Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction of Fatigue Resistance in Lead-Free Ni-Doped SAC 1205 Solder Alloys Using a Rate-Dependent Material Model and Subjected to Drop Tests

In this paper, a novel Ni-doped SAC 1205 (Sn–1.2Ag–0.5Cu) solder alloy is investigated to determine its mechanical behavior at a high strain rate. The Ni-doped SAC 1205 solder alloy has never been discussed in terms of high-strain-rate testing. The purpose of the SAC 1205 solder material doped with Ni is to enhance the mechanism of the solder alloy and delay the formation of the brittle intermetallic compound. In the drop test standard (Joint Electron Device Engineering Council condition B), the test vehicle was subjected to impact loading with a peak acceleration of 1500G and a time duration of 0.5 ms. Because the test board supported the impact loading for a very short time, the solder joint material was considered to have a rate-dependent elastic–plastic behavior. The quasi-static mechanical behavior and dynamic behavior were estimated for the simulation modeling by conducting a quasi-static tensile test and a split Hopkinson tensile bar test. A finite-element model was used to analyze the stress and strain of the solder joint during the drop test. The experimental results revealed that Young’s modulus and yielding strength of the Ni-doped SAC 1205 solder alloy increased during the high-strain-rate testing. Therefore, a rate-dependent elastic–plastic model of the Ni-doped SAC 1205 solder alloy was designed and implemented to describe the real mechanical behavior of a solder joint at a high strain rate during a drop test. The primary failure mode occurs at the top of the solder joint (package side) during the drop test. The predicted fatigue model we provided was focused on the primary failure mode. Eventually, the fatigue life curve of the rate-dependent elastic–plastic model offered the greatest accuracy in predicting the solder joint fatigue life.
Estimation of Knee Joint Angle Using a Fabric-Based Strain Sensor and Machine Learning: A Preliminary Investigation

Monitoring human knee kinematics has various health applications including in-home rehabilitation and longterm tracking of movements of people with knee disorders. We proposed a wearable system based on a stretchable strain sensor and investigated its feasibility to estimate the knee joint angle in tasks of walking and static knee flexion. A pilot study with six subjects was conducted in which participants were asked to walk and perform flexion exercises at multiple speeds. Two commonly used machine learning algorithms (neural network and random forest) were utilized to estimate the knee joint angle based on the strain sensor data. The performance of the proposed approach was assessed in an intra- and inter-subject evaluation. In the intra-subject evaluation. , the average mean absolute error (MAE) in estimating the knee joint angle during the walking task and flexion exercises was 1.94 and 3.02 degrees. , respectively. , with a similar coefficient of determination R2 of 0.97. In the inter-subject evaluation. , an average MAE of 4.14 degrees in the walking task and 6.97 degrees in the knee flexion exercises was achieved with a R2 of 0.90. Our results suggest the feasibility of our approach. , which includes a fabric-based strain sensor and machine learning. , to estimate the knee joint angle. In future, this method might be used in various applications including the fields of healthcare. , virtual reality and robotics.
Value of Diffusion-Weighted Magnetic Resonance Imaging Combined with miR-18a Level in Predicting Radiosensitivity of Cervical Cancer

Background Radioresistance during radiotherapy of cervical cancer often leads to treatment failure; therefore, there is an urgent need to develop effective predictive indicators of radiosensitivity for cervical cancer patients. Material/Methods Cervical cancer cells were collected from 40 patients who received surgical resections. The relationships between apparent diffusion coefficient (ADC) values of masses before surgery and different micro-RNAs (miRNA) levels (miR-18a, miR-132, and miR-145) of these cells were investigated. Cervical cancer cells were divided into 4 groups according to the ADC values of original tumor tissues and expression level of miR-18a. Then, these cells were exposed with irradiation both in vitro and in vivo. Results Advanced cervical cancer showed lower ADC values in magnetic resonance imaging. miR-18a, miR-132, and miR-145 all were increased in the cervical cancer tissues, while miR-18a showed a more marked negative correlation with ADC values. The results of in vitro and in vivo assays showed that higher expression of miR-18a in cervical cancer cells leads to more radiosensitivity, especially in cells from cancer tissues with lower ADC values. Conclusions The combination of ADC values with expression level of miR-18a may be a new and reliable predictor for radiosensitivity of cervical cancer, helping cervical cancer patients with low ADC values and high expressions of miR-18a to achieve better outcomes in radiotherapy.
Crowdsensing Approach to Monitoring and Optimize Wi-Fi Networks in Adversarial Environments

