bluuebunny/crossref_metadata_2025 huggingface dataset:
A Way of Bionic Control Based on EI, EMG, and FMG Signals
Authors: Briko Andrey , Kapravchuk Vladislava , Kobelev Alexander , Hammoud Ahmad , Leonhardt Steffen , Ngo Chuong , Gulyaev Yury , Shchukin Sergey
Published: December 2021

Creating highly functional prosthetic, orthotic, and rehabilitation devices is a socially relevant scientific and engineering task. Currently, certain constraints hamper the development of such devices. The primary constraint is the lack of an intuitive and reliable control interface working between the organism and the actuator. The critical point in developing these devices and systems is determining the type and parameters of movements based on control signals recorded on an extremity. In the study, we investigate the simultaneous acquisition of electric impedance (EI), electromyography (EMG), and force myography (FMG) signals during basic wrist movements: grasping, flexion/extension, and rotation. For investigation, a laboratory instrumentation and software test setup were made for registering signals and collecting data. The analysis of the acquired signals revealed that the EI signals in conjunction with the analysis of EMG and FMG signals could potentially be highly informative in anthropomorphic control systems. The study results confirm that the comprehensive real-time analysis of EI, EMG, and FMG signals potentially allows implementing the method of anthropomorphic and proportional control with an acceptable delay.
Structural Health Monitoring in Composite Structures: A Comprehensive Review
Authors: Hassani Sahar , Mousavi Mohsen , Gandomi Amir H.
Published: December 2021

This study presents a comprehensive review of the history of research and development of different damage-detection methods in the realm of composite structures. Different fields of engineering, such as mechanical, architectural, civil, and aerospace engineering, benefit excellent mechanical properties of composite materials. Due to their heterogeneous nature, composite materials can suffer from several complex nonlinear damage modes, including impact damage, delamination, matrix crack, fiber breakage, and voids. Therefore, early damage detection of composite structures can help avoid catastrophic events and tragic consequences, such as airplane crashes, further demanding the development of robust structural health monitoring (SHM) algorithms. This study first reviews different non-destructive damage testing techniques, then investigates vibration-based damage-detection methods along with their respective pros and cons, and concludes with a thorough discussion of a nonlinear hybrid method termed the Vibro-Acoustic Modulation technique. Advanced signal processing, machine learning, and deep learning have been widely employed for solving damage-detection problems of composite structures. Therefore, all of these methods have been fully studied. Considering the wide use of a new generation of smart composites in different applications, a section is dedicated to these materials. At the end of this paper, some final remarks and suggestions for future work are presented.
Autonomous Incident Detection on Spectrometers Using Deep Convolutional Models
Authors: Zhang Xuelin , Zhang Donghao , Leye Alexander , Scott Adrian , Visser Luke , Ge Zongyuan , Bonnington Paul
Published: December 2021

This paper focuses on improving the performance of scientific instrumentation that uses glass spray chambers for sample introduction, such as spectrometers, which are widely used in analytical chemistry, by detecting incidents using deep convolutional models. The performance of these instruments can be affected by the quality of the introduction of the sample into the spray chamber. Among the indicators of poor quality sample introduction are two primary incidents: The formation of liquid beads on the surface of the spray chamber, and flooding at the bottom of the spray chamber. Detecting such events autonomously as they occur can assist with improving the overall operational accuracy and efficacy of the chemical analysis, and avoid severe incidents such as malfunction and instrument damage. In contrast to objects commonly seen in the real world, beading and flooding detection are more challenging since they are of significantly small size and transparent. Furthermore, the non-rigid property increases the difficulty of the detection of these incidents, as such that existing deep-learning-based object detection frameworks are prone to fail for this task. There is no former work that uses computer vision to detect these incidents in the chemistry industry. In this work, we propose two frameworks for the detection task of these two incidents, which not only leverage the modern deep learning architectures but also integrate with expert knowledge of the problems. Specifically, the proposed networks first localize the regions of interest where the incidents are most likely generated and then refine these incident outputs. The use of data augmentation and synthesis, and choice of negative sampling in training, allows for a large increase in accuracy while remaining a real-time system for inference. In the data collected from our laboratory, our method surpasses widely used object detection baselines and can correctly detect 95% of the beads and 98% of the flooding. At the same time, out method can process four frames per second and is able to be implemented in real time.
Advances in Design Strategies of Multiplex Electrochemical Aptasensors
Authors: Grabowska Iwona , Hepel Maria , Kurzątkowska-Adaszyńska Katarzyna
Published: December 2021

