bluuebunny/crossref_metadata_2025 huggingface dataset:
Azobenzene-Containing Schiff-Bases—Syntheses and Dyes Applications
Authors: Leonard Estelle , Takeda China , Akitsu Takashiro
Published: February 2024

Azo-Schiff bases contain an azo photochrome showing isomerization accompanying with color change, and an imine moiety (which can contribute to the metal complexation capability). The syntheses of these molecules will be described, and their dyes applications will be discussed, such as for fuel cells, as photometric or colorimetric sensors. In addition, liquid crystals and their antibacterial efficiencies will also be discussed.
The Effect of Sintering Temperature on Phase-Related Peculiarities of the Microstructure, Flexural Strength, and Fracture Toughness of Fine-Grained ZrO2–Y2O3–Al2O3–CoO–CeO2–Fe2O3 Ceramics
Authors: Kulyk Volodymyr , Vasyliv Bogdan , Duriagina Zoia , Lyutyy Pavlo , Vavrukh Valentyna , Kostryzhev Andrii
Published: February 2024

The lifetime of products made of ceramic materials is related to their mechanical characteristics such as strength, hardness, wear resistance, and fracture toughness. The purpose of this work was to study the effect of sintering temperature on the phase-related peculiarities of the microstructures, causing changes in the flexural strength and fracture toughness of fine-grained ZrO2–Y2O3–Al2O3–CoO–CeO2–Fe2O3 ceramics. Flexural strength and fracture toughness tests were carried out using ceramics sintered in three modes (2 h at 1550 °C, 1580 °C, and 1620 °C in argon), and thorough phase, microstructure, and fractographic analyses were performed. For the ceramic sintered at 1550 °C, a mixed mechanism of intergranular fracture of the t-ZrO2 phase particles and cleavage fracture of the Ce–Al–O phase particles was found, which is reflected in its comparatively low fracture toughness. For the ceramic sintered at 1580 °C, a fracture developed along the boundaries of the aggregates, made of completely recrystallized fine ZrO2 grains with a high bond strength between adjacent t-ZrO2 grains; this corresponds to the highest fracture toughness (5.61 ± 0.24 MPa·m1/2) of this ceramic. For the ceramic sintered at 1620 °C, a transgranular fracture of the t-ZrO2 phase and Ce–Al–O phase particles and crack propagation along the t-ZrO2/Ce–Al–O interface were revealed; this caused a decrease in fracture toughness.
Quantum Magnetism in Wannier-Obstructed Mott Insulators
Authors: Huang Xiaoyang , Wang Taige , Liu Shang , Hu Hong-Ye , You Yi-Zhuang
Published: February 2024

We develop a strong coupling approach towards quantum magnetism in Mott insulators for Wannier-obstructed bands. Despite the lack of Wannier orbitals, electrons can still singly occupy a set of exponentially localized but nonorthogonal orbitals to minimize the repulsive interaction energy. We develop a systematic method to establish an effective spin model from the electron Hamiltonian using a diagrammatic approach. The nonorthogonality of the Mott basis gives rise to multiple new channels of spin-exchange (or permutation) interactions beyond Hartree–Fock and superexchange terms. We apply this approach to a Kagome lattice model of interacting electrons in Wannier-obstructed bands (including both Chern bands and fragile topological bands). Due to the orbital nonorthogonality, as parameterized by the nearest-neighbor orbital overlap g, this model exhibits stable ferromagnetism up to a finite bandwidth W∼Ug, where U is the interaction strength. This provides an explanation for the experimentally observed robust ferromagnetism in Wannier-obstructed bands. The effective spin model constructed through our approach also opens up the possibility for frustrated quantum magnetism around the ferromagnet-antiferromagnet crossover in Wannier-obstructed bands.
Additional Value of FDG-PET/MRI Complementary to Sentinel Lymphonodectomy for Minimal Invasive Lymph Node Staging in Patients with Endometrial Cancer: A Prospective Study
Authors: Weissinger Matthias , Bala Lidia , Brucker Sara Yvonne , Kommoss Stefan , Hoffmann Sascha , Seith Ferdinand , Nikolaou Konstantin , la Fougère Christian , Walter Christina Barbara , Dittmann Helmut
Published: February 2024

Background: Lymph node metastases (LNM) are rare in early-stage endometrial cancer, but a diagnostic systematic lymphadenectomy (LNE) is often performed to achieve reliable N-staging. Therefore, this prospective study aimed to evaluate the benefit of [18F]-Fluorodeoxyglucose (FDG) PET/MRI complementary to SPECT/CT guided sentinel lymphonodectomy (SLNE) for a less invasive N-staging Methods: 79 patients underwent a whole-body FDG-PET/MRI, SLN mapping with 99mTc-Nanocolloid SPECT/CT and indocyanine green (ICG) fluoroscopy followed by LNE which served as ground truth. Results: FDG-PET/MRI was highly specific in N-staging (97.2%) but revealed limited sensitivity (66.7%) due to missed micrometastases. In contrast, bilateral SLN mapping failed more often in patients with macrometastases. The combination of SLN mapping and FDG-PET/MRI increased the sensitivity from 66.7% to 77.8%. Additional SLN labeling with dye (ICG) revealed a complete SLN mapping in 80% (8/10) of patients with failed or incomplete SLN detection in SPECT/CT, reducing the need for diagnostic systematic LNE up to 87%. FDG-PET/MRI detected para-aortic LNM in three out of four cases and a liver metastasis. Conclusions: The combination of FDG-PET/MRI and SLNE can reduce the need for diagnostic systematic LNE by up to 87%. PET/MRI complements the SLN technique particularly in the detection of para-aortic LNM and occasional distant metastases.
A Novel Ensemble Framework for Multi-Classification of Brain Tumors Using Magnetic Resonance Imaging
Authors: Çetin-Kaya Yasemin , Kaya Mahir
Published: February 2024

