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This research investigates the impact of ten medicinal plant extracts on the growth and sporulation of prominent industrial fungal strains-Trichoderma harzianum, Aspergillus niger, and Penicillium chrysogenum. The phytochemical analysis revealed a diverse array of bioactive compounds, hinting at potential antimicrobial and antioxidant properties. Particularly, Terminalia arjuna consistently exhibited positive effects on fungal growth, implying a lower degree of antifungal activity, whereas Phyllanthus emblica showed variable impacts. These findings were supported by radial growth rates, highlighting Terminalia arjuna’s consistent stimulation of fungal growth. Sporulation indices and spore production further affirmed substantial sporulation in Terminalia arjuna. Comparative analyses with previous studies emphasized the unique characteristics of this extract. In conclusion, Terminalia arjuna shows distinct characteristics compared to other samples, indicating the need for additional investigation, suggests promising applications in both industrial and medicinal contexts, warranting further exploration for optimal utilization.

Facial swapping technology is a rapidly growing area of research with a wide range of applications, including entertainment, security, and healthcare. In this project, a deep learning approach was used to achieve highly accurate and realistic face swaps. Specifically, a CNN encoder and decoder network was trained using a large dataset of facial images, and facial clusters were generated using k-means clustering. Computer vision methodologies were also employed to accurately detect and align facial landmarks. The resulting model achieved impressive accuracy of 97% to 99.48% in different epochs, demonstrating its potential for various applications. The system configuration for executing the project included an 11th generation Intel Core i7 processor and 16GB RAM, which provided sufficient computational resources for the task at hand. Overall, this project highlights the power and potential of deep learning techniques for generating highly accurate and realistic facial swaps.

The Brans-Dicke scalar-tensor theory of gravity (Phys. Rev. 124, 925: 1961) is applied in this work to study the Sharma-Mittal holographic dark energy on a background of anisotropic and spatially homogenous Bianchi type-II spacetime. In this scenario, the Brans-Dicke scalar field ϕ is used as a function of the average scale factor a(t). To explore the universe's accelerated expansion, the physical behaviour of the model is addressed through a graphical representation. Furthermore, squared sound speed V22 is used to verify the stability of the models. The popular cosmic plane ωde - ω' de is built for our models. It's also important to note that all the findings are in line with current observational data.

Este artículo se centra en el análisis de la novela Cautivos (2020) del escritor argentino-chileno-estadounidense, Ariel Dorfman, en la que Miguel de Cervantes se convierte en un personaje de ficción y dialoga con su creación literaria universal, don Quijote. Enmarcamos la novela en la perspectiva de las teorías de la bioficción, para establecer que la obra de Dorfman es un ejemplo más de las recientes novelas que recrean aspectos conocidos de la vida de Cervantes y reflexiona sobre los temas del cautiverio, la libertad y la relación entre el escritor y su creación de una manera lúdica, jugando entre la ficción y la realidad.

Multilevel unified power flow controllers (ML-UPFCs) aim to improve grid stability, power quality, and fault management. This approach is particularly beneficial for renewable energy systems connected to a grid, where efficient power flow and robust fault handling are crucial for maintaining system reliability. However, current grid-integrated systems face challenges such as inefficient fault management, harmonic distortions, and instability when dealing with nonlinear loads. Existing control strategies often lack the flexibility and optimization required to handle these issues effectively in dynamic grid environments. Therefore, the proposed methodology involves a multistep control strategy to optimize the integration of solar photovoltaic (SPV) systems with ML-UPFCs. Initially, the SPV array generates DC power, which is optimized using a perturb and observe (P&O) maximum power point tracking (MPPT) controller. The DC-to-DC boost converter then steps up the voltage for input to a voltage source inverter (VSI) or voltage source converter (VSC). The VSI/VSC, enhanced by greedy control-based monarch butterfly optimization (GCMBO), converts DC to AC while minimizing harmonic distortion. The power is then fed into the grid, which supplies sensitive critical and nonlinear loads. Three-phase fault detection mechanisms and series transformers manage the power flow and fault conditions. Furthermore, the ML-UPFC, controlled by a random forest cuckoo search optimization (RFCSO) algorithm, enhances the fault ride-through capabilities and power regulation. Additional transformers and a shunt transformer optimize the voltage levels and reactive power management, ensuring stable and high-quality power delivery to both sensitive and nonlinear loads. Finally, the proposed approach addresses power flow optimization, fault mitigation, and nonlinear load management with the aim of enhancing grid stability and efficiency.

