Miracle-dz/UniFAR-FTUs huggingface dataset:
Predictive Formal Analysis of Resilience in Cyber-Physical Systems

The behavioral analysis of cyber-physical systems in safety-critical scenarios is a challenging task. In this paper, the endogenous and exogenous aspects of resilience are of cornerstone importance in system design and verification. Endogenous resilience is the inherent ability of the system to detect and process internal faults and malicious attacks. Exogenous resilience is the permanent capability of the system to maintain a safe operation within its ambient environment. In this paper, we present a predictive dual-sided contract-based formal methodology to address both aspects of resilience on top of a distributed object-oriented component-based software model. It is illustrated by a case study of urban drone rescue systems. We exploit the formalism of timed automat a and the toolbox UPPAAL to predict by abstraction and analyze (simulate and verify) endogenous resilience. Instead of presenting the final models of the case study, we reflect our experience with UPPAAL in generic patterns of system design and contract specification, reusable in other contexts with adaptations. The analysis of exogenous resilience is specific to the considered drone rescue system. It consists of synthesizing by iterative model-checking safe flight paths for the drones within a 3D virtual model of urban surroundings true to modern cities.
Interference Coverage Prediction of EESS Satellite Earth Station Based on WRAP Software

In the construction and network planning of the ground 5G system around the satellite earth station, we need to predict the distribution of the field strength to evaluate the coverage effect. The field strength distribution of satellite earth station and 5G system is simulated by using WRAP radio network programming software. Combining the actual antenna direction characteristic parameters and terrain database, the DETVAG 90/FOI propagation model and ITU-R P.2108 recommendation are selected to calculate the field strength coverage, and displayed on the electronic map directly. The prediction of field strength interference coverage around Beijing and Jiamusi satellite earth stations is realized, which provides technical basis for scientific layout of the two systems.
The SuiteSparse Matrix Collection Website Interface

The SuiteSparse Matrix Collection (formerly known as the University of Florida Sparse Matrix Collection) (Davis & Hu, 2011) has grown significantly since its introduction, with newly added matrices representing almost seven times as much data than the entirety of the original Collection. With this growth, searching the Collection for matrices with specific names, structures, and other properties has become increasingly difficult. To make the Collection more accessible to the scientific computing community, we have developed a web application that allows real-time search and filtering of the Collection matrices.
Computational approaches and data analytics in financial services: A literature review

Abstract The level of modeling sophistication in financial services has increased considerably over the years. Nowadays, the complexity of financial problems and the vast amount of data require an engineering approach based on analytical modeling tools for planning, decision making, reporting, and supervisory control. This article provides an overview of the main financial applications of computational and data analytics approaches, focusing on the coverage of the recent developments and trends. The overview covers different methodological tools and their uses in areas, such as portfolio management, credit analysis, banking, and insurance.
Generating synthetic populations using IPF and Monte Carlo techniques

Abstract The generation of synthetic populations represents a substantial contribution to the acquisition of useful data for large scale agent based microsimulations in the field of transport planning. Basically, the observed data are available from various sources, i.e. censuses (microcensus) in which the data is available in terms of simple summary tables of demographics, such as the number of persons per household for census-block-group-sized areas. Nevertheless, there is a need of more disaggregated personal data, and thus another type of data source is considered. The Public Use Sample (PUS), often used in transportation studies, is a 5% representative sample of complete census records, including bad records, for each individual, excluding ad-dresses and unique identifiers. The problem is, to generate a large number of individual agents (~1Mio.) with appropriate characteristic values of the demographic variables for each agent, interacting in the mi-crosimulation. The main techniques used to generate the agents are IPF and simple MC. In this paper we present further results of our effort to disaggregate the available census data. First, we present agents with age and sex as the sociodemographic characteristics for all mu-nicipalities in Switzerland using data from census 2000 and microcensus 2000. Second, we added more sociodemographic variables like driver licence ownership, car availability, em-ployment, accessibility of halbtax and GA periodic tickets to obtain more realistic agents for all of Switzerland. Third we made some effort to disaggregate the data to a hectare based level for employment, age, and sex of the agents. The current state of this work will be pre-sented.
Transformerless Hybrid Converter With AC and DC Outputs and Reduced Leakage Current