Brain tumors can have fatal consequences, affecting many body functions. For this reason, it is essential to detect brain tumor types accurately and at an early stage to start the appropriate treatment process. Although convolutional neural networks (CNNs) are widely used in disease detection from medical images, they face the problem of overfitting in the training phase on limited labeled and insufficiently diverse datasets. The existing studies use transfer learning and ensemble models to overcome these problems. When the existing studies are examined, it is evident that there is a lack of models and weight ratios that will be used with the ensemble technique. With the framework proposed in this study, several CNN models with different architectures are trained with transfer learning and fine-tuning on three brain tumor datasets. A particle swarm optimization-based algorithm determined the optimum weights for combining the five most successful CNN models with the ensemble technique. The results across three datasets are as follows: Dataset 1, 99.35% accuracy and 99.20 F1-score; Dataset 2, 98.77% accuracy and 98.92 F1-score; and Dataset 3, 99.92% accuracy and 99.92 F1-score. We achieved successful performances on three brain tumor datasets, showing that the proposed framework is reliable in classification. As a result, the proposed framework outperforms existing studies, offering clinicians enhanced decision-making support through its high-accuracy classification performance.
A Scoping Review of the Oral Health Status, Oral Health Behaviours and Interventions to Improve the Oral Health of Children and Young People in Care and Care Leavers
Authors: Booth Joelle , Erwin Jo , Burns Lorna , Axford Nick , Horrell Jane , Wheat Hannah , Witton Robert , Shawe Jill , Doughty Janine , Kaddour Sarah , Boswell Skye , Devalia Urshla , Nelder Abigail , Paisi Martha
Published: February 2024

Background: Children and young people (CYP) in care experience poorer physical health and overall wellbeing in comparison to their peers. Despite this, relatively little is known about what their oral health needs and behaviours are. The aim of this scoping review was to provide a global perspective on the oral health status and behaviours of CYP in care and care leavers. It also aimed to synthesise interventions that have been trialled in this population to improve oral health. Methods: Five databases were searched, Ovid Embase, Ovid MEDLINE, CINAHL (EBSCOhost), SocINDEX (EBSCOhost) and Dentistry and Oral Sciences Source (EBSCOhost), alongside grey literature sources up to January 2023. Eligibility criteria were studies that (i) reported on children and adolescents aged 25 years or younger who are currently in formal/informal foster or residential care and care leavers, (ii) pertained to oral health profile, behaviours or oral health promotion interventions (iii) and were published in the English language. Thematic analysis was used to develop the domains for oral health behaviours and interventions. Results: Seventy-one papers were included. Most papers were published from very high or medium Human Development Index countries. CYP in care were found to experience high levels of decay, dental trauma, periodontal disease and poorer oral health-related quality of life. Oral health behaviours included limited oral health self-care behaviours and a lack of oral health-based knowledge. The trialled interventions involved oral health education, supervised brushing and treatment or preventative dental care. Conclusions: This scoping review reveals that CYP in care experience poorer oral health in comparison to their peers. They are also less likely to carry out oral health self-care behaviours. This review highlights a scarcity of interventions to improve the oral health of this population and a paucity of evidence surrounding the oral health needs of care leavers.
Physical Reservoir Computing Enabled by Solitary Waves and Biologically Inspired Nonlinear Transformation of Input Data
Authors: Maksymov Ivan S.
Published: February 2024

Reservoir computing (RC) systems can efficiently forecast chaotic time series using the nonlinear dynamical properties of an artificial neural network of random connections. The versatility of RC systems has motivated further research on both hardware counterparts of traditional RC algorithms and more-efficient RC-like schemes. Inspired by the nonlinear processes in a living biological brain and using solitary waves excited on the surface of a flowing liquid film, in this paper, we experimentally validated a physical RC system that substitutes the effect of randomness that underpins the operation of the traditional RC algorithm for a nonlinear transformation of input data. Carrying out all operations using a microcontroller with minimal computational power, we demonstrate that the so-designed RC system serves as a technically simple hardware counterpart to the ‘next-generation’ improvement of the traditional RC algorithm.
Theoretical Improvements in Enzyme Efficiency Associated with Noisy Rate Constants and Increased Dissipation
Authors: Juretić Davor , Bonačić Lošić Željana
Published: February 2024

