Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficieny analysis of Quadratic buck converter for LED lamp driver applications

The objective of this paper is to verify Quadratic buck converter (QBC) performance as a LED lamp driver. Higher switching frequency operation reduces the size of components but at the penalty of increased switching losses and reduced efficiency. A compromise is also necessary between the switching frequency and efficiency. The PSIM simulation results of QBC are shown with average current-mode (ACM) technique. The performance curves of QBC such as efficiency Vs load, efficiency Vs switching frequency are presented with the best suited components data sheets which are available on on-line from different component vendors. The corresponding MATLAB programming results are also analyzed through bar diagrams.
A survey on big data stream processing in SDN supported cloud environment

Big data is the term which denotes data with features such as voluminous data, a variety of data and streaming data as well. Processing big data became essential for enterprises to garner general intelligence and avoid biased conclusions. Due to these features, big data processing is considered to be a challenging task. Big data Processing should rely on a robust network. Cloud computing offers a suitable environment for these processes. However, it is more challenging when we move big data to the cloud, as managing the cloud resources is the main issue. Software Defined Network (SDN) has a potential solution to this issue. In this paper, first, we survey the present state of the art of SDN, cloud computing, and Big data Stream processing (BDSP). Then, we discuss SDN in the context of Big Data Stream Processing in Cloud environment. Finally, critical issues and research opportunity are discussed.
Complex genetic patterns in human arise from a simple range-expansion model over continental landmasses

Although it is generally accepted that geography is a major factor shaping human genetic differentiation, it is still disputed how much of this differentiation is a result of a simple process of isolation-by-distance, and if there are factors generating distinct clusters of genetic similarity. We address this question using a geographically explicit simulation framework coupled with an Approximate Bayesian Computation approach. Based on six simple summary statistics only, we estimated the most probable demographic parameters that shaped modern human evolution under an isolation by distance scenario, and found these were the following: an initial population in East Africa spread and grew from 4000 individuals to 5.7 million in about 132 000 years. Subsequent simulations with these estimates followed by cluster analyses produced results nearly identical to those obtained in real data. Thus, a simple diffusion model from East Africa explains a large portion of the genetic diversity patterns observed in modern humans. We argue that a model of isolation by distance along the continental landmasses might be the relevant null model to use when investigating selective effects in humans and probably many other species.
Automated Detection of Driver Fatigue Based on Entropy and Complexity Measures

This paper presents a real-time method based on various entropy and complexity measures for detection and identification of driving fatigue from recorded electroencephalogram (EEG), electromyogram, and electrooculogram signals. The complexity features were used to distinguish whether the subjects are experienced drivers by calculating the Lempel-Ziv complexity of EEG approximate entropy (ApEn). Different threshold values can be set for the two kinds of drivers individually. The entropy-based features, namely, the wavelet entropy (WE), the peak-to-peak value of ApEn (PP-ApEn), and the peak-to-peak value of sample entropy (PP-SampEn), were extracted from the collected signals to estimate the driving fatigue stages. We proposed WE in a sliding window (WES), PP-ApEn in a sliding window (PP-ApEnS), and PP-SampEn in a sliding window (PP-SampEnS) for real-time analysis of driver fatigue. The real-time features obtained by WE, PP-ApEn, and PP-SampEn with sliding window were applied to artificial neural network for training and testing the system, which gives four situations for the fatigue level of the subjects, namely, normal state, mild fatigue, mood swing, and excessive fatigue. Then, the driver fatigue level can be determined in real time. The accuracy of estimation is about 96.5%-99.5%. Receiver operating characteristic (ROC) curve was used to present the performance of the neural network classifier. The area under the ROC curve is 0.9931. The results show that the developed method is valuable for the application of avoiding some traffic accidents caused by driver's fatigue.
Steric clashes with bound OMP peptides activate the DegS stress-response protease

