bluuebunny/crossref_metadata_2025 huggingface dataset:
Emotion Recognition on Edge Devices: Training and Deployment
Authors: Pandelea Vlad , Ragusa Edoardo , Apicella Tommaso , Gastaldo Paolo , Cambria Erik
Published: July 2021

Emotion recognition, among other natural language processing tasks, has greatly benefited from the use of large transformer models. Deploying these models on resource-constrained devices, however, is a major challenge due to their computational cost. In this paper, we show that the combination of large transformers, as high-quality feature extractors, and simple hardware-friendly classifiers based on linear separators can achieve competitive performance while allowing real-time inference and fast training. Various solutions including batch and Online Sequential Learning are analyzed. Additionally, our experiments show that latency and performance can be further improved via dimensionality reduction and pre-training, respectively. The resulting system is implemented on two types of edge device, namely an edge accelerator and two smartphones.
An Overview of the nMPRA and nHSE Microarchitectures for Real-Time Applications
Authors: Găitan Vasile Gheorghiță , Zagan Ionel
Published: July 2021

In the context of real-time control systems, it has become possible to obtain temporal resolutions of microseconds due to the development of embedded systems and the Internet of Things (IoT), the optimization of the use of processor hardware, and the improvement of architectures and real-time operating systems (RTOSs). All of these factors, together with current technological developments, have led to efficient central processing unit (CPU) time usage, guaranteeing both the predictability of thread execution and the satisfaction of the timing constraints required by real-time systems (RTSs). This is mainly due to time sharing in embedded RTSs and the pseudo-parallel execution of tasks in single-processor and multi-processor systems. The non-deterministic behavior triggered by asynchronous external interrupts and events in general is due to the fact that, for most commercial RTOSs, the execution of the same instruction ends in a variable number of cycles, primarily due to hazards. The software implementation of RTOS-specific mechanisms may lead to significant delays that can affect deadline requirements for some RTSs. The main objective of this paper was the design and deployment of innovative solutions to improve the performance of RTOSs by implementing their functions in hardware. The obtained architectures are intended to provide feasible scheduling, even if the total CPU utilization is close to the maximum limit. The contributions made by the authors will be followed by the validation of a high-performing microarchitecture, which is expected to allow a thread context switching time and event response time of only one clock cycle each. The main purpose of the research presented in this paper is to improve these factors of RTSs, as well as the implementation of the hardware structure used for the static and dynamic scheduling of tasks, for RTOS mechanisms specific to resource sharing and intertask communication.
Detection of Muscle Activation during Resistance Training Using Infrared Thermal Imaging
Authors: Jung Haemin , Seo Jeongwung , Seo Kangwon , Kim Dohwi , Park Suhyun
Published: July 2021

Infrared thermal imaging has been widely used to show the correlation between thermal characteristics of the body and muscle activation. This study aims to investigate a method using thermal imaging to visualize and differentiate target muscles during resistance training. Thermal images were acquired to monitor three target muscles (i.e., biceps brachii, triceps brachii, and deltoid muscle) in the brachium while varying the training weight, duration, and order of training. The acquired thermal images were segmented and converted to heat maps. By generating difference heat maps from pairs of heat maps during training, the target muscles were clearly visualized, with an average temperature difference of 0.86 °C. It was observed that training order had no significant effect on skin surface temperature. The difference heat maps were also used to train a convolutional neural network (CNN) to show the feasibility of target muscle classification, with an accuracy of 92.3%. This study demonstrated that infrared thermal imaging could be effectively utilized to locate and differentiate target muscle activation during resistance training.
Generation of Fluorescent Bacteria with the Genes Coding for Lumazine Protein and Riboflavin Biosynthesis
Authors: Lim Sunjoo , Oh Eugeney , Choi Miae , Lee Euiho , Lee Chan-Yong
Published: July 2021

Lumazine protein is a member of the riboflavin synthase superfamily and the intense fluorescence is caused by non-covalently bound to 6,7-dimethyl 8-ribityllumazine. The pRFN4 plasmid, which contains the riboflavin synthesis genes from Bacillus subtilis, was originally designed for overproduction of the fluorescent ligand of 6,7-dimethyl 8-ribityllumazine. To provide the basis for a biosensor based on the lux gene from bioluminescent bacteria of Photobacterium leiognathi, the gene coding for N-terminal domain half of the lumazine protein extending to amino acid 112 (N-LumP) and the gene for whole lumazine protein (W-LumP) from P. leiognathi were introduced by polymerase chain reaction (PCR) and ligated into pRFN4 vector, to construct the recombinant plasmids of N-lumP-pRFN4 and W-lumP-pRFN4 as well as their modified plasmids by insertion of the lux promoter. The expression of the genes in the recombinant plasmids was checked in various Escherichia coli strains, and the fluorescence intensity in Escherichia coli 43R can even be observed in a single cell. These results concerning the co-expression of the genes coding for lumazine protein and for riboflavin synthesis raise the possibility to generate fluorescent bacteria which can be used in the field of bio-imaging.
ZnO Structures with Surface Nanoscale Interfaces Formed by Au, Fe2O3, or Cu2O Modifier Nanoparticles: Characterization and Gas Sensing Properties
Authors: Tomić Milena , Claros Martha , Gràcia Isabel , Figueras Eduard , Cané Carles , Vallejos Stella
Published: July 2021

