bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimization of Preparation Process for Chitosan-Coated Pomelo Peel Flavonoid Microcapsules and Its Effect on Waterborne Paint Film Properties
Authors: Deng Jinzhe , Ding Tingting , Yan Xiaoxing
Published: August 2024

In order to prepare chitosan-coated pomelo peel flavonoid microcapsules with antibacterial properties, chitosan was used as the wall material for the purpose of coating the core material, pomelo peel flavonoids. The pH of the microcapsule crosslinking reaction was 7.5, the mass ratio of the microcapsule core material to the wall material was 1:1, and the concentration of the emulsifier was 1%. The microcapsules obtained under these preparation conditions exhibited superior performance, morphology, and dispersion. Additionally, the yield and coating rates were recorded at 22% and 50%, respectively. To prepare the paint film, the microcapsules were added into the coatings at varying concentrations of 0%, 3.0%, 6.0%, 9.0%, 12.0%, and 15.0%. The antibacterial efficacy of the paint film for both bacteria was progressively enhanced with the incorporation of microcapsules. The antibacterial efficacy against Staphylococcus aureus was observed to be higher than that against Escherichia coli. As the content of microcapsules increased, the color difference in the paint film increased, the gloss loss rate increased, and the light transmission rate reduced. The tensile property and elongation at break reduced, and the roughness increased. At a microcapsule content of 6.0%, the paint film exhibited superior overall performance, with an antibacterial efficacy against Escherichia coli and Staphylococcus aureus of 46.3% and 56.7%, respectively. The color difference was 38.58. The gloss loss rate was 41.0%, the light transmission rate was 90.4%, and the paint film exhibited a large elastic region, with an elongation at break of 21.5% and a roughness of 1.46 μm.
New Sustainable Intumescent Coating Based on Polyphenols Obtained from Wood Industry Waste
Authors: Montoya Luis F. , Flores Julio , Ramírez Jesús , Rojas David , Oñate Ángelo , Fernández Katherina , Jaramillo Andrés F. , Miranda Cristian , Melendrez Manuel F.
Published: August 2024

The global proliferation of Pinus radiata, known for its rapid growth and wood density, has led to an environmental challenge—significant waste production, especially bark, without a clear valorization route. This waste poses ecological concerns, and despite the crucial role of forest resources in structural applications, their limited fire resistance requires the use of coatings. However, traditional coatings lack an eco-friendly footprint. Addressing this challenge, this study aims to develop an intumescent coating with tannins extracted from waste bark, offering a sustainable alternative. This not only repurposes waste on a global scale but also aligns with the imperative for environmentally friendly materials, contributing to sustainable practices in the construction and wood treatment industry. This study achieved an eco-friendly FRR15 (fire resistance ratio 15) fire resistance classification with a 15% equivalence of low-molecular-weight tannins, presenting a sustainable alternative to commercial products. Characterization showed low-molecular-weight tannins comparable to conventional charring agents, with high hydroxyl content and oil absorption, while high-molecular-weight tannins exhibited lower viability. A reference coating achieved FRR30 fire resistance, aligning with commercial strength. The mechanical properties of tannin-based coatings matched commercial standards, with increased abrasion resistance and adhesion and decreased flexibility. Intumescent coatings with higher tannin content significantly reduced wood substrate charring and mass loss in flame response assessments.
Structure, Mechanical Properties and Water Vapor Corrosion Resistance of AlCrNbSiTiN High-Entropy Nitride Coatings Deposited by RF Magnetron Sputtering
Authors: Wang Xuanzheng , Liu Jie , Liu Yingfan , Li Wentao , Chen Yanming , Yang Bing
Published: August 2024

High-entropy nitride AlCrNbSiTiN coatings were deposited by RF magnetron sputtering at different bias voltages. The structure, mechanical properties and water vapor corrosion resistance of the coatings were systematically studied. The coatings exhibit a face-centered cubic (FCC) structure, while achieving a hardness up to 35.8 GPa. The main wear mechanisms of the coatings are adhesive wear and oxidation wear. After 200 h of water vapor corrosion, the content of O in the coatings is 4.30 at.%.
A Note on the Quasigroup of Lai–Massey Structures
Authors: Teşeleanu George
Published: August 2024

In our paper, we explore the consequences of replacing the commutative group operation used in Lai–Massey structures with a quasigroup operation. We introduce four quasigroup versions of the Lai–Massey structure and prove that for quasigroups isotopic with a group G, the complexity of launching a differential attack against these variants of the Lai–Massey structure is equivalent to attacking an alternative structure based on G. Then, we provide the conditions needed for correct decryption and further refine the resulting structure. The emerging structure is both intriguing and novel, and we hope that it will form the basis for future secure block ciphers based on non-commutative groups. In the case of commutative groups, we show that the resulting structure reduces to the classic Lai–Massey structure.
Natural Language Processing for Hardware Security: Case of Hardware Trojan Detection in FPGAs
Authors: Dofe Jaya , Danesh Wafi , More Vaishnavi , Chaudhari Aaditya
Published: August 2024

