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There is no standard protocol for managing non-union of diaphyseal humerus bone, with several authors reporting their results using various techniques and methods for its management. No meta-analysis has reported the results of managing these cases with non-vascularized fibula grafting as an adjuvant for osteosynthesis.

Background: Diabetes, the fastest growing health emergency, has created several life-threatening challenges to public health globally. It is a metabolic disorder and triggers many other chronic diseases such as heart attack, diabetic nephropathy, brain strokes, etc. The prime objective of this work is to develop a prognosis tool based on the PIMA Indian Diabetes dataset that will help medical practitioners in reducing the lethality associated with diabetes. Methods: Based on the features present in the dataset, two prediction models have been proposed by employing deep learning (DL) and quantum machine learning (QML) techniques. The accuracy has been used to evaluate the prediction capability of these developed models. The outlier rejection, filling missing values, and normalization have been used to uplift the discriminatory performance of these models. Also, the performance of these models has been compared against state-of-the-art models. Results: The performance measures such as precision, accuracy, recall, F1score, specificity, balanced accuracy, false detection rate, missed detection rate, and diagnostic odds ratio have been achieved as 0.90, 0.95, 0.95, 0.93, 0.95, 0.95, 0.03, 0.02, and 399.00 for DL model respectively, However for QML, these measures have been computed as 0.74, 0.86, 0.85, 0.79, 0.86, 0.86, 0.11, 0.05, and 35.89 respectively. Conclusion: The proposed DL model has a high diabetes prediction accuracy as compared with the developed QML and existing state-of-the-art models. It also uplifts the performance by 1.06% compared to reported work. However, the performance of the QML model has been found as satisfactory and comparable with existing literature.

While persistent efforts are being made to develop a novel arsenal against bacterial pathogens, the development of such materials remains a formidable challenge. One such strategy is to develop a multimodel antibacterial agent which will synergistically combat bacterial pathogens, including multidrug-resistant bacteria. Herein, we used pediocin, a class IIa bacteriocin, to decorate Ag° and developed a double-edged nanoplatform (Pd-SNPs) that inherits intrinsic properties of both antibacterial moieties, which engenders strikingly high antibacterial potency against a broad spectrum of bacterial pathogens including the ESKAPE category without displaying adverse cytotoxicity. The enhanced antimicrobial activity of Pd-SNPs is due to their higher affinity with the bacterial cell wall, which allows Pd-SNPs to penetrate the outer membrane, inducing membrane depolarization and the disruption of membrane integrity. Bioreporter assays revealed the upregulation of cpxP, degP, and sosXgenes, triggering the burst of reactive oxygen species which eventually cause bacterial cell death. Pd-SNPs prevented biofilm formation, eradicated established biofilms, and inhibited persister cells. Pd-SNPs display unprecedented advantages because they are heat-resistant, retain antibacterial activity in human serum, and alleviate vancomycin intermediate Staphylococcus aureus(VISA) infection in the mouse model. In addition, Pd-SNPs wrapped in biodegradable nanofibers mitigated Listeria monocytogenesin cheese samples. Collectively, Pd-SNPs exhibited excellent biocompatibility and in vivotherapeutic potency without allowing foreseeable resistance acquisition by pathogens. These findings underscore new avenues for using a potent biocompatible nanobiotic platform to combat a wide range of bacterial pathogens.

Fast charging and low temperatures create harsh conditions that promote lithium deposition on graphite anodes, which significantly accelerates the degradation of the battery's state of health (SoH) and eventually could result in safety concerns. In this work, commercial lithium-ion batteries are investigated under fast charging conditions and at low environmental temperatures. The findings are compared with lithium-ion battery cycled under ambient temperature conditions. The fast charging and low temperatures result in dead lithium formation, which is then characterized by electrochemical impedance spectroscopy (EIS) and scanning electron microscope (SEM). The low-temperature cycled battery exhibits significant growth of series resistance by an average of 73 %. In comparison, growth in charge transfer resistance is 16 %, and no significant change was observed in solid electrolyte interface (SEI) resistance due to the formation of dead lithium, compared to the battery cycled at ambient temperature. SEM images also confirm the dead lithium formation. The results provide insight into battery degradation in the case of high current due to low temperature and could lead to a better understanding of the degradation mechanism.

We present the first on-sky results of a four-telescope integrated optics discrete beam combiner (DBC) tested at the 4.2 m William Herschel Telescope. The device consists of a four-input pupil remapper followed by a DBC and a 23-output reformatter. The whole device was written monolithically in a single alumino-borosilicate substrate using ultrafast laser inscription. The device was operated at astronomical H-band (1.6 µm), and a deformable mirror along with a microlens array was used to inject stellar photons into the device. We report the measured visibility amplitudes and closure phases obtained on Vega and Altair that are retrieved using the calibrated transfer matrix of the device. While the coherence function can be reconstructed, the on-sky results show significant dispersion from the expected values. Based on the analysis of comparable simulations, we find that such dispersion is largely caused by the limited signal-to-noise ratio of our observations. This constitutes a first step toward an improved validation of the DBC as a possible beam combination scheme for long-baseline interferometry.

