bluuebunny/crossref_metadata_2025 huggingface dataset:
On Surface Waves Generated by Extra-Tropical Cyclones—Part II: Simulations
Authors: Cheshm Siyahi Vahid , Kudryavtsev Vladimir , Yurovskaya Maria , Collard Fabrice , Chapron Bertrand
Published: May 2023

In the previous companion study, satellite data were used to describe peculiar characteristics of ocean surface wave fields, generated by two extra-tropical cyclones (ETCs) rapidly propagating in the North Atlantic. Based on a 2D parametric wave model, further details are now provided to analyse and interpret the spatio-temporal evolution of very intense ETC-generated waves. Significant wave height and wavelength values are shown to reach extreme values, 18 m and 500 m, respectively. Resulting energetic swell systems waves then radiate in the whole eastern part of the North Atlantic, and more particularly in the Norwegian sea region. Moving to higher latitudes, wind forcing characteristics of ETCs evolve, with the shape of the wind field changing from quasi-cyclonic to “air jets/Icelandic lows”. In this paper, the resulting swell generation and propagation, after the deformation of an individual ETC, were studied, as well. Confirmed with comparisons with multi-satellite observations, the application of the parametric-2D wave-ray model was demonstrated to provide robust and highly detailed information on wave generation under very complex wind regime changes.
Improving LandTrendr Forest Disturbance Mapping in China Using Multi-Season Observations and Multispectral Indices
Authors: Qiu Dean , Liang Yunjian , Shang Rong , Chen Jing M.
Published: May 2023

Forest disturbance detection is of great significance for understanding forest dynamics. The Landsat-based detection of the Trends in Disturbance and Recovery (LandTrendr) algorithm is widely used for forest disturbance mapping. However, there are still two limitations in LandTrendr: first, it only used for summer-composited observations, which may delay the detection of forest disturbances that occurred in autumn and winter by one year, and second, it detected all disturbance types simultaneously using a single spectral index, which may reduce the mapping accuracy for certain forest disturbance types. Here, we modified LandTrendr (mLandTrendr) for forest disturbance mapping in China by using multi-season observations and multispectral indices. Validations using the randomly selected 1957 reference forest disturbance samples across China showed that the overall accuracy (F1 score) of forest disturbance detection in China was improved by 21% with these two modifications. The mLandTrendr can quickly and accurately detect forest disturbance and can be extended to national and global forest disturbance mapping for various forest types.
Impact of Urbanization on Regional Rainfall-Runoff Processes: Case Study in Jinan City, China
Authors: Zhao Yanjun , Xia Jun , Xu Zongxue , Qiao Yunfeng , Shen Jianming , Ye Chenlei
Published: May 2023

Rapid urbanization has altered the regional hydrological processes, posing a great challenge to the sustainable development of cities. The TVGM-USWM model, a new urban hydrological model considering the nonlinear rainfall-runoff relationship and the flow routing in an urban drainage system, was developed in this study. We employed this model in the Huangtaiqiao drainage basin of Jinan City, China, and examined the impact of land cover changes due to urbanization on rainfall-runoff processes. Two urbanization scenarios were set up in the TVGM-USWM model during the design rainfall events with different return periods. Results showed that (1) the TVGM-USWM model demonstrated good applicability in the study area, and the RNS values of the flood events are all greater than 0.75 in both calibration and validation periods; (2) the proportion of impervious areas increased from 44.65% in 1990 to 71.00% in 2020, and urbanization played a leading role in the process of land cover change and manifested itself as a circular extensional expansion; and (3) urbanization showed a significant amplifying effect on the design flood processes, particularly for relatively big floods with small frequency, and the impact of urbanization on the time-to-peak of the design flood gradually decreased as the frequency of the design rainfall decreased. The results of this study can provide technical support for flood mitigation and the construction of a sponge city in Jinan City.
Semi-Supervised Deep Kernel Active Learning for Material Removal Rate Prediction in Chemical Mechanical Planarization
Authors: Lv Chunpu , Huang Jingwei , Zhang Ming , Wang Huangang , Zhang Tao
Published: May 2023

The material removal rate (MRR) is an important variable but difficult to measure in the chemical–mechanical planarization (CMP) process. Most data-based virtual metrology (VM) methods ignore the large number of unlabeled samples, resulting in a waste of information. In this paper, the semi-supervised deep kernel active learning (SSDKAL) model is proposed. Clustering-based phase partition and phase-matching algorithms are used for the initial feature extraction, and a deep network is used to replace the kernel of Gaussian process regression so as to extract hidden deep features. Semi-supervised regression and active learning sample selection strategies are applied to make full use of information on the unlabeled samples. The experimental results of the CMP process dataset validate the effectiveness of the proposed method. Compared with supervised regression and co-training-based semi-supervised regression algorithms, the proposed model has a lower mean square error with different labeled sample proportions. Compared with other frameworks proposed in the literature, such as physics-based VM models, Gaussian-process-based regression models, and stacking models, the proposed method achieves better prediction results without using all the labeled samples.
Dual-Energy Processing of X-ray Images of Beryl in Muscovite Obtained Using Pulsed X-ray Sources
Authors: Komarskiy Alexander , Korzhenevskiy Sergey , Ponomarev Andrey , Chepusov Alexander
Published: May 2023