Field-programmable gate arrays (FPGAs) offer the inherent ability to reconfigure at runtime, making them ideal for applications such as data centers, cloud computing, and edge computing. This reconfiguration, often achieved through remote access, enables efficient resource utilization but also introduces critical security vulnerabilities. An adversary could exploit this access to insert a dormant hardware trojan (HT) into the configuration bitstream, bypassing conventional security and verification measures. To address this security threat, we propose a supervised learning approach using deep recurrent neural networks (RNNs) for HT detection within FPGA configuration bitstreams. We explore two RNN architectures: basic RNN and long short-term memory (LSTM) networks. Our proposed method analyzes bitstream patterns, to identify anomalies indicative of malicious modifications. We evaluated the effectiveness on ISCAS 85 benchmark circuits of varying sizes and topologies, implemented on a Xilinx Artix-7 FPGA. The experimental results revealed that the basic RNN model showed lower accuracy in identifying HT-compromised bitstreams for most circuits. In contrast, the LSTM model achieved a significantly higher average accuracy of 93.5%. These results demonstrate that the LSTM model is more successful for HT detection in FPGA bitstreams. This research paves the way for using RNN architectures for HT detection in FPGAs, eliminating the need for time-consuming and resource-intensive reverse engineering or performance-degrading bitstream conversions.
Translation Elongation Factor 1-Alpha Sequencing Provides Reliable Tool for Identification of Fusarium graminearum Species Complex Members
Authors: Yörük Emre , Yli-Mattila Tapani
Published: August 2024

The Fusarium graminearum species complex (FGSC) is a worldwide phytopathogenic fungus of small grain cereals. Genetics and bioinformatics tools have been providing an efficient strategy for identifying FGSC. However, the potential reliability of tef1−α sequencing in FGSC members has not been well investigated. In this study, the tef1−α sequencing data of 246 FGSC members, one F. culmorum, and one F. solani isolate were subjected to distance-, character-, and PCA-based phylogenetic analysis. Linux terminals and the R programming language were used in phylogenetic analysis. The Unweighted Pair Group Method with Arithmetic Mean (UPGMA) and maximum likelihood methods produced relatively more homogenous F. graminearum sensu stricto (Fgss) and F. asiaticum isolates. Fgss and F. asiaticum isolates co-clustered in two separate sub-divisions in the ML and UPGMA methods, with significant differences in the Chi2 test (p < 0.05). PCA profiling revealed a low level of variation in FGSC members, with 99–99.5% percentages in axis 1. An increased number of taxa and isolates would be tested for tef1−α in future studies. To our knowledge, this is also the first study to combine phylogenetic methods with PCA tests for comprehensive characterization of FGSC members.
The Postoperative Visual and Refractive Outcomes of Trifocal and Extended Depth-of-Focus Intraocular Lenses in Patients with Different Biometric Characteristics
Authors: Lee Chia-Yi , Chen Hung-Chi , Lian Ie-Bin , Huang Jing-Yang , Yang Shun-Fa , Chang Chao-Kai
Published: August 2024

We aimed to survey the potential correlation between biometric parameters and postoperative outcomes after implanting extended depth-of-focus (EDOF) intraocular lenses (IOLs) and trifocal IOLs. A retrospective cohort study was conducted, and patients receiving EDOF or trifocal IOL implantations were included. In total, 36 and 26 eyes were enrolled in the EDOF and trifocal groups, respectively. The primary outcomes of this study were the postoperative uncorrected distance visual acuity (UDVA), uncorrected near visual acuity (UNVA), and spherical equivalent (SE). The generalized linear model was applied to evaluate the adjusted odds ratio (aOR) and 95% confidence intervals (CIs) of primary outcomes in patients with different biometric characters. The final UDVA of the EDOF group was significantly better than that of the trifocal group (p = 0.020), and the UNVA and SE did not show significant differences between the two groups throughout the postoperative period (all p > 0.05). In a multivariable analysis, the UDVA was significantly better in the EDOF group than in the trifocal group (p = 0.038). For the subgroup analysis, the high axial length (AXL) value correlated to a lower postoperative UDVA in the EDOF group (both p < 0.05). Additionally, a large white-to-white (WTW) diameter was related to worse postoperative UNVA in the trifocal group (p = 0.042), and a high AXL was associated with higher SE in both the EDOF and trifocal groups (both p < 0.05). In conclusion, a high AXL correlates to worse postoperative outcomes in both the EDOF and trifocal IOLs, and trifocal IOL outcomes could be affected by large WTW diameters.
Best Practices for the Use of High-Frequency Ultrasound to Guide Aesthetic Filler Injections—Part 1: Upper Third of the Face
Authors: Sigrist Rosa , Desyatnikova Stella , Chammas Maria Cristina , Vasconcelos-Berg Roberta
Published: August 2024

