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The whole world is now widely using green energy compared to fossil because of the depletion of fossil fuels, the rising temperature of the earth, and changing weather conditions, all these things are becoming a big threat to the life of the earth. This study proposed a stand-alone hybrid renewable energy system using different types of batteries. This model includes photovoltaic arrays, wind turbines, diesel generators, converters, and batteries. Lead-acid and lithium-ion batteries have been compared for the selection of optimal battery based on hybrid renewable energy system and sustainable development requirements. The purpose of this study is to find the optimal configuration, and techno-economic characteristics, using the hybrid optimization of multiple energy resources technique. The results of Lithium-ion and Lead Acid have been compared and it is found that the best configuration is photovoltaic arrays/wind turbines/ diesel generators /Battery/converter with lithium-ion Batteries. The net present cost and cost of energy are found to be 1.64M and 0.144$ respectively, for the selected study location. The carbon dioxide emission for configuration with LI batteries is 107314 kg/year as against the LA batteries which have 351288 kg/year. The results show LI batteries are technically as well as economically better than the LA batteries.

Electric vehicle charging stations (EVCS) that are based on DC microgrids are presented in this research. The system comprises a solar photovoltaic system (SPVS), storage battery (SB), electric vehicle (EV) and grid. The adaptive interaction artificial neural network (AI-ANN)-based vehicle to grid (V2G) and grid to vehicle (G2V) power management controller (PMC) is suggested for DC microgrid based EVCS. This EVCS is suitable for the residential building and offices where EV may be parked. This EVCS provides the facility to manage the power of the building in addition to charge the EVIt has two different modes of operation. The first mode uses the EV as a power source. In the second mode, the EV functions as a load. This controller is developed to acquire electrical power from the solar photovoltaic system (SPVS), storage battery, EV and grid respectively. If the solar photovoltaic system (SPVS) and storage battery power are insufficient to meet the demand, power is extracted from electric vehicle (V2G). If the solar photovoltaic system (SPVS), storage battery and EV are not sufficient to meet up demand, then deficit power is obtained from the grid (G2V). ANN based power management controller (PMC) Also provides a consistent DC bus voltage and reduces overshoot from 9.6 % to 0 %., settling time from 1.18 sec. to 0.52 sec. and rise time from 0.27 sec. to 0.25 sec. of DC bus voltage compared to conventional controller. The suggested power management controller tested for two different modes i.e., V2G and G2V using MATLAB Simulink software.

Abstract High-grade serous ovarian carcinoma (HGSOC) is characterised by poor outcome and extreme chromosome instability (CIN). Therapies targeting centrosome amplification (CA), a key mediator of chromosome missegregation, may have significant clinical utility in HGSOC. However, the prevalence of CA in HGSOC, its relationship to genomic biomarkers of CIN and its potential impact on therapeutic response have not been defined. Using high-throughput multi-regional microscopy on 287 clinical HGSOC tissues and 73 cell lines models, here we show that CA through centriole overduplication is a highly recurrent and heterogeneous feature of HGSOC and strongly associated with CIN and genome subclonality. Cell-based studies showed that high-prevalence CA is phenocopied in ovarian cancer cell lines, and that high CA is associated with increased multi-treatment resistance; most notably to paclitaxel, the commonest treatment used in HGSOC. CA in HGSOC may therefore present a potential driver of tumour evolution and a powerful biomarker for response to standard-of-care treatment.