Zinc oxide rod structures are synthetized and subsequently modified with Au, Fe2O3, or Cu2O to form nanoscale interfaces at the rod surface. X-ray photoelectron spectroscopy corroborates the presence of Fe in the form of oxide—Fe2O3; Cu in the form of two oxides—CuO and Cu2O, with the major presence of Cu2O; and Au in three oxidation states—Au3+, Au+, and Au0, with the content of metallic Au being the highest among the other states. These structures are tested towards nitrogen dioxide, ethanol, acetone, carbon monoxide, and toluene, finding a remarkable increase in the response and sensitivity of the Au-modified ZnO films, especially towards nitrogen dioxide and ethanol. The results for the Au-modified ZnO films report about 47 times higher response to 10 ppm of nitrogen dioxide as compared to the non-modified structures with a sensitivity of 39.96% ppm−1 and a limit of detection of 26 ppb to this gas. These results are attributed to the cumulative effects of several factors, such as the presence of oxygen vacancies, the gas-sensing mechanism influenced by the nano-interfaces formed between ZnO and Au, and the catalytic nature of the Au nanoparticles.
A Comprehensive Overview of TCP Congestion Control in 5G Networks: Research Challenges and Future Perspectives
Authors: Lorincz Josip , Klarin Zvonimir , Ožegović Julije
Published: July 2021

In today’s data networks, the main protocol used to ensure reliable communications is the transmission control protocol (TCP). The TCP performance is largely determined by the used congestion control (CC) algorithm. TCP CC algorithms have evolved over the past three decades and a large number of CC algorithm variations have been developed to accommodate various network environments. The fifth-generation (5G) mobile network presents a new challenge for the implementation of the TCP CC mechanism, since networks will operate in environments with huge user device density and vast traffic flows. In contrast to the pre-5G networks that operate in the sub-6 GHz bands, the implementation of TCP CC algorithms in 5G mmWave communications will be further compromised with high variations in channel quality and susceptibility to blockages due to high penetration losses and atmospheric absorptions. These challenges will be particularly present in environments such as sensor networks and Internet of Things (IoT) applications. To alleviate these challenges, this paper provides an overview of the most popular single-flow and multy-flow TCP CC algorithms used in pre-5G networks. The related work on the previous examinations of TCP CC algorithm performance in 5G networks is further presented. A possible implementation of TCP CC algorithms is thoroughly analysed with respect to the specificities of 5G networks, such as the usage of high frequencies in the mmWave spectrum, the frequent horizontal and vertical handovers, the implementation of the 5G core network, the usage of beamforming and data buffering, the exploitation of edge computing, and the constantly transmitted always-on signals. Moreover, the capabilities of machine learning technique implementations for the improvement of TCPs CC performance have been presented last, with a discussion on future research opportunities that can contribute to the improvement of TCP CC implementation in 5G networks. This survey paper can serve as the basis for the development of novel solutions that will ensure the reliable implementation of TCP CC in different usage scenarios of 5G networks.
Features and Practicability of the Next-Generation Sensors and Monitors for Exposure Assessment to Airborne Pollutants: A Systematic Review
Authors: Fanti Giacomo , Borghi Francesca , Spinazzè Andrea , Rovelli Sabrina , Campagnolo Davide , Keller Marta , Cattaneo Andrea , Cauda Emanuele , Cavallo Domenico Maria
Published: July 2021

In the last years, the issue of exposure assessment of airborne pollutants has been on the rise, both in the environmental and occupational fields. Increasingly severe national and international air quality standards, indoor air guidance values, and exposure limit values have been developed to protect the health of the general population and workers; this issue required a significant and continuous improvement in monitoring technologies to allow the execution of proper exposure assessment studies. One of the most interesting aspects in this field is the development of the “next-generation” of airborne pollutants monitors and sensors (NGMS). The principal aim of this review is to analyze and characterize the state of the art and of NGMS and their practical applications in exposure assessment studies. A systematic review of the literature was performed analyzing outcomes from three different databases (Scopus, PubMed, Isi Web of Knowledge); a total of 67 scientific papers were analyzed. The reviewing process was conducting systematically with the aim to extrapolate information about the specifications, technologies, and applicability of NGMSs in both environmental and occupational exposure assessment. The principal results of this review show that the use of NGMSs is becoming increasingly common in the scientific community for both environmental and occupational exposure assessment. The available studies outlined that NGMSs cannot be used as reference instrumentation in air monitoring for regulatory purposes, but at the same time, they can be easily adapted to more specific applications, improving exposure assessment studies in terms of spatiotemporal resolution, wearability, and adaptability to different types of projects and applications. Nevertheless, improvements needed to further enhance NGMSs performances and allow their wider use in the field of exposure assessment are also discussed.
Adaptive Node Clustering for Underwater Sensor Networks
Authors: Khan Muhammad Fahad , Bibi Muqaddas , Aadil Farhan , Lee Jong-Weon
Published: July 2021

