Abstract: Phylogeny.fr, created in 2008, has been designed to facilitate the execution of phylogenetic workflows, and is nowadays widely used. However, since its development, user needs have evolved, new tools and workflows have been published, and the number of jobs has increased dramatically, thus promoting new practices, which motivated its refactoring. We developed NGPhylogeny.fr to be more flexible in terms of tools and workflows, easily installable, and more scalable. It integrates numerous tools in their latest version (e.g. TNT, FastME, MrBayes, etc.) as well as new ones designed in the last ten years (e.g. PhyML, SMS, FastTree, trimAl, BOOSTER, etc.). These tools cover a large range of usage (sequence searching, multiple sequence alignment, model selection, tree inference and tree drawing) and a large panel of standard methods (distance, parsimony, maximum likelihood and Bayesian). They are integrated in workflows, which have been already configured ('One click'), can be customized ('Advanced'), or are built from scratch ('A la carte'). Workflows are managed and run by an underlying Galaxy workflow system, which makes workflows more scalable in terms of number of jobs and size of data. NGPhylogeny.fr is deployable on any server or personal computer, and is freely accessible at https://ngphylogeny.fr.

Abstract: Direct coupling analysis (DCA) for protein folding has made very good progress, but it is not effective for proteins that lack many sequence homologs, even coupled with time-consuming conformation sampling with fragments. We show that we can accurately predict interresidue distance distribution of a protein by deep learning, even for proteins with ∼60 sequence homologs. Using only the geometric constraints given by the resulting distance matrix we may construct 3D models without involving extensive conformation sampling. Our method successfully folded 21 of the 37 CASP12 hard targets with a median family size of 58 effective sequence homologs within 4 h on a Linux computer of 20 central processing units. In contrast, DCA-predicted contacts cannot be used to fold any of these hard targets in the absence of extensive conformation sampling, and the best CASP12 group folded only 11 of them by integrating DCA-predicted contacts into fragment-based conformation sampling. Rigorous experimental validation in CASP13 shows that our distance-based folding server successfully folded 17 of 32 hard targets (with a median family size of 36 sequence homologs) and obtained 70% precision on the top L/5 long-range predicted contacts. The latest experimental validation in CAMEO shows that our server predicted correct folds for 2 membrane proteins while all of the other servers failed. These results demonstrate that it is now feasible to predict correct fold for many more proteins lack of similar structures in the Protein Data Bank even on a personal computer.

Abstract: . Despite their importance for sea-level rise, seasonal water availability, and as a source of geohazards, mountain glaciers are one of the few remaining subsystems of the global climate system for which no globally applicable, open source, community-driven model exists. Here we present the Open Global Glacier Model (OGGM), developed to provide a modular and open-source numerical model framework for simulating past and future change of any glacier in the world. The modeling chain comprises data downloading tools (glacier outlines, topography, climate, validation data), a preprocessing module, a mass-balance model, a distributed ice thickness estimation model, and an ice-flow model. The monthly mass balance is obtained from gridded climate data and a temperature index melt model. To our knowledge, OGGM is the first global model to explicitly simulate glacier dynamics: the model relies on the shallow-ice approximation to compute the depth-integrated flux of ice along multiple connected flow lines. In this paper, we describe and illustrate each processing step by applying the model to a selection of glaciers before running global simulations under idealized climate forcings. Even without an in-depth calibration, the model shows very realistic behavior. We are able to reproduce earlier estimates of global glacier volume by varying the ice dynamical parameters within a range of plausible values. At the same time, the increased complexity of OGGM compared to other prevalent global glacier models comes at a reasonable computational cost: several dozen glaciers can be simulated on a personal computer, whereas global simulations realized in a supercomputing environment take up to a few hours per century. Thanks to the modular framework, modules of various complexity can be added to the code base, which allows for new kinds of model intercomparison studies in a controlled environment. Future developments will add new physical processes to the model as well as automated calibration tools. Extensions or alternative parameterizations can be easily added by the community thanks to comprehensive documentation. OGGM spans a wide range of applications, from ice–climate interaction studies at millennial timescales to estimates of the contribution of glaciers to past and future sea-level change. It has the potential to become a self-sustained community-driven model for global and regional glacier evolution.