This paper presents the development of a method for dual-energy processing of X-ray images using pulsed X-ray sources for the contrast detection of beryl in muscovite mica in 2D X-ray and CT images. These substances have similar chemical properties and are difficult to differentiate when one is against the background of the other using methods based on X-ray absorption. In the experiments, we used three pulsed X-ray sources with different maximum voltages. We performed modeling of the emission spectra and selection of the necessary energy bands due to X-ray absorbing filters: a positive effect was shown for dual-energy image processing when the function of converting X-ray radiation into a signal using the VIVIX-V 2323D detector was taken into account. As a result, a pulsed X-ray source with the pulse voltage of 330 kV was chosen for the contrast detection of beryl, with the content of 5–7% against the background of muscovite and the thickness up to 70 mm. Using this source and the developed mathematical algorithms, it is possible to obtain a band of low-energy radiation at the level of 70–80 keV, as well as high-energy radiation in the range of 180 keV. Methods based on the X-ray absorption can become both additional and independent methods for studying and monitoring membranes; these objects range from tens of nanometers to several micrometers in size.
A Fast Loss Model for Cascode GaN-FETs and Real-Time Degradation-Sensitive Control of Solid-State Transformers
Authors: Haque Moinul Shahidul , Moniruzzaman Md , Choi Seungdeog , Kwak Sangshin , Okilly Ahmed H. , Baek Jeihoon
Published: May 2023

This paper proposes a novel, degradation-sensitive, adaptive SST controller for cascode GaN-FETs. Unlike in traditional transformers, a semiconductor switch’s degradation and failure can compromise its robustness and integrity. It is vital to continuously monitor a switch’s health condition to adapt it to mission-critical applications. The current state-of-the-art degradation monitoring methods for power electronics systems are computationally intensive, have limited capacity to accurately identify the severity of degradation, and can be challenging to implement in real time. These methods primarily focus on conducting accelerated life testing (ALT) of individual switches and are not typically implemented for online monitoring. The proposed controller uses accelerated life testing (ALT)-based switch degradation mapping for degradation severity assessment. This controller intelligently derates the SST to (1) ensure robust operation over the SST’s lifetime and (2) achieve the optimal degradation-sensitive function. Additionally, a fast behavioral switch loss model for cascode GaN-FETs is used. This proposed fast model estimates the loss accurately without proprietary switch parasitic information. Finally, the proposed method is experimentally validated using a 5 kW cascode GaN-FET-based SST platform.
A Collaborative Merging Method for Connected and Automated Vehicle Platoons in a Freeway Merging Area with Considerations for Safety and Efficiency
Authors: Gao Huan , Cen Yanqing , Liu Bo , Song Xianghui , Liu Hongben , Liu Jia
Published: May 2023

To solve the problems of congestion and accident risk when multiple vehicles merge into the merging area of a freeway, a platoon split collaborative merging (PSCM) method was proposed for an on-ramp connected and automated vehicle (CAV) platoon under a mixed traffic environment composed of human-driving vehicles (HDV) and CAVs. The PSCM method mainly includes two parts: merging vehicle motion control and merging effect evaluation. Firstly, the collision avoidance constraints of merging vehicles were analyzed, and on this basis, a following–merging motion rule was proposed. Then, considering the feasibility of and constraints on the stability of traffic flow during merging, a performance measurement function with safety and merging efficiency as optimization objectives was established to screen for the optimal splitting strategy. Simulation experiments under traffic demand of 1500 pcu/h/lane and CAV ratios of 30%, 50%, and 70% were conducted respectively. It was shown that under the 50% CAV ratio, the average travel time of the on-ramp CAV platoon was reduced by 50.7% under the optimal platoon split strategy compared with the no-split control strategy. In addition, the average travel time of main road vehicles was reduced by 27.9%. Thus, the proposed PSCM method is suitable for the merging control of on-ramp CAV platoons under the condition of heavy main road traffic demand.
extendGAN+: Transferable Data Augmentation Framework Using WGAN-GP for Data-Driven Indoor Localisation Model
Authors: Yean Seanglidet , Goh Wayne , Lee Bu-Sung , Oh Hong Lye
Published: May 2023