Monitoring of an underwater environment and communication is essential for many applications, such as sea habitat monitoring, offshore investigation and mineral exploration, but due to underwater current, low bandwidth, high water pressure, propagation delay and error probability, underwater communication is challenging. In this paper, we proposed a sensor node clustering technique for UWSNs named as adaptive node clustering technique (ANC-UWSNs). It uses a dragonfly optimization (DFO) algorithm for selecting ideal measure of clusters needed for routing. The DFO algorithm is inspired by the swarming behavior of dragons. The proposed methodology correlates with other algorithms, for example the ant colony optimizer (ACO), comprehensive learning particle swarm optimizer (CLPSO), gray wolf optimizer (GWO) and moth flame optimizer (MFO). Grid size, transmission range and nodes density are used in a performance matrix, which varies during simulation. Results show that DFO outperform the other algorithms. It produces a higher optimized number of clusters as compared to other algorithms and hence optimizes overall routing and increases the life span of a network.
Vibration Fatigue Damage Estimation by New Stress Correction Based on Kurtosis Control of Random Excitation Loadings
Authors: Wang Yuzhu , Serra Roger
Published: July 2021

In the pioneer CAE stage, life assessment is the essential part to make the product meet the life requirement. Commonly, the lives of flexible structures are determined by vibration fatigue which accrues at or close to their natural frequencies. However, existing PSD vibration fatigue damage estimation methods have two prerequisites for use: the behavior of the mechanical system must be linear and the probability density function of the response stresses must follow a Gaussian distribution. Under operating conditions, non-Gaussian signals are often recorded as excitation (usually observed through kurtosis), which will result in non-Gaussian response stresses. A new correction is needed to make the PSD approach available for the non-Gaussian vibration to deal with the inevitable extreme value of high kurtosis. This work aims to solve the vibration fatigue estimation under the non-Gaussian vibration; the key is the probability density function of response stress. This work researches the importance of non-Gaussianity numerically and experimentally. The beam specimens with two notches were used in this research. All excitation stays in the frequency range that only affects the second natural frequency, although their kurtosis is different. The results show that the probability density function of response stress under different kurtoses can be obtained by kurtosis correction based on the PSD approach of the frequency domain.
Machine Learning Methods for Fear Classification Based on Physiological Features
Authors: Petrescu Livia , Petrescu Cătălin , Oprea Ana , Mitruț Oana , Moise Gabriela , Moldoveanu Alin , Moldoveanu Florica
Published: July 2021

This paper focuses on the binary classification of the emotion of fear, based on the physiological data and subjective responses stored in the DEAP dataset. We performed a mapping between the discrete and dimensional emotional information considering the participants’ ratings and extracted a substantial set of 40 types of features from the physiological data, which represented the input to various machine learning algorithms—Decision Trees, k-Nearest Neighbors, Support Vector Machine and artificial networks—accompanied by dimensionality reduction, feature selection and the tuning of the most relevant hyperparameters, boosting classification accuracy. The methodology we approached included tackling different situations, such as resolving the problem of having an imbalanced dataset through data augmentation, reducing overfitting, computing various metrics in order to obtain the most reliable classification scores and applying the Local Interpretable Model-Agnostic Explanations method for interpretation and for explaining predictions in a human-understandable manner. The results show that fear can be predicted very well (accuracies ranging from 91.7% using Gradient Boosting Trees to 93.5% using dimensionality reduction and Support Vector Machine) by extracting the most relevant features from the physiological data and by searching for the best parameters which maximize the machine learning algorithms’ classification scores.
A Weakly Supervised Gas-Path Anomaly Detection Method for Civil Aero-Engines Based on Mapping Relationship Mining of Gas-Path Parameters and Improved Density Peak Clustering
Authors: Sun Hao , Fu Xuyun , Zhong Shisheng
Published: July 2021

