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Ber (Ziziphus mauritiana L.) commonly known as Indian jujube, is an underutilized seasonal fruit rich in nutrients and consumed directly. A punching type mechanism was developed to separate the seed and pulp of ber fruit, based on the analysis of its physical (length, width, thickness measured by vernier caliper) and mechanical properties (cutting and extruding forces measured by texture analyzer). The Pearson correlation coefficient showed that the physical parameters (width, thickness, geometric mean diameter, arithmetic mean diameter) were strongly correlated with the sphericity and surface area of the ber fruits and seeds, whereas length was not significantly correlated. The developed punching type de-seeding mechanism consists of a fruit resting seat, punching rod, guider, crank and lever mechanism, supporting stand, handle, and base. The performance of the developed de-seeder was analyzed, showing fruit pulp wastage from 1.89% to 8.62% and efficiency from 91.38% to 98.11%. The Pearson correlation coefficient indicated that the performance parameters (pulp wastage and efficiency) of de-seeder are negatively correlated. The capacity of the developed de-seeder varies from 15 – 17 kg/ h and 220 – 280 fruits/ h. The developed de-seeder reduces the labour requirement by 67%. In economic evaluation, the de-seeder has reduced the cost of operation (₹ h−1) by 73% and increase the net benefit (₹ year−1) by 6960 over manual operation. Its high efficiency and less pulp wastage make the machine preferable for progressive farmers. Highlights: Design and Development of Punching type mechanism for de-seeding of ber fruits Measurement of physical and mechanical properties of ber fruit and seeds. Statistical analysis of performance of ber fruit de-seeder Ergonomic and Economic analysis of ber fruit de-seeder in comparison to manual operation. [ABSTRACT FROM AUTHOR]

The rapid integration of IoT devices has ushered in unparalleled convenience and efficiency. However, ensuring secure communication and authenticity amidst constraints such as limited resources, heterogeneous configurations, and energy efficiency remains a formidable challenge. Physical Unclonable Functions (PUFs) offer a solution by establishing unique, device-specific identities through manufacturing process variations devoid of additional resource requirements. Challenge-response pairs (CRPs) are generated to authenticate IoT devices based on device-specific traits. Yet, PUF-generated CRPs often demonstrate susceptibility to modeling attacks due to high correlation. Despite complex PUF architectures, such as XOR PUF and Interpose PUF (iPUF), advances in machine learning (ML) techniques enable modeling attacks on PUFs. This work proposes a step towards more secure and resource-conscious PUF designs by introducing a novel 64-bit Arbiter-Skip Dynamic PUF (DPUF). This design integrates reliable noise into the delay-based arbiter PUF, creating a dynamic element that thwarts predictability. We conduct a detailed performance evaluation, comparing the effectiveness of logistic regression and multilayer perceptron-based modeling attacks on three PUF architectures: Arbiter PUF, XOR PUF, and the proposed DPUF. The experimental findings indicate that the proposed dynamic PUF exhibits better resilience against machine learning-based attacks while concurrently demonstrating efficient resource utilization. The contribution of the DPUF is a step towards more secure and resource-conscious PUF designs. The experimental results emphasize the potential suitability of the proposed dynamic PUF for integrating IoT applications, thereby contributing to enhanced security in these environments. [ABSTRACT FROM AUTHOR]

Increased temperature variability and extreme weather events associated with climate change can be detrimental to bees and lead to their population declines. While there is some research on the effects of heatwaves on insect biology and reproduction, impacts on male insect reproduction are not well described. Solitary bees may be more exposed to temperature variation than social bees, since there is no mitigation by group thermoregulation. Here, we evaluated the effects of sublethal heat exposure (at 30 °C and 38 °C) on reproductive fitness of adult males of a solitary bee species, Osmia cornifrons. After 4 h of heat exposure, bees were maintained at 24 °C for 48 h in laboratory cages to reach sexual maturity and later analyzed for sperm abundance (quantity) and proportion of viable sperm (quality). Bees that were reared and maintained at 24 °C served as controls. Despite rehabilitation after heat exposure, both sperm quantity and quality of male O. cornifrons bees were significantly reduced in heat-treated groups compared to controls. These results demonstrate that the exposure to elevated non-lethal temperatures, even for short periods and allowing time for recovery, may negatively affect the reproductive capacity of male solitary bees. Our findings reveal a possible mechanistic explanation for wild bee population declines. Given the importance of solitary bee species (such as O. cornifrons) in natural and agricultural landscapes, our study provides valuable insights into the potential consequences of climate change on these insect populations and the ecosystems they inhabit. [ABSTRACT FROM AUTHOR]

This paper presents enhanced parameterized quantum Hermite-Hadamard type integral inequalities for functions whose third right and left q-derivatives in absolute value are strongly convex functions. We obtain new bounds using H o ¨ lder’s and power mean inequalities as primary tools. Also, we derive new quantum estimates for q-trapezoidal and q-midpoints type inequalities in specific scenarios, which we illustrate with examples. These outcomes possess the potential for practical applications in optimizing various economic problems. [ABSTRACT FROM AUTHOR]

The agricultural productivity and world-wide food security is affected by different phytopathogens, in which Fusarium is more destructive affecting more than 150 crops, now got resistance against many fungicides that possess harmful effects on environment such as soil health, air pollution, and human health. Fusarium fungicide resistance is an increasing concern in agricultural and environmental contexts, requiring a thorough understanding of its causes, implications, and management approaches. The mechanisms of fungicide resistance in Fusarium spp., are reviewed in this article, including increased efflux pump activity, target-site mutations, and metabolic detoxification pathways. Fusarium is naturally resistant to some of the fungicides, on the other hand; it speedily develops resistance against the other fungicides groups. Most of the important plant pathogenic Fusarium species including F. oxysporum, F. psedogramanium, F. graminearium and Fusarium solani, which have shown resistance to major groups of fungicides including triazoles, phenylpyrole and benzimedazoles in various regions of the world. The review also covers a range of management techniques, including fungicide rotation, resistant cultivars, cultural methods, and biological control agents, to lessen fungicide resistance. By shedding light on the current state of knowledge concerning fungicide resistance in Fusarium spp., this review provides valuable information to researchers, policymakers, and practitioners to design long-term effective disease management approaches, as well as fungal menace control to preserve fungicides’ effectiveness in agriculture and conservancy activities. [ABSTRACT FROM AUTHOR]

