bluuebunny/crossref_metadata_2025 huggingface dataset:
Recent Developments in Shape Memory Elastomers for Biotechnology Applications
Authors: Suethao Supitta , Prasopdee Thridsawan , Buaksuntear Kwanchai , Shah Darshil , Smitthipong Wirasak
Published: August 2022

Shape memory elastomers have revolutionised the world since their introduction in the 20th century. The ability to tailor chemical structures to produce a family of materials in wide-ranging forms with versatile properties has propelled them to be ubiquitous. Recent challenges in the end-of-life management of polymeric materials should prompt us to ask, ‘what innovations in polymeric materials can make a strong case for their use as efficient materials?’ The development of smart elastomers that can acquire, convey, or process a stimulus (such as temperature, pressure, electromagnetic field, moisture, and chemical signals) and reply by creating a useful effect, specifically a reversible change in shape, is one such innovation. Here, we present a brief overview of shape memory elastomers (SMEs) and thereafter a review of recent advances in their development. We discuss the complex processing of structure-property relations and how they differ for a range of stimuli-responsive SMEs, self-healing SMEs, thermoplastic SMEs, and antibacterial and antifouling SMEs. Following innovations in SEMs, the SMEs are forecast to have significant potential in biotechnology based on their tailorable physical properties that are suited to a range of different external stimuli.
A Dynamic Decision Support System for Sustainable Supplier Selection under Fuzzy Environment
Authors: Thanh Nguyen Van
Published: August 2022

The plastics business has grown rapidly in recent years, with annual growth rates ranging from 16% to 18% each year (second only to the telecommunications and textile industries). The plastic industry is regarded as a dynamic industry in the Vietnamese economy due to its rapid development rate. Its high growth stems from a big market with significant expansion potential, since Vietnam’s plastic sector is still in its early stages in comparison to the rest of the globe and plastic goods are pushed and utilized in many aspects of life. In order to ensure sustainable development and comply with the provisions of the Law on Environment Protection, plastic manufactures and importers must ensure to fulfill two responsibilities: (1) proper product and packaging recycling—applicable to products and packaging with recyclable value; and (2) the collection and treatment of waste—applicable to products and packaging containing hazardous substances, difficult to recycle, causing difficulties for waste collection and treatment. Therefore, raw materials supplier selection in the plastic industry is a complex decision, and decision makers must consider many qualitative factors, quantitative factors and environmental attributes during the decision making. As a result, the goal of this study is to present an integrated multicriteria decision making model (MCDM) strategy for sustainable supplier selection in the plastics sector under fuzzy environment circumstances. This paper makes a contribution by proposing a hybrid fuzzy analytic hierarchy process (AHP) and technique for order of preference by similarity to ideal solution (TOPSIS) approach model for raw material supplier selection in the plastics sector. This research also provided a useful guideline for supplier selection in other industries.
Transient Synchronizing Stability of Droop-Controlled Converter during Grid Fault Considering Current Saturation Limitation
Authors: Ma Yanhong , Fu Lijun , Hu Qi , Wang Guangyu , Liu Chenruiyang
Published: August 2022

Traditional power systems usually rely on synchronous generators to provide inertia and maintain grid stability. However, with the continuous development of new energy power generation technologies, more and more converter power supplies provide voltage and frequency support for the power grid. The synchronizing instability issue of a converter-dominated power system becomes increasingly relevant, which exists in both grid-following (GFL) and grid-forming (GFM) converters. In this paper, the transient synchronizing stability of grid-forming (GFM) converters with droop-control during a grid fault is studied, with the influence of current saturation limitation considered especially. The current saturation limitation applied to limit overcurrent during grid faults brings transient voltage-source/current-source switched mode of the converter, which significantly influences the transient behavior. Considering the influence, an equivalent motion model similar to the rotor motion of a synchronous generator is developed first to depict the transient synchronizing dynamics. It has been identified that the current saturation limitation reconstructs the power angle characteristics, which significantly influence the transient stability. Then based on the developed model, transient synchronizing stability during a grid fault is studied based on the accelerating–decelerating area method. With the influence of grid impedance and grid voltage sag considered, several kinds of transient synchronizing stability issues are revealed clearly. Finally, simulated results are presented to verify the theoretical analysis. The analysis of this article paves the way for the follow-up research on the synchronizing stability of multi-converters and the corresponding control methods to improve synchronizing stability.
MFCD-Net: Cross Attention Based Multimodal Fusion Network for DPC Imagery Cloud Detection
Authors: Zhang Jingjing , Ge Kai , Xun Lina , Sun Xiaobing , Xiong Wei , Zou Mingmin , Zhong Jinqin , Li Teng
Published: August 2022

