bluuebunny/crossref_metadata_2025 huggingface dataset:
Digital Shade Matching in Dentistry: A Systematic Review
Authors: Rashid Farah , Farook Taseef Hasan , Dudley James
Published: October 2023

The pursuit of aesthetic excellence in dentistry, shaped by societal trends and digital advancements, highlights the critical role of precise shade matching in restorative procedures. Although conventional methods are prevalent, challenges such as shade guide variability and subjective interpretation necessitate a re-evaluation in the face of emerging non-proximity digital instruments. This systematic review employs PRISMA protocols and keyword-based search strategies spanning the Scopus®, PubMed.gov, and Web of ScienceTM databases, with the last updated search carried out in October 2023. The study aimed to synthesise literature that identified digital non-proximity recording instruments and associated colour spaces in dentistry and compare the clinical outcomes of digital systems with spectrophotometers and conventional visual methods. Utilising predefined criteria and resolving disagreements between two reviewers through Cohen’s kappa calculator, the review assessed 85 articles, with 33 included in a PICO model for clinical comparisons. The results reveal that 42% of studies employed the CIELAB colour space. Despite the challenges in study quality, non-proximity digital instruments demonstrated more consistent clinical outcomes than visual methods, akin to spectrophotometers, emphasising their efficacy in controlled conditions. The review underscores the evolving landscape of dental shade matching, recognising technological advancements and advocating for methodological rigor in dental research.
Improved Waste Heat Management and Energy Integration in an Aluminum Annealing Continuous Furnace Using a Machine Learning Approach
Authors: Andayesh Mohammad , Flórez-Orrego Daniel Alexander , Germanier Reginald , Gatti Manuele , Maréchal François
Published: October 2023

Annealing furnaces are critical for achieving the desired material properties in the production of high-quality aluminum products. In addition, energy efficiency has become more and more important in industrial processes due to increasing decarbonization regulations and the price of natural gas. Thus, the current study aims to determine the opportunities to reduce energy consumption in an annealing continuous furnace and the associated emissions. To this end, the heat transfer phenomenon is modeled and solutions for the decreasing fuel consumption are evaluated so that the overall performance of the process is enhanced. A heat transfer model is developed using the finite difference method, and the heat transfer coefficient is calculated using machine learning regression models. The heat transfer model is able to predict the heat transfer coefficient and calculate the aluminum temperature profile along the furnace and the fuel consumption for any given operating condition. Two solutions for boosting the furnace exergy efficiency are evaluated, including the modulation of the furnace temperature profiles and the energy integration by the recycling of exhaust flue gases. The results show that the advanced energy integration approach significantly reduces fuel consumption by up to 20.7%. Sensitivity analysis demonstrates that the proposed strategy can effectively reduce fuel consumption compared with the business-as-usual scenario for a range of sheet thicknesses and sheet velocities.
Economic Determinants Concerning Corporate Tax Revenue
Authors: Andrejovská Alena , Glova Jozef
Published: October 2023

This study quantifies the impact of selected economic determinants on corporate tax revenues. The methodology applies a panel regression method with the 27 EU Member States considered for 2004–2020. This paper used a panel regression model with fixed effects, and the Arellano adjustment was used to achieve robust standard deviations. Source data were obtained from the European Commission, Eurostat, World Bank and Transparency International databases. Based on this hypothesis, we wanted to prove that the nominal tax rate, which is legislatively determined based on political consensus, is a decisive determinant of the amount of tax revenue. However, the analysis results reject this hypothesis, although the model showed it as positive but statistically insignificant. On the other hand, an interesting research result is that the analysis confirmed the effective tax rate as a significant determinant of tax revenues. From this, we can conclude that policies should be aimed at an effective tax rate or a better harmonisation of the nominal tax rate towards the effective rate.
Conceptions of Portuguese Science Teachers on the Concept of Ecoethics
Authors: Carvalho Luísa , Dourado Luís , Varandas Maria José
Published: October 2023

Ecoethics is a philosophical subject that studies the moral relationship of human beings concerning the environment and its non-human components. Education for ecoethics addresses issues of how to live, how to make environmental choices, and how to think about the consequences of human activities. It is important that, firstly, the concept of ecoethics is clear to all involved, including teachers and students. Knowing that teachers’ conceptions strongly influence their practice, and since no studies with teachers in active service were found, it was considered pertinent to investigate the conceptions of Portuguese Biology and Geology teachers about the concept of ecoethics. For data collection, a questionnaire was applied at a national level, with a related open-ended question. Categories of analysis were defined a priori and then the responses were classified based on those categories. The main results show that most respondents define ecoethics as ethics applied to the environment; almost a tenth relate the concept to issues concerning the preservation of life or the quality of life. Given the influence of teachers on students’ education, a focus on teachers’ training in ecoethics is essential as a starting point for an effective approach to ecoethics issues that can contribute to solving environmental problems.
The Relation of Students’ Conceptions of Feedback to Motivational Beliefs and Achievement Goals: Comparing Chinese International Students to New Zealand Domestic Students in Higher Education
Authors: Gao Xiaoying , Brown Gavin T. L.
Published: October 2023