Conventional perovskite materials face several limitations in the design of solar cell stability and toxicity for the environment due to lead (Pb) content. To overcome the toxicity, Pb has been replaced by manganese (Mn) in this work. In this context, NH2(CH2)2NH3MnCl4 as a lead-free perovskite absorber has been explored owing to its low exciton binding energy, high carrier mobility, and tunable bandgap. With the help of the SCAPS 1d simulator, the doping (Nd), bulk defect density (BDD), and thickness of the absorber layer have been varied. The optimized champion cell obtained a power conversion efficiency (PCE) of 17.70%, with a JSC of 13.86 mA/cm2, VOC of 1.45 V, and fill factor of 84.60%. Further, different ML models, including Random Forest, XGBoost, Linear Regression, and Support Vector Regression, have been applied to predict the PCE from a dataset of 2400. The XGBoost model outperformed the other ML models, achieving an MSE of 0.05 and an R2 of 99.73%, demonstrating superior accuracy in the prediction of the PCE of the cell. These results demonstrate the effectiveness of integrating novel materials with machine learning techniques to accurately predict solar cell performance, offering a more efficient and less computationally demanding alternative to traditional simulation methods. [ABSTRACT FROM AUTHOR]

This study investigated the effectiveness of 3D woven structural composite-based aircrew helmets comprising a 3D woven solid shell and a 3D woven honeycomb liner. This research adopted a structured sequence of steps to integrate desired aircrew helmet properties. The study involved the analysis of 3D woven structural composites through quasistatic compression and dynamic impact tests to assess their compressive strength and impact energy properties, respectively. Initially, the study focuses on optimizing the honeycomb liner by adjusting its structural parameters to improve the compressive strength. The research then delved into the critical role of impact energy, aiming to enhance load transfer to the liner for maximal impact energy absorption. Key findings highlight that the L2T2H3 honeycomb liner configuration, when combined with the OR8L3M shell, significantly improves the protective performance by exhibiting superior impact energy, cushioning properties, and compressive strength. Factors such as weave architecture, impactor geometry, impactor velocity, and face sheet thickness were found to influence the energy absorption capacity, emphasizing the importance of structural design optimization. The combined use of helmet shell and liner components demonstrated superior energy absorption capabilities compared to individual components. This combination suggests a successful approach for achieving enhanced performance in aircrew helmets. By analyzing compressive strength and impact energy, this research contributes to the ongoing efforts to enhance the performance of aircrew helmets, thereby ensuring improved safety and protection for aircrew members operating in high-risk environments. [ABSTRACT FROM AUTHOR]

Background: Oral and maxillofacial lesions (OMFL) consist of a various malignant and benign lesions of the oral cavity. The aim of this study was to report the details of OMFL that had been diagnosed histologically at Oral Pathology Unit of a Dental Institute of eastern UP region. Materials and Methods: Oral cavity biopsied submitted to the Oral Pathology Unit during the period from 2016 to 2021 was evaluated. Information on age, gender, and histopathologic diagnosis was analyzed. Results: A total of 1128 biopsies were examined and analyzed. Reactive lesions were the most common type (n = 267; 23.7%) of lesions diagnosed, and epithelial malignancies (n = 250; 22.2%) were second most common type of lesion followed by cystic lesions (n = 202; 17.92%), epithelial lesions (n = 141; 12.5%), and odontogenic tumors (n = 122; 10.82%). Conclusion: The results provide important information on the prevalence and distribution of OMFL in eastern UP region of India. Reactive lesions were the most common type of OMFL diagnosed. Most of the lesions were soft tissue lesions, inflammatory in origin, and benign in nature. Multicentric studies involving larger sample should be carried out to assess the pattern of oral diseases in our community leading to the overall betterment of our society. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to evaluate whether the digital eye strain (DES) was associated with the low central corneal thickness (CCT). Methods: This observational cross-sectional pilot study was conducted from April 2023 to October 2023 at a tertiary eye care centre in North India, where CCT values were compared between subjects with DES and those without DES. Two hundred and eighty subjects (n = 280) aged 15–40 years with clear corneas and lenses were initially included in this study. Those with corneal opacity, corneal degeneration, pregnancy, and diabetes were excluded. These subjects were then assessed with a 'computer vision syndrome questionnaire (CVS-Q)' test. Participants having a score ≥ 6 were grouped as the "DES group" and participants having a score < 6 were grouped as the "Control group". The participants from each group were then examined with Spectral Domain Ocular Coherence Tomography (SD-OCT) to measure CCT. The primary outcome was to evaluate the comparative difference in CCT values between the two groups as well as the association of DES with CCT values. Results: The study included one hundred and forty (n = 140) subjects each in the DES group and the control group. The DES score assessed with the CVS-Q questionnaire ranged from 6 to 32 in the DES group, with a mean ± SD of 17.08 ± 7.01. The overall mean CCT was 507.31 ± 31.01 (median, 512 microns; range, 450–601 microns). There was a significant difference in the mean CCT between the two groups, with lower mean CCT values in the DES group (487.76 ± 23.61 microns) than the control group (526.87 ± 24.53 microns) (p < 0.0001). The CVS-Q test score had moderate inverse correlation in the DES group (Pearson's r = − 0.42, p < 0.01) and negligible correlation (Pearson's r = − 0.14, p = 0.12) in the control group with CCT. Conclusion: Central corneal thickness was observed to be reduced among the subjects with digital eye strain (DES). [ABSTRACT FROM AUTHOR]

Erythroderma is a severe and heterogeneous inflammatory skin condition with little guidance on the approach to management in cases of unknown etiology. To guide therapeutic selection, we sought to create an immunophenotyping platform able to identify aberrant cell populations and cytokines in subtypes of erythroderma. We performed high-parameter flow cytometry on peripheral blood mononuclear cells (PBMCs) and whole blood of a patient with refractory idiopathic erythroderma, erythrodermic patients with Sézary syndrome and pityriasis rubra pilaris, and healthy controls. We found that the index patient had a novel form of erythroderma characterized by increased interleukin (IL)-13- and IL-17-producing γVδ2 T cells, basophils, and activated platelets. Whole-genome sequencing of PBMCs and immunofluorescence staining of skin biopsies revealed increased expression of Th2- (IL-13, IL-4Rα) and Th17-associated markers (IL-17, IL-17Rα) and non-functional mutations associated with Th2 and Th17 signaling, demonstrating that PBMCs can reflect cutaneous disease pathology. Targeted intervention via dual dupilumab and secukinumab therapy resulted in complete disease control and reduction of immunopathogenic cell populations and cytokines. This study highlights a novel form of erythroderma with concomitant Th2 and Th17 dysregulation and introduces a minimally invasive peripheral blood-based immunophenotyping platform that offers a personalized medicine approach to the management of systemic inflammatory diseases. [ABSTRACT FROM AUTHOR]

