bluuebunny/crossref_metadata_2025 huggingface dataset:
Aggregation Strategy on Federated Machine Learning Algorithm for Collaborative Predictive Maintenance
Authors: Bemani Ali , Björsell Niclas
Published: August 2022

Industry 4.0 lets the industry build compact, precise, and connected assets and also has made modern industrial assets a massive source of data that can be used in process optimization, defining product quality, and predictive maintenance (PM). Large amounts of data are collected from machines, processed, and analyzed by different machine learning (ML) algorithms to achieve effective PM. These machines, assumed as edge devices, transmit their data readings to the cloud for processing and modeling. Transmitting massive amounts of data between edge and cloud is costly, increases latency, and causes privacy concerns. To address this issue, efforts have been made to use edge computing in PM applications., reducing data transmission costs and increasing processing speed. Federated learning (FL) has been proposed a mechanism that provides the ability to create a model from distributed data in edge, fog, and cloud layers without violating privacy and offers new opportunities for a collaborative approach to PM applications. However, FL has challenges in confronting with asset management in the industry, especially in the PM applications, which need to be considered in order to be fully compatible with these applications. This study describes distributed ML for PM applications and proposes two federated algorithms: Federated support vector machine (FedSVM) with memory for anomaly detection and federated long-short term memory (FedLSTM) for remaining useful life (RUL) estimation that enables factories at the fog level to maximize their PM models’ accuracy without compromising their privacy. A global model at the cloud level has also been generated based on these algorithms. We have evaluated the approach using the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) dataset to predict engines’ RUL Experimental results demonstrate the advantage of FedSVM and FedLSTM in terms of model accuracy, model convergence time, and network usage resources.
Exploiting Ultra-Wideband Channel Impulse Responses for Device-Free Localization
Authors: Cimdins Marco , Schmidt Sven Ole , Bartmann Peter , Hellbrück Horst
Published: August 2022

In radio-frequency (RF)-based device-free localization (DFL), the number of sensors acting as RF transmitters and receivers is crucial for accuracy and system costs. Two promising approaches for DFL have been identified in the past: radio tomographic imaging (RTI) and multi-static radar (MSR). RTI in its basic version requires many sensors for high accuracy, which increases the cost. In this paper, we show how RTI benefits from multipath propagation. By evaluating the direct and echo paths, we increase the coverage of the target area, and by utilizing UWB signals, the RTI system is less susceptible to multipath propagation. MSR maps reflections that occur within the target area to reflectors such as persons or other objects. MSR does not require that the person is located near a signal path. Both suggested methods exploit ultra-wideband (UWB) channel impulse response (CIR) measurements. CIR measurements and the modeling of multipath effects either increase the accuracy or reduce the required number of sensors for localization with RTI. We created a test setup and measure UWB CIRs at different positions with a commercially available off-the-shelf UWB radio chip, the Decawave DW1000. We compare the localization results of RTI, multipath-assisted (MA)-RTI, and MSR and investigate a combined approach. We show that RTI is improved by the analysis of multipath propagation; furthermore, MA-RTI results in a better performance compared to MSR: with 50% of all cases, the localization error is better than 0.82 m and in 80% of all cases 1.34 m. The combined approach results in the best localization result with 0.64 m in 50% of all cases.
iAKA-CIoT: An Improved Authentication and Key Agreement Scheme for Cloud Enabled Internet of Things Using Physical Unclonable Function
Authors: Park Kisung , Park Youngho
Published: August 2022

The Internet of Things (IoT) with cloud services are important functionalities in the latest IoT systems for providing various convenient services. These cloud-enabled IoT environments collect, analyze, and monitor surrounding data, resulting in the most effective handling of large amounts of heterogeneous data. In these environments, secure authentication with a key agreement mechanism is essential to ensure user and data privacy when transmitting data between the cloud server and IoT nodes. In this study, we prove that the previous scheme contains various security threats, and hence cannot guarantee essential security requirements. To overcome these security threats, we propose an improved authentication and key agreement scheme for cloud-enabled IoT using PUF. Furthermore, we evaluate its security by performing informal, formal (mathematical), and simulation analyses using the AVISPA tool and ROR model. The performance and security properties of our scheme are subsequently compared with those of other related schemes. The comparison confirms that our scheme is suitable for a practical cloud-enabled IoT environment because it provides a superior security level and is more efficient than contemporary schemes.
ZnO Nanorods Coated Single-Mode–Multimode–Single-Mode Optical Fiber Sensor for VOC Biomarker Detection
Authors: Swargiary Kankan , Metem Prattakorn , Kulatumyotin Chayapol , Thaneerat Suphavit , Ajchareeyasoontorn Noppasin , Jitpratak Pannathorn , Bora Tanujjal , Mohammed Waleed S. , Dutta Joydeep , Viphavakit Charusluk
Published: August 2022

