Miracle-dz/UniFAR-FTUs huggingface dataset:
Bronchopulmonary Nematodes in Alpine Ibex: Shedding of First Stage Larvae Analyzed at the Individual Host Level

Pneumonia is the most frequent cause of death for Alpine ibex (Capra ibex) in Gran Paradiso National Park, (Italy). The etiology of this form of pneumonia is currently unknown and the identification of the primary etiological agent remains difficult due to biological and logistic constraints. Uncovering individual differences in Protostrongylid prevalence and intensity is important to further investigate the epidemiology of respiratory diseases and their relationship to heterozygosity and inbreeding in a once almost extinct population like C. ibex. In a group of 21 individually recognizable adult male we monitored monthly prevalence and intensity of Protostrongylid first-stage larvae using Baerman's technique from June to September 2019. First-stage larvae of 5 genera were detected. Muellerius (P = 100%, CI95% = 84–100) and Protostrongylus (P = 86%, CI95%:71–100) were two dominant genera according to Bush's importance index. Neostrongylus (P = 38%,CI95%: 17–59), Cystocaulus (P = 33%,CI95% = 13–53) were classified as co-dominant genera while Dictyocaulus filaria (P = 0.05%, CI95% = 0.04–0.13) was detected, for the first time in Alpine ibex, in one subject. Protostrongylidae larval excretion varied significantly over time, with minimum L1 excretion in July. Individual median larval intensity ranged from 4.4 lpg to 82.2 lpg with Poulin's discrepancy index showing highly aggregated distribution patterns for Muellerius spp. (D = 0.283, CI95% = 0.760–0.895) and Protostrongylus spp. (D = 0.635, CI95% = 0.580–0.705). Presented data provide the necessary base point to further investigate how lungworm infection account for the different rates of progression of pneumonia in C. ibex. Individual aggregation of larval intensity must be further evaluated to determine whether these differences mirror different levels of parasitic infection related to individual differences in immune response, hormonal-states or genetic fitness.
Psychological Impact of COVID-19 on College Students After School Reopening: A Cross-Sectional Study Based on Machine Learning

COVID-19, the most severe public health problem to occur in the past 10 years, has greatly impacted people's mental health. Colleges in China have reopened, and how to prevent college students from suffering secondary damage due to school reopening remains elusive. This cross-sectional study was aimed to evaluate the psychological impact of COVID-19 after school reopening and explore via machine learning the factors that influence anxiety and depression among students. Among the 478 valid online questionnaires collected between September 14th and September 20th, 74 (15.5%) showed symptoms of anxiety (by the Self-Rating Anxiety Scale), and 155 (32.4%) showed symptoms of depression (by Patient Health Questionnaire-9). Descriptive analysis of basic personal characteristics indicated that students at a higher grade, having relatives or friends who have been infected, fearing being infected, and having a pessimistic attitude to COVID-19 easily experience anxiety or depression. The Synthetic Minority Oversampling Technique (SMOTE) was utilized to counteract the imbalance of retrieved data. The Akaike Information Criterion (AIC) and multivariate logistic regression were performed to explore significant influence factors. The results indicate that exercise frequency, alcohol use, school reopening, having relatives or friends who have been infected, self-quarantine, quarantine of classmates, taking temperature routinely, wearing masks routinely, sleep quality, retaining holiday, availability of package delivery, take-out availability, lockdown restriction, several areas in school closed due to COVID-19, living conditions in the school, taking the final examinations after school reopening, and the degree to which family economic status is influenced by COVID-19 are the primary influence factors for anxiety or depression. To evaluate the effect of our model, we used 5-fold cross-validation, and the average area under the curve (AUC) values of the receiver operating characteristic (ROC) curves of anxiety and depression on the test set reached 0.885 and 0.806, respectively. To conclude, we examined the presence of anxiety and depression symptoms among Chinese college students after school reopening and explored many factors influencing students' mental health, providing reasonable school management suggestions.
Polycomb Factor PHF19 Controls Cell Growth and Differentiation Toward Erythroid Pathway in Chronic Myeloid Leukemia Cells

