bluuebunny/crossref_metadata_2025 huggingface dataset:
Mapping Small-Scale Irrigation Areas Using Expert Decision Rules and the Random Forest Classifier in Northern Ethiopia
Authors: Mohammedshum Amina Abdelkadir , Maathuis Ben H. P. , Mannaerts Chris M. , Teka Daniel
Published: December 2023

The mapping of small-scale irrigation areas is essential for food security and water resource management studies. The identification of small-scale irrigation areas is a challenge, but it can be overcome using expert knowledge and satellite-derived high-spatial-resolution multispectral information in conjunction with monthly normalized difference vegetation index (NDVI) time series, and additional terrain information. This paper presents a novel approach to characterize small-scale irrigation schemes that combine expert knowledge, multi-temporal NDVI time series, multispectral high-resolution satellite images, and the random forest classifier in the Zamra catchment, North Ethiopia. A fundamental element of the approach is mapping small-scale irrigation areas using expert decision rules to incorporate the available water resources. We apply expert decision rules to monthly NDVI composites from September 2020 to August 2021 along with the digital elevation model (DEM) data on the slope, drainage order, and distance maps to derive the sample set. The samples were based on the thresholds obtained by expert knowledge from field surveys. These data, along with the four spectral bands of a cloud-free Planet satellite image composite, 12 NDVI monthly composites, slope, drainage order, and distance map were used as input into a random forest classifier which was trained to classify pixels as either irrigated or non-irrigated. The results show that the analysis allows the mapping of small-scale irrigation areas with high accuracy. The classification accuracy for identifying irrigated areas showed a user accuracy ranging from 81% to 87%, along with a producer accuracy ranging from 64% to 79%. Furthermore, the classification accuracy and the kappa coefficient for the classified irrigation schemes were 80% and 0.70, respectively. As a result, these findings highlight a substantial level of agreement between the classification results and the reference data. The use of different expert knowledge-based decision rules, as a method, can be applied to extract small-scale and larger irrigation areas with similar agro-ecological characteristics.
Remote Sensing of Aerosols and Water-Leaving Radiance from Chinese FY-3/MERSI Based on a Simultaneous Method
Authors: Zhang Xiaohan , Shi Chong , Si Yidan , Letu Husi , Wang Ling , Tang Chenqian , Xu Na , He Xianqiang , Yin Shuai , Zhang Zhihua , Chen Lin
Published: December 2023

In this paper, a new simultaneous retrieval method of the SIRAW algorithm is introduced and carried out on FY3D/MERSI-II satellite images to obtain the aerosol optical thickness (AOT) and normalized water-leaving radiance (WLR) over the ocean. In order to improve the operation efficiency of SIRAW, a machine learning solver is developed to improve the speed of forward radiative transfer computation during retrieval. Ground-based measurement data from AERONET-OC and satellite products from VIIRS are used for comparative verification. The results show that the retrieved AOT and WLR from SIRAW are both in good agreement with those of AERONET-OC and VIIRS. Further, considering the degradation of the MERSI sensor, a new calibration scheme on 412 nm and 443 nm is adopted and an evaluation is carried out. Inter-comparison of derived WLR between MERSI and VIIRS indicates that the new calibration scheme could effectively improve the WLR retrieval accuracy of MERSI with better consistency to the official data of VIIRS. Therefore, this paper confirms that a simultaneous retrieval scheme combined with effective calibration coefficients can be used for high-precision retrieval of real aerosol and water-leaving radiation.
Trampoline Stiffness Estimation by Using Robotic System for Quantitative Evaluation of Jumping Exercises
Authors: Park Gunseok , Choi Seung-Hwan , Kim Chang-Hyun , Kim Min Young , Lee Suwoong
Published: December 2023

Trampolines are recognized as a valuable tool in exercise and rehabilitation due to their unique properties like elasticity, rebound force, low-impact exercise, and enhancement of posture, balance, and cardiopulmonary function. To quantitatively assess the effects of trampoline exercises, it is essential to estimate factors such as stiffness, elements influencing jump dynamics, and user safety. Previous studies assessing trampoline characteristics had limitations in performing repetitive experiments at various locations on the trampoline. Therefore, this research introduces a robotic system equipped with foot-shaped jigs to evaluate trampoline stiffness and quantitatively measure exercise effects. This system, through automated, repetitive movements at various locations on the trampoline, accurately measures the elastic coefficient and vertical forces. The robot maneuvers based on the coordinates of the trampoline, as determined by its torque and position sensors. The force sensor measures data related to the force exerted, along with the vertical force data at X, Y, and Z coordinates. The model’s accuracy was evaluated using linear regression based on Hooke’s Law, with Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Correlation Coefficient Squared (R-squared) metrics. In the analysis including only the distance between X and the foot-shaped jigs, the average MAE, RMSE, and R-squared values were 17.9702, 21.7226, and 0.9840, respectively. Notably, expanding the model to include distances in X, Y, and between the foot-shaped jigs resulted in a decrease in MAE to 15.7347, RMSE to 18.8226, and an increase in R-squared to 0.9854. The integrated model, including distances in X, Y, and between the foot-shaped jigs, showed improved predictive capability with lower MAE and RMSE and higher R-squared, indicating its effectiveness in more accurately predicting trampoline dynamics, vital in fitness and rehabilitation fields.
A Privacy-Preserving Trajectory Publishing Method Based on Multi-Dimensional Sub-Trajectory Similarities
Authors: Shen Hua , Wang Yu , Zhang Mingwu
Published: December 2023

