bluuebunny/crossref_metadata_2025 huggingface dataset:
A Novel Approach to Dining Bowl Reconstruction for Image-Based Food Volume Estimation
Authors: Jia Wenyan , Ren Yiqiu , Li Boyang , Beatrice Britney , Que Jingda , Cao Shunxin , Wu Zekun , Mao Zhi-Hong , Lo Benny , Anderson Alex K. , Frost Gary , McCrory Megan A. , Sazonov Edward , Steiner-Asiedu Matilda , Baranowski Tom , Burke Lora E. , Sun Mingui
Published: February 2022

Knowing the amounts of energy and nutrients in an individual’s diet is important for maintaining health and preventing chronic diseases. As electronic and AI technologies advance rapidly, dietary assessment can now be performed using food images obtained from a smartphone or a wearable device. One of the challenges in this approach is to computationally measure the volume of food in a bowl from an image. This problem has not been studied systematically despite the bowl being the most utilized food container in many parts of the world, especially in Asia and Africa. In this paper, we present a new method to measure the size and shape of a bowl by adhering a paper ruler centrally across the bottom and sides of the bowl and then taking an image. When observed from the image, the distortions in the width of the paper ruler and the spacings between ruler markers completely encode the size and shape of the bowl. A computational algorithm is developed to reconstruct the three-dimensional bowl interior using the observed distortions. Our experiments using nine bowls, colored liquids, and amorphous foods demonstrate high accuracy of our method for food volume estimation involving round bowls as containers. A total of 228 images of amorphous foods were also used in a comparative experiment between our algorithm and an independent human estimator. The results showed that our algorithm overperformed the human estimator who utilized different types of reference information and two estimation methods, including direct volume estimation and indirect estimation through the fullness of the bowl.
Cyber-Attack Prediction Based on Network Intrusion Detection Systems for Alert Correlation Techniques: A Survey
Authors: Albasheer Hashim , Md Siraj Maheyzah , Mubarakali Azath , Elsier Tayfour Omer , Salih Sayeed , Hamdan Mosab , Khan Suleman , Zainal Anazida , Kamarudeen Sameer
Published: February 2022

Network Intrusion Detection Systems (NIDS) are designed to safeguard the security needs of enterprise networks against cyber-attacks. However, NIDS networks suffer from several limitations, such as generating a high volume of low-quality alerts. Moreover, 99% of the alerts produced by NIDSs are false positives. As well, the prediction of future actions of an attacker is one of the most important goals here. The study has reviewed the state-of-the-art cyber-attack prediction based on NIDS Intrusion Alert, its models, and limitations. The taxonomy of intrusion alert correlation (AC) is introduced, which includes similarity-based, statistical-based, knowledge-based, and hybrid-based approaches. Moreover, the classification of alert correlation components was also introduced. Alert Correlation Datasets and future research directions are highlighted. The AC receives raw alerts to identify the association between different alerts, linking each alert to its related contextual information and predicting a forthcoming alert/attack. It provides a timely, concise, and high-level view of the network security situation. This review can serve as a benchmark for researchers and industries for Network Intrusion Detection Systems’ future progress and development.
Correcting On-the-Go Field Measurement–Coordinate Mismatch by Minimizing Nearest Neighbor Difference
Authors: González Jiménez Alfonso , Pachepsky Yakov , Gómez Flores José Luis , Ramos Rodríguez Mario , Vanderlinden Karl
Published: February 2022

Many current precision agriculture applications involve on-the-go field measurements of soil and plant properties that require accurate georeferencing. Specific equipment configuration characteristics or data transmission, reception, or logging delays may cause a mismatch between the logged data and the GPS coordinates because of time and position lags that occur during data acquisition. We propose a simple coordinate translation along the measurement tracks to correct for such positional inaccuracies, based on the local travel speed and time lag, which is estimated by minimizing the average ln-transformed absolute difference with the nearest neighbors. The correction method is evaluated using electromagnetic induction soil-sensor data for different spatial measurement layouts and densities and by comparing variograms for raw and modified coordinates. Time lags of 1 s are shown to propagate into the spatial correlation structure up to lag distances of 10 m. The correction method performs best when repeated measurements in opposite driving directions are used and worst when measurements along parallel driving tracks are only repeated at the headland turns. In the latter case, the performance of the method is further improved by limiting the search neighborhood to adjacent measurement tracks. The proposed coordinate correction method is useful for improving the positional accuracy in a wide range of soil- and plant-sensing applications, without the need to grid the data first.
LoRaChainCare: An IoT Architecture Integrating Blockchain and LoRa Network for Personal Health Care Data Monitoring
Authors: Dammak Bouthaina , Turki Mariem , Cheikhrouhou Saoussen , Baklouti Mouna , Mars Rawya , Dhahbi Afef
Published: February 2022

