bluuebunny/crossref_metadata_2025 huggingface dataset:
Sargassum Detection Using Machine Learning Models: A Case Study with the First 6 Months of GOCI-II Imagery
Authors: Shin Jisun , Lee Jong-Seok , Jang Lee-Hyun , Lim Jinwook , Khim Boo-Keun , Jo Young-Heon
Published: December 2021

A record-breaking agglomeration of Sargassum was packed along the northern Jeju coast in Korea in 2021, and laborers suffered from removing them from the beach. If remote sensing can be used to detect the locations at which Sargassum accumulated in a timely and accurate manner, we could remove them before their arrival and reduce the damage caused by Sargassum. This study aims to detect Sargassum distribution on the coast of Jeju Island using the Geostationary KOMPSAT 2B (GK2B) Geostationary Ocean Color Imager-II (GOCI-II) imagery that was launched in February 2020, with measurements available since October 2020. For this, we used GOCI-II imagery during the first 6 months and machine learning models including Fine Tree, a Fine Gaussian support vector machine (SVM), and Gentle adaptive boosting (GentleBoost). We trained the models with the GOCI-II Rayleigh-corrected reflectance (RhoC) image and a ground truth map extracted from high-resolution images as input and output, respectively. Qualitative and quantitative assessments were carried out using the three machine learning models and traditional methods such as Sargassum indexes. We found that GentleBoost showed a lower false positive (6.2%) and a high F-measure level (0.82), and a more appropriate Sargassum distribution compared to other methods. The application of the machine learning model to GOCI-II images in various atmospheric conditions is therefore considered successful for mapping Sargassum extent quickly, enabling reduction of laborers’ efforts to remove them.
An Integrated CNN Model for Reconstructing and Predicting Land Use/Cover Change: A Case Study of the Baicheng Area, Northeast China
Authors: Zhang Yubo , Yang Jiuchun , Wang Dongyan , Wang Jing , Yu Lingxue , Yan Fengqin , Chang Liping , Zhang Shuwen
Published: December 2021

Land use and land cover change (LUCC) modeling has continuously been a major research theme in the field of land system science, which interprets the causes and consequences of land use dynamics. In particular, models that can obtain long-term land use data with high precision are of great value in research on global environmental change and climate impact, as land use data are important model input parameters for evaluating the effect of human activity on nature. However, the accuracy of existing reconstruction and prediction models is inadequate. In this context, this study proposes an integrated convolutional neural network (CNN) LUCC reconstruction and prediction model (CLRPM), which meets the demand for fine-scale LUCC reconstruction and prediction. This model applies the deep learning method, which far exceeds the performance of traditional machine learning methods, and uses CNN to extract spatial features and provide greater proximity information. Taking Baicheng city in Northeast China as an example, we verify that CLRPM achieved high-precision annual LUCC reconstruction and prediction, with an overall accuracy rate 9.38% higher than that of the existing models. Additionally, the error rate was reduced by 49.5%. Moreover, this model can perform multilevel LUCC classification category reconstructions and predictions. This study casts light on LUCC models within the high-precision and fine-grained LUCC categories, which will aid LUCC analyses and help decision-makers better understand complex land-use systems and develop better land management strategies.
Impacts of Orbital and Constellation Parameters on the Number and Spatiotemporal Coverage of LEO-LEO Occultation Events
Authors: Liu Congliang , Kirchengast Gottfried , Sun Yueqiang , Proschek Veronika , Wang Xin , Tian Longfei , Du Qifei , Bai Weihua , Wu Chunjun , Hu Peng , Tan Guangyuan
Published: December 2021

The development of small-satellite technologies allows the low Earth orbit intersatellite link (LEO-LEO) occultation method to observe the Earth’s atmosphere with global coverage and acceptable costs using electromagnetic signals, in which the L/X/K/M band and short-wave infrared band signals have been well demonstrated to be suitable. We hence need to investigate the impacts of orbital and constellation parameters on the number and spatiotemporal distribution of LEO-LEO occultation events for best-possible LEO-LEO occultation mission design and optimization at the targeted mission size. In this study, firstly, an occultation events location simulation model accounting for the right ascension of the ascending node (RAAN) precession was set up and the concept of a time-dependent global coverage fraction of occultation events was defined. Secondly, numerical experiments were designed to investigate the orbital parameters’ impacts and to assess the performance of LEO-LEO occultation constellations, in which the Earth is divided into 5° × 5° latitude and longitude cells. Finally, the number, timeliness, and global coverage fraction of occultation events for two-orbit and multi-orbit LEO-LEO constellations were calculated and analyzed. The results show that: ① the orbit inclination and RAAN are the main impacting parameters followed by orbital height, while the RAAN precession is a relevant modulation factor; ② co-planar counter-rotating receiving and transmitting satellite orbits are confirmed to be ideal for a two-satellite LEO-LEO constellation; ③ polar and near-polar orbit constellations most readily achieve global coverage of occultation events; near-equator orbit constellations with supplementary receiving and transmitting satellite orbit planes also readily form the occultation event geometry, though the occultation events are mainly distributed over low and low-to-middle latitude zones; and ④ a well-designed larger LEO-LEO occultation constellation, composed of 36–72 satellites, can meet the basic requirements of global numerical weather prediction for occultation numbers and timeliness, yielding 23,000–38,000 occultation events per day and achieving 100% global coverage in 12–18 h.
Experimental Assessment of UWB and Vision-Based Car Cooperative Positioning System
Authors: Masiero Andrea , Toth Charles , Gabela Jelena , Retscher Guenther , Kealy Allison , Perakis Harris , Gikas Vassilis , Grejner-Brzezinska Dorota
Published: December 2021