In recent years, the need for simple, fast, and economical detection of food and environmental contaminants, and the necessity to monitor biomarkers of different diseases have considerably accelerated the development of biosensor technology. However, designing biosensors capable of simultaneous determination of two or more analytes in a single measurement, for example on a single working electrode in single solution, is still a great challenge. On the other hand, such analysis offers many advantages compared to single analyte tests, such as cost per test, labor, throughput, and convenience. Because of the high sensitivity and scalability of the electrochemical detection systems on the one hand and the specificity of aptamers on the other, the electrochemical aptasensors are considered to be highly effective devices for simultaneous detection of multiple-target analytes. In this review, we describe and evaluate multi-label approaches based on (1) metal quantum dots and metal ions, (2) redox labels, and (3) enzyme labels. We focus on recently developed strategies for multiplex sensing using electrochemical aptasensors. Furthermore, we emphasize the use of different nanomaterials in the construction of these aptasensors. Based on examples from the existing literature, we highlight recent applications of multiplexed detection platforms in clinical diagnostics, food control, and environmental monitoring. Finally, we discuss the advantages and disadvantages of the aptasensors developed so far, and debate possible challenges and prospects.
Pilots for Healthy and Active Ageing (PHArA-ON) Project: Definition of New Technological Solutions for Older People in Italian Pilot Sites Based on Elicited User Needs
Authors: D’Onofrio Grazia , Fiorini Laura , Toccafondi Lara , Rovini Erika , Russo Sergio , Ciccone Filomena , Giuliani Francesco , Sancarlo Daniele , Cavallo Filippo
Published: December 2021

Background: The Pilots for Healthy and Active Ageing (PHArA-ON) project aimsto ensure reality smart and active living for Europe’s ageing population by creating a set of integrated and highly customizable interoperable open platforms with advanced services, devices, and technologies and tools. The aim of the present study was to determine the needs and preferences of older people and their caregivers for improving healthy and active aging and guiding the technological development of thePHArA-ON system. Methods: A pre-structured interview was administered to older adults, informal caregivers and professional caregivers (including social operators) taking part in the piloting sessions. Results: Interviews were carried out in Umana Persone Social Enterprise R&D Network (UP) in Tuscany, and Ospedale Casa SollievodellaSofferenza (CSS) in Apulia. A total of 22 older adults, 22 informal caregivers, 13 professional caregivers and 4 social operators were recruited. A prioritization analysis of services, according to the stakeholder’s needs, has determined two fundamental need categories: Heath Management (i.e., stimulation and monitoring), and Socialisation (i.e., promoting social inclusion). Conclusions: The main scientific contributions to this study are the following: to design and evaluate technology in the context of healthy and active ageing, to acquire relevant knowledge on user needs to develop technologies that can handle the real life situations of older people, obtain useful insights about the attitude and availability of end-users in using technologies in clinical practice, and to provide important guidelines to improve the PHArA-ON system. Specific experimentation stages were also carried out to understand which kind of technology is more acceptable, and to obtain feedback regarding the development priority related to the impact of the proposed services. Research through fruitful and continuous interaction with the different subjects involved in the development process of the system, as well as with stakeholders, enabled the implementation of a platform which could be further and easily integrated and improved.
A Partial Carrier Phase Integer Ambiguity Fixing Algorithm for Combinatorial Optimization between Network RTK Reference Stations
Authors: Wang Shouhua , You Zhiqi , Sun Xiyan
Published: December 2021

In the face of a complex observation environment, the solution of the reference station of the ambiguity of network real-time kinematic (RTK) will be affected. The joint solution of multiple systems makes the ambiguity dimension increase steeply, which makes it difficult to estimate all the ambiguity. In addition, when receiving satellite observation signals in the environment with many occlusions, the received satellite observation values are prone to gross errors, resulting in obvious deviations in the solution. In this paper, a new network RTK fixation algorithm for partial ambiguity among the reference stations is proposed. It first estimates the floating-point ambiguity using the robust extended Kalman filtering (EKF) technique based on mean estimation, then finds the optimal ambiguity subset by the optimized partial ambiguity solving method. Finally, fixing the floating-point solution by the least-squares ambiguity decorrelation adjustment (LAMBDA) algorithm and the joint test of ratio (R-ratio) and bootstrapping success rate index solver. The experimental results indicate that the new method can significantly improve the fixation rate of ambiguity among network RTK reference stations and thus effectively improve the reliability of positioning results.
Advancing Smart Home Awareness—A Conceptual Computational Modelling Framework for the Execution of Daily Activities of People with Alzheimer’s Disease
Authors: Liappas Nikolaos , Teriús-Padrón José Gabriel , García-Betances Rebeca Isabel , Cabrera-Umpiérrez María Fernanda
Published: December 2021

