bluuebunny/crossref_metadata_2025 huggingface dataset:
Monitoring Epoxy Coated Steel under Combined Mechanical Loads and Corrosion Using Fiber Bragg Grating Sensors
Authors: Xu Luyang , Zhang Dawei , Huang Ying , Shi Shuomang , Pan Hong , Bao Yi
Published: October 2022

Fiber Bragg grating (FBG) sensors have been applied to assess strains, stresses, loads, corrosion, and temperature for structural health monitoring (SHM) of steel infrastructure, such as buildings, bridges, and pipelines. Since a single FBG sensor measures a particular parameter at a local spot, it is challenging to detect different types of anomalies and interactions of anomalies. This paper presents an approach to assess interactive anomalies caused by mechanical loading and corrosion on epoxy coated steel substrates using FBG sensors in real time. Experiments were performed by comparing the monitored center wavelength changes in the conditions with loading only, corrosion only, and simultaneous loading and corrosion. The theoretical and experimental results indicated that there were significant interactive influences between loading and corrosion for steel substrates. Loading accelerated the progress of corrosion for the epoxy coated steel substrate, especially when delamination in the epoxy coating was noticed. Through the real-time monitoring from the FBG sensors, the interactions between the anomalies induced by the loading and corrosion can be quantitatively evaluated through the corrosion depth and the loading contact length. These fundamental understandings of the interactions of different anomalies on steel structures can provide valuable information to engineers for better management of steel structures.
Design and Implementation of a Modular UUV Simulation Platform
Authors: Zhang Zekai , Mi Weishi , Du Jun , Wang Ziyuan , Wei Wei , Zhang Yuang , Yang Yutong , Ren Yong
Published: October 2022

The complex and time-varying marine environment puts forward demanding requirements for the structural design and algorithm development of unmanned underwater vehicles (UUVs). It is inevitable to repeatedly evaluate the feasibility of autonomy schemes to enhance the intelligence and security of the UUV before putting it into use. Considering the high cost of the UUV hardware platform and the high risk of underwater experiments, this study aims to evaluate and optimize autonomy schemes in the manner of software-in-loop (SIL) simulation efficiently. Therefore, a self-feedback development framework is proposed and a multi-interface, programmable modular simulation platform for UUV based on a robotic operating system (ROS) is designed. The platform integrates the 3D marine environment, UUV models, sensor plugins, motion control plugins in a modular manner, and reserves programming interfaces for users to test various algorithms. Subsequently, we demonstrate the simulation details with cases, such as single UUV path planning, task scheduling, and multi-UUV formation control, and construct underwater experiments to confirm the feasibility of the simulation platform. Finally, the extensibility of the simulation platform and the related performance analysis are discussed.
Development of an Online Adaptive Parameter Tuning vSLAM Algorithm for UAVs in GPS-Denied Environments
Authors: Chen Chieh-Li , He Rong , Peng Chao-Chung
Published: October 2022

In recent years, unmanned aerial vehicles (UAVs) have been applied in many fields owing to their mature flight control technology and easy-to-operate characteristics. No doubt, these UAV-related applications rely heavily on location information provided by the positioning system. Most UAVs nowadays use a global navigation satellite system (GNSS) to obtain location information. However, this outside-in 3rd party positioning system is particularly susceptible to environmental interference and cannot be used in indoor environments, which limits the application diversity of UAVs. To deal with this problem, in this paper, a stereo-based visual simultaneous localization and mapping technology (vSLAM) is applied. The presented vSLAM algorithm fuses onboard inertial measurement unit (IMU) information to further solve the navigation problem in an unknown environment without the use of a GNSS signal and provides reliable localization information. The overall visual positioning system is based on the stereo parallel tracking and mapping architecture (S-PTAM). However, experiments found that the feature-matching threshold has a significant impact on positioning accuracy. Selection of the threshold is based on the Hamming distance without any physical meaning, which makes the threshold quite difficult to set manually. Therefore, this work develops an online adaptive matching threshold according to the keyframe poses. Experiments show that the developed adaptive matching threshold improves positioning accuracy. Since the attitude calculation of the IMU is carried out based on the Mahony complementary filter, the difference between the measured acceleration and the gravity is used as the metric to online tune the gain value dynamically, which can improve the accuracy of attitude estimation under aggressive motions. Moreover, a static state detection algorithm based on the moving window method and measured acceleration is proposed as well to accurately calculate the conversion mechanism between the vSLAM system and the IMU information; this initialization mechanism can help IMU provide a better initial guess for the bundle adjustment algorithm (BA) in the tracking thread. Finally, a performance evaluation of the proposed algorithm is conducted by the popular EuRoC dataset. All the experimental results show that the developed online adaptive parameter tuning algorithm can effectively improve the vSLAM accuracy and robustness.
Segmentation for Multi-Rock Types on Digital Outcrop Photographs Using Deep Learning Techniques
Authors: Malik Owais A. , Puasa Idrus , Lai Daphne Teck Ching
Published: October 2022