With the popularity of location services and the widespread use of trajectory data, trajectory privacy protection has become a popular research area. k-anonymity technology is a common method for achieving privacy-preserved trajectory publishing. When constructing virtual trajectories, most existing trajectory k-anonymity methods just consider point similarity, which results in a large dummy trajectory space. Suppose there are n similar point sets, each consisting of m points. The size of the space is then mn. Furthermore, to choose suitable k− 1 dummy trajectories for a given real trajectory, these methods need to evaluate the similarity between each trajectory in the space and the real trajectory, leading to a large performance overhead. To address these challenges, this paper proposes a k-anonymity trajectory privacy protection method based on the similarity of sub-trajectories. This method not only considers the multidimensional similarity of points, but also synthetically considers the area between the historic sub-trajectories and the real sub-trajectories to more fully describe the similarity between sub-trajectories. By quantifying the area enclosed by sub-trajectories, we can more accurately capture the spatial relationship between trajectories. Finally, our approach generates k−1 dummy trajectories that are indistinguishable from real trajectories, effectively achieving k-anonymity for a given trajectory. Furthermore, our proposed method utilizes real historic sub-trajectories to generate dummy trajectories, making them more authentic and providing better privacy protection for real trajectories. In comparison to other frequently employed trajectory privacy protection methods, our method has a better privacy protection effect, higher data quality, and better performance.
A 4H-SiC CMOS Oscillator-Based Temperature Sensor Operating from 298 K up to 573 K
Authors: Rinaldi Nicola , Liguori Rosalba , May Alexander , Rossi Chiara , Rommel Mathias , Rubino Alfredo , Licciardo Gian Domenico , Di Benedetto Luigi
Published: December 2023

In this paper, we propose a temperature sensor based on a 4H-SiC CMOS oscillator circuit and that is able to operate in the temperature range between 298 K and 573 K. The circuit is developed on Fraunhofer IISB’s 2 μm 4H-SiC CMOS technology and is designed for a bias voltage of 20 V and an oscillation frequency of 90 kHz at room temperature. The possibility to relate the absolute temperature with the oscillation frequency is due to the temperature dependency of the threshold voltage and of the channel mobility of the transistors. An analytical model of the frequency-temperature dependency has been developed and is used as a starting point for the design of the circuit. Once the circuit has been designed, numerical simulations are performed with the Verilog-A BSIM4SiC model, which has been opportunely tuned on Fraunhofer IISB’s 2 μm 4H-SiC CMOS technology, and their results showed almost linear frequency-temperature characteristics with a coefficient of determination that was higher than 0.9681 for all of the bias conditions, whose maximum is 0.9992 at a VDD = 12.5 V. Moreover, we considered the effects of the fabrication process through a Monte Carlo analysis, where we varied the threshold voltage and the channel mobility with different values of the Gaussian distribution variance. For example, at VDD = 20 V, a deviation of 17.4% from the nominal characteristic is obtained for a Gaussian distribution variance of 20%. Finally, we applied the one-point calibration procedure, and temperature errors of +8.8 K and −5.8 K were observed at VDD = 15 V.
CNN-Based Facial Expression Recognition with Simultaneous Consideration of Inter-Class and Intra-Class Variations
Authors: Pham Trong-Dong , Duong Minh-Thien , Ho Quoc-Thien , Lee Seongsoo , Hong Min-Cheol
Published: December 2023

Facial expression recognition is crucial for understanding human emotions and nonverbal communication. With the growing prevalence of facial recognition technology and its various applications, accurate and efficient facial expression recognition has become a significant research area. However, most previous methods have focused on designing unique deep-learning architectures while overlooking the loss function. This study presents a new loss function that allows simultaneous consideration of inter- and intra-class variations to be applied to CNN architecture for facial expression recognition. More concretely, this loss function reduces the intra-class variations by minimizing the distances between the deep features and their corresponding class centers. It also increases the inter-class variations by maximizing the distances between deep features and their non-corresponding class centers, and the distances between different class centers. Numerical results from several benchmark facial expression databases, such as Cohn-Kanade Plus, Oulu-Casia, MMI, and FER2013, are provided to prove the capability of the proposed loss function compared with existing ones.
Toward Sensor Measurement Reliability in Blockchains
Authors: Gómez-Marín Ernesto , Parrilla Luis , Tejero López Jose L. , Morales Diego P. , Castillo Encarnación
Published: December 2023