Filler injections in the upper face pose significant challenges due to its complex anatomy and proximity to vascular structures. High-frequency Doppler ultrasound offers real-time visualization of facial anatomy, improving both safety and aesthetic outcomes. This paper presents a detailed overview of the ultrasonographic anatomy of the temples, forehead, and glabella, along with reproducible, ultrasound-guided filler injection techniques for these areas. We use two scanning techniques previously described: “scan before injecting” and “scan while injecting”, applicable to subdermal, interfascial, and supraperiosteal planes in the temporal region, as well as the glabella, forehead, and supraorbital region. Ultrasound guidance for filler injections in the upper face can enhance procedural efficacy and safety. By integrating real-time imaging, practitioners can navigate the intricate vascular anatomy more effectively, thereby minimizing the risk of complications. This study highlights the need for ongoing research and continuous education to further refine these techniques and improve patient outcomes.
Assessment of an Artificial Intelligence Tool for Estimating Left Ventricular Ejection Fraction in Echocardiograms from Apical and Parasternal Long-Axis Views
Authors: Vega Roberto , Kwok Cherise , Rakkunedeth Hareendranathan Abhilash , Nagdev Arun , Jaremko Jacob L.
Published: August 2024

This work aims to evaluate the performance of a new artificial intelligence tool (ExoAI) to compute the left ventricular ejection fraction (LVEF) in echocardiograms of the apical and parasternal long axis (PLAX) views. We retrospectively gathered echocardiograms from 441 individual patients (70% male, age: 67.3 ± 15.3, weight: 87.7 ± 25.4, BMI: 29.5 ± 7.4) and computed the ejection fraction in each echocardiogram using the ExoAI algorithm. We compared its performance against the ejection fraction from the clinical report. ExoAI achieved a root mean squared error of 7.58% in A2C, 7.45% in A4C, and 7.29% in PLAX, and correlations of 0.79, 0.75, and 0.89, respectively. As for the detection of low EF values (EF < 50%), ExoAI achieved an accuracy of 83% in A2C, 80% in A4C, and 91% in PLAX. Our results suggest that ExoAI effectively estimates the LVEF and it is an effective tool for estimating abnormal ejection fraction values (EF < 50%). Importantly, the PLAX view allows for the estimation of the ejection fraction when it is not feasible to acquire apical views (e.g., in ICU settings where it is not possible to move the patient to obtain an apical scan).
Improving the Diagnosis of Autoimmune Gastritis: From Parietal Cell Antibodies to H+/K+ ATPase Antibodies
Authors: Tonegato Michela , Panozzo Maria Piera , Antico Antonio , Bizzaro Nicola
Published: August 2024

Parietal cell autoantibodies (PCAs), which recognize the enzyme H+/K+-ATPase as a target, are considered to be a diagnostic marker of autoimmune gastritis and pernicious anemia; these conditions are characterized by the presence of corpus atrophic gastritis. Circulating PCAs can be detected using several analytical methods that are commonly available in the clinical laboratory. Traditionally, indirect immunofluorescence (IIF) on rodent or primate stomach tissue is used as a screening test for the detection of PCAs. However, IIF suffers from a high inter-observer variability and lacks standardization. In addition, like immunoblotting, results are expressed only in a qualitative or semi-quantitative manner. Based on the few available studies that are reviewed herein, quantitative enzyme-linked immunosorbent assays (ELISAs) and fluorescence enzyme immunoassays (FEIAs) using purified H+/K+-ATPase perform better than IIF in the detection of PCAs, displaying higher sensitivity and utility in monitoring the disease. In light of their higher diagnostic accuracy, these solid-phase methods should be preferred to IIF in the screening of autoimmune atrophic gastritis. The use of methods to detect antibodies versus a specific subunit of H+/K+-ATPase (α or β) is currently confined to the world of research. Further investigation is required to define the clinical utility of H+/K+-ATPase subunit detection.
Robot-Assisted 2D Fluoroscopic Needle Placement—A Phantom Study
Authors: Scharll Yannick , Radojicic Nenad , Laimer Gregor , Schullian Peter , Bale Reto
Published: August 2024

Rationale and Objectives: To evaluate the targeting accuracy of a novel robot-assisted guidance technique relying on one pair of 2D C-arm images. Material and Methods: In total, 160 punctures were carried out semi-automatically by using a novel robotic device. The needle’s paths were planned based on one pair of 2D fluoroscopic images from different angles. Conically shaped aluminum tips inside a gelatin-filled plexiglass phantom served as targets. The accuracy of the needle placement was assessed by taking control CTs and measuring the Euclidean distance (ED) and normal distance (ND) between the needle and the target point. In addition, the procedural time per needle placement was evaluated. Results: The accomplished mean NDs at the target for the 45°, 60°, 75° and 90° angles were 1.86 mm (SD ± 0.19), 2.68 mm (SD ± 0.18), 2.19 mm (SD ± 0.18) and 1.86 mm (SD ± 0.18), respectively. The corresponding mean EDs were 2.32 mm (SD ± 0.16), 2.68 mm (SD ± 0.18), 2.65 mm (SD ± 0.16) and 2.44 mm (SD ± 0.15). The mean duration of the total procedure, including image acquisition, trajectory planning and placement of four needles sequentially, was 12.7 min. Conclusions: Robotic guidance based on two 2D fluoroscopy images allows for the precise placement of needle-like instruments at the first attempt without the need for using an invasive dynamic reference frame. This novel approach seems to be a valuable tool for the precise targeting of various anatomical structures that can be identified in fluoroscopic images.
Anatomical Relationships of the Proximal Attachment of the Hamstring Muscles with Neighboring Structures: From Ultrasound, Anatomical and Histological Findings to Clinical Implications
Authors: Miguel-Pérez Maribel , Iglesias-Chamorro Pere , Ortiz-Miguel Sara , Ortiz-Sagristà Juan-Carlos , Möller Ingrid , Blasi Joan , Agullò Josep , Martinoli Carlo , Pérez-Bellmunt Albert
Published: August 2024

