bluuebunny/crossref_metadata_2025 huggingface dataset:
Anti-Aging Effects of Low-Molecular-Weight Collagen Peptide Supplementation on Facial Wrinkles and Skin Hydration: Outcomes from a Six-Week Randomized, Double-Blind, Placebo-Controlled Trial
Authors: Carrillo-Norte Juan Antonio , García-Mir Baldomero , Quintana Lluis , Buracchio Bruno , Guerrero-Bonmatty Rafael
Published: August 2024

In recent decades, there has been a rising demand for anti-aging interventions aimed at postponing or potentially reversing indicators of skin aging. The use of collagen-based nutraceutical supplements has gained popularity as they have shown promise in enhancing skin health and reducing signs of aging. The aim of this randomized, placebo-controlled, blinded study was to investigate the effects of 2.5 g COLLinstant® LMW, a novel cosmeceutical containing low-molecular-weight (≤1000 Da) collagen peptides, on skin aging and health. The trial was conducted with 80 healthy women aged 30 years and older. They received a daily oral dose of either the food supplement (n = 40) or placebo (n = 40) for six weeks. Skin assessment was performed based on validated objective methods, such as Visioface 1000D (skin wrinkling), cutometry (elasticity and fatigue), and corneometry (skin hydration) at baseline (T0) and at week 6 (T6). After 6 weeks, participants that received collagen had significant improvements in biometric skin wrinkle parameters from baseline, with a reduction in volume by 46%, in area by 44%, and in depth by 9%, along with a greater increase in skin moisturization (by 34%) than those in the placebo group (p < 0.001). The food supplement did not significantly modify skin firmness or fatigue and had only slight beneficial effects on skin elasticity. The investigational product was well tolerated. The observed effects aligned closely with the subjective assessments reported by study participants. The study provides substantiated evidence supporting the efficacy of low-molecular-weight collagen peptides in restoring altered skin biometric parameters, as objectively assessed. Thus, regular supplementation with this nutraceutical may contribute to achieving smoother and more radiant skin.
Multivariate Analysis Techniques and Tolerance Indices for Detecting Bread Wheat Genotypes of Drought Tolerance
Authors: Al-Ashkar Ibrahim
Published: August 2024

Drought stress is one of the biggest hardships in wheat cultivation because of the strong negative relationship between water deficit and crop yields owing to a lower grain weight, a shorter grain-filling period, a slower grain-filling rate, and reduced grain quality. Genotype–environment interaction (GEN:ENV) generates hardships in selecting wheat genotypes and ideotypes due to biased genetic estimates. Diverse strategies have been proposed to respond to the urgent need for concurrent improvements in yield performance and stability. This study’s purpose was to appraise genetic variation and GEN:ENV effects on yield and yield components to discover drought-stress-tolerant genotypes and ideotypes. This study evaluated 20 genotypes in three consecutive seasons under non-stressful and drought-stress conditions in a total of six ENVs. The broad-sense heritability ranged from 0.54 to 0.82 based on expected mean squares and ranged from 0.60 to 0.90 based on plot mean, but in the other three ways, it was usually greater than 0.90. The high values of (σgen:env2) revealed the effect that broad-sense heritability has on the expression of traits. G01, G03, G06, G07, G08, G10, G12, G13, G16, G17, and G18 were stable genotypes for grain yield (GY), according to additive main effects and a multiplicative interaction biplot for the six ENVs. Based on scores in the weighted average of absolute scores biplot (WAASB), G02, G04, G05, G08, G10, and G18 were selected as stable and high-performance for GY, and they were all selected as the best genotype groups using the WAASB-GY superiority index. From the results obtained from principal component analysis and hierarchical clustering and from the tolerance discrimination indices, G02, G04, G05, G18, and G19 are genotypes that produce a suitable yield under non-stressful and drought-stress conditions. In essence, combining approaches that take into consideration stability and high performance can contribute significantly to enhancing the reliability of recommendations for novel wheat genotypes.
Skin Anatomy, Bone Histology and Taphonomy of a Toarcian (Lower Jurassic) Ichthyosaur (Reptilia: Ichthyopterygia) from Luxembourg, with Implications for Paleobiology
Authors: Bonnevier Wallstedt Ida , Sjövall Peter , Thuy Ben , De La Garza Randolph G. , Eriksson Mats E. , Lindgren Johan
Published: August 2024