In this work, a secure architecture to send data from an Internet of Things (IoT) device to a blockchain-based supply chain is presented. As is well known, blockchains can process critical information with high security, but the authenticity and accuracy of the stored and processed information depend primarily on the reliability of the information sources. When this information requires acquisition from uncontrolled environments, as is the normal situation in the real world, it may be, intentionally or unintentionally, erroneous. The entities that provide this external information, called Oracles, are critical to guarantee the quality and veracity of the information generated by them, thus affecting the subsequent blockchain-based applications. In the case of IoT devices, there are no effective single solutions in the literature for achieving a secure implementation of an Oracle that is capable of sending data generated by a sensor to a blockchain. In order to fill this gap, in this paper, we present a holistic solution that enables blockchains to verify a set of security requirements in order to accept information from an IoT Oracle. The proposed solution uses Hardware Security Modules (HSMs) to address the security requirements of integrity and device trustworthiness, as well as a novel Public Key Infrastructure (PKI) based on a blockchain for authenticity, traceability, and data freshness. The solution is then implemented on Ethereum and evaluated regarding the fulfillment of the security requirements and time response. The final design has some flexibility limitations that will be approached in future work.
Development of a Microservice-Based Storm Sewer Simulation System with IoT Devices for Early Warning in Urban Areas
Authors: Lin Shiu-Shin , Zhu Kai-Yang , Zhang Xian-Hao , Liu Yi-Chuan , Wang Chen-Yu
Published: December 2023

This study proposes an integrated approach to developing a Microservice, Cloud Computing, and Software as a Service (SaaS)-based Real-Time Storm Sewer Simulation System (MBSS). The MBSS combined the Storm Water Management Model (SWMM) microservice running on the EC2 Amazon Web Services (AWS) cloud platform and an Internet of Things (IoT) monitoring device to prevent disasters in smart cities. The Python language and Docker container were used to develop the MBSS and Web API of the SWMM microservice. The IoT comprised a pressure water level meter, an Arduino, and a Raspberry Pi. After laboratory channel testing, the simulated and IoT-monitored water levels under different flow rates indicate that the simulated water level in MBSS was such as that monitored by the IoT. These findings suggest that MBSS is feasible and can be further used as a reference for smart urban early warning systems. The MBSS can be applied in on-site stormwater sewers during heavy rain, with the goal of issuing early warnings and reducing disaster damage. The use case can be the process by which the SWMM model parameters will be optimized based on the water level data from IoT monitoring devices in stormwater sewer systems. The predicted rainfall will then be used by the SWMM microservices of MBSS to simulate the water levels at all manholes. The status of the water levels will finally be applied to early warning.
Analysis of the Third Digital Divide in Relation to Digital Socialization Itineraries among University Students in Uruguay
Authors: González-Chouciño Mariano Agustin , Ruiz-Callado Raúl
Published: December 2023

The social transformations generated by digitization and the increasing prominence of Information and Communication Technology (ICT) in social relations have underscored the need to delve deeper into the analysis of digital divides to enhance our understanding of social inequalities in the digital age. Thus, sociology has delved into identifying the variables that underpin differential capacities to make productive use of digital technologies for improving living conditions, a phenomenon referred to as the third digital divide. This study delves into this issue by analyzing the digital socialization itineraries of young people. To achieve this, the technique of techno-biographical interviews was employed with 30 university students in Uruguay. Through the classification of interviews into clusters based on coding similarity, five digital socialization itineraries were identified: contextual disengagement, aspirational advancement, productive channeling, controlled development, and abusive development. By closely examining these groups, a set of variables with significant biographical impacts was identified, affecting both the capacity to harness digital technologies and the potential risks associated with their use. The findings hold relevance for guiding research in the field and for policymakers in addressing ICT education challenges during childhood and adolescence.
Complexity Theory in Health Promotion Research: Four Essential Principles Based on Niklas Luhmann’s Systems Theory
Authors: Grabowski Dan , Aagaard-Hansen Jens , Rod Morten Hulvej , Jensen Bjarne Bruun
Published: December 2023

Complexity-oriented approaches built on complexity theories are not widely used in health promotion research. The field of health promotion faces significant difficulties in explaining and addressing unforeseen impacts and side effects due to the widespread tendency to implement health promotion initiatives that are considered best practices. It is important to theoretically embrace the fact that we operate in a complex world and that we, therefore, need to redefine our approaches by acknowledging the complexities involved in promoting health. In this theoretical paper, we propose a set of four complexity-oriented principles for health promotion research based on Niklas Luhmann’s systems theory: (1) direct transfer of health knowledge and health competencies is impossible; (2) all individuals and social entities are fundamentally different from each other; (3) the individual’s sense of health-related meaning determines what is deemed relevant; and (4) it is essential for communication to meet expectations if it is to be observed. The set of principles presented in this article can be applied to research projects intended to explore and address challenges related to complexity in health promotion settings. It can be used as a lens through which to observe health promotion practice. If health promotion research wants to address the field that we have defined for ourselves as extremely complex and unaddressed by anyone else, we need to embrace approaches that actually do this—by providing health promotion research with a formal framework appropriate to its existing main purposes and concerns.
Shaping an Image of Science in the 21st Century: The Perspective of Metamodernism
Authors: Pipere Anita , Mārtinsone Kristīne
Published: December 2023

