Miracle-dz/UniFAR-FTUs huggingface dataset:
Smart City for Future: Design of Data Acquisition Method using Threshold Concept Technique

The massive deployment of Internet of Things (IoT) is allowing Smart City projects and initiatives all over the world. Whereas IOT deals with intricate systems that integrates multiple disperse components towards their synergetic use. Due to resource constraints, there will be a problem in the future to provide all the services to the residents. To continue to serve and improve the standard of living of the growing population, it is necessary to develop smart cities. The Smart City aims to make optimal and sustainable use of all resources, while maintaining an appropriate balance between social, environmental and economic costs. The wireless sensors are attached to street lamp, water tank, dustbin. Sensors are then attached to Arduino microcontroller board where each and every necessary parameter for city are monitored and updated to cloud. The cloud is connected with Blynk server in turn which is connected to build in Blynk application of user's Android phone. The technology we use is IOT in this model a system of interconnected smart modules is developed where each and every parameter necessary for a city is monitored and updated to the cloud. Emphasis is given on how sensing and communication technologies of IOT can effectively be used in smart city monitoring. The project aims at developing a system which facilitates aids in collection of data with the help of interconnected modules consisting of multiple sensors useful for smart city monitoring. Further this project also includes controlling of some parameters like water, light and dust bin.
Global scenario and recent advances in molecular diagnosis and management of drug-resistant tuberculosis

Tuberculosis (TB) is one of the oldest communicable bacterial diseases, spreads predominantly by inhalation of infected respiratory droplets. It is a curable and preventable disease yet remains the leading infectious cause of human death worldwide. The TB burden is high among developing nations of Asia and Africa. Major obstacles in controlling TB are patient’s non-compliance to the anti-tubercular therapy, co-infection with Human immunodeficiency virus (HIV), low socioeconomic status, crowded living condition, inadequate rapid diagnostic testing facilities especially in resource-poor developing countries, delay in diagnosis and initiation of therapy, and the emergence of drug-resistant strains of Mycobacterium tuberculosis (MTB). Multidrug-resistant (MDR-TB), extensively drug-resistant (XDR-TB), and total drug-resistant (TDR-TB) MTB strains are difficult to treat and are associated with frequent treatment failures and high mortality. The recent advent of molecular techniques including nucleic acid amplification tests (NAATs) and whole-genome sequencing (WGS) have significantly ameliorated the rapid detection of TB cases and drug-resistant MTB. This, in turn, enabled the early initiation of therapy and development of novel treatment plans which is crucial for the global TB elimination target. The World Health Organization (WHO) 2020 guideline prioritizes the use of newer drugs as part of all-oral regimens for the treatment of MDR-TB. Apart from the use of newer drug delivery methods, host factors including immune functions and cytokine responses as well as mycobacterial enzymatic pathways are targeted in TB drug development. Adjuvant therapy employing host-directed approaches is increasingly studied through the time-tested pathogen-targeted approach remains the mainstay in the current treatment of MDR-TB.
Deep Android Malware Classification with API-Based Feature Graph

The rapid growth of Android malware apps poses a great security threat to users thus it is very important and urgent to detect Android malware effectively. What's more, the increasing unknown malware and evasion technique also call for novel detection method. In this paper, we focus on API feature and develop a novel method to detect Android malware. First, we propose a novel selection method for API feature related with the malware class. However, such API also has a legitimate use in benign app thus causing FP problem (misclassify benign as malware). Second, we further explore structure relationships between these APIs and map to a matrix interpreted as the hand-refined API-based feature graph. Third, a CNN-based classifier is developed for the API-based feature graph classification. Evaluations of a real-world dataset containing 3,697 malware apps and 3,312 benign apps demonstrate that selected API feature is effective for Android malware classification, just top 20 APIs can achieve high F1 of 94.3% under Random Forest classifier. When the available API features are few, classification performance including FPR indicator can achieve effective improvement effectively by complementing our further work.
Executive Summary

Evans R. Fernández Pérez, MD, FCCP; William D. Travis, MD, FCCP; David A. Lynch, MB, BCh; Kevin K. Brown, MD, FCCP; Kerri A. Johannson, MD, MPH; Moisés Selman, MD; Jay H. Ryu, MD, FCCP; Athol U. Wells, MD; Yuh-Chin Tony Huang, MD, MHS, FCCP; Carlos A. C. Pereira, MD, FCCP; Mary-Beth Scholand, MD, FCCP; Ana Villar, MD, PhD; Naohiko Inase, MD, PhD; Richard B. Evans, MD, MPH, FCCP; Stephen A. Mette, MD, FCCP; and Lindsy Frazer-Green, PhD
Automatic Building of ER and Data Flow Graph: A Business Process-based Approach

