Synthesizing biochar-based slow-releasing fertilizers using vermicompost leachate, cow dung, and plant weed biomass.

Biochar-based slow-releasing fertilizers (BSRF) have been recommended widely for efficient soil nutrient management and crop production. In this study, we examined the N, P, and K release behaviour of pyrolysed (at 350 °C) cow dung (CDB), vermicompost (VCB), and Lantana (LB) weed and impregnated LB (LBVW) and CDB (CDBVW) with vermicompost leachate (1:1 v/v) under a lab-scale trial. BSRFs (CDB, VCB, LBVW and VCBVW) characterization (FT-IR, SEM-EDX and surface area analysis) was done and then tested for its suitability for soil-plant applications. Soil incubation study indicated the slow-releasing behaviour of BSRFs and overall P, N, and K release was found to be in the ranges of 72.3-84.5%, 73.1-79.0%, and 43.1-85.3%, respectively in different BSRFs setups. Furthermore, lab trials suggested the highest P (64.5%), N (75.3%), and K (86.8%) uptakes by the plant (Vigna radiata) in CDBVW and LBVW setups. Moreover, pot trails with moong bean (Vigna radiata) suggested a high growth in shoot and root and plant yield as well in seedlings cultivated with BSRFs. This study indicates that animal manure, vermicompost and terrestrial weed Lantana biochar can be used effectively to prepare BSRFs for efficient soil-plant nutrient management with multiple environmental benefits.

Energy System 4.0: Digitalization of the Energy Sector with Inclination towards Sustainability.

The United Nations' sustainable development goals have emphasized implementing sustainability to ensure environmental security for the future. Affordable energy, clean energy, and innovation in infrastructure are the relevant sustainable development goals that are applied to the energy sector. At present, digital technologies have a significant capability to realize the target of sustainability in energy. With this motivation, the study aims to discuss the significance of different digital technologies such as the Internet of Things (IoT), artificial intelligence (AI), edge computing, blockchain, and big data and their implementation in the different stages of energy such as generation, distribution, transmission, smart grid, and energy trading. The study also discusses the different architecture that has been implemented by previous studies for smart grid computing. Additionally, we addressed IoT-based microgrids, IoT services in electrical equipment, and blockchain-based energy trading. Finally, the article discusses the challenges and recommendations for the effective implementation of digital technologies in the energy sector for meeting sustainability. Big data for energy analytics, digital twins in smart grid modeling, virtual power plants with Metaverse, and green IoT are the major vital recommendations that are discussed in this study for future enhancement.

Productivity enhancement of solar still through heat transfer enhancement techniques in latent heat storage system: a review.

Solar still is one of the sustainable and renewable technology which converts brackish or salty water into fresh water. The technology helps in CO2 mitigation, global warming effect, and the use of solar desalination contributes towards decarbonization, mitigation of CO2 and other adverse global warming effect, and it contributes to the sustainable development goals (SDG). However, due to the low production rate of the distillate, the performance of solar still gets affected. The phase change materials (PCMs) as latent heat storage systems can enhance the thermal performance of solar still (SS). Further, techniques like increasing the area of contact and thermal conductivity can be practiced to enhance the heat transfer in PCM-SS. The article reviewed the performance of various designs of solar still integrated with PCM. Furthermore, the effect of nanoparticles enhanced PCM-integrated solar still with different absorber designs and configurations was seen. Compared to conventional solar still (CSS), the heat transfer techniques in PCM's SS can significantly improve the overall distillate productivity of Tubular SS by 218%, followed by single basin single slope SS 149%, pyramidal 125%, hemispherical 94%, and stepped 68%, respectively. In addition, the night time productivity was increased by 235%. Also, it was observed that in comparison to tubular PCM-SS, the nanodisbanded tubular PCM-SS increases the productivity by 68%, whereas in stepped solar still by using external condenser arrangement the productivity was increased by 48%. In single basin single slope, the nanoparticle disbanded PCMSS increases the productivity from 11 to 33%.

A Multi-Thresholding-Based Discriminative Neural Classifier for Detection of Retinoblastoma Using CNN Models.

