bluuebunny/crossref_metadata_2025 huggingface dataset:
Post-COVID-19 Anosmia and Therapies: Stay Tuned for New Drugs to Sniff Out
Authors: Riccardi Gabriele , Niccolini Giovanni Francesco , Bellizzi Mario Giuseppe , Fiore Marco , Minni Antonio , Barbato Christian
Published: May 2023

Background: Anosmia is defined as the complete absence of olfactory function, which can be caused by a variety of causes, with upper respiratory tract infections being among the most frequent causes. Anosmia due to SARS-CoV-2 infection has attracted attention given its main role in symptomatology and the social impact of the pandemic. Methods: We conducted systematic research in a clinicaltrials.gov database to evaluate all active clinical trials worldwide regarding drug therapies in adult patients for anosmia following SARS-CoV-2 infection with the intention of identifying the nearby prospects to treat Anosmia. We use the following search terms: “Anosmia” AND “COVID-19” OR “SARS-CoV-2” OR “2019 novel coronavirus”. Results: We found 18 active clinical trials that met our criteria: one phase 1, one phase 1–2, five phases 2, two phases 2–3, three phases 3, and six phases 4 studies were identified. The drug therapies that appear more effective and promising are PEA-LUT and Cerebrolysin. The other interesting drugs are 13-cis-retinoic acid plus aerosolized Vitamin D, dexamethasone, and corticosteroid nasal irrigation. Conclusions: COVID-19 has allowed us to highlight how much anosmia is an important and debilitating symptom for patients and, above all, to direct research to find a therapy aimed at curing the symptom, whether it derives from SARS-CoV-2 infection or other infections of the upper airways. Some of these therapies are very promising and are almost at the end of experimentation. They also provide hope in this field, which not addressed until recently.
Association between Malnutrition and Dental Caries in Iraqi Kurdish Children
Authors: Mahmood Mohammed Khalid , Lan Romain , Tassery Herve , Tardivo Delphine
Published: May 2023

Background: This paper aimed to study the association of serum Vitamin D level and Body Mass Index (BMI) as representatives of malnutrition at micro and macro levels, respectively, on dental caries. Method and Participants: A total of 333 randomly selected children aged 6–12 years in Sulaimani, Kurdistan, Iraq were examined for three variables of the Decayed Missed Filled Tooth (DMFT) index, BMI, and Vitamin D serum levels in a single-time cross-sectional snapshot. Results: The majority of the population studied (70%) were Vitamin D deficient. In the linear regression analysis, both Vitamin D and BMI had insignificant effects on DMFT, with p-values of 0.22 and 0.55, respectively. After the categorization of the data, the risk estimate between normal (≥20 ng/mL) and deficient (<20 ng/mL) Vitamin D groups with regards to the caries and caries-free categories was 1.97 (95% CI: 0.91–4.24). According to the mean and median of the DMFT, which were both 4, the sample is categorized into the low-caries group (DMFT < 4) and high-caries group (DMFT > 4). When these groups are compared to Vitamin D levels and taking 20 and 15 as thresholds, the odds ratios were 1.19 (CI: 0.74–1.92) and 1.88 (CI: 1.20–2.94), respectively. Regarding the BMI, a normal BMI increases the chances of having a lower caries index (OR = 1.83, CI: 1.10–3.03). Conclusions: Our results show that having a serum Vitamin D level ≥15 ng/mL and having a normal BMI are associated with a lower caries index in children.
Missing Plant Detection in Vineyards Using UAV Angled RGB Imagery Acquired in Dormant Period
Authors: Di Gennaro Salvatore Filippo , Vannini Gian Luca , Berton Andrea , Dainelli Riccardo , Toscano Piero , Matese Alessandro
Published: May 2023

Since 2010, more and more farmers have been using remote sensing data from unmanned aerial vehicles, which have a high spatial–temporal resolution, to determine the status of their crops and how their fields change. Imaging sensors, such as multispectral and RGB cameras, are the most widely used tool in vineyards to characterize the vegetative development of the canopy and detect the presence of missing vines along the rows. In this study, the authors propose different approaches to identify and locate each vine within a commercial vineyard using angled RGB images acquired during winter in the dormant period (without canopy leaves), thus minimizing any disturbance to the agronomic practices commonly conducted in the vegetative period. Using a combination of photogrammetric techniques and spatial analysis tools, a workflow was developed to extract each post and vine trunk from a dense point cloud and then assess the number and position of missing vines with high precision. In order to correctly identify the vines and missing vines, the performance of four methods was evaluated, and the best performing one achieved 95.10% precision and 92.72% overall accuracy. The results confirm that the methodology developed represents an effective support in the decision-making processes for the correct management of missing vines, which is essential for preserving a vineyard’s productive capacity and, more importantly, to ensure the farmer’s economic return.
Identifying Tree Species in a Warm-Temperate Deciduous Forest by Combining Multi-Rotor and Fixed-Wing Unmanned Aerial Vehicles
Authors: Shi Weibo , Wang Shaoqiang , Yue Huanyin , Wang Dongliang , Ye Huping , Sun Leigang , Sun Jia , Liu Jianli , Deng Zhuoying , Rao Yuanyi , Hu Zuoran , Sun Xiyong
Published: May 2023

