order was S-PTD > Rac-PTD > S-PTZ > R-PTD > Rac-PTZ > R-PTZ. The S-PTZ, which had the highest toxicity, obviously inhibited the biosynthesis of photosynthetic pigments and the activity of antioxidant-defense enzymes (malondialdehyde, catalase and superoxide dismutase), leading to an increase in MDA content and oxidative damage. The results further confirmed that the metabolism of PTZ in aquatic ecosystems increased its exposure risk, providing data support and a theoretical basis for the risk assessment of PTZ.
A Two-Stage Deep-Learning Model for Link Prediction Based on Network Structure and Node Attributes
Authors: Liu Peng , Gui Liang , Wang Huirong , Riaz Muhammad
Published: December 2022

Link prediction, which is used to identify the potential relationship between nodes, is an important issue in network science. In existing studies, the traditional methods based on the structural similarity of nodes make it challenging to complete the task of link prediction in large-scale or sparse networks. Although emerging methods based on deep learning can solve this problem, most of the work mainly completes the link prediction through the similarity of the representation vector of network structure information. Many empirical studies show that link formation is affected by node attributes, and similarity is not the only criterion for the formation of links in reality. Accordingly, this paper proposed a two-stage deep-learning model for link prediction (i.e, TDLP), where the node representation vector of the network structure and attributes was obtained in the first stage, while link prediction was realized through supervised learning in the second stage. The empirical results on real networks showed that our model significantly outperforms the traditional methods (e.g., CN and RA), as well as newly proposed deep-learning methods (e.g., GCN and VGAE). This study not only proposed a deep-learning framework for link prediction from the perspective of structure and attribute fusion and link distribution capture, but also lays a methodological foundation for practical applications based on link prediction.
Effective Utilization of Copper Slag for the Production of Geopolymer Concrete with Different NaOH Molarity under Ambient Curing Conditions
Authors: Arunachelam Nagarajan , Maheswaran Jeyaprakash , Chellapandian Maheswaran , Ozbakkaloglu Togay
Published: December 2022

In spite of the considerable research on the mechanical and durability properties of geopolymer concrete, its widespread applicability is hindered due to the difficulties involved in achieving ambient curing conditions and awareness of the effective utilization of industrial by-products. This study investigates the physical and microstructure characterization of sustainable geopolymer concrete (GPC) developed with copper slag as a replacement for fine aggregate. In total, forty-four geopolymer concrete mixtures were prepared to examine their fresh and hardened properties. Four different NaOH molarities (10, 12, 14 and 16) and the replacement levels of copper slag, ranging from 0 to 100% with an increase of 10%, were considered as variables in this research. The study parameters examined includes the fresh (slump) and hardened concrete properties. Additionally, the microstructural characterization for different mixes were studied using the Fourier Transform Infrared Spectroscopy (FTIR), Electron Dispersive Spectrum (EDS) analyses and Scanning Electron Microscope (SEM). Results indicated that replacing fine aggregate with copper slag up to 100% showed no strength reduction. Increasing the molarity of the NaOH solution to 16M led to an increased strength of about 35% compared to the concrete with 10 M in all the mixes. The microstructural analysis performed using SEM/EDS and FTIR showed that a cohesive and fully compact geopolymer matrix was achieved together with the use of low-calcium fly ash and copper slag under ambient curing conditions.
Stakeholder Pressure Engaged with Circular Economy Principles and Economic and Environmental Performance
Authors: Hernández-Arzaba Juan Cristóbal , Nazir Sarfraz , Leyva-Hernández Sandra Nelly , Muhyaddin Sanar
Published: December 2022

The study aims to investigate the impacts of internal and external stakeholder pressures on the adoption of circular economy (CE) principles. The study explores the primary barriers to and causes (external or internal stakeholder pressures) of CE’s transition across Mexico and explores the effects of stakeholder pressures (internal and external) on the adoption of CE principles on economic and environmental performance. For this, data were collected from 433 respondents using a structured questionnaire. For analysis, the study used the PLS-SEM technique to examine internal and external stakeholders as barriers and motivators of economic and environmental performance through CE principles. Both the structural model and the measurement model were assessed. As well as mediation analysis, the direct and indirect effects were determined. The study found that economic (β = 0.178, p = 0.000) and environmental performance (β = 0.233, p = 0.000) is affected by most external stakeholders through the adoption of circular economy principles. Although the internal stakeholders don’t affect the environmental performance (β = 0.040, p = 0.492), with the adoption of circular economy principles mediating the relationship, internal stakeholders encourage environmental performance (β = 0.201, p = 0.000). The study motivates the public, non-governmental organisations (NGOs) and the government to adopt CE principles for the achievement of economic and environmental performance and participation in the sustainable development agenda.
Corporate Communication of CSR in China: Characteristics and Regional Differences
Authors: Su Ruixin , Zhong Weizhou
Published: December 2022