The signal quality of Wi-Fi networks tends to decrease in settings with high client density, bad Access Point (AP) coverage, or radio interferences from the presence of rogue APs (RAPs). This work proposes a novel solution for monitoring of the Wi-Fi network through a crowdsensing approach, combining data collected from user devices and fixed monitoring nodes. The developed system assesses Wi-Fi signal quality, detects and assails rogue APs, assesses the number of clients per area and the RAPs impact on the signal surrounding them. The system, tested on a large faculty building with over 1,000 Wi-Fi users, proved to be efficient, simple to use and adaptable, being easily deployed in any large and multi-floored building and network.
Semantic Manifold Alignment in Visual Feature Space for Zero-Shot Learning

Zero-Shot Learning (ZSL) is getting more attention for its potential to solve a task without training examples, such as to recognize a category of unseen object in computer vision task. Most existing methods are suffered from hubness problem and semantic gap problem. In this paper, we propose a novel strategy based on Aligning Semantic Manifolds in Feature Space (ASMFS) to boost the performance of ZSL. Considering that the semantic representations must be predicted in the location of their corresponding visual instances, we adjust the predicted unseen semantic representations by the average of their K nearest neighbors (K-NN). The experimental results over two basic ZSL models and four public datasets demonstrate the universal enhancement performance of the proposed strategy. It significantly boosts the existing ZSL approaches with low over cost and outperforms eight state-of-the-art methods.
Depth Aware Portrait Segmentation Using Dual Focus Images

The rapid development of camera in hand-held devices and the emergence of social media has led to an uprise in capturing self-portrait images. Augmenting these images for beautification or applying special effects to mimic DSLR camera has become a popular practice. Most of these effects require separation of foreground from background where the effect can be applied solely on background. To employ such effects on portrait (upper half of human body) images, a pixel-accurate segmentation is imperative. In this paper, we propose an effective method of fast depth aware CNN based portrait segmentation from monocular images. The proposed method is capable of being deployed on mobile phones, within the constraints of time and memory. On the segmented images, we demonstrate the application of bokeh effect, which blurs out-of-focus regions. We experiment with different combinations of state of the art encoder and decoder networks for segmentation and infer that our proposed method can improve the inference speed by 76 ms on mobile device while maintaining an accuracy of 97.0 %.
A Switched Systems Approach to Image-Based Localization of Targets That Temporarily Leave the Camera Field of View

Image sensors have widespread use in many robotics applications and, in particular, in target tracking. While existing methods assume continuous image feedback, the novelty of this brief stems from the development of dwell time conditions to guarantee convergence of state estimates to an ultimate bound for a class of image-based observers in the presence of intermittent measurements. A Lyapunov analysis for the switched system is performed to develop convergence conditions based on the minimum amount of time the object must be visible and on the maximum amount of time the object may remain outside the camera view. Experimental results are included to verify the theoretical results and elucidate real-world performance.
AppDNA: App Behavior Profiling via Graph-based Deep Learning