A partial ichthyosaur skeleton from the Toarcian (Lower Jurassic) bituminous shales of the ‘Schistes Carton’ unit of southern Luxembourg is described and illustrated. In addition, associated remnant soft tissues are analyzed using a combination of imaging and molecular techniques. The fossil (MNHNL TV344) comprises scattered appendicular elements, together with a consecutive series of semi-articulated vertebrae surrounded by extensive soft-tissue remains. We conclude that TV344 represents a skeletally immature individual (possibly of the genus Stenopterygius) and that the soft parts primarily consist of fossilized skin, including the epidermis (with embedded melanophore pigment cells and melanosome organelles) and dermis. Ground sections of dorsal ribs display cortical microstructures reminiscent of lines of arrested growth (LAGs), providing an opportunity for a tentative age determination of the animal at the time of death (>3 years). It is further inferred that the exceptional preservation of TV344 was facilitated by seafloor dysoxia/anoxia with periodical intervals of oxygenation, which triggered phosphatization and the subsequent formation of a carbonate concretion.
Optimizing Image Quality with High-Resolution, Deep-Learning-Based Diffusion-Weighted Imaging in Breast Cancer Patients at 1.5 T
Authors: Olthof Susann-Cathrin , Weiland Elisabeth , Benkert Thomas , Wessling Daniel , Leyhr Daniel , Afat Saif , Nikolaou Konstantin , Preibsch Heike
Published: August 2024

The objective of this study was to evaluate a high-resolution deep-learning (DL)-based diffusion-weighted imaging (DWI) sequence for breast magnetic resonance imaging (MRI) in comparison to a standard DWI sequence (DWIStd) at 1.5 T. It is a prospective study of 38 breast cancer patients, who were scanned with DWIStd and DWIDL. Both DWI sequences were scored for image quality, sharpness, artifacts, contrast, noise, and diagnostic confidence with a Likert-scale from 1 (non-diagnostic) to 5 (excellent). The lesion diameter was evaluated on b 800 DWI, apparent diffusion coefficient (ADC), and the second subtraction (SUB) of the contrast-enhanced T1 VIBE. SNR was also calculated. Statistics included correlation analyses and paired t-tests. High-resolution DWIDL offered significantly superior image quality, sharpness, noise, contrast, and diagnostic confidence (each p < 0.02)). Artifacts were significantly higher in DWIDL by one reader (M = 4.62 vs. 4.36 Likert scale, p < 0.01) without affecting the diagnostic confidence. SNR was higher in DWIDL for b 50 and ADC maps (each p = 0.07). Acquisition time was reduced by 22% in DWIDL. The lesion diameters in DWI b 800DL and Std and ADCDL and Std were respectively 6% lower compared to the 2nd SUB. A DL-based diffusion sequence at 1.5 T in breast MRI offers a higher resolution and a faster acquisition, including only minimally more artefacts without affecting the diagnostic confidence.
Perioperative Assessment and Clinical Outcomes of Percutaneous Atrial Septal Defect Closure with Three-Dimensional Transesophageal Echocardiography
Authors: Saraç İbrahim , Birdal Oğuzhan
Published: August 2024

Background/Objectives: To close the atrial septal defect (ASD) with the transcatheter method, correctly defining the defect and selecting the appropriate closure device size are the most critical steps for the procedure’s success. Although ASD can be successfully closed under the guidance of three-dimensional (3D) transesophageal echocardiography (TEE) and two-dimensional (2D) TEE, measurement comparisons between different types of defects are still needed. Methods: Our study was designed retrospectively. Sixty-one patients who underwent transcatheter ASD closure with 2D TEE and 3D TEE between 2020 and 2024 were included. The patients were divided into three groups according to the defect shape: circular, oval, and complex; and the measurement results, perioperative process, and clinical outcomes were compared in each group. Results: The average age of the patients was 35.05 ± 13.87 years, and 41 (67.2%) were women. The average follow-up period of the patients was 15.3 ± 9.18 months. No statistical significance was observed in the comparison of measurements obtained with 3D TEE and 2D TEE in the circular and oval defect groups. The differences between the minimum defect diameters of complex defects measured by 2D TEE and 3D TEE (p: 0.037), IVC rims (p < 0.001), aortic rims (p: 0.012), and the differences between implanted device dimensions and the maximum defect diameters measured by both methods were compared; statistical significance was observed (p: 0.025). Conclusions: In circular and non-complex oval defects, it has been observed that the size of the closure device selected with 2D TEE or 3D TEE is optimal, and the procedure is practical and feasible. While the closure of complex ASDs with 3D TEE provides reliable and optimal results, using only 2D TEE in complex ASDs may lead to selecting a smaller-sized device.
Practice Recommendations for the Management of MASLD in Primary Care: Consensus Results
Authors: Lionis Christos , Papadakis Sophia , Anastasaki Marilena , Aligizakis Eftihis , Anastasiou Foteini , Francque Sven , Gergianaki Irini , Mendive Juan Manuel , Marketou Maria , Muris Jean , Manolakopoulos Spilios , Papatheodoridis Georgios , Samonakis Dimitrios , Symvoulakis Emmanouil , Tsiligianni Ioanna
Published: August 2024