This article investigates CSR issues publicized by websites, annual reports, and social responsibility reports by companies in mainland China from the perspective of corporate communication. The empirical study is surrounded by issues about the motives of CSR, the content of CSR, and the applications of stakeholders. Empirical tests are distinguished by CSR motives, characteristics, and stakeholder issues for companies from Eastern, Central, and Western regions. Our research results indicate that although corporate social responsibility implementation and communication in China have made great progress compared with the past, there are still problems, such as the incomplete implementation of CSR, unbalanced communication of CSR, and lack of integrity and pertinence. Enterprises in different regions are affected by the degree of local economic development, social development environment, and government intervention, which leads to obvious differences in geographical and political attributes of enterprises in different regions in communicating social responsibility. This article focuses on the impact of institutional (consists formal and informal) and regional influencing factors on CSR communication. Therefore, political, cultural, and regional differences are expected to be conducted by corporate culture and government policy in future CSR activities.
Spatial Heterogeneity Analysis for Influencing Factors of Outbound Ridership of Subway Stations Considering the Optimal Scale Range of “7D” Built Environments
Authors: Wang Zhenbao , Song Jiarui , Zhang Yuchen , Li Shihao , Jia Jianlin , Song Chengcheng
Published: December 2022

The accuracy of the regression model of ridership of subway stations depends on the scale range of the built environment around the subway stations. Previous studies have not considered the Modifiable Area Unit Problem (MAUP) to establish the regression model of subway station ridership. Taking Beijing as an example, this paper expanded the built environment variables from “5D” category to “7D” category, added indicators such as parking fee standard and population density factor, and proposed a Multi-Scale Geographical Weighted Regression (MGWR) model of outbound ridership of subway stations with standardized variables. The goodness of fit of regression models under 10 spatial scales or built environment around subway stations are compared, and the spatial heterogeneity of built environment factors under the optimal spatial scale of outbound ridership of subway stations during the morning peak on weekdays is discussed. The results show that: (1) the scale range overlapped by 1000 m radius circular buffer zone and Thiessen polygon has the highest explanatory power for the regression model, and is regarded as the optimal scale range of built environment; (2) the density of office facilities, sports and leisure facilities, medical service facilities, building density and floor area ratio (FAR) has a significant impact on the outbound ridership of all subway stations; (3) office facilities, catering facilities, FAR, number of parking lots, and whether subway stations are transfer stations have a positive impact on outbound ridership. The number of medical service facilities, sports and leisure facilities, bus stops and building density have a negative impact on outbound ridership; (4) the two added factors in this study: parking charge standard and population density, as the influencing factors of the built environment, have a significant impact on the outbound ridership of some subway stations; and (5) the different local coefficients of the built environment factors at different stations are discussed, which indicate the spatial heterogeneity on the outbound ridership. The results can provide an important theoretical basis for the prediction and analysis of demand of ridership at subway stations and the integration of the built environment around the stations.
Solar and Wind Energy Forecasting for Green and Intelligent Migration of Traditional Energy Sources
Authors: Mohsin Syed Muhammad , Maqsood Tahir , Madani Sajjad Ahmed
Published: December 2022

Fossil-fuel-based power generation leads to higher energy costs and environmental impacts. Solar and wind energy are abundant important renewable energy sources (RES) that make the largest contribution to replacing fossil-fuel-based energy consumption. However, the uncertain solar radiation and highly fluctuating weather parameters of solar and wind energy require an accurate and reliable forecasting mechanism for effective and efficient load management, cost reduction, green environment, and grid stability. From the existing literature, artificial neural networks (ANN) are a better means for prediction, but the ANN-based renewable energy forecasting techniques lose prediction accuracy due to the high uncertainty of input data and random determination of initial weights among different layers of ANN. Therefore, the objective of this study is to develop a harmony search algorithm (HSA)-optimized ANN model for reliable and accurate prediction of solar and wind energy. In this study, we combined ANN with HSA and provided ANN feedback for its weights adjustment to HSA, instead of ANN. Then, the HSA optimized weights were assigned to the edges of ANN instead of random weights, and this completes the training of ANN. Extensive simulations were carried out and our proposed HSA-optimized ANN model for solar irradiation forecast achieved the values of MSE = 0.04754, MAE = 0.18546, MAPE = 0.32430%, and RMSE = 0.21805, whereas our proposed HSA-optimized ANN model for wind speed prediction achieved the values of MSE = 0.30944, MAE = 0.47172, MAPE = 0.12896%, and RMSE = 0.55627. Simulation results prove the supremacy of our proposed HSA-optimized ANN models compared to state-of-the-art solar and wind energy forecasting techniques.
Development of a Fuzzy Inference System Based Rapid Visual Screening Method for Seismic Assessment of Buildings Presented on a Case Study of URM Buildings
Authors: Bektaş Nurullah , Lilik Ferenc , Kegyes-Brassai Orsolya
Published: December 2022