Gas-path anomalies account for more than 90% of all civil aero-engine anomalies. It is essential to develop accurate gas-path anomaly detection methods. Therefore, a weakly supervised gas-path anomaly detection method for civil aero-engines based on mapping relationship mining of gas-path parameters and improved density peak clustering is proposed. First, the encoder-decoder, composed of an attention mechanism and a long short-term memory neural network, is used to construct the mapping relationship mining model among gas-path parameters. The predicted values of gas-path parameters under the restriction of mapping relationships are obtained. The deviation degree from the original values to the predicted values is regarded as the feature. To force the extracted features to better reflect the anomalies and make full use of weakly supervised labels, a weakly supervised cross-entropy loss function under extreme class imbalance is deployed. This loss function can be combined with a simple classifier to significantly improve the feature extraction results, in which anomaly samples are more different from normal samples and do not reduce the mining precision. Finally, an anomaly detection method is deployed based on improved density peak clustering and a weakly supervised clustering parameter adjustment strategy. In the improved density peak clustering method, the local density is enhanced by K-nearest neighbors, and the clustering effect is improved by a new outlier threshold determination method and a new outlier treatment method. Through these settings, the accuracy of dividing outliers and clustering can be improved, and the influence of outliers on the clustering process reduced. By introducing weakly supervised label information and automatically iterating according to clustering and anomaly detection results to update the hyperparameter settings, a weakly supervised anomaly detection method without complex parameter adjustment processes can be implemented. The experimental results demonstrate the superiority of the proposed method.
Deep Learning Based Prediction on Greenhouse Crop Yield Combined TCN and RNN
Authors: Gong Liyun , Yu Miao , Jiang Shouyong , Cutsuridis Vassilis , Pearson Simon
Published: July 2021

Currently, greenhouses are widely applied for plant growth, and environmental parameters can also be controlled in the modern greenhouse to guarantee the maximum crop yield. In order to optimally control greenhouses’ environmental parameters, one indispensable requirement is to accurately predict crop yields based on given environmental parameter settings. In addition, crop yield forecasting in greenhouses plays an important role in greenhouse farming planning and management, which allows cultivators and farmers to utilize the yield prediction results to make knowledgeable management and financial decisions. It is thus important to accurately predict the crop yield in a greenhouse considering the benefits that can be brought by accurate greenhouse crop yield prediction. In this work, we have developed a new greenhouse crop yield prediction technique, by combining two state-of-the-arts networks for temporal sequence processing—temporal convolutional network (TCN) and recurrent neural network (RNN). Comprehensive evaluations of the proposed algorithm have been made on multiple datasets obtained from multiple real greenhouse sites for tomato growing. Based on a statistical analysis of the root mean square errors (RMSEs) between the predicted and actual crop yields, it is shown that the proposed approach achieves more accurate yield prediction performance than both traditional machine learning methods and other classical deep neural networks. Moreover, the experimental study also shows that the historical yield information is the most important factor for accurately predicting future crop yields.
Emerging Contaminants in Seafront Zones. Environmental Impact and Analytical Approaches
Authors: Câmara José S. , Montesdeoca-Esponda Sarah , Freitas Jorge , Guedes-Alonso Rayco , Sosa-Ferrera Zoraida , Perestrelo Rosa
Published: July 2021

Some chemical substances have the potential to enter the coastal and marine environment and cause adverse effects on ecosystems, biodiversity and human health. For a large majority of them, their fate and effects are poorly understood as well as their use still unregulated. Finding effective and sustainable strategies for the identification of these emerging and/or anthropogenic contaminants that might cause polluting effects in marine environments to mitigate their adverse effects, is of utmost importance and a great challenge for managers, regulators and researchers. In this review we will evaluate the impact of emerging contaminants (ECs) on marine coastal zones namely in their ecosystems and biodiversity, highlighting the potential risks of organic pollutants, pharmaceuticals and personal care products. Emerging microextraction techniques and high-resolution analytical platforms used in isolation, identification and quantification of ECs will be also reviewed.
The Association between Dating Apps and Alcohol Consumption in an Italian Sample of Active Users, Former Users, and Non-Users
Authors: Flesia Luca , Fietta Valentina , Foresta Carlo , Monaro Merylin
Published: July 2021

To date, the relationship between alcohol use and dating app use has been investigated mostly in conjunction with sexual activities and in homosexual men. For this reason, the aim of this study was to explore the association between dating app use and alcohol consumption among the general population. A cross-sectional study was conducted including app users, non-users, and former users: 1278 respondents completed an online ad hoc questionnaire assessing dating app use, motivations for installing dating apps, alcohol use, and demographics. Multiple logistic regression analysis was run to investigate the association between dating app use, demographic features, and alcohol consumption. Whereas educational level, age, and gender significantly contributed to the regular consumption of alcohol, dating app use did not account for a significant amount of variance between regular and not regular drinkers. However, people who installed and used dating apps with the motivation of searching for sexual partners were more likely to be regular drinkers. Among the active users, heavy app users were less likely to drink regularly. The study indicates that underlying factors (sexual aspects, motives for using the apps) and the intensity of using the apps may mediate the relationship between dating app use and alcohol use.
Early Drop-Out from Sports and Strategic Learning Skills: A Cross-Country Study in Italian and Spanish Students
Authors: Consoni Carla , Pesce Caterina , Cherubini Domenico
Published: July 2021