Background: Despite its high prevalence and impact on health, metabolic dysfunction-associated steatotic liver disease (MASLD) is inadequately addressed in European primary care (PC), with a large proportion of cases going undiagnosed or diagnosed too late. A multi-country European research consortium led a project to design and evaluate a patient-centered, integrated model for MASLD screening, diagnosis, and linkage to specialty care for European PC settings. Based on the lessons from this project, the latest research evidence, and existing guidelines for the management of MASLD, we sought to develop a set of practice recommendations for screening, referral, and management of MASLD in PC. Methods: The Rand/UCLA modified Delphi panel method, with two rounds, was used to reach consensus on practice recommendations. The international panel consisted of experts from six countries, representing family medicine, gastroenterology, hepatology, cardiology, and public health. Initially, fifteen statements were drafted based on a synthesis of evidence from the literature and earlier findings from our consortium. Prior to the consensus meeting, the statements were rated by the experts in the first round. Then, in a hybrid meeting, the experts discussed findings from round one, adjusted the statements, and reassessed the updated recommendations in a second round. Results: In round one, there was already a high level of consensus on 10 out of 15 statements. After round 2, there were fourteen statements with a high degree of agreement (>90%). One statement was not endorsed. The approved recommendations addressed the following practice areas: risk screening and diagnosis, management of MASLD–lifestyle interventions, pharmacological treatment of MASLD/MASH, pharmacological treatment for co-morbidity, integrated care, surgical management, and other referrals to specialists. Conclusions: The final set of 14 recommendations focuses on increasing comprehensive care for MASLD in PC. The recommendations provide practical evidence-based guidance tailored to PC practitioners. We expect that these recommendations will contribute to the ongoing discussion on systematic approaches to tackling MASLD and supporting European PC providers by integrating the latest evidence into practice.
Investigation of Morphological Changes of the Soft Tissue in the Aesthetic Zone: A 3D Virtual Analysis after Conventional Tooth Extraction and Benex® Extraction
Authors: Buchbender Mayte , Gath Lotta , Jaeckel Fabian , Seidel Anna , Kesting Marco Rainer , Wichmann Manfred , Adler Werner , Matta Ragai Edward
Published: August 2024

Background: Postoperative resorption of hard and soft tissues occurs as a consequence of tooth extraction. The Benex®-Control extractor allows minimally invasive extraction of the tooth without causing pronounced iatrogenic trauma. The aim of this study was to verify whether the resorption of the tissues after extraction can be reduced by using the Benex® system compared to the conventional extraction method. Methods: Postoperative intraoral scans were superimposed after surgery (t0), after 7 days (t1), after 14 days (t2), after 30 days (t3), after 60 days (t4), and after 90 days (t5) within the two groups (study n = 14, control n = 16), and defined regions of interest (ROIs) (1–8) and volume changes were analyzed. In addition, the influence of gingival thickness and the thickness of the labial cortical plate was investigated. Results: The greatest decrease in volume was observed in both groups in ROI3, although there was no significant difference observed between the groups. In the presence of an adjacent tooth, there was less volume loss in the affected ROIs (1, 2 and 7, 8). The thickness of the gingiva and the bony lamella did not significantly influence the change in volume. Conclusions: Due to the small cohort, the results are limited, and the hypothesis is rejected.
Atmospheric Aircraft Conceptual Design Based on Multidisciplinary Optimization with Differential Evolution Algorithm and Neural Networks
Authors: Lukyanov Oleg , Hoang Van , Kurkin Evgenii , Quijada-Pioquinto Jose
Published: August 2024

A methodology for selecting rational parameters of atmospheric aircraft during the initial design stages using a differential evolutionary optimization algorithm and numerical mathematical modeling of aerodynamics problems is proposed. The technique involves implementing weight and aerodynamic balance in the main flight modes, considering atmospheric aircraft with one or two lifting surfaces, applying parallel calculations, and auto-generating a three-dimensional geometric model of the aircraft’s appearance based on the optimization results. A method for accelerating the process of optimizing aircraft parameters in terms of takeoff weight by more than three times by introducing an objective function into the set of design variables is proposed and demonstrated. The reliability of mathematical models used in aerodynamics and the accuracy of the objective function calculation considering various constraints are explored. A comprehensive test of the performance and efficiency of the methodology is conducted by solving demonstration problems to optimize more than ten main design parameters for the appearance of two existing heavy-class unmanned aerial vehicles with known characteristics from open sources.
An Evaluation Model for Node Influence Based on Heuristic Spatiotemporal Features
Authors: Jin Sheng , Xiao Yuzhi , Han Jiaxin , Huang Tao
Published: August 2024

The accurate assessment of node influence is of vital significance for enhancing system stability. Given the structural redundancy problem triggered by the network topology deviation when an empirical network is copied, as well as the dynamic characteristics of the empirical network itself, it is difficult for traditional static assessment methods to effectively capture the dynamic evolution of node influence. Therefore, we propose a heuristic-based spatiotemporal feature node influence assessment model (HEIST). First, the zero-model method is applied to optimize the network-copying process and reduce the noise interference caused by network structure redundancy. Second, the copied network is divided into subnets, and feature modeling is performed to enhance the node influence differentiation. Third, node influence is quantified based on the spatiotemporal depth-perception module, which has a built-in local and global two-layer structure. At the local level, a graph convolutional neural network (GCN) is used to improve the spatial perception of node influence; it fuses the feature changes of the nodes in the subnetwork variation, combining this method with a long- and short-term memory network (LSTM) to enhance its ability to capture the depth evolution of node influence and improve the robustness of the assessment. Finally, a heuristic assessment algorithm is used to jointly optimize the influence strength of the nodes at different stages and quantify the node influence via a nonlinear optimization function. The experiments show that the Kendall coefficients exceed 90% in multiple datasets, proving that the model has good generalization performance in empirical networks.
Hacking the Predictive Mind
Authors: Clark Andy
Published: August 2024