For indoor localisation, a challenge in data-driven localisation is to ensure sufficient data to train the prediction model to produce a good accuracy. However, for WiFi-based data collection, human effort is still required to capture a large amount of data as the representation Received Signal Strength (RSS) could easily be affected by obstacles and other factors. In this paper, we propose an extendGAN+ pipeline that leverages up-sampling with the Dirichlet distribution to improve location prediction accuracy with small sample sizes, applies transferred WGAN-GP for synthetic data generation, and ensures data quality with a filtering module. The results highlight the effectiveness of the proposed data augmentation method not only by localisation performance but also showcase the variety of RSS patterns it could produce. Benchmarking against the baseline methods such as fingerprint, random forest, and its base dataset with localisation models, extendGAN+ shows improvements of up to 23.47%, 25.35%, and 18.88% respectively. Furthermore, compared to existing GAN+ methods, it reduces training time by a factor of four due to transfer learning and improves performance by 10.13%.
Utilizing High Resolution Satellite Imagery for Automated Road Infrastructure Safety Assessments
Authors: Brkić Ivan , Ševrović Marko , Medak Damir , Miler Mario
Published: May 2023

The European Commission (EC) has published a European Union (EU) Road Safety Framework for the period 2021 to 2030 to reduce road fatalities. In addition, the EC with the EU Directive 2019/1936 requires a much more detailed recording of road attributes. Therefore, automatic detection of school routes, four classes of crosswalks, and divided carriageways were performed in this paper. The study integrated satellite imagery as a data source and the Yolo object detector. The satellite Pleiades Neo 3 with a spatial resolution of 0.3 m was used as the source for the satellite images. In addition, the study was divided into three phases: vector processing, satellite imagery processing, and training and evaluation of the You Only Look Once (Yolo) object detector. The training process was performed on 1951 images with 2515 samples, while the evaluation was performed on 651 images with 862 samples. For school zones and divided carriageways, this study achieved accuracies of 0.988 and 0.950, respectively. For crosswalks, this study also achieved similar or better results than similar work, with accuracies ranging from 0.957 to 0.988. The study also provided the standard performance measure for object recognition, mean average precision (mAP), as well as the values for the confusion matrix, precision, recall, and f1 score for each class as benchmark values for future studies.
A Hybrid Trust Model against Insider Packet Drop Attacks in Wireless Sensor Networks
Authors: Cho Youngho , Qu Gang
Published: May 2023

Quick and accurate detection of inside packet drop attackers is of critical importance to reduce the damage they can have on the network. Trust mechanisms have been widely used in wireless sensor networks for this purpose. However, existing trust models are not effective because they cannot distinguish between packet drops caused by an attack and those caused by normal network failure. We observe that insider packet drop attacks will cause more consecutive packet drops than a network abnormality. Therefore, we propose the use of consecutive packet drops to speed up the detection of inside packet drop attackers. In this article, we describe a new trust model based on consecutive drops and develop a hybrid trust mechanism to seamlessly integrate the new trust model with existing trust models. We perform extensive OPNET (Optimized Network Engineering Tool) simulations using a geographic greedy routing protocol to validate the effectiveness of our new model. The simulation results show that our hybrid trust model outperforms existing trust models for all types of inside packet drop attacks, not only in terms of detection speed and accuracy as it is designed for, but also in terms of other important network performance metrics, such as packet delivery rate, routing reliability, and energy efficiency.
Explaining and Visualizing Embeddings of One-Dimensional Convolutional Models in Human Activity Recognition Tasks
Authors: Aquino Gustavo , Costa Marly Guimarães Fernandes , Filho Cícero Ferreira Fernandes Costa
Published: May 2023

Human Activity Recognition (HAR) is a complex problem in deep learning, and One-Dimensional Convolutional Neural Networks (1D CNNs) have emerged as a popular approach for addressing it. These networks efficiently learn features from data that can be utilized to classify human activities with high performance. However, understanding and explaining the features learned by these networks remains a challenge. This paper presents a novel eXplainable Artificial Intelligence (XAI) method for generating visual explanations of features learned by one-dimensional CNNs in its training process, utilizing t-Distributed Stochastic Neighbor Embedding (t-SNE). By applying this method, we provide insights into the decision-making process through visualizing the information obtained from the model’s deepest layer before classification. Our results demonstrate that the learned features from one dataset can be applied to differentiate human activities in other datasets. Our trained networks achieved high performance on two public databases, with 0.98 accuracy on the SHO dataset and 0.93 accuracy on the HAPT dataset. The visualization method proposed in this work offers a powerful means to detect bias issues or explain incorrect predictions. This work introduces a new type of XAI application, enhancing the reliability and practicality of CNN models in real-world scenarios.
A Comprehensive Survey on Resource Allocation Strategies in Fog/Cloud Environments
Authors: Vergara Jaime , Botero Juan , Fletscher Luis
Published: May 2023

The growing number of connected objects has allowed the development of new applications in different areas. In addition, the technologies that support these applications, such as cloud and fog computing, face challenges in providing the necessary resources to process information for different applications due to the highly dynamic nature of these networks and the many heterogeneous devices involved. This article reviews the existing literature on one of these challenges: resource allocation in the fog–cloud continuum, including approaches that consider different strategies and network characteristics. We also discuss the factors influencing resource allocation decisions, such as energy consumption, latency, monetary cost, or network usage. Finally, we identify the open research challenges and highlight potential future directions. This survey article aims to serve as a valuable reference for researchers and practitioners interested in the field of edge computing and resource allocation.
Detection and Classification of Cotton Foreign Fibers Based on Polarization Imaging and Improved YOLOv5
Authors: Wang Rui , Zhang Zhi-Feng , Yang Ben , Xi Hai-Qi , Zhai Yu-Sheng , Zhang Rui-Liang , Geng Li-Jie , Chen Zhi-Yong , Yang Kun
Published: May 2023