Extant research on students’ feedback conceptions has reported effects on performance, but the relationship of feedback conceptions to important motivational factors is not empirically evidenced. This study fills this gap by providing empirical data about students’ conceptions of feedback in relation to their motivational beliefs and achievement goals. Measurement and structural modelling analyses were based on undergraduate student data from New Zealand domestic (n = 832) and Chinese (People’s Republic of China, PRC) international students (n = 504) in New Zealand universities. Based on cross-ethnic invariant measurement models of conceptions of feedback, motivational beliefs, and achievement goals, a structural equation model uncovered links between feedback conceptions, motivational beliefs, and achievement goals. Specifically, feedback conceptions believing in actively using feedback and the value of teacher comments significantly promoted self-efficacy and task value beliefs, as well as mastery and performance-approach goals. In contrast, maladaptive feedback perceptions (i.e., feedback is ignored or used for judging performance against external standards or relative to others) had a minimal-to-negative impact on motivational beliefs and triggered stronger performance-avoidance goals. This study empirically demonstrates that conceptions of feedback support motivational beliefs and goal approaches consistent with previous claims concerning their self-regulatory role.
Joint Sub-Band and Transmission Rate Selection for Anti-Jamming Non-Contiguous Orthogonal Frequency Division Multiplexing System: An Upper Confidence Bound Based Reinforcement Learning Approach
Authors: Yuan Xinyi , Yu Long , Li Yusheng , Xu Yifan , Shi Yuxin
Published: October 2023

Reinforcement Learning (RL) has been employed to assign transmission parameters to all sub-carriers in a set frequency band for anti-jamming Orthogonal Frequency Division Multiplexing (OFDM) systems. However, prior works often overlooked the influence of wireless environment fading and convergence issues stemming from overly large parameter sets. To address these challenges, an anti-jamming scheme was proposed based on the Non-Contiguous Orthogonal Frequency Division Multiplexing (NC-OFDM) communication system integrated with reinforcement learning. First, all sub-carriers were divided into sub-bands, and a Finite State Markov Sub-bands (FSMS) model was established to describe the time-varying fading characteristics of each sub-band by combining Adaptive Modulation and Coding (AMC) technology. To mitigate instability due to the fading channel, a joint sub-band and modulation anti-jamming decision scheme was adopted, enabling the transmitter to select the optimal sub-band and transmission rate. Ultimately, this decision-making process was modeled as a Markov Decision Process (MDP), and an Upper Confidence Bound based Q-learning (UCB-Q) anti-jamming algorithm was proposed for obtaining the joint sub-band and transmission rate selection strategies. Simulation results indicate that the proposed algorithm demonstrates enhanced speed and superior average throughput. Additionally, the algorithm showcases the same commendable anti-jamming performance in scenarios with time-varying dynamic jamming.
An Investigation of ECAPA-TDNN Audio Type Recognition Method Based on Mel Acoustic Spectrograms
Authors: Wang Jian , Wang Zhongzheng , Han Xingcheng , Han Yan
Published: October 2023

Audio signals play a crucial role in our perception of our surroundings. People rely on sound to assess motion, distance, direction, and environmental conditions, aiding in danger avoidance and decision making. However, in real-world environments, during the acquisition and transmission of audio signals, we often encounter various types of noises that interfere with the intended signals. As a result, the essential features of audio signals become significantly obscured. Under the interference of strong noise, identifying noise segments or sound segments, and distinguishing audio types becomes pivotal for detecting specific events and sound patterns or isolating abnormal sounds. This study analyzes the characteristics of Mel’s acoustic spectrogram, explores the application of the deep learning ECAPA-TDNN method for audio type recognition, and substantiates its effectiveness through experiments. Ultimately, the experimental results demonstrate that the deep learning ECAPA-TDNN method for audio type recognition, utilizing Mel’s acoustic spectrogram as features, achieves a notably high recognition accuracy.
Stratified Sampling-Based Deep Learning Approach to Increase Prediction Accuracy of Unbalanced Dataset
Authors: Sadaiyandi Jeyabharathy , Arumugam Padmapriya , Sangaiah Arun Kumar , Zhang Chao
Published: October 2023

Due to the imbalanced nature of datasets, classifying unbalanced data classes and drawing accurate predictions is still a challenging task. Sampling procedures, along with machine learning and deep learning algorithms, are a boon for solving this kind of challenging task. This study’s objective is to use sampling-based machine learning and deep learning approaches to automate the recognition of rotting trees from a forest dataset. Method/Approach: The proposed approach successfully predicted the dead tree in the forest. Seven of the twenty-one features are computed using the wrapper approach. This research work presents a novel method for determining the state of decay of the tree. The process of classifying the tree’s state of decay is connected to the issue of unequal class distribution. When classes to be predicted are uneven, this frequently hides poor performance in minority classes. Using stratified sampling procedures, the required samples for precise categorization are prepared. Stratified sampling approaches are employed to generate the necessary samples for accurate prediction, and the precise samples with computed features are input into a deep learning neural network. Finding: The multi-layer feed-forward classifier produces the greatest results in terms of classification accuracy (91%). Novelty/Improvement: Correct samples are necessary for correct classification in machine learning approaches. In the present study, stratified samples were considered while deciding which samples to use as deep neural network input. It suggests that the proposed algorithm could accurately determine whether the tree has decayed or not.
Robust Person Identification and Following in a Mobile Robot Based on Deep Learning and Optical Tracking
Authors: Condés Ignacio , Fernández-Conde Jesús , Perdices Eduardo , Cañas José M.
Published: October 2023

