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Production of dissimilar weldments using microwave hybrid heating is currently gaining immense impetus in the field of advanced welding. This is because such heat source could provide benefits like cost-effectiveness, rapid, volumetric, uniform and selective heating, and efficient throughput which would be significant to various industries. Till-date researchers have carried out joining of dissimilar general purpose engineering materials using microwave hybrid heat source. But attention has not been paid on the joining and characterization of dissimilar exotic-general purpose materials using the aforementioned heat source and the promising applications of the weldments. Therefore, the present article is focused on the joining of dissimilar materials- Inconel 625 and SS 304 alloys using selective microwave hybrid joining (SMHJ) technique. In SMHJ, nickel-based powder is used as a filler material, Silicon carbide (SiC) block and SiC powder are used as susceptor to increase the initial temperature. The developed weldments through SMHJ are characterized using various physico-chemical diagnostic methods. The results reveal the average micro-hardness of joint was found to be 303 ± 17 HV owing to the presence of various carbides and nitrides phase like NbC, Cr 23 C 6 , Cr 2 Ni 3 , Ni 8 Nb, and Fe 3 Ni 2 in the joint zone which is evident from XRD. The average UTS of the joints found to be 448.6 MPa with an elongation of 10.93% and flexural strength observed to be 435 MPa. Further, fractography study reveals, the joint regions have mixed mode of failure. The failure was attributed to the existence of secondary phases in the joint zone.

Pipe bends are a crucial component of the pipeline industry because they experience more stresses and deformations than straight pipes of the same dimensions and material properties under the same loading conditions. For a reliable and safe piping system, the plastic collapse moment of pipe bends must be estimated accurately. The current study aims to find which bending mode is critical to failure for pipe bends; for that, the collapse moment under in-plane closing (IPC), in-plane opening (IPO) and out-of-plane (OP) bending moments are compared using finite element (FE) analysis. The comparison accounts for various values of internal pressure, bend angle and initial geometric imperfection. The FE analysis considers elastic-perfectly plastic (EPP) and strain-hardening (SH) material models. Twice-elastic-slope (TES) method is implemented to evaluate plastic collapse moment for all considered cases. The comparison of collapse moment shows that under unpressurized conditions, pipe bends are critical to IPC bending moment. However, it is difficult to identify which bending mode is critical under pressurized conditions. Therefore, plastic collapse moment under all three bending modes should be known and for that plastic collapse moment equations under all bending modes should be proposed.

Cloud computing revolutionizes fast-changing technology. Companies' computational resource use is changing. Busmesses can quickly adapt to changing market conditions and operational needs with cloud-based solutions' adaptability, scalability, and cost-efficiency. IT operations and service delivery have changed due to widespread computational resource access. Cloud computing efficiently allocates resources in cloud environments, making it crucial to this transformation. Resource allocation impacts efficiency, cost, performance, and SLAs. Users and providers can allocate cloud resources based on workloads using elasticity, scalability, and on-demand provisioning. IT economics and operational effectiveness have changed due to rapid and flexible resource allocation. Proactive versus reactive resource allocation is key to understanding cloud resource management challenges and opportunities. Reactive strategies allocate resources only when shortages or surpluses occur at demand. This responsive strategy often leads to inefficiencies like over- or under-allocation, which raises costs and lowers performance. Predictive analysis and workload forecasting predict resource needs hr proactive resource allocation. Optimize resource use to avoid shortages and over-provisioning. Attention has been drawn to proactive predictive resource allocation. These methods predict resource needs u sing historical data, machme learning, and predictive analytics. Predictive strategies optimize resource allocation by considering future decisions. Reduced bottlenecks boost user satisfaction and lower operational costs. Matching resource distribution to workloads optimizes cloud resource management. Resource allocation prediction improves with deep learning. CNN, LSTM, and Transformer cloud resource forecasting algorithms are promising. New tools for accurate and flexible workload predictions have come 6om their ability to spot intricate patterns hi historical data. This paper compares CNN, LSTM, and Transformer deep learning algorithms for cloud computing resource allocation forecasting. This study determines the best predictive accuracy and workload adaptability algorithm using Google Cluster Data (GCD). The study evaluates upgrading cloud computing resource allocation with the Transformer model. This study advances predictive resource allocation strategies, which can help cloud seivice providers and organizations improve resource utilization, cost-effectiveness, and performance hi the face of rapid technological change.

Background: The term mucormycosis refers to any fungal infection caused by fungi belonging to the Mucorales order. The disease often manifests in the skin and also affects the lungs and the brain. A large number of Mucormycosis cases were detected in Delhi, Maharashtra and Gujarat, and Madhya Pradesh. Objectives: (1) To describe the epidemiology, management, and outcome of individuals with mucormycosis. (2) To evaluate the risk factors associated with cases and control. Methodology: A case-control study, conducted in Hamidia Hospital, Bhopal, for 5 weeks. One hundred and sixty-eight patients diagnosed clinically with radiological or pathological findings was considered a case of Mucormycosis. Control was taken from March 2020 to May 28, 2021, the list of COVID-19-positive patients obtained from IDSP, MP. Results: Majority of the study participants were among the age group of 51-60 years and comprising 69.6% of males. Diabetes mellitus is the major comorbidity found in both cases (87.58%) and in controls (20.0%). Conclusion: There is a need to stress to control hyperglycemia, and monitor blood glucose levels after discharge following COVID-19 treatment.

Objectives: Dental caries is a common chronic disease amongst children and are typically evaluated using the DMFT/deft index (decayed, missing, and filled teeth for permanent dentition/decayed, extracted, and filled teeth for primary dentition). To address the limitations associated with these indices, alternative assessment tools such as the Caries Assessment Spectrum and Treatment (CAST) index and Ora test have been developed. These methods aim to estimate caries activity within the oral cavity more accurately. The objective of our study was to evaluate and correlate caries activity in 5-to-8-year-old children using Ora test and CAST index. Materials and Methods: Thirty schoolchildren between the ages of 5 and 8 years were selected and allocated into two groups (n=15) with DMFT/deft scores of <5 (group A) and >5 (group B). Two separate blinded examiners administered the assessments by first determining CAST scoring, which was followed by Ora test. Statistical analysis was performed using Pearson correlation test and significance was set at P≤0.05. Results: The mean time for color change of Ora test, was 118.53±23.28 minutes in group A and 53.33±15.07 minutes in group B. CAST severity scores were 3.67±2.08 and 15.7±9.70 for groups A and B, respectively. Time taken for color change in Ora test and CAST scores showed a significant negative linear relationship (P=0.039). Conclusion: Based on the negative correlation between CAST scores and Ora test, it may be postulated that microbial activity is directly related to caries activity in 5-to-8-year-old children.

Non-conventional process like Photochemical Machining (PCM) is found to show a promise for machining very thin metal components. In the present study, the effect of various selected parameters such as time of etching, temperature of etchant and concentration of etchant on material removal rate, undercut in PCM of phosphor bronze has been investigated by using multi-objective grey relational analysis and their optimal conditions are evaluated. Full factorial (L27) orthogonal array (DoE) has been used to perform the experiments. GRG value indicates most significant parameters affecting the PCM process. The above factors are selected on the basis of effect - cause analysis and literature survey. Mathematical models relating to the machining performance and machining parameters have been formulated. Optimal settings for each performance measure have also been obtained. The results obtained after conference test prove that improvement in the quality will take place is if the setting of parameters are done at optimum level predicted by multi-objective grey relational analysis. The ANN model is prepared to predict the result by training neural which can be compared with actual experiments to confirm the satisfactory performance during the experimentation.