In a contemporary world facing countless multifaceted crises and challenges, science can still serve as one of the most powerful tools to deal with the ordeals of our time. However, the scientific community needs to provide space for reflection on novel ways of developing its centuries-old heritage and unlocking its potential for the benefit of the world and humanity. The purpose of this article was to deliberate on the image of contemporary science within the framework of the new philosophical paradigm of metamodernism. Following historical strands related to metamodernism and science, the authors encircled the general features and elaborated the main philosophical principles of metamodernism. The main task was to identify elements of contemporary science that conform to the philosophical principles of metamodernism. Thus, several features of science and research, such as the structure of science, scientific truth, metanarratives of science, scientific thinking, system of science, interaction of scientific disciplines, dialogue of science with society and politics, open science, digitalisation of science, etc., were interpreted through the perspective of the ontological, epistemological, axiological, and methodological principles of metamodernism. This article ends with a summary of the main points of the discussion and practical implications of the presented ideas.
Gender Roles, Gender Bias, and Cultural Influences: Perceptions of Male and Female UAE Public Relations Professionals
Authors: Lee Sang , Alsereidi Raya Hamad , Ben Romdhane Samar
Published: December 2023

Research into gender dynamics in the Public Relations (PR) industry has suggested that the gendered perceptions of PR roles, gender bias, and cultural norms may contribute to gender-related issues in the field. The current study, framed as an exploratory investigation, examines how PR professionals in the United Arab Emirates (UAE) perceive gender equality issues within their industry. Drawing on survey responses from 152 PR practitioners in the UAE, this research investigates the extent to which these professionals perceive gender equality issues in the PR sector. The survey results reveal positive perceptions of gender equality issues, although some specific gender-related concerns emerge. One key finding is that there are minimal gender differences in perceiving gender equality issues between male and female professionals. Respondents also expressed confidence that their organizations are implementing practices in support of gender equality and recognizing the impact of such practices on employee performance. Given the lack of data on gender equality in the UAE, this exploratory study contributes to a better understanding of gender equality among professionals and provides insights into the strategies for advancing gender equality in the sector.
The Participation of Students with Autism in Educational Robotics: A Scoping Review
Authors: Nanou Andromachi , Karampatzakis Dimitris
Published: December 2023

Educational robotics (ER) is a growing interdisciplinary field that is attracting increasing attention in inclusive or special education settings. It provides a suitable educational environment for the participation of students with autism, through which they can utilize their main strengths and interests. Strengths-based vs. deficits-based approaches recognize the strengths and interests of autistic children as the starting point for their inclusion in school and the community. The scoping review was developed as the best knowledge-synthesis method for summarizing the pertinent research on the participation of students with autism in educational robotics for their successful inclusion. Forty-five studies were included and analyzed to address the main objectives, the mapping of contextual dimensions, and the specific characteristics of the educational robotic activities where the participation of students with autism occurred. The data were extracted into a charting framework, and a narrative analysis was adopted for the knowledge synthesis. According to the results, the research on the participation of children with autism is limited and has focused primarily on educational robotic activities, failing to adequately explore other dimensions that affect the successful participation and inclusion of students with autism in educational robotics.
Spectroscopic Ellipsometry: Advancements, Applications and Future Prospects in Optical Characterization
Authors: Politano Grazia Giuseppina , Versace Carlo
Published: December 2023

Spectroscopic ellipsometry (SE), a non-invasive optical technique, is a powerful tool for characterizing surfaces, interfaces, and thin films. By analyzing the change in the polarization state of light upon reflection or transmission through a sample, ellipsometry provides essential parameters such as thin film thickness (t) and optical constants (n, k). This review article discusses the principles of ellipsometry, including the measurement of key values ∆ and Ψ, and the complex quantity ρ. The article also presents the Fresnel equations for s and p polarizations and the importance of oblique angles of incidence in ellipsometry. Data analysis in ellipsometry is explored, including the determination of bandgap and data referencing the electrical properties of materials. The article emphasizes the importance of choosing the appropriate models to fit ellipsometric data accurately, with examples of the Cauchy and Lorentz models. Additionally, the Kramers–Kronig relations are introduced, illustrating the connection between real and imaginary components of optical constants. The review underscores the significance of ellipsometry as a non-destructive and versatile technique for material characterization across a wide range of applications.
Methodology for the Identification of Vehicle Congestion Based on Dynamic Clustering
Authors: Reyes Gary , Tolozano-Benites Roberto , Lanzarini Laura , Estrebou César , Bariviera Aurelio F. , Barzola-Monteses Julio
Published: December 2023

