Mechanisms of Bioinformatics

The article plans to acquaint computer researchers with the new field of bioinformatics. The article gives a 10,000 foot perspective of the essential ideas in atomic cell science diagrams the way of the current information, tranquilize revelation representation and portrays the sort of PC calculations and systems that are important to comprehend cell conduct. The points secured include: portrayals of the present programming particularly produced for scholars, PC and numerical cell models, and regions of software engineering that assume a critical part in bioinformatics

Existence of Initial Dip for BCI: An Illusion or Reality

A tight coupling between the neuronal activity and the cerebral blood flow (CBF) is the motivation of many hemodynamic response (HR)-based neuroimaging modalities. The increase in neuronal activity causes the increase in CBF that is indirectly measured by HR modalities. Upon functional stimulation, the HR is mainly categorized in three durations: (i) initial dip, (ii) conventional HR (i.e., positive increase in HR caused by an increase in the CBF), and (iii) undershoot. The initial dip is a change in oxygenation prior to any subsequent increase in CBF and spatially more specific to the site of neuronal activity. Despite additional evidence from various HR modalities on the presence of initial dip in human and animal species (i.e., cat, rat, and monkey); the existence/occurrence of an initial dip in HR is still under debate. This article reviews the existence and elusive nature of the initial dip duration of HR in intrinsic signal optical imaging (ISOI), functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy (fNIRS). The advent of initial dip and its elusiveness factors in ISOI and fMRI studies are briefly discussed. Furthermore, the detection of initial dip and its role in brain-computer interface using fNIRS is examined in detail. The best possible application for the initial dip utilization and its future implications using fNIRS are provided

Kernel recursive least square tracker and long-short term memory ensemble based battery health prognostic model

Summary: A data-driven approach is developed to predict the future capacity of lithium-ion batteries (LIBs) in this work. The empirical mode decomposition (EMD), kernel recursive least square tracker (KRLST), and long short-term memory (LSTM) are used to derive the proposed approach. First, the LIB capacity data is split into local regeneration and monotonic global degradation using the EMD approach. Next, the KRLST is used to track the decomposed intrinsic mode functions, and the residual signal is predicted using the LSTM sub-model. Finally, all the predicted intrinsic mode functions and the residual are ensembled to get the future capacity. The experimental and comparative analysis validates the high accuracy (RMSE of 0.00103) of the proposed ensemble approach compared to Gaussian process regression and LSTM fused model. Furthermore, two times lesser error than other fused models makes this approach an efficient tool for battery health prognostics

Metaheuristic Optimization-Based Feature Selection for Imagery and Arithmetic Tasks: An fNIRS Study

In recent decades, the brain–computer interface (BCI) has emerged as a leading area of research. The feature selection is vital to reduce the dataset’s dimensionality, increase the computing effectiveness, and enhance the BCI’s performance. Using activity-related features leads to a high classification rate among the desired tasks. This study presents a wrapper-based metaheuristic feature selection framework for BCI applications using functional near-infrared spectroscopy (fNIRS). Here, the temporal statistical features (i.e., the mean, slope, maximum, skewness, and kurtosis) were computed from all the available channels to form a training vector. Seven metaheuristic optimization algorithms were tested for their classification performance using a k-nearest neighbor-based cost function: particle swarm optimization, cuckoo search optimization, the firefly algorithm, the bat algorithm, flower pollination optimization, whale optimization, and grey wolf optimization (GWO). The presented approach was validated based on an available online dataset of motor imagery (MI) and mental arithmetic (MA) tasks from 29 healthy subjects. The results showed that the classification accuracy was significantly improved by utilizing the features selected from the metaheuristic optimization algorithms relative to those obtained from the full set of features. All of the abovementioned metaheuristic algorithms improved the classification accuracy and reduced the feature vector size. The GWO yielded the highest average classification rates (p < 0.01) of 94.83 ± 5.5%, 92.57 ± 6.9%, and 85.66 ± 7.3% for the MA, MI, and four-class (left- and right-hand MI, MA, and baseline) tasks, respectively. The presented framework may be helpful in the training phase for selecting the appropriate features for robust fNIRS-based BCI applications

A State-Observer-Based Protection Scheme for AC Microgrids with Recurrent Neural Network Assistance

The microgrids operate in tie-up (TU) mode with the main grid normally, and operate in isolation (IN) mode without the main grid during faults. In a dynamic operational regime, protecting the microgrids is highly challenging. This article proposes a new microgrid protection scheme based on a state observer (SO) aided by a recurrent neural network (RNN). Initially, the particle filter (PF) serves as a SO to estimate the measured current/voltage signals from the corresponding bus. Then, a natural log of the difference between the estimated and measured current signal is taken to estimate the per-phase particle filter deviation (PFD). If the PFD of any single phase exceeds the preset threshold limit, the proposed scheme successfully detects and classifies the faults. Finally, the RNN is implemented on the SO-estimated voltage and current signals to retrieve the non-fundamental harmonic features, which are then utilized to compute RNN-based state observation energy (SOE). The directional attributes of the RNN-based SOE are employed for the localization of faults in a microgrid. The scheme is tested using Matlab® Simulink 2022b on an International Electrotechnical Commission (IEC) microgrid test bed. The results indicate the efficacy of the proposed method in the TU and IN operation regimes on radial, loop, and meshed networks. Furthermore, the scheme can detect both high-impedance (HI) and low-impedance (LI) faults with 99.6% of accuracy

