bluuebunny/crossref_metadata_2025 huggingface dataset:
Evolution of Landscape Ecological Risk and Identification of Critical Areas in the Yellow River Source Area Based on LUCC
Authors: Lu Zhibo , Song Qian , Zhao Jianyun
Published: June 2023

A reasonable evaluation of the ecological risk status of the landscape in the Yellow River source area is of practical significance for optimizing the regional landscape pattern and maintaining ecosystem function. To explore the regional heterogeneity of ecological risk in the watershed landscape, a landscape ecological risk evaluation model is constructed to evaluate the ecological risk status of the watershed for 20 years, and correlation analysis is used to further reveal the characteristics of the relationship between ecological risk and land use. The results show that the rapid expansion of urbanization and the increasing intensity of land development and use has caused significant changes in the Yellow River source area ecological environment and various land use types. The area of grassland decreased the most, by a total of 6160.04 km2, while the area of unused land increased the most, by a total of 2930.27 km2. A total of 12,453.11 km2 of land in the Yellow River source area was transformed, accounting for 9.52% of the total area. The most significant area of grassland was transferred out, accounting for 49.47% of the transferred area. During the study period, the proportion of area in the low-risk zone decreased from 54.75% to 36.35%, the proportion of area in the medium-low-risk zone increased from 21.75% to 31.74%, and the proportion of area in the medium-high-risk and high-risk zones increased from 10.63% to 14.38%. The high-risk areas are mainly located in areas with fragmented landscapes and are vulnerable to human activities. The mean ecological risk values in the study area show an increasing trend, and the spatial distribution shows a hierarchical distribution of “lower around the center and higher in the center”. The global Moran’s I index is higher than 0.68, which indicates that the ecological risk values have a significant positive correlation in space, the area of cold spots of ecological risk varies significantly, and the spatial pattern fluctuates frequently, while the spatial distribution of hot spots is relatively stable. Therefore, the landscape ecological risk in the Yellow River source area is rising, but the different risk levels and their spatial aggregation patterns and cold and hot spot areas continue to transform, which requires continuous planning of the landscape pattern to enhance the safety and stability of the regional ecosystem.
A New Efficient Cuckoo Search MPPT Algorithm Based on a Super-Twisting Sliding Mode Controller for Partially Shaded Standalone Photovoltaic System
Authors: Hadj Salah Zahra Bel , Krim Saber , Hajjaji Mohamed Ali , Alshammari Badr M. , Alqunun Khalid , Alzamil Ahmed , Guesmi Tawfik
Published: June 2023

The impact of Partial Shading Conditions (PSCs) significantly influences the output of Photovoltaic Systems (PVSs). Under PSCs, the Power-Voltage (P-V) characteristic of the PVS unveils numerous power peaks, inclusive of local maxima and a global maximum. The latter represents the optimum power point. Traditional Maximum Power Point Tracking (MPPT) algorithms struggle to track the Global Maximum Power Point (GMPP). To address this, our study emphasizes the creation of a novel algorithm capable of identifying the GMPP. This approach combines the Cuckoo Search (CS) MPPT algorithm with an Integral Super-Twisting Sliding Mode Controller (STSMC) using their benefits to enhance the PVS performance under PSCs in terms of high efficiency, low power losses, and high-speed convergence towards the GMPP. The STSMC is a second-order Sliding Mode Control strategy that employs a continuous control action that attenuates the “chattering” phenomenon, caused when the first-order SMC technique is employed. Indeed, the proposed CS-STSMC-MPPT algorithm consists of two parts. The first one is based on the CS algorithm used for scanning the power-voltage curve to identify the GMPP, and subsequently generating the associated optimal voltage reference. The second part aims to track the voltage reference by manipulating the duty cycle of the boost converter. The proposed CS-STSMC-MPPT algorithm is featured by its strength against uncertainties and modeling errors. The obtained simulation results underline a high convergence speed and an excellent precision of the proposed method in identifying and tracking the GMPP with high efficiency under varying shading scenarios. For comparative purposes, this method is set against the hybrid CS-Proportional Integral Derivative, the conventional CS, the Particle Swarm Optimization, and the Perturb and Observe algorithms under different PSCs, including zero, weak, and severe shading. Simulation conducted in the Matlab/Simulink environment confirms the superior performance of the proposed CS-STSMC-MPPT algorithm in terms of precision, convergence speed, efficiency, and resilience.
Dynamic Multi-Criteria Decision Making of Graduate Admission Recommender System: AHP and Fuzzy AHP Approaches
Authors: Wongvilaisakul Wanvipa , Netinant Paniti , Rukhiran Meennapa
Published: June 2023