Over the past several years, the adoption of HealthCare Monitoring Systems (HCS) in health centers and organizations like hospitals or eldery homes growth significantly. The adoption of such systems is revolutionized by a propelling advancements in IoT and Blockchain technologies. Owing to technological advancement in IoT sensors market, innovations in HCS to monitor patients health status have motivated many countries to strength their efforts to support their citizens with such care delivery systems under the directives of a physician who has access to patient’s data. Nevertheless, secure data sharing is a principal patient’s concern to be comfort to use such systems. Current HCS are not able to provide reassuring security policies. For that, one of our focus in this work, is to provide security countermeasures, likewise cost-efficient solution for HCS by integrating storage model based on Blockchain and Interplanetary File Systems (IPFS). Blockchain technology is an emerging solution in pharmaceutical industry and starts to take place for HCS and allows HealthCare providers to track connected devices and control access to shared data, hence protecting patients’ privacy. Furthermore, the addition of Edge and Fog computing has improved HCS to react in real-time and enhance their reliability. A variety of communication protocols can connect sensor devices to edge/Fog layer and the best choice will depend upon connectivity requirements: range, bandwidth, power, interoperability, security, and reliability. Instead, systems efficiency would decline and hurt if communication protocol is inconsistent. LoRa (Long Range) communications technology is emerging as the leader among Low-Power Wide-Area Networks (LPWANs) entering the IoT domain benefiting from many features such as long-range distances and low power consumption. This work proposes LoRaChainCare, an architecture model for HCS which combines the technologies Blockchain, Fog/Edge computing, and the LoRa communication protocol. A real implementation of LoRaChainCare system is presented and evaluated in terms of cost, run time and power consumption.
Sensitive Parameter Analysis for Solar Irradiance Short-Term Forecasting: Application to LoRa-Based Monitoring Technology
Authors: Bueso María C. , Paredes-Parra José Miguel , Mateo-Aroca Antonio , Molina-García Angel
Published: February 2022

Due to the relevant penetration of solar PV power plants, an accurate power generation forecasting of these installations is crucial to provide both reliability and stability of current grids. At the same time, PV monitoring requirements are more and more demanded by different agents to provide reliable information regarding performances, efficiencies, and possible predictive maintenance tasks. Under this framework, this paper proposes a methodology to evaluate different LoRa-based PV monitoring architectures and node layouts in terms of short-term solar power generation forecasting. A random forest model is proposed as forecasting method, simplifying the forecasting problem especially when the time series exhibits heteroscedasticity, nonstationarity, and multiple seasonal cycles. This approach provides a sensitive analysis of LoRa parameters in terms of node layout, loss of data, spreading factor and short time intervals to evaluate their influence on PV forecasting accuracy. A case example located in the southeast of Spain is included in the paper to evaluate the proposed analysis. This methodology is applicable to other locations, as well as different LoRa configurations, parameters, and networks structures; providing detailed analysis regarding PV monitoring performances and short-term PV generation forecasting discrepancies.
Tilt Sensor with Recalibration Feature Based on MEMS Accelerometer
Authors: Łuczak Sergiusz , Zams Maciej , Dąbrowski Bogdan , Kusznierewicz Zbigniew
Published: February 2022

The main errors of MEMS accelerometers are misalignments of their sensitivity axes, thermal and long-term drifts, imprecise factory calibration, and aging phenomena. In order to reduce these errors, a two-axial tilt sensor comprising a triaxial MEMS accelerometer, an aligning unit, and solid cubic housing was built. By means of the aligning unit it was possible to align the orientation of the accelerometer sensitive axes with respect to the housing with an accuracy of 0.03°. Owing to the housing, the sensor could be easily and quickly recalibrated, and thus errors such as thermal and long-term drifts as well as effects of aging were eliminated. Moreover, errors due to local and temporal variations of the gravitational acceleration can be compensated for. Procedures for calibrating and aligning the accelerometer are described. Values of thermal and long-term drifts of the tested sensor, resulting in tilt errors of even 0.4°, are presented. Application of the sensor for monitoring elevated loads is discussed.
Simultaneous Generation of Surface Plasmon and Lossy Mode Resonances in the Same Planar Platform
Authors: Fuentes O. , Del Villar I. , Dominguez I. , Corres J. M. , Matías I. R.
Published: February 2022

A planar waveguide consisting of a coverslip for a microscope glass slide was deposited in one of its two faces with two materials: silver and indium tin oxide (ITO). The incidence of light by the edge of the coverslip permitted the generation of both surface plasmon and lossy mode resonances (SPRs and LMRs) in the same transmission spectrum with a single optical source and detector. This proves the ability of this optical platform to be used as a benchmark for comparing different optical phenomena generated by both metal and dielectric materials, which can be used to progress in the assessment of different sensing technologies. Here the SPR and the LMR were compared in terms of sensitivity to refractive index and figure of merit (FoM), at the same time it was demonstrated that both resonances can operate independently when silver and ITO coated regions are surrounded by different refractive index liquids. The results were supported with numerical results that confirm the experimental ones.
Prediction of Preterm Delivery from Unbalanced EHG Database
Authors: Mohammadi Far Somayeh , Beiramvand Matin , Shahbakhti Mohammad , Augustyniak Piotr
Published: February 2022

