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Abstract Introduction Cardiovascular disease (CVD) is a major health concern worldwide, particularly in low- and middle-income countries. The COVID-19 pandemic that emerged in late 2019 may have had an impact on the trend of CVD mortality. This study aimed to investigate the trend and changes in CVD mortality rates in Malaysia, using age-standardized mortality rates (ASMR) from 2010 to 2021. Methods The Malaysian population and mortality data from 2010 to 2021 were obtained from the Department of Statistics Malaysia (DOSM). ASMRs from CVD per 100,000 population were calculated based on the World Health Organization (2000–2025) standard population using the direct method. The ASMRs were computed based on sex, age groups (including premature mortality age, 30–69 years), and CVD types. The annual percent change (APC) and average annual percent change (AAPC) of the ASMR with corresponding 95% confidence intervals (95% CI) were estimated from joinpoint regression model using the Joinpoint Regression Program, Version 4.9.1.0. Results Throughout the study period (2010–2021), ASMRs for CVD exhibited an increase from 93.1 to 147.0 per 100,000, with an AAPC of 3.6% (95% CI: 2.1 to 5.2). The substantial increase was observed between 2015 and 2018 (APC 12.6%, 95% CI: 5.4%, 20.3%), with significant changes in both sexes, and age groups 50–69, 70 years and over, and 30–69 (premature mortality age). Notably, the ASMR trend remained consistently high in the premature mortality age group across other age groups, with males experiencing higher rates than females. No significant changes were detected before or after the COVID-19 pandemic (between 2019 and 2021), except for females who died from IHD (10.3% increase) and those aged 0–4 (25.2% decrease). Conclusion Overall, our analysis highlights the persistently high burden of CVD mortality in Malaysia, particularly among the premature mortality age group. These findings underscore the importance of continued efforts to address CVD risk factors and implement effective prevention and management strategies. Further research is needed to fully understand the impact of the COVID-19 pandemic on CVD mortality rates and to inform targeted interventions to reduce the burden of CVD in Malaysia.

Background and Aims: Acute kidney injury (AKI) is a significant postoperative complication. Multiple perioperative factors are implicated in the causation of AKI in the postoperative period in patients with oesophageal cancer. The study aimed to find out the incidence, causes and effects of AKI following oesophagectomy surgery. Methods: A prospective observational study was conducted in consecutive adult patients undergoing elective oesophagectomy at a tertiary cancer care hospital. Patients with preoperative chronic renal insufficiency (serum creatinine >1.5 mg/dl), AKI in the past and a history of renal replacement therapy were excluded. Serum creatinine values were measured on postoperative days 1, 3, 5, the day of discharge or day 15 and on the day of first follow-up or day 28, following oesophagectomy surgery. The incidence of AKI was measured using the ‘Kidney Disease Improving Global Outcome’ (KDIGO) criteria. Results: The incidence of AKI was 14.7% [95% confidence interval (CI) 9.9%, 20.7%] (i.e., 27/183) in patients who underwent elective oesophagectomy. AKI was associated with prolonged hospital stay [median- 13 days (interquartile range {IQR} 11–21.5) versus 9 days (IQR 8–12), P < 0.001] and increased in-hospital mortality (14.8% versus 1.3%, P 0.004, odds ratio = 13.2, 95% CI 2.3, 77.3). After multivariate analysis, age, anastomotic leak and use of vasopressors in the postoperative period were independent predictors of AKI. Conclusion: The incidence of AKI was 14.7% after elective oesophagectomy. AKI was associated with prolonged hospital stay and in-hospital mortality. Higher age, anastomotic leak and use of vasopressors in the postoperative period were independent predictors of AKI.

BackgroundIn 2008-2010, Malaysia experienced a nationwide chikungunya virus (CHIKV) outbreak caused by the Indian Ocean lineage E1-226V (valine) variant, adapted to Aedes albopictus. In 2017-2022, transition to an E1-226A (alanine) variant occurred. Ae. albopictus prevails in rural areas, where most cases occurred during the E1-226V outbreak, while Ae. aegypti dominates urban areas. The shift in circulating CHIKV variants from E1-226V to E1-226A (2009-2022) was hypothesized to result in a transition from rural to urban CHIKV distribution, driven by differences in Ae. aegypti vector competence for the two variants. This study aimed to: (1) map the spatiotemporal spread of CHIKV cases in Malaysia between 2009-2022; and (2) compare replication of E1-226A and E1-226V variants in the midguts and head/thoraxes of Ae. aegypti.Methodology/principal findingsSpatiotemporal analysis of national notified CHIKV case addresses was performed. Between 2009-2022, 12,446 CHIKV cases were reported, with peaks in 2009 and 2020, and a significant shift from predominantly rural cases in 2009-2011 (85.1% rural), to urban areas in 2017-2022 (86.1% urban; pConclusions/significanceThis study revealed that a change in circulating CHIKV variants can be associated with changes in vector competence and outbreak epidemiology. Continued genomic surveillance of arboviruses is important.

In recent years, the power sector has shifted to decentralized power generation, exemplified by microgrids that combine renewable and traditional power sources. With the introduction of renewable energy resources and distributed generators, novel strategies are required to improve reliability and quality of power (PQ). In our proposed system, a model consisting of photovoltaics, wind energy, and fuel cells has been designed to share a network, bolstered by the integration of UPQC to rectify PQ issues. Notably, our model introduces a Back-stepping controller method featuring Model Reference Adaptive Control (MRAC) with online parameter tuning, offering superior adaptability and responsiveness. This approach not only ensures optimal grid management but also enhances efficiency and stability. Furthermore, the proposed model demands minimal additional infrastructure, leveraging existing resources to streamline implementation and maintenance, thereby promoting sustainability and cost-effectiveness. The research culminates in a comparative analysis between the MRAC-Back-stepping controller, Adaptive Neuro-Fuzzy Inference System (ANFIS), and Fuzzy controller, highlighting the efficacy and versatility of our proposed model in microgrid operations. A Matlab model has been designed along with a hardware setup to demonstrate the robustness of the model.

Voltage and reactive power regulation in a deregulated microgrid can be achieved by strategically placing the Static Synchronous Compensator (STATCOM) in coordination with other renewable energy sources, thus ensuring high-end stability and independent control. STATCOM plays a crucial role in effectively addressing power quality issues such as voltage fluctuation and reactive power imbalances caused by the intermittent nature of wind energy conversion systems. To successfully integrate STATCOM into the existing system, it is essential that the control system employed for STATCOM coordination aligns with the Doubly-Fed Induction Generator (DFIG) controller within the microgrid. Therefore, an efficient control algorithm is required in the microgrid, capable of coordinating with the DFIG controller while maintaining system stability. The utilization of a Genetic Algorithm (GA) in calibrating the Restricted Boltzmannn Machine (RBM) can streamline the process of determining optimal hyperparameters for specific tasks, eliminating the need for computationally intensive and time-consuming grid searches or manual tuning. This approach is particularly advantageous when dealing with large datasets within short time durations. In this research, a Simulink model comprising a DFIG-based microgrid and STATCOM has been developed to demonstrate the effectiveness of the proposed control system using RBM in managing STATCOM and facilitating microgrid operations.