The availability of global navigation satellite systems (GNSS) on consumer devices has caused a dramatic change in every-day life and human behaviour globally. Although GNSS generally performs well outdoors, unavailability, intentional and unintentional threats, and reliability issues still remain. This has motivated the deployment of other complementary sensors in such a way that enables reliable positioning, even in GNSS-challenged environments. Besides sensor integration on a single platform to remedy the lack of GNSS, data sharing between platforms, such as in collaborative positioning, offers further performance improvements for positioning. An essential element of this approach is the availability of internode measurements, which brings in the strength of a geometric network. There are many sensors that can support ranging between platforms, such as LiDAR, camera, radar, and many RF technologies, including UWB, LoRA, 5G, etc. In this paper, to demonstrate the potential of the collaborative positioning technique, we use ultra-wide band (UWB) transceivers and vision data to compensate for the unavailability of GNSS in a terrestrial vehicle urban scenario. In particular, a cooperative positioning approach exploiting both vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) UWB measurements have been developed and tested in an experiment involving four cars. The results show that UWB ranging can be effectively used to determine distances between vehicles (at sub-meter level), and their relative positions, especially when vision data or a sufficient number of V2V ranges are available. The presence of NLOS observations is one of the principal factors causing a decrease in the UWB ranging performance, but modern machine learning tools have shown to be effective in partially eliminating NLOS observations. According to the obtained results, UWB V2I can achieve sub-meter level of accuracy in 2D positioning when GNSS is not available. Combining UWB V2I and GNSS as well V2V ranging may lead to similar results in cooperative positioning. Absolute cooperative positioning of a group of vehicles requires stable V2V ranging and that a certain number of vehicles in the group are provided with V2I ranging data. Results show that meter-level accuracy is achieved when at least two vehicles in the network have V2I data or reliable GNSS measurements, and usually when vehicles lack V2I data but receive V2V ranging to 2–3 vehicles. These working conditions typically ensure the robustness of the solution against undefined rotations. The integration of UWB with vision led to relative positioning results at sub-meter level of accuracy, an improvement of the absolute positioning cooperative results, and a reduction in the number of vehicles required to be provided with V2I or GNSS data to one.
A New Multispectral Data Augmentation Technique Based on Data Imputation
Authors: Acción Álvaro , Argüello Francisco , Heras Dora B.
Published: December 2021

Deep Learning (DL) has been recently introduced into the hyperspectral and multispectral image classification landscape. Despite the success of DL in the remote sensing field, DL models are computationally intensive due to the large number of parameters they need to learn. The high density of information present in remote sensing imagery with high spectral resolution can make the application of DL models to large scenes challenging. Methods such as patch-based classification require large amounts of data to be processed during the training and prediction stages, which translates into long processing times and high energy consumption. One of the solutions to decrease the computational cost of these models is to perform segment-based classification. Segment-based classification schemes can significantly decrease training and prediction times, and also offer advantages over simply reducing the size of the training datasets by randomly sampling training data. The lack of a large enough number of samples can, however, pose an additional challenge, causing these models to not generalize properly. Data augmentation methods are used to generate new synthetic samples based on existing data to increase the classification performance. In this work, we propose a new data augmentation scheme using data imputation and matrix completion methods for segment-based classification. The proposal has been validated using two high-resolution multispectral datasets from the literature. The results obtained show that the proposed approach successfully increases the classification performance across all the scenes tested and that data imputation methods applied to multispectral imagery are a valid means to perform data augmentation. A comparison of classification accuracy between different imputation methods applied to the proposed scheme was also carried out.
Research on Efficient Reinforcement Learning for Adaptive Frequency-Agility Radar
Authors: Li Xinzhi , Dong Shengbo
Published: December 2021