Objective: The early prediction of preterm labor can significantly minimize premature delivery complications for both the mother and infant. The aim of this research is to propose an automatic algorithm for the prediction of preterm labor using a single electrohysterogram (EHG) signal. Method: The proposed method firstly employs empirical mode decomposition (EMD) to split the EHG signal into two intrinsic mode functions (IMFs), then extracts sample entropy (SampEn), the root mean square (RMS), and the mean Teager–Kaiser energy (MTKE) from each IMF to form the feature vector. Finally, the extracted features are fed to a k-nearest neighbors (kNN), support vector machine (SVM), and decision tree (DT) classifiers to predict whether the recorded EHG signal refers to the preterm case. Main results: The studied database consists of 262 term and 38 preterm delivery pregnancies, each with three EHG channels, recorded for 30 min. The SVM with a polynomial kernel achieved the best result, with an average sensitivity of 99.5%, a specificity of 99.7%, and an accuracy of 99.7%. This was followed by DT, with a mean sensitivity of 100%, a specificity of 98.4%, and an accuracy of 98.7%. Significance: The main superiority of the proposed method over the state-of-the-art algorithms that studied the same database is the use of only a single EHG channel without using either synthetic data generation or feature ranking algorithms.
Electronic Noses and Their Applications for Sensory and Analytical Measurements in the Waste Management Plants—A Review
Authors: Jońca Justyna , Pawnuk Marcin , Arsen Adalbert , Sówka Izabela
Published: February 2022

Waste management plants are one of the most important sources of odorants that may cause odor nuisance. The monitoring of processes involved in the waste treatment and disposal as well as the assessment of odor impact in the vicinity of this type of facilities require two different but complementary approaches: analytical and sensory. The purpose of this work is to present these two approaches. Among sensory techniques dynamic and field olfactometry are considered, whereas analytical methodologies are represented by gas chromatography–mass spectrometry (GC-MS), single gas sensors and electronic noses (EN). The latter are the core of this paper and are discussed in details. Since the design of multi-sensor arrays and the development of machine learning algorithms are the most challenging parts of the EN construction a special attention is given to the recent advancements in the sensitive layers development and current challenges in data processing. The review takes also into account relatively new EN systems based on mass spectrometry and flash gas chromatography technologies. Numerous examples of applications of the EN devices to the sensory and analytical measurements in the waste management plants are given in order to summarize efforts of scientists on development of these instruments for constant monitoring of chosen waste treatment processes (composting, anaerobic digestion, biofiltration) and assessment of odor nuisance associated with these facilities.
Few-Shot Object Detection Using Multimodal Sensor Systems of Unmanned Surface Vehicles
Authors: Hong Bowei , Zhou Yuandong , Qin Huacheng , Wei Zhiqiang , Liu Hao , Yang Yongquan
Published: February 2022

The object detection algorithm is a key component for the autonomous operation of unmanned surface vehicles (USVs). However, owing to complex marine conditions, it is difficult to obtain large-scale, fully labeled surface object datasets. Shipborne sensors are often susceptible to external interference and have unsatisfying performance, compromising the results of traditional object detection tasks. In this paper, a few-shot surface object detection method is proposed based on multimodal sensor systems for USVs. The multi-modal sensors were used for three-dimensional object detection, and the ability of USVs to detect moving objects was enhanced, realizing metric learning-based few-shot object detection for USVs. Compared with conventional methods, the proposed method enhanced the classification results of few-shot tasks. The proposed approach achieves relatively better performance in three sampled sets of well-known datasets, i.e., 2%, 10%, 5% on average precision (AP) and 28%, 24%, 24% on average orientation similarity (AOS). Therefore, this study can be potentially used for various applications where the number of labeled data is not enough to acquire a compromising result.
Research on Shipborne Helicopter Electric Rapid Secure Device: System Design, Modeling, and Simulation
Authors: Zhang Zhuxin , Liu Qian , Zhao Dingxuan , Wang Lixin , Yang Pengcheng
Published: February 2022

The research objective of this paper is to propose a new type of ERSD to solve the problem of the uncontrollable velocity of the claw in the current RSD. Firstly, the working characteristics of the RSD in ASIST are analyzed, and the design scheme of the transmission system of the ERSD is provided. The control system is designed by combining the vector control algorithm with the fuzzy adaptive PID control algorithm. On this basis, the trajectory planning of claw capture velocity is completed. Finally, the dynamics model of the transmission system of the ERSD is built by power bond graph theory, and the system simulation is carried out. The results show that the maximum capture time, velocity, and force were reduced by 47%, 53%, and 80%. In addition, when the ERSD is towing the helicopter, the mechanical claw can still provide good velocity tracking performance under a maximum interference load of 34,000 N.
Temperature and Gate-Length Dependence of Subthreshold RF Detection in GaN HEMTs
Authors: Paz-Martínez Gaudencio , Íñiguez-de-la-Torre Ignacio , Sánchez-Martín Héctor , Novoa-López José Antonio , Hoel Virginie , Cordier Yvon , Mateos Javier , González Tomás
Published: February 2022