Many inversion codes in marine controlled-source electromagnetic (CSEM) inverse problems use numerical partial differential equation (PDE) solvers as the forward solutions modelers. These codes perform well only when handling limited observations, as solving the governing equations in the forward modeling is very challenging, and quantifying uncertainties is not the strength of these solvers. A Gaussian process (GP)-based inversion was proposed to allow for greater flexibility in modeling numerous forward solutions by calibrating the stochastic process with computer experiment responses to estimate hydrocarbon parameters (i.e., depth and electrical resistivity) in marine CSEM application. Here, GP was used to evaluate electromagnetic (EM) responses at untried input specifications with uncertainty quantification, and gradient decent was employed for optimization in the inversion algorithm. The developed forward and inverse models were assessed against the true values through computing deviations. Hypothesis testing was also conducted to quantify the uncertainty of the estimation function as per its corresponding estimator. This research also compared the predictive performance of the proposed inversion methodology and existing framework. The calibration enabled GP to provide numerous reliable EM responses for the inversion. All the EM responses significantly fell within the GP predictive variance at $\alpha =0.05$ . The resulting deviations and Bland-Altman plots demonstrated that GP-based inversion was capable of efficiently estimating the hydrocarbon input parameters in the marine CSEM inverse problems.

Abstract This study attempts to accelerate the learning ability of an artificial electric field algorithm (AEFA) by attributing it with two mechanisms: elitism and opposition-based learning. Elitism advances the convergence of the AEFA towards global optima by retaining the fine-tuned solutions obtained thus far, and opposition-based learning helps enhance its exploration ability. The new version of the AEFA, called elitist opposition leaning-based AEFA (EOAEFA), retains the properties of the basic AEFA while taking advantage of both elitism and opposition-based learning. Hence, the improved version attempts to reach optimum solutions by enabling the diversification of solutions with guaranteed convergence. Higher-order neural networks (HONNs) have single-layer adjustable parameters, fast learning, a robust fault tolerance, and good approximation ability compared with multilayer neural networks. They consider a higher order of input signals, increased the dimensionality of inputs through functional expansion and could thus discriminate between them. However, determining the number of expansion units in HONNs along with their associated parameters (i.e., weight and threshold) is a bottleneck in the design of such networks. Here, we used EOAEFA to design two HONNs, namely, a pi-sigma neural network and a functional link artificial neural network, called EOAEFA-PSNN and EOAEFA-FLN, respectively, in a fully automated manner. The proposed models were evaluated on financial time-series datasets, focusing on predicting four closing prices, four exchange rates, and three energy prices. Experiments, comparative studies, and statistical tests were conducted to establish the efficacy of the proposed approach.

The present review focuses on the theoretical model developments made in floating flexible net fish cages and the floating bodies application to offshore aquaculture. A brief discussion of the essential mathematical equations related to various theoretical models of flexible net cages in the frequency domain is presented. The single and array of floating or submerged flexible net cages connected with or without mooring lines are discussed. Further, as the combined effect of the hydroelastic behaviour of floaters and the flexible behaviour of fish cages are necessary to assess their efficiency and survivability from structural damages, the issues and the knowledge gap between the recent and future models are also discussed. In conclusion, the practical suggestions concerning advancements in future research and directions within floating flexible net cages and the hydroelastic response of elastic floaters are highlighted.

A 3D analytical model is formulated based on linearised small-amplitude wave theory to analyse the behaviour of a horizontal, flexible membrane subject to wave–current interaction. The membrane is connected to spring moorings for stability. Green’s function approach is used to obtain the dispersion relation and is utilised in the solution by applying the velocity decomposition method. On the other hand, a brief description of the experiment is presented. The accuracy level of the analytical results is checked by comparing the results of reflection and the transmission coefficients against experimental data sets. Several numerical results on the displacements of the membrane and the vertical forces are studied thoroughly to examine the impact of current loads, spring stiffness, membrane tension, modes of oscillations, and water depths. It is observed that as the value of the current speed (CS) rises, the deflection also increases, whereas it declines in deeper water. On the other hand, the spring stiffness has minimal effect on the vibrations of the flexible membrane. When vertical force is considered, higher oscillation modes increase the vertical loads on the membrane, and for a mid-range wavelength, the vertical wave loads on the membrane grow as the CS increases. Further, the influence of the phase and group velocities are presented. The influences of CS and comparisons between them in terms of water depth are presented and analysed. This analysis will inform the design of membrane-based wave energy converters and breakwaters by clarifying how current loads affect the dynamics of floating membranes at various water depths.

Compressive strength (CS) has been considered as the utmost critical parameter while designing concrete structures. Usually, it is determined through laboratory tests, which are expensive, time consuming, and requires consumptions of materials. Therefore, correct prediction of CS before actual placement of concrete is highly desirable. The relationship among the constituent materials that forms concrete structures is highly nonlinear and necessitates application of intelligent methods. Though a few such methods like artificial neural network (ANN) based models are available in the literature; their performance in the context is limited to certain extent and they have their own merits and demerits. Hence, to address some of the limitations of ANN (non-higher order Neural Network) based models, this contribution proposed a hybrid model in which a flat network i.e., a type of higher order neural network- functional link artificial neural network (FLANN) is used as the base structure and genetic algorithm (GA) is employed to find out the optimal FLANN parameters (i.e., GA + FLANN). The training process comprises selection of connection weights, bias, as well as optimal number of basis functions of FLANN by GA rather fixing them earlier. Thus, an optimal FLANN is crafted on fly from exploitation of training data. The proposed model is used to assess the CS of concrete cements from datasets available in the literature considering samples of curing age at 3, 7, 14, 28, 56, and 91 days. A rolling window is used for input selection and five evaluation metrics are used for performance evaluation. On an average, the GA + FLANN obtained 0.477052 MAPE (mean absolute percentage of error), 0.598067 ARV (average relative variance), 0.593862 UT (U of Theil’s statistic), 0.551452 NMSE (normalized mean squared error), and 0.206135 SD (standard deviation) values which are the lowest compared to others. The superiority of the model is established through comparative studies and statistical significance tests.

Abstract Background A survey was executed to estimate the extent of entomophagy practiced by the tribal population in Nagaland. From the survey, two consumed stink bugs, namely Tessaratoma javanica and Coridius singhalanus, were assessed. Nutrient contents were assessed following the AOAC guidelines and elements were quantised using ICP-OES. Results About 55% of the people surveyed consumed T. javanica, and 49% were consumers of C. singhalanus. The survey also indicated that the non-consumer respondents did not have any aversion towards the consumption of these insects. Biochemical analysis revealed that except for carbohydrate and moisture contents, all the other evaluated parameters (ash, crude fats, crude protein, and fibre content) were found to be comparatively higher in C. singhalanus, although the difference was significant (p ≤ 0.05) only for crude fat, carbohydrate, and fibre content. Trace elements analysis revealed that calcium (107.74 ± 0.213 mg/100 g), iron (22.17 ± 0.080 mg/100 g), and sodium (120.34 ± 0.214 mg/100 g) contents were significantly higher in T. javanica, whereas magnesium (76.06 ± 0.166 mg/100 g) content was significantly higher in C. singhalanus. Conclusions Both the bugs show good nutritive contents and are liked for their distinctive smell and flavour.