The basic identification and classification of sedimentary rocks into sandstone and mudstone are important in the study of sedimentology and they are executed by a sedimentologist. However, such manual activity involves countless hours of observation and data collection prior to any interpretation. When such activity is conducted in the field as part of an outcrop study, the sedimentologist is likely to be exposed to challenging conditions such as the weather and their accessibility to the outcrops. This study uses high-resolution photographs which are acquired from a sedimentological study to test an alternative basic multi-rock identification through machine learning. While existing studies have effectively applied deep learning techniques to classify the rock types in field rock images, their approaches only handle a single rock-type classification per image. One study applied deep learning techniques to classify multi-rock types in each image; however, the test was performed on artificially overlaid images of different rock types in a test sample and not of naturally occurring rock surfaces of multiple rock types. To the best of our knowledge, no study has applied semantic segmentation to solve the multi-rock classification problem using digital photographs of multiple rock types. This paper presents the application of two state-of-the-art segmentation models, namely U-Net and LinkNet, to identify multiple rock types in digital photographs by segmenting the sandstone, mudstone, and background classes in a self-collected dataset of 102 images from a field in Brunei Darussalam. Four pre-trained networks, including Resnet34, Inceptionv3, VGG16, and Efficientnetb7 were used as a backbone for both models, and the performances of the individual models and their ensembles were compared. We also investigated the impact of image enhancement and different color representations on the performances of these segmentation models. The experiment results of this study show that among the individual models, LinkNet with Efficientnetb7 as a backbone had the best performance with a mean over intersection (MIoU) value of 0.8135 for all of the classes. While the ensemble of U-Net models (with all four backbones) performed slightly better than the LinkNet with Efficientnetb7 did with an MIoU of 0.8201. When different color representations and image enhancements were explored, the best performance (MIoU = 0.8178) was noticed for the L*a*b* color representation with Efficientnetb7 using U-Net segmentation. For the individual classes of interest (sandstone and mudstone), U-Net with Efficientnetb7 was found to be the best model for the segmentation. Thus, this study presents the potential of semantic segmentation in automating the reservoir characterization process whereby we can extract the patches of interest from the rocks for much deeper study and modeling to be conducted.
The Associations between Evacuation Movements and Children’s Physiological Demands Analyzed via Wearable-Based Sensors
Authors: Zhang Bo , Gao Xiaoyu , Zhou Jiaxu , Jia Xiaohu
Published: October 2022

During fire evacuations, crawling is recommended to prevent harm from toxic smoke and to access more breathable air. Few studies have evaluated the physiological burden of crawling, especially for children. The method of using wearable sensors to collect data (e.g., electrodermal activity, EDA; skin temperature, SKT) was used to evaluate the effects of different locomotive postures on children’s velocity and physiological demands. Twenty-eight (28) children (13 boys and 15 girls), aged 4 to 6 years old, traveled up to 22.0 m in different postures: Upright walking (UW), stoop walking (SW), knee and hand crawling (KHC). The results showed that: (1) Gender and age had significant impacts on children’s velocity (p < 0.05): Boys were always faster than girls in any of the three postures and the older the child, the faster the velocity for KHC. (2) Physiological results demonstrated that KHC was more physically demanding than bipedal walking, represented by higher scores of the EDA and SKT indicators, similar to the findings of adults. (3) Gender and age had significant impacts on children’s physiological demands (p < 0.05). The physiological demands were greater for boys than girls. In addition, the higher the age, the less physiological demands he/she needs. Overall, the findings suggest that children are unnecessarily required to choose crawling precisely as adults as the best posture to respond to emergency scenarios. In a severe fire, stoop walking is suggested, as there is more respired air and children could move quickly and avoid overworking physiological burdens. The results of this study are expected to be considered in the evaluation of current evacuation recommendations and for the safety guide of preparedness to improve the effectiveness of risk reduction for children.
User-Centered Design Methodologies for the Prototype Development of a Smart Harness and Related System to Provide Haptic Cues to Persons with Parkinson’s Disease
Authors: Imbesi Silvia , Corzani Mattia , Lopane Giovanna , Mincolelli Giuseppe , Chiari Lorenzo
Published: October 2022

This paper describes the second part of the PASSO (Parkinson smart sensory cues for older users) project, which designs and tests an innovative haptic biofeedback system based on a wireless body sensor network using a smartphone and different smartwatches specifically designed to rehabilitate postural disturbances in persons with Parkinson’s disease. According to the scientific literature on the use of smart devices to transmit sensory cues, vibrotactile feedback (particularly on the trunk) seems promising for improving people’s gait and posture performance; they have been used in different environments and are well accepted by users. In the PASSO project, we designed and developed a wearable device and a related system to transmit vibrations to a person’s body to improve posture and combat impairments like Pisa syndrome and camptocormia. Specifically, this paper describes the methodologies and strategies used to design, develop, and test wearable prototypes and the mHealth system. The results allowed a multidisciplinary comparison among the solutions, which led to prototypes with a high degree of usability, wearability, accessibility, and effectiveness. This mHealth system is now being used in pilot trials with subjects with Parkinson’s disease to verify its feasibility among patients.
Monocular Camera Viewpoint-Invariant Vehicular Traffic Segmentation and Classification Utilizing Small Datasets
Authors: Yousef Amr , Flora Jeff , Iftekharuddin Khan
Published: October 2022