Fixed-wing unmanned aerial vehicles (UAVs) and multi-rotor UAVs are widely utilized in large-area (>1 km2) environmental monitoring and small-area (<1 km2) fine vegetation surveys, respectively, having different characteristics in terms of flight cost, operational efficiency, and landing and take-off methods. However, large-area fine mapping in complex forest environments is still a challenge in UAV remote sensing. Here, we developed a method that combines a multi-rotor UAV and a fixed-wing UAV to solve this challenge at a low cost. Firstly, we acquired small-scale, multi-season ultra-high-resolution red-green-blue (RGB) images and large-area RGB images by a multi-rotor UAV and a fixed-wing UAV, respectively. Secondly, we combined the reference data of visual interpretation with the multi-rotor UAV images to construct a semantic segmentation model and used the model to expand the reference data. Finally, we classified fixed-wing UAV images using the large-area reference data combined with the semantic segmentation model and discuss the effects of different sizes. Our results show that combining multi-rotor and fixed-wing UAV imagery provides an accurate prediction of tree species. The model for fixed-wing images had an average F1 of 92.93%, with 92.00% for Quercus wutaishanica and 93.86% for Juglans mandshurica. The accuracy of the semantic segmentation model that uses a larger size shows a slight improvement, and the model has a greater impact on the accuracy of Quercus liaotungensis. The new method exploits the complementary characteristics of multi-rotor and fixed-wing UAVs to achieve fine mapping of large areas in complex environments. These results also highlight the potential of exploiting this synergy between multi-rotor UAVs and fixed-wing UAVs.
Effects of Community Connectivity on the Spreading Process of Epidemics
Authors: Gao Zhongshe , Gu Ziyu , Yang Lixin
Published: May 2023

Community structure exists widely in real social networks. To investigate the effect of community structure on the spreading of infectious diseases, this paper proposes a community network model that considers both the connection rate and the number of connected edges. Based on the presented community network, a new SIRS transmission model is constructed via the mean-field theory. Furthermore, the basic reproduction number of the model is calculated via the next-generation matrix method. The results reveal that the connection rate and the number of connected edges of the community nodes play crucial roles in the spreading process of infectious diseases. Specifically, it is demonstrated that the basic reproduction number of the model decreases as the community strength increases. However, the density of infected individuals within the community increases as the community strength increases. For community networks with weak strength, infectious diseases are likely not to be eradicated and eventually will become endemic. Therefore, controlling the frequency and range of intercommunity contact will be an effective initiative to curb outbreaks of infectious diseases throughout the network. Our results can provide a theoretical basis for preventing and controlling the spreading of infectious diseases.
Optimum k-Nearest Neighbors for Heading Synchronization on a Swarm of UAVs under a Time-Evolving Communication Network
Authors: Martínez-Clark Rigoberto , Pliego-Jimenez Javier , Flores-Resendiz Juan Francisco , Avilés-Velázquez David
Published: May 2023

Heading synchronization is fundamental in flocking behaviors. If a swarm of unmanned aerial vehicles (UAVs) can exhibit this behavior, the group can establish a common navigation route. Inspired by flocks in nature, the k-nearest neighbors algorithm modifies the behavior of a group member based on the k closest teammates. This algorithm produces a time-evolving communication network, due to the continuous displacement of the drones. Nevertheless, this is a computationally expensive algorithm, especially for large groups. This paper contains a statistical analysis to determine an optimal neighborhood size for a swarm of up to 100 UAVs, that seeks heading synchronization using a simple P-like control algorithm, in order to reduce the calculations on every UAV, this is especially important if it is intended to be implemented in drones with limited capabilities, as in swarm robotics. Based on the literature of bird flocks, that establishes that the neighborhood of every bird is fixed around seven teammates, two approaches are treated in this work: (i) the analysis of the optimum percentage of neighbors from a 100-UAV swarm, that is necessary to achieve heading synchronization, and (ii) the analysis to determine if the problem is solved in swarms of different sizes, up to 100 UAVs, while maintaining seven nearest neighbors among the members of the group. Simulation results and a statistical analysis, support the idea that the simple control algorithm behaves like a flock of starlings.
Enhanced CycleGAN Network with Adaptive Dark Channel Prior for Unpaired Single-Image Dehazing
Authors: Xu Yijun , Zhang Hanzhi , He Fuliang , Guo Jiachi , Wang Zichen
Published: May 2023

