Miracle-dz/UniFAR-FTUs huggingface dataset:
A Systematic Overview of Android Malware Detection

ABSTRACT Due to the completely open-source nature of Android, the exploitable vulnerability of malware attacks is increasing. To stay ahead of other similar review work attempting to deal with the serious security problem of the Android environment, this work not only summarizes the approaches in the malware classification phase but also lays emphasis on the Android feature selection algorithm and presents some areas neglected in previous works in the field of Android malware detection, like limitations and commonly applied datasets in machine learning-based models. In this paper, the Android OS environment, feature selection, classification models, and confronted challenges of machine learning detection are described in detail. Based on the brief introduction to Android background knowledge, feature selection methods are elaborated from key perspectives as feature extraction, raw data preprocessing, valid feature subsets selection, and machine learning-based selection models. For the algorithms of the malware classification, machine learning methods are categorized according to different standards to present an all-around view. Furthermore, this paper focuses on the study of deterioration problems and evasion attacks in machine learning detectors.
An HEMT input charge preamplifier for nanoseconds signal processing time

The capabilities of the high electron mobility transistor (HEMT) as front end device in charge preamplifiers for radiation detectors have been experimentally tested. We present the design and performance of a fast low-noise charge preamplifier having an HEMT as input transistor. An equivalent noise charge of 139 rms electrons, i.e., 1.13 keV FWHM in silicon detectors, has been measured at room temperature with 10 ns RC-CR shaping and 1 pF input capacitance. >
HEROES V-V - HEmorRhagic cOmplications in Veno-Venous Extracorporeal life Support - development and internal validation of multivariable prediction model in adult patients.

BACKGROUND During extracorporeal life support (ECLS), bleeding is one of the most frequent complications, associated with high morbidity and increased mortality, despite continuous improvements in devices and patient care. Risk factors for bleeding complications in veno-venous (V-V) ECLS applied for respiratory support have been poorly investigated. We aim to develop and internally validate a prediction model to calculate the risk for bleeding complications in adult patients receiving V-V ECLS support. METHODS Data from adult patients reported to the Extracorporeal Life Support Organization (ELSO) Registry between the years 2010 and 2020 were analyzed. The primary outcome was bleeding complications recorded during V-V ECLS. Multivariable logistic regression with backward stepwise elimination was used to develop the predictive model. Performance of the model was tested by discriminative ability and calibration with ROC curves and visual inspection of the calibration plot. RESULTS In total, 18.658 adult patients were included, of which 3.933 (21.1%) developed bleeding complications. The prediction model showed a prediction of bleeding complications with an AUC of 0.63. Pre-ECLS arrest, surgical cannulation, lactate, pO2 , HCO3 , ventilation rate, mean airway pressure, pre-ECLS cardiopulmonary bypass or renal replacement therapy, pre-ECLS surgical interventions and different types of diagnosis were included in the prediction model. CONCLUSIONS The model is based on the largest cohort of V-V ECLS patients and reveals the most favourable predictive value addressing bleeding events given the predictors that are feasible and when compared to the current literature. This model will help identifying patients at risk of bleeding complications, and decision making in terms of anticoagulation and hemostatic management.
Event-Driven Queue-Based Traffic Flow Microsimulation

Simulating traffic flow is an important problem in transport planning. The most popular simulation approaches for large-scale scenarios today are aggregated models. Unfortunately, these models lack temporal and spatial resolution. But microsimulations are interesting for traffic flow simulation, as they can accurately simulate features requiring both high temporal and high spatial resolution, including traffic jams and peak periods. However, most microscopic approaches involve high computational costs and expensive large computers to run them within a reasonable time. This work presents possibilities for reducing these costs by using a queue-based model and an event-driven approach jointly. The approach makes it possible to run large-scale scenarios with more than 1 million simulated person-days on networks with 10,000 links in less than 10 min on single CPU desktop computers present in most offices today.
Constructing new working practices through project narratives

