bluuebunny/crossref_metadata_2025 huggingface dataset:
On Conditional Tsallis Entropy
Authors: Teixeira Andreia , Souto André , Antunes Luís
Published: November 2021

There is no generally accepted definition for conditional Tsallis entropy. The standard definition of (unconditional) Tsallis entropy depends on a parameter α that converges to the Shannon entropy as α approaches 1. In this paper, we describe three proposed definitions of conditional Tsallis entropy suggested in the literature—their properties are studied and their values, as a function of α, are compared. We also consider another natural proposal for conditional Tsallis entropy and compare it with the existing ones. Lastly, we present an online tool to compute the four conditional Tsallis entropies, given the probability distributions and the value of the parameter α.
An Interval Iteration Based Multilevel Thresholding Algorithm for Brain MR Image Segmentation
Authors: Feng Yuncong , Liu Wanru , Zhang Xiaoli , Liu Zhicheng , Liu Yunfei , Wang Guishen
Published: November 2021

In this paper, we propose an interval iteration multilevel thresholding method (IIMT). This approach is based on the Otsu method but iteratively searches for sub-regions of the image to achieve segmentation, rather than processing the full image as a whole region. Then, a novel multilevel thresholding framework based on IIMT for brain MR image segmentation is proposed. In this framework, the original image is first decomposed using a hybrid L1 − L0 layer decomposition method to obtain the base layer. Second, we use IIMT to segment both the original image and its base layer. Finally, the two segmentation results are integrated by a fusion scheme to obtain a more refined and accurate segmentation result. Experimental results showed that our proposed algorithm is effective, and outperforms the standard Otsu-based and other optimization-based segmentation methods.
The Fundamental Theorem of Natural Selection
Authors: Baez John C.
Published: November 2021

Suppose we have n different types of self-replicating entity, with the population Pi of the ith type changing at a rate equal to Pi times the fitness fi of that type. Suppose the fitness fi is any continuous function of all the populations P1,…,Pn. Let pi be the fraction of replicators that are of the ith type. Then p=(p1,…,pn) is a time-dependent probability distribution, and we prove that its speed as measured by the Fisher information metric equals the variance in fitness. In rough terms, this says that the speed at which information is updated through natural selection equals the variance in fitness. This result can be seen as a modified version of Fisher’s fundamental theorem of natural selection. We compare it to Fisher’s original result as interpreted by Price, Ewens and Edwards.
Region Adaptive Single Image Dehazing
Authors: Kim Changwon
Published: November 2021

Image haze removal is essential in preprocessing for computer vision applications because outdoor images taken in adverse weather conditions such as fog or snow have poor visibility. This problem has been extensively studied in the literature, and the most popular technique is dark channel prior (DCP). However, dark channel prior tends to underestimate transmissions of bright areas or objects, which may cause color distortions during dehazing. This paper proposes a new single-image dehazing method that combines dark channel prior with bright channel prior in order to overcome the limitations of dark channel prior. A patch-based robust atmospheric light estimation was introduced in order to divide image into regions to which the DCP assumption and the BCP assumption are applied. Moreover, region adaptive haze control parameters are introduced in order to suppress the distortions in a flat and bright region and to increase the visibilities in a texture region. The flat and texture regions are expressed as probabilities by using local image entropy. The performance of the proposed method is evaluated by using synthetic and real data sets. Experimental results show that the proposed method outperforms the state-of-the-art image dehazing method both visually and numerically.
The Interstitial Pathways as the Substrate of Consciousness: A New Synthesis
Authors: Tyler Christopher W.
Published: November 2021

This paper considers three classes of analyses of the nature of consciousness: abstract theories of the functional organization of consciousness, and concrete proposals as to the neural substrate of consciousness, while providing a rationale for contesting non-neural and transcendental conceptualizations of consciousness. It indicates that abstract theories of the dynamic core of consciousness have no force unless they are grounded in the physiology of the brain, since the organization of dynamic systems, such as the Sun, could equally well qualify as conscious under such theories. In reviewing the wealth of studies of human consciousness since the mid-20th century, it concludes that many proposals for the particular neural substrate of consciousness are insufficient in various respects, but that the results can be integrated into a novel scheme that consciousness extends through a subcortical network of interlaminar structures from the brainstem to the claustrum. This interstitial structure has both the specificity and the extended connectivity to account for the array of reportable conscious experiences.
Model Unity and the Unity of Consciousness: Developments in Expected Float Entropy Minimisation
Authors: Mason Jonathan W. D.
Published: November 2021