Many conventional rapid visual screening (RVS) methods for the seismic assessment of existing structures have been designed over the past three decades, tailored to site-specific building features. The objective of implementing RVS is to identify the buildings most susceptible to earthquake-induced damage. RVS methods are utilized to classify buildings according to their risk level to prioritize the buildings at high seismic risk. The conventional RVS methods are employed to determine the damage after an earthquake or to make safety assessments in order to predict the damage that may occur in a building before an impending earthquake. Due to the subjectivity of the screener based on visual examination, previous research has shown that these conventional methods can lead to vagueness and uncertainty. Additionally, because RVS methods were found to be conservative and to be partially accurate, as well as the fact that some expert opinion based developed RVS techniques do not have the capability of further enhancement, it was recommended that RVS methods be developed. Therefore, this paper discusses a fuzzy logic based RVS method development to produce an accurate building features responsive examination method for unreinforced masonry (URM) structures, as well as a way of revising existing RVS methods. In this context, RVS parameters are used in a fuzzy-inference system hierarchical computational pattern to develop the RVS method. The fuzzy inference system based RVS method was developed considering post-earthquake building screening data of 40 URM structures located in Albania following the earthquake in 2019 as a case study. In addition, FEMA P-154, a conventional RVS method, was employed to screen considered buildings to comparatively demonstrate the efficiency of the developed RVS method in this study. The findings of the study revealed that the proposed method with an accuracy of 67.5% strongly outperformed the conventional RVS method by 42.5%.
Farmers’ Perception of Tourism in Sustainable Development of Forests and Their Decisions in China: The Sustainability of the Prism Model and SEM Model
Authors: Hong Xiafang , He Cheng
Published: December 2022

The sustainable development of forests involves a combination of environmental protection and economics. Ecotourism is a new and growing sustainable economic model for forests, compared with the traditional utilization and protection of forests. The purpose of this study was to explore the applicability of the Prism model and SEM model in seeking to understand farmers’ perceptions and decision-making regarding tourism, so as to fully understand farmers’ thinking and behavior around forest tourism, and to truly make them in harmony with the sustainable development of forests. In this study, based on a field investigation of 392 farmers living in 11 national forest parks in Jiangxi Province, China, three hypotheses were constructed using the SEM model; and then six first-level indicators and 15 s-level indicators were constructed based on the Prism model, to verify the cognition and influencing factors for farmers, regarding sustainable forest tourism. The results showed that (1) the formation process of farmers’ willingness to adapt to forest tourism follows the path of “individual cognition → individual behavior”. (2) Economic influences, social culture, environmental cognition, and institutional cognition all have significant positive effects on their adaptive willingness. (3) Farmers’ cognition of economic life, environmental protection, and social culture lead to significant differences in their cognition and adaptability to forest tourism. These findings highlight the importance of the perceived value of tourism in assessing how tourism develops, as well as whether it is sustainable for the forest and its impacts. This study provides a better understanding of the factors that influence farmers’ attitudes towards tourism and highlights the importance of valuing local communities as important players in sustainable development, especially as this boosts the economy in forests.
Relationship between the Type A Personality Concept of Time Urgency and Mothers’ Parenting Situation
Authors: Kawasaki Hiromi , Yamasaki Satoko , Nishiyama Mika , D’Angelo Pete , Cui Zhengai
Published: December 2022