As one kind of remote sensing image (RSI), Directional Polarimetric Camera (DPC) data are of great significance in atmospheric radiation transfer and climate feedback. The availability of DPC images is often hindered by clouds, and effective cloud detection is the premise of many applications. Conventional threshold-based cloud detection methods are limited in performance and generalization capability. In this paper, we propose an effective learning-based 3D multimodal fusion cloud detection network (MFCD-Net) model. The network is a three-input stream architecture with a 3D-Unet-like encoder-decoder structure to fuse the multiple modalities of reflectance image, polarization image Q, and polarization image U in DPC imagery, with consideration of the angle and spectral information. Furthermore, cross attention is utilized in fusing the polarization features into the spatial-angle-spectral features in the reflectance image to enhance the expression of the fused features. The dataset used in this paper is obtained from the DPC cloud product and the cloud mask product. The proposed MFCD-Net achieved excellent cloud detection performance, with a recognition accuracy of 95.74%, according to the results of the experiments.
Resource Allocation and Computation Offloading for Wireless Powered Mobile Edge Computing
Authors: Chen Jun , Chang Zheng , Guo Wenlong , Guo Xijuan
Published: August 2022

In this paper, we investigate a resource allocation and computation offloading problem in a heterogeneous mobile edge computing (MEC) system. In the considered system, a wireless power transfer (WPT) base station (BS) with an MEC sever is able to deliver wireless energy to the mobile devices (MDs), and the MDs can utilize the harvested energy for local computing or task offloading to the WPT BS or a Macro BS (MBS) with a stronger computing server. In particular, we consider that the WPT BS can utilize full- or half-duplex wireless energy transmission mode to empower the MDs. The aim of this work focuses on optimizing the offloading decision, full/half-duplex energy harvesting mode and energy harvesting (EH) time allocation with the objective of minimizing the energy consumption of the MDs. As the formulate problem has a non-convex mixed integer programming structure, we use the quadratically constrained quadratic program (QCQP) and semi-definite relaxation (SDR) methods to solve it. The simulation results demonstrate the effectiveness of the proposed scheme.
Design of an Experimental Study for the Simultaneous Determination of Cefepime, Piperacillin and Tazobactam Using Micellar Organic Solvent-Free HPLC
Authors: Hafez Hani M. , El Deeb Sami , Naji Esraa Abd Alkareem , Aziz Zahraa Ali , Mahmood Amaal Sajid , Khalil Nooralhuda Ibrahim , Ibrahim Adel Ehab
Published: August 2022

Application of Sustainable analytical chemistry concepts has become crucial in order to remove the environmentally harmful impacts originating from the routine use of analytical techniques. Here, a new LC method is developed and its parameters are analyzed, depending on a mixed micellar mobile phase. This was primarily aimed at getting rid of the use of organic solvents in conventional routine analyses. Combinations of tazobactam (TZB) with piperacillin (PPC) or cefepime (CFM) are commonly used as effective antimicrobial therapies, especially for resistant strains. Therefore, the three drugs were separated and quantified using an organic solvent-free mobile phase. The mixed micellar mobile phase was comprised of 15 mM Brij-35 with 38 mM SDS, adjusted to pH 3.5. Separation was performed by HPLC on monolithic RP-C18 column Chromolith® Performance RP-18e (100 mm × 4.6 mm) at a rate of 1 mL per minute of flow in conjunction with a measurement wavelength 210 nm. The method was found valid and applicable in accordance of precision, and accuracy within ranges of 5–100 µg mL−1 for PPC and CFM and of 0.625–12.5 µg mL−1 for TZB. The quality-by-design technique was used to analyze the effect of modifying the mixed micellar ratios on separation efficiency and conclude their behavior. Finally, the suggested approach was assessed applying the green analytical procedure index against the greenest published methodology to show superiority.
For CS Educators, by CS Educators: An Exploratory Analysis of Issues and Recommendations for Online Teaching in Computer Science
Authors: Lal Sangeeta , Mourya Rahul
Published: August 2022

The COVID-19 pandemic has completely transformed the education sector. Almost all universities and colleges have had to convert their normal classroom teaching to online/remote or hybrid teaching during the COVID-19 pandemic. Online teaching has been found quite useful during an emergency situation. This switch to online teaching forced educators to come out of their comfort zone and learn new tools and techniques for online teaching. It is important, therefore, to analyse the problems faced by educators in online teaching because this has become the new normal. There are several studies identifying the issues faced by educators in online teaching but less is known about the issues faced by Computer Science (CS) educators. In this paper, we perform an exploratory study of the problems, questions, and associated responses from CS educators posted on popular Q&A forums, e.g., CS educators StackExchange. We identified six main challenges related to online teaching: platform recommendation, Q&A management, academic dishonesty, pair programming, and feedback mechanism. Several recommendations are provided by other CS educators in each of the categories, which are discussed in detail in this paper. This study will help organizations come up with better solutions to support their educators so that they can deliver better quality education and reduce the overall stress levels of staff.
Equitable City in an Aging Society: Public Transportation-Based Primary Care Accessibility in Seoul, Korea
Authors: Yoon Yoonchae , Park Jina
Published: August 2022