Addressing sustainable mobility in urban areas has become a priority in today’s society, given the growing population and increasing vehicular flow in these areas. Intelligent Transportation Systems have emerged as innovative and effective technological solutions for addressing these challenges. Research in this area has become crucial, as it contributes not only to improving mobility in urban areas but also to positively impacting the quality of life of their inhabitants. To address this, a dynamic clustering methodology for vehicular trajectory data is proposed which can provide an accurate representation of the traffic state. Data were collected for the city of San Francisco, a dynamic clustering algorithm was applied and then an indicator was applied to identify areas with traffic congestion. Several experiments were also conducted with different parameterizations of the forgetting factor of the clustering algorithm. We observed that there is an inverse relationship between forgetting and accuracy, and the tolerance allows for a flexible margin of error that allows for better results in precision. The results showed in terms of precision that the dynamic clustering methodology achieved high match rates compared to the congestion indicator applied to static cells.
Confidence in Greenhouse Gas Emission Estimation: A Case Study of Formaldehyde Manufacturing
Authors: Marujo Ernesto C. , Almeida José R. U. C. , Souza Luiz F. L. , Costa Alan R. S. P. , Miranda Paulo C. G. , Covatti Arthur A. , Holschuch Solange G. , Melo Potira M. S.
Published: December 2023

In this article, we discuss the uncertainties involved in the models and in the measurements necessary to estimate the emissions of greenhouse gases (GHG) in a chemical industry. When these uncertainties cannot be neglected and some measurements exhibit correlations with others, estimating the final emission is not a trivial task. Even if we intend to determine a simple point estimate for the mean emissions, we will need to use the average values of the measurements as well as information about their uncertainties and correlations in complex computations. To solve this problem, we propose a Monte Carlo method to estimate the mean and confidence interval of CO2 emissions in the context of uncertainties and correlations. We validated our approach through a case study involving a traditional chemical company in Brazil. Our results indicated that previously, there was an overestimation of the emission because the company did not consider uncertainties and correlations. The overestimation was modest since the parameters involved present relatively little uncertainty, but the bias effect was clear. This research has demonstrated the importance of accounting for uncertainties and correlations in emission estimates, providing a practical framework for analyses in industrial settings.
Tensile Properties of Natural Fibre-Reinforced FDM Filaments: A Short Review
Authors: Haque Abu Naser Md Ahsanul , Naebe Maryam
Published: December 2023

Fused deposition modelling (FDM) is considered the most popular technique of three-dimensional (3D) printing. This is a simple and sustainable method of materials manufacturing with rapidly spreading applications in diverse areas. In this method, a thermoplastic filament is extruded through a nozzle on a layer-by-layer basis to construct a 3D object in a benchtop environment. To further promote its acceptance, FDM printing currently has a significant focus on the use of natural fillers with thermoplastic polymer. Nevertheless, successful FDM printing is largely dependent on the strength and consistency of the feed material, the filament. Preparing such composite filaments is challenging due to possible manufacturing defects and inconsistency while mixing the filler and matrix. Studies showed that there are significant differences between the tensile properties of FDM filament when compared with their printed parts, caused by the variations in printing parameters, filament consumption, density, and architectural difference. Previous reports have confirmed that mechanical characteristics are the most common parameters used by scientists to evaluate the properties of the materials in the additive manufacturing field. Though several reviews are accessible on the tensile properties of FDM-printed materials, currently there is no review available on the tensile properties of the filament itself. This is the first review focused exclusively on the tensile properties of FDM filaments. The goal of this short review is to better understand the influential factors in the natural fibre-reinforced filament preparation process that affect the tensile properties and subsequently impact on 3D printing. Therefore, evaluation of the reported tensile properties, i.e., tensile strength and elongation at the break and modulus, was conducted in relation to different process parameters, such as filler concentration, filler size, extrusion methods, the combination of filler and polymer, and the interrelations among the parameters and properties were explored.
Location of Electric Vehicle Charging Stations in Inter-Andean Corridors Considering Road Altitude and Nearby Infrastructure
Authors: Dávila-Sacoto Miguel , Toledo Marco A. , Hernández-Callejo Luis , González L. G. , Alvarez Bel Carlos , Zorita-Lamadrid Ángel L.
Published: December 2023