Robust Controller for Pursuing Trajectory and Force Estimations of a Bilateral Tele-Operated Hydraulic Manipulator

In hazardous/emergency situations, public safety is of the utmost concern. In areas where human access is not possible or is restricted due to hazardous situations, a system or robot that can be distantly controlled is mandatory. There are many applications in which force cannot be applied directly while using physical sensors. Therefore, in this research, a robust controller for pursuing trajectory and force estimations while deprived of any signals or sensors for bilateral tele-operation of a hydraulic manipulator is suggested to handle these hazardous, emergency circumstances. A terminal sliding control with a sliding perturbation observer (TSMCSPO) is considered as the robust controller for a coupled leader and hydraulic follower system. The ultimate use of this controller is as a sliding perturbation observer (SPO) that can estimate the reaction force without any physical force sensors. Robust and perfect position tracking is attained with terminal sliding mode control (TSMC) in addition to control of the hydraulic follower manipulator. The force estimation and pursuing trajectory for the leader–follower system is built upon a bilateral tele-operation control approach. The difference between the reaction forces (caused by the remote environment) and the operating forces (applied by the human operator) required the involvement of an impedance model. The impedance model is implemented in the leader manipulator to provide human operators with an actual sense of the reaction force while the manipulator connects with the remote environment. A camera is used to ensure the safety of the workplace through visual feedback. The experimental results showed that the controller was robust at pursuing trajectory and force estimations for the bilateral tele-operation control of a hydraulic manipulator

Addressing Social Inequality and Improper Water Distribution in Cities: A Case Study of Karachi, Pakistan

Inhabited by almost 20 million people, Karachi, also known as the “city of lights”, houses almost 60 percent of the industries in Pakistan and is considered as the financial and industrial center of the country. The city contributes almost 12–15 percent to the gross domestic product (GDP), showing its significance in Pakistan’s economy. Unfortunately, with the increase in population, the city is facing a serious shortage of water supply. The current allocation of water among the city’s districts is not equitable, which has caused water scarcity and even riots in some areas. Surface water and ground water are the two primary sources of water supply in the city. The water supply provided by Karachi Water and Sewerage Board (KWSB) is approximately 650 million gallons per day (MGD) against a demand of 480–866 million gallons per day (MGD), resulting in a serious shortfall. Keeping a holistic view in mind, this paper focuses specifically on proposing measures to address the gap in proposing concrete solutions to manage Karachi’s increasing water woes. It also proposes a water allocation mechanism and uses Nash bargaining theory to address the inefficient and unequal water distribution. Results indicate that our suggested policies and water allocation mechanism have the potential to simultaneously resolve the supply–demand mismatch and water shortage problems of the city

Reduce-Order Modeling and Higher Order Numerical Solutions for Unsteady Flow and Heat Transfer in Boundary Layer with Internal Heating

We obtain similarity transformations to reduce a system of partial differential equations representing the unsteady fluid flow and heat transfer in a boundary layer with heat generation/absorption using Lie symmetry algebra. There exist seven Lie symmetries for this system of differential equations having three independent and three dependent variables. We use these Lie symmetries for the reduced-order modeling of the flow equations by constructing invariants corresponding to linear combinations of these Lie point symmetries. This procedure reduces one independent variable of the concerned fluid flow model when applied once. Double reductions are achieved by employing invariants twice that lead to ordinary differential equations with one independent and two dependent variables. Similarity transformations are constructed using these two sets of derived invariants corresponding to linear combinations of the Lie point symmetries. These similarity transformations have not been obtained earlier for this flow model. Moreover, the corresponding reduced systems of ordinary differential equations are different from those which exist in the literature for fluid flow and heat transfer that we have been dealing with. We obtain multiple similarity transformations which lead us to new classes of systems of ordinary differential equations. Accurate numerical solutions of these systems are obtained using the combination of an adaptive fourth-order Runge–Kutta method and shooting procedure. Effects of variation of unsteadiness parameter, Prandtl number and heat generation/absorption on fluid velocity, skin friction, surface temperature and heat flux are studied and presented with the help of tables and figures

An Optimization-Based Strategy for Solving Optimal Power Flow Problems in a Power System Integrated with Stochastic Solar and Wind Power Energy

In an effort to reduce greenhouse gas emissions, experts are looking to substitute fossil fuel energy with renewable energy for environmentally sustainable and emission free societies. This paper presents the hybridization of particle swarm optimization (PSO) with grey wolf optimization (GWO), namely a hybrid PSO-GWO algorithm for the solution of optimal power flow (OPF) problems integrated with stochastic solar photovoltaics (SPV) and wind turbines (WT) to enhance global search capabilities towards an optimal solution. A solution approach is used in which SPV and WT output powers are estimated using lognormal and Weibull probability distribution functions respectively, after simulation of 8000 Monte Carlo scenarios. The control variables include the forecast real power generation of SPV and WT, real power of thermal generators except slack-bus, and voltages of all voltage generation buses. The total generation cost of the system is considered the main objective function to be optimized, including the penalty and reserve cost for underestimation and overestimation of SPV and WT, respectively. The proposed solution approach for OPF problems is verified on the modified IEEE 30 bus test system. The performance and robustness of the proposed hybrid PSO-GWO algorithm in solving the OPF problem is assessed by comparing the results with five other metaheuristic optimization algorithms for the same test system, under the same control variables and system constraints. Simulation results confirm that the hybrid PSO-GWO algorithm performs well compared to other algorithms and shows that it can be an efficient choice for the solution of OPF problems