The use of code-mixed languages in social media platforms is very common to communicate in an informal way and has immense importance in a multilingual society, like India. Implementing various NLP tasks on code-mixed language for machine comprehension and NLP applications is the need of the hour. The implementation of complex learning models is difficult due to the scarcity of available code-mixed resources. Designing more effective architectures to perform learning from low resource dataset along with transfer learning settings are the possible solutions. We propose an improvised transformer network (Character Inclusion Transformer) that utilizes and learns character-level information available in the words of code-mixed sentences. The proposed model improves the performance of the transformer model when trained from scratch using low resource code-mixed datasets. We also propose two more architecture settings, useful for transfer learning strategy using the mBERT pre-trained model. Three basic word-level tagging NLP tasks, i.e., NER, POS Tagging, and Language Identification (LID) are considered in the paper where Language Identification is specific to code-mixed language. Six separate datasets, namely IIITH NER, LID FIRE, LID ICON, LID UD, POS ICON, POS UD, have been tested, and results are reported using weighted and macro-average while evaluating precision, recall and F1 score [ABSTRACT FROM AUTHOR]

The phosphatidylethanolamine-binding protein (PEBP) gene family plays a vital role in various aspects of plant biology, including development, differentiation, metabolism, and signaling pathways. This study examined the genetic relations between PEBP genes in palms (oil palm, coconut, and date) using various techniques of sequence retrieval, alignment, phylogenetic tree construction, and protein structure modeling. A significant genetic resemblance was found between coconut and oil palm, surpassing the similarity observed between either of these species and date palm. Three primary groups of PEBP proteins were identified: FLOWERING LOCUS T (FT), TERMINAL FLOWER1 (TFL1), and MOTHER OF FT AND TFL1 (MFT). The FT group was further categorized into two subclades. Analysis of the FT protein structures in these subclades revealed significant similarities. A notable difference was observed in the absence of important amino acids in specific segments of the second subclade, making it more similar to the TFL group. Furthermore, mRNA sequence analysis within and between palm species indicated significant genetic similarities in the evolutionary trajectories of these organisms, suggesting the possibility of gene duplication in their ancestral genomes. In silico expression analysis revealed that gene expression patterns differed from those of RNA sequences, possibly because data were not collected at a specific time. This study provides significant insights into the genetic evolution and diversification of the palm PEBP family, highlighting a possible link between the regulatory mechanisms that govern the flowering processes, which may be beneficial for accelerating breeding programs. [ABSTRACT FROM AUTHOR]

Background and Objective: Neonatal pharmacotherapy has gained attention from clinicians and regulatory agencies for optimizing the dosage of the drug which improves therapeutic outcomes in this special population. Piperacillin–tazobactam antibiotic is commonly used as a therapeutic option for treatment of severe infection in neonatal intensive care units. There are few population pharmacokinetic (PopPK) studies of piperacillin and tazobactam published for this specific population and which were not validated in other study settings. The aim of this study was to externally evaluate the published population pharmacokinetic models for piperacillin–tazobactam. Methods: A systematic review was conducted through Scopus, PubMed, and Embase databases to identify PopPK models. Clinical data collected in neonates treated with piperacillin–tazobactam were used for evaluation of these models. Various prediction-based metrics were used for assessing the bias and precision of PopPK models using individual predictions. Results: Three PopPK models were identified for external evaluation. A total of 53 plasma samples were collected from 46 neonates admitted in the neonatal intensive care unit. The PopPK models reported by Cohen-Wolkowiez et al. for piperacillin and Li et al. for tazobactam were able to predict well for our clinical data. Conclusion: The PopPK models by Cohen-Wolkowiez et al. and Li et al. predicted our data well for piperacillin and tazobactam with the lower relative median absolute predictive error (rMAPE) of 8.61% and 16.48% and relative root mean square error (rRMSE) of 0.01 and 0.03, respectively. External evaluation of the published PopPK models of piperacillin and tazobactam resulted in enhancing their credibility to be implemented in clinical practice. [ABSTRACT FROM AUTHOR]

Plant diseases pose a significant threat to global food security, necessitating innovative and sustainable strategies for disease management. Due to its numerous emerging and innovative applications, nanotechnology has garnered interest in a variety of sectors. Engineered nanoparticles are versatile materials with sizes ranging from 1 to 100 nm that can be used as fungicides, bactericides, and nano-fertilizers because of their high reactivity, wide surface area, and tiny size. In order to achieve the goals of sustainable farming, nano-bioformulations are being developed. Biopolymers, including cellulose, starch, alginate, chitin, and chitosan, with ecological endurability are employed for synthesizing nano-formulations. The second most prevalent biopolymer after cellulose is chitosan, which is utilized extensively because of its special qualities, which include non-toxicity, pH sensitivity, abundance, biodegradability, biocompatibility, low allergenicity, and bioabsorbability. Chitosan, a natural biopolymer derived from chitin, has gained attention for its antifungal, antibacterial, and elicitor properties. The combination of natural biopolymers with nanotechnology presents an opportunity to revolutionize agriculture and plant protection. High surface area, positive charge, and nanoscale size are some of the distinct physicochemical characteristics of nano-chitosan that boost its bioactivity and improve the interaction with plant tissues. Chitosan nanoparticles (ChNPs) have multifaceted modes of action, viz., direct antimicrobial activity, induction of plant defense, and modulation of microbial gene expression. It is broadly used in disease suppression and improving overall plant health. The techniques, viz., ionic gelation, emulsion cross-linking, and solvent evaporation, are commonly used to synthesize ChNPs, showing better control over particle size, stability, and biocompatibility. The present review highlights the synthesis of ChNPs, their potential applications in crop protection, their mechanism of action against plant pathogens, and their toxicity in plants. [ABSTRACT FROM AUTHOR]

In recent years, the utilization of green synthesis routes for the production of nanomaterials has garnered considerable attention owing to their eco-friendly nature and multifaceted applications. Present study presents the phyto-mediated synthesis of red fluorescent graphene quantum dots using the leaves of Bauhinia variegata as a sustainable precursor. The synthesized GQDs were characterized through various analytical techniques i.e. UV–visible spectroscopy, FT-IR, fluorescence spectrophotometry, XRD, DLS and TEM to confirm their structural and optical properties. The bio-functional properties of the GQDs were explored, focusing on their antidiabetic and antioxidant activities. The antidiabetic potential of the GQDs was evaluated by measuring their inhibitory effects on α-amylase and α-glucosidase enzymes, which are key to glucose metabolism. Furthermore, antioxidant activity was assessed using DPPH⋅ scavenging assays, demonstrating the free radical neutralizing ability of the GQDs. The study found that the synthesized graphene quantum dots (GQDs) were more effective than the leaf extract in antioxidant and enzyme inhibition activities. The GQDs showed lower IC50 values for DPPH⋅ radical scavenging (51.232 ± 6.478 µg/mL), α-amylase inhibition (135.309 ± 4.970 µg/mL), and α-glucosidase inhibition (147.551 ± 5.549 µg/mL) compared to the leaf extract, which showed higher IC50 values of 71.157 ± 6.134 µg/mL, 157.660 ± 7.463 µg/mL, 152.455 ± 4.630 µg/mL respectively. The findings highlight the multifunctionality of Bauhinia derived GQDs as promising materials for biomedical and environmental applications, offering eco-friendly alternatives for antidiabetic treatments and antioxidant therapy. [ABSTRACT FROM AUTHOR]

