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The growing integration of renewable energy sources into grid-connected microgrids has created new challenges in power generation forecasting and energy management. This paper explores the use of advanced machine learning algorithms, specifically Support Vector Regression (SVR), to enhance the efficiency and reliability of these systems. The proposed SVR algorithm leverages comprehensive historical energy production data, detailed weather patterns, and dynamic grid conditions to accurately forecast power generation. Our model demonstrated significantly lower error metrics compared to traditional linear regression models, achieving a Mean Squared Error of 2.002 for solar PV and 3.059 for wind power forecasting. The Mean Absolute Error was reduced to 0.547 for solar PV and 0.825 for wind scenarios, and the Root Mean Squared Error (RMSE) was 1.415 for solar PV and 1.749 for wind power, showcasing the model’s superior accuracy. Enhanced predictive accuracy directly contributes to optimized resource allocation, enabling more precise control of energy generation schedules and reducing the reliance on external power sources. The application of our SVR model resulted in an 8.4% reduction in overall operating costs, highlighting its effectiveness in improving energy management efficiency. Furthermore, the system’s ability to predict fluctuations in energy output allowed for adaptive real-time energy management, reducing grid stress and enhancing system stability. This approach led to a 10% improvement in the balance between supply and demand, a 15% reduction in peak load demand, and a 12% increase in the utilization of renewable energy sources. Our approach enhances grid stability by better balancing supply and demand, mitigating the variability and intermittency of renewable energy sources. These advancements promote a more sustainable integration of renewable energy into the microgrid, contributing to a cleaner, more resilient, and efficient energy infrastructure. The findings of this research provide valuable insights into the development of intelligent energy systems capable of adapting to changing conditions, paving the way for future innovations in energy management. Additionally, this work underscores the potential of machine learning to revolutionize energy management practices by providing more accurate, reliable, and cost-effective solutions for integrating renewable energy into existing grid infrastructures.

One of the main concerns about the fast spreading coronavirus disease 2019 (Covid-19) pandemic is how to intervene. We analysed severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) isolates data using the multifractal approach and found a rich in viral genome diversity, which could be one of the root causes of the fast Covid-19 pandemic and is strongly affected by pressure and health index of the hosts inhabited regions. The calculated mutation rate ( m r ) is observed to be maximum at a particular pressure, beyond which m r maintains diversity. Hurst exponent and fractal dimension are found to be optimal at a critical pressure ( P m), whereas, for P > P m and P < P m, we found rich genome diversity relating to complicated genome organisation and virulence of the virus. The values of these complexity measurement parameters are found to be increased linearly with health index values.

Community participation is widely believed to be beneficial to the development, implementation and evaluation of health services. However, many challenges to successful and sustainable community involvement remain. Importantly, there is little evidence on the effect of community participation in terms of outcomes at both the community and individual level. Our systematic review seeks to examine the evidence on outcomes of community participation in high and upper-middle income countries. This review was developed according to PRISMA guidelines. Eligible studies included those that involved the community, service users, consumers, households, patients, public and their representatives in the development, implementation, and evaluation of health services, policy or interventions. We searched the following databases from January 2000 to September 2016: Medline, Embase, Global Health, Scopus, and LILACs. We independently screened articles for inclusion, conducted data extraction, and assessed studies for risk of bias. No language restrictions were made. 27,232 records were identified, with 23,468 after removal of duplicates. Following titles and abstracts screening, 49 met the inclusion criteria for this review. A narrative synthesis of the findings was conducted. Outcomes were categorised as process outcomes, community outcomes, health outcomes, empowerment and stakeholder perspectives. Our review reports a breadth of evidence that community involvement has a positive impact on health, particularly when substantiated by strong organisational and community processes. This is in line with the notion that participatory approaches and positive outcomes including community empowerment and health improvements do not occur in a linear progression, but instead consists of complex processes influenced by an array of social and cultural factors. This review adds to the evidence base supporting the effectiveness of community participation in yielding positive outcomes at the organizational, community and individual level. Prospero record number: CRD42016048244.