In photovoltaic (PV) generation systems, due to the high efficiency, compactness, and low costs, transformerless hybrid converters with ac and dc outputs have become popular in recent times. However, common-mode leakage current occurs in these converters when the converter neutral point is grounded or it is tied to the grid. The presence of leakage current causes considerable safety and electromagnetic interference problem along with the improper operation of safety devices. This paper proposes a transformerless hybrid converter capable to give simultaneous ac and dc outputs with reduced leakage current for the PV system. In order to ensure the reduced leakage current for all operating states (power, shoot through, and zero) of the proposed converter, a novel pulsewidth modulation (PWM) switching scheme is also introduced in this paper. As the proposed hybrid converter can provide both dc and ac outputs, it can supply both dc and ac loads simultaneously. The detailed steady state analysis and dynamic modeling of the proposed hybrid converter is carried and discussion on the developed PWM technique for reducing the leakage current is presented in this paper. A comparative analysis between the proposed and conventional converters is also carried out. The performance of the proposed algorithm is validated through experimental results.
Robust Boundary Segmentation in Medical Images Using a Consecutive Deep Encoder-Decoder Network

Image segmentation is typically used to locate objects and boundaries. It is essential in many clinical applications, such as the pathological diagnosis of hepatic diseases, surgical planning, and postoperative assessment. The segmentation task is hampered by fuzzy boundaries, complex backgrounds, and appearances of objects of interest, which vary considerably. The success of the procedure is still highly dependent on the operator’s skills and the level of hand–eye coordination. Thus, this paper was strongly motivated by the necessity to obtain an early and accurate diagnosis of a detected object in medical images. In this paper, we propose a new polyp segmentation method based on the architecture of a multiple deep encoder–decoder networks combination called CDED-net. The architecture can not only hold multi-level contextual information by extracting discriminative features at different effective fields-of-view and multiple image scales but also learn rich information features from missing pixels in the training phase. Moreover, the network is also able to capture object boundaries by using multiscale effective decoders. We also propose a novel strategy for improving the method’s segmentation performance based on a combination of a boundary-emphasization data augmentation method and a new effective dice loss function. The goal of this strategy is to make our deep learning network available with poorly defined object boundaries, which are caused by the non-specular transition zone between the background and foreground regions. To provide a general view of the proposed method, our network was trained and evaluated on three well-known polyp datasets, CVC-ColonDB, CVC-ClinicDB, and ETIS-Larib PolypDB. Furthermore, we also used the Pedro Hispano Hospital (PH2), ISBI 2016 skin lesion segmentation dataset, and CT healthy abdominal organ segmentation dataset to depict our network’s ability. Our results reveal that the CDED-net significantly surpasses the state-of-the-art methods.
A Review on Additive Manufacturing Techniques in Medical Applications

International Journal of Advanced Science and Engineering www.mahendrapublications. com A Review on Additive Manufacturing Techniques in Medical Applications R. Dinesh Babu1,2*, D. Devaprakasam1,3 1School of Mechanical and Building Sciences, VIT University, Chennai Campus, Chennai 600127, India 2Department of Mechanical Engineering, Rajiv Gandhi College of Engineering and Technology (RGCET), Puducherry 607402, India 3InCUBE-EngSciRes RandD, Alpha Academy Tech, Udumalpet, Coimbatore 642126, India
The Practices of Human Resource Management Among Headmasters in Primary Schools: A Case Study of Selected National and Private Schools in Malaysia

This study focuses on the practices of human resource management among headmasters in primary schools across Malaysia. It concentrates on how headmasters perceive the concept of human resource management and translate it into practice. The qualitative research design is applied on six headmasters, four males and two females who serve in selected national and private primary schools. The qualitative data from the interviews are organized into themes and sub-themes. The major findings of this study reveal that a majority of the headmasters perceive human resource management as a shared responsibility among teachers and school administrators while the headmaster is the key manager. It also reveals that headmasters in national schools believe that human resource management is important to reach the educational goal. In contrast, a majority of headmasters in private schools believe that it is important for sustainability. In addition, this study also reveals that the headmasters of national schools have less autonomy in decision-making process as compared to headmasters of private schools. The results have implications on two major aspects; the leadership in human resource management and the system. The leadership implication will help headmasters identify the best practices of human resource management including support and professional development for the teachers. System refers to teacher placement and other policy implementation. Finally, this study recommends more authority to be given to headmasters in managing human resource in schools, especially in the national schools.
Author Impact: Evaluations, Predictions, and Challenges

Author impact evaluation and prediction play a key role in determining rewards, funding, and promotion. In this paper, we first introduce the background of the author impact evaluation and prediction. Then, we review the recent developments of the author impact evaluation, including data collection, data pre-processing, data analysis, feature selection, algorithm design, and algorithm evaluation. Third, we provide an in-depth literature review on the author impact predictive models and the common evaluation metrics. Finally, we look into the representative research issues, including author impact inflation, unified evaluation standards, academic success gene, identification of the origins of hot streaks, and higher-order academic networks analysis. This paper should help the researchers obtain a broader understanding of the author impact evaluation and prediction and provides future research directions.
Efficient Evaluation of Massive MIMO Channel Capacity