The unity of consciousness, or, more precisely, phenomenal unity, is an important property of consciousness and an important area of research in mathematical consciousness science and the scientific study of consciousness. Due to the numerous aspects and complexity of consciousness, the property tends to engender loose or inadequate characterizations. In this article, we introduce the concept and mathematical formulation of model unity. A system has model unity if a single relational model, stretched across the whole system, is optimal. Alternatively, model unity may only be present for subsystems, although there may still be unity at some higher level. As a development in the theory of expected float entropy minimisation, such relational models provide an interpretation of system states and the theory may help to provide insights into questions such as why experience of the visual field is unified and why different people do not have a unified consciousness, for example. This article constitutes a relatively small initial study of model unity. Four investigations were undertaken and are given as examples. A postulate is also given, distilling the foundations of EFE minimisation into a clear statement allowing others to consider whether or not the postulate identifies a self-evident fundamental property of consciousness.
Recent Advances in Conservation–Dissipation Formalism for Irreversible Processes
Authors: Peng Liangrong , Hong Liu
Published: November 2021

The main purpose of this review is to summarize the recent advances of the Conservation–Dissipation Formalism (CDF), a new way for constructing both thermodynamically compatible and mathematically stable and well-posed models for irreversible processes. The contents include but are not restricted to the CDF’s physical motivations, mathematical foundations, formulations of several classical models in mathematical physics from master equations and Fokker–Planck equations to Boltzmann equations and quasi-linear Maxwell equations, as well as novel applications in the fields of non-Fourier heat conduction, non-Newtonian viscoelastic fluids, wave propagation/transportation in geophysics and neural science, soft matter physics, etc. Connections with other popular theories in the field of non-equilibrium thermodynamics are examined too.
Dynamic Event-Triggered Predictive Control for Interval Type-2 Fuzzy Systems with Imperfect Premise Matching
Authors: Zhou Jingfeng , Cao Jianming , Chen Jing , Hu Aihua , Zhang Jingxiang , Hu Manfeng
Published: November 2021

This paper investigates the dynamic event-triggered predictive control problem of interval type-2 (IT2) fuzzy systems with imperfect premise matching. First, an IT2 fuzzy systems model is proposed, including a dynamic event-triggered mechanism, which can save limited network resources by reducing the number of data packets transmitted, and a predictive controller, which can predict the state of the system between the two successful transmitted instants to deal with unreliable communication networks. Then, according to the Lyapunov stability theory and imperfect premise matching method, sufficient conditions for system stabilization and the controller gain are obtained. Finally, the validity of the proposed method is demonstrated by the numerical examples.
Optimization Algorithms for Joint Power and Sub-Channel Allocation for NOMA-Based Maritime Communications
Authors: Li Huanyu , Li Hui , Zhou Youling
Published: November 2021

This paper investigates resource optimization schemes in a marine communication scenario based on non-orthogonal multiple access (NOMA). According to the offshore environment of the South China Sea, we first establish a Longley–Rice-based channel model. Then, the weighted achievable rate (WAR) is considered as the optimization objective to weigh the information rate and user fairness effectively. Our work introduces an improved joint power and user allocation scheme (RBPUA) based on a single resource block. Taking RBPUA as a basic module, we propose three joint multi-subchannel power and marine user allocation algorithms. The gradient descent algorithm (GRAD) is used as the reference standard for WAR optimization. The multi-choice knapsack algorithm combined with dynamic programming (MCKP-DP) obtains a WAR optimization result almost equal to that of GRAD. These two NOMA-based solutions are able to improve WAR performance by 7.47% compared with OMA. Due to the high computational complexity of the MCKP-DP, we further propose a DP-based fully polynomial-time approximation algorithm (DP-FPTA). The simulation results show that DP-FPTA can reduce the complexity by 84.3% while achieving an approximate optimized performance of 99.55%. This advantage of realizing the trade-off between performance optimization and complexity meets the requirements of practical low-latency systems.
Teacher–Child Interaction in a Goal-Oriented Preschool Context: A Micro-Analytical Study
Authors: Nilfyr Katarina , Aspelin Jonas , Lantz-Andersson Annika
Published: November 2021

The international trend of positioning the preschool as a pre-academic learning environment is challenging for preschool teachers, as it necessitates a balance between emotionally supportive interaction and goal-oriented learning instruction. However, previous research suggests that the complexities of such interactions need to be further studied. This article contributes by presenting a very detailed exploration of how social adaptation is pursued in a goal-oriented documentation activity. The study used a micro-sociological approach, characterized by the careful analysis of verbal and nonverbal interactions. Two research questions were raised: (1) How is social adaptation pursued in verbal and nonverbal interactions in preschool teacher–child relationships during a goal-oriented activity? and (2) How can social adaptation in the preschool context be understood in terms of a “deference-emotion system”? Interactions between a teacher and child in two video-recorded episodes were transcribed and sequentially analyzed thoroughly. The findings suggest that the interactions were regulated through an informal system of social sanctions, in which nonverbal signs of deference played a key role. The interaction was shown to be embedded in an institutional context that advocates goal-oriented instruction, thus highlighting the challenge of preschool teaching in attempting to promote goal-oriented processes while simultaneously maintaining respectful, caring teacher–child relationships.
Recognizing Predictors of Students’ Emergency Remote Online Learning Satisfaction during COVID-19
Authors: Kovačević Ivana , Anđelković Labrović Jelena , Petrović Nikola , Kužet Ivana
Published: November 2021