The development of an equitable city has been the primary goal of recent urban planning. Whereas previous urban planning has addressed problems caused by urbanization and population growth, current urban planning must closely consider demographic changes such as an aging society. An aging population, characterized by an increased need for regular healthcare and a growing reliance on public transport, can pose a major challenge to equitable cities. Thus, it is necessary to scrutinize the medical and public transportation links in urban planning strategies to create a more equitable city in an aging society. This study analyzed primary care accessibility for older adults using public transportation in Seoul and considered whether it is socially and economically equitable. Primary care accessibility was derived using the modified 2SFCA model and the Seoul Public Transportation API and social equity was analyzed using the concentration index and concentration curve. The results showed that primary care accessibility of the elderly is related to public transportation mobility, medical facilities and the distribution of elderly populations, revealing both social and economic inequality. This analysis suggests the need for integrated urban planning for an equitable city in an aging society, with implications for efficient resource distribution.
Surface Soil Moisture Inversion and Distribution Based on Spatio-Temporal Fusion of MODIS and Landsat
Authors: Wang Sinan , Wang Wenjun , Wu Yingjie , Zhao Shuixia
Published: August 2022

Soil moisture plays an important role in hydrology, climate, agriculture, and ecology, and remote sensing is one of the most important tools for estimating the soil moisture over large areas. Soil moisture, which is calculated by remote sensing inversion, is affected by the uneven distribution of vegetation and therefore the results cannot accurately reflect the spatial distribution of the soil moisture in the study area. This study analyzes the soil moisture of different vegetation covers in the Wushen Banner of Inner Mongolia, recorded in 2016, and using Landsat and MODIS images fused with multispectral bands. Firstly, we compared and analyzed the ability of the visible optical and short-wave infrared drought index (VSDI), the normalized differential infrared index (NDII), and the short-wave infrared water stress index (SIWSI) in monitoring the soil moisture in different vegetation cover soils. Secondly, we used the stepwise multiple regression analysis method in order to correlate the multispectral fusion bands with the field-measured soil water content and established a soil moisture inversion model based on the multispectral fusion bands. As the results show, there was a strong correlation between the established model and the measured soil water content of the different vegetation cover soils: in the bare soil, R2 was 0.86; in the partially vegetated cover soil, R2 was 0.84; and in the highly vegetated cover soil, R2 was 0.87. This shows that the established model could better reflect the actual condition of the surface soil moisture in the different vegetation covers.
Sustainable Professional Development of German Language Teachers in China: Research Assessment and External Research Funding
Authors: Liu Siqi , Chen Yu , Shen Qi , Gao Xuesong (Andy)
Published: August 2022

The academic evaluation of teachers of languages other than English (LOTEs) has been extensively researched, especially from the perspective of academic publications. However, little attention has been paid to another key performance indicator in teacher assessment, namely, external research funding. Focusing on German language teachers (GLTs), this paper adopts a mixed methods approach to investigate the assessment requirements for LOTE teachers in terms of external research funding and the factors that may impact their accomplishments. Based on Bronfenbrenner’s ecological systems theory and conservation of resources theory, we analyzed policy documents from the universities under investigation, examined “German or Germany-related” funding approvals, and conducted semi-structured interviews with eight GLTs to explore the environmental factors (individual context, institutional context, social context, chronological context) that may influence the survival of GLTs in terms of the requirements for external research funding. The findings indicate that factors from each ecological context interact with one another and have a combined influence on GLTs’ external research funding application activity. Moreover, there is an imbalance between the academic demands faced by GLTs and the resource support that is available to them. This imbalance may affect the survival and development of GLTs and is likely to have a continuing influence throughout their career. The study concludes by offering some suggestions at different levels to facilitate the sustainable professional development of GLTs.
Vectors of Sustainable Development and Global Knowledge in the Metallic Materials Industry in Romania
Authors: Nicolae Maria , Sohaciu Mirela Gabriela , Dumitrescu Ruxandra , Ciucă Sorin , Nicolae Avram
Published: August 2022