The placement of electric vehicle charging stations is typically accomplished using mathematical optimization methods or by calculating average distances. However, this design approach is not suitable for mountainous roads in the Andes Mountain range, given that steep gradients pose both advantages and disadvantages for electric vehicles. This study examines the placement of charging stations along five Andean corridors connecting the cities of La Troncal, Macas, Santa Isabel, Loja, and Cañar to the city of Cuenca. This document presents a novel methodology that can be used to design charging corridors by considering parameters such as state of charge (SOC) per second and SOC per road altitude, among others. By applying this methodology, our study successfully selects appropriate charging station locations to serve both outbound and return journeys on roads with significant gradients. The current study represents an initial approach to analyzing the autonomy of electric vehicles on high-altitude roads, specifically considering the contribution of regenerative braking, for the placement of charging stations.
Risk Assessment in Sustainable Production: Utilizing a Hybrid Evaluation Model to Identify the Waste Factors in Steel Plate Manufacturing
Authors: Lai Kuei-Kuei , Lin Sheng-Wei , Lo Huai-Wei , Hsiao Chia-Ying , Lai Po-Jung
Published: December 2023

In the realm of industrial development, steel has consistently played a pivotal role due to its extensive applications. This research aims to refine the process of steel plate manufacturing, focusing on reducing waste as a critical step towards embracing sustainable development and aligning with the Sustainable Development Goals (SDGs). Our approach integrates a hybrid analytical model grounded in Failure Mode and Effects Analysis (FMEA) to thoroughly investigate the waste-producing elements in steel plate production. The methodology of this study is structured in a three-pronged approach, as follows: Initially, it involves meticulous on-site inspections across various factories to pinpoint potential sources of waste. Subsequently, we employ the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to intricately analyze the interconnectedness and impact of various risk factors. The final phase utilizes the Performance Calculation technique within the Integrated Multiple Multi-Attribute Decision-Making (PCIM-MADM) framework for aggregating and evaluating risk scores. This multifaceted approach aids in establishing the priorities for corrective actions aimed at waste reduction. Our findings present innovative solutions for identifying and mitigating critical waste factors, contributing to a more efficient and sustainable steel manufacturing process. These strategies promise scalability and adaptability for broader industrial applications and provide critical insights into resource optimization. This research directly supports the objectives of SDG 9, which focuses on building resilient infrastructure and promoting sustainable industrialization. Furthermore, it resonates with SDG 12, advocating for sustainable consumption and production patterns. By enhancing the efficiency and cost effectiveness of steel plate production, this study significantly contributes to minimizing waste and elevating the sustainability of industrial practices.
Software Design for Users with Autism Using Human-Centered Design and Design Thinking Techniques
Authors: Constain Moreno Gustavo E. , Collazos César A. , Blasco Susana Bautista , Moreira Fernando
Published: December 2023

This paper details a method, which has been called FRIDA, applied by the authors within this study based on the collaborative and interdisciplinary development of accessible software that is usable by people with autism spectrum disorder (ASD). To this end, the context of this study is based on the adaptation of the Design Thinking model as a strategy for interdisciplinary collaborative work between clinical therapists and software developers. This is complemented by a human-centered design (DCH) approach to obtain accessible software aimed at people with ASD level 1. Each of the stages of Design Thinking is detailed in order to obtain the personalized design of accessible software, as well as the activities carried out in case studies in Colombia, Spain, and Portugal with eight children diagnosed with ASD. As a result, we present the design of FRIDA’s components for interdisciplinary collaborative work and the design of personalized software that was developed for some children with ASD who participated in this study. For these children, it is demonstrated that the use of personalized and collaboratively designed software facilitates the development of specific social skills more quickly than with conventional clinical therapies.
Laboratory Risk Assessment Based on SHELL-HACCP-Cloud Model
Authors: Zhao Xiaoning , Wei Zhongcheng , Gao Yukun , Yin Penggang
Published: December 2023

With the increasing demand and expanding scale of laboratories in colleges and universities, laboratory accidents frequently occur, seriously impacting personal health, schools, society, and the environment. Scientific and effective risk assessment is crucial to prevent accidents. Therefore, in order to effectively reduce the risk of chemical laboratories and minimize the frequency of accidents. This study employs the combination of the SHELL model and HACCP system to analyze the potential sources of hazards in hazardous chemical laboratories and establish a risk assessment index system. Based on the Cloud model, a dynamic risk assessment model for the laboratory is established to quantitatively evaluate the risk level of the evaluation results. In order to ensure the rationality of the assessment results, the subjective and objective weights are combined by the principle of minimizing information entropy. Case analysis proves the scientific validity of the evaluation results of the model, which can assist laboratory managers in formulating emergency plans and risk management mechanisms to reduce or eliminate the occurrence of experimental accidents. This approach ensures the safe and sustainable development of schools and laboratories, which is conducive to the progress of researchers’ scientific research results.
Dissolved Inorganic Nutrient Biogeochemistry in an Urbanized Coastal Region: A Study of Dapeng Cove, Shenzhen
Authors: Tong Fei , Chen Pimao , Zhang Xiumei
Published: December 2023

