A novel hybrid methodology for wind speed and solar irradiance forecasting based on improved whale optimized regularized extreme learning machine.

With rising demand for electricity, integrating renewable energy sources into power networks has become a key challenge. The fast incorporation of clean energy sources, particularly solar and wind power, into the existing power grid in the last several years has raised a major problem in controlling and managing the power grid due to the intermittent nature of these sources. Therefore, in order to ensure the safe RES integration providing high-quality power at a fair price and for the secure and reliable functioning of electrical systems, a precise one-day-ahead solar irradiation and wind speed forecast is essential for a stable and safe hybrid energy system.

A reinforcement learning approach for reducing traffic congestion using deep Q learning.

Nowadays, traffic congestion is a significant issue globally. The vehicle quantity has grown dramatically, while road and transportation infrastructure capacities have yet to expand proportionally to handle the additional traffic effectively. Road congestion and traffic-related pollution have increased, which is detrimental to society and public health.

Brayton-Moser passivity based controller for constant power load with interleaved boost converter.

A DC microgrid with renewable energy sources can achieve reduced current ripple, higher efficiency, faster dynamics, high voltage gain, and less operational stress by interfacing with an interleaved boost converter (IBC). The stability of an IBC linked to a DC microgrid supplying a constant power load (CPL) can be imperceptibly guaranteed by a conventional controller. A tightly regulated CPL with nonlinear and negative incremental impedance characteristics will lead to stability issues.

Amide/urea-based simple fluorometric receptors for iodide and Hg ions in aqueous medium: Aggregation induced emission and DFT studies.

Herein, we report pyrene-tagged amide and urea-based sugar derivatives 1 and 2 in a simple synthetic pathway to recognize I and Hg ions. Both molecules showed absorbance and fluorescence selectivity towards iodide ions in THF/HO (7/3, v/v) medium. The selectivity and sensitivity of 2 for iodide ions are superior to 1 due to more H-bond donors in 2.

Applications of blockchain technology in peer-to-peer energy markets and green hydrogen supply chains: a topical review.

Countries all over the world are shifting from conventional and fossil fuel-based energy systems to more sustainable energy systems (renewable energy-based systems). To effectively integrate renewable sources of energy, multi-directional power flow and control are required, and to facilitate this multi-directional power flow, peer-to-peer (P2P) trading is employed. For a safe, secure, and reliable P2P trading system, a secure communication gateway and a cryptographically secure data storage mechanism are required.

Frequency-tuned selectivity enhancement of Mg@ZnO-TiO nanoflake-based heterojunction sensor devices.

Selectivity improvement by the frequency tuning of Mg@ZnO-TiO nanoflake-based heterojunction devices under exposure to different volatile organic compounds is the prime focus of the present paper. The synthesis of Mg@ZnO-TiO nanoflakes was carried out using a solution process followed by a low-cost hydrothermal method. A capacitive measurement approach was used to find the resonant frequencies of the device in 2-butanone, acetone, 2-propanol, ethanol, and methanol environments in the frequency range from 0.

Selenium - An environmentally friendly micronutrient in agroecosystem in the modern era: An overview of 50-year findings.

Agricultural productivity is constantly being forced to maintain yield stability to feed the enormously growing world population. However, shrinking arable and nutrient-deprived soil and abiotic and biotic stressor (s) in different magnitudes put additional challenges to achieving global food security. Though well-defined, the concept of macro, micronutrients, and beneficial elements is from a plant nutritional perspective.

Optimized Machine learning techniques Enable prediction of organic dyes photophysical Properties: Absorption Wavelengths, emission Wavelengths, and quantum yields.

Applications of organic dyes, ranging from basic research to industry, are functions of their photophysical properties. Two important aspects- (1) knowledge of the photophysical properties of existing dyes long before real applications and (2) discovery of new organic dyes with desired photophysical properties for either upgradation of existing or development of new applications-are needed to be addressed. These two cases are coupled together with the common goal of estimating photophysical properties with high accuracy at the minimum cost of time and money long before the hard-core laboratory experiment.

