Miracle-dz/UniFAR-FTUs huggingface dataset:
The PKCβ‐p66shc‐NADPH oxidase pathway plays a crucial role in diabetic nephropathy

Oxidative stress plays a critical role in the pathogenesis of diabetic nephropathy (DN). p66shc is closely related to oxidative stress. However, the exact mechanism of its involvement in diabetic nephropathy is poorly understood. This study aimed to investigate the role of the p66shc‐related pathway in diabetic nephropathy.
Multidimensional Data Mining Based on Tensor Model

In this work, we propose a framework suitable for multidimensional data mining based on tensor. A Tensor Data Model (TDM) provides us with high order data structure and naive description for information retrieval. Among others, we discuss multidimensional rule mining here. Generally, association rule mining (or extraction of association rules) concerns about co-related transaction records of single predicate, and hard to examine the ones over multiple predicates since it takes heavy time-and space-complexities. Here we show TDM allows us to model several operations specific to multidimensional data mining yet to reduce amount of description.
Compact Balanced Bandpass Filter Design Using Asymmetric SIR Pairs and Spoof Surface Plasmon Polariton Feeding Structure

This letter demonstrates a design of a compact balanced bandpass filter (BPF) with stopband extension and common-mode (CM) suppression using asymmetric stepped-impedance resonator (SIR) pairs and a spoof surface plasmon polariton (SPP) feeding structure. The configuration of balanced SIR pairs is analyzed theoretically to obtain a wide stopband response and intrinsic CM suppression within differential-mode passband. Moreover, a novel SPP feeding structure is adopted as a low-pass filter and impedance matching section for enhancing filter transmission responses. Finally, a balanced BPF operating at 0.85 GHz with the SPP feeding structure is designed and fabricated to verify the design method.
Stability Control for FWID-EVs With Supervision Mechanism in Critical Cornering Situations

This paper proposes a novel control strategy to improve the stability performance for four-wheel independent drive electric vehicles in critical cornering by introducing the supervision mechanism for yaw moment control and slip ratio regulation simultaneously. The proposed control strategy is a three-level structure: the first level is composed of a yaw moment controller and a longitudinal velocity controller; the second level is made up of a tyre force distributor; and the third level consists of a slip ratio regulator. To begin with, the supervision mechanism is developed to supervise vehicle driving states and determine whether the yaw moment controller should be activated. After that, the tyre force distributor is presented to assign the resultant force/moment generated by the first level to the four independent wheels, in which average distribution, dynamic load distribution, and optimal distribution schemes are separately discussed for comparison. Also, the slip ratio regulator is designed to regulate the slip ratio within the threshold. In the end, simulations performed on a nonlinear vehicle model with eight degree of freedom validated the effectiveness of the proposed control strategy and approaches.
Inhibition of PI3K/Akt/NF‐κB signaling with leonurine for ameliorating the progression of osteoarthritis: In vitro and in vivo studies

Osteoarthritis (OA) is characterized as the degeneration and destruction of articular cartilage. In recent decades, leonurine (LN), the main active component in medical and edible dual purpose plant Herba Leonuri, has been shown associated with potent anti‐inflammatory effects in several diseases. In the current study, we examined the protective effects of LN in the inhibition of OA development as well as its underlying mechanism both in vitro and in vivo experiments. In vitro, interleukin‐1 beta (IL‐1β) induced over‐production of prostaglandin E2, nitric oxide, inducible nitric oxide synthase, cyclooxygenase‐2, interleukin‐6 and tumor necrosis factor alpha were all inhibited significantly by the pretreatment of LN at a dose‐dependent manner (5, 10, and 20 µM). Moreover, the expression of thrombospondin motifs 5 (ADAMTS5) and metalloproteinase 13 (MMP13) was downregulated by LN. All these changes led to the IL‐1β induced degradation of extracellular matrix. Mechanistically, the LN suppressed IL‐1β induced activation of the PI3K/Akt/NF‐κB signaling pathway cascades. Meanwhile, it was also demonstrated in our molecular docking studies that LN had strong binding abilities to PI3K. In addition, LN was observed exerting protective effects in a surgical induced model of OA. To sum up, this study indicated LN could be applied as a promising therapeutic agent in the treatment of OA.
Predicting Future Developer Behavior in the IDE Using Topic Models

