Indian Institute of Technology Bombay

Friction taper stitch welding (FTSW) is a novel technique that uses multiple inserts to conceal surface crack in a given substrate. The inserts are rotated and forced to fill the crack as plasticized material, and forge with the substrate in solid-state. The process is well suited for alloys such as duplex stainless steel, which suffers degradation of properties during fusion welding. A detailed experimental and theoretical investigation is presented here on FTSW of a duplex stainless steel (DSS). The experimental results show the presence of a ferrite-rich phase along the interface. The results computed by the numerical process model reveal a direct influence of thermal cycle in the amount of ferrite along the joint interface. The welded joint shows near homogeneous structure and properties similar to those of the substrate.

We numerically study the transverse flow-induced vibration (FIV) of elastically coupled tandem cylinders at Reynolds number 100, using an in-house immersed boundary method-based solver in two-dimensional coordinates. While several previous studies considered tandem cylinders coupled through flow between them, a hitherto unexplored elastic coupling with fluid flow between them significantly influences FIV. We consider a wide range of gap ratio, reduced velocity, an equal mass ratio of both cylinders and zero damping. A systematic comparison between the classic elastically mounted tandem cylinders and elastically coupled cylinders is presented. The latter configuration exhibits two vibration modes, in-phase and out-of-phase, with corresponding natural frequencies approaching the Strouhal frequency of the system. We quantify variation of the following output variables with reduced velocity and gap ratios: cylinders' displacement; fluid forces; amplitude spectral density of displacement and force signals; phase characteristics; energy harvesting potential; and discuss the wake characteristics using flow separation, pressure distribution, gap flow quantification, and dynamic mode decomposition characterization. The FIV response is classified into several regimes: initial desynchronization with and without gap vortices; final desynchronization; mixed mode; initial branch; lock-in; upper and lower branch; wake-induced vibration; galloping. We draw upon similarities of computed FIV characteristics with those of an isolated cylinder, in which the lower branch exhibits larger a amplitude than the upper branch. The elastically coupled cylinders show a galloping response similar to an isolated D-section cylinder. By invoking the elastic coupling, we demonstrate FIV suppression and augmentation for in-phase and out-of-phase systems. Our calculations show larger energy harvesting potential at reduced cost for elastically coupled cylinders.

MATLAB partial differential equation (PDE) toolbox is used to evolute the general partial differential equation of different problems like heat transfer, structural mechanics, and electromagnetics. It can find the heat flow rates, heat flux, and temperature distribution over the surfaces by modeling conduction-dominant heat transfer problems. Here, this chapter has solved the previous research problems of two-dimensional transient heat conduction by using MATLAB partial differential equations (PDE) toolbox simulation. Previously, it was solved by using the Numerical Manifold Method (NMM). Here, firstly, 2D or 3D geometry is imported from mesh data or STL files. Automatically, the PDEs toolbox creates meshes using tetrahedral and triangular elements through which solved the PDEs by using finite element methods, and post-processing is done to analyze the results. The findings indicate that the use of the MATLAB PDE toolbox as a solution technique is highly efficient, with closely matched results to the solution.

India is the second-highest contributor to the post-2000 global greening. However, with satellite data, here we show that this 18.51% increase in Leaf Area Index (LAI) during 2001–2019 fails to translate into increased carbon uptake due to warming constraints. Our analysis further shows 6.19% decrease in Net Primary Productivity (NPP) during 2001–2019 over the temporally consistent forests in India despite 6.75% increase in LAI. We identify hotspots of statistically significant decreasing trends in NPP over the key forested regions of Northeast India, Peninsular India, and the Western Ghats. Together, these areas contribute to more than 31% of the NPP of India (1274.8 TgC.year ⁻¹ ). These three regions are also the warming hotspots in India. Granger Causality analysis confirms that temperature causes the changes in net-photosynthesis of vegetation. Decreasing photosynthesis and stable respiration, above a threshold temperature, over these regions, as seen in observations, are the key reasons behind the declining NPP. Our analysis shows that warming has already started affecting carbon uptake in Indian forests and calls for improved climate resilient forest management practices in a warming world.