The work presented here develops a computer vision framework that is view angle independent for vehicle segmentation and classification from roadway traffic systems installed by the Virginia Department of Transportation (VDOT). An automated technique for extracting a region of interest is discussed to speed up the processing. The VDOT traffic videos are analyzed for vehicle segmentation using an improved robust low-rank matrix decomposition technique. It presents a new and effective thresholding method that improves segmentation accuracy and simultaneously speeds up the segmentation processing. Size and shape physical descriptors from morphological properties and textural features from the Histogram of Oriented Gradients (HOG) are extracted from the segmented traffic. Furthermore, a multi-class support vector machine classifier is employed to categorize different traffic vehicle types, including passenger cars, passenger trucks, motorcycles, buses, and small and large utility trucks. It handles multiple vehicle detections through an iterative k-means clustering over-segmentation process. The proposed algorithm reduced the processed data by an average of 40%. Compared to recent techniques, it showed an average improvement of 15% in segmentation accuracy, and it is 55% faster than the compared segmentation techniques on average. Moreover, a comparative analysis of 23 different deep learning architectures is presented. The resulting algorithm outperformed the compared deep learning algorithms for the quality of vehicle classification accuracy. Furthermore, the timing analysis showed that it could operate in real-time scenarios.
Synthesis of Gum Arabic Magnetic Nanoparticles for Adsorptive Removal of Ciprofloxacin: Equilibrium, Kinetic, Thermodynamics Studies, and Optimization by Response Surface Methodology
Authors: Birniwa Abdullahi Haruna , Mohammad Rania Edrees Adam , Ali Mujahid , Rehman Muhammad Faisal , Abdullahi Shehu Sa’ad , Eldin Sayed M. , Mamman Suwaiba , Sadiq Abubakar Chadi , Jagaba Ahmad Hussaini
Published: October 2022

Given the increasing risks that antibiotic abuse poses to microecology and human health, it is imperative to develop incredibly powerful adsorbents. This study investigated the use of environmentally sustainable polymeric nanocomposite based on gum arabic (GA) and magnetic nanoparticles (MNPs) synthesized via co-precipitation method to form gum arabic magnetitic nanoparticles (GA-MNPs) as an efficient adsorbent for ciprofloxacin (CIP) removal from aqueous solution. The physicochemical properties and morphology of the synthesized GA-MNPs were characterized by Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and Energy Dispersive X-Ray Analysis (EDX). The experiment was designed by response surface methodology (RSM) and the Central Composite Design (CCD) was utilized to optimize the operating variables: contact time (0–120 min), pH (3–10), adsorbent dosage (0.10–0.40 g/L), and concentration of adsorbate (5–100 mg/L). Results showed that 96.30% was the maximum percentage of CIP removed. The adsorption effect of the CIP molecule on the surface of the GA-MNPs was investigated using regression analysis and analysis of variance. Furthermore, Freundlich Isotherm and Pseudo Second order kinetic equations have the highest consistency with experimental investigations suggesting double-layer adsorption. This implies that chemisorption was the mechanism involved. In addition, the calculated thermodynamic parameters were postulating an exothermic and spontaneous method in nature. Owing to its adsorption selectivity and recyclability, GA-MNPs could be classified as an environmentally friendly, less expensive, and highly efficient promising adsorbent for remediation of CIP from aqueous solution.
Agriculture Waste as Slow Carbon Releasing Source of Mixotrophic Denitrification Process for Treating Low C/N Wastewater
Authors: Hong Xiaohong , Tang Liaofan , Feng Haixia , Zhang Xiaolei , Hu Xianqiong
Published: October 2022

Mixotrophic denitrification has showed great potential for treating wastewater with a low C/N ratio. Mixotrophic denitrification is the process combining autotrophic denitrification and heterotrophic denitrification in one system. It can compensate the disadvantage of the both denitrifications. Instead of using sodium acetate and glucose as carbon source for the heterotrophic denitrification, agriculture solid wastes including rice straw (RS), wheat straw (WS), and corncob (CC) were employed in this study to investigate their potential as carbon source for treating low C/N wastewater. The carbon releasing pattern of the three carbon rich materials has been studied as well as their capacity in denitrification. The results showed that the highest denitrification occurred in the corncob system which was 0.34 kg N/(m3·d). Corncob was then selected to combine with sulfur beads to build the mixotrophic denitrification system. The reactor packed with sulfur bead on the top and corncob on the bottom achieved 0.34 kg N/(m3·d) denitrification efficiency, which is higher than that of the reactor packed with completely mixed sulfur bead and corncob. The autotrophic denitrification and heterotrophic denitrification were 42.2% and 57.8%, respectively. The microorganisms in the sulfur layer were Thermomonas, Ferritrophicum, Thiobacillus belonging to autotrophic denitrification bacteria. Kouleothrix and Geothrix were mostly found in the corncob layer, which have the function for fiber hydrolysis and denitrification. The study has provided an insight into agriculture solid waste application and enhancement on denitrification of wastewater treatment.
The Impact of Transformative Tourism Experiences on Prosocial Behaviors of College Students: Multiple Chain Mediating Effects of Dispositional Awe and Social Connectedness
Authors: Li Ying , Wu Xuan , Chu Yu-Jie , Guo Ya-Jun
Published: October 2022