Enhancing Bone Cancer Diagnosis Through Image Extraction and Machine Learning: A State-of-the-Art Approach.

Bone cancer is a severe condition often leading to patient mortality. Diagnosis relies on X-rays, MRIs, or CT scans, which require time-consuming manual review by experts. Thus, developing an automated system is crucial for accurate classification of malignant and healthy bone.

Sustainable production-inventory system for perishables under dynamic fuel pricing and preservation technology investment.

In the production and inventory management of perishables, environmental considerations are gaining prominence. By reducing carbon emissions from various supply chain processes, such as production, transportation, warehousing, and waste disposal of perishable items, the present study aims to minimize the overall cost to the manufacturer through an optimized investment in green technology. Additionally, cycle time and preservation technology investment are optimized to decrease deterioration and revenue loss in order to minimize cost.

A Comprehensive Survey on Pandemic Patient Monitoring System: Enabling Technologies, Opportunities, and Research Challenges.

Sporadic occurrences of transmissible diseases have severe and long-lasting effects on humankind throughout history. These outbreaks have molded the political, economic, and social aspects of human life. Pandemics have redefined some of the basic beliefs of modern healthcare, pushing researchers and scientists to develop innovative solutions to be better equipped for future emergencies.

An assessment of the strategies for the energy-critical elements necessary for the development of sustainable energy sources.

There have been several strategies developed to increase the diversified supply of energy so that it can meet all of the future demands for energy. As a result, to ensure a healthy and sustainable energy future, it is imperative to warrant reliable and diverse energy supply sources if the "green energy economy" is to be realized. The purpose of developing and deploying clean energy technologies is to improve our overall energy security, reduce our carbon footprint, and ensure that the generation of energy is secure and reliable in the future, making sure that we can spur economic growth in the future.

Sustainability assessment of waste cooking oil-based biodiesel plant in developing economy based on F-DEMATEL and F-ISM approaches.

Developing clean and renewable energy resources for long-term usage is a crucial concern in developing countries. In this regard, biodiesel technology is drawing a lot of public interest throughout the world. The issues associated with the sustainability of a biodiesel plant are intimately connected with restricted natural resources, political influences, energy independence, economic prosperity and global stability.

Deposition of trace metals associated with atmospheric particulate matter: Environmental fate and health risk assessment.

Anthropogenic and natural sources influence trace metals (TMs) bound to different sized particulate matter (PM) in dry and wet atmospheric deposition, which can create ecosystem and human health issues in the long run. Limited reviews are available summarizing worldwide concentrations in TMs in atmospheric PMs, their sources and pathways. Simultaneously, quantitative assessment of the potential human and ecosystem health risks imposed by the atmospheric particulate matter has not been adequately reviewed.

Global food security post COVID-19: Dearth or dwell in the developing world?

Today, global food systems are highly susceptible to food safety risks, economic shocks, price volatility, and natural disasters and pandemics, such as the novel coronavirus (COVID-19). This paper draws from research on food and nutritional security, food fraud and associated economic ecosystem, and the disruptions due to COVID-19 for socio-economic inequality globally. It is concluded that the safety risks have pushed enforcement of measures to reduce food supplies, adversely impacting food availability.

Temporal variability of atmospheric particulate-bound polycyclic aromatic hydrocarbons (PAHs) over central east India: sources and carcinogenic risk assessment.

Atmospheric polycyclic aromatic hydrocarbons (PAHs) are of significant interest owing to their high potential health effects, including mutagenicity and carcinogenicity. We report 16 PAHs measured in ambient PM from June 2018 to May 2019 over three different sites located in central east India. The annual average PM mass concentrations of 97.

Cloud-Based Zero Trust Access Control Policy: An Approach to Support Work-From-Home Driven by COVID-19 Pandemic.