Utilizing context-aware tools in smart homes (SH) helps to incorporate higher quality interaction paradigms between the house and specific groups of users such as people with Alzheimer’s disease (AD). One method of delivering these interaction paradigms acceptably and efficiently is through context processing the behavior of the residents within the SH. Predicting human behavior and uncertain events is crucial in the prevention of upcoming missteps and confusion when people with AD perform their daily activities. Modelling human behavior and mental states using cognitive architectures produces computational models capable of replicating real use case scenarios. In this way, SHs can reinforce the execution of daily activities effectively once they acquire adequate awareness about the missteps, interruptions, memory problems, and unpredictable events that can arise during the daily life of a person living with cognitive deterioration. This paper presents a conceptual computational framework for the modelling of daily living activities of people with AD and their progression through different stages of AD. Simulations and initial results demonstrate that it is feasible to effectively estimate and predict common errors and behaviors in the execution of daily activities under specific assessment tests.
Early Diagnosis of Multiple Sclerosis Using Swept-Source Optical Coherence Tomography and Convolutional Neural Networks Trained with Data Augmentation
Authors: López-Dorado Almudena , Ortiz Miguel , Satue María , Rodrigo María J. , Barea Rafael , Sánchez-Morla Eva M. , Cavaliere Carlo , Rodríguez-Ascariz José M. , Orduna-Hospital Elvira , Boquete Luciano , Garcia-Martin Elena
Published: December 2021

Background: The aim of this paper is to implement a system to facilitate the diagnosis of multiple sclerosis (MS) in its initial stages. It does so using a convolutional neural network (CNN) to classify images captured with swept-source optical coherence tomography (SS-OCT). Methods: SS-OCT images from 48 control subjects and 48 recently diagnosed MS patients have been used. These images show the thicknesses (45 × 60 points) of the following structures: complete retina, retinal nerve fiber layer, two ganglion cell layers (GCL+, GCL++) and choroid. The Cohen distance is used to identify the structures and the regions within them with greatest discriminant capacity. The original database of OCT images is augmented by a deep convolutional generative adversarial network to expand the CNN’s training set. Results: The retinal structures with greatest discriminant capacity are the GCL++ (44.99% of image points), complete retina (26.71%) and GCL+ (22.93%). Thresholding these images and using them as inputs to a CNN comprising two convolution modules and one classification module obtains sensitivity = specificity = 1.0. Conclusions: Feature pre-selection and the use of a convolutional neural network may be a promising, nonharmful, low-cost, easy-to-perform and effective means of assisting the early diagnosis of MS based on SS-OCT thickness data.
Dynamic and Full-Time Acquisition Technology and Method of Ice Data of Yellow River
Authors: Deng Yu , Li Chunjiang , Li Zhijun , Zhang Baosen
Published: December 2021

Regarding the ice periods of the Yellow River, it is difficult to obtain ice data information. To effectively grasp the ice evolution process in the ice periods of the typical reach of the Yellow River, a fixed-point air-coupled radar remote monitoring device is proposed in this paper. The device is mainly composed of an air-coupled radar ice thickness measurement sensor, radar water level measurement sensor, temperature measurement sensor, high-definition infrared night vision instrument, remote switch control, telemetry communication machine, solar and wind power supply, lightning protection, and slewing arm steel tower. The integrated monitoring device can monitor ice thickness, water level, air temperature, ice surface temperature, and other related parameters in real time. At present, devices have obtained the ice change process of fixed points in ice periods from 2020 to 2021. Through a comparison with manual data, the mean error of the monitoring results of the water level and ice thickness was approximately 1 cm. The device realizes the real-time monitoring of ice thickness and water level change in the whole cycle at the fixed position. Through video monitoring, it can take pictures and videos regularly and realize the connection between the visual river and monitoring data. The research results provide a new model and new technology for hydrological monitoring in the ice periods of the Yellow River, which has broad application prospects.
Entropy-Based Variational Scheme with Component Splitting for the Efficient Learning of Gamma Mixtures
Authors: Bourouis Sami , Pawar Yogesh , Bouguila Nizar
Published: December 2021

Finite Gamma mixture models have proved to be flexible and can take prior information into account to improve generalization capability, which make them interesting for several machine learning and data mining applications. In this study, an efficient Gamma mixture model-based approach for proportional vector clustering is proposed. In particular, a sophisticated entropy-based variational algorithm is developed to learn the model and optimize its complexity simultaneously. Moreover, a component-splitting principle is investigated, here, to handle the problem of model selection and to prevent over-fitting, which is an added advantage, as it is done within the variational framework. The performance and merits of the proposed framework are evaluated on multiple, real-challenging applications including dynamic textures clustering, objects categorization and human gesture recognition.
Development of Co-Rich Microwires with Graded Magnetic Anisotropy
Authors: Zhukova Valentina , Corte-Leon Paula , Blanco Juan Maria , Ipatov Mihail , Gonzalez Alvaro , Zhukov Arcady
Published: December 2021

In this paper, a gradual change in the hysteresis loop of Co-rich glass-coated microwire stress-annealed at variable temperature is observed. Such microwires annealed with a temperature gradient also present a variable squareness ratio and magnetic anisotropy field along the microwire’s length. The obtained graded anisotropy has been attributed to a gradual modification of the domain structure along the microwire originated by a counterbalance between shape, magnetoelastic, and induced magnetic anisotropies. Accordingly, we propose a rather simple route to design graded magnetic anisotropy in a magnetic microwire.
Carbon Dioxide Sensing—Biomedical Applications to Human Subjects
Authors: Dervieux Emmanuel , Théron Michaël , Uhring Wilfried
Published: December 2021

