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Switching-sequence-based control (SBC) laws when designed based on topological switching behavior can have positive effects on slow- and fast-scale dynamics of a power-electronic system (PES). The slow-scale control can encompass fast PES state regulation and tracking, based on predefined objective, while fast-scale control can address differential-mode (DM) and common-mode (CM) spectral-peak energy associated with PES switching operation. Such control laws may offer enhanced programmability to conventional PES design where bulky electromagnetic interference (EMI) filters have been traditionally used to reduce EMI of switching power converters to meet EMI regulatory standards. An EMI filter is always a less programmable solution since it is usually designed for the worst-case EMI mitigation and usually overkill for a PES operating under reduced load condition. The control scheme outlined in this article offers EMI mitigation across wide operating regions without compromising PES regulation. Moreover, it does so by use of switching sequences that guarantee the reachability of the PES dynamics using an advanced Lyapunov-function-based approach. SBC is a powerful tool to generate control actions for a PES based on multivariate PES state constraints. Hence, contemporary EMI regulatory standards are used as constraints in the SBC formulation to operate the PES under wide operating regime while autonomously mitigating the EMI levels. The work may be of paramount importance for operating the ultra-fast-transition recent wide-bandgap semiconductor devices like GaN–FET and SiC mosfet under higher power with increasing switching frequencies, which is usually desirable for increased power density and reduced switching losses. Here, a hardware Cuk–PES operated with GaN–FETs is fabricated and is used for case illustration. It is shown by experimental results how SBC mitigate DM and CM EMI noise of the PES while maintaining regulation even for the higher order nonminimum phase PES, while reducing sensor requirements using state observer derived from the switching model of the PES.

Natural products have contributed immensely towards the treatment of various diseases including diabetes. Here, a database of small molecules from nature possessing antidiabetic properties was analysed and shortlisted according to their structural diversity. Later, those structures were screened by in-silico docking studies to understand their affinity towards glucagon-like peptide-1 (GLP-1) receptor. The selected molecules were isolated and investigated further by integrated in-vitro and in-silico approaches. Alpha-mangostin was found to be suitable due to its excellent docking score and isolation yield. A pancreatic beta cell line was used to test the activity of alpha-mangostin and observed a 3-fold increase in insulin secretion compared to 15 mM glucose control. Further, in-silico molecular dynamics simulations studies have validated its target by showing conformational changes at the functionally active part of the GLP-1 receptor. This screening strategy can be applied to identify pertinent natural products rapidly for various therapeutic targets.

In a conventional linear-control design of a higher order power-electronic system (PES), the gains of the control laws are typically determined by small-signal analysis of the averaged model of the PES. The closed-loop control of such a PES (driving a pulsating load that periodically shifts the equilibrium) using its small-signal model is often found to be unsatisfactory with regard to the overall stability and performance. To address the challenges in control design for a PES driving such a nonlinear time-varying pulsating load, this paper delineates an optimal switching-sequence-based control (SBC) scheme, which applies stability-bound switching sequence(s) to the PES. A novel method has been formulated to ensure the reachability of the PES dynamics based on its switching sequence in terms of the time horizon of the switching sequence and the allocation of this time among the switching states of the same switching sequence. This is ascertained by modeling the PES and the pulsating load as a nonlinear map and then using this map and multiple Lyapunov functions determined by solving a set of linear-matrix inequalities corresponding to each of the switching states of a given switching sequence. It has been further shown that the knowledge of the stability bounds of a reachable switching sequence helps in reducing the online computation time for optimal SBC associated with solving the optimization problem by reducing the overall search space. Finally, to validate the optimal SBC, an experimental GaN-FET-based 100-kHz Cuk-PES has been fabricated and tested on a pulsating load. The overall SBC is implemented on a low-cost TMS320F28335 DSP, which also implements an observer to preclude the need for plurality of sensors for the higher order Cuk-PES. The overall performance of the SBC is found to be satisfactory under varied dynamical conditions.

A switched resistor based active drive scheme is delineated in this paper. The active gate drive does switching transition control (STC) of a wide bandgap (WBG) SiC MOSFET. Contrary to prevalent active gate drive schemes which are validated on a double pulse (DP) testbed, this paper synthesizes an STC scheme for a WBG PES prototype to meet a system-level goal like efficiency increment. An HF Cuk PES operating at 50 kHz using Cree SiC MOSFET half-bridge module is fabricated for testing purpose of the STC framework. The switched resistor based STC uses HF GaN-FET based network, consisting of state-of-the-art EPC8004 GaN and LMG1020 Texas drivers. The design achieved transitions of 1 ns to create short pulse widths that can control the turn-on trajectory of the SiC device. The simple approach is realized only with the use of an industrial-scale F28379D processor.