Evolutionary algorithms (EAs) have been used extensively for the optimal design of water distribution networks (WDNs). There is evidence in the literature that search space reduction is highly effective. However, practical methods that do not introduce extra computational requirements are lacking. A dynamic search space reduction methodology is proposed to search the entire solution space without eliminating any part of the search space beforehand. The proposed methodology works on the information explored during the execution of the algorithm. Further, a self-adaptive penalty is suggested which is based on both flow and pressure deficits instead of only pressure deficit, and is obtained using pressure-dependent analysis. In this study, the methodology is demonstrated using a Genetic Algorithm (GA). The effectiveness of the methodology is demonstrated on the Ramnagar Network of Nagpur City, India, and two benchmark problems from the literature. The proposed methodology resulted in a substantial reduction in the computational efforts and provided nine improved solutions as compared to the best solution available in the literature for one of the networks. The techniques proposed are generic and can be incorporated in other EAs.

Lean is all about creating value for customers and eliminating waste. The construction industry is plagued with time and cost overruns due to physical and non-physical waste. One of the non-physical wastes is the over allocation of equipment. Therefore, there is a need to assess the optimum allocation of equipment at the site as part of equipment planning. Simulation can be a valuable tool to verify various options for equipment allocation. Further, simulation helps to understand the implications before physical implementation and saves time and efforts. Moreover, simulation is beneficial to capture the various factors influencing the activity and visualise the expected output. Therefore, a methodology to use simulation for equipment allocation and productivity under different allocation scenarios is presented in this study. Further excavation is taken as a case study, by site data collection, and developed computer simulation model using Anylogic software. The proposed model can be used by practitioners for planning and estimation works, and the academicians for using it as a teaching tool for construction productivity and by researchers can do further development. Using simulation and lean is recommended to increase productivity and thus alleviate time and cost overruns in the construction industry.

The construction sector is considered one of the most hazardous sectors globally due to its risky work practices and unsafe nature. Researchers have investigated various avenues to promote safe behavior and conditions. One of the popular ways is the application of Lean principles. Research investigations have been done to investigate the applications of Lean principles to resolve safety issues in the construction industry. However, there is a lack of systematic analysis in presenting a comprehensive picture of the applications of Lean principles for Construction Safety Management (CSM). Therefore, this study aims to perform a systematic literature review to comprehend the synergies between Lean and CSM. To this end, this study considered peer-reviewed research journal articles from the Scopus and Web of Science literature databases, and an in-depth content analysis has been performed. The results highlighted the United States of America as a leading country, Tianjin University of Finance and Economics as the leading organization, and Dr. Wu Xiuyu as the foremost researcher in this domain. The current research trends investigate the implementation of Lean Construction (LC) practices, examine their impact, and identify the benefits and barriers to implementing LC for CSM. At last, the theoretical and practical contributions of the study and future research opportunities are discussed.

A SEPIC-Ćuk based transformer-less micro-inverter capable of interfacing a 35 V, 250 W solar PV module to a single phase 220-230 V ac grid is proposed in this paper. The circuit employs only one high frequency switch and four line frequency switches thereby reducing the switching losses. This also enhances the overall reliability of the system. The circuit is made to operate in discontinuous conduction mode (DCM) under all possible operating conditions to achieve high gain and at the same time ensures negligible turn on loss for the high frequency switch. The direct connection existing between PV neutral and the utility ground makes the magnitude of the leakage current to be zero. In order to reduce the size of the capacitor across the PV module, an active power decoupling circuit is employed. Hence, this capacitor can be realized by a thin film capacitor instead of an electrolytic capacitor thereby improving the reliability of the system. The detailed analyses of the proposed micro-inverter is carried out. The effectiveness of the proposed scheme is verified by performing detailed simulation studies. A 250 W laboratory prototype of the inverter is fabricated, and detailed experimental studies are carried out to confirm the viability of the proposed scheme.