Unpaired single-image dehazing has become a challenging research hotspot due to its wide application in modern transportation, remote sensing, and intelligent surveillance, among other applications. Recently, CycleGAN-based approaches have been popularly adopted in single-image dehazing as the foundations of unpaired unsupervised training. However, there are still deficiencies with these approaches, such as obvious artificial recovery traces and the distortion of image processing results. This paper proposes a novel enhanced CycleGAN network with an adaptive dark channel prior for unpaired single-image dehazing. First, a Wave-Vit semantic segmentation model is utilized to achieve the adaption of the dark channel prior (DCP) to accurately recover the transmittance and atmospheric light. Then, the scattering coefficient derived from both physical calculations and random sampling means is utilized to optimize the rehazing process. Bridged by the atmospheric scattering model, the dehazing/rehazing cycle branches are successfully combined to form an enhanced CycleGAN framework. Finally, experiments are conducted on reference/no-reference datasets. The proposed model achieved an SSIM of 94.9% and a PSNR of 26.95 on the SOTS-outdoor dataset and obtained an SSIM of 84.71% and a PSNR of 22.72 on the O-HAZE dataset. The proposed model significantly outperforms typical existing algorithms in both objective quantitative evaluation and subjective visual effect.
Stieltjes Transforms and R-Transforms Associated with Two-Parameter Lambert–Tsallis Functions
Authors: Nakashima Hideto , Graczyk Piotr
Published: May 2023

In this paper, we study a two-parameter family of Stieltjes transformations related to holomorphic Lambert–Tsallis functions, which are a two-parameter generalization of the Lambert function. Such Stieltjes transformations appear in the study of eigenvalue distributions of random matrices associated with some growing statistically sparse models. A necessary and sufficient condition on the parameters is given for the corresponding functions being Stieltjes transformations of probabilistic measures. We also give an explicit formula of the corresponding R-transformations.
An Observation-Driven Random Parameter INAR(1) Model Based on the Poisson Thinning Operator
Authors: Yu Kaizhi , Tao Tielai
Published: May 2023

This paper presents a first-order integer-valued autoregressive time series model featuring observation-driven parameters that may adhere to a particular random distribution. We derive the ergodicity of the model as well as the theoretical properties of point estimation, interval estimation, and parameter testing. The properties are verified through numerical simulations. Lastly, we demonstrate the application of this model using real-world datasets.
Exploring Perceived Speaking Skills, Motives, and Communication Needs of Undergraduate Students Studying Japanese Language
Authors: Tran Nam Hoang , Marinova Katya , Nghiem Van Hong
Published: May 2023

This study investigated the perceived speaking skills of undergraduate students who were majoring or minoring in Japanese Studies at two universities in Vietnam and Bulgaria. It also examined the factors associated with students’ speaking skills, their motives for studying Japanese, and their needs for communication with native Japanese speakers while proposing a hypothetical model linking these constructs. A total of 108 students participated in the cross-sectional online survey questionnaire, which included questions on personal information, perceived Japanese language skills, motives for studying Japanese, needs for communication with native Japanese speakers, and self-esteem. The data were analyzed using SPSS. The results showed that the respondents perceived their Japanese speaking skills level as lower than the other three skills. Factors such as age, year of enrollment, years studying Japanese, English level, co-living status, study abroad experience, and self-esteem were found to be associated with the perceived speaking skills of the respondents. Family-related factors, such as parents’ education and the family’s study abroad experience, were also found to be associated with perceived speaking skills. The study also validated three constructs of motives for studying Japanese, including being interested in Japan, being interested in communication, and being interested in going to Japan and highlighting the respondents’ needs for communication with native speakers. The proposed model suggests that motives for studying Japanese influence perceived speaking skills and the need for communication with native Japanese speakers. The findings of this study have implications for Japanese language education, particularly in the development of teaching strategies that enhance students’ speaking skills and provide opportunities for communication with native speakers. It also underscores the importance of understanding students’ motives for studying Japanese, as these motivations can influence their language proficiency and the effectiveness of language education programs.
Classifying Geography Textbook Visuals According to the Concept of Direct, Indirect, and Symbolic Experience of Nature
Authors: Hilander Markus
Published: May 2023

Geography textbooks have many visuals, such as maps, graphs, tables, photographs, and illustrations, which makes geography a highly visual school subject. However, previous studies show that geography students can pay surprisingly little attention to textbook visuals. In this article, it is asked what sorts of visuals are there in geography textbooks if they do not attract students’ attention. In order to answer the research question, a visual method to approach textbook visuals is developed. The visual method is motivated by Stephen Kellert’s concept of direct, indirect, and symbolic experience of nature. Based on the three types of experience of nature, three categories for geography textbook visuals are established respectively: snapshot visuals (deriving from direct experience), landscape visuals (deriving from indirect experience), and abstract visuals (deriving from symbolic experience). The sample covers eight Finnish geography textbooks for grades 7–9 and for one upper secondary school course. The results show that most of the visuals depict landscapes. In addition, there are many abstract visuals, such as maps and illustrations, depicting geographical processes. Instead, only a few snapshots of people in action and snapshots of geographical hazards were identified. In the article, it is suggested that enhancing the role of snapshot visuals in geography education could increase students’ interest towards textbook visuals, as well. This is because snapshot visuals can draw students’ attention and enable students to express their own feelings and thoughts.
The Power of Electronic Media: Nexus of Digital Crowdfunding Platforms, Innovation Strategy, Technology Orientation and Crowdfunding Performance
Authors: Binsaeed Rima H. , Yousaf Zahid , Grigorescu Adriana , Chitescu Razvan Ion , Samoila Alina , Nassani Abdelmohsen A.
Published: May 2023