In response to the growing demand for electricity and the depletion of fossil fuel resources, nations are transitioning towards renewable energy systems as viable alternatives for power generation. Wind and solar photovoltaic (SPV) energy systems have emerged as promising, sustainable options. However, conventional multilevel inverters fail to control both wind and SPV energy simultaneously. Therefore, in this study, a hybrid SPV wind power system with a level converter (HPWPS-SLC) was developed using a wind-based permanent magnet synchronous generator and SPV energy grid sources. The HPWPS-SLC leverages the benefits of the genetic algorithm-optimized adaptive neuro-fuzzy inference system controller for efficient energy generation and management. In addition, a pulse width modulation controller with a hybrid asymmetric switching scheme was implemented to reduce the total harmonic distortion (THD). This approach enables high switching frequency while minimizing the switch count, thereby reducing the losses and costs associated with conventional techniques. Simulation results show that the proposed HPWPS-SLC system achieves a power factor of 0.7 and a THD of 25.02 % for grid voltages under fault conditions. Despite the fault conditions, maintaining a THD value of 25.02 % ensures a better grid voltage waveform quality and minimizes distortions for stable operation. Utilizing a 42-cycle signal with a fast Fourier transform of 17 cycles enables finer resolution in harmonic analysis up to the 16th order, thereby enhancing the overall system performance.

This paper seeks to explore the stability assessment of a generalist predator-prey system with predator harvesting. The study presents a model examining the dynamics between primary prey and predator, considering the existence of a generalist predator. Prey growth follows a logistic rate, while predator consumption is modeled with a cyrtoid functional response. In the absence of primary prey, the predator population adopts a generalist strategy, akin to the Beverton-Holt model. The model also incorporates harvesting on the predator population. We analyze the model's equilibrium, stability, positivity, and boundedness, and use numerical simulations to explore its predictions. This study enhances our understanding of ecological interactions and supports the development of effective conservation and management strategies.

Mineral admixtures such as fly ash (FA), ground granulated blast furnace slag (GGBS), metakaolin (MK), and silica fumes (SF) derived from industries are often referred to as secondary cementitious materials, as partial replacements for cement. Also, the use of fibres significantly enhances the flexural behaviour of concrete. Various kinds of literature highlight the performance of concrete with one or two types of admixtures. In this study, an attempt is made to explore the effect of the use of more than two admixtures viz. FA, GGBS and MK as a partial replacement to cement and fibrofor fibre (FF) on workability and strength properties of based Fibre-Reinforced Self-Compacting Concrete (FRSCC). The FF in Self-Compacting Concrete (SCC) is varied from 1.0 to 2.0 kg/cum. The SCC samples are tested for three curing durations. Based on the experimental investigations, the workability of all the mixes is found to be within the EFNARC limits. The variation in the compressive strength of SCC for various fibre dosages is within 10%, after 28 days of the curing period. It is noted that A-10 mix with fibre dosage of 1.0 Kg/cum yielded the highest compressive, split tensile, and flexural strengths after 56 days of curing, as compared to that of all other variations including conventional SCC mix.

