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Hemidesmus indicus L. R. Br. is a rare and endangered plant exhibits potent medicinal properties explored for the presence of flavoring compound in the roots namely 2-hydroxy-4- methoxybenzaldehyde (HMB) having applications in pharmaceutical and nutraceutical industries. The objective of the study is to improve the regeneration potential and optimize the genetic transformation in Hemidesmus indicus. Present study exhibits direct regeneration of Hemidesmus indicus through leaf and nodal explants with subsequent plant regeneration using Murashige and Skoog (MS) medium supplemented with various plant growth regulators (auxins, cytokinins, and gibberellic acid) TRIA and with adjuvant adenine sulphate,. The caulogenic response of 78.8%, 73.3% and 71.4% was observed when the leaf explant was inoculated on MS media containing 2.3 mgL− 1 BAP + 0.2 mgL− 1 2,4-D, 0.02 mgL− 1 TRIA + 2 mgL− 1 BAP and 1 mgL− 1 KIN + 1 mgL− 1 NAA respectively with creamish yellow nodular friable callus by 4 weeks. The initiation of shoot bud was observed within three days after inoculation of nodal explant on media supplemented with 1 mgL− 1 BAP + 0.1 mgL− 1 NAA, 1 mgL− 1 BAP + 0.1 mgL− 1 NAA + 40 mgL− 1 AgNO3, 1 mgL− 1 BAP + 0.1 mgL− 1 NAA + 40 mgL− 1 AgNO3 + 40 mgL− 1 adenine sulphate respectively and incubated in the dark for 2 weeks. Shoot regeneration from the leaf explants was also observed within 4 weeks after inoculation in MS medium with 1 mgL− 1 BAP + 0.1 mgL− 1 NAA. In the present study, we also report the development of an effective and reproducible Agrobacterium tumefaciens mediated genetic transformation system in Hemidesmus indicus. Genetic transformation was achieved by inoculating in vitro calli of Hemidesmus indicus with the A. tumefaciens strain EHA105 harboring pCAMBIA1301 with 35 S CAMV promoter driving the expression of reporter β-glucuronidase gene (GUS) and hygromycin selection marker hptII. After co-cultivation, the calli were thoroughly washed and inoculated on MS basal medium with (2.3mgL− 1 BAP + 0.2 mgL− 1 2,4-D) along with cefotaxime (250 mgL− 1) and hygromycin (30 mgL− 1) and the co-cultivated callus cultures were maintained in dark at 25 ̊C ± 2 ̊C for 4 weeks. After two rounds of subculture, the putative transformed calli were assessed by performing histochemical GUS staining assay and genomic DNA PCR to detect the exogenous GUS gene sequence. The transformation efficiency was 26% with transformed calli tolerant to hygromycin (30 mg L−1) and were positive to histochemical GUS staining assay and PCR. The results indicated a successful establishment of a reliable and efficient A. tumefaciens mediated genetic transformation system. The in-vitro micropropagation is a well-known technique for the mass production of the plant and thus aid in conservation of H. indicus. Through transformation, the regulatory and the biosynthetic genes can be manipulated for enhancement of nutraceutical, pharmaceutical bioactive compounds present in H. indicus and can also be applied for generating transgenic plants with desirable traits. [ABSTRACT FROM AUTHOR]

In this paper, we address the challenge of maximizing the effectiveness of warheads by targeting the structurally vulnerable points of the intended targets in three-dimensional engagements. We first transform the problem of achieving the target interception at the desired impact angles into the one of tracking desired line-of-sight (LOS) angles and their corresponding rates. The guidance strategies, derived considering coupled and nonlinear kinematics in three-dimensional engagements, guarantee the interception of non-maneuvering and maneuvering targets from desired impact directions within a finite time. The proposed approach allows the designer to specify the times of convergence, regardless of initial engagement geometries. Owing to the sliding mode control approach and the predefined-time stability notion, the proposed guidance methods are robust enough to compensate for the effects of target's maneuver and satisfy the response time constraints. To achieve the guidance objectives with enhanced transient and terminal performances, we construct the sliding surfaces using the LOS angles errors and their rates, with predefined times. The choices of such sliding functions allow us to derive the guidance laws that drive the error variables and their rates to the origin within assigned times. Moreover, we analyze the effect of tuning parameters on the dynamics using the Lyapunov stability theory. Such analysis allows us to obtain continuous guidance commands without necessitating any approximation of the signum function, eliminates singularity and chattering in the lateral accelerations and enables a better guidance precision and disturbance attenuation. Guidance strategies for the planar engagement are obtained as the special cases of proposed guidance schemes. Finally, we perform numerical simulations using the proposed guidance strategies for various engagement geometries and terminal conditions, where the pursuer moves at a constant speed, and the results are satisfactory. [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 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]

