Miracle-dz/UniFAR-FTUs huggingface dataset:
Chromium-Resistant Bacteria and Their Environmental Condition for Hexavalent Chromium Removal: A Review

The anthropogenic inputs of hexavalent chromium [Cr(VI)] have increased enormously during the past few decades and has become a challenge for life on earth and hence removal of this carcinogen has become the need of the hour. Cr(VI) removal through common physicochemical techniques is highly expensive and inappropriate at low concentration. Microbial reduction of Cr(VI) to trivalent form is considered a favorable technique for Cr(VI) removal from wastewater, as it reduces the highly toxic form of Cr to less toxic form and therefore the article conveys essential fundamental information on removal of Cr(VI) by bacteria. For efficient bioremoval of Cr(VI),the main machinery of the process, the microbes, and their conditions, which decide the fate of this heavy metal, should be appropriate. Hence, the authors cover vast information about the isolation of chromium-resistant bacteria from various environment and their Cr(VI) resistance capability. An extensive report is given on information pertaining to the factors such as cell density, pH, temperature, salt concentration, oxidation-reduction potential, electron donor, oxyanions, metabolic inhibitors, and other heavy metals that influence or affect the efficient Cr(VI) removal. Cr(VI) removal by immobilized bacterial cells and their advantages has also been summarized. In transferring this technology from laboratory to a large-scale application, better understanding of all these aspects is necessary. Hence, this developing biotechnological method that encompasses fields from genetic engineering to reactor engineering demands focused research in these directions, which may lead to implementation of this technology on a larger scale and drive it toward being the most opted-for technology.
A Stochastic Program for Siting and Sizing Fast Charging Stations and Small Wind Turbines in Urban Areas

Small wind turbines (SWTs) are promoted to be used in urban areas to mitigate the carbon footprint and expensive upgrades expected from high penetration levels of fast charging stations (FCSs). In this paper, a planning framework is proposed to amplify the total benefit for the owners of FCSs and SWTs as well as local distribution companies (LDCs). A stochastic program is developed to site and size SWTs along with FCSs in urban and suburban areas considering their specific wind characteristics, statutory regulations, turbine clustering studies, and geographic constraints. A worthiness metric is also proposed to rank FCS candidate locations based on their attractiveness to electric-vehicle (EV) drivers. An electric distribution network is overlaid onto a geographic map of downtown Chicago to assess the introduced planning framework. Results show that new efficient SWTs in urban areas can realistically justify their own investments over the long-term, and reduce the overall system losses and support FCS loads. In the case study presented, the investments yield a present value of $15M in profit, in 20 years, with an investment of $23M—only $6M of which is capital due in year one, while the rest consists of annual operation and maintenance costs.
Real-Time Gait Phase Estimation for Robotic Hip Exoskeleton Control During Multimodal Locomotion

We developed and validated a gait phase estimator for real-time control of a robotic hip exoskeleton during multimodal locomotion. Gait phase describes the fraction of time passed since the previous gait event, such as heel strike, and is a promising framework for appropriately applying exoskeleton assistance during cyclic tasks. A conventional method utilizes a mechanical sensor to detect a gait event and uses the time since the last gait event to linearly interpolate the current gait phase. While this approach may work well for constant treadmill walking, it shows poor performance when translated to overground situations where the user may change walking speed and locomotion modes dynamically. To tackle these challenges, we utilized a convolutional neural network-based gait phase estimator that can adapt to different locomotion mode settings to modulate the exoskeleton assistance. Our resulting model accurately predicted the gait phase during multimodal locomotion without any additional information about the user's locomotion mode, with a gait phase estimation RMSE of 5.04 <inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 0.79%, significantly outperforming the literature standard (<italic>p</italic> <inline-formula><tex-math notation="LaTeX">$< $</tex-math></inline-formula> 0.05). Our study highlights the promise of translating exoskeleton technology to more realistic settings where the user can naturally and seamlessly navigate through different terrain settings.
Compressive-Sensing-Aided MIMO Radar Enabling Multi-Functional and Compact Sensors in Air Scenarios Using Optimized Antenna Arrays

We address the problem of direction-of-arrival (DoA) estimation for air targets using a compact, multi-functional radar sensor. In order to enhance the angular resolution of such sensors while exploiting the sparseness of typical air scenarios, we consider the combination of a multiple-input multiple-output (MIMO) radar approach with suitable compressive-sensing (CS) techniques. In particular, we investigate the combination of MIMO processing for two-dimensional (2D) antenna arrays with CS-based angular processing for three-dimensional (3D) target localization and 2D DoA estimation in azimuth and elevation. We analyze the benefits of randomized antenna element positions in one and two dimensions and devise optimized array geometries for practicable aperture sizes. In particular, we take physical side constraints into account, such as the smallest realizable/ desired element spacing as well as area restrictions for antenna placement within the overall aperture. The aperture area reserved by this means could be used to accommodate additional hardware components enabling a multi-functional sensor approach. Extensive computer simulation results for different 3D target scenarios illustrate the advantages of our CS-based compact MIMO radar approach with randomized antenna elements compared to fully randomized arrays, given the same number of physical antenna elements.
Variability of DRAM Peripheral Transistor at Liquid Nitrogen Temperature