It has been suggested that parenting can be stressful for individuals with Type A personality because they have difficulties managing their own time. This study aimed to determine the relationship between mothers’ sense of time urgency (a Type A personality concept) and variables such as background characteristics, tiredness, and frequency of anger. The study participants were 103 mothers (mean age, 33.34 ± 4.38 years) of children aged 0–3 years, who used the child-rearing open space of a non-profit organization in Japan. A self-administered questionnaire survey assessed the participants’ demographic characteristics, feelings of tiredness, sense of time urgency, and anger in different parenting situations. Statistical analyses were performed using Kruskal–Wallis, Wilcoxon signed-rank, and linear regression analyses. The variable “sense of time urgency” was positively correlated with higher instability (feeling of tiredness; p = 0.00079), uneasiness (p < 0.001), eyestrain (p < 0.001), and anger, in several specific parenting situations. However, it was negatively correlated with sleep duration (p < 0.01). Our findings provide insight into the challenges faced by mothers with Type A personality traits, and may aid in the development of parenting guidance for these mothers.
Solid Waste Characterization and Management in a Highly Vulnerable Tropical City
Authors: Esteban Cantillo Oscar Julián , Quesada Benjamin
Published: December 2022

Inadequate waste management can cause human health problems, economic losses, and environmental contamination. Colombian municipalities face the critical issues of very low levels of recycling for solid waste, increasing waste quantities, precarious conditions for waste pickers, a lack of investment in alternative strategies, increasing pollution, and landslides in landfills. Moreover, Colombia lacks an in-situ quantification of solid waste, as well as alternative strategies based on an analysis of the local contexts. This study provides an analysis of the current waste management and a characterization of the waste production in a highly vulnerable tropical city in Colombia, Puerto Carreño, the capital municipality in Vichada. Systematically following the collection routes, we determined that 61% of waste produced is potentially recyclable, and that the total solid waste per inhabitant (at 0.504 kg/capita/day) is 43% lower than that estimated by the private local waste collector. The great majority of solid waste is disposed of in El Merey landfill, which does not currently fulfill legal requirements. Given the current incentivization legislation, formal economic gains can be achieved, including an increase in employment, and the reduction of negative social and environmental impacts near the landfill, and it is estimated that its useful lifetime can be doubled (+30 years) within a circular economy framework. This study is an important contribution for local and national authorities to implement key waste-management recommendations, including the formalization of indigenous waste pickers, implementation of selective collection routes, agricultural exploitation of the organic waste, and adequate landfill management.
Influence of Joint Characteristics on Crack Propagation during the Double-Hole High-Energy Gas Impact Permeability Enhancement Process
Authors: Duan Dong , Chen Xi , Feng Xiaojing , Liu Wenbo , Zhang Hongzhi , Chen Xiaoyu , Gao Shilei , Wang Xin , Wang Ao
Published: December 2022

In view of current research on the cracking mechanism of high-energy gas on coal, little attention has been paid to imitating the law of explosive blasting and cracks propagation, and the influence of joint on cracks propagation in the process of high-energy gas impact permeability enhancement has not been taken into account. In this paper, the effects of joint dip angles and joint lengths on cracks size propagation are studied by using a similar simulation test and RFPA2D-dynamic numerical simulation software. In the process of impact permeability enhancement of high-energy gas, the extension direction of the cracks is approximately parallel to the joint, and with the increase in the dip angle and length, the higher the number of cracks, the larger the extension range, and the closer it is to the permeability enhancement holes, the fracture network is formed. When the dip angle of the joint is 30°, the impact permeability enhancement effect results in an obvious zoning phenomenon. When the joint dip angle is 60° and 90°, there is a higher number of cracks and the cracks network is formed, and with the increase in the dip angle, the more the cracks develop and the better the impact permeability enhancement effect.
Data Mining in Coal-Mine Gas Explosion Accidents Based on Evidence-Based Safety: A Case Study in China
Authors: Hu Jiaqi , Huang Rui , Xu Fangting
Published: December 2022

From an informatics perspective, decision-making failures in accident prevention are due to insufficient necessary safety evidence. Analyzing accident data can help in obtaining safety evidence. Currently, such a practice mostly relies on experts’ judgement and experience, which are subjective and inefficient. Furthermore, due to the inadequate safety-related theoretical support, the sustainable safety of a system can hardly be achieved purposefully. To automatically explore and obtain latent safety evidence in coal-mine data, and improve the reliability and sustainability of coal-mine safety management, a novel framework of combining data mining technology and evidence-based safety (EBS) theory is proposed, and was applied to a coal gas explosion accident. First, the term frequency-inverse document (TF-IDF) and TextRank algorithms were fused to extract keywords, and keyword evolution word cloud maps from the time dimension were drawn to obtain keyword safety evidence. Then, on the basis of the latent Dirichlet allocation (LDA) model, the best safety evidence, such as accident causation topics and causation factors, were mined, and safety decisions were given. The results show that accident data mining, based on evidence-based safety, can effectively and purposefully mine the best safety evidence, and guide safety decision making to optimize safety management models and achieve sustainable safety.
The Anthropic Pressure on the Grey Water Footprint: The Case of the Vulnerable Areas of the Emilia-Romagna Region in Italy
Authors: Voccia Diego , Mortella Giacomo , Ferrari Federico , Fontanella Maria Chiara , Trevisan Marco , Lamastra Lucrezia
Published: December 2022

