bluuebunny/crossref_metadata_2025 huggingface dataset:
Simulation and Multi-Objective Optimization of Three-Column Double-Effect Methanol Distillation by NSGA-III Algorithm
Authors: Chen Weiye , Hu Zehua , Gao Xuechao , Liu Yefei
Published: May 2023

The multi-objective optimization of methanol distillation is a critical and complex issue in the methanol industry. The three-column methanol distillation scheme is first simulated with Aspen Plus to provide the initial value of the NSGA-III algorithm. The operating parameters are optimized through the Python-Aspen platform. The total annual cost and CO2 emissions are considered the objective function. A small value of indicator generational distance can be achieved by increasing the number of generations, which is helpful in improving algorithm convergence. The NSGA-III algorithm has good convergence and distribution performance. By comparing the optimized results with the original ones, the total annual cost and CO2 emissions are, respectively, reduced by 5.35% and 12.80% when the operating parameters of the methanol distillation sequence are optimized through NSGA-III. As a result, substantial economic and energy savings can be made, offering great potential to improve the performance of the three-column methanol distillation.
Modelling the Mechanism of Sulphur Evolution in the Coal Combustion Process: The Effect of Sulphur–Nitrogen Interactions and Excess Air Coefficients
Authors: Jiang Yu , Yang Xinyu , Ma Honghe
Published: May 2023

The efficient use of coal resources and the safe operation of coal-fired boilers are hindered by high-temperature corrosion caused by corrosive sulphur components. To predict the impact of sulphur–nitrogen interactions on sulphur’s evolution and its mechanism of action, a conventional sulphur component evolution model (uS–N) and an improved sulphur component evolution model (S–N) that considers sulphur–nitrogen interactions were proposed in the present study. The models were built using OpenFOAM–v8 software for the coal combustion process, and the generation of SO2, H2S, COS, and CS2 was simulated and analysed under different air excess coefficients. The simulations were conducted to analyse the patterns of SO2, H2S, COS, and CS2 generation at different air excess factors. The results show that, compared with the uS–N condition, the simulated values of coal combustion products (SO2, H2S, COS, and CS2) under the S–N condition were closer to the experimental values, and the errors of different sulphur components at the furnace exit were all less than 5%. As such, the S–N model can more accurately predict the evolution of sulphur components. In the simulation range, when the air excess factor increased from 0.7 to 0.9, the production rate of SO2 increased, while the production rates of corrosive sulphur components H2S, COS, and CS2 decreased significantly by 41.3%, 34.8%, and 53.8%, respectively. Further, the mechanism of the effect of sulphur–nitrogen interactions on the generation rates of different components was revealed at different air excess coefficients. Here, the effect of sulphur–nitrogen interactions on SO2 and COS was found to be more significant at smaller air excess coefficients, and the effect of sulphur–nitrogen interactions on H2S and CS2 was more significant at larger air excess coefficients. The present study can provide a theoretical basis for predicting the evolution of sulphur components during coal combustion and improving the high-temperature corrosion problems caused by such a process.
Solid Precipitation Behaviors in Coal Slag from Different Primary Phases and Their Effects on Slag Viscosity from Thermochemistry and Experimental
Authors: Guo Jing , He Chong , Yuan Mengran , Fan Feifei , Qin Yuhong , Yan Jingchong
Published: May 2023

Undesired solid precipitation in coal slag at high temperatures can cause serious blockages, or even the shutdown of coal gasifiers, due to a rapid increase in slag viscosity. In this study, the solid precipitation behaviors of coal slag from different primary phases and under different atmospheres were both experimentally and theoretically investigated. Our results demonstrated that the viscosity of the coal slag in the primary phase of mullite was strongly influenced by the atmosphere at a typical tapping temperature of 1300 °C because of the high content of network formers. The viscosity of the partially crystallized slag was significantly affected by solid precipitation behavior. Iron was converted to magnetite and hematite in air and was reduced to metallic iron under a reducing atmosphere. Increasing CaO content improved both the iron reduction reaction and the slag crystallization behavior. Anorthite precipitation was largely inhibited under a mild reducing atmosphere, leading to a large difference in the viscosity of coal slag under different atmospheres. In contrast, the viscosity of the coal slag in the primary phase of mullite was slightly influenced by the atmosphere due to the weak crystallization tendency of mullite, as well as its high slag viscosity.
Toward a Real-Time Analysis of Column Height by Visible Cameras: An Example from Mt. Etna, in Italy
Authors: Aravena Alvaro , Carparelli Giuseppe , Cioni Raffaello , Prestifilippo Michele , Scollo Simona
Published: May 2023

