In the present investigation, rice husk ash (RHA), bamboo leaf ash (BLA) and palm oil fuel ash (POFA) known as agricultural-waste materials locally available were used to produce ultra lightweight foamed concretes (ULFCs). BLA, RHA, and POFA were used as replacements for cement in ULFCs at different weight percentages of 0 %, 5 %, 10 %, 15 %, and 20 %. To achieve the target density of 350 kg/m3, cement was substituted with agricultural wastes, and a protein foaming agent was added. The properties of ULFCs were examined including, slump test, setting time, fresh density, splitting tensile, compressive and flexural strengths, thermal insulating, microstructural and transport properties, permeability and pore's structure. The experimental results demonstrated that BLA and POFA ashes significantly outperforms in terms of efficiency and improved the mechanical and transport qualities of ULFCs than RHA ash. By increasing the weight fractions of BLA, RHA, and POFA from 5 % to 20 % in ULFC mixes resulted in an improvement in slump, porosity, capillary sorption, water absorption, bulk density, intrinsic air permeability, and specific heat. The ideal results were seen in terms of compressive strength, bending strength, splitting tensile strength, and UPV when 15 % POFA and BLA, and 10 % RHA were used as substitutes for cement. The increase in weight fraction of BLA, RHA, and POFA resulted in a rise in both thermal conductivity and thermal diffusivity of ULFCs. SEM studies revealed that incorporating different agricultural wastes positively affects the pore size distribution within the microstructure decreasing with increased content of ashes related to the development of ULFCs with stronger matrix. As a result, the formulated ULFCs met the masonry strength criteria while also providing economic and environmental benefits.

In today's world, enhancing the performance of recycled aggregate concrete is an essential need, as it ensures the effective utilization of demolished construction waste. The advantages of fly ash and polypropylene fiber in conventional strength concrete are well known. However Further research is required to evaluate the impact of using fly ash, silica fume and polypropylene fiber in case of RA and its effect on the properties of recycled aggregate concrete (RAC). This study evaluates the effects of using 50%, 75%, and 100% recycled aggregate (RA) and 1%, 2%, and 3% polypropylene fiber with a fixed amount of fly ash and silica fume 87 kg/m3 and 21.75 kg/m3. The objective is to determine how these combinations affect concrete strength and durability properties (compression, tensile strength, acid resistance and water absorption). This study also investigates the reliability of non-destructive tests, ultrasonic pulse velocity and rebound number, in the presence of polypropylene (PP) fibers and fly ash with recycled aggregate. The experimental investigation showed that the combination of RA based concrete, fly ash (FA), and polypropylene fiber (PPF) improved concrete durability and strength. The concrete compressive strength made with 50% RA was 13% lesser than that of control concrete, but with the incorporation 3% PP fiber the same mix achieved 5% higher strength than control mix. On the other hand, the concrete compressive strength made with 75% RA and 100% RA was experimentally found 19% and 22% lower, respectively than control mix. The combinations containing constant fly ash and PPF, in addition to 50% recycled concrete aggregate (RCA), demonstrate greater resistance when exposed to sulfuric acid (H2SO4) environments, in comparison to the control mixture. However, the worst performance is shown by mix (100% RA).

The convergence of Internet of Things (IoT) and metaverse technologies is revolutionizing healthcare. This study introduces a pioneering framework tailored for health monitoring within the metaverse. By reshaping remote patient monitoring and virtual consultations, the framework utilizes vital parameters like heart rate, blood pressure, and body temperature. It integrates IoT sensors, augmented reality (AR), and virtual reality (VR), establishing a cohesive metaverse environment for healthcare interactions. Notably, robust 256-bit AES encryption ensures data privacy and security. Our analysis highlights the pivotal role of metaverse architecture in healthcare, emphasizing the efficacy of AES-256 encryption in preserving patient confidentiality. Findings underscore the framework's potential to enhance remote patient care while upholding stringent data privacy standards. Moreover, it fosters trust among patients, healthcare providers, and regulatory bodies. In summary, this comprehensive framework marks a significant advancement in remote patient care, promising improved health outcomes and a secure foundation for healthcare in the metaverse.

This paper studies the use of municipal solid waste incinerator bottom ash as a supplementary cementitious material in concrete products using an innovative chemical treatment approach. The primary objective is to address emissions associated with waste-to-energy facilities and the heavy reliance on ordinary Portland cement as the primary binder in concrete. The proposed method involves the removal of metallic aluminium from the bottom ash and the subsequent use of the treated bottom ash as a partial cement replacement to produce concrete. Concrete specimens were produced with varying proportions of treated or untreated municipal solid waste incinerator bottom ash, replacing 20%, 35%, and 55% of ordinary Portland cement according to EN 197 European standard for common cement. Moreover, class F fly ash was incorporated in equivalent percentages as a reference supplementary cementitious material, and a control mix was prepared using solely Portland cement. The evaluation encompassed multiple visual and analytical techniques, including scanning electron microscopy, X-ray diffraction, X-ray fluorescence, and setting time analyses on pastes made with Portland cement, fly ash, and bottom ash. All specimens were evaluated in terms of mechanical performance, namely compressive strength. The chemical treatment process facilitated the release of a significant quantity of hydrogen, a by-product of aluminium oxidization. Consequently, this resulted in significantly reduced formation of gas bubbles in concrete in the fresh state and, therefore, diminished expansion during the setting process. As the proportion of cement replacement with bottom ash increased, a decline in strength was observed. However, this decline was less pronounced when using treated bottom ash, particularly with lower levels of incorporation.