As a 21st century trend, sustainability has encompassed the entire world economy, including industry. The concept of “Industry 4.0” is already known today. It promotes the computerization of manufacturing by interconnection, information transparency, technical assistance and decentralized decisions. In recent years, companies in the metal materials industry have also implemented strategies and technologies belonging to the Industry 4.0 concept. The main aim of the manuscript is to identify the key issues in the evolution of the development of the metal materials industry. The transition to a higher level of its evolution is based on two vectors, namely: the ecological paradigm, as a vector of in-depth knowledge, and sustainable material, as a vector that ensures sustainability in the areas of convergence of systems in the spheres of life and social consciousness. The systems that have an impact on the sustainable development of the metallic materials industry through the interactions between them are the technological system, the social system and the natural-ecological system. The main objectives of the paper are the investigations into the interconditions of ecology–economy, and the correlations of ecology–economy–energy, investigations that led to the establishment of new scientific branches (ecometallurgy, metallurgical economics, metallurgical ecosociology and sustainable materials engineering) in terms of global knowledge, and which allow the expansion of the field of implementing knowledge of sustainable development in the metal materials industry. The paper is based on literary foundations, obtained from libraries; databases such as Web of Science (WoS), Scopus and Google Scholar; sustainable universal principles; and legislative parameters.
Performance Measurement Model for Sustainability Assessment of the Swine Supply Chain
Authors: Kruger Silvana Dalmutt , Zanin Antonio , Durán Orlando , Afonso Paulo
Published: August 2022

In this paper, a model and a set of indicators for evaluating the sustainability in swine supply chains are presented and discussed. Using the Delphi method, environmental, social and economic indicators were identified (namely environmental performance indicators to evaluate soil, water, air, energy and environmental practices; social performance indicators related to human capital and social interaction; and economic performance indicators that address labor remuneration and return on investment). Subsequently, the proposed sustainability assessment model was applied for validation purposes in three different companies belonging to the swine supply chain in the southern region of Brazil. This study differs from previous ones by focusing on the sustainability assessment of the upstream and downstream of the supply chain, which are responsible for significant impacts. The performance of the studied companies from the three triple bottom-line (TBL) dimensions is significantly different. Both negative and positive impacts were found to be related to most of the specific metrics. The economic dimension presented a better performance than the environmental and social ones. Furthermore, there was a predominance of negative impacts in the environmental dimension, in relation to the soil, water, air, and energy indicators. Thus, specific actions, strategies, and policies must be designed for the different companies towards an effective and comprehensive sustainability throughout the swine supply chain. The proposed model can be extended to other companies in the same supply chain, replicated in other livestock and agribusiness industries and supply chains (such as cattle and poultry), and it can be used in different locations and including additional indicators and metrics.
Ethnographic Reflections of K–12 Distance Education in Saudi Arabia: Shaping the Future of Post-Pandemic Digital Education
Authors: Aladsani Hibah , Al-Abdullatif Ahlam , Almuhanna Manal , Gameil Azza
Published: August 2022

The health, social, and economic challenges we have faced have contributed to the improvement of educational styles and learning environments. Globally, the reflections of COVID-19 have contributed to the re-perception of the future of education and the anticipation of new scenarios. This qualitative study aims to deeply examine and understand the repercussions of distance education—specifically K–12 education (kindergarten to twelfth grade) during the pandemic in Saudi Arabia—and, with the findings, build anticipated scenarios for future post-pandemic digital education. This study adopts an ethnographic approach to investigate the cultural perspectives of those whose education was and has been greatly affected by this transition. Qualitative large-scale data (comprising 36 observations, 387 individual interviews, and 177 focus groups) were collected for 7 months in 2021 from 600 participants, all of whom were connecting in various ways to the K–12 educational system and varied by gender, age, profession, and academic degree. The findings were categorized into four themes: (1) educational outcomes, (2) teaching landscape, (3) parental involvement, and (4) societal and life aspects. The findings are discussed in a style that presents the most crucial aspects that we must consider for anticipated scenarios of future post-pandemic education. Each presents critical implications for teachers, students, parents, researchers, and educational authorities.
A Review of the Linear Generator Type of Wave Energy Converters’ Power Take-Off Systems
Authors: Ahamed Raju , McKee Kristoffer , Howard Ian
Published: August 2022