It is important to detect and classify foreign fibers in cotton, especially white and transparent foreign fibers, to produce subsequent yarn and textile quality. There are some problems in the actual cotton foreign fiber removing process, such as some foreign fibers missing inspection, low recognition accuracy of small foreign fibers, and low detection speed. A polarization imaging device of cotton foreign fiber was constructed based on the difference in optical properties and polarization characteristics between cotton fibers. An object detection and classification algorithm based on an improved YOLOv5 was proposed to achieve small foreign fiber recognition and classification. The methods were as follows: (1) The lightweight network Shufflenetv2 with the Hard-Swish activation function was used as the backbone feature extraction network to improve the detection speed and reduce the model volume. (2) The PANet network connection of YOLOv5 was modified to obtain a fine-grained feature map to improve the detection accuracy for small targets. (3) A CA attention module was added to the YOLOv5 network to increase the weight of the useful features while suppressing the weight of invalid features to improve the detection accuracy of foreign fiber targets. Moreover, we conducted ablation experiments on the improved strategy. The model volume, mAP@0.5, mAP@0.5:0.95, and FPS of the improved YOLOv5 were up to 0.75 MB, 96.9%, 59.9%, and 385 f/s, respectively, compared to YOLOv5, and the improved YOLOv5 increased by 1.03%, 7.13%, and 126.47%, respectively, which proves that the method can be applied to the vision system of an actual production line for cotton foreign fiber detection.
Low-Cost Water Quality Sensors for IoT: A Systematic Review
Authors: de Camargo Edson Tavares , Spanhol Fabio Alexandre , Slongo Juliano Scholz , da Silva Marcos Vinicius Rocha , Pazinato Jaqueline , de Lima Lobo Adriana Vechai , Coutinho Fábio Rizental , Pfrimer Felipe Walter Dafico , Lindino Cleber Antonio , Oyamada Marcio Seiji , Martins Leila Droprinchinski
Published: May 2023

In many countries, water quality monitoring is limited due to the high cost of logistics and professional equipment such as multiparametric probes. However, low-cost sensors integrated with the Internet of Things can enable real-time environmental monitoring networks, providing valuable water quality information to the public. To facilitate the widespread adoption of these sensors, it is crucial to identify which sensors can accurately measure key water quality parameters, their manufacturers, and their reliability in different environments. Although there is an increasing body of work utilizing low-cost water quality sensors, many questions remain unanswered. To address this issue, a systematic literature review was conducted to determine which low-cost sensors are being used for remote water quality monitoring. The results show that there are three primary vendors for the sensors used in the selected papers. Most sensors range in price from US$6.9 to US$169.00 but can cost up to US$500.00. While many papers suggest that low-cost sensors are suitable for water quality monitoring, few compare low-cost sensors to reference devices. Therefore, further research is necessary to determine the reliability and accuracy of low-cost sensors compared to professional devices.
ssFPN: Scale Sequence (S2) Feature-Based Feature Pyramid Network for Object Detection
Authors: Park Hye-Jin , Kang Ji-Woo , Kim Byung-Gyu
Published: May 2023

Object detection is a fundamental task in computer vision. Over the past several years, convolutional neural network (CNN)-based object detection models have significantly improved detection accuracyin terms of average precision (AP). Furthermore, feature pyramid networks (FPNs) are essential modules for object detection models to consider various object scales. However, the AP for small objects is lower than the AP for medium and large objects. It is difficult to recognize small objects because they do not have sufficient information, and information is lost in deeper CNN layers. This paper proposes a new FPN model named ssFPN (scale sequence (S2) feature-based feature pyramid network) to detect multi-scale objects, especially small objects. We propose a new scale sequence (S2) feature that is extracted by 3D convolution on the level of the FPN. It is defined and extracted from the FPN to strengthen the information on small objects based on scale-space theory. Motivated by this theory, the FPN is regarded as a scale space and extracts a scale sequence (S2) feature by three-dimensional convolution on the level axis of the FPN. The defined feature is basically scale-invariant and is built on a high-resolution pyramid feature map for small objects. Additionally, the deigned S2 feature can be extended to most object detection models based on FPNs. We also designed a feature-level super-resolution approach to show the efficiency of the scale sequence (S2) feature. We verified that the scale sequence (S2) feature could improve the classification accuracy for low-resolution images by training a feature-level super-resolution model. To demonstrate the effect of the scale sequence (S2) feature, experiments on the scale sequence (S2) feature built-in object detection approach including both one-stage and two-stage models were conducted on the MS COCO dataset. For the two-stage object detection models Faster R-CNN and Mask R-CNN with the S2 feature, AP improvements of up to 1.6% and 1.4%, respectively, were achieved. Additionally, the APS of each model was improved by 1.2% and 1.1%, respectively. Furthermore, the one-stage object detection models in the YOLO series were improved. For YOLOv4-P5, YOLOv4-P6, YOLOR-P6, YOLOR-W6, and YOLOR-D6 with the S2 feature, 0.9%, 0.5%, 0.5%, 0.1%, and 0.1% AP improvements were observed. For small object detection, the APS increased by 1.1%, 1.1%, 0.9%, 0.4%, and 0.1%, respectively. Experiments using the feature-level super-resolution approach with the proposed scale sequence (S2) feature were conducted on the CIFAR-100 dataset. By training the feature-level super-resolution model, we verified that ResNet-101 with the S2 feature trained on LR images achieved a 55.2% classification accuracy, which was 1.6% higher than for ResNet-101 trained on HR images.
Harmonic-Reduced Bias Circuit for Ultrasound Transducers
Authors: Choi Hojong
Published: May 2023