The ubiquitous cloud computing services provide a new paradigm to the work-from-home environment adopted by the enterprise in the unprecedented crisis of the COVID-19 outbreak. However, the change in work culture would also increase the chances of the cybersecurity attack, MAC spoofing attack, and DDoS/DoS attack due to the divergent incoming traffic from the untrusted network for accessing the enterprise's resources. Networks are usually unable to detect spoofing if the intruder already forges the host's MAC address.

Threshold dynamics of difference equations for SEIR model with nonlinear incidence function and infinite delay.

In this research, we explore the global conduct of age-structured SEIR system with nonlinear incidence functional (NIF), where a threshold behavior is obtained. More precisely, we will analyze the investigated model differently, where we will rewrite it as a difference equations with infinite delay by the help of the characteristic method. Using standard conditions on the nonlinear incidence functional that can fit with a vast class of a well-known incidence functionals, we investigated the global asymptotic stability (GAS) of the disease-free equilibrium (DFE) using a Lyapunov functional (LF) for .

A study on fractional COVID-19 disease model by using Hermite wavelets.

The preeminent target of present study is to reveal the speed characteristic of ongoing outbreak COVID-19 due to novel coronavirus. On January 2020, the novel coronavirus infection (COVID-19) detected in India, and the total statistic of cases continuously increased to 7 128 268 cases including 109 285 deceases to October 2020, where 860 601 cases are active in India. In this study, we use the Hermite wavelets basis in order to solve the COVID-19 model with time- arbitrary Caputo derivative.

Entropy generation and dissipative heat transfer analysis of mixed convective hydromagnetic flow of a Casson nanofluid with thermal radiation and Hall current.

The present article provides a detailed analysis of entropy generation on the unsteady three-dimensional incompressible and electrically conducting magnetohydrodynamic flow of a Casson nanofluid under the influence of mixed convection, radiation, viscous dissipation, Brownian motion, Ohmic heating, thermophoresis and heat generation. At first, similarity transformation is used to transform the governing nonlinear coupled partial differential equations into nonlinear coupled ordinary differential equations, and then the resulting highly nonlinear coupled ordinary differential equations are numerically solved by the utilization of spectral quasi-linearization method. Moreover, the effects of pertinent flow parameters on velocity distribution, temperature distribution, concentration distribution, entropy generation and Bejan number are depicted prominently through various graphs and tables.

Sentiment Analysis of Lockdown in India During COVID-19: A Case Study on Twitter.

With the rapid increase in the use of the Internet, sentiment analysis has become one of the most popular fields of natural language processing (NLP). Using sentiment analysis, the implied emotion in the text can be mined effectively for different occasions. People are using social media to receive and communicate different types of information on a massive scale during COVID-19 outburst.

Estimating the parameters of susceptible-infected-recovered model of COVID-19 cases in India during lockdown periods.

Owing to the pandemic scenario of COVID-19 disease cases all over the world, the outbreak prediction has become extremely complex for the emerging scientific research. Several epidemiological mathematical models of spread are increasing daily to forecast the predictions appropriately. In this study, the classical susceptible-infected-recovered (SIR) modeling approach was employed to study the different parameters of this model for India.

Multifunctional hydroxyapatite and hopeite coatings on SS254 by laser rapid manufacturing for improved osseointegration and antibacterial character: A comparative study.

Orthopaedic metallic implant's long-term success strongly depends upon the two main factors: osseointegration and antibacterial character. Bioceramic (hydroxyapatite and hopeite) coatings have been proven effective for getting strong osseointegration and antibacterial character. However, deterioration of bioceramic coatings during the implantation period can adversely affect its overall biological performance.

Detailed Review of Chronic Kidney Disease.

Background: Nephropathy problems in the Udhanam region of Andhra Pradesh in India have motivated researchers to investigate the various factors related to chronic kidney disease (CKD). Initially, studies came across the markers of identification of CKD, i.e.

Characteristics, toxicity, source identification and seasonal variation of atmospheric polycyclic aromatic hydrocarbons over East India.

Atmospheric PM-bound polycyclic aromatic hydrocarbons (PAHs) were analyzed over urban and rural sites during January to December 2018. Total annual average concentration of PM was 74.41 ± 24.