Miracle-dz/UniFAR-FTUs huggingface dataset:
A Sub-1GHz Wearable Radar Sensor for Artery Detection

A sub-1 GHz wearable radar with non-contact and real-time detection for artery detection is presented in this paper. Healthcare and wearable requirements have been a significant topic recently. The wearable radar adopts the near-field sensing technique to detect artery wall variation. A series of radio pulse is emitted to the wrist skin by the transmitting antenna. The reflected signal with artery pulse information is then back to the receiving antenna, which will be processed by the following analog/digital processing circuits. The physiological data are digitized by using the embedded system with a 10-bit analog to digital converter. The digital bandpass filter is performed to improve overall signal-to-noise ratio. Therefore, the raw data with artery information can be obtain and further transmitted to the personal computer and cellphone by a built-in Bluetooth wireless communication module. The experimental results demonstrate that the near-field sensing technique successfully detects the wrist artery based on the proposed pulse-based sensor module.
Kinematic Wave Modeling in Water Resources: Environmental Hydrology

PRELIMINARIES. Hydraulic Equations for Subsurface Flow. Hydraulic Properties of Soil. SUBSURFACE FLOW. Unsaturated Flow. Subsurface Stormflow. SURFACE IRRIGATION. Flow Advance Over Porous Beds: Application to Surface Irrigation. Flow in Infiltrating Channels: Furrow Irrigation. SNOW HYDROLOGY. Movement of Water Through Snow. Glacier Flow. EROSION AND SEDIMENT YIELD. Erosion and Sediment Transport Processes. Erosion on a Plane. WATER QUALITY. Solute Transport. Solute Transport by Surface Runoff. Solute Transport in Porous Media. Sedimentation. Chromatographic Transport in Soils. STREAMLINE. Streamflow Generation. Effect of Spatial and Temporal Variability on Streamflow Hydrograph. Appendix. Index.
A Novel Approach to Generate Dataset for Object Detection in Assembly Lines

Quality Assurance (QA) is required to ensure precision in the vehicle Assembly Unit process. The most significant challenge is that manual work is error-prone, and even minor errors can be a problem for a vehicle. This study aims to explore suitable Deep Learning (DL) models to automate various parts of the task well. For the current work, the aim is focused on predicting/detecting points on the chassis accurately. In this article, we elaborated on the process to generate the dataset and the proposed model 'You Look Only Once-v5' (YOLOv5) to identify cross marks on the vehicles. The model architecture and parameters are discussed in-depth and changed to detect and classify marked objects against the chassis background. The accuracy and efficiency evaluations show that the model achieved the top performance in average precision (mAP) of ≥ 98%.
Numerical Analysis of Homogeneous and Stratified Turbulence under
 Horizontal Shear via Lagrangian Stochastic Model: Richardson Number
 Effect

The present investigation is carried out to reveal Richardson number (Ri) effects on an homogeneous and stratified turbulence under horizontal shear. The problem is simulated via Lagrangian Stochastic model (LSM). Hence, the method of Runge Kutta with fourth order is adopted for the numerical integration of three differential systems under non linear initial conditions of Jacobitz (2002) and Jacobitz et al. (1998). This study is performed for Ri ranging from 0.2 to 3.0. It has been found that computational results by the adopted model (LSM) gave same findings than that of preceding works. It has been shown a global tendency of different parameters governing the problem to equilibrium asymptotic states for various values of Ri. The comparative study between the computations of the present LSM and direct numerical simulation of Jacobitz demonstrates a good agreement for both methods for the ratios of; potential energy Kθ/E and kinetic energy K/E toward the total energy E and the principal component of anisotropy b12 It has been found that Ri is the most important parameter affecting the thermal and dynamic fields of the flow. Hence, increase Ri conduct to increase the uniform stable stratification and decrease for the uniform mean shear S. It can be concluded that Ri is a main non-dimensional parameter which enable us to understand physical phenomenons produced inside stratified shear flows.
Beyond cyanotoxins: increased Legionella, antibiotic resistance genes in western Lake Erie water and disinfection-byproducts in their finished water

