Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya

Introduction. Haglund’s deformity, characterized by inflammation of the bursa situated in the posterior aspect of the calcaneus (heel bone), poses a significant challenge in orthopedic treatment. This study examines the effectiveness of laser therapy combined with concentric exercises on pain and functional ability in patients with Haglund’s deformity, pain and discomfort in the heel caused by the bony enlargement in it. Aim of the study. The aim of this study was to evaluate the effectiveness of concentric exercises and laser therapy in lowering pain and enhancing functionality in people with Haglund’s deformity. Materials and Methods. A study was conducted involving 40 subjects diagnosed with Haglund’s deformity, aged between 20 to 35 years and of both genders, randomly selected and divided equally into two groups: Group A (experimental) and Group B (traditional). Exclusion criteria encompassed foot fracture history, hindfoot open wounds, or non-cooperative behavior. Pre-test assessments utilizing the American Orthopaedic Foot & Ankle Society (AOFAS) scale and PainChek AI software were administered to both groups. Treatment intervention for both groups involved three weeks of exercises, comprising 3 sets per session, 15 repetitions per set, and 5 days a week. Post-treatment evaluations were conducted to compare the effectiveness of the interventions using AOFAS and PainChek scores, alongside monitoring for any adverse effects or complications arising from the treatments. Results. The post-treatment mean PainChek value for Group A was 5.10, whereas it was 6.45 for Group B. Group A’s post-treatment mean AOFAS score was 79.50, whereas Group B was 61.50. It was discovered that Group A was beneficial in lowering discomfort and enhancing function in those with Haglund’s deformity. Conclusion. The results of this study suggest that among individuals with Haglund’s deformity, the utilization of laser therapy alongside concentric exercises in the experimental group proved to be more effective in reducing discomfort and enhancing functionality compared to employing stretching exercises in the conventional group.

This research focuses on the fabrication and characterization of Fiber Metal Laminates reinforced with 5% BaSO4 nanoparticles for automotive applications. Eight distinct samples were produced using synthetic fibers (Kevlar, carbon, and glass fiber), natural abaca fiber, and an aluminum mesh (AL 1100) embedded in an epoxy resin matrix. Mechanical testing based on ASTM standards showed significant improvements in tensile, flexural, and impact strength. Kevlar fiber-reinforced Fiber metal laminates with BaSO4 nanoparticles exhibited the highest tensile strength at 18.27 kN, a 40% increase compared to non-reinforced fiber metal laminate. Flexural strength increased by 44% for Kevlar-reinforced fiber metal laminates, reaching 2.88 kN. Impact strength analysis revealed that nanoparticle-infused FMLs maintained superior energy absorption, with the Kevlar-reinforced Fiber metal laminates absorbing 90 J. Morphological analysis using Scanning Electron Microscopy confirmed enhanced microstructural integrity, with reduced void formation and better fiber-matrix adhesion in the nanoparticle-reinforced laminates. These results suggest that BaSO4 nanoparticles significantly improve the mechanical performance and structural integrity of fiber metal laminates, making them suitable for high-performance automotive components such as body panels and structural reinforcements. Keywords: Fiber Metal Laminates; BaSO4 nanoparticles; hand layup method; automotive applications; mechanical properties; microstructural characterization

Due to the COVID-19 pandemic, education has recently undergone a rapid digital-ization, necessitating the simultaneous adoption of several technologies by educators for online learning and instruction. This study will build a model that predicts student teachers' extensive technology acceptance by extending the technology acceptance model (TAM) with their technological pedagogical content knowledge (TPACK) and innovativeness. The survey (N = 870) will be used to collect the data for this study. The TAM has been shown to be a useful instrument for tracking the uptake of new technologies across a range of fields, including education. TAM, however, has been primarily used to gauge user acceptance of a certain technology deployment. With the development of numerous technologies, this study has expanded TAM to measure student teachers' technology-enabled practice. The suggested model explains the behavioural purpose of student teachers to teach online. Our research identified the interrelated influences of TPACK, perceived utility (PU) and innovation on teachers' behaviour and intention to teach online following the epidemic. In addition, the study found that student teachers' TPACK and PU were significantly predicted by their training and institutional support. This study's model conceptualization, which combines elements based on personal

Coral reefs are essential ecosystems, supporting a diverse range of marine life and offering considerable ecological, economic, and cultural benefits. However, they face increasing threats from climate change, pollution, and other human activities. Thus, effective monitoring and management of coral reefs are crucial for their conservation and sustainability. In this study, we employed a hybrid model, HCNN-SVM, which combines convolutional neural networks (CNNs) for feature extraction and support vector machines (SVMs) for classification. We utilized a coral-reef dataset from Kaggle, containing images from CoralNet and the Moorea Coral Reef Long Term Ecological Research (MCR LTER). To enhance image quality and ensure accurate reflectance values, we applied radiometric, geometric, and water column corrections during preprocessing. Feature extraction involved the use of spectral indices such as the Coral Bleaching Index (CBI) and Normalized Difference Vegetation Index (NDVI) to detect healthy and stressed corals, along with texture analysis to differentiate substrates. The model was trained on this diverse dataset, which captures various environmental conditions and reef types, achieving an accuracy of 98.55%. Cross-validation methods were employed to evaluate model performance, ensuring robustness and generalizability. The HCNN-SVM model demonstrated high accuracy in identifying and mapping coral reef components, making it a powerful tool for monitoring coral reef health and supporting conservation efforts.