In order not to lose continuity in education during COVID-19, universities mainly found the solution in Emergency remote teaching. Student satisfaction with online learning experience is one of the measures of the excellence of learning practice. Our goal was to test the hypothesis that the predictors of students’ satisfaction with emergency remote online learning are their prior experience, attitude toward online learning, their motivation, aspects of the learning situation they value (expectations), and their digital competencies. For estimating students’ satisfaction, a survey was created and disseminated, resulting with 547 responses from students of engineering education. Results showed that all students had some experience with online learning platforms and MS Teams. Attitude toward online learning in this situation was neutral, as well as the level of motivation. Their expectations were relatively high in comparison to their satisfaction with all relevant aspects of the online learning situation, while their digital competency was rather perceived as advanced. Our results showed that the prediction of the emergency remote online learning satisfaction for engineering students can be achieved based on previous positive experience with learning platform, motivation for learning in a specific situation, the importance they put to the learning achievement, and their level of digital competency.
A Case Study Investigating Mental Wellbeing of University Academics during the COVID-19 Pandemic
Authors: Dinu Larisa M. , Dommett Eleanor J. , Baykoca Ardic , Mehta Kosha J. , Everett Sally , Foster Juliet L. H. , Byrom Nicola C.
Published: November 2021

COVID-19 has impacted Higher Education worldwide. While several studies have examined the effects of the pandemic on students, few have addressed its impact on academic staff. Here, we present both survey (n = 89) and interview (n = 12) data highlighting the pandemic-induced effects on academics from various disciplines and career stages. Data was collected between May and September 2020, aiming to capture and understand the immediate effects of the U.K. lockdown on the academics examining demographic and employment factors, digital abilities and confidence, and mental wellbeing. Analyses revealed that most academics were satisfied with the support they received from the university and colleagues, and they had adequate equipment and space at home to work. However, half incurred additional financial costs to maintain access to technology and many felt an altered relationship with the university. There were discrepancies in digital abilities and confidence according to employment status, age, faculty, and social identity as an academic. Teaching workload did not increase across the board, rather seniority predicted increases. Levels of wellbeing were low but were not significantly predicted by workload increase or abilities and confidence in working digitally as might have been expected. Stronger social identity as an academic may predict higher mental wellbeing with qualitative data suggesting teamwork and collegiate activities helped. Furthermore, interviewees identified several positive aspects to working remotely. These findings suggest universities should consider carefully how to support all staff to work digitally and consider flexible working post-pandemic.
Student Leadership at the University: An Explanatory Model
Authors: Cáceres-Reche María Pilar , López-Gómez Miguel , Sadio-Ramos Fernando José , Berral-Ortiz Blanca , Martínez-Domingo José Antonio
Published: November 2021

The exercise of leadership in the educational area and its diverse forms of being reflected in practice continue to arouse interest at the time of designing and developing research that reveals the characteristics that a leader must have in order to be considered a competent university leader, that is, a college student who carries out activities and a leadership position on his or her campus. In this sense, the present work aims to analyze the profile of the student leader to generate an explanatory model, being answered by means of a series of questions. Next, a cross-sectional study of university students at the University of Santo Tomás is carried out, obtaining statistical-descriptive data that indicate the mean scores in relation to each of the dimensions of the sociodemographic factors of this research. In addition, gender and age are identified as study factors that coincide with other research, as well as the qualities, expectations and formation of the student leader. In the present study, female gender is significant in relation to leadership practice, and age does not show significant differences between the groups. Finally, it is concluded by determining as key the role of students who exercise leadership in the University. In addition, it should be noted that the present research is derived from the Doctoral Thesis entitled: “Student leadership at the University Santo Tomás de Bucaramanga: Pedagogical proposal”, defended at the University of Granada, Spain. Funded by Proyecto de investigación, Colombia. Code: 2008/12058.
Emotional Development Questionnaire for Primary Education (CDE_9–13)
Authors: Pérez-Escoda Núria , López-Cassà Èlia , Alegre Alberto
Published: November 2021