Domain-specific vocabulary, which is crucial in fields such as Information Retrieval and Natural Language Processing, requires continuous updates to remain effective. Incremental Learning, unlike conventional methods, updates existing knowledge without retraining from scratch. This paper presents an incremental learning algorithm for updating domain-specific vocabularies. It introduces DocLib, an archive used to capture a compact footprint of previously seen data and vocabulary terms. Task-based evaluation measures the effectiveness of the updated vocabulary by using vocabulary terms to perform a downstream task of text classification. The classification accuracy gauges the effectiveness of the vocabulary in discerning unseen documents related to the domain. Experiments illustrate that multiple incremental updates maintain vocabulary relevance without compromising its effectiveness. The proposed algorithm ensures bounded memory and processing requirements, distinguishing it from conventional approaches. Novel algorithms are introduced to assess the stability and plasticity of the proposed approach, demonstrating its ability to assimilate new knowledge while retaining old insights. The generalizability of the vocabulary is tested across datasets, achieving 97.89% accuracy in identifying domain-related data. A comparison with state-of-the-art techniques using a benchmark dataset confirms the effectiveness of the proposed approach. Importantly, this approach extends beyond classification tasks, potentially benefiting other research fields. [ABSTRACT FROM AUTHOR]

Pathogens significantly influence natural and agricultural ecosystems, playing a crucial role in the regulation of species populations and maintaining biodiversity. Entomopathogenic fungi (EF), particularly within the Hypocreales order, exemplify understudied pathogens that infect insects and other arthropods globally. Despite their ecological importance, comprehensive data on EF host specificity and geographical distribution are lacking. To address this, we present EntomoFun 1.0, an open-access database centralizing global records of EF–insect associations in Hypocreales. This database includes 1,791 records detailing EF species, insect host taxa, countries of occurrence, life stages of hosts, and information sources. EntomoFun 1.0 is constructed based on 600 literature sources, as well as herbarium specimens of the Royal Botanical Gardens, Kew. This database is intended to test hypotheses, identify knowledge gaps, and stimulate future research. Contents of the EntomoFun 1.0 database are visualized with a global map, taxonomic chart, bipartite community network, and graphs. [ABSTRACT FROM AUTHOR]

Lead halide hybrid solar cells have demonstrated exceptional performance in recent years, but concerns over their toxicity and instability have spurred the development of perovskite-based cells without lead. This work explores a lead-free perovskite material consisting of cesium tin-germanium triiodide solid solution perovskite (CsSn0.5Ge0.5I3) is utilized to fabricate solar cells with varying thicknesses and donor densities of the absorber layer. The results imply that enhancing the thickness of the layer boosts the power conversion efficiency (PCE) by facilitating better photon absorption. However, this increase in thickness also causes a reduction in both the open-circuit voltage (VOC) and fill factor (FF). In other words, while thickening the layer improves PCE through increased photon absorption, it also negatively impacts other key performance metrics, such as VOC and FF. Conversely, the effect of donor density (Nd) on the cell's performance is less significant than the CsSn0.5Ge0.5I3 layer's thickness. This study sheds light on the critical role of thickness and donor density in optimizing the performance of CsSn0.5Ge0.5I3-based solar cells. The performance of c-Si and CIGS-based single junction bottom cells under standalone conditions is also analyzed using JV and EQE curves. The C-Si-based single junction solar cell exhibited greater efficiency (21.28%) in converting photons of different wavelengths into electrons compared to the CIGS-based solar cell (16.26%). Lastly, for increasing the PCE of the single-junction solar cells the study moves towards multi-junction solar cells. In this context, two highly efficient TSCs (LFPVK/c-Si and LFPVK/CIGS) are designed and analyzed using the current matching technique, which involves the series connection of the top and bottom cells with increased PV parameters (LFPVK/c-Si-JSC: 21.44 mA/cm2, VOC: 1.45 V, FF: 82.17%, PCE: 25.54% and LFPVK/CIGS-JSC: 21.83 mA/cm2, VOC: 1.33 V, FF: 73.34%, PCE: 21.45%). [ABSTRACT FROM AUTHOR]

Lysosomal damage induces stress granule (SG) formation. However, the importance of SGs in determining cell fate and the precise mechanisms that mediate SG formation in response to lysosomal damage remain unclear. Here, we describe a novel calcium-dependent pathway controlling SG formation, which promotes cell survival during lysosomal damage. Mechanistically, the calcium-activated protein ALIX transduces lysosomal damage signals to SG formation by controlling eIF2α phosphorylation after sensing calcium leakage. ALIX enhances eIF2α phosphorylation by promoting the association between PKR and its activator PACT, with galectin-3 inhibiting this interaction; these regulatory events occur on damaged lysosomes. We further find that SG formation plays a crucial role in promoting cell survival upon lysosomal damage caused by factors such as SARS-CoV-2ORF3a, adenovirus, malarial pigment, proteopathic tau, or environmental hazards. Collectively, these data provide insights into the mechanism of SG formation upon lysosomal damage and implicate it in diseases associated with damaged lysosomes and SGs. Synopsis: Stress granules (SGs) form in response to diverse environmental signals. Here, the signaling pathways that connect lysosomal stress to SG formation are characterized. A calcium-dependent pathway initiates SG formation in response to lysosomal damage. ALIX, a sensor of calcium release from damaged lysosomes, controls the initial stages of SG formation, particularly eIF2α phosphorylation. Modulation of eIF2α phosphorylation involves opposing effects of ALIX and galectin-3 on PKR-PACT association. SG formation promotes cell survival upon lysosomal damage induced by various endogenous, viral, or environmental factors. Lysosome damage is a critical internal physiological trigger for stress granule formation. [ABSTRACT FROM AUTHOR]

Polymorphous AdenoCarcinoma (PAC), a slow growing tumor primarily found in minor salivary glands, predominantly palate. Herein, we report a case of 52-year female with palatal swelling. This report highlights the recent updates on molecular and IHC findings of PAC and its sub variant CASG. [ABSTRACT FROM AUTHOR]

