bluuebunny/crossref_metadata_2025 huggingface dataset:
Numerical and Experimental Evaluation and Heat Transfer Characteristics of a Soft Magnetic Transformer Built from Laminated Steel Plates
Authors: Cano-Pleite Eduardo , Barrado Andrés , Garcia-Hernando Néstor , Olías Emilio , Soria-Verdugo Antonio
Published: December 2021

The present work evaluates, both experimentally and numerically, the heat transfer characteristics of a 5 kW three-phase transformer built from laminated steel sheets. The transformer is operated at different powers, and its temperature distribution is monitored using 108 thermocouples. The experimental measurements are used firstly to determine the heat dissipated at the core and the windings of the transformer. This information is used as an input for a finite element numerical model, which evaluates the heat transfer characteristics of the transformer. The model proposed in this work simply solves the diffusion equation inside the transformer, accounting for the anisotropic thermal conductivity of the different components of the transformer, together with well-known correlations at its boundaries. The results reveal that the proposed numerical model can correctly reproduce the maximum temperature, the temperature distribution, and the time-evolution of the temperature at specific points of the transformer measured during the experimental campaign. These results are of great use for the subsequent development of transformers of the same type in lab-scale or industrial-scale size and reveal the applicability of simplified numerical models to accurately predict the heat transfer characteristics of this kind of transformers.
Classification of Individual Finger Movements from Right Hand Using fNIRS Signals
Authors: Khan Haroon , Noori Farzan M. , Yazidi Anis , Uddin Md Zia , Khan M. N. Afzal , Mirtaheri Peyman
Published: December 2021

Functional near-infrared spectroscopy (fNIRS) is a comparatively new noninvasive, portable, and easy-to-use brain imaging modality. However, complicated dexterous tasks such as individual finger-tapping, particularly using one hand, have been not investigated using fNIRS technology. Twenty-four healthy volunteers participated in the individual finger-tapping experiment. Data were acquired from the motor cortex using sixteen sources and sixteen detectors. In this preliminary study, we applied standard fNIRS data processing pipeline, i.e., optical densities conversation, signal processing, feature extraction, and classification algorithm implementation. Physiological and non-physiological noise is removed using 4th order band-pass Butter-worth and 3rd order Savitzky–Golay filters. Eight spatial statistical features were selected: signal-mean, peak, minimum, Skewness, Kurtosis, variance, median, and peak-to-peak form data of oxygenated haemoglobin changes. Sophisticated machine learning algorithms were applied, such as support vector machine (SVM), random forests (RF), decision trees (DT), AdaBoost, quadratic discriminant analysis (QDA), Artificial neural networks (ANN), k-nearest neighbors (kNN), and extreme gradient boosting (XGBoost). The average classification accuracies achieved were 0.75±0.04, 0.75±0.05, and 0.77±0.06 using k-nearest neighbors (kNN), Random forest (RF) and XGBoost, respectively. KNN, RF and XGBoost classifiers performed exceptionally well on such a high-class problem. The results need to be further investigated. In the future, a more in-depth analysis of the signal in both temporal and spatial domains will be conducted to investigate the underlying facts. The accuracies achieved are promising results and could open up a new research direction leading to enrichment of control commands generation for fNIRS-based brain-computer interface applications.
Cross-Cycled Uplink Resource Allocation over NB-IoT
Authors: Yu Ya-Ju , Huang Yu-Hsiang , Shih Yuan-Yao
Published: December 2021

Before each user equipment (UE) can send data using the narrowband physical uplink shared channel (NPUSCH), each UE should periodically monitor a search space in the narrowband physical downlink control channel (NPDCCH) to decode a downlink control indicator (DCI) over narrowband Internet of Things (NB-IoT). This monitoring period, called the NPDCCH period in NB-IoT, can be flexibly adjusted for UEs with different channel qualities. However, because low-cost NB-IoT UEs operate in the half-duplex mode, they cannot monitor search spaces in NPDCCHs and transmit data in the NPUSCH simultaneously. Thus, as we observed, a percentage of uplink subframes will be wasted when UEs monitor search spaces in NPDCCHs, and the wasted percentage is higher when the monitored period is shorter. In this paper, to address this issue, we formulate the cross-cycled resource allocation problem to reduce the consumed subframes while satisfying the uplink data requirement of each UE. We then propose a cross-cycled uplink resource allocation algorithm to efficiently use the originally unusable NPUSCH subframes to increase resource utilization. Compared with the two resource allocation algorithms, the simulation results verify our motivation of using the cross-cycled radio resources to achieve massive connections over NB-IoT, especially for UEs with high channel qualities. The results also showcase the efficiency of the proposed algorithm, which can be flexibly applied for more different NPDCCH periods.
Convolution-Based Encoding of Depth Images for Transfer Learning in RGB-D Scene Classification
Authors: Gopalapillai Radhakrishnan , Gupta Deepa , Zakariah Mohammed , Alotaibi Yousef Ajami
Published: December 2021