The responsivity of AlGaN/GaN high-electron mobility transistors (HEMTs) when operating as zero-bias RF detectors in the subthreshold regime exhibits different behaviors depending on the operating temperature and gate length of the transistors. We have characterized in temperature (8–400 K) the detection performance of HEMTs with different gate lengths (75–250 nm). The detection results at 1 GHz can be reproduced by a quasi-static model, which allows us to interpret them by inspection of the output ID − VDS curves of the transistors. We explain the different behaviors observed in terms of the presence or absence of a shift in the zero-current operating point originating from the existence of the gate-leakage current jointly with temperature effects related to the ionization of bulk traps.
Effects of Wind Conditions on Wind Turbine Temperature Monitoring and Solution Based on Wind Condition Clustering and IGA-ELM
Authors: Hou Zhengnan , Zhuang Shengxian
Published: February 2022

To reduce maintenance costs of wind turbines (WTs), WT health monitoring has attracted wide attention, and different methods have been proposed. However, most existing WT temperature monitoring methods ignore the fact that various wind conditions can directly affect internal temperature of WT, such as main bearing temperature. This paper analyzes the effects of wind conditions on WT temperature monitoring. To reduce these effects, this paper also proposes a novel WT temperature monitoring solution. Compared with existing solutions, the proposed solution has two advantages: (1) wind condition clustering (WCC) is applied and then a normal turbine behavior model is built for each wind condition; (2) extreme learning machine (ELM) is optimized by an improved genetic algorithm (IGA) to avoid local minimum due to the irregularity of wind condition change and the randomness of initial coefficients. Cases of real SCADA data validate the effectiveness and advantages of the proposed solution.
Policy Advocacy and NGOs Assisting Immigrants: Legitimacy, Accountability and the Perceived Attitude of the Majority
Authors: Zogata-Kusz Agnieszka
Published: February 2022

The article addresses the involvement of non-governmental organizations (NGOs) assisting immigrants in policy advocacy (PA) connecting the perspectives of political science and social work. In a context in which many politicians and a major part of society opposes immigration, it examines how NGOs perceive their legitimacy and accountability concerning their attempts to influence policymaking. It also studies how the attitude of the society towards these NGOs affects their work. The analysis builds on the multimethod research combining qualitative and quantitative approaches carried out among Czech NGOs. Among the key findings is that, when talking about legitimacy, NGOs’ representatives refer mainly to themselves and their own vision of society. This is however a manifestation of internalized external legitimacy sources such as democratic principles and existing laws, together with experience and direct contact with clients, as well as moral obligations. As for accountability, despite many people identify these NGOs as irresponsible welcomers, in fact most of them feel accountable primarily to society (in particular its weakest parts), then to immigrants and finally to themselves. The negative attitude of the majority toward these NGOs clearly affects their PA activities, e.g., their access to authorities, the raised topics and applied tools or types of arguments.
Can Blended Finance Be a Game Changer in Sustainable Development? An Empirical Investigation of the “Lucas Paradox”
Authors: Kim Hyojin , Jun Hannah
Published: February 2022

In recent years, the global development community has been emphasizing blended finance approaches for economic development without taking into consideration practical implications of the Lucas Paradox, or the observation that capital does not flow from rich to poor countries. To prevent misuse of official development funds as catalysts for private flows, it is crucial to consider the direction of blended finance approaches in light of the Lucas Paradox. To fill this important gap in the literature, this paper investigates determinants of capital flows in recipient countries where a blended finance strategy is applied in light of the Lucas Paradox, with a focus on foreign direct investment and portfolio equity investment. For the analysis, this paper utilizes a cross-sectional sample of 157 countries between 2002 and 2018, including ODA recipients and OECD DAC members, by conducting a regression analysis based on the ordinary least squares (OLS). Our findings suggest that the Lucas Paradox strongly exists in all recipient countries that can utilize ODA as a catalyst, which is the core of the blended finance strategy. Institutional quality, human capital and asymmetric information improvement appear to mitigate the Lucas Paradox, although the paradox does not disappear entirely. In addition, total ODA, institutional and human capital appear to be determinants of the paradox in the multiple regression model.
Techno-Economic Analysis of Hydrogen and Electricity Production by Biomass Calcium Looping Gasification
Authors: Shaikh Abdul Rahim , Wang Qinhui , Han Long , Feng Yi , Sharif Zohaib , Li Zhixin , Cen Jianmeng , Kumar Sunel
Published: February 2022

Combined cycle, biomass calcium looping gasification is proposed for a hydrogen and electricity production (CLGCC–H) system. The process simulation Aspen Plus is used to conduct techno-economic analysis of the CLGCC–H system. The appropriate detailed models are set up for the proposed system. Furthermore, a dual fluidized bed is optimized for hydrogen production at 700 °C and 12 bar. For comparison, calcium looping gasification with the combined cycle for electricity (CLGCC) is selected with the same parameters. The system exergy and energy efficiency of CLGCC–H reached as high as 60.79% and 64.75%, while the CLGCC system had 51.22% and 54.19%. The IRR and payback period of the CLGCC–H system, based on economic data, are calculated as 17.43% and 7.35 years, respectively. However, the CLGCC system has an IRR of 11.45% and a payback period of 9.99 years, respectively. The results show that the calcium looping gasification-based hydrogen and electricity coproduction system has a promising market prospect in the near future.
Spatiotemporal Variability Assessment of Trace Metals Based on Subsurface Water Quality Impact Integrated with Artificial Intelligence-Based Modeling
Authors: Tawabini Bassam , Yassin Mohamed A. , Benaafi Mohammed , Adetoro John Adedapo , Al-Shaibani Abdulaziz , Abba S. I.
Published: February 2022