Modern radar jamming scenarios are complex and changeable. In order to improve the adaptability of frequency-agile radar under complex environmental conditions, reinforcement learning (RL) is introduced into the radar anti-jamming research. There are two aspects of the radar system that do not obey with the Markov decision process (MDP), which is the basic theory of RL: Firstly, the radar cannot confirm the interference rules of the jammer in advance, resulting in unclear environmental boundaries; secondly, the radar has frequency-agility characteristics, which does not meet the sequence change requirements of the MDP. As the existing RL algorithm is directly applied to the radar system, there would be problems, such as low sample utilization rate, poor computational efficiency and large error oscillation amplitude. In this paper, an adaptive frequency agile radar anti-jamming efficient RL model is proposed. First, a radar-jammer system model based on Markov game (MG) established, and the Nash equilibrium point determined and set as a dynamic environment boundary. Subsequently, the state and behavioral structure of RL model is improved to be suitable for processing frequency-agile data. Experiments that our proposal effectively the anti-jamming performance and efficiency of frequency-agile radar.
Effect of Temperature on Diluate Water in Batch Electrodialysis Reversal
Authors: Dévora-Isiordia Germán Eduardo , Ayala-Espinoza Alejandra , Lares-Rangel Luis Alberto , Encinas-Guzmán María Isela , Sánchez-Duarte Reyna Guadalupe , Álvarez-Sánchez Jesús , Martínez-Macías María del Rosario
Published: December 2021

A high percentage of the agricultural wells in the state of Sonora are overexploited, thus generating a significant degree of saline intrusion and abandonment by nearby communities. In this paper, the effect of temperature on the final concentration of diluted water was evaluated with variations in voltage and input concentration in a batch electrodialysis reversal (EDR) process in order to find the optimal operating conditions, with an emphasis on reducing the energy consumption and cost of desalinated water. Thirty-six samples were prepared: eighteen samples of 2000 mg/L total dissolved solids (TDS) and eighteen samples of 5000 mg/L TDS; brackish well water of 639 mg/L TDS and synthetic salt were mixed to obtain these concentrations. Three different temperatures (25, 30, and 35 °C) and two different voltages (10 and 20 V) were tested for each sample after evaluating the limiting current density. The best salt removal occurred in the 20 V sets, with 18.34% higher removal for the 2000 mg/L TDS experiments and 25.05% for the 5000 mg/L experiments (average between the 25 to 35 °C tests). The temperature positively affected the EDR, especially in the experiments at 10 V, where increasing by 10 °C increased the efficiency by 10.83% and 24.69% for 2000 and 5000 mg/L TDS, respectively. The energy consumption was lower with increasing temperature (35 °C), as it decreased by 1.405% and 1.613% for the 2000 and 5000 mg/L TDS concentrations, respectively (average between the 10 and 20 V tests), thus decreasing the cost per m3 of water.
Management of Local Citizen Energy Communities and Bilateral Contracting in Multi-Agent Electricity Markets
Authors: Algarvio Hugo
Published: December 2021

Over the last few decades, the electricity sector has experienced several changes, resulting in different electricity markets (EMs) models and paradigms. In particular, liberalization has led to the establishment of a wholesale market for electricity generation and a retail market for electricity retailing. In competitive EMs, customers can do the following: freely choose their electricity suppliers; invest in variable renewable energy such as solar photovoltaic; become prosumers; or form local alliances such as Citizen Energy Communities (CECs). Trading of electricity can be done in spot and derivatives markets, or by bilateral contracts. This article focuses on CECs. Specifically, it presents how agent-based local consumers can form alliances as CECs, manage their resources, and trade on EMs. It also presents a review of how agent-based systems can model and support the formation and interaction of alliances in the electricity sector. The CEC can trade electricity directly with sellers through private bilateral agreements. During the negotiation of private bilateral contracts, the CEC receives the prices and volumes of their members and according to its negotiation strategy, tries to satisfy the electricity demands of all members and reduce their costs for electricity.
Optimal Investment and Consumption for Multidimensional Spread Financial Markets with Logarithmic Utility
Authors: Albosaily Sahar , Pergamenchtchikov Serguei
Published: December 2021

We consider a spread financial market defined by the multidimensional Ornstein–Uhlenbeck (OU) process. We study the optimal consumption/investment problem for logarithmic utility functions using a stochastic dynamical programming method. We show a special verification theorem for this case. We find the solution to the Hamilton–Jacobi–Bellman (HJB) equation in explicit form and as a consequence we construct optimal financial strategies. Moreover, we study the constructed strategies with numerical simulations.
The Immobilisation of Heavy Metals from Sewage Sludge Ash in CO2-Cured Mortars
Authors: Grünhäuser Soares Erick , Castro-Gomes João , Sitarz Mateusz , Zdeb Tomasz , Hager Izabela
Published: December 2021