Significance Gram-negative bacteria sense and respond to compromised outer-membrane assembly to maintain cell integrity. In Escherichia coli, outer-membrane stress is sensed via regulated intramembrane proteolysis, a universal signal-transduction system. The C-terminal peptides of unassembled outer-membrane proteins (OMPs) bind and activate DegS, a periplasmic protease that cleaves a transmembrane anti-sigma factor, increasing transcription of stress-response genes. The molecular mechanism by which OMPs allosterically activate DegS has been unclear. We show that OMP-peptide binding to DegS initiates a steric clash that breaks autoinhibitory interactions, resulting in proteolytic activation. This mechanism does not require strong sequence conservation. As proteases related to DegS play important roles in protein-quality control and regulation in most organisms, these studies provide an important step forward in understanding these enzymes. Escherichia coli senses envelope stress using a signaling cascade initiated when DegS cleaves a transmembrane inhibitor of a transcriptional activator for response genes. Each subunit of the DegS trimer contains a protease domain and a PDZ domain. During stress, unassembled outer-membrane proteins (OMPs) accumulate in the periplasm and their C-terminal peptides activate DegS by binding to its PDZ domains. In the absence of stress, autoinhibitory interactions, mediated by the L3 loop, stabilize inactive DegS, but it is not known how this autoinhibition is reversed during activation. Here, we show that OMP peptides initiate a steric clash between the PDZ domain and the L3 loop that results in a structural rearrangement of the loop and breaking of autoinhibitory interactions. Many different L3-loop sequences are compatible with activation but those that relieve the steric clash reduce OMP activation dramatically. Our results provide a compelling molecular mechanism for allosteric activation of DegS by OMP-peptide binding.
Analysis of reorganization overhead in log-structured file systems

In a log-structured file system (LFS), in general each block written to disk causes another disk block to become invalid data, resulting in one block of free space. Over time free disk space becomes highly fragmented, and a high level of dynamic reorganization may be required to coalesce free blocks into physically contiguous areas that subsequently can be used for logs. By consuming available disk bandwidth, this reorganization can degrade system performance. In a segmented disk LFS organization, the copy-and-compact reorganization method reads entire segments and then writes back all valid blocks. Other methods, suggested by earlier work on reduction of storage fragmentation for non-LFS disks, may access far fewer blocks (at the cost of increased CPU time). An analytic model is used to evaluate the effects on available disk bandwidth of dynamic reorganization, as a function of the read/write ratio, storage utilization, and degree of data movement required by dynamic reorganization for steady-state operation. It is shown that decreasing reorganization overhead can have dramatic effects on available disk bandwidth. <<ETX>>
A Framework for Wireless Sensor Network Based Mobile Mashup Applications

We describe a framework for wireless sensor network (WSN) based mobile mashup applications. The framework consists of four functional components: WSN, mobile gateway, context management platform, and mashup server. The WSN deployed in the application domain senses the physical phenomena and the human-environment interaction and expresses them in sensor readings. The mobile phone is used as the gateway to the WSN, which can directly communicate with the sensor nodes and collect the user related sensor data. On the mobile phone, the raw sensor data is preprocessed and the user-centric contexts are extracted. The context management platform stores all contexts according to the predefined context ontology, detects the inconsistent information, deduces the high level knowledge using heuristic rules and provides web service application programming interfaces (APIs) to mashup server. The mashup server integrates the context information obtained from the context management platform with the information from other mashup data sources, and provides services to users through mobile phone. The proposed framework can be used in exhibition center, shopping mall, smart office/home, and many other scenarios.
Phospholipase Cγ1 links inflammation and tumorigenesis in colitis-associated cancer