According to active inference, constantly running prediction engines in our brain play a large role in delivering all human experience. These predictions help deliver everything we see, hear, touch, and feel. In this paper, I pursue one apparent consequence of this increasingly well-supported view. Given the constant influence of hidden predictions on human experience, can we leverage the power of prediction in the service of human flourishing? Can we learn to hack our own predictive regimes in ways that better serve our needs and purposes? Asking this question rapidly reveals a landscape that is at once familiar and new. It is also challenging, suggesting important questions about scope and dangers while casting further doubt (as if any was needed) on old assumptions about a firm mind/body divide. I review a range of possible hacks, starting with the careful use of placebos, moving on to look at chronic pain and functional disorders, and ending with some speculations concerning the complex role of genetic influences on the predictive brain.
Bayesian Modeling for Nonstationary Spatial Point Process via Spatial Deformations
Authors: Gamerman Dani , Quintana Marcel de Souza Borges , Alves Mariane Branco
Published: August 2024

Many techniques have been proposed to model space-varying observation processes with a nonstationary spatial covariance structure and/or anisotropy, usually on a geostatistical framework. Nevertheless, there is an increasing interest in point process applications, and methodologies that take nonstationarity into account are welcomed. In this sense, this work proposes an extension of a class of spatial Cox process using spatial deformation. The proposed method enables the deformation behavior to be data-driven, through a multivariate latent Gaussian process. Inference leads to intractable posterior distributions that are approximated via MCMC. The convergence of algorithms based on the Metropolis–Hastings steps proved to be slow, and the computational efficiency of the Bayesian updating scheme was improved by adopting Hamiltonian Monte Carlo (HMC) methods. Our proposal was also compared against an alternative anisotropic formulation. Studies based on synthetic data provided empirical evidence of the benefit brought by the adoption of nonstationarity through our anisotropic structure. A real data application was conducted on the spatial spread of the Spodoptera frugiperda pest in a corn-producing agricultural area in southern Brazil. Once again, the proposed method demonstrated its benefit over alternatives.
GAD-PVI: A General Accelerated Dynamic-Weight Particle-Based Variational Inference Framework
Authors: Wang Fangyikang , Zhu Huminhao , Zhang Chao , Zhao Hanbin , Qian Hui
Published: August 2024

Particle-based Variational Inference (ParVI) methods have been widely adopted in deep Bayesian inference tasks such as Bayesian neural networks or Gaussian Processes, owing to their efficiency in generating high-quality samples given the score of the target distribution. Typically, ParVI methods evolve a weighted-particle system by approximating the first-order Wasserstein gradient flow to reduce the dissimilarity between the particle system’s empirical distribution and the target distribution. Recent advancements in ParVI have explored sophisticated gradient flows to obtain refined particle systems with either accelerated position updates or dynamic weight adjustments. In this paper, we introduce the semi-Hamiltonian gradient flow on a novel Information–Fisher–Rao space, known as the SHIFR flow, and propose the first ParVI framework that possesses both accelerated position update and dynamical weight adjustment simultaneously, named the General Accelerated Dynamic-Weight Particle-based Variational Inference (GAD-PVI) framework. GAD-PVI is compatible with different dissimilarities between the empirical distribution and the target distribution, as well as different approximation approaches to gradient flow. Moreover, when the appropriate dissimilarity is selected, GAD-PVI is also suitable for obtaining high-quality samples even when analytical scores cannot be obtained. Experiments conducted under both the score-based tasks and sample-based tasks demonstrate the faster convergence and reduced approximation error of GAD-PVI methods over the state-of-the-art.
Multipartite Correlations in Parikh–Wilczek Non-Thermal Spectrum
Authors: Ming Xi
Published: August 2024

In this study, we systematically investigate the multipartite correlations in the process of black hole radiation via the Parikh–Wilczek tunneling model. We examine not only the correlations among Hawking radiations but also the correlations between the emissions and the remainder of the black hole. Our findings indicate that the total correlation among emitted particles continues to increase as the black hole evaporates. Additionally, we observe that the bipartite correlation between the emissions and the remainder of the black hole initially increases and then decreases, while the total correlation of the entire system monotonically increases. Finally, we extend our analysis to include quantum correction and observe similar phenomena. Through this research, we aim to elucidate the mechanism of information conservation in the black hole information paradox.
Transient and Persistent Technical Efficiencies in Rice Farming: A Generalized True Random-Effects Model Approach
Authors: Ho Phuc Trong , Burton Michael , Hailu Atakelty , Ma Chunbo
Published: August 2024