As sustainable behaviors that contribute to the development of human society, prosocial behaviors are an important part of the moral cultivation of college students and have attracted the growing attention of higher education in recent years. It has been indicated by previous studies that transformative tourism experiences can have a profound impact on individual prosocial behaviors. Therefore, how transformative tourism experiences play a role in strengthening college students’ prosocial behaviors has become a topic worthy of note. Based on the self-determination theory, the awe prototype theory, and the transformative tourism research, this study constructed a mechanism model of the transformative tourism experiences affecting college students’ prosocial behaviors. Four hundred and fifty-four valid questionnaires were collected through questionnaire surveys, with the structural equation model and bootstrap analysis method used for the empirical test. The results showed that transformative tourism experiences became one of the important ways to strengthen college students’ prosocial behaviors and that the multiple chain intermediary effects of awe and social connectedness between transformative tourism experiences and the college students’ prosocial behaviors were significant. This study provided a new way to cultivate college students’ prosocial behaviors and promote the sustainable development of human society, and it provided a theoretical basis for the social education function played by research tourism in higher education.
A Hybrid Deep Learning-Based Model for Detection of Electricity Losses Using Big Data in Power Systems
Authors: Khattak Adnan , Bukhsh Rasool , Aslam Sheraz , Yafoz Ayman , Alghushairy Omar , Alsini Raed
Published: October 2022

Electricity theft harms smart grids and results in huge revenue losses for electric companies. Deep learning (DL), machine learning (ML), and statistical methods have been used in recent research studies to detect anomalies and illegal patterns in electricity consumption (EC) data collected by smart meters. In this paper, we propose a hybrid DL model for detecting theft activity in EC data. The model combines both a gated recurrent unit (GRU) and a convolutional neural network (CNN). The model distinguishes between legitimate and malicious EC patterns. GRU layers are used to extract temporal patterns, while the CNN is used to retrieve optimal abstract or latent patterns from EC data. Moreover, imbalance of data classes negatively affects the consistency of ML and DL. In this paper, an adaptive synthetic (ADASYN) method and TomekLinks are used to deal with the imbalance of data classes. In addition, the performance of the hybrid model is evaluated using a real-time EC dataset from the State Grid Corporation of China (SGCC). The proposed algorithm is computationally expensive, but on the other hand, it provides higher accuracy than the other algorithms used for comparison. With more and more computational resources available nowadays, researchers are focusing on algorithms that provide better efficiency in the face of widespread data. Various performance metrics such as F1-score, precision, recall, accuracy, and false positive rate are used to investigate the effectiveness of the hybrid DL model. The proposed model outperforms its counterparts with 0.985 Precision–Recall Area Under Curve (PR-AUC) and 0.987 Receiver Operating Characteristic Area Under Curve (ROC-AUC) for the data of EC.
Fluidization Roasting Technology of Jingtieshan Iron Ore in the Absence of Carbon Additives
Authors: Zhu Xinran , Zhang Qiang , Sun Yongsheng , Li Yanjun , Han Yuexin
Published: October 2022

This study presents a fluidization roasting technology for siderite-bearing iron ore without the use of carbon additives. Samples of Jingtieshan iron ore were subjected to fluidization magnetization roasting, and the effects of roasting temperature, time, and N2 flow rate on the magnetic separation performance were explored. An iron concentrate with an iron grade of 57.40% and recovery of 91.17% was acquired at a roasting temperature of 700 °C, roasting time of 10.0 min, an N2 flow rate of 600 mL/min, grinding particle size of −125 μm, and constant magnetic intensity of 99.47 kA/m. The samples were characterized by X-ray diffraction, optical microscopy, scanning electron microscopy–energy dispersive spectroscopy, and vibrating sample magnetometry. The results revealed that hematite and goethite were reduced to magnetite by the CO generated during siderite decomposition; meanwhile, siderite was transformed into magnetite with the consumption of CO during the reduction process. The saturation magnetization of the roasted ore significantly increased owing to the formation of ferrimagnetic magnetite, which was easily recovered in the subsequent magnetic separation.
How Attachment and Community Identification Affect User Stickiness in Social Commerce: A Consumer Engagement Experience Perspective
Authors: Gao Xian , Yee Choy-Leong , Choo Wei-Chong
Published: October 2022