Previous studies have revealed the extraordinarily large catalytic efficiency of some enzymes. High catalytic proficiency is an essential accomplishment of biological evolution. Natural selection led to the increased turnover number, kcat, and enzyme efficiency, kcat/KM, of uni–uni enzymes, which convert a single substrate into a single product. We added or multiplied random noise with chosen rate constants to explore the correlation between dissipation and catalytic efficiency for ten enzymes: beta-galactosidase, glucose isomerase, β-lactamases from three bacterial strains, ketosteroid isomerase, triosephosphate isomerase, and carbonic anhydrase I, II, and T200H. Our results highlight the role of biological evolution in accelerating thermodynamic evolution. The catalytic performance of these enzymes is proportional to overall entropy production—the main parameter from irreversible thermodynamics. That parameter is also proportional to the evolutionary distance of β-lactamases PC1, RTEM, and Lac-1 when natural or artificial evolution produces the optimal or maximal possible catalytic efficiency. De novo enzyme design and attempts to speed up the rate-limiting catalytic steps may profit from the described connection between kinetics and thermodynamics.
Metaphors in Educational Videos
Authors: Norscini Michele , Daniela Linda
Published: February 2024

Traditionally, metaphors have been used as a pedagogical tool to facilitate the processes of educational mediation. From a medial perspective, there are various ways to implement educational mediation, and currently, we are witnessing an increase in the use of videos. Given the historical pedagogical role of metaphor and the widespread use of videos, we expected to find a significant amount of the scientific literature exploring metaphors in educational videos. However, studies establishing a direct connection are rare. Motivated by this gap, we decided to present users with a metaphorical educational video, intending to observe and analyze, through a phenomenological approach, how the metaphor is perceived by users. To gather data on users’ experience, we applied the think-aloud protocol during video consumption and then we conducted semi-structured interviews. Subsequently, we analyzed the collected data using phenomenological procedures. Our results highlighted that the use of metaphor can stimulate engagement and facilitate the educational mediation, as long as the metaphor is shared and perceived as coherent by users. Finally, we have highlighted some distinctive aspects of using metaphor in educational videos, such as the ability to visually represent metaphors, create metaphorical contexts, and reinforce the processes of embodied simulation that occur during video viewing.
Grouping Neural Network-Based Smith PID Temperature Controller for Multi-Channel Interaction System
Authors: Li Fubing , Yang Linhao , Ye Ao , Zhao Zongmin , Shen Bingxia
Published: February 2024

The thermal vacuum test (TVT) is an important verification process in the development of spacecraft and load. There are often multiple temperature points on the device under test (DUT) that require control. The interaction among multiple channels poses a challenge for temperature control in the TVT. To solve this problem, a multi-channel Smith proportional–integral–derivative (PID) controller based on a grouping neural network (Grouping-NN) is proposed. Firstly, the mathematical derivation for a typical multi-channel temperature control model of the TVT is carried out. Then, the multi-channel interaction system is identified using a Grouping-NN to predict the output temperature of each channel by grouping the hidden layer neurons according to the number of channels. Finally, two Grouping-NNs are utilized to update the Smith predictor, and the time-delay error is fed back to the PID controller, which is used to optimize the control effect of the multi-channel interaction system under high time delay. The proposal is compared with the traditional PID controller and Smith predictor-based PID controller through simulation. The simulation results show that the proposed method has better suppression of overshooting. In addition, the algorithm is verified by controlling the temperature of six channels in a practical thermal vacuum test.
Design of Multivariable PID Control Using Iterative Linear Programming and Decoupling
Authors: Garrido Juan , Garrido-Jurado Sergio , Vázquez Francisco , Arrieta Orlando
Published: February 2024

The design of multivariable process control systems is specially complicated when there are strong interactions between the different control loops, and even more with multiple time delays. This paper proposes an iterative design method of centralized proportional-integral-derivative (PID) controllers for stable linear systems. The methodology is based on the linear parameterization of equivalent loop transfer functions (ELTFs) for centralized control. These functions capture the dynamics of the other loops and, from a prior design, allow solving the design problem at each iteration with linear programming that shapes the Nyquist plot of the ELTFs in the frequency domain, which also avoids the need for approximations. Two optimizations are proposed: (I) maximizing integral gains by fulfilling linear robustness margins in each ELTF and (II) maximizing linear robustness margins by fulfilling minimum bandwidths in each loop. In both optimizations, static decoupling and decoupling at a frequency close to the bandwidth of each loop are included as constraints, which improves the decoupling performance and the procedure convergence. The effectiveness of the method is verified in three simulation examples (square and non-square) and a lab experimental process. The proposed designs achieve a similar or better response when compared to that achieved by other authors.
Design and Analysis of a Quad-Band Antenna for IoT and Wearable RFID Applications
Authors: Ali Waqas , Nizam-Uddin N. , Abdulkawi Wazie M. , Masood Asad , Hassan Ali , Abdul Nasir Jamal , Khan Munezza Ata
Published: February 2024