In this article we focus on the (internal and external) dynamics of New Public Management (NPM) in the daily life of project management. We concentrate on the ways NPM concepts work out in the realities of project actors. Based on recent research within the Dutch infrastructure sector, we analyse alternative responses to perceived problems in the infrastructure sector by focusing on 'project narratives'. Next to compliance, these narratives feature deterrence (a strong resistance to the change concept), dilution (blurring of the initial ambitions) and dissociation (confusion over the societal value of the project). The article shows how project narratives provide organisational members with space to make sense of and contest the new managerial initiatives and value systems imposed upon them.
BATMAV: a biologically inspired micro air vehicle for flapping flight: kinematic modeling

The overall objective of the BATMAV project is the development of a biologically inspired bat-like Micro-Aerial Vehicle (MAV) with flexible and foldable wings, capable of flapping flight. This first phase of the project focuses particularly on the kinematical analysis of the wing motion in order to build an artificial-muscle-driven actuation system in the future. While flapping flight in MAV has been previously studied and a number of models were realized using light-weight nature-inspired rigid wings, this paper presents a first model for a platform that features bat-inspired wings with a number of flexible joints which allows mimicking the kinematics of the real flyer. The bat was chosen after an extensive analysis of the flight physics of small birds, bats and large insects characterized by superior gust rejection and obstacle avoidance. Typical engineering parameters such as wing loading, wing beat frequency etc. were studied and it was concluded that bats are a suitable platform that can be actuated efficiently using artificial muscles. Also, due to their wing camber variation, they can operate effectively at a large range of speeds and allow remarkably maneuverable flight. In order to understand how to implement the artificial muscles on a bat-like platform, the analysis was followed by a study of bat flight kinematics. Due to their obvious complexity, only a limited number of degrees of freedom (DOF) were selected to characterize the flexible wing's stroke pattern. An extended analysis of flight styles in bats based on the data collected by Norberg and the engineering theory of robotic manipulators resulted in a 2 and 4-DOF models which managed to mimic the wingbeat cycle of the natural flyer. The results of the kinematical model can be used to optimize the lengths and the attachment locations of the wires such that enough lift, thrust and wing stroke are obtained.
Size effect on compressive behaviors of waste fiber-reinforced recycled aggregate concrete

Abstract The compressive size effect of conventional concrete is well known, but there is limited knowledge of the fiber-reinforced recycled concrete. A total of 270 specimens with the different cube and prism sizes, water-cement ratios, compressive strength grades, the replacement rates of recycled aggregates, and volume fractions of waste fibers were tested until failure to study the size effect behaviors of waste fiber-reinforced recycled concrete subjected to axial compression. The results showed that as the replacement rate of recycled aggregates increases, the size effect degree of the cube and axial compressive strength increases. With the increase of the volume fraction of waste fibers, the size effect degree of compressive strength decreases first and then increases, with the optimum volume incorporation of 0.12%. The higher the strength grade of concrete is, the more obvious the size effect of strength is. For large-scale components, no incision correction of Bažant and Carpineri’s fractal size effect theory is more suitable for the analysis of the size effect of waste fiber recycled concrete. It is essential to understand the compression size effect of waste fiber-reinforced recycled concrete so that the laboratory investigation results can be applied to real full-scale structures subjected to compressive loading.
Robust nonlinear speed control of PM synchronous motor using adaptive and sliding mode control techniques

A DSP-based nonlinear speed control of a permanent magnet synchronous motor (PMSM) which is robust to unknown parameter variations and speed measurement error is presented. The model reference adaptive system (MRAS) based adaptation mechanisms for the estimation of slowly varying parameters are derived using the Lyapunov stability theory. For the disturbances or quickly varying parameters, a quasilinearised and decoupled model including the influence of parameter variations and speed measurement error on the nonlinear speed control of a PMSM is derived. Based on this model, a boundary layer integral sliding mode controller to improve the robustness and performance of a PMSM drive is designed and compared with the conventional controller. To show the validity of the proposed control scheme, simulations and experimental work are carried out and compared with the conventional control scheme.
Modular design of heat exchanger networks