In a conventional power system, distribution systems tend to incur higher power losses. However, integrating decentralized sources into power networks not only reduces these losses but also alleviates the strain on primary sources. Since centralized power sources are often situated at a distance, incorporating distributed generations (DGs) closer to load centers offers more efficient system operation. However, the lack of proper sizing alignment among secondary sources could result in operational, financial, and ecological complexities. Selecting optimal DG locations and sizes necessitates intricate computational processes, escalating workload demands. Fortunately, advancements in optimization software have simplified this task. The primary focus of this paper lies in optimizing DG allocation through the application of a fuzzified firefly algorithm (FFA). The strategic placement of diverse DG types aims to reduce both real and reactive power losses within the distribution system, followed by an evaluation of system performance indices. The study examines how different types of DGs incorporated into an IEEE-33 bus system elucidate the impact of varying power system loads, spanning from 50 to 150% of their rated value. [ABSTRACT FROM AUTHOR]

Bumble bees are important pollinators in natural environments and agricultural farmlands, and they are in particular adapted to harsh environments like high mountain habitats. In these environments, animals are exposed to low temperature and face the risk of desiccation. The Eastern Himalayas are one of the recognized biodiversity hotspots worldwide. The area covers subtropical rainforest with warm temperature and high precipitation as well as high mountain ranges with peaks reaching up to 7,000 m, shaping a diverse floral and faunal community at the different elevational zones. To identify possible adaptation strategies, we investigated the cuticular hydrocarbon profiles of four bumble bee species occurring at different elevational ranges in Arunachal Pradesh, the northeastern most state in India. At 17 locations along an elevational gradient, we collected workers of two species from lower elevations (B. albopleuralis and B. breviceps; ~ 100 m − 3,000 m asl) and two species from higher elevations (B. prshewalskyi and B. mirus; ~ 2,800 m − 4,500 m asl). The CHC profiles of all four species showed a significant degree of variation in the composition of hydrocarbons, indicating species specificity. We also found clear correlation with elevation. The weighted mean chain length of the hydrocarbons significantly differed between the low and high elevation species, and the proportion of saturated hydrocarbons in CHC profiles significantly increased with the elevational range of the bumble bee species. Our results indicate that bumble bees living at high elevations reduce the risk of water loss by adapting their CHC composition on their cuticle, a phenomenon that has also been found in other insects like ants and fruit flies. [ABSTRACT FROM AUTHOR]

The optical properties of Carbon dot (CD) solution significantly change on dilution. Herein, the effect of dilution on the optical properties of folic acid-derived CD was meticulously analyzed using absorption and photoluminescence (PL) spectroscopy. Absorption spectra of as-prepared CD solution consist of four overlapping yet discernable absorption bands centered at ~ 249, 302, 348, and 330 nm, respectively, attributed to originate from the π-π* transition and n-π* transitions and/or surface states. As the CD solution is diluted from 100 to 5%, these four absorption bands become more resolved. Moreover, we observed a blueshift of ~ 30 nm for the transitions at ~ 302 nm due to surface state with dilution up to 20% CD concentration. This is attributed to the decrease in the interaction between surface states due to the increase in the interparticle distance with dilution. PL emission from the as-prepared CD solution is centered at 463 nm and is asymmetric. This can be resolved into three components centered at 446 nm (intense), 474 nm (intense) and 508 nm (weak) respectively. With dilution, the PL intensity corresponding to the 463 nm emission seems to increase up to an optimum concentration of 15% CD and then decreases. The high concentration effectively quenches the luminescence through inner filter effect which is evident from the overlapping of absorption peak with the peak in the excitation spectrum together with no notable change in the average decay time. The decrease in the percentage of overlapping area of the absorption and excitation spectra with dilution causes the reduction of inner filter effect and enhances the luminescence for diluted solutions. Furthermore, we found that the surface states become more dominant in the contribution of luminescence of CD, whose influence diminishes in extremely diluted solutions, thereby the intensity decrease below 15% dilution. [ABSTRACT FROM AUTHOR]

To meet the demands of the contemporary world, lead-free and non-toxic materials must be found in the pursuit of sustainable energy. Perovskite solar cells (PSCs) based on FAMASnI3 appear to be a promising alternative because they are non-toxic and inexpensive. Computational modeling enables efficient analysis of perovskite solar cell performance, optimizing materials, interfaces, and device architectures without extensive experimental trials. It accelerates innovation by providing insights into mechanisms like charge transport, recombination, and defect dynamics, saving time and costs. In the present study, the evaluation of FAMASnI3-based PSCs with organic electron transport layers (ETLs), such as FNiPc, BrNiPc, and C60, has been presented with SCAPS-1D software. Optimizing the absorber layer thickness ( 300 n m ) and defect density ( 1 × 10 13 c m - 3 ), the performance of these PSCs has been enhanced. Furthermore, the effect of ETL thickness on solar cell efficiency is also studied. The results shows that the maximum power conversion efficiency of the PSCs is 22.89%, with a V OC of 0.94 V, J SC of 28.54 mA/cm2, and FF of 85.06%, is achieved by utilizing BrNiPc as the ETL material and FAMASnI3 as the absorber layer. These results show that great efficiency may be achieved at cheaper manufacturing costs and with little environmental impact when tin-based lead-free PSCs are produced. [ABSTRACT FROM AUTHOR]

Greenland ice sheet has the potential to exert a notable impact on the rise of sea level in the background of global warming. Microwave Remote sensing observations, due to their synoptic and all-weather capability, have been preferred to study the snow melt/freeze condition of the cryosphere. In the current study, near-surface melt characteristics of the Greenland ice sheets were analysed using ISRO's SCATSAT-1 RADAR Cross Section (RCS) observations in conjunction with ERA5 near the surface temperature at a daily scale for 2017–2020. RCS of HH and VV polarization shows similar spatial variability and sensitivity across Greenland. PDFs are similar for both the polarizations with multi- modal distribution of RCS, which signify the mixing of two or more classes of snow conditions. Mean climatology indicates the existence of 2 distinct regions: central high altitude-plateau (> 2500 m) characterized by lower mean RCS with small seasonal amplitude (< 0.5) considered as the permanent snow/ice cover region and the peripheral region (altitude < 2500 m) characterized by higher mean RCS and higher seasonal amplitude (0.5–2) as the dynamic region. Greenland was divided into 6 major classes based on the climatological mean of RCS in association with topography, and each class is characterized with a unique unimodal PDF. Analysis using RCS and temperature was carried out for melt/freeze identification and quantification of temporal dynamics of snow melt regions for each class. The melt prone regions of Greenland had an early melt onset and delayed freeze onset by almost 15 days in 2019, when compared to other years. During the peak melt period, Greenland had an excess melt of approximately 26% in 2019 when compared to 3-year average of 2017–2020 (excluding 2019). A notable 63% of the total Greenland area was in melt condition in 2019 in comparison with an average of 37% during 2017–2020. [ABSTRACT FROM AUTHOR]