Polycomb group (PcG) of proteins are a group of highly conserved epigenetic regulators involved in many biological functions, such as embryonic development, cell proliferation, and adult stem cell determination. PHD finger protein 19 (PHF19) is an associated factor of Polycomb repressor complex 2 (PRC2), often upregulated in human cancers. In particular, myeloid leukemia cell lines show increased levels of PHF19, yet little is known about its function. Here, we have characterized the role of PHF19 in myeloid leukemia cells. We demonstrated that PHF19 depletion decreases cell proliferation and promotes chronic myeloid leukemia (CML) differentiation. Mechanistically, we have shown how PHF19 regulates the proliferation of CML through a direct regulation of the cell cycle inhibitor p21. Furthermore, we observed that MTF2, a PHF19 homolog, partially compensates for PHF19 depletion in a subset of target genes, instructing specific erythroid differentiation. Taken together, our results show that PHF19 is a key transcriptional regulator for cell fate determination and could be a potential therapeutic target for myeloid leukemia treatment.
The role of avB3 and VEGFR2 endocytic recycling in angiogenesis

Placental growth factor (PIGF) binds to VEGFR1 and is known to play a role in pathological angiogenesis, but its mechanism of action remains unclear. Endothelial cell migration in response to angiogenic stimuli requires coordination of adhesive function with VEGFR signalling, and I have studied the intracellular trafficking of integrins and VEGFRs in primary cultured human umbilical vein endothelial cells (HUVECs). VEGFR2 and avp3 integrin cycled rapidly between the plasma membrane and an internal pool, and treatment of HUVECs with PIGF promoted the rapid mobilisation of both VEGFR2 and avp3 integrin from this internal pool to the plasma membrane. This mobilisation occurred via a mechanism that was dependent on the presence of VEGFR1 and Rab4a, and required the inactivation of GSK3p, but did not require the activity of PKD1 nor the tyrosine kinase activity of VEGFRs. Furthermore, RNAi of Rab4a, avp3 and VEGFR1 opposed PIGF-promoted endothelial cell tubule-like structure branching and cross-bridge formation in an organotypic tube formation assay. Taken together, these data show that PIGF can influence endothelial cell function by controlling the endocytic function of av^3 integrin. This recycling mechanism is required to induce endothelial cell structure branching and formation of a complex endothelial cell vessel network. In addition, regulation of VEGFR2 recycling by VEGFR1 represents a novel mechanism for mediating receptor cross talk during angiogenesis. Therefore, I have identified a novel pathway by which PIGF/VEGFR1 is able to positively influence the angiogenic response.
Solving the Rubik's cube with stepwise deep learning

This paper explores a novel technique for learning the fitness function for search algorithms such as evolutionary strategies and hillclimbing. The aim of the new technique is to learn a fitness function (called a Learned Guidance Function) from a set of sample solutions to the problem. These functions are learned using a supervised learning approach based on deep neural network learning, that is, neural networks with a number of hidden layers. This is applied to a test problem: unscrambling the Rubik's Cube using evolutionary and hillclimbing algorithms. Comparisons are made with a previous LGF approach based on random forests, with a baseline approach based on traditional error‐based fitness, and with other approaches in the literature. This demonstrates how a fitness function can be learned from existing solutions, rather than being provided by the user, increasing the autonomy of AI search processes.
Evaluation of the Feasibility of Three Custom-made Tetrapolar Probes for Electrical Characterization of Cardiac Tissue