Cryptocurrencies have carved out a significant presence in financial transactions during the past few years. Cryptocurrency market performs similarly to other financial markets with considerable nonlinearity and volatility and its prediction is a growing research area. It is challenging to capture the inherent uncertainties connected with cryptocurrency using the currently used conventional methodologies. The popularity of random vector functional link networks (RVFLN) is attributed to its simple structural layout, quick rate of learning, and enhanced generalization ability. It computes the output layer weights using non-iterative techniques like least square methods or iterative techniques like gradient methods, and assigns hidden neuron parameters at random. Random initialization of non-optimal hidden neuron settings, however, degrades the performance. Population-based metaheuristics are a superior option to random initialization for determining the ideal parameters and avoiding the problem of local optima stagnation. In the current article, an elitist artificial electric field algorithm (eAEFA) for training RVFLN is proposed. Here, eAEFA is utilized to create an ideal RVFLN by determining the weights and biases of the hidden layer connections. The elitism method is used by AEFA to maximize its strength. Here, the most suitable entities are directly inserted to create the population of the following generation. By predicting the closing values of six widely used cryptocurrencies, including Bitcoin, Litecoin, Ethereum, ZEC, XLM, and Ripple, one may determine how well the eAEFA+RVFLN model is performing. For comparison study, models including ARIMA, multi-layer perceptron (MLP), basic RVFLN, support vector regression (SVR), LSTM, GA trained RVFLN, and AEFA trained RVFLN are also constructed concurrently. In terms of performance and statistical significance testing, the suggested eAEFA+RVFLN findings outperform the comparator models. On an average, it achieves a MAPE (mean absolute percentage of error) value of 0.0573, R2 (coefficient of determination) of 0.9589, POCID (prediction of change in direction) of 0.9676, RMSE (root mean squared error) of 0.0685, MAE (mean absolute error) of 0.0727 and an average rank of 1.346; as a result, it is possible to recommend it as a useful financial forecasting tool.

The present article addresses various novel and advanced approaches to mitigate frequency oscillation issues in an integrated power system. The suggested model of the multi-source power system is basically a two-area power system, where area2 is completely a microgrid system. The diesel generator, thermo-electric generator, wind generator, solar PV system, micro-turbine and fuel cell are the basic power generating sources in the microgrid system. The microgrid also has been associated with various energy saving equipments like battery and flywheel energy storage (FES) system to improve the power quality of the system. The unpredictable wind speed variant and fluctuation in solar power incorporates oscillations in voltage and frequency profile of the interconnected power system. In challenging to above issues, this research article has designed an innovative Fractional Order-type-2 fuzzy controller (FO-T2FC) to supress unwanted errors in the frequency and voltage of the system. Further, the proposed controller parameters are selected optimally with suggesting a novel Quassi- Oppositional Path Finder Algorithm (QO-PFA) under different operatings. An analysis on relative study is exercised to validate strength of the FO-T2FC control strategy and QO-PFA algorithm. Owing to the performance analysis, it is justified that proposed QO-PFA designed FO-T2FC controller is proved to be most potential candidate and able to improve the dynamic frequency response of area1 (Δf1) by 251.74% and 418.74% as compared to Type1 fuzzy controller and conventional PID controller respectively.

Currently, the world of material requires intensive research to discover a new-class of materials those posses the properties like lower in weight, greater in strength and better in mechanical properties. This led to the study of light and strong alloys or composites. This study focuses to produce current novel aluminium composite with an appreciable density, good machinable characteristics, less corrosive, high strength, light weight and low manufacturing cost product. In this research, an aluminium metal matrix composites (AMMC) (Al-0.5Si-0.5Mg-2.5Cu-15SiC) was developed using the metallurgical powdered method and subjected to the investigation of erosion wear characteristics. Here the solid particle erosion test was conducted on AMMC samples. The article presents, the design of Taguchi experiments and statistical techniques of erosion wear characteristics and the behaviors of the composite. The rate of erosion wear found to decrease with increasing impact angle, regardless of the rate of impact. With higher impact velocity erosion rate increases but decreases with stand of distance.

Death of dopaminergic (DAergic) neurons in the substantia nigra pars compacta of the human brain is the characteristic pathological feature of Parkinson’s disease (PD). On exposure to neurotoxicants, Drosophila too exhibits mobility defects and diminished levels of brain dopamine. In the fly model of sporadic PD, our laboratory has demonstrated that there is no loss of DAergic neuronal number, however, a significant reduction in fluorescence intensity (FI) of secondary antibodies that target the primary antibody-anti-tyrosine hydroxylase (TH). Here, we present a sensitive, economical, and repeatable assay to characterize neurodegeneration based on the quantification of FI of the secondary antibody. As the intensity of fluorescence correlates with the amount of TH synthesis, its reduction under PD conditions denotes the depletion in the TH synthesis, suggesting DAergic neuronal dysfunction. Reduction in TH protein synthesis is further confirmed through Bio-Rad Stain-Free Western Blotting. Quantification of brain DA and its metabolites (DOPAC and HVA) using HPLC-ECD further demonstrated the depleted DA level and altered DA metabolism as evident from enhanced DA turnover rate. Together all these PD marker studies suggest that FI quantification is a refined and sensitive method to understand the early stages of DAergic neurodegeneration. FI quantification is performed using ZEN 2012 SP2, a licensed software from Carl Zeiss, Germany. This method will be of good use to biologists, as it with few modifications, can also be implemented to characterize the extent of degeneration of different cell types. Unlike the expensive and cumbersome confocal microscopy, the present method using fluorescence microscopy will be a feasible option for fund-constrained neurobiology laboratories in developing countries.

A hydroelastic model associated with the interaction between a surface wave and a floating circular structure connected with mooring lines in finite water depth is developed using BIEM. The BIEM solution is achieved using free surface Green’s function and Green’s theorem. Furthermore, the algebraic equations for circular structural displacement are derived from the integro-differential equation. The correctness of the BIEM code is verified with the results of shear force and deflection amplitude existing in the literature, and the hydroelastic response of the circular structure is analyzed. The comparison results show a good level of agreement between the present results and those from other calculations. It is observed that the shear force, bending moment, and deflection decrease for higher values of stiffness of the mooring lines. The current study may be supportive of the visualization of the effect of mooring stiffness and to generalize articulated circular structure models for ocean space utilization.

