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Medical Thermography image is used for the detection of breast cancer at an earlier stage. Thermography image shows the temperature change in the body due to cells. The metabolic rate of cancer cells is high compared to normal cells. A high metabolic rate increases the blood flow in cancer cells. High blood leads to changes in body temperature. The change in body temperature is used for cancer cell detection at an earlier stage. However, T1-stage cancer cells are smaller and have small temperature differences undetected with thermography. In this paper, a T1-stage cancer cell is heated by an external source; then, thermal images are acquired for earlier detection of small-size cancer cells. External heat source amplifies T1 stage cancer cell temperature. Amplified cancer cell images are analyzed using the proposed Multiwavelet-Deep Denoised Convolutional Neural Network (MWTDnCNN) algorithm for T1 cancer cell detection. Amplified T1 stage cancer cell has higher thermal conductivity (k) and heat capacity (Cp), which helps to detect T1 cancer cell tissue due to the enhanced pixel feature. The proposed MWTDnCNN algorithm has a T1-stage cancer cell detection accuracy of about 98% compared with traditional algorithms. The proposed MWTDnCNN algorithm detects T1-stage cancer of size 1.29 mm.

In recent scenarios, induction motors are used in many different fields due to their small size, advanced technology, and capacity to handle large amounts of power. One of the annoying downsides of conventional AC traction drive systems is the engine's speed and the uncertainty of the ripple content. The goal of the proposed work is to use various control techniques to analyze and enhance the performance of two five-phase induction motors that are supplied in parallel via an inverter. The control techniques are scalar control, six-step voltage type source inverter (VSI), space vector modulation (SVM) based VSI inverter, field-oriented control, model reference adaptive system-based field-oriented control (FOC), direct torque control (DTC), direct torque control -SVM, model reference adaptive system based direct torque control-SVM for traction application. The modified space vector modulation-based induction motor (IM) sensorless drive is realized together with a comparison of several control strategies. It makes use of direct torque control approaches with a model reference adaptive system (MRAS). An IM is designed, and performances are analyzed in high-speed and low-speed regions. Two bogies sets were considered in the closed-loop scheme, tested under parameter variations and in low-speed areas. The simulation result examined the most significant control methods for an IM fed by an inverter for electric traction applications. The performance of the torque in terms of reduced torque ripple in the IM drive is analyzed. The proposed direct torque control technique will reduce torque harmonic ripple by 100 % under the steady-state condition. Under a change in a parameter, the torque harmonic ripple is decreased by 99 %. For the IM drive, the hardware implementation of the advanced control approaches is examined.

Statistical features and wavelet based fault detection are attempted to find computationally less complex, low-memory, and power for real-time implementation. The mean absolute value (MAV), simple sign integral (SSI), waveform length (WL), slope sign change, and zero crossing are extracted from the vibration signal, phase current signal-1, and phase current signal-2. The extracted features are combined varyingly to obtain 31 combinations and classified using a decision tree, k-nearest neighbor {k-NN}, and support vector machine. The identified features {MAV, SSI, WL} performed better with vibration and combined current signals, with an average accuracy of 99.8% and 99.5% with the k-NN classifier, respectively. Wavelet has shown an accuracy of 98%, and the Alexnet method obtained an average accuracy of 97.5% using a combined current signal, which is less than the time domain features-based machine learning approach. In addition, simple time-domain features require memory of 9.6 MB times less than wavelets and 4.18MB times less than Alexnet. The time domain-based technique requires a computation time of 30.21 minutes less than Alexnet and 53.54 minutes less than wavelets. Experimentally, the effectiveness of identified minimal features is verified using an induction motor current signal and achieved 100% accuracy with {MAV, SSI, WL}.