Colorectal cancer (CRC) is the third diagnosed cancer and the second leading cause of cancer-related deaths in the United States. Colorectal cancer is linked to inflammation and phospholipase Cγ1 (PLCγ1) is associated with tumorigenesis and the development of colorectal cancer; however, evidence of mechanisms connecting them remains unclear. The tight junctions (TJ), as intercellular junctional complexes, have an important role for integrity of the epithelial barrier to regulate the cellular permeability. Here we found that PLCγ1 regulated colitis and tumorigenesis in intestinal epithelial cells (IEC). To induce the colitis-associated cancer (CAC), we used the AOM/DSS model. Mice were sacrificed at 100 days (DSS three cycles) and 120 days (DSS one cycle). In a CAC model, we showed that the deletion of PLCγ1 in IEC decreased the incidence of tumors by enhancing apoptosis and inhibiting proliferation during tumor development. Accordingly, the deletion of PLCγ1 in IEC reduced colitis-induced epithelial inflammation via inhibition of pro-inflammatory cytokines and mediators. The PLCγ1 pathway in IEC accelerated colitis-induced epithelial damage via regulation of TJ proteins. Conclusions: Our findings suggest that PLCγ1 is a critical regulator of colitis and colorectal cancer and could further help in the development of therapy for colitis-associated cancer.
Explicit (Semantic) Memory for Music in Patients with Mild Cognitive Impairment and Early-Stage Alzheimer's Disease

Background/Study Context: Explicit memory for music was investigated by using a new test with 24 existing and 3 newly composed pieces. Methods: Ten patients with mild cognitive impairment (MCI) and 10 patients with early stage of Alzheimer's disease (AD) were compared with 23 healthy subjects, in terms of verbal memory of music by the identification of familiar music excerpts and the discrimination of distortion and original timbre of musical excerpts. Results: MCI and Alzheimer's patients showed significantly poorer performances in tasks requiring verbal memory of musical excerpts than the healthy participants. For discrimination of musical excerpts, MCI and AD patients surprisingly performed significantly better than the healthy comparison subjects. Conclusion: Our results support the notion of a specialized memory system for music.
The Application of Social Characteristic and L1 Optimization in the Error Correction for Network Coding in Wireless Sensor Networks

One of the remarkable challenges about Wireless Sensor Networks (WSN) is how to transfer the collected data efficiently due to energy limitation of sensor nodes. Network coding will increase network throughput of WSN dramatically due to the broadcast nature of WSN. However, the network coding usually propagates a single original error over the whole network. Due to the special property of error propagation in network coding, most of error correction methods cannot correct more than C/2 corrupted errors where C is the max flow min cut of the network. To maximize the effectiveness of network coding applied in WSN, a new error-correcting mechanism to confront the propagated error is urgently needed. Based on the social network characteristic inherent in WSN and L1 optimization, we propose a novel scheme which successfully corrects more than C/2 corrupted errors. What is more, even if the error occurs on all the links of the network, our scheme also can correct errors successfully. With introducing a secret channel and a specially designed matrix which can trap some errors, we improve John and Yi’s model so that it can correct the propagated errors in network coding which usually pollute exactly 100% of the received messages. Taking advantage of the social characteristic inherent in WSN, we propose a new distributed approach that establishes reputation-based trust among sensor nodes in order to identify the informative upstream sensor nodes. With referred theory of social networks, the informative relay nodes are selected and marked with high trust value. The two methods of L1 optimization and utilizing social characteristic coordinate with each other, and can correct the propagated error whose fraction is even exactly 100% in WSN where network coding is performed. The effectiveness of the error correction scheme is validated through simulation experiments.
Analyzer: A Complex System for Data Analysis

Recently eXtensible Markup Language (XML) has achieved the leading role among languages for data representation and, thus, we can witness a massive boom of corresponding techniques for managing XML data. Most of the processing techniques, however, suffer from various bottlenecks worsening their time and/or space efficiency. We assume that the main reason is they consider XML collections too globally, involving all their possible features, although real-world data are often much simpler. Even though some techniques do restrict the input data, the restrictions are mostly unnatural. This paper aims to introduce Analyzer—a complex framework for performing statistical analyses of realworld documents. Exploitation of results of these analyses is a classical way how data processing can be optimized in many areas. Although this intent is legitimate, ad hoc and dedicated analyses soon become obsolete, they are usually built on insufficiently extensive collections and are difficult to repeat. Analyzer represents an easily extensible framework, which helps the user with gathering documents, managing analyses and browsing computed reports.
Burr Type XII Software Reliability Growth Model