There is an exciting synergy between deep learning and robotics, combining the perception skills a deep learning system can achieve with the wide variety of physical responses a robot can perform. This article describes an embedded system integrated into a mobile robot capable of identifying and following a specific person reliably based on a convolutional neural network pipeline. In addition, the design incorporates an optical tracking system for supporting the inferences of the neural networks, allowing the determination of the position of a person using an RGB depth camera. The system runs on an NVIDIA Jetson TX2 board, an embedded System-on-Module capable of performing computationally demanding tasks onboard and handling the complexity needed to run a solid tracking and following algorithm. A robotic mobile base with the Jetson TX2 board attached receives velocity orders to move the system toward the target. The proposed approach has been validated on a mobile robotic platform that successfully follows a determined person, relying on the robustness of the combination of deep learning with optical tracking for working efficiently in a real environment.
Measuring Received Signal Strength of UWB Chaotic Radio Pulses for Ranging and Positioning
Authors: Efremova Elena V. , Kuzmin Lev V. , Itskov Vadim V.
Published: October 2023

The use of ultra-wideband (UWB) signals for local positioning is very attractive for practice, because such signals have the potential to provide centimeter precision. In this paper, we consider wireless ranging (distance measurement) and positioning, using one of the kinds of UWB signals, i.e., chaotic radio pulses, which are noise-like signals with no constant shape. The distance measurement is based on an assessment in the receiver of the power of UWB chaotic radio pulses emitted by the transmitter. A new method for estimating their power and its experimental implementation is proposed and described. Experimental layouts of the transmitter and receiver and the principles of their operation are described. To determine the main features of this method under real signal propagation conditions, full-scale indoor measurements were carried out, and statistical estimates of the accuracy were made. We present the results of experimental testing of the proposed approach for positioning the emitter relative to a system of anchors in an office space 6 × 6.5 m2 in the mode of measuring object coordinates on a line and on a plane. The mean absolute error (MAE) of distance measurement (1D) was 25 cm, and the root mean squared error (RMSE) was 39 cm. When positioning on a plane (2D), the MAE of coordinate estimation was 34 cm and the RMSE was 42 cm. The proposed distance measurement method is intended for use in wireless UWB transceivers used in wireless sensor networks.
Intelligent Optimization Method of Human–Computer Interaction Interface for UAV Cluster Attack Mission
Authors: Chen Anqi , Xie Feng , Wang Jingbo , Chen Jun
Published: October 2023

In modern warfare, it is often necessary for the operator to control the UAV cluster from a ground control station to perform an attack task. However, the absence of an effective method for optimizing the human–computer interface in ground control stations for UAV clusters leads to usability difficulties and heightens the probability of human errors. Hence, we propose an optimization framework for human–computer interaction interfaces within UAV ground control stations, rooted in interface-essential elements. Specifically, the interface evaluation model was formulated by combining the Salient, Effort, Expectancy, and Value (SEEV) framework with the essential factor mutation cost of the quantified interface. We employed the SEEV–ant colony algorithm to address the challenge of optimizing the interface design within this context. For a typical UAV cluster attack mission, we optimized the human–computer interaction interfaces of the three mission stages based on the proposed SEEV-AC model. We conducted extensive simulation experiments in these optimized interfaces, and used eye-movement indicators to evaluate the effectiveness of the interface optimization model. Based on the experimental results, divergence is reduced by 11.59%, and the fitness of the optimized interface is increased from 1.34 to 3.42. The results show that the proposed intelligent interface optimization method can effectively improve the interface design and reduce the operator’s workload.
Comparison of Different Methods for Building Ensembles of Convolutional Neural Networks
Authors: Nanni Loris , Loreggia Andrea , Brahnam Sheryl
Published: October 2023

In computer vision and image analysis, Convolutional Neural Networks (CNNs) and other deep-learning models are at the forefront of research and development. These advanced models have proven to be highly effective in tasks related to computer vision. One technique that has gained prominence in recent years is the construction of ensembles using deep CNNs. These ensembles typically involve combining multiple pretrained CNNs to create a more powerful and robust network. The purpose of this study is to evaluate the effectiveness of building CNN ensembles by combining several advanced techniques. Tested here are CNN ensembles constructed by replacing ReLU layers with different activation functions, employing various data-augmentation techniques, and utilizing several algorithms, including some novel ones, that perturb network weights. Experimental results performed across many datasets representing different tasks demonstrate that our proposed methods for building deep ensembles produces superior results.
Residual Energy-Based Computation Efficiency Maximization in Dense Edge Computing Systems
Authors: Wu Shie , Li Xiaolin , Dong Ningfei , Liu Xia
Published: October 2023