The traditional wave energy converters (WECs) use hydraulic or turbine-type power take-off (PTO) mechanisms which consist of many moving parts, creating mechanical complexity and increasing the installation and maintenance costs. Linear generator-based direct-drive WECs could be a solution to overcome this problem, but the efficiency of the single conventional linear generator is not high enough, and it cannot work satisfactorily in the low-frequency range. This article reviews the recent research developments of the linear permanent magnet (PM) generator-based WEC to harness maximum energy from ocean waves. It starts with a brief introduction and background of wave energy converters using linear generators. Following this, the working principle of the WECs with linear PM generators is briefly outlined. Subsequently, the analytical model of the linear PM generator-based WEC is studied. After that, the up-to-date developments of the linear PM generator-based PTO systems are studied. Despite some modifications resulting in complexity in the linear PM generator’s structure and a rise in manufacturing costs, the study shows the systems’ efficiencies increased by increasing magnetic flux and reducing cogging force. The key parameters and improvement issues that can increase the performances and efficiencies of the PTO systems are identified to help future researchers for further development. Moreover, the review discusses the numerical and experimental analysis tools, the typical control systems used by the researchers and the challenges of the linear generator-based wave energy conversion system. Finally, conclusions about the significant beneficial characteristics and design choice of the WEC linear generator structure are provided and related to the application conditions.
Lane-Change Risk When the Subject Vehicle Is Faster Than the Following Vehicle: A Case Study on the Lane-Changing Warning Model Considering Different Driving Styles
Authors: Liu Tong , Wang Chang , Fu Rui , Ma Yong , Liu Zhuofan , Liu Tangzhi
Published: August 2022

The research of early warning and control strategy considering driving styles during lane changes is a hotspot in the field of automatic driving. However, many lane-changing studies only emphasize the warning analysis when the following vehicle is faster than the subject vehicle, while neglecting the potential risk when the subject vehicle is faster than the following vehicle in the adjacent lane during lane changes. To study the lane-changing characteristics of drivers considering driving styles and to establish a personalized lane-changing warning model under different relative speed conditions, fifty participants (three females and forty-seven males) were recruited to carry out a real road driving test. A novel Gaussian mixture model with the results of k-means clustering was established to classify driving styles based on two-dimensional variables: average time gap and average minimum time to collision. The clustering result was then verified. In addition, by analyzing the relationship between the subject vehicle and the following vehicle in the adjacent lane during lane changes, a lane-changing warning model considering driving styles under different relative speed conditions was established. Results show that the clustering algorithm proposed in this paper has high separability between samples, achieving a much softer clustering result that can provide a reference for the parameter setting of the personalized driver assistance system. Furthermore, the overall recognition accuracy of the hazardous lane-changing behaviors improved after drivers were classified into different driving styles. The established lane-changing warning model has a better recognition performance for aggressive drivers when compared with the other two driver types. The results provide a basis for the algorithm design of the intelligent lane-changing warning system and can improve the user acceptance of an advanced driver assistance system for self-driving vehicles.
Machine-Learning-Based Gender Distribution Prediction from Anonymous News Comments: The Case of Korean News Portal
Authors: Suh Jong Hwan
Published: August 2022

Anonymous news comment data from a news portal in South Korea, naver.com, can help conduct gender research and resolve related issues for sustainable societies. Nevertheless, only a small portion of gender information (i.e., gender distribution) is open to the public, and therefore, it has rarely been considered for gender research. Hence, this paper aims to resolve the matter of incomplete gender information and make the anonymous news comment data usable for gender research as new social media big data. This paper proposes a machine-learning-based approach for predicting the gender distribution (i.e., male and female rates) of anonymous news commenters for a news article. Initially, the big data of news articles and their anonymous news comments were collected and divided into labeled and unlabeled datasets (i.e., with and without gender information). The word2vec approach was employed to represent a news article by the characteristics of the news comments. Then, using the labeled dataset, various prediction techniques were evaluated for predicting the gender distribution of anonymous news commenters for a labeled news article. As a result, the neural network was selected as the best prediction technique, and it could accurately predict the gender distribution of anonymous news commenters of the labeled news article. Thus, this study showed that a machine-learning-based approach can overcome the incomplete gender information problem of anonymous social media users. Moreover, when the gender distributions of the unlabeled news articles were predicted using the best neural network model, trained with the labeled dataset, their distribution turned out different from the labeled news articles. The result indicates that using only the labeled dataset for gender research can result in misleading findings and distorted conclusions. The predicted gender distributions for the unlabeled news articles can help to better understand anonymous news commenters as humans for sustainable societies. Eventually, this study provides a new way for data-driven computational social science with incomplete and anonymous social media big data.
Improved Marine Predator Algorithm for Wireless Sensor Network Coverage Optimization Problem
Authors: He Qing , Lan Zhouxin , Zhang Damin , Yang Liu , Luo Shihang
Published: August 2022