In machine learning, the extraction of features is necessary for intelligent motor fault diagnosis. In industrial applications, it is necessary to identify the optimal number of features to differentiate various types of fault characteristics with less computational complexity and cost. However, motor fault diagnosis for real-time applications has challenges in capturing characteristics due to variations in speed, load, force, run-to-failure state as well as the type of the motor and its parts. The deep learning techniques that automatically extract features and perform classification have algorithmic complexity. In this work, the authors address these challenges by: 1) selecting and ensembling optimal time-domain features that are capable of identifying motor faults using current signals of the permanent magnet synchronous motor (PMSM) in bearing; and 2) investigating the feature ensemble constituting optimal features for robust fault diagnosis in the PMSM bearing as well as the stator and bearing of squirrel cage induction motor (SCIM) for various conditions. The optimal features mean absolute value, simple sign integral, and waveform length yields 99.8% and 100% for bearing fault and stator fault diagnosis, respectively, in PMSM. These features show 100% accuracy for identification of fault in SCIM and 98.2% accuracy in the run-to-failure state.

Abstract Significance of this research is based on torque controlling strategies in hybrid electric vehicle. The traction motor of a hybrid electric commercial vehicle must have a high power density-to-weight ratio and excellent torque at extremely low speeds. A proportional-integral controller estimates the high-resolution rotor position based on the difference between actual as well as reference output power. This technique can correct for hall position sensor installation error and also accurately estimate rotor position. In this study, a current proportional integral controller is also used to determine the permanent magnetic flux linkage. Experiments are used to test other key metrics such as d-axis and q-axis inductances, stator resistance, and energy loss. The measured parameters are kept in lookup tables that span the whole operating range at various current levels. A maximum torque per ampere control methodology, paired with the feed forward parameter iteration method, may be used to produce accurate and efficient torque control based on these precise parameters. The standard driving cycle performance requirements of the modelled Federal Test Procedure vehicle is analysed in the article for understanding the real model requirements of the vehicle. The efficacy of the proposed methodology using Matlab/Simulink environment is analysed. Both modelling and experimental findings validate the efficacy, torque ripple, stator and rotor flux, sensitivity of the vehicle parameter as well as the computational complexity of the proposed methodology.

Abstract Sugarcane crop is irrigated using surface, overhead, and drip irrigation methods. Increased water use in sugarcane is a major concern around the world, implying the need for water accounting, developing water-efficient hybrids and water-saving agro-techniques for long-term conservation and use of water. “Water Footprint (WF)” is a measure of both direct and indirect water usage accountable for any product and/or process. In praxis, ‘Green Water Footprint’ (GWF) and ‘Blue Water Footprint’ (BWF) are extremely crucial for the restoration of essential ecosystem services (ES), such as sugarcane production. The WF metric was used as a priority tool in our study to evaluate water-efficient sugarcane hybrids, germplasm clones, deficit irrigation scheduling, crop geometry, and water conservation measures. Precise and accurate WF quantification would supplement the decision-making processes for managing available water resources in sugarcane agriculture. In split plot experimental design two research investigations on water management in sugarcane were undertaken at the ICAR-Sugarcane Breeding Institute, Coimbatore, Tamil Nadu, India. The major objective of the research trails was to find out suitable sugarcane hybrids and agronomic management practices to minimise water usage in sugarcane cultivation in water stressed and drought prone areas of tropical India. Our investigation comprised two phases; the first one being assessment of the impact of deficit irrigation scheduling, planting techniques and water conservation measures in sugarcane production, while the second phase dealt with genotypic evaluation under variable irrigation scheduling. Results showed that BWF reduced significantly in the first ratoon crop due to deficit irrigation scheduling coupled with planting of two budded setts and application of sugarcane trash at the rate of 5 t ha−1. Sugarcane hybrids viz., Co 85019, Co 10026, Co 12009, Co 13014, Co 14002, Co 14025, Co 15015, and Co 15018 were more water efficient, with a lower total WF. Among the germplasm clones, Fiji 55, ISH 111, ISH 107, Pathri, and Gungera exhibited lower GWF, BWF and total WF.

