bluuebunny/crossref_metadata_2025 huggingface dataset:
Pedestrian Positioning Using an Enhanced Ensemble Transform Kalman Filter
Authors: Sung Kwangjae
Published: August 2023

Due to the unavailability of GPS indoors, various indoor pedestrian positioning approaches have been designed to estimate the position of the user leveraging sensory data measured from inertial measurement units (IMUs) and wireless signal receivers, such as pedestrian dead reckoning (PDR) and received signal strength (RSS) fingerprinting. This study is similar to the previous study in that it estimates the user position by fusing noisy positional information obtained from the PDR and RSS fingerprinting using the Bayes filter in the indoor pedestrian positioning system. However, this study differs from the previous study in that it uses an enhanced state estimation approach based on the ensemble transform Kalman filter (ETKF), called QETKF, as the Bayes filer for the indoor pedestrian positioning instead of the SKPF proposed in the previous study. The QETKF estimates the updated user position by fusing the predicted position by the PDR and the positional measurement estimated by the RSS fingerprinting scheme using the ensemble transformation, whereas the SKPF calculates the updated user position by fusing them using both the unscented transformation (UT) of UKF and the weighting method of PF. In the field of Earth science, the ETKF has been widely used to estimate the state of the atmospheric and ocean models. However, the ETKF algorithm does not consider the model error in the state prediction model; that is, it assumes a perfect model without any model errors. Hence, the error covariance estimated by the ETKF can be systematically underestimated, thereby yielding inaccurate state estimation results due to underweighted observations. The QETKF proposed in this paper is an efficient approach to implementing the ETKF applied to the indoor pedestrian localization system that should consider the model error. Unlike the ETKF, the QETKF can avoid the systematic underestimation of the error covariance by considering the model error in the state prediction model. The main goal of this study is to investigate the feasibility of the pedestrian position estimation for the QETKF in the indoor localization system that uses the PDR and RSS fingerprinting. Pedestrian positioning experiments performed using the indoor localization system implemented on the smartphone in a campus building show that the QETKF can offer more accurate positioning results than the ETKF and other ensemble-based Kalman filters (EBKFs). This indicates that the QETKF has great potential in performing better position estimation with more accurately estimated error covariances for the indoor pedestrian localization system.
The Objective Dementia Severity Scale Based on MRI with Contrastive Learning: A Whole Brain Neuroimaging Perspective
Authors: Zhang Yike , Fan Wenliang , Chen Xi , Li Wei ,   on behalf of the for Alzheimer’s Disease Neuroimaging Initiative
Published: August 2023

In the clinical treatment of Alzheimer’s disease, one of the most important tasks is evaluating its severity for diagnosis and therapy. However, traditional testing methods are deficient, such as their susceptibility to subjective factors, incomplete evaluation, low accuracy, or insufficient granularity, resulting in unreliable evaluation scores. To address these issues, we propose an objective dementia severity scale based on MRI (ODSS-MRI) using contrastive learning to automatically evaluate the neurological function of patients. The approach utilizes a deep learning framework and a contrastive learning strategy to mine relevant information from structural magnetic resonance images to obtain the patient’s neurological function level score. Given that the model is driven by the patient’s whole brain imaging data, but without any possible biased manual intervention or instruction from the physician or patient, it provides a comprehensive and objective evaluation of the patient’s neurological function. We conducted experiments on the Alzheimer’s disease Neuroimaging Initiative (ADNI) dataset, and the results showed that the proposed ODSS-MRI was correlated with the stages of AD 88.55% better than all existing methods. This demonstrates its efficacy to describe the neurological function changes of patients during AD progression. It also outperformed traditional psychiatric rating scales in discriminating different stages of AD, which is indicative of its superiority for neurological function evaluation.
Chromatographic Separations
Authors: Samanidou Victoria
Published: August 2023

The topic of chromatographic separations is a recent addition to the Separations journal [...]
Using a Standing Heel-Rise Test as a Predictor of Ankle Muscle Strength in the Elderly
Authors: Tapanya Weerasak , Maharan Sinthuporn , Sangkarit Noppharath , Poncumhak Puttipong , Konsanit Saisunee
Published: August 2023

The senior population is at increased risk of falling due to a reduction in ankle muscle strength. Evaluating the strength of the ankle muscles in older adults is of paramount importance. The purpose of this study was to formulate an equation to estimate ankle muscle strength by utilizing the basic physical characteristics of the subject and the variables related to their ability to perform the standing heel-rise test (SHRT). One hundred and thirty-two healthy elderly participants (mean age 67.30 ± 7.60) completed the SHRT and provided demographic information. Ankle plantar flexor (PF) muscle strength was evaluated using a push–pull dynamometer. Multiple regression analysis was utilized to develop a prediction equation for ankle PF muscle strength. The study revealed that the ankle PF strength equation was derived from variables including the power index of the SHRT, gender, age, calf circumference, and single-leg standing balance test. The equation exhibited a strong correlation (r = 0.816) and had a predictive power of 65.3%. The equation is represented as follows: ankle PF strength = 24.31 − 0.20(A) + 8.14(G) + 0.49(CC) + 0.07(SSEO) + 0.20(BW/t-SHRT). The equation had an estimation error of 5.51 kg. The strength of ankle PFs in elderly individuals can be estimated by considering demographic variables, including gender, age, calf circumference, single-leg standing balance test, and the power index of the SHRT. These factors were identified as significant determinants of ankle PF strength in this population.
Acute Effects of Supra- and High-Loaded Front Squats on Mechanical Properties of Lower-Limb Muscles
Authors: Krzysztofik Michal , Wilk Michal , Kolinger Dominik , Pisz Anna , Świtała Katarzyna , Petruzela Jan , Stastny Petr
Published: August 2023