A wireless sensor network (WSN) is a distributed network system composed of a great many sensor nodes that rely on self-organization. The random deployment of WSNs in city planning often leads to the problem of low coverage of monitoring areas. In the construction of smart cities in particular, a large number of sensor nodes need to be deployed to maintain the reception, processing, and transmission of data throughout the city. However, the uneven distribution of nodes can cause a lot of wasted resources. To solve this problem, this paper proposes a WSN coverage optimization model based on an improved marine predator algorithm (IMPA). The algorithm introduces a dynamic inertia weight adjustment strategy in the global exploration and local exploitation stages of the standard marine predator algorithm to balance the exploration and exploitation capabilities of the algorithm and improve its solution accuracy. At the same time, the improved algorithm uses a multi-elite random leading strategy to enhance the information exchange rate between population individuals and improve the algorithm’s ability to jump out of the local optimum. The optimization experiment results of 11 benchmark test functions and part of the CEC2014 test functions show that the optimization performance of the improved algorithm is significantly better than the standard marine predator algorithm and other algorithms in the literature. Finally, the improved algorithm is applied to the WSN coverage optimization problem. The simulation results demonstrate that the IMPA has a better coverage rate than other metaheuristic algorithms and other improved algorithms in the literature for solving the WSN coverage optimization problem.
The Tourism Service Trade Network: Statistics from China and ASEAN Countries
Authors: Liu Qing , Liu Yaping , Zhang Jun
Published: August 2022

This study uses the social network analysis method to explore the structural changes of the China–ASEAN tourism services trade network and its influencing factors based on tourism services trade data of China and ASEAN countries in 2015 and 2018. The findings show that (i) the network density of the tourism services trade network increased by 30% in 2018 compared with 2015. (ii) China, Thailand, Singapore, and Malaysia rank highly in terms of degree, betweenness, and closeness centrality. (iii) The distance between countries and differences in GDP per capita significantly affect the tourism services trade network. The recommendation is that tourism services trade be developed in a vigorous and systematic manner in the China–ASEAN region. This approach would enhance overall stability and cooperation in the tourism services trade network and create a win–win situation for trade in tourism services between China and ASEAN countries.
Data-Driven Robust DEA Models for Measuring Operational Efficiency of Endowment Insurance System of Different Provinces in China
Authors: Qu Shaojian , Feng Can , Jiang Shan , Wei Jinpeng , Xu Yuting
Published: August 2022

China is facing an increasingly serious aging problem, which puts forward higher requirements for the smoothness of the endowment insurance system. Accurate evaluation of the efficiency of the system can help the government to find problems and improve the system. Some scholars have used data envelopment analysis (DEA) method to measure the efficiency of endowment insurance system. However, according to the literature, the impact of government policy adjustment and economic shocks on output of the data was ignored. In this study, a robust optimization method is applied to deal with uncertainty. Robust DEA models proposed in this paper are based on three kinds of uncertainty sets. A data-driven robust optimization method is also applied to resolve the over-conservative problem. Compared with the robust DEA method, based on analysis it is found that the data-driven robust DEA method is more flexible and reliable for efficiency estimating strategies. The results of data-driven robust DEA models illustrate that the government should increase its support for the endowment insurance system, especially for the underdeveloped regions.
The Impact of Government and Public Dual-Subject Environmental Concerns on Urban Haze Pollution: An Empirical Research on 279 Cities in China
Authors: Ma Dianyuan , Sun Hui , Xia Xuechao , Zhao Yan
Published: August 2022

Based on 279 cities in China from 2011 to 2019 as research samples, this study used a fixed-effect regression model to investigate the influence of government and public dual-subject environmental concerns on urban haze pollution. The results show that: (1) Government and public environmental concerns have a significant negative impact on urban haze pollution. The results are still valid after a series of robustness tests and controlling for endogenous problems. (2) Further research found that dual-subject environmental concerns have a stronger negative effect on urban haze pollution in areas where there is a low level of Internet development and in western regions. (3) Through the test of the intermediary mechanism, it can be seen that the environmental concerns of the government and the public can reduce haze pollution by reducing policy uncertainty and capital element misallocation. In general, the policy effect is greater than the capital allocation effect. The research conclusions of this study will help to deepen the interpretation of the role of the government and the public in environmental governance and also provide a reference for cities to further promote haze governance.
Provably Secure with Efficient Data Sharing Scheme for Fifth-Generation (5G)-Enabled Vehicular Networks without Road-Side Unit (RSU)
Authors: Al-Shareeda Mahmood A. , Manickam Selvakumar , Mohammed Badiea Abdulkarem , Al-Mekhlafi Zeyad Ghaleb , Qtaish Amjad , Alzahrani Abdullah J. , Alshammari Gharbi , Sallam Amer A. , Almekhlafi Khalil
Published: August 2022