Abstract: The revolution in computer hardware, especially in graphics processing units and tensor processing units, has enabled significant advances in computer graphics and artificial intelligence algorithms. In addition to their many beneficial applications in daily life and business, computer-generated/manipulated images and videos can be used for malicious purposes that violate security systems, privacy, and social trust. The deepfake phenomenon and its variations enable a normal user to use his or her personal computer to easily create fake videos of anybody from a short real online video. Several countermeasures have been introduced to deal with attacks using such videos. However, most of them are targeted at certain domains and are ineffective when applied to other domains or new attacks. In this paper, we introduce a capsule network that can detect various kinds of attacks, from presentation attacks using printed images and replayed videos to attacks using fake videos created using deep learning. It uses many fewer parameters than traditional convolutional neural networks with similar performance. Moreover, we explain, for the first time ever in the literature, the theory behind the application of capsule networks to the forensics problem through detailed analysis and visualization.

Abstract: One of the major causes of death all over the world is heart disease or cardiac dysfunction. These diseases could be identified easily with the variations in the sound produced due to the heart activity. These sophisticated auscultations need important clinical experience and concentrated listening skills. Therefore, there is an unmet need for a portable system for the early detection of cardiac illnesses. This paper proposes a prototype model of a smart digital-stethoscope system to monitor patient's heart sounds and diagnose any abnormality in a real-time manner. This system consists of two subsystems that communicate wirelessly using Bluetooth low energy technology: A portable digital stethoscope subsystem, and a computer-based decision-making subsystem. The portable subsystem captures the heart sounds of the patient, filters and digitizes, and sends the captured heart sounds to a personal computer wirelessly to visualize the heart sounds and for further processing to make a decision if the heart sounds are normal or abnormal. Twenty-seven t-domain, f-domain, and Mel frequency cepstral coefficients (MFCC) features were used to train a public database to identify the best-performing algorithm for classifying abnormal and normal heart sound (HS). The hyper parameter optimization, along with and without a feature reduction method, was tested to improve accuracy. The cost-adjusted optimized ensemble algorithm can produce 97% and 88% accuracy of classifying abnormal and normal HS, respectively.

Abstract: Internet of Things (IoT) based healthcare system is now at the top peak because of its potentialities among all other IoT applications. Supporting sensors integrated with IoT healthcare can effectively analyze and gather the patients’ physical health data that has made the IoT based healthcare ubiquitously acceptable. A set of challenges including the continuous presence of the healthcare professionals and staff as well as the proper amenities in remote areas during emergency situations need to be addressed for developing a flexible IoT based healthcare system. Besides that, the human entered data are not as reliable as automated generated data. The development of the IoT based health monitoring system allows a personalized treatment in certain circumstances that helps to reduce the healthcare cost and wastage with a continuous improving outcome. We present an IoT based health monitoring system using the MySignals development shield with (Low power long range) LoRa wireless network system. Electrocardiogram (ECG) sensor, body temperature sensor, pulse rate, and oxygen saturation sensor have been used with MySignals and LoRa. Evaluating the performances and effectiveness of the sensors and wireless platform devices are also analyzed in this paper by applying physiological data analysis methodology and statistical analysis. MySignals enables the stated sensors to gather physical data. The aim is to transmit the gathered data from MySignals to a personal computer by implementing a wireless system with LoRa. The results show that MySignals is successfully interfaced with the ECG, temperature, oxygen saturation, and pulse rate sensors. The communication with the hyper-terminal program using LoRa has been implemented and an IoT based healthcare system is being developed in MySignals platform with the expected results getting from the sensors.