Automatic Disease Diagnosis (ADD) has gained immense popularity and demand over the past few years, and it is emerging as an effective diagnostic assistant to doctors. Diagnosis assistants assist clinicians in conducting a thorough symptom investigation and identifying possible diseases. Doctors correctly diagnose patients by observing only a few symptoms in most cases, even though the diagnosed disease has numerous symptoms. Also, some common symptoms, such as fever and headache, usually emerge due to other symptoms, which do not play a major role in identifying suffering diseases. In this work, we investigate the role of symptom importance in disease diagnosis through several feature engineering techniques and propose a novel symptom assessment incorporated symptom investigation and disease diagnosis (SA-SIDD) assistant using hierarchical reinforcement learning. The proposed SA-SIDD assistant first collects an adequate set of symptoms/sign information through conversing with users and then diagnoses a disease based on the extracted symptoms. We incorporated a symptom assessment module with the diagnosis framework that evaluates the relevance of current inspected symptom at each turn and reinforces the assistant to investigate distinctive and context-aligned symptoms using an assessment critic. The proposed methodology outperforms the state-of-the-art method, HRL, on two publicly available datasets, which firmly establishes the crucial role of symptom importance in disease diagnosis and the need for the proposed symptom assessment incorporated disease diagnosis framework. Furthermore, we have also conducted a human evaluation, revealing that the diagnosis method greatly enhances end-user satisfaction because of context-aligned relevant and minimal symptom investigation ¹ ¹ The code and data are available at https://github.com/NLP-RL/SA-SIDD .

Memristor-based logic-in-memory (LiM) has become popular as a means to overcome the von Neumann bottleneck in traditional data-intensive computing. Recently, the memristor-aided logic (MAGIC) design style has gained immense traction for LiM due to its simplicity. However, understanding the energy distribution during the design of logic operations within the memristive memory is crucial in assessing such an implementation’s significance. The current energy estimation methods rely on coarse-grained techniques, which underestimate the energy consumption of MAGIC-styled operations performed on a memristor crossbar. To address this issue, we analyze the energy breakdown in MAGIC operations and propose a solution that utilizes mapping from the SIMPLER MAGIC tool to achieve accurate energy estimation through SPICE simulations. In contrast to existing research that primarily focuses on optimizing execution energy, our findings reveal that the memristor’s initialization energy in the MAGIC design style is, on average, 68× higher. We demonstrate that this initialization energy significantly dominates the overall energy consumption. By highlighting this aspect, we aim to redirect the attention of designers towards developing algorithms and strategies that prioritize optimizations in initializations rather than execution for more effective energy savings.

Let k be an algebraically closed field of characteristic \(p > 3\). Let A be an abelian surface over k. Fix an integer \(n \ge 1\) such that \(p \not \mid n\) and let \(K^{[n]}\) be the n-th generalized Kummer variety associated to A. In this article, we show that the S-fundamental group scheme and the Nori’s fundamental group scheme of \(K^{[n]}\) are trivial.

Enzyme-induced calcium carbonate precipitation (EICP) through the urea hydrolysis pathway has been widely studied for various applications. The EICP solution comprises urea, a calcium source (usually calcium chloride) and the enzyme urease. This study addressed the effect of the chemical concentration of the EICP solution on the morphology of the calcium carbonate product. This was achieved by varying the concentration of urea–calcium chloride and urease activity. The duration of the reaction was the third variable. The precipitation efficiency and the interface shearing resistance were reported. Precipitation efficiency decreased as the concentration of urea–calcium chloride reached beyond 0.75 mol/l. The calcium carbonate polymorph was predominantly calcite. Its crystal size and shape did, however, vary, depending on the precipitation conditions. The findings showed that the urease activity promoted the formation of rhombohedral calcite in the presence of adequate calcium ions and urea. Spherical calcite was formed when the urease activity was further increased. The morphology of calcite evolved from a single, uniform, smooth spherical crystal to a polycrystalline formation with orthorhombic protrusions. The crystals tended to grow as the reaction time increased, resulting in aggregation, when the urease levels crossed 30 kU/l. It was noted that spherical crystals exhibited stronger interface shearing resistance than rhombohedral crystals.

Gold interdigitated electrode (IDE) structure is one of the commonly-used platforms for sensing. The response of IDE-based sensors is measured in different ways, of which non-faradaic electrochemical impedance spectroscopy (nf-EIS) is specially used to extract the analytes impedance characteristics without redox labels. Many research projects across various disciplines need students from different domains, such as Electrical Engineering, Chemistry, and/or Biology, to experience electrical measurements that can easily go wrong without notice. It is crucial to evaluate the intrinsic and extrinsic constraints that need to be resolved to get reliable and high-quality nf-EIS measurements, which are also reproducible in the presence of noise and other sources of error. This article provides examples and summarizes how to systematically address the effect of the commonly existing non-idealities to enable any student to get confidence in the interpretation and reliability of nf-EIS measurements they perform. As part of our experimental analysis demonstration, various conditions and volume optimization for adequate measurements are performed.