Sewage treatment processes are a serious environmental threat due to the difficulties involved in its waste management and disposal. Therefore, one developing trend in sewage sludge processing is its thermal treatment, which generates sewage sludge ash that may contain many environmental pollutants, such as heavy metals. Moreover, due to the European Union requirements that not only focus on the waste generation reduction but also on its reuse and final disposal, it is essential to pursue new applications of such resources, such as the waste-based material incorporation into alternative cementitious materials. Thus, this study evaluated the heavy metals leachability of CO2-cured mortars incorporating sewage sludge ash as filler. For this purpose, Portland cement, reactive magnesia, and electric arc furnace slag were used to produce three different CO2-cured mortars, which were cured though pressurised accelerated carbonation curing for 24h. These mortars presented up to 12.7 MPa as compressive strength and their carbonation was confirmed by TG-DTG and FT-IR analyses. Their leachability of heavy metals met the European requirements for all waste materials, including inert materials, and post-industrial wastewater. Therefore, the immobilisation of heavy metals in this binding technology may be considered an effective method to safely manage sewage sludge ash.
An Empirical Mode Decomposition for Establishing Spatiotemporal Air Quality Trends in Shandong Province, China
Authors: Wu Huisheng , Hu Maogui , Zhang Yaping , Han Yuan
Published: December 2021

Air pollution is a serious problem in China, and the government has taken a series of steps to solve it. However, it is still u2nclear how the situation has improved after years of atmospheric pollution control. Shandong Province, which has the second largest population and the highest coal consumption in China, was chosen to analyze the spatiotemporal air quality trends. We obtained daily air quality index (AQI) values from 91 stations in the province from 1 January 2014, to 31 December 2019, based on an adaptive data analysis method, empirical mode decomposition (EMD). The distribution of AQI in Shandong Province was heterogeneous at both spatial and temporal scales. All the stations could be divided into four clusters whose AQI trends decreased from 75 to 53, 95 to 68, 128 to 82, and 148 to 82, respectively. The overall trend of pollution became more serious from east to west in the province. The AQI is the largest in winter, followed by spring and autumn, and the smallest index occurs in summer. There are four types of annual trends of the AQI of each city. The overall downward trend indicates that the air quality of each city was improving annually.
Solving Traveling Salesman Problem with Time Windows Using Hybrid Pointer Networks with Time Features
Authors: Alharbi Majed , Stohy Ahmed , Elhenawy Mohammed , Masoud Mahmoud , El-Wahed Khalifa Hamiden
Published: December 2021

This paper introduces a time efficient deep learning-based solution to the traveling salesman problem with time window (TSPTW). Our goal is to reduce the total tour length traveled by -*the agent without violating any time limitations. This will aid in decreasing the time required to supply any type of service, as well as lowering the emissions produced by automobiles, allowing our planet to recover from air pollution emissions. The proposed model is a variation of the pointer networks that has a better ability to encode the TSPTW problems. The model proposed in this paper is inspired from our previous work that introduces a hybrid context encoder and a multi attention decoder. The hybrid encoder primarily comprises the transformer encoder and the graph encoder; these encoders encode the feature vector before passing it to the attention decoder layer. The decoder consists of transformer context and graph context as well. The output attentions from the two decoders are aggregated and used to select the following step in the trip. To the best of our knowledge, our network is the first neural model that will be able to solve medium-size TSPTW problems. Moreover, we conducted sensitivity analysis to explore how the model performance changes as the time window width in the training and testing data changes. The experimental work shows that our proposed model outperforms the state-of-the-art model for TSPTW of sizes 20, 50 and 100 nodes/cities. We expect that our model will become state-of-the-art methodology for solving the TSPTW problems.
COVID-19 Concerns and Personality of Commerce Workers: Its Influence on Burnout
Authors: Rodríguez-López Ana María , Rubio-Valdehita Susana
Published: December 2021

We analyze burnout in a sample of commercial workers in Spain and its relationship with sociodemographic variables, personality, and concern about the influence of the COVID-19 pandemic on their jobs through a cross-sectional design. Participants (n = 614) answered an online survey, including questions about sociodemographic data, concern, NEO-FFI (personality), and MBI (burnout syndrome). The survey took place from October 2020 to May 2021. We assessed the relationships between sociodemographic variables, pandemic concern, and personality as predictors of burnout by hierarchical regression analysis and then tested using SEM (structural equation models). The proposed model showed adequate goodness-of-fit indices. The results of the present study suggest that the COVID-19 pandemic had little effect to the development of burnout syndrome in commerce employees. However, in agreement with previous literature, the present study shows that personality has a significant role in predicting burnout. Neuroticism, introversion, conscientiousness, and agreeableness were strong predictors for burnout dimensions. In addition, we found that personality directly affected the pandemic concern: individuals with high levels of Neuroticism and low levels of extraversion, agreeableness, and conscientiousness have more pandemic concerns. In conclusion, personality is an important factor that affects the level of workers’ concern about the influence of the pandemic on their job and the development of burnout syndrome. Furthermore, although we found significant differences between groups formed by various sociodemographic characteristics, the conclusion regarding this type of variable is that their ability to predict burnout is deficient.
Selected Aspects of Sustainable Mobility Reveals Implementable Approaches and Conceivable Actions
Authors: Chakraborty Suprava , Kumar Nallapaneni Manoj , Jayakumar Arunkumar , Dash Santanu Kumar , Elangovan Devaraj
Published: December 2021