Abstract: SUMMARY Since cancer is a heterogeneous disease, tumor subtyping is crucial for improved treatment and prognosis. We have developed a subtype discovery tool, called PINSPlus, that is: (i) robust against noise and unstable quantitative assays, (ii) able to integrate multiple types of omics data in a single analysis and (iii) dramatically superior to established approaches in identifying known subtypes and novel subgroups with significant survival differences. Our validation on 12,158 samples from 44 datasets shows that PINSPlus vastly outperforms other approaches. The software is easy-to-use and can partition hundreds of patients in a few minutes on a personal computer. AVAILABILITY AND IMPLEMENTATION The package is available at https://cran.r-project.org/package=PINSPlus. Data and R script used in this manuscript are available at https://bioinformatics.cse.unr.edu/software/PINSPlus/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Abstract: Background Cephalometric analysis has long been, and still is one of the most important tools in evaluating craniomaxillofacial skeletal profile. To perform this, manual tracing of x-ray film and plotting landmarks have been required. This procedure is time-consuming and demands expertise. In these days, computerized cephalometric systems have been introduced; however, tracing and plotting still have to be done on the monitor display. Artificial intelligence is developing rapidly. Deep learning is one of the most evolving areas in artificial intelligence. The authors made an automated landmark predicting system, based on a deep learning neural network. Methods On a personal desktop computer, a convolutional network was built for regression analysis of cephalometric landmarks' coordinate values. Lateral cephalogram images were gathered through the internet and 219 images were obtained. Ten skeletal cephalometric landmarks were manually plotted and coordinate values of them were listed. The images were randomly divided into 153 training images and 66 testing images. Training images were expanded 51 folds. The network was trained with the expanded training images. With the testing images, landmarks were predicted by the network. Prediction errors from manually plotted points were evaluated. Results Average and median prediction errors were 17.02 and 16.22 pixels. Angles and lengths in cephalometric analysis, predicted by the neural network, were not statistically different from those calculated from manually plotted points. Conclusion Despite the variety of image quality, using cephalogram images on the internet is a feasible approach for landmark prediction.

Abstract: Depth cameras have recently become popular and many vision problems can be better solved with depth information. But, how to integrate depth information into a visual tracker to overcome the challenges such as occlusion and background distraction is still underinvestigated in current literature on visual tracking. In this paper, we investigate a 3-D extension of a classical mean-shift tracker whose greedy gradient ascend strategy is generally considered as unreliable in conventional 2-D tracking. However, through careful study of the physical property of 3-D point clouds, we reveal that objects which may appear to be adjacent on a 2-D image will form distinctive modes in the 3-D probability distribution approximated by kernel density estimation, and finding the nearest mode using 3-D mean-shift can always work in tracking. Based on the understanding of 3-D mean-shift, we propose two important mechanisms to further boost the tracker's robustness: one is to enable the tracker to be aware of potential distractions and make corresponding adjustments to the appearance model; and the other is to enable the tracker to detect and recover from tracking failures caused by total occlusion. The proposed method is both effective and computationally efficient. On a conventional personal computer, it runs at more than 60 FPS without graphical processing unit acceleration.

Abstract: The astonishing progress of personal computer technology in the past 30 years as well as the availability of thermodynamic data and modeling programs have revolutionized our ability to investigate and quantify metamorphic processes. Equilibrium thermodynamics has played a central role in this revolution, providing simultaneously a physico-chemical framework and efficient modeling strategies to calculate mineral stability relations in the Earth’s lithosphere (and beyond) as well as thermobarometric results. This Perspectives contribution provides a review of the ingredients and recipes required for constructing models. A fundamental requirement to perform thermodynamic modeling is an internally consistent database containing standard state properties and activity–composition models of pure minerals, solid solutions, and fluids. We demonstrate how important internal consistency is to this database, and show some of the advantages and pitfalls of the two main modeling strategies (inverse and forward modeling). Both techniques are commonly applied to obtain thermobarometric estimates; that is, to derive P–T (pressure–temperature) information to quantify the conditions of metamorphism. In the last section, we describe a new modeling strategy based on iterative thermodynamic models, integrated with quantitative compositional mapping. This technique provides a powerful alternative to traditional modeling tools and permits use of local bulk compositions for testing the assumption of local equilibrium in rocks that were not fully re-equilibrated during their metamorphic history. We argue that this is the case for most natural samples, even at high-temperature conditions, and that this natural complexity must be taken into consideration when applying equilibrium models.