Background: Injuries of the proximal attachment of the hamstring muscles are common. The present study aimed to investigate the relationship of the proximal attachment of the hamstring muscles with neighboring structures comprehensively. Methods: A total of 97 hemipelvis from 66 cryopreserved specimens were evaluated via ultrasound, anatomical and histological samples. Results: The proximal attachment of the hamstring muscles presents a hyperechogenic line surrounding the origin of the semimembranosus and the long head of the biceps femoris muscles, as well as another hyperechogenic line covering the sciatic nerve. The anatomical and histological study confirms the ultrasound results and shows different layers forming the sacrotuberous ligament. Furthermore, it shows that the proximal attachment of the semimembranosus muscle has a more proximal origin than the rest of the hamstring muscles. Moreover, this muscle shares fibers with the long head of the biceps femoris muscle and expands to the adductor magnus muscle. The histological analysis also shows the dense connective tissue of the retinaculum covering the long head of the biceps femoris and semimembranosus muscles, as well as the expansion covering the sciatic nerve. Conclusions: These anatomical relationships could explain injuries at the origin of the hamstring muscles.
Evaluation of Cytotoxicity of the Dental Materials TheraCal LC, TheraCal PT, ApaCal ART and Biodentine Used in Vital Pulp Therapy: In Vitro Study
Authors: Novotná Barbora , Holík Pavel , Morozova Yuliya , Rosa Matej , Galandáková Adéla , Langová Kateřina
Published: August 2024

(1) Background: The aim of this study was to compare the cytotoxicity of selected resin-modified materials used in direct contact with the dental pulp (TheraCal LC, TheraCal PT, and ApaCal ART) with calcium silicate cement (Biodentine). (2) Methods: The mouse fibroblast Balb/3T3 cell line and the extracts of tested materials in four concentrations were used for the testing. An MTT assay was performed in three independent experiments with six replicates for each concentration of tested material. The cell viability (%) and cytotoxicity were expressed (cytotoxic effect is considered in cases where the cell viability is lower than 70%). The mean of the cell viability and the standard deviation were expressed for each material at all concentrations. ANOVA and Dunnet’s post hoc tests were used for the statistical analysis. All of these tests were performed at the 0.05 significance level. (3) Results: At all concentrations, the cell viability was statistically significantly lower (p ≤ 0.002) for all tested materials compared to Biodentine. ApaCal ART showed a high level of cytotoxicity at all concentrations (cell viability lower than 47.71%, p < 0.0001). The same result was found for TheraCal LC at concentrations of 100%, 50% and 25% and TheraCal PT at concentrations of 100% and 50%. TheraCal LC at a 10% concentration (cell viability 68.18%) and TheraCal PT at a 25% concentration (cell viability 60.63%) indicated potential cytotoxicity. TheraCal PT at a 10% concentration was not found to be cytotoxic (cell viability 79.18%, p = 0.095). (4) Conclusion: The resin-modified calcium silicate and calcium phosphate materials showed higher cytotoxic potential, so they should be used with caution when in direct contact with the dental pulp.
Adaptive Dynamic Learning Rate Optimization Technique for Colorectal Cancer Diagnosis Based on Histopathological Image Using EfficientNet-B0 Deep Learning Model
Authors: Abd El-Ghany Sameh , Mahmood Mahmood A. , Abd El-Aziz A. A.
Published: August 2024

The elevated death rate associated with colorectal cancer (CRC) continues to impact human life worldwide. It helps prevent disease and extend human life by being detected early. CRC is frequently diagnosed and detected through histopathological examination. The decision is based on clinicians’ subjective perceptions and daily image analyses. Histological image (HI) classification is difficult because HIs contain multiple tissue types and characteristics. Therefore, deep learning (DL) models are employed to classify different kinds of CRC HIs. Therefore, to increase the efficiency of the CRC diagnostic procedure from HIs, we propose a fine-tuning model for the CRC diagnosis process with the EfficientNet-B0 DL model. The proposed model performs a multi-classification for HIs. It uses an adaptive learning rate (ALR) to overcome the overfitting problem caused by using the static learning rate (SLR) and to enhance the performance of detecting the CRC. The ALR compares the training loss value at the beginning of each epoch. If it is smaller, we increase the ALR; if it is larger, we decrease it. Our proposed model speeds diagnosis, reduces diagnostic costs, and reduces medical errors; hence, it enhances the diagnostic procedure from the patient’s perspective. We trained and evaluated the proposed model over the two datasets (NCT-CRC-HE-100K and CRC-VAL-HE-7K). Normalization and scaling methods were used to pre-process the NCT-CRC-HE-100K dataset. The EfficientNet-B0 model attained accuracy, sensitivity, specificity, precision, and an F1-score of 99.87%, 99.64%, 99.95%, 99.62%, and 99.63%, respectively when applied to the NCT-CRC-HE-100K dataset. On the CRC-VAL-HE-7K dataset, the EfficientNet-B0 model achieved 99%, 94.52%, 99.45%, 94.41%, and 94.36% for accuracy, sensitivity, specificity, precision, and F1-score, respectively. As a result, the EfficientNet-B0 model outperforms the state of the art in this field.
Traffic-Aware Intelligent Association and Task Offloading for Multi-Access Edge Computing
Authors: Nugroho Avilia Kusumaputeri , Kim Taewoon
Published: August 2024