Classification of indoor environments is a challenging problem. The availability of low-cost depth sensors has opened up a new research area of using depth information in addition to color image (RGB) data for scene understanding. Transfer learning of deep convolutional networks with pairs of RGB and depth (RGB-D) images has to deal with integrating these two modalities. Single-channel depth images are often converted to three-channel images by extracting horizontal disparity, height above ground, and the angle of the pixel’s local surface normal (HHA) to apply transfer learning using networks trained on the Places365 dataset. The high computational cost of HHA encoding can be a major disadvantage for the real-time prediction of scenes, although this may be less important during the training phase. We propose a new, computationally efficient encoding method that can be integrated with any convolutional neural network. We show that our encoding approach performs equally well or better in a multimodal transfer learning setup for scene classification. Our encoding is implemented in a customized and pretrained VGG16 Net. We address the class imbalance problem seen in the image dataset using a method based on the synthetic minority oversampling technique (SMOTE) at the feature level. With appropriate image augmentation and fine-tuning, our network achieves scene classification accuracy comparable to that of other state-of-the-art architectures.
Effects of Different Parameter Settings for 3D Data Smoothing and Mesh Simplification on Near Real-Time 3D Reconstruction of High Resolution Bioceramic Bone Void Filling Medical Images
Authors: Chin Daniel Jie Yuan , Mohamed Ahmad Sufril Azlan , Shariff Khairul Anuar , Ab Wahab Mohd Nadhir , Ishikawa Kunio
Published: December 2021

Three-dimensional reconstruction plays a vital role in assisting doctors and surgeons in diagnosing the healing progress of bone defects. Common three-dimensional reconstruction methods include surface and volume rendering. As the focus is on the shape of the bone, this study omits the volume rendering methods. Many improvements have been made to surface rendering methods like Marching Cubes and Marching Tetrahedra, but not many on working towards real-time or near real-time surface rendering for large medical images and studying the effects of different parameter settings for the improvements. Hence, this study attempts near real-time surface rendering for large medical images. Different parameter values are experimented on to study their effect on reconstruction accuracy, reconstruction and rendering time, and the number of vertices and faces. The proposed improvement involving three-dimensional data smoothing with convolution kernel Gaussian size 5 and mesh simplification reduction factor of 0.1 is the best parameter value combination for achieving a good balance between high reconstruction accuracy, low total execution time, and a low number of vertices and faces. It has successfully increased reconstruction accuracy by 0.0235%, decreased the total execution time by 69.81%, and decreased the number of vertices and faces by 86.57% and 86.61%, respectively.
Calibration of On-Board Energy Measurement Systems Installed in Locomotives for AC Distorted Current and High Voltage Waveforms and Determination of Its Uncertainty Budget
Authors: Khamlichi Abderrahim , Garnacho Fernando , Simon Pascual , Rovira Jorge , Ramirez Angel
Published: December 2021

Periodic calibrations of Energy Measurement Systems (EMS) installed in locomotives must be carried out to demonstrate the required accuracy established in the EN 50463-2 standard according to European Parliament and Council Directive 2008/57/EC on the interoperability of rail systems within the Community. As a result of the work performed in the “MyRailS” EURAMET project an AC calibration facility was developed consisting of a fictive power source was developed. This fictive power source can generate distorted sinusoidal voltages up to 25 kV-50 Hz and 15 kV-16.7 Hz as well as distorted sinusoidal currents up to 500 A with harmonic content up to 5 kHz or phase-fired current waveform stated in EN50463-2 standard. These waveforms are representative of those that appear during periods of acceleration and breaking of the train. Reference measuring systems have been designed and built consisting of high voltage and high current transducers adapted to multimeters, which function as digital recorders to acquire synchronized voltage and current signals. An approved procedure has been developed and an in-depth uncertainty analysis has been performed to achieve a set of uncertainty formulas considering the influence parameters. Different influence parameters have been analyzed to evaluate uncertainty contributions for each quantity to be measured: rms voltage, rms current, active power, apparent power and non-active power of distorted voltage and current waveforms. The resulting calculated global expanded uncertainty for the developed Energy Measuring Function calibration set up has been better than 0.5% for distorted waveforms. This paper is focused on presenting the complete set of expressions and formulas developed for the different influence parameters, necessary for uncertainty budget calculation of an Energy Measuring Function calibration.
Recognition of Noisy Radar Emitter Signals Using a One-Dimensional Deep Residual Shrinkage Network
Authors: Zhang Shengli , Pan Jifei , Han Zhenzhong , Guo Linqing
Published: December 2021