This work demonstrated a ZnO-coated optical fiber sensor for the detection of a volatile organic compound (VOC) biomarker for diabetes for detecting isopropanol (IPA) markers. A coreless silica fiber (CSF) was connected to a single-mode fiber (SMF) at both ends to achieve a SMF–CSF–SMF structure. CSF is the sensing region where multimode interference (MMI) generates higher light interaction at the interface between the fiber and sensing medium, leading to enhanced sensitivity. Optimization of the CSF length was conducted numerically to attain the highest possible coupling efficiency at the output. Surface functionalization was achieved via hydrothermal growth of ZnO nanorods directly onto the CSF at low temperatures. The optical fiber-based sensor was successfully fabricated and tested with 20%, 40%, 60%, 80%, and 100% of IPA. The sensor response was recorded using an optical spectrometer and analyzed for sensor sensitivity. The fabricated sensor shows the potential to detect isopropanol with the sensitivity of 0.053 nm/%IPA vapor. Further improvement of the sensor sensitivity and selectivity is also proposed for future work.
Usability of Two New Interactive Game Sensor-Based Hand Training Devices in Parkinson’s Disease
Authors: Saric Lea , Knobel Samuel E. J. , Pastore-Wapp Manuela , Nef Tobias , Mast Fred W. , Vanbellingen Tim
Published: August 2022

This pilot cross-sectional study aimed to evaluate the usability of two new interactive game sensor-based hand devices (GripAble and Smart Sensor Egg) in both healthy adults as well as in persons with Parkinson’s Disease (PD). Eight healthy adults and eight persons with PD participated in this study. Besides a standardised usability measure, the state of flow after one training session and the effect of cognitive abilities on flow were evaluated. High system usability scores (SUS) were obtained both in healthy participants (72.5, IQR = 64.375–90, GripAble) as well as persons with PD (77.5, IQR = 70–80.625, GripAble; 77.5, IQR = 75–82.5, Smart Sensor Egg). Similarly, high FSSOT scores were achieved after one training session (42.5, IQR = 39.75–50, GripAble; 50, IQR = 47–50, Smart Sensor Egg; maximum score 55). Across both groups, FSSOT scores correlated significantly with SUS scores (r = 0.52, p = 0.039). Finally, MoCA did not correlate significantly with FSSOT scores (r = 0.02, p = 0.9). The present study shows high usability for both interactive game sensor-based hand training devices, for persons with PD and healthy participants.
EPI Light Field Depth Estimation Based on a Directional Relationship Model and Multiviewpoint Attention Mechanism
Authors: Gao Ming , Deng Huiping , Xiang Sen , Wu Jin , He Zeyang
Published: August 2022

Light field (LF) image depth estimation is a critical technique for LF-related applications such as 3D reconstruction, target detection, and tracking. The refocusing property of LF images provide rich information for depth estimations; however, it is still challenging in cases of occlusion regions, edge regions, noise interference, etc. The epipolar plane image (EPI) of LF can effectively deal with the depth estimation because of its characteristics of multidirectionality and pixel consistency—in which the LF depth estimations are converted to calculate the EPI slope. This paper proposed an EPI LF depth estimation algorithm based on a directional relationship model and attention mechanism. Unlike the subaperture LF depth estimation method, the proposed method takes EPIs as input images. Specifically, a directional relationship model was used to extract direction features of the horizontal and vertical EPIs, respectively. Then, a multiviewpoint attention mechanism combining channel attention and spatial attention is used to give more weight to the EPI slope information. Subsequently, multiple residual modules are used to eliminate the redundant features that interfere with the EPI slope information—in which a small stride convolution operation is used to avoid losing key EPI slope information. The experimental results revealed that the proposed algorithm outperformed the compared algorithms in terms of accuracy.
A Cloud Enabled Crop Recommendation Platform for Machine Learning-Driven Precision Farming
Authors: Thilakarathne Navod Neranjan , Bakar Muhammad Saifullah Abu , Abas Pg Emerolylariffion , Yassin Hayati
Published: August 2022

Modern agriculture incorporated a portfolio of technologies to meet the current demand for agricultural food production, in terms of both quality and quantity. In this technology-driven farming era, this portfolio of technologies has aided farmers to overcome many of the challenges associated with their farming activities by enabling precise and timely decision making on the basis of data that are observed and subsequently converged. In this regard, Artificial Intelligence (AI) holds a key place, whereby it can assist key stakeholders in making precise decisions regarding the conditions on their farms. Machine Learning (ML), which is a branch of AI, enables systems to learn and improve from their experience without explicitly being programmed, by imitating intelligent behavior in solving tasks in a manner that requires low computational power. For the time being, ML is involved in a variety of aspects of farming, assisting ranchers in making smarter decisions on the basis of the observed data. In this study, we provide an overview of AI-driven precision farming/agriculture with related work and then propose a novel cloud-based ML-powered crop recommendation platform to assist farmers in deciding which crops need to be harvested based on a variety of known parameters. Moreover, in this paper, we compare five predictive ML algorithms—K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Support Vector Machine (SVM)—to identify the best-performing ML algorithm on which to build our recommendation platform as a cloud-based service with the intention of offering precision farming solutions that are free and open source, as will lead to the growth and adoption of precision farming solutions in the long run.
Research into Gas Chromatography–Mass Spectrometry (GC-MS) for Ensuring the Effect of 1 MeV-Accelerated Electrons on Volatile Organic Compounds in Turkey Meat
Authors: Bliznyuk Ulyana , Borshchegovskaya Polina , Bolotnik Timofey , Chernyaev Alexander , Ipatova Victoria , Nikitchenko Alexander , Shinkarev Oleg , Yurov Dmitry , Khmelevskiy Oleg , Rodin Igor
Published: August 2022