Abstract: Continuous glucose monitoring from sweat and tears can improve the quality of life of diabetic patients and provide data for more accurate diagnosis and treatment. Current continuous glucose sensors use enzymes with a one-to-two week lifespan, which forces periodic replacement. Metal oxide sensors are an alternative to enzymatic sensors with a longer lifetime. However, metal oxide sensors do not operate in sweat and tears because they function at high pH (pH > 10), and sweat and tears are neutral (pH = 7). Here, we introduce a non-enzymatic metal oxide glucose sensor that functions in neutral fluids by electronically inducing a reversible and localized pH change. We demonstrate glucose monitoring at physiologically relevant levels in neutral fluids mimicking sweat, and wireless communication with a personal computer via an integrated circuit board.

Abstract: Personalized learning refers to instruction in which the pace of learning and the instructional approach are optimized for the needs of each learner. With the latest advances in information technology and data science, personalized learning is becoming possible for anyone with a personal computer, supported by a data-driven recommendation system that automatically schedules the learning sequence. The engine of such a recommendation system is a recommendation strategy that, based on data from other learners and the performance of the current learner, recommends suitable learning materials to optimize certain learning outcomes. A powerful engine achieves a balance between making the best possible recommendations based on the current knowledge and exploring new learning trajectories that may potentially pay off. Building such an engine is a challenging task. We formulate this problem within the Markov decision framework and propose a reinforcement learning approach to solving the problem.

Abstract: This book is a celebration of Leslie Lamport's work on concurrency, interwoven in four-and-a-half decades of an evolving industry: from the introduction of the first personal computer to an era when parallel and distributed multiprocessors are abundant. His works lay formal foundations for concurrent computations executed by interconnected computers. Some of the algorithms have become standard engineering practice for fault tolerant distributed computing - distributed systems that continue to function correctly despite failures of individual components. He also developed a substantial body of work on the formal specification and verification of concurrent systems, and has contributed to the development of automated tools applying these methods. Part I consists of technical chapters of the book and a biography. The technical chapters of this book present a retrospective on Lamport's original ideas from experts in the field. Through this lens, it portrays their long-lasting impact. The chapters cover timeless notions Lamport introduced: the Bakery algorithm, atomic shared registers and sequential consistency; causality and logical time; Byzantine Agreement; state machine replication and Paxos; temporal logic of actions (TLA). The professional biography tells of Lamport's career, providing the context in which his work arose and broke new grounds, and discusses LaTeX - perhaps Lamport's most influential contribution outside the field of concurrency. This chapter gives a voice to the people behind the achievements, notably Lamport himself, and additionally the colleagues around him, who inspired, collaborated, and helped him drive worldwide impact. Part II consists of a selection of Leslie Lamport's most influential papers. This book touches on a lifetime of contributions by Leslie Lamport to the field of concurrency and on the extensive influence he had on people working in the field. It will be of value to historians of science, and to researchers and students who work in the area of concurrency and who are interested to read about the work of one of the most influential researchers in this field.