Nitrogen (N) is an important component of plant development, yet its application and contamination are a global issue. Diffuse source pollution and its effects on ecosystem health are notoriously difficult to track and control. This paper answers the Sustainable development Goal 6 goal focused on reducing water pollution by improving the understanding of nitrate emissions to groundwater and their resulting water pollution level in the Emilia-Romagna region in Italy. The Grey water footprint (GWF) and water pollution level (WPL) were used as indicators and geospatial maps were constructed in order to determine if N levels exceeded groundwater quality demand from 2014 to 2020. Moreover, a selection of specific agricultural sites in the Piacenza district has been performed to better understand the potential anthropogenic nitrate pollution due to the agricultural sector. In the selected sites, the predicted nitrate pollution due to agricultural practice has been compared with the nitrate concentration measured in samples collected across the period 2015–2018. The regional results show that approximately 70% of the analysed sites resulted in a total N load exceeding the estimated agricultural load to groundwater. The analysis conducted in three selected wells in the Piacenza district shows the sporadic exceedances of the legal limit and demonstrates the presence of anthropogenic pressures of various natures insisting on the surrounding area and confirms a potential non-agricultural point or diffuse pollution source.
Liquefied Natural Gas (LNG) as a Transitional Choice Replacing Marine Conventional Fuels (Heavy Fuel Oil/Marine Diesel Oil), towards the Era of Decarbonisation
Authors: Livaniou Styliani , Papadopoulos Georgios A.
Published: December 2022

As environmental regulations on sulphur emissions become more severe, the maritime sector is looking for alternative solutions. This study evaluates greenhouse gas (GHG) reduction alternatives and their combined ability to decarbonise international transport. Liquefied natural gas (LNG) is becoming widely used, reducing CO2 emissions by 20–30 percent, while it has similar action in other emissions such as SOX. Although costs are attractive, methane slip, which depends on the engine type, reduces GHG gains. Replacing conventional fuels such as heavy fuel oil and marine diesel oil with alternative ones is an effective method to decrease SOx emissions. Liquefied natural gas is highly appreciated as an alternative fuel for maritime transportation. In this frame, the possibility of using alternative fuels, such as LNG, to reduce NOx, CO2 and SOx emissions in Heraklion Port, including certain regionally defined waters, over the life of the vessel will also be explored. The study is conducted for ships calling at Heraklion Port and using alternative fuel such as LNG in different modes (cruising, hotelling, manoeuvring). A fuel-based emission reduction factor, rEif, is defined in relation to the comparison of two different fuels: conventional (heavy oil, marine diesel) and alternative fuels (LNG). The bottom-up method is used for this data analysis. This study, by defining the reduction of several emissions with the use of LNG, indicates that it is actually an efficient transitional fuel to lead international transport to decarbonisation.
Evaluation of the Laboratory Degradation Performance of a Straw Drainage Board
Authors: Zhu Runtian , Su Yinqiang , Wu Cankun , Yuan Wei , Deng Yongfeng
Published: December 2022

Plastic drainage boards are installed into the foundation as vertical drainage channels in vacuum preloading projects. After construction, numerous plastic drainage boards are left in the foundation, causing not only white pollution but also potential groundwater contamination. Straw was utilized to produce degradable drainage boards in this study, and the feasibility of straw drainage boards was confirmed by laboratory degradation tests. The results revealed that Zhuhai’s soft marine soil is rich in degrading bacteria such as Bacteroidota and Firmicutes, which have adverse effects on the performance of the straw drainage board. The straw drainage board was deteriorated by bacteria in the foundation, and the discharge capacity and tensile strength dropped with time, with the discharge capacity degradation relationship as qw(t) = qw0(1 − 3.83 × 10−6t2). The discharge capacity and tensile strength of straw drainage boards are lower than those of plastic drainage boards, but they all meet the engineering requirements. Straw drainage boards can replace plastic drainage boards in vacuum preloading reinforcement projects, which not only solves the problem of environmental pollution but also expands the comprehensive utilization of straw resources in a new way.