Objective: Weight gain can harm health and be socially stigmatizing. We sought to determine whether weight gain during the inpatient stay, as collateral damage related to lockdown, was more in patients admitted during the lockdown in the first pandemic year than in those admitted during the same period in the immediately preceding year. To our knowledge, this has not been previously investigated. Methods: We extracted from medical record weight data during two similar 4-month periods (April–July) in 2019 and 2020. We examined the impact of the ward restrictions during the pandemic lockdown on weight gain using multivariable regression analysis, adjusting for potential confounding variables selected a priori: age, sex, baseline weight, metabolic syndrome or antipsychotic load, and duration of inpatient stay. Results: There were 59 patients, almost all with schizophrenia spectrum disorders. Patients admitted in 2019 (n = 27) were significantly more likely to consume alcohol or use illicit substances, but otherwise did not differ much from those admitted in 2020 (n = 32). Weight gain during inpatient stay was not statistically significant for 2019 (+1.85 kg; P = 0.22), 2020 (+0.74 kg; P = 0.26), or for the 2 years combined (+1.25 kg; P = 0.10). Finally, weight gain during inpatient stay did not differ between 2019 and 2020 (P = 0.83). In the regression analysis, the year of admission was not significantly associated with change in weight during inpatient stay (beta, −0.11; P = 0.43). Conclusions: Our findings validate our ward strategies to reduce the risk of weight gain during lockdown in inpatients with severe mental illness.

Abstract Fuel cell-based electric vehicles (EVs) are gaining popularity in the automotive industry due to strict carbon emissions and fuel efficiency regulations. Fuel cells have inherently low voltage characteristics, making it challenging to interface with EV drive systems. This work proposes a unique topology implementing a non-isolated high step-up DC-DC converter to integrate the Proton Exchange Membrane Fuel (PEMF) cell with the EV motor drive. The converter combines a modified Single-Ended Primary Inductor Converter (m-SEPIC) with two winding-coupled inductors, enabling high voltage gain, minimizing conduction losses, and ensuring high efficiency. The output power of a fuel cell is highly dependent on cell temperature and membrane water content, making a maximum power point tracking controller essential for extracting optimal power from the fuel cell stack. The optimum power point of the PEMF cell is identified using a Neural Network (NN)-based Maximum Power Point Tracking (MPPT) technique, employing the Radial Basis Function Network (RBFN) approach compared to the Perturb & Observe (P&O) method. The performance of the RBFN MPPT method is validated under various temperature conditions of the fuel cell system. An extensive analysis and comparison with alternative converters validate the efficacy of the proposed system. The converter is designed for an input voltage of 24 V and boosts it to an output voltage of 240 V, with high static voltage gain, high power density, and minimized total losses. The system is tested and validated through both simulation and real-time implementation using the dSPACE 1104 real-time controller. A 200 W, 3000 rpm, 240 V VSI-based BLDC motor drive integrated with the proposed converter topology is developed as a real-time setup in the laboratory which would confirm the converter’s functionality. Both theoretical analysis and real-time outcomes affirm the suitability of the proposed converter topology for electric vehicle applications.

Abstract Eukaryotes must balance the need for gene transcription by RNA polymerase II (Pol II) against the danger of mutations caused by transposable element (TE) proliferation. In plants, these gene expression and TE silencing activities are divided between different RNA polymerases. Specifically, RNA polymerase IV (Pol IV), which evolved from Pol II, transcribes TEs to generate small interfering RNAs (siRNAs) that guide DNA methylation and block TE transcription by Pol II. While the Pol IV complex is recruited to TEs via SNF2-like CLASSY (CLSY) proteins, how Pol IV partners with the CLSYs remains unknown. Here, we identified a conserved CYC-YPMF motif that is specific to Pol IV and is positioned on the complex exterior. Furthermore, we found that this motif is essential for the co-purification of all four CLSYs with Pol IV, but that only one CLSY is present in any given Pol IV complex. These findings support a “one CLSY per Pol IV” model where the CYC-YPMF motif acts as a CLSY-docking site. Indeed, mutations in and around this motif phenocopy pol iv null and clsy quadruple mutants. Together, these findings provide structural and functional insights into a critical protein feature that distinguishes Pol IV from other RNA polymerases, allowing it to promote genome stability by targeting TEs for silencing.