Signal features can be obscured in noisy environments, resulting in low accuracy of radar emitter signal recognition based on traditional methods. To improve the ability of learning features from noisy signals, a new radar emitter signal recognition method based on one-dimensional (1D) deep residual shrinkage network (DRSN) is proposed, which offers the following advantages: (i) Unimportant features are eliminated using the soft thresholding function, and the thresholds are automatically set based on the attention mechanism; (ii) without any professional knowledge of signal processing or dimension conversion of data, the 1D DRSN can automatically learn the features characterizing the signal directly from the 1D data and achieve a high recognition rate for noisy signals. The effectiveness of the 1D DRSN was experimentally verified under different types of noise. In addition, comparison with other deep learning methods revealed the superior performance of the DRSN. Last, the mechanism of eliminating redundant features using the soft thresholding function was analyzed.
Applying Noncontact Sensing Technology in the Customized Product Design of Smart Clothes Based on Anthropometry
Authors: Wang I-Jan , Chang Wei-Ting , Wu Wen-Hao , Lin Bor-Shyh
Published: December 2021

Electrocardiograms (ECGs) provide important information for diagnosing cardiovascular diseases. In clinical practice, the conventional Ag/AgCl electrode is generally used; however, it is not suitable for long-term ECG measurement because of the risk of allergic reactions on the skin and the dying issue of electrolytic gels. In previous studies, several dry electrodes have been proposed to address these issues. However, most dry electrodes, which are the mode of conductive materials, have to contact the skin well and are easily affected by motion artifacts in daily life. In the smart clothes developed in this study, a noncontact electrode was used to assess the biopotential across the clothes to prevent skin irritation and discomfort. Moreover, a three-dimensional parametric model based on anthropometric data was built, and the technique of customized product design was introduced into the smart clothes development process to reduce the influence of motion artifacts. The experimental results show that the proposed smart clothes can maintain a good ECG signal quality stably under motion from different activities.
An Ultrasonic-Capacitive System for Online Characterization of Fuel Oils in Thermal Power Plants
Authors: Campos Mateus Mendes , Borges-da-Silva Luiz Eduardo , Arantes Daniel de Almeida , Teixeira Carlos Eduardo , Bonaldi Erik Leandro , Lambert-Torres Germano , Ribeiro Junior Ronny Francis , Krupa Gabriel Pedro , Sant’Ana Wilson Cesar , Oliveira Levy Ely Lacerda , de Paiva Renato Guth
Published: December 2021

This paper presents a ultrasonic-capacitive system for online analysis of the quality of fuel oils (FO), which are widely used to produce electric energy in Thermal Power Plants (TPP) due to their elevated heating value. The heating value, in turn, is linked to the quality of the fuel (i.e., the density and the amount of contaminants, such as water). Therefore, the analysis of the quality is of great importance for TPPs, either in order to avoid a decrease in generated power or in order to avoid damage to the TPP equipment. The proposed system is composed of two main strategies: a capacitive system (in order to estimate the water content in the fuel) and an ultrasonic system (in order to estimate the density). The conjunction of the two strategies is used in order to estimate the heating value of the fuel, online, as it passes through the pipeline and is an important tool for the TPP in order to detect counterfeit fuel. In addition, the ultrasonic system allows the estimation of the flow rate through the pipeline, hence estimating the amount of oil transferred and obtaining the total mass transferred as a feature of the system. Experimental results are provided for both sensors installed in a TPP in Brazil.
A Convolutional Neural Network-Based Intelligent Medical System with Sensors for Assistive Diagnosis and Decision-Making in Non-Small Cell Lung Cancer
Authors: Zhan Xiangbing , Long Huiyun , Gou Fangfang , Duan Xun , Kong Guangqian , Wu Jia
Published: December 2021

In many regions of the world, early diagnosis of non-small cell lung cancer (NSCLC) is a major challenge due to the large population and lack of medical resources, which is difficult toeffectively address via limited physician manpower alone. Therefore, we developed a convolutional neural network (CNN)-based assisted diagnosis and decision-making intelligent medical system with sensors. This system analyzes NSCLC patients’ medical records using sensors to assist staging a diagnosis and provides recommended treatment plans to physicians. To address the problem of unbalanced case samples across pathological stages, we used transfer learning and dynamic sampling techniques to reconstruct and iteratively train the model to improve the accuracy of the prediction system. In this paper, all data for training and testing the system were obtained from the medical records of 2,789,675 patients with NSCLC, which were recorded in three hospitals in China over a five-year period. When the number of case samples reached 8000, the system achieved an accuracy rate of 0.84, which is already close to that of the doctors (accuracy: 0.86). The experimental results proved that the system can quickly and accurately analyze patient data and provide decision information support for physicians.
Responsive Dashboard as a Component of Learning Analytics System for Evaluation in Emergency Remote Teaching Situations
Authors: Corbu Emilia Corina , Edelhauser Eduard
Published: December 2021