Knowledge about the acute effects of supramaximal-loaded resistance exercises on muscle mechanical properties is scarce. Therefore, this study aimed to examine changes in dominant limb biceps femoris and vastus lateralis oscillation frequency and stiffness before and after high- and supramaximal-loaded front squats. Nineteen male handball players participated in the experimental session with a barbell front squat 1RM. The first set was performed at 70% of the 1RM for four repetitions, and the second and third sets were performed at 90%1RM in an eccentric–concentric or an eccentric-only manner at 120% of the 1RM for three repetitions. The handheld myometer was used for the measurement of the biceps femoris and vastus lateralis stiffness and the oscillation frequency of the dominant limb 5 min before and at the 5th and 10th min after front squats. A two-way ANOVA neither indicated a statistically significant interaction (p = 0.335; η2 = 0.059 and p = 0.103; η2 = 0.118), the main effect of a condition (p = 0.124; η2 = 0.126 and p = 0.197; η2 = 0.091), nor the main effect of the time point (p = 0.314; η2 = 0.06 and p = 0.196; η2 = 0.089) for vastus lateralis and biceps femoris stiffness. However, there was a statistically significant interaction (F = 3.516; p = 0.04; η2 = 0.163) for vastus lateralis oscillation frequency. The post hoc analysis showed a significantly higher vastus lateralis oscillation frequency at POST (p = 0.037; d = 0.29) and POST_10 (p = 0.02; d = 0.29) compared to PRE during the SUPRA condition. Moreover, Friedman’s test indicated statistically significant differences in biceps femoris oscillation frequency (test = 15.482; p = 0.008; Kendall’s W = 0.163). Pairwise comparison showed a significantly lower biceps femoris oscillation frequency in POST (p = 0.042; d = 0.31) and POST_10 (p = 0.015; d = 0.2) during the HIGH condition compared to that in the corresponding time points during the SUPRA condition. The results of this study indicate that the SUPRA front squats, compared to the high-loaded ones, cause a significant increase in biceps femoris and vastus lateralis oscillation frequency.
Using Ground-Penetrating Radar and Deep Learning to Rapidly Detect Voids and Rebar Defects in Linings
Authors: Liu Peng , Ding Zude , Zhang Wanping , Ren Zhihua , Yang Xuxiang
Published: August 2023

The geological radar method has found widespread use in evaluating the quality of tunnel lining. However, relying on manual experience to interpret geological radar data may cause identification errors and reduce efficiency when dealing with large numbers of data. This paper proposes a method for identifying internal quality defects in tunnel lining using deep learning and transfer learning techniques. An experimental physical model for detecting the quality of tunnel lining radars was developed to identify the typical radar image features of internal quality defects. Using the geological radar method, a large volume of lining quality detection radar image data was collected, in conjunction with several examples of tunnel engineering. The preprocessing of geological radar data was performed, including gain and normalization, and a set of data samples exhibiting typical lining quality defects was prepared with 6236 detection targets in 4246 images. The intelligent recognition models for tunnel lining quality defects were established using a combination of geological radar image datasets and transfer learning concepts, based on the SSD and YOLOv4 models. The accuracy of the SSD algorithm for cavity defect recognition is 86.58%, with the YOLOv4 algorithm achieving slightly lower accuracy at 86.05%. For steel bar missing recognition, the SSD algorithm has an accuracy of 97.7%, compared to 98.18% accuracy for the YOLOv4 algorithm. This indicates that deep learning-based models are practical for tunnel quality defect detection.
Tracking Sediment Provenance Applying a Linear Mixing Model Approach Using R’s FingerPro Package, in the Mining-Influenced Ocoña Watershed, Southern Peru
Authors: Crespo Jorge , Holley Elizabeth , Guillen Madeleine , Lizaga Ivan , Ticona Sergio , Simon Isaac , Garcia-Chevesich Pablo A. , Martínez Gisella
Published: August 2023