Volcanic plume height is one the most important features of explosive activity; thus, it is a parameter of interest for volcanic monitoring that can be retrieved using different remote sensing techniques. Among them, calibrated visible cameras have demonstrated to be a promising alternative during daylight hours, mainly due to their low cost and low uncertainty in the results. However, currently these measurements are generally not fully automatic. In this paper, we present a new, interactive, open-source MATLAB tool, named ‘Plume Height Analyzer’ (PHA), which is able to analyze images and videos of explosive eruptions derived from visible cameras, with the objective of automatically identifying the temporal evolution of eruption columns. PHA is a self-customizing tool, i.e., before operational use, the user must perform an iterative calibration procedure based on the analysis of images of previous eruptions of the volcanic system of interest, under different eruptive, atmospheric and illumination conditions. The images used for the calibration step allow the computation of ad hoc expressions to set the model parameters used to recognize the volcanic plume in new images, which are controlled by their individual characteristics. Thereby, the number of frames used in the calibration procedure will control the goodness of the model to analyze new videos/images and the range of eruption, atmospheric, and illumination conditions for which the program will return reliable results. This also allows improvement of the performance of the program as new data become available for the calibration, for which PHA includes ad hoc routines. PHA has been tested on a wide set of videos from recent explosive activity at Mt. Etna, in Italy, and may represent a first approximation toward a real-time analysis of column height using visible cameras on erupting volcanoes.
Local Climate Zone Classification by Seasonal and Diurnal Satellite Observations: An Integration of Daytime Thermal Infrared Multispectral Imageries and High-Resolution Night-Time Light Data
Authors: Wang Ziyu , Cao Shisong , Du Mingyi , Song Wen , Quan Jinling , Lv Yang
Published: May 2023

Accurate, rapid, and automatic local climate zone (LCZ) mapping is essential for urban climatology and studies in terms of urban heat islands. Remotely sensed imageries incorporated with machine learning algorithms are widely utilized in LCZ labeling. Nevertheless, large-scale LCZ mapping is still challenging due to the complex vertical structure of underlying urban surfaces. This study proposed a new method of LCZ labeling that uses a random forest classifier and multi-source remotely sensed data, including Sentinel 1A Synthetic Aperture Radar (SAR), Sentinel 2 Multispectral Instrument, and Luojia1-01 night-time light data. In particular, leaf-on and -off imageries and surface thermal dynamics were utilized to enhance LCZ labeling. Additionally, we systematically evaluated how daytime and night-time features influence the performance of the classification procedure. Upon examination, the results for Beijing, China, were confirmed to be robust and refined; the Overall Accuracy (OA) value of the proposed method was 88.86%. The accuracy of LCZs 1–9 was considerably increased when using the land surface temperature feature. Among these, the Producer Accuracy (PA) value of LCZ 3 (compact low-rise) significantly increased by 16.10%. Notably, it was found that NTL largely contributed to the classification concerning LCZ 3 (compact low-rise) and LCZ A/B (dense trees). The performance of integrating leaf-on and -off imageries for LCZ labeling was better than merely uses of leaf-on or -off imageries (the OA value increased by 4.75% compared with the single use of leaf-on imagery and by 3.62% with that of leaf-off imagery). Future studies that use social media big data and Very-High-Resolution imageries are required for LCZ mapping. This study shows that combining multispectral, SAR, and night-time light data can improve the performance of the random forest classifier in general, as these data sources capture significant information about surface roughness, surface thermal feature, and night-time features. Moreover, it is found that incorporating both leaf-on and leaf-off remotely sensed imageries can improve LCZ mapping.
Site Agnostic Approach to Early Detection of Cyberbullying on Social Media Networks
Authors: López-Vizcaíno Manuel , Nóvoa Francisco J. , Artieres Thierry , Cacheda Fidel
Published: May 2023

The rise in the use of social media networks has increased the prevalence of cyberbullying, and time is paramount to reduce the negative effects that derive from those behaviours on any social media platform. This paper aims to study the early detection problem from a general perspective by carrying out experiments over two independent datasets (Instagram and Vine), exclusively using users’ comments. We used textual information from comments over baseline early detection models (fixed, threshold, and dual models) to apply three different methods of improving early detection. First, we evaluated the performance of Doc2Vec features. Finally, we also presented multiple instance learning (MIL) on early detection models and we assessed its performance. We applied timeawareprecision (TaP) as an early detection metric to asses the performance of the presented methods. We conclude that the inclusion of Doc2Vec features improves the performance of baseline early detection models by up to 79.6%. Moreover, multiple instance learning shows an important positive effect for the Vine dataset, where smaller post sizes and less use of the English language are present, with a further improvement of up to 13%, but no significant enhancement is shown for the Instagram dataset.
Digital Twinning for 20th Century Concrete Heritage: HBIM Cognitive Model for Torino Esposizioni Halls
Authors: Spanò Antonia , Patrucco Giacomo , Sammartano Giulia , Perri Stefano , Avena Marco , Fillia Edoardo , Milan Stefano
Published: May 2023