The gain of class-C power amplifiers is generally lower than that of class-A power amplifiers. Thus, higher-amplitude input voltage signals for class-C power amplifiers are required. However, high-amplitude input signals generate unwanted harmonic signals. Therefore, a novel bias circuit was proposed to suppress the harmonic signals generated by class-C power amplifiers, which improves the output voltage amplitudes. To verify the proposed idea, the input harmonic signals when using a harmonic-reduced bias circuit (−61.31 dB, −89.092 dB, −90.53 dB, and −90.32 dB) were measured and were found to be much lower than those when using the voltage divider bias circuit (−57.19 dB, −73.49 dB, −70.97 dB, and −73.61 dB) at 25 MHz, 50 MHz, 75 MHz, and 100 MHz, respectively. To further validate the proposed idea, the pulse-echo measurements were compared using the bias circuits. The peak-to-peak echo amplitude and bandwidth of the piezoelectric transducer, measured when using a harmonic-reduced bias circuit (27.07 mV and 37.19%), were higher than those achieved with a voltage divider circuit (18.55 mV and 22.71%). Therefore, the proposed scheme may be useful for ultrasound instruments with low sensitivity.
Deep Learning Framework for Complex Disease Risk Prediction Using Genomic Variations
Authors: Alzoubi Hadeel , Alzubi Raid , Ramzan Naeem
Published: May 2023

Genome-wide association studies have proven their ability to improve human health outcomes by identifying genotypes associated with phenotypes. Various works have attempted to predict the risk of diseases for individuals based on genotype data. This prediction can either be considered as an analysis model that can lead to a better understanding of gene functions that underlie human disease or as a black box in order to be used in decision support systems and in early disease detection. Deep learning techniques have gained more popularity recently. In this work, we propose a deep-learning framework for disease risk prediction. The proposed framework employs a multilayer perceptron (MLP) in order to predict individuals’ disease status. The proposed framework was applied to the Wellcome Trust Case-Control Consortium (WTCCC), the UK National Blood Service (NBS) Control Group, and the 1958 British Birth Cohort (58C) datasets. The performance comparison of the proposed framework showed that the proposed approach outperformed the other methods in predicting disease risk, achieving an area under the curve (AUC) up to 0.94.
The Nexus of Holistic Wellbeing and School Education: A Literature-Informed Theoretical Framework
Authors: Norozi Sultana Ali
Published: May 2023

The concept of wellbeing in education is complex and multifaceted, with inconsistent definitions, philosophical conceptualizations, and research approaches. This paper proposes a theoretical framework for understanding and promoting comprehensive wellbeing in school education, drawing insights from global in general and the Norwegian context in particular. The paper begins by reviewing the literature on wellbeing and wellbeing in educational policies to identify common threads in contemporary understandings and approaches to wellbeing, highlighting important issues in its conceptualization. The analysis shows the need for a holistic understanding of wellbeing, encompassing its multiple dimensions, which should be introduced and advocated in schools. The paper contributes to a more comprehensive and holistic concept of wellbeing that should be an integral part of school education. The discourse highlights the emergence of an alternative, commonly agreed theoretical framework for holistic wellbeing, drawing together different dimensions of wellbeing and interconnectedness and focusing on students’ strengths. The paper concludes by discussing the implications of the framework for future research and practice. The framework offers a comprehensive and integrative approach to understanding and promoting wellbeing in school education, which can guide the development of interventions and policies that address the multiple factors influencing students’ wellbeing. The framework also highlights the need for interdisciplinary collaboration and a systems approach to wellbeing in education, which can promote a holistic and sustainable approach to education that benefits both students and society.
Student Participation: Issues for the Governance of Higher Education
Authors: Palma Albertina , Mourato Joaquim , Vinagre João , Almeida Fernando , Pessoa Ana Maria
Published: May 2023