One of the most important tasks in the food industry is the search for alternative biochemical markers of radiation treatment in dietary, chilled meat products such as chicken and turkey. Major organic volatile chemicals found in meat products can be precisely identified using gas chromatography coupled with mass spectrometry. In the response to the needs of the food industry, our research team conducted a series of experiments involving the irradiation of chilled poultry meat using an electron accelerator. The experiments showed that the concentration of pure volatile organic compounds in saline solution dropped exponentially with an increase in the irradiation dose, which proves that these chemicals decomposed when exposed to ionizing radiation. However, when turkey meat was exposed to an electron beam with doses up to 1 kGy, the concentration of alcohols, aldehydes, and ketones peaked, only to decrease with an increase in the irradiation dose up to 2 kGy, and then went up slightly when the irradiation dose was within the range from 2 kGy to 10 kGy. To determine the reason behind the nonlinear dependencies of organic compound concentrations in turkey meat on the irradiation dose, we developed a mathematical model that acknowledges the presence of two opposing processes, those of decomposition and accumulation of organic compounds as a result of the decomposition of other compounds that can be found in turkey meat.
Enhancing the Stability of Aerobic Granular Sludge Process Treating Municipal Wastewater by Adjusting Organic Loading Rate and Dissolved Oxygen Concentration
Authors: Peng Juan , Zhao Lei , Wang Qiaoru , Song Wei , Wang Zhuoyue , Li Ji , Zhang Xiaolei , Yuan Fang
Published: August 2022

Aerobic granular sludge (AGS) application in treating municipal wastewater has been greatly restricted due to its low stability. It has been found that operation parameters have a great impact on stability. The organic loading rate (OLR) and dissolved oxygen (DO) concentration are two very important parameters that impact stability. In this study, the organic loading rate (OLR) and aeration rate were studied to verify their influence on AGS system stability, which is indicated by determining pollutant removal performance, including chemical oxygen demand (COD), ammonia nitrogen, and total nitrogen (TN). The physical and chemical property changes of AGS and the effects of pollutant removal during the formation of AGS were systematically investigated. The AGS was formed after about 25 days and remained stable for about 45–50 days. The AGS was light-yellow globular sludge with an average particle size of 1.25 mm and a sludge volume index (SVI) of 33.9 mL/g. The optimal condition was obtained at an OLR of 4.2 kg COD/m3·d, aeration rate of 4 L/min, and a hydraulic retention time (HRT) of 4 h. The corresponding removal efficiencies of COD, ammonia nitrogen, and TN were 94.1%, 98.4% and 74.1%, respectively. The study shows that the AGS system has great potential for pollutant removal from wastewater.
Agent-Based Model of Citizen Energy Communities Used to Negotiate Bilateral Contracts in Electricity Markets
Authors: Algarvio Hugo
Published: August 2022

The worldwide targets for carbon-neutral societies increased the penetration of distributed generation and storage. Smart cities now play a key role in achieving these targets by considering the alliances of their demand and supply assets as local citizen energy communities. These communities need to have enough weight to trade electricity in wholesale markets. Trading of electricity can be done in spot markets or by bilateral contracts involving customers and suppliers. This paper is devoted to bilateral contracting, which is modeled as a negotiation process involving an iterative exchange of offers and counter-offers. This article focuses on local citizen energy communities. Specifically, it presents team and single-agent negotiation models, where each member has its sets of strategies and tactics and also its decision model. Community agents are equipped with intra-team strategies and decision protocols. To evaluate the benefits of CECs, models of both coalition formation and management have been adapted. This paper also describes a case study on forward bilateral contracts, involving a retailer agent and three different types of citizen energy communities. The results demonstrate the benefits of CECs during the negotiation of private bilateral contracts of electricity. Furthermore, they also demonstrate that in the case of using a representative strategy, the selection of the mediator may be critical for achieving a good deal.
The Political Significance of Overeducation: Status Inconsistency, Attitudes towards the Political System and Political Participation in a High-Overeducation Context
Authors: Voces Carmen , Caínzos Miguel
Published: August 2022