Carbon dioxide (CO2) monitoring in human subjects is of crucial importance in medical practice. Transcutaneous monitors based on the Stow-Severinghaus electrode make a good alternative to the painful and risky arterial “blood gases” sampling. Yet, such monitors are not only expensive, but also bulky and continuously drifting, requiring frequent recalibrations by trained medical staff. Aiming at finding alternatives, the full panel of CO2 measurement techniques is thoroughly reviewed. The physicochemical working principle of each sensing technique is given, as well as some typical merit criteria, advantages, and drawbacks. An overview of the main CO2 monitoring methods and sites routinely used in clinical practice is also provided, revealing their constraints and specificities. The reviewed CO2 sensing techniques are then evaluated in view of the latter clinical constraints and transcutaneous sensing coupled to a dye-based fluorescence CO2 sensing seems to offer the best potential for the development of a future non-invasive clinical CO2 monitor.
Review and Simulation of Counter-UAS Sensors for Unmanned Traffic Management
Authors: Besada Juan A. , Campaña Ivan , Carramiñana David , Bergesio Luca , de Miguel Gonzalo
Published: December 2021

Noncollaborative surveillance of airborne UAS (Unmanned Aerial System) is a key enabler to the safe integration of UAS within a UTM (Unmanned Traffic Management) ecosystem. Thus, a wide variety of new sensors (known as Counter-UAS sensors) are being developed to provide real-time UAS tracking, ranging from radar, RF analysis and image-based detection to even sound-based sensors. This paper aims to discuss the current state-of-the art technology in this wide variety of sensors (both academically and commercially) and to propose a set of simulation models for them. Thus, the review is focused on identifying the key parameters and processes that allow modeling their performance and operation, which reflect the variety of measurement processes. The resulting simulation models are designed to help evaluate how sensors’ performances affect UTM systems, and specifically the implications in their tracking and tactical services (i.e., tactical conflicts with uncontrolled drones). The simulation models cover probabilistic detection (i.e., false alarms and probability of detection) and measurement errors, considering equipment installation (i.e., monostatic vs. multistatic configurations, passive sensing, etc.). The models were integrated in a UTM simulation platform and simulation results are included in the paper for active radars, passive radars, and acoustic sensors.
A Layered Middleware for OT/IT Convergence to Empower Industry 5.0 Applications
Authors: Patera Lorenzo , Garbugli Andrea , Bujari Armir , Scotece Domenico , Corradi Antonio
Published: December 2021

We are still in the midst of Industry 4.0 (I4.0), with more manufacturing lines being labeled as smart thanks to the integration of advanced ICT in Cyber–Physical Systems (CPS). While I4.0 aims to provision cognitive CPS systems, the nascent Industry 5.0 (I5.0) era goes a step beyond, aiming to build cross-border, sustainable, and circular value chains benefiting society as a whole. An enabler of this vision is the integration of data and AI in the industrial decision-making process, which does not exhibit yet a coordination between the Operation and Information Technology domains (OT/IT). This work proposes an architectural approach and an accompanying software prototype addressing the OT/IT convergence problem. The approach is based on a two-layered middleware solution, where each layer aims to better serve the specific differentiated requirements of the OT and IT layers. The proposal is validated in a real testbed, employing actual machine data, showing the capacity of the components to gracefully scale and serve increasing data volumes.
Traditional Uses, Phytochemistry, and Pharmacology of Elegia Species: A Review
Authors: Lymperis Panagiotis , Tomou Ekaterina-Michaela , De Canha Marco Nuno , Lall Namrita , Skaltsa Helen
Published: December 2021

In South Africa, plants belonging to the Restionaceae family possess an ecological dominance. As a result, they have been the subject of numerous morphological, anatomical, and evolutionary studies. However, few studies have focused on their phytochemical profile and their potential pharmacological activities. The genus Elegia L. is the second largest of this family comprising 52 species, which are mainly used as materials for thatching. Limited studies on the chemical constituents of Elegia species and their importance as medicinal plants have been undertaken. This review provides constructive and extensive information about the botanical characterization, distribution, traditional uses, phytochemistry and pharmacology of the genus Elegia. A comprehensive search of previously published literature was performed for studies on this genus, using databases with different key search words. This survey documented 52 Elegia species summarizing their previous taxonomic classification. In addition, 14 species were found to be studied for their phytochemical profile, revealing 14 chemical compounds. Concerning their biological activities, only one species (E. tectorum (L.f.) Moline and H.P.Linder) is reported for its anti-wrinkle activity. Moreover, two species are locally used for thatching and as materials for brooms. The present review highlights the Elegia genus as an important source of bioactive phytochemicals with flavonol glycosides being the main metabolites and reveals the uncharted territory of this genus for new research studies.
Grey’s Anatomy: Gender Differences in Specialty Choice for Medical Students in China
Authors: Shao Xiaofeng , Wang Tianyu
Published: December 2021