The vehicles in the fifth-generation (5G)-enabled vehicular networks exchange the data about road conditions, since the message transmission rate and the downloading service rate have been considerably brighter. The data shared by vehicles are vulnerable to privacy and security issues. Notably, the existing schemes require expensive components, namely a road-side unit (RSU), to authenticate the messages for the joining process. To cope with these issues, this paper proposes a provably secure efficient data-sharing scheme without RSU for 5G-enabled vehicular networks. Our work included six phases, namely: TA initialization (TASetup) phase, pseudonym-identity generation (PIDGen) phase, key generation (KeyGen) phase, message signing (MsgSign) phase, single verification (SigVerify) phase, and batch signatures verification (BSigVerify) phase. The vehicle in our work has the ability to verify multiple signatures simultaneously. Our work not only achieves privacy and security requirements but also withstands various security attacks on the vehicular network. Ultimately, our work also evaluates favourable performance compared to other existing schemes with regards to costs of communication and computation.
The Perspective Projects Promoting Sustainable Mobility by Active Travel to School on the Example of the Southern Poland Region
Authors: Cieśla Maria , Macioszek Elżbieta
Published: August 2022

The paper presents a discussion concerning the development of projects regarding active travel with the policy of sustainable mobility, with active school transport in southern Poland being taken into special consideration. The implementation of the idea of sustainable school travel planning involves linking several social groups, including traffic planners and organizers, school administrators, governments, parents, and children. This, in turn, requires considering the criteria reported by all parties when choosing a scenario for the region’s development in terms of transport solutions. The following study was based on the methodological foundations of multicriteria decision-making analysis. The research purpose of this paper is to identify and classify the actions, policies, and scenarios of active travel projects for the sustainable development of mobility based on the cities of southern Poland. The evaluation was carried out through expert methods with multicriteria decision-making tools based on the MULTIPOL (MULTI-criteria and POLicy) prospective analysis technique. It allowed for the selection of the most probable policy, which covered the six actions considered by the experts to be the most significant for the development of the active school transport system in the analyzed region. Such actions are as follows: identification of walking school bus routes, modernization of crosswalk lines, planning of walking and cycling routes to schools, promoting safety educational programs, distribution of active school transport booklets and cycling, and pedestrian skills training workshops. The paper presents a new method to evaluate the policies and actions regarding promoting sustainable (active) travel to school. The innovative approach results from assembling a mixed group of people (stakeholders) as experts. The mix of experts consisting of users (pupils and parents), practitioners, and scientist experts in this field allowed us to score policies, actions, and scenarios, enabling a wider spectrum of assessment than before.
Functional Hemispheric Activity and Asymmetry Markers of Effective Foreign Language Performance in 3rd-Grade, 10th-Grade, and University Students
Authors: Demareva Valeriia
Published: August 2022

The activity of the left hemisphere is often associated with linguistic functioning, including in a foreign language. At the same time, research results demonstrate that different structures in both hemispheres can be jointly activated in the performance of particular linguistic tasks. The current study aimed to identify functional hemispheric activity and asymmetry markers for effective foreign language performance. The study sample consisted of 27 3rd-grade, 26 10th-grade, and 21 university students, all native Russian. To measure functional hemispheric asymmetry and activity before and after an English class and before an English test, we used computer laterometry in the ‘two-source’ lead–lag dichotic paradigm. The study results reveal that left hemispheric functional dominance can be considered as a marker for effective activity during an English class and an English test in 3rd-grade and 10th-grade students. In university students, right hemispheric functional dominance predicted better efficacy during the English class. Therefore, the results obtained provide evidence about different hemispheric activity and asymmetry modes for different ages of foreign language mastering, and the results may support the hypothesis about the possibility of a ‘sensitive period’ for foreign language acquisition occurring at any age. These findings can be applied to the creation of biofeedback trainings for hemispheric profile optimization when learning a foreign language and may help in creating personalized learning schedules.
Numerical Investigation on the Symmetric Breakup of Bubble within a Heated Microfluidic Y-Junction
Authors: Chen Jingbo , Du Wen , Kong Bo , Wang Zhiguo , Cao Jun , Wang Weiran , Yan Zhe
Published: August 2022

This study numerically investigated the symmetric breakup of bubble within a heated microfluidic Y-junction. The established three-dimensional model was verified with the results in the literature. Two crucial variables, Reynolds number (Re) and heat flux (q), were considered. Numerical results demonstrated that the bubble breakup was significantly affected by phase change under the heated environment. The “breakup with tunnel” and “breakup with obstruction” modes respectively occurred at the low and high q. The breakup rate in pinch-off stage was much larger than that in squeezing stage. As Re increased, the bubble broke more rapidly, and the critical neck thickness tended to decrease. The bubble annihilated the vortices existing in the divergence region and made the fluid flow more uniform. The heat transfer was enhanced more drastically as Re was decreased or q was increased, where the maximum Nusselt number under two-phase case was 6.53 times larger than single-phase case. The present study not only helps understanding of the physical mechanisms of bubble behaviors and heat transfer within microfluidic Y-junction, but also informs design of microfluidic devices.
Human–Object Interaction Detection with Ratio-Transformer
Authors: Wang Tianlang , Lu Tao , Fang Wenhua , Zhang Yanduo
Published: August 2022