This article examines the impact of overeducation on attitudes towards the political system and political participation. Using survey data from Spain, diagonal reference models were estimated to contrast hypotheses based on the theory of status inconsistency. The evidence links overeducation to political attitudes (lower satisfaction with the functioning of democracy, lower external political efficacy) and political participation (greater participation in protests, higher membership in associations). Most of these effects are modest and some of them are moderated by age. Overall, the findings indicate that overeducation has relevant political consequences, mainly among young, university-educated workers doing jobs with low educational requirements. However, overeducation does not pose a major threat to political stability.
The Influence of Weekly Sprint Volume and Maximal Velocity Exposures on Eccentric Hamstring Strength in Professional Football Players
Authors: Shah Sunnan , Collins Kieran , Macgregor Lewis J.
Published: August 2022

Background: Hamstring strains are the most common moderate-major severity injuries in football. The majority of hamstring injuries occur during sprinting, with low eccentric hamstring strength being associated with an elevated risk. Objective: To examine the relationship between sprinting and eccentric hamstring strength by monitoring total weekly sprint distance and weekly efforts > 90% and >95% of maximum velocity. Methods: Fifty-eight professional male footballers were observed over one-and-a-half seasons. Players’ running was monitored during training and matches using GPS, and eccentric hamstring strength was measured weekly. Results: Weekly sprint distance (ρ = −0.13, p < 0.01) and weekly efforts >90% of maximum velocity (ρ = −0.08, p = 0.01) both displayed significant inverse relationships with the percentage change in eccentric hamstring strength; weekly efforts >95% of maximum velocity showed no relationship with hamstring strength (ρ = −0.02, p = 0.45). Only weekly efforts >90% of maximum velocity significantly influenced the mean percentage change in eccentric hamstring force, F(3,58) = 3.71, p = 0.01, with significant differences occurring when comparing 7–8 sprint efforts with 0–2 efforts (0.11%, p = 0.03) and 5–6 efforts (0.12%, p = 0.03). Conclusions: Eccentric hamstring strength levels significantly decrease when 7–8 weekly sprint efforts are completed at >90% of maximum velocity. Monitoring weekly sprint loading at velocities > 90% of maximum velocity may be valuable to help to reduce the risk of hamstring injuries in professional football.
A New Bivariate INAR(1) Model with Time-Dependent Innovation Vectors
Authors: Chen Huaping , Zhu Fukang , Liu Xiufang
Published: August 2022

Recently, there has been a growing interest in integer-valued time series models, especially in multivariate models. Motivated by the diversity of the infinite-patch metapopulation models, we propose an extension to the popular bivariate INAR(1) model, whose innovation vector is assumed to be time-dependent in the sense that the mean of the innovation vector is linearly increased by the previous population size. We discuss the stationarity and ergodicity of the observed process and its subprocesses. We consider the conditional maximum likelihood estimate of the parameters of interest, and establish their large-sample properties. The finite sample performance of the estimator is assessed via simulations. Applications on crime data illustrate the model.
Investigating Corporate Environmental Risk Disclosure Using Machine Learning Algorithm
Authors: Uddin Mohammad Main , Rashid Md. Mamunar , Hasan Mahmudul , Hossain Md. Alamgir , Fang Yuantao
Published: August 2022

The volume of the environmental risk disclosure in the annual reports of firms in the pharmaceutical and chemical, tannery, telecommunications, and paper and printing industries listed on the Dhaka Stock Exchange (DSE) in Bangladesh was analyzed in this paper. The research used a content analysis of the annual reports of 43 companies that represented four DSE sectors. To quantify the level of environmental risk disclosure reporting practiced by corporations in their annual reports, the authors established the ERDIPCI for the pharmaceutical and chemical industry, the ERDITI for the tannery industry, the ERDITeI for the telecommunications industry, and the ERDIPPI for the paper and printing industry. Similarly, the machine learning clustering algorithm, k-means clustering, is used to cluster the companies based on the completion of different environmental indices. It is observed that from four sectors, the highest number of companies from the pharmaceutical and chemical industry disclosed environmental risk disclosures, and the lowest number of companies was from the tannery industry, followed by the telecommunications and the paper and printing industries. The enterprises differ significantly in their environmental risk disclosures, and the overall scenarios of the environmental reporting practices by companies in Bangladesh are quite poor. It also shows that among the 43 companies, a limited number of enterprises are placed first. The majority of the businesses are in the midst of a cluster that reflects the increasing order of indices fulfillment. This paper provided a few specific proposals to the relevant authorities in order to establish a regularity framework in which all the firms listed on the DSE in Bangladesh will be expected to address environmental risk disclosures and conservation actions in their annual reports towards adaptation to climate change and achieving environmental sustainability.
The Educational Digital Divide for Vulnerable Students in the Pandemic: Towards the New Agenda 2030
Authors: Norman Helmi , Adnan Nor Hafizah , Nordin Norazah , Ally Mohamed , Tsinakos Avgoustos
Published: August 2022