While early software command recommender systems drew negative user reaction, recent studies show that users of unusually complex applications will accept and utilize command recommendations. Given this new interest, more than a decade after first attempts, both the recommendation generation (backend) and the user experience (frontend) should be revisited. In this work, we focus on recommendation generation. One shortcoming of existing command recommenders is that algorithms focus primarily on mirroring the short-term past,—i.e., assuming that a developer who is currently debugging will continue to debug endlessly. We propose an approach to improve on the state of the art by modeling future task context to make better recommendations to developers. That is, the approach can predict that a developer who is currently debugging may continue to debug OR may edit their program. To predict future development commands, we applied Temporal Latent Dirichlet Allocation, a topic model used primarily for natural language, to software development interaction data (i.e., command streams). We evaluated this approach on two large interaction datasets for two different IDEs, Microsoft Visual Studio and ABB Robot Studio. Our evaluation shows that this is a promising approach for both predicting future IDE commands and producing empirically-interpretable observations.
Engineering a Woman: Marketing Opportunities and Challenges [Amperes: Current Affairs from Around the World]

There are severe constraints on women's entry into higher levels of education and their work in senior professional and managerial jobs [1]. For women in engineering education, support is crucial from the precollege level onward. In particular, female engineers? parents tend to raise their daughters with fewer gender stereotypes and place greater weight on education and learning [2].
Measuring and Visualizing the Scrappiness Level of a Website

Scraper sites are questionable quality sites that copy their content partially or entirely from other websites and sometimes gain more ranking and popularity to the detriment of the original websites. This usually happens from a search engine point of view. Misleading a user to a scraper site almost always implies an unhappy, time consuming user experience, the scraper site being an unnecessary link in the user's navigation path. In this paper we present a method through which one can numerically measure and quantify the scrappiness level of a website and also visually display this level. In the same time, this paper wants to advert to the web and research communities about this type of websites and to urge actions against them.
Professional Standards in Engineering Education and Industry 4.0

The development of information and communication technologies in the fourth industrial revolution can provide new educational opportunities, so it requires for more qualified graduates. The quality control of education is mostly investigated so far. It is caused by current professional standards that cover some necessary criteria for graduates at all levels. The paper observes current educational trends, suggests proper methods to put an integrated approach in practice and results of their application. So, 01.03. 02 Applied mathematics and Computer science is an example of major.
A Framework of Transfer Learning in Object Detection for Embedded Systems

Transfer learning is one of the subjects undergoing intense study in the area of machine learning. In object recognition and object detection there are known experiments for the transferability of parameters, but not for neural networks which are suitable for object detection in real time embedded applications, such as the SqueezeDet neural network. We use transfer learning to accelerate the training of SqueezeDet to a new group of classes. Also, experiments are conducted to study the transferability and co-adaptation phenomena introduced by the transfer learning process. To accelerate training, we propose a new implementation of the SqueezeDet training which provides a faster pipeline for data processing and achieves 1.8 times speedup compared to the initial implementation. Finally, we created a mechanism for automatic hyperparameter optimization using an empirical method.
Automatic Tweet Classification Based on News Category in Indonesian Language

Tweet is being informative as well as news articles, so that the automatic tweet classifier based on news category could be useful to make ease in searching tweet based on certain interesting category. We identified those are 11 categories: religion, business, entertainment, law and crime, health, motivation, sport, government, education, politics and technology. In the learning process, we use ZeroR, Naive Bayes Multinomial (NBM), Support Vector Machine (SVM), Random Forest (RF) and Sequential Minimal Optimization (SMO) algorithm based on previous work that has similar topic with this paper. In experiments, we experiment classifier using all tweet and various maximum number of tweets and terms in each category. In evaluating performance system, we used 10-fold cross validation and use accuracy (correctly classified instances) as performance paramater. In the experiments result, NBM performs the highest performance with 77,47% accuracy with maximum number of tweets and terms in every category is 500 tweets and 1000 terms. At the last, we built automatic tweet classifier with NBM due to this classifier and experiment result perform the best performances using web-based programming.
Surrogate Model-Based Energy-Efficient Scheduling for LPWA-Based Environmental Monitoring Systems

The rapid development of the Internet of Things (IoTs) with newly proposed wireless communication technology has compactly generated connections between humans and the physical world. Beginning with research on wireless sensor networks, environmental monitoring research has currently developed into wider applications aiming to detect the world with multitudinous IoT technologies. Recently, low power wide area technologies have provided such benefits as longer communication range, larger network capacity, and low unit-price, providing more choices for managers to set up more detailed monitoring. However, the operational lives of battery-powered end-devices still hinder long-term monitoring. In this paper, we propose a decentralized framework for energy-efficient environmental monitoring considering both the operation cost and current computational capabilities of the end-devices. The core function is a low-complexity scheduling approach that can balance the monitoring performance and energy consumption for different environmental states. Driven by the prediction accuracy of the surrogate model, monitoring nodes are selected. Meanwhile, the energy consumption of the other nodes is saved. Simulation results demonstrate that the proposed monitoring system has high energy efficiency with acceptable performance. The battery life of the whole system can be prolonged by up to 136.22%.
Verification Based Authentication Scheme for Bogus Attacks in VANETs for Secure Communication

