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This paper presents the optimal hybrid power system design including various configurations of renewable energy generation. To decide the optimal configuration of parameters a new multi-objective function with six separate objectives of hybrid renewable system is presented using GA, PSO, BFPSO and TLBO optimization techniques. The different parameters namely technical (LPSP, Renewable factor), economical (COE, Penalty & Fuel consumption) and social (Job creation, HDI & PM) features are investigated as objectives simultaneously for optimal design of hybrid system. The design consideration of hybrid system using a novel PM factor, human health impacts are directly shown whereas pollutant emission is measured in the hybrid system design. Based on the minimum value of multi-objective function optimal values are decided for objective indices. For optimal configuration including various combinations of wind, PV, diesel generator, biomass and battery bank, separate cases from I to VI of hybrid system are tested. Performance of TLBO is found to be better than BFPSO, PSO and GA as per the analysis of results for individual cases. Also the case I found to be the most efficient solution among all cases. [ABSTRACT FROM AUTHOR]

PV and wind hybrid are found to be the most lucrative solution for the diminishing traditional energy sources. Whereas these alternatives sources of the energy have many remarkable rewards like cost of energy and feasibility etc. The attributes of these sources of being cost effective and stable are possible due to their complementary nature as compared to independent energy systems. Therefore, these systems have admirable capability to meet energy crisis up to some extent. The proposed work gives the idea about various configuration, control strategy, techno-economic analysis and social effect. The findings of comprehensive review will help for further improvements in hybrid system design and control with respect to practical system implementation. This paper also presents a case study of remote area Barwani, India and results are compared using Homer and PSO. The resulting analysis reveals that configurations of hybrid system are the most techno-economical feasible solution concerning COE, renewable fraction, maximum renewable penetration, levelized cost, operating cost, mean electrical efficiency, and emission amongst various hybrid system configurations using PSO as compared to HOMER. [ABSTRACT FROM AUTHOR]

This paper presents the optimal planning of PV-Wind-Biomass hybrid energy system, which includes back up power sources as battery bank and diesel generator. The cost of energy (COE) or electricity price is minimized as objective function using GA and PSO. The optimal configuration of hybrid system is obtained on the basis of minimum COE. The optimal solution is consists with high reliability, maximum value of renewable fraction, less emission and low penalty cost according to minimum COE. The reliability is computed on the basis of loss of power supply probability (LPSP), which is assumed 2% maximum in this case study. This paper aim's to present the techno-economic feasibility of pv-wind-biomass hybrid energy system (HES) for a case study of remote area of barwani district, India. The optimization results are presented for load following and cycle charging strategy and also compared the results using GA and PSO optimization techniques. [ABSTRACT FROM AUTHOR]

Background & Aims We performed a genome-wide association study (GWAS) to identify genetic risk factors for drug-induced liver injury (DILI) from licensed drugs without previously reported genetic risk factors. Methods We performed a GWAS of 862 persons with DILI and 10,588 population-matched controls. The first set of cases was recruited before May 2009 in Europe (n = 137) and the United States (n = 274). The second set of cases were identified from May 2009 through May 2013 from international collaborative studies performed in Europe, the United States, and South America. For the GWAS, we included only cases with patients of European ancestry associated with a particular drug (but not flucloxacillin or amoxicillin-clavulanate). We used DNA samples from all subjects to analyze HLA genes and single nucleotide polymorphisms. After the discovery analysis was concluded, we validated our findings using data from 283 European patients with diagnosis of DILI associated with various drugs. Results We associated DILI with rs114577328 (a proxy for A*33:01 a HLA class I allele; odds ratio [OR], 2.7; 95% confidence interval [CI], 1.9−3.8; P = 2.4 × 10 −8 ) and with rs72631567 on chromosome 2 (OR, 2.0; 95% CI, 1.6−2.5; P = 9.7 × 10 −9 ). The association with A*33:01 was mediated by large effects for terbinafine-, fenofibrate-, and ticlopidine-related DILI. The variant on chromosome 2 was associated with DILI from a variety of drugs. Further phenotypic analysis indicated that the association between DILI and A*33:01 was significant genome wide for cholestatic and mixed DILI, but not for hepatocellular DILI; the polymorphism on chromosome 2 was associated with cholestatic and mixed DILI as well as hepatocellular DILI. We identified an association between rs28521457 (within the lipopolysaccharide-responsive vesicle trafficking, beach and anchor containing gene) and only hepatocellular DILI (OR, 2.1; 95% CI, 1.6−2.7; P = 4.8 × 10 −9 ). We did not associate any specific drug classes with genetic polymorphisms, except for statin-associated DILI, which was associated with rs116561224 on chromosome 18 (OR, 5.4; 95% CI, 3.0−9.5; P = 7.1 × 10 −9 ). We validated the association between A*33:01 terbinafine- and sertraline-induced DILI. We could not validate the association between DILI and rs72631567, rs28521457, or rs116561224. Conclusions In a GWAS of persons of European descent with DILI, we associated HLA-A*33:01 with DILI due to terbinafine and possibly fenofibrate and ticlopidine. We identified polymorphisms that appear to be associated with DILI from statins, as well as 2 non−drug-specific risk factors. [ABSTRACT FROM AUTHOR]