Edge computing is a promising technology, especially for offloading users’ computationally heavy tasks. The close proximity of edge computing units to users minimizes network latency, thereby enabling delay-sensitive applications. Although optimal resource provisioning and task offloading in edge computing are widely studied in the literature, there are still some critical research gaps. In this study, we propose a traffic-aware optimal association and task-offloading approach. The proposed method does not rely solely on the average rate of offloading requests, which can differ from actual values in real time. Instead, it uses an intelligent, high-precision prediction model to forecast future offloading requests, allowing resource provisioning to be based on future sequences of requests rather than average values. Additionally, we propose an optimization-based approach that can meet task deadlines, which is crucial for mission-critical applications. Finally, the proposed approach distributes the computing load over multiple time steps, ensuring future resource scheduling and task-offloading decisions can be made with a certain level of flexibility. The proposed approach is extensively evaluated under various scenarios and configurations to validate its effectiveness. As a result, the proposed deep learning model has resulted in a request prediction error of 0.0338 (RMSE). In addition, compared to the greedy approach, the proposed approach has reduced the use of local and cloud computing from 0.02 and 18.26 to 0.00 and 0.62, respectively, while increasing edge computing usage from 1.31 to 16.98, which can effectively prolong the lifetime of user devices and reduce network latency.
Generative Adversarial Network-Based Voltage Fault Diagnosis for Electric Vehicles under Unbalanced Data
Authors: Fang Weidong , Guo Yihan , Zhang Ji
Published: August 2024

The research of electric vehicle power battery fault diagnosis technology is turning to machine learning methods. However, during operation, the time of occurrence of faults is much smaller than the normal driving time, resulting in too small a proportion of fault data as well as a single fault characteristic in the collected data. This has hindered the research progress in this field. To address this problem, this paper proposes a data enhancement method using Least Squares Generative Adversarial Networks (LSGAN). The method consists of training the original power battery fault dataset using LSGAN models to generate diverse sample data representing various fault states. The augmented dataset is then used to develop a fault diagnosis framework called LSGAN-RF-GWO, which combines a random forest (RF) model with a Gray Wolf Optimization (GWO) model for effective fault diagnosis. The performance of the framework is evaluated on the original and enhanced datasets and compared with other commonly used models such as Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and Naïve Bayes (NB). The results show that the proposed fault diagnosis scheme improves the evaluation metrics and accuracy level, proving that the LSGAN-RF-GWO framework can utilize limited data resources to effectively diagnose power battery faults.
Fully Tunable Analog Biquadratic Filter for Low-Power Auditory Signal Processing in CMOS Technologies
Authors: Jendernalik Waldemar , Jakusz Jacek
Published: August 2024

A novel Gm-C structure of a second-order continuous-time filter is proposed that allows for the independent control of the filter’s natural frequency (ω0) and quality factor (Q). The structure consists of two capacitors and four transconductors. Two transconductors together with the capacitors form a lossless second-order circuit with tunable ω0. The other two transconductors form a variable gain amplifier (VGA) which realizes an adjustable loss and thereby adjustable Q. The proposed solution can be used to implement low-voltage and low-power tunable front-end filter banks for fully integrated CMOS cochlear implants and edge intelligence accelerators. An example filter bank powered by 0.5 V and consuming 40 nW of power per single filter is designed and simulated using a 180 nm CMOS process. Circuitries for the adaptive control of transistor bias at a reduced supply voltage are proposed. The ω0 and Q control circuitries are also proposed: a delay-locked loop (DLL)-based system for fine ω0 tuning and a binary-weighted current mirror for Q adjustment. The proposed solution allows for the independent regulation of ω0 and Q within the ranges of 0.25–8 kHz and 1–14, respectively, with a relative tolerance of up to 5% across a filter bank.
Design a Robust Control System for Rubidium Fountain Clock
Authors: Liu Dandan , Zhang Hui , Bai Yang , Fan Sichen , Ruan Jun , Zhang Shougang
Published: August 2024

Rubidium fountain clocks are operated by a control system. In this study, first, the control requirements of a rubidium fountain clock are analyzed; then, a control system is designed and divided into a timing sequence control system, a data acquisition system, and a servo control system. Multiple boards based on PCI extensions for the instrumentation (PXI) bus and Labwindows/CVI software 2019 are adopted. The timing sequence control system outputs 16 transistor–transistor logic (TTL) signals and three arbitrary waveforms. The results show that these signals are synchronized within 380 ns. Moreover, the digital locking frequency of the master laser is triggered by one of the timing signal, sweeping the laser frequency in a wide range during polarization gradient cooling. Two time-of-flight signals are acquired, and Ramsey fringes are scanned using the data acquisition system. An error signal is obtained every two cycles, and it is feed back to the frequency synthesizer and a high resolution offset generator using the servo control system. Then the frequency synthesizer is locked to the Ramsey center resonance. The rubidium atomic fountain clock exhibited a frequency stability of 7.4 × 10−16 at 86,400 s. Moreover, the phase of the Rb fountain clock is compared with that of UTC (NTSC) in real time, demonstrating a frequency drift of 0.00044 ns/day/day. The whole control system is full-featured, robust and flexible, thus satisfying the requirements of rubidium fountain clocks.
On the Security of a Secure and Computationally Efficient Authentication and Key Agreement Scheme for Internet of Vehicles
Authors: Park Kisung , Kim Myeonghyun , Park Youngho
Published: August 2024