Increasing anthropogenic emissions due to rapid industrialization have triggered environmental pollution and pose a threat to the well-being of the ecosystem. In this study, the first scenario involved the spatio-temporal assessment of topsoil contamination with trace metals in the Dammam region, and samples were taken from 2 zones: the industrial (ID), and the agricultural (AG) area. For this purpose, more than 130 spatially distributed samples of topsoil were collected from residential, industrial, and agricultural areas. Inductively coupled plasma—optical emission spectroscopy (ICP-OES)—was used to analyze the samples for various trace metals. The second scenario involved the creation of different artificial intelligence (AI) models, namely an artificial neural network (ANN) and a support vector regression (SVR), for the estimation of zinc (Zn), copper (Cu), chromium (Cr), and lead (Pb) using feature-based input selection. The experimental outcomes depicted that the average concentration levels of HMs were as follows: Chromium (Cr) (31.79 ± 37.9 mg/kg), Copper (Cu) (6.76 ± 12.54 mg/kg), Lead (Pb) (6.34 ± 14.55 mg/kg), and Zinc (Zn) (23.44 ± 84.43 mg/kg). The modelling accuracy, based on different evaluation criteria, showed that agricultural and industrial stations showed performance merit with goodness-of-fit ranges of 51–91% and 80–99%, respectively. This study concludes that AI models could be successfully applied for the rapid estimation of soil trace metals and related decision-making.
Exploring the Impact of Climate Change on Arctic Shipping through the Lenses of Quadruple Bottom Line and Sustainable Development Goals
Authors: Tiller Stephen J. , Rhindress Adam P. , Oguntola Ibrahim O. , Ülkü M. Ali , Williams Kent A. , Sundararajan Binod
Published: February 2022

Climate change is everywhere, and the Arctic is no exception. The melting sea ice has caused renewed interest in expanding maritime shipping for potentially more accessible ocean routes. Canada emerges as a natural land bridge for trade between Asia, Europe, and the Americas. Plausibly, it is not a choice but an imperative to properly integrate the stakeholders (the environment, countries, remote communities, industrial partners) in opening the Arctic Circle to the global economy while considering the challenges. Keeping sustainability front and center and drawing on the extant literature and government policies, this interdisciplinary study offers a Canadian perspective on Arctic transportation routes over tribal lands and their quadruple bottom line (QBL) impacts on the environment, economy, society, and Indigenous cultures. Unlike the arguable premise that new transport corridors will increase trade traffic and enhance the economy in Northern Canada, the QBL approach enables a more holistic and realistic strategy for the Arctic region’s sustainable development regarding regional economies, rural logistics, supply chain efficiency, and social licensing. Drawing on an integrative literature review as methodology, we highlight the QBL framework and the United Nations Sustainable Development Goals as crucial policy tools. Such a holistic perspective helps stakeholders and decision makers frame better policies in identifying, assessing, adapting, and mitigating risks for transportation infrastructure exposed to climate change. We recap the impacts of Arctic Shipping (ArSh) on QBL pillars in an interaction matrix and emphasize that while ArSh may be complementary to economic development, it poses threats to the viability of the Indigenous cultures.
Towards a Resilient and Resource-Efficient Local Food System Based on Industrial Symbiosis in Härnösand: A Swedish Case Study
Authors: Haller Henrik , Fagerholm Anna-Sara , Carlsson Peter , Skoglund Wilhelm , van den Brink Paul , Danielski Itai , Brink Kristina , Mirata Murat , Englund Oskar
Published: February 2022

The endeavour to align the goals of the Swedish food strategy with the national environmental quality objectives and the 17 global SDGs, presents an extraordinary challenge that calls for systemic innovation. Industrial symbiosis can potentially provide the means for increasing sustainable food production, using locally sub-exploited resources that can reduce the need for land, agrochemicals, transport and energy. This case study of the municipality of Härnösand, aims to assess opportunities and challenges for using waste flows and by-products for local food production, facilitated by industrial symbiosis. A potential symbiotic network was developed during three workshops with the main stakeholders in Härnösand. The potential of the COVID-19 pandemic to instigate policy changes, behavioural changes and formation of new alliances that may catalyse the transition towards food systems based on industrial symbiosis is discussed. The material flow inventory revealed that many underexploited resource flows were present in quantities that rendered them commercially interesting. Resources that can be used for innovative food production include, e.g., lignocellulosic residues, rock dust, and food processing waste. The internalised drive among local companies interested in industrial symbiosis and the emerging symbiotic relations, provide a fertile ground for the establishment of a local network that can process the sub-exploited material flows. Although there are multiple challenges for an industrial symbiosis network to form in Härnösand, this study shows that there is a significant potential to create added value from the region’s many resources while at the same time making the food system more sustainable and resilient, by expanding industrial symbiosis practices.
Forecast of Electric Vehicle Sales in the World and China Based on PCA-GRNN
Authors: Wu Minfeng , Chen Wen
Published: February 2022