In the wide scenario of heritage documentation and conservation, the multi-scale nature of digital models is able to twin the real object, as well as to store information and record investigation results, in order to detect and analyse deformation and materials deterioration, especially from a structural point of view. The contribution proposes an integrated approach for the generation of an n-D enriched model, also called a digital twin, able to support the interdisciplinary investigation process conducted on the site and following the processing of the collected data. Particularly for 20th Century concrete heritage, an integrated approach is required in order to adapt the more consolidated approaches to a new conception of the spaces, where structure and architecture are often coincident. The research plans to present the documentation process for the halls of Torino Esposizioni (Turin, Italy), built in the mid-twentieth century and designed by Pier Luigi Nervi. The HBIM paradigm is explored and expanded in order to fulfil the multi-source data requirements and adapt the consolidated reverse modelling processes based on scan-to-BIM solutions. The most relevant contributions of the research reside in the study of the chances of using and adapting the characteristics of the IFC (Industry Foundation Classes) standard to the archiving needs of the diagnostic investigations results so that the digital twin model can meet the requirements of replicability in the context of the architectural heritage and interoperability with respect to the subsequent intervention phases envisaged by the conservation plan. Another crucial innovation is a proposal of a scan-to-BIM process improved by an automated approach performed by VPL (Visual Programming Languages) contribution. Finally, an online visualisation tool enables the HBIM cognitive system to be accessible and shareable by stakeholders involved in the general conservation process.
Electric-Field Mapping of Optically Perturbed CdTe Radiation Detectors
Authors: Cola Adriano , Dominici Lorenzo , Valletta Antonio
Published: May 2023

In radiation detectors, the spatial distribution of the electric field plays a fundamental role in their operation. Access to this field distribution is of strategic importance, especially when investigating the perturbing effects induced by incident radiation. For example, one dangerous effect that prevents their proper operation is the accumulation of internal space charge. Here, we probe the two-dimensional electric field in a Schottky CdTe detector using the Pockels effect and report on its local perturbation after exposure to an optical beam at the anode electrode. Our electro-optical imaging setup, together with a custom processing routine, allows the extraction of the electric-field vector maps and their dynamics during a voltage bias-optical exposure sequence. The results are in agreement with numerical simulations, allowing us to confirm a two-level model based on a dominant deep level. Such a simple model is indeed able to fully account for both the temporal and spatial dynamics of the perturbed electric field. This approach thus allows a deeper understanding of the main mechanisms affecting the non-equilibrium electric-field distribution in CdTe Schottky detectors, such as those leading to polarization. In the future, it could also be used to predict and improve the performance of planar or electrode-segmented detectors.
Toward the Definition of a Soundscape Ranking Index (SRI) in an Urban Park Using Machine Learning Techniques
Authors: Benocci Roberto , Afify Andrea , Potenza Andrea , Roman H. Eduardo , Zambon Giovanni
Published: May 2023

The goal of estimating a soundscape index, aimed at evaluating the contribution of the environmental sound components, is to provide an accurate “acoustic quality” assessment of a complex habitat. Such an index can prove to be a powerful ecological tool associated with both rapid on-site and remote surveys. The soundscape ranking index (SRI), introduced by us recently, can empirically account for the contribution of different sound sources by assigning a positive weight to natural sounds (biophony) and a negative weight to anthropogenic ones. The optimization of such weights was performed by training four machine learning algorithms (decision tree, DT; random forest, RF; adaptive boosting, AdaBoost; support vector machine, SVM) over a relatively small fraction of a labeled sound recording dataset. The sound recordings were taken at 16 sites distributed over an area of approximately 22 hectares at Parco Nord (Northern Park) of the city Milan (Italy). From the audio recordings, we extracted four different spectral features: two based on ecoacoustic indices and the other two based on mel-frequency cepstral coefficients (MFCCs). The labeling was focused on the identification of sounds belonging to biophonies and anthropophonies. This preliminary approach revealed that two classification models, DT and AdaBoost, trained by using 84 extracted features from each recording, are able to provide a set of weights characterized by a rather good classification performance (F1-score = 0.70, 0.71). The present results are in quantitative agreement with a self-consistent estimation of the mean SRI values at each site that was recently obtained by us using a different statistical approach.
Multi-Label Classification in Anime Illustrations Based on Hierarchical Attribute Relationships
Authors: Lan Ziwen , Maeda Keisuke , Ogawa Takahiro , Haseyama Miki
Published: May 2023

In this paper, we propose a hierarchical multi-modal multi-label attribute classification model for anime illustrations using a graph convolutional network (GCN). Our focus is on the challenging task of multi-label attribute classification, which requires capturing subtle features intentionally highlighted by creators of anime illustrations. To address the hierarchical nature of these attributes, we leverage hierarchical clustering and hierarchical label assignments to organize the attribute information into a hierarchical feature. The proposed GCN-based model effectively utilizes this hierarchical feature to achieve high accuracy in multi-label attribute classification. The contributions of the proposed method are as follows. Firstly, we introduce GCN to the multi-label attribute classification task of anime illustrations, enabling the capturing of more comprehensive relationships between attributes from their co-occurrence. Secondly, we capture subordinate relationships among the attributes by adopting hierarchical clustering and hierarchical label assignment. Lastly, we construct a hierarchical structure of attributes that appear more frequently in anime illustrations based on certain rules derived from previous studies, which helps to reflect the relationships between different attributes. The experimental results on multiple datasets show that the proposed method is effective and extensible by comparing it with some existing methods, including the state-of-the-art method.
The Classification of Movement in Infants for the Autonomous Monitoring of Neurological Development
Authors: Turner Alexander , Hayes Stephen , Sharkey Don
Published: May 2023