With the rapid development of 5G, artificial intelligence, the internet of things (IoT) and other technologies, the number of intelligent terminal devices is growing explosively, bringing huge challenges to the existing communication network and cloud computing service mode. The dense edge computing system (DECS), which combines mobile edge computing (MEC) with an ultra-dense network (UDN), has the potential to significantly improve low latency of communications and enhance the quality of experience (QoE) of user equipments (UEs). In this paper, to achieve energy-efficient MEC, computation efficiency (CE) is maximized by jointly optimizing computation offloading, subchannel allocation and power allocation, which yields a challenging non-convex problem. Specially, due to the heterogeneity of UE battery capacities and residual energy, the residual energy of UEs should be taken into consideration in order to achieve better QoE. Therefore, we develop a residual energy-based computation efficiency (RECE) optimization scheme to maximize CE, where the optimization problem is divided into three subproblems. Firstly, the computation offloading subproblem is addressed by a many-to-one matching strategy. Secondly, the subchannel allocation subproblem is dealt with by adopting the graph coloring algorithm. Finally, the power allocation subproblem is solved by the concave–convex procedure (CCCP) method. The numerical results illustrate that UEs’ CE can be optimized based on their residual energy in the proposed RECE scheme. Additionally, compared to a scheme without considering UE residual energy, the system CE can be much enhanced, and the UE energy consumption can be significantly reduced in the RECE scheme.
EEG-Based Functional Connectivity Analysis for Cognitive Impairment Classification
Authors: Echeverri-Ocampo Isabel , Ardila Karen , Molina-Mateo José , Padilla-Buritica J. I. , Carceller Héctor , Barceló-Martinez Ernesto A. , Llamur S. I. , Iglesia-Vaya Maria de la
Published: October 2023

Understanding how mild cognitive impairment affects global neural networks may explain changes in brain electrophysiology. Using graph theory and the visual oddball paradigm, we evaluated the functional connectivity of neuronal networks in brain lobes. The study involved 30 participants: 14 with mild cognitive impairment (MCI) and 16 healthy control (HC) participants. We conducted an examination using the visual oddball paradigm, focusing on electroencephalography signals with targeted stimuli. Our analysis employed functional connectivity utilizing the change point detection method. Additionally, we implemented training for linear discriminant analysis, K-nearest neighbor, and decision tree techniques to classify brain activity, distinguishing between subjects with mild cognitive impairment and those in the healthy control group. Our results demonstrate the efficacy of combining functional connectivity measurements derived from electroencephalography with machine learning for cognitive impairment classification. This research opens avenues for further exploration, including the potential for real-time detection of cognitive decline in complex real-world scenarios.
Transformer-Based Integrated Framework for Joint Reconstruction and Segmentation in Accelerated Knee MRI
Authors: Lim Hongki
Published: October 2023

Magnetic Resonance Imaging (MRI) reconstruction and segmentation are crucial for medical diagnostics and treatment planning. Despite advances, achieving high performance in both tasks remains challenging, especially in the context of accelerated MRI acquisition. Motivated by this challenge, the objective of this study is to develop an integrated approach for MRI image reconstruction and segmentation specifically tailored for accelerated acquisition scenarios. The proposed method unifies these tasks by incorporating segmentation feedback into an iterative reconstruction algorithm and using a transformer-based encoder–decoder architecture. This architecture consists of a shared encoder and task-specific decoders, and employs a feature distillation process between the decoders. The proposed model is evaluated on the Stanford Knee MRI with Multi-Task Evaluation (SKM-TEA) dataset against established methods such as SegNetMRI and IDSLR-Seg. The results show improvements in the PSNR, SSIM, Dice, and Hausdorff distance metrics. An ablation study confirms the contribution of feature distillation and segmentation feedback to the performance gains. The advancements demonstrated in this study have the potential to impact clinical practice by facilitating more accurate diagnosis and better-informed treatment plans.
Research Progress in Breakdown Enhancement for GaN-Based High-Electron-Mobility Transistors
Authors: Tian Zhiwen , Ji Xuan , Yang Dongwei , Liu Pei
Published: October 2023

The breakdown characteristics are very important for GaN high-electron-mobility transistors (HEMTs), which affect the application voltage, power density, efficiency, etc. In order to further enhance the breakdown voltage of the device, it is necessary to carry out research on the breakdown mechanisms of the device. This article summarizes several breakdown mechanisms of GaN devices, including electric field concentration, buffer leakage current, gate leakage current, and vertical breakdown. In order to suppress the breakdown mechanisms, techniques such as the use of a field plate, reduced surface field (RESURF), back barrier, gate dielectric, substrate removal, and addition of AlGaN channels can be developed. With the continuous development of various technologies, the breakdown characteristics of GaN devices can be fully explored, laying the foundation for improving the performance of power electronic systems.
Similarity and Complementarity Attention-Based Graph Neural Networks for Mashup-Oriented Cloud API Recommendation
Authors: Shen Limin , Wang Yuying , Zhang Shuai , Chen Zhen
Published: October 2023