The increasing energy prices and pollutants from fossil fuels that threaten the climate, there is a growing preference for renewable energy. The implementation of hybrid renewable energy systems (HRES) has been a challenging task due to its interference, uncertainty, and unpredictable nature. Also, it comes with high net present cost and multi-dimensional architecture facets. It is critical to evaluate HRES using a variety of economic and sizing criteria. This paper aims to provide a succinct review of recent progress in the field of optimization of different HRES using various optimization techniques based on classical methods, artificial intelligence (AI), hybrid algorithms, and software-based optimization tools. While comparing classical and AI-based techniques, the AI-based techniques are found promising and provide a global solution in less time. However, there are deficiencies in AI-based techniques. To overcome those, a combination of two or more algorithms, known as hybrid optimization algorithm can be adopted to solve problems more quickly, reliably, and effectively. Apart from different algorithms, there are several popular optimization software tools. Among them, the HOMER software tool is one of the most popular as it is simple to use. As the research activities in the optimization of HRES are increasing, the concentration on sources such as hydro, geothermal, biomass, and biofuel has to be given due consideration. This analysis will inform readers about the current and evolving state of optimization approaches for HRES applications and enable them to choose the most appropriate strategy as per requirement. • Feasiblity and applicablity of various renewable energy systems to different energy applications are examined. • Various optimization methodologies applied to different hybrid renewable energy systems are analyzed. • The outcomes of the recent artificial intelligence techniques for variousoptimization problems are evaluated. • Economic, reliability, environmental, and social impacts of HRES are discussed. [ABSTRACT FROM AUTHOR]

This study explores the performance evaluation of recently developed metaheuristic optimization techniques used to address the sizing problem for five different stand-alone hybrid renewable energy systems applied to a rural community in India. The algorithms include the honey badger optimization algorithm (HBO), golden jackal optimization (GJO), and arithmetic optimization algorithms (AOA), intending to minimize the total annual cost (TAC) of the system while maintaining an acceptable loss of power supply probability and renewable fraction. The adopted algorithm has been simulated for 25 independent runs to determine its superiority. The outcomes of simulations were compared with popular algorithms such as cuckoo search (CS), the grey wolf algorithm (GWO), and HOMER software. According to the findings, a hybrid system integrating solar power (116.4 kW), battery storage (148.5 kW), and a diesel generator (15.7 kW) is the most cost-effective system. While comparing all techniques, the TAC of HBO ($33797) with a standard deviation (SD) of 2.29 and GWO ($33799) with a SD of 2.76 are the most economical. However, while comparing these, HBO obtained the least SD, which indicates a lower oscillation rate and a better trade-off exploration-exploitation balance than GWO and affirmed that HBO is well suited for solving this optimization problems. [ABSTRACT FROM AUTHOR]

GCNA proteins are expressed across eukarya in pluripotent cells and have conserved functions in fertility. GCNA homologs Spartan (DVC-1) and Wss1 resolve DNA-protein crosslinks (DPCs), including Topoisomerase-DNA adducts, during DNA replication. Here, we show that GCNA mutants in mouse and C. elegans display defects in genome maintenance including DNA damage, aberrant chromosome condensation, and crossover defects in mouse spermatocytes and spontaneous genomic rearrangements in C. elegans. We show that GCNA and topoisomerase II (TOP2) physically interact in both mice and worms and colocalize on condensed chromosomes during mitosis in C. elegans embryos. Moreover, C. elegans gcna-1 mutants are hypersensitive to TOP2 poison. Together, our findings support a model in which GCNA provides genome maintenance functions in the germline and may do so, in part, by promoting the resolution of TOP2 DPCs. • gcna-1 is essential for germline immortality and integrity of the heritable genome • GCNA works in tandem with Spartan (DVC-1) to maintain genomic integrity • GCNA promotes the resolution of TOP2 DPCs in the germline and early embryo in worms and mice DNA topoisomerases help unwind DNA but occasionally get trapped, resulting in DNA-protein crosslinks (DPCs). DPCs damage DNA and threaten genomic integrity. Dokshin et al. find that GCNA protein family complements standard DPC processing machinery in resolving topoisomerase II DPCs to ensure heritable genome stability and germline immortality. [ABSTRACT FROM AUTHOR]

