Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Agent Modeling and Jamming-Aware Routing Protocols for Movable-Jammer-Affected WSNs

Wireless sensor networks (WSNs) are widely used in various fields, and the reliability and performance of WSNs are critical for their applications. However, WSNs are vulnerable to jamming attacks, and the impact of movable jammers on WSNs’ reliability and performance remains largely unexplored. This study aims to investigate the impact of movable jammers on WSNs and propose a comprehensive approach for modeling jammer-affected WSNs, comprising four parts. Firstly, agent-based modeling of sensor nodes, base stations, and jammers has been proposed. Secondly, a jamming-aware routing protocol (JRP) has been proposed to enable sensor nodes to weigh depth and jamming values when selecting relay nodes, thereby bypassing areas affected by jamming. The third and fourth parts involve simulation processes and parameter design for simulations. The simulation results show that the mobility of the jammer significantly affects WSNs’ reliability and performance, and JRP effectively bypasses jammed areas and maintains network connectivity. Furthermore, the number and deployment location of jammers has a significant impact on WSNs’ reliability and performance. These findings provide insights into the design of reliable and efficient WSNs under jamming attacks.
Face recognition algorithm based on kernel orthogonal semi-supervised discriminant analysis

In view of the problems of nonlinear feature extraction and use of a few labeled samples in face recognition, a new algorithm of orthogonal optimal semi-supervised discriminant vectors in a kernel space is proposed. Nonlinear kernel mapping is used to map the face data into an implicit feature space. In this space, the MFA can make use of small amount of labeled samples and the UDP can study a large number of unlabeled samples. The object function is defined using the semi-supervised method. Then optimal projection vector is found using orthogonal approach and face recognition is realized.The effectiveness of the proposed methods is validated through the experimental results on ORL and YALE face databases.
Nonlinear stability of three-dimensional boundary layers

The stability of incompressible three-dimensional boundary layers has been investigated using the Parabolized Stability Equations (PSE). We computed the evolution of stationary and traveling disturbances in the linear and nonlinear region prior to transition. As model problems, we chose Swept Hiemenz Flow and the DLR-Transition Experiment. For the latter, the mean flow profiles were obtained by solving the boundary layer equations for the measured pressure distribution. Furthermore, global and local eigenvalue solver using the Implicitly Restarted Arnoldi-Method and a Newton-Raphson technique, respectively, were developed for an investigation of the secondary instability in the region of nonlinear saturation. The primary stability results for Swept Hiemenz Flow agree very well with computations by Malik, et.al. Including an unstable higher frequency mode in the primary analysis, we were able to capture essential flow features in the later stages of transition. For the DLR-Experiment, we present linear and nonlinear results, as well as results from the secondary instability analysis.
Sound Classification using Sound Spectrum Features and Convolutional Neural Networks

This paper proposes an alternative approach to sound classification using sound spectrum features, differing from the use of the Mel-Frequency Cepstral Coefficients (MFCC). Aligning with the crowd sourcing data collection application NoiseCapture, the data are kept in form of the post-processed sound spectrum instead of the raw audio files to maintain privacy of volunteers. Under such circumstances, MFCC, which requires audio processing, cannot be directly obtained from nor maximize the features of sound spectrum data stored in the application. As sound spectrum does not undergo further feature transformation, it retains audio features from the audio file and should therefore be classifiable when passed into a trained sound spectrum model. Hence, in this study, we aim to evaluate whether sound spectrum could be used as a replacement of MFCC, especially when audio file is inaccessible. The UrbanSound8K dataset and a mix of deep learning and machine learning models were used for the comparison. Experiment results show sound spectrum achieving comparable results in Convolutional Neural Network (CNN), with better predictions than its MFCC counterpart. Further comparisons draw insights that illustrate the need for more finetuning for sound spectrum data when using non-CNN models for sound classification due to the shape of the input features.
C-DIEGO: An Algorithm with Near-Optimal Sample Complexity for Distributed, Streaming PCA

