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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]

Oil spills are a potential hazard, causing the deaths of millions of aquatic animals and this leaves a calamitous effect on the marine ecosystem. This research focuses on evaluating the potential of polarimetric parameters in discriminating the oil slick from water and also possible thicker/thinner zones within the slick. For this purpose, L-band UAVSAR quad-pol data of the Gulf of Mexico region is exploited. A total number of 19 polarimetric parameters are examined to study their behavior and ability in distinguishing oil slick from water and its own less or more oil accumulated zones. The simulation of compact-pol data from UAVSAR quad-pol data is carried out which has shown good performance in detection and discrimination of oil slick from water. To know the extent of separation between oil and water classes, a statistical separability analysis is carried out. The outcomes of each polarimetric parameter from separability analysis are then quantified with the radial basis function (RBF) supervised Support Vector Machine classifier followed with an accurate estimation of the results. Moreover, a comparison of the achieved and estimated accuracy has shown a significant drop in accuracy values. It has been observed that the highest accuracy is given by LHV compact-pol decomposition and coherency matrix with a classification accuracy of ~ 94.09% and ~ 94.60%, respectively. The proposed methodology has performed well in discriminating the oil slick by utilizing UAVSAR dataset for both quad-pol and compact-pol simulation. [ABSTRACT FROM AUTHOR]

The dynamic soil-structure interaction of a shear wall embedded in elastic isotropic and homogeneous soil layers underlain by bedrock, subjected to SH waves, is modeled in the present article. The soil layers consist of irregular interfaces and it has been shown that the scattering due to the roughness of the layers has significant effect on the displacement of both the foundation and the shear wall. To demonstrate the phenomena indirect boundary element method (IBEM) has been used on the basis of its validation in previous problems of similar type. The system response is compared with the analytical solution of the same type of model for vertically propagating incident SH waves. It is observed that for the low frequency of wave, displacement is abruptly high, and as a result the combination of shear wall and foundation perceives resonance. The thickness of the soil layer, mass of the shear wall, stiffness of the bedrock and the soil layers all affects the system frequency and displacement. [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]

High cultivation cost and low lipid yield are framed as a major bottleneck for the production of microalgae biodiesel. Hence, we first and foremost highlight a trophic mode transition, coupled with a combinatorial effect of organic carbon, nitrogen and light (C/N/L) on an isolated microalga Chlorococcum sp. SVF in a one pot tri-phasic intermittent feeding system by developing a lab scale Raceway tank (40L). Hitherto, waste molasses syrup without hydrolysis is unexplored in algal bioenergy arena. The direct utilisation capability of sucrose, served by waste unhydrolysed molasses syrup (WUMS), effectively modulates the intrinsic biochemical and physiological characteristics towards microalgae biomass and lipid assimilation. Response surface methodology—central composite design (RSM-CCD) tool has been employed to observe the cumulative impact of light irradiation and nutrient sources (carbon and nitrogen) on cellular stoichiometric analysis. Experimental results exhibit a potentially achievable biomass (18.88 g L−1) and lipid accumulation (80.34%) under the light intensity of 75.5 µmol m−2 s−1 with stepwise light attenuation strategy. Characterisation of fatty acid methyl esters (FAME) reveals the dominance of oleic acid (32.72%) and palmitic acid methyl esters (32.49%) in mixotrophic condition, which are considered as the upmost indicators of quality biodiesel. The biofuel properties were obtained in acquiescence with American and European standard. These findings are therefore a way forward towards the effective growth of Chlorococcum sp. SVF in sucrose rich inexpensive industrial waste stream that positively influences the lipid yield for large scale sustainable biodiesel production. [ABSTRACT FROM AUTHOR]

Hexaploid bread wheat, being highly recalcitrant, has been difficult to regenerate in vitro which limits the use of transgenesis and genome editing for its agronomic improvement and functional genomics. We have studied the comparative in vitro regeneration in six most important wheat varieties, namely, HD-2967, PBW-343, WH-1105, PBW-550, DBW-17, and WL-711, using immature vs mature scutellum. The regeneration response of varieties PBW-550, DBW-17, and HD-2967 has not yet been reported. The mature and immature scutella cultured on MS medium containing 2,4-dichlorophenoxyacetic acid (2,4-D) (2 mg l−1) or dicamba (6 mgl−1), respectively, and maltose (30 gl−1) under dark conditions developed calli which subsequently on transfer to modified MS medium with double concentration of NH4NO3, zeatin (5.0 mgl−1), dicamba (0.1 mgl−1), AgNO3 (2.0 mgl−1), CuSO4 (2.5 mgl−1), and sucrose (30 gl−1) under light conditions regenerated into shoots. Immature scutellum showed 100% callus induction irrespective of varieties and regenerated a maximum of 15 shoots per callus in 90% cultures, whereas mature scutellum showed a maximum 80% callus induction, producing maximum 5 shoots only in 67% cultures. Thus, immature scutellum has been more responsive comparative to mature scutellum for inducing callus and shoot regeneration. Among all the six varieties, HD-2967 was the most regenerative, which can be explored further for improving wheat via genetic transformation studies. [ABSTRACT FROM AUTHOR]

