Concrete structures are susceptible to damage and deterioration over time. Different repair methods have been adopted to restore the integrity of structures and ensure their safety and longevity. Although jacketing is commonly adopted as a repair method, its implementation results in the addition of loads and reduction in dimensions and free spacing. In view of the challenges associated with the implementation of jacketing, development of ultra-high-performance engineered cementitious composite (UHPECC) is frequently discussed in research as it can enable jacketing to be performed using thin layers of repair material due to the higher strength-to-weight ratio of UHPECC as compared to conventional repair materials. Therefore, the adoption of UHPECC for jacketing can lead to the reduction in overall weight and thickness of the repair material while ensuring the longevity of the repair. At the same time, graphene, which is a nanomaterial that comprises carbon atoms arranged in a honeycomb lattice pattern with a structure that is nearly transparent and a thickness of one atom, can impart strength that is 200 times greater than steel. In the light of the exceptional strength property of graphene, the effect of incorporating graphene in engineered cementitious composite (ECC) on strength was studied in an effort to further advance UHPECC research. Compressive and tensile strength tests were conducted on ECC samples that contain graphene added at contents in the range of 0.03–0.09% by weight of the binder. Results reveal an increasing trend in compressive and tensile strengths with respect to increasing graphene content.

Context:

The Internet of Things (IoT) refers to a network of interconnected devices as well as technology that enables objects to communicate with one another with the cloud for modern medical treatment. New technologies, such as context-aware systems and apps, are continually being introduced into the field of medicine. This work creates an IoT-enabled healthcare system based on context awareness.

Objectives:

Deep learning is the subset of Machine Learning, which has the transformative ability to rapidly analyze massive amounts of data, produce insightful conclusions and effectively resolves complex problems. The objective of this work is to employ an IoT framework for heart disease prediction.

Method:

An RHMIoT framework is proposed in a secure IoT and cloud context using a lightweight block encryption and decryption approach. Using IoT medical sensors patient clinical data are gathered to classify the severity of hypertension, hypercholesterolemia and heart disease. The accuracy levels of cardiac disease are calculated using Deep Learning and auto-encoder-based methods.

Findings: We present a novel strategy for identifying key features using machine learning and deep learning techniques in a secured cloud environment to improve the accuracy of CVD.

Conclusion:

A lightweight block encryption and decryption technique is provided for a secure RHMIoT. The outcomes were determined using several performance matrices. The performance of auto-encoder Kernel SVM model provided the greatest accuracy of 87.00%. The suggested remote health monitoring system identifies the presence of heart disease in a patient and helps to get quick medical attention in case of an emergency situation.

In today's industrial landscape, wireless technology is gaining importance. Popular standards like WirelessHART, ZigBee, and ISA100.11a are widely used. Despite their benefits, wireless networks can experience packet loss or drops, making closed-loop systems vulnerable and resulting in system failure. To prevent such issues, dead-time compensation is necessary. Conventional techniques like predictive PI are commonly used for this purpose. However, these compensators may perform poorly for wireless networks with long dead time and variations in set-point, which can affect network stability. To address this, a fractional calculus-based predictive PI compensator is proposed in this paper for wireless networks in process industries, which can improve the performance of these compensators. The performance of the proposed compensator is evaluated on the industrial processes, including pressure, flow and temperature, where the measurement and control actions are carried out wirelessly. The wireless network’s performance is evaluated for packet loss, reduced throughput, and increased latency. The proposed compensator outperformed traditional ones in terms of achieving better set-point characteristics.

This study investigates the use of CuBr vapor nanosecond laser with combined 510 nm and 578.2 nm wavelengths for surface treatment of 1050 aluminum and 2219 aluminum alloys. Laser-induced periodic surface structuring (LIPSS) was used to optimize processing parameters to achieve hydrophobic and hydrophilic properties on the surface. The wetting properties were measured and the roughness results (Ra, Rz, Rq) evaluated. Prior to and after laser treatment, surface wetting and roughness changes were investigated. The wetting study showed that the maximum contact angle between a droplet of deionized water and the treated surface can be reached between more than 140 degrees and less than 10 degrees, which, respectively, is a super hydrophobic and hydrophilic surface. Compared to untreated surface, wetting increased by more than 2 times and decreased more than 8 times. Overall, experiments show the dependence of wetting properties on laser input parameters such as scan speed, scan line distance with different delivered energy amounts. This study demonstrates the possibility of laser parameter optimization which do not require auxiliary gases and additional processing of the resulting surfaces to obtain different wetting properties on the surface. The findings described in this article suggest that this surface treatment method is a promising method for industrial applications where surfaces with special wetting and roughness properties are required, as an example of laser marking of serial number of parts used in wet environments such as aerospace, shipbuilding and defense industries.