Massive multiple-input multiple-output (MIMO) is a key promising technology for 5G wireless networks that allows significant increase in spectral and energy efficiency without increasing the spectrum and/or the number of cell sites. However, the computational time and resources required to rigorously analyze the capacity offered by a base station (BS) equipped with a large number of antenna elements increase with the increase of the number of antenna elements in the array. In this paper, we present a novel approximation approach for modeling large microstrip antenna arrays in BSs of massive MIMO systems. The approach subdivides an M × N array into columns, rows, rectangular, or square subarrays, each consisting of a group of elements. The coupling is rigorously taken into account within each subarray, but it is ignored between subarrays, using just an array factor instead. Results are demonstrated for an 8 × 8 = 64 element patch array. It is shown that the difference in the capacity evaluated using rigorous electromagnetic simulations and the proposed approach is less than 0.79% using the 2 × (8 × 4) approach for both the suburban macrocell model of the Extended Spatial Channel Model (SCME/suburban macro cell (SMa)) outdoor propagation model and three-dimensional independent identically distributed model with a significant reduction of 44% and 50%, respectively, in computational time as compared to the full-wave antenna array modeling approach.
Electrochemical Detection and Characterization of Nanoparticles with Printed Devices

Innovative methods to achieve the user-friendly, quick, and highly sensitive detection of nanomaterials are urgently needed. Nanomaterials have increased importance in commercial products, and there are concerns about the potential risk that they entail for the environment. In addition, detection of nanomaterials can be a highly valuable tool in many applications, such as biosensing. Electrochemical methods using disposable, low-cost, printed electrodes provide excellent analytical performance for the detection of a wide set of nanomaterials. In this review, the foundations and latest advances of several electrochemical strategies for the detection of nanoparticles using cost-effective printed devices are introduced. These strategies will equip the experimentalist with an extensive toolbox for the detection of nanoparticles of different chemical nature and possible applications ranging from quality control to environmental analysis and biosensing.
The Tad pilus apparatus of Candidatus Liberibacter asiaticus and its regulation by VisNR.

Huanglongbing (HLB) is one of the most destructive diseases affecting citrus plants. Candidatus Liberibacter asiaticus (CLas), an uncultivated α-proteobacteria, is the most widely spread causal agent of HLB and transmitted by the Asian citrus psyllid Diaphorina citri. CLas attachment to psyllid midgut is believed to be critical to further infect other organs including the salivary gland for its transmission. In this study we characterized the Type IVc tight adherence-pilus (Tad) locus encoded by CLas. The Tad loci are conserved among members of Rhizobiaceae including CLas and Agrobacterium. Ectopic expression of CLas cpaF gene, an ATPase essential for the biogenesis and secretion of the Tad pilus, restored the adherence phenotype in cpaF mutant of A. tumefaciens, indicating CpaF of CLas is functional and critical for bacterial adherence mediated by Tad pilus. qRT-PCR analysis revealed that CLas Tad pilus-encoding genes and a CLas pilin gene flp3 were upregulated in psyllids compared to in planta. Bacterial one-hybrid assay showed that CLas VisN and VisR, members of LuxR transcriptional factor family, bind to the flp3 promoter. VisNR regulate flp3. Negative regulation of the flp3 promoter by both VisN and VisR was demonstrated by using shuttle strategy with analysis of the phenotypes and immunoblotting together with quantification of the expression of the flp3 promoter fused to beta-galactosidase reporter gene. Comparative expression analysis confirmed that CLas visNR was less expressed in psyllid than in plant host. Further, motility and biofilm phenotypes of the visNR mutant of A. tumefaciens were fully complemented by expressing CLas visNR together. The physical interaction between VisN and VisR was confirmed by pull-down and stability assays. The interaction of the flp3 promoter with VisR was verified by electrophoretic mobility shift assay. Taken together, our results revealed the contribution of the Tad pilus apparatus in the colonization of the insect vector by CLas and shed light on the involvement of VisNR in regulation of the Tad locus.
Energy Efficient Power Control & Bayesian Channel Estimation for Massive MIMO

Pilot contamination posts a fundamental limit on the performance of massive multiple-input–multiple-output (MIMO) antenna systems due to failure in accurate channel estimation. To address this problem, we propose estimation of channel parameters of the desired links in a target cell. In this paper, we show that if the propagation properties of massive MIMO systems can be exploited, it is possible to obtain an accurate estimate of the channel parameters. The signals are observed in the beam domain (using Fourier transform), the channel is approximately sparse, i.e., the channel matrix contains only a small fraction of large components, and other components are close to zero. This observation then enables channel estimation based on sparse Bayesian learning methods, where sparse channel components can be reconstructed using a small number of observations. Results illustrate that compared to conventional estimators, the proposed approach achieves much better performance in terms of the channel estimation accuracy and achievable rates in the presence of pilot contamination. In addition to channel estimation, efficient energy control technique for massive MIMO is adopted in this work.
Advanced Content based Medical Image Retrieval