This study presents the construction of the Emotional Development Questionnaire (CDE_9–13) and examines its psychometric properties. This questionnaire measures the emotional competence and its five dimensions—emotional awareness, emotional regulation, emotional autonomy, social competence, and life and well-being competence—of boys and girls from 9 to 13 years of age. Its construction followed the guidelines of the International Test Commission. The final version consists of 41 items. The total sample is 1905 boys and girls between the ages of 9 and 13, although partial samples have been used for specific analyses. Various studies have been carried out to demonstrate the reliability and validity of the instrument: the calculation of the reliability coefficient, a confirmatory factor analysis (CFA), and the correlational comparison of the CDE_9–13 with recognized measures of emotional intelligence, personality, adjustment difficulties, and self-esteem. Likewise, a regression study has been carried out to confirm the incremental validity. The CDE_9–13 is a theoretically well-founded questionnaire with appropriate psychometric characteristics. Therefore, it is considered an optimal tool to assess emotional competence in interventions aimed at promoting mental health and well-being.
Adaptive Droop Coefficient and SOC Equalization-Based Primary Frequency Modulation Control Strategy of Energy Storage
Authors: Meng Gaojun , Lu Yang , Liu Haitao , Ye Yuan , Sun Yukun , Tan Wenyi
Published: November 2021

In order to efficiently use energy storage resources while meeting the power grid primary frequency modulation requirements, an adaptive droop coefficient and SOC balance-based primary frequency modulation control strategy for energy storage is proposed. Taking the SOC of energy storage battery as the control quantity, the depth of energy storage output is adaptively adjusted to prevent the saturation or exhaustion of energy storage SOC. The balanced control strategy is introduced to realize the rational utilization of resources and the fast balance of SOC in the process of primary frequency modulation of energy storage battery under different charge states. Then, four evaluation indexes are proposed to evaluate the effect of primary frequency modulation and SOC maintenance. Taking a regional power grid as an example, a simulation analysis is carried out under step load disturbance and continuous load disturbance. According to the simulation results, the proposed control strategy is effective in power system frequency regulation and battery SOC maintenance.
A 9-Bit 1-GS/s Hybrid-Domain Pseudo-Pipelined SAR ADC Based on Variable Gain VTC and Segmented TDC
Authors: Bai Suping , Wan Zhi , Wan Peiyuan , Zhang Hongda , Ma Yongkuo , Zhang Xiaoyu , Liu Xu , Chen Zhijie
Published: November 2021

This paper presents a 9-bit 1 GS/s successive approximation register (SAR) analog-to-digital converter (ADC). In this hybrid architecture, the pseudo-pipeline operation is realized, which increases the sampling rate effectively. The ADC adopts two key technologies: the variable gain voltage-to-time converter (VTC), which ensures the linearity is not sacrificed; the segmented time-to-digital converter (STDC), which further improves the linearity of time domain quantization. The prototype ADC is simulated in a standard 65-nm CMOS process with an active area of 0.038 mm2. The simulated SNDR and SFDR are 44.3 and 58 dB with a sampling rate of 1 GS/s. The FoMW and FoMS are 24.7 fJ/conv-step and 150.7 dB, respectively.
A Low-Noise, High-Gain, and Small-Size UWB Mixer Utilizing Negative Impedance Technique and Source Input Method
Authors: Zhou Zhaokun , Li Xiaoran , Wang Xinghua , Gu Wei
Published: November 2021

This paper presents an ultra-wideband (UWB) down-conversion mixer with low-noise, high-gain and small-size. The negative impedance technique and source input method are applied for the proposed mixer. The negative impedance achieves the dynamic current injection and increases the mixer output impedance, which reduces the mixer flicker noise and increases its conversion gain. The source input method allows the input matching networks to be cancelled, avoiding the noise and loss introduced by the matching resistors, saving the chip area occupied by the matching inductors. The proposed mixer is designed in 45-nm SOI process provided by GlobalFoundries. The simulation results show a conversion gain of 11.4–14.3 dB, ranging from 3.1 to 10.6 GHz, a minimum noise figure of 9.8 dB, a RF port return loss of less than −11 dB, a port-to-port isolation of better than −48 dB, and a core chip area of 0.16 × 0.16 mm2. The power consumption from a 1 V supply voltage is 2.85 mW.
A Data Augmentation Method for War Trauma Using the War Trauma Severity Score and Deep Neural Networks
Authors: Yin Jibin , Zhao Pengfei , Zhang Yi , Han Yi , Wang Shuoyu
Published: November 2021

The demand for large-scale analysis and research of data on trauma from modern warfare is increasing day by day, but the amount of existing data is not sufficient to meet such demand. In this study, an integrated modeling approach incorporating a war trauma severity scoring algorithm (WTSS) and deep neural networks (DNN) is proposed. First, the proposed WTSS, which uses multiple non-linear regression based on the characteristics of war trauma data and the medical evaluation by an expert panel, performed a standardized assessment of an injury and predicts its trauma consequences. Second, to generate virtual injury, based on the probability of occurrence, the injured parts, injury types, and complications were randomly sampled and combined, and then WTSS was used to assess the consequences of the virtual injury. Third, to evaluate the accuracy of the predicted injury consequences, we built a DNN classifier and then trained it with the generated data and tested it with real data. Finally, we used the Delphi method to filter out unreasonable injuries and improve data rationality. The experimental results verified that the proposed approach surpassed the traditional artificial generation methods, achieved a prediction accuracy of 84.43%, and realized large-scale and credible war trauma data augmentation.
Service Robots: A Systematic Literature Review
Authors: Lee In
Published: November 2021