This study estimates transient and persistent technical efficiencies (TEs) using a generalized true random-effects (GTRE) model. We estimate the GTRE model using maximum likelihood and Bayesian estimation methods, then compare it to three simpler models nested within it to evaluate the robustness of our estimates. We use a panel data set of 945 observations collected from 344 rice farming households in Vietnam’s Mekong River Delta. The results indicate that the GTRE model is more appropriate than the restricted models for understanding heterogeneity and inefficiency in rice production. The mean estimate of overall technical efficiency is 0.71 on average, with transient rather than persistent inefficiency being the dominant component. This suggests that rice farmers could increase output substantially and would benefit from policies that pay more attention to addressing short-term inefficiency issues.
MIMO Signal Detection Based on IM-LSTMNet Model
Authors: Huang Xiaoli , Yuan Yumiao , Li Jingyu
Published: August 2024

Signal detection is crucial in multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, yet classical detection methods often struggle with nonlinear issues in wireless channels. To handle this challenge, we propose a novel signal detection method for MIMO-OFDM system based on the fractional Fourier transform (FrFT), leveraging the robust time series processing capabilities of long short-term memory (LSTM) networks. Our innovative approach, termed IM-LSTMNet, integrates LSTM with convolutional neural networks (CNNs) and incorporates a Squeeze and Excitation Network to emphasize critical information, enhancing neural network performance. The proposed IM-LSTMNet is applied to the FrFT-based MIMO-OFDM system to improve signal detection performance. We compare the detection results of IM-LSTMNet with zero forcing (ZF), minimum mean square error (MMSE), simple LSTM neural network, and CNN–LSTM network by evaluating the bit error rate. Experimental results demonstrate that IM-LSTMNet outperforms ZF, MMSE, LSTM, and other methods, significantly enhancing system signal detection performance. This work offers a promising advancement in MIMO-OFDM signal detection, presenting a deep learning-based solution that effectively improves the system signal detection performance.
Fractional Diversity Entropy: A Vibration Signal Measure to Assist a Diffusion Model in the Fault Diagnosis of Automotive Machines
Authors: Wang Baohua , Zhang Jiacheng , Wang Weilong , Cheng Tingting
Published: August 2024

Real-world vibration signal acquisition of automotive machines often results in imbalanced sample sets due to restricted test conditions, adversely impacting fault diagnostic accuracy. To address this problem, we propose fractional diversity entropy (FrDivEn) and incorporate it into the classifier-guided diffusion model (CGDM) to synthesize high-quality samples. Additionally, we present a corresponding imbalanced fault diagnostic method. This method first converts vibration data to Gramian angular field (GAF) image samples through GAF transformation. Then, FrDivEn is mapped to the gradient scale of CGDM to trade off the diversity and fidelity of synthetic samples. These synthetic samples are mixed with real samples to obtain a balanced sample set, which is fed to the fine-tuned pretrained ConvNeXt for fault diagnosis. Various sample synthesizers and fault classifiers were combined to conduct imbalanced fault diagnosis experiments across bearing, gearbox, and rotor datasets. The results indicate that for the three datasets, the diagnostic accuracies of the proposed CGDM using FrDivEn at an imbalance ratio of 40:1 are 91.22%, 87.90%, and 98.89%, respectively, which are 7.32%, 11.59%, and 3.48% higher than that of the Wasserstein generative adversarial network (WGAN), respectively. The experimental results across the three datasets validated the validity and generalizability of the proposed diagnostic method.
Fully Integrated Miniaturized Wireless Power Transfer Rectenna for Medical Applications Tested inside Biological Tissues
Authors: Fernandez-Munoz Miguel , Missous Mohamed , Sadeghi Mohammadreza , Lopez-Espi Pablo Luis , Sanchez-Montero Rocio , Martinez-Rojas Juan Antonio , Diez-Jimenez Efren
Published: August 2024

This work presents the results of the characterization of two 1 × 5 mm2 miniaturized rectennas developed for medical applications. They have been designed for relatively high voltage and high-power applications, given the size of the rectennas. Both rectennas were tested in open-air conditions and surrounded by pork fat and muscle tissues, whose properties are similar to those of the human body. The resonant frequencies of the rectennas were found, and the incident electric field on the rectennas tests was increased. The first chip showed a maximum output voltage of 5.29 V and a maximum output power of 0.056 mW, at 1.446 GHz, under an incident field on the rectenna of 340 V/m, and the second chip, 4.62 V and 4.27 mW, at 1.175 GHz, under 535 V/m. The second rectenna can provide an output power greater than 5 mW. The rectennas presented in this article are beyond the state of the art, as they can deliver about three times more power and voltage than those of similar dimensions reported in the literature. Based on the test results, the efficiency of the rectennas was analyzed at different locations of the human body, considering different thicknesses of tissues with high and low water content. Finally, potential applications are described in which the rectennas could power implantable medical devices or microsurgery tools, for example, pulmonary artery pressure monitors.
High-Frequency Magnetic Pulse Generator for Low-Intensity Transcranial Magnetic Stimulation
Authors: Shin Seungjae , Kim Hyungeun , Jeong Jinho
Published: August 2024