The pandemic crisis has forced the development of teaching and evaluation activities exclusively online. In this context, the emergency remote teaching (ERT) process, which raised a multitude of problems for institutions, teachers, and students, led the authors to consider it important to design a model for evaluating teaching and evaluation processes. The study objective presented in this paper was to develop a model for the evaluation system called the learning analytics and evaluation model (LAEM). We also validated a software instrument we designed called the EvalMathI system, which is to be used in the evaluation system and was developed and tested during the pandemic. The optimization of the evaluation process was accomplished by including and integrating the dashboard model in a responsive panel. With the dashboard from EvalMathI, six online courses were monitored in the 2019/2020 and 2020/2021 academic years, and for each of the six monitored courses, the evaluation of the curricula was performed through the analyzed parameters by highlighting the percentage achieved by each course on various components, such as content, adaptability, skills, and involvement. In addition, after collecting the data through interview guides, the authors were able to determine the extent to which online education during the COVID 19 pandemic has influenced the educational process. Through the developed model, the authors also found software tools to solve some of the problems raised by teaching and evaluation in the ERT environment.
Design of Ultrasonic Synthetic Aperture Imaging Systems Based on a Non-Grid 2D Sparse Array
Authors: de Souza Júlio Cesar Eduardo , Parrilla Romero Montserrat , Higuti Ricardo Tokio , Martínez-Graullera Óscar
Published: December 2021

This work provides a guide to design ultrasonic synthetic aperture systems for non-grid two-dimensional sparse arrays such as spirals or annular segmented arrays. It presents an algorithm that identifies which elements have a more significant impact on the beampattern characteristics and uses this information to reduce the number of signals, the number of emitters and the number of parallel receiver channels involved in the beamforming process. Consequently, we can optimise the 3D synthetic aperture ultrasonic imaging system for a specific sparse array, reducing the computational cost, the hardware requirements and the system complexity. Simulations using a Fermat spiral array and experimental data based on an annular segmented array with 64 elements are used to assess this algorithm.
Smart Textiles for Improved Quality of Life and Cognitive Assessment
Authors: Oatley Giles , Choudhury Tanveer , Buckman Paul
Published: December 2021

Smart textiles can be used as innovative solutions to amuse, meaningfully engage, comfort, entertain, stimulate, and to overall improve the quality of life for people living in care homes with dementia or its precursor mild cognitive impairment (MCI). This concept paper presents a smart textile prototype to both entertain and monitor/assess the behavior of the relevant clients. The prototype includes physical computing components for music playing and simple interaction, but additionally games and data logging systems, to determine baselines of activity and interaction. Using microelectronics, light-emitting diodes (LEDs) and capacitive touch sensors woven into a fabric, the study demonstrates the kinds of augmentations possible over the normal manipulation of the traditional non-smart activity apron by incorporating light and sound effects as feedback when patients interact with different regions of the textile. A data logging system will record the patient’s behavioral patterns. This would include the location, frequency, and time of the patient’s activities within the different textile areas. The textile will be placed across the laps of the resident, which they then play with, permitting the development of a behavioral profile through the gamification of cognitive tests. This concept paper outlines the development of a prototype sensor system and highlights the challenges related to its use in a care home setting. The research implements a wide range of functionality through a novel architecture involving loosely coupling and concentrating artifacts on the top layer and technology on the bottom layer. Components in a loosely coupled system can be replaced with alternative implementations that provide the same services, and so this gives the solution the best flexibility. The literature shows that existing architectures that are strongly coupled result in difficulties modeling different individuals without incurring significant costs.
3D-Printed Quasi-Cylindrical Bragg Reflector to Boost the Gain and Directivity of cm- and mm-Wave Antennas
Authors: Ribeiro Jéssica A. P. , Filgueiras Hugo R. D. , Cerqueira Sodré Junior Arismar , Beltrán-Mejía Felipe , Mejía-Salazar Jorge Ricardo
Published: December 2021

We demonstrate a concept for a large enhancement of the directivity and gain of readily available cm- and mm-wave antennas, i.e., without altering any property of the antenna design. Our concept exploits the high reflectivity of a Bragg reflector composed of three bilayers made of transparent materials. The cavity has a triangular aperture in order to resemble the idea of a horn-like, highly directive antenna. Importantly, we report gain enhancements of more than 400% in relation to the gain of the antenna without the Bragg structure, accompanied by a highly directive radiation pattern. The proposed structure is cost-effective and easy to fabricate with 3D-printing. Our results are presented for frequencies within the conventional WiFi frequencies, based on IEEE 802.11 standards, thus, enabling easily implementation by non-experts and needing only to be placed around the antenna to improve the directivity and gain of the signal.
A Hybrid Approach for Noise Reduction in Acoustic Signal of Machining Process Using Neural Networks and ARMA Model
Authors: Zafar Tayyab , Kamal Khurram , Mathavan Senthan , Hussain Ghulam , Alkahtani Mohammed , Alqahtani Fahad M. , Aboudaif Mohamed K.
Published: December 2021