The search for overarching factors involved in both sport and broader lifestyle and achievement domains may help to understand the early drop-out phenomenon. This study aimed to analyze the association between early sport drop-out and strategic learning skills, checking for the individual and joint role of nationality, school type, gender, age and sport habits. Six hundred and fourteen Italian and Spanish students aged 14–18 years completed two self-assessment questionnaires concerning physical activity, sports habits and learning strategies. Outcomes were analyzed with frequency analysis. Higher affective–motivational strategic learning skills were associated with lower drop-out rates in Italian but not Spanish students. In high schools with an enhanced sports curriculum, drop-out rates were negligible compared to other Italian and Spanish curricula. A lack of persistence in the same sport type was significantly associated with a higher drop-out rate in males but not in female students, who had overall higher drop-out rates. This study suggests that overarching personal skills, cultural characteristics and sports habits may independently and jointly contribute to sport drop-out. Specifically, affective–motivational learning skills may play a key role in sport persistence and in strategies tailored to drop-out prevention.
Sustainable Supply Chain Management, Digital-Based Supply Chain Integration, and Firm Performance: A Cross-Country Empirical Comparison between South Korea and Vietnam
Authors: Lee Su-Yol
Published: July 2021

Sustainability and digitization issues have coevolved in supply chain management (SCM), receiving attention from the academic and business circles. However, few studies have addressed this topic in an integrated way. In this regard, this study examined the relationships between sustainable SCM, digital-based supply chain integration, and supplying firms’ performance in two different country settings: South Korea and Vietnam. This comparative study provides evidence that sustainable SCM facilitates the adoption and implementation of supply chain integration using digital technologies, leading to suppliers’ operational competitiveness. This study also indicates the differences and similarities of sustainable SCM and its effects in the two different economies. These findings provide scholars, managers, and policymakers with theoretical and practical implications to encourage firms in Asian countries to enhance corporate competitiveness by adequately responding to environmental and social issues and digitalization along the global value chain.
Bullying and Cyberbullying in Primary School: The Impact of Gender and Student Academic Performance
Authors: Palomares-Ruiz Ascensión , García-Perales Ramón , Cebrián-Martínez Antonio , Martín-García María Inés
Published: July 2021

(1) Background: This study examines school bullying and cyberbullying, which are unfortunate realities in our schools that require interdisciplinary, multidimensional educational interventions in order to enhance the quality of the educational process. (2) Methods: The study analyzes the results of the application of the School Harassment Questionnaire (CAES) to 494 students in the sixth year of primary school, aged between 11 and 13, in the Spanish region of Castilla-La Mancha. The results are presented for each dimension and item in the instrument, then analyzed by gender and student academic performance. (3) Results: The results indicated statistically significant differences in the variables examined, particularly in the variable Skills for Conflict Resolution. (4) Conclusions: The use of this instrument and results such as those in this study could enhance teachers’ awareness of the reality of their classrooms, from the school climate to the relational conflicts that may be present. This allows teachers to look into preventive action to encourage the comprehensive development of all students, with tutorial action being essential.
Activities of Food Retail Companies in Poland during the COVID-19 Pandemic in the Context of Food Security
Authors: Skawińska Eulalia , Zalewski Romuald I.
Published: July 2021

This paper deals with a very important topic concerning the adaptive actions of food retail entities, operating in a network model, during threats caused by a pandemic. The aim of the study was to identify and characterize the actions taken by food retail chain entities during the COVID-19 pandemic, in the context of the food security of consumers in Poland (using the example of Wielkopolska). A basic research hypothesis (H0) was generated, stating that the adaptation activities of food retail chain actors varied during the COVID-19 pandemic in Poland with regard to ensuring consumer food security. It was detailed in five sub-hypotheses. In their verification and in the realization of the aim of the study, the methods of literature study and other secondary sources, and induction, survey, comparative, visualization, modeling and descriptive statistics, were used. The research results include (a) the authors’ diagnosis of threats to the food security of companies in the food system resulting from the COVID-19 pandemic, and applied ways of adaptation to the new requirements of the environment, based on secondary sources; (b) analysis of data from the empirical survey conducted by the authors in November 2020 on the adaptive actions of companies, among managers/owners of food retail stores of a selected network of a particular franchise type in Greater Poland; (c) the construction of an empirical model of the typical behaviors of food retail units in the chain under study, distinguishing three of their types in the model; and (d) suggestions concerning the directions of future scientific research areas. The article was prepared following the stream of sustainable development theory.
Detecting and Classifying Typhoon Information from Chinese News Based on a Neural Network Model
Authors: Chen Danjie , Qin Fen , Cai Kun , Shen Yatian
Published: July 2021

Typhoons are major natural disasters in China. Much typhoon information is contained in a large number of network media resources, such as news reports and volunteered geographic information (VGI) data, and these are the implicit data sources for typhoon research. However, two problems arise when using typhoon information from Chinese news reports. Since the Chinese language lacks natural delimiters, word segmentation error results in trigger mismatches. Additionally, the polysemy of Chinese affects the classification of triggers. Second, there is no authoritative classification system for typhoon events. This paper defines a classification system for typhoon events, and then uses the system in a neural network model, lattice-structured bidirectional long–short-term memory with a conditional random field (BiLSTM-CRF), to detect these events in Chinese online news. A typhoon dataset is created using texts from the China Weather Typhoon Network. Three other datasets are generated from general Chinese web pages. Experiments on these four datasets show that the model can tackle the problems mentioned above and accurately detect typhoon events in Chinese news reports.
Inward Foreign Direct Investment-Induced Technological Innovation in Sri Lanka? Empirical Evidence Using ARDL Approach
Authors: Adikari AM.Priyangani , Liu Haiyun , Marasinghe MMSA.
Published: July 2021