The paper reports findings of a research project aimed at developing insight into student participation in the governance of higher education institutions. The project was carried out in two institutions in Portugal, analyzing numbers and forms of participation, identifying facilitators and constraints to participation, and analyzing the students’ perceptions of their own participation. The study was carried out in the context of the European purpose of creating a cohesive European Higher Education Area (EHEA), and in light of theoretical perspectives of the sociology of public action. The concept of participation put forward by the Council of Europe in 2003 was mobilized in the sense that true participation involves making a difference in decision-making. The research objectives required both quantitative and qualitative data collection; therefore, a mixed-methods approach was adopted, including document analysis, interviews, and a questionnaire. The cross-analysis of the data collected enabled the researchers to characterize the participation of students in formal governing bodies and in other institutional contexts, identify facilitators and constraints to participation resulting either from the legal framework, the institutional culture, or personal contexts, and simultaneously capture individual perceptions of participation on the part of the students. The results enabled the authors to make a set of recommendations for political action both at national and institutional levels.
Can Food Safety Practices and Knowledge of Raw Fish Promote Perception of Infection Risk and Safe Consumption Behavior Intentions Related to the Zoonotic Parasite Anisakis?
Authors: Ganucci Cancellieri Uberta , Amicone Giulia , Cicero Lavinia , Milani Alessandro , Mosca Oriana , Palomba Marialetizia , Mattiucci Simonetta , Bonaiuto Marino
Published: May 2023

The study of the zoonotic parasites of the genus Anisakis and human anisakiasis is an increasingly hot topic in evolutionary biology and epidemiological studies carried out on natural and accidental (human) hosts, given the risk of this parasite to human health. However, the assessment of social-psychological factors relevant to Anisakis’ risky consumption of human behavior is still an understudied topic. Given the centrality of the topic, highlighted by its presence in Goals 2 (subgoal 2.1, achieve food security and improve safe nutrition) and 3 (health and well-being) of the 2030 Agenda, it appears necessary to deepen our social-psychological knowledge regarding this specific topic. There is plenty of psychological research focused on antecedents of fish and seafood consumption; however, parasite risk is not often specifically examined. This research is aimed at increasing the safety of consumers’ seafood products by examining their psychological aspects, such as knowledge, perception, awareness, and concern about Anisakis. Past and future behavior intentions were also investigated. Analyses were carried out on a sample of 251 subjects, and a path analysis model was used to explain possible relations assumed among the variables. The results of the study showed that habits related to raw fish consumption and self-perceived health were, respectively, positively, and negatively correlated with a higher perceived risk of contracting anisakiasis. This perceived risk in turn correlates positively with a greater willingness to pay for Anisakis-free fish. Similarly, prior knowledge of the disease was found to be associated with prior avoidance of fish consumption, which in turn was found to be positively correlated with a greater willingness to pay for Anisakis-free fish.
Drivers, Challenges and Outcomes of Environmental Management System Implementation in Public Sector Organizations: A Systematic Review of Empirical Evidence
Authors: Waxin Marie-France , Bartholomew Aaron , Zhao Fang , Siddiqi Ayesha
Published: May 2023

Our research objectives were to conduct a systematic literature review of the empirical articles on the drivers, challenges and outcomes of environmental management system (EMS) implementation in public sector organizations (PSOs) in the Scopus database, published in English. Following the PRISMA guidelines, we identified, reviewed and analyzed 11 selected articles. We used content analysis to identify important themes and concepts. First, we mapped the selected literature. We then identified four main categories of drivers (environmentally strategic, social/stakeholders, regulatory and organizational efficiency), five categories of challenges (budgetary, human resource, technical, managerial and regulatory) and five categories of outcomes (improvement in environmental management practices, environmental performance, awareness of environmental issues, image and organizational efficiency). Finally, we identified important avenues for future research that should be explored further. This article synthesizes the knowledge on EMS implementation in PSOs and offers new insights. It will help EMS scholars and practitioners develop a broader and more critical understanding of the issues specific to EMS implementation in PSOs.
A Bibliometric Visualized Analysis and Classification of Vehicle Routing Problem Research
Authors: Ni Qiuping , Tang Yuanxiang
Published: May 2023