Intelligent machining has become an important part of manufacturing systems because of the increased demand for productivity. Tool condition monitoring is an integral part of these systems. Airborne acoustic emission from the machining process is a vital indicator of tool health, however, it is highly affected by background noise. Reducing the background noise helps in developing a low-cost system. In this research work, a feedforward neural network is used as an adaptive filter to reduce the background noise. Acoustic signals from four different machines in the background are acquired and are introduced to a machining signal at different speeds and feed-rates at a constant depth of cut. These four machines are a three-axis milling machine, a four-axis mini-milling machine, a variable speed DC motor, and a grinding machine. The backpropagation neural network shows an accuracy of 75.82% in classifying the background noise. To reconstruct the filtered signal, a novel autoregressive moving average (ARMA)-based algorithm is proposed. An average increase of 71.3% in signal-to-noise ratio (SNR) is found before and after signal reconstruction. The proposed technique shows promising results for signal reconstruction for the machining process.
Light-Based IoT: Developing a Full-Duplex Energy Autonomous IoT Node Using Printed Electronics Technology
Authors: Perera Malalgodage Amila Nilantha , Katz Marcos , Häkkinen Juha , Godaliyadda Roshan
Published: December 2021

The light-based Internet of things (LIoT) concept defines nodes that exploit light to (a) power up their operation by harvesting light energy and (b) provide full-duplex wireless connectivity. In this paper, we explore the LIoT concept by designing, implementing, and evaluating the communication and energy harvesting performance of a LIoT node. The use of components based on printed electronics (PE) technology is adopted in the implementation, supporting the vision of future fully printed LIoT nodes. In fact, we envision that as PE technology develops, energy-autonomous LIoT nodes will be entirely printed, resulting in cost-efficient, flexible and highly sustainable connectivity solutions that can be attached to the surface of virtually any object. However, the use of PE technology poses additional challenges to the task, as the performance of these components is typically considerably poorer than that of conventional components. In the study, printed photovoltaic cells, printed OLEDs (organic light-emitting diodes) as well as printed displays are used in the node implementation. The dual-mode operation of the proposed LIoT node is demonstrated, and its communication performance in downlink and uplink directions is evaluated. In addition, the energy harvesting system’s behaviour is studied and evaluated under different illumination scenarios and based on the results, a novel self-operating limitation aware algorithm for LIoT nodes is proposed.
COVID-19, Rural Communities, and Implications of Telebehavioral Health Services: Addressing the Benefits and Challenges of Behavioral Health Services via Telehealth in Nebraska
Authors: Freske Emily , Malczyk Benjamin Robert
Published: December 2021

Telehealth has been in use, in various forms, for over a century and is growing increasingly more popular. The current research sought to examine the prevalence, benefits, and challenges of telehealth for behavioral and mental health services in the state of Nebraska with a particular focus on rural communities. The COVID-19 pandemic coincided with the research endeavors and impacted the questions that were asked related to the use of telebehavioral health. Specifically, the research included an investigation of the rates of use of telebehavioral health across agencies and whether/how the pandemic impacted the use of telehealth services. The research included an initial examination of more than 50 behavioral health agencies to assess overall utilization of telehealth. Researchers then conducted interviews with 15 practitioners to discuss the challenges and benefits associated with telehealth services. Key results suggest that implementation of telehealth in Nebraska has resulted in increased access to services among rural residents and has deeply impacted clinical practice. Additionally, clinicians identified specific benefits and challenges of telebehavioral health. It was also noted that the majority of clinicians plan to continue providing services via telehealth if the policies and regulations remain as they are post-COVID-19. Implications of this research highlight the efficiency and effectiveness of using telehealth to increase access.
Opportunities for Waste to Energy in the Milk Production Industry: Perspectives for the Circular Economy
Authors: Casallas-Ojeda Miguel , Torres-Guevara Luz Elba , Caicedo-Concha Diana M. , Gómez María F.
Published: December 2021

Cheese whey is a waste produced in the dairy industry which generates problems if it is dumped directly into the sewer due to its high organic load. An alternative for cheese whey management is anaerobic digestion, a biological process that transforms organic matter into biogas and digestate, two products with significant energy and agricultural potential. This work was aimed at contributing to the building of knowledge about the anaerobic degradation of cheese whey, developing a bibliometric analysis, and tracing trends in related research from 2010 up to the present, using PRISMA® to develop a systematic review based on Scopus® and using Excel® and bibliometric software (VosViewer® and RefViz®) for the identification of information. Our results show that the research around cheese whey is relatively recent and that the highest percentage of publications is from 2018 onwards. Twelve variables of the anaerobic cheese whey degradation process were identified and grouped into five factors: substrate, reactor configuration, digestate analysis, microbiological analysis, and inoculum. Likewise, it was identified that most of the anaerobic processes allow the implementation of the circular economy into the dairy sector. In conclusion, the application of anaerobic digestion in the dairy sector can help to close the productive cycles, produce biofuels, and reduce pollution.
Blockchain Technology in Wine Chain for Collecting and Addressing Sustainable Performance: An Exploratory Study
Authors: Luzzani Gloria , Grandis Erica , Frey Marco , Capri Ettore
Published: December 2021