Since electric vehicles (EVs) could reduce the growing concerns on environmental pollution issues and relieve the social dependency of fossil fuels, the EVs market is fast increased in recent years. However, a large growth in the number of EVs will bring a great challenge to the present traffic system; thus, an acceptable model is necessary to forecast the sales of EVs in order to better plan the appropriate supply of necessary facilities (e.g., charging stations and sockets in car parks) as well as the electricity required on the road. In this study, we propose a model to predict the sales volume and increase rate of EVs in the world and China, using both statistics and machine learning methods by combining principle component analysis and a general regression neural network, based on the previous 11 years of sales data of EVs. The results indicate that a continuing growth in the sales of EVs will appear in both the world and China in the coming eight years, but the sales increase rate is slowly and continuously deceasing because of the persistent growth of the basic sales volume. The results also indicate that the increase rate of sales of EVs in China is higher than that of the world, and the proportion of sales of EVs in China will increase gradually and will be above 50% in 2025. In this case, large accessory facilities for EVs are required in China in the coming few years.
Distribution Characteristics and Influencing Factors of Supply Chain Innovation Firms: A Case Study of Zhejiang Province
Authors: Zhou Guanglan , Zhu Luyao
Published: February 2022

The establishment of supply chain innovation enterprises is conducive to maximizing production efficiency, deepening the division of labor mechanism in all links, and promoting supply-side structural reform. In order to study the factors related to the distribution of supply chain innovation enterprises, this study was based on the pilot list of the first and second batches of supply chain innovation enterprises in Zhejiang Province from 2017 to 2019, and based on the geographical location points of 187 enterprises. The POI data were analyzed with the GeoDa 1.20 and ArcMap 10.8 systems, using tools such as the Moran index, nearest neighbor index, point density estimation, standard deviational ellipse, etc. The analysis results show that the distribution characteristics and influencing factors of supply chain innovation enterprises in Zhejiang Province demonstrate global autocorrelation and a high degree of local aggregation, forming a “one main multi-point” distribution with Hangzhou as the center. The influencing factors are mainly affected by employment density and local economy, and there are multi-factor interactions. Finally, this study puts forward suggestions for the improvement of supply chain innovation enterprises, hoping to promote the sustainable development of supply chain innovation enterprises.
Capabilities and Opportunities: Linking Knowledge Management Practices of Textile-Based SMEs on Sustainable Entrepreneurship and Organizational Performance in China
Authors: Qader Aleem , Zhang Jingwei , Ashraf Sheikh , Syed Nausheen , Omhand Khaoula , Nazir Mehrab
Published: February 2022

Most researchers hold the role of dynamic capability (DC) as a key factor of achieving sustainable entrepreneurship performance (SEP) and Organizational Performance (OP) with the competitive advantage of knowledge management practices (KMPs). However, the effects of KMPs with SEP and OP are still under discussion. Moreover, the ambidexterity view, exploring the direct impact of KMPs mediated by dynamic capabilities (DC), gives evidence of sustainable entrepreneurship and organizational performance. Measuring the gathered data of 480 textile-based SME entrepreneurs and organizations through structural equation modeling, the study’s finding shows that KMPs positively and significantly influence sustainable entrepreneurship and organizational performance. Furthermore, this study explores the partial mediation of dynamic capabilities among KMPs with SEP and organization performance. At the same time, opportunity recognition is a moderator that strengthens the relationship between DCs with sustainable entrepreneurship and organizational performance. The study is limited to male textile-based SMEs and can be enhanced by female entrepreneurs. Furthermore, this research contributes to entrepreneurship students by encouraging better research work in current and existing studies, and practically assists government and private policymakers and business practitioners to formulate better strategies for the sustainability and economic growth of a country.
Remote Sensing Inversion of Suspended Matter Concentration Using a Neural Network Model Optimized by the Partial Least Squares and Particle Swarm Optimization Algorithms
Authors: Guo Qiaozhen , Wu Huanhuan , Jin Huiyi , Yang Guang , Wu Xiaoxu
Published: February 2022

Suspended matter concentration is an important index for the assessment of a water environment and it is also one of the core parameters for remote sensing inversion of water color. Due to the optical complexity of a water body and the interaction between different water quality parameters, the remote sensing inversion accuracy of suspended matter concentration is currently limited. To solve this problem, based on the remote sensing images from Gaofen-2 (GF-2) and the field-measured suspended matter concentration, taking a section of the Haihe River as the study area, this study establishes a remote sensing inversion model. The model combines the partial least squares (PLS) algorithm and the particle swarm optimization (PSO) algorithm to optimize the back-propagation neural network (BPNN) model, i.e., the PLS-PSO-BPNN model. The partial least squares algorithm is involved in screening the input values of the neural network model. The particle swarm optimization algorithm optimizes the weights and thresholds of the neural network model and it thus effectively overcomes the over-fitting of the neural network. The inversion accuracy of the optimized neural network model is compared with that of the partial least squares model and the traditional neural network model by determining the coefficient, the mean absolute error, the root mean square error, the correlation coefficient and the relative root mean square error. The results indicate that the root mean squared error of the PLS-PSO-BPNN inversion model was 3.05 mg/L, which is higher than the accuracy of the statistical regression model. The developed PLS-PSO-BPNN model could be widely applied in other areas to better invert the water quality parameters of surface water.
Reliability Analysis of an Embankment Built and Reinforced in Soft Ground Using LE and FE
Authors: Belo Jean Lucas dos Passos , Lins da Silva Jefferson , de Queiroz Paulo Ivo Braga
Published: February 2022