Heat exchanger networks (HENs) are essential for energy recovery in process plants. Current HEN design methods use approximate models to improve convergence. This work presents a novel linear rigorous algorithm for computation of mass and energy balances which enables rapid synthesis of optimal HEN structure by using modular size heat exchangers. HEN non-iterative simulation algorithm: 1) solves mass balance equations for the network; 2) computes heat exchanger energy transfer factor which depends only on the current flows through the exchanger and conditions for a typical counter-current heat exchanger; 3) solves linear energy balance given results from 1 and 2. This once-through rapid computation enables network design via differential evolution algorithm by using heat exchangers in modular sizes. Several examples illustrate the algorithm capability to determine the best structures. Proposed method enables design of lower capital cost networks that are as efficient as the networks with custom exchanger sizes.
High-impact rare genetic variants in severe schizophrenia

Significance In this study, we found that selecting individuals with extremely severe forms of schizophrenia led to a significantly improved ability to detect disease-associated rare variants. The high prevalence of rare variant risk factors in individuals with severe, extremely treatment-resistant schizophrenia suggests future clinical opportunities for risk prediction, prognostic stratification, and genetic counseling. These findings have implications for the design of future genetic studies in schizophrenia and highlight a strategy to reduce phenotypic heterogeneity and improve gene discovery efforts in other neuropsychiatric disorders. Extreme phenotype sequencing has led to the identification of high-impact rare genetic variants for many complex disorders but has not been applied to studies of severe schizophrenia. We sequenced 112 individuals with severe, extremely treatment-resistant schizophrenia, 218 individuals with typical schizophrenia, and 4,929 controls. We compared the burden of rare, damaging missense and loss-of-function variants between severe, extremely treatment-resistant schizophrenia, typical schizophrenia, and controls across mutation intolerant genes. Individuals with severe, extremely treatment-resistant schizophrenia had a high burden of rare loss-of-function (odds ratio, 1.91; 95% CI, 1.39 to 2.63; P = 7.8 × 10−5) and damaging missense variants in intolerant genes (odds ratio, 2.90; 95% CI, 2.02 to 4.15; P = 3.2 × 10−9). A total of 48.2% of individuals with severe, extremely treatment-resistant schizophrenia carried at least one rare, damaging missense or loss-of-function variant in intolerant genes compared to 29.8% of typical schizophrenia individuals (odds ratio, 2.18; 95% CI, 1.33 to 3.60; P = 1.6 × 10−3) and 25.4% of controls (odds ratio, 2.74; 95% CI, 1.85 to 4.06; P = 2.9 × 10−7). Restricting to genes previously associated with schizophrenia risk strengthened the enrichment with 8.9% of individuals with severe, extremely treatment-resistant schizophrenia carrying a damaging missense or loss-of-function variant compared to 2.3% of typical schizophrenia (odds ratio, 5.48; 95% CI, 1.52 to 19.74; P = 0.02) and 1.6% of controls (odds ratio, 5.82; 95% CI, 3.00 to 11.28; P = 2.6 × 10−8). These results demonstrate the power of extreme phenotype case selection in psychiatric genetics and an approach to augment schizophrenia gene discovery efforts.
Particle filter tracking without dynamics

People tracking is an interesting topic in computer vision. It has applications in industrial areas such as surveillance or human-machine interaction. Particle Filters is a common algorithm for people tracking; challenging situations occur when the target's motion is poorly modelled or with unexpected motions. In this paper, an alternative to address people tracking is presented. The proposed algorithm is based in particle filters, but instead of using a dynamical model, it uses background subtraction to predict future locations of particles. The algorithm is able to track people in omnidirectional sequences with a low frame rate one or two frames per second. Our approach can tackle unexpected discontinuities and changes in the direction of the motion. The main goal of the paper is to track people from laboratories, but it has applications in surveillance, mainly in controlled environments.
Noncontact method of in-plane strain measurement on rotating structures