The COVID-19 pandemic has caused school closures worldwide and has disrupted nearly 1.6 billion students across the globe. This has widened existing digital gaps and has caused vulnerable students to be further digitally displaced. In efforts to mitigate this issue, various strategies have been used to cater for the educational digital divide of vulnerable students. However, there is a lack of studies investigating the relationship between access and connectivity of learning and use and exploitation of technology, particularly with regards to iPads during the pandemic. Thus, the present study investigates this scenario by examining the digital educational divide for vulnerable students in the pandemic, in terms of access and connectivity and use and exploitation. A survey was distributed to 518 vulnerable students in schools between the ages of 10 and 15 years old, and results were analyzed using partial least squares–structural equation modeling (PLS-SEM). The findings indicate that asynchronous learning is a stronger construct than synchronous learning, while creativity skills was stronger than productivity skills with regard to the use and exploitation of technology for pandemic learning of vulnerable students. This study’s findings could assist future developers and educators in the development of effective emergency teaching and learning strategies and design.
Fermentative Production of Erythritol from Cane Molasses Using Candida magnoliae: Media Optimization, Purification, and Characterization
Authors: Seshadrinathan Shruthy , Chakraborty Snehasis
Published: August 2022

Erythritol is a natural, zero-calorie sweetener that can be used as a sugar substitute and humectant for different products such as confectionaries, tablets, etc. Methods such as extraction and chemical synthesis for erythritol synthesis are not feasible or sustainable due to lower yield and higher operating costs. In the present study, erythritol is produced through the submerged fermentation of cane molasses, a by-product of the cane sugar industry, in the presence of the osmophilic yeast Candida magnoliae. Erythrose reductase enzyme assay was used for quantifying erythritol yield. Plackett–Burman’s design screened the three most influential factors viz. molasses, yeast extract, and KH2PO4 out of 12 contributing factors. Further, the concentration of molasses (200–300 g/L), yeast extract (9–12 g/L), and KH2PO4 (2–5 g/L) were optimized using response surface methodology coupled with numerical optimization. The optimized erythritol yield (99.54 g·L−1) was obtained when the media consisted of 273.96 g·L−1 molasses, 10.25 g·L−1 yeast extract, and 3.28 g·L−1 KH2PO4 in the medium. After purification, the liquid chromatography–mass spectrometry (LC-MS) analysis of erythritol crystals from this optimized fermentation condition showed 94% purity. Glycerol was produced as the side product (5.4%) followed by a trace amount of sucrose and mannitol. The molecular masses of the erythritol were determined through mass spectrometry by comparing [M + Na] + ions. Analysis in electrospray (ES) positive mode gave (m/z) of 145.12 [M + 23]. This study has reported a higher erythritol yield from molasses and used osmotolerant yeast Candida magnoliae to assimilate the sucrose from molasses.
Intelligent Vehicle Sales Prediction Based on Online Public Opinion and Online Search Index
Authors: Zhang Mingyang , Xu Heyan , Ma Ning , Pan Xinglin
Published: August 2022

Intelligent vehicles refer to a new generation of vehicles with automatic driving functions that is gradually becoming an intelligent mobile space and application terminal by carrying advanced sensors and other devices and using new technologies, such as artificial intelligence. Firstly, the traditional autoregressive intelligent vehicle sales prediction model based on historical sales is established. Secondly, the public opinion data and online search index data are selected to establish a sales prediction model based on online public opinion and online search index. Then, we consider the influence of KOL (Key Opinion Leader), a sales prediction model based on KOL online public opinion andonline search index is established. Finally, the model is further optimized by using the deep learning algorithm LSTM (Long Short-Term Memory network), and the LSTM sales prediction model based on KOL online public opinion and online search index is established. The results show that the consideration of the online public opinion and search index can improve the prediction accuracy of intelligent vehicle sales, and the public opinion of KOL plays a greater role in improving the prediction accuracy of sales than that of the general public. Deep learning algorithms can further improve the prediction accuracy of intelligent vehicle sales.
Impact of Biased Technological Change on High-Quality Economic Development of China’s Forestry: Based on Mediating Effect of Industrial Structure Upgrading
Authors: Jiang Yu , Wang Na
Published: August 2022