A coupling method is presented to integrate well-known geotechnical modeling commercial programs with a reliability one. Coupling enables us to use transformation methods, such as the first-order reliability method (FORM), to evaluate the reliability index (β) of a model, via the limit equilibrium (LEM) or the finite-element method (FEM). It was applied to a case study of a stage-constructed embankment on soft ground by considering two conditions, unreinforced and reinforced, to investigate its probabilistic stability. In addition, the value of β associated with a postconstruction settlement was acquired, and it showed one possibility that the coupling method is able to provide. The influence of the uncertainty level on reliability analysis was also analyzed. The outcomes were compared to the crude Monte Carlo simulation. As a result, the proposed approach was satisfactory, and the advantage of using it is presented; with a much lower computational cost than simulation methods, evaluation time may be up to 13.8 times faster than usual with great precision. Moreover, this approach can apply to other commercial programs widely used in practice.
An Atomistic Investigation of Adsorption of Bone Morphogenetic Protein-2 on Gold with Nanoscale Topographies
Authors: Marquetti Izabele , Desai Salil
Published: February 2022

Nanoscale surface topographies mediated with biochemical cues influence the differentiation of stem cells into different lineages. This research focuses on the adsorption behavior of bone morphogenetic protein (BMP-2) on nanopatterned gold substrates, which can aid in the differentiation of bone and cartilage tissue constructs. The gold substrates were patterned as flat, pillar, linear grating, and linear-grating deep based, and the BMP-2 conformation in end-on configuration was studied over 20 ns. The linear grating deep substrate pattern had the highest adsorption energy of around 125 kJ/mol and maintained its radius of gyration of 18.5 Å, indicating a stable adsorption behavior. Secondary structures including α-helix and β-sheet displayed no denaturation, and thus, the bioavailability of the BMP-2, for the deep linear-grating pattern. Ramachandran plots for the wrist and knuckle epitopes indicated no steric hindrances and provided binding sites to type I and type II receptors. The deep linear-grating substrate had the highest number of contacts (88 atoms) within 5 Å of the gold substrate, indicating its preferred nanoscale pattern choice among the substrates considered. This research provides new insights into the atomistic adsorption of BMP-2 on nanoscale topographies of a gold substrate, with applications in biomedical implants and regenerative medicine.
Movement Coordination during Functional Single-Leg Squat Tests in Healthy, Recreational Athletes
Authors: Ksoll Korbinian Sebastian Hermann , Cotic Matthias , Schmalzl Kathrin , Beitzel Knut , Achtnich Andrea , Imhoff Andreas , Schwirtz Ansgar , Kreuzpointner Florian , Seiberl Wolfgang
Published: February 2022

The single-leg squat (SLS) represents a functional movement task for determining leg function. Objective movement analysis is required to evaluate inter-limb symmetry and movement coordination. Therefore, this study aimed to investigate inter-limb symmetry of SLS kinematics and movement coordination using the modified vector coding technique. A 3D motion capture system and electromyography were used to assess SLS execution and muscle activation of hip ab- and adductors of 17 healthy, recreational athletes. Coordination patterns of hip, knee, and ankle joint movement were assessed by the modified vector coding technique. Statistical parametric mapping revealed no significant differences between both legs (p > 0.05). Inter-limb symmetry also appeared in movement coordination (p > 0.05). Additionally, the analysis of movement coordination indicates knee-dominant, in-phase coordination. However, coordination patterns were different between downward movement, change of direction, and upward movement (p < 0.001). Since perturbations during SLS execution, such as moments of imbalance, occur as anti-phase coordination patterns, the analysis of coordination patterns can be used as a new evaluation method for SLS performance. Furthermore, the modified vector coding technique might be helpful to analyze different compensation strategies during the SLS in symptomatic individuals.
Determinants of Customer Satisfaction and eWOM in the Sharing Economy: Timeshare versus Peer-to-Peer Accommodations
Authors: Redditt Jennifer , Orlowski Marissa , Fyall Alan , Gregory Amy M. , Ro Heejung
Published: February 2022