Stream sediments record water–rock interactions in tributaries followed by fluid mixing in larger downstream catchments, but it can be difficult to determine the relative contributions of each tributary. A good way to analyze this problem is sediment fingerprinting, which allows researchers to identify the source of sediments within a basin and to estimate the contribution of each source to the watershed. Herein, we developed a workflow using the frequentist model FingerPro v1.3 to quantify the sediment source contribution in a semiarid watershed. We applied an unmixing model algorithm to an ICP-MS geochemical database containing information on 32 elements in 362 stream sediment samples. By modeling the source contributions to these mixed samples, we infer that the main sediment contribution comes from the upper portion of the catchment (61–70%), followed by the middle (21–29%) and lower (8–10%) parts, with geochemical anomalies (As and Cu) being closely related to mining sites. Results from this study can be helpful for future management decisions to ensure a better environment in this semiarid watershed.
Sustainable Tourism in the Post-COVID-19 Era: Investigating the Effect of Green Practices on Hotels Attributes and Customer Preferences in Budapest, Hungary
Authors: Alreahi Mahmoud , Bujdosó Zoltán , Lakner Zoltán , Pataki Laszlo , Zhu Kai , Dávid Lóránt Dénes , Kabil Moaaz
Published: August 2023

Environmental practices have become an important matter in all aspects of life and industries, especially in the post-COVID-19 era. However, these practices continue to face many criticisms about their seriousness and effectiveness. In this context, this study aims to analyze the relationship between adopting green practices in hotels on one side and hotel image, customer satisfaction, and customer loyalty on the other side, considering the star-level rating system of the hotels and the hotel operating categories (chain or independent). This study depended on a sample of 235 hotels in the Hungarian capital of Budapest. Several analytical methods were used to achieve the study aim, including descriptive statistics, t-test, arithmetic averages comparison, text mining, NLP, and sentiment analysis. This study revealed that: (I) The higher the hotel star rank, the better the reviews and valuation factors. (II) Hotels that operate in chains show more attention to environmental practices. (III) Customers are more loyal to and satisfied with green hotels, and this increases as the hotel’s star rating increases.
Impact of Digital Supply Chain on Sustainable Trade Credit Provision: Evidence from Chinese Listed Companies
Authors: Chen Jinlong , Wu Weipeng , Zhuang Yiqun
Published: August 2023

Given the trend of digitization, it is imperative to ascertain the role of the digital supply chain on sustainable trade credit provision. Based on data from Chinese listed firms from 2008 to 2020, we utilized the TF-IDF algorithm to measure the digital supply chain and ascertained its impact on trade credit. We found that the digital supply chain was positively associated with trade credit provision. Specifically, we arrived at the following conclusions: (1) the digital supply chain strengthens trade credit provision, including to customers and suppliers; (2) top management team power positively and significantly moderates the effect of digital supply chain; (3) among the sub-indicators of the digital supply chain, the dimensions of logistics, products and information have significant and positive impacts, while cash is insignificant; (4) curbing financialization and enhancing asset specialization are the mechanisms of the effect of the digital supply chain; and (5) the effect is more pronounced in firms with higher agency costs and lower supply chain collaboration and non-state ownership, and it is more salient in industries with higher competition and non-national support. We extend the theory of trade credit and enrich the literature on the digital supply chain. Our study offers managerial insights into the digital supply chain for emerging countries and enterprises.
Long-Term Ecological Environment Quality Evaluation and Its Driving Mechanism in Luoyang City
Authors: Li Hui , Jing Haitao , Yan Geding , Guo Huanchao , Luan Wenfei
Published: August 2023

The quality of the ecological environment determines human well-being, and the degree of ecological environment quality has a significant impact on regional sustainable development. Currently, the assessment content of ecological environment quality in Luoyang is relatively single-indicator-based and is insufficient to comprehensively reflect the changes in the ecological environment quality of Luoyang city. Therefore, the study aims to use the Remote Sensing Ecological Index (RSEI), a comprehensive evaluation model, with Landsat remote sensing images and statistical yearbooks as the data sources, to evaluate the spatiotemporal dynamic changes in the ecological environment quality of Luoyang city from 2002 to 2022 through trend analysis and mutation testing; the study employs geographical detectors to analyze the driving factors about the changes in ecological environment quality. The study found that: (1) the average RSEI value in Luoyang city has increased by 0.102 in the past 20 years, indicating an overall improvement in the ecological environment quality of Luoyang city. (2) The northern region of the study area has lower RSEI values, while the southern region has better ecological environment quality, which corresponds to the fact that the northern part of Luoyang city has intensive human activities, while the southern part is characterized by higher vegetation coverage in mountainous areas. (3) The proportion of areas with medium and above ecological environment quality grades have increased from 47.2% to 67.5%, indicating a positive trend in future ecological environment quality changes. (4) The population change was the strongest single factor influencing the ecological environment quality change in Luoyang city. The interaction between temperature and GDP was relatively the strongest. The current ecological environment status in the study area is the result of the combined effects of natural and anthropogenic factors. The research conclusions contribute to improving regional ecological environment quality and are of great significance for the regional ecological environment planning and the achievement of sustainable development goals.
The Impact of Housing Prices on Regional Innovation Capacity: Evidence from China
Authors: Li Jianlong , Lyu Ping , Jin Chen
Published: August 2023

