Bharat Heavy Electricals (India)

The research work aimed to evaluate the chitosan-coated ketoconazole-loaded ethosomal gel using a Box–Behnken design approach to increase bioavailability and reduce dose-related toxicity. In the design of experiment (DoE)-based formulation optimization, the independent variables were soy lecithin, Tween 80, and ethanol, while the dependent variables were particle size, polydispersity index (PDI), zeta potential (ZP), entrapment efficiency (EE), and cumulative percentage drug release (CDR). The design was a three-factor, three-level Box–Behnken design (BBD). The optimized ketoconazole (KCZ)-loaded ethosomes predicted by Design-Expert software (Version 13) consisted of 399.9 mg of soya lecithin, 4.73 ml of ethanol, and 300 µl of Tween 80, which were prepared by the cold method. According to characterization investigations, the mean particle size was 184.9 ± 6.21 nm, the zeta potential was −12.9 mV, the PDI was 0.230 ± 0.07, and the percentage of entrapment efficiency was 78.45 ± 3.5%. The observed CDR was 81.93 ± 5.6% at 24 h, exhibiting a sustained drug release profile. The optimized formulation was coated with chitosan to form a gel, with a pH of 5.5 and a viscosity of 9780 cps. In the in vitro antifungal study, the maximum zone of inhibition after 72 h, shown by chitosan-coated KCZ-loaded ethosomal, was 23.5 ± 5.3 mm. Edema was seen up to 72 h following the application of chitosan-coated KCZ-loaded ethosomes, but there was no indication of erythema in the in vivo skin irritation assay. The results showed that this formulation showed good potential for antifungal activity against Candida albicans.

Fingerprint identification is one of the most wellunderstood and widely used biometric methods for identifying individuals, relying on the accurate identification of ridge structures in fingerprint images. There has been significant work to date on estimating ridge information (i.e., orientation) in fingerprint images. However, estimating ridge quantity (i.e., ridge frequency) presents challenges, especially for noisy or lowquality images. In this work, we propose an end-to-end solution using Convolutional Neural Networks (CNNs) to simultaneously estimate both ridge frequency and ridge orientation from fingerprint images. The proposed method includes preliminary processing to extract the fingerprint area from an image and orientation encoding to facilitate the network's learning process. The system has been trained with enhanced data and dense pixelwise supervision to enhance noise and low contrast robustness. The system was evaluated on a typical fingerprint dataset and it yielded a mean absolute percentage error (MAPE) of 4.58 for frequency estimation leading to a substantial improvement over traditional image processing methodology. Additionally, this method is easily adaptable to real-time implementations. In this work, we illustrate how combining domain knowledge with modern machine learning approaches can lead to better accuracy and overall reliability in fingerprint analysis.

The rise in complexity of the operations involving large-scale geographically dispersed workforce and dynamic cloud infrastructure has also increased the requirement of intelligent scheduling and resource optimisation. The proposed paper has come up with an efficient framework; Adaptive Multi-Agent Scheduling and Optimization (AMASO) to overcome this challenge through AI-based techniques. AMASO incorporates the federated optimization, multi-agent systems, and deep reinforcement learning in the autonomy of task assignments and cloud resource allocation in the edge-fog-cloud dynamics. Every agent, be it acting on a unit of workforce or a compute resource, acts locally and acts in a globally coordinated manner through a federated multi-agent protocol. The resulting learning algorithm uses Multi-Agent Proximal Policy Optimization (MAPPO), implemented with a custom policy to learn through contextual data in real time such as the urgency of tasks, and the availability of skills, SLA needs and infrastructure loads. The mechanism it uses is a bi-level decision refinement based on the integration of Genetic Algorithm-Ant Colony Optimization (GA-ACO) offering resource-task pairing efficiency. To maintain performance, the techniques of Federated learning are used to ensure privacy and scalability. Compared to the traditional heuristic technique, the simulation outcomes explain that there is an increase in the adherence to SLA, less idling of resources, and higher utilization of the workforce. The suggested AMASO system attained a scheduling accuracy of 98.76%, indicating its exceptional precision in work allocation and adherence to deadlines.