Better understanding of mobile applications' behaviors would lead to better malware detection/classification and better app recommendation for users. In this work, we design a framework AppDNA to automatically generate a compact representation for each app to comprehensively profile its behaviors. The behavior difference between two apps can be measured by the distance between their representations. As a result, the versatile representation can be generated once for each app, and then be used for a wide variety of objectives, including malware detection, app categorizing, plagiarism detection, etc. Based on a systematic and deep understanding of an app's behavior, we propose to perform a function-call-graph-based app profiling. We carefully design a graph-encoding method to convert a typically extremely large call-graph to a 64-dimension fix-size vector to achieve robust app profiling. Our extensive evaluations based on 86,332 benign and malicious apps demonstrate that our system performs app profiling (thus malware detection, classification, and app recommendation) to a high accuracy with extremely low computation cost: it classifies 4024 (benign/malware) apps using around 5.06 second with accuracy about 93.07%; it classifies 570 malware's family (total 21 families) using around 0.83 second with accuracy 82.3%; it classifies 9,730 apps' functionality with accuracy 33.3% for a total of 7 categories and accuracy of 88.1 % for 2 categories.
Design and Implementation of a Switched Beam Smart Antenna for Wireless Power Transfer System at 5.8 GHz

in recent years, the development of efficient microwave power transmission (MPT) system has become a vital research topic. This paper proposes the use of a switched beam smart antenna based on a planar 4×4 Butler Matrix in order to improve the performances of wireless power transfer system to a MAV at 5.8 GHz. Our smart system consists of four patch antenna array, a 4×4 butler matrix, and PIN diode switch. The proposed system produces and steers four orthogonal beams in the required directions (±39°, and ±15°), with a maximum gain of 17.98 dB, and good return losses less than −24.690 dB. All components of this smart system were designed, optimized and simulated with microstrip technology on a FR4 substrate, using Momentum software of Agilent integrated into ADS software, which is based on the method of moments (MoM).
Scaling CMOS beyond Si FinFET: an analog/RF perspective

FinFET has been introduced in the 22/16nm node to continue CMOS logic scaling. The very tight pitches foreseen for the coming generation necessitate the introduction of different scaling boosters. In this paper, we review how these elements affect the analog device performance. The benefits of alternative channel material for dedicated RF applications and the related integration challenges are also discussed.
Biological Characterization of an Edible Species from Brazilian Biodiversity: From Pharmacognostic Data to Ethnopharmacological Investigation.

Talisia esculenta (A. St.-Hil.) Radlk. is a large tree belonging to family Sapindaceae and popularly known as "pitombeira" or "pitomba. " Although species have relevant economic and medicinal uses in Brazil, no study has investigated its effectiveness as a diuretic, hypotensive, and antihypertensive agent. The aim of this study was to present a detailed anatomical and histochemical study for T. esculenta and provide important safety and efficacy parameters. After morpho-anatomical and microchemical study, a purified aqueous extract (ethanol soluble fraction obtained from T. esculenta [ESTE]) was obtained, and detailed phytochemical investigation was performed. Subsequently, acute oral toxicity test was performed in male and female rats. Moreover, diuretic, hypotensive, and antihypertensive effects on normotensive and spontaneously hypertensive rats (SHR) were investigated. Finally, the effects of prolonged treatment with ESTE on serum levels of nitrite, thiobarbituric acid reactive species, and nitrotyrosine were also measured in SHR. Oral treatment with ESTE did not induce acute toxic effects and did not affect urine production, blood pressure, or heart rate of normotensive and SHR. Prolonged treatment with ESTE was able to increase serum nitrite levels and significantly reduce oxidative and nitrosative stress markers in SHR. Data obtained showed that ESTE has a significant antioxidant activity without showing any clinical signs of acute toxicity. The use of this species as a diuretic, hypotensive, or antihypertensive agent should be carried out with caution, since administration in rodents did not produce renal and/or hemodynamic responses that justify this indication.
Wideband Tunable Differential Bandstop Filter Based on Double-Sided Parallel-Strip Line