Fostering innovation is considered one of the key policy priorities in most governments’ agendas in developing countries. Foreign direct investment (FDI) is a principal resource for financing sustainable development, corresponding to 17 sustainable development goals (SDGs). This study analyzes how inward FDI affects innovation in Sri Lanka using secondary data from 1990 to 2019. We used the Autoregressive Distributed Lag (ARDL) cointegration procedure to examine the long-run relationships between variables. As per the study results, the coefficient of inward FDI is a negative sign while the coefficients of education expenditure (EDU) and research and development expenditure (RDE) show positive signs of 0.26 and 5.7, respectively, and are statistically significant in the long run. It is demonstrated that research and development expenditure is vital in explaining technological innovation, and inward FDI inflows do not contribute to widening technological innovation in Sri Lanka. More FDI inflows will not bring higher innovation. Shaping the future of FDI in Sri Lanka is essential to foster innovation capability.
Efficient and Comprehensive Evaluation Method of Temporary Overvoltage in Distribution Systems with Inverter-Based Distributed Generations
Authors: Cho Namhun , Lee Moonjeong , Yoon Myungseok , Choi Sungyun
Published: July 2021

In general, a temporary overvoltage (TOV) on the healthy phases occurs because of the neutral-shift phenomenon during a single line-to-ground (SLG) fault. The TOV can destroy the insulation of electric devices and cause damage to other equipment and customer loads in just a few cycles. In practice, the TOV can be affected by numerous factors: the sequence reactance ratio of the interconnection transformer, the ratio of load to DG, and the distance to the fault. More importantly, inverter-based distributed generations (DGs) have different influences on the TOV from traditional synchronous-machine-based DGs. In this sense, this work performed an efficient and comprehensive investigation on the effect of these various parameter types and their extensive variations, based on steady-state analysis with sequence equivalent circuits and three-dimensional representations. The proposed methodology can facilitate judging the impact of multi-parameter conditions on the TOV readily and comparing the fault characteristics of synchronous-machine-based and inverter-based DGs. Finally, the results can be used for future studies on TOV mitigation techniques.
Evaluation of the Potential Role of Bacillus altitudinis MT422188 in Nickel Bioremediation from Contaminated Industrial Effluents
Authors: Babar Zarka , Khan Maryam , Chotana Ghayoor Abbas , Murtaza Ghulam , Shamim Saba
Published: July 2021

The incessant pervasiveness of heavy metals in the environment is one of the precursory factors of pollution. This research study was endeavored upon to investigate the bioremediation potential of a nickel (Ni)-resistant bacterial isolate, identified as Bacillus altitudinis MT422188, whose optimum growth parameters were demonstrated at pH 7, temperature 32 °C, and 1 mM phosphate. Minimal Inhibitory Concentration (MIC) and EC50 for Ni were observed to be 20 and 11.5 mM, respectively, whereas the cross heavy-metal resistance was discerned as Cu2+ (25 mM) > Zn2+ (15 mM) > Cr6+ (10 mM) > Pb2+ (5 mM) > Co2+ (8 mM) > Cd2+ (3 mM) > Hg2+ (0 mM). Ni biosorption studies by live and heat-killed bacterial cells were suggestive of Ni uptake being facilitated by an ATP-independent efflux system. A pilot-scale study displayed the effective removal of Ni (70 mg/L and 85 mg/L) at 4- and 8-day intervals, respectively. Moreover, chemotaxis and motility assays indicated the role of Ni as a chemoattractant for bacterial cells. The presence of Ni reduced the GR (0.001 ± 0.003 Ug−1FW), POX (0.001 ± 0.001 Ug−1FW), and SOD (0.091 ± 0.003 Ug−1FW) activity, whereas Sodium dodecyl sulphate—Polyacrylamide gel electrophoresis (SDS-PAGE) revealed the presence of metallothionein (60 kDa). Kinetic and isotherm studies suggested a pseudo second-order and Freundlich model to be better fitted for our study. The thermodynamic parameters (∆H° = 3.0436 kJ/mol, ∆S° = 0.0224 kJ/mol/K) suggested the process to be endothermic, spontaneous, and favorable in nature. FTIR analysis elucidated the interaction of hydroxyl and carboxyl groups with Ni. Scanning Electron Microscopy (SEM) and Energy Dispersive X-Ray Spectroscopy (EDS) demonstrated changes in the morphological and elemental composition of the bacterial cells, which affirmed their interaction with Ni during biosorption. In summary, our study concludes the efficient role of Bacillus altitudinis MT422188 in removing Ni from polluted industrial effluents.
“I’ll Take the E-Scooter Instead of My Car”—The Potential of E-Scooters as a Substitute for Car Trips in Germany
Authors: Gebhardt Laura , Wolf Christian , Seiffert Robert
Published: July 2021