Gender differences in sub-major choices within the science, technology, engineering, and mathematics (STEM) fields have scarcely been discussed. This study uses administrative records from a top medical school in China to examine gender differences in medical students’ specialty choices. Results showed that, although the gender gap in choosing a clinical track shrinks over time, female students in the clinical track are far less likely to choose highly paid surgical specialties, and this gap persists over time. However, female students outperformed male students in all of the courses. Thus, academic performance cannot explain the underrepresentation of female students in surgery. We further collected questions such as “Why don’t female students choose surgical specialties” and answers to them in “Chinese Quora”, Zhihu.com. A preliminary text analysis showed that ultra-physical load, discrimination in recruitment, women-unfriendly work climates, and difficulties in taking care of family are barriers that prevent women from choosing surgery.
When Does Abusive Supervision Foster Ineffectual and Defensive Silence? Employee Self-Efficacy and Fear as Contingencies
Authors: Dedahanov Alisher Tohirovich , Abdurazzakov Odiljon Sobirovich , Fayzullaev Abdulkhamid Komil ugli , Sun Wei
Published: December 2021

This study investigates the relationships between abusive supervision and two forms of silence, ineffectual and defensive; the moderating role of self-efficacy in the association between abusive supervision and ineffectual silence and the contingency role of fear in the relationship between abusive supervision and defensive silence. We collected data from 685 employees in manufacturing companies. Of these, 271 were incomplete questionnaires and were excluded from the study; the remaining 414 responses were assessed in the analyses. The results indicate that abusive supervision fosters ineffectual and defensive silence. Moreover, the results suggest that even if supervisors are abusive, individuals with a higher level of self-efficacy tend to have a lower level of ineffectual silence. Furthermore, fear strengthens the link between abusive supervision and defensive silence. According to our knowledge, this work is the first to investigate the relationship between abusive supervision and ineffectual silence, the contingency role of self-efficacy in the link between abusive supervision and ineffectual silence and the moderating role of fear in the relationship between abusive supervision and defensive silence.
COVID-19 Misinformation in Portuguese-Speaking Countries: Agreement with Content and Associated Factors
Authors: Sousa Álvaro Francisco Lopes de , Schneider Guilherme , Carvalho Herica Emilia Félix de , Oliveira Layze Braz de , Lima Shirley Verônica Melo Almeida , Sousa Anderson Reis de , Araújo Telma Maria Evangelista de , Camargo Emerson Lucas Silva , Oriá Mônica Oliveira Batista , Ramos Carmen Viana , de Oliveira Rodrigo Mota , Almeida Camila Aparecida Pinheiro Landim , Jacqueline Fortes Ferreira Andrêa , Teixeira Jules Ramon Brito , Lua Iracema , Souza Fernanda de Oliveira , Araújo Tânia Maria de , Fronteira Inês , Mendes Isabel Amélia Costa
Published: December 2021

In the wake of the COVID-19 pandemic, a complex phenomenon called the “infodemic” has emerged, compromising coping with the pandemic. This study aims to estimate the prevalence of agreement with misinformation about COVID-19 and to identify associated factors. A web survey was carried out in Portuguese-speaking countries in two stages: 1. the identification of misinformation circulating in the included countries; 2. a multicentric online survey with residents of the included countries. The outcome of the study was agreement or disagreement with misinformation about COVID-19. Multivariate analyzes were conducted using the Poisson regression model with robust variance, a logarithmic link function, and 95% confidence intervals. The prevalence of agreement with misinformation about COVID-19 was 63.9%. The following factors increased the prevalence of this outcome: having a religious affiliation (aPR: 1.454, 95% CI: 1.393–1.517), having restrictions on leisure (aPR: 1.230, 95% CI: 1.127–1.342), practicing social isolation (aPR: 1.073, 95% CI: 1.030–1.118), not avoiding agglomeration (aPR: 1.060, 95% CI: 1.005–1.117), not seeking/receiving news from scientific sources (aPR: 1.153, 95% CI: 1.068–1.245), seeking/receiving news from three or more non-scientific sources (aPR: 1.114, 95% CI: 1.049–1.182), and giving credibility to news carried by people from social networks (aPR: 1.175, 95% CI: 1.104–1.251). There was a high prevalence of agreement with misinformation about COVID-19. The quality, similarity, uniformity, and acceptance of the contents indicate a concentration of themes that reflect “homemade”, simple, and easy methods to avoid infection by SARS-CoV-2, compromising decision-making and ability to cope with the disease.
Green Technology Innovation, Globalization, and CO2 Emissions: Recent Insights from the OBOR Economies
Authors: Bilal Ahmer , Li Xiaoping , Zhu Nanli , Sharma Ridhima , Jahanger Atif
Published: December 2021