Dissolved inorganic nutrients are pivotal in maintaining the material and energy balance of marine ecosystems, impacting the survival and dynamic succession of marine organisms. To gain a deeper understanding of the source and sink characteristics of dissolved inorganic nutrients in bays affected by human activities and to elucidate the processes involving filter-feeding shellfish in relation to these nutrients, this study investigated the source and sink dynamics of dissolved inorganic nutrients in the Dapeng Cove sea area of Shenzhen. Over the past decade, a significant change in the N/P ratio within the survey area has been observed, suggesting a shift in nutrient limitation from nitrogen to phosphorus or phosphorus–silicon limitation. This induced change in the N/P ratio, along with Si/N and Si/P ratios, may facilitate the growth of cyanobacteria and, subsequently, alter the proportions of diatoms, dinoflagellates, and cyanobacteria. Seasonal fluctuations in human disturbance intensity and precipitation determine the seasonal and spatial distribution of nutrients in the bay, thereby influencing the bay ecosystem metabolism. The Land–Ocean Interactions in the Coastal Zone (LOICZ) model analysis revealed that the bay represents a major source of inorganic nitrogen and a source of phosphate in spring, summer, and autumn, while acting as a sink for phosphate in winter. Furthermore, rivers and groundwater represent the primary sources of phosphate and inorganic nitrogen in the bay. The bay exhibits an annual net ecosystem metabolic rate of 7.06 mmol C/m2/d, with denitrification dominating the nitrogen cycle at 12.65 mmol C/m2/d. Overall, the Dapeng Cove ecosystem displays net production exceeding respiration, classifying it as an autotrophic system. Additionally, the nitrogen cycle in the sea area is predominantly driven by denitrification. The analysis also revealed that the impact of oyster proliferation on the physical and chemical factors in the surveyed area is relatively weaker than that of surface runoff and groundwater inputs.
Mapping the Landscape of ESG Strategies: A Bibliometric Review and Recommendations for Future Research
Authors: Au Alan Kai Ming , Yang Yi-Fan , Wang Huan , Chen Rui-Hong , Zheng Leven J.
Published: December 2023

Environmental, social, and governance (ESG) together comprise what is regarded as a metric system that can be used to gauge a corporation’s performance in various facets of social responsibility. The increasing urgency for businesses to contemplate and proactively address ESG issues, due to their immediate relevance, underscores its importance in contemporary business landscapes. In the current academic landscape, scholars across various disciplines have thus been engaged in rigorous investigations of ESG. This research aims to present an overarching comprehension of the theoretical foundation of ESG by reviewing existing research and highlight the latest trends in ESG literature in the field of management. We have engaged in a comprehensive bibliometric examination, supplementing our research with the application of co-citation and bibliographic coupling methodologies. Based on co-citation analysis, this study elucidates four theoretical foundations of ESG research: Sustainability of competitive advantage; compliance of social construction; alignment of governance accountability; and allocation of sustainable capital. We then employ bibliographic coupling to assess current research trends, revealing five groups of research trends correlated with the topics: ESG activities and economic outcomes; ESG reporting and non-financial disclosure; ESG performance and corporate sustainability; ESG attributes and investment market; and ESG practices and board diversity. Furthermore, this study summarizes future research directions in the ESG domain.
Transfer-Ensemble Learning: A Novel Approach for Mapping Urban Land Use/Cover of the Indian Metropolitans
Authors: Barman Prosenjit , Mustak Sheikh , Kuffer Monika , Singh Sudhir Kumar
Published: December 2023

Land use and land cover (LULC) classification plays a significant role in the analysis of climate change, evidence-based policies, and urban and regional planning. For example, updated and detailed information on land use in urban areas is highly needed to monitor and evaluate urban development plans. Machine learning (ML) algorithms, and particularly ensemble ML models support transferability and efficiency in mapping land uses. Generalization, model consistency, and efficiency are essential requirements for implementing such algorithms. The transfer-ensemble learning approach is increasingly used due to its efficiency. However, it is rarely investigated for mapping complex urban LULC in Global South cities, such as India. The main objective of this study is to assess the performance of machine and ensemble-transfer learning algorithms to map the LULC of two metropolitan cities of India using Landsat 5 TM, 2011, and DMSP-OLS nightlight, 2013. This study used classical ML algorithms, such as Support Vector Machine-Radial Basis Function (SVM-RBF), SVM-Linear, and Random Forest (RF). A total of 480 samples were collected to classify six LULC types. The samples were split into training and validation sets with a 65:35 ratio for the training, parameter tuning, and validation of the ML algorithms. The result shows that RF has the highest accuracy (94.43%) of individual models, as compared to SVM-RBF (85.07%) and SVM-Linear (91.99%). Overall, the ensemble model-4 produces the highest accuracy (94.84%) compared to other ensemble models for the Kolkata metropolitan area. In transfer learning, the pre-trained ensemble model-4 achieved the highest accuracy (80.75%) compared to other pre-trained ensemble models for Delhi. This study provides innovative guidelines for selecting a robust ML algorithm to map urban LULC at the metropolitan scale to support urban sustainability.
Assessing Hygrothermal Performance in Building Walls Engineered for Extreme Cold Climate Environments
Authors: Palani Hevar , Khaleghi Hamed , Salehi Parisa , Karatas Aslihan
Published: December 2023