A service robot performs various professional services and domestic/personal services useful for organizations and humans in many application domains. Currently, the service robot industry is growing rapidly along with the technological advances of the Fourth Industrial Revolution. In light of the great interest and potential of service robots, this study conducts a systematic review of the past and current research in service robots. This study examines the development activities for service robots across applications and industries and categorizes the service robots into four types. The categorization provides us with insights into the unique research activities and practices in each category of service robots. Then, this study analyzes the technological foundation that applies to all four categories of service robots. Finally, this study discusses opportunities and challenges that are understudied but potentially important for the future research of service robots.
Mathematical Analysis of Parametric Characteristics of the Consensus Algorithms Operation with the Choice of the Most Priority One for Implementation in the Financial Sphere
Authors: Safaryan Olga A. , Lemeshko Kirill S. , Beskopylny Alexey N. , Cherckesova Larissa V. , Korochentsev Denis A.
Published: November 2021

Blockchain is one of the leading data transfer technologies that eliminate the need for centralized management through consensus algorithms. This article describes the consensus algorithms, their benefits, and their applications within a micropayment system in the financial sector. Preliminary studies have shown that the performance of distributed databases largely depends on the chosen consensus algorithm. The main task of the study is to create a mathematical model to assess their performance. The most popular crypto projects and the consensus algorithms are analyzed to determine their performance. The obtained model was tested by calculating the parameters of the distributed register based on the directed acyclic graph algorithm and calculating the parameters of other algorithms used. The result is a mathematical model for evaluating the parametric characteristics of the work of consensus algorithms with the choice of the most priority one for implementation in the financial sector. The analysis focuses on the mathematical steps taken by each consensus algorithm. The data obtained using the developed mathematical model demonstrates that PoW, PoS, and DAG algorithms depend on various resources, such as computing power, the number of connected nodes, and the speed of receiving transactions.
Neural Network Explainable AI Based on Paraconsistent Analysis: An Extension
Authors: Marcondes Francisco S. , Durães Dalila , Santos Flávio , Almeida José João , Novais Paulo
Published: November 2021

This paper explores the use of paraconsistent analysis for assessing neural networks from an explainable AI perspective. This is an early exploration paper aiming to understand whether paraconsistent analysis can be applied for understanding neural networks and whether it is worth further develop the subject in future research. The answers to these two questions are affirmative. Paraconsistent analysis provides insightful prediction visualisation through a mature formal framework that provides proper support for reasoning. The significant potential envisioned is the that paraconsistent analysis will be used for guiding neural network development projects, despite the performance issues. This paper provides two explorations. The first was a baseline experiment based on MNIST for establishing the link between paraconsistency and neural networks. The second experiment aimed to detect violence in audio files to verify whether the paraconsistent framework scales to industry level problems. The conclusion shown by this early assessment is that further research on this subject is worthful, and may eventually result in a significant contribution to the field.
A Regulated Pulse Current Driver with Spread Spectrum Clock Generator
Authors: Lin Ming-Shian
Published: November 2021

This paper presents a regulated pulse current driver with a spread spectrum clock generator (SSCG) to lower the electromagnetic interference (EMI) effect. An SSCG is used and implemented by applying a triangular wave to modulate a voltage-controlled oscillator (VCO). The results show a 7 dBm reduction in the peak power level with a frequency deviation of 10%, demonstrating that the dominate harmonic is spread and distributed to adjacent frequencies, and the magnitude of harmonics is significantly reduced. The results demonstrate that the driver with a spread spectrum clock generator would help to reduce interference in sensitive electronic components and be suitable for portable consumer electronics applications.
A Multichannel Deep Learning Framework for Cyberbullying Detection on Social Media
Authors: Alotaibi Munif , Alotaibi Bandar , Razaque Abdul
Published: November 2021

Online social networks (OSNs) play an integral role in facilitating social interaction; however, these social networks increase antisocial behavior, such as cyberbullying, hate speech, and trolling. Aggression or hate speech that takes place through short message service (SMS) or the Internet (e.g., in social media platforms) is known as cyberbullying. Therefore, automatic detection utilizing natural language processing (NLP) is a necessary first step that helps prevent cyberbullying. This research proposes an automatic cyberbullying method to detect aggressive behavior using a consolidated deep learning model. This technique utilizes multichannel deep learning based on three models, namely, the bidirectional gated recurrent unit (BiGRU), transformer block, and convolutional neural network (CNN), to classify Twitter comments into two categories: aggressive and not aggressive. Three well-known hate speech datasets were combined to evaluate the performance of the proposed method. The proposed method achieved promising results. The accuracy of the proposed method was approximately 88%.
A Systematic Review on Life Extension Strategies in Industry: The Case of Remanufacturing and Refurbishment
Authors: Ferreira Carlos , Gonçalves Gil
Published: November 2021