Mashups, which combine various web application programming interfaces (APIs) to implement some complex requirements, have grown to be a popular technique for developing service-oriented software. However, recommending suitable cloud APIs for mashup creation is challenging due to the rapidly increasing number of comparable APIs. Many existing mashup-oriented cloud API recommendations focus on functional similarity and ignore functional complementarity, which significantly impacts the accuracy of the recommendation results. Therefore, this paper proposed a feature representation and recommendation method for cloud APIs that fuses both similarity and complementarity. A heterogeneous information network of the cloud API ecosystem was constructed, and the neighbors, based on metapaths, were aggregated using a self-attention mechanism to generate the features of similarity and complementarity for the cloud APIs. Then, the mashup-related attention was utilized to fuse the two features, taking into consideration the varying preferences of different mashups towards similarity and complementarity features of cloud APIs. This fusion resulted in features that align cloud APIs with mashup requirements, which were employed to predict the probability of the mashup invoking a particular candidate cloud API. The proposed method was evaluated on a real dataset, and the results showed that it outperforms the baseline method and enhances the performance of mashup-oriented cloud API recommendations.
Seepage Model and Pressure Response Characteristics of Non-Orthogonal Multi-Fracture Vertical Wells with Superimposed Sand Body in Tight Gas Reservoirs
Authors: Zhou Ziwu , Xia Ao , Guo Rui , Chen Lin , Kong Fengshuo , Zhao Xiaoliang
Published: October 2023

Faced with difficulties stemming from the complex interactions between tight gas sand bodies and fractures, when describing and identifying reservoirs, a composite reservoir model was established. By setting the supply boundary to characterize the superposition characteristics of sand bodies, a mathematical model of unstable seepage in fractured vertical wells in tight sandstone gas reservoirs was developed, considering factors such as stress sensitivity, fracture density and fracture symmetry. The seepage law and pressure response characteristics of gas wells in tight sandstone discontinuous reservoirs with stress sensitivity, semi-permeable supply boundary and complex fracture topology were determined, and the reliability of the model was verified. The research results more accurately display the pressure characteristic of a vertical well in the superimposed sand body with complex fractures and provide a more comprehensive model for tight gas production dynamic analysis and well test data analysis, which can more accurately guide the dynamic inversion of reservoir and fracture parameters.
Electric Arc Furnace Electrode Movement Control System Based on a Fuzzy Arc Length Identifier
Authors: Kozyra Jacek , Lozynskyy Andriy , Łukasik Zbigniew , Kuśmińska-Fijałkowska Aldona , Kutsyk Andriy , Kasha Lidiia
Published: October 2023

From the point of view of the synthesis of control influences, arc steelmaking furnaces are complex nonlinear objects with strongly expressed mutual influences. It has been demonstrated that at a given supply voltage, the distribution of the current values of the phase currents in the quasi-steady-state mode makes it possible to estimate the situation in the arc space of an arc steelmaking furnace and identify the value of arc lengths. This dependence is preserved in transient modes. In order to identify arc lengths from the phase currents, it is proposed to use an approach based on the theory of fuzzy sets. The construction of the fuzzy arc length identifier rule base was carried out in two stages: the first stage used data from quasi-steady-state modes and the Wang–Mendel algorithm; the second stage involved adding a new rule to the database if the activity level of the formed rules was lower than the established level α for the data obtained in the dynamic mode. Further optimization of the parameters of the fuzzy identifier for operation in dynamic modes was carried out using the “back-propagation” algorithm. Based on the identified values of arc lengths, a control system for the movement of electrodes in an arc steelmaking furnace was synthesised. The proposed control system makes it possible to eliminate unproductive electrode movements due to changes in the situation in other phases of the arc steelmaking furnace and simplifies the application of modern methods of synthesising a control system for such complex objects. The results obtained in the mathematical model have confirmed the effectiveness of the proposed control system for the movement of electrodes in an arc steelmaking furnace.
Assessment of Data Capture Conditions Effect on Reverse Electrodialysis Process Using a DC Electronic Load
Authors: Hernandez-Perez Jesus Nahum , Hernández-Nochebuena Marco Antonio , González-Scott Jéssica , González-Huerta Rosa de Guadalupe , Reyes-Rodríguez José Luis , Ortiz Alfredo
Published: October 2023