Enhancing regional innovation capacity has become a realistic need to promote high-quality economic development. At present, there is no consensus on how housing prices affect regional innovation capacity. Papers in the existing literature frequently regard regional innovation capacity as a “black box”. Little consideration has been given to the internal processes of this black box as well as to the mechanisms of housing prices affecting regional innovation capacity. In order to crack this black box, regional innovation can be divided into three stages: innovation input, innovation output, and innovation achievement transformation based on innovation value-chain theory. In this paper, an analytical framework of housing prices affecting regional innovation capacity was constructed using the relevant theories. An empirical test was conducted using Chinese provincial panel data from 2004 to 2019. It was found that housing prices have negative impacts on innovation input, innovation output, and innovation achievement transformation capacity. These three impact paths are influenced by the three mechanisms of real-estate investment, human-capital level, and regional consumption capacity, and the impacts are heterogeneous among different regions in China. These findings contribute to our understanding of the relationship between housing prices and regional innovation capacity and provide useful references for enhancing regional innovation capacity.
The Renewable Energy Communities in Italy and the Role of Public Administrations: The Experience of the Municipality of Assisi between Challenges and Opportunities
Authors: Moretti Elisa , Stamponi Ettore
Published: August 2023

The pressing necessity to address climate change calls for the reduction in carbon emissions in the energy sector. Renewable energy communities (RECs) provide environmental, financial, and societal advantages that facilitate the shift towards sustainable energy sources. This paper examines the development of RECs in Italy through a case study in the Municipality of Assisi, and investigates the pivotal role played by public administrations as catalysts in the formation of RECs. Despite facing unique challenges and constraints, Assisi leverages RECs and the proactive approach of the local government to overcome barriers hindering the implementation of renewable energy projects. A municipality-led REC of a total power of 2 MWp by 2030, using clusters of prosumers and consumers and including energy-intensive municipal facilities, is investigated. Through rigorous simulations and the resulting shared energy, the study conducts a comprehensive analysis encompassing technical, energy, and economic aspects. The results, including relevant energy indices, are presented and various scenarios are discussed as the energy shared varies. Finally, sensitivity analyses show that the profitability strongly depends on the cost of energy, the remuneration from the sale, and the value of the incentive earned on the shared energy: the simple payback time ranges from 8 to 14 years and NPV varies from EUR 0.8 to 4.5 M.
Sorption and Photocatalysis of Dyes on an Oil-Based Composite Enriched with Nanometric ZnO and TiO2
Authors: Staroń Anita
Published: August 2023

Sustainable development and environmental protection are among the most important challenges facing humanity today. One important issue is the problem of groundwater and surface water pollution which can lead to the degradation of aquatic ecosystems and negatively affect human health. As a result, new methods and materials are being sought that can help remove contaminants from water in an efficient and environmentally friendly manner. In recent years, there has been increasing interest in composite materials made from used cooking oil. This paper presents attempts to obtain composite materials with the addition of nano-sized zinc oxide and titanium oxide. The characterization of the composite materials was performed using FTIR, XRD, and SEM-EDS; their sorption and photocatalytic abilities were studied using batch mode experiments. The materials obtained exhibited sorption and photocatalytic properties. The highest value of photodegradation efficiency of more than 70% was recorded for the oil composite containing 20% zinc oxide. Composites containing 10% zinc oxide and titanium oxide had comparable sorption efficiencies of about 45% but different photodegradation efficiencies of 0.52% and 15.42%, respectively.
Weather-Informed Recommendations for Pest and Disease Management in the Cashew Production Zone of Côte d’Ivoire
Authors: Amani Kouassi , Coulibaly Kignielman Riflard Luc , Tondoh Ebagnerin Jérôme , Ouattara Zanga Adama , Soro Sibirina , Minhibo Yves Magloire , Kouakou Charles Konan , Aynekulu Ermias , Kouamé Christophe
Published: August 2023

Poorly informed pest and disease management strategies can have detrimental effects on the environment, crop quality, crop yield, farmers’ income, and the overall sustainability of agriculture. For this reason, integrated pest and disease management (IPDM) draws on knowledge from various research fields to effectively manage risks of bio-aggressor outbreaks. However, many agricultural sectors of Sub-Saharan African countries lack such necessary knowledge, including the epidemiology of bio-aggressors in relation to the increased climate variability. The objective of this work is to provide weather-based guidance for the development of sustainable pest and disease control strategies in cashew cultivation areas of Côte d’Ivoire, the second most important cash crop of the country. Leveraging the bioclimatic knowledge in the literature about fungi and insects, we explored four-year hourly data (2017–2020) of 34 sites of the cashew production zone. The outputs showed potentially conducive weather events for fungi and insects throughout the entire cashew production areas, with the forest–savanna transition zone being the most critical. These outputs were used to elicit recommendations for pest and disease management in consultation with a multidisciplinary stakeholder task force. They recommended that effort for disease prevention in the forest–savanna transition zone, the new cashew production zone, should be an incrementation of the one already recommended in the north savanna. Weather-based disease monitoring, entomopathogenic-fungi-based pest control, and the promotion of early-maturing cashew genotypes are also recommended, especially in the forest–savanna transition zone.
Did the Quality of Digital Communication Skills in Education Improve after the Pandemic? Evidence from HEIs
Authors: Urbanek Anna , Losa Anna , Wieczorek-Kosmala Monika , Hlaváček Karel , Lokaj Aleš
Published: August 2023