The role of antennas in wireless communication is critical for enabling efficient signal transmission and reception across various frequency bands, including those associated with IoT (Internet of Things), X-band, S-band, and RFID (radio-frequency identification) systems. This paper presents a small quadruple-band antenna with 25 × 40 × 1.5 mm3 dimensions designed for diverse wireless applications. It is adept at operating in the S-band (2.2 GHz), wireless local area network (WLAN) (5.7 GHz), microwave RFID frequency band (5.8 GHz), and X-band (7.7 GHz and 8.3 GHz). While the majority of existing research focuses on antennas covering two or three bands, our work stands out by achieving quad-band operation in the proposed antenna design. This antenna is constructed on a semiflexible Rogers RT5880 substrate, making it well-suited for wearable applications. Computer Simulation Technology (CST) Microwave studio (2019) simulation package software is chosen for design and analysis. The antenna design features a comb-shaped radiating structure, where each “tooth” is responsible for resonating at a distinct frequency with an appropriate bandwidth. The antenna retains stability in both free space and on-body wearability scenarios. It achieves a low specific absorption rate (SAR), meeting wearable criteria with SAR values below 1.6 W/Kg for all resonating frequencies. The proposed antenna demonstrates suitable radiation efficiency, reaching a maximum of 82.6% and a peak gain of 6.3 dBi. It exhibits a bidirectional pattern in the elevation plane and omnidirectional behavior in the azimuth plane. The antenna finds applications across multiple frequencies and shows close agreement between simulated and measured results, validating its effectiveness.
Agile FPGA Computing at the 5G Edge: Joint Management of Accelerated and Software Functions for Open Radio Access Technologies
Authors: Bartzoudis Nikolaos , Rubio Fernández José , López-Bueno David , Román Villarroel Antonio , Antonopoulos Angelos
Published: February 2024

This paper presents ReproRun, a flexible and extensible run-time framework for the reconfiguration of functions in field programmable gate array (FPGA) devices used in popular software-defined radio (SDR) platforms. The FPGA devices embed a hardwired or soft processing system (PS) which communicates with the programmable logic (PL) using a standard embedded bus interface. In order to apply a seamless run-time partial reconfiguration, we made use of all the related building blocks, design guidelines, and tools offered by AMD-Xilinx. In ReproRun, each partial bitstream targeting a reconfigurable region (RR) of the PL area comes with its respective firmware (i.e., software functions) that runs on the PS side. Our work guarantees run-time updates of the firmware without interrupting the functionality of other software processes running in the PS or PL, by employing a specialized controller, denoted as Run-timE firmWare reconfIguration contRollEr (REWIRE). The latter leverages the open asymmetric multiprocessing framework (OpenAMP). The partial bitstreams and respective firmware are fetched from a remote location using the trivial file transfer protocol (TFTP). ReproRun can be applied in different FPGA accelerators residing in disaggregated open radio access network (RAN) equipment, adaptive radio access technologies, and Edge servers hosting virtualized functions.
Classification of the Relative Position between the Third Molar and the Inferior Alveolar Nerve Using a Convolutional Neural Network Based on Transfer Learning
Authors: Chen Shih-Lun , Chou He-Sheng , Chuo Yueh , Lin Yuan-Jin , Tsai Tzu-Hsiang , Peng Cheng-Hao , Tseng Ai-Yun , Li Kuo-Chen , Chen Chiung-An , Chen Tsung-Yi
Published: February 2024

In recent years, there has been a significant increase in collaboration between medical imaging and artificial intelligence technology. The use of automated techniques for detecting medical symptoms has become increasingly prevalent. However, there has been a lack of research on the relationship between impacted teeth and the inferior alveolar nerve (IAN) in DPR images. The severe compression of teeth against the IAN may necessitate the requirement for nerve canal treatment. To reduce the occurrence of such events, this study aims to develop an auxiliary detection system capable of precisely locating the relative positions of the IAN and impacted teeth through object detection and image enhancement. This system is designed to shorten the duration of examinations for dentists while concurrently mitigating the chances of diagnostic errors. The innovations in this research are as follows: (1) using YOLO_v4 to identify impacted teeth and the IAN in DPR images achieves an accuracy of 88%. However, the developed algorithm in this study achieves an accuracy of 93%. (2) Image enhancement is utilized in this study to expand the dataset, with an accuracy of up to 2~3% enhancement in detecting diseases. (3) The segmentation technique proposed in this study surpasses previous methods by achieving 6% higher accuracy in dental diagnosis.
An Effective Selection of Memory Technologies for TCAM to Improve the Search Operations: Demonstration of Memory Efficiency in SDN Recovery
Authors: Alahmadi Abdulhadi , Chung Tae Sun
Published: February 2024

Ternary Content-Addressable Memory (TCAM) is used for storing the flow tables in software-defined networking (SDN)-based OpenFlow switches. However, the TCAM can store only a certain number of flow tables (8000). Moreover, when the switch flow tables need to be updated due to the link failure in the SDN, further updates may be lost due to the flow tables limit of the TCAM space. Hence, to resolve this issue, other memories need to be used in conjunction with TCAM to enhance the memory operations of TCAM. When considering which flash memory technology is to be used in conjunction with TCAM, we need to balance several factors to ensure optimal performance, speed, endurance, reliability, integration complexity, and cost-effectiveness. Hence, it leads to a multi-criteria decision-making problem regarding the selection of other memory technologies such as 3D XPoint, Magnetoresistive RAM, Resistive RAM, and Ferroelectric RAM. In this paper, we use the analytical network process (ANP) method to select the suitable technology in conjunction with TCAM, considering the features of the memory technologies for Software-Defined Internet-of-Things (SD-IoT). We provide a comprehensive numerical model leveraging the ANP to rank the memory technologies regarding their weights. The highest weights identify the most suitable technology for TCAM. We perform simulations to show the effectiveness of the mathematical model utilizing the ANP. The results show that the suggested methodology reduces the recovery delay, improves the packets received ratio (PRR), decreases the jitter, and increases the throughput.
Enhancing Road Safety: Deep Learning-Based Intelligent Driver Drowsiness Detection for Advanced Driver-Assistance Systems
Authors: Yang Eunmok , Yi Okyeon
Published: February 2024