Reverse electrodialysis (RED), an emerging membrane-based technology, harnesses salinity gradient energy for sustainable power generation. Accurate characterization of electrical parameters in RED stacks is crucial to monitoring its performance and exploring possible applications. In this study, a DC electronic load module (DCELM) is implemented in a constant current condition (CC mode) for characterization of lab scale RED process, using a RED prototype in-house designed and manufactured (RU1), at different data capture setups (DCS), on which the total number of steps for data capture (NS) and the number of measurements per step (ρ) are the parameters that were modified to study their effect on obtained electrical parameters in RED. NS of 10, 50, and 100 and ρ of 10 and 20 were used with this purpose. The accuracy of resulting current and voltage steps can be enhanced by increasing NS and ρ values, and according to obtained results, the higher accuracy of resulting output current and voltage steps, with low uncertainty of the average output steps (AOS) inside the operational region of power curve, was obtained using a DCS of NS = 100 and ρ = 20. The developed DCELM is a low-cost alternative to commercial electronic load devices, and the proposed methodology in this study represents an adaptative and optimizable CC mode characterization of RED process. The results obtained in this study suggest that data capture conditions have a direct influence of RED performance, and the accuracy of electrical parameters can be improved by optimizing the DCS parameters, according to the required specifications and the scale of RED prototypes.
Thermo-Hydraulic Performance of Pillow-Plate Heat Exchangers with Secondary Structuring: A Numerical Analysis
Authors: Afsahnoudeh Reza , Wortmeier Andreas , Holzmüller Maik , Gong Yi , Homberg Werner , Kenig Eugeny Y.
Published: October 2023

Pillow-plate heat exchangers (PPHEs) represent a suitable alternative to conventional shell-and-tube and plate heat exchangers. The inherent waviness of their channels promotes fluid mixing in the boundary layers and facilitates heat transfer. The overall thermo-hydraulic performance of PPHEs can further be enhanced by applying secondary surface structuring, thus increasing their competitiveness against conventional heat exchangers. In this work, various secondary structures applied on the PPHE surface were studied numerically to explore their potential to enhance near-wall mixing. Computational fluid dynamics (CFD) simulations of single-phase turbulent flow in the outer PPHE channel were performed and pressure drop, heat transfer coefficients, and overall thermo-hydraulic efficiency were determined. The simulation results clearly demonstrate a positive impact of secondary structuring on heat transfer in PPHEs.
Deep Reinforcement Learning-Driven Mitigation of Adverse Effects of Cyber-Attacks on Electric Vehicle Charging Station
Authors: Basnet Manoj , Ali Mohd. Hasan
Published: October 2023

An electric vehicle charging station (EVCS) infrastructure is the backbone of transportation electrification; however, the EVCS has various vulnerabilities in software, hardware, supply chain, and incumbent legacy technologies such as network, communication, and control. These standalone or networked EVCSs open up large attack surfaces for local or state-funded adversaries. The state-of-the-art approaches are not agile and intelligent enough to defend against and mitigate advanced persistent threats (APT). We propose data-driven model-free digital clones based on multiple independent agents deep reinforcement learning (IADRL) that uses the Twin Delayed Deep Deterministic Policy Gradient (TD3) to efficiently learn the control policy to mitigate the cyberattacks on the controllers of EVCS. Also, the proposed digital clones trained with TD3 are compared against the benchmark Deep Deterministic Policy Gradient (DDPG) agent. The attack model considers the APT designed to malfunction the duty cycles of the EVCS controllers with Type-I low-frequency attacks and Type-II constant attacks. The proposed model restores the EVCS operation under threat incidence in any/all controllers by correcting the control signals generated by the legacy controllers. Our experiments verify the superior control policies and actions of TD3-based clones compared to the DDPG-based clones. Also, the TD3-based controller clones solve the problem of incremental bias, suboptimal policy, and hyperparameter sensitivity of the benchmark DDPG-based digital clones, enforcing the efficient mitigation of the impact of cyberattacks on EVCS controllers.
Methane Emission Outbursts in the Mine Face of Two Galleries: Computational Fluid Dynamics Analysis and On-Site Calibration
Authors: Torno Susana , Toraño Javier
Published: October 2023

Numerous cases of serious accidents related to methane outbursts exist worldwide. Due to their disastrous consequences, a vast quantity of research on underground gas explosions has been conducted using conventional models and laboratory tests, as performing studies at explosion sites is difficult. When conventional models are employed, the results are poor since these models are based on calculations at fixed times for a single point of a single section of an underground space. Computational fluid dynamics (CFD) analysis is necessary to calibrate these models, using measurements obtained in galleries. In this case, the measurements are obtained from methane emissions of 3885 m3. CFD modelling was carried out in three phases. First, the relationship between methane emission and the main ventilation was analysed. Second, the effect of adding an auxiliary ventilation fan to the main ventilation was investigated. Finally, methane evolution over time was analysed. In the first moments, methane produces a reversal at 200 m from the main ventilation entrance of the gallery. When 60 m of auxiliary ventilation is superimposed, the ventilation reversal distance is reduced to 42 m from the methane emission source. The effect of superimposing appropriate auxiliary ventilation on the main ventilation, as well as the correct placement of the auxiliary ventilation fan, can generate safe zones behind the ventilation reversal area. These CFD models are powerful tools for analysing methane explosion emission and propagation.
Robust Bilevel Optimal Dispatch of Park Integrated Energy System Considering Renewable Energy Uncertainty
Authors: Wang Puming , Zheng Liqin , Diao Tianyi , Huang Shengquan , Bai Xiaoqing
Published: October 2023