Nowadays, many tools are available for drawing entity relationship(ER) diagram and data flow graph, but the graph layout is manually designed and the drawing process is tedious and time-consuming. Moreover, the management of business model is independent of the data model, the inconsistency is frequently occurred. This paper proposes an entity-based process model called Entity-Process Model (EP-Model) to build up the linkage between the business model and data model. Based on the EP-Model, this paper gives the construction algorithms of ER diagrams and data flow graph. Finally, a modeling tool is developed for business process modeling and data modeling. Using this platform, the consistency between business model and data model is guaranteed, and the ER diagram and data flow graph can be built automatically. It plays an important role in the process modeling, data modeling and the integration of information systems in the future.
Tectonic Inheritance During Plate Boundary Evolution in Southern California Constrained From Seismic Anisotropy

The style of convective force transmission to plates and strain‐localization within and underneath plate boundaries remain debated. To address some of the related issues, we analyze a range of deformation indicators in southern California from the surface to the asthenosphere. Present‐day surface strain rates can be inferred from geodesy. At seismogenic crustal depths, stress can be inferred from focal mechanisms and splitting of shear waves from local earthquakes via crack‐dependent seismic velocities. At greater depths, constraints on rock fabrics are obtained from receiver function anisotropy, Pn and P tomography, surface wave tomography, and splitting of SKS and other teleseismic core phases. We construct a synthesis of deformation‐related observations focusing on quantitative comparisons of deformation style. We find consistency with roughly N‐S compression and E‐W extension near the surface and in the asthenospheric mantle. However, all lithospheric anisotropy indicators show deviations from this pattern. Pn fast axes and dipping foliations from receiver functions are fault‐parallel with no localization to fault traces and match post‐Farallon block rotations in the Western Transverse Ranges. Local shear wave splitting orientations deviate from the stress orientations inferred from focal mechanisms in significant portions of the area. We interpret these observations as an indication that lithospheric fabric, developed during Farallon subduction and subsequent extension, has not been completely reset by present‐day transform motion and may influence the current deformation behavior. This provides a new perspective on the timescales of deformation memory and lithosphere‐asthenosphere interactions.
The solution structure of desulforedoxin, a simple iron-sulfur protein - An NMR study of the zinc derivative

Desulforedoxin is a simple dimeric protein isolated from Desulfovibrio gigas containing a distorted rubredoxin-like center with one iron coordinated by four cysteinyl residues (7.9 kDa with a 36-amino-acid monomer). H NMR spectra of the oxidized Dx(Fe) and reduced Dx(Fe) forms were analyzed. The spectra show substantial line broadening due to the paramagnetism of iron. However, very low-field-shifted resonances, assigned to Hb protons, were observed in the reduced state and their temperature dependence analyzed. The active site of Dx was reconstituted with zinc, and its solution structure was determined using 2D NMR methods. This diamagnetic form gave high-resolution NMR data enabling the identification of all the amino acid spin systems. Sequential assignment and the determination of secondary structural elements was attempted using 2D NOESY experiments. However, because of the symmetrical dimer nature of the protein standard, NMR sequential assignment methods could not resolve all cross peaks due to interand intra-chain effects. The X-ray structure enabled the spatial relationship between the monomers to be obtained, and resolved the assignment problems. Secondary structural features could be identified from the NMR data; an antiparallel b-sheet running from D5 to V18 with a welldefined b-turn around cysteines C9 and C12. The section G22 to T25 is poorly defined by the NMR data and is followed by a turn around V27-C29. The C-terminus ends up near residues V6 and Y7. Distance geometry (DG) calculations allowed families of structures to be generated from the NMR data. A family of structures with a low target function violation for the Dx monomer and dimer were found to have secondary structural elements identical to those seen in the X-ray structure. The amide protons for G4, D5, G13, L11 NH and Q14 NH amide protons, H-bonded in the X-ray structure, were not seen by NMR as slowly exchanging, while structural disorder at the N-terminus, for the backbone at E10 and for the section G22–T25, was observed. Comparison between the Fe and Zn forms of Dx suggests that metal substitution does not have an effect on the structure of the protein.
The Importance of Space and Time for Signal Processing in Neuromorphic Agents: The Challenge of Developing Low-Power, Autonomous Agents That Interact With the Environment

Artificial neural networks (ANNs) and computational neuroscience models have made tremendous progress, enabling us to achieve impressive results in artificial intelligence applications, such as image recognition, natural language processing, and autonomous driving. Despite this, biological neural systems consume orders of magnitude less energy than today's ANNs and are much more flexible and robust. This adaptivity and efficiency gap is partially explained by the computing substrate of biological neural processing systems that is fundamentally different from the way today? s computers are built. Biological systems use in-memory computing elements operating in a massively parallel way rather than time-multiplexed computing units that are reused in a sequential fashion. Moreover, the activity of biological neurons follows continuous-time dynamics in real, physical time instead of operating on discrete temporal cycles abstracted away from real time.
Cloud Computing: Things to consider by Information Professionals (IP)