Considering the controversial discussion about the sustainability and usefulness of e-scooters, in this study, we analyzed the substitution potential of e-scooters, especially with regard to car trips. Based on data from the national mobility survey in Germany (Mobility in Germany, MiD 2017), we identified trips that could be covered purely by an e-scooter. Thereby, trip length, trip purposes, weather conditions, and other influencing factors were taken into account. Our analysis showed that, in Germany, 10–15% of the motorized individual transport (MIT) trips could be made by e-scooter. Accompanied by a literature analysis, we then critically reflected on the overall potential of e-scooters and formulated recommendations for urban and transport planning.
Approaches to Social Innovation in Positive Energy Districts (PEDs)—A Comparison of Norwegian Projects
Authors: Baer Daniela , Loewen Bradley , Cheng Caroline , Thomsen Judith , Wyckmans Annemie , Temeljotov-Salaj Alenka , Ahlers Dirk
Published: July 2021

The Positive Energy District (PED) concept is a localized city and district level response to the challenges of greenhouse gas emission reduction and energy transition. With the Strategic Energy Transition (SET) Plan aiming to establish 100 PEDs by 2025 in Europe, a number of PED projects are emerging in the EU member states. While the energy transition is mainly focusing on technical innovations, social innovation is crucial to guarantee the uptake and deployment of PEDs in the built environment. We set the spotlight on Norway, which, to date, has three PED projects encompassing 12 PED demo sites in planning and early implementation stages, from which we extract approaches for social innovations and discuss how these learnings can contribute to further PED planning and implementation. We describe the respective approaches and learnings for social innovation of the three PED projects, ZEN, +CityxChange and syn.ikia, in a multiple case study approach. Through the comparison of these projects, we start to identify social innovation approaches with different scopes regarding citizen involvement, stakeholder interaction and capacity building. These insights are also expected to contribute to further planning and design of PED projects within local and regional networks (PEDs in Nordic countries) and contribute to international PED concept development.
Prosopis Species—An Invasive Species and a Potential Source of Browse for Livestock in Semi-Arid Areas of South Africa
Authors: Ravhuhali Khuliso Emmanuel , Mudau Humbelani Silas , Moyo Bethwell , Hawu Onke , Msiza Ntokozo Happy
Published: July 2021

Globally, there have been differing views on whether the proliferation of invasive species will be of benefit as a livestock feed source or it will have detrimental effects on the ecosystem. The infestation of invasive plants such as Prosopis species does not only affect the groundwater levels but also threatens the grazing capacity and species richness of most of the semi-arid areas around South Africa. Though Prosopis is invasive, it is however of good nutritive value and can serve as an alternative source of protein and minerals for livestock during the dry season. Bush encroachment by browsable invasive species can be controlled through biological methods by using organisms such as livestock. The utilisation of Prosopis through browse benefits livestock production and at the same time reduces its spread, thereby preventing possible environmental harm that may arise. Although several studies have been carried out globally on the assessment of the Prosopis species’ nutritive value and also on the threat of this invasive species to the environment, there is a need to update the state of knowledge on this species, particularly in the context of the semi-arid areas of South Africa where the dry season is characterised by less herbage of poor quality. It is therefore critical to understand whether Prosopis is a beneficial invader, or a detriment that needs to be eradicated. This review will contribute knowledge towards finding practical solutions to controlling Prosopis species and whether utilising Prosopis as a feed source will limit its spread and result in a vegetation structure where Prosopis becomes part of the ecosystem with limited detrimental impact. This means that the several components of the species such as nutritive value and the negative impact associated with this plant species along with the means to control its spreading must be well understood to recognise the plant species’ vital contribution to the ecosystem.
Team Learning as a Model for Facilitating Entrepreneurial Competences in Higher Education: The Case of Proakatemia
Authors: Nevalainen Timo , Seikkula-Leino Jaana , Salomaa Maria
Published: July 2021

In the past decades, there has been a growing interest in entrepreneurship education, and many higher education institutions have developed specific programs and courses to support entrepreneurial competencies. However, there have been significant changes in how universities train competences related to business skills and entrepreneurship in practice. Whereas entrepreneurship courses used to focus on the different forms of businesses and drafting business plans, the overall perception of entrepreneurship and entrepreneurial competences has shifted this toward a more holistic educational approach to develop students’ entrepreneurial competencies. In this comparative quantitative case study, we investigate the university students’ perception of the development of their entrepreneurial competencies in the case of Proakatemia (Tampere University of Applied Sciences). The aim was to examine how the entrepreneurial competencies are reflected and strengthened in their thinking and everyday functions through the concept of team learning. The survey involved, altogether, 64 students, of which 21 studied in Proakatemia. The results of this study indicate that the team learning concept of Proakatemia facilitates learning entrepreneurial competencies. Therefore, these results provide insights for universities aiming to develop their curricula, programs and pedagogy, thus promoting sustainable societal development. However, we recommend further studies, e.g., from a qualitative point of view, to assess the effective of the concept in other learning environments.
The Romanian Ecological Flow Method, RoEflow, Developed in Line with the EU Water Framework Directive. Concept and Case Studies
Authors: Gălie Andreea-Cristina , Mătreață Marius , Tănase Ileana , Rădulescu Daniela
Published: July 2021

