In recent times, research has shifted away from conventional materials and alloys and more towards nanocomposites to create lighter, more efficient materials for specific applications. The major goal of this research is to see how successful adding aluminium tetrahydride (ATH) filler to a luffa fibre/polyester-based hybrid composite is. The compression moulding process was used to create the nanocomposite. The following limitations were used to achieve the goals mentioned above: (i) weight percent of ATH, (ii) weight percent of luffa fibres, and (iii) cryogenic treatment hours. The mechanical properties of the materials, such as flexural, tensile, and impact, were examined. The scanning electron microscope observed the morphology pictures, revealing flaws such as interface behaviour, fibre pullouts, voids, and interior cracks. As a result, the current study found that adding nanofiller to a natural fibre composite can improve its mechanical properties, because it established a strong link between the matrix and its reinforcements, which would aid in the effective transmission of stress in the hybrid system. It also improved moisture resistance, which might be useful in construction and commercial industries. The composite with 1 wt.% of ATH, 24 wt.% of luffa fibres, and 30 minutes of cryogenic treatment showed better mechanical strength. Cryogenic treatment reduces compressive interface stresses, which helps maintain fibre and matrix in contact and improve adhesion, resulting in superior results. TGA analysis was used to confirm it.

Earlier, crop cultivation was undertaken on the basis of farmers' hands-on expertise. However, climate change has begun to affect crop yields badly. Consequently, farmers are unable to choose the right crop/s based on soil and environmental factors, and the process of manually predicting the choice of the right crop/s of land has, more often than not, resulted in failure. Accurate crop prediction results in increased crop production. This is where machine learning playing a crucial role in the area of crop prediction. Crop prediction depends on the soil, geographic and climatic attributes. Selecting appropriate attributes for the right crop/s is an intrinsic part of the prediction undertaken by feature selection techniques. In this work, a comparative study of various wrapper feature selection methods are carried out for crop prediction using classification techniques that suggest the suitable crop/s for land. The experimental results show the Recursive Feature Elimination technique with the Adaptive Bagging classifier outperforms the others.

This article investigates and presents the upshots observed in the brook of hybrid composites especially, the current investigation focuses on the impact of fiber composition, sequence, and stacking pattern on composite mechanical Features. Five varied stacking sequences of hybrid composites encompassing laminates are used to create four classes of fiber with jute/bamboo/glass by utilizing a conscientious hand lay-up process with glass fiber-laced mats as their peripheral layer. For examination, fiber sequences are arranged in the combination of GJBJG, GBJBG, GJGJG, and GBGBG, where G, J, and B refer to glass fiber, jute fiber, and bamboo fiber, respectively. The position of fiber in the core layer is kept in a perpendicular direction with respect to adjacent piles which might be jute or bamboo fiber and the best position of fiber is considered due to the stacking order. Stress and strain were linear in the load versus deflection curves, and all of the samples failed quickly, it is observed that the sample containing a higher or considerable number of bamboo fiber layers exhibited increased strain and toughness. In comparison to other samples, embolism of glass fiber as the main and covering layer expressed a higher impact on the mechanical properties of the composites is observed in this investigation. The shattered sample morphology demonstrated that the matrix and reinforcements were compatible.

Internet of Things (IoT) can be defined as a thing or device, physical and virtual, connected and communicating together, and integrated to a network for a specific purpose. The IoT uses technologies and devices such as sensors, radio-frequency identification (RFID) and actuators to collect data. IoT is not only about collecting data generated from sensors, but also about analyzing it. IoT applications must, of necessity, keep out all attackers and intruders so as to thwart attacks. IoT must allow for information to be shared, with every assurance of confidentiality, and is about a connected environment where people and things interact to enhance the quality of life. IoT infrastructure must be an open source, without ownership, meaning that anyone can develop, deploy and use it. The objective of this paper is to discuss the various challenges, issues and applications confronting the Internet of Things.

The enhancement of the PLA thermomechanical properties is significant due to its suitability as a replacement for primary synthetic polymer use in diverse industrial production. The amphiphilic chitin was used as a compatibilizer in PLA/starch biocomposite. The properties of plasticised polylactic acid blended with starch, and amphiphilic chitin was studied for enhanced thermomechanical and viscoelastic properties. Chitin was modified using acetylated substitution reaction and blended with plasticised PLA/starch biocomposite. The biocomposite was prepared with combined compression and melt extrusion techniques. The biocomposite’s thermomechanical, thermal, mechanical, and morphological properties were studied using dynamic mechanical analysis, TGA-DSC, tensile test, and scanning electron microscopy. The storage and loss modulus were significantly enhanced with increased amphiphilic chitin content. Similarly, the single peak of tan delta showed good miscibility of the polymeric blend. Additionally, the modulus increases with frequency change from 1 Hz to 10 Hz. The thermal stability of the biocomposite was observed to be lower than the neat PLA. The tensile properties of the biocomposite increased significantly more than the neat PLA, with P4S4C having the highest tensile strength and modulus of 87 MPa and 7600 MPa. The SEM images show good miscibility with no significant void in the fractured surface. The viscoelastic properties of PLA were enhanced considerably with plasticizer and amphiphilic chitin with improved biodegradability. The properties of the biocomposite can be adapted for various industrial applications.

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Detecting the breakdown of industrial IoT devices is a major challenge. Despite these challenges, real-time sensor data from the industrial internet of things (IIoT) present several advantages, such as the ability to monitor and respond to events in real time. Sensor statistics from the IIoT can be processed, fused with other data sources, and used for rapid decision-making. The study also discusses how to manage denoising, missing data imputation, and outlier discovery using preprocessing. After that, data fusion techniques like the direct fusion technique are used to combine the cleaned sensor data. Fault detection in the IIoT can be accomplished by using a variety of deep learning models such as PropensityNet, deep neural network (DNN), and convolution neural networks-long short term memory network (CNS-LSTM). According to various outcomes, the suggested model is tested with Case Western Reserve University (CWRU) data. The results suggest that the method is viable and has a good level of accuracy and efficiency.

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