Neurodevelopmental delay following extremely preterm birth or birth asphyxia is common but diagnosis is often delayed as early milder signs are not recognised by parents or clinicians. Early interventions have been shown to improve outcomes. Automation of diagnosis and monitoring of neurological disorders using non-invasive, cost effective methods within a patient’s home could improve accessibility to testing. Furthermore, said testing could be conducted over a longer period, enabling greater confidence in diagnoses, due to increased data availability. This work proposes a new method to assess the movements in children. Twelve parent and infant participants were recruited (children aged between 3 and 12 months). Approximately 25 min 2D video recordings of the infants organically playing with toys were captured. A combination of deep learning and 2D pose estimation algorithms were used to classify the movements in relation to the children’s dexterity and position when interacting with a toy. The results demonstrate the possibility of capturing and classifying children’s complexity of movements when interacting with toys as well as their posture. Such classifications and the movement features could assist practitioners to accurately diagnose impaired or delayed movement development in a timely fashion as well as facilitating treatment monitoring.
Realization of Forest Internet of Things Using Wireless Network Communication Technology of Low-Power Wide-Area Network
Authors: Zhao Ming , Ye Ren-Jie , Chen Shuo-Tsung , Chen Yen-Chun , Chen Zi-Yu
Published: May 2023

This work implements an intelligent forest monitoring system using the Internet of things (IoT) with the wireless network communication technology of a low-power wide-area network (LPWAN), a long range (LoRa), and a narrow-band Internet of things (NB-IoT). A solar micro-weather station with LoRa-based sensors and communications was built to monitor the forest status and information such as the light intensity, air pressure, ultraviolet intensity, CO2, etc. Moreover, a multi-hop algorithm for the LoRa-based sensors and communications is proposed to solve the problem of long-distance communication without 3G/4G. For the forest without electricity, we installed solar panels to supply electricity for the sensors and other equipment. In order to avoid the problem of insufficient solar panels due to insufficient sunlight in the forest, we also connected each solar panel to a battery to store electricity. The experimental results show the implementation of the proposed method and its performance.
“Zero Residue” Concept—Implementation and Certification Challenges
Authors: Djekic Ilija , Smigic Nada , Udovicki Bozidar , Tomic Nikola
Published: May 2023

This paper gives an overview of scientific challenges in implementing and certifying “Zero residue” approach. The rationale behind the concept is that final control of commodities during/immediately after harvesting should confirm that traces of all used plant protection products are less than or equal to 0.01 mg/kg. To evaluate the risks in applying this concept, FMEA (Failure Mode and Effect Analysis) as a tool has been used. Among the most common factors affecting the pesticide residue levels in fresh produce, the following three appeared to be the biggest challenges in the “Zero residue” concept implementation and certification process: the use of unregistered plant protection products, inadequate sampling plan, and inappropriate laboratory methods. The analysis showed that all three factors have strong influence on achieving “Zero residue” limits.
Holistic Education for Sustainable Development: A Study of Shaping the Pro-Quality Attitude of Students in the Polish Educational System
Authors: Spychalski Bartosz
Published: May 2023

The scope of this article is fact finding and evaluation of activities for sustainable development in the field of shaping a pro-quality attitude in the educational system. As indicated by UNESCO, education for sustainable development needs a holistic approach. In this perspective, pro-quality education plays a double role—it includes contents that are innovative and closely related to sustainable development and it is a link connecting other activities of the educational system to achieve the objectives of education for sustainable development. This issue, as overlooked in most countries, is an attractive educational innovation, which can affect shaping students’ attitudes in a more effective way. For the purpose of this research, the author collected secondary data as a result of literature analysis, mostly including governing and archival acts of law, as well as raw data with the use of a diagnostic survey method with a questionnaire survey technique. The main stage of this method was collecting research data through surveying students, representatives of teaching staff, and school principals from schools in Poland. Research conducted by the author supported the proposed hypothesis, according to which the Polish educational system is not oriented towards shaping the pro-quality attitude of high school students.
Design of Efficient Based Artificial Intelligence Approaches for Sustainable of Cyber Security in Smart Industrial Control System
Authors: Alzahrani Ali , Aldhyani Theyazn H. H.
Published: May 2023