Abstract This paper aims to develop an automated web application to generate validated daily effective reproduction numbers (Rt) which can be used to examine the effects of super-spreading events due to mass gatherings and the effectiveness of the various Movement Control Order (MCO) stringency levels on the outbreak progression of COVID-19 in Malaysia. The effective reproduction number, Rt, was estimated by adopting and modifying an Rt estimation algorithm using a validated distribution mean of 3.96 and standard deviation of 4.75 with a seven-day sliding window. The Rt values generated were validated using thea moving window SEIR model with a negative binomial likelihood fitted using methods from the Bayesian inferential framework. A Pearson’s correlation between the Rt values estimated by the algorithm and the SEIR model was r = 0.70, p

Sexual dysfunction (SD) is one of the most common non-motor symptoms of Parkinson’s disease (PD) and remains the most neglected, under-reported, and under-recognized aspect of PD. Studies have shown that Dopamine (DA) in the hypothalamus plays a role in regulating sexual behavior. But the detailed mechanism of SD in PD is not known. Drosophila melanogaster shares several genes and signaling pathways with humans which makes it an ideal model for the study of a neurodegenerative disorder such as PD. Courtship behavior of Drosophila is one such behavior that is closely related to human sexual behavior and so plays an important role in understanding sexual behavior in diseased conditions as well. In the present study, a sporadic SD model of PD using Drosophila was developed and SD phenotype was observed based on abnormalities in courtship behavior markers. The Drosophila SD model was developed in such a way that at the window of neurotoxin paraquat (PQ) treatment [PQ is considered a crucial risk factor for PD due to its structural similarity with 1-methyl-4-phenyl pyridinium (MPP+), the active form of PD-inducing agent, 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)], it does not exhibit mobility defects but shows SD. The whole brain tyrosine hydroxylase immunostaining showed no observable dopaminergic (DAergic) degeneration (number of DA neurons and fluorescence intensity of fluorescently labeled secondary antibodies that target anti-TH primary antibody) of the SD model. Similarly, there was no significant depletion of brain DA and its metabolite levels (HVA and DOPAC) as determined using HPLC-ECD (High-Performance Liquid Chromatography using Electrochemical Detector). The present study illustrates that the traits associated with courtship and sexual activity provide sensitive markers at the earlier stage of PD onset. This PQ-induced SD fly model throws an opportunity to decipher the molecular basis of SD under PD conditions and to screen nutraceuticals/potential therapeutic molecules to rescue SD phenotype and further to DAergic neuroprotection.

Chronic kidney disease (CKD) which is characterized by progressive loss of renal function and renal fibrosis is a worldwide public health problem. Hepatocyte growth factor (HGF) is a polypeptide that exhibits multiple functions including antifibrotic effects on kidneys. The present study was aimed at evaluating HGF levels and studying its association with markers of inflammation and oxidative stress in patients of predialysis and dialysis CKD. A total of 80 subjects including 20 healthy controls, 40 patients of CKD stage 1 to stage 5 (predialysis), and 20 CKD patients with end-stage renal disease on hemodialysis were recruited. HGF, high-sensitivity C-reactive protein (hsCRP), malondialdehyde (MDA), and ferric reducing ability of plasma (FRAP) were measured in all the subjects. HGF levels were significantly higher in all patients with CKD compared to controls. The levels were found to be lower in patients on dialysis than in the predialysis group; however, the difference was not statistically significant. hsCRP, MDA, and FRAP were significantly higher in all patients with CKD than in controls. HGF levels did not show a significant correlation with the markers studied. HGF levels were increased in response to renal injury in CKD patients. The levels were higher in predialysis patients of CKD than in CKD patients on dialysis. HGF levels may be used as an indicator of renal fibrosis in patients with CKD.

Chemical reaction optimization (CRO) algorithm is a robust metaheuristic by simulating the process of natural chemical reaction and has been applied for solving numerous realistic problems. However, it is suffering from low population diversity, slow convergence, and deprived from local search ability. To boost the search operation of CRO, this article proposes an improved CRO method termed as ICRO. We achieve the improvements in two phases. First, to start the search operation with a better-quality population, we propose a new method to initialize population which helps in enhancing diversification of the search space. Second, to intensify the local search ability we integrate Nelder-Mead simplex method with CRO. The ICRO is then evaluated on a few benchmark functions for optimization and found apposite. Dendritic neuron model (DNM) using additive and multiplicative-based aggregation functions has been emerging as a machine learning approach and found successful in many engineering applications. This study attempts to advance the predictive accuracy of DNM through maintaining a decent steadiness between exploitation and exploration of its search space with the proposed ICRO, termed as ICRO-DNM. The powerful global search ability of ICRO synergies with better approximation capability of DNM thus, able to overcome the limitations of conventional back propagation learning based DNM. ICRO-DNM is evaluated on closing price prediction for four stock index and net asset values of four mutual fund datasets and found efficient. The consequences of this attempt are: 1) Proposed ICRO shows robust parameter optimization ability for benchmark functions and DNM compared to basic CRO, particle swarm optimization (PSO), and genetic algorithm (GA) and 2) The learning paradigm formed due to reasonable amalgamation of ICRO and DNM is pretty able to capture the underlying uncertainties coupled with financial data, produces more precise and steady predictions and significantly different from other forecasts.

Production of clean, green solar PV (SPV) power in developing countries now becomes a trend because of their economic and technical benefits. Therefore, generating maximum power out of the SPV is a key searchable area. The SPV must produce power at its terminal at their maximum possible power. To reach to the maximum possible power, maximum power point tracker(MPPT) is used in conjunction with SPV. Extracting maximum power from SPV under varying partial shading condition is one of the important factor in performance improvement of SPV. The characteristics of classical MPPT controller is not acceptable under variable shading condition. A clear distinction between global maxima power point from global minima using MPPT technique must be needed for extracting maximum power. This paper proposes a P&O MPPT based particle swarm optimization with improved search space, optimised through Fuzzy Fokker Planck solution. The pre-defined search space has been introduced to provide fine tune to membership function used in Fuzzy logic controller. The partial shading performance has been examined under four different condition such as active partial shading, colour spectrum, dust level and green house gas (GHG) concentration. Both hardware and simulation studies has been carried out for the proposed techniques. The MATLAB simulation result and that of proposed MPPT, offer more and better performance in terms of algorithm convergence by enhancing the efficiency of system under varying shading condition.

The present study aims at comparing the life cycle environmental impacts of polyunsaturated fatty acids production (PUFA) from microalgae and farmed fish. PUFA production from microalgae cultivated via heterotrophy and photoautotrophy was assessed and compared. The primary energy demand (PED) and environmental impacts (EI) of PUFA production from microalgae via heterotrophy were significantly lower compared to PUFA produced via photoautotrophy. Furthermore, PED and EI of PUFA production from fish farmed in marine net pens were assessed. The results indicated that the PED and EI of PUFA production from farmed fish are higher than that produced from microalgae cultivated via heterotrophy. Therefore, the results suggest that PUFA produced from microalgae via heterotrophy could substitute fish oil from an environmental perspective. Furthermore, life cycle analysis results indicate that PUFA derived from microalgae could potentially replace fish oil in the fish feed, thus reducing the pressure on oceans.