The scaffold-based drug design has been widely used in new drug discovery. Biphenyl and its analog scaffold are found in many drugs and naturally occurring compounds. To date an array of synthetic methodologies have been introduced to access biphenyl analogs with good yield. Biphenyl scaffolds have shown wide range of biological activities such as anti-inflammatory, antihypertensive, treatment of CNS disorders, anti-cancer, anti-HIV, etc. Also, the importance of biphenyl atropisomers is discussed. This review aims to highlight the evidence of biphenyl motif as a privileged scaffold in various natural products and anticancer agents.

Piperazine (bis) p-toluenesulfonate (PPTSA), an organic single crystal was synthesized and grown at environmental temperature by slow evaporation process using methanol as the solvent. The grown PPTSA crystal is from the triclinic system and belongs to the space group P1-. Powder X-ray diffraction was performed to ensure lattice parameters. Analysis and confirmation of functional groups and bonds were carried out through FT-IR spectral study. The optical characteristics were investigated using the UV-Vis spectrum such as the optical absorption, cut-off wavelength were calculated. The photoluminescence investigation was conducted to assess the luminous characteristics of grown crystal. The calculated NLO parameters like β, n2, and χ(3) were found to be 0.0495×10-4 (cm/W), 8.705×10-10 (cm2/W), 5.316×10-7 (esu) and Optical Limiting threshold value was found to be 3.074×10-3 (Wcm-2). Antibacterial studies were carried out to investigate the biological significance against selected foodborne germs.

Abstract Background According to the information gathered from the literature, no technique for UPLC of triamterene and hydrochlorothiazide employing QbD in the formulations has been published. The technique development by incorporating QbD and validating for accuracy, linearity, precision, LOQ, LOD, ruggedness and selectivity as per ICH is part of the work’s modernity. Results Screening investigations led to the selection of cmps. Peak tailing was evaluated as a metric of technique robustness based on these important analytical attributes, namely retention time. With a 0.1 percent OPN: methanol (40:60) mobile phase, a flow rate of 0.3 ml/min, a wave length of 224 nm, an injection volume of 41, and a run time of 6 min, the best chromatographic separation was attained. Conclusions The method was verified using ICH criteria, which ensure a high level of linearity, accuracy, precision, specificity and robustness. As a result, the suggested approach is regarded as a quick and accurate method for estimating triamterene and hydrochlorothiazide at the same time.

Introduction. In recent years, a growing variety of technical applications have necessitated the employment of more powerful equipment. Power electronics and megawatt power levels are required in far too many medium voltage motor drives and utility applications. It is challenging to incorporate a medium voltage grid with only one power semiconductor that has been extensively modified. As a result, in high power and medium voltage settings, multiple power converter structure has been offered as a solution. A multilevel converter has high power ratings while also allowing for the utilization of renewable energy sources. Renewable energy sources such as photovoltaic, wind, and fuel cells may be readily connected to a multilevel inverter topology for enhanced outcomes. The novelty of the proposed work consists of a novel modular inverter structure for solar applications that uses fewer switches. Purpose. The proposed architecture is to decrease the number of switches and Total Harmonic Distortions. There is no need for passive filters, and the proposed design enhances power quality by creating distortion-free sinusoidal output voltage as the level count grows while also lowering power losses. Methods. The proposed topology is implemented with MATLAB / Simulink, using gating pulses and various pulse width modulation methodologies. Moreover, the proposed model also has been validated and compared to the hardware system. Results. Total harmonic distortion, number of power switches, output voltage and number of DC sources are compared with conventional topologies. Practical value. The proposed topology has been very supportive for implementing photovoltaic based multilevel inverter, which is connected to large demand in grid.

Facial age estimation has grasped the attention of numerous researchers in recent times. It is a challenging task as a consequence of illumination, pose variations, occlusion, complex background, facial expression, and facial makeup. Estimating the age of an individual with an arbitrary pose is quite a challenging job because most of the age estimation system focuses on the frontal view. In this paper, a novel framework for multi-view age estimation by amalgamating the local and global features is proposed. A novel texture feature, Median Gradient Ternary Pattern is proposed in this paper. The Pseudo Zernike Moment extracts the shape features and the View-based Active Appearance Model constructs an appearance model from the facial images. Further, all three features are combined into a feature vector by executing feature-level fusion. The dimension of the combined feature is reduced using Principal Component Analysis. Multi-class Support Vector Machine is utilized to divide the images into four poses. For each pose, a Support Vector Regression with RBF kernel is applied to train a model for estimating the actual age of an individual. The proposed methodology is performed on two databases, namely, FG-NET and CACD which showcase eminent performance.