Integration of renewable energy systems can provide reliable, environmentally sustainable, and cost-effective alternatives for meeting the demand for electricity in remote locations. In this study, recently developed meta-heuristic techniques are explored to find the optimal design for two combinations of off-grid hybrid renewable energy systems. To evaluate the performance, the Tasmanian devil Optimization (TDO) was compared to three meta-heuristic algorithms, called the COOT bird optimization algorithm (COOT), the Grey wolf algorithm (GWO), and the Beluga whale optimization (BWO), and determined the optimal design of the proposed off-grid energy system in terms of best and worst-case solutions. The system consisting of a solar-battery is more cost-effective, with the lowest total annual cost (TAC) of 36,859 $ and the lowest levelized cost of electricity (LCOE) of 0.0930 $/kWh for 0% LPSPmax level as compared to the wind turbine-battery-diesel generator with the highest TAC (102580 $) and LCOE (0.2589 $/kWh). Hence, a solar-battery hybrid system is more viable for producing clean energy with effective storage and better power system reliability enhancement. Also, the obtained simulation results reveal the supremacy of the TDO compared to the other three meta-heuristic algorithms, where it achieved the optimal solution with a quick convergence time and fewer oscillations.

ObjectiveThere are few guidelines for clinical trials of interventions for prevention of post-traumatic osteoarthritis (PTOA), reflecting challenges in this area. An international multi-disciplinary expert group including patients was convened to generate points to consider for the design and conduct of interventional studies following acute knee injury.DesignAn evidence review on acute knee injury interventional studies to prevent PTOA was presented to the group, alongside overviews of challenges in this area, including potential targets, biomarkers and imaging. Working groups considered pre-identified key areas: eligibility criteria and outcomes, biomarkers, injury definition and intervention timing including multi-modality interventions. Consensus agreement within the group on points to consider was generated and is reported here after iterative review by all contributors.ResultsThe evidence review identified 37 studies. Study duration and outcomes varied widely and 70% examined surgical interventions. Considerations were grouped into three areas: justification of inclusion criteria including the classification of injury and participant age (as people over 35 may have pre-existing OA); careful consideration in the selection and timing of outcomes or biomarkers; definition of the intervention(s)/comparator(s) and the appropriate time-window for intervention (considerations may be particular to intervention type). Areas for further research included demonstrating the utility of patient-reported outcomes, biomarkers and imaging outcomes from ancillary/cohort studies in this area, and development of surrogate clinical trial endpoints that shorten the duration of clinical trials and are acceptable to regulatory agencies.ConclusionsThese considerations represent the first international consensus on the conduct of interventional studies following acute knee joint trauma.

Background One in six five 16-year-olds have a probable mental health difficulty. Of these, almost half of older teenagers and a quarter of 11–16-year-olds report having self-harmed or attempted suicide. Currently, there is little research into mental health crisis services for young people, with little understanding of what services exist, who uses them, or what works best. Question ‘How are mental health crisis responses for children and young people up to the age of 25 sustained, experienced and integrated within their local systems of services’? Objectives To describe National Health Service (NHS), local authority, education and third sector approaches to the implementation and organisation of crisis care for children and young people across England and Wales. To identify eight contrasting case studies in which to evaluate how crisis services have developed and are currently organised, sustained, experienced and integrated within the context of their local systems of services. To compare and contrast these services in the context of the available international evidence, drawing out and disseminating clear implications for the design and delivery of future crisis responses for children and young people and their families. Methods A sequential mixed methods approach, underpinned by normalisation process theory will be employed. A survey will create a detailed record of how crisis responses across England and Wales are organised, implemented and used. Subsequently, eight contrasting services in relation to geographic and socioeconomic setting, populations served, and service configuration will be identified as case studies. Interviews will be conducted with children, young people and parents/carers who have used the service, as well as commissioners, managers and practitioners. Operational policies and service usage data will also be examined. Analysis of how each service is provided, experienced, implemented and sustained will be conducted both inductively and deductively, reflecting normalisation process theory constructs.

