Explore the vast range of titles available.

The rising demand for renewable energy has recently spurred notable advancements in hybrid energy systems that utilize solar and wind power. The Hybrid Solar Wind Energy System (HSWES) integrates wind turbines with solar energy systems. This research project aims to develop effective modeling and control techniques for a grid-connected HSWES. The goal is to optimize power tracking efficiency in an electrically linked solar photovoltaic system combined with a wind-powered Doubly Fed Induction Generator (DFIG). The back-to-back ( ) converters’ DC link is connected to this to integrate the solar photovoltaic (PV). The study controls the rotor and grid converters via a vector control technique. This study aims to optimize power extraction efficiency and hybrid system integration with electrical grids by applying the Maximum Power Point Tracking (MPPT) technique to solar and wind systems. Combining the control strategy with the optimization algorithm makes our work new and compelling. We utilized this technology with a focus on optimization to evaluate how our system performs when applying optimization techniques to the control strategies. We optimized the solar system using the conventional Perturb and Observe (P & O) method and the metaheuristic Particle Swarm Optimization (PSO) technique. Our primary objective was to validate the effectiveness of the optimization process in enhancing the control strategy. The paper investigates the applications of Particle Swarm Optimization (PSO) and Perturb and Observe (P & O) algorithms in solar photovoltaics under constant and real-time sunlight. The hybrid Indirect Speed Controller MPPT algorithms are utilized for step-up and step-down wind speeds. An HSWES simulation is used to confirm the effectiveness and efficiency of the recommended regulation technique. The suggested control approach is simulated using the Sim Power of the MATLAB/Simulink platform. The advantage of the established techniques lies in their capacity to swiftly and precisely monitor the ideal power output of the HSWES. The comprehensive simulations conducted provide compelling evidence for the efficacy of the proposed system in attaining optimal efficiency and stability, propelling the progress of sustainable energy solutions.

Abiotic stresses are the primary sources of crop losses globally. The identification of key mechanisms deployed and established by plants in response to abiotic stresses is necessary for the maintenance of their growth and persistence. Recent discoveries have revealed that phytohormones or plant growth regulators (PGRs), mainly jasmonic acid (JA), have increased our knowledge of hormonal signaling of plants under stressful environments. Jasmonic acid is involved in various physiological and biochemical processes associated with plant growth and development as well as plant defense mechanism against wounding by pathogen and insect attacks. Recent findings suggest that JA can mediate the effect of abiotic stresses and help plants to acclimatize under unfavorable conditions. As a vital PGR, JA contributes in many signal transduction pathways, i.e., gene network, regulatory protein, signaling intermediates and enzymes, proteins, and other molecules that act to defend cells from the harmful effects of various environmental stresses. However, JA does not work as an independent regulator, but acts in a complex signaling pathway along other PGRs. Further, JA can protect and maintain the integrity of plant cells under several stresses by up-regulating the antioxidant defense. In this review, we have documented the biosynthesis and metabolism of JA and its protective role against different abiotic stresses. Further, JA-mediated antioxidant potential and its crosstalk with other PGRs have also been discussed.

Nanotechnology explores a variety of promising approaches in the area of material sciences on a molecular level, and silver nanoparticles (AgNPs) are of leading interest in the present scenario. This review is a comprehensive contribution in the field of green synthesis, characterization, and biological activities of AgNPs using different biological sources. Biosynthesis of AgNPs can be accomplished by physical, chemical, and green synthesis; however, synthesis via biological precursors has shown remarkable outcomes. In available reported data, these entities are used as reducing agents where the synthesized NPs are characterized by ultraviolet-visible and Fourier-transform infrared spectra and X-ray diffraction, scanning electron microscopy, and transmission electron microscopy. Modulation of metals to a nanoscale drastically changes their chemical, physical, and optical properties, and is exploited further via antibacterial, antifungal, anticancer, antioxidant, and cardioprotective activities. Results showed excellent growth inhibition of the microorganism. Novel outcomes of green synthesis in the field of nanotechnology are appreciable where the synthesis and design of NPs have proven potential outcomes in diverse fields. The study of green synthesis can be extended to conduct the in silco and in vitro research to confirm these findings.