Driver drowsiness detection is a significant element of Advanced Driver-Assistance Systems (ADASs), which utilize deep learning (DL) methods to improve road safety. A driver drowsiness detection system can trigger timely alerts like auditory or visual warnings, thereby stimulating drivers to take corrective measures and ultimately avoiding possible accidents caused by impaired driving. This study presents a Deep Learning-based Intelligent Driver Drowsiness Detection for Advanced Driver-Assistance Systems (DLID3-ADAS) technique. The DLID3-ADAS technique aims to enhance road safety via the detection of drowsiness among drivers. Using the DLID3-ADAS technique, complex features from images are derived through the use of the ShuffleNet approach. Moreover, the Northern Goshawk Optimization (NGO) algorithm is exploited for the selection of optimum hyperparameters for the ShuffleNet model. Lastly, an extreme learning machine (ELM) model is used to properly detect and classify the drowsiness states of drivers. The extensive set of experiments conducted based on the Yawdd driver database showed that the DLID3-ADAS technique achieves a higher performance compared to existing models, with a maximum accuracy of 97.05% and minimum computational time of 0.60 s.
Building Advanced Web Applications Using Data Ingestion and Data Processing Tools
Authors: Šprem Šimun , Tomažin Nikola , Matečić Jelena , Horvat Marko
Published: February 2024

Today, advanced websites serve as robust data repositories that constantly collect various user-centered information and prepare it for subsequent processing. The data collected can include a wide range of important information from email addresses, usernames, and passwords to demographic information such as age, gender, and geographic location. User behavior metrics are also collected, including browsing history, click patterns, and time spent on pages, as well as different preferences like product selection, language preferences, and individual settings. Interactions, device information, transaction history, authentication data, communication logs, and various analytics and metrics contribute to the comprehensive range of user-centric information collected by websites. A method to systematically ingest and transfer such differently structured information to a central message broker is thoroughly described. In this context, a novel tool—Dataphos Publisher—for the creation of ready-to-digest data packages is presented. Data acquired from the message broker are employed for data quality analysis, storage, conversion, and downstream processing. A brief overview of the commonly used and freely available tools for data ingestion and processing is also provided.
Fault Location Method for Distribution Network Using an Additional Inductance Strategy
Authors: Yang Zonglei , Xie Chao , Yin Chunya
Published: February 2024

In distribution networks, time asynchrony exists between the phasor measuring unit (PMU) at both ends of a line, and the effective measurement time of the devices is short, leading to insufficient accuracy in phasor measurements. This paper proposes a fault location method for distribution networks that employ an additional inductance strategy to address the limited location accuracy caused by time asynchrony and the inadequate accuracy of phasor measurement devices. The method enhances the stability and accuracy of phase measurement by connecting an additional inductance after the online circuit breaker, thus extending the effective measurement time. It uses the symmetrical component method to obtain the positive-sequence and negative-sequence networks following a fault. Time asynchrony is treated as an equivalent asynchronous phase angle, which is then applied to the positive and negative-sequence voltage components. The impact of time asynchrony is mitigated by compensating for the phase angle difference using the ratio of the positive-sequence voltage component to the negative-sequence voltage component. This approach provides the fault location function and has improved the accuracy of fault location, which is advantageous for rapid fault repair.
A Review of Generative Adversarial Networks for Computer Vision Tasks
Authors: Simion Ana-Maria , Radu Șerban , Florea Adina Magda
Published: February 2024

In recent years, computer vision tasks have gained a lot of popularity, accompanied by the development of numerous powerful architectures consistently delivering outstanding results when applied to well-annotated datasets. However, acquiring a high-quality dataset remains a challenge, particularly in sensitive domains like medical imaging, where expense and ethical concerns represent a challenge. Generative adversarial networks (GANs) offer a possible solution to artificially expand datasets, providing a basic resource for applications requiring large and diverse data. This work presents a thorough review and comparative analysis of the most promising GAN architectures. This review is intended to serve as a valuable reference for selecting the most suitable architecture for diverse projects, diminishing the challenges posed by limited and constrained datasets. Furthermore, we developed practical experimentation, focusing on the augmentation of a medical dataset derived from a colonoscopy video. We also applied one of the GAN architectures outlined in our work to a dataset consisting of histopathology images. The goal was to illustrate how GANs can enhance and augment datasets, showcasing their potential to improve overall data quality. Through this research, we aim to contribute to the broader understanding and application of GANs in scenarios where dataset scarcity poses a significant obstacle, particularly in medical imaging applications.
Improving Security in the Internet of Vehicles: A Blockchain-Based Data Sharing Scheme
Authors: Wang Lianhai , Guan Chenxi
Published: February 2024