The transportation sector plays a prominent role in driving the economy of any given nation. However, with the recent tensions arising in and around the transportation sector, sustainable mobility concepts have evolved. However, it is quite unclear whether sustainable mobility is feasible and exhibits economic returns, environmental benefits, and societal advantages. Hence, taking into account the environmental, economic, and social impact, and technical possibilities, this study intends to analyse sustainable mobility in relation to economic returns, environmental benefits and societal advantages using bibliometric analysis. For this study, we considered two decades of research, from 2001 to 2021. An in-depth search was performed on articles generated in the last two decades to assess the state of the literature on sustainable mobility. The most reverent, frequently referenced papers and influential journals in the field of sustainable mobility were identified. The acquired findings highlight the most prominent publications, journals, and authors who have made significant contributions to sustainable mobility studies, as well as the sub-areas or themes linked to sustainable mobility. Overall, the analysis discovered current paradigms, significant research topics, and a relationship between the domains of sustainable mobility studies. Meanwhile, this study also demonstrates advancements in the primary themes and sub-areas during the previous 20 years and alterations in future research fields. In addition, this study identified the promotion of rapid-reliable-safe-convenient (RRSC) transportation services, reduction in urban car traffic, and support to low transportation demand as the critical steps that require immediate attention in order to build a sustainable mobility future. We also observed that hydrogen would be a promising fuel and potential technology for the future mobility sector in the post-COVID era.
The Relationship between Intangible Cultural Heritage and Urban Resilience: A Systematic Literature Review
Authors: Tavares Daniel Sampaio , Alves Fernando Brandão , Vásquez Isabel Breda
Published: December 2021

The need to study and understand urban resilience has been defended by academics, justified by a new global context characterized by a growing urban population and a changing climate. Moreover, the importance of intangible cultural heritage (ICH) has been recognized by UNESCO since 2003. Nevertheless, the relationship between ICH and urban resilience discourses is recent, with academic studies on this topic seeing an exponential growth from 2017 onward. This article aims to develop a systematic literature review in order to answer the research question “how does intangible heritage relate to urban resilience?” and present current academic debates on this relationship. Following a methodology which entailed an academic database search and the application of exclusion criteria, 94 results from Scopus and Web of Science were retrieved and analysed. The article presents a discussion of results and showcases an existing linkage between both areas of study. This study demonstrates the fragmentation and diversity of the debates when addressing the relationship between the two topics, with an existing focus on sustainability discourses, built heritage and the role of local communities. Moreover, the article also shows a prevalence of discourses based on an engineering resilience approach. Considerations for future approaches to ICH and urban resilience are presented, namely, the need to better integrate ICH into urban resilience discourses.
Comparative Study of the Hazardous Chemical Transportation Accident Analyses Using the CREAM Model and the 24Model
Authors: Jiang Wei , Li Yun , Zhou Jiankai , Huang Zhishun , Wu Zonghao
Published: December 2021

Compared with other types of transportation, hazardous chemical transportation is more dangerous and more likely to cause accidents, such as combustion and explosion. To better study the advantages of different accident analysis models and realize the sustainable development of the accident analysis, this paper compares the 24Model and the cognitive reliability and error analysis method in their analyses of causes of hazardous chemical transportation accidents. Regarding their analyses of the causes of hazardous chemical transportation accidents, the causal factors of hazardous chemical transportation accidents are obtained. Then the analysis results of the two models are compared on three aspects: the object of accident influence, the module of accident analysis, and the number of accident causes. Gray correlation analysis and regression analysis are used to quantitatively compare and verify the focus of the two models on the cause of the accident. The results show that the 24Model emphasizes the safety culture of the enterprise, the cognitive reliability and error analysis method emphasizes the technology of the enterprise, and the two accident analysis models provide different emphases on preventing accidents to better achieve the goal of sustainable development.
Serial Multiple Mediation of the Impact of Customer Knowledge Management on Sustainable Product Innovation by Innovative Work Behavior
Authors: Bratianu Constantin , Stanescu Dan Florin , Mocanu Rares , Bejinaru Ruxandra
Published: December 2021