Dynamic random-access memory (DRAM) peripheral nMOSFETs with various channel length and channel doping concentration are analyzed at liquid nitrogen temperature (LNT). Important device parameters in circuit designs such as subthreshold mismatch, subthreshold swing (SS), transconductance, and drain-induced barrier lowering (DIBL) are analyzed at temperatures ranging from room temperature to LNT. In devices with relatively low channel doping, exacerbation of threshold mismatch at cryogenic temperatures is alleviated. Devices with lower doping exhibit better SS and transconductance characteristics. DIBL improved in short-channel devices at cryogenic temperatures, and the degradation of the DIBL with low doping is relatively small.
Cognitive Load During Multitasking Can Be Accurately Assessed Based on Single Channel Electroencephalography Using Graph Methods

Mental workload has been widely estimated based on electroencephalography (EEG) in the frequency domain. However, simple frequency features are not entirely accurate indicators of the cognitive load because surface EEG signals are weak, nonstationary and randomness. We hypothesize that graph methods, which analyse the relationship between each point and other points of the EEG signals, may provide a more precise identification of the mental load. To investigate this hypothesis, we aim to identify the optimum graph features from 14 channel EEG recordings (sampling rate = 128 Hz) in order to detect the high cognitive load related to multitasking. Three graph features: mean degree <inline-formula> <tex-math notation="LaTeX">$\overline {d}$ </tex-math></inline-formula>, clustering coefficient <inline-formula> <tex-math notation="LaTeX">$\overline {c}$ </tex-math></inline-formula>, and degree distribution <inline-formula> <tex-math notation="LaTeX">$p(k)$ </tex-math></inline-formula>, are extracted from 48 subjects EEG records. Each experimental subject conducts two tasks: without tasks and with a simultaneous capacity task, respectively. After the experiment is completed, the feeling of the subject with the cognitive load tags in three types: low load, medium load, and heavy load. The optimal features of these three levels of the subject sensation and two types of cognitive load in different tasks are selected on the basis of statistical analysis. Then all graph features are forwarded into a support vector machine (SVM) and a decision tree to conduct objective scoring classification and a three subjective rating classification, respectively. Based on the present results,channels O2, T8, FC6, F8, and AF4 are considered optimal for a more efficiently estimation of the cognitive load. <inline-formula> <tex-math notation="LaTeX">$\overline {c}$ </tex-math></inline-formula> associated with F8 and T8 during low cognitive load is significantly lower than those associated with high cognitive load (p < 0.001). Using three graph features, the accuracy of identifying two types of mental load is 89.6%. Current findings suggest that the mental workload associated with multi-tasks can be accurately assessed using the graph approaches to EEG data.
0.5T0.5R—An Ultracompact RRAM Cell Uniquely Enabled by van der Waals Heterostructures

Conventional designs of the extensively studied resistive-random access-memory (RRAM) cell involve one transistor and one RRAM—“1T1R,” i.e., two separate devices thereby constraining its integration density. In this work, we overcome this longstanding limitation by experimentally demonstrating a novel memory architecture that combines the 1T and 1R into a single hybrid device by uniquely leveraging both lateral and vertical van der Waals (vdW) heterostructures. This ultracompact device, which can be considered as a “0.5T0. 5R” memory cell, reduces the device count by half—the first of its kind in RRAM technology history, and simultaneously allows higher lateral as well as vertical (3-D) integration density w.r. t. the conventional 1T1R architecture. The unique “smart” device that can retain information after power is turned off is structurally designed by utilizing a shared graphene edge-contact and resistively switchable hexagonal boron nitride ( ${h}$ -BN) insulator. Aided by design optimization, record performance (<10 ns switching-speed), energy- (~0.07 pJ/bit), and area-efficiency (smallest footprint among all reported vdW-material-based RRAM memory units), as well as great retention (106 s) and endurance (>1000), benchmarked against current vdW-material-based RRAM devices, have been achieved by this 0.5T0. 5R memory cell. Moreover, the RRAM’s fine tunability with ultrashort pulsewidth, pulse amplitude, and gate voltage, enables synaptic plasticity and makes it an integrated three-terminal RRAM with considerable potential for neuromorphic and in-memory computing applications.
Geometrical Confinement of Vortex Domain Wall in Constricted Magnetic Nanowire With In-Plane Magnetic Anisotropy