Purpose of Review: The COVID-19 pandemic accelerated the surge in medical data utilization, notably impacting chronic pain management given its enormous economist cost. While the collection and use of data enhances research and care quality, data exchange risks compromising integrity and privacy, exemplified by the Change Healthcare cyberattack. Here, we review the potential of blockchain for the utilization for cybersecurity in the healthcare system with an emphasis on the field of chronic pain. Recent Findings: Blockchain technology emerges as a potential solution, offering decentralized, secure, and immutable data management. Blockchain ensures transparency, integrity, and data privacy, which is crucial in healthcare. Smart contracts may offer automated, secure management of implantable neuromodulation devices such as spinal cord stimulators and intrathecal pumps. Blockchain's potential in pharmaceutical supply chain integrity is exemplified in preliminary efforts ensuring that the medication retrieved by the patient is indeed the intended medication. Summary: Despite limitations such as speed of transactions, blockchain presents innovative avenues for healthcare security and quality improvement, necessitating further development for widespread implementation. Blockchain's applicability is not only applicable to chronic pain management, but can be used in medicine as a whole. [ABSTRACT FROM AUTHOR]

The present study was aimed at investigating the source, quality, and quantity of organic matter and labile fraction of biochemical constituents in the surficial sediments of Arabian Sea Shoreline at Kollam, India, which gives an input into the processes associated with the subsurface geochemistry pattern. About 15 surface sediment samples were collected from the five beaches for this study during January 2021. The samples were analyzed for CHN, total organic carbon (TOC), and labile fraction such as carbohydrates (CHO), proteins (PRO), and lipids (LIP). The TOC/TN ratio revealed that the source organic matter (OM) in the samples was primarily of marine origin and autochthonous, except TM3 at Thirumullavaram. Thirumullavaram is a sheltered beach, and it has low hydrodynamic process. The high organic matter content in TM3 (13.9) may be the originated from the terrestrial input. The hierarchical cluster analysis revealed that TM3 indicate that unique character to other stations. Principal component analysis revealed that three components accounted for 78% of the total variance. PC1 variance was associated with carbon-based organic compounds. PC2 was predominantly influenced by CHO and LOM, suggesting that PC2 may encompass variability associated with these nutritional profiles. PC3 was influenced by LIP and LIP/CHO, showing it may be a lipid-related component. The PRO to CHO ratio is < 1, exhibiting old aged organic matter deposited in sediments and the meso-oligotrophic status. The LIP to CHO ratio, recorded as < 1, exhibits the poor quality of energetic (food) OM in sediments. The sediment samples exhibit the following trend CHO > PRO > LIP. [ABSTRACT FROM AUTHOR]

Knowledge exploration refers to actively seeking information, ideas, and experiences, often beyond immediate task requirements. Previous research on exploratory behaviour has predominantly focused on visual and perceptual forms of exploration, overlooking the academic aspect, where the aim is to bridge knowledge gaps. To test the effect of task and individual factors on knowledge exploration, an experimental study was conducted on 100 high school students in two sessions. In the first session, participants answered 15 multiple-choice questions without receiving immediate feedback but were given the opportunity to explore the answers. In the second session, immediate feedback was provided after each response. The results indicated that knowledge exploration was more likely to occur when students answered the questions wrongly, and the feedback on the accuracy of answers significantly enhanced knowledge exploration for incorrect answers. Session 2 also investigated the impact of factors such as Feeling of Confidence (FOC), confidence error, and intrinsic and extrinsic academic motivation on knowledge exploration. A multilevel ordinal logit model was employed to analyse the within-person and between-person relationships among these variables. The findings revealed that FOC, confidence error, and intrinsic motivation positively predicted knowledge exploration, whereas extrinsic motivation had a negative effect. These findings have significant implications for educational practice and policy. Educators can structure their course materials to cultivate students' metacognitive awareness, thereby promoting deep engagement and enhancing their learning experience. [ABSTRACT FROM AUTHOR]

In this research paper, we have evaluated fuzzy reliability and fuzzy mean time to failure of two different systems by using distinct fuzzy distributions under triskaidecagonal fuzzy environment. Using the concept of α -cut of triskaidecagonal fuzzy number, firstly we have evaluated the system reliability in which the lifetimes of components are modelled as fuzzy exponential distribution and secondly, we have evaluated the fuzzy reliability functions of k-out-of-m system and parallel system where the survivors have the binomial distribution. Finally, some numerical examples are taken to illustrate how to calculate the fuzzy reliability and fuzzy mean time to failure along with the graphs. [ABSTRACT FROM AUTHOR]

The tectonic character of the Indian Peninsular Shield is in a state of dichotomy among various researchers. Although the landscape is a very ancient surface, an outcome of prolonged denudation, various topographic disequilibrium conditions at different areas in the shield repeatedly question the notion of long-term tectonic stability. One of the important sub-units of the shield is the Chhotanagpur Granite-Gneiss Complex in the north. It is a plateau formed by solidification of lava flows, and no noticeable tectonic movement is observed during the Holocene. However, many of the rivers draining this plateau display numerous characteristics of topographic disequilibrium conditions. One such river is the Barakar River, which flows through the Indian states of Jharkhand and West Bengal. Apart from high-level surfaces, this river is elongated and closely resembles a river flowing through a rift valley. The present study has been carried out in the Barakar River Basin to identify various evidence of topographic disequilibrium. Further analysis involved checking whether there is any spatial arrangement in such conditions of disequilibrium and associating their occurrence with litho-structure and tectonics. For this purpose, the SRTM–DEM-derived longitudinal profiles were extracted for the Barakar River and its major tributaries. The reason for extracting the long profiles is the fact that these profiles represent the long-term morphological adjustment of the rivers to varying conditions of litho-structure and tectonics. DEM analysis reveals that the concave-up profile associated with river systems under tectonic stability is missing. Furthermore, the above-grade conditions and over-steepened lower segments of many of the tributaries reveal that the evolutionary phase of drainage development has not been completed yet. A number of steep segments are noticed with no relation to the lithologic boundaries, implying tectonic control. However, the results derived for the Barakar River do not resemble actively uplifting areas such as the Himalayas. The marked resemblance to the rivers of the Deccan and Mysore Plateau implies that there is no evidence in the Barakar River Basin, wherein the tectonic upheaval is geologically recent. [ABSTRACT FROM AUTHOR]

Spinal muscular atrophy (SMA) is an autosomal recessive genetic neuromuscular disorder that is characterized by gradual muscle weakness and atrophy due to the degeneration of alpha motor neurons that are present on the anterior horn of the spinal cord. Despite the comprehensive investigations conducted by global scientists, effective treatments or interventions remain elusive. The time- and resource-intensive nature of the initial stages of drug research underscores the need for alternate strategies like drug repurposing. This review explores the repurposed drugs that have shown some improvement in treating SMA, including branaplam, riluzole, olesoxime, harmine, and prednisolone. The current strategy for medication repurposing, however, lacks systematicity and frequently depends more on serendipitous discoveries than on organized approaches. To speed up the development of successful therapeutic interventions, it is apparent that a methodical approach targeting the molecular origins of SMA is strictly required. [ABSTRACT FROM AUTHOR]