Software Reliability Growth model (SRGM) is a mathematical model of how the software reliability improves as faults are detected and repaired. The development of many SRGMs over the last several decades have resulted in the improvement of software facilitating many engineers and managers in tracking and measuring the growth of reliability. This paper proposes Burr type XII based Software Reliability growth model with time domain data. The unknown parameters of the model are estimated using the maximum likelihood (ML) estimation method. Reliability of a software system using Burr type XII distribution, which is based on NonHomogenous Poisson process (NHPP), is presented through estimation procedures. The performance of the SRGM is judged by its ability to fit the software failure data. How good does a mathematical model fit to the data is also being calculated. To access the performance of the considered SRGM, we have carried out the parameter estimation on the real software failure datasets. General Terms Software failure data, Mean value function.
Ieee Transactions on Communications, Accepted for Publication Detect-and-forward Relaying Aided Cooperative Spatial Modulation for Wireless Networks

—A novel detect-and-forward (DeF) relaying aided cooperative SM scheme is proposed, which is capable of striking a flexible tradeoff in terms of the achievable bit error ratio (BER), complexity and unequal error protection (UEP). More specifically, SM is invoked at the source node (SN) and the information bit stream is divided into two different sets: the antenna index-bits (AI-bits) as well as the amplitude and phase modulation-bits (APM-bits). By exploiting the different importance of the AI-bits and the APM-bits in SM detection, we propose three low-complexity, yet powerful relay protocols, namely the partial, the hybrid and the hierarchical modulation (HM) based DeF relaying schemes. These schemes determine the most appropriate number of bits to be re-modulated by carefully considering their potential benefits and then assigning a specific modulation scheme for relaying the message. As a further benefit, the employment of multiple radio frequency (RF) chains and the requirement of tight inter-relay synchronization (IRS) can be avoided. Moreover, by exploiting the benefits of our low-complexity relaying protocols and our inter-element interference (IEI) model, a low-complexity maximum-likelihood (ML) detector is proposed for jointly detecting the signal received both via the source-destination (SD) and relay-destination (RD) links. Additionally, an upper bound of the BER is derived for our DeF-SM scheme. Our numerical results show that the bound is asymptotically tight in the high-SNR region and the proposed schemes provide beneficial system performance improvements compared to the conventional MIMO schemes in an identical cooperative scenario.
Image segmentation via grabcut and linear multi-scale smoothing

In order to improve image segmentation performance, a novel segmentation model is proposed combining GrabCut model and linear multi-scale smoothing. Multi-scale smoothing components, generated by Gaussian Kernel through an iterative scheme, provide different level image information that contributes to image segmentation. Each component is segmented by GrabCut model and segmentation result is different because the fine information is smeared step by step leading to that the appearance characteristic is different. A convergence condition, which rooted in the significant level of segmentation sub-regions on adjacent scale components, is constructed based on the invariance of object contour in each component. Compared to the traditional GrabCut, as the experiment result shows, the proposed model has a superior performance on real images and achieves better robustness against noise.
Dorsal hippocampus is necessary for visual categorization in rats