This paper focuses on optimizing the park integrated energy system (PIES) operation, and a robust bilevel optimal dispatch is proposed. Firstly, the robust uncertainty set is constructed based on the K-means++ algorithm to solve the uncertainty of renewable energy sources output in PIES. Then, the bi-level dispatch model is proposed, with the operator as the leader and consumers as the follower. The upper model establishes an electricity-heat-gas integrated energy network, and the lower model considers the demand response of consumers. Optimizing the pricing strategies of energy sources to determine the output of each energy conversion equipment and the demand response plan. Moreover, analyzing the decision-making process of the robust bi-level model and the solution method is given. Finally, case studies show that the proposed dispatch model can increase operator profits and reduce consumers’ energy costs. The in-sample and out-of-sample simulations demonstrate that the proposed ellipsoid uncertainty set possesses high compactness, good robustness, and low conservatism.
The Antibiotic Resistome and Its Association with Bacterial Communities in Raw Camel Milk from Altay Xinjiang
Authors: Qin Yanan , Huang Wanting , Yang Jie , Zhao Yan , Zhao Min , Xu Haotian , Zhang Minwei
Published: October 2023

Raw camel milk is generally contaminated with varied microbiota, including antibiotic–resistant bacteria (ARB), that can act as a potential pathway for the spread of antibiotic resistance genes (ARGs). In this study, high–throughput quantitative PCR and 16S rRNA gene–based Illumine sequencing data were used to establish a comprehensive understanding of the antibiotic resistome and its relationship with the bacterial community in Bactrian camel milk from Xinjiang. A total of 136 ARGs and up to 1.33 × 108 total ARG copies per gram were identified, which predominantly encode resistance to β–lactamas and multidrugs. The ARGs’ profiles were mainly explained by interactions between the bacteria community and physicochemical indicators (77.9%). Network analysis suggested that most ARGs exhibited co–occurrence with Corynebacterium, Leuconostoc and MGEs. Overall, raw camel milk serves as a reservoir for ARGs, which may aggravate the spread of ARGs through vertical and horizontal gene transfer in the food chain.
Simultaneous Analysis of Hydrophobic Atractylenolides, Atractylon and Hydrophilic Sugars in Bai-Zhu Using a High-Performance Liquid Chromatography Column Tandem Technique
Authors: Gu Zhixing , Nie Xi , Guo Ping , Lu Yuehua , Chen Bo
Published: October 2023

An analytical method was established using high-performance liquid chromatography coupled with diode array and evaporative light scattering detectors (HPLC-DAD-ELSD) with -C18 and -NH2 column tandem for the simultaneous determination of hydrophobic atractylenolide I, II, III, atractylone and hydrophilic compounds glucose, fructose and sucrose in the dried rhizome of Atractylodes macrocephala Koidz (a natural raw material for health foods, Bai-Zhu aka. in Chinese). The method combines the different separation capabilities of reversed-phase liquid chromatography and hydrophilic interaction liquid chromatography. It can provides a new choice for the simultaneous determination of hydrophilic and hydrophobic compounds in traditional Chinese medicines and health foods. It provided a reference method for the quality control of Bai-Zhu. The results showed that the linear correlation coefficients of the established column tandem chromatographic method were all greater than 0.9990, the relative standard deviation was 0.1–2.8%, and the average recovery was 96.7–103.1%. The contents of atractylenolide I, II, III, atractylone, fructose, glucose, and sucrose in 17 batches of Baizhu were 172.3–759.8 μg/g, 201.4–612.8 μg/g, 160.3–534.2 μg/g, 541.4–8723.1 μg/g, 6.9–89.7 mg/g, 0.7–7.9 mg/g, and 1.2–21.0 mg/g, respectively.
Consumer Nutritional Awareness, Sustainability Knowledge, and Purchase Intention of Environmentally Friendly Cookies in Croatia, France, and North Macedonia
Authors: Molnar Dunja , Velickova Elena , Prost Carole , Temkov Mishela , Ščetar Mario , Novotni Dubravka
Published: October 2023

The increasing demand for greater utilization of byproducts in the food industry has been driven by growing interest in environmental sustainability. This paper examines the market potential and consumer attitudes toward whole-grain and sustainable cookies made with food byproducts and edible films. Additionally, particular attention was given to evaluating levels of sustainability knowledge and nutritional awareness, willingness to purchase environmentally friendly cookies with food byproducts, and to appraise differences in answers between countries and generations. An online questionnaire was used to collect data from Croatian (n = 472), French (n = 166), and North Macedonian consumers (n = 119) aged between 18 and 62, predominantly women (82%) with higher education degrees. Results showed that even if chocolate-coated cookies remain very popular, North Macedonians prefer whole-grain and plain cookies, while the French prefer chocolate-coated cookies and Croats prefer both types of cookie. The majority of consumers (96%) were interested in purchasing environmentally friendly cookies. However, consumers’ interest in purchasing cookies with food byproducts was generally low, which may be related to their limited knowledge of byproducts. In conclusion, there is market potential for whole-grain cookies with food byproducts, but brand, price, and consumer education may be critical to their success.
Assessing the Seafood Trade Diversion Arising from Economic Sanctions: Evidence from Russia and Western Countries
Authors: Kim Chang Min , Kim Dae Eui , Lim Song Soo
Published: October 2023