Merapoh, Pahang, is an area rich with limestone karst located within the Lipis National Geopark and home to the Sungai Relau gate of Taman Negara Pahang, a totally protected rainforest in Malaysia. Much of the research conducted here is mainly inside the National Park, with few published faunal records for the Merapoh caves. This study compiled the data on the bat species diversity of eight Merapoh caves (March 2020 to March 2022) using mist nets and harp traps. Our results indicate that Chiroptera diversity at Merapoh caves is rich, with a total of 32 species recorded from 865 individuals and four new locality records for the State of Pahang, namely Rousettus leschenaultii, Lyroderma lyra, Rhinolophus coelophyllus and Hipposideros pomona. Gua Gunting has the highest diversity of bats recorded in this study (19 species). Significant Merapoh caves with bat colony roosts in Merapoh include Gua Jinjang Pelamin (Eonycteris spelaea & Rousettus leschenaultii), Gua Tahi Bintang (Hipposideros larvatus) and Gua Pasir Besar (Miniopterus medius). Rhinolophus convexus, previously recorded only in upper montane rainforests, was also recorded in Merapoh caves indicating that this species can also be found in lower elevations than previously thought. Based on the findings of the current study and additional records from two previous studies, the Merapoh bat species diversity checklist totalled up to 38 species. On the whole, the rich bat diversity in Merapoh is reflective of its immense limestone karst landscape, which highlights the reason Lipis National Geopark has been recently gazetted. Future bat research should continue here and in other karsts within Lipis Geopark to sustainably conserve biological diversity, manage geological structures and raise awareness amongst the locals to appreciate their national heritage.

Abstract The escalating threat of climate change has placed global coastal communities at risk, with rising sea levels and intensified storm events presenting unprecedented challenges. Coastal vulnerability assessments, conducted every 3–5 years, are crucial. This empirical study assesses the Coastal Vulnerability Index (CVI) for the distinct coastal contexts of Dawlish, Happisburgh (England), and Aberystwyth (Wales). The CVI method consists of the Physical Coastal Vulnerability Index (PCVI) and the Economic Coastal Vulnerability Index (ECVI), which provide a multidimensional assessment of vulnerability for coastal zones. This integrated index allows for a nuanced evaluation of vulnerability, distinguishing between sites based on various factors. Additionally, this study conducted a correlation analysis to understand the associations between the parameters. The findings demonstrate that physical features like beach and dune widths significantly impact a location’s natural defences, and economic factors such as property values and population density are equally crucial in determining societal risks and potential financial repercussions. The Combined Coastal Vulnerability Index (CCVI) results confirm the effectiveness of incorporating a diverse range of variables. Despite its substantial economic value, it reveals that Dawlish requires targeted protective measures, whereas Happisburgh needs an increased focus on its most vulnerable sectors. Aberystwyth emerges as the area with the highest overall vulnerability, underscoring the need for comprehensive coastal management practices. The study’s conclusions emphasize the essential role of adaptive, integrated management strategies in enhancing coastal resilience against the complex threats posed by climate dynamics. Moving forward, the indices established herein advocate for their use in strategic planning and policymaking to strengthen coastal regions in the face of sea-level rise and climatic variability. This investigation lays the groundwork for future research, aimed at refining and expanding these methodologies, aspiring to develop a detailed national coastal vulnerability atlas, a critical tool for informed decision-making and safeguarding at-risk communities.

Abstract Asenapine maleate (ASPM) is a second-generation atypical antipsychotic that is approved for treating acute schizophrenia and bipolar disorder in adults by the US FDA. The major downside of ASPM therapy is rapid, extensive first-pass hepatic metabolism following its oral administration with a very low oral bioavailability of

Abstract Glaucoma is an eye disease that damages the optic nerve as a result of vision loss, it is the leading cause of blindness worldwide. Due to the time‐consuming, inaccurate, and manual nature of traditional methods, automation in glaucoma detection is important. This paper proposes an explainable artificial intelligence (XAI) based model for automatic glaucoma detection using pre‐trained convolutional neural networks (PCNNs) and machine learning classifiers (MLCs). PCNNs are used as feature extractors to obtain deep features that can capture the important visual patterns and characteristics from fundus images. Using extracted features MLCs then classify glaucoma and healthy images. An empirical selection of the CNN and MLC parameters has been made in the performance evaluation. In this work, a total of 1,865 healthy and 1,590 glaucoma images from different fundus datasets were used. The results on the ACRIMA dataset show an accuracy, precision, and recall of 98.03%, 97.61%, and 99%, respectively. Explainable artificial intelligence aims to create a model to increase the user's trust in the model's decision‐making process in a transparent and interpretable manner. An assessment of image misclassification has been carried out to facilitate future investigations.