This study explores the connection between technological innovation, globalization, and CO2 emissions by controlling the critical influence of information and communication technology (ICT) and economic growth in a panel of One Belt One Road (OBOR) countries from 1991 to 2019, utilizing advanced and robust econometric strategies (second generation). In addition, this study also uses an interaction variable (TI*GLOB) to check the interaction role of technological innovation on the linkage between globalization and CO2 emission, besides their direct effect on CO2 emissions in OBOR countries. The outcomes revealed that the linkage between technological innovation and CO2 emissions is negative, and statically significant in all the regions (e.g., OBOR, South Asia, East and Southeast Asia, MENA, Europe, and Central Asia). Moreover, the results of globalization show a significant positive relationship with CO2 emissions in OBOR and South Asia region. Nevertheless, it significantly negatively affects environmental pollution in East and Southeast Asia, MENA, Europe, and Central Asia. The results of TI*GLOB indicate that, for the OBOR sample, East and Southeast Asia, and Central Asia, the moderation effects of technological innovation with globalization are significantly negatively associated with CO2 emissions. However, in MENA and Europe, the interaction effect is a significant positive. The coefficient of ICT for OBOR, Europe, and Central Asia are positive and statistically significant; however, for East, Southeast Asia, and MENA regions, these results are statistically negative. Furthermore, the findings are robust, according to various robustness checks that we have performed for checking the reliability of our main findings. The study establishes numerous polities and makes various recommendations, in light of relevant conclusions.
Application of Green Design and Manufacturing in Mechanical Engineering: Education, Scientific Research, and Practice
Authors: Gao Mengdi , Wang Qingyang , Wang Nan , Ma Zhilin , Li Lei
Published: December 2021

Green design and manufacturing strategies are necessary to cope with the current resource, energy, and environmental problems of the manufacturing industry. To meet various enterprises’ needs for green design and manufacturing, colleges and universities should integrate these concepts into their curricula. This study discusses the application of green strategies in the mechanical engineering field from the viewpoints of teaching, scientific research, and practical education. Based on its development and a basic connotation analysis, this study highlights the challenges and urgency of incorporating green concepts into teaching and research modules of mechanical engineering, as well as methods and implementation strategies to incorporate them in professional curricula using teaching method reform and the teaching and research integration method. An experimental training course of advanced manufacturing processes at the authors’ institution was amended to emphasize the integrated application of green design and manufacturing. This integration not only enriches the field of mechanical engineering but also provides scientific research directions and methods to educators, along with new ideas to imbibe students with mechanical talents for their technical development. These efforts lay the foundation for the sustainable development of China’s manufacturing industry.
Technopreneurial Intentions: The Effect of Innate Innovativeness and Academic Self-Efficacy
Authors: Salhieh Sa’Ed M. , Al-Abdallat Yousef
Published: December 2021

Several factors can affect students’ intention to start a new technology-based venture (technopreneurial intentions). Understanding these factors is important when developing technical educational programs. This study investigates the effect of innate innovativeness and academic self-efficacy on technopreneurial self-efficacy and the forming of technopreneurial intentions. It does this by developing a conceptual model that relates technopreneurial intentions, technopreneurial self-efficacy, academic self-efficacy, and innate innovativeness. The data was collected from 378 undergraduate engineering students enrolled in a Jordanian university with a self-administered questionnaire survey. The results of the structural equation modeling (SEM) using AMOS showed that technopreneurial self-efficacy had a positive and significant impact on technopreneurial intentions. Academic self-efficacy had both a direct and indirect positive effect on technopreneurial intention. The indirect effect occurred through increased technopreneurial self-efficacy. Innate innovativeness had a direct effect on technopreneurial intentions, but it did not have a significant indirect effect through technopreneurship self-efficacy as was initially hypothesized. The findings suggest that those who show interest in starting a new technology-based venture have a strong belief in their abilities to perform the technological and entrepreneurial tasks needed, are confident about their ability to acquire the academic technical skills required, and have the inner motivation to seek what is technologically new and different.
Smart Sustainable City Roadmap as a Tool for Addressing Sustainability Challenges and Building Governance Capacity
Authors: Viale Pereira Gabriela , Schuch de Azambuja Luiza
Published: December 2021

Building smart sustainable cities initiatives requires governance capacity, which is constantly challenged by a diversity of actors and the transformation towards a digital society; however, the process of identifying the conditions for building a smart sustainable city (SSC) is not straightforward. As an attempt to map the key governance conditions, the goal of this study is to suggest guidelines for the development of SSC initiatives in the format of a generic roadmap. This research applies design science research methodology and builds the roadmap based on identified antecedents that may hinder or facilitate the development of SSC initiatives from a systematic literature review and the analysis of key governance aspects from 12 smart city initiatives in Europe and Latin America. This paper builds its results through a four-step approach including: (1) defining the main concepts and dimensions within the smart sustainable city context; (2) identifying sustainability challenges for the development of smart sustainable city initiatives; (3) analysing key governance aspects from smart sustainable city initiatives; and (4) designing an actionable research-based roadmap and practical recommendations. The resulting roadmap contains 11 key governance conditions for developing strategies for smart sustainable city initiatives that were classified into three main phases: (1) planning; (2) implementing; and (3) adopting, monitoring, and evaluating. In terms of contribution, this research provides a tool to support the development of initiatives, addressing sustainability challenges and strengthening governance capacity to ensure the long-term impacts of smart sustainable cities.
Exploring the Antecedents of Organic Food Purchase Intention: An Extension of the Theory of Planned Behavior
Authors: Teixeira Sandrina Francisca , Barbosa Belem , Cunha Hugo , Oliveira Zaila
Published: December 2021