Parkinson’s disease (PD) affects almost 1% of the population worldwide over the age of 50 years. Exposure to environmental toxins like paraquat and rotenone is a risk factor for sporadic PD which constitutes 95% of total cases. Herbicide rotenone has been shown to cause Parkinsonian symptoms in multiple animal models. Drosophila is an excellent model organism for studying neurodegenerative diseases (NDD) including PD. The aging process is characterized by differential expression of genes during different life stages. Hence it is necessary to develop life-stage-matched animal models for late-onset human disease(s) such as PD. Such animal models are critical for understanding the pathophysiology of age-related disease progression and important to understand if a genotropic drug/nutraceutical can be effective during late stages. With this idea, we developed an adult life stage-specific (health and transition phase, during which late-onset NDDs such as PD sets in) rotenone-mediated Drosophila model of idiopathic PD. Drosophila is susceptible to rotenone in dose-time dependent manner. Rotenone-mediated fly model of sporadic PD exhibits mobility defects (independent of mortality), inhibited mitochondrial complex I activity, dopaminergic (DAergic) neuronal dysfunction (no loss of DAergic neuronal number; however, reduction in rate-limiting enzyme tyrosine hydroxylase (TH) synthesis), and alteration in levels of dopamine (DA) and its metabolites; 3,4-Dihydroxyphenylacetic acid (DOPAC) and Homovanilic acid (HVA) in brain-specific fashion. These PD-linked behaviors and brain-specific phenotypes denote the robustness of the present fly model of PD. This novel model will be of great help to decipher life stage-specific genetic targets of small molecule mediated DAergic neuroprotection; understanding of which is critical for formulating therapeutic strategies for PD.

Abstract The state of Selangor, in Malaysia consist of urban and peri-urban centres with good transportation system, and suitable temperature levels with high precipitations and humidity which make the state ideal for high number of dengue cases, annually. This study investigates if districts within the Selangor state do influence each other in determining pattern of dengue cases. Study compares two different models; the Autoregressive Integrated Moving Average (ARIMA) and Ensemble ARIMA models, using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) measurement to gauge their performance tools. ARIMA model is developed using the epidemiological data of dengue cases, whereas ensemble ARIMA incorporates the neighbouring regions’ dengue models as the exogenous variable (X), into traditional ARIMA model. Ensemble ARIMA models have better model fit compared to the basic ARIMA models by incorporating neighbuoring effects of seven districts which made of state of Selangor. The AIC and BIC values of ensemble ARIMA models to be smaller compared to traditional ARIMA counterpart models. Thus, study concludes that pattern of dengue cases for a district is subject to spatial effects of its neighbouring districts and number of dengue cases in the surrounding areas.

Abstract The susceptible-infectious-removed (SIR) model offers the simplest framework to study transmission dynamics of COVID-19, however, it does not factor in its early depleting trend observed during a lockdown. We modified the SIR model to specifically simulate the early depleting transmission dynamics of COVID-19 to better predict its temporal trend in Malaysia. The classical SIR model was fitted to observed total (I total), active (I) and removed (R) cases of COVID-19 before lockdown to estimate the basic reproduction number. Next, the model was modified with a partial time-varying force of infection, given by a proportionally depleting transmission coefficient, $$\beta_{t}$$ β t and a fractional term, z. The modified SIR model was then fitted to observed data over 6 weeks during the lockdown. Model fitting and projection were validated using the mean absolute percent error (MAPE). The transmission dynamics of COVID-19 was interrupted immediately by the lockdown. The modified SIR model projected the depleting temporal trends with lowest MAPE for I total, followed by I, I daily and R. During lockdown, the dynamics of COVID-19 depleted at a rate of 4.7% each day with a decreased capacity of 40%. For 7-day and 14-day projections, the modified SIR model accurately predicted I total, I and R. The depleting transmission dynamics for COVID-19 during lockdown can be accurately captured by time-varying SIR model. Projection generated based on observed data is useful for future planning and control of COVID-19.

Abstract Extreme learning machine (ELM) allows for fast learning and better generalization performance than conventional gradient-based learning. However, the possible inclusion of non-optimal weight and bias due to random selection and the need for more hidden neurons adversely influence network usability. Further, choosing the optimal number of hidden nodes for a network usually requires intensive human intervention, which may lead to an ill-conditioned situation. In this context, chemical reaction optimization (CRO) is a meta-heuristic paradigm with increased success in a large number of application areas. It is characterized by faster convergence capability and requires fewer tunable parameters. This study develops a learning framework combining the advantages of ELM and CRO, called extreme learning with chemical reaction optimization (ELCRO). ELCRO simultaneously optimizes the weight and bias vector and number of hidden neurons of a single layer feed-forward neural network without compromising prediction accuracy. We evaluate its performance by predicting the daily volatility and closing prices of BSE indices. Additionally, its performance is compared with three other similarly developed models—ELM based on particle swarm optimization, genetic algorithm, and gradient descent—and find the performance of the proposed algorithm superior. Wilcoxon signed-rank and Diebold–Mariano tests are then conducted to verify the statistical significance of the proposed model. Hence, this model can be used as a promising tool for financial forecasting.

Energy systems around the world are going through tremendous transformations, mainly driven by carbon footprint reductions and related policy imperatives and low-carbon technological development. These transformations pose unprecedented technical challenges to the energy sector, but they also bring opportunities for energy systems to develop, adapt, and evolve. With rising complexity and increased digitalization, there has been significant growth in the amount of data in the power/energy sector (data ranging from power grid to household levels). Utilization of this large data (or “big data”), along with the use of proper data analytics, will allow for useful insights to be drawn that will help energy systems to deliver an increased amount of technical, operational, economic, and environmental benefits. This paper reviews various categories of data available in the current and future energy systems and the potential benefits of utilizing those data categories in energy system planning and operation. This paper also discusses the Big Data Analytics (BDA) that can be used to process/analyze the data and extract useful information that can be integrated and used in energy systems. More specifically, this paper discusses typical applications of BDA in energy systems, including how BDA can be used to resolve the critical issues faced by the current and future energy network operations and how BDA contributes to the development of smarter and more flexible energy systems. Combining data characterization and analysis methods, this review paper presents BDA as a powerful tool for making electrical energy systems smarter, more responsive, and more resilient to changes in operations.

An analytical model of a current load’s interaction with a moored floating flexible structure based on the Timoshenko–Mindlin beam theory is developed under the assumption of small-amplitude wave theory and the structural response. Theoretical solutions on the displacement of the structure, reflection, and transmission coefficients are obtained by applying the matching technique along with the orthogonal model coupling relation. The results of the transmission coefficient and displacement amplitude are compared with the other calculations and experimental datasets available in the literature. The structural deflection and transmission coefficients are investigated via the hydroelastic response for wave–current loads along with design parameters. The comparison results showed that the present model result is supported by the numerical model’s results. This present analysis can provide further information for marine engineers to design floating flexible platforms in the marine environment.