The accuracy of many downstream machine learning algorithms is tied to the training data having uncorrelated features. With the modern-day data often being streaming in nature, geographically distributed, and having large dimensions, it is paramount to apply both uncorrelated feature learning and dimensionality reduction techniques in this scenario. Principal Component Analysis (PCA) is a state-of-the-art tool that simultaneously yields uncorrelated features and reduces data dimensions by projecting data onto the eigenvectors of the population covariance matrix. This paper introduces a novel algorithm called Consensus-DIstributEd Generalized Oja (C-DIEGO), which is based on Oja's method, to estimate the dominant eigenvector of a population covariance matrix in a distributed, streaming setting. The algorithm considers a distributed network of arbitrarily connected nodes without a central coordinator and assumes data samples continuously arrive at the individual nodes in a streaming manner. It is established in the paper that C-DIEGO can achieve an order-optimal convergence rate if nodes in the network are allowed to have enough consensus rounds per algorithmic iteration. Numerical results are also reported in the paper that showcase the efficacy of the proposed algorithm.
Production of Epoxidized Rubber Seed Oil via Lipase-Catalyzed Epoxidation

Rubber seeds are residuals from rubber plantations containing oils. Epoxidized rubber seed oil (ERSO) was produced from rubber seed oil under different conditions withacetic acid as an oxygen carrier and hydrogen peroxide as an oxygen donor. A lipase B immobilized on macroporous acrylic resin beads was used as a biocatalyst. The lipase biocatalyst was used to optimize the epoxidation process at a temperature of 65 C and a reaction period of 18 h. Under these conditions, ERSO with the highest epoxidation percentage of 81.63 % and a low iodine value of 4.43 g iodine/kg sample was obtained. The key functional groups of ERSOs were investigated by Fourier transform infrared spectroscopy (FTIR), in which the FTIR spectra showed the oxirane ring of ERSOs. The proton nuclear magnetic resonance (1H-NMR) analyses confirmed the decrease of the peak of the double bond in rubber seed oil and the existence of an epoxidized ring peak in ERSOs. These results showed that ERSOs were successfully synthesized from rubber seed oil.
CT Radiomics and Clinical Feature Model to Predict Lymph Node Metastases in Early-Stage Testicular Cancer

Accurate retroperitoneal lymph node metastasis (LNM) prediction in early-stage testicular germ cell tumours (TGCTs) harbours the potential to significantly reduce over- or undertreatment and treatment-related morbidity in this group of young patients as an important survivorship imperative. We investigated the role of computed tomography (CT) radiomics models integrating clinical predictors for the individualised prediction of LNM in early-stage TGCT. Ninety-one patients with surgically proven testicular germ cell tumours and contrast-enhanced CT were included in this retrospective study. Dedicated radiomics software was used to segment 273 retroperitoneal lymph nodes and extract features. After feature selection, radiomics-based machine learning models were developed to predict LN metastasis. The robustness of the procedure was controlled by 10-fold cross-validation. Using multivariable logistic regression modelling, we developed three prediction models: a radiomics-only model, a clinical-only model, and a combined radiomics–clinical model. The models’ performances were evaluated using the area under the receiver operating characteristic curve (AUC). Finally, decision curve analysis was performed to estimate the clinical usefulness of the predictive model. The radiomics-only model for predicting lymph node metastasis reached a greater discrimination power than the clinical-only model, with an AUC of 0.87 (±0.04; 95% CI) vs. 0.75 (±0.08; 95% CI) in our study cohort. The combined model integrating clinical risk factors and selected radiomics features outperformed the clinical-only and the radiomics-only prediction models, and showed good discrimination with an area under the curve of 0.89 (±0.03; 95% CI). The decision curve analysis demonstrated the clinical usefulness of our proposed combined model. The presented combined CT-based radiomics–clinical model represents an exciting non-invasive tool for individualised LN metastasis prediction in testicular germ cell tumours. Multi-centre validation is required to generate high-quality evidence for its clinical application.
Failure Detector Based on Vehicle Movement Prediction in Vehicular Ad-Hoc Networks

