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In this work, strategies for the formation of duplex coatings with enhanced bioactivity, mechanical properties and corrosion resistance have been focused. TiO2 arrays were fabricated on Ti alloy were carried out in a single step using suitable electrolyte by anodization method. Here, we have synthesized a novel bioactive material, minerals incorporated Hydroxyapatite (La/Tb-HAP)-chitosan-casein duplex coatings on anodized Ti via electrodeposition method. The fabricated novel composite coatings were characterized by FT-IR, XRD, FESEM and EDAX analyses. Also, the mechanical properties of duplex coatings were scrutinized by Dermitron thickness and microhardness tester. The corrosion resistance of the as-developed duplex coatings was studied by electrochemical techniques using Ringers solution as the electrolyte. In addition, the antibacterial activity, cell viability, live and dead staining were executed to substantiate the biocompatibility of TNT/CS-CA@M-HAP duplex coatings. From the overall summary of this work, it is proved that the resultant CS-CA@M-HAP coatings on TNT exhibit excellent bioactivity and improved corrosion resistance over pure Ti and serve as a potential candidate for orthopedic applications.

Objective: Chitin a natural polymer is abundant in several sources such as shells of crustaceans, mollusks, insects, and fungi. Several possible attempts have been made to recover chitin because of its importance in biomedical applications in various forms such as hydrogel, nanoparticles, nanosheets, nanowires, etc. Among them, deep eutectic solvents have gained much consideration because of their eco-friendly and recyclable nature. However, several factors need to be addressed to obtain a pure form of chitin with a high yield. The development of an innovative system for the production of quality chitin is of prime importance and is still challenging. Methods: The present study intended to develop a novel and robust approach to investigate chitin purity from various crustacean shell wastes using deep eutectic solvents. This investigation will assist in envisaging the important influencing parameters to obtain a pure form of chitin via a machine learning approach. Different machine learning algorithms have been proposed to model chitin purity by considering the enormous experimental dataset retrieved from previously conducted experiments. Several input variables have been selected to assess chitin purity as the output variable. Results: The statistical criteria of the proposed model have been critically investigated and it was observed that the results indicate XGBoost has the maximum predictive accuracy of 0.95 compared with other selected models. The RMSE and MAE values were also minimal in the XGBoost model. In addition, it revealed better input variables to obtain pure chitin with minimal processing time. Conclusion: This study validates that machine learning paves the way for complex problems with substantial datasets and can be an inexpensive and time-saving model for analyzing chitin purity from crustacean shells.

The Siddhas are the ones who contribute to the well-being and development of human society. The Siddhas are those who did not live life for the sake of birth, but lived the life of the best and deserved the excellence of others. Those who condemn socially closed practices are revered as equals to the guru in enlightening the people. This article describes the life of Pattinathar Siddhar and the philosophical ideas he expressed in his songs, considering the service he rendered to the people. Who is God? How does he bless? His form, His Omnipotence, man's birth, his innate usefulness, and his attributes are realized through this study. This article also addresses the views of Pattinathar, who suggested that there is only one God who is inward all. He points out that one should live in a world free from conflict and the benevolence of good deeds that will bring the devoted one to the Lord in the form of Gnana as per the doctrines of the fourfold religion.

As heavy rainfall can lead to several catastrophes; the prediction of rainfall is vital. The forecast encourages individuals to take appropriate steps and should be reasonable in the forecast. Agriculture is the most important factor in ensuring a person's survival. The most crucial aspect of agriculture is rainfall. Predicting rain has been a big issue in recent years. Rainfall forecasting raises people's awareness and allows them to plan ahead of time to preserve their crops from the elements. To predict rainfall, many methods have been developed. Instant comparisons between past weather forecasts and observations can be processed using machine learning. Weather models can better account for prediction flaws, such as overestimated rainfall, with the help of machine learning, and create more accurate predictions. Thanjavur Station rainfall data for the period of 17 years from 2000 to 2016 is used to study the accuracy of rainfall forecasting. To get the most accurate prediction model, three prediction models ARIMA (Auto-Regression Integrated with Moving Average Model), ETS (Error Trend Seasonality Model) and Holt-Winters (HW) were compared using R package. The findings show that the model of HW and ETS performs well compared to models of ARIMA. Performance criteria such as Akaike Information Criteria (AIC) and Root Mean Square Error (RMSE) have been used to identify the best forecasting model for Thanjavur station.