This article reports on a desktop evidence review of Australian public universities work-integrated learning policies and procedures. The review examined the availability and accessibility of these documents to prospective students with disability, as well as their inclusivity and quality, against three dimensions developed from analysis of equity best practice literature. The findings reveal an inconsistent approach by Australian universities to inclusive work-integrated learning for students with disability. The variability in availability and accessibility of inclusive work-integrated learning materials implies course and university decision-making for future university students with disability may be difficult. Quality concerns included a limited reference to relevant standards, transparent terminology and research evidence in available materials, as well as expired policies and procedures, outdated language and disability theory, and a lack of evidence of industry collaboration. Recommendations from these findings support the development of equitable WIL practices with students with disability across Australian universities.

When morphological capabilities are used for the class of high decision hyperspectral photographs from metropolitan areas, one must not forget two crucial problems. Among which the primary one is that traditional morphological openings and closings degrade the object obstacles and distorts the items shape. Morphological profiles (MP) opening and closing via reconstruction can keep us away from this problem, however this system ends in a few unwanted consequences. In this paper, first check out morphological summaries with subjective restoration and steering MPs for the classification of excessive decision hyperspectral snap shots from city areas. Secondly, broaden a supervised face extraction to lessen the dimensionality of the engendered morphological profiles for the prediction.

Organic non-linear optical Benzimidazolium salicylate (BISA) single crystals have been harvested from methanol solution by slow evaporation method. The grown crystals belong to the monoclinic crystal system with space group P21/c. Good crystalline nature of grown BISA crystal was confirmed by PXRD. The FTIR spectrum analysis affirms the presence of functional groups in BISA crystal. From the UV‒Vis-Absorption spectrum, the lower cut-off wavelength (356 nm) and its energy band gap and linear refractive index were calculated. Luminescence spectrum was recorded to explore the emission peak at 426 nm. The mechanical strength of BISA crystal was determined by Vickers microhardness tester and mechanical parameters like C11, Hk, Kc, and Bi were calculated for the first time. Dielectric properties of grown crystals were systematically investigated for different temperatures. Further, electronic polarizability (α) were calculated using Penn analysis and Clausius‒Mossotti equation. Z‒scan measurement was taken to explore the third-order NLO properties of BISA crystal. For the first attempt, the BISA crystals were tested against five human pathogenic bacterial, i.e. Bacillus cereus, Staphylococcus aureus, Shigella flexneri, Vibrio cholerae, and Klebsiella pneumoniae. Existing results established that Benzimidazolium salicylate crystals might find useful applications in nonlinear optics and antimicrobial field.

Scholars have been working through multiple avenues to address longstanding and entrenched patterns of global and racial exclusion in psychology and academia more generally. As part of the Society for Personality and Social Psychology’s efforts to enhance inclusive excellence in its journals, the Anti Colorism/Eurocentrism in Methods and Practices (ACEMAP) task force worked to develop recommendations and resources to counteract racism and global exclusion in standard publication practices. In this paper, the task force describes a structure and process we developed for conducting committee work that centers marginalized perspectives while mitigating cultural taxation. We then describe our recommendations and openly accessible resources (e.g., resources for inclusive reviewing practices, writing about constraints on generalizability, drafting a globally inclusive demographic information survey, inclusive citation practices, and improving representation among editorial gatekeeping positions; recommendations and resource links are provided in Table 3). These recommendations and resources are both (a) tailored for a particular set of journals at a particular time and (b) useful as a foundation that can be continually adapted and improved for other journals and going forward. This paper provides concrete plans for readers looking to enhance inclusive excellence in their committee work, authorship, reviewing, and/or editing.