Sustainability standards have not yet been commonly adopted by the whole wine chain, and indicator assessments are not widely spread. A deep understanding of how embedding sustainability into business while controlling costs related to the adoption of sustainability certification standards such as data collection and management practices could allow one to overcome most barriers relevant to sustainability compliance. Blockchain technology (BCT) may answer these needs. In order to verify BCT potential to be used as a sustainability management tool in the wine industry, with a qualitative triple bottom line research approach, this article explores the connections among BCT adoption in agri-food, issues posed by wine sustainability certification, and whether wine companies that already own a wine sustainability certification are prepared to adopt it. Results show that (1) the blockchain allows collecting data and information that are relevant for monitoring and improving sustainability: Soil and water features, climate conditions, treatment with pesticides and fertilizers, production process, traceability, transparency, labor and human rights, quality and safety, waste reduction, authenticity, relationship with stakeholders; (2) wine companies that already own a sustainability certification have little familiarity with blockchain applications (57.1%, n.21) and only 14% of the respondents support their intention to invest in BCT in the coming years; (3) the case study shows improvements in traceability and transparency along the supply chain and an increase in consumers’ trust that was reflected in sales growth, and the main costs are linked to complexity in data management.
Artificial Intelligence and Its Role in Education
Authors: Ahmad Sayed Fayaz , Rahmat Mohd. Khairil , Mubarik Muhammad Shujaat , Alam Muhammad Mansoor , Hyder Syed Irfan
Published: December 2021

The objective of this study is to explore the role of artificial intelligence applications (AIA) in education. AI applications provide the solution in many ways to the exponential rise of modern-day challenges, which create difficulties in access to education and learning. They play a significant role in forming social robots (SR), smart learning (SL), and intelligent tutoring systems (ITS) to name a few. The review indicates that the education sector should also embrace the modern methods of teaching and the necessary technology. Looking into the flow, the education sector organizations need to adopt AI technologies as a necessity of the day and education. The study needs to be tested statistically for better understanding and to make the findings more generalized in the future.
Transmission Mechanism of Stock Price Fluctuation in the Rare Earth Industry Chain
Authors: Jia Yanjing , Ding Chao , Dong Zhiliang
Published: December 2021

The transmission of stock price fluctuations of listed companies in the rare earth industry has complex characteristics. Mastering its transmission law is of great meaning to understand the relationship between the upstream and downstream of the rare earth industry chain and market investment. This article uses the time series of daily closing prices of stocks in the global rare earth industry chain in the past ten years as the research object. The Granger causality test and complex network theory were used to construct the risk transmission network of the industrial chain. We have identified the key stocks in the network of stock price fluctuation in the rare earth industry chain and obtained the transmission path of stock price fluctuation. According to the results: (1) The stocks of Chinese and Japanese listed companies considerably influence the transmission of the stock price fluctuation in the rare earth industry chain. (2) The transmission distance of the stock price fluctuation of each network is relatively small, and the transmission speed is relatively fast. (3) The fluctuation of stock price in the rare earth industry chain is mainly transmitted from the upstream and midstream links to the midstream and downstream links.
Energy Evolution: Forecasting the Development of Non-Conventional Renewable Energy Sources and Their Impact on the Conventional Electricity System
Authors: Golubev Vadim A. , Verbnikova Viktoria A. , Lopyrev Ilia A. , Voznesenskaya Daria D. , Alimov Rashid N. , Novikova Olga V. , Konnikov Evgenii A.
Published: December 2021

The development of the world’s electric power systems goes back over a century. During this period, the overwhelming majority of states have formed stable, typically centralized systems for generation, transmission, and distribution of electrical energy. At the same time, technologies, primarily for energy generation, are steadily developing, which leads to the emergence of potentially effective technological solutions based on fundamentally new energy sources. The most rapidly expanding group at the moment are renewable energy sources (RES). This fact is due to the significant coverage of the potential environmental and economic benefits of using technologies based on RES in the information environment. At the same time, the process of transformation of traditional electric power systems, by integrating generation technologies based on the use of renewable energy sources, is extremely resource-intensive, and also potentially reducing the level of sustainability and efficiency of the entire system functioning as a whole. This thesis is primarily true for exclusively centralized power systems. The purpose of this study is to create a forecasting model for the development of non-conventional renewable energy sources (NCRES) for short, medium, and long term, which makes it possible to form an action plan to ensure a reliable and uninterrupted supplying of consumers, taking into account the existing electric power system. The developed model made it possible to identify the most promising directions of NCRES from the integration point of view, and for them the quantification and clustering of the information environment was carried out, which made it possible to identify key trends and the specifics of the development of technological solutions for these directions of renewable energy sources. The developed tool and systemic conclusions formulated on the basis of its application make it possible to develop mathematically sound solutions in the direction of managing the development of traditional electric power systems based on the integration of NCRES.
Attitudes and Considerations towards Pharmaceuticals-Related Environmental Issues among Finnish Population
Authors: Alajärvi Lasse , Timonen Johanna , Lavikainen Piia , Martikainen Janne
Published: December 2021