Aluminum matrix composites (AMCs) developed with micro/nano-reinforcements emerge as an attractive candidate for innumerable applications, including automotive, aerospace, electronics, biomedical, and many more, owing to their high strength-to-weight ratio and outstanding tribological, mechanical, electrical, and thermal characteristics. This work aims to offer a review of the state of the art of research in the processing, fabrication, properties, and potential applications of AMCs. The review starts with an emphasis on light-weighted AMCs, followed by a brief discussion of the hybrid metal matrix composite structure and micro/nano-reinforcement. This review also includes an in-depth assessment of manufacturing processes and parametric factors that regulate the properties of AMCs. It also highlights the challenges that are currently encountered when processing AMCs, such as limited wettability, reinforcement agglomeration, and interfacial reactions, before analyzing the effect of adding micro/nano-reinforcements on the attributes of AMCs. In addition to the stated characteristics, the most feasible and novel applications of AMCs have been envisioned. Lastly, new research directions in the field of AMCs have been recommended and critically discussed.

Supercapacitors currently hold a prominent position in energy storage systems due to their exceptionally high power density, although they fall behind batteries and fuel cells in terms of energy density. This paper examines contemporary approaches aimed at enhancing the energy density of supercapacitors by adopting hybrid configurations, alongside considerations of their power density, rate capability, and cycle stability. Given that electrodes play a pivotal role in supercapacitor cells, this review focuses on the design of hybrid electrode structures with elevated specific capacitance, shedding light on the underlying mechanisms. Factors such as available surface area, porosity, and conductivity of the constituent materials significantly influence electrode performance, prompting the adoption of strategies such as nanostructuring. Additionally, the paper delves into the impact of novel bio-based hybrid electrolytes, drawing upon literature data to outline the fabrication of various hybrid electrode materials incorporating conducting polymers like polyaniline and polypyrrole, as well as metal oxides, carbon compounds, and hybrid electrolytes such as ionic liquids, gel polymers, aqueous, and solid polymer electrolytes. The discussion explores the contributions of different components and methodologies to overall capacitance, with a primary emphasis on the mechanisms of energy storage through non-faradic electrical double-layer capacitance and faradaic pseudo-capacitance. Furthermore, the paper addresses the electrochemical performance of hybrid components, examining their concentrations and functioning via diverse charge storage techniques.

Screening for genomic sequence variants in genes of predictive and prognostic significance is an integral part of precision medicine. Next-generation sequencing (NGS) technologies are progressively becoming platforms of choice to facilitate this, owing to their massively parallel sequencing capability, which can be used to simultaneously screen multiple markers in multiple samples for a variety of variants (single nucleotide and multi nucleotide variants, insertions and deletions, gene copy number variations, and fusions). A crucial step in the workflow of targeted NGS is the enrichment of the genomic regions of interest to be sequenced, against the whole genomic background. This ensures that the NGS effort is focused to predominantly screen target regions of interest with minimal off-target sequencing, making it more accurate and economical. Polymerase chain reaction-based (PCR, or amplicon-based) and hybridization capture-based methodologies are the two prominent approaches employed for target enrichment. This review summarizes the basic principles of target enrichment utilized by these methods, their multiple variations that have evolved over time, automation approaches, overall comparison of their advantages and drawbacks, and commercially available choices for these methodologies.

Spinal muscular atrophy (SMA), a prominent genetic disease of infant mortality, is caused by low levels of survival motor neuron (SMN) protein owing to deletions or mutations of the SMN1 gene. SMN2 , a nearly identical copy of SMN1 present in humans, cannot compensate for the loss of SMN1 because of predominant skipping of exon 7 during pre-mRNA splicing. With the recent US Food and Drug Administration approval of nusinersen (Spinraza), the potential for correction of SMN2 exon 7 splicing as an SMA therapy has been affirmed. Nusinersen is an antisense oligonucleotide that targets intronic splicing silencer N1 (ISS-N1) discovered in 2004 at the University of Massachusetts Medical School. ISS-N1 has emerged as the model target for testing the therapeutic efficacy of antisense oligonucleotides using different chemistries as well as different mouse models of SMA. Here, we provide a historical account of events that led to the discovery of ISS-N1 and describe the impact of independent validations that raised the profile of ISS-N1 as one of the most potent antisense targets for the treatment of a genetic disease. Recent approval of nusinersen provides a much-needed boost for antisense technology that is just beginning to realize its potential. Beyond treating SMA, the ISS-N1 target offers myriad potentials for perfecting various aspects of the nucleic-acid-based technology for the amelioration of the countless number of pathological conditions.