Abstract: This paper presents a wearable electromyographic gesture recognition system based on the hyperdimensional computing paradigm, running on a programmable parallel ultra-low-power (PULP) platform. The processing chain includes efficient on-chip training, which leads to a fully embedded implementation with no need to perform any offline training on a personal computer. The proposed solution has been tested on 10 subjects in a typical gesture recognition scenario achieving 85% average accuracy on 11 gestures recognition, which is aligned with the state-of-the-art, with the unique capability of performing online learning. Furthermore, by virtue of the hardware friendly algorithm and of the efficient PULP system-on-chip (Mr. Wolf) used for prototyping and evaluation, the energy budget required to run the learning part with 11 gestures is 10.04 mJ, and 83.2 $\mu$ J per classification. The system works with a average power consumption of 10.4 mW in classification, ensuring around 29 h of autonomy with a 100 mAh battery. Finally, the scalability of the system is explored by increasing the number of channels (up to 256 electrodes), demonstrating the suitability of our approach as universal, energy-efficient biopotential wearable recognition framework.

Abstract: The Abel transform is a mathematical operation that transforms a cylindrically symmetric three-dimensional (3D) object into its two-dimensional (2D) projection. The inverse Abel transform reconstructs the 3D object from the 2D projection. Abel transforms have wide application across numerous fields of science, especially chemical physics, astronomy, and the study of laser-plasma plumes. Consequently, many numerical methods for the Abel transform have been developed, which makes it challenging to select the ideal method for a specific application. In this work, eight published transform methods have been incorporated into a single, open-source Python software package (PyAbel) to provide a direct comparison of the capabilities, advantages, and relative computational efficiency of each transform method. Most of the tested methods provide similar, high-quality results. However, the computational efficiency varies across several orders of magnitude. By optimizing the algorithms, we find that some transform methods are sufficiently fast to transform 1-megapixel images at more than 100 frames per second on a desktop personal computer. In addition, we demonstrate the transform of gigapixel images.

Abstract: Virtual and Augmented Reality deliver engaging interaction experiences that can transport and extend the capabilities of the user. To ensure these paradigms are more broadly usable and effective, however, it is necessary to also deliver many of the conventional functions of a smartphone or personal computer. It remains unclear how conventional input tasks, such as text entry, can best be translated into virtual and augmented reality. In this paper we examine the performance potential of four alternative text entry strategies in virtual reality (VR). These four strategies are selected to provide full coverage of two fundamental design dimensions: i) physical surface association; and ii) number of engaged fingers. Specifically, we examine typing with index fingers on a surface and in mid-air and typing using all ten fingers on a surface and in mid-air. The central objective is to evaluate the human performance potential of these four typing strategies without being constrained by current tracking and statistical text decoding limitations. To this end we introduce an auto-correction simulator that uses knowledge of the stimulus to emulate statistical text decoding within constrained experimental parameters and use high-precision motion tracking hardware to visualise and detect fingertip interactions. We find that alignment of the virtual keyboard with a physical surface delivers significantly faster entry rates over a mid-air keyboard. Also, users overwhelmingly fail to effectively engage all ten fingers in mid-air typing, resulting in slower entry rates and higher error rates compared to just using two index fingers. In addition to identifying the envelopes of human performance for the four strategies investigated, we also provide a detailed analysis of the underlying features that distinguish each strategy in terms of its performance and behaviour.

Abstract: We present a learning-based Shack-Hartmann wavefront sensor (SHWS) to achieve the high-order aberration detection without image segmentation or centroid positioning. Zernike coefficient amplitudes of aberrations measured from biological samples are referred and expanded to generate the training datasets. With one SHWS pattern inputted, up to 120th Zernike modes could be predicted within 10.9 ms with 95.56% model accuracy by a personal computer. The statistical experimental results show that compared with traditional modal-based SHWS, the root mean squared error in phase residuals of this method is reduced by ∼40.54% and the Strehl ratio of the point spread functions is improved by ∼27.31%. The aberration detection performance of this method is also validated on a mouse brain slice with 300 µm thickness and the median improvement of peak-to-background ratio of this method is ∼30% to 40% compared with traditional SHWS. With the high detection accuracy, simple processes, fast prediction speed and good compatibility, this work offers a potential approach to improve the wavefront sensing ability of SHWS, which could be combined with an existing adaptive optics system and be further applied in biological applications.