The optimal management of personal resources impacts everyone’s quality of life. An investment in graduate education is a sustainable opportunity for improved outcomes in human life, including cognition, behavior, life opportunities, salary, and career. Advanced technology dramatically reduces the risk of personal resources in graduate program admission recommendations that depend on multiple individual needs and preferences. In the digital age, a dynamic recommender system enhances the suitably effective solution for students’ university selections. This study focused on designing, developing, and testing a recommender system for graduate admission using a dynamic multi-criteria AHP and fuzzy AHP approach. The explicit multi-criteria recommender system was a platform as a service (PaaS) web application created to aid in graduate admissions management and decision-making. The design proposed that the bit representation store a dynamic explicit multi-criteria data structure. The recommendations adopting dynamic multi-criteria were validated by comparing them to the programs to which the students were actually admitted and enrolled. They individually ranked the evaluation outcomes of dynamic explicit multi-criteria and alternative preferences to provide graduate admission recommendations. Eighty graduate students in information technology evaluated the recommender system. Using top-1, top-2, and F1-score accuracy, the effective system accuracy performance on the dynamic multi-criteria recommender system was evaluated using AHP and fuzzy AHP approaches. The fuzzy AHP demonstrated marginally greater practical accuracy than the AHP method.
Assessing the Sustainability of the Consumption of Agricultural Products with Regard to a Possible Reduction in Its Imports: The Case of Countries That Import Corn and Wheat
Authors: Yemelyanov Olexandr , Petrushka Tetyana , Lesyk Lilia , Havryliak Anatolii , Yanevych Nataliya , Kurylo Oksana , Bodakovskyy Volodymyr , Skoropad Iryna , Danylovych Taras , Petrushka Kateryna
Published: June 2023

The main goal of this study is to develop and apply a toolkit for assessing the sustainability level of countries that import certain types of agricultural products with regard to a possible reduction in the volume of such imports. An indicator was proposed for evaluating sustainability. A model of its decomposition was built, which made it possible to reveal the hierarchy of factors of the formation of the specified sustainability. The concept of sustainable growth in consuming a certain agricultural product by a group of importing countries was developed. In particular, such growth conditions were formalized. Approbation of the formed methodological principles was carried out on two groups of countries that import corn and wheat. In particular, it was established that an increase in the relative vulnerability level of countries to a reduction in imported food supplies is negatively correlated with an increase in the relative level of adaptation of countries to such a reduction. It was also found that as of 1 January 2023, the resistance level of the studied countries to a reduction in corn and wheat imports has significantly decreased. Proposals are developed to increase the resilience level of food-importing countries to possible disruptions in their import supplies.
The Effect of the Evaluation of Trash Can Removal Policy under the “Compulsory Times” of Waste-Sorting in Longhua District in China
Authors: Geng Xu , Li Honghao , Liu Xiaoyu , Liu Huayun , Huang Miaoxin
Published: June 2023

In China, waste sorting has gradually entered “compulsory times”. The beginning of the compulsory times of waste sorting is marked by the implementation of the policy to remove trash cans in residential building hallways. Since then, this policy has been controversial. Based on the theory of planned behavior and the public’s perspective as well as using the Delphi method and entropy weight method, we investigated Longhua District in Shenzhen and designed an evaluation index system for the effect of the policy to remove trash cans from the following three dimensions: the policy cognitive level, the policy admissive degree, and the awareness of waste sorting. The data were supplemented by observations and interview methods as well as questionnaire surveys that were distributed in residential sub-districts in Longhua District. According to the quantitative research and variance analysis of the data, the policy promotes waste sorting. This paper provides a new idea on how to evaluate environmental policies such as the garbage-classification policy from the perspective of the public based on rigorous evaluation methods and processes.
Monitoring Urban Expansion (2000–2020) in Yangtze River Delta Using Time-Series Nighttime Light Data and MODIS NDVI
Authors: Zou Yanhong , Shen Jingya , Chen Yuying , Zhang Baoyi
Published: June 2023

The Yangtze River Delta Urban Agglomeration (YRDUA), which is located in the convergence zone of “The Belt and Road Initiative”, is one of the regions with the best urbanization foundations in China. Referring to the four five-year plans (China’s national economic plan), this study aimed to investigate the spatiotemporal patterns of urban expansion in the YRDUA from 2000 to 2020. To conduct a long-term analysis of urbanization, an extended time series (2000–2020) of a nighttime light (NTL) dataset was built from the multi-temporal Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) data (2000–2013), and Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) data (2014–2020); data from these sources are crucial to understanding the urbanization processes in the region in order for more effective decision making to take place. The support vector machine (SVM) method was used to extract urban clusters from the extended time-series NTL data and MODIS NDVI products. The evolution of the urban expansion intensity was detected at city scales, and the inequality of urban growth was demonstrated using the Lorenz curve and Gini coefficient. Finally, a quantitative relationship between urban NTL intensity and socio-economic data was built to explore the main factors that control urban intensity. The results indicated that the urban extents extracted from time-series NTL data were consistent with those extracted from Landsat data, with an average overall accuracy (OA) of 89%. A relatively fast urbanization pace was observed during the 10th five-year plan (from 2000 to 2005), which then declined slightly in the 11th five-year plan (from 2006 to 2010). By the 12th and 13th five-year plan (from 2011 to 2020), urban clusters in all cities tended to grow steadily. Urban expansion has presented a radial pattern around the main cities, with sprawl inequality across cities. The results further revealed that the primary factors controlling NTL brightness were gross domestic product (GDP), total fixed asset investment, tertiary industry, gross industrial output, urban area, and urban permanent residents in city clusters, but the same driving factors had a different contribution order on the NTL intensity across cities. This study provides significant insight for further urbanization study to be conducted in the YRDUA region, which is crucial for sustainable urban development in the region.
The Impact of Green Finance on Upgrading the Manufacturing Industry of the Yangtze River Economic Belt Based on the Spatial Econometric Model
Authors: Zhang Xiaoling , Shan Zhangming , Wang Xuerong , Tang Decai
Published: June 2023