The vehicle routing problem (VRP), as a classic combinatorial optimization problem, has always been a hot research topic in operations research. In order to gain a deeper understanding of the VRP problem, this work uses the knowledge graph to comprehensively analyze and summarize the literature related to VRP from 1959 to 2022 in the Web of Science (WoS) database. Firstly, according to the basic statistical information of the literature, the annual publications, the authors, their institutions and countries, the keyword co-occurrence, and the literature co-citation network are analyzed to comprehensively summarize and generalize the research on VRP and predict its future development trend. The results show that, in the past 60 years, there have been abundant changes in the research on VRP. The United States and China have made the most important contributions in the field of VRP. According to the WoS literature retrieval results and classification methods, the VRP models and their solutions are comprehensively classified, and the model solving algorithms are divided into exact algorithms, heuristic algorithms, metaheuristic algorithms, hyper-heuristic algorithms, machine learning, etc. The results show that the development of information computing technology plays an important role in research on the VRP problem, and dynamic VRP, hyper-heuristic algorithms, deep reinforcement learning, etc. are the future development directions of the VRP model and its optimization. The results of this research can provide help and guidance for beginners and scholars outside the industry to comprehensively understand the development and research hotspots of VRP.
Integrating Perspectives from Education for Sustainable Development to Foster Plant Awareness among Trainee Science Teachers: A Mixed Methods Study
Authors: Fiel’ardh Khalifatulloh , Fardhani Indra , Fujii Hiroki
Published: May 2023

This mixed-method study aimed to investigate the efficacy of an intervention unit that integrates perspectives from Education for Sustainable Development (ESD) to foster plant awareness, within the context of botanical lessons for trainee science teachers. Third-year undergraduate students (n = 91) studying to become lower secondary school (grade 7–9) science teachers from a public university in East Java, Indonesia, participated in this study. Data were collected through a self-reported questionnaire, reflective journal entries, and focus group interviews. The findings revealed a statistically significant increase in the participants’ attention and attitude towards plants, relative interest in plants, and self-efficacy in teaching plant-related topics. The triangulation of the analysis results from the reflective journals and focus group interviews demonstrated that through transformative learning, the participants’ experiences, perceptions, and learning evolved throughout the intervention unit, leading to their more comprehensive understanding of plant-related issues and their connection to broader sustainability concerns. These findings imply that the integration of ESD perspectives into botanical education positively affects plant awareness. Future research could further investigate the long-term impact of integrating ESD perspectives on teacher training programs.
Audiovisual Fiction and World Heritage Sites in Medium-Sized Spanish Cities: The Alhambra of Granada and the Royal Alcazar of Seville (1905–2023)
Authors: Puche-Ruiz Maria C. , Gámir Agustín
Published: May 2023

This article analyses the production of audiovisual fiction as an instrument for disseminating the image of the Alhambra (Granada) and the Royal Alcazar (Seville) for tourism purposes. The methodology used was twofold: qualitative, carrying out an exhaustive bibliographic review, but also quantitative, using primary sources, through the identification of the films shot, visualization and collection of metadata of the scenes filmed. The information, previously structured, also allows a double analysis: temporal, considering five stages, and spatial, identifying and mapping more than twenty significant places within both sites. In its temporal dimension, the main conclusion refers to the different significance of cinema in the dissemination of the image of both sites: very prominent during Franco’s regime, with a profusion of foreign productions (Alhambra), while from the democratic period onwards it is the Royal Alcazar which offers a greater number of filming. In its spatial dimension, it has been verified that the 19th century formats in charge of disseminating images established a canon of places according to a certain degree of exoticism, which has been perpetuated by cinematography without significant changes.
Consumer Perceptions of Online Shopping and Willingness to Use Pick-Up Points: A Case Study of Morocco
Authors: El Moussaoui Alaa Eddine , Benbba Brahim , Jaegler Anicia , El Moussaoui Taoufiq , El Andaloussi Zineb , Chakir Loqman
Published: May 2023

The use of pick-up points by consumers is one of the most developed areas of research in the literature on last-mile logistics over the last decade. In this regard, several researchers have attempted to expose the factors that influence consumers’ online shopping behavior and their willingness to use pick-up points. However, no study has addressed this issue in African countries. The aim of this research is to examine the online shopping behavior of Moroccan consumers, focusing on their opinions about using pick-up points to receive/return goods purchased online. This research adopted a qualitative approach through focus group sessions with Moroccan consumers. The results indicate that temporal and spatial flexibility, competitive prices, and the quality of the retailer’s website are the main factors encouraging consumers to buy online. On the other hand, product risk, delivery risk, privacy, and security were identified as the factors that prevent consumers from buying online. In contrast, the location, density, security, and opening hours of pick-up points were considered to be the factors that influence the Moroccan consumer’s choice to use this delivery option when buying online. These findings are important both for parcel delivery companies that want to establish pick-up point networks in Casablanca and for public authorities and local communities that want to formulate policies and implement strategies leading to more sustainable urban environments.
The Digital Twin Modeling Method of the National Sliding Center for Intelligent Security
Authors: Liu Zhansheng , Sun Xiaotao , Sun Zhe , Liu Liang , Meng Xiaolin
Published: May 2023