An experimental technique of in-plane strain measurement on large rotating structures in service, requiring only minor surface preparation and little or no interruption to or shut-down of costly production-line plant is extremely attractive. The development of ESP1 (electronic speckle pattern interferometry), which displays this potential, using a pulsed laser is described. Some preliminary results for component tangential velocities up to 3.4 ms-1 are presented.
Computation of the diameter of a lightning return stroke

The diameter of lightning return strokes has been measured by a number of different methods, with little consistency in the results. In this research, the diameter of the return stroke is computed according to simple electrical circuit models based on other experimental results. The computed diameters for different situations may help to clear up the discrepancies between the experimental diameter measurements. Two models are considered. The first is a lumped, resonant circuit; the second is a charged transmission line. Both models give current waveforms that closely approximate the current actually measured at the base of lightning strokes. In both cases, the diameter computed is the same. The diameter of a first return stroke is found to lie in the range 1–4 cm. Subsequent return strokes are smaller, with diameters in the range 0.2–0.5 cm. In many cases, strokes to towers have shown evidence of the smaller strokes occurring alone. These are explained as strokes initiated by an upward leader.
Consideration of insolation characteristics in sizing stand-alone PV stations

Stand-alone PV schemes have found a fairly wide application in remote isolated regions to meet small but essential electric load. The capacities of such stations are commonly specified in terms of the peak power available during maximum solar insolation. The actual insolation available at a site is, however, affected by the vagaries of the weather in addition to the seasonal and diurnal variation. The support for these systems to cover for the periods of low insolation levels is provided by the battery system. The variation of insolation can be quite drastic and the peak rating may not be a proper measure of the station capacity. This paper analyzes the statistical nature of the available insolation using historical data collected at an experimental PV station. It attempts to determine the appropriate statistical distribution for the insolation level. This representation is then used to simulate the operation of stand alone PV stations probabilistically using different station configurations to evaluate their performance. An interactive software package has been developed using MATLAB to carry out the insolation data analysis and to simulate the operation of such schemes. The details regarding: (i) the modeling of various components; (ii) the features of the software package; and (iii) typical simulation results and their applications, are presented.
Discursive, Communal, and Individual Coping Strategies: How U.S. Adults Co-constructed Coping During Preliminary COVID-19 Stressors

ABSTRACT This interpretive research study explores U.S. adults’ lived experiences during the beginning months of the COVID-19 global pandemic. Participants (N= 44), recruited from a convenience sample of U.S. adults, engaged in in-depth semi-structured interviews or focus groups. Through an iterative analysis of participants’ experiences and the theoretical model of communal coping (TMCC), the authors identified three convergent stressors (i.e., isolation, uncertainty, conflict) and several coping strategies related to participants’ stressor appraisal (i.e., individual or joint) and action orientation (i.e., individual or joint). Based on these findings, this study offers the novel theoretical concept of Discursive coping and proposes a model for how this perspective might be integrated with current theorizing about individual and communal coping. Implications for communal coping and discursive theory are discussed as well as practical recommendations for public health messaging.
Ensemble‐Based Assimilation of Satellite All‐Sky Microwave Radiances Improves Intensity and Rainfall Predictions for Hurricane Harvey (2017)