Recently, titania nanotubes (TNTs) have been extensively studied because both their functional properties and highly controllable morphology make them important building blocks for understanding nanoscale phenomena and realizing nanoscale devices. Compared with sol–gel and template-assisted methods, electrochemical anodization is a simple, cost-effective, and low-temperature technique offering additional advantages such as straightforward processing and ease of scale-up. This review focuses on the process modalities and underlying mechanism of electrochemical anodization to achieve a different set of TNTs for a variety of applications. Finally, important applications of TNTs are highlighted including biomedical devices, water purification, and solar cells.

China is experiencing a swift transformation from a rural to an urban society. This societal transition is the result of a country’s ambition toward economic development. Rapid urbanization impacts on human wellbeing, environment, and infrastructure. Unlike many cases of urban extension, China’s urbanization has led to increase in environmental issues, rather than to contain, and posed serious challenges to achieve sustainable development. To date, nevertheless, there is a little empirical enquiry on the spatiotemporal effects of multidimensional urbanization on green total factor productivity. Taking this into account, this article constructs a spatial panel data model based on 283 Chinese cities during 2006–2019. The results showed that the three types of urbanization (i.e., population, land, and economic) have positive effect on green total factor productivity (GTFP). Population urbanization has indirect positive effect on GTFP. Local land urbanization inhibits while surrounding land urbanization promotes GTFP. Economic urbanization has positive direct effect on GTFP. Considering regional heterogeneity, population and economic urbanization have positive effect on GTFP in eastern region. In central region, land urbanization significantly promotes GTFP, while economic urbanization has significant negative effect on GTFP. In the western region, the three types of urbanization are not conducive to GTFP. Considering sized heterogeneity, population and economic types of urbanization have significant negative effect on GTFP in small- and medium-sized cities. In large cities, the three types of urbanization have significant positive effects on GTFP. Considering temporal heterogeneity, the three types of urbanization significantly promote GTFP before 2014, but economic urbanization deteriorates GTFP after 2014. Our results recommend loosening Hukou system, improving land use efficiency, and developing technology and knowledge-intensive industries.

Engaging with the nuanced intersection of cultural tourism and religious identity, this study probes the socioeconomic inequalities shaped by ethnic tourism within Pakistan’s Kalash community, where religious distinctions between Kafir Kalash and Muslim Kalash subgroups underlie unequal access to tourism-derived benefits. While tourism is often promoted as a tool for development, its role in reinforcing intra-community disparities in multi-religious settings remains understudied. Combining Bourdieu’s capital theory with household surveys from both tourism-active and non-tourism villages, we use stratified expenditure models to assess economic outcomes. Results show that Kafir Kalash households convert cultural capital into tourism income, while forest-reliant Muslim Kalash households remain economically marginalized. These patterns highlight tourism’s role in deepening internal stratification and seasonal environmental pressure. We recommend inclusive tourism policies, targeted training, and forest-conservation income pathways to address these disparities. The study contributes new insights into the intersection of cultural tourism, religious identity, and inequality in indigenous communities.

Economic growth and economic energy consumption have received greater attention due to its contribution to global CO 2 emissions in recent decades. The literature on CO 2 emissions and innovation for regional differences is very scanty as there is not enough study that considered different regions in a single analysis. We adopt a holistic approach by incorporating different regions so as to assess how innovation contributes to emission reduction. The study, therefore, examined the effects of innovation and economic growth on CO 2 emissions for 18 developed and developing countries over the period of 1990 to 2016. The study used panel technique capable of dealing with cross-section dependence effects: panel cross-sectional augmented Dickey-Fuller (CADF) unit root to determine the order of integration, Westerlund cointegration tests confirmed that the variables are co-integrated. We employed panel fully modified ordinary least square (FMOLS) and panel dynamic ordinary least square (DOLS) to estimate the long-run relationship. The results show that energy consumption increases CO 2 emissions at all panel levels. However, innovation reduces CO 2 emissions in G6 while it increases emissions in the MENA and the BRICS countries. Environmental Kuznets curve (EKC) hypothesis is valid for the BRICS. The pollution haven hypothesis (PHH) and pollution halo effect were confirmed at different panel levels. Based on the findings different policy recommendations are proposed.