Natural products are a major source of therapeutic agents since ancient times and many of them have been approved as potent drugs or drug candidates. The chief sources of natural products can be broadly divided into plants, animals, marines, and microbes. The secondary metabolites produced from these living organisms have provided many useful drugs such as the anticancer drug Taxol obtained from the plant Taxus brevifolia and Halichondrin B from the marine sponge Halichondria okadai among others. Drug discovery from natural products is, however, a tedious and challenging task. The process of drug discovery from natural sources involves various steps which include botanical identification and collection of the source, extraction, isolation, purification and structure elucidation of the desired bioactive substance and biological evaluation of natural compounds. This is further followed by formulation development, stability study of the developed formulation, preclinical and clinical trial studies, and submission of New Drug Application. The developments in various chromatographic techniques like HPLC, HPTLC, and GC and spectroscopic techniques like NMR, MS, FTIR, and UV and emergence of hyphenated techniques like LC-MS, GC-MS, and LC-NMR have largely revolutionized the process of drug discovery from natural products. The natural products also form the basis of traditional medicine systems of Ayurveda, Yoga, Unani, Siddha, and Homeopathy (AYUSH). Generally, lower yields of targeted molecule from natural sources necessitate increased focus on development of semisynthetic analogs of lead molecules derived from nature. Also, many herbal formulations fail to make it large in market due to lack of proper scientific evidence to support their claims for health. Despite supremacy of synthetic drugs, plant-based drugs are re-emerging in recent years.

In the present scenario, Renewable Energy requires real-time condition monitoring for their uninterrupted performance. Wind turbines are often subjected to huge mechanical, and thermal stresses which in turn result in causing faults. In this paper, a Cloud-based Real-time Monitoring System (CRMS) has been developed for the early detection of a problem and identify the need for maintenance before a wind turbine fails. CRMS associated with vibration sensor and temperature sensor can easily detect the fault and alarming system indicates the operator personnel about the abnormal state of the motors in the industrial plant. With real-time data monitoring system and LabVIEW, it enables the detailed spectral analysis of the system. A wireless sensor networks are included in this research work for a real-time condition monitoring. Therefore, the authors of this paper have developed a prototype which can provide smart maintenance to elongate the life of wind turbine and prevent the harm of nearby equipments.

This paper aims to present an intelligent system for autonomous diagnosis of fetal arrhythmia based on fetal ECG recordings. The present scheme uses one dimensional (1D) convolution with a wavelet kernel to extract time domain features from subjects possessing normal fetal ECG and fetal arrhythmia ECG. Time- domain features obtained from the convoluted signals are fed to a trained artificial neural network (ANN) with gradient descent learning to identify and classify fetal ECG signals. The experimental evaluation of the proposed scheme has been tested with a six- channel fetal ECG signal, available in the NIFEADB database. An overall accuracy of 96% is obtained by evaluating standard performance metrics. The use of 1D convolution not only reduces the computational burden but also helps to specify the feature space to develop an intelligent system for portable embedded system applications.

Mistletoes are extremely important plants and their extracts have been widely used as a complementary medicine towards cancer therapy. In this context, we have studied the leaves of a South Indian Mistletoe, Macrosolen capitellatus (Wight & Arn.) Danser, a parasitic plant belonging to family Loranthaceae. A bioassay-guided isolation and LC-MS dereplication strategies have led to the isolation of pinocembrin-7-O-[4”, 6"-(S)-hexahydroxy diphenoyl (HHDP)]-β-D-glucose (1). This is the first report showing the presence of 1 not only in this plant but also in the genus Macrosolen. In this study, the aforementioned parasitic plant was collected from two different host plants, Mangifera indica and Phyllanthus emblica, which were extensively studied chemically by various research groups. We have observed that 1 is present in both samples of M. capitellatus, while a thorough literature search showed that this compound 1 has not been reported from either Mangifera indica or Phyllanthus emblica. These observations indicated that the producer of 1 is nothing but parasitic plant M. capitellatus. This compound has shown moderate anti-proliferative activity against MCF-7 cell line (IC50 value-15 μM). A computational protein binding study has been performed on 1 against 25 crystallographic proteins of the BRCA-1 gene of breast cancer. The results showed interactions having -9.33 and -8.99 kcal/mol binding energy with the proteins IDs 1T15 and 1T29 respectively; which suggests that this scaffold has the potential to develop further.

Natural disasters present a huge challenge for Government and non-Governmental organizations, as the individuals and communities are heavily affected. Disasters like earthquakes are seismic events that result in shaking of the earth’s surface causing large-scale death and destruction. Therefore, disaster management and control are of utmost importance. Nowadays individuals on site use social media platforms (like Twitter, Facebook, etc.) for sharing information and seeking remedy. If the extent of destruction can be estimated, one can assess the amount of relief needed. Thus, the data gathered from social media platforms can be used to evaluate the extremity of architectural damage induced by disasters like earthquakes. This paper proposes a CNN based segmentation of earthquake affected regions in images that are posted on online social media and classifying them into various classes according to the severity of damage. The approach is trained and tested using a dataset of images collected from social media posts during earthquakes and the results are encouraging.