Vehicular ad hoc network (VANETs) improves road safety and efficiency by organizing vehicles and infrastructure to provide a platform for application deployment. The availability of vehicles and infrastructure is critical to the operation of applications. Accurate failure detector (FD) has been one of the fundamental components for maintaining high availability in VANETs. However, it is hard to find the vehicle failure accurately and timely due to the dynamic nature of VANETs caused by the high mobility of vehicles and communications link failures. Therefore, it is important to achieve an accurate FD which can cope with the high mobility of VANETs. In this paper, we propose a dead reckoning based FD, called DR-FD. It can predict the mobility of vehicle accurately and avoid the impact of link failures on the detection results by the cooperation between vehicles. Experimental results are provided to confirm that the proposed DR-FD method can achieve at most 20% reduction in detection time, 30% improvement in mistake rate and 20% improvement in overhead.
Adaptive fault‐tolerant attitude tracking control for hypersonic vehicle with unknown inertial matrix and states constraints

This paper proposes an adaptive fault-tolerant control (FTC) method for hypersonic vehicle (HSV) with unexpected centroid shift, actuator fault, time-varying full state constraints, and input saturation. The occurrence of unexpected centroid shift has three main effects on the HSV system, which are system uncertainties, eccentric moments
U-Chord Curvature: A Computational Method of Discrete Curvature

Discrete curvature computation of digital curves is widely applied to various tasks of image analysis and computer vision. A computational method of discrete curvature,U-chord curvature,is proposed. For each point in a digital curve,its support region is determined by two points with a given chord distance to the point,and then the U-chord curvature of the point is estimated. A theoretical analysis shows that there is a close relationship between the U-chord curvature and the real curvature of the curve. Compared with the existing computational methods of discrete curvature,the U-chord curvature is more stable under rotation transformations and noise condition. Therefore,it is suitable for image and vision tasks which require a high stability of curvature estimation,such as curve matching. Simulation experiments show the efficiency of the proposed method.
Yoga Meets Intelligent Internet of Things: Recent Challenges and Future Directions

The physical and mental health of people can be enhanced through yoga, an excellent form of exercise. As part of the breathing procedure, yoga involves stretching the body organs. The guidance and monitoring of yoga are crucial to ripe the full benefits of it, as wrong postures possess multiple antagonistic effects, including physical hazards and stroke. The detection and monitoring of the yoga postures are possible with the Intelligent Internet of Things (IIoT), which is the integration of intelligent approaches (machine learning) and the Internet of Things (IoT). Considering the increment in yoga practitioners in recent years, the integration of IIoT and yoga has led to the successful implementation of IIoT-based yoga training systems. This paper provides a comprehensive survey on integrating yoga with IIoT. The paper also discusses the multiple types of yoga and the procedure for the detection of yoga using IIoT. Additionally, this paper highlights various applications of yoga, safety measures, various challenges, and future directions. This survey provides the latest developments and findings on yoga and its integration with IIoT.
A 16-Channel Impedance-Readout IC With Synchronous Sampling and Baseline Cancelation for Fast Neural Electrical Impedance Tomography

Fast neural electrical impedance tomography (EIT) is a promising method to not only record neural activities but also to localize them in peripheral nerves in a minimally invasive way. A much higher image frame rate is required for this application than conventional impedance measurement applications, which generally adopt the I/Q demodulation technique. To achieve a high-enough frame rate to capture neural activities along the peripheral nerve fascicle, this letter proposes synchronous sampling (SS) and fast baseline tracking based on successive approximation (SA). The SS method needs an injection of only a single or a few periods of square-wave current signal for the amplitude measurement by synchronously sampling the received voltage signal. The proposed SA-based baseline tracking provides fast signal recovery from unwanted motion artifacts or baseline drifts. The designed readout circuit uses a two-step conversion where its coarse bits are generated from a current-based baseline cancelation circuit and its fine bits from a following SAR ADC. Thanks to the baseline tracking based on the SS and SA, the proposed IC achieves a faster frame rate of 312 fps with continuous baseline update and 500 fps with constant baseline compared to state-of-the-art works. The implemented IC is validated by measuring a water-filled tank and reconstructing its tomographic image. The results show successful discrimination of varied impedance areas over the tank.
Entropy function for the teleparallel Kaluza-Klein reduction