Breast cancer is a common, potentially fatal disease that not only effects women but can also affect men. Breast cancer is the most common disease affecting women globally, and is the main cause of morbidity and death. Early and accurate detection of this risky disease is very crucial. A timely and precise identification of breast cancer disease can decrease death rate and also can protect people from additional damage. The traditional methods used for identification of breast cancer detection are very expensive in term of time and cost. The goal of this study is to develop a system which can detect the breast cancer accurately and at early stage. The primary objective of this research study is to make use of histopathological images to identify breast cancer correctly and faster. In the proposed research work we have developed a model with name BCDecNet, which comprises twelve learnable layers, i.e., nine convolution layers and three fully connected (FC) layers. The architecture has a total of thirty layers, including one input layer, eight leaky relu (LR) layers, four relu layer, five maximum pooling layers, 6 batch normalization (BN) layers, one cross channel normalization layer and three dropout layers. The proposed work uses image based data taken from Kaggle online repository. The suggested model achieved 97% accuracy, 96% precision, 96% recall and F1 score. Furthermore, the result of proposed model compared with other hybrid approaches used for diagnosis of breast cancer at early stages. Our model achieved satisfactory result then all other approaches use for breast cancer disease detection. Additionally, the proposed BCDecNet model can be generally applied on other medical images datasets for diagnosis of various diseases.

Most hydrological and water resources researchers prioritise the development of an accurate sediment prediction model. Several conventional techniques have failed to accurately predict suspended sediment. Because of the complexity, non-stationarity, and non-linearity of sediment transport behavior in rivers, many techniques fall short. Over the last few decades, there have been significant developments in the theoretical understanding of machine learning approaches, as well as algorithmic strategies for their implementation and applications of the approach to practical and hydrological problems. To produce the desired output, machine learning models and other algorithms have been employed to predict complicated non-linear connections and patterns of huge input parameters. This paper examines a number of key works of literature on sediment transport prediction while focusing on a variety of machine learning applications. Sediment transport models aided by machine learning have attracted a growing number of researchers in recent years. As a result, they must gain in-depth knowledge of their theory and modeling methodologies. Furthermore, this chapter includes an overview of the machine learning technique and other developing hybrid models that have produced promising outcomes. This overview also includes various examples of successful machine learning applications in sediment prediction.

Most of the current day industries are suffering from nonlinear processes. Thus, both the stability and the process performance of high-degree nonlinear systems with dominating delay might be difficult to achieve. Adaptive and intelligent fuzzy classifiers and controllers have been more popular in recent years as a means of overcoming a significant number of difficulties faced by the industrial sector. A large number of dynamic process plants with a variety of orders and kinds have been represented heuristically and recognized. Fuzzy structures have also been employed for these interactive systems by making use of fuzzy and linguistic techniques. In view of all these initiatives, the purpose of this paper is to conduct an experimental investigation into the performance of a LabVIEW-based Type-1 Adaptive Mamdani Fuzzy Controller (AMFC) that has been designed and applied over a lag dominant and a second-order nonlinear Dual Input Tank System (DITS) and Single Input Tank System (SITS). As compared to other Type-I approaches that were previously experimented with and are now in existence, the adaptability of AMFC demonstrates that it is quite effective. Performance indices such as Integrated / Summated Absolute Error (IAE) and Integrated / Summated Squared Error (ISE) are also computed for several variable set point profiles of DITS. These indices measure errors in integrated absolute value and integrated squared value, respectively. Adaptive Type-1 Intelligent Fuzzy Controller's response and error reduction efficiency have been found for several flow configurations of DITS, namely Multiple Input Multiple Output (MIMO) and Single Input Single Output (SISO). From the results, it can be concluded that the proposed experimental validation may be used for a wide variety of process challenges that are experienced in industrial systems to achieve robust and low error controller performances.