To ensure the aggregation of a high-quality global model during the data-sharing process in the Internet of Vehicles (IoV), current approaches primarily utilize gradient detection to mitigate malicious or low-quality parameter updates. However, deploying gradient detection in plain text neglects adequate privacy protection for vehicular data. This paper proposes the IoV-BDSS, a novel data-sharing scheme that integrates blockchain and hybrid privacy technologies to protect private data in gradient detection. This paper utilizes Euclidean distance to filter the similarity between vehicles and gradients, followed by encrypting the filtered gradients using secret sharing. Moreover, this paper evaluates the contribution and credibility of participating nodes, further ensuring the secure storage of high-quality models on the blockchain. Experimental results demonstrate that our approach achieves data sharing while preserving privacy and accuracy. It also exhibits resilience against 30% poisoning attacks, with a test error rate remaining below 0.16. Furthermore, our scheme incurs a lower computational overhead and faster inference speed, markedly reducing experimental costs by approximately 26% compared to similar methods, rendering it suitable for highly dynamic IoV systems with unstable communication.
An Open-Circuit Fault Diagnosis Method for LLC Converters
Authors: Xiong Shibo , Pei Yuxuan , Wang Weikang , Liu Wenwei , Zhang Peng , Liu Yang
Published: February 2024

In electrified transportation systems, power system failures can lead to greater disasters. Therefore, the reliability of converters in transportation systems has been a concern. Fault-tolerant techniques are widely applied to ensure that converters can continue to supply loads under fault conditions. Fault diagnosis as a prerequisite for fault tolerance has also become a research hotspot. This paper proposes a fast method for fault diagnosis of high-frequency LLC converters. The proposed fault diagnosis method is based on the observation of the voltage across the resonant capacitor to determine and locate the faulty power switch, providing a basis for fault tolerance. This diagnosis method requires a voltage sensor, which is also necessary for some control methods. When applying these control methods, the proposed fault diagnosis method can be used without additional sensors, beneficial for cost reduction. A full-bridge LLC converter controlled by a digital signal processor was used as an experimental platform to verify the effectiveness and speed of the proposed diagnostic method. The results show that the proposed fault diagnosis method can achieve the fast diagnosis of high-frequency LLC converters in a short time and with only minimal computational resources.
A Control-Oriented Model for Predicting Variations in Membrane Water Content of an Open-Cathode Proton Exchange Membrane Fuel Cell
Authors: Adunyah Adwoa S. , Gawli Harshal A. , Hall Carrie M.
Published: February 2024

Proton exchange membrane (PEM) fuel cells have emerged as a viable alternative energy production source for stationary and transportation applications. Reliable and sustainable fuel cell operation requires effective water management. Membrane water content can vary along the stack during transients which can lead to losses in fuel cell performance. To control these variations, a model that predicts the internal humidity dynamics of the stack is needed. In this study, a control-oriented model for predicting membrane water content variation was developed and implemented in MATLAB/Simulink. A lumped parameter model was initially developed and then further discretized into smaller control volumes to track humidity distribution along the stack. To validate the model’s predictions, the predicted results were compared to computer simulation results from GT-Suite. The root mean square error (RMSE) between the model’s prediction and GT-Suite’s simulation results was found to be within 1.5 membrane water content for all cases, demonstrating the model’s capability to capture the variation in membrane water content along the stack. The developed model will be useful for real-time control of membrane water content distribution in PEM fuel cells.
Detection of Pumping Unit in Complex Scenes by YOLOv7 with Switched Atrous Convolution
Authors: Song Zewen , Zhang Kai , Xia Xiaolong , Zhang Huaqing , Yan Xia , Zhang Liming
Published: February 2024

The petroleum and natural gas industries exhibit a high dependency on lifting equipment for oil and gas. Any malfunction in these devices can lead to severe economic losses. Therefore, continuous and timely monitoring of the status of pumping equipment is of paramount importance to proactively prevent potential issues. In an effort to enhance this monitoring process, this study delves into multi-source data images at the well site and extends traditional information analysis methods. It introduces an improved YOLOv7 method based on switchable atrous convolution. While the YOLOv7 algorithm achieves a balance between speed and accuracy, its robustness in non-standard environments is suboptimal. To address this limitation, we propose the utilization of a switchable atrous convolution method for enhancement, thereby augmenting the adaptability of the model. Images of pumping units from diverse scenarios are actively collected and utilized to construct training, validation, and test sets. Different models, including YOLOv7SAC, YOLOv7, and YOLOv5-n, undergo testing, and their detection performances are systematically compared in complex environments. Experimental findings demonstrate that YOLOv7SAC consistently attains optimal detection results across various scenes. In conclusion, the study suggests that the combination of the YOLOv7 model with switchable atrous convolution proves effective for detecting pumping unit equipment in complex scenarios. This provides robust theoretical support for the detection and identification of pumping equipment issues under challenging conditions.
A Survey of Photovoltaic Panel Overlay and Fault Detection Methods
Authors: Yang Cheng , Sun Fuhao , Zou Yujie , Lv Zhipeng , Xue Liang , Jiang Chao , Liu Shuangyu , Zhao Bochao , Cui Haoyang
Published: February 2024