Technology is fast growing and individuals are doing their best in order to catch up with this rapid growth. Those involved never failed to innovate and invent new technologies to be integrated with the current technologies. One of the innovations that technology has recently been presented is the phenomenon of cloud computing. Cloud computing provides almost all technological facilities needed in organizations at the cost of very less-skilled personal and computing devices. The essence of the concept of cloud computing is to provide end users with remote dynamic access to services, computing resources and applications which includes operating systems and infrastructure over the Internet. Cloud computing have three different service models which are Software as a Service (SaaS), Platform as a Service (PaaS) and Infrastructure as a Service (IaaS). Cloud computing also has four different deployment models which are private cloud, community cloud, public cloud and hybrid cloud. Cloud computing could poses both opportunities and challenges for organizations and IP. An objective of this paper was to identify factors that could be consider for IP in adopting cloud computing.
Research of New Criteria for Detecting First-order Residuals Autocorrelation in Regression Models

When estimating regression models using the least squares method, one of its prerequisites is the lack of autocorrelation in the regression residuals. The presence of autocorrelation in the residuals makes the least-squares regression estimates to be ineffective, and the standard errors of these estimates to be untenable. Quantitatively, autocorrelation in the residuals of the regression model has traditionally been estimated using the Durbin-Watson statistic, which is the ratio of the sum of the squares of differences of consecutive residual values to the sum of squares of the residuals. Unfortunately, such an analytical form of the Durbin-Watson statistic does not allow it to be integrated, as linear constraints, into the problem of selecting informative regressors, which is, in fact, a mathematical programming problem in the regression model. The task of selecting informative regressors is to extract from the given number of possible regressors a given number of variables based on a certain quality criterion.The aim of the paper is to develop and study new criteria for detecting first-order autocorrelation in the residuals in regression models that can later be integrated into the problem of selecting informative regressors in the form of linear constraints. To do this, the paper proposes modular autocorrelation statistic for which, using the Gretl package, the ranges of their possible values and limit values were first determined experimentally, depending on the value of the selective coefficient of auto-regression. Then the results obtained were proved by model experiments using the Monte Carlo method. The disadvantage of the proposed modular statistic of adequacy is that their dependencies on the selective coefficient of auto-regression are not even functions. For this, double modular autocorrelation criteria are proposed, which, using special methods, can be used as linear constraints in mathematical programming problems to select informative regressors in regression models.
A TRIAL OF APPLYING THE HYPER-SPECTRAL SENSING TECHNIQUE IN THE IDENTIFICATION OF SOME SOIL ATTRIBUTES

Historically, our understanding of the soil and assessment of its quality and function has been gained through field survey and routine soil physicochemical laboratory analysis. Reflectance spectroscopy can be used to non-destructively characterize materials for a wide range of applications. Hyperspectral remote sensing data provide a rich source of information produced in the form of the spectrum which can be used to identify surface materials. In this study, Field Portable Hyperspectral Radiometer (FPHR) was evaluated in an attempt for prediction of diverse soil properties related to three different soil orders (Vertisols, Aridisols, and Entisols) across Lower Egypt. Eight pedons consisting of 34 samples were collected from different semi-arid areas. Soil horizonation and twelve soil attributes including clay, sand, silt, SOC, pH, EC, A.W, gypsum, CaCO3, Fe2O3, Al2O3, and SiO2 were traditionally analyzed and then correlated with spectral reflectance of the spectrum range. Four bands (blue, green, red, and near-infrared) were calculated for prediction of these variables. The results showed that the variations in spectral reflectance for each horizon across the spectrum range (276-1093 nm) were matched well with those of morphologically described horizons in the field. Additionally, the correlation results of different soil variables were highly correlated with spectral reflectance at different band wavelengths. For example, clay content correlated negatively (r = -0.93) with reflectance at the green band while silt (r = 0.67 at the blue band) and sand (0.87 at the green band) correlated positively. Regression equations were fitted in graphs to attempt the quantification of the soil constituents from their reflectance values. The best predictive models were obtained for clay
Sulfonated Polyimide/Ionic Liquid Composite Membranes for CO2 Separation: Transport Properties in Relation to Their Nanostructures