A Vehicular Ad Hoc Network (VANET) is a subclass of mobile ad hoc network (MANET), which includes smart vehicles and roadside units (RSU). It maintains vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communications. The significant features of VANET contain self-organization, distributed networking, and highly dynamic topology. As the transmission of the message is in an open-access environment, like VANET, leads to the most critical and challenging security issues. Due to these critical concern, it is important to develop security mechanisms to detect and mitigate the VANET attacks. There are three authentication schemes in VANET such as Cryptography techniques, Digital signatures and Message Verification techniques to develop security mechanisms against VANET attacks, but some of the attacks are not addressed by these schemes. In this paper bogus attack is mitigated by using Position Verification Technique in which the effects of falsified position information are analyzed primarily. In order to reduce these problems, detection mechanisms are used to detect the cheating nodes and mitigate the effect of bogus node (false position information). The solution is evaluated based on simulation and the position verification system, that successfully prevent nodes from spreading false positions and thus the bogus attack can be mitigated.
Expressing Probabilistic Graphical Models in RCC

The purpose of this paper is to show the expressiveness of two different formalisms that combine logic and probabilistic reasoning: Stochastic Logic Programs (SLPs) and Proba-bilistic Concurrent Constraint Programs (PCCs). We analyse the relation between the two and we show that we are able to express, using PCC programs, some of the main probabilistic graphical models: Bayesian Networks, Markov random ﬁelds, Markov chains, Hidden Markov models, Stochastic Context Free Grammars and Markov Logic Networks. We express this last framework also in SLPs.
3-D Spatial Spectrum Fusion Indoor Localization Algorithm Based on CSI-UCA Smoothing Technique

In the indoor wireless localization environment, the non-line-of-sight receiving signal and low signal-to-noise ratio are usually strongly dominant issues due to the heavy existence of multipath signals, which also restrict the final wireless localization performance seriously. In this paper, a novel indoor localization algorithm based on 3-D multi-array spatial spectrum fusion (3-D-MSSF), which also uses the channel state information (CSI) under uniform circular array (UCA) structure, is proposed. First, in the data assembly process, the number of antennas and the number of observations can be virtually extended by applying a beam space transformation and smoothing technique by using the observed CSI information. Then, the existing multiple signal classification approach is applied on the smoothed data to jointly estimate the 2-D direction-of-arrival angles and the time-of-flight information from the resulting spatial spectrums at each UCA array. And in the grid-fusion explorer process, the estimated parameters are subsequently transmitted to the aggregation center to calculate the location results of each point relative to each access point, which introduces a grid-refinement algorithm in the search grid to improve the localization precision. While the parameters of interest for the final target position can be estimated from a single fused spatial spectrum, which results from fusing all maximum noise subspaces corresponding to the minimum error between each estimated point in the search grid and every set in the 3-D space-time searching grid. Computer simulation results together with the real application experiments in the indoor environment in terms of source position estimation and corresponding RMSE values are given. The proposed 3-D-MSSF method proves a significant indoor positioning performance, which can achieve the final localization accuracy below 1 m even if the line-of-sight signal is blocked, and there exist only multipath path signals at the receiver.
The Influence of Position of the Displaced Lesser Trochanter on Clinical Outcome of Unstable Trochanteric Femur Fractures in the Elderly

Purpose This study aimed to evaluate whether position of the displaced lesser trochanter affected clinical outcome in the treatment of unstable trochanteric fractures with intramedullary fixation. Patients and Methods Patients with unstable trochanteric fractures and displaced lesser trochanter who received intramedullary fixation were retrospectively reviewed in this study. Based on displacement distance of the lesser trochanter and whether the lesser trochanter was reduced operatively, patients were divided into three groups: patients with the displaced lesser trochanter less than 1cm (Group A), those with the displaced lesser trochanter more than 1 cm without operative reduction (Group B), or those with operative reduction (Group C). The surgical time, reduction quality, Harris Hip Score (HHS), Visual Analog Score (VAS), and complication rate were reviewed. Results There were 42 patients in Group A, 33 in Group B, and 36 in Group C with comparable demographic characteristics. The surgical time was significantly longer in Group C (P=0.009), compared with Groups A and B. Fracture reduction quality was comparable with over 85% good reduction among the three groups. The VAS score was significantly higher in Group B (P=0.023) without significant difference between Groups A and B. The HHS score was slightly lower in Group B, but it did not reach significant difference. The complication rate was statistically higher in Group B (p=0.043) than Groups A and C. Conclusion The severe displaced lesser trochanter may increase postoperative complications and postoperative pain in the treatment of unstable trochanteric femur fractures. However, the displaced lesser trochanter may not affect hip function.
Adaptive Spatiotemporal Filtering for Coronary Ultrafast Doppler Angiography