The high-quality development of the forestry economy refers not only to the quantity of economic growth but also to the improvement of its quality, that is, driving forestry economic development through technological change and industrial structure upgrading. In this paper, a multi-angle indicator evaluation system and obstacle model were constructed to explore the current situation and obstacle factors of the high-quality development of the forestry economy in 31 Chinese provinces (municipalities and autonomous regions) from 2005 to 2020. At the same time, based on the mediating effect of industrial structure upgrading, a mediating effects model was constructed to analyze the direct and indirect effects of forestry’s biased technological change on high-quality economic development. The results showed three key findings: (1) There was regional heterogeneity in the high-quality development of the forestry economy in China, and the biggest obstacle factor was technological change. (2) Forestry’s biased technological change had a significant positive effect on high-quality economic development, with a total effect of 0.222; industrial structure upgrading played a partial mediating effect, and 16.7% of the effect was indirectly realized by promoting industrial structure upgrading. (3) The contribution of forestry’s biased technological change to high-quality economic development in the western and northeastern regions was greater than that in the eastern and central regions. Based on this, the promotion of the high-quality development of the forestry economy and the enhancement of industrial structure upgrading through optimizing biased technological change were suggested.
A Comprehensive Evaluation of Benefit of High-Standard Farmland Development in China
Authors: Wang Yu , Li Ganqiong , Wang Shengwei , Zhang Yongen , Li Denghua , Zhou Han , Yu Wen , Xu Shiwei
Published: August 2022

In 2013, the government officially approved the construction task of developing high-standard farmland, which had been written into the outline of the “12th Five-Year Plan”, the “13th Five-Year Plan” and the “14th Five-Year Plan”, effectively ensuring the sustainable development of farmland with high and stable yield in China. Moreover, with the rapid progress of urbanization and industrialization, the quality and usage of cultivated land have changed greatly, and the relationship between the economic value, social value and ecological value of land has become increasingly prominent. Whether the development of high-standard farmland, especially the high-standard farmland used for grain production, has achieved the goals of increasing farmers’ income, agricultural output and rural development is not clear. Therefore, it is necessary to evaluate the comprehensive benefits of high-standard farmland development in grain production, so as to scientifically measure the results of the development. From the perspective of economic, social and ecological benefits, this paper establishes an entropy weight evaluation index system and a model to evaluate the level and effectiveness of high-standard farmland development from 2013 to 2020 in China. The results show that the high-standard farmland development project has improved the yield of grain and the basic productivity of cultivated land, effectively increased the yields of land in the project area and promoted the protection and improvement of cultivated land quality, which includes soil quality improvement, soil fertility enhancement, pollution control and soil remediation. The project also helped raise the farmers’ income levels and improved farmers’ agricultural knowledge and skills in the project area. The projects are very beneficial for agricultural production, the farmers’ income and rural development. However, there is still a certain gap between the national average level of improvement and the original goal set in the policy. The average grain yield per mu (Note: 1 mu ≈ 0.0667 ha, similarly hereinafter) was expected to be increased by less than 100 kg (the national average was a 40 kg increase), and the degrees of improvement in economic, social, ecological and comprehensive benefits in different project types were also different. In the future, we suggest that the project should be implemented according to local conditions and the features of each region. We should pay attention to the protection of basic farmland quality and further improve grain output to achieve the goal of stabilizing and increasing production.
What Triggers Climate Action: The Impact of a Climate Change Education Program on Students’ Climate Literacy and Their Willingness to Act
Authors: Kolenatý Miloslav , Kroufek Roman , Činčera Jan
Published: August 2022

There has been an intensive debate in the field of climate change education about what predictors influence youth’s climate-related behavior and what educational strategies and practices stimulate such behavior. This study investigated the impact of the ‘CO2 League’ program which involved 47 schools (N = 123). For the analyses, we used pre-/-post quasi-experimental design which combined quantitative and qualitative methods: an extensive pre-/-post survey for analyzing several components of students’ climate literacy (system/action/effectiveness knowledge, climate change concern, self-efficacy, willingness for climate-protective behavior) and focus group interviews. The analyses revealed a significant impact of sufficient climate change knowledge on climate change concern which subsequently positively influenced participants’ self-efficacy and their willingness to act. The findings of this study suggest that knowledge is a key initial driver for climate action, especially for young people, and confirm the conclusion of previous studies that willingness to adopt pro-climatic behavior presupposes a clear and explicit understanding of climate dynamics and its causal relations. The focus group interviews also revealed that the reported increased willingness to act often translated into actual climate action and that learning about the concept of carbon footprint and the process of calculating and decreasing it proved to be a very accessible and fast path to participants’ engagement in personal climate action.
Spatiotemporal Dynamics of COVID-19 Infections in Mainland Portugal
Authors: Silva Melissa , Betco Iuria , Capinha César , Roquette Rita , Viana Cláudia M. , Rocha Jorge
Published: August 2022

The World Health Organization declared COVID-19 as a pandemic disease on 12 March 2020. Currently, this disease caused by the SARS-CoV-2 virus remains one of the biggest public health problems in the world. Thus, it is essential to apply methods that enable a better understanding of the virus diffusion processes, not only at the spatial level but also at the spatiotemporal one. To that end, we tried to understand the spatial distribution of COVID-19 pathology in continental Portugal at the municipal level and to comprehend how mobility influences transmission. We used autocorrelation indices such as Getis-Ord (with Euclidian distance and commuting values), Local Moran, and a new hybrid approach. Likewise, aiming to identify the spatiotemporal patterns of the virus propagation by using Man–Kendall statistics, we found that most hotspots of infected individuals occur in the municipalities of metropolitan areas. The spatiotemporal analysis identified most of the municipalities as oscillating hotspots.
Exploring the Instrumental and Emotional Supports for Sustainability and Social Participation
Authors: Canedo-García Alejandro , García-Sánchez Jesús-Nicasio
Published: August 2022

