In most structural failures, deterioration of reinforced concrete structure is due to corrosion of reinforcing bars. Ingress of chloride ions and reaction of carbon dioxide with the cement paste due to poor-quality concrete or inadequate cover are the main causes of corrosion. Corrosion can be reduced by using high-quality concrete and providing proper cover to reinforcement. In addition, corrosion inhibitors are used to protect the reinforcement in order to extend the service life of concrete structures. This research work is focused on determining the effectiveness of corrosion inhibitors under cracked and uncracked sections of U-shaped beam. The U-shaped beam concept was used in this study, which helps to develop the crack width within 0.3 mm as per IS: 456 code provision. The two vertical stubs of the U-shaped beam were stressed in order to generate the cracks at the soffit of the beam. Then, the beams are tested for accelerated corrosion along with the half-cell potential meter calculations for the corrosion potential measurement.

Large and complicated datasets may now be generated utilising device reading machine learning approaches, which can subsequently be used to model and study substances in a variety of ways, along with people who require robotics and automation. For data analysis, there was a delay in implementing device learning methodologies since nanomaterials have not yet achieved the overall benefits of automation. There has been an explosion in the number of tools available for learning about nanomaterials, but there are still significant roadblocks in the way of actually putting those tools to use in a practical way. The homes of nanoparticles can be examined and anticipated with the help of system learning algorithms, and this painting shows how classic and deep system mastery techniques may be done to preserve nanomaterials. Among the topics covered are the history of nanoprotection, as well as a forecast for the future of artificial intelligence’s (AI) role in the field in the near future.

One of India’s fastest‐growing industries is the manufacturing sector centered on exports. Additionally, it has a greater impact on the economic development of India. Technology in the export‐oriented manufacturing sector makes a significant contribution to the rise in productivity, which also boosts profits. The study’s major goal is to learn more about the adoption of the 14.0 ICT system by manufacturing enterprises with reference to MSMEs and to evaluate its efficacy. In order to gather information for this goal, a survey approach was used, with the dimensions of ICT infrastructure, ICT hardware, software, and information systems, as well as people and human resource factors, as the instruments. Multiple regression and descriptive statistics were used to evaluate the data on a sample size of 7 businesses from the Pondicherry region. The conclusion is that firms have a significant gap in their ICT preparedness in terms of ICT infrastructure, ICT hardware, software, and information systems, and this must be treated seriously by the businesses if technology advancement is to continue in the future.

The surging demand for ecofriendly and sustainable energy sources has propelled the exploration of innovative solutions. This study comprehensively investigates the production and characterizations of bioethanol derived from the waste lignocellulose sugarcane top and leave. The biomass composition, including hemicellulose, cellulose, and lignin, was thoroughly analyzed. The hydrolysis uses diluted sulfuric acid at a temperature ranging from 110°C to 122°C, with a time of 20–30 min, and acid concentrations of 2%‐3%. To understand the effect of hydrolysis during fermentation, the untreated and acid‐treated biomass samples underwent various characterization techniques, including FTIR and SME‐EDX. The anaerobic fermentation transformed the hydrolyzed biomass into bioethanol using the yeast strain Saccharomyces cerevisiae . Fermentation occurred at a temperature of 30°C and a pH of 4.86, with a fixed biomass concentration of 10 v/v yeast strain and a final fermentation time of 72 h. The resulting ethanol–water mixture was separated through distillation, and its characteristics were determined, revealing a density of 807 kg/m ³ and a viscosity of 1.55 × 10 ⁶ m ² s ⁻¹ . The observed ethanol yield from sugarcane leaves and tops reached 28.24%. To optimize the process parameters, response surface methodology was employed. The optimal conditions were a temperature of 121.95°C, time of 26.36 min, and an acid concentration of 2.99%. These conditions yielded a total reduced sugar content of 69.63%, thereby maximizing the bioethanol yield. Under these optimal conditions, chromatography‐coupled mass spectrometry (GC‐MS) confirmed the presence of 18.65 g/L of bioethanol in the broth.