Power conditioning systems are performing a key role in smart grid operation. The increase in renewable energy installation leads to an increase in the power electronic converters installation for distributed generation, rural electrification, and grid integration. As the numbers are increasing, the reliability of the grid is impacted mainly due to the power conditioning weak link. Thus, there is a need to improve the reliability and efficiency of overall power processing. One way to achieve this is to improve health by smart monitoring and make the controller fault-tolerant. For example, approaches that monitor the health of the devices along with fault-tolerant control architecture can be implemented in the controller to detect failures and to get timely alarm signals. For this, relevant data gathering and analysis are extremely critical. Machine learning (ML) and deep learning (DL) are approaches that analyze the data, learn from the data, and then apply it during the decision-making process. The Special Issue on Machine Learning Techniques in Power Electronics invites the articles related to data gathering/analysis and improvements of reliable operation of power conditioning and renewable energy resources with applications to the smart grid.

The quality of emergency medical services remains a major public health issue in developing countries in terms of access, availability, or timely delivery, owing to high socio-economic and ethnic disparities. Particularly, the timeliness of EMS remains a drawback, leading to higher mortality and morbidity. The aim of the study is to assess the district-level differences and factors that influence ambulance travel time, as there was no study done in the Indian scenario. Sequential Explanatory Design was applied here, which involved a descriptive study and spatial analysis of the call volume and distribution to understand the operational challenges of MEMS, followed by in-depth interviews among medical officers and officials to explore the reasons for the challenges. The data, shared by the Department of Health, Government of Maharashtra, consisted of 38,823 records (emergency: 16,197 and hospital-to-hospital transfer: 22,626), including emergency and hospital-to-hospital transfer calls across 36 districts of Maharashtra for November 2022. Spatial analyses were performed to identify the districts with challenges of timeliness. The average ambulance response time (T) across the districts was reported at 134.5 min for emergency cases and 222.80 min for hospital-to-hospital transfer cases. The total ambulance response time, was classified as preparation time (t1:3.53 min for emergency, 3.69 min for hospital-to-hospital transfer), travel time from base to scene (t2: 23.15 min for emergency, 17.18 min for hospital-to-hospital transfer), time required at scene (t3: 12.12 min for emergency, 14.72 min for hospital-to-hospital transfer), travel time from scene to hospital (t4:39.41 min for emergency, 74.34 min for hospital-to-hospital transfer), patient handover time (t5: 10.85 min for emergency, 13.84 min for hospital-to-hospital transfer), and return from base to hospital (t6: 41.89 min for emergency, 94.72 min for hospital-to-hospital transfer). Multivariate linear regression was conducted to investigate the factors that influence ambulance travel time. The finding identifies that the ambulance travel time increased for the districts with lesser population density, lower road density, fewer hospitals, a higher district area served per ambulance, and a higher population served per ambulance. Additionally, socio-cultural reasons affecting health-seeking behaviour, early closing of healthcare centres, undercapacity and resource-deficit healthcare centres, and overloading of specialised tertiary hospitals were identified as determinants of delay in patient assessment and handover time in qualitative findings. A decisive and multi-sectoral approach is required to address the timeliness of EMS in the Indian context.

We developed a stable and reproducible p-type P:ZnO thin film using a cost-effective solution-derived spin-on-doping (SOD) technique. We created a pure p-n heterojunction by depositing a highly transparent Ga $_{\text{2}}$ O $_{\text{3}}$ thin film on P:ZnO for photodetector applications. The films’ surface morphology and thickness were analyzed using AFM and FEGSEM. At the same time, UV-visible (UV–Vis) and PL spectroscopy were employed to investigate their optical properties, including absorption, energy bandgap, and defect-related carrier transitions. The resulting P:ZnO/Ga $_{\text{2}}$ O $_{\text{3}}$ heterojunction demonstrated excellent photo-response performance, with a responsivity of 4.76 A/W, detectivity of 10.13 $\times$ 10 $^{\text{12}}$ Jones, and rapid response speed. The device exhibited sensitivity to UV-C and UV–Vis wavelength regions, showcasing its potential for high-performance, dual-band, and low-power photodetectors.