In the global transition, digital technologies are perceived as important drivers of change. Contemporary IT technologies help to enhance the productivity and efficiency of numerous systems on the way to achieving sustainable development goals. Wider use of digital communication tools in HEIs (higher education institutions) can reduce inequalities and increase inclusiveness of tertiary education. In this paper, we present the results of exploratory research aimed at assessing whether COVID-19 has improved digital communication skills in higher education. We revised the quality of digital direct and indirect communication skills between students and teachers (both ways), by referring to the level of satisfaction of both sides of digital communication (teachers and students). The results indicate that there is a statistically significant improvement in the quality of digital communication skills, in particular, if we compare the position of both students and teachers after the pandemic, relative to the pre-COVID-19 pandemic period. Our investigation confirms that COVID-19 was a shock that enhanced the improvement of digital communication skills in higher education, and the pandemic experience had a positive impact on the more efficient use of digital education technologies.
Subjective and Simulation-Based Analysis of Discomfort Glare Metrics in Office Buildings with Light Shelf Systems
Authors: Faraji Amir , Rezaei Fatemeh , Rahnamayiezekavat Payam , Rashidi Maria , Soleimani Hossein
Published: August 2023

Glare is a kind of physiological phenomenon that influences occupants’ visual comfort. Discomfort glare scenes in comparison to other levels of glare have been difficult to estimate and need accurate and reliable metrics. In contemporary architecture, the glass façade is so popular since it can remarkably minimize energy consumption in buildings and maximize daylight utilization as a natural energy. However, it is necessary to consider occupants’ visual discomfort due to the daylighting glare risks during the initial stage of design. Since the measured glare metrics should have an acceptable correlation with the human subject data study, the agreement on the glare indices is complicated. This paper presents a comparison between subjective and simulation-based analysis of discomfort glare metrics in offices with a light shelf system. The discomfort glare metrics considered in this study include Daylight Glare Index (DGI), CIE Glare Index (CGI), Visual Comfort Probability (VCP), Unified Glare Rating (UGR), and Daylight Glare Probability (DGP). The parallel comparison was conducted by using simulation and questionnaire surveys to determine which criteria are more useful under different conditions. According to the findings, DGP yields the most reliable results in different levels of glare based on the subjective analysis and VCP has the lowest accuracy in each stage. UGR also has the highest accuracy rate for evaluating perceptible glare, DGI is applicable for assessing imperceptible glare, and CGI can be an acceptable index for approximating intolerable glare. The study results significantly reduce the complexity of the problem and can provide useful guidance for designers to select the most reliable glare metric based on climatic conditions.
Enhancing Sustainability Communication among UAE’s Higher Education Students: The Relationship between Sustainable Living Knowledge and Intention to Live Sustainably
Authors: Ben Romdhane Samar , Lee Sang , Al-Shaebi Salem
Published: August 2023

This study investigates the association between sustainable living knowledge and the intention to live sustainably among university students in the UAE. Using a survey method, the research examines students’ perceptions, attitudes, and intentions. The results indicate a positive correlation between sustainable living knowledge and the intention to live sustainably. Specifically, higher levels of sustainable living knowledge are correlated with a strong inclination towards engaging in sustainable behaviors. Additionally, the data analysis supports a proposed serial mediation model, suggesting that sustainable living knowledge influences the intention to live sustainably through perceived benefits and attitudes. These findings emphasize the significance of providing education on sustainable living practices to university students, as it can foster environmentally conscious behaviors and contribute to community development. This research enhances our understanding of the factors influencing individuals’ inclination towards adopting sustainable behaviors, offering valuable insights for designing interventions and educational programs targeted at promoting sustainable living among UAE students.
A Graph Neural Network (GNN)-Based Approach for Real-Time Estimation of Traffic Speed in Sustainable Smart Cities
Authors: Sharma Amit , Sharma Ashutosh , Nikashina Polina , Gavrilenko Vadim , Tselykh Alexey , Bozhenyuk Alexander , Masud Mehedi , Meshref Hossam
Published: August 2023