Background Western Lake Erie is suffering from harmful cyanobacterial blooms, primarily toxic Microcystis spp. , affecting the ecosystem, water safety, and the regional economy. Continued bloom occurrence has raised concerns about public health implications. However, there has been no investigation regarding the potential increase of Legionella and antibiotic resistance genes in source water, and disinfection byproducts in municipal treated drinking water caused by these bloom events. Methods Over 2 years, source water (total n = 118) and finished water (total n = 118) samples were collected from drinking water plants situated in western Lake Erie (bloom site) and central Lake Erie (control site). Bloom-related parameters were determined, such as microcystin (MC), toxic Microcystis, total organic carbon, N, and P. Disinfection byproducts (DBPs) [total trihalomethanes (THMs) and haloacetic acids (HAAs)] were assessed in finished water. Genetic markers for Legionella, antibiotic resistance genes, and mobile genetic elements were quantified in source and finished waters. Results Significantly higher levels of MC-producing Microcystis were observed in the western Lake Erie site compared to the control site. Analysis of DBPs revealed significantly elevated THMs concentrations at the bloom site, while HAAs concentrations remained similar between the two sites. Legionella spp. levels were significantly higher in the bloom site, showing a significant relationship with total cyanobacteria. Abundance of ARGs (tetQ and sul1) and mobile genetic elements (MGEs) were also significantly higher at the bloom site. Discussion Although overall abundance decreased in finished water, relative abundance of ARGs and MGE among total bacteria increased after treatment, particularly at the bloom site. The findings underscore the need for ongoing efforts to mitigate bloom frequency and intensity in the lake. Moreover, optimizing water treatment processes during bloom episodes is crucial to maintain water quality. The associations observed between bloom conditions, ARGs, and Legionella, necessitate future investigations into the potential enhancement of antibiotic-resistant bacteria and Legionella spp. due to blooms, both in lake environments and drinking water distribution systems.
Pulmonary outcomes of congenital diaphragmatic hernia patients based on defect size (CDH Study Group Stage)

Background Congenital diaphragmatic hernia (CDH) is associated with significant pulmonary morbidity. Previous investigation has shown that postnatal inpatient morbidity is linked to diaphragmatic defect size. The objective of this study was to evaluate long-term pulmonary outcomes by CDH study group defect size. Methods A retrospective analysis was conducted for CDH patients (n=133) managed in a neonatal intensive care unit (NICU) at a single children’s hospital within an adult hospital system and subsequently followed up at a comprehensive multidisciplinary CDH clinic (n=102) from January 2012 to April 2022. CDH patients were stratified according to Congenital Diaphragmatic Hernia Study Group (CDHSG) Stage, and then categorized as low-risk (LR), defect size A and B, or high-risk (HR), defect size C and D. Inpatient data, including the presence of pulmonary hypertension, extracorporeal life support (ECLS) utilization, and mechanical ventilation days, were collected. Post-discharge data including the prevalence of asthma, pulmonary hypertension, emergency department visits, the total number of hospitalizations, and average rehospitalization days were collected. Frequentist analysis was used. Results The outcomes for 133 NICU patients were analyzed (HR: n=54, LR: n=79). During NICU stay, the prevalence of pulmonary hypertension [HR: 16/54 (30%) vs. LR: 9/79 (12%), P=0.009], ECLS utilization [HR: 19/54 (35%) vs. LR: 4/79 (5%), P<0.001], and the average number of mechanical ventilation days [HR: 17 days (IQR: 12–27) vs. LR: 5 days (IQR: 2–9), P<0.001] were significantly higher in the HR CDH group. Post NICU discharge, the prevalence of asthma [HR: 20/54 (37%), vs. LR: 17/79 (22%), P=0.050)] and the total days of rehospitalization [HR: 9 (IQR: 2–27) vs. LR: 4 (IQR: 1–8), P=0.035] were significantly higher in HR group. Of the patients seen in the comprehensive multidisciplinary CDH clinic, obstructive lung disease measured by impulse oscillometry was increased in the HR CDH population compared to the reference group [median R5Hz was 12.95 kPa/(L/s) in CDH vs. 9.8 kPa/(L/s) (P=0.010)]. Conclusions HR CDHSG Stage is associated with worse inpatient and long-term pulmonary outcomes.
Temporal transformer-spatial graph convolutional network: an intelligent classification model for anti N-methyl-D-aspartate receptor encephalitis based on electroencephalogram signal

