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This paper analyses the effects of a non-uniform laminar flow past a circular cylinder using the direct numerical simulation approach. Compared to the cylinder immersed in a uniform flow case, modifications caused by the non-uniform approaching flow on the evolution of the flow and sound fields are studied in detail in the present work. The prescribed non-uniform velocity profile at the inlet has a Gaussian distribution and imitates a jet or a wake flow velocity profile. The sound is generated due to periodic vortex shedding behind the circular cylinder. The frequency of vortex shedding and the strength of the shed vortices depend on the inflow velocity profile. Hence, variation of the inflow velocity profile alters the shed vortices, changing the generated sound field. It is observed that the amount of gain or deficit present in the prescribed inflow velocity profile compared to the uniform inflow profile alters the sound field information. The jet kind of inflow profile creates a sound of higher frequency and intensity in contrast to the wake kind of inflow profile. The presence of shear in the inflow velocity profile is also expected to change the sound propagation direction. However, in present simulations, the distance over which sound propagation is considered is small compared to the wavelength of the sound waves, and sound beam drift effects are not observed much. [ABSTRACT FROM AUTHOR]

Due to the intelligent, adaptive nature of various operations and their ability to provide maximum comfort to the occupants residing in them, smart buildings are becoming a pioneering area of research. Since these architectures leverage the Internet of Things (IoT), there is a need for monitoring different operations (occupancy, humidity etc.) to provide sustainable comfort to the occupants. This work proposes a novel approach for intelligent building operations monitoring using rule-based complex event processing (CEP) and query-based approaches for dynamically monitoring the operations. Siddhi is a CEP engine designed for handling multiple sources of events data in real time and processing it according to predefined rules using a decision tree. Since datastream is dynamic in nature, to keep track of different operations, we have converted the IoT data into an RDF (Resource Description Framework) dataset. The RDF dataset is ingested to Apache Kafka for streaming and for stored data we have used GraphDB tool that extracts information with the help of SPARQL query. Additionally, a large number of events are passed through CEP for correlating and analyzing events in real time, and we measure the performance of CEP engines through SPARQL for enhancing decision support. The proposed approach is evaluated by deploying rules to events through the CEP engine and assessing their processing efficiency in terms of time. Subsequently, a risk estimation scenario is proposed to generate alerts for end users if any smart building operations need immediate attention. However, the proposed work has limitations regarding adaptive and standard parameters, which may cause the rules to fail in adapting to changing data streams. [ABSTRACT FROM AUTHOR]

The work reported in this paper describes the design and simulation of a novel current mirror readout circuit based MOSFET integrated cantilever structure for micro-gram (μ g) mass sensing applications. The proposed mass sensing structure consist of silicon cantilever as the micromechanical structure, MOSFET as strain sensing element and current mirror as a readout circuit. In this paper, studies have been carried out on three structures (a) n-channel MOSFET based resistive loaded current mirror integrated cantilever mass sensor (b) p-channel MOSFET based resistive loaded current mirror integrated cantilever mass sensor and (c) n- and p- channel MOSFETs based dual current mirror integrated cantilever mass sensor. The input MOSFET(s) of the current mirror acts as the reference transistor(s) while the output MOSFET(s) of the mirror circuit acts as strain sensing element(s) to measure the cantilever deflection under externally applied load. The structural and the electrical characteristics of the sensors were simulated using COMSOL Multiphysics and TSPICE software respectively. Simulation results showed a sensitivity of 21.89 μ V/ μ g, 1 μ V/ μ g and 115.57 μ V/ μ g for nMOS, pMOS and dual channel cantilever mass sensor. The fabrication steps and mask layout of the proposed sensor have also been presented in this paper. The novelty of this work is emphasized by the innovative integration of a CMOS current mirror circuit with a cantilever for mass sensing. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "A conceptual study on novel current mirror integrated cantilever (CMIC) mass sensor for micro-gram (µg) range sensing applications" published in the journal Microsystems Technologies. The correction addresses an error in the affiliation of two authors, providing the correct affiliations for Kalpana Gogoi and Shashi Kumar. The original article has been corrected. The publisher, Springer Nature, remains neutral regarding jurisdictional claims and institutional affiliations. [Extracted from the article]