China’s economic development has made world-renowned achievements which have subsequently brought serious environmental pollution problems. As a large manufacturing country, the industrial value added in China accounts for nearly 30% of the global share. However, the manufacturing industry lacks competence. Therefore, considering environmental protection issues while promoting manufacturing industry upgrading (MIU) is a critical goal for China at present. By applying panel data from China’s Yangtze River Economic Belt (YREB) from 2010 to 2019, this paper uses a spatial econometric model to study the effect of green finance (GF) on upgrading (hereafter, upgrading refers to the improvement of the manufacturing structure and the transition toward green manufacturing) the manufacturing industry. This study found that the impact of GF on MIU in the YREB is significantly positive. GF has a good positive spatial spillover effect. Moreover, the high level of urbanization and high freight volume have positive impacts, the impacts of foreign investment and government fiscal expenditure are significantly negative, and the effect of the level of human capital on upgrading is not obvious. Therefore, vigorously developing green finance, optimizing the environment for manufacturing upgrades, and cultivating green and high-quality talents should be the future direction of government policy formulation.
Greenhouse Gas Emission and Water Footprint of the National Diet in Turkey: Results from Turkey Nutrition and Health Survey 2017
Authors: Ilhan Azad , Yenicag Rumeysa , Yalcin Pehlivan Ece , Ozturk Ebru , Karahan Sevilay , Rakıcıoğlu Neslişah
Published: June 2023

The study aimed to assess and characterize the sustainability of the national diet in Turkey and its association with diet quality, dietary requirements, and sociodemographic factors. Dietary intake was assessed using 24 h recalls from the Turkey Nutrition and Health Survey 2017 (TNHS-2017) (n = 12,527). The environmental footprints were assessed with two environmental indicators: greenhouse gas emissions (GHGEs) and water footprint (WF). Diet quality was assessed using the diet quality index (DQI) and dietary diversity score (DDS). The dietary GHGEs was 3.21 ± 2.07 kg CO2-eq/person/day and the dietary total WF was 2832 ± 1378 L/person/day. The DQI and DDS were 62.26 ± 8.28 and 6.66 ± 1.31, respectively. Total energy intake was significantly higher in the highest GHGEs and total WF tertiles (2238 ± 722 and 2383 ± 701 kcal, respectively) compared to lower GHGEs and total WF tertiles. Individuals with higher diet-related GHGEs and total WF had a higher daily intake of nutrients with the exception of the percentages of energy supplied from carbohydrates, percentages of meeting nutrients according to the recommended dietary allowance (RDA), DQI (excluding DQI total, moderation, and overall balance score), and DDS scores (p < 0.001). GHGEs and total WF value of the national diet in Turkey are lower than the world average. The results would help develop dietary guidelines to encourage sustainable dietary choices.
Profiling and Prevalence of Substance-Related and Addictive Disorders and Behavioural Addictions in Incarcerated Traffic Offenders
Authors: Fariña Francisca , Romero Juan , Isorna Manuel , Arce Ramón
Published: June 2023

A field study was designed to determine if traffic offenders were characterised by substance-related and addictive disorders and behavioural addictions, and to examine their prevalence in this population. A total of 268 regular drivers (weekly or daily use) participated in the study; 132 incarcerated traffic offenders and 136 drivers with no criminal background. Subsamples were matched in age, sex, and time elapsed since their driving test. Participants responded to a measure of impulse control and addictions. The results revealed a more-than-problematic effect regarding drug addiction, alcohol consumption, and compulsive purchasing in the population of traffic offenders. In contrast, a trivial effect (insignificant) was observed in addiction to gambling, internet, videogames, eating, and sex. Comparatively, traffic offenders reported higher addiction to drugs, alcohol, gambling, compulsive purchasing, and sex, but less addiction to internet than controls. As for caseness analysis, a significant prevalence of caseness (>0.05) was observed in traffic offenders in connection to drugs, alcohol, internet, compulsive purchasing, and eating addictions. Moreover, addiction comorbidity or multi-comorbidity was found to be common (=0.50). The implications of the results for interventions with traffic offenders are discussed.
Technologies Associated with Industry 4.0 in Green Supply Chains: A Systematic Literature Review
Authors: Morella Paula , Lambán María Pilar , Royo Jesús , Sánchez Juan Carlos , Latapia Jaime
Published: June 2023