Customer knowledge management (CKM) is a relatively new research domain, aiming at exploring the potential of customer knowledge for the open innovation process of companies. The present paper aims at performing a complex analysis of the serial mediation phenomenon of the impact of CKM on sustainable product innovation (SPI) by innovative work behavior (IWB). The dimensions considered for IWB in the present research are the following: idea exploration, idea generation, idea championing, and idea implementation. In the first phase of our research, we performed a semantic analysis of the main concepts, ideas, and theories, based on a critical literature review. Thus, we reached a deeper understanding of the complexity of the concept of knowledge by learning the theory of knowledge fields and knowledge dynamics. As a result of this conceptual phase, we designed the research model and a questionnaire to be addressed to managers from the business environment. In the quantitative phase of the present research, we used the statistical software packages, SPSS version 26.0, and the PROCESS macro for SPSS, version 3.5. We used well-known criteria for reliability, validation, and interpretation of the numerical results. The final results demonstrate a significant serial mediation phenomenon regarding the impact of CKM on SPI by the IWB. These results are important in developing the co-creation process of new products by using customer knowledge. The present research reveals some original ideas concerning the impact of CKM on SPI by using a serial mediation process performed by basic innovative work dimensions. The implications of the present research are significant for both academics and practitioners in designing open innovation in knowledge ecosystems.
Noninvasive Passenger Detection Comparison Using Thermal Imager and IP Cameras
Authors: Kuchár Pavol , Pirník Rastislav , Tichý Tomáš , Rástočný Karol , Skuba Michal , Tettamanti Tamás
Published: December 2021

Many modern vehicles today are equipped with an on-board e-call system that can send information about the number of passengers in the event of an accident. However, in case of fire or other major danger in a road tunnel, it is very important for rescue services to know not only the number of passengers in a given vehicle that has an accident and called help via e-call but how many people are in the tunnel in total. This paper deals with the issue of passenger detection and counting using the TPH3008-S Thermal camera and the VIVOTEK IP7361 IP Cameras noninvasively, i.e., the cameras are placed outside the vehicle. These cameras have their limitations; therefore, we investigated how to improve conditions and how to make detection better for future work. The main goal of this article is to summarize the achieved results and possibilities of improvement of the proposed system by adding other sensors and systems that would improve the final score of passenger detection. The experimental results demonstrate that our approach has to be modified and we have to add additional sensors or change methods to achieve more promising results. The results, findings and conclusions might be later used in tunnels and highways and also be applied in telematics and lead to better, safer road transport and improvement of existing tunnel systems sustainability by utilizing resources in a smarter way.
Factors Affecting the Efficiency of Teaching Process in Higher Education in the Republic of Serbia during COVID-19
Authors: Kuleto Valentin , Ilić Milena P. , Šević Nevenka Popović , Ranković Marko , Stojaković Dušan , Dobrilović Milutin
Published: December 2021

From the moment the Republic of Serbia declared a state of emergency in the summer semester of 2019/2020, higher education institutions (HEIs) used various teaching models from Distance Learning Systems (DLS), online platforms and modern information and communication technologies (ICT), to sending materials via student e-mails and notifications via faculty portals. Using survey research as a method, the paper describes the experiences of teachers and associates at HEIs in Serbia (780 respondents) regarding the efficiency of provided education services. In this article, we used the method of content analysis and participatory observation, as well. We analysed the attitudes of teachers and associates apropos the efficiency of providing educational services through the work from home (WFH) model and distance learning (DL) and other models used in response to COVID-19 epidemiological measures in education. During the WFH setup, we looked for factors that affect educational efficiency. When it comes to the statistical technique, factor analysis was selected. Technology, managerial support, and work–home conflict are all expected to impact process efficiency, so these were the first criteria considered when selecting potential factors. Principal Component Analysis (PCA) was used as the extraction method, and the Varimax rotation method was also used. We discarded all factors with eigenvalues below one. Four factors caught our attention: School management support, Family–work conflict, Home infrastructure, and Technology choice. The results showed that F1 (School management support) is positively correlated to F2 (Family–work conflict) and efficiency and negatively correlated to F3 (Home infrastructure). Conversely, F2 is negatively correlated to F3 and positively correlated to efficiency. The F4 factor shows no significant correlations to other factors.
Methodology for Documentation and Sustainability of Cultural Heritage Landscapes: The Case of the Tajos de Alhama (Granada, Spain)
Authors: Vílchez-Lara María del Carmen , Molinero-Sánchez Jorge Gabriel , Rodríguez-Moreno Concepción
Published: December 2021