Since 2014, economic sanctions between Russia and Western nations have significantly altered the global seafood trade. The consequent decline in bilateral trade also had spillover effects on the rest of the world (ROW). According to earlier studies, economic sanctions appear to negatively impact bilateral trade and income. However, few studies examine how Russian sanctions affect the world as a whole and estimate their effects on the fisheries industry. This study seeks to close this gap by quantifying the extent to which Russian sanctions have impacted trade in terms of trade deflection, trade destruction, trade depression, and trade creation. To this end, panel data from 185 countries were created for the years from 2005 to 2020. With trade policy variables that account for changes in trade channels, a structural gravity trade model was specified. Based on calculations using the Poisson pseudo-maximum likelihood (PPML) fixed effect model, economic sanctions led to a 119.28% surge in Russia’s seafood imports from the rest of the world (ROW), alongside a 39% decline in imports from Western countries. The extent of trade deflection, which includes the exports of Western nations diverted from Russia to the ROW markets, increased by 5.49%. The results demonstrate that trade between sanctioned states, as well as global trade, is significantly impacted by economic sanctions.
Biocide Tolerance and Impact of Sanitizer Concentrations on the Antibiotic Resistance of Bacteria Originating from Cheese
Authors: György Éva , Unguran Károly Arnold , Laslo Éva
Published: October 2023

In this study, we determined and identified the bacterial diversity of different types of artisanal and industrially produced cheese. The antibiotic (erythromycin, chloramphenicol, kanamycin, ampicillin, clindamycin, streptomycin, tetracycline, and gentamicin) and biocide (peracetic acid, sodium hypochlorite, and benzalkonium chloride) resistance of clinically relevant bacteria was determined as follows: Staphylococcus aureus, Macrococcus caseolyticus, Bacillus sp., Kocuria varians, Escherichia coli, Enterococcus faecalis, Citrobacter freundii, Citrobacter pasteurii, Klebsiella oxytoca, Klebsiella michiganensis, Enterobacter sp., Enterobacter cloacae, Enterobacter sichuanensis, Raoultella ornithinolytica, Shigella flexneri, and Salmonella enterica. Also, the effect of the sub-inhibitory concentration of three biocides on antibiotic resistance was determined. The microbiota of evaluated dairy products comprise diverse and heterogeneous groups of bacteria with respect to antibiotic and disinfectant tolerance. The results indicated that resistance was common in the case of ampicillin, chloramphenicol, erythromycin, and streptomycin. Bacillus sp. SCSSZT2/3, Enterococcus faecalis SRGT/1, E. coli SAT/1, Raoultella ornithinolytica MTT/5, and S. aureus SIJ/2 showed resistance to most antibiotics. The tested bacteria showed sensitivity to peracetic acid and a different level of tolerance to benzalkonium chloride and sodium hypochlorite. The inhibition zone diameter of antibiotics against Enterococcus faecalis SZT/2, S. aureus JS11, E. coli CSKO2, and Kocuria varians GRT/10 was affected only by the sub-inhibitory concentration of peracetic acid.
Structural Basis of the Immunological Cross-Reactivity between Kiwi and Birch Pollen
Authors: Zeindl Ricarda , Franzmann Annika L. , Fernández-Quintero Monica L. , Seidler Clarissa A. , Hoerschinger Valentin J. , Liedl Klaus R. , Tollinger Martin
Published: October 2023

Allergies related to kiwi consumption have become a growing health concern, with their prevalence on the rise. Many of these allergic reactions are attributed to cross-reactivity, particularly with the major allergen found in birch pollen. This cross-reactivity is associated with proteins belonging to the pathogenesis-related class 10 (PR-10) protein family. In our study, we determined the three-dimensional structures of the two PR-10 proteins in gold and green kiwi fruits, Act c 8 and Act d 8, using nuclear magnetic resonance (NMR) spectroscopy. The structures of both kiwi proteins closely resemble the major birch pollen allergen, Bet v 1, providing a molecular explanation for the observed immunological cross-reactivity between kiwi and birch pollen. Compared to Act d 11, however, a kiwi allergen that shares the same architecture as PR-10 proteins, structural differences are apparent. Moreover, despite both Act c 8 and Act d 8 containing multiple cysteine residues, no disulfide bridges are present within their structures. Instead, all the cysteines are accessible on the protein’s surface and exposed to the surrounding solvent, where they are available for reactions with components of the natural food matrix. This structural characteristic sets Act c 8 and Act d 8 apart from other kiwi proteins with a high cysteine content. Furthermore, we demonstrate that pyrogallol, the most abundant phenolic compound found in kiwi, binds into the internal cavities of these two proteins, albeit with low affinity. Our research offers a foundation for further studies aimed at understanding allergic reactions associated with this fruit and exploring how interactions with the natural food matrix might be employed to enhance food safety.