Several factors have led to an increase in the focus on sustainable development. In this context, the concept of Circular Economy (CE) has gained tremendous momentum in research and implementation. Fitted into CE’s concept, the Life Extension Strategies (LES) have been popular among industrial practitioners, regulators, policymakers, and academics from different industries due to the several benefits of these strategies. The general scope of this study is to approach LES within the industrial environment. The main goal is to understand how Remanufacturing and Refurbishment (R/R) strategies have been applied in different industry types. To achieve this goal, we carried out a systematic literature review whereby we captured some examples of R/R applications that demonstrate the potential of application to various industries (e.g., aerospace/aeronautics, energy power, and automotive industries) and equipment types (e.g., nuclear reactor, hydropower plant, and turbine blades). We also described some strategies to implement LES (e.g., economic analysis and life cycle assessment) and the different impacts achieved or expected (life extension, cost-saving, and efficiency enhancement). Moreover, we discussed three specific points concerning R/R application, the LES categorization, and the inexistence of a multi-dimensional framework to LES. Finally, we provided a set of systematized information about R/R.
A Semantic Segmentation Method for Early Forest Fire Smoke Based on Concentration Weighting
Authors: Wang Zewei , Zheng Change , Yin Jiyan , Tian Ye , Cui Wenbin
Published: November 2021

Forest fire smoke detection based on deep learning has been widely studied. Labeling the smoke image is a necessity when building datasets of target detection and semantic segmentation. The uncertainty in labeling the forest fire smoke pixels caused by the non-uniform diffusion of smoke particles will affect the recognition accuracy of the deep learning model. To overcome the labeling ambiguity, the weighted idea was proposed in this paper for the first time. First, the pixel-concentration relationship between the gray value and the concentration of forest fire smoke pixels in the image was established. Second, the loss function of the semantic segmentation method based on concentration weighting was built and improved; thus, the network could pay attention to the smoke pixels differently, an effort to better segment smoke by weighting the loss calculation of smoke pixels. Finally, based on the established forest fire smoke dataset, selection of the optimum weighted factors was made through experiments. mIoU based on the weighted method increased by 1.52% than the unweighted method. The weighted method cannot only be applied to the semantic segmentation and target detection of forest fire smoke, but also has a certain significance to other dispersive target recognition.
Examining the Factors Influencing the Mobile Learning Applications Usage in Higher Education during the COVID-19 Pandemic
Authors: Althunibat Ahmad , Almaiah Mohammed , Altarawneh Feras
Published: November 2021

Recently, the emergence of the COVID-19 has caused a high acceleration towards the use of mobile learning applications in learning and education. Investigation of the adoption of mobile learning still needs more research. Therefore, this study seeks to understand the influencing factors of mobile learning adoption in higher education by employing the Information System Success Model (ISS). The proposed model is evaluated through an SEM approach. Subsequently, the findings show that the proposed research model of this study could explain 63.9% of the variance in the actual use of mobile learning systems, which offers important insight for understanding the impact of educational, environmental, and quality factors on mobile learning system actual use. The findings also indicate that institutional policy, change management, and top management support have positive effects on the actual use of mobile learning systems, mediated by quality factors. Furthermore, the results indicate that factors of functionality, design quality, and usability have positive effects on the actual use of mobile learning systems, mediated by student satisfaction. The findings of this study provide practical suggestions, for designers, developers, and decision makers in universities, on how to enhance the use of mobile learning applications and thus derive greater benefits from mobile learning systems.
Operation Optimization of Regional Integrated Energy System Considering the Responsibility of Renewable Energy Consumption and Carbon Emission Trading
Authors: Li Feng , Lu Shirong , Cao Chunwei , Feng Jiang
Published: November 2021

To “bring carbon emissions to a peak by 2030 and to be carbon-neutral by 2060”, the role of renewable energy consumption and carbon emission trading are promoted. As an important energy consumer of regional energy system, it is necessary for integrated energy system to ensure the low-carbon economic operation of the system. Combined with the responsibility of renewable energy consumption, green certificate trading mechanism, carbon emission rights trading, and China Certified Emission Reduction (CCER), a regional integrated energy system operation optimization model was proposed. The model aims to minimize the total cost of the system, which included with electric bus, thermal bus, and cold bus. Setting different scenarios for the given example, the results show that the optimized model could effectively reduce the operating costs of the system. Moreover, the results also provide an effective reference for the system’s economic and low-carbon operation.
A Lightweight CNN Architecture for Automatic Modulation Classification
Authors: Wang Zhongyong , Sun Dongzhe , Gong Kexian , Wang Wei , Sun Peng
Published: November 2021