A straightforward and effective methodology was established for the synthesis of 2-(2-arylidenehydrazinyl)-4,5-diphenylthiazoles (Va-Vg) through the reaction of 1,3-diphenyl-propane-1,3-dione with suitable aromatic aldehydes to produce 2-methoxy-4,5-dimethylthiazole, which is subsequently reacted with hydrazine hydrate and further treated with diverse aromatic aldehydes. The synthesized compounds were structurally verified employing IR, NMR, and mass spectrometry. Furthermore, investigations into antioxidant activity were performed, demonstrating the promising bioactivity of the synthesized thiazole derivatives. Compound Ve exhibited the highest antioxidant activity in comparison to the standard, with an IC50 value of 23.71, followed by Vd with an IC50 value of 32.11. Compounds Vc and Vf demonstrated moderate antioxidant activity, with IC50 values of 47.3 and 51.6, respectively.

Sitosterolemia is a rare inherited autosomal recessive disorder characterized by the accumulation of plant sterols in the blood, due to mutations in the ABCG5 and/or ABCG8 genes, which encode the protein sterolin. This condition can potentially lead to premature cardiac death due to atherosclerosis. We report a series of four patients with sitosterolemia who presented to us with nonspecific symptoms. Most of them had received steroids arbitrarily at the previously visited clinics. All of them had stomatocytes and macrothrombocytes in peripheral blood smear examination. Next-generation sequencing (NGS) revealed ABCG5 gene mutation in two patients and ABCG8 gene mutation in the other two. None of them had xanthomas or liver disease. All of them responded to a low plant sterol diet and ezetimibe. To conclude, although rare, sitosterolemia should be considered in patients with thrombocytopenia and nonspecific symptoms once common etiologies are ruled out as this condition could cause premature cardiac death. Irrational steroid initiation without a diagnosis must be avoided. Basic peripheral blood smear examination which is also relatively inexpensive may provide immense amounts of information, as in our cases.

Background : Surya Namaskara is not only covering the elements of all Physical Exercises like body building, quick reflexes, stamina, strength, flexibility, dexterity, daring and initiative but also enhances the mental faculties of both the sides of the brain, I.Q., memory, concentration and creativity etc. Surya Namaskara also develops the most needed spiritual quality in man- awareness, alertness and wakefulness. Aim : To assess the effects of Surya Namaskara as a fitness booster Materials and Methods. In this study 113 Subjects included both male and female are participating. 113 students in the Experiment group are practicing Surya Namaskara for 45 minutes. Assessment Tools such as Mindful Attention Awareness Scale (MAAS), BMI, Kraus-Weber Test, Self- Believe, Self Regulation were used to measure the parameters. Result : Total of 113 participants included in this study. Intervention of Surya Namaskara was given for 45 minutes for experimental group. Pre and Post data were taken. Found good result in many variables. Conclusion : In experimental group there was significant improvement found after the practice of Surya Namaskara on BMI, MASS levels, Self- Regulation, Kraus-Weber Test.

Educational technology theories have lagged behind the rapid development of relevant technologies. Due to advancements in information and communication technologies, college students can now reach audiences beyond their professors. Assignments are now being graded based on podcasts, infographics, blog posts, and social media marketing. In addition to improving their theoretical and practical abilities, students are able to reach a wider audience with the use of these instruments. Improved assignment evaluation was achieved by a multi-stage prediction model utilising CNN and BILSTM. Latent Dirichlet Allocation was used for feature extraction after data was prepped by removing duplicate and incomplete information. To examine school records, the CNN-BILSTM model applies geographic information gleaned from traffic records. With a Recall of 95.56% and an F-Measure of 99.49%, the model performed admirably while evaluating assignments. A more precise assessment of assignments may be possible with the use of sophisticated DL techniques, according to these results. The necessity for ongoing technological advancement in educational assessment is emphasised by the fact that ICT-based communication tools enhance students' engagement with real-world audiences.