Renewable energy sources–based power systems are increasing rapidly every year with higher chances of destabilization and low power quality due to harmonics, subharmonics, and interharmonics. The elimination of the root causes of these harmonics is difficult because harmonics are generated at different levels due to various electronic components. However, the accurate estimation of power signal harmonic can be done and eliminated with a counter signal. Therefore, in this paper, the evolutionary mating heuristic is implied to accurately estimate the parameters of power signal harmonics. The proposed heuristic was tested under multiple scenarios to accurately estimate the phase and amplitude parameters of power signal harmonics at various noise levels. The tuning of the evolutionary mating heuristic is carried out for encountering predator probability and crossover probability Epp <0.8, Cp <0.9. The simulation analysis depicts that the best fitness achieved by the proposed heuristic for noise levels (no) 104, 78, 52, 26, and 13 dB are 2.8169e−11, 9.6969e−09, 3.7703e−06, 1.3501e−03, and 2.8624e−02, respectively, for iteration I = 1249. The comparative analysis shows that the proposed heuristic performance is significantly better than other heuristics. [ABSTRACT FROM AUTHOR]

Energy poverty (EP) has emerged as a major challenge to achieving sustainable development goals, and its significance in social development has increased over time. This paper aims to analyze the spatial autocorrelation between EP and social factors on a global scale. Utilizing the panel data of 116 countries from 2012 to 2019, the Bivariate local Moran index, a representative spatial econometrics tool, has been employed to examine temporal changes and spatial differences of transboundary synergy and tradeoff relations between EP and social factors. The results indicate that EP has synergy relationships with social factors, including life expectancy at birth, access to immunization, CO2 emission, and forest area, and tradeoff relationship with social factors, such as infant mortality rate, prevalence of undernourishment, forest rents, and gender inequality. Significant spatial differences have been observed that clusters of high‐income countries, particularly those in the Global North, tend to have better energy access and are surrounded by areas with favorable social conditions, and clusters of lower‐income countries, especially those in South Africa and Southeast Asia, have lower energy access and are surrounded by areas with more severe social conditions. The robustness analysis has been conducted to verify the reliability of the results. The spatial imbalance of findings offers robust evidence by emphasizing the importance of key areas, such as Southeast Asia and South Africa, that should be prioritized to take essential policy measures to address the EP and social issues. [ABSTRACT FROM AUTHOR]

Regulatory T cells (Tregs) control adaptive immunity and restrain type 2 inflammation in allergic disease. Interleukin-33 promotes the expansion of tissue-resident Tregs and group 2 innate lymphoid cells (ILC2s); however, how Tregs locally coordinate their function within the inflammatory niche is not understood. Here, we show that ILC2s are critical orchestrators of Treg function. Using spatial, cellular, and molecular profiling of the type 2 inflamed niche, we found that ILC2s and Tregs engage in a direct (OX40L-OX40) and chemotaxis-dependent (CCL1-CCR8) cellular dialogue that enforces the local accumulation of Gata3high Tregs, which are transcriptionally and functionally adapted to the type 2 environment. Genetic interruption of ILC2-Treg communication resulted in uncontrolled type 2 lung inflammation after allergen exposure. Mechanistically, we found that Gata3high Tregs can modulate the local bioavailability of the costimulatory molecule OX40L, which subsequently controlled effector memory T helper 2 cell numbers. Hence, ILC2-Treg interactions represent a critical feedback mechanism to control adaptive type 2 immunity. Editor's summary: Regulatory T cells (Tregs) maintain the balance between protective and harmful inflammation, yet the mechanisms underlying Treg-mediated restraint of type 2 immunity remain to be determined. Stockis et al. investigate how cross-talk between ILC2) and Tregs constrains adaptive type 2 immunity in mice. ILC2 were required for the expansion of tissue-resident Gata3high Tregs after IL-33 exposure. These Gata3high Tregs directly controlled the availability of the costimulatory molecule OX40L on ILC2, which facilitated the expansion of effector memory TH2 cells and promoted type 2 airway inflammation. These findings identify that ILC2 locally regulate both effector and regulatory T cells during type 2 inflammation. —Hannah Isles [ABSTRACT FROM AUTHOR]