This study aims to review the literature published in the last 15 years (2007–2022) that relates to Industry 4.0 and the green supply chain (GSC). This review identifies the enabling technologies for Industry 4.0 that are being applied to improve GSC practices and establishes the points of its future research agenda. A systematic literature review (SLR) of this topic was conducted using Web of Science and Scopus as databases. Our study combined descriptive and conceptual analysis, guided by three review questions. These questions were proposed to identify how Industry 4.0 technologies can enhance the development of GSCs. After refinement, 75 papers were analyzed. This research underlines what technologies of Industry 4.0 are being implemented to improve GSC aspects. In addition, it shows in which GSC practices the application of these technologies is being considered and what challenges of implementing Industry 4.0 in GSCs have been identified in the literature. Finally, all these findings enhance establishing a future research agenda. Current studies have focused on analyzing the benefits of implementing Industry 4.0 in GSCs. However, this paper delves into enabling technologies for Industry 4.0 and GSC practices and does not only discuss them in general terms.
Biodiesel Production from Jatropha: A Computational Approach by Means of Artificial Intelligence and Genetic Algorithm
Authors: Khanna Abhirup , Lamba Bhawna Yadav , Jain Sapna , Bolshev Vadim , Budnikov Dmitry , Panchenko Vladimir , Smirnov Alexandr
Published: June 2023

In the past couple of years, the world has come to realize the importance of renewable sources of energy and the disadvantages of excessive use of fossil fuels. Numerous studies have been conducted to implicate the benefits of artificial intelligence in areas of green energy production. Artificial intelligence (AI) and machine learning algorithms are believed to be the driving forces behind the fourth industrial revolution and possess capabilities for interpreting non-linear relationships that exist in complex problems. Sustainable biofuels are derived from renewable resources such as plants, crops, and waste materials other than food crops. Unlike traditional fossil fuels such as coal and oil, biofuels are considered to be more sustainable and environmentally friendly. The work discusses the transesterification of jatropha oil into biodiesel using KOH and NaOH as alkaline catalysts. This research aims to examine and optimize the nonlinear relationship between transesterification process parameters (molar ratio, temperature, reaction time, and catalyst concentration) and biodiesel properties. The methodology employed in this study utilizes AI and machine learning algorithms to predict biodiesel properties and improve the yield and quality of biodiesel. Deep neural networks, linear regression, polynomial regression, and K-nearest neighbors are the algorithms implemented for prediction purposes. The research comprehensively examines the impact of individual transesterification process parameters on biodiesel properties, including yield, viscosity, and density. Furthermore, this research introduces the use of genetic algorithms for optimizing biodiesel production. The genetic algorithm (GA) generates optimal values for transesterification process parameters based on the desired biodiesel properties, such as yield, viscosity, and density. The results section presents the transesterification process parameters required for obtaining 72%, 85%, and 98% biodiesel yields. By leveraging AI and machine learning, this research aims to enhance the efficiency and sustainability of biodiesel production processes.
Evaluation of Urban Sustainability through Perceived Importance, Performance, Satisfaction and Loyalty: An Integrated IPA–SEM-Based Modelling Approach
Authors: Bose Arghadeep , Basak Debanjan , Roy Subham , Chowdhury Indrajit Roy , Abdo Hazem Ghassan , Aldagheiri Mohammed , Almohamad Hussein
Published: June 2023

In recent years, there has been a surge in research pertaining to sustainable urban development. Importance–performance analysis (IPA) has emerged as one of the most widely used methods. However, few studies have combined IPA with structural equation modelling (SEM). This study introduces and evaluates an integrated IPA–SEM approach to assess the impact of perceived importance and performance of sustainable city dimensions on residents’ satisfaction and loyalty. The data for this study were collected from 425 survey respondents residing in Siliguri City of West Bengal, India. The results indicate that the ‘Keep Up the Good Work’ quadrant, consisting of social quality, connectivity conditions, and environmental quality, significantly and positively affected satisfaction. On the other hand, the ‘Concentrate Here’ quadrant concerning environmental pollution presented a negative yet insignificant relationship with satisfaction while both the ‘Low Priority’ and ‘Potential Overkill’ quadrants demonstrated no notable influence. Furthermore, a robust positive correlation between satisfaction and loyalty is confirmed. Overall, the findings offer valuable implications for urban planning, policy-making, and strategies aimed at enhancing Siliguri residents’ quality of life.
An Analysis of Sustainability in Real Estate in Job Advertisements and Personal Profiles in Switzerland
Authors: Wrase Isabelle
Published: June 2023