The transformation of technological orientation and crowdfunding as innovative concepts combined with cutting-edge digital tools provides the foundation for outstanding fundraising success. Therefore, this study aims to explore the relationship between technology orientation (TO) and crowdfunding performance. Additionally, this research also investigates how crowdfunding digital platform mediates and innovation strategy moderates in the linkage between crowdfunding performance and technology orientation. Data were collected through a questionnaire on the basis of cross-sectional research design. Structural econometric models for direct and indirect analyses were conducted to test hypotheses. The results confirmed the direct positive impact of technology orientation on crowdfunding performance. Moreover, the findings prove the mediating role of a crowdfunding digital platform and moderating role of innovation strategy between technology orientation and crowdfunding performance. Fundraisers are required to give attention to technology orientation for the acceptance and adoption of the latest advancements in technology. They must consider digital platforms for fundraising and pay proper attention to innovation strategy for achieving the needed crowdfunding performance. This research discusses how return base crowdfunding performance occurs; in future, other researchers may provide evidence on donations/charity campaign base crowdfunding. This is the first study in the context of digital transformation that explored the nexus of technology orientation, crowdfunding digital platform and innovation strategy for attaining crowdfunding performance.
Research on High-Quality Control Technology for Three-Phase PWM Rectifier
Authors: Zhou Zhiquan , Song Jiyu , Yu Yanjun , Xu Qingrui , Zhou Xinhang
Published: May 2023

With the development of power-electronics technology, PWM rectifiers are widely used in many fields. They are especially suitable for high-precision- and high-efficiency-application scenarios, such as communication and industrial control. Although it has many advantages, overshoot control during startup is a challenge. Therefore, this paper first derives the input–output mathematical model through equation establishment and dq-coordinate transformation. Next, a voltage and current double closed-loop simulation model is established in MATLAB/Simulink, and its control effect is analyzed. On this basis, a new voltage-setting strategy is designed to reduce the output-voltage overshoot of the PWM rectifier when starting. The simulation results demonstrate that the strategy has a good application effect and can contain starting-voltage overshooting from 12.88% to 0.925%. Moreover, the principle of this strategy is simpler and easier to understand and implement compared with traditional strategies.
Noise Reduction of Atomic Force Microscopy Measurement Data for Fitting Verification of Chemical Mechanical Planarization Model
Authors: Ren Bowen , Chen Lan , Chen Rong , Ji Ruian , Wang Yali
Published: May 2023

In advanced integrated circuit manufacturing processes, the quality of chemical mechanical flattening is a key factor affecting chip performance and yield. Therefore, it has become increasingly important to develop an accurate predictive model for the chip surface topography after chemical mechanical flattening. In the modeling process, the noise problem of atomic force microscopy measurement data is relatively serious. To solve this problem, the noise characteristics of atomic force microscope measurement data for chip surface topography in this field are studied and discussed in this paper. It is found that the noise present in such problems is mainly triggered by the vibration and tilt of the probe. Two types of noise, low-frequency and high-frequency, are presented in the time domain. In order to solve the noise problem in this modeling data, this paper analyzes the spectral characteristics of the measurement data using Fourier transform, and a wavelet-Fourier transform composite noise reduction process is proposed. The algorithm is applied to the noise reduction of the chip surface data of 32 nm copper interconnect process. The noise reduction results were compared with scanning electron microscope photographs to verify the effectiveness of the noise reduction.
Design of Regenerating Code Based on Security Level in Cloud Storage System
Authors: Zhang Fan , Xu Jian , Yang Gangqiang
Published: May 2023

Cloud storage is an indispensable part of cloud computing solutions and the security of its stored data has become a key issue in the research and application of cloud storage systems. To solve this problem, this paper studies the anti-eavesdropping regenerating code technology for cloud storage systems, from the perspective of information theory. As opposed to the existing research ideas on regenerating code theory, that enable the system to obtain strong/weak security, this paper focuses on quantifying the relationship between security and system performance parameters, evaluating the system performance gains that can be obtained by appropriately reducing security, and designing regenerating code schemes with different information security levels to meet the personalized requirements of cloud storage customers. This paper puts forward a generalized matrix transposing method and applies it to the coding construction of fractional repetition codes. The scheme proposed in this paper will provide new ideas and methods for research on secure regenerating code technology in cloud storage systems.
Ti/HfO2-Based RRAM with Superior Thermal Stability Based on Self-Limited TiOx
Authors: He Huikai , Tan Yixin , Lee Choonghyun , Zhao Yi
Published: May 2023