Aims: To design a new variant of the Shuffled Frog Leaping Algorithm in which memeplexes formation is modified with a new strategy. Background: Shuffled Frog Leaping (SFL) is a memetic meta-heuristic algorithm that inherits two other algorithms’ features. Its intensification component of search is similar to Particle Swarm Optimization, while the inspiration for diversification is inherited from the global exchange of information in Shuffled Complex Evolution. A basic variant has been applied to solve many optimisation problems. SFLA suffers from a slow acceleration rate. Objective: To propose a robust hybrid SFLA that accelerates convergence. Methods: Two modifications are proposed in the structure of basic SFLA. Firstly, memeplexes formation is modified to handle continuous optimization problems. Secondly, in the basic SFL algorithm, the position of the worst frog is improved by moving it towards the best frog in the respective memeplex. With the progress of execution, the difference between best and worst frog position reduces; there may be more chances to trap in local minima. To improve convergence and avoid trapping in local optima, a parent-centric operator is embedded in each memeplex while performing a local search. The proposed algorithm is named PC-SFLA (Parent Centric - Shuffled Frog Leaping Algorithm). Results: The improved efficiency of PC-SFLA is validated on a robust and diverse set of standard test functions defined in CEC 2006 and 2010. Further, its efficacy is verified to optimize the total cost of supply chain management of a system. Non-parametric statistical result analysis demonstrates the efficiency of the proposal. Conclusion: PC-SFLA performed better than PSO, DE, PESO+, Modified DE, ABC, and SFLA at 5% and 10% level of significance whereas at par with Shuffled-ABC for g01-g07 functions of CEC 2006 in terms of NFE’s. Similarly, PC-SFLA performed better than SaDE, SFLA, CMODE at both levels of significance (5% & 10%) and par with MPDE in terms of mean function value for 17 problems taken from CEC 2006. Further, PC-SFLA is investigated on 18 problems from CEC 2010, and Wilcoxon signed ranks test is performed at a 5% level of significance. PC-SFLA performed better than SFLA and CHDE and at par with PESO. The computational results present the competency of the proposed method to solve quadratic, nonlinear, polynomial, linear, and cubic functions efficiently. The simulated results show that the proposed algorithm can solve mix integer constrained continuous optimization problem efficiently.

AbstractElectron cyclotron (EC) heating and current drive is a well-established auxiliary heating mechanism for tokamak plasmas, which is also effective in assisting plasma breakdown and controlling disruptive plasma instabilities. ITER requires 20 MW of power at 170-GHz frequency to be coupled into the plasma for EC radio-frequency (rf) applications. Gyrotrons are rf/microwave oscillators capable of delivering high continuous-wave power in the microwave and millimeter-wave frequency range (a few to hundreds of gigahertz). An EC system with 26 gyrotron sources at 170 GHz, with a typical unit power of 1 MW each, and a total installed power capacity of 24 MW is planned for ITER. As a part of the in-kind contributions, the Indian domestic agency is responsible for two sets of EC sources that provide 2 MW (∼8%) of the EC power at 170-GHz frequency. Here, we provide an overview of the gyrotron source system, its basic concepts and main features, design aspects, auxiliary requirements, performance issues, and future research and development goals.

The working of basic ABC algorithm depends on the functioning of three categories of bees; the employed bees, the onlooker bees and the scout bees. Although, employed and onlooker bees have different functionality, they follow the same equation for exploration and exploitation. Obviously, the performance of ABC greatly depends on single equation. In order to provide a variation in the working of ABC, we propose the use of different equations in the employed bee and onlooker bee phase. The new mechanism proposed by us for the movement of the bees is based on the convex linear combination of three candidate solutions. This scheme is initially embedded in the employed bees phase while the original equation is maintained for the onlooker bees. In the second variation the basic equation for employed bees is retained while for onlooker bees, different equation is used. The simulation results when compared with DE and ABC demonstrate that the modification increases efficiency and capability in terms of balancing exploration and exploitation as well as the accelerating the convergence rate of the ABC.

Chemotherapy is one of the most extensively utilized cancer treatment strategies worldwide. It is intended to eliminate fast-developing cancer cells in a patient’s body. The amount of chemotherapeutic drug that must be administered precisely into a patient’s body determines the efficacy of the treatment and governs the patient survival during chemotherapy. Therefore, controlling the chemotherapeutic drug dose delivered to the patient is essential. This research aims to propose a two-degree-of-freedom fractional order proportional–integral–derivative (2FOPID) controller with a set point filter for implementing an automatic drug delivery control scheme during chemotherapy. The Whale optimization algorithm (WOA) is used to tune the parameters of the 2FOPID controller, resulting in a WOA-tuned 2FOPID controller (W2FOPID). The performance of the proposed W2FOPID is compared with the Integral–Proportional–Derivative (IPD), Internal Model Control (IMC), and Fractional Order IMC (FOIMC) schemes. The experimental results demonstrate that the proposed W2FOPID controller is effective, accurate, and robust for drug concentration control during chemotherapy. W2FOPID outperforms IPD, IMC, and FOIMC schemes in terms of Integral Absolute Error by 79.9%, 25.3%, and 23.36%, respectively. In addition, W2FOPID exhibits excellent set-point tracking, noise suppression and uncertainty handling capabilities.