Planning effective routes and monitoring vehicle traffic are essential for creating sustainable smart cities. Accurate speed prediction is a key component of these efforts, as it aids in alleviating traffic congestion. While their physical proximity is important, the interconnection of these road segments is what significantly contributes to the increase of traffic congestion. This interconnectedness poses a significant challenge to increasing prediction accuracy. To address this, we propose a novel approach based on Deep Graph Neural Networks (DGNNs), which represent the connectedness of road sections as a graph using Graph Neural Networks (GNNs). In this study, we implement the proposed approach, called STGGAN, for real-time traffic-speed estimation using two different actual traffic datasets: PeMSD4 and PeMSD8. The experimental results validate the prediction accuracy values of 96.67% and 98.75% for the PeMSD4 and PeMSD8 datasets, respectively. The computation of mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) also shows a progressive decline in these error values with increasing iteration count, demonstrating the success of the suggested technique. To confirm the feasibility, reliability, and applicability of the suggested STGGAN technique, we also perform a comparison analysis, including several statistical, analytical, and machine-learning- and deep-learning-based approaches. Our work contributes significantly to the field of traffic-speed estimation by considering the structure and characteristics of road networks through the implementation of DGNNs. The proposed technique trains a neural network to accurately predict traffic flow using data from the entire road network. Additionally, we extend DGNNs by incorporating Gated Graph Attention Network (GGAN) blocks, enabling the modification of the input and output to sequential graphs. The prediction accuracy of the proposed model based on DGNNs is thoroughly evaluated through extensive tests on real-world datasets, providing a comprehensive comparison with existing state-of-the-art models for traffic-flow forecasting.
Does COVID-19 Exacerbate Regional Income Inequality? Evidence from 20 Provinces of China
Authors: Wan Wei , Wang Jue , Jiang Weimin
Published: August 2023

The COVID-19 pandemic has had a profound impact on almost all aspects of society and the world’s economy. This study aimed to examine the impact of COVID-19 on regional income inequality in China. Based on provincial data from 20 provinces (covering 218 prefecture-level cities) for the period from 2013 to 2022, the study revealed the significant impact of COVID-19 on widening regional income inequality, using a continuous difference-in-differences (DID) model. The results were robust when applying a parallel trend test, lagging control variables, and alternative measures of regional income inequality to test the results. Additional analysis suggested that the adverse impact of COVID-19 on regional income inequality was increased by regions’ foreign trade dependence and their share in the service industry but may have been weakened by the development of the digital economy. The findings highlight the adverse effects of COVID-19 on regional income inequality.
China’s Outward Foreign Direct Investment and the Environmental Performance of the “Belt and Road Initiative” Countries
Authors: Gao Li , Li Fuyou
Published: August 2023

Since the launch of the “Belt and Road Initiative” (BRI) in 2013, China’s outward foreign direct investment (OFDI) has grown rapidly. Moreover, the environmental protection issues introduced by these investment behaviors to BRI countries have attracted widespread attention from the international community. With the unbalanced panel data of 66 BRI countries from 2006 to 2020, this paper studied the impacts of China’s OFDI on the environmental performance of BRI countries from a systemic and partial perspective. We found that from a systemic perspective, China’s OFDI is conducive to the improvement of the comprehensive environmental performance of countries along the “Belt and Road”. From a partial perspective, the environmental performance influences of China’s OFDI in countries along the “Belt and Road” are threefold: (1) China’s OFDI can help mitigate climate change; (2) China’s OFDI improves wastewater treatment capacity; and (3) China’s OFDI has no significant impact on air quality. Therefore, China’s OFDI needs to continue its efforts to promote and improve the environmental performance of BRI countries to achieve their sustainable development goals. Some BRI developing countries should gradually change their extensive economic growth models; reduce their share of high energy-consuming, high-pollution, and inefficient industries in the national economy; and expand the proportion of their environmentally friendly industries while refraining from improving environmental performance by imposing high environmental pollution taxes.
Symmetry Analysis for the 2D Aw-Rascle Traffic-Flow Model of Multi-Lane Motorways in the Euler and Lagrange Variables
Authors: Paliathanasis Andronikos
Published: August 2023

A detailed symmetry analysis is performed for a microscopic model used to describe traffic flow in two-lane motorways. The traffic flow theory employed in this model is a two-dimensional extension of the Aw-Rascle theory. The flow parameters, including vehicle density, and vertical and horizontal velocities, are described by a system of first-order partial differential equations belonging to the family of hydrodynamic systems. This fluid-dynamics model is expressed in terms of the Euler and Lagrange variables. The admitted Lie point symmetries and the one-dimensional optimal system are determined for both sets of variables. It is found that the admitted symmetries for the two sets of variables form different Lie algebras, leading to distinct one-dimensional optimal systems. Finally, the Lie symmetries are utilized to derive new similarity closed-form solutions.
Convex Polygon Triangulation Based on Symmetry
Authors: Krtolica Predrag V. , Stanimirović Predrag S. , Mašović Sead H. , Elshaarawy Islam A. , Stupina Alena
Published: August 2023

A polygon with n nodes can be divided into two subpolygons by an internal diagonal through node n. Splitting the polygon along diagonal δi,n and diagonal δn−i,n, i∈{2,…,⌊n/2⌋} results in mirror images. Obviously, there are ⌊n/2⌋−1 pairs of these reflectively symmetrical images. The influence of the observed symmetry on polygon triangulation is studied. The central result of this research is the construction of an efficient algorithm used for generating convex polygon triangulations in minimal time and without generating repeat triangulations. The proposed algorithm uses the diagonal values of the Catalan triangle to avoid duplicate triangulations with negligible computational costs and provides significant speedups compared to known methods.
A Comprehensive Survey on Knowledge-Defined Networking
Authors: Wijesekara Patikiri Arachchige Don Shehan Nilmantha , Gunawardena Subodha
Published: August 2023