Photovoltaic (PV) panels are prone to experiencing various overlays and faults that can affect their performance and efficiency. The detection of photovoltaic panel overlays and faults is crucial for enhancing the performance and durability of photovoltaic power generation systems. It can minimize energy losses, increase system reliability and lifetime, and lower maintenance costs. Furthermore, it can contribute to the sustainable development of photovoltaic power generation systems, which can reduce our reliance on conventional energy sources and mitigate environmental pollution and greenhouse gas emissions in line with the goals of sustainable energy and environmental protection. In this paper, we provide a comprehensive survey of the existing detection techniques for PV panel overlays and faults from two main aspects. The first aspect is the detection of PV panel overlays, which are mainly caused by dust, snow, or shading. We classify the existing PV panel overlay detection methods into two categories, including image processing and deep learning methods, and analyze their advantages, disadvantages, and influencing factors. We also discuss some other methods for overlay detection that do not process images to detect PV panel overlays. The second aspect is the detection of PV panel faults, which are mainly caused by cracks, hot spots, or partial shading. We categorize existing PV panel fault detection methods into three categories, including electrical parameter detection methods, detection methods based on image processing, and detection methods based on data mining and artificial intelligence, and discusses their advantages and disadvantages.
Enhancing DMC Production from CO2: Tuning Oxygen Vacancies and In Situ Water Removal
Authors: Wang Kaiying , Li Shiguang , Yu Miao , Liang Xinhua
Published: February 2024

The direct synthesis of dimethyl carbonate (DMC) from methanol and CO2 presents an attractive route to turn abundant CO2 into value-added chemicals. However, insufficient DMC yields arise due to the inert nature of CO2 and the limitations of reaction equilibrium. Oxygen vacancies are known to facilitate CO2 activation and improve catalytic performance. In this work, we have demonstrated that tuning oxygen vacancies in catalysts and implementing in situ water removal can enable highly efficient DMC production from CO2. CexZryO2 nanorods with abundant oxygen vacancies were synthesized via a hydrothermal method. In liquid-phase DMC synthesis, the Ce10Zr1O2 nanorods exhibited a 1.7- and 1.4-times higher DMC yield compared to CeO2 nanoparticles and undoped CeO2 nanorods, respectively. Zr doping yielded a CeZr solid solution with increased oxygen vacancies, promoting CO2 adsorption and activation. In addition, adding 2-cyanopyridine as an organic dehydrating agent achieved an outstanding 87% methanol conversion and >99% DMC selectivity by shifting the reaction equilibrium to the desired product. Moreover, mixing CeO2 nanoparticles with hydrophobic fumed SiO2 in gas-phase DMC synthesis led to a doubling of DMC yield. This significant increase was attributed to the faster diffusion of water molecules away from the catalyst surface, facilitated by the hydrophobic SiO2. This study illustrates an effective dual strategy of enhancing oxygen vacancies and implementing in situ water removal to boost DMC production from CO2. The strategy can also be applied to other reactions impacted by water accumulation.
Optimization Examples for Water Allocation, Energy, Carbon Emissions, and Costs
Authors: Alamanos Angelos , Garcia Jorge Andres
Published: February 2024

The field of Water Resources Management (WRM) is becoming increasingly interdisciplinary, realizing its direct connections with energy, food, and social and economic sciences, among others. Computationally, this leads to more complex models, wherein the achievement of multiple goals is sought. Optimization processes have found various applications in such complex WRM problems. This entry considers the main factors involved in modern WRM, and puts them in a single optimization problem, including water allocation from different sources to different uses and non-renewable and renewable energy supplies, with their associated carbon emissions and costs. The entry explores the problem mathematically by presenting different optimization approaches, such as linear, fuzzy, dynamic, goal, and non-linear programming models. Furthermore, codes for each model are provided in Python, an open-source language. This entry has an educational character, and the examples presented are easily reproducible, so this is expected to be a useful resource for students, modelers, researchers, and water managers.
Assessing the Distribution and Driving Effects of Net Primary Productivity along an Elevation Gradient in Subtropical Regions of China
Authors: Xu Bo , Feng Zhongke , Chen Yuan , Zhou Yuchen , Shao Yakui , Wang Zhichao
Published: February 2024