This study focused on the synthesis and purification of nano-sized titanium carbide (TiC) particles through vacuum carbothermal reduction under a hydrogen/argon atmosphere. The process involved the production of carbon-coated titanium precursors, followed by heating under vacuum conditions at 1500 °C for 2.5 h. The resulting products underwent additional treatment in either a hydrogen/argon (1:1) mixed gas or pure hydrogen gas. Experimental findings revealed that TiC powders exhibiting a single phase are obtained within a molar ratio of Ti to C ranging from 1:2 to 1:4. Adjusting the molar ratio of Ti/C in the precursors allowed for controlled variation in the particle size of the synthesized TiC powders, ranging from 50 to 80 nm. Treatment in hydrogen/argon mixed gas at 850 °C for 3 h resulted in TiC products with an impressive purity of 99.40%. The subsequent incorporation of 5% Nano-TiC-3 (Ti to C ranging from 1:3) as reinforcement into an aluminum alloy by Friction stir process technique demonstrated a remarkable 19.41% improvement in tensile strength, highlighting the efficacy of this specific reinforcement. The nano-TiC-3 reinforced composite exhibited uniform distribution without porosity. Additionally, a notable 50.81% improvement in hardness and the best wear resistance were observed for the 5% Nano-TiC-3 reinforced aluminum composite. This comprehensive study contributes valuable insights into the synthesis, purification, and application of nano-sized TiC particles, paving the way for the development of high-performance aluminum-based composites with enhanced mechanical and tribological properties.

Pigmented epithelioid melanocytoma (PEM) is a rare and diagnostically challenging skin neoplasm. Differentiating this lesion from melanoma is challenging due to overlapping clinical, histopathological, and immunohistochemical features. Some PEMs have a benign course, while others may be associated with more aggressive behaviour and have potential for local recurrence or distant metastasis. So, careful evaluation and follow-up are crucial for individuals with a diagnosis of PEM. This case study focuses on a 42-year-old woman with a gradually enlarging, dark, pigmented gluteal lesion. Histopathological analysis following complete excision showed nodular proliferation of spindled and epithelioid melanocytes in the dermis, extending into subcutaneous fat. Cellular atypia or mitotic figures were absent, confirming the diagnosis as PEM. This case underscores the intricate balance between monitoring and intervention in complex dermatological cases, shedding light on diagnostic challenges and ultimately striving for improved patient care.

Introduction: Flipped Classroom (FC) method is a new approach in which there is a switch between the work done in class and work done as homework. Background: Previous studies have had variable results. Some conclude neither an improvement nor degradation in students’ performance, while others agree that the Flipped Classroom teaching model has value over traditional teaching styles. Objectives: To assess the effectiveness of the FC learning module on phase I medical students. Methods: There were mixed methods used in this study. For the students to compare the teaching methods, first a didactic lecture class was conducted followed by a more interactive flipped classroom for the interested students. After the classes, two questionnaires were given to them. One to receive their feedback on the teaching model, and one to assess the difference in performance between traditional and Flipped Classroom teaching models. Results: The test done to assess performance proved that the FC model was more effective as a teaching method. A high percentage of students also provided feedback in support of the FC model as compared to the traditional teaching method. Conclusion: The results of this study provide conclusive evidence of the Flipped Classroom teaching model being more effective for the understanding of concepts as well as the performance of phase I MBBS students. It is also a better method as per the students as understood from the feedback form.