Encephalitis is a disease typically caused by viral infections or autoimmunity. The most common type of autoimmune encephalitis is anti-N-methyl-D-aspartate receptor (NMDAR) antibody-mediated, known as anti-NMDA receptor encephalitis, which is a rare disease. Specific EEG patterns, including “extreme delta brush” (EDB), have been reported in patients with anti-NMDA receptor encephalitis. The aim of this study was to develop an intelligent diagnostic model for encephalitis based on EEG signals. A total of 131 Participants were selected based on reasonable inclusion criteria and divided into three groups: health control (35 participants), viral encephalitis (58 participants), and anti NMDAR receptor encephalitis (55 participants). Due to the low prevalence of anti-NMDAR receptor encephalitis, it took several years to collect participants’ EEG signals while they were in an awake state. EEG signals were collected and analyzed following the international 10–20 system layout. We proposed a model called Temporal Transformer-Spatial Graph Convolutional Network (TT-SGCN), which consists of a Preprocess Module, a Temporal Transformer Module (TTM), and a Spatial Graph Convolutional Module (SGCM). The raw EEG signal was preprocessed according to traditional procedures, including filtering, averaging, and Independent Component Analysis (ICA) method. The EEG signal was then segmented and transformed using short-time Fourier transform (STFT) to produce concatenated power density (CPD) maps, which served as inputs for the proposed model. TTM extracted the time-frequency features of each channel, and SGCM fused these features using graph convolutional methods based on the location of electrodes. The model was evaluated in two experiments: classification of the three groups and pairwise classification among the three groups. The model was trained using two stages and achieved the performance, with an accuracy of 82.23%, recall of 80.75%, precision of 82.51%, and F1 score of 81.23% in the classification of the three groups. The proposed model has the potential to become an intelligent auxiliary diagnostic tool for encephalitis.
A Compensation for Elevated Sidelobe of Radiation Pattern of Antenna Array Caused by Amplitude and Phase Discretization Based on Deep Reinforcement Learning

In our previous research, we proposed a multi-layer perceptron method for synthesizing radiation patterns with complex requirements. However, this method has a limitation in that it requires continuous amplitude and phase excitations. The continuous excitations may not be practical in most applications, and when the amplitude and phase of the antenna element are discontinuous due to the resolution of the electronic device during the optimization process, this method may fail. In order to solve this problem, a combined method is proposed in this paper, utilizing the multi-layer perceptron network to optimize the radiation patterns of array antenna with continuous amplitude and phase, and introducing a deep reinforcement learning method to compensate for the elevated sidelobes in the radiation patterns caused by amplitude and phase discretization in post-processing. Specifically, the continuous amplitude and phase values obtained through the multi-layer perceptron network are rounded to approximate discrete results based on the resolution, serving as prior training experience for the deep reinforcement learning model. The compensation problem of elevated sidelobes caused by amplitude and phase discretization is then formulated as an optimization model, and a deep reinforcement learning model is constructed accordingly, with discrete excitations acting as the agent for exploration and search. The agent is trained using the Deep Q-learning network as the basic framework, combined with Double DQN technology and Dueling DQN technology to efficiently search for the best compensatory effect. The reward is carefully designed to incentivize the agent to search for discrete excitations with the optimal compensatory effect. A simulation experiment is conducted on a 50-element hemispheric conformal antenna array, demonstrating the effectiveness of the combined method.
Deep Image: An Efficient Image-Based Deep Conventional Neural Network Method for Android Malware Detection