A number of policy recommendations identify the general public as a key participant in environmentally sustainable pharmaceutical policies. However, research into pharmaceuticals-related environmental issues from the population perspective is scarce. We studied the awareness among Finnish adults (n = 2030) of pharmaceutical residues in Finnish waterways, their perceived environmental sources, views on pharmaceuticals-related environmental issues, and their association with background characteristics. Of the respondents, 89.5% were aware of the presence of pharmaceuticals in Finnish waterways. Wastewaters from the pharmaceutical industry were most frequently, and pharmaceuticals excreted by humans least frequently, considered a major environmental source of pharmaceuticals. The vast majority of the respondents were worried about the environmental and health impact of pharmaceutical residues and emphasized issues such as the importance of environmentally sustainable actions by pharmaceutical companies and the recyclability of the pharmaceutical packaging materials. Age, education, use of prescription medicines, and environmental attitude were associated with awareness of pharmaceutical residues, while gender, age, education level, and environmental attitude were associated most frequently with pharmaceuticals-related environmental considerations. These results indicate a need for improved communication about the environmental effects of pharmaceuticals and the significance of different pharmaceutical emission sources in order to promote a more environmentally friendly, effective pharmaceutical policy.
Using Fuzzy AHP-PROMETHEE for Market Risk Assessment of New-Build River Cruises on the Yangtze River
Authors: Zhu Jing , Wang Haiyan , Xu Biwen
Published: December 2021

Cruise tourism on the Yangtze River Basin has developed rapidly in recent years. However, it is still facing such challenges as homogenization of itinerary and shore attraction arrangement, as well as aging cruise ships and simplification of service facilities, while it is also difficult to satisfy hierarchical and personalized tourist needs. To change such circumstances, new-build river cruise ships are inevitable. Complexity of market supply and demand environment, together with variability of tourist preferences, have increased market uncertainties of new-build cruise products. This study aims to assess market risks of new-build river cruise ships first by identifying risk factors, from the perspective of supply and demand under the actual conditions of the Yangtze River cruise market, then by evaluating potential impacts, caused by risk factors based on multi-criteria decision-making considerations. Fuzzy AHP-PROMETHEE was employed to prioritize the risk factors. The results reveal that among the most significant market risk factors are the following, in sequence: backwardness of support facilities; sudden security crisis; homogenization of cruise products; simplification of tourism route design; and inadequate management of the tourism market. Such findings will provide beneficial insights for strategic and sustainable development of river cruises on the Yangtze River.
Comparing and Identifying Influential Factors of Technological Innovation Efficiency in Manufacturing and Service Industries Using DEA: A Study of SMEs in South Korea
Authors: Im Chae Hyun , Cho Keun Tae
Published: December 2021

Although technological innovation is critical for growth and future survival, small and medium scale enterprises (SMEs) are at a disadvantage compared to larger organizations given the resources available. It is important to examine the possible methods for making research and development more efficient. This study analyzes the technological innovation efficiency of SMEs in the manufacturing and service industries in South Korea and determines the factors affecting efficiency. The models of data envelopment analysis and Tobit regression analysis were used. According to the analysis results, the technical and pure technical efficiencies were higher in the service industry than in the manufacturing industry. The factors affecting efficiency were also different between the two industries. This study is significant because it evaluates the innovation activity efficiency of small and medium manufacturing and service companies in South Korea and provides specific criteria and a rationale to improve the efficiency.
Building Integrated Photovoltaic (BIPV) in Southeast Asian Countries: Review of Effects and Challenges
Authors: Rababah Haitham Esam , Ghazali Azhar , Mohd Isa Mohd Hafizal
Published: December 2021