A large collection of medical images surrounds health care centers and hospitals. Medical images produced by different modalities like magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and X-rays have increased incredibly with the advent of latest technologies for image acquisition. Retrieving clinical images of interest from these large data sets is a thought provoking and demanding task. A novel Content based medical image retrieval system is proposed that can aid physicians in the identification or analysis of medical images. Here we use the fast wavelet transform for image characterization. The image signature is calculated using kurtosis and standard deviation as features. This technique can be used when the radiologist has some suspicion on diagnosis and wants further case histories, the acquired clinical images are sent (e.g. x ray images, CT scan images) as a query to the content based medical image retrieval system. The system is tuned to retrieve the top most relevant images to the query. The proposed system is computationally efficient and more accurate in terms of the quality of retrieved
Hypoxia-induced Changes in SUMO Conjugation Affect Transcriptional Regulation Under Low Oxygen*

HeLa cells grown under normoxia or hypoxia for 48h, were subjected to endogenous SUMO-immunoprecipitation in combination with quantitative mass spectrometry (SILAC) to gain insights into differences of the SUMO1 and SUMO2/3 proteome. Proteins whose SUMOylation changed without concomitant change in abundance were predominantly transcriptions factors. Particularly interesting was transcription factor TFAP2A (Activating enhancer binding Protein 2 alpha), whose sumoylation decreased on hypoxia. DeSUMOylation of TFAP2A enhanced transcriptional activity of HIF-1 under hypoxia contributing at the cellular response to low oxygen. Graphical Abstract Highlights Identification of endogenous SUMO1,SUMO2/3 conjugates and their response to hypoxia. TFAP2A is a target of sumoylation and its modification is inhibited under hypoxia. TFAP2A enhances the transcriptional activity of HIF-1 in a sumo-dependent manner. TFAP2A interacts physically with HIF-1 and HIF-2 in a sumo-independent manner. Hypoxia occurs in pathological conditions, such as cancer, as a result of the imbalance between oxygen supply and consumption by proliferating cells. HIFs are critical molecular mediators of the physiological response to hypoxia but also regulate multiple steps of carcinogenesis including tumor progression and metastasis. Recent data support that sumoylation, the covalent attachment of the Small Ubiquitin-related MOdifier (SUMO) to proteins, is involved in the activation of the hypoxic response and the ensuing signaling cascade. To gain insights into differences of the SUMO1 and SUMO2/3 proteome of HeLa cells under normoxia and cells grown for 48 h under hypoxic conditions, we employed endogenous SUMO-immunoprecipitation in combination with quantitative mass spectrometry (SILAC). The group of proteins whose abundance was increased both in the total proteome and in the SUMO IPs from hypoxic conditions was enriched in enzymes linked to the hypoxic response. In contrast, proteins whose SUMOylation status changed without concomitant change in abundance were predominantly transcriptions factors or transcription regulators. Particularly interesting was transcription factor TFAP2A (Activating enhancer binding Protein 2 alpha), whose sumoylation decreased on hypoxia. TFAP2A is known to interact with HIF-1 and we provide evidence that deSUMOylation of TFAP2A enhances the transcriptional activity of HIF-1 under hypoxic conditions. Overall, these results support the notion that SUMO-regulated signaling pathways contribute at many distinct levels to the cellular response to low oxygen.
Identifying and Evaluating Early Stage Fintech Companies: Working with Consumer Internet Data and Analytic Tools

The purpose of this project is to work as an interdisciplinary team whose primary role is to mentor a team of WPI undergraduate students completing their Major Qualifying Project (MQP) in collaboration with Vestigo Ventures, LLC. (“Vestigo Ventures”) and Cogo Labs. We worked closely with the project sponsors at Vestigo Ventures and Cogo Labs to understand each sponsor’s goals and desires, and then translated those thoughts into actionable items and concrete deliverables to be completed by the undergraduate student team. As a graduate student team with a diverse set of educational backgrounds and a range of academic and professional experiences, we provided two primary functions throughout the duration of this project. The first function was to develop a roadmap for each individual project, with concrete steps, justification, goals and deliverables. The second function was to provide the undergraduate team with clarification and assistance throughout the implementation and completion of each project, as well as provide our opinions and thoughts on any proposed changes. The two teams worked together in lock-step in order to provide the project sponsors with a complete set of deliverables, with the undergraduate team primarily responsible for implementation and final delivery of each completed project.