Abstract: These lectures were given at the Weizmann Institute in the spring of 2019. They are intended to familiarize students with the nuts and bolts of the numerical bootstrap as efficiently as possible. After a brief review of the basics of conformal field theory in $d>2$ spacetime dimensions, we discuss how to compute conformal blocks, formulate the crossing equations as a semi-definite programming problem, solve this problem using SDPB on a personal computer, and interpret the results. We include worked examples for all steps, including bounds for 3d CFTs with $\mathbb{Z}_2$ or $O(N)$ global symmetries. Each lecture includes a problem set, which culminate in a precise computation of the 3d Ising model critical exponents using the mixed correlator $\mathbb{Z}_2$ bootstrap. A Mathematica file is included that transforms crossing equations into the proper input form for SDPB.

Abstract: Now-a-days, the society is witnessing a keen urge to enhance the quality of healthcare services with the intervention of technology in the health sector. The main focus in transforming traditional healthcare to smart healthcare is on facilitating the patients as well as medical professionals. However, this changover is not easy due to various issues of security and integrity associated with it. Security of patients's personal health record and privacy can be handled well by permitting only authorized access to the confidential health-data via suitably designed authentication scheme. In pursuit to contribute in this direction, we came across the role of Universal Serial Bus (USB), the most widely accepted interface, in enabling communication between peripheral devices and a host controller like laptop, personal computer, smart phone, tablet etc. In the process, we analysed a recently proposed a three-factor authentication scheme for consumer USB Mass Storage Devices (MSD) by He et al. In this paper, we demonstrate that He et al.'s scheme is vulnerable to leakage of temporary but session specific information attacks, late detection of message replay, forward secrecy attacks, and backward secrecy attacks. Then motivated with the benefits of USB, we propose a secure three-factor authentication scheme for smart healthcare.

Abstract: Details of an open-source planar perovskite solar cell simulator, which includes ion vacancy migration within the perovskite layer coupled to charge carrier transport throughout the perovskite and adjoining transport layers in one dimension, are presented. The model equations are discretised in space using a finite element scheme, and temporal integration of the resulting system of differential algebraic equations is carried out in MATLAB. The user is free to modify device parameters, as well as the incident illumination and applied voltage. Time-varying voltage and/or illumination protocols can be specified, e.g. to simulate current–voltage sweeps, or to track the open-circuit conditions as the illumination is varied. Typical simulations, e.g. current–voltage sweeps, only require computation times of seconds to minutes on a modern personal computer. An example set of hysteretic current–voltage curves is presented.

Abstract: Full-waveform inversion (FWI) in the time domain of ground-penetrating radar (GPR) data involves a vast number of calculations; thus, it requires a large amount of memory and is difficult to calculate on a personal computer (PC). In this paper, GPR data are analyzed with multiscale FWI using two parameters (permittivity and conductivity) based on total variation (TV) regularization, which is implemented on a PC using a graphics processing unit (GPU) parallel acceleration strategy. The inverse problem is considered to be a nonlinear optimization problem and is solved with limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) process, which is a quasi-Newton method. The gradient of the objective function is calculated using the adjoint-state method, and the finite-difference method is required to solve the forward problem many times. A multiscale serial inversion strategy is applied to optimize the inversion algorithm and to decompose the inversion problem into 2–3 frequency bands to search in the direction of the global minimum point instead of local minimums. Taking the complex model as an example, experiments are carried out to assess the parameter adjustment factor and regularization parameter. The appropriate parameter adjustment factor and regularization parameter can effectively guarantee the convergence speed and stability of dual-parameter inversion method and improve the accuracy of GPR data inversion. Finally, FWI of the noise-free and 25-dB signal-to-noise ratio (SNR) noise data of the overthrust model is performed. The results show that the multiscale and dual-parameter inversion method proposed in this paper can provide reliable constraints, has better adaptability to noisy data, and can reliably and accurately reconstruct the dielectric properties distribution of the subsurface.