Introduction: The coronavirus disease 2019 (COVID-19) pandemic has caused unprecedented pressure on healthcare systems globally. The lack of quality guidelines on the management of COVID-19 in rheumatologic disease, renal disease, hematological malignancy, and solid organ transplant recipients has resulted in a wide variation in clinical practice. Methods: Using a Delphi process, a panel of 16 key opinion leaders developed clinical practice statements regarding vaccine recommendations in areas where standards are absent or limited. Agreement among practicing physicians with consensus statements was also assessed via an online physician survey. The strength of the consensus was determined by the following rating system: a strong rating was defined as all four key opinion leaders (KOLs) rating the statement ≥ 8, a moderate rating was defined as three out of four KOLs rating the statement ≥ 8, and no consensus was defined as less than three out of four KOLs provided a rating of ≤ 8. Specialists voted on agreement with each consensus statement for their disease area using the same ten-point scoring system. Results: Key opinion leaders in rheumatology, nephrology, and hematology achieved consensuses for all nine statements pertaining to the primary and booster series with transplant physicians reaching consensus on eight of nine statements. Experts agreed that COVID-19 vaccines are safe, effective, and well tolerated by patients with rheumatological conditions, renal disease, hematologic malignancy, and recipients of solid organ transplants. The Delphi process yielded strong to moderate suggestions for the use of COVID-19 messenger ribonucleic acid (mRNA) vaccines and the necessity of the COVID-19 booster for the immunocompromised population. The expert panel had mixed feelings concerning the measurement of antibody titers, higher-dose mRNA vaccines, and the development of disease-specific COVID-19 guidance. Conclusions: These results confirmed the necessity of COVID-19 vaccines and boosters in immunocompromised patients with rheumatologic disease, renal disease, hematological malignancy, and solid organ transplant recipients. Statements where consensus was not achieved were due to absent or limited evidence. [ABSTRACT FROM AUTHOR]

Purpose: Tuberculosis (TB) is a widespread infectious disease that requires early detection for effective treatment and control. This study aims to improve TB detection using cough audio analysis, comparing the performance of capsule networks to other deep learning models. Methods: We used cough audio recordings from 1105 individuals with a new or worsening cough for at least two weeks, totaling 9772 recordings. These recordings were processed into spectral images, and HOG features were extracted. Various models, including Capsule Networks + FCNN, CNN, VGG16, and ResNet50 were trained and evaluated. Results: Capsule Networks + FCNN achieved the best performance with an accuracy of 0.97, sensitivity of 0.98, specificity of 0.96, F1 score of 0.97, and precision of 0.97, outperforming other models. This attribute is due to the model's ability to learn complex features from spectral images. Conclusions: This study concludes that Capsule Networks are more effective than typical CNN-based models in diagnosing TB from cough audio. This suggests that advanced deep learning frameworks could significantly enhance TB screening accuracy, especially in resource-limited areas. [ABSTRACT FROM AUTHOR]

Huanglongbing is a severe citrus disease that causes significant tree and crop losses worldwide. It is caused by three Candidatus liberibacter species and spread by psyllids and infected budwood. Various methods have been used to diagnose and understand HLB, including recent advances in molecular and biochemical assays that explore the pathogen's mode of action and its impact on the host plant. Characterization is essential for developing sustainable HLB management strategies. Nanotechnology, particularly nano sensors and metal nanoparticles, shows potential for precise disease diagnosis and control. Additionally, antibiotics, nanomaterials, and genetic engineering techniques like transgenesis offer promising avenues for mitigating HLB. These diverse approaches, from conventional to cutting-edge, contribute to developing integrated HLB management strategies for sustainable citrus cultivation. The review highlights the significant advancements in conventional and advanced molecular and biochemical characterization of HLB, aiding in early detection and understanding of the infection mechanism. It emphasizes the multidimensional efforts required to characterize disease and devise innovative management strategies. As the citrus industry faces unprecedented challenges, exploring new frontiers in HLB research provides hope for sustainable solutions and a resilient future for global citrus cultivation. [ABSTRACT FROM AUTHOR]

Plant pathogens and other biological pests represent significant obstacles to crop Protection worldwide. Even though there are many effective conventional methods for controlling plant diseases, new methods that are also effective, environmentally safe, and cost-effective are required. While plant breeding has traditionally been used to manipulate the plant genome to develop resistant cultivars for controlling plant diseases, the emergence of genetic engineering has introduced a completely new approach to render plants resistant to bacteria, nematodes, fungi, and viruses. The RNA interference (RNAi) approach has recently emerged as a potentially useful tool for mitigating the inherent risks associated with the development of conventional transgenics. These risks include the use of specific transgenes, gene control sequences, or marker genes. Utilizing RNAi to silence certain genes is a promising solution to this dilemma as disease-resistant transgenic plants can be generated within a legislative structure. Recent investigations have shown that using target double stranded RNAs via an effective vector system can produce significant silencing effects. Both dsRNA-containing crop sprays and transgenic plants carrying RNAi vectors have proven effective in controlling plant diseases that threaten commercially significant crop species. This article discusses the methods and applications of the most recent RNAi technology for reducing plant diseases to ensure sustainable agricultural yields. [ABSTRACT FROM AUTHOR]

Palladium nanoparticles (PdNPs) in the past decade have been extensively used in the diagnosis, imaging, and treatment of numerous diseases owing to their unique physical, chemical, and catalytic properties. Previously, Palladium was almost singularly used for catalysis during chemical synthesis in industries. It is only in the past decade that researchers explore its potential in biomedicine. PdNPs can also be used in the imaging and diagnosis of cancer through techniques such as photoacoustic imaging, computed tomography, and flow cytometry-assisted cell sorting. They offer various advantages such as tunability, desirable photothermal and optical properties, thermal stability, antimicrobial properties, and many more. Cancer treatment through conventional techniques and the broad use of cytotoxic therapeutics has reached a plateau. This paved the way to design novel selective and targeted therapies, one of them being PdNPs. PdNPs are most commonly used in synergy with chemotherapy, photothermal therapy, photodynamic therapy, as well as radiotherapy which improves the efficacy of cancer treatment. In this review, several studies are included that summarize the synthesis, characterization, and cytotoxicity of palladium nanoparticles as well as the functionalized PdNP complexes. In addition, the review provides insight into the mechanism of palladium nanoparticles in improving the efficacy and efficiency of synergistic therapies. [ABSTRACT FROM AUTHOR]