This research aims to start the process of the revitalization of peri-urban spaces with high landscape and cultural potential, dotted with a series of heritage landmarks that allude to the recent industrial, economic and cultural history of the region, currently semi-degraded or abandoned, as is the case with the impressive and steep miller landscape of the Tajos de Alhama de Granada. To achieve this, it is proposed to carry out a comprehensive documentation (historical, cartographic, planimetric, photographic and photogrammetric) of the study area since, until now, there were no similar research studies. The application of an organized and structured method of work, documentation and diagnosis using the tools and graphic techniques of the 21st century has offered extensive results that have been turned into a rigorous and systematic catalog. This catalog will serve as the basis for the promotion of integrated action plans for the recovery of this urban edge, with the triple objective of the rehabilitation of buildings of architectural interest, rehabilitation of the surrounding public space and consolidation of the historic complex that makes up the mills, the river, the landscape and the city. We conclude that the enhancement of the cultural heritage landscape of the Tajos and the guidelines provided for the rehabilitation of its historic water mills, with possible compatible uses (tourist, cultural or administrative), will favor the conservation and sustainable revitalization of such an exceptional heritage site.
A Modified Triple-Diode Model Parameters Identification for Perovskite Solar Cells via Nature-Inspired Search Optimization Algorithms
Authors: Zaky Alaa A. , Fathy Ahmed , Rezk Hegazy , Gkini Konstantina , Falaras Polycarpos , Abaza Amlak
Published: December 2021

Recently, perovskite solar cells (PSCs) have been widely investigated as an efficient alternative for silicon solar cells. In this work, a proposed modified triple-diode model (MTDM) for PSCs modeling and simulation was used. The Bald Eagle Search (BES) algorithm, which is a novel nature-inspired search optimizer, was suggested for solving the model and estimating the PSCs device parameters because of the complex nature of determining the model parameters. Two PSC architectures, namely control and modified devices, were experimentally fabricated, characterized and tested in the lab. The I–V datasets of the fabricated devices were recorded at standard conditions. The decision variables in the proposed optimization process are the nine and ten unknown parameters of triple-diode model (TDM) and MTDM, respectively. The direct comparison with a number of modern optimization techniques including grey wolf (GWO), particle swarm (PSO) and moth flame (MFO) optimizers, as well as sine cosine (SCA) and slap swarm (SSA) algorithms, confirmed the superiority of the proposed BES approach, where the Root Mean Square Error (RMSE) objective function between the experimental data and estimated characteristics achieves the least value.
Impact of the COVID-19 Pandemic to the Sustainability of the Energy Sector
Authors: Siksnelyte-Butkiene Indre
Published: December 2021

In order to control the COVID-19 pandemic, the governments of the world started to implement measures regarding social distance and social contacts, including closures of cities, work and study relocations, and work suspension. The epidemical situation and the lockdown of the economy by governments in various countries caused changes in production, changes in the habits of energy consumers and other energy-related changes. This article analyses the impact of the global pandemic on the energy sector and the relationship with the progress to the sustainability of the energy sector. The systematic literature review was performed in the Web of Science (WoS) database. The research follows recommendations of the SALSA (Search, Appraisal, Synthesis and Analysis) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approaches. A total of 113 relevant articles were selected for the analysis. All selected articles were categorized according to their application and impact areas. The five main impact areas of the COVID-19 pandemic to the sustainability of the energy sector were identified: consumption and energy demand; air pollution; investments in renewable energy; energy poverty; and energy system flexibility. Based on the current research findings and perception of the problem, the main insights for future research in the field are provided.
Experimental Work in Science Education from Green Chemistry Perspectives: A Systematic Literature Review Using PRISMA
Authors: Ferk Savec Vesna , Mlinarec Katarina
Published: December 2021

Experimental work is an important component of science subjects at all educational levels. The implication of green chemistry ideas indicated the need for optimization of traditional experimental work by implementing green chemistry principles to promote sustainable development. The aim of the study is to present findings from a systematic literature review on the use of experimental work in science education from green chemistry perspectives in the literature from 1995 to 2020. Thus, three electronic databases were reviewed following the Preferred Reporting Items for Systematic Reviews (PRISMA) guidelines. The literature search identified a total of 1199 papers from Web of Science (N = 419), Scopus (N = 578), and Education Resources Information Center (ERIC) (N = 202). After applying inclusion/exclusion criteria, 263 papers were obtained and then analyzed in further detail. The findings highlighted trends in the integration of green chemistry principles into experimental work from primary to tertiary education levels and identified a literature gap, as well as the challenges and the possibilities for future development. The review outlined various opportunities for active learning within experimental work from green chemistry perspectives using a range of methods, with a particular focus on practical, hands-on, and laboratory activities.
Assessing and Advancing Gender Equity in Lake Malawi’s Small-Scale Fisheries Sector
Authors: Torell Elin , Manyungwa-Pasani Chikondi , Bilecki Danielle , Gumulira Innocent , Yiwombe Gordon
Published: December 2021