The prevalence of instrumental and emotional support received was investigated, and several psychosocial characteristics were studied in a Spanish population of all ages (10–85 years) and different social groups, by using an 88-item online questionnaire, with a total of 2013 respondents. Participants reported on the instrumental and emotional supports they receive, and on a set of psychosocial variables (emotion management, motivation, self-efficacy, social and communication skills, and social support), as well as their sociodemographic data; all of which were considered as contributions to a sustainable world. Instrumental and emotional supports for sustainability were significantly less prevalent among older participants, men, people with low educational levels, low autonomy levels, retired individuals, and those with a low-income level. Older and female participants were able to manage their own and other peoples’ emotions significantly more frequently. Participant motivation was less prevalent among the participants with low autonomy levels and low-income levels. Younger participants with low autonomy levels were significantly less able to perform activities related to their self-efficacy. Extreme age groups, those with higher educational levels, singles, and those living alone, reported feeling lonely more often. These results help identify sociocultural characteristics of adults with low social participation, which is critical for developing useful strategies to promote community engagement and related benefits for older adults.
Study on the Construction Method and Effects of Ipsilateral, Multi-Nozzle, High-Pressure Jet Grouting Cut-Off Wall
Authors: Liu Da , Xie Weijiang , Gao Jianglin , Hu Songtao , Chen Ming , Li Yan , Li Linhua
Published: August 2022

High-pressure jet grouting is widely used in the construction of cut-off wall in hydraulic engineering. Compared to the high-pressure jet grouting used for foundation improvement, the high-pressure jet grouting cut-off wall of hydraulic engineering has lower requirements for the advance consolidation and provision of bearing capacity, and more focus on providing an anti-seepage effect and ensuring the continuous integrity of the wall. In the past, the research on high-pressure jet grouting focused more on the application to foundation treatments, and the research on the construction efficiency of cut-off wall is relatively insufficient. Therefore, in this study, a self-developed ipsilateral multi-nozzle jet grouting machine with a large drill diameter and construction method are proposed according to the construction characteristics of the embankment cut-off wall in hydraulic engineering. Based on the dyke protection projects, Jinggangshan navigation hydropower and Xingan navigation hydropower, the large-diameter, multi-nozzle, high-pressure jet cut-off wall test is carried out, and the wall forming effect of the cut-off wall is verified through the combination of indoor and outdoor tests. The results show that the proposed construction method can adapt to the formation conditions in which it is difficult to implement conventional high-pressure jet grouting, and obtains good construction efficiency. A favorable wall quality can be obtained by using more efficient large-diameter and ipsilateral multi-nozzle jet grouting in the construction of high-pressure jet cut-off wall.
A Multidimensional Evaluation of Technology-Enabled Assessment Methods during Online Education in Developing Countries
Authors: Khattak Ambreen Sultana , Ali Muhammad Khurram , Al Awadh Mohammed
Published: August 2022

Humanity has faced unprecedented chaos in the education sector due to the inevitable sudden adoption of online mode of learning during the pandemic. The complexities associated with technology-enabled learning and assessment have different connotations in developing countries due to a lack of infrastructure and awareness. Such countries can switch over to an online mode of education more frequently in the future due to highly volatile local political and cultural situations on top of the pandemic. This study evaluates the complexities associated with technology-enabled online assessment methods in Pakistan. Technology readiness and performance for the learning assessment of students are appraised through approaching approximately one thousand students from more than one hundred public and private sector engineering universities. A screened list of assessment alternatives and their influencing factors are then prioritized using the multi-actor multi-criteria analysis (MAMCA) by considering the perceptions of national policymakers, faculty members and students. The aggregate results reveal that, among the influencing factors, ‘mental health’ received the highest weightage, and stakeholders are indifferent to associated costs despite financial challenges. Automated MCQs secured the top position in the ranking list. Sensitivity analysis incorporates some disagreements among the stakeholders, which makes this study highly beneficial for policy modeling.
Avoiding Food Neophobia and Increasing Consumer Acceptance of New Food Trends—A Decade of Research
Authors: Siddiqui Shahida Anusha , Zannou Oscar , Karim Ikawati , Kasmiati  , Awad Nour M. H. , Gołaszewski Janusz , Heinz Volker , Smetana Sergiy
Published: August 2022