This paper presents a high-frequency (HF) magnetic pulse generator designed for low-intensity transcranial magnetic stimulation (LI-TMS) applications. HF pulse stimulation can induce a strong electric field with minimal current and enhance the penetration depth of the electric field in human tissue. The HF magnetic pulse generator was designed and fabricated using a microcontroller unit, gate driver, full-bridge coil driver, and stimulation coil. Measurements with a full-bridge circuit supply voltage of 10 V demonstrated an electric field intensity of 6.8 Vpp/m at a frequency of 1 MHz with a power dissipation of 2.45 W. Achieving a similar electric field intensity at a frequency of 100 kHz required approximately ten times the coil current. Additionally, a quasi-resonant LC load was introduced by connecting a capacitor in series with the stimulation coil, which set the resonant frequency to approximately 10% higher than the frequency of 1 MHz. This approach reduced the coil impedance, achieving higher current with the same bias supply voltage. Experimental results showed an enhanced electric field intensity of 19.1 Vpp/m with a supply voltage of only 1.8 V and reduced power dissipation of 1.11 W. The proposed HF pulse train with quasi-resonant coil system is expected to enable a low-power LI-TMS system.
Software Weakness Detection in Solidity Smart Contracts Using Control and Data Flow Analysis: A Novel Approach with Graph Neural Networks
Authors: Seo Aria , Kim Young-Tak , Yang Ji Seok , Lee YangSun , Son Yunsik
Published: August 2024

Smart contracts on blockchain platforms are susceptible to security issues that can lead to significant financial losses. This study converts the Solidity code into abstract syntax trees and generates control flow graphs and data flow graphs. These graphs train a graph convolutional network model to detect security weaknesses. The proposed system outperforms traditional tools, achieving higher accuracy, recall, precision, and F1 scores when detecting weaknesses such as integer overflow/underflow, reentrancy, delegate call to the untrusted callee, and time-based issues. This study demonstrates that leveraging control and data flow analysis with graph neural networks significantly enhances smart contract security and provides a robust and reliable solution.
Unlinkable and Revocable Signcryption Scheme for VANETs
Authors: Li Lihui , Chen Dongmei , Liu Yining , Liang Yangfan , Wang Yujue , Wu Xianglin
Published: August 2024

Vehicular ad-hoc networks (VANETs) can significantly improve the level of urban traffic management. However, the sender unlinkability has become an intricate issue in the field of VANETs’ encryption. As the sender signcrypts a message, the receiver has to use the sender’s identity or public key to decrypt it. Consequently, the sender can be traced using the same identity or public key, which poses some security risks to the sender. To address this issue, we present an unlinkable and revocable signcryption scheme (URSCS), where an efficient and powerful signcryption mechanism is adopted for communication. The sender constructs a polynomial to generate a unique session key for each communication, which is then transmitted to a group of receivers, enabling the same secret message to be sent to multiple receivers. Each time a secret message is sent, a new key pair is generated, and an anonymization mechanism is introduced to conceal the true identity of the vehicle, thus preventing malicious attackers from tracing the sender through the public key or the real identity. With the introduction of the identification public key, this scheme supports either multiple receivers or a single receiver, where the receiver can be either road side units (RSUs) or vehicles. Additionally, a complete revocation mechanism is constructed with extremely low communication overhead, utilizing the Chinese remainder theorem (CRT). Formal and informal security analyses demonstrate that our URSCS scheme meets the expected security and privacy requirements of VANETs. The performance analysis shows that our URSCS scheme outperforms other represented schemes.
Global Low-Complexity Fault-Tolerant Control for Pure-Feedback Systems with Sensor Faults
Authors: Han Chongchong , Liu Zongcheng , Yao Liangfu , Xue Jianping , Li Qiuni , Chen Yong , Jian Jialong
Published: August 2024

A low-complexity global fault-tolerant control method is proposed to solve the tracking problem of uncertain pure-feedback systems in the presence of sensor faults. First, a novel modeling approach is introduced to reconstruct the non-affine term, which removes the restriction that the non-affine function must be differentiable. Second, a novel nonlinear mapping based on inverse-tangent function is utilized in the controller design such that the control parameters are free from initial values of states compared to the traditional prescribed performance control methods, resulting in global fault-tolerant control of pure-feedback systems under sensor and actuator faults. Furthermore, the designed global controller is low-complexity in the sense that no time derivatives of system signals are involved in the controller, and no neural networks or fuzzy logic systems are used, though unknown nonlinearities are present in the considered systems, and the control parameters are allowed to be arbitrary positive constants. Finally, the proposed method is applied to numerical and tailless fly-wing UAV examples, which fully demonstrates the effectiveness of the proposed method.
Characterization of Breakdown Arcs Induced by Venting Particles Generated by Thermal Runaway of Large-Capacity Ternary Lithium-Ion Batteries
Authors: Chen Yuhao , Li Yalun , Wang Juan , Lu Languang , Wang Hewu , Li Minghai , Xu Wenqiang , Shi Chao , Li Cheng
Published: August 2024

