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A sustainable concrete solution was developed by partially replacing aggregates and cement with lightweight plastic aggregates (LWPA) and bagasse ash, respectively. Three types of mixes were studied: (i) concrete with 10-30% cement replaced by bagasse ash (BA), (ii) Bagasse ash concrete mix with scrap steel fiber, and (iii) Bagasse ash concrete with scrap fiber and 10-50% lightweight aggregate. The addition of bagasse ash positively influenced the fresh properties of concrete. However, workability decreased by 56% with the incorporation of scrap fibers and lightweight aggregate compared to the control mix. Slump values reduced in concrete mixes containing plastic aggregate (PA) due to mortar accumulation in voids within the hollow aggregate, significantly diminishing workability. The density of concrete decreased with bagasse ash and lightweight aggregate inclusion, aiding in reducing structural dead weight. However, the strength decreased notably with 30% replacement using hollow lightweight plastic aggregate. Voids in concrete specimens increased with LWPA percentage. Observations from fresh and hardened concrete tests suggest that plastic aggregate, bagasse ash, and scrap fiber can effectively replace up to 20% of gravel aggregate.

In the current research, lightweight foam concrete blocks made from recycled concrete powder, reinforced with plastic macro fibers and polypropylene micro fibers have been proposed as sustainable concrete approach. By adding Recycled Concrete Powder (RCP) in the amount ranging from 15 to 30% by weight of cement, have only a mild impact on the strength properties of foam concrete. Addition of mono fibers and hybrid fibers improves the mechanical properties of foam concrete with RCP. The tensile strength, flexural strength and compressive strengths of concrete are enhanced by the addition of micro and macro fibres. As the percentage of macro fiber increases the strength of the light weight concrete also increases. when the proportion of micro fibers is more compared to macro fibers, the strength properties are slightly reducing as the micro fibers reduces the plasticity of foam mix. In hybrid fiber reinforcement 0.3% plastic and 0.2% polypropylene showed an improvement in mechanical properties compared to other specimens.

This work focuses on the effect of natural plant fiber as reinforcement on the strength properties of concrete of different grade strength (M30-M50). Fibers of different length were added to the concrete mix at the rate 3% of the binder content. Three different lengths of fibers (10-30 mm) at fixed volume fraction were added to M30, M40 and M50 grades of concrete. The fresh and mechanical property of twelve different fiber reinforced concrete mixes was investigated. The effect of fibers on the pre cracking, post cracking behaviour of the concrete specimen was investigated using digital image processing technique and video measuring system images. Using the developed linear regression plot, empirical equations were formulated to establish relation between the compressive strength and other mechanical properties of concrete. From the study it can be concluded that the caryota fiber with rich cellulose content contributes to arrest the cracks at the initial stage of loading and prevents major crack plane in the post peak region. Fibers mainly contribute to increase the tensile property of concrete. The effect of fibers is more prominent in M30 mix concrete when compared to M40 and M50 concrete mixes.

Cracks in structures pose significant risks to safety, integrity, and functionality, necessitating efficient and accurate analysis methods. This paper presents an innovative approach to crack analysis by integrating Artificial Intelligence (AI) techniques with Natural Language Processing (NLP) methodologies. Initially, NLP techniques are employed to extract key concepts, methodologies, and findings related to crack analysis from textual sources. Subsequently, features relevant to crack detection, classification, and severity assessment are extracted from the processed text data. AI models, including supervised learning algorithms such as Convolutional Neural Networks (CNNs) and unsupervised clustering methods, are trained using labelled data to detect cracks, classify their types, and assess their severity. Reinforcement learning techniques may also be explored for optimizing inspection strategies and maintenance schedules based on crack analysis results. Furthermore, the integration of sensor data, such as images from cameras and measurements from ultrasonic devices, enriches the analysis process, providing more accurate and comprehensive insights into crack formation and propagation. Real-time monitoring systems, coupled with AI models, enable continuous assessment of structural health, with alert mechanisms in place to notify relevant stakeholders of potential issues. This approach not only streamlines crack analysis processes but also enhances the efficiency and effectiveness of structural health monitoring systems, ultimately contributing to safer and more resilient infrastructure.