Social commerce (s-commerce) is a rapidly developing form of e-commerce powered by social media influencers (SMIs). It can create valuable opportunities for retailers. In light of this growing trend, this study explores the influence of consumers’ engagement experiences (social support and presence) on community identification and consumers’ attachment to SMIs, along with their impact on consumers’ stickiness in the s-commerce context. We explore this through social presence and social support theory. The survey data from 411 s-commerce users via an online questionnaire were analyzed empirically with the PLS-SEM approach. The results indicated that presence and social support have significantly positive impacts on consumers’ attachment to SMIs and community identification, respectively. This increases users’ stickiness in s-commerce. This study enriches our understanding of user stickiness in s-commerce and can assist online vendors in developing marketing strategies and cultivating sustained relationships with their users.
Biomonitoring-Supported Land Restoration to Reduce Land Degradation in Intensively Mined Areas of India
Authors: Singh Sunidhi , Dhyani Shalini , Janipella Ramesh , Chakraborty Soumya , Pujari Paras Ranjan , Shinde V. M. , Singh Kripal
Published: October 2022

Land degradation due to mining is a major concern leading to massive losses of biodiversity and ecosystem services. The biomonitoring of metals in mine voids can help to keep track of ecosystem health. The present study was carried out in a large mine void that is presently used for fly ash disposal in the Angul district of Odisha, India. For the biomonitoring of the fly ash, composite soil and plant samples (non-edible as well as edible, naturally growing in and around the mine void) were collected seasonally four times between April 2018 and February 2019 from the sampling locations. We monitored the metal uptake (Al, Cd, Cr, Fe, Mn, Ni, Pb, Zn) and bioaccumulation to assess the bioaccumulation factor (BAF) in the collected plant samples. The Fe concentration was reported to be high in Tragia involucrate (24.82 mg/kg) and Digitaria ciliaris (24.818 mg/kg), while the soil at the study site is also rich in Fe and Al. Higher concentrations of metals in fruit trees such as Psidium guajava and other plants such as Ficus religiosa, Ipomoea batatas, Delonix regia, Digitaria ciliaris, and Cynodon dactylon were reported from nearby areas. Understanding the presence of metals should be a guiding factor for reducing land degradation. Our study stresses the need for corporate commitment to ensure regular biomonitoring and biomonitoring-supported land restoration for degraded mining areas. Sustainable land restoration supported by biomonitoring has the potential to help achieve the global goals of the UN Decade on Restoration: Land Degradation Neutrality (UNCCD) and Sustainable Development Goals (SDGs) 12, 13, and 15.
Refined Information Service Using Knowledge-Base and Deep Learning to Extract Advertisement Articles from Korean Online Articles
Authors: Kim Yongjun , Byun Yung-Cheol , Lee Sang-Joon
Published: October 2022

We live amidst a flood of information in the internet and digital revolution era. Due to such indiscriminate information access, there are many problems in accurately recognizing the information desired by the user. Moreover, there are many difficulties with finding accurate information and the articles that individuals want due to indiscriminate advertisements in various online papers such as SNS and internet newspapers. Negative experiences with these advertisements lead to advertisement avoidance; if media users avoid advertisements, the media’s existence is threatened. This system aims to provide high-quality online articles, excluding promotions, by designing a system using a knowledge-based management system (KBMS) and Deep Learning system to solve the problems of advertisement. In other words, this system compares advertisement phrases or general keywords related to a specific company and product promotion with the contents to be searched in the database system of the knowledge-based management service.
Historical Lead Smelting Slag Harmlessness and Valuable Metals Recovery: A Co-Treatment of Lead Slag and Zinc-Bearing Material in Rotary Kiln
Authors: Niu Xuekui , Li Minting , Wang Hongbin , Ma Liping , Wang Shuting , Zhou Tao , Wang Wei
Published: October 2022

The harmless treatment of historical lead smelting slag (LSS) is of significance to ecological and environmental protection, but it is still challenging in terms of the economic feasibility of alone processing due to the low content of valuable metals. Here, we performed an industrialized test with a co-treatment of LSS and zinc oxide ore in a rotary kiln to evaluate the economic feasibility and solidification effect of harmful elements. The results revealed that more than 70% of Zn and Pb were recovered from LSS in the form of dust, while the nonvolatile part of Pb, Zn, and Cd were solidified in gangue as complex silicate phases. The nonvolatile part of As came into being Fe-As intermetallic compound which was encapsulated by gangue particles or was solidified in silicate phases. The entirely enclosed structure of water-quenched slag plays an important role in the stability of slag. The TCLP and SNAL leaching tests demonstrated the high stability of water-quenched slag. A zinc oxide ore addition of 20% was recommended for energy consumption and processing capacity. Our findings highlight that the valuable metals not only can be effectively recovered but also harmful elements are solidified in gangue, providing an economical and feasible technical route for the treatment of historical LSS.
Spatial Pattern of Water Footprints for Crop Production in Northeast China
Authors: Li Zhihui , Wu Haowei , Deng Xiangzheng
Published: October 2022