In recent years, with the continuous growth in power demand, lithium-ion batteries (LIBs) have become an indispensable component of various electronic devices, transportation vehicles, and energy systems. The safety performance of LIBs is one of the most significant issues facing their continued development. In battery systems, the presence of arcs constitutes a significant safety hazard that necessitates attention; the thermal runaway (TR) of LIBs releases a large quantity of particles with elevated temperature and high velocity, probably resulting in arc failures. Changes in the insulation structure inside battery packs and the accumulation of particulate matter resulting from the TR of battery cells are potential causes of arc-induced disasters. In this study, we utilized fully charged 71 Ah ternary LIB Li (Ni0.8Co0.1Mn0.1) O2 (NCM811) pouch cell samples and collected the vented particles in an inert atmosphere after TR. All the settled particles were classified into six groups; by conducting experiments with different particle sizes, electrode spacings, and circuit loads, the patterns of the particle-induced arcs were understood. The results indicate that as the particle size increases, the critical breakdown voltage decreases. Regarding electrode spacing and circuit load resistance, larger values require higher critical breakdown voltages. The research results provide valuable guidance for the electrical protection and safety design of battery systems.
FedGAT-DCNN: Advanced Credit Card Fraud Detection Using Federated Learning, Graph Attention Networks, and Dilated Convolutions
Authors: Li Mengqiu , Walsh John
Published: August 2024

Credit card fraud detection is a critical issue for financial institutions due to significant financial losses and the erosion of customer trust. Fraud not only impacts the bottom line but also undermines the confidence customers place in financial services, leading to long-term reputational damage. Traditional machine learning methods struggle to improve detection accuracy with limited data, adapt to new fraud techniques, and detect complex fraud patterns. To address these challenges, we present FedGAT-DCNN, a model integrating a Graph Attention Network (GAT) and dilated convolutions within a federated learning framework. FedGAT-DCNN employs federated learning, allowing financial institutions to collaboratively train models using local datasets, enhancing accuracy and robustness while maintaining data privacy. Incorporating a GAT enables continuous model updates across institutions, quickly adapting to new fraud patterns. Dilated convolutions extend the model’s receptive field without extra computational overhead, improving detection of subtle and complex fraudulent activities. Experiments on the 2018CN and 2023EU datasets show that FedGAT-DCNN outperforms traditional models and other federated learning methods, achieving a ROC-AUC of 0.9712 on the 2018CN dataset and 0.9992 on the 2023EU dataset. These results highlight FedGAT-DCNN’s robustness, accuracy, and applicability in real-world fraud detection scenarios.
Lightweight Progressive Fusion Calibration Network for Rotated Object Detection in Remote Sensing Images
Authors: Liu Jing , Jing Donglin , Cao Yanyan , Wang Ying , Guo Chaoping , Shi Peijun , Zhang Haijing
Published: August 2024

Rotated object detection is a crucial task in aerial image analysis. To address challenges such as multi-directional object rotation, complex backgrounds with occlusions, and the trade-off between speed and accuracy in remote sensing images, this paper introduces a lightweight progressive fusion calibration network for rotated object detection (LPFC-RDet). The network comprises three main modules: the Retentive Meet Transformers (RMT) feature extraction block, the Progressive Fusion Calibration module (PFC), and the Shared Group Convolution Lightweight detection head (SGCL). The RMT feature extraction block integrates a retentive mechanism with global context modeling to learn rotation-insensitive features. The PFC module employs pixel-level, local-level, and global-level weights to calibrate features, enhancing feature extraction from occluded objects while suppressing background interference. The SGCL detection head uses decoupled detection tasks and shared group convolution layers to achieve parameter sharing and feature interaction, improving accuracy while maintaining a lightweight structure. Experimental results demonstrate that our method surpasses state-of-the-art detectors on three widely used remote sensing object datasets: HRSC2016, UCAS_AOD, and DOTA.
Computer-Aided Diagnosis Systems for Automatic Malaria Parasite Detection and Classification: A Systematic Review
Authors: Grignaffini Flavia , Simeoni Patrizio , Alisi Anna , Frezza Fabrizio
Published: August 2024

Malaria is a disease that affects millions of people worldwide with a consistent mortality rate. The light microscope examination is the gold standard for detecting infection by malaria parasites. Still, it is limited by long timescales and requires a high level of expertise from pathologists. Early diagnosis of this disease is necessary to achieve timely and effective treatment, which avoids tragic consequences, thus leading to the development of computer-aided diagnosis systems based on artificial intelligence (AI) for the detection and classification of blood cells infected with the malaria parasite in blood smear images. Such systems involve an articulated pipeline, culminating in the use of machine learning and deep learning approaches, the main branches of AI. Here, we present a systematic literature review of recent research on the use of automated algorithms to identify and classify malaria parasites in blood smear images. Based on the PRISMA 2020 criteria, a search was conducted using several electronic databases including PubMed, Scopus, and arXiv by applying inclusion/exclusion filters. From the 606 initial records identified, 135 eligible studies were selected and analyzed. Many promising results were achieved, and some mobile and web applications were developed to address resource and expertise limitations in developing countries.
Optimal Sizing of Electric Vehicle Charging Stacks Considering a Multiscenario Strategy and User Satisfaction
Authors: Zhou Yinghong , Yang Weihao , Yang Zhijing , Chen Ruihan
Published: August 2024