Traditional networking is hardware-based, having the control plane coupled with the data plane. Software-Defined Networking (SDN), which has a logically centralized control plane, has been introduced to increase the programmability and flexibility of networks. Knowledge-Defined Networking (KDN) is an advanced version of SDN that takes one step forward by decoupling the management plane from control logic and introducing a new plane, called a knowledge plane, decoupled from control logic for generating knowledge based on data collected from the network. KDN is the next-generation architecture for self-learning, self-organizing, and self-evolving networks with high automation and intelligence. Even though KDN was introduced about two decades ago, it had not gained much attention among researchers until recently. The reasons for delayed recognition could be due to the technology gap and difficulty in direct transformation from traditional networks to KDN. Communication networks around the globe have already begun to transform from SDNs into KDNs. Machine learning models are typically used to generate knowledge using the data collected from network devices and sensors, where the generated knowledge may be further composed to create knowledge ontologies that can be used in generating rules, where rules and/or knowledge can be provided to the control, management, and application planes for use in decision-making processes, for network monitoring and configuration, and for dynamic adjustment of network policies, respectively. Among the numerous advantages that KDN brings compared to SDN, enhanced automation and intelligence, higher flexibility, and improved security stand tall. However, KDN also has a set of challenges, such as reliance on large quantities of high-quality data, difficulty in integration with legacy networks, the high cost of upgrading to KDN, etc. In this survey, we first present an overview of the KDN architecture and then discuss each plane of the KDN in detail, such as sub-planes and interfaces, functions of each plane, existing standards and protocols, different models of the planes, etc., with respect to examples from the existing literature. Existing works are qualitatively reviewed and assessed by grouping them into categories and assessing the individual performance of the literature where possible. We further compare and contrast traditional networks and SDN against KDN. Finally, we discuss the benefits, challenges, design guidelines, and ongoing research of KDNs. Design guidelines and recommendations are provided so that identified challenges can be mitigated. Therefore, this survey is a comprehensive review of architecture, operation, applications, and existing works of knowledge-defined networks.
Simultaneous Quantification of 16 Bisphenol Analogues in Food Matrices
Authors: Lucarini Fiorella , Gasco Rocco , Staedler Davide
Published: August 2023

Exposure to bisphenol analogues can occur in several ways throughout the food production chain, with their presence at higher concentrations representing a risk to human health. This study aimed to develop effective analytical methods to simultaneously quantify BPA and fifteen bisphenol analogues (i.e., bisphenol AF, bisphenol AP, bisphenol B, bisphenol BP, bisphenol C, bisphenol E, bisphenol F, bisphenol G, bisphenol M, bisphenol P, bisphenol PH, bisphenol S, bisphenol Z, bisphenol TMC, and tetramethyl bisphenol F) present in canned foods and beverages. Samples of foods and beverages available in the Swiss and EU markets (n = 22), including canned pineapples, ravioli, and beer, were prepared and analyzed using QuEChERS GC-MS. The quantification method was compared to a QuEChERS LC-MS/MS analysis. This allowed for the selective and efficient simultaneous quantitative analysis of bisphenol analogues. Quantities of these analogues were present in 20 of the 22 samples tested, with the most frequent analytes at higher concentrations: BPA and BPS were discovered in 78% and 48% of cases, respectively. The study demonstrates the robustness of QuEChERS GC-MS for determining low quantities of bisphenol analogues in canned foods. However, further studies are necessary to achieve full knowledge of the extent of bisphenol contamination in the food production chain and its associated toxicity.
The Effect of Dietary Protein Concentration on the Fecal Microbiome and Serum Concentrations of Gut-Derived Uremic Toxins in Healthy Adult Cats
Authors: Summers Stacie , Quimby Jessica , Gagné Jason , Lappin Michael
Published: August 2023

The purpose of this study was to evaluate the effect of feeding healthy adult cats with foods containing variable protein concentrations on the fecal microbiome and serum concentrations of the gut-derived uremic toxins indoxyl sulfate, p-cresol sulfate (pCS), and trimethylamine-n-oxide. Twenty healthy young adult cats were randomized into two groups and fed either a low-protein diet (LPD; 7.4 g/100 kcal ME) or a high-protein diet (HPD; 11.0 g/100 kcal ME) for a 12-week period. Serum uremic toxin concentrations were measured via liquid chromatography tandem mass spectrometry, and the fecal microbiome was characterized using shallow sequence shotgun metagenomics. Cats that consumed the HPD had higher pCS concentrations at 8 weeks (p = 0.028) when compared to baseline. After 12 weeks, cats fed the HPD had higher fecal alpha diversity indices at both the taxonomic and functional levels and lower fecal Bifidobacterium relative abundance compared to those cats fed the LPD. In conclusion, a change in diet and dietary protein concentration shifted the fecal microbial community and microbial function. Feeding cats a high amount of protein increased serum concentrations of the uremic toxin pCS; however, the effect was short-lived.
Effect of Peganum harmala Total Alkaloid Extract on Sexual Behavior and Sperm Parameters in Male Mice
Authors: Derbak Hanane , Imre Kálmán , Benabdelhak Amira Chahrazad , Moussaoui Mohamed , Kribeche Amina , Kebbi Rosa , Ayad Abdelhanine
Published: August 2023