Carbon sequestration in aboveground biomass remains understudied because of the difficulties in conducting field observations and saturation of remote sensing datasets. This study aimed to address the challenges associated with estimating forest AGB using remote sensing data from a dense mangrove ecosystem that has reached saturation limits. A mangrove ecosystem can reach saturation limits when the vegetation density and AGB are so high that remote sensing instruments, such as radar and lidar, cannot accurately measure further increases in biomass because of the sensors' limited penetration and resolution capabilities. This study evaluated the potential and limitations of using dual-polarised microwave data from Sentinel-1A and PALSAR-2, as well as spectral reflectance data from Sentinel-2, to estimate the AGB of the Bhitarkanika Wildlife Sanctuary (BWS), which is the second largest mangrove site in India. Using stratified random sampling, 314 elementary sampling units of 20 m × 20 m (0.04 ha) were used to record the diameter at breast height, tree height, and stand density for estimating AGB. Different band combinations of multisensor datasets were utilised to identify the best predictor variables for estimating the AGB and their corresponding saturation limits. Sentinel-1A demonstrated saturation at 123 Mg/ha (R2 = 0.17) for AGB using VV polarisation, followed by 93 Mg/ha (R2 = 0.55), 91 Mg/ha (R2 = 0.26), and 96 Mg/ha (R2 = 0.17) for the three red-edge bands at wavelengths of 705, 749, and 783 nm, respectively, of Sentinel-2 data. Red-edge bands are sensitive to chlorophyll and vegetation structure, and therefore, S2REP and REIP attained the maximum saturation limit at an AGB of 80 Mg/ha (R2 = 0.26, 0.3, respectively). The highest correlation (R2 = 0.9) and the maximum AGB of 326.06 Mg/ha was captured by the variable HH × HV of the L-band of the PALSAR-2 due to its penetration and interaction capacity with biomass components and less susceptible to interference from surface conditions and atmospheric effects. This study confirmed the advantages of longer-wavelength L-band data over C-band and multispectral optical bands for AGB estimation and identified the best predictor variables. This approach highlights the efficacy of different predictor variables for AGB estimation and the complementary strengths of multisensor datasets for navigating saturation limits. This framework could offer a practical guide for variable selection based on forest density and canopy structure from upcoming SAR missions, including NISAR. [ABSTRACT FROM AUTHOR]

Two-fluid plasma flow equations describe the flow of ions and electrons with different densities, velocities, and pressures. We consider the ideal plasma flow i.e. we ignore viscous, resistive and collision effects. The resulting system of equations has flux consisting of three independent components, one for ions, one for electrons, and a linear Maxwell’s equation flux for the electromagnetic fields. The coupling of these components is via source terms. In this article, we present conservative second-order finite difference schemes that ensure the consistent evolution of the divergence constraints on the electric and magnetic fields. The key idea is to design a numerical solver for Maxwell’s equations using the multidimensional Riemann solver at the vertices, ensuring discrete divergence constraints; for the fluid parts, we use an entropy-stable discretization. The proposed schemes are co-located, second-order accurate, entropy stable, and ensure divergence-free evolution of the magnetic field. We use explicit and IMplicit–EXplicit (IMEX) schemes for time discretizations. To demonstrate the accuracy, stability, and divergence constraint-preserving ability of the proposed schemes, we present several test cases in one and two dimensions. We also compare the numerical results with those obtained from schemes with no divergence cleaning and those employing perfectly hyperbolic Maxwell (PHM) equations-based divergence cleaning methods for Maxwell’s equations. [ABSTRACT FROM AUTHOR]

Solar energy, a prominent renewable source, has reached an installed capacity of 71.78 GW in India. This research explored the load demands of the computer center at an engineering college in Tanjore, Tamil Nadu, India. The computer center at the engineering college has an annual energy requirement of 260,552 kWh/Year. Consequently, the research focused on the planning and implementation of a standalone photovoltaic (SAPV) system, assessing it against the institution's total annual energy consumption. The performance ratio and losses of the SAPV system with both direct coupling and an MPPT charge controller was compared. This comparative analysis aimed to evaluate the efficacy of two solar photovoltaic control methods—SAPV direct coupling and Maximum Power Point Tracking control—in optimizing energy harvesting from solar panels. [ABSTRACT FROM AUTHOR]

The biosynthesis of novel nanoparticles with varied morphologies, which has good implications for their biological capabilities, has attracted increasing attention in the field of nanotechnology. Bioactive compounds present in the extract of fungi, bacteria, plants and algae are responsible for nanoparticle synthesis. In comparison to other biological resources, brown seaweeds can also be useful to convert metal ions to metal nanoparticles because of the presence of richer bioactive chemicals. Carbohydrates, proteins, polysaccharides, vitamins, enzymes, pigments, and secondary metabolites in brown seaweeds act as natural reducing, capping, and stabilizing agents in the nanoparticle's synthesis. There are around 2000 species of seaweed that dominate marine resources, but only a few have been reported for nanoparticle synthesis. The presence of bioactive chemicals in the biosynthesized metal nanoparticles confers biological activity. The biosynthesized metal and non-metal nanoparticles from brown seaweeds possess different biological activities because of their different physiochemical properties. Compared with terrestrial resources, marine resources are not much explored for nanoparticle synthesis. To confirm their morphology, characterization methods are used, such as absorption spectrophotometer, X-ray diffraction, Fourier transforms infrared spectroscopy, scanning electron microscope, and transmission electron microscopy. This review attempts to include the vital role of brown seaweed in the synthesis of metal and non-metal nanoparticles, as well as the method of synthesis and biological applications such as anticancer, antibacterial, antioxidant, anti-diabetic, and other functions. [ABSTRACT FROM AUTHOR]

The genus Phytophthora contains more than 100 plant pathogenic species that parasitize a wide range of plants, including economically important fruits, vegetables, cereals, and forest trees, causing significant losses. Global agriculture is seriously threatened by fungicide resistance in Phytophthora species, which makes it imperative to fully comprehend the mechanisms, frequency, and non-chemical management techniques related to resistance mutations. The mechanisms behind fungicide resistance, such as target-site mutations, efflux pump overexpression, overexpression of target genes and metabolic detoxification routes for fungicides routinely used against Phytophthora species, are thoroughly examined in this review. Additionally, it assesses the frequency of resistance mutations in various Phytophthora species and geographical areas, emphasizing the rise of strains that are resistant to multiple drugs. The effectiveness of non-chemical management techniques, including biological control, host resistance, integrated pest management plans, and cultural practices, in reducing fungicide resistance is also thoroughly evaluated. The study provides important insights for future research and the development of sustainable disease management strategies to counter fungicide resistance in Phytophthora species by synthesizing current information and identifying knowledge gaps. [ABSTRACT FROM AUTHOR]