HfO2-based resistive random-access memory (RRAM) with a Ti buffer layer has been extensively studied as an emerging nonvolatile memory (eNVM) candidate because of its excellent resistive switching (RS) properties and CMOS process compatibility. However, a detailed understanding of the nature of Ti thickness-dependent RS and systematic thermal degradation research about the effect of post-metallization annealing (PMA) time on oxygen vacancy distribution and RS performance still needs to be included. Herein, the impact of Ti buffer layer thickness on the RS performance of the Al/Ti/HfO2/TiN devices is first addressed. Consequently, we have proposed a simple strategy to regulate the leakage current, forming voltage, memory window, and uniformity by varying the thickness of the Ti layer. Moreover, it is found that the device with 15 nm Ti shows the minimum cycle-to-cycle variability (CCV) and device-to-device variability (DDV), good retention (105 s at 85 °C), and superior endurance (104). In addition, thermal degradation of the Al/Ti(15 nm)/HfO2/TiN devices under different PMA times at 400 °C is carried out. It is found that the leakage current increases and the forming voltage and memory window decrease with the increase in PMA time due to the thermally activated oxidation of the Ti. However, when the PMA time increases to 30 min, the Ti can no longer capture oxygen from HfO2 due to the formation of self-limited TiOx. Therefore, the device shows superior thermal stability with a PMA time of 90 min at 400 °C and no degradation of the memory window, uniformity, endurance, or retention. This work demonstrates that the Ti/HfO2-based RRAM shows superior back-end-of-line compatibility with high thermal stability up to 400 °C for over an hour.
Noisy Quantum Channel Characterization Using Quantum Neural Networks
Authors: Song Junyang , Lu Bo , Liu Lu , Wang Chuan
Published: May 2023

Channel noise is considered to be the main obstacle in long-distance quantum communication and distributed quantum networks. Here, employing a quantum neural network, we present an efficient method to study the model and detect the noise of quantum channels. Based on various types of noisy quantum channel models, we construct the architecture of the quantum neural network and the model training process. Finally, we perform experiments to verify the training effectiveness of the scheme, and the results show that the cost function of the quantum neural network could approach above 90% of the channel model.
Predicting Power Generation from a Combined Cycle Power Plant Using Transformer Encoders with DNN
Authors: Yi Qiu , Xiong Hanqing , Wang Denghui
Published: May 2023

With the development of the Smart Grid, accurate prediction of power generation is becoming an increasingly crucial task. The primary goal of this research is to create an efficient and reliable forecasting model to estimate the full-load power generation of a combined-cycle power plant (CCPP). The dataset used in this research is a subset of the publicly available UCI Machine Learning Repository. It contains 9568 items of data collected from a CCPP during its full load operation over a span of six years. To enhance the accuracy of power generation forecasting, a novel forecasting method based on Transformer encoders with deep neural networks (DNN) was proposed. The proposed model exploits the ability of the Transformer encoder to extract valuable information. Furthermore, bottleneck DNN blocks and residual connections are used in the DNN component. In this study, a series of experiments were conducted, and the performance of the proposed model was evaluated against other state-of-the-art machine learning models based on the CCPP dataset. The experimental results illustrated that using Transformer encoders along with DNN can considerably improve the accuracy of predicting CCPPs power generation (RMSE = 3.5370, MAE = 2.4033, MAPE = 0.5307%, and R2 = 0.9555).
Performance Improvement of Speech Emotion Recognition Systems by Combining 1D CNN and LSTM with Data Augmentation
Authors: Pan Shing-Tai , Wu Han-Jui
Published: May 2023

In recent years, the increasing popularity of smart mobile devices has made the interaction between devices and users, particularly through voice interaction, more crucial. By enabling smart devices to better understand users’ emotional states through voice data, it becomes possible to provide more personalized services. This paper proposes a novel machine learning model for speech emotion recognition called CLDNN, which combines convolutional neural networks (CNN), long short-term memory neural networks (LSTM), and deep neural networks (DNN). To design a system that closely resembles the human auditory system in recognizing audio signals, this article uses the Mel-frequency cepstral coefficients (MFCCs) of audio data as the input of the machine learning model. First, the MFCCs of the voice signal are extracted as the input of the model. Local feature learning blocks (LFLBs) composed of one-dimensional CNNs are employed to calculate the feature values of the data. As audio signals are time-series data, the resulting feature values from LFLBs are then fed into the LSTM layer to enhance learning on the time-series level. Finally, fully connected layers are used for classification and prediction. The experimental evaluation of the proposed model utilizes three databases: RAVDESS, EMO-DB, and IEMOCAP. The results demonstrate that the LSTM model effectively models the features extracted from the 1D CNN due to the time-series characteristics of speech signals. Additionally, the data augmentation method applied in this paper proves beneficial in improving the recognition accuracy and stability of the systems for different databases. Furthermore, according to the experimental results, the proposed system achieves superior recognition rates compared to related research in speech emotion recognition.
Adversarial Perturbation Elimination with GAN Based Defense in Continuous-Variable Quantum Key Distribution Systems
Authors: Tang Xun , Yin Pengzhi , Zhou Zehao , Huang Duan
Published: May 2023