Buildings located in extreme cold climates encounter challenges (e.g., heat loss, condensation, and frozen utilities), especially within their wall envelopes. These challenges also play a pivotal role in occupant health, comfort, and the structural integrity of the building. While the existing literature has primarily focused on thermal performance, this study underscores the importance of evaluating hygrothermal performance within wall envelopes, given the existence of mold growth even in cases of high thermal resistance. Therefore, the aim of this study was to evaluate the hygrothermal performance of an adaptable house wall (AHW) panel that incorporates composite infill panels paired with vacuum-insulated panels to endure harsh cold conditions in Alaska. Therefore, three steps were proposed to: (1) collect the material and thermal properties of the AHW; (2) model the hygrothermal performance of the AHW in WUFI® PRO v6.7 software; and (3) analyze the results. The results revealed a moderate risk of mold growth in the inner plywood layer of the AHW, whereas the outer plywood layer showed zero risk, indicating an acceptable condition. The findings aid decisionmakers in recognizing potential mold-related issues in building walls before advancing to the construction phase.
Automating Microclimate Evaluation and Optimization during Urban Design: A Rhino–Grasshopper Workflow
Authors: Bedra Komi Bernard , Zheng Jian , Li Jiayu , Sun Zhaoqian , Zheng Bohong
Published: December 2023

Though building-scale energy demand and indoor thermal comfort have been extensively covered by recent studies, the automation of middle- and larger-scale outdoor microclimate evaluation in parametric design is less covered. The relatively slow computation and the need for sophisticated expertise are some of the current issues. This paper proposes a Rhino–Grasshopper custom script to automatically compute spatial indicators for a quick thermal comfort estimation. The Galapagos evolutionary algorithm is used to optimize thermal comfort and select the best combinations of spatial indicators. In a summer case study located in Shantou, China, the proposed workflow was three times faster than a non-automated indicator calculation in ArcGIS, while the optimization method achieved 25% to 33% reduction in land areas under extreme heat stress. This automated process applies to existing states and new urban designs. It is adaptable to customized prediction models under different climatic zones.
Classification Study of New Power System Stability Considering Stochastic Disturbance Factors
Authors: Li Sheng , Duan Changhong , Gao Yuan , Cai Yuhao
Published: December 2023

Power system instability causes many local or large-scale power outage accidents. To maintain sustainable development, a new power system construction aimed at maximizing new energy consumption is being put on the agenda. However, with a large increase in stochastic disturbance factors (SDFs), the system gradually shows strong stochasticity, and the stability presents greater complexity. It is necessary to analyze the impact on the system based on different processing methods of SDFs to maintain system stability. This paper delves into the impact of SDFs on system stability by analyzing and summarizing both stochastic variables and processes. Initially, the SDFs in the power system are meticulously analyzed and categorized. When the SDFs are treated as stochastic variables, the probabilistic stability is classified and evaluated based on a probability analysis method, which includes the probabilistic small-disturbance stability, the probabilistic transient stability, and the probabilistic voltage stability. When the SDFs are treated as stochastic processes, the stochastic stability is classified and evaluated by using a stochastic analysis method, including the stochastic small-disturbance stability, the stochastic transient stability, and the stochastic voltage stability. Finally, the research perspectives on SDFs and system stability are discussed and prospected.
Primary Occlusion of the Fourth Ventricle: Case Report and Review of the Literature
Authors: Wilbers Eike , Zawy Alsofy Samer , Schipmann Stephanie , Ewelt Christian , Fortmann Thomas , Lewitz Marc , Schwake Michael
Published: December 2023

Idiopathic obstruction of the outlets of the fourth ventricle (FVOO) is a rare cause of hydrocephalus, which can be misdiagnosed as communicating hydrocephalus due to the enlargement of all four ventricles. Different surgical approaches are discussed in the literature. We present a case report of a 25-year-old male admitted with headache, vertigo, and nystagmus. The MRI scan showed a tetraventricular hydrocephalus with a patent aqueduct. After endoscopic third ventriculostomy (ETV), symptoms resolved. We performed a systematic review of the literature, covering 26 years, with the aim to investigate the symptoms, therapy, and outcome of primary FVOO, according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines. We found 9 case reports and 2 case series and could extract a total of 34 cases. After ETV all symptoms resolved in 10 of 23 cases (43.5%), and in 13 of 23 cases (56.5%) symptoms improved partially. Seven cases (30.4%) required additional surgery. A decrease in ventricular volume occurred in most cases. In the 10 patients who were operated via fenestration, all symptoms resolved in 6 cases. ETV seems to be an effective treatment option for patients with idiopathic FVOO in a majority of cases. In special cases, fenestration of the foramen of Magendie may be suitable.