Ensemble‐based data assimilation of radar observations across inner‐core regions of tropical cyclones (TCs) in tandem with satellite all‐sky infrared (IR) radiances across the TC domain improves TC track and intensity forecasts. This study further investigates potential enhancements in TC track, intensity, and rainfall forecasts via assimilation of all‐sky microwave (MW) radiances using Hurricane Harvey (2017) as an example. Assimilating Global Precipitation Measurement constellation all‐sky MW radiances in addition to GOES‐16 all‐sky IR radiances reduces the forecast errors in the TC track, rapid intensification (RI), and peak intensity compared to assimilating all‐sky IR radiances alone, including a 24‐hr increase in forecast lead‐time for RI. Assimilating all‐sky MW radiances also improves Harvey's hydrometeor fields, which leads to improved forecasts of rainfall after Harvey's landfall. This study indicates that avenues exist for producing more accurate forecasts for TCs using available yet underutilized data, leading to better warnings of and preparedness for TC‐associated hazards in the future.
Antimicrobial consumption in patients with COVID-19: a systematic review and meta-analysis

ABSTRACT Introduction Since the onset of the pandemic, prescribing antimicrobials has become a common practice to treat patients infected with COVID-19. Areas covered A systematic literature search was performed in the electronic databases MEDLINE, CINAHL, WHO COVID-19 database, including EMBASE, Scopus, WHO-COVID, LILACS, and Google Scholar to identify original articles published up to 31 July 2021. A random-effects model was used to estimate the pooled prevalence or proportion of antimicrobial consumption among COVID-19 patients. Expert opinion We identified 43 original articles, 33 studies from high-income countries, six from upper-middle-income countries, and four from lower-middle-income countries. Most of the studies presented data from hospital or secondary health-care settings (n = 34). Included studies measured antimicrobial consumption as Daily Defined Doses (DDD) or day of therapy (DOT) or percentage. A total of 19 studies measured antimicrobial consumption as DDDs or DOT. Meta-analysis revealed an overall high antimicrobial consumption of 68% (95% CI: 60% to 75%). The subgroup analysis found a lower consumption in high-income countries (58%, 95% CI: 48% to 67%), compared with lower and middle-income countries (89%, 95% CI: 82% to 94%). High antimicrobial consumption found in COVID-19 patients demands implementation of appropriate antimicrobial stewardship interventions.
Investigating Social Networking applications on smartphones detecting Facebook, Twitter, LinkedIn and Google+ artefacts on Android and iOS platforms

The rapid growth in usage and application of Social Networking (SN) platforms make them a potential target by cyber criminals to conduct malicious activities such as identity theft, piracy, illegal trading, sexual harassment, cyber stalking and cyber terrorism. Many SN platforms are extending their services to mobile platforms, making them an important source of evidence in cyber investigation cases. Therefore, understanding the types of potential evidence of users’ SN activities available on mobile devices is crucial to forensic investigation and research. In this paper, we examine four popular SN applications: Facebook, Twitter, LinkedIn and Google+, on Android and iOS platforms, to detect remnants of users’ activities that are of forensic interest. We detect a variety of artefacts (e.g. usernames, passwords, login information, personal information, uploaded posts, exchanged messages and uploaded comments from SN applications) that could facilitate a criminal investigation.
Classification of Brain MRI Tumor Images Based on Deep Learning PGGAN Augmentation

The wide prevalence of brain tumors in all age groups necessitates having the ability to make an early and accurate identification of the tumor type and thus select the most appropriate treatment plans. The application of convolutional neural networks (CNNs) has helped radiologists to more accurately classify the type of brain tumor from magnetic resonance images (MRIs). The learning of CNN suffers from overfitting if a suboptimal number of MRIs are introduced to the system. Recognized as the current best solution to this problem, the augmentation method allows for the optimization of the learning stage and thus maximizes the overall efficiency. The main objective of this study is to examine the efficacy of a new approach to the classification of brain tumor MRIs through the use of a VGG19 features extractor coupled with one of three types of classifiers. A progressive growing generative adversarial network (PGGAN) augmentation model is used to produce ‘realistic’ MRIs of brain tumors and help overcome the shortage of images needed for deep learning. Results indicated the ability of our framework to classify gliomas, meningiomas, and pituitary tumors more accurately than in previous studies with an accuracy of 98.54%. Other performance metrics were also examined.