This study aims to investigate the differential effects of determinants of satisfaction on subsequent electronic word-of-mouth (eWOM) behavior in the sharing economy with peer-to-peer accommodations and timeshares. Data were collected from consumers who stayed at either a timeshare or peer-to-peer accommodation within the previous year (N = 785). Structural equation modeling was used to test the relationship between the determinants and eWOM through satisfaction, followed by multiple-group comparison to examine the moderating effect of accommodation type. The results indicated that amenities, economic benefits, and trust demonstrated an indirect effect on eWOM behavior through satisfaction, but community belonging affected eWOM behavior directly. Multiple-group SEM revealed that accommodation type moderated the indirect effects of community belonging, economic benefits, and trust on eWOM behavior, such that the indirect effect was significant only for timeshare accommodations. This study contributes to the sharing economy literature by filling the gap of lodging research beyond Airbnb and including timeshares. The findings highlight competitive differences that influence eWOM between peer-to-peer accommodations and timeshares that otherwise offer similar home-like accommodations. The study revealed a newly found direct relationship between community belonging and eWOM.
Analysis of the Aerosol Generated from Tetrahydrocannabinol, Vitamin E Acetate, and Their Mixtures
Authors: Mikheev Vladimir B. , Ivanov Alexander
Published: February 2022

E-cigarette, or vaping, product use–associated lung injury (EVALI) outbreak was linked to vitamin E acetate (VEA) used as a solvent for tetrahydrocannabinol (THC). Several studies were conducted to assess the products of VEA (and THC/VEA mixtures) thermal degradation as a result of vaporizing/aerosolizing from a traditional type (coil—cotton wick) and ceramic type coil vape pens. The particle size distribution (PSD) of VEA aerosol and the temperature VEA and THC/VEA mixtures are heated to were also measured for a few types of traditional and ceramic vape pens. The current study assessed the PSD of the aerosol generated from THC, VEA, and a number of THC/VEA mixtures using a dab-type vape pen under two different temperature settings and two puffing flow rates. Thermal degradation of THC, VEA, and THC/VEA mixtures were also assessed, and coil temperature was measured. Results showed the dependence of the PSD upon the chemical content of the aerosolized mixture as well as upon the puffing flow rate. Minimal thermal degradation was observed. Flaws in the vape pen’s design, which most likely affected results, were detected. The suitability of VEA, THC, and THC/VEA mixtures with certain types of vape pens was discussed.
Transcriptomics Reveals the Effect of Thymol on the Growth and Toxin Production of Fusarium graminearum
Authors: Wang Lian-Qun , Wu Kun-Tan , Yang Ping , Hou Fang , Rajput Shahid Ali , Qi De-Sheng , Wang Shuai
Published: February 2022

Fusarium graminearum is a harmful pathogen causing head blight in cereals such as wheat and barley, and thymol has been proven to inhibit the growth of many pathogens. This study aims to explore the fungistatic effect of thymol on F. graminearum and its mechanism. Different concentrations of thymol were used to treat F. graminearum. The results showed that the EC50 concentration of thymol against F. graminearum was 40 μg/mL. Compared with the control group, 40 μg/mL of thymol reduced the production of Deoxynivalenol (DON) and 3-Ac-DON by 70.1% and 78.2%, respectively. Our results indicate that thymol can effectively inhibit the growth and toxin production of F. graminearum and cause an extensive transcriptome response. Transcriptome identified 16,727 non-redundant unigenes and 1653 unigenes that COG did not annotate. The correlation coefficients between samples were all >0.941. When FC was 2.0 times, a total of 3230 differential unigenes were identified, of which 1223 were up-regulated, and 2007 were down-regulated. Through the transcriptome, we confirmed that the expression of many genes involved in F. graminearum growth and synthesis of DON and other secondary metabolites were also changed. The gluconeogenesis/glycolysis pathway may be a potential and important way for thymol to affect the growth of F. graminearum hyphae and the production of DON simultaneously.
Influenza A(H1N1)pdm09 Virus but Not Respiratory Syncytial Virus Interferes with SARS-CoV-2 Replication during Sequential Infections in Human Nasal Epithelial Cells
Authors: Fage Clément , Hénaut Mathilde , Carbonneau Julie , Piret Jocelyne , Boivin Guy
Published: February 2022

The types of interactions between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and other respiratory viruses are not well-characterized due to the low number of co-infection cases described since the onset of the pandemic. We have evaluated the interactions between SARS-CoV-2 (D614G mutant) and influenza A(H1N1)pdm09 or respiratory syncytial virus (RSV) in the nasal human airway epithelium (HAE) infected simultaneously or sequentially (24 h apart) with virus combinations. The replication kinetics of each virus were determined by RT-qPCR at different post-infection times. Our results showed that during simultaneous infection, SARS-CoV-2 interferes with RSV-A2 but not with A(H1N1)pdm09 replication. The prior infection of nasal HAE with SARS-CoV-2 reduces the replication kinetics of both respiratory viruses. SARS-CoV-2 replication is decreased by a prior infection with A(H1N1)pdm09 but not with RSV-A2. The pretreatment of nasal HAE with BX795, a TANK-binding kinase 1 inhibitor, partially alleviates the reduced replication of SARS-CoV-2 or influenza A(H1N1)pdm09 during sequential infection with both virus combinations. Thus, a prior infection of nasal HAE with SARS-CoV-2 interferes with the replication kinetics of A(H1N1)pdm09 and RSV-A2, whereas only A(H1N1)pdm09 reduces the subsequent infection with SARS-CoV-2. The mechanism involved in the viral interference between SARS-CoV-2 and A(H1N1)pdm09 is mediated by the production of interferon.