Online food security and industrial environments and sustainability-related industries are highly confidential and in urgent need for network traffic analysis to attain proper security information to avoid attacks from anywhere in the world. The integration of cutting-edge technology such as the Internet of things (IoT) has resulted in a gradual increase in the number of vulnerabilities that may be exploited in supervisory control and data acquisition (SCADA) systems. In this research, we present a network intrusion detection system for SCADA networks that is based on deep learning. The goal of this system is to defend ICSs against network-based assaults that are both conventional and SCADA-specific. An empirical evaluation of a number of classification techniques including k-nearest neighbors (KNN), linear discriminant analysis (LDA), random forest (RF), convolution neural network (CNN), and integrated gated recurrent unit (GRU) is reported in this paper. The suggested algorithms were tested on a genuine industrial control system (SCADA), which was known as the WUSTL-IIoT-2018 and WUSTL-IIoT-20121 datasets. SCADA system operators are now able to augment proposed machine learning and deep learning models with site-specific network attack traces as a result of our invention of a re-training method to handle previously unforeseen instances of network attacks. The empirical results, using realistic SCADA traffic datasets, show that the proposed machine learning and deep-learning-based approach is well-suited for network intrusion detection in SCADA systems, achieving high detection accuracy and providing the capability to handle newly emerging threats. The accuracy performance attained by the KNN and RF algorithms was superior and achieved a near-perfect score of 99.99%, whereas the CNN-GRU model scored an accuracy of 99.98% using WUSTL-IIoT-2018. The Rf and GRU algorithms achieved >99.75% using the WUSTL-IIoT-20121 dataset. In addition, a statistical analysis method was developed in order to anticipate the error that exists between the target values and the prediction values. According to the findings of the statistical analysis, the KNN, RF, and CNN-GRU approaches were successful in achieving an R2 > 99%. This was demonstrated by the fact that the approach was able to handle previously unknown threats in the industrial control systems (ICSs) environment.
Spatiotemporal Distribution and Influencing Factors of Coupling Coordination between Digital Village and Green and High-Quality Agricultural Development—Evidence from China
Authors: Wang Heng , Tang Yuting
Published: May 2023

With digital technologies injecting a strong impetus into China’s sustainable agricultural development, the digital village is a new path for Chinese agricultural development. This paper focuses on digital village and sustainable agricultural development, in which the situation of digital village and green and high-quality agricultural development in China from 2010 to 2019 is measured based on the entropy weight–TOPSIS method and coupling coordination model and further explores the spatiotemporal evolution of their coupling coordination. In addition, it studies the factors which influence the coupling coordination of digital village and green and high-quality agricultural development using a geographical detector. The study shows that both digital villages and green and high-quality agricultural development in China show good momentum. In terms of the spatial pattern, cities on the southeast coast witness better development of digital villages, and the southern regions enjoy a higher degree of green and high-quality development in agriculture. The coupling coordination between digital villages and green and high-quality agricultural development shows a fluctuating upward trend in the eastern regions. Some of the influence factors play a significant role in the coupling coordination of digital villages and green and high-quality agricultural development, such as e-commerce, per capita income, innovation, and levels of income and education. On this basis, we suggest that the government should continuously promote the development of digital villages and improve rural governance to bridge the digital divide. In this case, policies to promote green and high-quality agricultural development through digitalization can be introduced according to local conditions, thus enabling sustainable agricultural development with the empowerment of digitalization.
The Sustainable Business Model Database: 92 Patterns That Enable Sustainability in Business Model Innovation
Authors: Schroedel Sebastian
Published: May 2023

Humanity’s consumption of resources is many times greater than nature’s capacity for regeneration. Companies need to innovate their own business models to incorporate sustainability in all dimensions, as they are the main producers of greenhouse gases and environmental damage. However, obstacles such as dominant logics hinder the necessary processes. One way of overcoming these problems is to use sustainable business model patterns as an exemplary description of the business activities of existing and successful companies. The aim of this publication is to provide a holistic collection of relevant business model patterns for sustainability. The basis for this is the rich body of research in this area, which is, however, hampered by small-scale representations, overlaps, and problems of understanding. This will be addressed by conducting a comprehensive literature review, standardising, clustering, and eliminating duplication. The resulting database will enable sustainable business model innovation.
How Mobile Health Livingstreaming Engages the Consumer-Insights from a Dual-Process Model
Authors: Lu Fuyong , Wang Xintao , Li Siheng , Zhao Qun
Published: May 2023

Mobile health livestreaming has rapidly grown and become a popular platform for consumers to receive sustainable health consultation services. However, the factors influencing consumer engagement in this context still need clarification. To address this gap, we propose a framework based on dual-process theory, which suggests that cognitive mechanism and affective mechanism are two pathways that can cultivate consumer engagement in mobile health livestreaming. Using data from 499 Chinese consumers and the partial least squares structural equation modeling (PLS-SEM) approach, we empirically corroborated our framework. The results show that perceived intelligence significantly predicts consumer engagement, while concentration is positively associated with consumer engagement. Our results also indicate that concentration moderates the relationship between perceived intelligence and consumer engagement. In addition, mobile health livestreaming features can activate the two mechanisms. Perceived synchronicity and perceived vicariousness have a significant influence on perceived intelligence, while arousal and affinity are positively associated with concentration. This study carries considerable implications for the industry in support of promotional policies to engage consumers in mobile health livestreaming.
The Global Whitewashing of Smart Cities: Citizens’ Perspectives
Authors: Mutambik Ibrahim
Published: May 2023