COVID-19 caused devastating effects of human loss and suffering along with disruption in clinical research, forcing reconceptualization and modification of studies. This paper attempts to outline the steps followed and detail the modifications undertaken to deal with the impacts of the pandemic on the first ongoing randomized controlled trial on effectiveness of neuropsychological rehabilitation in adult patients with drug-resistant epilepsy in India. All modifications were based on evolving guidelines and circumstantial context and were planned, reviewed and approved by important stakeholders. Results obtained from the trial need to be interpreted and analysed within this context. These modifications have implications for wider outreach of neuropsychology services in India.

Objective: Hypertension is the most important risk factor for cardiovascular morbidity and mortality. There is limited data on hypertension prevalence in India. This study was conducted to estimate the prevalence of hypertension among Indian adults. Methods: A national level survey was conducted with fixed one-day blood pressure measurement camps across 24 states and union territories of India. Hypertension was defined as systolic blood pressure (BP) ≥140 mmHg or a diastolic BP ≥90 mmHg or on treatment for hypertension. The prevalence was age- and gender-standardized according to the 2011 census population of India. Results: Blood pressure was recorded for 180,335 participants (33.2% women; mean age 40.6 ± 14.9 years). Among them, 8,898 (4.9%), 99,791 (55.3%), 35,694 (11.9%), 23,084 (12.8%), 9,989 (5.5%), and 2,878 (1.6%) participants were of the age group 18–19, 20–44, 45–54, 55–64, 65–74, and ≥ 75 years, respectively. Overall prevalence of hypertension was 30.7% (95% confidence interval [CI]: 30.5, 30.9) and the prevalence among women was 23.7% (95% CI: 23.3, 24). Prevalence adjusted for 2011 census population and the WHO reference population was 29.7% and 32.8%, respectively. Conclusion: There is a high prevalence of hypertension, with almost one in every three Indian adult affected. Keywords: Hypertension, India, Prevalence

Introduction: Optimizing patient safety and quality improvement is increasingly important in surgery. Benchmarks and clinical quality registries are being developed to assess the best achievable results for several surgical procedures and reduce unwarranted variation between different centers. However, there is no clinical database from international centers for establishing standardized reference values of patients undergoing surgery for mesial temporal lobe epilepsy.Design: The Enhancing Safety in Epilepsy Surgery (EASINESS) study is a retrospectively conducted, multicenter, open registry. All patients undergoing mesial temporal lobe epilepsy surgery in participating centers between January 2015 and December 2019 are included in this study. The patient characteristics, preoperative diagnostic tools, surgical data, postoperative complications, and long-term seizure outcomes are recorded.Outcomes: The collected data will be used for establishing standardized reference values (“benchmarks”) for this type of surgical procedure. The primary endpoints include seizure outcomes according to the International League Against Epilepsy (ILAE) classification and defined postoperative complications.Discussion: The EASINESS will define robust and standardized outcome references after amygdalohippocampectomy for temporal lobe epilepsy. After the successful definition of benchmarks from an international cohort of renowned centers, these data will serve as reference values for the evaluation of novel surgical techniques and comparisons among centers for future clinical trials.Clinical trial registration: This study is indexed at clinicaltrials.gov (NT 04952298).

The aim of the Special Issue (SI) was to publish original and high-quality research papers that deal with model developments based on the mathematical, numerical, and experimental models associated with flexible floating or submerged structure applications in the discipline of marine engineering [...]

The authors developed a nanocomposite using polypropylene (PP) and graphene nanoplatelets (GNPs) with a melt mixing method. Virgin PP was filled with three sets of GNPs with a fixed thickness (15 nm) and surface area (50–80 m2/g). The selected H-type GNPs had three different sizes of 5, 15 and 25 µm. The nanocomposites were made by loading GNPs at 1, 2 and 3 wt.%. Mechanical analysis was carried out by performing tensile, flexural and impact strength tests. The crystalline, micro-structural, thermal and dynamic mechanical properties were assessed through XRD, FESEM, PLM, DSC, TGA and DMA tests. It was observed that all three types of GNPs boosted the mechanical strength of the polymer composite. Increasing the nanofiller size decreased the tensile strength and the tensile modulus, increased the flexural strength and flexural modulus, and increased the impact strength. Maximum tensile strength (≈41.18 MPa) resulted for the composite consisting 3 wt.% H5, whereas maximum flexural (≈50.931 MPa) and impact (≈42.88 J/m) strengths were observed for nanocomposite holding 3 wt.% H25. Graphene induced the PP’s crystalline phases and structure. An improvement in thermal stability was seen based on the results of onset degradation (TD) and melting (Tm) temperatures. Graphene increased the crystallization (Tc) temperatures, and acted like a nucleating agent. The experimental analysis indicated that the lateral size of graphene plays an important role for the nanocomposite’s homogeneity. It was noted that the small-sized GNPs improved dispersion and decreased agglomeration. Thus overall, small-sized GNPs are preferable, and increasing the lateral size hardly establishes feasible characteristics in the nanocomposite.

OBJECTIVES Starting in March 2020, movement control measures were instituted across several phases in Malaysia to break the chain of transmission of coronavirus disease 2019 (COVID-19). In this study, we developed a susceptible-exposed-infected-recovered (SEIR) model to examine the effects of the various phases of movement control measures on disease transmissibility and the trend of cases during the third wave of the COVID-19 pandemic in Malaysia. METHODS Three SEIR models were developed using the R programming software ODIN interface based on COVID-19 case data from September 1, 2020, to March 29, 2021. The models were validated and subsequently used to provide forecasts of daily cases from October 14, 2020, to March 29, 2021, based on 3 phases of movement control measures. RESULTS We found that the reproduction rate (R-value) of COVID-19 decreased by 59.1% from an initial high of 2.2 during the nationwide Recovery Movement Control Order (RMCO) to 0.9 during the Movement Control Order (MCO) and Conditional MCO (CMCO) phases. In addition, the observed cumulative and daily highest numbers of cases were much lower than the forecasted cumulative and daily highest numbers of cases (by 64.4-98.9% and 68.8-99.8%, respectively). CONCLUSIONS The movement control measures progressively reduced the R-value during the COVID-19 pandemic. In addition, more stringent movement control measures such as the MCO and CMCO were effective for further lowering the R-value and case numbers during the third wave of the COVID-19 pandemic in Malaysia due to their higher stringency than the nationwide RMCO.

Abstract Accurate prediction of stock market behavior is a challenging issue for financial forecasting. Artificial neural networks, such as multilayer perceptron have been established as better approximation and classification models for this domain. This study proposes a chemical reaction optimization (CRO) based neuro-fuzzy network model for prediction of stock indices. The input vectors to the model are fuzzified by applying a Gaussian membership function, and each input is associated with a degree of membership to different classes. A multilayer perceptron with one hidden layer is used as the base model and CRO is used to the optimal weights and biases of this model. CRO was chosen because it requires fewer control parameters and has a faster convergence rate. Five statistical parameters are used to evaluate the performance of the model, and the model is validated by forecasting the daily closing indices for five major stock markets. The performance of the proposed model is compared with four state-of-art models that are trained similarly and was found to be superior. We conducted the Deibold-Mariano test to check the statistical significance of the proposed model, and it was found to be significant. This model can be used as a promising tool for financial forecasting.