The rapid growth of EVs relies on the development of supporting infrastructure, e.g., charging stations (CSs). The sizing problem of a CS typically involves minimizing the investment costs. Therefore, a flexible and precise sizing strategy is crucial. However, the existing methods suffer from the following issues: (1) they do not consider charging station deployments based on the charging stack; (2) existing sizing strategies based on smart charging technology consider a single scenario and fail to meet the demand for flexible operation under multiple scenarios in real-life situations. This paper proposes a novel CS sizing framework specific for charging stacks to overcome these challenges. Specifically, it first addresses the charging-stack-based CS sizing problem, and then it proposes the corresponding multiscenario constraints, i.e., exclusive and shared, for capacity-setting optimization. In addition, a novel quality of service (QoS) formulation is also proposed to better relate the user QoS levels to the CS sizing problem. Finally, it also explores the relationship between the investment costs and the total power of the needed charging stack under three business models. Extensive experiments show that the proposed framework can offer valuable guidance to CS operators in competitive environments.
Impact of the Exciter and Governor Parameters on Forced Oscillations
Authors: Thotakura Naga Lakshmi , Burge Christopher Ray , Liu Yilu
Published: August 2024

In recent years, the frequency of forced oscillation events due to control system malfunctions or improper parameter settings has increased. Tuning the parameters of exciters and governor models is crucial for maintaining power system stability. Traditional simulation studies typically involve small transient disturbances or step changes to find optimal parameter sets, but existing optimization algorithms often fall short in fine-tuning for forced oscillations. Identifying the sensitive parameters within these control models is essential for ensuring stability during large, sustained disturbances. This study focuses on identifying these critical exciter and governor model parameters by analyzing their influence on sustained forced oscillations. Using Kundur’s two-area system, we analyze common exciter models such as SCRX, ESST1A, and AC7B, along with governor models like GAST, HYGOV, and GGOV1, utilizing PSS®E software version 34. Sustained forced oscillations are injected at generator-1 of area-1, with individual parameter changes dynamically simulated. By considering a local oscillation frequency of 1.4 Hz and an inter-area oscillation mode of 0.25 Hz, we analyze the impact of each parameter change on the magnitude and frequency of forced oscillations as well as on active and reactive power outputs. This novel approach highlights the most influential parameters of each tested model—such as exciter, governor, and turbine gains, as well as time constant parameters—on the impact of forced oscillations. Based on our findings, the sensitive parameters of each tested model are ranked. These would provide valuable insights for industry operators to fine-tune control settings during oscillation events, ultimately enhancing system stability.
BSRT++: Improving BSRT with Feature Enhancement, Weighted Fusion, and Cyclic Sampling
Authors: Son Suji , Park Hanhoon
Published: August 2024

Multi-frame super-resolution (MFSR) generates a super-resolution (SR) image from a burst consisting of multiple low-resolution images. Burst Super-Resolution Transformer (BSRT) is a state-of-the-art deep learning model for MFSR. However, in this study, we show that there is room for further improvement of BSRT in the feature extraction and fusion process. Then, we propose a feature enhancement module (FEM), a cyclic sampling module (CSM), and a feature reweighting module (FRM) and integrate them into BSRT. Finally, we demonstrate that the modules can help recover the high-frequency information well, enhance inter-frame communication, and suppress misaligned features, thus significantly improving the SR performance and producing more visually plausible and pleasant results compared to other MFSR methods, including BSRT. On the SyntheticBurst and RealBurst datasets, the improved BSRT with the modules, dubbed BSRT++, achieved higher PSNR values of 1.15 dB and 1.31 dB than BSRT, respectively.
A Globally Consistent Merging Method for House Point Clouds Based on Artificially Enhanced Features
Authors: Sa Guodong , Chao Yipeng , Li Shuo , Liu Dandan , Wang Zonghua
Published: August 2024

In the process of using structured light technology to obtain indoor point clouds, due to the limited field of view of the device, it is necessary to obtain multiple point clouds of different wall surfaces. Therefore, merging the point cloud is necessary to acquire a complete point cloud. However, due to issues such as the sparse geometric features of the wall point clouds and the high similarity of multiple point clouds, the merging effect of point clouds is poor. In this paper, we leverage artificially enhanced features to improve the accuracy of registration in this scenario. Firstly, we design feature markers and present their layout criteria. Then, the feature information of the marker is extracted based on the Color Signature of Histograms of OrienTations (Color-SHOT) descriptor, and coarse registration is realized through the second-order similarity measure matrix. After that, precise registration is achieved using the Iterative Closest Point (ICP) method based on markers and overlapping areas. Finally, the global error of the point cloud registration is optimized by loop error averaging. Our method enables the high-precision reconstruction of integrated home design scenes lacking significant features at a low cost. The accuracy and validity of the method were verified through comparative experiments.