The disposal of waste from industrial sources is becoming a serious problem throughout the world. To produce one liter (L) of ethanol, about 15-L Ethanol Spent Water (ESW) is discharged from the ethanol industry adversely impacting aquatic and terrestrial ecosystems. This study investigated the bioremediation of ESW in two stages, first filtration through biochar (byproduct after HTL processing of algal biomass) followed by bioremediation using algae. In the first step up to 99.4% toxic melanoidin pigment and suspended pollutants from ESW were removed. The colorless ESW was then tested for the growth of marine Parachlorella kessleri-I (PK-I), and freshwater Chlorella vulgaris (CV) and Chlorella sorokiniana-I (CS-I). Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) analysis was conducted before and after bioremediation of ESW using algae. The chemical oxygen demand (COD) 28,900 mg L−1 was reduced to 55–118 mg L−1, biological oxygen demand (BOD) 7200 mg L−1 was reduced to 28–55 mg L−1, total organic carbon (98.7%), phenol (64%), sugar (99.5%), nutrients and toxic metal concentrations were also reduced significantly after 12 days of algae-cultivation in ESW. The highest biomass (115.4 mg L−1 day−1) and lipid (9.1 mg L−1 day−1) productivity were observed in PK-1 using biochar-filtered ESW. The biodiesel properties and cetane number 48.1–63.9 in FAME studied by GC–MS were observed as per the international standard of fuels. The ESW cultivated algal biomass contained a significant amount of lipids, proteins, and carbohydrates compared to the control medium grown biomass. Thus algae-derived biochar has the potential to effectively remediate the hazardous ESW thereby facilitating the efficient growth of different algal species to generate the feedstock for biofuel applications. Bioremediation of raw Ethanol Spent Wash (ESW). About 15-L wastewater generates to produce 1-L ethanol from Ethanol Distillery. The filtration of ESW through algae-based biochar facilitated the growth of marine and freshwater algae as a bioresources for various applications while remediation of wastewater as a circular bioeconomy. [ABSTRACT FROM AUTHOR]

Algal biorefinery is rising as a prominent solution to economically fulfill the escalating global requirement for nutrition, feed, fuel, and medicines. In recent years, scientific productiveness associated with microalgae-based studies has elaborated in multiplied aspects, while translation to the commercial level continues to be missing. The present microalgal biorefinery has a challenge in long-term viability due to escalated market price of algal-mediated biofuels and bioproducts. Advancements are required in a few aspects like improvement in algae processing, energy investment, and cost analysis of microalgae biorefinery. Therefore, it is essential to recognize the modern work by understanding the knowledge gaps and hotspots driving business scale up. The microalgae biorefinery integrated with energy-based products, bioactive and green compounds, focusing on a circular bioeconomy, is urgently needed. A detailed investigation of techno-economic analysis (TEA) and life cycle assessment (LCA) is important to increase the market value of algal products. This review discusses the valorization of algal biomass for the value-added application that holds a sustainable approach and cost-competitive algal biorefinery. The current industries, policies, technology transfer trends, challenges, and future economic outlook are discussed. This study is an overview through scientometric investigation attempt to describe the research development contributing to this rising field. [ABSTRACT FROM AUTHOR]

Urban area mapping is an important application of remote sensing which aims at both estimation and change in land cover under the urban area. A major challenge being faced while analyzing Synthetic Aperture Radar (SAR) based remote sensing data is that there is a lot of similarity between highly vegetated urban areas and oriented urban targets with that of actual vegetation. This similarity between some urban areas and vegetation leads to misclassification of the urban area into forest cover. The present work is a precursor study for the dual-frequency L and S-band NASA-ISRO Synthetic Aperture Radar (NISAR) mission and aims at minimizing the misclassification of such highly vegetated and oriented urban targets into vegetation class with the help of deep learning. In this study, three machine learning algorithms Random Forest (RF), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) have been implemented along with a deep learning model DeepLabv3+ for semantic segmentation of Polarimetric SAR (PolSAR) data. It is a general perception that a large dataset is required for the successful implementation of any deep learning model but in the field of SAR based remote sensing, a major issue is the unavailability of a large benchmark labeled dataset for the implementation of deep learning algorithms from scratch. In current work, it has been shown that a pre-trained deep learning model DeepLabv3+ outperforms the machine learning algorithms for land use and land cover (LULC) classification task even with a small dataset using transfer learning. The highest pixel accuracy of 87.78% and overall pixel accuracy of 85.65% have been achieved with DeepLabv3+ and Random Forest performs best among the machine learning algorithms with overall pixel accuracy of 77.91% while SVM and KNN trail with an overall accuracy of 77.01% and 76.47% respectively. The highest precision of 0.9228 is recorded for the urban class for semantic segmentation task with DeepLabv3+ while machine learning algorithms SVM and RF gave comparable results with a precision of 0.8977 and 0.8958 respectively. [ABSTRACT FROM AUTHOR]