The dynamics of the vortex domain wall (VDW) with head-to-head magnetization configuration in the stepped nanowire is investigated by carrying out a micromagnetic simulation. The stepped area is created at the center of the nanowire by shifting one part in the <inline-formula> <tex-math notation="LaTeX">$y$ </tex-math></inline-formula>-direction. This constricted area will help pin the domain wall (DW) by adjusting its depth (<inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula>) and length (<inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula>) to implement a high information storage memory based on the multi-bit per cell scheme. It is found that the VDW stability type is highly dependent on stepped area geometries (<inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\lambda$ </tex-math></inline-formula>). In addition, confined area dimensions have influenced the VDW dynamics in the stepped nanowire, whereas the VDW velocity increases by increasing <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> and keeping <inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> as constant. Furthermore, the VDW depinning current density (<inline-formula> <tex-math notation="LaTeX">$J_{d}$ </tex-math></inline-formula>) is found to increase with increasing <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> and decreasing <inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula>. <inline-formula> <tex-math notation="LaTeX">$J_{d}$ </tex-math></inline-formula> could be systematically defined as a function of stepped nanowire dimensions such as thickness (th). Also, the depinning behavior through the stepped area depends on the DW magnetization configuration. Whereas VDW with clockwise chirality (CWVDW) and up polarity has a higher depinning current density than CWVDW with down polarity.
Deep Learning Framework for Forecasting Tourism Demand

Accurate Tourism demand forecasting plays an important role to make decision and plan policy. However, tourism demand is characterized by complexity and non-linearity. Traditional tourism demand forecasting techniques are Linear methods and unable to fully simulate the nonlinear characteristics of tourism demand. Deep learning (DL) methods can be a promising solution to achieve an accurate forecast. These models are able to evaluate the non-linear relationship, without the drawbacks of Time Series and econometric models. In this paper, a deep learning Models are proposed to accurately predict tourist arrivals for Morocco from 2010 to 2019. The proposed framework uses a long short-term memory (LSTM) and gated recurrent unit (GRU). Experiments demonstrate that the LSTM and GRU methods perform better than support vector regression (SVR) and artificial neural network models (ANN).
Determination of the precious metals in geological materials by inductively coupled plasma mass spectrometry

The determination of the precious metals, consisting of the platinum group elements and gold, in geological materials is reviewed. The instrumental technique, inductively coupled plasma mass spectrometry, has been applied to analyses of rocks, minerals, soils, sediments, water and vegetation. The advantages of multi-element capability and excellent detection limits mean that accurate determinations of small concentrations of precious metals are possible in a wide variety of geological samples such as rocks, soils, sediments, water and botanical materials. This review contains information and data from 66 references. The period covered is 1988–97. The references were selected from a larger group because of their significance in describing procedures and applications involving determinations of precious metals in various matrices of interest in geological studies. They demonstrate some of the strengths of inductively coupled plasma mass spectrometry, particularly high sensitivity combined with good accuracies in simultaneous determinations of low concentrations. The review includes descriptions of some relatively new geological reference materials for use in development and validation of analytical methods.
Reduction of PV Curtailment through Optimally Sized Residential Battery Storage

The proliferation of rooftop solar PV has caused reverse power flow in low voltage distribution networks leading to elevated voltage levels. As the voltage is raised, PV inverters reduce output power to combat voltage rise and may result in inverter shutdowns causing PV curtailment (loss of PV output power). Residential battery storage can store excess PV energy and hence reduce reverse power flow, overvoltage and PV curtailment. This study aims to determine the reduction in PV curtailment achieved by the installation of optimally sized residential battery storage. A method to calculate the reduction in PV curtailment and the reduction in corresponding financial losses incurred by PV owners and the energy market is presented. The method is applied to an 11-kV feeder in South Australia along with prevailing feeder and inverter voltage limits. Results show that with the installation of optimally sized battery storage, PV curtailment and the corresponding financial losses can be reduced by more than 50%.
Optimum Device and Modulation Scheme Selection for Optical Wireless Communications

There has been an extensive modelling of the optical wireless channel, and the optimum modulation scheme for a particular channel is well-understood. However, this modelling has not taken into account the trade-offs that transmitter and receiver selection usually involve. For a particular type of transmitter, the modulation bandwidth and available power are closely related, as are receiver bandwidth, active area and sensitivity. In this article, we present a design approach that takes this device selection into account. The article details a general design method for an optical wireless communication system using a holistic design approach (i.e., considering channel, modulation schemes, and device constraints). The article shows results for particular examples, showing a substantial increase in margin (or data-rate) is available using this approach. For instance, by using this approach mutually optimising both modulation schemes and device constraints, it is found that for an optimally chosen Gallium Nitride micro-LED and a commercial photo receiver pair, a 20 dB SNR margin (or ∼3 times data-rate improvement) can be obtained compared with a more typical approach mainly concerning the modulation scheme optimisation.
Non-Isolated Three Port Converter using Modified Time Sharing Control Scheme for PV Based LVDC Application