Organizations in and associated with the real estate sector rely on a competent workforce capable of effectively managing sustainability practices. This study examined the alignment of sustainability-related qualifications between employers and individuals in the Swiss real estate-related job market. A comprehensive analysis was conducted on 600 job advertisements and 1520 personal profiles, employing a keyword-based search approach derived from sustainability definitions and contexts. The findings revealed that companies emphasized the importance of “sustainability” in their job advertisements, whereas employees rarely mentioned it in their profiles. Nevertheless, both employers and workers demonstrate a demand for expertise, competencies, and skills that contribute to fostering sustainability in the real estate domain. Noteworthy keywords encompassed digitalization, green buildings, economic thinking, creativity, and collaboration. To facilitate improved job matching between organizations and applicants and to ensure sustainable practices in the real estate sector, it is recommended that organizations integrate these suggested keywords in their job advertisements and that employees incorporate the corresponding keywords into their profiles. Furthermore, educational institutions can enhance their programs by incorporating these keywords and the proposed dimensions of sustainability into their educational frameworks.
An Approach for Measuring Complexity Degree of International Engineering Projects
Authors: Ju Qianqian , Sun Yankun , Chen Ran
Published: June 2023

With the increasing trend of globalization, countries actively join the international engineering market, increasing the complexity of projects. An appropriate method for assessing project complexity can help project managers recognize the current situation and solve problems. However, existing complexity studies ignored the contribution of human element. The impact of human activities on the complexity of socio-economic systems is concerned in the Harmony Management Theory (HMT), therefore, this study proposed a complexity measurement based on the Harmony Management Theory. Firstly, an evaluation indicator system including three dimensions of organizational, technological, and environmental complexity is proposed through a literature review, Back-Propagation Neural Network-Decision Making Trial and Evaluation Laboratory (BP-DEMATEL) method, and Interval-valued intuitionistic Fuzzy Sets (IIFS) are used to calculate complexity scores. Then, a case study of Yawan high-speed railway project is conducted to verify the effectiveness of the proposed method. The main conclusions are as follows: (1) The complexity measurement based on harmony theory can be carried out from three aspects: Organization, technology, and environment. (2) The overall complexity performance of case project is 0.52, showing a medium level. (3) Technical complexity is the most important factor of the case project. The method proposed in this study can identify the influencing factors of complexity and calculate the comprehensive evaluation value of complexity. Based on the final quantified results, managers can formulate appropriate measures and match the project with appropriate resources so as to improve the performance of International Engineering Projects (IEPs).
Tourism Sector: The Supply Chain Social Footprint of an Italian Accommodation Facility
Authors: Arzoumanidis Ioannis , D’Eusanio Manuela
Published: June 2023

In Italy, as in the rest of the world, the COVID-19 pandemic had an important impact on tourism. In particular, in the Abruzzo region, there was a decrease of circa 60% in tourist arrivals from 2019 to 2021. A driving force for the recovery of the tourism sector may be investment in sustainability activities, especially those connected to social aspects, which appear to be less considered. Social sustainability can be investigated through the Social Life Cycle Assessment methodology, thus allowing the assessment of the social risks that are connected to the life cycle of a delivered service. This study aims at providing a social footprint of the supply chain of one overnight stay at an accommodation facility using the Social Hotspot Database. The main results show that electricity consumption has the greatest impact within the life cycle, and it is the “Health and Safety” category that is the most influenced, followed by “Labour Rights and Decent Work”, “Governance” and “Human Rights”. Finally, at a subcategory level, “Occupational Toxics and Hazards” is the most influenced one, followed by “Occupational Injuries and Deaths”, “High Conflict”, “Corruption” and “Legal System”. These findings allow us to understand the overall main social risks related to the sector in Italy, since the assessment of the foreground system was conducted at a country and a sector level.
Sustainability Interventions of Construction Project Managers—Establishing a Minimum Baseline
Authors: Arabpour Shabnam , Silvius Gilbert
Published: June 2023

The implementation of higher standards for sustainability presents a challenge to the construction industry. Sustainable construction guidelines often emphasize the outcomes of a project rather than addressing the sustainability aspects of its delivery, management, and governance processes. Project management standards and frameworks recognize the significance of sustainability. However, they lack practical instructions for project managers on effectively integrating sustainability into their project management practices. This study addresses this gap in the literature by investigating the perceived effectiveness and ease of sustainability interventions, with the aim of developing a ‘minimum baseline’ set of interventions that managers of construction projects can make in order to develop more sustainable projects. From the existing literature, 42 sustainability interventions by a project manager were derived. A quantitative survey-based research approach utilizing a self-administrated online questionnaire was employed to assess their effectiveness and ease of implementation. The questionnaire was distributed to the project managers worldwide, and valuable input was received from 105 respondents globally. The findings indicate that a minimum baseline of interventions can be formed with ten interventions that focus on the themes of communication, guidelines and regulations, and the supply chain. By revealing this minimum baseline, the study provides practical guidance for project managers of construction projects and fills the gap in the literature regarding the lack of a practical framework for improving sustainability in construction projects.
Enhancing the Sustainability of Deep-Learning-Based Network Intrusion Detection Classifiers against Adversarial Attacks
Authors: Alotaibi Afnan , Rassam Murad A.
Published: June 2023