Cancer is one of the vital diseases which lead to the uncontrollable growth of the cell, and it affects the body tissue. A type of cancer that affects the children below five years and adults in a rare case is called retinoblastoma. It affects the retina in the eye and the surrounding region of eye like the eyelid, and sometimes, it leads to vision loss if it is not diagnosed at the early stage. MRI and CT are widely used scanning procedures to identify the cancerous region in the eye. Current screening methods for cancer region identification needs the clinicians' support to spot the affected regions. Modern healthcare systems develop an easy way to diagnose the disease. Discriminative architectures in deep learning can be viewed as supervised deep learning algorithms which use classification/regression techniques to predict the output. A convolutional neural network (CNN) is a part of the discriminative architecture which helps to process both image and text data. This work suggests the CNN-based classifier which classifies the tumor and nontumor regions in retinoblastoma. The tumor-like region (TLR) in retinoblastoma is identified using the automated thresholding method. After that, ResNet and AlexNet algorithms are used to classify the cancerous region along with classifiers. In addition, the comparison of discriminative algorithm along with its variants is experimented to produce the better image analysis method without the intervention of clinicians. The experimental study reveals that ResNet50 and AlexNet yield better results compared to other learning modules.

Microstructure and Mechanical Properties Analysis of Al/Cu Dissimilar Alloys Joining by Using Conventional and Bobbin Tool Friction Stir Welding.

The feasibility of producing welding joints between 6061-T6 aluminum and pure copper sheets of 6 mm thickness by conventional friction stir welding (CFSW) and bobbin tool friction stir welding (BTFSW) by using a slot-groove configuration at the joining surface was investigated. The microstructure of the welded samples was examined by using an optical microscope and X-ray diffraction. Furthermore, the mechanical properties of the weld samples are compared based on the results of the tensile test, hardness measurement, and fractography test. The slot-groove configuration resulted in the presence of a bulk-sized Al block on the Cu side. The microscopic observations revealed the dispersion of fine Cu particles in the stir zone. The presence of intermetallic compounds (IMCs) CuAl2, which are hard and brittle, lowered the strength of the weld joints. The strength of the weld joints produced with BTFSW was superior to that of the C-FSW. The maximum hardness values of 214 HV and 211 HV are reported at the stir zone for BTFSW and CFSW, respectively. The fracture location of all the joints was at the intersection of the stir zone and the thermomechanically affected zone was on the Cu side.

Comparative Analysis of Waste Materials for Their Potential Utilization in Green Concrete Applications.

The utilization of solid waste in useful product is becoming a great deal of worth for individuals, organizations, and countries themselves. The powder of waste glass and silica fumes are also considered major waste materials across the globe. In this paper, the physico-chemical, thermal, and morphological properties of both waste powders are investigated in order to determine their suitability for use as a partial replacement for cement in basic concrete. They are suitable for use in concrete due to their pozzolanic and other basic properties. Extensive testing, in terms of the compressive strength test, the slump test, and the flexural strength test, has been carried out to study the replacement of cement in the range of 5-15% by waste glass powder for curing ages of 7 and 28 days. The FTIR analyses of both materials are studied for determining the effect of characteristics of chemical bonding and intense bands with bending vibrations of O-Si-O bonds. Experimental results indicate towards the potential utilization of wastes in concrete in terms of green concrete.

Al-Mg-MoS2 Reinforced Metal Matrix Composites: Machinability Characteristics.

Several components are made from Al-Mg-based composites. MoS2 is used to increase the composite's machinability. Different weight percent (3, 4, and 5) of MoS2 are added as reinforcement to explore the machinability properties of Al-Mg-reinforced composites. The wire cut electrical discharge machining (WEDM) process is used to study the machinability characteristics of the fabricated Al-Mg-MoS2 composite. The machined surface's roughness and overcut under different process conditions are discussed. The evaluation-based distance from average solution (EDAS) method is used to identify the optimal setting to get the desired surface roughness and overcut. The following WEDM process parameters are taken to determine the impact of peak current, pulse on time, and gap voltage on surface roughness, and overcut. The WEDM tests were carried out on three different reinforced samples to determine the impact of reinforcement on surface roughness and overcut. The surface roughness and overcut increase as the reinforcement level increases, but the optimal parameters for all three composites are the same. According to EDAS analysis, I3, Ton2, and V1 are the best conditions. Furthermore, peak current and pulse on-time significantly influence surface roughness and overcut.

Optimization of Selective Laser Melting Parameter for Invar Material by Using JAYA Algorithm: Comparison with TLBO, GA and JAYA.