—The continuous increment of malware and its complexity motivated researchers to implement techniques to detect and classify it. Manual detection of malicious files is time consuming and shows poor results. Recently, Deep Convolution Neural Networks (DCNN) shows promising results in malware detection. DCNNs include large number of fully connected layers that are capable to deal with fast iterations of Android malware. Compared to the existing approach, DCNN shows high performance and accuracy in detecting different types of malwares. The proposed work combines Scale-Invariant Feature Transform (SIFT) and DCNN to detect malware features. Combining SIFT with DCNN allow higher accuracy of features classification and overcome the problem of single-feature extraction. The proposed method is compared to existing approaches to malware detection in terms of anticipated time and detection accuracy. The experimental results showed the significant enhancement offered by the proposed work in terms of accuracy and performance.
Asynchronous and Heterogeneous Wake-Up Schedules for IoT Neighbor Communication

For Internet of Things devices, one of the main power demands comes from the radio interfaces. Hence, duty cycling, i.e., activating and deactivating the radio, becomes essential to energy savings. In asynchronous scenarios, scheduled-based duty cycle methods stand out for their low deployment cost. Recently, the literature reports many studies on asymmetric scheduled-based methods — i.e., different nodes operating under different duty cycles. In this work we propose an extension of this concept: the heterogeneous duty cycling. It allows nodes to operate under schedules generated by distinct methods, resulting in a wider range of duty cycle choices and also a better coexistence between devices from different manufactures. In particular, we study which combinations of methods present the rotation closure property, and what are the average and maximum latencies for those pairs. We also show that heterogeneous duty cycling can improve performance if schedules are properly selected.
A Switch from Latent to Typical Infection during Pectobacterium atrosepticum—Tobacco Interactions: Predicted and True Molecular Players

Phytopathogenic microorganisms, being able to cause plant diseases, usually interact with hosts asymptomatically, resulting in the development of latent infections. Knowledge of the mechanisms that trigger a switch from latent to typical, symptomatic infection is of great importance from the perspectives of both fundamental science and disease management. No studies to date have compared, at the systemic molecular level, the physiological portraits of plants when different infection types (typical and latent) are developed. The only phytopathogenic bacterium for which latent infections were not only widely described but also at least fluently characterized at the molecular level is Pectobacterium atrosepticum (Pba). The present study aimed at the comparison of plant transcriptome responses during typical and latent infections caused by Pba in order to identify and then experimentally verify the key molecular players that act as switchers, turning peaceful plant-Pba coexistence into a typical infection. Based on RNA-Seq, we predicted plant cell wall-, secondary metabolism-, and phytohormone-related genes whose products contributed to the development of the disease or provided asymptomatic plant—Pba interactions. By treatment tests, we confirmed that a switch from latent to typical Pba-caused infection is determined by the plant susceptible responses mediated by the joint action of ethylene and jasmonates.
Analysis of a Leaky-wave Antenna with High Scanning Rate Using Microstrip Phase Delay Lines

In this paper, we analyze the microstrip phase delay (PD) lines of different lengths are applied between the unit-cells (UCs) of leaky-wave antenna (LWA). The employment of PD lines can offer the phase compensations (PCs) for each UC, the simulation experiments observed that the PD lines can significantly improve the realized gain of LWA and reduce the operating bandwidth due to longer PD lines exciting the higher order modes of phase constant. Therefore, the scanning rates (SRs), i.e., scanning range divided by operating bandwidth (%), can remarkably increase. Besides, in order to maintain the beam scanning ability of LWA in travelling-wave direction (or longitudinal direction), the PD lines are placed and varied in the lateral direction. The phase constants in the lateral direction component are also proven to be able to excite higher order modes. Benefit from this, the multi-beam LWAs in the lateral direction can be obtained by the PD lines with optimal lengths, and patterns of multi-beam are fixed with the operating frequency changing.
ECM hydrogel derived from human umbilical cord as a scaffold for neural tissue repair and its comparison to ECM from CNS and non-CNS porcine tissues