Non-thermal cardiac ablation can be used to treat atrial fibrillation and consists in applying a pattern of electrical pulses to the targeted tissue. Knowing the conductivity of the tissue gives an extent of the pulse parameters needed for the ablation. However, there is a lack of conductivity data for cardiac tissues in the range of frequencies of non-thermal ablation. To this extent, this paper presents a feasibility study for the use of three tetrapolar probes, a planar probe, a needle probe and a pin probe, for cardiac tissue electrical characterization. Probe performance was evaluated across the range of 0.1-100 kHz. The planar probe was the most accurate in reference liquid measurements. Thus, it was used to characterize ovine ex vivo cardiac tissue, which measured conductivity is lower than that from the literature. These results suggest the need for a probe design optimization to improve the contact with the tissue.
An Improved Bearing Fault Diagnosis Scheme Based on Hierarchical Fuzzy Entropy and Alexnet Network

Bearings are important part of aerospace equipments. Therefore, bearings fault diagnosis and fault detection is necessary for the safe operation. Generally, the bearing are core component and runs in a complex and heavy background noise environment. Thence, the bearing signals will be overwhelmed by noise. At present, the diagnosis methods of bearings are based on the experience of experts which costs a lot of labor and time-consuming. Consequently, to address these inadequacies, a novel method based on variational mode decomposition (VMD) algorithms and Alexnet neural network has been presented. Firstly, it decomposes the nonstationary bearing signals into intrinsic mode functions adaptively. However, background noise will seriously affect the number of signal decompositions and reduce accuracy even over-decomposition. Thence, this paper proposed the hierarchical fuzzy entropy method to adaptively extract weak fault characteristics of bearings. Then Alexnet neural network has been utilized to learn the relationship of fault features and bearings health conditions. The architecture of Alexnet diagnosis model is convenient and efficient. Finally, two tentative dataset are adopted to verify the effectiveness and feasibility of the method proposed in this article. The results show that the presented method has a higher fault diagnosis accuracy rate than traditional convolutional neural networks.
Diffuse Multipath Analysis of Vehicle-to-Vulnerable Road User Channel in Urban Environment

The impulse response of the radio channel can typically be decomposed into a specular part and diffuse part. Neglecting the diffuse multipath components (DMC) in the channel model may result in underestimating the received power and the channel capacity. Therefore, it is important to analyze the power contribution of the DMC. This work presents an analysis of the fractional DMC power. A wideband channel measurement campaign was conducted at 5.2 GHz. The Kalman enhanced super resolution tracking (KEST) algorithm is used to estimate the parameters of the specular multipath components (SMC). The results show that the value of the fractional DMC power depends on the shadowing condition. While the DMC contributes as low as 5 % in line-of-sight (LoS) situation prior to the collision, it varies between 15 % and 40 % in obstructed-LoS (OLoS), and it can reach up to 90 % in non-LoS (NLoS) situation.
Wireless Power Transfer (WPT) Enabled IoT Sensors Based on Ultra-Thin Electrically Small Antennas

Wireless power transfer (WPT) enabled Internet-of-Things (IoT) temperature and light sensors based on two ultra-thin electrically small antennas are reported. Two distinctive wireless power capture capabilities are realized with metamaterial-inspired electrically small antennas. The near-field resonant parasitic (NFRP) element-based Egyptian Axe Dipole (EAD) antenna realizes an omnidirectional pattern. The Huygens dipole antenna, which combines a balanced pair of EAD and capacitively loaded loop (CLL) NFRP elements, realizes a unidirectional pattern. A sensor-augmented rectifier circuit is developed and seamlessly integrated with these antennas. Both of these systems are ultra-thin, being printed on a single piece of PCB substrate. The compact IoT temperature and light detection sensors were successfully implemented and tested. Since these high performance WPT-powered sensors are battery-free, they are very attractive for a variety of IoT applications.
SWM: A High-Performance Sparse-Winograd Matrix Multiplication CNN Accelerator