Today, smart cities offer many significant benefits. As a result, the smart city is usually described in highly benign terms, and it is often uncritically assumed that its implementation must lead to widescale improvements, at all levels of society. Yet, while smart cities undoubtedly offer advantages, they also carry associated risks and dangers which could outweigh the benefits. This raises the important question of to which extent the ordinary citizen is aware of these risks and dangers. This study sets out to examine this question. While the ‘downsides’ of smart cities have been the subject of research over the past few years, there have been no studies which explore the perception of smart city disadvantages among citizen-stakeholders in the GCC countries. This study seeks to fill this gap in the literature, by examining the perspectives of a representative sample of citizens from the GCC. The results show that, despite the ‘whitewashing’ effect of public messaging, the study’s participants have a range of significant concerns about smart cities which could affect their acceptance of such initiatives. The findings will inform smart city development bodies in the GCC and contribute to strategies for promoting smart cities and citizen engagement.
High-Temperature Rheological Properties and Storage Stability of Bitumen Modified with the Char Produced from Co-Pyrolysis of Different Wastes
Authors: Atasağun Neslihan
Published: May 2023

By enhancing the bitumen properties with the additives produced from waste, not only can the performance of flexible pavements be improved, but it can also contribute to the environment. This research aims to produce a novel pyrolytic char from different wastes and to investigate the usability of this char as an additive in bitumen. In this study, first, the pyrolytic char was produced from the co-pyrolysis of three different wastes, including waste paper cups, polypropylene (PP), and polyethylene terephthalate (PET) waste. Properties of the char were determined by using CHN (carbon, hydrogen, and nitrogen) and Fourier transform infrared spectroscopy (FTIR) analyses. The high-temperature rheological properties of binders were evaluated by using DSR (dynamic shear rheometer), RV (rotational viscometer), softening point, and penetration tests. Additionally, specific gravity, storage stability, high-temperature performance grades (PGs), and temperature susceptibilities of binders were determined. Test results showed that this pyrolytic char enhanced the high-temperature properties of neat bitumen by increasing softening point, viscosity, rutting resistance, and high-temperature PG of pure bitumen. Additionally, it can be said that the bituminous binder modified with this pyrolytic char had good storage stability when it was used at an 8% ratio in neat bitumen.
A Comprehensive Review of Electric Vehicle Charging Stations with Solar Photovoltaic System Considering Market, Technical Requirements, Network Implications, and Future Challenges
Authors: Alrubaie Ali Jawad , Salem Mohamed , Yahya Khalid , Mohamed Mahmoud , Kamarol Mohamad
Published: May 2023

Electric cars (EVs) are getting more and more popular across the globe. While comparing traditional utility grid-based EV charging, photovoltaic (PV) powered EV charging may significantly lessen carbon footprints. However, there are not enough charging stations, which limits the global adoption of EVs. More public places are adding EV charging stations as EV use increases. However, using the current utility grid, which is powered by the fossil fuel basing generating system, to charge EVs has an impact on the distribution system and could not be ecologically beneficial. The current electric vehicle (EV) market, technical requirements including recent studies on various topologies of electric vehicle/photovoltaic systems, charging infrastructure as well as control strategies for Power management of electric vehicle/photovoltaic system., and grid implications including electric vehicle and Plug-in hybrid electric vehicles charging systems, are all examined in depth in this paper. The report gives overview of present EV situation as well as a thorough analysis of significant global EV charging and grid connectivity standards. Finally, the challenges and suggestions for future expansion of the infrastructure of EV charging, grid integration, are evaluated and summarized. It has been determined that PV-grid charging has the ability to create a profit. However, due to the limited capacity of the PV as well as the batteries, the Power system may not be cost effective. Furthermore, since PV is intermittent, it is probable that it will not be able to generate enough electricity to meet consumer demand.
Kink Soliton Dynamic of the (2+1)-Dimensional Integro-Differential Jaulent–Miodek Equation via a Couple of Integration Techniques
Authors: Cakicioglu Hasan , Ozisik Muslum , Secer Aydin , Bayram Mustafa
Published: May 2023

In this article, the aim was to obtain kink soliton solutions of the (2+1)-dimensional integro-differential Jaulent–Miodek equation (IDJME), which is a prominent model related to energy-dependent Schrödinger potential and is used in fluid dynamics, condensed matter physics, optics and many engineering systems. The IDJME is created depending on the parameters and with constant coefficients, and two efficient methods, the generalized Kudryashov and a sub-version of an auxiliary equation method, were applied for the first time. Initially, the traveling wave transform, which comes from Lie symmetry infinitesimals ∂∂x,∂∂y and ∂∂t, was applied, and a nonlinear ordinary differential equation (NODE) form was derived. In order to make physical interpretations, appropriate solution sets and soliton solutions were obtained by performing systematic operations in line with the algorithm of the proposed methods. Then, 3D, 2D and contour simulations were made. Interpretations of different kink soliton solutions were made by obtaining results that are consistent with previous studies in the literature. The obtained results contribute to the studies in this field, though the contribution is small.
Enhancement of Asymmetrically Color-Cast Sandstorm Image Using Saturation-Based Color Correction and Hybrid Transmission Network
Authors: Lee Hosang
Published: May 2023