Abstract: The Abel transform is a mathematical operation that transforms a cylindrically symmetric three-dimensional (3D) object into its two-dimensional (2D) projection. The inverse Abel transform reconstructs the 3D object from the 2D projection. Abel transforms have wide application across numerous fields of science, especially chemical physics, astronomy, and the study of laser-plasma plumes. Consequently, many numerical methods for the Abel transform have been developed, which makes it challenging to select the ideal method for a specific application. In this work eight transform methods have been incorporated into a single, open-source Python software package (PyAbel) to provide a direct comparison of the capabilities, advantages, and relative computational efficiency of each transform method. Most of the tested methods provide similar, high-quality results. However, the computational efficiency varies across several orders of magnitude. By optimizing the algorithms, we find that some transform methods are sufficiently fast to transform 1-megapixel images at more than 100 frames per second on a desktop personal computer. In addition, we demonstrate the transform of gigapixel images.

Abstract: In this paper, a system for microwave breast tumor detection is presented using iteratively corrected coherence factor delay and sum (CF-DAS) algorithm. CF-DAS is data independent, which makes it stable in a noisy environment. However, data adaptive techniques have made significant progress by enhancing the image quality in microwave tomography. Thus, a novel data adaptive iterative variant of CF-DAS is proposed in this paper to produce stable and accurate images. The microwave imaging (MI) system contains a rotatable array of nine modified antipodal Vivaldi antennas in a circular arrangement, an array-mounting stand based on the stepper motor, the flexible phantom mounting podium, a control system for RF switching of the transceivers, and signal processing unit based on personal computer involved in the reconstruction of the image. The impedance bandwidth of the modified antenna is recorded from 2.5 to 11 GHz with stable directional radiation pattern. For performing the transmission and reception of the microwave signals, an SP8T nine port RF switch is used ranging from 2.5 to 8.0 GHz, and the switching is controlled by MATLAB software. Several low-cost lab-based homogenous and heterogeneous phantoms containing the dielectric property of human breast and tumor tissue are prepared to test the system efficiency. Since typical data independent radar-based techniques are ill-equipped for multiple reflection scenarios, an iteratively corrected variant of CF-DAS algorithm is used for processing the recorded backscattered signals to reconstruct the image of the breast phantom and to identify the existence and locate the area of the multiple breast tumors. The proposed method achieves more than 10-dB improvement over conventional CF-DAS in terms of signal to mean ratio for four different phantoms measured in this study.

Abstract: Cellular beams are an attractive option for the steel construction industry due to their versatility in terms of strength, size, and weight. Further benefits are the integration of services thereby reducing ceiling-to-floor depth (thus, building’s height), which has a great economic impact. Moreover, the complex localised and global failures characterizing those members have led several scientists to focus their research on the development of more efficient design guidelines. This paper aims to propose an artificial neural network (ANN)-based formula to estimate the critical elastic buckling load of simply supported cellular beams under uniformly distributed vertical loads. The 3645-point dataset used in ANN design was obtained from an extensive parametric finite element analysis performed in ABAQUS. The independent variables adopted as ANN inputs are the following: beam’s length, opening diameter, web-post width, cross-section height, web thickness, flange width, flange thickness, and the distance between the last opening edge and the end support. The proposed model shows a strong potential as an effective design tool. The maximum and average relative errors among the 3645 data points were found to be 3.7% and 0.4%, respectively, whereas the average computing time per data point is smaller than a millisecond for any current personal computer.