Introduction: Herpes simplex virus type 1 (HSV-1) is the leading cause of sporadic fatal encephalitis, typically presenting with temporal lobe abnormalities. It usually manifests as fever, headache, seizure, altered sensorium, and focal neurological deficit. Hyperphagia as a sole complication of HSV-1 encephalitis is a rare presentation. Case Presentation: We report a 25-year-old woman with a 10-day history of fever, headache, and vomiting, progressing to confusion, visual hallucinations, and drowsiness. She had a history of meningoencephalitis at age 8 and well-controlled focal seizures. Upon admission, magnetic resonance imaging showed T2/fluid-attenuated inversion recovery hyperintensities in both temporal lobes with diffusion restriction. Electroencephalography indicated generalized slowing and cerebrospinal fluid (CSF) analysis revealed lymphocytic pleocytosis with elevated protein levels. Viral encephalitis was suspected, and intravenous acyclovir was initiated. CSF polymerase chain reaction (PCR) confirmed HSV-1. With treatment, she gradually improved but developed hyperphagia during hospital stay. Hyperphagia, a rare complication of herpes simplex virus (HSV) encephalitis, is a part of Kluver-Bucy syndrome typically associated with other cognitive dysfunctions. Despite early treatment, voracious appetite remained partially, emphasizing the need for rapid diagnosis and treatment to prevent severe outcomes. Conclusion: The case highlights that acute onset hyperphagia can be an isolated complication of HSV encephalitis, requiring tailored therapeutic strategies. Follow-up showed significant weight gain with partial improvement in hyperphagia, underscoring the challenges in managing this condition.

Background: The data on Leydig cell hypoplasia (LCH) resulting from biallelic Luteinizing hormone/chorionic gonadotropin receptor (LHCGR) inactivating variants is limited to case series. Methods: We aim to describe our patients and perform systematic review of the patients with LHCGR inactivating variants in the literature. Detailed phenotype and genotype data of three patients from our centre and 85 (46,XY: 67; 46,XX: 18) patients from 59 families with LHCGR-inactivating variants from literature were described. Results: Three 46,XY patients (age 6–18 years) from our center, with two reared as females, had two novel variants in LHCGR. Systematic review (including our patients) revealed 72 variants in 88 patients. 46,XY patients (n = 70, 56 raised as females) presented with pubertal delay (n = 41) or atypical genitalia (n = 17). Sinnecker score ≥3 (suggesting antenatal human chorionic gonadotropin (hCG) inaction) was seen in 80% (56/70), and hCG-stimulated testosterone was low (

The present study reports about the factors that determine the readiness of students towards E-learning. Through a small-scale literature review, efforts were made to identify the key factors that determine the readiness of students towards E-learning. The researcher used technique of content analysis for reviewing the literature. The literature was collected from various databases like Google Scholar, Research Gate, Academia, ERIC and various publication house websites like Sage, Taylor and Francis, Wiley, and Emerald. The findings of the review of literature suggest that the factors and its components that determine the ELR among students are diversified. However, "Technological Readiness, attitudinal Readiness, Basic Internet Skills and Motivation" are the factors that have been largely used by the researchers to determine ELR of Students.

Schools in New Zealand (NZ) have a range of disciplinary options when dealing with challenging behaviours, one of which is excluding students by way of stand-downs, suspensions, exclusions or expulsions. Following marginal downward trend from 2006 to 2015, the numbers of stand-downs and suspensions have been on the rise again since 2016 despite well-established evidence of strong links between poor academic achievement, educational exclusions and youth justice encounters. One of the key reasons cited by schools for excluding students in New Zealand is for continual disobedience (CD). According to the NZ Ministry of Education Guidelines to schools, behaviours must be persistent and set a harmful or dangerous example to other students to meet the criteria for CD, and it is up to schools to interpret these guidelines. This paper reports the findings from a national study in New Zealand, that sought to understand how the category of CD was interpreted in high schools and the type of behaviours they associated with this category. The findings highlighted the need to question the existence of this category (CD) in the guidelines, as it provides a carte blanche to schools to exclude students.

Recent advancements in generative artificial intelligence, particularly using large language models (LLMs), are gaining increased public attention. We provide a perspective on the potential of LLMs to analyze enormous amounts of data from medical records and gain insights on specific topics in neurology. In addition, we explore use cases for LLMs, such as early diagnosis, supporting patient and caregivers, and acting as an assistant for clinicians. We point to the potential ethical and technical challenges raised by LLMs, such as concerns about privacy and data security, potential biases in the data for model training, and the need for careful validation of results. Researchers must consider these challenges and take steps to address them to ensure that their work is conducted in a safe and responsible manner. Despite these challenges, LLMs offer promising opportunities for improving care and treatment of various neurologic disorders.