We review how major cell behaviors, such as bacterial growth laws, are derived from the physical chemistry of the cell's proteins. On one hand, cell actions depend on the individual biological functionalities of their many genes and proteins. On the other hand, the common physics among proteins can be as important as the unique biology that distinguishes them. For example, bacterial growth rates depend strongly on temperature. This dependence can be explained by the folding stabilities across a cell's proteome. Such modeling explains how thermophilic and mesophilic organisms differ, and how oxidative damage of highly charged proteins can lead to unfolding and aggregation in aging cells. Cells have characteristic time scales. For example, E. coli can duplicate as fast as 2-3 times per hour. These time scales can be explained by protein dynamics (the rates of synthesis and degradation, folding, and diffusional transport). It rationalizes how bacterial growth is slowed down by added salt. In the same way that the behaviors of inanimate materials can be expressed in terms of the statistical distributions of atoms and molecules, some cell behaviors can be expressed in terms of distributions of protein properties, giving insights into the microscopic basis of growth laws in simple cells.

All tea plants in India rely on the national grid for their electrical needs and diesel for their thermal energy and transportation, which are encountering high costs, high emissions, and issues of accessibility. In this paper, hybrid renewable systems based on both standalone and grid-connected technologies have been modeled using HOMER Pro software for supplying power to a tea manufacturing plant in a typical rural area in India, namely, Gudalur village (Nilgiris), geographically located at 11°30.2′N and 76°29.5′E, which is presently run by the state grid to meet their energy requirements. The different configurations comprised of Solar PV, biomass, hydro, electrolyzer, boiler, thermal load controller to utilize excess electricity, and waste heat recovery options, and lead-acid batteries were designed to meet 650 kWh/day of electricity for processing units, 101 kWh/day of electricity for general applications, 4,450 kWh/day of thermal energy, and 86.35 kg/day of hydrogen energy. To determine the most feasible system design among various scenarios, several criteria such as NPC, COE, LCOH, and CO2 emission of the system have been investigated. In the case of off-grid hybrid systems, results show the highest NPC of $7.01 M with an LCOE of $1.06/kWh is obtained for the diesel generator/boiler/reformer/TLC system. It is reduced to $1.75 M with an LCOE of −$.420/kWh for the PV/biomass-CHP/hydro/TLC scenario. In a grid-connected system, the maximum NPC of $6.20 M with an LCOE of $0.835/kWh is obtained for a diesel generator/boiler system, and it is reduced to −$10.5 M with an LCOE of −$.240/kWh for the PV/biomass-CHP/hydro/TLC scenario. Additionally, in the off-grid systems, the PV/biomass-CHP/hydro/TLC system has LCOH of $4.27/kg, which is economical with the highest renewable fraction of 93%. The PV/biomass-CHP/hydro/TLC hybrid system has the lowest LCOH of −$64.5/kg with a maximum renewable fraction of 96% in on-grid systems. The findings show that recovering excess electricity and waste heat would increase renewable fraction, decrease the energy cost and emissions from the system, and emphasize the importance of TLC and CHP in HRES. According to the simulation results, the grid-connected system is more cost-effective than a stand-alone system due to the revenue obtained from selling renewable power to the grid.

Electrification in rural areas is relatively costly compared to urban areas. Therefore, the aim of this research is to identify the best combination of hybrid renewable energy systems (HRESs) to satisfy the load demand in a sustainable and cost-efficient way. The techno-economic study of stand-alone hybrid photovoltaic–wind turbine–diesel–battery-converter energy systems based on the hybrid optimization model for electric renewable (HOMER) simulation has been analyzed for various locations in the Tamil Nadu state, India. Various combinations of the systems have been compared and analyzed based on the performance of their technical parameters, costs, the electrical power production of each source, and unmet load. The findings indicated that the off-grid solar–wind–diesel–battery configuration is the most economical for all the sites among other system configurations. Comparing with conventional diesel generators among all the locations, a combination of solar/wind/diesel/battery is the economically best design for Thoothukudi, with the least and most reliable solution in terms of net present cost and cost of energy. Also, the impact of intermittent variables becomes significant, so sensitivity analysis for the various parameters has been carried out. The study finds that the least cost of electricity and the net present cost of electricity for Thoothukudi are achieved at 0.266 $/kWh and 138,197 $, respectively. This is economical compared to a stand-alone diesel system where the obtained COE is $1.88 and the NPC is $977523. In the stand-alone diesel operating mode, 41854 kg of CO2 is produced, which is higher than CO2 emissions associated with any other renewable energy systems. According to the results, the Kanyakumari location outperforms in terms of producing environmental pollutants with emission of 1,020 kg/y CO2 at their best. Moreover, the results of the proposed study imply that the proposed renewable energy system in remote sites could be a more economical measure.