Water is an important resource for crop production; identifying the spatial pattern of the crop water footprint (WF) is of great significance for the optimization of water resource consumption and management in agricultural production. This study quantified the green, blue and grey water footprints (GWF, BWF and GRWF) and water consumption (GWC, BWC and GRWC) of rice, maize and soybean at the 1 km grid level and city level in Northeast China in 2019 based on the CROPWAT 8.0 model. The results showed that the average total water footprints of rice (TWFr), maize (TWFm) and soybean (TWFs) were 624.31 m3·ton−1, 527.26 m3·ton−1 and 1298.21 m3·ton−1, respectively. The spatial differences in the WF of each crop were obvious in Northeast China, with the highest values of TWFr mainly occurring in Baicheng, Dalian and Qitaihe; the highest TWFm values were mainly found in Baicheng, Yingkou and Hulundao, and the highest TWFs were mainly found in Baicheng, Chifeng and Tongliao. The total water consumption of all three crops (TWCc) in Northeast China was 94 billion m3·yr−1 (42% green, 26% blue and 32% grey), in which the total water consumption of maize production (TWCm) accounted for 60%. The production of rice, maize and soybean in Northeast China mainly depends on green water, grey water and blue water, respectively. Combining the results of the spatial patterns of crop TWF and TWC, the study revealed that the planting pattern of crops in Northeast China was relatively reasonable for sustainable water use. Meanwhile, cities that have the potential to enhance crop production and cities that should improve their water use efficiency and reduce fertilizer application were also identified.
“Why Has the Water Turned Green?” A Problem of Eutrophication in Primary School
Authors: Banos-González Isabel , Esteve-Guirao Patricia , Valverde-Pérez Magdalena , Ruiz-Navarro Ana
Published: October 2022

This paper analyses how 224 students from the fifth and sixth grade recognise the key aspects, i.e., the causes, consequences, and solutions of the eutrophication problem affecting the relevant ecosystem in their region. Two instruments were used: a report sheet in which students work through the causes and consequences associated with the problem; and a personal letter addressed to the competent authority, which proposes solutions. The results show that students from the sixth grade recognised more complete interconnections between the agricultural development of the area and the changes that occurred in the ecosystem. In the identification of the key phases of the eutrophication process, students from both courses presented similar difficulties. Regarding the solutions, some reluctance was observed to limit agricultural activity. This reveals that students’ social perceptions about the importance of agriculture for their region may be a possible influence on their solutions. Finally, five models were established regarding the understanding that students reach of the problem as a whole. More than half of students were included in the same model, comprising those who were able to adequately identify the causes and whole process of eutrophication, as well as those who defended the advantages of the proposed solution. Educational implications are discussed in this paper.
Circular Economy Strategies with Social Implications: Findings from a Case Study
Authors: Mansilla-Obando Katherine , Jeldes-Delgado Fabiola , Guiñez-Cabrera Nataly
Published: October 2022

To progress towards sustainable development, more companies are voluntarily committing to move from a linear economy to a circular economy (CE), mitigating resource consumption and waste generation. Despite the commitment of companies, there is a lack of understanding of how stakeholders view reduction, reuse, and recycling (3R), and the social aspects related to them. Stakeholders were asked how they perceive CE strategies, and more specifically, how they perceive that these strategies, observed in the practice of the 3Rs, transcend into social aspects. The objective of this research is to analyse stakeholders’ perception of CE strategies using the 3Rs framework and stakeholder theory. Using a qualitative methodology, we conducted a case study for Green Glass, a company that uses glass as an input to manufacture its products. By analysing the content of 20 interviews, 23 videos, and 24 news items related to the company, we found that Green Glass stakeholders perceive the contribution of the 3Rs towards CE and that these have social implications, such as supplier evaluation with social impact, responsibility for the product, and decent work.
Optimizing the Mechanical Performance and Microstructure of Alkali-Activated Soda Residue-Slag Composite Cementing Materials by Various Curing Methods
Authors: Zhang Zhaoyun , Xie Chuang , Sang Zhaohu , Li Dejun
Published: October 2022

Aiming to promote further the application of alkali-activated soda residue-ground granulated blast furnace slag (SR-GGBS) cementing materials, this study explored the optimal curing method for enhancing mechanical performance. The optimal curing method was determined based on the development of compressive strengths at different curing periods and microstructural examination by XRD, FTIR, SEM, and TG-DTG. The results show that the strength of cementing materials after room-temperature (RT) dry curing was the poorest, with the slow development of mechanical performance. The 7d and 28d compressive strengths were only 14.62 and 20.99 MPa, respectively. Compared with the values after RT dry curing, the samples’ 7d and 28d compressive strengths after RT water curing, standard curing, and RT sealed curing were enhanced by 16.35%/24.06%, 30.98%/23.77%, and 38.24%/37.97%, respectively. High-temperature (HT) curing can significantly improve the early strength of the prepared cementing materials. Curing at 60 °C for 12 h was the optimal HT curing method. Curing at 60 °C for 12 h enhanced the 3d strength by 100.84% compared with standard curing. This is because HT curing promoted the decomposition and aggregation of GGBS, and more C-A-S-H gel and crystal hydration products, including ettringite and calcium chloroaluminate hydrate, were produced and filled the inner pores, thereby enhancing both the overall compactness and mechanical performance. However, curing at too high temperatures for too long can reduce the material’s overall mechanical performance. After excess HT curing, many shrinkage cracks were produced in the sample. Different thermal expansion coefficients of different materials led to a decline in strength. The present study can provide a theoretical foundation for extensive engineering applications of alkali-activated SR-GGBS composite cementing materials.
Rapid Detection of Tools of Railway Works in the Full Time Domain
Authors: Zheng Zhaohui , Luo Yuncheng , Li Shaoyi , Fan Zhaoyong , Li Xi , Ju Jianping , Lin Mingyu , Wang Zijian
Published: October 2022