In the Internet of Vehicles (IoV) environments, vehicles and roadside units (RSUs) communicate predominantly through public channels. These vehicles and RSUs exchange various data, such as traffic density, location, speed, etc. Therefore, secure and efficient authentication and key establishment (AKE) are needed to guarantee user privacy when exchanging data between vehicles and RSUs. Recently, a secure and computationally AKE scheme have been proposed to construct secure IoV environments. In their research, the authors asserted that their AKE scheme provides comprehensive security properties, protecting against various potential threats while simultaneously ensuring session key integrity, robust mutual authentication. This paper proved that the previous scheme does not prevent various attacks using logical and mathematical analyses. Moreover, we demonstrated that this scheme does not meet the essential security requirements and correctness of security assumptions. We perform the simulation proof using AVISPA, which is well known as a formal verification tool. To enhance the resilience of attacks, we propose solutions aimed at developing more robust and efficient AKE for IoV environments.
PerFication: A Person Identifying Technique by Evaluating Gait with 2D LiDAR Data
Authors: Hasan Mahmudul , Uddin Md. Kamal , Suzuki Ryota , Kuno Yoshinori , Kobayashi Yoshinori
Published: August 2024

PerFication is a person identification technique that uses a 2D LiDAR sensor in a customized dataset KoLaSu (Kobayashi Laboratory of Saitama University). Video-based recognition systems are highly effective and are now at the forefront of research. However, it experiences bottlenecks. New inventions can cause embarrassing situations, settings, and momentum. To address the limitations of technology, one must introduce a new technology to enhance it. Using biometric characteristics are highly reliable and valuable methods for identifying individuals. Most approaches depend on close interactions with the subject. A gait is the walking pattern of an individual. Most research on identifying individuals based on their walking patterns is conducted using RGB or RGB-D cameras. Only a limited number of studies utilized LiDAR data. Working with 2D LiDAR imagery for individual tracking and identification is excellent in situations where video monitoring is ineffective, owing to environmental challenges such as disasters, smoke, occlusion, and economic constraints. This study presented an extensive analysis of 2D LiDAR data using a meticulously created dataset and a modified residual neural network. In this paper, an alternative method of person identification is proposed that circumvents the limitations of video cameras in terms of capturing difficulties. An individual is precisely identified by the system through the utilization of ankle-level 2D LiDAR data. Our LiDAR-based detection system offers a unique method for person identification in modern surveillance systems, with a painstaking dataset, remarkable results, and a break from traditional camera setups. We focused on demonstrating the cost-effectiveness and durability of LiDAR sensors by utilizing 2D sensors in our research.
AM-ESRGAN: Super-Resolution Reconstruction of Ancient Murals Based on Attention Mechanism and Multi-Level Residual Network
Authors: Xiao Ci , Chen Yajun , Sun Chaoyue , You Longxiang , Li Rongzhen
Published: August 2024

To address the issues of blurred edges and contours, insufficient extraction of low-frequency information, and unclear texture details in ancient murals, which lead to decreased ornamental value and limited research significance of the murals, this paper proposes a novel ancient mural super-resolution reconstruction method, based on an attention mechanism and a multi-level residual network, termed AM-ESRGAN. This network builds a module for Multi-Scale Dense Feature Fusion (MDFF) to adaptively fuse features at different levels for more complete structural information regarding the image. The deep feature extraction module is improved with a new Sim-RRDB module, which expands capacity without increasing complexity. Additionally, a Simple Parameter-Free Attention Module for Convolutional Neural Networks (SimAM) is introduced to address the issue of insufficient feature extraction in the nonlinear mapping process of image super-resolution reconstruction. A new feature refinement module (DEABlock) is added to extract image feature information without changing the resolution, thereby avoiding excessive loss of image information and ensuring richer generated details. The experimental results indicate that the proposed method improves PSNR/dB by 3.4738 dB, SSIM by 0.2060, MSE by 123.8436, and NIQE by 0.1651 at a ×4 scale factor. At a ×2 scale factor, PSNR/dB improves by 4.0280 dB, SSIM increases by 3.38%, MSE decreases by 62.2746, and NIQE reduces by 0.1242. Compared to mainstream models, the objective evaluation metrics of the reconstructed images achieve the best results, and the reconstructed ancient mural images exhibit more detailed textures and clearer edges.
Comparative PSO Optimisation of Microgrid Management Models in Island Operation to Minimise Cost
Authors: Žigman Dubravko , Tvorić Stjepan , Lonić Manuel
Published: August 2024