Due to rising population growth and economic development, there is indeed a growing demand for electricity. Both in aspects of generation and transmission, conventional power firms are striving to manage these demands. Moreover, the ubiquitous utilization of electricity and other power generators, which are mainly driven from fossil fuels, seems to have some limitations, like declining performance and restricted energy production. As a result, use of renewable energy sources is incredibly important. Decentralized power generation in remote regions has become the primary requirement of society, based on renewable energy. Particularly in comparison with the electrification of urban areas, rural electrification is quite expensive. Microgrid’s development utilizing hybrid power is a potential solution for the electrification of rural regions where the transmission chain of network’s extension is unfeasible or inefficient. This research aims to structure a power generation model associated with different HRES combinations using a HOMER software application at a location in India. In the findings of this research, it has been observed that NPC, O&M, COEs, and RF of on-grid energy systems are better than off-grid energy systems. In the study, between eight hybrid system combinations, the lowest COE of 0.034 $/kWh is obtained with the PV-WT-MH-GRID-CT system in the on-grid scenario. This analysis shows NPC, COE, O&M, and renewable fraction are sensitive to the variation in all the considered sensitivity parameters1,2,3,4.

Multi-energy microgrids (MEM) are a new class of power grids focusing on the distributed form of generation and integrating different energy sectors. The primary idea of MEM is to increase renewable energy share in the final energy demand while maintaining the energy balance at all times. However, integrating renewable technology into the grid has some technical limitations that must be analyzed before being deployed in the real world. This study examines the impact of increasing renewable penetration and portfolio design on a multi-energy microgrid energy system from a technical standpoint. As the accuracy of the system analysis is primarily a factor of modeling accuracy, an artificial neural network-based model is trained and deployed to develop forecasts for solar power generation. The forecasting model is integrated with the EnergyPLAN simulation tool to analyze the multi-energy microgrid system regarding renewable share in primary energy consumption and import/export of energy from the primary grid. The Norwegian energy system is considered a case study, as the energy generation and consumption patterns are interesting from both renewable energy and demand contexts for a cold country. One interesting conclusion is that the portfolio and capacities of coupling components such as combined heat and power plants negatively impact renewable integration, while heat pumps positively impact renewable integration by increasing renewable energy utilization. Additionally, the photovoltaic system size has a high degree of correlation to imports and exports compared to wind generation systems. [ABSTRACT FROM AUTHOR]

Abstract Here we report a novel nitridation technique for transforming niobium into hexagonal Nb2N which appears to be superconducting below 1K. The nitridation is achieved by high temperature annealing of Nb films grown on Si3N4/Si (100) substrate under high vacuum. The structural characterization directs the formation of a majority Nb2N phase while the morphology shows granular nature of the films. The temperature dependent resistance measurements reveal a wide metal-to-superconductor transition featuring two distinct transition regions. The region close to the normal state varies strongly with the film thickness, whereas, the second region in the vicinity of the superconducting state remains almost unaltered but exhibiting resistive tailing. The current-voltage characteristics also display wide transition embedded with intermediate resistive states originated by phase slip lines. The transition width in current and the number of resistive steps depend on film thickness and they both increase with decrease in thickness. The broadening in transition width is explained by progressive establishment of superconductivity through proximity coupled superconducting nano-grains while finite size effects and quantum fluctuation may lead to the resistive tailing. Finally, by comparing with Nb control samples, we emphasize that Nb2N offers unconventional superconductivity with promises in the field of phase slip based device applications.