The overall purpose of the research is to develop a method to compute ecological flows in line with the EU Water Framework Directive (Directive 2000/60/EC) for the whole Romanian territory, for a variety of hydrological, morphological and ecological conditions. The method has three components: a Quantity component, a Dynamic component, and a Real-time operation component. The Quantity component is a hydrological method with elements of the aquatic fauna habitat indirectly linked to biological organisms based on the current Romanian knowledge on the linkages between hydrology and aquatic biology. The Dynamic and Real-time operation components are related to the hydrological forecast. The method is practical, robust and easy to apply. The concept and the ideas use the hydrological forecast to ensure the water dynamics required by the Water Framework Directive, and to develop the quantitative component, keeping in mind that putting it into practice might have importance for a broader audience. In order to better highlight the concept, the paper shows three practical examples of the RoEflow method’s application.
Practicing Green Residence Business Model Based on TPB Perspective
Authors: Wu Chao , Liang Shuling , Wu Weijiong , Hong Yuxiang
Published: July 2021

Green residence is the future of urban development, it is also an attempt to implement the green business model in the residential business field. The key to the success of a green business model is that a wide range of customers can accept its green value proposition and react to it through their purchase decisions. This study aims to develop a theory of a planned behavior (TPB) research model to predict individuals’ intention to purchase green residence. This study took steel structure residence as an example, which was widely recognized as one emerging type of green residence. The samples were selected in Baotou city of Inner Mongolia, P.R. China. Data analysis was performed using the structural equation modeling (SEM) with data obtained from a survey of 208 respondents using SPSS19.0 and AMOS17.0. The results found that the individuals’ intention to purchase green residence was significantly affected by attitude, subjective norm, and perceived behavioral control. Moreover, the empirical evidence showed that these influences differed between different gender groups. Specifically, women responders had a stronger effect on the path attitude impact on purchase intention than men. These findings can provide implications for practicing the green business model of real estate enterprise.
The Implications of Green Employment: Making a Just Transition in ASEAN
Authors: Sharpe Samantha A. , Martinez-Fernandez Cristina M.
Published: July 2021

The transition to an ecologically sustainable economy is and will create significant changes in the world of work. These changes will include the creation of new green jobs and new industries, minor to major changes in existing jobs and the phase-out of some jobs in carbon-intensive activities. A just transition ensures that while working towards a more ecologically sound economy, countries also plan for positive outcomes for those communities and people negatively affected by our global efforts to decarbonise. Identifying and implementing a country-specific policy mix for a just transition is an emerging challenge for nations. This paper constructs a policy framework for supporting the greening of employment and a just transition. The resulting policy framework is then used to assess policy readiness for promoting green jobs and a just transition across the Association of Southeast Asian Nations (ASEAN). The assessment highlights the current state of policy for supporting green jobs, green skills and just transition planning, as well as the significant challenges in identifying and implementing policy settings to support green jobs and a just transition.
Zinc Concentration and Distribution in Vineyard Soils and Grapevine Leaves from Valdepeñas Designation of Origin (Central Spain)
Authors: García-Navarro Francisco Jesús , Jiménez-Ballesta Raimundo , Garcia-Pradas Jesus , Amoros Jose A. , Perez de los Reyes Caridad , Bravo Sandra
Published: July 2021

(1) Background: The purpose of this study was to investigate zinc contents in leaves and soils of the Valdepeñas Protected Designation of Origin (PDO), situated in central Spain. Zn distribution maps of leaves and soils were obtained. (2) Methods: Ninety soil profiles were described, sampled and analyzed. Furthermore, vineyard leaves were collected randomly in each of the analyzed soil vineyard profiles. Soil and leaf samples were analyzed by X-ray fluorescence. (3) Results: The mean total Zn concentrations in vineyard soils were in the range of 16.2–153.7 mg·kg−1, with a mean of 47.5 mg·kg−1. The obtained values above the 95th percentile (between 81.3 and 153.7 mg·kg−1) could be affected by different parent materials or Zn agricultural treatments in vineyards. Contents in different soils follow the order Entisol > Alfisol > Inceptisol. The average Zn content value in leaves was 23.8 mg·kg−1 and oscillated between 11.5 and 93.3 mg·kg−1; minor differences were detected between soil types, with the highest value in plants grown on soils without carbonates. (4) Conclusions: The obtained values are optimal for vine cultivation. The bioaccumulation factors in leaves were lower than unity (0.24–0.53 range). This means that the Zn bioaccumulation process is relatively low in the soil–grapevine system. This study serves as a reference to identify areas that present Zn deficiencies or risk of contamination.