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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

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.

Abstract Focal cortical dysplasia (FCD) is a malformation of the cerebral cortex with poorly-defined epileptogenic zones (EZs), and poor surgical outcome in FCD is associated with inaccurate localization of the EZ. Hence, identifying novel epileptogenic markers to aid in the localization of EZ in patients with FCD is very much needed. High-throughput gene expression studies of FCD samples have the potential to uncover molecular changes underlying the epileptogenic process and identify novel markers for delineating the EZ. For this purpose, we, for the first time performed RNA sequencing of surgically resected paired tissue samples obtained from electrocorticographically graded high (MAX) and low spiking (MIN) regions of FCD type II patients and autopsy controls. We identified significant changes in the MAX samples of the FCD type II patients when compared to non-epileptic controls, but not in the case of MIN samples. We found significant enrichment for myelination, oligodendrocyte development and differentiation, neuronal and axon ensheathment, phospholipid metabolism, cell adhesion and cytoskeleton, semaphorins, and ion channels in the MAX region. Through the integration of both MAX vs non-epileptic control and MAX vs MIN RNA sequencing (RNA Seq) data, PLP1, PLLP, UGT8, KLK6, SOX10, MOG, MAG, MOBP, ANLN, ERMN, SPP1, CLDN11, TNC, GPR37, SLC12A2, ABCA2, ABCA8, ASPA, P2RX7, CERS2, MAP4K4, TF, CTGF, Semaphorins, Opalin, FGFs, CALB2, and TNC were identified as potential key regulators of multiple pathways related to FCD type II pathology. We have identified novel epileptogenic marker elements that may contribute to epileptogenicity in patients with FCD and could be possible markers for the localization of EZ.

Intersections are places where two or more roadways intersect with one another. Federal Highway Administration (FHWA) quoted that more than fifty percent of crashes occur at or near intersections. An at-grade intersection is more accident prone compared to grade separated intersections. Intersection-related crashes are almost 335-times more likely to occur as compared to non-intersection-related crashes caused by vehicles turned with obstructed views. Approach and departure sight triangles are two types of sight triangles that are considered to provide clear visibility for drivers entering and exiting an intersection. Presently, there is no common practice followed by transportation or public work agencies other than considering accident history and public complaints as indicators for the need to improve intersection conditions. This approach warrants a safe and rapid methodology that can effectively identify obstructions within the intersection sight triangles. Unmanned aerial vehicles coupled with close range photogrammetry (UAV-CRP) technology offers a safe, quick, and efficient alternative for obstruction identification compared to traditional methods. In this study, a UAV based methodology is developed to identify obstructions in intersections from the 3-dimensional models developed using the imagery collected from unmanned aerial surveys. Two case studies including a T-intersection and a railroad crossing were considered to demonstrate the developed methodology. This research successfully validated the developed UAV methodology by analyzing departure sight triangles at T-intersection and approach sight triangles at railroad crossing. It is expected to be applied in many other civil engineering applications that are deemed critical due to the presence of obstructions and can potentially save human lives.

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.

Perhaps in no other area of neurosurgery, has a greater research been done as in traumatic brain injury (TBI). Despite this, TBI remains one of the biggest killers around the world and especially in India. Decompressive craniotomy still remains one of the mainstay paradigms in the management of TBI. The following article explores several new modalities of treatment, and these include the role of beta-blockers for TBI, updates on decompressive craniotomy, the results of DECRA and RESCUEicp trials, diagnostic and prognostic biomarkers in TBI, vascular dysfunction, neuroimaging, and role of neuroinflammation in TBI.

Objective Developmental bony craniovertebral junction (CVJ) anomalies seem to have a genetic basis and also abnormal joint morphology causing atlantoaxial dislocation (AAD) and basilar invagination (BI). Methods DNA extracted polymerase chain reaction single-stranded conformation polymorphism (SSCP) performed for mutation screening of FBN1 gene (n = 50 cases+ 50 age/sex-matched normal; total: 100). Samples with a deviated pattern of bands in SSCP were sequenced to detect the type of variation. Computed tomography (CT) scans of 100 patients (15–45 years old) compared with an equal number of age/sex-matched controls (21.9 ±8.2 years). Joint parameters studied: sagittal joint inclination (SI), craniocervical tilt (CCT), coronal joint inclination (CI). Results Thirty-nine samples (78%) showed sequence variants. Exon 25, 26, 27, and 28 showed variable patterns of DNA bands in SSCP, which on sequencing gives various types of DNA sequence variations in intronic region of the FBN1 gene in 14%, 14%, 6%, and 44% respectively. CT radiology:SI and CCT correlated with both BI and AAD (p < 0.01). The mean SI value in controls: 83.35° ± 8.65°, and in patients with BI and AAD:129° ± 24.05°. Mean CCT in controls: 60.2° ± 9.2°, and in patients with BI and AAD: 86.0° ± 18.1°. Mean CI in controls:110.3° ± 4.23°, and in cases: 125.15° ± 16.4°. Conclusion The study showed mutations in FBN1 gene (reported in Marfan syndrome). There is also an alteration of joint morphology, correlating with AAD and BI severity. Hence, we propose a double-hit hypothesis: the presence of weak ligaments (due to FB1 gene alterations) and abnormal joint morphology may contribute to AAD and BI.