Automatic modulation classification (AMC) algorithms based on deep learning (DL) have been widely studied in the past decade, showing significant performance advantage compared to traditional ones. However, the existing DL methods generally behave worse in computational complexity. For this, this paper proposes a lightweight convolutional neural network (CNN) for AMC task, where we design a depthwise separable convolution (DSC) residual architecture for feature extraction to prevent the vanishing gradient problem and lighten the computational burden. Besides that, in order to further reduce model complexity, global depthwise convolution (GDWConv) is adopted for feature reconstruction after the last (non-global) convolutional layer. Compared to recent works, the experimental results show that the proposed network can save approximately 70~98% model parameters and 30~99% inference time on two well-known benchmarks.
Optimal Configuration of the Integrated Charging Station for PV and Hydrogen Storage
Authors: Wang Min , Dong Xiaobin , Zhai Youchun
Published: November 2021

This paper designs the integrated charging station of PV and hydrogen storage based on the charging station. The energy storage system includes hydrogen energy storage for hydrogen production, and the charging station can provide services for electric vehicles and hydrogen vehicles at the same time. To improve the independent energy supply capacity of the hybrid charging station and reduce the cost, the components are reasonably configured. To minimize the configuration cost of the integrated charging station and the proportion of power purchase to the demand of the charging station, the energy flow strategy of the integrated charging station is designed, and the optimal configuration model of optical storage capacity is constructed. The NSGA-II algorithm optimizes the non-inferior Pareto solution set, and a fuzzy comprehensive evaluation evaluates the optimal configuration.
Prospects for the Use of Hydrogen in the Armed Forces
Authors: Soboń Andrzej , Słyś Daniel , Ruszel Mariusz , Wiącek Alicja
Published: November 2021

The energy security landscape that we envisage in 2050 will be different from that of today. Meeting the future energy needs of the armed forces will be a key challenge, not least for military security. The World Energy Council’s World Energy Scenarios forecast that the world’s population will rise to 10 billion by 2050, which will also necessitate an increase in the size of the armed forces. In this context, energy extraction, distribution, and storage become essential to stabilizing the imbalance between production and demand. Among the available solutions, Power to Hydrogen (P2H) is one of the most appealing options. However, despite the potential, many obstacles currently hinder the development of the P2H market. This article aims to identify and analyse existing barriers to the introduction of P2H technologies that use hydrogen. The holistic approach used, which was based on a literature survey, identified obstacles and possible strategies for overcoming them. The research conducted presents an original research contribution at the level of hydrogen strategies considered in leading countries around the world. The research findings identified unresolved regulatory issues and sources of uncertainty in the armed forces. There is a lack of knowledge in the armed forces of some countries about the process of producing hydrogen energy and its benefits, which raises concerns about the consistency of its exploitation. Negative attitudes towards hydrogen fuel energy can be a significant barrier to its deployment in the armed forces. Possible approaches and solutions have also been proposed to eliminate obstacles and to support decision makers in defining and implementing a strategy for hydrogen as a clean energy carrier. There are decisive and unresolved obstacles to its deployment, not only in the armed forces.
Analysis of Controlling Factors at Separate Imbibition Stages for Ultra-Low-Permeability Reservoirs
Authors: Dang Hailong , Jiang Hanqiao , Hou Binchi , Wang Xiaofeng , Gao Tao , Wang Chengjun , Lu Chunhua
Published: November 2021

Spontaneous imbibition is an important mechanism in naturally fractured reservoirs. In our previous studies on the effect of imbibition efficiency of ultra-low permeability reservoirs, we mostly focused on the relationship between macroscopic core recovery rate and influential factors. Additionally, we also mainly focused on the factors that control the final imbibition recovery for ultra-low permeability reservoirs. Through a large number of experiments, it was found that the factors affecting imbibition are different in separate stages. However, the relative importance of those factors in different imbibition stages was hardly studied. In this work, we tested six key factors, i.e., the core length, RQI, salinity, interfacial characteristics, initial oil saturation, and oil viscosity, in natural sandstone samples from Chang 6 in the Zichang area. Based on experimental results, we divided the imbibition process into three stages (i.e., the early stage, the middle stage, and the late stage) to quantify the effects of the controlling factors. The results show that the relative importance of the controlling factors is changing during the imbibition process. The weight of importance is obtained for those factors at each stage. In addition, a comparative model is established for the dual-porosity media from Chang 6 formation. The results show that the increase of the rock size can extend the imbibition period for the early and middle stages. Moreover, the weight of importance for the initial oil saturation, interfacial characteristics, and salinity are also analyzed in three imbibition stages. This study provides theoretical support to guide water injection in ultra-low-permeability reservoirs and to understand the formation of energy supplements and oil recovery during the imbibition process.
3D Unsteady Simulation of a Scale-Up Methanation Reactor with Interconnected Cooling Unit
Authors: Sun Liyan , Luo Kun , Fan Jianren
Published: November 2021