Polymeric Membranes are a technological innovation for separation and filtration. They are composed of advanced materials, such as Polyvinylidene fluoride (PVDF), which imparts mechanical stability to the membrane and helps to prevent biofouling, along with a hydrophobic character that facilitates the coagulation phase, and the polyvinylpyrrolidone (PVP) provides a hydrophilic nature, enhancing its affinity for the reaction medium and assisting in the filtration process. This structure allows the passage of substances through it while retaining larger particles and external contaminants, making selective filtration a differentiating factor in wastewater treatment. One strategy to further enhance the properties of these membranes is the incorporation of activated carbon during manufacturing. Activated carbon has a high surface area and adsorption capacity, making it effective in adsorbing different substances. This study aims to produce mixed polymeric membranes incorporating activated carbon, using PVDF and PVP as polymers and N-Methyl-2-pyrrolidone (NMP) as the solvent. These membranes will be employed for the filtration of phenolic compounds, such as phenol. In a membrane with the formulation of 1.3g of PVP, 1.15g of PVDF, 8.8 ml of NMP, and 2.5g of activated carbon, with different thicknesses of 150 µm and 300 µm, approximately 56.77% and 90.35% of 50mg/l of phenol in a model wastewater were removed in 5 minutes, respectively, with breakthrough occurring in 15 minutes. Thus, it is possible to demonstrate the viability of using these membranes in the treatment of model wastewater containing phenolic compounds, where the 300 µm membrane showed better results, only requiring scaling up for practical application.

Drought is a complex natural disaster with significant implications for agriculture, water resources, and socioeconomic development. Accurate and timely assessment of meteorological drought is crucial for effective management and mitigation strategies. This systematic review analyzes and evaluates meteorological drought indices and their associated techniques in drought monitoring and assessment. The review involved an exhaustive search of peer-reviewed literature, conference papers, and technical reports from reputable databases. A robust selection criterion was applied to identify relevant studies on meteorological drought indices, methodologies, and their performance in different regions and climatic conditions. A wide array of meteorological drought indices was identified and classified into three major categories: rainfall-based, temperature-based, and combined indices. The most commonly used rainfall-based indices were the Standardized Precipitation Index (SPI) and the Palmer Drought Severity Index (PDSI), which effectively capture meteorological droughts with varying lead times and durations. Temperature-based indices, such as the Standardized Precipitation Evapotranspiration Index (SPEI) and the Thornthwaite Moisture Index (TMI), provide valuable insights into the impact of temperature on drought conditions. Several studies focused on developing combined indices that integrate precipitation and temperature data to offer a more holistic representation of meteorological droughts. The Composite Drought Index (CDI) and the Joint Drought Index (JDI) were widely used due to their ability to account for multiple climatic variables. The review emphasizes the importance of considering the specific characteristics of the study region and the intended application when selecting an appropriate drought index. It also highlights the need for continuous advancements in drought assessment techniques to address emerging challenges, such as climate change impacts and data sparsity in certain regions.

In Pakistan energy production from nuclear, hydro, and gas sources is about 99 %, and only 1 % from renewable energy resources. Biomass is an emerging renewable resource for Pakistan by converting loose biomass into pellets of high density. In this regard, biomass is getting more and more attention day by day due to its abundant availability. Due to improper management of biomass regarding transportation, handling, storing, and lack of awareness, its use for energy production is very low. The environmental and health effects of dumping and incineration techniques are becoming more dangerous for developing countries and rural communities’ day by day. The densification technique is an emerging technology nowadays for developing countries. Pellets development of yard waste is a new concept that can encounter energy demands in addition to reducing environmental pollution and sources of waste management. For this purpose, the biomass of yard waste shredded and then it was dried under sunlight or open drying to reduce the moisture content in the biomass material to less than 16% before being carbonized. Five samples at different moisture content levels were made and pellets were prepared from it. The results carried out by characterization of pellets show that yard waste pellets at 5 % moisture content have the highest calorific value of about 17.76 MJ/kg. Flue gas analysis was carried out and emissions from all were determined at all five-moisture content.