Machine learning is being applied to continuous-variable quantum key distribution (CVQKD) systems as defense countermeasures for attack classification. However, recent studies have demonstrated that most of these detection networks are not immune to adversarial attacks. In this paper, we propose to implement typical adversarial attack strategies against the CVQKD system and introduce a generalized defense scheme. Adversarial attacks essentially generate data points located near decision boundaries that are linearized based on iterations of the classifier to lead to misclassification. Using the DeepFool attack as an example, we test it on four different CVQKD detection networks and demonstrate that an adversarial attack can fool most CVQKD detection networks. To solve this problem, we propose an improved adversarial perturbation elimination with a generative adversarial network (APE-GAN) scheme to generate samples with similar distribution to the original samples to defend against adversarial attacks. The results show that the proposed scheme can effectively defend against adversarial attacks including DeepFool and other adversarial attacks and significantly improve the security of communication systems.
Anomalous Behavior Detection with Spatiotemporal Interaction and Autoencoder Enhancement
Authors: Li Bohao , Xie Kai , Zeng Xuepeng , Cao Mingxuan , Wen Chang , He Jianbiao , Zhang Wei
Published: May 2023

To reduce the cargo loss rate caused by abnormal consumption behavior in smart retail cabinets, two problems need to be solved. The first is that the diversity of consumers leads to a diversity of actions contained in the same behavior, which makes the accuracy of consumer behavior identification low. Second, the difference between normal interaction behavior and abnormal interaction behavior is small, and anomalous features are difficult to define. Therefore, we propose an anomalous behavior detection algorithm with human–object interaction graph convolution and confidence-guided difference enhancement. Aiming to solve the problem of low accuracy of consumer behavior recognition, including interactive behavior, the human–object interaction graph convolutional network is used to recognize action and extract video frames of abnormal human behavior. To define anomalies, we detect anomalies by delineating anomalous areas of the anomaly video frames. We use a confidence-guided anomaly enhancement module to perform confidence detection on the encoder-extracted coded features using a confidence full connection layer. The experimental results showed that the action recognition algorithm had good generalization ability and accuracy, and the screened video frames have obvious destruction characteristics, and the area under the receiver operating characteristic (AUROC) curve reached 82.8% in the detection of abnormal areas. Our research provides a new solution for the detection of abnormal behavior that destroys commodity packaging, which has considerable application value.
Improving the Performance of the Single Shot Multibox Detector for Steel Surface Defects with Context Fusion and Feature Refinement
Authors: Li Yiming , He Lixin , Zhang Min , Cheng Zhi , Liu Wangwei , Wu Zijun
Published: May 2023

Strip surface defects have large intraclass and small interclass differences, resulting in the available detection techniques having either a low accuracy or very poor real-time performance. In order to improve the ability for capturing steel surface defects, the context fusion structure introduces the local information of the shallow layer and the semantic information of the deep layer into multiscale feature maps. In addition, for filtering the semantic conflicts and redundancies arising from context fusion, a feature refinement module is introduced in our method, which further improves the detection accuracy. Our experimental results show that this significantly improved the performance. In particular, our method achieved 79.5% mAP and 71 FPS on the public NEU-DET dataset. This means that our method had a higher detection accuracy compared to other techniques.
Influence of Charge Transfer on Thermoelectric Properties of Endohedral Metallofullerene (EMF) Complexes
Authors: Alshammari Majed , Alotaibi Turki , Alotaibi Moteb , Ismael Ali K.
Published: May 2023

A considerable potential advantage of manufacturing electric and thermoelectric devices using endohedral metallofullerenes (EMFs) is their ability to accommodate metallic moieties inside their cavities. Published experimental and theoretical works have explained the usefulness of this resilience feature for improving the electrical conductance and thermopower. Through thorough theoretical investigations of three EMF complexes employing three different metallic moieties involving Sc3C2, Sc3N, and Er3N and their configurations on a gold (111) surface, this research demonstrates that the thermoelectric properties of these molecular complexes can be tuned by taking advantage of the charge transfer from metallic moieties to Ih-C80 cages. Mulliken, Hirshfeld, and Voronoi simulations articulate that the charge migrates from metallic moieties to cages; however, the amount of the transferred charge depends on the nature of the moiety within the complex.
Environmental Assessment of Hydrogen Utilization in Various Applications and Alternative Renewable Sources for Hydrogen Production: A Review
Authors: Amin Muhammad , Shah Hamad Hussain , Bashir Bilal , Iqbal Muhammad Azhar , Shah Umer Hameed , Ali Muhammad Umair
Published: May 2023

Rapid industrialization is consuming too much energy, and non-renewable energy resources are currently supplying the world’s majority of energy requirements. As a result, the global energy mix is being pushed towards renewable and sustainable energy sources by the world’s future energy plan and climate change. Thus, hydrogen has been suggested as a potential energy source for sustainable development. Currently, the production of hydrogen from fossil fuels is dominant in the world and its utilization is increasing daily. As discussed in the paper, a large amount of hydrogen is used in rocket engines, oil refining, ammonia production, and many other processes. This paper also analyzes the environmental impacts of hydrogen utilization in various applications such as iron and steel production, rocket engines, ammonia production, and hydrogenation. It is predicted that all of our fossil fuels will run out soon if we continue to consume them at our current pace of consumption. Hydrogen is only ecologically friendly when it is produced from renewable energy. Therefore, a transition towards hydrogen production from renewable energy resources such as solar, geothermal, and wind is necessary. However, many things need to be achieved before we can transition from a fossil-fuel-driven economy to one based on renewable energy.
Studies on the Performance of Engines Powered with Hydrogen-Enriched Biogas
Authors: Pandey Vivek , Shahapurkar Kiran , Guluwadi Suresh , Mengesha Getinet , Gadissa Bekele , Banapurmath Nagaraj , Vadlamudi Chandramouli , Krishnappa Sanjay , Khan T.
Published: May 2023