Globally, forest ecosystems, especially subtropical forests, play a central role in biogeochemical cycles and climate regulation, demonstrating their irreplaceable function. The subtropical region of China, characterized by its unique forest ecosystem, complex terrain, climate heterogeneity, diverse vegetation types, and frequent human activities, underscores the importance of the in-depth study of its net primary productivity (NPP). This paper employs the eddy covariance–light use efficiency (EC-LUE) model to quantitatively estimate the gross primary productivity (GPP) of this region from 2001 to 2018, followed by an estimation of the actual net primary productivity (ANPP) using the carbon use efficiency (CUE). The results showed that over these 18 years, the annual average ANPP was 677.17 gC m−2 a−1, exhibiting an overall increasing trend, particularly in mountainous areas, reserves, and the cultivated lands of the northeastern plains, whereas a significant decrease was observed around the urban agglomerations on the southeast coast. Furthermore, the Thornthwaite memorial model was applied to calculate the potential net primary productivity (PNPP), and diverse scenarios were set to quantitatively evaluate the impact of climate change and human activities on the vegetation productivity in the study area. It was found that in areas where the ANPP increased, both human activities and climate change jointly influenced ANPP dynamics; in areas with a decreased ANPP, the impact of human activities was particularly significant. Additionally, the heterogeneous distribution of ANPP across different altitudinal gradients and the driving effects of various climatic factors were analyzed. Finally, a partial correlation analysis was used to examine the relationships between the temperature, precipitation, and ANPP. This study indicated that temperature and precipitation have a substantial impact on the growth and distribution of vegetation in the region, yet the extent of this influence shows considerable variation among different areas. This provides a robust scientific basis for further research and understanding of the carbon dynamics of subtropical forest ecosystems and their role in the global carbon cycle.
Growth Response of Thai Pine (Pinus latteri) to Climate Drivers in Tak Province of Northwestern Thailand
Authors: Inthawong Sasiwimol , Pumijumnong Nathsuda , Muangsong Chotika , Buajan Supaporn , Cai Binggui , Chatwatthana Rattanakorn , Chareonwong Uthai , Phewphan Uthaiwan
Published: February 2024

The long-term effects of climate change and climate extremes have been associated with changes in tree growth and forest productivity worldwide, and dendrochronological analyses are important tools that can be used to investigate the influence of climatic forces on tree growth at a particular site. In this study, a 180-year tree ring width chronology (spanning from 1843 to 2022) of living pine trees (Pinus latteri) in Tak province, northwestern Thailand, was developed. The analysis of the climate–tree growth relationship indicated the influences of the annual total rainfall (r = 0.60, p < 0.001) and annual averaged relative humidity (r = 0.47, p < 0.001) on tree growth in this area. Anomalously high (for example, in 1853, 1984, 2011, and 2018) and low growths (for example, in 1954, 1983, 1992, and 1996) were found. Growth anomalies in the Thai pine in this study were related to changes in abnormal and extreme rainfall (r = 0.94, p < 0.001) and the El Niño Southern Oscillation (ENSO). Our results confirm that rainfall and relative humidity are the main climatic factors regulating the radial growth of Thai pine. This finding could be an important contribution to further research on the effects of climate change and extreme weather events on the vulnerability of tropical and subtropical trees in this region.
Are There Dragon Kings in the Stock Market?
Authors: Liu Jiong , Dashti Moghaddam Mohammadamin , Serota Rostislav A.
Published: February 2024

In this study, we undertake a systematic study of historic market volatility spanning roughly five preceding decades. We focus specifically on the time series of the realized volatility (RV) of the S&P500 index and its distribution function. As expected, the largest values of RV coincide with the largest economic upheavals of the period: Savings and Loan Crisis, Tech Bubble, Financial Crisis and Covid Pandemic. We address the question of whether these values belong to one of the three categories: Black Swans (BS), that is, they lie on scale-free, power-law tails of the distribution; Dragon Kings (DK), defined as statistically significant upward deviations from BS; or Negative Dragons Kings (nDK), defined as statistically significant downward deviations from BS. In analyzing the tails of the distribution with RV>40, we observe the appearance of “potential” DK, which eventually terminate in an abrupt plunge to nDK. This phenomenon becomes more pronounced with the increase in the number of days over which the average RV is calculated—here from daily, n=1, to “monthly”, n=21. We fit the entire distribution with a modified Generalized Beta (mGB) distribution function, which terminates at a finite value of the variable but exhibits a long power-law stretch prior to that, as well as a Generalized Beta Prime (GB2) distribution function, which has a power-law tail. We also fit the tails directly with a straight line on a log-log scale. In order to ascertain BS, DK or nDK behavior, all fits include their confidence intervals and p-values are evaluated for the data points to check whether they can come from the respective distributions.
Preparation of a Novel Lignocellulose-Based Aerogel by Partially Dissolving Medulla Tetrapanacis via Ionic Liquid
Authors: Quan Long , Shi Xueqian , Zhang Jie , Shu Zhuju , Zhou Liang
Published: February 2024

A novel lignocellulosic aerogel, MT-LCA, was successfully prepared from MT by undergoing partial dissolution in an ionic liquid, coagulation in water, freezing in liquid nitrogen, and subsequent freeze-drying. The MT-LCA preserves its original honeycomb-like porous structure, and the newly formed micropores contribute to increased porosity and specific surface area. FT-IR analysis reveals that MT, after dissolution and coagulation, experiences no chemical reactions. However, a change in the crystalline structure of cellulose is observed, transitioning from cellulose I to cellulose II. Both MT and MT-LCA demonstrate a quasi-second-order kinetic process during methylene blue adsorption, indicative of chemical adsorption. The Langmuir model proves to be more appropriate for characterizing the methylene blue adsorption process. Both adsorbents exhibit monolayer adsorption, and their effective adsorption sites are uniformly distributed. The higher porosity, nanoscale micropores, and larger pore size in MT-LCA enhance its capillary force, providing efficient directional transport performance. Consequently, the prepared MT-LCA displays exceptional compressive performance and efficient directional transport capabilities, making it well-suited for applications requiring high compressive performance and selective directional transport.