The images discussed in this manuscript show atmospheric conditions of smog, sandstorm, and dust. Moreover, the images were taken in various environments and have features such as dimness or color cast. The smoggy image has a greenish or bluish color veil, and the sandstorm image has a yellowish or reddish color veil because of the various sand particles. Various methods have been used to enhance images containing dust. However, if the color-cast ingredients are not considered during image enhancement, then the enhanced image will have a new, artificial color veil that did not appear in the input image, as the color-veiled image does not have a uniform color channel. Certain channels are attenuated by sand particles. Therefore, this paper proposes a color-balancing method based on saturation to enhance asymmetrically cast colors due to the attenuation of the color channel by sand particles. Moreover, because the balanced image contains dust and the distribution of hazy ingredients is asymmetrical, a dehazing procedure is needed to enhance the image. This work used the original image and a reversed image to train the hybrid transmission network and generate the image’s transmission map. Moreover, an objective and subjective assessment procedure was used to compare the performance of the proposed method with that of other methods. Through the assessment, the performance of the proposed method was shown to be superior to other methods’ performance.
Multidimensional Evolution and Driving Factors of Securities Firms’ Collaborative Bond Joint Underwriting Networks in China: A Comprehensive Analysis from 2011 to 2020
Authors: Cao Yuan , Yang Ying , Ma Hongkun , Kong Xiangyi , Li Xueran , Du Yiran , Chen Dou
Published: May 2023

This study utilizes the joint bond joint underwriting data of China’s securities firms from 2011 to 2020 to systematically explore the evolutionary characteristics of China’s collaborative bond joint underwriting networks from temporal, topological, and spatial dimensions. By employing social network analysis, Ucinet, and ArcGIS, we construct a longitudinal network panel data to quantitatively analyze the driving factors and their underlying mechanisms. The findings reveal that, in terms of topological structure, China’s bond joint underwriting networks exhibit increasingly mature, active, balanced, and accessible features, with domestic securities firms such as China Securities Co., Ltd. emerging as the backbone and foreign-backed firms gradually fading. In the spatial dimension, urban collaboration presents a transformation from triangular to butterfly-shaped, quadrilateral, and ultimately multicore networks. At the regional scale, inter-regional collaboration is most extensive between the eastern regions, followed by eastern–central regions, with eastern–western and central–western regions relatively less engaged. At the urban scale, the central positions of Beijing, Shenzhen, and Shanghai are gradually strengthening, and their external radiation scope is expanding annually. The underlying mechanism driving this evolution is the increasing opportunities for securities firms to establish and adjust their cooperative relationships due to the maturing and active bond joint underwriting networks in China. To compensate for the opportunity cost of bond joint underwriting and to maximize collaboration benefits, securities firms need to select potential partners with close geographical proximity, similar business domains, larger underwriting scales, “friends of friends,” and “network star” status, thereby promoting the continuous evolution of China’s bond joint underwriting syndicates.
Combination Therapy with UV-4B and Molnupiravir Enhances SARS-CoV-2 Suppression
Authors: Franco Evelyn J. , Drusano George L. , Hanrahan Kaley C. , Warfield Kelly L. , Brown Ashley N.
Published: May 2023

The host targeting antiviral, UV-4B, and the RNA polymerase inhibitor, molnupiravir, are two orally available, broad-spectrum antivirals that have demonstrated potent activity against SARS-CoV-2 as monotherapy. In this work, we evaluated the effectiveness of UV-4B and EIDD-1931 (molnupiravir’s main circulating metabolite) combination regimens against the SARS-CoV-2 beta, delta, and omicron BA.2 variants in a human lung cell line. Infected ACE2 transfected A549 (ACE2-A549) cells were treated with UV-4B and EIDD-1931 both as monotherapy and in combination. Viral supernatant was sampled on day three when viral titers peaked in the no-treatment control arm, and levels of infectious virus were measured by plaque assay. The drug–drug effect interaction between UV-4B and EIDD-1931 was also defined using the Greco Universal Response Surface Approach (URSA) model. Antiviral evaluations demonstrated that treatment with UV-4B plus EIDD-1931 enhanced antiviral activity against all three variants relative to monotherapy. These results were in accordance with those obtained from the Greco model, as these identified the interaction between UV-4B and EIDD-1931 as additive against the beta and omicron variants and synergistic against the delta variant. Our findings highlight the anti-SARS-CoV-2 potential of UV-4B and EIDD-1931 combination regimens, and present combination therapy as a promising therapeutic strategy against SARS-CoV-2.
Efficacy of Sotrovimab (SOT), Molnupiravir (MOL), and Nirmatrelvir/Ritponavir (N/R) and Tolerability of Molnupiravir in Outpatients at High Risk for Severe COVID-19
Authors: Kauer Victoria , Totschnig David , Waldenberger Ferdinand , Augustin Max , Karolyi Mario , Nägeli Michelle , Wenisch Christoph , Zoufaly Alexander
Published: May 2023