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]

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]

With increasing availability of satellite data of high temporal resolution, a more robust classifier is needed which can exploit the temporal information along with the spectral information of the remote sensing images. Specific fuzzy-based and learning-based algorithms are two broad categories and have the potential to perform well in spectral–temporal domain. In the present study, for mapping paddy fields as a specific class two classification algorithms, viz. fuzzy-based modified possibilistic c-mean (MPCM) algorithm and learning-based 1D-convolutional neural networks (CNN), were tested using Sentinel-2A/2B temporal data. The overall accuracy for learning-based 1D-CNN and fuzzy-based MPCM classifiers was found to be 96% and 93%, respectively. The F-measure values were found to be 0.95 and 0.92 for 1D-CNN- and MPCM-based classifier, respectively. Thus, it can be inferred from this study that the 1D-CNN classifier performed better than the traditional fuzzy-based classifier and can handle heterogeneity within class. [ABSTRACT FROM AUTHOR]

This paper considers a pursuit-evasion scenario among three agents– an evader, a pursuer, and a defender. We design cooperative guidance laws for the evader and the defender team to safeguard the evader from an attacking pursuer. Unlike differential games, optimal control formulations, and other heuristic methods, we propose a novel perspective on designing effective nonlinear feedback control laws for the evader-defender team using a time-constrained guidance approach. The evader lures the pursuer on the collision course by offering itself as bait. At the same time, the defender protects the evader from the pursuer by exercising control over the engagement duration. Depending on the nature of the mission, the defender may choose to take an aggressive or defensive stance. Such consideration widens the applicability of the proposed methods in various three-agent motion planning scenarios such as aircraft defense, asset guarding, search and rescue, surveillance, and secure transportation. We use a fixed-time sliding mode control strategy to design the control laws for the evader-defender team and a nonlinear finite-time disturbance observer to estimate the pursuer's maneuver. Finally, we present simulations to demonstrate favorable performance under various engagement geometries, thus vindicating the efficacy of the proposed designs. [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]

This paper employs a nonlinear cooperative deviated pursuit guidance strategy, for multiple interceptors, to ensure salvo interception. The problem of ensuring salvo is transformed into one of achieving consensus in time-to-go within a finite time, over a directed cycle. By using a deviated pursuit-based guidance approach, the computation of exact time-to-go is facilitated based on interceptors' present states, thereby avoiding the need for estimation of time-to-go and small-angle approximations. Greater flexibility in controlling consensus value and time can be achieved by the choice of heterogeneous gains in the consensus algorithm. The stability and performance of the proposed nonlinear guidance are analysed using Lyapunov-based methods. A sensitivity analysis of time-to-go with respect to various engagement parameters is also carried out. Effectiveness of the proposed salvo guidance strategy is demonstrated through numerical simulations on constant speed, and also realistic, time-varying speed interceptor models. [ABSTRACT FROM AUTHOR]

Coenzyme Q (CoQ), an electron carrier of the mitochondrial respiratory chain and a potent antioxidant, consists of a benzoquinone head and an isoprenoid side chain of varying numbers of isoprene units, 10 in humans, animals and some microbes including Agrobacterium tumefaciens (CoQ10), 9 in Arabidopsis and cereals (CoQ9) and 8 in Escherichia coli (CoQ8). The synthesis of CoQ10 side chain is catalyzed by a single side chain length determining enzyme, decaprenyl diphosphate synthase (DPS) which is encoded by a dps gene. We report here, the isolation and sequencing of a dps gene from A. tumefaciens strain LBA4404 and its cloning in pET28a expression vector of E. coli BL21 (DE3) strain to produce and purify recombinant DPS protein for the production of its antibodies in rabbit. The dps gene isolated from A. tumefaciens consists of 1077 bp efficient for encoding a polypeptide of 359 amino acids with a molecular mass of around 39 kDa. DPS protein expression was induced by adding isopropyl β-D-1-thiogalactopyranoside in bacterial culture to a final concentration of 1.0 mM. Recombinant protein tagged with histidine at N-terminal was purified by immobilized metal-affinity chromatography. The purified DPS protein was capable of producing polyclonal antibodies in rabbit whose specificity to DPS enzyme was confirmed by the western blot analysis. Expression of dps gene in E. coli extended the side chain length of CoQ8 to 10 isopenoid units and produced CoQ10 in addition to CoQ8. This is the first report on the production of polyclonal antibodies against recombinant DPS protein and demonstration of their utility. [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]