Many convolutional neural network (CNN) accelerators are proposed to exploit the sparsity of the networks recently to enjoy the benefits of both computation and memory reduction. However, most accelerators cannot exploit the sparsity of both activations and weights. For those works that exploit both sparsity opportunities, they cannot achieve the stable load balance through a static scheduling (SS) strategy, which is vulnerable to the sparsity distribution. In this work, a balanced compressed sparse row format and a dynamic scheduling strategy are proposed to improve the load balance. A set-associate structure is also presented to tradeoff the load balance and hardware resource overhead. We propose SWM to accelerate the CNN inference, which supports both sparse convolution and sparse fully connected (FC) layers. SWM provides Winograd adaptability for large convolution kernels and supports both 16-bit and 8-bit quantized CNNs. Due to the activation sharing, 8-bit processing can achieve theoretically twice the performance of the 16-bit processing with the same sparsity. The architecture is evaluated with VGG16 and ResNet50, which achieves: at most 7.6 TOP/s for sparse-Winograd convolution and three TOP/s for sparse matrix multiplication with 16-bit quantization on Xilinx VCU1525 platform. SWM can process 310/725 images per second for VGG16/ResNet50 with 16-bit quantization. Compared with the state-of-the-art works, our design can achieve at least $1.53 \boldsymbol {\times }$ speedup and $1.8 \boldsymbol {\times }$ energy efficiency improvement.
Effects of Temperature and Supply Voltage on Soft Errors for 7-nm Bulk FinFET Technology

Integrated circuits are expected to operate across a wide range of temperatures and supply voltages. At the 7-nm FinFET technology node, the self-heating of individual transistors may further increase local temperatures on a die. The combined effects of supply voltage variations and elevated temperatures on soft-error rates for the 7-nm node are investigated. Results show increased sensitivity to soft errors at reduced supply voltage and elevated temperature conditions due to decreased charge collection.
Test-Based Least Privilege Discovery on Cloud Infrastructure as Code

Infrastructure as Code (IaC) for cloud is an important practice due to its efficient and reproducible provisioning of cloud environments. On a cloud IaC definition (template), developers need to manage permissions for each cloud services as well as a desired cloud environment. To minimize the risk of cyber-attacks, retaining least privilege, i.e., giving a minimum set of permissions, on IaC templates is important and widely regarded as best practice. However, discovering least privilege on a target IaC template at one time is an error-prone and burdensome task for developers. One reason is that some actions of a cloud service implicitly use other services and require corresponding permissions, which are hard to recognize without actual executions on the cloud and burden the development process with iterations of permission setting and provisioned result checking. In this paper, we present a technique to automatically discover least privilege. Our method incrementally finds the least privilege by the iteration of testing on the cloud and (re)configuring permissions on the basis of test results. We conducted case studies and found that our approach can identify least privilege on Amazon Web Services within a practical time. Our experiments also show that the proposed algorithm can reduce the number of test executions, which directly affects the time and cost on cloud to determine least privilege, by 69.3% and 39.8% compared with the random and heuristic methods, respectively, on average.
Sensorless Field-Oriented Control for Open-End Winding Five-Phase Induction Motor With Parameters Estimation

This paper proposes a sensorless field-oriented control (FOC) of an open-end stator winding five-phase induction motor (OESW-FPIM). The FOC technique used is associated with dual Space Vector Modulation (SVM) to provide a constant switching frequency and lower harmonics distortion. Furthermore, a simple hybrid observer is proposed which combines a model reference adaptive system (MRAS) and a sliding mode (SM) observer. The examined observer is designed for the estimation of the rotor flux and rotational speed as well as for the estimation of the load torque disturbances. Lyapunov theorem is used in this paper to prove the observer's stability. The work presented in this paper aims to enhance the researched motor's sensorless control and its robustness against external load disturbances and parameters variation. In the proposed MRAS-SM observer, the reference model is replaced by a SM model which uses a sigmoid function as a switching function to overcome the chattering problem. This combination is intended to make use of the advantages of both strategies. At the same time, to preserve the high-level performance of the sensorless FOC technique and to reduce system uncertainties, an estimation algorithm is developed to identify the rotor resistance and the stator resistance simultaneously during motor operation. The parameter estimation algorithm is combined with the proposed control to improve the speed estimation and control accuracy, particularly at low-speed operation. Finally, the effectiveness of the proposed control is validated in real-time by utilizing a hardware-in-the-loop (HIL) platform.
Graphene Inkjet-Printed Ultrawideband Tapered Coplanar-Waveguide Antenna on Kapton Substrate