Background Sahiwal cattle is an indigenous cattle breed of Pakistan and mastitis is one of the major problems faced by Sahiwal cattle which hinders its production potential. The study was designed to investigate the milk microbiota of healthy and mastitic Sahiwal cattle as part of a multistep project to develop probiotics for the mitigation and control of mastitis. Milk samples of Sahiwal cattle (healthy clinical mastitis and subclinical mastitis) reared under similar husbandry and management practices were processed for 16S rRNA gene base metagenomics analysis. Results Results revealed that Proteobacteria were dominant in the healthy group and subclinical mastitis group (56.48% and 48.77%, respectively) as compared to the clinical mastitis group (2.68%). In contrast, Firmicutes  were abundant in the clinical mastitis group (64%) as compared to the healthy and subclinical mastitis groups (15.87% and 38.98%, respectively). Dominant species assigned in the healthy group were Ignavibacterium album , Novosphingobium capsulatum , Akkermansia muciniphila and Lactobacillus fermentum .The clinical mastitis group was dominated by Streptococcus dysgalactiae and Corynebacterium bovis , while subclinical mastitis group included Lactobacillus fermentum and uncultured acidobacteriales and Akkermansia muciniphila as dominant species. Alpha diversity indices showed higher microbial diversity in the healthy group compared to the clinical and sub-clinical mastitis groups. Conclusion It is concluded that the milk microbiota of healthy sahiwal cattle has higher diversity and dominant taxa in the different groups may be used as signature microbes for mastitis susceptibility. Akkermansia muciniphila is one of candidate specie that was identified and may be used for development of probiotics.

Deep eutectic solvents (DES), which have low toxicity and are low cost, biodegradable, and easily synthesized, were used for the extraction of neutral red (NR) dye before its spectrophotometric analysis. DES, containing choline chloride as a hydrogen bond acceptor and phenol as a hydrogen bond donor with a molar ratio of 1:2, was used for the extraction of NR dye from aqueous media. The possible interaction of different DESs with NR was studied using density functional theory (DFT) calculations. Experimentally, a UV-visible spectrophotometer was used for the quantitative analysis. The most important parameters affecting method performance, such as pH, extraction temperature, DES type, its volume, THF volume, sonication time, and centrifugation time, were optimized. The developed method provides exceptional sensitivity in terms of LOD and LOQ, which were 2.2 and 7.3 µg/L respectively. The relative standard deviation was 1.35–1.5% (n = 10), and the pre-concentration factor was 40. The method was found to be linear in the range of 2–300 µg/L (R2 = 0.9967). The method was successfully used for the determination of NR in wastewater samples. Finally, the DES-based method presents operational simplicity, high sensitivity, and rapid determination (<5 min) compared with other analytical procedures.

Cardiovascular diseases are one of the most severe causes of mortality, annually taking a heavy toll on lives worldwide. Continuous monitoring of blood pressure seems to be the most viable option, but this demands an invasive process, introducing several layers of complexities and reliability concerns due to non-invasive techniques not being accurate. This motivates us to develop a method to estimate the continuous arterial blood pressure (ABP) waveform through a non-invasive approach using Photoplethysmogram (PPG) signals. We explore the advantage of deep learning, as it would free us from sticking to ideally shaped PPG signals only by making handcrafted feature computation irrelevant, which is a shortcoming of the existing approaches. Thus, we present PPG2ABP, a two-stage cascaded deep learning-based method that manages to estimate the continuous ABP waveform from the input PPG signal with a mean absolute error of 4.604 mmHg, preserving the shape, magnitude, and phase in unison. However, the more astounding success of PPG2ABP turns out to be that the computed values of Diastolic Blood Pressure (DBP), Mean Arterial Pressure (MAP), and Systolic Blood Pressure (SBP) from the estimated ABP waveform outperform the existing works under several metrics (mean absolute error of 3.449 ± 6.147 mmHg, 2.310 ± 4.437 mmHg, and 5.727 ± 9.162 mmHg, respectively), despite that PPG2ABP is not explicitly trained to do so. Notably, both for DBP and MAP, we achieve Grade A in the BHS (British Hypertension Society) Standard and satisfy the AAMI (Association for the Advancement of Medical Instrumentation) standard.