An intrusion detection system (IDS) is an effective tool for securing networks and a dependable technique for improving a user’s internet security. It informs the administration whenever strange conduct occurs. An IDS fundamentally depends on the classification of network packets as benign or attack. Moreover, IDSs can achieve better results when built with machine learning (ML)/deep learning (DL) techniques, such as convolutional neural networks (CNNs). However, there is a limitation when building a reliable IDS using ML/DL techniques, which is their vulnerability to adversarial attacks. Such attacks are crafted by attackers to compromise the ML/DL models, which affects their accuracy. Thus, this paper describes the construction of a sustainable IDS based on the CNN technique, and it presents a method for defense against adversarial attacks that enhances the IDS’s accuracy and ensures it is more reliable in performing classification. To achieve this goal, first, two IDS models with a convolutional neural network (CNN) were built to enhance the IDS accuracy. Second, seven adversarial attack scenarios were designed against the aforementioned CNN-based IDS models to test their reliability and efficiency. The experimental results show that the CNN-based IDS models achieved significant increases in the intrusion detection system accuracy of 97.51% and 95.43% compared with the scores before the adversarial scenarios were applied. Furthermore, it was revealed that the adversarial attacks caused the models’ accuracy to significantly decrease from one attack scenario to another. The Auto-PGD and BIM attacks had the strongest effect against the CNN-based IDS models, with accuracy drops of 2.92% and 3.46%, respectively. Third, this research applied the adversarial perturbation elimination with generative adversarial nets (APE_GAN++) defense method to enhance the accuracy of the CNN-based IDS models after they were affected by adversarial attacks, which was shown to increase after the adversarial attacks in an intelligible way, with accuracy scores ranging between 78.12% and 89.40%.
Effect of Hydrothermal Curing on the Hydration and Strength Development of Belite Cement Mortar Containing Industrial Wastes
Authors: Rubinaite Dovile , Dambrauskas Tadas , Baltakys Kestutis , Siauciunas Raimundas
Published: June 2023

This paper describes the impact of hydrothermal conditions on the strength properties and hydration processes of belite cement mortar samples. The belite-rich binder was synthesized by sintering the initial mixture of raw materials (granite cutting waste, the silica-gel waste from AlF3 production, and natural materials) in a high-temperature furnace at a temperature of 1150 °C for 2 h. The prepared clinker consists of larnite, mayenite, srebrodolskite, ye’elimite, and gehlenite. To control hydration kinetics and optimize the hardening of belite cement mortar, the produced clinker was blended with 7.5% of gypsum. The mechanical properties were assessed by curing the standard prisms (following the EN 196-1 standard, cement/sand = 1:3, W/C= 0.67) under water-saturated conditions in a stainless steel autoclave. The curing process was performed in a temperature range of 90 °C to 200 °C at various hydrothermal curing durations (6–48 h). The results indicated that the curing conditions highly influence the compressive strength evolution of belite cement mortar and the formed mineralogy of hydrates. The highest compressive strength value (exceeded 20 MPa) was obtained at 200 °C, i.e., when the main belite cement mineral was entirely hydrated and recrystallized into 1.13 nm tobermorite. The microstructural evolution and the phase assemblage during the hydrothermal curing were determined by X-ray diffraction analysis and differential scanning calorimetry.
Influence of Borehole Casing on Received Signals in Downhole Method
Authors: Yan Shi , Yu Yan , Zheng Wenjun , Su Jie , Zhou Zhenghua
Published: June 2023

Borehole shrinkage and collapse are likely to occur when downhole testing is conducted in soft or loose sandy soils, resulting in testing interruption. To prevent this situation from occurring, installing casing in the borehole is a common approach. However, in actual testing, the quality of the signal obtained from measuring points within the depth of the casing is often not ideal, and there is still no clear and unified justification for the causes of interference generated by the casing. Therefore, the team attempt to investigate and elucidate the impact of casing through on-site experiments and numerical simulations. Firstly, on-site tests on the impact of different materials of casing on the wave velocity test utilizing the downhole method are conducted, the waveform characteristics of the measurement points inside the PVC casing and steel casing boreholes are analyzed, and the usability of the test results are evaluated. Next, the contact state between shallow soil and casing is changed, and its impact on the waveform characteristics of signal at different depth measurement points is analyzed. Then, the ABAQUS finite element software is utilized to establish a three-dimensional finite element model for wave velocity testing using the casing method, and the dynamic response of the measuring points on the casing wall inside the hole under surface excitation is solved. By numerically simulating different casing materials, the contact state between the casing and the hole wall, and the presence of low wave velocity filling soil around the casing, the variation patterns of the affected measurement point signals in the time and frequency domains are investigated. Furthermore, combined with the measured data, the impact characteristics of the casing on the results of the wave velocity testing using the downhole method are systematically explored. This research can provide some insights for the application and data interpretation of signals in the downhole methods of cased wells.
Assessing Impacts of Land Use and Land Cover (LULC) Change on Stream Flow and Runoff in Rur Basin, Germany
Authors: Shukla Saurabh , Meshesha Tesfa Worku , Sen Indra S. , Bol Roland , Bogena Heye , Wang Junye
Published: June 2023