Fossil fuel consumption for electricity generation in the building sector is at an all-time high in line with the country’s economic growth. This scenario will increase the global CO2 emissions and large carbon footprints, thus leading to global warming. In recent years, most of the research related to the building sector has focused on the development of new techniques to reduce buildings’ energy consumption through energy conservation, energy efficiency, and the implementation of renewable energy technologies. The introduction of photovoltaic (PV) technology has become the most prominent renewable energy (RE) that can be integrated into building components. Even though the Building Integrated Photovoltaic (BIPV) has been available for decades, but its implementation in Southeast Asian countries has not gained widespread acceptance compared to European countries and other parts of Asia. This paper aims to investigate the effects and challenges of BIPV implementation in Southeast Asian Countries (Cambodia, Indonesia, Laos, Malaysia, Singapore, Thailand, Vietnam, and the Philippines), focusing on climate effects, the initial cost of PV technology, government policies, and initiatives. An in-depth literature review from past research, policies, and reports taken between 2016 to 2021 has been conducted and found that the environmental parameters directly influence the performance of BIPV systems and affect efficiency. This study pointed at Feed-in Tariff (FiT), policies and initiatives offered by the government in Southeast Asian countries are not beneficial and discourage building owners to adopt the BIPV technology or any other RE technology. Governments should revise the current policies to promote and attract more building owners to take part in the efforts to minimize CO2 emissions from the building industry.
Cannabis Use and Emotional Intelligence in Adolescents during COVID-19 Confinement: A Social Network Analysis Approach
Authors: Martínez-Fernández María Cristina , García-Rodríguez Isaías , Arias-Ramos Natalia , García-Fernández Rubén , Trevissón-Redondo Bibiana , Liébana-Presa Cristina
Published: December 2021

Confinement by COVID-19 had negative consequences on adolescent mental health, including increased cannabis use. Cannabis is related to variables that influence health and well-being. Emotional Intelligence is associated with adaptive coping styles, peer relationships, and social–emotional competencies. In adolescence, peer selection plays a unique role in the initiation of substance use. However, there are no studies during a confinement stage that analyse the relationships between networks, Emotional Intelligence, and cannabis use. The aim of this paper is to describe and analyse the consumption and friendship networks of an adolescent classroom and their relationship with Emotional Intelligence, cannabis use, and gender during COVID-19 confinement. Participants completed different questionnaires for Emotional Intelligence, cannabis use, and the consumption and friendship network. The sample consisted of 21 students from 10th grade, of which 47.6% were consumers. The friendship network correlates with the consumption network, and significant associations between emotional repair and being a cannabis user. The regression model points to the friendship network as a significant variable in predicting the classroom use network. This study highlights the role of the Social Network Analysis in predicting consumption networks during a COVID-19 confinement stage and serves as a tool for cannabis use prevention interventions in a specific population.
Tactical Innovation to Incorporate Post-Consumer Expanded Polystyrene in Artisanal Chains for the Doping of Products
Authors: Montalvo-Romero Nayeli , Montiel-Rosales Aarón , Fernández-Lambert Gregorio , Sánchez-Galván Fabiola , Bautista-Santos Horacio
Published: December 2021

In its original manufacturing purpose, Expanded Polystyrene (EPS) is an industrial product with a short life cycle and waste with high negative environmental impact. Given this externality, this article contributes to the state of the art by proposing reuse EPS as a raw material valuable to the process of manufacturing hats in a Mexican company. The SCAMPER technique is used to formulate a hardener, which is optimized with a Taguchi design. Statistically, there is no difference between the quality of the conventional hardener and the mixture based on post-consumer EPS to make hats based on the standards defined by the company; a subjective analysis supported by the judgment of experts validates the quality of the hats. A contour graph and response surface reflect different combinations of solute and solvent to formulate the glue for the doping of the hat, with the same hardness results. This allows the artisan to assess the formulation from an economical point of view, as well as with respect to the arrangement of materials. These results specifically propose the sustainable alternative of integrating waste from the post–consumer EPS chain into the artisanal hat value chain, and are replicable to other similar products.
Do Miscanthus lutarioriparius-Based Oriented Strand Boards Provide Environmentally Benign Alternatives? An LCA Case Study of Lake Dongting District in China
Authors: Liao Qianying , Zhang Jun , Yi Zili , Li Youzhi
Published: December 2021

Miscanthus lutarioriparius(M. lutarioriparius) in Lake Dongting District are in the situation of being discarded due to the government’s environmental policy, the decomposition of which will bring another pollution risk. The purpose of this study is to environmentally analyze the production of M. lutarioriparius-based oriented strand particleboards(M.OSB) as alternatives to the conventional artificial boards. The production systems were evaluated from a cradle-to-gate perspective using the Life Cycle Assessment(LCA)methodology. Our results showed that the M.OSB had an overall better profile than wood panels, identifying the production of starch adhesives and bio-fuels as the main environmental hotspots. It was also found that annual harvesting and utilization of M. lutarioripariuscould ease the burden to the environment during the decomposition of this plant, and further improve the environmental performance of M.OSB. Sensitivity analyses were conducted on the key parameters, suggesting that there are opportunities for improvement. This study provides useful information for enterprises and policymakers on where to focus their activities, with the aim of making the future of M. lutarioriparius utilization more technically and environmentally favourable.