Worldwide organic food consumption has registered a consistent rise in recent years. Despite the relevant body of literature on the topic, it is necessary to further understand the antecedents of purchase intention. This article aims to identify the factors that influence the consumer’s intention to purchase organic food. It extends the theory of planned behavior model by including environmental concerns, health concerns, and perceived quality as determinants of attitude toward organic food products. Additionally, it considers the effect of product availability on consumers’ perceived behavioral control. This article includes a quantitative study that was conducted in Portugal in 2020 (n = 206). Structural equation modeling was used to test the proposed set of research hypotheses. In line with extant literature, this study confirmed that attitude toward organic food is the main determinant of purchase intention. Additionally, it demonstrates that health concerns and perceived quality have a significant impact on attitude toward organic food. The impact of environmental concerns on attitude was not confirmed by this study. Based on these findings, it is recommended that managers stress health benefits and quality of organic food in order to foster positive attitudes and consequently leverage purchase intention.
Effect of Carbonized Tangerines Prepared Using Plasma as Fertilizer: Case Study—Lettuce Farming
Authors: Moon Soo-Young , Kim Hyeon Soo , Jang Daehee , Lee Ji-Young
Published: December 2021

This study investigates the soil chemical changes and crop growth characteristics after treatment with carbonized tangerines prepared using plasma. To determine the effect of the carbonized tangerines on crop, four test plots were treated with chaff charcoal or different ratios of coco peat and carbonized tangerines. Lettuce was grown on the plots, and the chemical changes in soil and physical changes in the lettuce were observed. Chemical changes in the soil before and after the carbonized tangerine treatments were very similar to those before and after chaff charcoal treatment. Lettuce leaf length, width, weight, and count in carbonized tangerine-treated plots significantly increased than those in the untreated plot. Our results found that the carbonization of tangerines using plasma can be a good alternative for processing many by-products generated during cultivation. In addition, the possibility of using carbonized tangerines as biochar has been explored.
Research on Global Grain Trade Network Pattern and Its Driving Factors
Authors: Duan Jian , Nie Changle , Wang Yingying , Yan Dan , Xiong Weiwei
Published: December 2021

Trading systems are essential in promoting global food security. With the growing proportion of global food consumption obtained through international trade, the global food trade pattern has become increasingly complex over recent years. This study constructed a weighted global grain network using the trade data of 196 countries in 2000 and 2018 to explore the structure and evolution based on the complex network theory. We established that the global grain network was scale-free. There was significant heterogeneity among nodes, and the heterogeneity of the out-degree was greater than that of the in-degree. The global grain network has a significant core-periphery structure, with the United States, Japan, Mexico, Egypt, South Korea, and Colombia as the core countries. Thereafter, by applying the quadratic assignment procedure model to explore the driving factors of the global grain network, we established that geographical distance had a positive impact on the food trade patterns in 2000 and 2018. This differs from the classical gravity model theory. Furthermore, grain trade had significant “boundary effects”; economic gaps, resource endowment, and regional free trade agreements had a positive impact on the evolution of the grain trade network, whereas cultural similarity and political differences had a negative impact on the grain trade network pattern.
Ecological and/or Nutritional Scores for Food Traffic-Lights: Results of an Online Survey Conducted on Pizza in France
Authors: Marette Stéphan
Published: December 2021

Understanding the hierarchy for consumers between different labels signaling various characteristics defining food sustainability is still an open question. A web survey was conducted in France to examine how providing different scores about the environment and/or nutrition could influence purchase intents for one pizza. 1200 participants were recruited in France in April 2021. They were asked about their purchase intents for one pizza, before and after seeing nutritional and/or ecological scores associated with colors going from green to red. A Global-Score synthetizing both nutritional and ecological dimensions was also tested. The results show that the appearance of scores and colors significantly affect the purchase intents for this pizza. Indeed, for each type of score (namely nutritional, ecological or global), the dominant effect comes from the reduction in purchase intents related to the red color, although green or yellow colors also change purchase intents but to a lesser extent. With the red color, the nutritional score leads to more significant decreases in purchasing intents than the ones related to the ecological score or the Global-Score. With an additional round, the appearance of another score complementing the alternative one underlines that the negative impact of the red color for one score on purchase intents is not outweighed by the positive impact of the green color for the other score.
Experienced Well-Being in the Rural Areas of the Srem Region (Serbia): Perceptions of the Local Community
Authors: Đerčan Bojan , Bubalo Živković Milka , Gatarić Dragica , Lukić Tamara , Dragin Aleksandra , Kalenjuk Pivarski Bojana , Lutovac Miloš , Kuzman Boris , Puškarić Anton , Banjac Maja , Grubor Biljana , Simović Olivera
Published: December 2021