The increasingly fierce competition in food trends requires producers to innovate and develop new foods to be accepted and to avoid neophobia by consumers at the same time. Food neophobia’s motivational adoption barriers include the consumption of novel foods, social norms and conflicting eating goals. Therefore, appropriate strategies are needed to avoid neophobia amid the presence of new food trends in the market. Efforts to avoid food neophobia can also be accepted as part of the sustainability concept, in which the consumer has new foods to choose from in order to reduce scarcity in one particular type of food. The food industry is also challenged to produce healthy food by producing food from natural ingredients. In this article, new food trends and advances in food processing are described, and through them, strategies to avoid neophobia and increase consumer acceptance of new food trends are referenced. Neophobia meets marketing food products delivered to consumers facing motivational adoption barriers, such as the consumption of novel foods, social norms and conflicting eating goals, which are indicated to be challenges to purchase drivers in new food trends. Tasting foods is indicated as one of the most efficient means to ensure neophobia reduction in new foods and new food trends. Other factors identified to reduce food neophobia are education, income, taste and exposure to novel foods. Some preconditions for novel foods to be accepted by consumers are related to the very nature of food innovation, the manufacturer’s features and market circumstances. Food processed with advanced technologies may differ depending on the brand of the food production company and the knowledge of consumers about the novel foods. Moreover, food technology is seen as more acceptable for plant food products based or natural ingredients for consumers. In addition to the focus on health benefits, it is supports the sustainability of food systems. Another accidental element is the transparent traceability system providing accurate and adequate information about such novel foods.
Environmental Sensitivity to Form a Sustainable Entrepreneurial Intention
Authors: Pascucci Tancredi , Cardella Giuseppina Maria , Hernàndez-Sànchez Brizeida , Sànchez-Garcìa Jose Carlos
Published: August 2022

In this paper, the authors aim to analyze entrepreneurial intention and focus on sustainable entrepreneurial strategies, which consider the responsible use of resources while avoiding the strong exploitation of materials and workers, and which use a long-term approach. This consideration is important in the current era, especially when considering that the incorrect hypercompetitive approaches implemented among business organizations in recent years have caused many problems around the world both ecologically and socio-economically. In this research, the authors administered questionnaires to 743 university students. These questionnaires measured considerations of future consequences, considerations of immediate consequences, environmental awareness, personal norms for acting in a pro-environmental way, sustainable entrepreneurial attitudes, subjective norms, perceived behavioral control, and sustainable entrepreneurial intentions. When combining these independent, dependent, and moderating variables, it emerged that considerations of future consequences, considerations of immediate consequences, environmental awareness, and personal norms for acting in a pro-environmental way have a positive influence on sustainable entrepreneurial attitudes; the authors also considered the influence on the other variables, and it was determined that sustainable entrepreneurial attitudes, subjective norms, and perceived behavioral control have a positive influence on sustainable entrepreneurial intentions. Using partial least squares structural equation modeling (PLS-SEM), all of the proposed hypotheses were verified, with the exception of influence between entrepreneurial attitudes and sustainable entrepreneurial intentions.
The Association between Mindfulness and Resilience among University Students: A Meta-Analysis
Authors: Liu Xuepeng , Wang Qing , Zhou Zhenzhen
Published: August 2022

Based on the concept of education for sustainable development (ESD), both individual mindfulness and resilience play a vital role in developing students’ competences in ESD. Across 20 samples, this meta-analysis investigated the association between mindfulness and resilience among the university student population. The results revealed that (1) the two constructs were significantly correlated, with a moderate effect (r = 0.465); (2) cultural background and national economic development level, as well as the type of resilience measurement scale, did not moderate the relationship between the two constructs; (3) the type of the mindfulness measurement scale was a statistically significant moderator of the relationship between mindfulness and resilience among university students; (4) the mindfulness dimension of observing presented the lowest correlation with the university students’ resilience, while acting with awareness and non-reacting were shown as the two most relevant dimensions for the target group. The findings of the meta-analysis study convey important implications for ESD within the university context.
From Biotechnology to Bioeconomy: A Review of Development Dynamics and Pathways
Authors: Wei Xun , Luo Jie , Pu Aqing , Liu Qianqian , Zhang Lei , Wu Suowei , Long Yan , Leng Yan , Dong Zhenying , Wan Xiangyuan
Published: August 2022

Based on the logistic growth model, the relationship between technology innovation and the evolution of economic forms was analyzed, and the main characteristics and basic laws of the five economic forms of hunter–gatherer, agriculture, industry, information, and bioeconomy were summarized. Based on a comprehensive and intensive scan of the latest bioeconomy development strategies of various countries, we summarized their two main driving forces from the technological supply side and economic and social demand side, as well as their four distinctive features, namely the comprehensiveness of science and technology innovation, the aggregation of industrial development, the globalization of development goals, and strong policy dependence. Finally, we proposed countermeasures to design the implementation path of the bioeconomy and improve the quality of the bioeconomy factor supply in terms of the development and application of biotechnology, the upgradation of bioindustry clusters, the positive policy environment, and the theorization of the bioeconomy.