This paper presents an ultra-wideband graphene antenna with tapered coplanar-waveguide feed. The proposed antenna covers the 2.7-8.2 GHz bandwidth (2.6-10 GHz measured), with two main resonance frequencies at 3.1 and 5.5 gigahertz (3.1 and 5.8 measured). Simulations show a radiation pattern that looks quasi-omnidirectional with a maximum gain limited to 3.15 dBi and efficiency above 84.7%. In order to post-process the graphene ink and to provide flexibility, Kapton Polyimide is used as a substrate. The flexibility, as well as the lightweight and ease in the fabrication of accurate designs, turns this antenna into a suitable candidate for wearable and flexible wireless applications.
Numerical Assessment of Microwave Imaging for Axillary Lymph Nodes Screening Using Anthropomorphic Phantom

We numerically assess the potential of microwave imaging (MWI) for the detection of axillary lymph nodes (ALNs). The proposed MWI system is radar-based, in which a broad-band signal (2-6 GHz) is transmitted by a single probing antenna to scan the axillary region. The full-wave simulations include a realistic phantom of the underarm region which was previously developed by the authors. The phantom includes the main tissues of the axillary region and the corresponding dielectric properties. We show that the proposed system can successfully detect an ALN embedded in a homogeneous fatty medium. Additionally, we show that despite the strong reflection of the muscle - caused by the high dielectric contrast between fat and muscle - we are able to distinguish an ALN from the background. To the best of our knowledge, this is the first study in literature which employs an anatomically realistic phantom to study ALN MWI.
Base Station Antenna Array Design with Characteristic Mode Analysis

This paper revisits the history of Characteristic Mode Analysis (CMA) and describes thereby the fundamental differences between the antenna element design for handheld terminals and array applications, such as base station antenna design. With this knowledge in mind, open research topics are highlighted. A mutual coupling investigation with CMA is performed on a typical multiband array arrangement, to show the influence of mutual coupling on the individual array modes, respectively the modal significance.
Polarizability of dielectric prolate half ellipse

This article presents a method for solving the polarizability of a dielectric prolate half ellipse as a function of its relative electric permittivity. The considered geometry consists of two conjoined half ellipses with different permittivities. The polarizability depends on the excitation field direction, therefore can be presented in the form of dyadic consisting of two components that are series and parallel polarizabilities. The method is based on analytical series expansions with coefficients obtained as a numerical solution of a matrix equation.
Game Theory Analysis and Modeling of Sophisticated Multi-Collusion Attack in MANETs

Mobile Adhoc Network (MANET) has been a core topic of research since the last decade. Currently, this form of networking paradigm is increasingly being construed as an integral part of upcoming urban applications of Internet-of-Things (IoT), consisting of massive connectivity of diverse types of nodes. There is a significant barrier to the applicability of existing routing approaches in conventional MANETs when integrated with IoT. This routing mismatch can lead to security risks for the MANET-based application tied with the IoT platform. This paper examines a pragmatic scenario as a test case wherein the mobile nodes must exchange multimedia signals for supporting real-time streaming applications. There exist two essential security requirements viz. i) securing the data packet and ii) understanding the unpredictable behavior of the attacker. The current study considers sophistication on the part of attacker nodes. They are aware of each other’s identity and thereby collude to conduct lethal attacks, which is rarely reflected in existing security modeling statistics. This research harnesses the potential modeling aspect of game theory to model the multiple-collusion attacker scenario. It contributes towards i) modeling strategies of regular/malicious nodes and ii) applying optimization principle using novel auxiliary information to formulate the optimal strategies. The model advances each regular node’s capability to carry out precise computation about the opponent player’s strategy prediction, i.e., malicious node. The simulation outcome of the proposed mathematical model in MATLAB ascertains that it outperforms the game theory’s baseline approach.