Graphene and graphene oxide have become the base of many advanced biosensors due to their exceptional characteristics. However, lack of some properties, such as inertness of graphene in organic solutions and non-electrical conductivity of graphene oxide, are their drawbacks in sensing applications. To compensate for these shortcomings, various methods of modifications have been developed to provide the appropriate properties required for biosensing. Efficient modification of graphene and graphene oxide facilitates the interaction of biomolecules with their surface, and the ultimate bioconjugate can be employed as the main sensing part of the biosensors. Graphene nanomaterials as transducers increase the signal response in various sensing applications. Their large surface area and perfect biocompatibility with lots of biomolecules provide the prerequisite of a stable biosensor, which is the immobilization of bioreceptor on transducer. Biosensor development has paramount importance in the field of environmental monitoring, security, defense, food safety standards, clinical sector, marine sector, biomedicine, and drug discovery. Biosensor applications are also prevalent in the plant biology sector to find the missing links required in the metabolic process. In this review, the importance of oxygen functional groups in functionalizing the graphene and graphene oxide and different types of functionalization will be explained. Moreover, immobilization of biomolecules (such as protein, peptide, DNA, aptamer) on graphene and graphene oxide and at the end, the application of these biomaterials in biosensors with different transducing mechanisms will be discussed.

Successful treatment of diffuse large B-cell lymphoma (DLBCL) is frequently hindered by the development of resistance to conventional chemotherapy resulting in disease relapse and high mortality. High expression of antiapoptotic and/or drug transporter proteins induced by oncogenic signaling pathways has been implicated in the development of chemoresistance in cancer. Previously, our studies showed that high expression of adenosine triphosphate-binding cassette drug transporter ABCG2 in DLBCL correlated inversely with disease- and failure-free survival. In this study, we have implicated activated hedgehog (Hh) signaling pathway as a key factor behind high ABCG2 expression in DLBCL through direct upregulation of ABCG2 gene transcription. We have identified a single binding site for GLI transcription factors in the ABCG2 promoter and established its functionality using luciferase reporter, site-directed mutagenesis and chromatin-immunoprecipitation assays. Furthermore, in DLBCL tumor samples, significantly high ABCG2 and GLI1 levels were found in DLBCL tumors with lymph node involvement in comparison with DLBCL tumor cells collected from pleural and/or peritoneal effusions. This suggests a role for the stromal microenvironment in maintaining high levels of ABCG2 and GLI1. Accordingly, in vitro co-culture of DLBCL cells with HS-5 stromal cells increased ABCG2 mRNA and protein levels by paracrine activation of Hh signaling. In addition to ABCG2, co-culture of DLBCL cells with HS-5 cells also resulted in increase expression of the antiapoptotic proteins BCL2, BCL-xL and BCL2A1 and in induced chemotolerance to doxorubicin and methotrexate, drugs routinely used for the treatment of DLBCL. Similarly, activation of Hh signaling in DLBCL cell lines with recombinant Shh N-terminal peptide resulted in increased expression of BCL2 and ABCG2 associated with increased chemotolerance. Finally, functional inhibition of ABCG2 drug efflux activity with fumitremorgin C or inhibition of Hh signaling with cyclopamine-KAAD abrogated the stroma-induced chemotolerance suggesting that targeting ABCG2 and Hh signaling may have therapeutic value in overcoming chemoresistance in DLBCL.

Trees become dormant in winter, with encapsulated buds protected against harsh conditions. Tylewicz et al. found that, as the days get shorter, communication channels between cells in aspen trees shut down. The blocked plasmodesmata sequester the dormant meristems from growth signals. Growth-promoting signals can be turned on and off relatively rapidly, but the closed plasmodesmata are not so nimble. Thus, despite the occasional sunny day, the trees stay dormant until spring. Science , this issue p. 212 Aspen trees go dormant in winter because plasmodesmata, which would otherwise convey growth-promoting signals, shut down communication. In temperate and boreal ecosystems, seasonal cycles of growth and dormancy allow perennial plants to adapt to winter conditions. We show, in hybrid aspen trees, that photoperiodic regulation of dormancy is mechanistically distinct from autumnal growth cessation. Dormancy sets in when symplastic intercellular communication through plasmodesmata is blocked by a process dependent on the phytohormone abscisic acid. The communication blockage prevents growth-promoting signals from accessing the meristem. Thus, precocious growth is disallowed during dormancy. The dormant period, which supports robust survival of the aspen tree in winter, is due to loss of access to growth-promoting signals.