The sustainability of rural settlements in Serbia has become the main focus of strategic rural development planning. Although it is still difficult to measure the quality of life of the citizens of the Republic of Serbia, preliminary results show that it is necessary to go beyond the GDP as the only or one of its most important indicators. A multidimensional approach and analysis at the local level is necessary for more comprehensive insight into quality of life in order to avoid erroneous or simplified conclusions. Striving to provide more detailed insight into the attitudes and needs of the local population, this research uses a qualitative approach. Subjective measures of how people feel and function in everyday life can predict future reactions, many of which are in the domain of the interests of local public services. The aim of the research is to apply measurement indicators through six dimensions of well-being in order to determine to what extent the inhabitants of rural communities are satisfied with some indicators and how they assess their own well-being. The research results illustrate the importance of understanding the needs of the local population and the perception of overall life experience in monitoring balanced rural development. The research can be adapted and applied to any rural community. Thus, only by developing a participatory approach can one strive for community-based rural development.
A Brief Review of Our Agile Teaching Formats in Entrepreneurship Education
Authors: Fischer Sophie , Rosilius Maximilian , Schmitt Jan , Bräutigam Volker
Published: December 2021

Companies are confronted with increasingly demanding environments, including globalization, technologization, intergenerationality, and crises such as the coronavirus pandemic. To accept uncertainties as a challenge and to recognize opportunities for development, well-educated and resilient founders are needed who can foster innovation and sustainable development within society and the economy. The majority of today’s entrepreneurs have an academic background. Hence, institutions for higher education need to provide comprehensive educational offerings and support initiatives to train and sensitize future entrepreneurs. Therefore, since 2013, agile teaching formats have been developed in our project at a Bavarian university of applied sciences. In two stages, we founded a limited company for hands-on experimentation with entrepreneurship and also conceptualized an elective course and an annual founders’ night. Based on a theoretical model and continuous teaching evaluations, we adjusted the individual modules to suit the target group. The objective is to promote the acquisition of key competencies and exert a positive influence on the startup quotient in the region. There are six startups by students who can be traced back to our project. This indicates that a target-group-oriented educational program encourages motivation and awareness of entrepreneurial thinking and action among students.
Adoption of Advanced Technologies in Palm Oil Milling Firms in Malaysia: The Role of Technology Attributes, and Environmental and Organizational Factors
Authors: Parvand Sima , Rasiah Rajah
Published: December 2021

Malaysian palm oil mills have lagged behind in the adoption of advanced milling technologies, both to support productivity growth as well as meet sustainable development goals. Using primary data from a simple random sample of 54 mills, this study sought to examine the factors explaining the adoption of advanced milling technologies. The results from the logit regressions deployed show that top management support and mill size act as key drivers of new technology adoption. Importantly, organizational factors were more critical in the adoption of advanced milling technologies than technological and environmental factors. However, financial support and resources, technical skills, complexity, cost, and government support produced negative coefficients among the non-adopters. The results should help mill owners, managers, and policymakers to remove the barriers facing the non-adopters, and to create effective strategies to stimulate the adoption of advanced milling technologies.
Parental Perceptions and Behaviors about Children’s Exposure to Secondhand Smoke in Portugal
Authors: Precioso José , Sousa Isabel , Araújo Carolina , Correia Cláudia , Cunha-Machado José , Rocha Vânia
Published: December 2021

One of the sustainable development goals adopted by the United Nations is to ensure healthy lives and promote well-being for all. Tobacco consumption is a serious health problem that affects smokers and non-smokers exposed to secondhand smoke (SHS), particularly children. This study aims to describe parental perceptions of smoke-free rules, to analyze parental awareness about health risks associated with children’s exposure to SHS, and to describe the prevalence of avoidance behaviors related to tobacco smoke, according to parental smoking status. This study includes 1175 parents from a representative sample of 1511 Portuguese children aged 4 to 9 years old in 2016. Parents who were non-smokers reported a higher level of agreement regarding smoke-free rules at home, inside the car, at playgrounds, and near the school entrance than smokers. A higher percentage of nonsmoking parents agreed that children whose parents smoke at home are more likely to become smokers themselves. Nonsmoking parents reported adopting more avoidance behaviors regarding exposure to SHS. The findings indicate that parental exposure perceptions and avoidance behaviors towards SHS were lower and less frequent among smokers. Health education, smoking cessation programs and smoking bans are needed to raise parental awareness and to protect children from SHS exposure.