Objective: The main goal of this study was to assess the potential clinical impact of an outpatient administration of available antivirals including SOT, N/R, and MOL to COVID-19 patients at high risk for disease progression. Methods: We conducted a retrospective analysis on 2606 outpatient individuals with mild to moderate COVID-19 at risk for disease progression, hospitalization, or death. After receiving either SOT (420/2606), MOL (1788/2606), or N/R (398/2606), patients were followed-up with regarding primary (hospitalization rate) and secondary (treatment and side effects) outcomes by phone. Result: A total of 2606 patients were treated at the outpatient clinic (SOT: 420; N/R: 398; MOL: 1788). 3.2% of the SOT patients (1 ICU admission), 0.8% of the MOL patients (2 ICU admissions), and none of the N/R patients were hospitalized. 14.3% of the N/R patients reported strong to severe side effects, exceeding SOT (2.6%) and MOL (5%) patients. A reduction in COVID symptoms after the treatment was experienced by 43% of patients in both the SOT and MOL groups and by 67% of patients in the N/R group, respectively. Women had a higher chance of symptom improvement with MOL (OR 1.2, 95%CI 1.0–1.5). Conclusion: All antiviral treatment options effectively prevented hospitalization in high-risk COVID-19 patients and were well tolerated. Side effects were pronounced in patients with N/R.
Weathering Intensity Response to Climate Change on Decadal Scales: A Record of Rb/Sr Ratios from Chaonaqiu Lake Sediments, Western Chinese Loess Plateau
Authors: Liu Lipeng , Yu Keke , Li Aizhen , Zhang Chao , Wang Le , Liu Xingxing , Lan Jianghu
Published: May 2023

The Rb/Sr ratio of lake sediments has been widely adopted as an indicator of weathering intensity in studies of past climate change, but the geochemical significance of this ratio varies with timescale. Here, we present Rb/Sr data for the past 300 years for sediments collected from Chaonaqiu Lake in the Liupan Mountains of the western Chinese Loess Plateau as a decadal-scale record of weathering intensity. To validate the application of this weathering proxy, we correlated the record with those of other major elements, rock-forming minerals, and paleoclimatic proxies. We found that Rb/Sr ratios are influenced mainly by Sr activity within the lake catchment (where Sr is likely sourced from albite). In addition, higher (lower) Rb/Sr ratios of bulk sediments from Chaonaqiu Lake are correlated with lower (higher) fractions of terrigenous detritus (SiO2, Ti, K2O, Al2O3, and Na2O). These indicate that the Rb/Sr ratios of bulk sediments in Chaonaqiu Lake are closely linked to terrigenous detritus input on decadal scales and also correlate well with TOC (a precipitation indicator) and other high-resolution paleoclimate records (e.g., tree rings and drought/flood index) in neighboring regions, with higher (lower) Rb/Sr ratios corresponding to more (less) precipitation. Lake bulk sediment Rb/Sr ratios are dominated by the input of terrigenous detritus over decadal timescales. Our data show that physical and chemical weathering in the Chaonaqiu Lake watershed have opposing influences on Rb/Sr ratios of bulk sediment, competing to dominate these ratios of lake sediments over different timescales, with ratios reflecting the relative importance of the two types of weathering.
Enhanced Removal of As(V) and Pb(II) from Drinking and Irrigating Water Effluents Using Hydrothermally Synthesized Zeolite 5A
Authors: Mayta-Armas Angie F. , Canchanya-Huaman Yamerson , Pomalaya-Velasco Jemina , Bendezú-Roca Yéssica , Checca-Huaman Noemi-Raquel , Ramos-Guivar Juan A.
Published: May 2023

Zeolites 5A were obtained by ion exchange of a zeolite 4A, previously synthesized by the hydrothermal method from precursor kaolin, with the aim of removal As(V) from drinking water and Pb(II) from irrigation surface water. Zeolite 5A was characterized before and after adsorption by X-ray diffraction, scanning electron microscopy, energy dispersive spectroscopy, and X-ray photoelectron spectroscopy. To find the adsorption mechanisms of both As and Pb in real waters, experiments on adsorption kinetics, optimum pH, adsorbent dose, and adsorption isotherms were developed. Adsorption kinetics and adsorption isotherm models were evaluated, and the selection criterion for the appropriate model was made using the residual sum of squares (RSS) and Bayesian information criterion (BIC). As a result, removal adsorption of As(V) and Pb(II) was higher than 95% in 9 and 12 h, respectively. The estimated maximum adsorption capacities for As and Pb were 36.35 mg g−1 and 46.67 mg g−1, respectively. Zeolite 5A is a low-cost adsorbent, through which a concentration of less than 0.01 mg L−1 of As and Pb was obtained in drinking and irrigation water, which is below the permissible limit established by the World Health Organization (WHO).