Diarrhea is a common illness for travelers. Traveler’s diarrhea is typically defined as experiencing at least three unformed stools per day during a stay abroad or within 10 days of returning from the destination. In this review, we consulted five databases, namely, Medicine Complete, Medscape, Drugs.com, Epocrates, and DDInter, to conduct a comprehensive drug interaction analysis. We selected commonly prescribed medications used for the treatment of traveler’s diarrhea, including ciprofloxacin, levofloxacin, norfloxacin, ofloxacin, azithromycin, rifaximin, bismuth salicylate, and loperamide. The antidiabetic medications chosen included metformin, glipizide, glimepiride, sitagliptin, linagliptin, dapagliflozin, empagliflozin, and acarbose. The chosen antihypertensive drugs were telmisartan, olmesartan, amlodipine, nifedipine, enalapril, ramipril, metoprolol, and propranolol. Aspirin, clopidogrel, ticagrelor, rivaroxaban, warfarin, atorvastatin, and rosuvastatin were also chosen as they play an essential role in cardiovascular treatment. We performed comprehensive interaction checks across all five databases for each combination of a traveler’s diarrhea medication and medication from one of the three comorbid conditions (antidiabetic, antihypertensive, or cardioprotective). We categorized the severity of interactions as mild, moderate, or severe. Similarly, we used colors to highlight the number of databases reporting drug interactions, providing insights into the reliability of these interactions across sources. Interactions with antidiabetic drugs revealed that fluoroquinolones and sulfonylureas produce severe interaction effects. Comparatively, rifaximin can be safer as it exhibited mild interaction only with metformin, whereas the other antidiabetic drugs showed no interaction effect. Levofloxacin was found to be the safest drug among hypertensive individuals as it exerted no interaction effects with any of the antihypertensive medications. Levofloxacin and rifaximin were considered to be safe as these drugs interacted with only two cardioprotective drugs. This review features the importance of a precise approach in prescribing medications for traveler’s diarrhea, especially for patients with chronic comorbidities. These findings play a pivotal role in improving awareness and providing tailored treatment for the interaction to ensure patient well-being.

In this study, slow evaporation technique is used for the growth of organic single crystal of Propylenediamine Picrate (PDP) and this research investigates crystallographic, experimental, and theoretical density functional theory (DFT) of PDP. The crystal structure was found from single-crystal XRD analysis. Spectral analyses, including UV–Vis and FTIR spectroscopy, revealed a bandgap energy of 3.505 eV and identified key functional groups. The SHG efficiency of the crystal is measured to study the NLO property of the crystal. The (SHG) efficiency was measured as 0.53 that of KDP, confirming the nonlinear optical (NLO) properties. Thermal stability was evaluated using TG/DTA, and the crystal’s mechanical hardness was measured as 2.753 via Vickers microhardness testing. Dielectric properties, including loss, permittivity, and AC conductivity, were examined at varying temperatures. The first-order hyperpolarizability (β0) and related properties (β, α0, and Δα) of PDP is calculated usingB3LYP/6-31G (d,p) method on the finite field approach. The study reveals that the transfer of charge occurs within these molecules through the analysis of the molecular structure of PDP, performed using the molecular electrostatic potential (MESP) and calculated HOMO and LUMO energies. The study confirms PDP’s potential in optoelectronic applications.

In the title compound, C31H28O4, the phenyl rings of the chalcone unit subtend a dihedral angle of 26.43 (10)°. The phenyl rings of the pendant benz­yloxy groups are orientated at 75.57 (13) and 75.70 (10)° with respect to their attached ring. In the crystal, weak C—H⋯O and C—H⋯π inter­actions link the mol­ecules. The inter­molecular inter­actions were qu­anti­fied and analysed using Hirshfeld surface analysis, which showed a breakdown into H⋯H (49.8%), H⋯C/C⋯H (33.8%) and H⋯O/O⋯H (13.6%) inter­actions with other types making negligible contributions.

Cloud computing (CC) is a network-based concept where users access data at a specific time and place. The CC comprises servers, storage, databases, networking, software, analytics, and intelligence. Cloud security is the cybersecurity authority dedicated to securing cloud computing systems. It includes keeping data private and safe across online-based infrastructure, applications, and platforms. Securing these systems involves the efforts of cloud providers and the clients that use them, whether an individual, small-to-medium business, or enterprise uses. Security is essential for protecting data and cloud resources from malicious activity. A cloud service provider is utilized to provide secure data storage services. Data integrity is a critical issue in cloud computing. However, using data storage services securely and ensuring data integrity in these cloud servers remain an issue for cloud users. We introduce a unique piecewise regressive Kupyna cryptographic hash blockchain (PRKCHB) technique to secure cloud services with higher data integrity to solve these issues. The proposed PRKCHB method involves user registration, cryptographic hash blockchain, and regression analysis. Initially, the registration process for each cloud user is performed. After registering user particulars, Davies–Meyer Kupyna’s cryptographic hash blockchain generates the hash value of data in each block. When a user requests data from the server, a piecewise regression function is used to validate their identity. Furthermore, the Gaussian kernel function recognizes authorized or unauthorized users for secure cloud information transmission. The regression function results in original data by enhanced integrity in the cloud. An analysis of the proposed PRKCHB technique evaluates different existing methods implemented in Python. The results contain different metrics: data confidentiality rate, data integrity rate, authentication time, storage overhead, and execution time. Compared to conventional techniques, findings corroborate the assertion that the proposed PRKCHB technique improves data confidentiality and integrity by up to 9% and 9% while lowering storage overhead, authentication time, and execution time by 10%, 12%, and 12%.