Our research concerns generating imperative programs from Answer Set Programming Specifications. ASP is highly declarative and is ideal for writing specifications. Further with negation-as-failure it is easy to succinctly represent combinatorial search problems. We are currently working on synthesizing imperative programs from ASP programs by turning the negation into useful computations. This opens up a novel way to synthesize programs from executable specifications.

A mathematical model of solitary wave interaction with a pontoon-type rigid floating structure over a flat bottom is formulated based on Boussinesq-type equations under weakly nonlinear dispersive waves. Based on the higher-order Boussinesq equations, the solitary wave equation is derived, and a semi-analytical solution is obtained using the perturbation technique. On the other hand, brief descriptions of the application of wave2Foam and OceanWave3D on the aforementioned problem are presented. The analytical solitary wave profiles in the outer region are compared with Computational Fluid Dynamics (CFD) and OceanWave 3D model simulations in different cases. The comparison shows a good level of agreement between analytical, wave2Foam, and OceanWave3D. In addition, based on the wave2Foam and coupled OceanWave3D model, the horizontal, vertical wave forces, and the pressure distributions around the pontoon are analysed. Further, the effect of the Ursell number, pontoon length, and water depth on the solitary wave profiles are analysed based on the analytical solution. The paper validates each of the three models and performs intercomparison among them to assess their fidelity and computational burden.

A hydroelastic model is developed of a floating flexible structure in the presence of a submerged perforated base connected with mooring lines under oblique wave action. Using the velocity decomposition method, the analytical solution of the referred model is obtained in finite water depth. The convergence of the analytical solution for different oblique wave incidences is examined, and the present results of deflection amplitude are compared with experimental datasets and the numerical results available in the literature. The effects of oblique wave incidence, along with various design parameters on the reflection, transmission, and dissipation coefficients, as well as structural displacements, are analysed through hydroelastic analysis. Further, the effect of oblique incidence angle on the free oscillation hydroelastic waves in two wave modes is investigated by deriving the free motion velocity potential in a wave basin.

Background: The impact of the 2017 American College of Cardiology (ACC)/American Heart Association (AHA) guidelines for diagnosis and management of hypertension on the prevalence of hypertension in India is unknown. Methods: We analyzed data from the Cardiac Prevent 2015 survey to estimate the change in the prevalence of hypertension. The JNC8 guidelines defined hypertension as a systolic blood pressure of ≥140 ​mmHg or diastolic blood pressure of ≥90 ​mmHg. The 2017 ACC/AHA guidelines define hypertension as a systolic blood pressure of ≥130 ​mmHg or diastolic blood pressure of ≥80 ​mmHg. We standardized the prevalence as per the 2011 census population of India. We also calculated the prevalence as per the World Health Organization (WHO) World Standard Population (2000–2025). Results: Among 180,335 participants (33.2% women), the mean age was 40.6 ​± ​14.9 years (41.1 ​± ​15.0 and 39.7 ​± ​14.7 years in men and women, respectively). Among them, 8,898 (4.9%), 99,791 (55.3%), 35,694 (11.9%), 23,084 (12.8%), 9,989 (5.5%) and 2,878 (1.6%) participants belonged to age group 18–19, 20–44, 45–54, 55–64, 65–74 and ​≥ ​75 years respectively. The prevalence of hypertension according to the JNC8 and 2017 ACC/AHA guidelines was 29.7% and 63.8%, respectively- an increase of 115%. With the 2011 census population of India, this suggests that currently, 486 million Indian adults have hypertension according to the 2017 ACC/AHA guidelines, an addition of 260 million as compared to the JNC8 guidelines. Conclusion: According to the 2017 ACC/AHA guidelines, 3 in every 5 Indian adults have hypertension.

The Iowa Gambling Task (IGT) is a decision-making task that preferentially involves the right prefrontal cortex (PFC). However, the performance of the task is driven by two attributes: intertemporal (long vs. short-term) and frequency-based processing of rewards-punishments, and differs over the two phases of uncertainty (early trials) and risk (later trials). Although intertemporal decision making involves the right PFC, the extent of hemispheric specialization in attribute and phase-specific decision making is unknown. Therefore, the current study assessed decision making in a patient with a uni-hemispheric disease, who underwent hemispherotomy surgery, comparing pre-surgical IGT performance (3 days prior to surgery) with post-surgical performance (1 month, and 12 months post-surgery). The patient’s pre- and post-surgical IGT performances were analyzed to examine changes in attribute and phase-specific decision making, including the widely reported deck B phenomenon. The results for the two attributes of deck selection at the pre- and post-surgical assessments suggested marked changes in the two IGT phases of risk and uncertainty. Pre-surgery, the patient made more intertemporally disadvantageous choices, and task-progression contributed to it; within 1 month of surgery, intertemporal disadvantageous deck choices were contingent on task progression, after 1 year, disadvantageous choices were independent of task progression. Intertemporal attribute alteration was unresponsive to uncertainty and risk phase. The effect of task progression on frequency attribute remained unchanged before and immediately after the surgery, and preference for infrequent decks was observed only after 1 year. Further, pre and post surgery alteration in frequency attribute was phase-specific: within 1 month of surgery, infrequent deck choices decreased in uncertainty and increased in risk, whereas the reverse was observed after 12 months. Deck B choice increase was in the uncertainty phase. Results are discussed in reference to valence-linked hemispheric specialization and its potential role in attribute and phase-specific IGT decision making.

Rapid Industrialization and demand for Energy makes the distribution system more complicated. To address such issue robust control system is being developed for newly implemented microgrid and smart grid concepts. These control systems being a complex structure interconnects the renewable sources with the conventional sources of energy production. The power quality issues arising due to interconnection of number of variable generation system makes it more dynamic and fault oriented. The dynamics of the system is due to the use of power conditioning unit in the renewable system. Most of the power conditioning unit are of current source inverter type. However if these inverters can be made up of voltage source type then reactive power compensation can be achieved in addition to the active power supplied by the inverter into the grid. Dynamic Active Compensation System (DACS) is simulated through MATLAB to validate the performance. Keywords: Active power compensation, Artificial Neural Fuzzy Inference System, Distributed generation, Power quality improvement, Current harmonics

Abstract Accurate forecasting of changes in stock market indices can provide financial managers and individual investors with strategically valuable information. However, predicting the closing prices of stock indices remains a challenging task because stock price movements are characterized by high volatility and nonlinearity. This paper proposes a novel condensed polynomial neural network (CPNN) for the task of forecasting stock closing price indices. We developed a model that uses partial descriptions (PDs) and is limited to only two layers for the PNN architecture. The outputs of these PDs along with the original features are fed to a single output neuron, and the synaptic weight values and biases of the CPNN are optimized by a genetic algorithm. The proposed model was evaluated by predicting the next day’s closing price of five fast-growing stock indices: the BSE, DJIA, NASDAQ, FTSE, and TAIEX. In comparative testing, the proposed model proved its ability to provide closing price predictions with superior accuracy. Further, the Deibold-Mariano test justified the statistical significance of the model, establishing that this approach can be adopted as a competent financial forecasting tool.