Sustainability of energy supply has become a prime concern for energy producers and consumers alike. There is heightened awareness in the global community about the decreasing supply of conventional fossil fuels along with increasing fuel and energy demand and the consequent rise in unit energy cost. In addition to the sustainability aspect, the environmental impact of emissions from fossil fuel combustion is the focus of global targets for emissions reduction. In this context, the research and application of sustainable and non-polluting fuels become significant. Internal combustion (IC) engines are part of a significant energy-consuming sector, and the application of sustainable and non-polluting fuels within IC engines would be impactful. Biogas and hydrogen are viewed as sustainable and non-polluting alternatives to conventional fossil fuels. However, either of these used individually offer certain disadvantages. Experimental results and analysis of the performance and emissions characteristics of an IC engine fueled with biogas blended with 5, 10, and 15% hydrogen volume fractions are studied. An increase in hydrogen content increases the engine’s performance and power and reduces carbon monoxide (CO) and total hydrocarbons (THCs). However, nitrogen oxides (NOx) are found to increase due to higher combustion temperatures attributed to hydrogen. A 17.5% increase in brake power is observed for 15% hydrogen-enriched biogas, compared to plain biogas, at an equivalence ratio of 0.6. Similarly, a 17% increase in BTE, a 50% decrease in CO, a 68% decrease in UHC, but a 71% increase in NOx are observed for 15% hydrogen-enriched biogas.
What If Country Commitments for CO2 Removal Were Based on Responsibility for Historical Emissions?
Authors: Torvanger Asbjørn
Published: May 2023

This study explored the consequences of allocating commitments to remove CO2 to countries according to their responsibility for human-made climate change based on historical (cumulative) CO2 emissions from fossil fuel use and industry. The ‘carbon debt’ to be restored through CO2 removal was calculated as the remaining carbon budget for warming by 2 °C minus emissions until 2100. The study included the remaining carbon budget from the recent literature and scenarios for greenhouse gas emissions. This experiment showed that industrialized countries would need to take on the biggest share of CO2 removal if the calculation of historical emissions starts with the industrial era. If accounting instead starts with the global negotiations on climate policy in 1990, however, developing countries would have to take on the largest commitment for CO2 removal. Given this scheme and with the aim of settling the carbon debt over two decades with equal annual efforts, the eight countries with the largest shares of historical emissions would have to take on annual CO2 removal efforts from 1 to 12 Gt CO2. These CO2 removal commitments would imply substantial efforts for many countries but nevertheless depend on the choice of a fairness principle and calculation method to render this operational.
Optimal Tuning of Fractional Order Sliding Mode Controller for PMSM Speed Using Neural Network with Reinforcement Learning
Authors: Zahraoui Younes , Zaihidee Fardila M. , Kermadi Mostefa , Mekhilef Saad , Alhamrouni Ibrahim , Seyedmahmoudian Mehdi , Stojcevski Alex
Published: May 2023

An improved fractional-order sliding mode control (FOSMC) for PMSM is presented in this study to set the unavoidable parameters and to improve permanent magnet synchronous motors (PMSMs) drive performance, such as current and speed tracking accuracy. To determine the optimal parameters of the FOSMC for control speed in a PMSM drive, a neural network algorithm with reinforcement learning (RLNNA) is proposed. The FOSMC parameters are set by the ANN algorithm and then adapted through reinforcement learning to enhance the results. The proposed controller using RLNNA based on fractional-order sliding mode control (RLNNA-FOSMC) can drive the motor speed to achieve the referred value in a finite period of time, leading to faster convergence and improved tracking accuracy. For a fair comparison and evaluation, the proposed RLNNA-FOSMC is compared with conventional FOSMC by applying the integral of time multiplied absolute error as an objective function. The most commonly used objective functions in the literature were also compared, including the integral time multiplied square error, integral square error, and integral absolute error. To validate the performance of the RLNNA-FOSMC speed controller, different scenarios with different speeds steps were carried out. The computational results are promising and demonstrate the effectiveness of the proposed controller. Overall, the proposed RLNNA-FOSMC controller for the PMSM speed control system performed better than conventional FOSMC in numerical simulations.
Large Eddy Simulation of Rotationally Induced Ingress and Egress around an Axial Seal between Rotor and Stator Disks
Authors: Nketia Sabina , Shih Tom I-P. , Bryden Kenneth , Dalton Richard , Dennis Richard A.
Published: May 2023