Construction tool detection is an important link in the operation and maintenance management of professional facilities in public works. Due to the large number and types of construction equipment and the complex and changeable construction environment, manual checking and inventory are still required. It is very challenging to count the variety of tools in a full-time environment automatically. To solve this problem, this paper aims to develop a full-time domain target detection system based on a deep learning network for difficult, complex railway environment image recognition. First, for the different time domain images, the image enhancement network with brightness channel decision is used to set different processing weights according to the images in different time domains to ensure the robustness of image enhancement in the entire time domain. Then, in view of the collected complex environment and the overlapping placement of the construction tools, a lightweight attention module is added on the basis of YOLOX, which makes the detection more purposeful, and the features cover more parts of the object to be recognized to improve the model. Overall detection performance. At the same time, the CIOU loss function is used to consider the distance fully, overlap rate, and penalty between the two detection frames, which is reflected in the final detection results, which can bring more stable target frame regression and further improve the recognition accuracy of the model. Experiments on the railway engineering dataset show that our RYOLO achieves a mAP of 77.26% for multiple tools and a count frame rate of 32.25FPS. Compared with YOLOX, mAP increased by 3.16%, especially the AP of woven bags with a high overlap rate increased from 0.15 to 0.57. Therefore, the target detection system proposed in this paper has better environmental adaptability and higher detection accuracy in complex railway environments, which is of great significance to the development of railway engineering intelligence.
Good Agricultural Practices Related to Water and Soil as a Means of Adaptation of Mediterranean Olive Growing to Extreme Climate-Water Conditions
Authors: Kourgialas Nektarios , Psarras Georgios , Morianou Giasemi , Pisinaras Vassilios , Koubouris Georgios , Digalaki Nektaria , Malliaraki Stella , Aggelaki Katerina , Motakis Georgios , Arampatzis George
Published: October 2022

Despite the fact that the olive tree is one of the best-adapted species in Mediterranean hydroclimate conditions, climate extremes impose negative effects on olive fruit set and development and subsequently on crop yield. Considering that the frequency of climate extremes is increasing in the last years due to climate change, Good Agricultural Practices (GAPs) have to be applied in order to mitigate their impact on olive trees. In this context, 18 experimental olive groves (irrigated and rainfed) were established, located on the island of Crete (south Greece). A set of 13 GAPs were applied in different combinations, mainly targeting to reduce water losses and erosion, alleviate heat stress and increase water use efficiency. Each experimental orchard was divided into two parts, the control (business-as-usual) and experimental (GAPs implementation). Four indicators were used for the assessment of GAPs performance, namely, Water Productivity (WP), Economic Water Productivity (EWP), Runoff (RF), and Yield (Y). WP and EWP were found to be up to 2.02 and 2.20 times higher, respectively, in the demonstration part of the orchards compared to the control, while Y was found to be up to 119% higher. RF was higher up to 190% in the control compared to the demonstration part of the experimental orchards. The above results clearly demonstrate that the implementation of the proposed GAPs can significantly support the adaptation of olive crops to extreme conditions.
Evaluation and Empirical Research on Eco-Efficiency of Financial Industry Based on Carbon Footprint in China
Authors: Chen Xiaolan , Wang Kaikai , Wan Guanjiang , Liu Yufei , Liu Wenbin , Shen Wanfang , Shi Jianing
Published: October 2022

Since finance is the core of economic development, the green development of the financial industry is an essential driving force not only for achieving the dual “carbon” goal of China but also for economic and social sustainable development. An accurate understanding of the ecological efficiency of the financial industry is of great importance for guiding sustainable economic development. In this paper, we first calculate the carbon footprint of China’s financial industry in 2012 and 2017 based on the life cycle theory and the input–output analysis method. Second, we analyze the primary sources and final flows of the carbon footprint of the financial industry in each province from the perspectives of the industrial chain and final demand. Finally, we estimate the ecological efficiency, emission reduction, and value-added potential of the financial industry by using the radially adjusted slack variable DEA model (SRAM-DEA) under two assumptions, natural disposability and managerial disposability. The results show that (1) the ecological efficiency of the financial industry in most provinces is low, and the regional differences are significant; (2) the overall ecological efficiency of the financial industry in 2017 was better than that in 2012; (3) technological innovation of financial products and the upgrading of capital supervision play an essential role in promoting the improvement of ecological efficiency. Especially, under managerial disposability, the ecological efficiency of the financial industry in each province has a greater potential for emission reduction and added value.
An Analysis of Renewable Energy Technology Integration Investments in Malaysia Using HOMER Pro
Authors: Rahmat Muhammad Aqil Afham , Abd Hamid Ag Sufiyan , Lu Yuanshen , Ishak Muhammad Amir Aziat , Suheel Shaikh Zishan , Fazlizan Ahmad , Ibrahim Adnan
Published: October 2022