Utilizing sawdust efficiently to produce construction materials can help safeguard the environment and decrease costs by minimizing the need for traditional resources and reducing carbon dioxide (CO2) emissions. Additionally, recycling sawdust plays an essential role in creating a sustainable ecosystem. Hence, this study aimed to examine the potential use of sawdust ash (SDA) as a partial cement replacement on foamed mortar (FM) properties, including its fresh, mechanical, transport, thermal, and microstructural properties. A variety of FM mixtures were tested for workability, density, consistency, intrinsic air permeability, porosity, split tensile strength, compressive strength, flexural strength, and thermal conductivity by replacing cement with SDA at varying percentages of 0%, 10%, 20%, 30%, 40%, and 50%. The results revealed that FM’s workability was reduced by the introduction of SDA with a higher percentage cement replacement, while the density of the FM mixtures was reduced due to SDA’s specific gravity being lower than that of cement. A linear improvement was observed in the air permeability, sorptivity, and porosity of FM–SDA composites with an increased SDA percentage to 20%. It is notable that these properties started to deteriorate once the cement replacement by SDA surpassed 30%. A noticeable improvement of mechanical strength properties of the FM was found at 20% of SDA content, but they deteriorated when the SDA content was more than 30%. FM blends with higher SDA contents exhibited larger and more apparent voids, according to SEM analysis. In conclusion, incorporating sawdust into formulations emerges as a viable method for FM production. This approach not only mitigates the environmental impact of sawdust disposal but also reduces the need for extracting natural resources in construction material manufacturing.

AbstractDetecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

One of the universal occurrences, death seems to have inspired great poets and writers in Iranian and international literature. This study examined how Rumi’s poems address one of the most important life facts, which is death. He regarded death as similar to life or subsequent birth, which is unpleasant and bitter, however, it is also something that poets are eager to embrace and relish for its scrumptiousness. The qualitative content analysis method was used to analyze the concept of life and death in Rumi’s poetry. Several poems on life and death were chosen randomly from Rumi’s masterpiece Mathnavi to be analyzed in this study. Moreover, this study attempted to express the ideas of life and death through some of his famous poems selected from Rumi’s world-famous pieces of writing. The analysis showed that Rumi’s concept of death is bent toward Sufism. He considers death as a rebirth and reunion. For Rumi, death is a reality that people do not have to worry about because it is a reunion with God but rather being happy and having a firm belief in God.

Industrial cyber–physical systems (ICPS) are emerging platforms for various industrial applications. For instance, remote healthcare monitoring, real-time healthcare data generation, and many other applications have been integrated into the ICPS platform. These healthcare applications encompass workflow tasks, such as processing within hospitals, laboratory tests, and insurance companies for patient payments, which necessitate a sequential flow. The external wireless, fog, and cloud services within ICPS face security issues that impact end-users’ healthcare applications. Blockchain technology offers an optimal solution for ICPS-enabled applications. However, blockchain technology for the ICPS platform is still vulnerable to cyberattacks, while microservices are essential for executing applications. This paper introduces the novel “Pattern-Proof Malware Validation” (PoPMV) algorithm designed for blockchain in ICPS. It exploits a deep learning model (LSTM) with reinforcement learning techniques to receive feedback and rewards in real-time. The primary objective is to mitigate security vulnerabilities, enhance processing speed, identify both familiar and unfamiliar attacks, and optimize the functionality of ICPS. Simulations demonstrate the superiority of the proposed approach compared to current blockchain frameworks, showcasing dynamic allocation of microservices and improved security with comprehensive attack detection by 30%.

This paper presents a pioneering approach, Bayesian machine learning (ML), for the estimation and characterization of critical laser welding features in Aluminum alloys, encompassing peak temperature, heat-affected zone width, and bead aspect ratio. The methodology involved constructing a laser welding database utilizing finite element simulations (FEM). The distinctive advantage of the Bayesian ML model lies in its capability to address challenges associated with excessive approximation and to account for uncertainties in parameters. This results in precise and resilient predictions of laser weld parameters across a spectrum of aluminum alloys. The findings underscored the model's efficacy in forecasting output targets, although regression analysis unveiled unique characteristics in data distribution and outliers specific to aluminum alloys. These outliers were primarily linked with the melting range of aluminum alloys, leading to the Al7075 alloy having the lowest prediction, and the Al1100 alloy the highest within the ML model. Additionally, the normalized average weight functions of input parameters were illustrated, clarifying their differing importance concerning diverse types of Al alloys in precisely forecasting output objectives. In light of these explanations, it remains consistent that laser power (LP) and welding speed (WS) inputs hold substantial sway across all alloys, while workpiece thickness (WT), beam diameter (BD), and initial temperature (IT) played comparatively lesser roles. Ultimately, this work contributes to a more profound comprehension of the relationships between input features and the geometrical and thermal behavior of laser-welded Al joints.