The heart’s supply of oxygen and nutrients relies on the coronary vasculature, which branches from millimeter-sized arteries down to micrometer-sized capillaries. To date, imaging technologies can only detect large epicardial coronary vessels, whereas the intramural coronary vasculature remains invisible due to cardiac motion. We recently introduced coronary ultrafast Doppler angiography, a noninvasive vascular imaging technology based on ultrafast ultrasound that enables the visualization of epicardial and intramural coronary vasculature in humans. In this letter we describe, using an open-chest swine data set, the adaptive spatiotemporal filtering method that was developed for the detection of slow blood flows embedded in rapid myocardial motion.
Fast Beam Alignment for Millimeter Wave Communications: A Sparse Encoding and Phaseless Decoding Approach

In this paper, we study the problem of beam alignment for millimeter wave (mmWave) communications, where a hybrid analog and digital beamforming structure is employed at the transmitter (i.e., base station), and an omni-directional antenna or an antenna array is used at the receiver (i.e., user). By exploiting the sparse scattering nature of mmWave channels, the beam alignment problem is formulated as a sparse encoding and phaseless decoding problem. More specifically, the problem of interest involves finding a sparse sensing matrix and an efficient recovery algorithm to recover the support and magnitude of a sparse signal from compressive phaseless measurements. A sparse bipartite graph code algorithm is developed for sparse encoding and phaseless decoding. Our theoretical analysis shows that, in the noiseless case, our proposed algorithm can perfectly recover the support and magnitude of the sparse signal with a probability exceeding a pre-specified value from $\mathcal {O}(K^2)$ measurements, where $K$ is the number of nonzero entries of the sparse signal. The proposed algorithm has a simple decoding procedure which is computationally efficient and noise robust. Simulation results show that our proposed method renders reliable beam alignment even in the low signal-to-noise ratio regime and presents a clear performance advantage over existing methods.
Adaptive algorithm for smart antenna system

This paper presents a comparative study of two adaptive algorithms, Linear Constraint Minimum Variance (LMCV) and the Minimum Variance Distortionless Response (MVDR) applied to the smart antenna system. These algorithms are studied in terms of power while tracking the desired signal. In this case, we used a system with three upcoming signals containing the desired signal, whose position changes constantly, and two signals considered as interference. The simulation result shows that the MVDR technique provides better results than LMCV by creating a beam forming that follows the desired signal through its movement preserving an adequate power.
Lessons Learned from Memory Errors Observed Over the Lifetime of Cielo

Maintaining the performance of high-performance computing (HPC) applications as failures increase is a major challenge for next-generation extreme-scale systems. Recent work demonstrates that hardware failures are expected to become more common. Few existing studies, however, have examined failures in the context of the entire lifetime of a single platform. In this paper, we analyze a corpus of empirical failure data collected over the entire five-year lifetime of Cielo, a leadership-class HPC system. Our analysis reveals several important findings about failures on Cielo: (i) its memory (DRAM and SRAM) exhibited no aging effects; detectable, uncorrectable errors (DUE) showed no discernible increase over its five-year lifetime; (ii) contrary to popular belief, correctable DRAM faults are not predictive of future uncorrectable DRAM faults; (iii) the majority of system down events have no identifiable hardware root cause, highlighting the need for more comprehensive logging facilities to improve failure analysis on future systems; and (iv) continued advances will be needed in order for current failure mitigation techniques to be viable on future systems. Our analysis of this corpus of empirical data provides critical analysis of, and guidance for, the deployment of extreme-scale systems.
Adaptive Confidence Threshold Algorithm for Vehicle Detection by Employing Temporal Information

An adaptive confidence algorithm for vehicle detection is proposed. Shortcomings of traditional vehicle detection method of deep learning with fixed confidence threshold are analyzed. To deal with false alarms and false negatives brought by these shortcomings, an adaptive confidence threshold algorithm for vehicle detection is raised. Two datasets are set up to test and compare the detection performance of the adaptive confidence algorithm with fixed confidence on MS-CNN. The experimental results show that the proposed algorithm could effectively reduce the false alarm rate and improve the detection performance of MS-CNN. It is of certain significance for the application of deep learning technology to vehicle detection.