The transport properties of ionic liquid (IL)/sulfonated polyimide (SPI) composite membranes for CO2 separation were explored in relation to their nanostructures. 1-Butyl-3-methylimidazolium bis(trifluoromethanesulfonyl)amide ([C4mim][NTf2]), 1-butyl-3-methylimidazolium hexafluorophosphate ([C4mim]PF6), and 1-butyl-1-methylpyrrolidinium bis(trifluoromethanesulfonyl)amide ([P14][NTf2]) were selected as the ILs. These composite membranes enable favorable CO2 separation and superior mechanical properties. SPI is plasticized by the IL, which induces a decrease in the elastic modulus; however, due to the formation of bicontinuous nanostructures of IL-rich and SPI-rich phases, a modulus of >10 MPa is retained even with the incorporation of 75 wt % IL. [C4mim][NTf2] and [C4mim]PF6 exhibit stronger plasticization effects on SPI than [P14][NTf2]. The diffusion coefficients of CO2 and N2, which were measured by the time-lag method, abruptly increase with increasing IL content in the [C4mim][NTf2]/SPI composite memb.
A Novel Assay Format as an Alternative to ELISA: MINA Test for Biotin

Article type: Full Paper A novel assay format as an alternative to ELISA – MINA test for biotin Stanislav S. Piletsky,1* Anthony E. G. Cass,1 Elena V. Piletska,2* Joanna Czulak3 and Sergey A. Piletsky2 S. S. Piletsky, Prof A. E. G. Cass Department of Chemistry, Imperial College, London, SW7 2AZ (UK) Email: stanislav.piletsky14@imperial.ac. uk Dr E. Piletska, Prof S. A. Piletsky Department of Chemistry, University of Leicester, Leicester, LE1 7RH (UK) Email: ep219@le.ac. uk Dr J. Czulak MIP Diagnostics Ltd, University of Leicester, Leicester, LE1 7RH (UK)
Five models of hysteretic water-retention capacity and their comparison for sandy soil

A description of five mathematical models of the water-retention capacity of soil is given, taking into account the hysteresis phenomena. A computational experiment was carried out with these models using data on sandy soil. The experiment consisted of: (i) tuning of the models (parameter identification) by the method of dot-approximation of experimental data on the main drying and wetting branches of the hysteresis loop using an optimizing algorithm; (ii) the predictive calculation of the scanning branches of the hysteresis loop; (iii) a comparison of the errors in tuning results and the predictive calculation using the Williams-Kloot criteria. The commensurate and sufficiently low errors in the adjustment of the models have been achieved. The differences in the calculation of the scanning hysteresis branches are revealed. The practical significance of the mathematical models presented is to ensure the calculation of precision irrigation rates. The application of such rates in irrigation farming will help to prevent excess moisture from flowing beyond the root layer of the soil under the influence of gravity and, thus, to minimize the losses (unproductive consumption) of irrigation water, fertilizers, meliorants and plant protection products, and also reduce the risk of groundwater contamination with agrochemicals and eutrophication of water bodies. * Corresponding author: coolhabit@yandex.ru
Impact of Fin Line Edge Roughness and Metal Gate Granularity on Variability of 10-nm Node SOI n-FinFET

We report the numerical simulation study on the characteristic variability of 10-nm SOI Multi Fin n-FET due to the impact of random fluctuation sources such as gate work function variability induced by metal gate granularity (MGG) and Fin line edge roughness (LER) using quantum corrected 3-D drift diffusion (DD) simulation framework. The statistical simulation predictions reveal that for ultra downscaled SOI FinFET, the MGG predominantly affects device threshold voltage. Similarly, the LER sources are found to strongly influence the variability of device short channel effect immunity and channel mobility of carriers. Both MGG and long correlation length LER are found to strongly influence the overlap and outer fringing parasitic capacitances variability, resulting in increased variability of device intrinsic speed. It is predicted that the presence of combined random fluctuation sources results in the increased variability of threshold mismatch index ( $A_{\text {VT}}$ ) for the sub-10-nm SOI FinFET technology.
Structural Harmony Method in the Context of Deep Learning on Example of Music by Valentyn Sylvestrov and Philipp Glass

The article shows the potential of structural harmony method application in deep learning models training. Harmonic sequences, being represented as the paths in the system of graphs and then schematised, serve as the source of the deep learning models input. The visual character of the dataset generated using schemes allows the application of supervised machine learning technics and is suitable for time series analysis. Therefore, two neural network architectures – Convolutional and Convolutional Long-Short Term Memory were tested on example of music by postmodern composers Valentyn Sylvestrov (represented by vocal cycle “Silent songs”) and Philipp Glass (represented by piano cycle “Metamorphosis”).
Exposure to bisphenol A, bisphenol F, and bisphenol S can result in obesity in human body

Abstract Exposure to endocrine disruptors such as bisphenols (BPs), for example, bisphenol A, bisphenol S and bisphenol F in early life may contribute to obesity. BPs are man-made chemicals found in lots of household products including food packaging, drink containers, lotions, toys, plastic PVC flooring and water pipes. Human exposure to these chemicals has been linked to serious adverse health outcomes including cardiovascular disease, abnormal sperm morphology/sperm DNA damage, miscarriage, early puberty, diabetes and obesity.