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]

In this work, a series of Cu/MgO catalysts with different copper metal loading were prepared by the precipitation-deposition method. Their catalytic behaviour was investigated for glycerol hydrogenolysis to 1,2-propanediol (1,2-PDO). The physico-chemical properties of the catalysts were characterized by various techniques such as BET surface area, X-ray diffraction (XRD), temperature programmed reduction (TPR), NH-temperature programmed desorption (NH-TPD) and scanning electron microscopy (SEM) methods. The characterization results showed that the copper metal was well-dispersed over MgO support and a new phase Cu-MgO was also identified from XRD results after calcination. The 25Cu/MgO (Cu:25 wt%) catalyst exhibited the highest glycerol conversion of 88.7% and 1,2-PDO selectivity of 91.7% at 210C, 4.5 MPa of hydrogen pressure after 12 h. The high glycerol conversion was mainly due to the Cu dispersion on MgO support and high acidic strength. Further, the effects of temperature, hydrogen pressure, catalyst loading and glycerol concentration were studied over 25Cu/MgO catalyst for optimization of reaction parameters. Kinetic study over highly active 25Cu/MgO catalyst showed that the reaction followed the pseudo second order rate with respect to glycerol and the apparent activation energy was found to be 28.7 ± 0.8 kcal/mol. [Figure not available: see fulltext.] [ABSTRACT FROM AUTHOR]

Background: We retrospectively evaluated the pretreatment radiological presentation and the clinicoradiological outcome at the completion of 1 year chemotherapy in osteoarticular tuberculosis of hip in children to prognosticate correlation between them. Materials and Methods: We retrospectively analyzed the clinical and plain radiographic findings in 27 patients with an age of 12 years or younger in whom hip tuberculosis was diagnosed and treated between 2006 and 2010. The diagnosis was based on histopathology in 14 and clinicoradiological basis in 13 patients. The pre and post treatment plain radiographs were evaluated according to Shanmugasundaram radiological classification and our observations regarding unclassified cases which were not fit in this classification were suggested. The functional outcome at the completion of chemotherapy was assessed using modified Moon’s criteria. Results: The male female ratio was 11:16. The left hip was involved more frequently than the right (17:10). The average age was 7.37 years (range, 2‑12 years). In the pretreatment radiographs, 9 hips were normal, 6 traveling, 4 dislocating, 1 protrusio acetabuli, 3 atrophic and 4 unclassified types (3 triradiate; 1 pseudarthrosis coxae). There were no Perthes and mortar pestle at the initial presentation. Posttreatment, the types changed to 9 normal, 3 Perthes, 1 protrusio acetabuli, 1 atrophic, 4 mortar pestle and 9 unclassified types (3 triradiate, 3 pseudarthrosis coxae and 3 ankylosed). There were 37% excellent, 18.5% good, 26% fair and 18.5% poor results. The prognosis was best with initial ‘”triradiate” and normal types and worst with posttreatment atrophic and “ankylosed” types. Conclusions: The Shanmugasundaram radiological types accurately predict prognosis only in normal types and “triradiate” pattern. The functional outcome is independent of radiological morphology of the hip in smaller children. [ABSTRACT FROM AUTHOR]

Chloroplasts offer high-level transgene expression and transgene containment due to maternal inheritance, and are ideal hosts for biopharmaceutical biosynthesis via multigene engineering. To exploit these advantages, we have expressed 12 enzymes in chloroplasts for the biosynthesis of artemisinic acid (precursor of artemisinin, antimalarial drug) in an alternative plant system. Integration of transgenes into the tobacco chloroplast genome via homologous recombination was confirmed by molecular analysis, and biosynthesis of artemisinic acid in plant leaf tissues was detected with the help of C NMR and ESI-mass spectrometry. The excess metabolic flux of isopentenyl pyrophosphate generated by an engineered mevalonate pathway was diverted for the biosynthesis of artemisinic acid. However, expression of megatransgenes impacted the growth of the transplastomic plantlets. By combining two exogenous pathways, artemisinic acid was produced in transplastomic plants, which can be improved further using better metabolic engineering strategies for commercially viable yield of desirable isoprenoid products. [ABSTRACT FROM AUTHOR]