Renewable energy systems are technologies that can generate electricity from solar, wind, hydroelectric, biomass, and other renewable energy resources. This research project aims to find the best renewable energy technology combinations for several scenarios in Malaysia. The strategies are analysed by evaluating the investments in the renewable energy systems in each of the decided scenarios in Malaysia, Pekan, Pahang and Mersing, Johor, using HOMER Pro software. The finding shows that the PV–wind hybrid system has a better net present cost (NPC) than the other systems for both scenarios, which are USD −299,762.16 for Scenario 1 and USD −642,247.46 for Scenario 2. The PV–wind hybrid system has 4.86-year and 2.98-year payback periods in Scenarios 1 and 2. A combination of RE technologies yielded fewer emissions than one kind alone. The PV–wind hybrid system provides a quicker payback period, higher money savings, and reduced pollutants. The sensitivity results show that resource availability and capital cost impact NPC and system emissions. This finding reveals that integrated solar and wind technologies can improve the economic performance (e.g., NPC, payback period, present worth) and environmental performance (e.g., carbon dioxide emissions) of a renewable energy system.
Analysis of Wind Turbine Distances Using a Novel Techno-Spatial Approach in Complex Wind Farm Terrains
Authors: Hoxha Bukurije , Shesho Igor K. , Filkoski Risto V.
Published: October 2022

Among the current challenges facing the energy sector is finding environmentally friendly and high-performance forms of energy generation. One such form of energy generation is from the wind. In addition to the fluctuations that cause changes in the generated energy, another factor that significantly affects the overall efficiency of wind farms is the distance between the turbines. In that context, a distance of at least three diameters (3D) onwards is necessary to enable a stable operation. This is more difficult to implement for mountainous terrain due to the terrain configuration’s influence, the turbine units’ positioning, and the mutual influence resulting from their position in the area under consideration. This work investigates the interdependence of the terrain features, the placement of ten turbines in different scenarios, and the impact on the overall efficiency of the wind farm. The place where the wind farm is considered is in Koznica, a mountainous area near Prishtina. An analysis has been carried out for two-diameter (2D), three-diameter (3D), and five-diameter (5D) turbine blade spacing for turbines with a rated power of 3.4 MW. The study considers placement in the following forms: Arc, I, L, M, and V. The results show that for 2D distance layout, the capacity factors for Arc, I, L, M, and V placements have the values: 32.9%, 29.8%, 31.1%, 30.6%, and 37.1%. For the 3D distance, according to these scenarios, the capacity factor values are: 29.9%, 30.8%, 30.4%, 29.3%, and 35.6%. For the longest distance, 5D, the capacity factor values are: 28.9%, 29.9%, 29.4%, 27.6%, and 30.6%. The value of the capacity factor for an optimal layout; is achieved at 39.3%.
The Possibility of Providing Acoustic Comfort in Hotel Rooms as an Element of Sustainable Development
Authors: Nering Krzysztof , Kowalska-Koczwara Alicja , Shymanska Anna , Pawluś Michał
Published: October 2022

The noise problem in hotel rooms is strictly connected to noise generated by heating, ventilating, and air conditioning (HVAC) devices. Hospitality industry companies in many countries have specific requirements and monitor the noise level with technical equipment inside the hotel room. To ensure an adequately low level of noise from the HVAC system, proper calculations and tests are performed before the final application of the system in the hotel room. One of the best methods for assessing the noise level from HVAC devices in hotel rooms is to create a testing room (mock-up) in a certain available space, e.g., in a warehouse, and to perform appropriate standard measurements for the noise level. This method is a popular choice in the hotel industry because the noise level can not only be verified, but also, the installation and operation of the HVAC system inside the room can be checked. The main factors in choosing the space in which the mock-up will be made are availability and appropriate volume. It is not always possible to provide a hall space with a very low background level of noise. This article shows that the selection of a mock-up space is also determined by the noise level in the selected space. The background noise level—thus, the noise level in the mock-up room—must be low enough to be able to reliably measure the noise level from the HVAC equipment to avoid the phenomenon of acoustic masking by other noise sources (background noise) not related to the measurement. Background noise at too high a level will lead to invalidation of the measurement results or overestimation of the actual noise level in the room from the tested HVAC devices. In this study, background noise level measurements made in the testing rooms are described. The results are discussed, and a conclusion is drawn.