The rapid progress in renewable energy sources and the increasing complexity of energy distribution networks have highlighted the need for efficient and intelligent energy management systems. This paper presents a comparative analysis of two optimisation algorithms, P and M70, used for the optimal control of the operation of microgrids in islanded mode. The main objective is to minimise production costs while ensuring a reliable energy supply. Algorithm P prioritises the use of photovoltaic (PV) and battery storage and operates the diesel generator at minimum capacity to reduce fuel consumption and maximise the use of renewable energy sources. Algorithm M70, on the other hand, uses a heuristic approach to adaptively manage energy resources in real time. In this study, the performance of both algorithms is evaluated through simulation in different operating scenarios. The results show that both algorithms significantly improve the efficiency of the microgrid, with the M70 algorithm showing better adaptability and cost efficiency in dynamic environments. This research contributes to ongoing efforts to develop robust and scalable energy management systems for future smart grids.
Political, Economic, Social, Technical, Environmental and Legal Analysis of the Hellenic Heating and Cooling Sector
Authors: Christodoulaki Rosa , Drosou Vassiliki , Papadopoulos Agis
Published: August 2024

This work provides the policy and economic analysis of the renewable heating and cooling sector in Greece. The novelty of this study is that it unveils the different policy instruments and incentive structures in promoting renewable heating and cooling adoption, and it analyses the social, cultural and technical barriers to the adoption of renewable heating and cooling systems in Greece. To this end, a PESTEL (Political, Economic, Social, Technical, Environmental and Legal) analysis is performed, with a view to demonstrating the factors that affect Greece’s progress against the National Energy and Climate Plans (NECP), the decarbonisation of the heating and cooling sector and finally, the alignment with the long-term energy strategy. Overall, the results of this analysis show that the heating and cooling sector is still greatly dependent on the use of fossil fuels. However, there are more opportunities than barriers for the deployment of the renewable heating and cooling sector in Greece; opportunities arise from the ambitious political goals that are harmonized with those of the EU, targeting climate-neutrality by 2050.
Electricity Behavior Modeling and Anomaly Detection Services Based on a Deep Variational Autoencoder Network
Authors: Lin Rongheng , Chen Shuo , He Zheyu , Wu Budan , Zou Hua , Zhao Xin , Li Qiushuang
Published: August 2024

Understanding electrical load profiles and detecting anomaly behaviors are important to the smart grid system. However, current load identification and anomaly analysis are based on static analysis, and less consideration is given to anomaly findings under load change conditions. This paper proposes a deep variational autoencoder network (DVAE) for load profiles, along with anomaly analysis services, and introduces auto-time series data updating strategies based on sliding window adjustment. DVAE can help reconstruct the load curve and measure the difference between the original and the newer curve, whose measurement indicators include reconstruction probability and Pearson similarity. Meanwhile, the design of the sliding window strategy updates the data and DVAE model in a time-series manner. Experiments were carried out based on datasets from the U.S. Department of Energy and from Southeast China. The results showed that the proposed services could result in a 5% improvement in the AUC value, which helps to identify the anomaly behavior.
Research on Gas Channeling Identification Using the Fuzzy Comprehensive Evaluation Method
Authors: Liu Yang , Hao Mingqiang , Bi Ran , Bian Chaoliang , Wang Xiaoqing
Published: August 2024

With the development of the petroleum industry and advancements in technology, gas injection techniques have gradually matured and become an important means to enhance oil recovery in reservoir development. Gas channeling is a major challenge in the process of gas injection development. The presence of gas channeling can lead to a decrease in the swept volume of gas flooding, severely affecting the effectiveness of gas injection development. This paper focuses on low-permeability reservoirs, comprehensively analyzing the development characteristics of low-permeability reservoirs and the dynamic characteristics of gas flooding production. It selects and evaluates indicators for assessing the development degree of gas channeling and establishes a fuzzy comprehensive evaluation method for evaluating gas channeling in the gas injection development of low-permeability reservoirs. Based on the evaluation values derived from the fuzzy comprehensive evaluation, it classifies the development levels of gas channeling. Application in oilfield cases shows that the evaluation results of this method are generally consistent with the dynamic response of production data, with high evaluation accuracy. This provides strong support for implementing gas channeling prevention and control measures on site and improving the effectiveness of gas injection development.
A Bi-Level Reactive Power Optimization for Wind Clusters Integrating the Power Grid While Considering the Reactive Capability
Authors: Ma Xiping , Zhen Wenxi , Xu Rui , Dong Xiaoyang , Li Yaxin
Published: August 2024

With the integration of large-scale wind power clusters into the power system, wind farms play a crucial role in grid reactive power regulation. However, the range of its reactive power remains uncertain, posing challenges in formulating a viable program for regulating reactive power to ensure the safe and cost-effective operation of the power system. Based on this, this paper develops a bi-level reactive power optimization for wind clusters integrating the power grid while considering the reactive capability. Firstly, this paper carries out a refined analysis of the wind power clusters, taking into account the characteristics of different areas to estimate the exact value of the reactive power capability in wind power clusters. Secondly, a bi-level reactive power optimization model is established. The upper-layer optimization aims to minimize active losses and voltage deviation in power system operation, while the lower-layer optimization focuses on maximizing reactive power margin utilization in wind farms. To solve this bi-level optimization model, an improved artificial fish swarm algorithm (AFSA) is employed, which decouples real variables and integer variables to enhance the optimization ability of the algorithm. Finally, the effectiveness of our proposed optimization strategy and algorithm is validated through the simulation results.
Research into the Fast Calculation Method of Single-Phase Transformer Magnetic Field Based on CNN-LSTM
Authors: Peng Qingjun , Zhu Xiaoxian , Hong Zhihu , Zou Dexu , Guo Renjie , Chu Desheng
Published: August 2024