Women play important, but often invisible, roles in Lake Malawi’s small-scale fisheries sector. This paper augments previous research by exploring the productive and reproductive roles that men and women have in fishing communities and how this shapes women’s access and control over fisheries resources. Contributing to advancing the understanding of how to strengthen women’s roles in the fisheries sector, this paper reports on a qualitative assessment conducted in seven Malawian lakeshore districts. Data collected via focus group discussions, which included gendered resource mapping exercises, revealed belief systems and gender norms that shape men’s and women’s access to and control over lacustrine resources. While both men and women have access to lake and land resources, their roles differ. Men dominate fishing resources whereas women dominate resources that are tied to household management. While all value chain nodes are open to men, women tend to be concentrated in lower-value processing and trading activities. Social norms and values shape people’s access and control over communal resources. It is noteworthy that women who earn an income from the fisheries value chain have more access to savings and credit and have more equal household bargaining power.
Climate Change Scenarios and Their Implications on the Energy Performance of Hellenic Non-Residential Buildings
Authors: Droutsa Kalliopi G. , Kontoyiannidis Simon , Balaras Constantinos A. , Argiriou Athanassios A. , Dascalaki Elena G. , Varotsos Konstantinos V. , Giannakopoulos Christos
Published: December 2021

It is important to understand how the climate is changing in order to prepare for the future, adapt if necessary, and, most importantly, take proper precautionary measures to alleviate major negative impacts. This work investigates the potential impacts of climate change on the anticipated energy performance of the existing Hellenic building stock until the end of the century. The assessment considers average climatic projections for two future time periods, one for the near and one for the distant future, following two representative concentration pathways (RCPs). The first one is a baseline scenario (RCP8.5) representing the highest greenhouse gas emissions. The second is an intermediate stabilization scenario (RCP4.5), assuming the imposition of conservative emissions mitigation policies. The future climate data are generated for 62 cities throughout Greece. As a case study, the work focuses on Hellenic non-residential (NR) whole buildings, analyzing available data collected during about 2500 energy audits of real NR buildings. The available data are used to assess the buildings’ heating and cooling demand and energy use. The annual average air temperature for Greece in 2050 is projected to increase by 1.5 K for the RCP4.5 scenario and by 1.9 K for the RCP8.5 scenario. In 2090, the increase is estimated to reach 1.7 K and 4.2 K, respectively. Accordingly, if the existing NR buildings are not renovated, the average heating energy use is expected to decrease by 22–26% in 2050 and by 23–52% in 2090. On the other hand, the average cooling energy use is expected to increase by 24–30% in 2050 and by 28–66% in 2090.
Flood Risk Analysis by Building Use in Urban Planning for Disaster Risk Reduction and Climate Change Adaptation
Authors: Yu Insang , Park Kiyong , Lee Eui Hoon
Published: December 2021

In this study, focusing on buildings as the smallest unit of urban space, the distribution characteristics of risk factors were examined by building use as an adaptable measure for urban flooding disasters. Flood risk is calculated as a function of hazard, exposure, and vulnerability. The flood risk for a building was classified into five classes, and the distribution characteristics of buildings were examined according to England’s flood risk vulnerability classification system, known as Planning Policy Statement 25 (PPS25). After analyzing the risk of flooding in Ulsan Metropolitan City, one of Korea’s representative urban areas, it was found that while Dong-gu District can be considered relatively safe, districts of Jung-gu and Nam-gu, as well as Ulju-gun, have highly vulnerable buildings with red and orange ratings, which include motor vehicles-related facilities, education and welfare facilities, and residential facilities. There has been evidence to prove that urban flood disaster affects topography and the environment, in addition to having a significant effect on adaptability depending on the facility groups that resulted from urbanization. This study is expected to serve as a scientific database for disaster risk reduction and climate change adaptation to floods during land-use planning, which would eventually allow for systematic management of high-risk buildings through verification of location suitability of buildings by facility group.
Digitalization as an Engine for Change? Building a Vision Pathway towards a Sustainable Health Care System by Using the MLP and Health Economic Decision Modelling
Authors: Leväsluoto Johanna , Kohl Johanna , Sigfrids Anton , Pihlajamäki Jussi , Martikainen Janne
Published: December 2021

Grand social challenges, such as type 2 diabetes (T2D), are increasing, which creates sustainability problems for health care service systems. To reduce socio-economic burdens, changes are required in the socio-technical system. However, there is an uncertainty of the most cost-effective policy action that can create sustainability while providing health benefits. To find potential solutions to these challenges, the multi-level perspective (MLP) and health economic decision modelling was used to study socio-technical change and project potential health economic consequences of different scenarios. The study focuses on creating a vision pathway for reducing T2D in Finland. In total, 23 interviews were carried out and the results were analyzed utilizing the MLP model. As a result, five themes towards prevention of T2D were identified. Digitalization was found to be a cross-cutting theme for preventing T2D and was thus taken as the object of study and the main focus of this paper. As a result, this paper reports on the opportunities and barriers for using digital tools in a transition towards T2D prevention. A health economic decision modelling revealed that the highest expected savings could be obtained by prioritizing prevention programs based on T2D risk. Finally, the model was converted into a web-based online tool by combining vision pathway, transition-focused storylines and forward-looking health economic scenario analysis to give the policy makers an overall picture of the needed societal changes and support the impact assessment of alternative policies in a case of T2D prevention in Finland.