Understanding the impact of land use/land cover (LULC) change on hydrology is the key to sustainable water resource management. In this study, we used the Soil and Water Assessment Tool (SWAT) to evaluate the impact of LULC change on the runoff in the Rur basin, Germany. The SWAT model was calibrated against the observed data of stream flow and runoff at three sites (Stah, Linnich, and Monschau) between 2000 and 2010 and validated between 2011 and 2015. The performance of the hydrological model was assessed by using statistical parameters such as the coefficient of determination (R2), p-value, r-value, and percentage bias (PBAIS). Our analysis reveals that the average R2 values for model calibration and validation were 0.68 and 0.67 (n = 3), respectively. The impacts of three change scenarios on stream runoff were assessed by replacing the partial forest with urban settlements, agricultural land, and grasslands compared to the 2006 LULC map. The SWAT model captured, overall, the spatio-temporal patterns and effects of LULC change on the stream runoffs despite the heterogeneous runoff responses related to the variable impacts of the different LULC. The results show that LULC change from deciduous forest to urban settlements, agricultural land, or grasslands increased the overall basin runoff by 43%, 14%, and 4%, respectively.
When Security Risk Assessment Meets Advanced Metering Infrastructure: Identifying the Appropriate Method
Authors: Shokry Mostafa , Awad Ali Ismail , Abd-Ellah Mahmoud Khaled , Khalaf Ashraf A. M.
Published: June 2023

Leading risk assessment standards such as the NIST SP 800-39 and ISO 27005 state that information security risk assessment (ISRA) is one of the crucial stages in the risk-management process. It pinpoints current weaknesses and potential risks, the likelihood of their materializing, and their potential impact on the functionality of critical information systems such as advanced metering infrastructure (AMI). If the current security controls are insufficient, risk assessment helps with applying countermeasures and choosing risk-mitigation strategies to decrease the risk to a controllable level. Although studies have been conducted on risk assessment for AMI and smart grids, the scientific foundations for selecting and using an appropriate method are lacking, negatively impacting the credibility of the results. The main contribution of this work is identifying an appropriate ISRA method for AMI by aligning the risk assessment criteria for AMI systems with the ISRA methodologies’ characteristics. Consequently, this work makes three main contributions. First, it presents a comprehensive comparison of multiple ISRA methods, including OCTAVE Allegro (OA), CORAS, COBRA, and FAIR, based on a variety of input requirements, tool features, and the type of risk assessment method. Second, it explores the necessary conditions for carrying out a risk assessment for an AMI system. Third, these AMI risk assessment prerequisites are aligned with the capabilities of multiple ISRA approaches to identify the best ISRA method for AMI systems. The OA method is found to be the best-suited risk assessment method for AMI, and this outcome paves the way to standardizing this method for AMI risk assessment.
Analysis of Redundancy Techniques for Electronics Design—Case Study of Digital Image Processing
Authors: Balasubramanian Padmanabhan
Published: June 2023

Electronic circuits/systems operating in harsh environments such as space are likely to experience faults or failures due to the impact of high-energy radiation. Given this, to overcome any faults or failures, redundancy is usually employed as a hardening-by-design approach. Moreover, low power and a small silicon footprint are also important considerations for space electronics since these translate into better energy efficiency, less system weight, and less cost. Therefore, the fault-tolerant design of electronic circuits and systems should go hand in hand with the optimization of design metrics, especially for resource-constrained electronics such as those used in space systems. A single circuit or system (also called a simplex implementation) is not fault-tolerant as it may become a single point of failure and is not used for a space application. As an alternative, a triple modular redundancy (TMR) implementation, which uses three identical copies of a circuit or system and a voter to perform majority voting of the circuits and systems outputs, may be used. However, in comparison with a simplex implementation, a TMR implementation consumes about 200% more area and dissipates 200% more power when circuits or systems are triplicated. To mitigate the area and power overheads of a TMR implementation compared to a simplex implementation, researchers have suggested alternative redundancy approaches such as selective TMR (STMR) insertion, partially approximate TMR (PATMR), fully approximate TMR (FATMR), and majority voting-based reduced precision redundancy (VRPR). Among these, VRPR appears to be promising, especially for inherently error-tolerant applications such as digital image/video/audio processing, which is relevant to space systems. However, the alternative redundancy approaches mentioned are unlikely to be suitable for the implementation of control logic. In this work, we analyze various redundancy approaches and evaluate the performance of TMR and VRPR for a digital image processing application. We provide MATLAB-based image processing results corresponding to TMR and VRPR and physical implementation results of functional units based on TMR and VRPR using a 28-nm CMOS technology.
Modeling and Evaluation of a Dynamic Channel Selection Framework for Multi-Channel Operation in ITS-G5
Authors: Váczi Ottó , Bokor László
Published: June 2023