The production of synthetic natural gas (SNG) via methanation has been demonstrated by experiments in bench scale bubbling fluidized bed reactors. In the current work, we focus on the scale-up of the methanation reactor, and a circulating fluidized bed (CFB) is designed with variable diameter according to the characteristic of methanation. The critical issue is the removal of reaction heat during the strongly exothermic process of the methanation. As a result, an interconnected bubbling fluidized bed (BFB) is utilized and connected with the reactor in order to cool the particles and to maintain system temperature. A 3D model is built, and the influences of operating temperature on H2, CO conversion and CH4 yield are evaluated by numerical simulations. The instantaneous and time-averaged flow behaviors are obtained and analyzed. It turns out that the products with high concentrations of CH4 are received at the CFB reactor outlet. The temperature of the system is kept under control by using a cooling unit, and the steady state of thermal behavior is achieved under the cooling effect of BFB reactor. The circulating rate of particles and the cooling power of the BFB reactor significantly affect the performance of reactor. This investigation provides insight into the design and operation of a scale-up methanation reactor, and the feasibility of the CFB reactor for the methanation process is confirmed.
Optimal Design of Step-Sloping Notches for Cogging Torque Minimization of Single-Phase BLDC Motors
Authors: Park Yong-woon , Ko Jae-sub , Kim Dae-kyong
Published: November 2021

This paper presents a method for reducing the cogging torque for a sloping notch with two notches applied on the stator teeth. The accuracy of FEA was confirmed by a comparison with a previous model using an asymmetric notch for the experiment data and 3D FEA results, followed by a comparison of the cogging torque of a two notches model and a sloping notch model. The sloping notch model was modified to a step-sloping notch model in consideration of a potential manufacturing process. The optimal design for minimizing the cogging torque was developed considering the sloping degree, angle, position, and size of the notches. As the optimal design result, the cogging torque on the optimal model was reduced. Finally, the analysis and optimal design results were confirmed by FEA.
Experimental Research of the Heat Storage Performance of a Magnesium Nitrate Hexahydrate-Based Phase Change Material for Building Heating
Authors: Li Yang , Wang Caixia , Zong Jun , Ma Jien , Fang Youtong
Published: November 2021

Phase change heat storage material is a preferred material in solar building heating or off-peak electric-heat storage heating technology and is the research focus. A compact phase change thermal storage device has been designed and experimentally studied for improving heating system load in this work. A new type, magnesium nitrate hexahydrate-based phase change material has been studied to improve the cooling degree and crystallization difficulty. The focus of this study is on the heat charging and discharging characteristics of this new phase change material. The heat storage device has two groups of coils, the inner side which carries water and the outer side which is the phase change material. A testing system was built up to value the thermal cycling performance of the heat storage device. The measurement data include phase change material temperature field, water inlet and water outlet mean temperature, heat charging and heat discharging depth, and flow rates over the operating period. The results show the phase change material has a quick response with the operating temperature range of 20–99 °C. Its latent heat is 151.3 J/g at 91.8 °C. The heat storage density of this phase change material is about 420 MJ/m3. The thermal performance degradation is about 1.8% after 800 operation cycles. The phase change thermal storage device shows flexibility and a great potential to improve the capacity and economy of heating systems.
Fuel Properties of Torrefied Biomass from Sapindus Pericarp Extraction Residue under a Wide Range of Pyrolysis Conditions
Authors: Tsai Wen-Tien , Jiang Tasi-Jung , Lin Yu-Quan , Zhang Xiang , Yeh Kung-Sheng , Tsai Chi-Hung
Published: November 2021

In this work, a novel biomass, the extraction residue of Sapindus pericarp (SP), was torrefied by using an electronic oven under a wide range of temperature (i.e., 200–320 °C) and residence times (i.e., 0–60 min). From the results of the thermogravimetric analysis (TGA) of SP, a significant weight loss was observed in the temperature range of 200–400 °C, which can be divided into the decompositions of hemicellulose (major)/lignin (minor) (200–320 °C) and cellulose (major)/lignin (minor) (320–400 °C). Based on the fuel properties of the feedstock SP and SP-torrefied products, the optimal torrefaction conditions can be found at around 280 °C for holding 30 min, showing that the calorific value, enhancement factor and energy yield of the torrefied biomass were enhanced to be 28.60 MJ/kg, 1.36 and 82.04 wt%, respectively. Consistently, the values of the calorific value, carbon content and molar carbon/hydrogen (C/H) ratio indicated an increasing trend at higher torrefaction temperatures and/or longer residence times. The findings showed that some SP-torrefied solids can be grouped into the characteristics of a lignite-like biomass by a van Krevelen diagram for all the SP-torrefied products. However, the SP-torrefied fuels would be particularly susceptible to the problems of slagging and fouling because of the relatively high contents of potassium (K) and calcium (Ca) based on the analytical results of the energy dispersive X-ray spectroscopy (EDS).