Developments in power sector due to the increased use of renewable energy resources had a great influence in Low Voltage DC (LVDC) systems. Multiport converters aid reliable conversion of renewable energy in day-to-day applications. The non-isolated single inductor Three-Port Converter (TPC) serves as an interface for renewable energy source, storage device and load simultaneously to provide uninterruptible power supply. The paper introduces a modified time-sharing control-based power flow management system for single inductor-based boost TPC. The control scheme includes Maximum Power Point Tracking (MPPT) algorithm for PV, battery charge control and a fuzzy based control strategy for load regulation. The proposed algorithm operates the converter in three different modes, such as Dual Input (DI) mode, Dual Output (DO) mode and Single Input and Single Output (SISO) mode. Small signal model of TPC is developed to verify the viability of the work. Simulation studies of the overall system is carried out in MATLAB Simulink to check the feasibility of modified time-sharing control scheme. The obtained results promise the use of TPC in LVDC system in an efficient manner.
Prediction of Plug-in Electric Vehicle's State-of-Charge using Gradient Boosting Method and Random Forest Method

The accurate prediction of state-of-charge (SOC) of plug-in electric vehicle (PEV) is a vital factor in order to decide the mode of operation. Depending upon SOC status at the trip end of PEVs, the operator decides the charging and discharging of vehicles. Therefore, accurate prediction is the need of the demand to know the exact status of PEVs SOC. The total trip distance of PEVs and energy consumption pattern of different sizes of PEVs are also included so that calculation of SOC can be made to accurate. The prediction analysis is carried out by using machine learning approaches like gradient boosting method (GBM) and random forest method (RFM). Tree based ensemble method improves the worst performance by adding additional trees. The additional trees in GBM further improve the prediction accuracy of PEVs SOC. The application of both the machine learning approaches creates significant impact on PEVs SOC prediction. Moreover, a comparative analysis is carried out between both the approaches and results have been presented. Three case studies with different sizes are considered to analyze the results of the proposed work.
Sparse Common Feature Representation for Undersampled Face Recognition

This work investigates the problem of undersampled face recognition (i.e., insufficient training data) encountered in practical Internet-of-Things (IoT) applications. Insufficient and uncertain samples captured by IoT devices may include background and facial disguise that makes face recognition more challenging than that with sufficient and reliable images. Many models work well in face recognition on a big data set, but when training data are insufficient, they achieve unsatisfactory performance. This work proposes a novel method named sparse common feature-based representation (SCFR) that provides a unique and stable result and completely avoids very time-consuming training required by a deep learning model. Specially, it constructs a common feature dictionary using both training and test images. Thereinto, a common feature is based on a discriminative common vector and learned by a Gaussian mixture model for both training and test images in a semisupervised learninig manner, which would reduce the difference among samples in each class. In the optimization, the latent indicator of test data is initialized by the estimated label. This can avoid learning invalid information and lead to good prototype images. A new variation dictionary characterizes variables that can be shared by different classes. Finally, this work adopts minimum reconstruction residuals to recognize test images, thus bringing about a substantial improvement in SCFR’s performance. Extensive results on benchmark face databases demonstrate that the proposed method is better than the state-of-the-art methods handling undersampled face recognition.
Analysis of Single Event Effects in Capacitor-Less 1T-DRAM Based on an InGaAs Transistor

In this work, the study of the single event effects (SEEs) in capacitor-less one-transistor dynamic random access memory (1T-DRAM) based on an indium gallium arsenide on insulator (InGaAs-OI) transistor is conducted for the first time. The electrical properties of the 90-nm device are numerically simulated to determine appropriate operation condition. The successful memory behaviors are observed due to the gate-induced drain leakage (GIDL) programming method. The impacts of different linear energy transfer (LET) values, high-energy particle (HEP) strike positions and directions, and HEP hitting moments on the memory performance are investigated via simulations. Unlike the conventional SOI device, the source and drain in 1T-DRAM cells are found to be sensitive to SEEs with a higher transient current. The single event upsets (SEUs) are found to occur only when the HEP hits the device at reading “0” state and holding state before read “0. ” An increased read-out current is obtained after the HEP strike due to the GIDL- and SEE-induced accumulative floating holes. The related mechanisms are discussed in detail. These results and discussions are useful for the development of the SEE hardening of 1T-DRAM-based embedded memory for space applications in the future.