A novel you only look once (YOLO)-based gated attention deep convolution network (GADCN) classification algorithm is developed and utilized in this present study for the detection of breast cancer. In this framework, contrast enhancement-based histogram equalization is applied initially to produce the normalized breast image with reduced noise artifacts. Then, the breast region is accurately segmented from the preprocessed images with low complexity and segmentation error using the YOLO-based attention network model. To diagnose breast cancer with better accuracy, the GADCN model is used to predict the exact class of image (i.e., benign or malignant). During classification, the activation function is optimally computed with the use of the Lichtenberg optimization algorithm (LOA). It aids in achieving improved classification performance with little complexity in training and assessment. The significance of the present study includes the use of a unique, YOLO-based GADCN-LOA model that helps in the prediction of breast cancer with higher accuracy. It was observed that the model exhibited 99% accuracy for the datasets utilized. In addition, the selected model outperforms well with sensitivity, specificity, precision, and F1-score. Hence the proposed model could be exploited for the diagnosis of breast cancer at an early stage to enable preventive care. [ABSTRACT FROM AUTHOR]

This study investigates the incorporation of thin-film photovoltaic (TFPV) technologies in building-integrated photovoltaics (BIPV) and their contribution to sustainable architecture. The research focuses on three key TFPV materials: amorphous silicon (a-Si), cadmium telluride (CdTe), and copper indium gallium selenide (CIGS), examining their composition, efficiency, and BIPV applications. Recent advancements have yielded impressive results, with CdTe and CIGS achieving laboratory efficiencies of 22.10% and 23.35%, respectively. The study also explores the implementation of building energy management systems (BEMS) for optimizing energy use in BIPV-equipped buildings. Financial analysis indicates that despite 10.00–30.00% higher initial costs compared to conventional materials, BIPV systems can generate 50–150 kWh/m2 annually, with simple payback periods of 5–15 years. The research emphasizes the role of government incentives and innovative financing in promoting BIPV adoption. As BIPV technology progresses, it offers a promising solution for transforming buildings from energy consumers to producers, significantly contributing to sustainable urban development and climate change mitigation. [ABSTRACT FROM AUTHOR]

Bio-derived materials could play a major role in future supportable green and health technologies. Nontraditional materials such as proteins and biopolymers are observed for their probable use in the fabrication of bioplastics for packaging. In the present work, novel ternary composite films of chitosan (CS), polyvinyl alcohol (PVA), with different ratios of natural egg white (EW) and glycerol as a plasticizer, were fabricated by solution casting technique. The synthesized ternary composite films were characterized by UV–Vis absorption spectra, Fourier transform-infrared spectroscopy (FT-IR), X-ray diffraction (XRD), and scanning electron microscopy (SEM) analysis. The hydrophobicity of as-synthesized samples was confirmed by contact angle measurement. The microbiological screening analysis was established against bacteria viz. Pseudomonas sp., Bacillus subtilis and Staphylococcus aureus bacteria. The attained results denoted that the film might be an appropriate applicant for food packaging applications. [ABSTRACT FROM AUTHOR]

In this study, individual and copyrolysis experiments were performed with pressed neem oil cake (NOC) and waste thermocol (WT) to produce high grade liquid oil. The effects of reactor temperature, heating rate, feed ratio, and reaction time on product yields were investigated to identify the optimum parameters for maximum oil yield. The maximum oil yield of 49.3 wt%, 73.4 wt% and 88.5 wt% was obtained from NOC pyrolysis, copyrolysis, and WT pyrolysis under optimized conditions. During copyrolysis, the maximum oil product was obtained under NOC/WT ratio of 1 : 2 and at the temperature of 550°C. The liquid oils obtained from thermal and copyrolysis were subjected to detailed physicochemical analysis. When compared to biomass pyrolysis, the copyrolysis of WT and NOC had a substantial improvement in oil properties. The copyrolysis oil shows higher heating value of 40.3 MJ/kg with reduced water content. In addition to that, the copyrolysis oil obtained under optimized conditions is analyzed with Fourier transform infrared spectroscopy (FT-IR) and Gas chromatography–mass spectrometry (GC-MS) analysis to determine the chemical characterization. The analysis showed the presence of aliphatic and aromatic hydrocarbons in the oil.