This paper presents a wideband tunable differential bandstop filter (BSF) using optimized varactor-loaded stepped-impedance resonators (SIRs) based on the double-sided parallel-strip line (DSPSL). A detailed investigation on the relationship between the frequency tunability and the properties of the SIR is presented. It can be found that there are two extra factors for determining frequency-tuning range of the varactor-loaded SIR, i.e., the impedance and length ratios, in addition to the variation range of the varactor’s capacitance. Thanks to the balanced properties of the DSPSL, the common mode shows all-stop response. For demonstration, a second-order wideband tunable differential BSF using two varactor-loaded DSPSL SIRs is designed, and the design procedures are given in detail. The simulation and measured results, showing good accordance, exhibit a tuning range from 0.58 to 1.07 GHz, corresponding to a fractional tuning range of 59.4%.
Improve Production Efficiency and Predict Machine Tool Status using Markov Chain and Hidden Markov Model

Industrial intelligence is the most popular issue in the industry. Building models based on the different conditions of each algorithm can significantly increase production efficiency and reduce costs. On the other hand, Prevention of machine downtime is the most important part of industrial intelligence as well. This study believes that reducing downtime can improve efficiency and reduce costs. So we simulate this situation and predict base on different unit time by means of Markov chain as well as Hidden Markov Model. The Markov chain can show which idle reason may cause the machine to malfunction. In addition, Hidden Markov models can help managers arrange personnel and prevent potential failures based on past status sequences. Therefore, This study concludes the accuracy upper 86 percent, so the research can help the managers to make decision and improve production efficiency.
Temporal Attentive Network for Action Recognition

In action recognition, one of the most important challenges is to jointly utilize the texture and motion information as well as capturing the long-term dependence of various common and action-specific postures. Motivated by this fact, this paper proposes Temporal Attentive Network (TAN) for action recognition. The key idea in TAN is that not all postures, each of which represented by a small collection of consecutive frames, contribute equally to the successful recognition of an action. As a result, TAN incorporates two separate spatial and temporal streams into one network. Information in the two streams is partially shared so that discriminative spatiotemporal features can be extracted to characterize various postures in an action. Moreover, a temporal attention mechanism is introduced in the form of Long-Short Term Memory (LSTM) network. With this mechanism, features from the action-specific postures can be emphasized, while common postures shared by many different actions will be ignored to some extent. By jointly using such spatial and temporal information as well as attentive cues in a single network, TAN achieves impressive performance on two public datasets, HMDB51 and UCF101, with accuracy scores of 72.5% and 94.1 %, respectively.
Individual, Social, and Societal Correlates of Health-Related Quality of Life Among African American Survivors of Ovarian Cancer: Results from the African American Cancer Epidemiology Study.

OBJECTIVE While the incidence of epithelial ovarian cancer (EOC) is lower among African American (AA) women compared with European American (EA) women, AA women have markedly worse outcomes. In this study, we describe individual, social, and societal factors in health-related quality of life (HRQL) in AA women diagnosed with EOC in the African American Cancer Epidemiology Study (AACES) that we hypothesize may influence a patient's capacity to psychosocially adjust to a diagnosis of cancer. METHODS There were 215 invasive EOC cases included in the analysis. HRQL was measured using the SF-8 component scores for physical (PCS) and mental (MCS) health. We used least squares regression to test the effects of individual dispositional factors (optimism and trait anxiety); social level (perceived social support); and societal-level factors (SES defined as low family income and low educational attainment, and perceived discrimination) on HRQL, while adjusting for patient age, tumor stage, body mass index, and comorbidity. Mediation analysis was applied to test whether social support and physical activity buffer impacts of EOC on HRQL. RESULTS Optimism, trait anxiety, social support, poverty, and past perceived discrimination were significantly associated with HRQL following diagnosis of EOC. Specifically, higher family income, lower phobic anxiety, and higher social support were associated with better wellbeing on the MCS and PCS (p < 0.01). Higher perceived discrimination was associated with both lower MCS and PCS, whereas higher optimism was associated with higher MCS. Physical activity (MET-min/week) and social support displayed significant overall mediation for effects of SES on MCS and PCS, but not for trait anxiety. CONCLUSIONS Both pre- and postdiagnosis characteristics of AA women with EOC are important predictors of HRQL after cancer diagnosis. Individual, social, and societal-level factors each contribute to HRQL status with EOC and should be assessed.
Rank-Consistency Multi-Label Deep Hashing