The hippocampus may play a role in categorization because of the need to differentiate stimulus categories (pattern separation) and to recognize category membership of stimuli from partial information (pattern completion). We hypothesized that the hippocampus would be more crucial for categorization of low‐density (few relevant features) stimuli–due to the higher demand on pattern separation and pattern completion–than for categorization of high‐density (many relevant features) stimuli. Using a touchscreen apparatus, rats were trained to categorize multiple abstract stimuli into two different categories. Each stimulus was a pentagonal configuration of five visual features; some of the visual features were relevant for defining the category whereas others were irrelevant. Two groups of rats were trained with either a high (dense, n = 8) or low (sparse, n = 8) number of category‐relevant features. Upon reaching criterion discrimination (≥75% correct, on 2 consecutive days), bilateral cannulas were implanted in the dorsal hippocampus. The rats were then given either vehicle or muscimol infusions into the hippocampus just prior to various testing sessions. They were tested with: the previously trained stimuli (trained), novel stimuli involving new irrelevant features (novel), stimuli involving relocated features (relocation), and a single relevant feature (singleton). In training, the dense group reached criterion faster than the sparse group, indicating that the sparse task was more difficult than the dense task. In testing, accuracy of both groups was equally high for trained and novel stimuli. However, both groups showed impaired accuracy in the relocation and singleton conditions, with a greater deficit in the sparse group. The testing data indicate that rats encode both the relevant features and the spatial locations of the features. Hippocampal inactivation impaired visual categorization regardless of the density of the category‐relevant features for the trained, novel, relocation, and singleton stimuli. Hippocampus‐mediated pattern completion and pattern separation mechanisms may be necessary for visual categorization involving overlapping irrelevant features.
Hierarchical bilinear network for high performance face detection

Deep Convolutional Networks (DCNs) have achieved great success in face detection. Most architectures of the DCN-based methods, however, suffer from multiple separated steps and large-size models, which increase the training complexity and also slow down the testing speed. In this paper, we propose an efficient end-to-end architecture, called Hierarchical Bilinear Network (HBN), for fast and accurate face detection. It mainly consists of two parts: the Backbone Network and the Bilinear Network. The Backbone Network generates hierarchical feature maps for efficiently characterizing faces of different scales, while the Bilinear Network classifies the regions and regresses the face bounding-boxes on each feature map by introducing the Inception module and weights sharing. Benefited from the characters of the proposed architecture, it obtains a better comprehensive performance regarding the model effectiveness, running efficiency, and parameter size, compared with other DCN-based methods. Extensive experimental results demonstrate that our detector achieves competitive accuracy on both the FDDB database and the WIDER FACE database, while still runs in real time (about 69 FPS on a Titan Black GPU) with a tiny size (2.2 MB) model.
Molecular Epidemiology of Human Intestinal Amoebas in Iran

Many microscopic-based epidemiological surveys on the prevalence of human intestinal pathogenic and non-pathogenic protozoa including intestinal amoeba performed in Iran show a high prevalence of human intestinal amoeba in different parts of Iran. Such epidemiological studies on amoebiasis are confusing, mainly due to recently appreciated distinction between the Entamoeba histolytica, E. dispar and E. moshkovskii. Differential diagnosis can be done by some methods such as PCR-based methods, monoclonal antibodies and the analysis of isoenzyme typing, however the molecular study of these protozoa in Iran is low. Based on molecular studies, it seems that E. dispar is predominant species especially in the central and northern areas of Iran and amoebiasis due to E. histolytica is a rare infection in the country. It is suggested that infection with E. moshkovskii may be common among Iranians. Considering the importance of molecular epidemiology of amoeba in Iran and also the current data, the present study reviews the data currently available on the molecular distribution of intestinal human amoeba in Iran.
A 2-D Nonlinear Ambipolar Diffusion Equation Model of an IGBT and Its Numerical Solution Methodology

To consider the nonlinear and 2-D characteristics of the carriers in the draft region of a planar-gate insulated-gate bipolar transistor, which are not properly modeled in the existing physics-based IGBT models, a 2-D ambipolar diffusion equation model is proposed and solved using the finite-element method. Moreover, a numerically iterative procedure is introduced to simply and efficiently solve the 2-D nonlinear finite-element equations. The numerical results of the transient performances obtained using the proposed model and solution methodology show a good agreement with those of the experiment ones, showing the high accuracy and feasibility of the proposed model and method.