In gas turbines, the hot gas exiting the combustor can have temperatures as high as 2000 °C, and some of this hot gas enter into the space between the stator and rotor disks (wheelspace). Since the entering hot gas could damage the disks, its ingestion must be minimized. This is carried out by rim seals and by introducing a cooler flow from the compressor (sealing flow) into the wheelspace. Ingress and egress into rim seals are driven by the stator vanes, the rotor and its rotation, and the rotor blades. This study focuses on the ingress and egress driven by the rotor and its rotation. This is carried out by performing wall-resolved large eddy simulation (LES) around an axial seal in a rotor–stator configuration without vanes and blades. Results obtained show the mechanisms by which the rotor and its rotation induce ingress, egress, and flow trajectories. Kelvin–Helmholtz instability was found to create a wavy shear layer and displacement thickness that produces alternating regions of high and low pressures around the rotor side of the seal. Vortex shedding on the backward-facing side of the seal and its impingement on the rotor side of the seal also produces alternating regions of high and low pressures. The locations of the alternating regions of high and low pressures were found to be statistically stationary and to cause ingress to start on the rotor side of the seal. Vortex shedding and recirculating flow in the seal clearance also cause ingress by entrainment. With the effects of the rotor and its rotation on ingress and egress isolated, this study enables the effects of stator vanes and rotor blades to be assessed.
Design of IOT-Based Framework for Evaluation of Energy Efficiency in Power Transformers
Authors: Plienis Mantas , Deveikis Tomas , Jonaitis Audrius , Gudžius Saulius
Published: May 2023

Two prototypes with energy efficiency calculations have been developed to enable real-time efficiency assessment and data collection. The results of the experiment demonstrate that the use of microprocessor technology and the Internet of Things can significantly improve the efficiency and accuracy of energy audits in power transformers. The prototypes developed in this study provide real-time efficiency assessment and data collection, enabling more effective energy management and cost savings for industrial users. During the experiment, it was found that resonance can cause the same losses as a poor power factor of the system, highlighting the importance of addressing energy quality issues in addition to energy efficiency. These findings have important implications for energy efficiency policies and practices in the context of climate change mitigation and rising energy prices.
Aluminosilicate Clay Minerals: Kaolin, Bentonite, and Halloysite as Fuel Additives for Thermal Conversion of Biomass and Waste
Authors: Maj Izabella , Matus Krzysztof
Published: May 2023

The current focus on renewable energy sources and the circular economy favors the thermal conversion of low-quality fuels, such as biomass and waste. However, the main limitation of their usability in the power sector is the risk of slagging, fouling, ash deposition, and high-temperature corrosion. These problems may be avoided or significantly mitigated by the application of aluminosilicate clay minerals as fuel additives. In this paper, the three most commonly occurring aluminosilicates are reviewed: kaolin, halloysite, and bentonite. Their application has been proven to minimize combustion-related problems by bonding alkalis in high-melting compounds, thus increasing ash melting temperatures, reducing ash deposition tendencies, and decreasing the particulate matter emission. Due to excellent sorption properties, aluminosilicates are also expected to fix heavy metals in ash and therefore decrease their emissions into the atmosphere. The application of aluminosilicates as fuel additives may be a key factor that increases the attractiveness of biomass and other low-quality fuels for the power sector.
Review of Measuring Methods, Setups and Conditions for Evaluation of the Inductive Instrument Transformers Accuracy for Transformation of Distorted Waveforms
Authors: Kaczmarek Michal , Stano Ernest
Published: May 2023

In this review article, ideas for the development of measuring methods and setups to test inductive instrument transformer accuracy for the transformation of distorted current or voltage are analyzed. This article proposes limiting values for ratio error and phase displacement at harmonics. Moreover, the manuscript discusses some remarks about the required test waveform of voltage and current, as well as the operating conditions of the inductive instrument transformer during the test. This includes designation of the number of harmonics, their RMS values and phase angles in relation to the fundamental component, as well as the required load of the VT’s and CT’s secondary winding. The aim of this work is also to show the common requirements and principles of developed methods and measuring setups to justify the proposed solution.
Research on Legal Promotion Mechanism of Biomass Energy Development under “Carbon Peaking and Carbon Neutrality” Targets in China
Authors: Song Dongdong , Rui Jing
Published: May 2023

In order to achieve the goal of carbon peaking and carbon neutralization, we must vigorously develop renewable energy. As one of the important renewable energy sources, the development and utilization of biomass energy has a high cost, and it is difficult to compete with traditional fossil energy in the early stage of development. Therefore, it is necessary for the government to escort the development of the biomass energy industry through legal means. First of all, on the basis of fully analyzing the development prospects of China’s biomass energy, the article sorts out the laws and regulations related to biomass energy in China, and finds the shortcomings. Secondly, this paper draws on the institutional experience of the United States, Brazil, Sweden and other countries with relatively developed biomass energy industry, and provides suggestions for improvement from three aspects: law, policy and administrative system, with a view to supporting China’s development of biomass energy industry and realizing the “dual carbon target” theoretically.