In this paper, we present a deep hashing method for multi-label image retrieval, which uses a rank list to provide global supervision information. Unlike most existing approaches using shallow models to learn hash functions for multi-label images, we deepen the DNN structure to extract powerful features. In addition, we apply a rank-consistency objective function to align the similarity orders in the hamming space and the ones from the original space. Compared with conventional contrast loss and triplet loss, our listwise ranking can capture sufficient global information. We also propose a multi-label softmax cross-entropy loss to strengthen the discriminative power. Specifically, we consider the number of common labels of multi-label images as the metric of similarity in the original space with the hamming distance between binary codes as the distance metric in the hamming space. Experimental results on MIRFLICKR-25K and IAPRTC12 are presented to show the effectiveness of our proposed approach.
Vulnerability Mining Method Based on Genetic Algorithm and Model Constraint

The fuzzy technology based on model constraint is lack of guidance in variation process, and the fuzzy technology based on coverage information has a weak code penetration ability when meeting a complex logic verification, what's more, these two methods' code coverage are deeply dependent on the initial samples, if there are no specific file structure types in the initial samples, the possibility of covering the corresponding code blocks will be very low. This paper proposes a vulnerability mining method based on genetic algorithm and constraint model. The method uses the model constraint technology to create test samples, and uses the fuzzy technology based on coverage information feedback to guide the direction of data variation. Besides that, using genetic algorithm to enrich the diversity of file structure types and combinations among test samples, and generate high-quality test samples gradually at the same time, which can greatly improve the efficiency of fuzzing test.
Reconfigurable Analog Filter Design using Current Mode Device

This paper reports a synthesis methodology of linear analog filter function using programmable first order function. Synthesized transfer function has been realized as a network of single-output (SO) operational transconductance amplifier (OTA). All types of filters can be realized only by programming the inputs without any parameter matching conditions. This programmability property is maintained through the hierarchical synthesis of higher order multifunction filters. The proposed filter shows better frequency response and tunability, lower sensitivities compared to literature. All the proposed theory are validated with SPICE simulation.
Cascading Sum-Product Networks using Robustness

Sum-product networks are an increasingly popular family of probabilistic graphical models for which marginal inference can be performed in polynomial time. They have been shown to achieve state-of-the-art performance in several tasks. When learning sum-product networks from scarce data, the obtained model may be prone to robustness issues. In particular, small variations of parameters could lead to different conclusions. We discuss the characteristics of sum-product networks as classifiers and study the robustness of them with respect to their parameters. Using a robustness measure to identify (possibly) unreliable decisions, we build a hierarchical approach where the classification task is deferred to another model if the outcome is deemed unreliable. We apply this approach on benchmark classification tasks and experiments show that the robustness measure can be a meaningful manner to improve classification accuracy.
Three-Level Two-Stage Decoupled Active NPC Converter With Si IGBT and SiC MOSFET

This paper presents the operation principle and benefits of a novel power converter topology named three-level two-stage decoupled active neutral point clamped (3L-TDANPC) converter, which is implemented based on a hybrid utilization of silicon (Si) insulated gate bipolar transistors (IGBTs) and silicon carbide (SiC) metal–oxide semiconductor field-effect transistors (mosfets). The 3L-TDANPC converter can achieve high efficiency with limited number of SiC mosfet modules while keeping balanced loss distribution among the switching devices, which helps increase the converter power ratings. In addition, in this 3L-TDANPC converter, the SiC mosfet has a potential to ride through short-circuit fault because of the presence of Si IGBTs. The key challenges that are associated with system resonant current are investigated and the methods to damp such resonant current are proposed and explained in detail. The simulation and experimental results based on a 1-MW 3L-TDANPC converter prototype confirm the expected benefits of this proposed converter and the effectiveness of the proposed resonant current damping methods.