Magnetic field is one of the basic data for constructing a transformer digital twin. The finite element transient simulation takes a long time and cannot meet the real-time requirements of a digital twin. According to the nonlinear characteristics of the core and the timing characteristics of the magnetic field, this paper proposes a fast calculation method of the spatial magnetic field of the transformer, considering the nonlinear characteristics of the core. Firstly, based on the geometric and electrical parameters of the single-phase double-winding test transformer, the corresponding finite element simulation model is built. Secondly, the key parameters of the finite element model are parametrically scanned to obtain the nonlinear working condition data set of the test transformer. Finally, a deep learning network integrating a convolutional neural network (CNN) and a long short-term memory network (LSTM) is built to train the mapping relationship between winding voltage, current, and the spatial magnetic field so as to realize the rapid calculation of the transformer magnetic field. The results show that the calculation time of the deep learning model is greatly shortened compared with the finite element model, and the model calculation results are consistent with the experimental measurement results.
clicSAND for OSeMOSYS: A User-Friendly Interface Using Open-Source Optimisation Software for Energy System Modelling Analysis
Authors: Cannone Carla , Allington Lucy , de Wet Nicki , Shivakumar Abhishek , Goyns Philip , Valderrama Cesar , Kell Alexander , Plazas Niño Fernando Antonio , Mohanty Reema , Kapor Vedran , Wright Jarrad , Yeganyan Rudolf , Tan Naomi , To Long Seng , Harrison John , Howells Mark
Published: August 2024

Energy modelling plays a crucial role in assisting governmental and policymaking bodies to strategise long-term investments within the context of energy transition. Among the well-established open-source optimisation models, OSeMOSYS—the Open-Source Energy Modelling System—stands out. This paper introduces clicSAND, a novel user interface designed for OSeMOSYS, aimed at reducing the learning curve and supporting novice energy modelers in efficiently conducting long-term investment analyses. clicSAND, freely available and open-source, features a user-friendly Excel interface for data input, integrated solvers, and a visualisation dashboard for result interpretation. The outcomes, projected up to 2070, hold the potential to inform policy decisions and mobilise financial resources for sustainable development endeavors, such as ensuring affordable and secure energy supply and mitigating climate change impacts. This advancement not only democratises access to energy modelling tools but also empowers policymakers and stakeholders to conduct thorough long-term investment analyses with ease. This paper elaborates on clicSAND’s key advantages, architecture, and functionalities. Additionally, it discusses the evolutionary journey from clicSAND 1.0 to 3.0, emphasising a commitment to continuous improvement and user-centric adaptation, thereby enhancing its utility and relevance. The inclusion of a South African case study, conducted during the EMP-A (Energy Modelling Platform for Africa) 2021 international capacity-building event, showcases clicSAND’s efficacy in facilitating knowledge transfer and skill development among inexperienced users, while providing a tangible example of its application in addressing specific regional energy challenges and policy contexts. Finally, current applications and future extensions of the software are also presented.
Review on CO2–Brine Interaction in Oil and Gas Reservoirs
Authors: Wang Chanfei , Wu Songtao , Shen Yue , Li Xiang
Published: August 2024

Carbon neutrality has become a global common goal. CCUS, as one of the technologies to achieve carbon neutrality, has received widespread attention from academia and industry. After CO2 enters the formation, under the conditions of formation temperature and pressure, supercritical CO2, formation water, and rock components interact, which directly affects the oil and gas recovery and carbon sequestration efficiency. In this paper, the recent progress on CO2 water–rock interaction was reviewed from three aspects, including (i) the investigation methods of CO2 water–rock interaction; (ii) the variable changes of key minerals, pore structure, and physical properties; and (iii) the nomination of suitable reservoirs for CO2 geological sequestration. The review obtains the following three understandings: (1) Physical simulation and cross-time scale numerical simulation based on formation temperature and pressure conditions are important research methods for CO2 water–rock interaction. High-precision mineral-pore in situ comparison and physical property evolution evaluation are important development directions. (2) Sensitive minerals in CO2 water–rock interaction mainly include dolomite, calcite, anhydrite, feldspar, kaolinite, and chlorite. Due to the differences in simulated formation conditions or geological backgrounds, these minerals generally show the pattern of dissolution or precipitation or dissolution before precipitation. This differential evolution leads to complex changes in pore structure and physical properties. (3) To select the suitable reservoir for sequestration, it is necessary to confirm the sequestration potential of the reservoir and the later sequestration capacity, and then select the appropriate layer and well location to start CO2 injection. At the same time, these processes can be optimized by CO2 water–rock interaction research. This review aims to provide scientific guidance and technical support for shale oil recovery and carbon sequestration by introducing the mechanism of CO2 water–rock interaction, expounding the changes of key minerals, pore structure, and physical properties, and summarizing the sequestration scheme.