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.

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

Analysing the Output Power of a Solar Photo-voltaic System at the design stage and at the same time predicting the performance of solar PV System under different weather condition is a primary work i.e. to be carried out before any installation. Due to large penetration of solar Photovoltaic system into the traditional grid and increase in the construction of smart grid, now it is required to inject a very clean and economic power into the grid so that grid disturbance can be avoided. The level of solar Power that can be generated by a solar photovoltaic system depends upon the environment in which it is operated and two other important factor like the amount of solar insolation and temperature. As these two factors are intermittent in nature hence forecasting the output of solar photovoltaic system is the most difficult work. In this paper a comparative analysis of different solar photovoltaic forecasting method were presented. A MATLAB Simulink model based on Real time data which were collected from Odisha (20.9517∘N, 85.0985∘E), India. were used in the model for forecasting performance of solar photovoltaic system.

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.

This work is about making hybrid composite materials out of carbon fiber mats and basalt fiber mats that are 40% reinforced with a 60% epoxy polymer matrix. Traditional hand layup has been used for the fabrication process to make five laminates of these two fibers. The mechanical properties of the hybrid composite were evaluated by measuring its tensile strength, flexural strength, impact energy, and hardness. The results showed that adding more carbon fiber layers to the composite made a big difference in its mechanical properties. In sample A, the tensile strength is 280 MPa, the flexural strength is 247 MPa, and the basalt fiber can keep more impact energy of 24 J in sample E, along with the carbon fiber and epoxy matrix. A scanning electron microscope was used to figure out how carbon/basalt fiber composite laminates break down.

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.

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).

Glutamate-receptor-mediated hyperexcitability contributes to seizure generation in temporal lobe epilepsy (TLE). Tryptophan–kynurenine pathway (TKP) metabolites regulate glutamate receptor activity under physiological conditions. This study was designed to investigate alterations in the levels of TKP metabolites and the differential regulation of glutamatergic activity by TKP metabolites in the hippocampus, anterior temporal lobe (ATL), and neocortex samples of a lithium–pilocarpine rat model of TLE. We observed that levels of tryptophan were reduced in the hippocampus and ATL samples but unaltered in the neocortex samples. The levels of kynurenic acid were reduced in the hippocampus samples and unaltered in the ATL and neocortex samples of the TLE rats. The levels of kynurenine were unaltered in all three regions of the TLE rats. The magnitude of reduction in these metabolites in all regions was unaltered in the TLE rats. The frequency and amplitude of spontaneous excitatory postsynaptic currents were enhanced in hippocampus ATL samples but not in the neocortex samples of the TLE rats. The exogenous application of kynurenic acid inhibited glutamatergic activity in the slice preparations of all these regions in both the control and the TLE rats. However, the magnitude of reduction in the frequency of kynurenic acid was higher in the hippocampus (18.44 ± 2.6% in control vs. 30.02 ± 1.5 in TLE rats) and ATL (16.31 ± 0.91% in control vs. 29.82 ± 3.08% in TLE rats) samples of the TLE rats. These findings suggest the differential regulation of glutamatergic activity by TKP metabolites in the hippocampus, ATL, and neocortex of TLE rats.

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.

The peritumoral regions of WHO grade II gliomas, like astrocytoma and oligodendroglioma, have been reported to show epileptiform activities. An imbalance of glutamatergic and GABAergic mechanisms is primarily responsible for the generation of epileptiform activities. Here we have compared the electrophysiological properties of pyramidal neurons in intraoperative peritumoral specimens obtained from glioma patients with (GS) and without (GN) a history of seizures at presentation. Histology and immunohistochemistry were performed to assess the infiltration of proliferating cells at the peritumoral tissues. Whole-cell patch clamp technique was performed to measure the spontaneous glutamatergic and GABAergic activity onto pyramidal neurons in the peritumoral samples of GS (n = 11) and GN (n = 15) patients. The cytoarchitecture of the peritumoral tissues was devoid of Ki67 immuno-positive cells. We observed a higher frequency of spontaneous glutamatergic and GABAergic activities onto pyramidal neurons of the peritumoral samples of GS patients. Our findings suggest that, in spite of similar histopathological features, the pyramidal neurons in the peritumoral samples of GS and GN patients showed differences in spontaneous excitatory and inhibitory synaptic neurotransmission. An alteration in postsynaptic currents may contribute to the spontaneous epileptiform activity in GS patients.

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.

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

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.

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.

Heart failure is a common clinical syndrome and a global health priority. The burden of heart failure is increasing at an alarming rate worldwide as well as in India. Heart failure not only increases the risk of mortality, morbidity and worsens the patient’s quality of life, but also puts a huge burden on the overall healthcare system. The management of heart failure has evolved over the years with the advent of new drugs and devices. This document has been developed with an objective to provide standard management guidance and simple heart failure algorithms to aid Indian clinicians in their daily practice. It would also inform the clinicians on the latest evidence in heart failure and provide guidance to recognize and diagnose chronic heart failure early and optimize management.

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.

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.