The Kraft recovery process in the paper mills is highly complex and nonlinear. The evaporation of black liquor using Multiple Effect Evaporators (MEE) in the recovery unit of the Kraft process is an energy-consuming procedure. The challenge lies in designing a proficient control strategy to conserve energy and guarantee good product quality. In this article, the 2-DOF-PI controller is designed to enable efficient control of MEE and eliminate process uncertainties. The Heptads’ Effect Falling Film Evaporator in the backward feed flow configuration is used as a working platform. The steady-state and transient behavior of MEE is modeled and analyzed to assist in controller design. The steady-state process parameters, that guarantee optimum energy efficiency, are estimated using Moth–Flame Optimization (MFO). MFO is also employed to estimate the optimal 2-DOF-PI controller parameters to attain improved product quality and energy-efficient performance of MEE. The competence of MFO towards controller tuning is shown by a fair comparison with some well-known optimization techniques. The quantitative investigation of the statistical results demonstrates that MFO outperforms the other algorithms. To validate the proficiency of the designed control scheme, its performance is compared with basic PI/PID control strategy for set-point tracking, noise suppression, and process uncertainties. The simulation results indicate that the designed MFO-tuned 2-DOF-PI controller offers efficient control action and, therefore, proves to be an appropriate control algorithm to ensure sustainable production.

A novel approach has been developed for the detection of 56kDa tissue-specific antigen(TSA) gene of Orientia tsutsugamushia causative agent of scrub typhus disease. The approach was developed by immobilization of 5′ NH2 labeled ssDNA probe selective to 56kDaTSAgene, to the surface of AuNPs/CNF modified screen-printed electrode. An electrochemical response was recorded with single stranded genomic DNA (ssDNA) of O. tsutsugamushiisolated from patient sample, using cyclic voltammetry and electrochemical impedance spectroscopy. The electrode surface was characterized by Field-Emission Scanning electron microscope (FE-SEM), Fourier Transform Infrared Spectroscopy (FTIR) and Raman Spectroscopy at each step of fabrication. The DNA biosensor shows optimum response within 50–60 s at room temperature (25 ± 3 °C). The sensor shows higher sensitivity [7849 (µA/cm2)/ng DNA], fast response time (60 s), wider linear range (0.04–2.6 ng) with limit of detection of 0.02 ng/µl of ssDNA sample.

Pathogenic strains of Escherichia coli(EC) have emerged as a threat to public health due to its ability to contaminate food and water. Early detection of EC contamination in food products is of fundamental importance to avoid any severe medical situations associated with its infection. We herein report a rapid and facile; aptamer and NanoZyme-based assay for EC detection in fruit juice. The developed assay is highly rapid, sensitive, and affordable. The test can be completed within 5 min without any requirement of a sophisticated instrument and cost roughly $ 2. Moreover, the detection is visible to naked eyes. We have also adapted the aptamer on to an electrochemical sensing platform to evaluate its diagnostic utility. The aptamer performed impressively with both NanoZyme-based colorimetric and electrochemical assay and displayed a low-end detection limit of ~10 CFU. The approach presented in the current study is a generic strategy and in principle, may be used for the detection of various analytes, including other bacterial pathogens.

A large number of clear nights at a given location is one of the key prerequisites for establishing an astronomical observatory. This parameter needs to be reliably determined before the site location is finalized. For an already exiting observatory site, monitoring of clouds on real-time basis helps automating many observing procedures, thus optimizing the scientific returns from the facility. We have developed an infrared (IR) scanning cloud monitor which generates a local sky-map and provides reliable information about the changing sky conditions. Our device measures the IR sky brightness temperature using a circular array of thermopile sensors mounted on a rotating system. In this paper, we give a detailed description of the instrument hardware, laboratory and on-site calibration procedures and further tests carried out to check the reliability of the device. The cloud monitor has been in regular use since its installation at the Indian Astronomical Observation (IAO), Hanle, Ladakh in 2015 December. We also present the analysis of a year long sky data to demonstrate the usefulness of the device.

At room temperature, we demonstrate an unambiguous detection of the ground- and excited-state transitions of mid-infrared GaInSb/AlGaInSb type I quantum well (QW) lasers grown on GaAs using a new technique based on Fourier transform infrared surface photovoltage spectroscopy. It is found that none of the currently established spectroscopic techniques is able to detect even the ground state transition of these mid-infrared QW lasers at room temperature. The spectroscopic results are in reasonable agreement with the laser emission spectra at low temperatures.