Th is p ap er h as b ee n pe er -r ev ie w ed a nd a cc ep te d fo r p ub lic at io n, b ut h as y et to u nd er go c op ye di tin g an d pr oo f c or re ct io n. T he fi na l p ub lis he d ve rs io n m ay d iff er fr om th is p ro of . ECM hydrogel derived from human umbilical cord as a scaffold for neural tissue repair and its comparison to ECM from CNS and non‐CNS porcine tissues Zuzana Kočí , Karel Výborný , Jana Dubišová , Irena Vacková, Aleš Jäger, Oleg Lunov, Klára Jiráková, Šárka Kubinová
TEACHERS’ PERCEPTIONS ON INTERNATIONAL ONLINE TEAMWORK AND HOW IT CAN BE IMPROVED

In-service and preservice teachers were invited through an online platform (VALIANT) to participate in six to seven-week free online teacher development 20-hour courses with the aim to test the feasibility of a teacher development model that not only reunites novice and experienced teachers, but also promotes peer learning among them on concrete situations that happen in their classroom and their own teaching experience. This study focuses on one such course on Diversity and Inclusion in Primary Classrooms that brought together teachers from several European countries to develop their knowledge and competence on how to deal with diversity and inclusion issues through cooperative learning. At the conclusion of the VE editions of the course, nine participating teachers volunteered to be interviewed online. The main gains pointed out by the interviewed teachers are connected to the development of intercultural competence and learning; the ability to look at their and others’ classrooms and education in general from different perspectives; the perusal of digital solutions they could reuse in their classrooms; and the development of social interaction and bonding with other teachers as essential for peer learning. The main challenges experienced when working in a team online were related to distributing and keeping deadlines; equal commitment to tasks, time management; and engaging with one another effectively despite only knowing each other virtually. Interviewees suggested several strategies to cope with the challenges of online teamwork that impact course design and the facilitators’ and participants’ roles. The main conclusion is that increased scaffolding is needed for team leadership and shared leadership to help virtual teams collaborate more effectively and cohesively across cultures.
Understanding Frédéric Flachéron’s Paper Negative Process through Experimentation and Specular Reflection FTIR Analysis

Cultural heritage objects present a special set of challenges for chemical analysis. Often micro-sampling or even contacting the object is deemed an unacceptable risk to the object. This study examined if specular reflection FTIR, a non-sampling and non-contact analytical technique, can provide insight into chemical composition of the layered coatings on Frédéric Flachéron’s paper negatives (1848–1852) in the Harrison D. Horblit Collection of Early Photography, in Houghton Library, at Harvard University. Specular reflection FTIR data confirmed the identification of beeswax and sandarac as the transparentizing media on Flachéron’s paper negatives, as previously published by Lee Ann Daffner. However, specular reflection FTIR analysis also indicated that some negatives had additional localized coatings of orange shellac in specific areas of the images. To understand why Flachéron retouched his negatives with orange shellac, modern experimental replications of the paper negative process were performed. Through modern experimentation with the paper negative technique, it was found that Flachéron’s coatings of shellac served as an integral part of his image-altering technique. The color of the orange shellac subtly alters contrast and density, but it does not mask an area the way an opaque watercolor or highly pigmented paint might. The fine adjustments to the negatives with orange shellac were an attempt to perfect the contrast in the print, and better render depth and detail. These discoveries add to a growing body of recent research that points to the historic and art historic importance of negatives and coatings in photography. The specificity with which specular reflection FTIR was able to nondestructively identify the chemical composition of the local coating, and specifically target the analysis on the areas in which it appeared, allowed for an understanding of Flachéron’s use of local coatings as a retouching method rather than a protective coating.
stealthML: Data-driven Malware for Stealthy Data Exfiltration

The use of machine learning methods have been actively studied to detect and mitigate the consequences of malicious attacks. However, this sophisticated technology can become a threat when it falls into the wrong hands. This paper describes a new class of malware that employs machine learning to autonomously infer when and how to trigger an attack payload to maximize impact while minimizing attack traces. We designed, implemented, and demonstrated a smart malware that monitors the realtime network traffic flow of the victim system, analyzes the collected traffic data to forecast traffic and identify the most opportune time to trigger data extraction, and optimizes its strategy in planning the data exfiltration to minimize traces that might reveal the malware's presence.