Abstract Selecting an optimal importance density and ensuring optimal particle weights are central challenges in particle-based filtering. In this paper, we provide a two-step procedure to learn importance densities for particle-based filtering. The first stage importance density is constructed based on ensemble Kalman filter kernels. This is followed by learning a second stage importance density via weighted likelihood criteria. The importance density is learned by fitting Gaussian mixture models to a set of particles and weights. The weighted likelihood learning criteria ensure that the second stage importance density is closer to the true filtered density, thereby improving the particle filtering procedure. Particle weights recalculated based on the latter density are shown to mitigate particle weight degeneracy as the filtering procedure propagates in time. We illustrate the proposed methodology on 2D and 3D nonlinear dynamical systems.

Melanoma is one the most increasing cancers since past decades. For accurate detection and classification, discriminative features are required to distinguish between benign and malignant cases. In this study, the authors introduce a fusion of structural and textural features from two descriptors. The structural features are extracted from wavelet and curvelet transforms, whereas the textural features are extracted from different variants of local binary pattern operator. The proposed method is implemented on 200 images from PH2 dermoscopy database including 160 non‐melanoma and 40 melanoma images, where a rigorous statistical analysis for the database is performed. Using support vector machine (SVM) classifier with random sampling cross‐validation method between the three cases of skin lesions given in the database, the validated results showed a very encouraging performance with a sensitivity of 78.93%, a specificity of 93.25% and an accuracy of 86.07%. The proposed approach outperforms the existing methods on the PH2 database.

A boundary integral equation method (BIEM) model for the problem of surface wave interaction with a moored finite floating flexible plate is presented. The BIEM solution is obtained by employing the free surface Greens function and Green’s theorem, and the expressions for the plate deflection, reflection, and transmission coefficients are derived from the integro-differential equation. Furthermore, the shallow water approximation model and its solution is obtained based on the matching technique in a direct manner. The accuracy of the present BIEM code is checked by comparing the results of deflection amplitude, reflection, and transmission coefficients with existing published results and experimental datasets as well as the shallow water approximation model. The hydroelastic response of the moored floating flexible plate is studied by analyzing the effects of the mooring stiffness, incidence angle, and flexural rigidity on the deflection amplitude, plate deformations, reflection, and transmission coefficients. The present analysis may be helpful in understanding the different physical parameters to model a wave energy conversion device with mooring systems over BIEM formulations.

Capturing the complex correlations among the data on the crude oil price time series is challenging, hence accurate prediction of it is difficult. Contrast to multilayer artificial neural network, Pi-Sigma neural network (PSNN) is characterized with stronger approximation ability, fast learning and higher fault tolerance. Fireworks algorithm (FWA) is a new metaheuristic motivated from the occurrence of fireworks explosion, characterized with fast convergence capability, parallelism and finds global optima. This article intends to achieve the synergetic effect of both on hybridizing them. It uses FWA for optimization of weight and bias of PSNN (FWA-PSNN) and overcomes the limitations of gradient based training. It has single hidden layer structure and trainable weight, hence fast and robust. The FWA-PSNN is evaluated on prediction of crude oil price time series. Extensive simulation results, performance analysis, and statistical significance test suggested the suitability of FWA-PSNN.

Precise and proficient modelling and forecasting financial time series has been paying attention of researchers,which leads to the development of various statistical and machine learning based models. Accuracy of a particularmethod is problem and domain specific, hence identifying best method is controversial. To boost up overallaccuracies and minimizing risk of model selection, combination of outputs of different models has beenrecommended in the literature. This work presents a linear combiner of five predictive models i.e. ARIMA,RBFNN, MLP, SVM, and FLANN for improving prediction accuracy. Four statistical methods i.e. trimmed mean,simple average, median, and an error based method are used for suitable choice of combining weights. Theindividual forecasts and the linear combiner are used separately to predict closing price of five stock markets andexchange rate of five global markets. Extensive simulation work demonstrates the feasibility and supremacy of thelinear combiner.

The effect of stress on task performance is complex, too much or too little stress negatively affects performance and there exists an optimal level of stress to drive optimal performance. Task difficulty and external affective factors are distinct stressors that impact cognitive performance. Neuroimaging studies showed that mood affects working memory performance and the correlates are changes in haemodynamic activity in the prefrontal cortex (PFC). We investigate the interactive effects of affective states and working memory load (WML) on working memory task performance and haemodynamic activity using functional near-infrared spectroscopy (fNIRS) neuroimaging on the PFC of healthy participants. We seek to understand if haemodynamic responses could tell apart workload-related stress from situational stress arising from external affective distraction. We found that the haemodynamic changes towards affective stressor- and workload-related stress were more dominant in the medial and lateral PFC, respectively. Our study reveals distinct affective state-dependent modulations of haemodynamic activity with increasing WML in n-back tasks, which correlate with decreasing performance. The influence of a negative effect on performance is greater at higher WML, and haemodynamic activity showed evident changes in temporal, and both spatial and strength of activation differently with WML.

This work proposes a stochastic process-based inversion to estimate hydrocarbon resistivity based on multifrequency electromagnetic (EM) data. Currently, mesh-based algorithms are used for processing the EM responses which cause high time-consuming and unable to quantify uncertainty. Gaussian process (GP) is utilized as the alternative forward modeling approach to evaluate the EM profiles with uncertainty quantification. For the optimization, gradient descent is used to find the optimum by minimizing its loss function. The prior EM profiles are evaluated using finite element (FE) through computer simulation technology (CST) software. For validation purposes, mean squared deviation and its root between EM profiles evaluated by the GP and FE at the unobserved resistivities are computed. Time taken for the GP and CST to evaluate the EM profiles is compared, and absolute error between the estimate and its simulation input is also computed. All the resulting deviations were significantly small, and the GP took lesser time to evaluate the EM profiles compared to the software. The observational datasets also lied within the 95% confidence interval (CI) where the resistivity inputs were estimated by the proposed inversion. This indicates the stochastic process-based inversion can effectively estimate the hydrocarbon resistivity in the seabed logging.

Alternating current (AC) microgrids are the next step in the evolution of the electricity distribution systems. They can operate in a grid-tied or island mode. Depending on the services they are designed to offer, their grid-tied or island modes could have several sub-operational states and or topological configurations. Short-circuit current levels and protection requirements between different microgrid modes and configurations can vary significantly. Designing a microgrid’s protection system, therefore, requires a thorough understanding of all microgrid operational modes, configurations, transitional states, and how transitions between those modes are managed. As part of the microgrid protection design, speed and reliability of information flow between the microprocessor-based relays and the microgrid controller, including during microgrid failure modes, must be considered. Furthermore, utility protection practices and customer requirements are not always inclusive of the protection schemes that are unique to microgrids. These and other aspects contribute to the overall complexity and challenge of designing effective microgrid protection systems. Following a review of microgrid protection system design challenges, this paper discusses a few real-world experiences, based on the authors’ own engineering, design, and field experience, in using several approaches to address microgrid protection system design, engineering, and implementation challenges.