This work is devoted to examine the galactic behavior of entropy by checking the validity of thermodynamics laws for the teleparallel reduction of Kaluza-Klein theory in thermal equilibrium state. We assume a flat Friedmann-Lemaitre-Robertson-Walker type space-time model in which dark matter is interacting with dark energy. With this scenario, calculating the variation of galactic entropy function for each dark components and for the dynamical apparent horizon itself, we prove that the thermodynamical laws are independent of the specific interaction term and dependent of the selected Kaluza-Klein model. Finally, some limiting conditions and their implications have been presented.
IRS-Assisted High-Speed Train Communications: Performance Analysis and Optimal Configuration

High-speed train (HST) communications are envisioned to provide diversified broadband services by integrating with 5G while the high mobility induces fast-fading channels and potentially degrades the system performance. To address this issue, we investigate an HST communication network empowered by intelligent reflecting surfaces (IRSs) with the multiple-input–multiple-output (MIMO) technology. Statistical channel state information (CSI) is exploited to mitigate the impact of the fast time-varying fading. The transceiver beamforming vectors and the IRS phase shift matrix are optimized to improve the system performance in terms of the outage probability and the ergodic capacity considering the channel uncertainty. First, we derive the analytical expression of the outage probability with a generalized Marcum $Q $ -function. Then, we develop an alternating optimization algorithm to minimize the outage probability by capitalizing on the generalized eigenvalue–eigenvectors. Moreover, the ergodic capacity is deduced with statistical CSI and then optimized by analyzing the upper bound with Jensen’s approximation. Extensive simulations show that simulation results are consistent with the theoretical analysis, and the IRS-assisted system significantly outperforms the system without IRS in terms of the outage probability and the ergodic capacity.
Transducer and Radiation Efficiency Figures of a Multiport Antenna Array

Optimal performance of antennas used in multiple-input multiple-output (MIMO) systems is addressed in this communication with optimality being expressed in terms of the power radiated, which is subject to realistic, yet unknown, excitation and fixed, however, arbitrarily complicated, matching. It is shown that if excitation is not specified, the optimality of an MIMO radiating system has to be understood differently as compared with the case of a specified excitation. Consequently, two important figures of merits—transducer and radiation efficiency figures—are adopted to measure the quality of the MIMO radiating systems. The communication is accompanied by examples illustrating the theoretical concepts.
Indirect Applications of Additive Manufacturing for Antennas

We report the fabrication methodology of stereolithography (SLA) printed molds for metal and resin cast antennas. In the first method, a conical horn created using metal cast molds printed from a glass-filled resin utilizes a casting technique allowing for low-cost 3D printing to fabricate metal antennas, reducing the losses incurred by metallized plastics, while still producing complex geometries quickly. This metal cast conical horn is compared to a horn constructed using a more traditional 3D printing method. The second casting method demonstrates the interchangeability between creating parts via SLA printing with a glass-filled resin and using the same resin cast into a reusable Polydimethylsiloxane (PDMS) mold. We demonstrate this method by casting an interchangeable slug for a capacitively coupled, mechanically reconfigurable disk loaded monopole. Simulated and experimental data are presented for S textsubscript 11, and Gain. Simulated BW, directivity, gain and efficiency as a function of frequency are presented. The results indicate that the 3D printed metal casting process produces antennas with a higher gain and lower return loss than metallized resin antennas. The method is suitable for difficult geometries requiring resolution of at least $50 \mu \text{m}$ . The capacitively coupled disk loaded monopole demonstrates the versatility of 3D printing in antenna fabrication.
Human SCARB2 Acts as a Cellular Associator for Helping Coxsackieviruses A10 Infection