Pancreatic ductal adenocarcinoma (PDAC) has a dismal prognosis. Cancer-associated fibroblasts (CAFs) are recognized potential therapeutic targets, but poor understanding of these heterogeneous cell populations has limited the development of effective treatment strategies. We previously identified transforming growth factor beta (TGF-β) as a main driver of myofibroblastic CAFs (myCAFs). Here, we show that epidermal growth factor receptor/Erb-B2 receptor (EGFR/ERBB2) signaling is induced by TGF-β in myCAFs through an autocrine process mediated by amphiregulin. Inhibition of this EGFR/ERBB2-signaling network in PDAC organoid-derived cultures and mouse models differentially impacts distinct CAF subtypes, providing insights into mechanisms underpinning their heterogeneity. Remarkably, EGFR-activated myCAFs promote PDAC metastasis in mice, unmasking functional significance in myCAF heterogeneity. Finally, analyses of other cancer datasets suggest that these processes might operate in other malignancies. These data provide functional relevance to myCAF heterogeneity and identify a candidate target for preventing tumor invasion in PDAC. [Display omitted] • PDAC malignant cell-secreted TGF-β induces EGFR/ERBB2 activation in myCAFs • EGFR/ERBB2 activation in TGF-β-induced myCAFs is mediated by autocrine amphiregulin • EGFR/ERBB2 inhibition targets a CD90− myCAF subset in PDAC tumors • EGFR-activated myCAFs promote PDAC metastasis Mucciolo et al. demonstrate EGFR/ERBB2 signaling activation in myofibroblastic cancer-associated fibroblasts (myCAFs) in pancreatic ductal adenocarcinoma (PDAC). EGFR-activated myCAFs promote metastasis in PDAC mouse models, revealing a previously unappreciated functional complexity of myCAFs. This knowledge could be used to design therapies that target PDAC malignant cells and tumor-promoting fibroblast populations. [ABSTRACT FROM AUTHOR]

• To reduce peak load demands and energy costs, MG uses DGs for a 24-hour period. • Minimize operational l.,costs as well as environmental emissions. • Equality and inequality constraints are considered. • The DE algorithm is simple and performs better than GA & PSO technique. This manuscript presents a unique method for stochastic management of renewable energy sources using Micro Grid (MG). This method is based on the demand reduction function to provide a demand side reserve to supply electricity to consumers. To reduce peak demand and energy costs, a typical MG for distributed generation (DG) is used to schedule 24-hour power. The optimal generation scheduling problem is solved as both single-objective and multi-objective optimization problems. Differential Evolution and Multi-Objective Differential Evolution are used to minimize operating costs and environmental emissions, which consider equality and inequality constraints to find the best way to allocate power among the many units connected to MG. Power allocation using DE is shown to be more cost effective and environmentally friendly compared to Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) based technique. Fig. A.1: Schematic configuration of micro grid with connecting all DG and feeders Fig. A.1 shows a schematic diagram of the micro grid with connecting all DGs and feeders. The considered system contains several technical power components such as micro turbine, wind turbine, photo-voltaic, phosphoric acid fuel cell and battery for reliable operation of the system. [Display omitted] [ABSTRACT FROM AUTHOR]

Abstract: The CO-releasing properties of iron(0)tricarbonyl complexes bearing a 2-pyrone ligand have been evaluated. In this report, we demonstrate that the intrinsic stability of the (η4-2-pyrone)Fe(CO)3 complex influences the extent and rate of CO release, which is affected by the presence of a halogen substituent on the 2-pyrone ring. The cell viability index has been highlighted for the active carbon monoxide-releasing molecules (CO-RMs), demonstrating that these complexes and related derivatives are a promising new class of compounds with potential therapeutic applications. [Copyright &y& Elsevier]