Abstract: This research presents an improved mobile inverted pendulum robot called Two-wheeled Self-balancing robot (TWSBR) using a Proportional-Derivative Proportional-Integral (PD-PI) robust control design based on 32-bit microcontroller in a sensed environment (SE). The robot keeps itself balance with two wheels and a PD-PI controller based on the Kalman filter algorithm during the navigation process and is able to stabilize while avoiding acute and dynamic obstacles in the sensed environment. The Proportional (P) control is used to implement turn control for obstacle avoidance in SE with ultrasonic waves. Finally, in a SE, the robot can communicate with any of the Internet of Things (IoT) devices (mobile phone or Personal Computer) which have a Java-based transmission application installed and through Bluetooth technology connectivity for wireless control. The simulation results prove the efficiency of the proposed PD-PI controller in path planning, and balancing challenges of the TWSBR under several environmental disturbances. This shows an improved control system as compared to the existing improved Adaptive Fuzzy Controller.

Abstract: Background: Traditional methods for assessing memory are expensive and have high administrative costs. Memory assessment is important for establishing cognitive impairment in cases such as detecting dementia in older adults. Virtual reality (VR) technology can assist in establishing better quality outcome in such crucial screening by supporting the well-being of individuals and offering them an engaging, cognitively challenging task that is not stressful. However, unmet user needs can compromise the validity of the outcome. Therefore, screening technology for older adults must address their specific design and usability requirements. Objective: This study aimed to design and evaluate the feasibility of an immersive VR platform to assess spatial navigation memory in older adults and establish its compatibility by comparing the outcome to a standard screening platform on a personal computer (PC). Methods: VR-CogAssess is a platform integrating an Oculus Rift head-mounted display and immersive photorealistic imagery. In a pilot study with healthy older adults (N=42; mean age 73.22 years, SD 9.26), a landmark recall test was conducted, and assessment on the VR-CogAssess was compared against a standard PC (SPC) setup. Results: Results showed that participants in VR were significantly more engaged (P=.003), achieved higher landmark recall scores (P=.004), made less navigational mistakes (P=.04), and reported a higher level of presence (P=.002) than those in SPC setup. In addition, participants in VR indicated no significantly higher stress than SPC setup (P=.87). Conclusions: The study findings suggest immersive VR is feasible and compatible with SPC counterpart for spatial navigation memory assessment. The study provides a set of design guidelines for creating similar platforms in the future.

Abstract: This paper proposed an enhanced system for securing sensitive text data on personal computer benefitting from the combination of both techniques: cryptography and steganography. The system security is generated by involving RSA cryptography followed by audio-based steganography as two sequential layers to ensure the best possible security gaining the advantages from both. The study modeled the system and implemented it to be tested to explore the relation between security, capacity, and data dependency. The experimentations covered securing data within 15 differently sized audios showing interesting results. The results gave enhancement in capacity versus security trade-off, allowing the user and application to be the decision maker in the choice. The work showed the possibility of accepting security of 1-LSB, 2-LSB, and 3-LSB methods and their reasonable effect on the cover. The study also shows that using 3-LSB is giving acceptable security benefitting extra capacity more than 1-LSB and 2-LSB found in the literature.

Abstract: Nanoparticles find applications in multiple technological and scientific fields, and laser ablation in liquid (LAL) emerged as a versatile method for providing colloidal solutions of nanomaterials with various composition, by a low cost, simple, self-standing, and "green" procedure. However, the use of high energy and high power laser beams is harmful, especially when coupled with flammable or toxic liquids, and in situ operation is required for starting, monitoring the LAL synthesis, and stopping it at the desired point. Here we describe the hardware and software design and the test results of a system for the production of nanoparticles by laser ablation synthesis in liquid solution (LASiS), which is remotely controllable with a personal computer or a smartphone. In this system, laser energy and solution flux are selectable, and the synthesis status can be monitored and managed at any time off site. Only commercially available components and software are employed, making the whole apparatus easily reproducible in any LAL laboratory. The system has proven its reliability in various conditions, including intercontinental remote control experiments. Overall, this apparatus represents a step forward to improve the safety and to more efficiently exploit the time of people working with LASiS, thus contributing to the increasing demand for off-site real time monitoring of experimental equipment in many scientific and industrial laboratories, due to safety and efficiency requirements.