There are some problems in the security management of large stadiums, such as complex situations and a lack of coordination among systems. An intelligent security system can effectively improve the efficiency of security management. The digital twin concept is applied to intelligent security systems in large stadiums, and an intelligent security modeling method for large stadiums based on digital twin is proposed. The modeling method of the physical model is presented for the security equipment and building entities. The virtual model is based on geometric, physical, behavioral, and rule models. Considering the particularity of building security, the environmental model is added to describe the environmental information. The application mode of the digital twin model is proposed. In the security management process, multi-source data and virtual models are integrated to analyze and control the security management process of buildings, forming a closed loop of “perception-analysis-control” in security management. Taking the National Sliding Center as an example, this paper verifies the digital twin model and its operation mode of intelligent building security through several possible situations in the operation process of the stadium. The analysis of security data and evacuation path guidance in emergencies are simulated. The digital twin model for intelligent security integrated the building security data and simulation models to assist in identifying the types of dangers and the treatment of emergencies. Furthermore, the control of building equipment was integrated into the security system. The digital twin model for intelligent security improved the integration and intelligence of the security system.
Performance, Emission, and Spectroscopic Analysis of Diesel Engine Fuelled with Ternary Biofuel Blends
Authors: Hasnain S M Mozammil , Chatterjee Rajeshwari , Ranjan Prabhat , Kumar Gaurav , Sharma Shubham , Kumar Abhinav , Salah Bashir , Ullah Syed Sajid
Published: May 2023

The demand for sustainable alternative-fuels in the transportation and agriculture domains is essential due to the quick depletion of petroleum supplies and the growing environmental challenges. The ternary-blends (diesel, biodiesel, and Methyl oleate) have the ability to report the existing challenges in this area because they offer significant promise for reducing exhaust emissions and improving engine performance. In the current work, soy methyl ester is blended with methyl oleate and diesel. The emissions and performance of blended biodiesel was conducted in common rail direct injection engine (CRDI). The characterization and physical properties were also evaluated by utilizing various methods like Fourier-Transform Infrared Spectroscopy (FTIR), UV-vis Spectroscopy (UV-vis), and Nuclear Magnetic Resonance. FTIR spectra showed the existence of the strong C=O, indicating the presence of FAME at 1745 cm−1. Again, UV-vis has reported the appearance of conjugated dienes in the oxidized biodiesel. The results indicated all blended samples retained the properties of diesel. The addition of methyl oleate improved brake specific fuel consumption of blended biodiesel almost near to diesel. D50::S80:M20 produced a mean reduction in hydrocarbon 42.64% compared to diesel. The average carbon monoxide emission reduction for D50::S80:M20 was 49.36% as against diesel.
Investment Decision of Blockchain Technology in Fresh Food Supply Chains Considering Misreporting Behavior
Authors: Feng Zhangwei , Jin Peng , Li Guiping
Published: May 2023

Effectively managing fresh food supply chains (FFSCs) has long been a big challenge owing to the complexity in practice. Adoption of innovative technologies, blockchain traceability (BT), and cold-chain preservation (CP) technology to improve the efficiency of FFSC management has attracted much attention practically and academically. Distinct from existing studies, this paper provides a specific insight into the application of these two technologies by taking into account their functions in restraining misreporting behavior with BT technology due to its traceability and transparency and improving the freshness with CP technology based on the investment in fresh preservation equipment. With this in mind, this paper develops game theoretic models to identify the conditions under which technologies are the optimal selection for the supplier/retailer. It assumes that the retailer is the Stackelberg leader and the supplier is the follower. The key findings show that: (1) with the foci to eliminate suppliers’ misreporting behavior and minimize the incentive fees, BT investment can increase prices and promote order quantities and demand; (2) only when the consumers demonstrate “appropriate” sensitivity to freshness, a win–win situation can be achieved without adoption of BT technology; and (3) suppliers are not keen to invest in BT or CP technology when the circulation time is short, while retailers prefer to invest in CP technology when the circulation time is long.
Spatial Imbalance, Dynamic Evolution and Convergence of the Digital Economy: Analysis Based on Panel Data of 278 Cities in China
Authors: Yu Zhuoxi , Liu Shan , Zhu Zhichuan , Fu Lianyan
Published: May 2023

Accelerating the development of the digital economy is the way to build a modern industrial system and promote sustainable development. In order to accurately analyze the development status of China’s digital economy, this study introduced a text analysis method to construct an index of the digital economy and surveyed the digital economy based on the panel data of 278 Chinese cities from 2011 to 2019. Moran’s I index, the Dagum Gini coefficient, the kernel density and a Markov chain were used to reveal the space-time difference and dynamic change characteristics. Considering the impact of the spatial correlation and regional division on convergence, we compared the σ values and spatial σ values to study the convergence characteristics after grouping with the decision tree method. The research showed that the digital economy had greatly improved, but it showed a significant imbalance. The research on the regional division of cities according to their geographical distribution and grade showed that the development status of the digital economy was increasingly different, and there was no convergence feature. We chose continuous classification variables and used the decision tree method to divide cities into 10 groups to investigate the convergence. The results showed that the σ values and spatial σ values decreased significantly and showed convergence characteristics. The development of the digital economy showed convergence, indicating that the convergence was greatly affected by the geographical location and grouping basis. Overall, this study contributes to our understanding of the development status of the digital economy, and targeted policy recommendations were proposed to improve the level of digital economy development.