This review explores Peer-to-Peer (P2P) Energy Trading Platforms in Local Energy Markets, drawing insights from three key studies. These markets, using P2P trading, efficiently distribute electricity within communities. Research assesses P2P trading’s impact in a Norwegian neighbourhood, comparing it to scenarios without local markets. It also examines integrating PV systems, batteries, and electric vehicles on grid operations. Findings reveal minimal grid impacts with PVs alone, but adding batteries increases voltage fluctuations and losses. However, P2P trading benefits end-users with cost savings and supports Distribution System Operator operations. The paper surveys global P2P energy trading projects, emphasising communication and control networks within local Microgrids. It discusses the transition from passive consumers to prosumers in power networks and introduces the concept of a federated power plant, combining virtual power plants and P2P transactions among prosumers to address challenges and unlock additional value. This review fills research gaps, shedding light on P2P energy trading’s multifaceted aspects in local markets and its transformative potential for the energy sector.

This research suggests a modeling approach for health risk prediction that utilizes an ambient environment and Artificial Intelligence (AI). The proposed AI-based Health Risk Modeling and Predicting System (AI-HRMPS) included gathering medical records from chronic illness patients, including Electronic Health Records (EHR), Personal Health Records (PHR), medical records, and environmental variables from a health portal. Diverse data is combined via choosing, cleaning, modeling, and assessing raw data, followed by data production. Sensor data is standardized by converting the time-domain details to frequency-domain details. The standardized input is processed using an AI to provide an ambient environment. A health risk prediction system has been proposed to analyze specific health issues about environmental factors. The program utilizes ambient context patterns identified via metadata and AI. The risk prediction framework is integrated into a person's risk alert/prevention mechanism. The system might substantially influence healthcare and AI studies, ultimately enhancing the future society's standard of life.

The globe has adopted the cloud computing environment, which organizes data and manages space for data storage, processing, and access. This technical development has brought up questions regarding data security and privacy in cloud computing environments, though. The purpose of this abstract is to offer a thorough review of the issues, solutions, and future developments related to data privacy and security in cloud computing. Keeping data private and secure while it is being processed and stored in outside data centres is the main difficulty in cloud computing systems. The abstract discusses the dangers of insider threats, data breaches, and illegal access to sensitive information. It digs further into the legal and compliance criteria that businesses must follow in order to protect user data in the cloud. In result, data privacy and security in cloud computing environments remain critical concerns for organizations and individuals alike. In the survey the overview of how to use cloud storage globally and its challenges, solution and future innovation is well explained. It underscores the importance of robust encryption, access controls, user awareness, and emerging technologies in safeguarding data in the cloud. By addressing these concerns, organizations can leverage the power of cloud computing while maintaining the confidentiality, integrity, and availability of their data.

A hardware-based variant known as an automatic fire extinguisher is used to put out fires when they start accidentally. Boards made of calcium silicate that can endure extremely high temperatures are used to coat the shield. Thermocouple ends are heated to a cut-off temperature, over which the robot begins responding to the fire, to change the robot's sensitivity to temperature. The biggest plus to this is that it automatically switches on when it senses a fire in its immediate vicinity using a thermocouple and moves in that direction in an attempt to put it out. In extreme situations, the temperature sensor acts as a backup for the thermocouple, as it detects fire or heating temperature the extinguishing agent is released and it flows through the solenoid valve for extinguishment of fire. Both life and property at the same instance of time. our system reduce the risk factors of rescuing life and property by immediately extinguishing the fire at first foremost the incident happens and the DAF EXTINGUISHER automatically does all the work and extinguishing the fire.