Many years ago, it seemed inconceivable that our cars could drive autonomously or communicate with each other to form a self-organizing convoy or platoon. By 2023, however, technological advances have taken us to the point where most of these goals will be achieved. In the time of what was initially known as Day 1, single-channel Intelligent Transport System (ITS) devices fully met the requirements for safe communication. The trends show that with the rapid development and the emergence of new, more robust Vehicle-to-Everything (V2X) applications, which require higher bandwidth (collectively called Day 2), the current single-channel medium access method in the available ITS bands will no longer achieve the desired capacities. The main reason is that Day 2 and beyond V2X information dissemination protocols introduce increasing packet sizes and sending frequencies. To complete a resource-friendly and more efficient operation with Day 2 or other advanced V2X services, ITS standards present the Multi-Channel Operation (MCO) constellation as a potential solution. In the case of MCO, we use two or more channels simultaneously, thus preventing the radio medium from saturating its capacity. However, there are still several pending questions about MCO applicability, practical usage, configuration, and deployment, to name a few. The primary purpose of this article is to present a dynamic channel selection framework design and implementation capable of modeling and simulating advanced multi-channel communication use cases. We used this framework to investigate Channel Busy Ratio (CBR) based dynamic channel switching within the Artery/OMNeT++ simulation environment.
AI-Assisted Multi-Operator RAN Sharing for Energy-Efficient Networks
Authors: Peesapati Saivenkata Krishna Gowtam , Olsson Magnus , Andersson Sören , Qvarfordt Christer , Dahlen Anders
Published: June 2023

Recent times have seen a significant rise in interest from mobile operators, vendors, and research projects toward achieving more energy-efficient and sustainable networks. Not surprisingly, it comes at a time when higher traffic demand and more stringent and diverse network requirements result in diminishing benefits for operators using complex AI-driven network optimization solutions. In this paper, we propose the idea of tower companies that facilitate radio access network (RAN) infrastructure sharing between operators and evaluate the additional energy savings obtained in this process. In particular, we focus on the RAN-as-a-Service (RANaaS) implementation, wherein each operator leases and controls an independent logical RAN instance running on the shared infrastructure. We show how an AI system can assist operators in optimizing their share of resources under multiple constraints. This paper aims to provide a vision, a quantitative and qualitative analysis of the RANaaS paradigm, and its benefits in terms of energy efficiency. Through simulations, we show the possibility to achieve up to 75 percent energy savings per operator over 24 h compared to the scenario where none of the energy-saving features are activated. This is an additional 55 percent energy savings from sharing the RAN infrastructure compared to the baseline scenario where the operators use independent hardware.
Poly- and Perfluoroalkyl Substances (PFAS): Do They Matter to Aquatic Ecosystems?
Authors: Nayak Sipra , Sahoo Gunanidhi , Das Ipsita Iswari , Mohanty Aman Kumar , Kumar Rajesh , Sahoo Lakshman , Sundaray Jitendra Kumar
Published: June 2023

Poly- and perfluoroalkyl substances (PFASs) are a group of anthropogenic chemicals with an aliphatic fluorinated carbon chain. Due to their durability, bioaccumulation potential, and negative impacts on living organisms, these compounds have drawn lots of attention across the world. The negative impacts of PFASs on aquatic ecosystems are becoming a major concern due to their widespread use in increasing concentrations and constant leakage into the aquatic environment. Furthermore, by acting as agonists or antagonists, PFASs may alter the bioaccumulation and toxicity of certain substances. In many species, particularly aquatic organisms, PFASs can stay in the body and induce a variety of negative consequences, such as reproductive toxicity, oxidative stress, metabolic disruption, immunological toxicity, developmental toxicity, cellular damage and necrosis. PFAS bioaccumulation plays a significant role and has an impact on the composition of the intestinal microbiota, which is influenced by the kind of diet and is directly related to the host’s well-being. PFASs also act as endocrine disruptor chemicals (EDCs) which can change the endocrine system and result in dysbiosis of gut microbes and other health repercussions. In silico investigation and analysis also shows that PFASs are incorporated into the maturing oocytes during vitellogenesis and are bound to vitellogenin and other yolk proteins. The present review reveals that aquatic species, especially fishes, are negatively affected by exposure to emerging PFASs. Additionally, the effects of PFAS pollution on aquatic ecosystems were investigated by evaluating a number of characteristics, including extracellular polymeric substances (EPSs) and chlorophyll content as well as the diversity of the microorganisms in the biofilms. Therefore, this review will provide crucial information on the possible adverse effects of PFASs on fish growth, reproduction, gut microbial dysbiosis, and its potential endocrine disruption. This information aims to help the researchers and academicians work and come up with possible remedial measures to protect aquatic ecosystems as future works need to be focus on techno-economic assessment, life cycle assessment, and multi criteria decision analysis systems that screen PFAS-containing samples. New innovative methods requires further development to reach detection at the permissible regulatory limits.