The study was designed to evaluate the effects of the total alkaloid extract of Algerian Peganum harmala seeds on sexual behavior and male reproductive function. After two weeks of acclimatization, the male mice were randomly divided into four groups (seven mice in each group). For 35 days, the extract was administered orally at dose levels of 6.25, 12.5, and 25 mg/kg body weight per day to the respective groups of male mice (n = 7) and normal saline daily to the control group. On day 28, sexual behavior parameters were recorded. At the end of the trial, reproductive organ weights, sperm quality, seminal fructose, and testosterone hormone levels were evaluated. The three treated groups were compared with the control using statistical variance analysis (one-way ANOVA, p < 0.05), followed by Tukey’s test. The results of the groups treated with 12.5 and 6.25 mg/kg of P. harmala alkaloid revealed the MF and IF parameters to be the lowest compared to the control group (p < 0.05). However, the male mice treated with 25 mg/kg recorded the highest values. A low significant value of ML was observed in the group treated with 25 mg/kg of the total alkaloid extract of P. harmala compared to the control group (p < 0.01), while a rise was observed in the concentration group treated with 6.25 mg/kg. Regarding IL, the male mice treated with different concentrations of the total alkaloid extract of P. harmala recorded a higher time than the control group. Moreover, an increase in the gonadosomatic index was noticed in all groups compared to the control group. However, there was a significant (p < 0.01) decrease in the sperm counts of the groups treated with 12.5 mg/kg and 6.25 mg/kg. However, there was no significant difference in the motility, membrane integrity, and total antioxidant capacity of sperm cells compared to the control. The extract treatment also brought about a non-significant increase in fructose content of the seminal vesicle and serum testosterone level. The findings of this study demonstrate that the extract acts in a dose-dependent manner, and it has varying effects on the reproductive parameters of male mice.
Water Flow Modeling and Forecast in a Water Branch of Mexico City through ARIMA and Transfer Function Models for Anomaly Detection
Authors: Barrientos-Torres David , Martinez-Ríos Erick Axel , Navarro-Tuch Sergio A. , Pablos-Hach Jose Luis , Bustamante-Bello Rogelio
Published: August 2023

Early identification of anomalies (such as leakages or sensor failures) in urban water distribution systems is critical to mitigating water scarcity in cities and is a challenge in water resource management. Several data-driven methods based on machine learning algorithms have been proposed in the literature for leakage detection in urban water distribution systems. Still, most of them are challenging to implement due to their complexity and requirements of vast amounts of reliable data for proper model generation. In addition, the required infrastructure and instrumentation to collect the data needed to train the models could be unaffordable. This paper presents the use and comparison of Autoregressive Integrated Moving Average models and Transfer Function models generated via the Box–Jenkins approach to modeling the water flow in water distribution systems for anomaly detection. The models were fit using water flow data from tanks operating in a branch of the water distribution system of Mexico City. The results showed that both methods helped select the best model type for each variable in the analyzed water branch, with Seasonal ARIMA models achieving a lower mean absolute percentage error than the fitted Transfer Function models. Furthermore, this methodology can be adjusted to different time windows to generate alerts at different rates and does not require a large sample size. The generated anomaly detection models could improve the efficiency of the water distribution system by detecting anomalies such as wrong measurements and water leakages.
Real-Time Control Operation Method of Water Diversion Project Based on River Diversion Disturbance
Authors: Jin Pengyu , Wang Chao , Sun Jiahui , Lei Xiaohui , Wang Hao
Published: August 2023

Changes in water diversion flow are the major disturbance sources in the daily operation of water diversion projects. Ensuring efficient and safe project operation while dealing with different degrees of water diversion disturbance is crucial for real-time control operation. Based on the historical water diversion projects in China and abroad, this study constructs the water diversion disturbance conditions, selects the typical disturbance lines, and constructs the control objectives for different water diversion disturbance lines. The discrete state space equation of the multi-channel pool integral time-delay model is introduced and used as the system prediction model. Concurrently, the simulation results of the river channel hydrodynamic model are used to correct the system state. The model predictive control algorithm is established according to the objective functions of different typical water distribution disturbance lines, and the control strategy of the control gate and pump station along the water diversion project is formulated to assist in the decision making of the project scheduling operation scheme. The proposed method can better cope with different degrees of river diversion disturbance, compensate for the loss of control performance caused by the low accuracy of the generalized model simulation, and improve water level control and sluice regulation.