Human–object interaction (HOI) is a human-centered object detection task that aims to identify the interactions between persons and objects in an image. Previous end-to-end methods have used the attention mechanism of a transformer to spontaneously identify the associations between persons and objects in an image, which effectively improved detection accuracy; however, a transformer can increase computational demands and slow down detection processes. In addition, the end-to-end method can result in asymmetry between foreground and background information. The foreground data may be significantly less than the background data, while the latter consumes more computational resources without significantly improving detection accuracy. Therefore, we proposed an input-controlled transformer, “ratio-transformer” to solve an HOI task, which could not only limit the amount of information in the input transformer by setting a sampling ratio, but also significantly reduced the computational demands while ensuring detection accuracy. The ratio-transformer consisted of a sampling module and a transformer network. The sampling module divided the input feature map into foreground versus background features. The irrelevant background features were a pooling sampler, which were then fused with the foreground features as input data for the transformer. As a result, the valid data input into the Transformer network remained constant, while irrelevant information was significantly reduced, which maintained the foreground and background information symmetry. The proposed network was able to learn the feature information of the target itself and the association features between persons and objects so it could query to obtain the complete HOI interaction triplet. The experiments on the VCOCO dataset showed that the proposed method reduced the computational demand of the transformer by 57% without any loss of accuracy, as compared to other current HOI methods.
A Fuzzy-Based Mobile Edge Architecture for Latency-Sensitive and Heavy-Task Applications
Authors: Shi Yanjun , Chu Jinlong , Ji Chao , Li Jiajian , Ning Shiduo
Published: August 2022

Appropriate task offloading management strategy is a challenging problem for high delay-sensitive and heavy-task applications. This paper proposes a fuzzy-based mobile edge manager with task partitioning, which can handle the multi-criteria decision-making process by considering multiple parameters in the MEC network framework and make appropriate offloading decisions for incoming tasks of IoT applications. Considering that the mobile devices are becoming more and more powerful, this paper also takes WLAN delay and the computing power of mobile devices into account, forming a three-level fuzzy logic system. In addition, since many tasks of Internet of Things applications are composed of several independent modules, this paper also sets two optimal task partitioning ratios, which have symmetry, so that each module can be independently executed in each layer of the MEC network. In addition, results will return to the mobile devices after execution, so as to minimize the service time and improve QoS. Finally, several indexes such as task failure rate and service time are simulated, and the results show that the proposed scheme has better performance compared with the other four comparison schemes, especially for high-latency sensitivity and heavy-task applications.
Prenatal Exposure to Mercury, Manganese, and Lead and Adverse Birth Outcomes in Suriname: A Population-Based Birth Cohort Study
Authors: Sewberath Misser Vinoj H. , Hindori-Mohangoo Ashna D. , Shankar Arti , Wickliffe Jeffrey K. , Lichtveld Maureen Y. , Mans Dennis R. A.
Published: August 2022

Globally, adverse birth outcomes are increasingly linked to prenatal exposure to environmental contaminants, such as mercury, manganese, and lead. This study aims to assess an association between prenatal exposure to mercury, manganese, and lead and the occurrence of adverse birth outcomes in 380 pregnant women in Suriname. The numbers of stillbirths, preterm births, low birth weights, and low Apgar scores were determined, as well as blood levels of mercury, manganese, lead, and relevant covariates. Descriptive statistics were calculated using frequency distributions. The associations between mercury, manganese, and lead blood levels, on the one hand, and adverse birth outcomes, on the other hand, were explored using contingency tables, tested with the χ2-test (Fisher’s exact test), and expressed with a p value. Multivariate logistic regression models were computed to explore independent associations and expressed as (adjusted) odds ratios (aOR) with 95% confidence intervals (CI). The findings of this study indicate no statistically significant relationship between blood mercury, manganese, or lead levels and stillbirth, preterm birth, low birth weight, and low Apgar score. However, the covariate diabetes mellitus (aOR 5.58, 95% CI (1.38–22.53)) was independently associated with preterm birth and the covariate hypertension (aOR 2.72, 95% CI (1.081–6.86)) with low birth weight. Nevertheless, the observed high proportions of pregnant women with blood levels of mercury, manganese, and lead above the reference levels values of public health concern warrants environmental health research on risk factors for adverse birth outcomes to develop public health policy interventions to protect pregnant Surinamese women and their newborns from potential long-term effects.