In this study, the hardness and surface roughness of selective laser-melted parts have been evaluated by considering a wide variety of input parameters. The Invar-36 has been considered a workpiece material that is mainly used in the aerospace industry for making parts as well as widely used in bimetallic thermostats. It is the mechanical properties and metallurgical properties of parts that drive the final product's quality in today's competitive marketplace. The study aims to examine how laser power, scanning speed, and orientation influence fabricated specimens. Using ANOVA, the established models were tested and the parameters were evaluated for their significance in predicting response. In the next step, the fuzzy-based JAYA algorithm has been implemented to determine which parameter is optimal in the proposed study. In addition, the optimal parametric combination obtained by the JAYA algorithm was compared with the optimal parametric combination obtained by TLBO and genetic algorithm (GA) to establish the effectiveness of the JAYA algorithm. Based on the results, an orientation of 90°, 136 KW of laser power, and 650 mm/s scanning speed were found to be the best combination of process parameters for generating the desired hardness and roughness for the Invar-36 material.

Advancements in the Additive Manufacturing of Magnesium and Aluminum Alloys through Laser-Based Approach.

Complex structures can now be manufactured easily utilizing AM technologies to meet the pre-requisite objectives such as reduced part numbers, greater functionality, and lightweight, among others. Polymers, metals, and ceramics are the few materials that can be used in AM technology, but metallic materials (Magnesium and Aluminum) are attracting more attention from the research and industrial point of view. Understanding the role processing parameters of laser-based additive manufacturing is critical to maximize the usage of material in forming the product geometry. LPBF (Laser powder-based fusion) method is regarded as a potent and effective additive manufacturing technique for creating intricate 3D forms/parts with high levels of precision and reproducibility together with acceptable metallurgical characteristics. While dealing with LBPF, some degree of porosity is acceptable because it is unavoidable; hot ripping and cracking must be avoided, though. The necessary manufacturing of pre-alloyed powder and ductility remains to be the primary concern while dealing with a laser-based additive manufacturing approach. The presence of the Al-Si eutectic phase in AlSi10Mg and AlSi12 alloy attributing to excellent castability and low shrinkage, attaining the most attention in the laser-based approach. Related studies with these alloys along with precipitation hardening and heat treatment processing were discussed. The Pure Mg, Mg-Al alloy, Mg-RE alloy, and Mg-Zn alloy along with the mechanical characteristics, electrochemical durability, and biocompatibility of Mg-based material have been elaborated in the work-study. The review article also summarizes the processing parameters of the additive manufacturing powder-based approach relating to different Mg-based alloys. For future aspects, the optimization of processing parameters, composition of the alloy, and quality of powder material used will significantly improve the ductility of additively manufactured Mg alloy by the LPBF approach. Other than that, the recycling of Mg-alloy powder hasn't been investigated yet. Meanwhile, the post-processing approach, including a homogeneous coating on the porous scaffolds, will mark the suitability in terms of future advancements in Mg and Al-based alloys.

Elastic Properties of Jute Fiber Reinforced Polymer Composites with Different Hierarchical Structures.

A two-stage micromechanics technique is used to predict the elastic modulus, as well as the major and minor Poisson's ratio of unidirectional natural fiber (NF) reinforced composites. The actual NF microstructure consists of cellulose, hemicellulose, lignin, lumen, etc., and these constituents and their contributions are neglected in classical models while quantifying their mechanical properties. The present paper addresses the effect of the real microstructure of the natural jute fiber (JF) by applying a micromechanics approach with the Finite Element Method. Six different hierarchically micro-structured JFs are considered to quantify the JF elastic properties in the first level of homogenization. Later, the JF reinforced polypropylene matrix properties are investigated in the second stage by adopting a homogenization approach. Taking into account the different hierarchical structures (HS), the fiber direction modulus (E1), transverse modulus (E2 and E3), in-plane and out-of-plane shear modulus (G12 and G23), and major (ν12, ν13) and minor (ν23, ν21) Poisson's ratios are estimated for JF and JF reinforced polypropylene composites. The predicted elastic modulus from micromechanics models is validated against the analytical results and experimental predictions. From the present work, it is observed that the HS of NF needs to be considered while addressing the elastic properties of the NF-reinforced composite for their effective design, particularly at a higher volume fraction of NF.