GaN-FET-based solar microinverter using a differential-mode inverter (DMI) topology is presented. Key features of the DMI topology and its switching scheme are outlined and how the GaN-FET technology affects the power-stage performance are narrated. Further, summary of the issues with grid connection are elaborated. Next, how the modular DMI can be scaled for three-phase inverter and energy-storage applications are illustrated. Finally, one manifestation of how the energy storage can be integrated using topology emulating the DMI is narrated.

This paper delineates a mechanism to attain decentralized control of a high-frequency distributed power system (HFDPS) based on local observation and/or estimation at the source and load ends. An acceptable dynamic performance of the independent control eliminates/reduces the need for high-speed communication (that typically needs to be high because of HF power transfer) between the source- and load-end controllers. This provides practically complete autonomy for the HFDPS. To verify the dynamic performance of the control scheme, start-up, transient and steady-state results have been provided.

Control of a distributed power system (DPS) is a challenging task and requires unique control formulation as they arise in complex engineering problems. Since DPSs are gaining widespread importance, development of robust control strategies, which encompasses the nonlinear behavior and spatial spread of such networks, commands utmost gravity. Due to the spatial spread of such networks, state observer design based on local state variables plays a role of paramount importance. In this paper, the authors have outlined the design of a non-linear state observer which will aid a distributed control architecture for a single-input multi-output high frequency distributed power system (HFDPS) which incorporates need-based asynchronous power transfer. An adaptive control strategy for independent control of the need-based asynchronous HFDPS will be implemented based on the estimated state variables of the observer. Hence global control objectives are addressed using local sensing and a robust estimator design. Experimental results are portrayed in order to verify the stability and robust dynamic performance of the observer so designed. The nonlinear state observer was designed in the framework of exact formulation based on nonlinear map. Hence the observer encompasses the impact of switching frequency and other fast scale dynamics without any bandwidth limitation.

Differential-mode Cuk inverter (DMCI) represents a class of single-stage inverter with low device count, modular symmetry, and topological simplicity. Recently, discontinuous modulation scheme (DMS) has been proposed for the DMCI. This paper extends the modular nature of the power stage to the control architecture and proposes a transformation-based tracking controller using adaptive control algorithm for the same. A key challenge in designing a controller for DMS-based DMCI is to take into account the inherent converter nonlinearity and startup transient at the start of every line cycle. Experimental results are provided for startup, steady state, load transition, and total harmonic distortion (THD) using GaN-FET based DMCI, which validate the efficacy of the implemented modular transformation-based tracking controller.

Power electronic systems are gaining widespread importance in contemporary world due to the extensive integration of renewable energy resources into grid. As a consequence of grid integration of renewable energy sources, developing robust control methods for inverters have gained paramount importance, since the control scheme that we apply plays an important role in ensuring stability and good performance of the inverters. Taking this into account, we have designed an optimal switching sequence based controller (OSBC) which ensures stability and robustness of a differential-mode inverter (DMI), which is based on Cuk topology. The controller, so designed, responded well to large signal perturbations under varying operating conditions. The control scheme developed provides good steady state and transient response. The control leads to the rapid convergence of the input inductor current to its reference resulting in fast convergence of the output voltage to its reference ass well.

This paper discusses a face image abstraction method by using SIFT features and Ford-Fulkerson algorithm. Ford-Fulkerson algorithm is used to compute the maximum flow in a flow network drawn on SIFT features extracted from a face image. The idea is to obtain an augmenting path which is a path from the source vertex to destination vertex with the available capacities on all edges along a set of paths and flow is calculated along one of these paths. The process is repeated until it is obtained more paths with the available capacities. At the initial stage, face image is characterized by SIFT (Scale Invariant Feature Transform) features and the keypoints descriptor information is taken as features set for further processing. Keypoints descriptor is used to generate several face representations by using a series of matrix operations which are further used to determine a Directed Acyclic Graph (DAG). The resultant directed graph contains sparse and distinctive face characteristics of a subject from which the face image is captured. We then apply the Ford-Fulkerson algorithm on the directed graph to maintain the capacity constraints, skew symmetry and flow conservation to obtain an augmenting path with available capacities (relation between SIFT points). Finally, we obtain a mathematical representation of a face image and this representation is further encoded to be used as a set of distinctive features for matching. The time complexity of the proposed face abstraction algorithm is found to be O(VE2) where V is the set of vertices and E is the set of edges in a directed graph.