Coxsackievirus A10 (CVA10) causes hand, foot, and mouth disease (HFMD) and herpangina, which can result in severe neurological symptoms in children. CVA10 does not use the common enterovirus 71 (EV71) receptor, human SCARB2 (hSCARB2, scavenger receptor class B, member 2), for infection but instead uses another receptor, such as KREMEN1. Our research has shown that CVA10 can infect and replicate in mouse cells expressing human SCARB2 (3T3-SCARB2) but not in the parental NIH3T3 cells, which do not express hSCARB2 for CVA10 entry. Knocking down endogenous hSCARB2 and KREMEN1 with specific siRNAs inhibited CVA10 infection in human cells. Co-immunoprecipitation confirmed that VP1, a main capsid protein where virus receptors for attaching to the host cells, could physically interact with hSCARB2 and KREMEN1 during CVA10 infection. It is the efficient virus replication following virus attachment to its cellular receptor. It resulted in severe limb paralysis and a high mortality rate in 12-day-old transgenic mice challenged with CVA10 but not in wild-type mice of the same age. Massive amounts of CVA10 accumulated in the muscles, spinal cords, and brains of the transgenic mice. Formalin inactivated CVA10 vaccine-induced protective immunity against lethal CVA10 challenge and reduced the severity of disease and tissue viral loads. This is the first report to show that hSCARB2 serves as an associate to aid CVA10 infection. hSCARB2-transgenic mice could be useful in evaluating anti-CVA10 medications and studying the pathogenesis induced by CVA10.
Biodiversity of rolB/C-like Natural Transgene in the Genus Vaccinium L. and Its Application for Phylogenetic Studies

A variety of plant species found in nature contain agrobacterial T-DNAs in their genomes which they transmit in a series of sexual generations. Such T-DNAs are called cellular T-DNAs (cT-DNAs). cT-DNAs have been discovered in dozens of plant genera, and are suggested to be used in phylogenetic studies, since they are well-defined and unrelated to other plant sequences. Their integration into a particular chromosomal site indicates a founder event and a clear start of a new clade. cT-DNA inserts do not disseminate in the genome after insertion. They can be large and old enough to generate a range of variants, thereby allowing the construction of detailed trees. Unusual cT-DNAs (containing the rolB/C-like gene) were found in our previous study in the genome data of two Vaccinium L. species. Here, we present a deeper study of these sequences in Vaccinium L. Molecular-genetic and bioinformatics methods were applied for sequencing, assembly, and analysis of the rolB/C-like gene. The rolB/C-like gene was discovered in 26 new Vaccinium species and Agapetes serpens (Wight) Sleumer. Most samples were found to contain full-size genes. It allowed us to develop approaches for the phasing of cT-DNA alleles and reconstruct a Vaccinium phylogenetic relationship. Intra- and interspecific polymorphism found in cT-DNA makes it possible to use it for phylogenetic and phylogeographic studies of the Vaccinium genus.
Affinity-Based Analysis Methods for the Detection of Aminoglycoside Antibiotic Residues in Animal-Derived Foods: A Review

With the increasingly serious problem of aminoglycoside antibiotic residues, it is imperative to develop rapid, sensitive and efficient detection methods. This article reviews the detection methods of aminoglycoside antibiotics in animal-derived foods, including enzyme-linked immunosorbent assay, fluorescent immunoassay, chemical immunoassay, affinity sensing assay, lateral flow immunochromatography and molecular imprinted immunoassay. After evaluating the performance of these methods, the advantages and disadvantages were analyzed and compared. Furthermore, development prospects and research trends were proposed and summarized. This review can serve as a basis for further research and provide helpful references and new insights for the analysis of aminoglycoside residues. Accordingly, the in-depth investigation and analysis will certainly make great contributions to food safety, public hygiene and human health.