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DTC, also known as Direct Torque Control, is a technique employed in variable frequency drives for regulating the torque and speed of three-phase AC electric motors. This method entails determining an approximation of the motor’s magnetic flux and torque by analyzing the voltage and current readings of the motor. In order to reduce flux ripples and ensure smooth speed operation, we have suggested the use of two fuzzy logic controllers based on DTC for induction motors. The proposed method offers a faster response compared to conventional DTC for induction motors. The MATLAB simulink software was used to illustrate the comprehensive results.

Numerous techniques for maximum power point tracking (MPPT) are extensively employed to extract the utmost energy from Photovoltaic (PV) panels. The P&O method is a highly effective technique among those who perturb and observe. Nevertheless, in the presence of partial shading conditions, it becomes imperative to employ an optimization algorithm that can accurately pinpoint the global maximum location (GML) amidst the numerous local maximum locations. The Whale-inspired algorithm is a renowned optimization method for efficiently identifying GML in comparison to numerous other optimization algorithms. An inverter is required to interfacing between PV and grid in single stage grid connected system as it will also work as a MPPT device by controlling voltage corresponding to GML. In this situation, the utilization of a whale-inspired optimization technique can enhance energy harvesting through an appropriate inverter controller. The combination of TS-Fuzzy and H∞ controllers through hybridization can lead to rapid response times and increased system stability. Hence, this paper introduces a new controller designed for the inverter to function effectively in a single-stage grid-connected PV system. The proposed controller can also provide the grid with high-quality power. This work showcases comprehensive findings derived from Hardware-in-the-Loop (HIL) testing conducted using OPAL-RT technologies.

Power generation from Photovoltaic (PV) modules is the best way to utilize the renewable energy in many places. The output of the PV modules will depends on many factors including changes in solar irradiance, temperature, partial shading conditions, operating voltage across terminals of module etc. Apart from operating voltage of PV modules, remaining all factors are depending on nature which we cannot change easily. Hence obtaining an effective voltage regulation of terminals to achieve maximum power from PV module is very important for best utilization. There are many existing maximum power point tracking (MPPT) mechanisms on PV systems but an effective integrating mechanism is required to achieve the best operation under all possible conditions. Artificial neural network (ANN) can able to produce best reference signal corresponding to maximum power location under various random changes very quickly. In other hand, sliding mode controller (SMC) is able to force the boost converter to deliver the best output voltage with fewer ripples under variable input voltage. Fastest response with fewer ripples in dc voltage can help to harvest maximum energy from PV panels. Therefore, integration of ANN with SMC can prepare the MPPT converter to deliver more energy under random changes in irradiance and temperature. The performance of ANN associated SMC for boost converter based MPPT of the PV system is presented in this paper. In order to obtain more realistic response, Hardware-in the-Loop (HIL) is established for proposed system with the help of two OPAL-RT modules. Extensive results by using HIL are presented and discussed under various case studies to enhance the proposed method.

Herein, silver phosphate nanoparticles decorated with melamine–formaldehyde polymer nanocomposite (Ag3PO4-MFP) were fabricated. The synthesized nanocomposite was assessed for the catalytic reduction of mono, di, and tri-nitrophenols, such as p-nitrophenol (PNP), di-nitrophenol (DNP), and tri-nitrophenol (TNP), to their corresponding aminophenols (APs) using NaBH4 in an aqueous medium, particularly at ambient conditions. The % reduction of PNP, DNP, and TNP was ∼99%, ∼96%, and ∼63%, respectively. [ABSTRACT FROM AUTHOR]

Amid the escalating prevalence of eye diseases and the intricate nature of the eye as a crucial target organ for drug delivery, researchers face significant challenges in developing delivery systems tailored specifically for ocular complications. Addressing the gaps in the current conventional ocular drug delivery system (ODDS) is crucial and this can be achieved by incorporating polymers while designing newer ODDS. This review aims to offer a concise overview of the diverse polymers utilized in the development of ODDS, designed to address various eye conditions and disorders, enhance treatment outcomes, and ensure patient adherence. Introducing the anatomy of the eye and different ocular routes of administration, alongside the barriers encountered, this review presents polymer‐based ODDS, renowned for their unique properties facilitating the engineering of specialized devices for enhanced drug delivery. Further discussions delve into the applications of polymers in ophthalmology. Emphasis is placed on emerging polymer‐based technologies available in the market for treating ocular diseases, underscoring their potential for revolutionizing ocular healthcare. The review also addresses challenges in translating these advancements into clinical practice, while highlighting the versatility of polymers in treating diverse eye diseases and disorders through customizable properties and sustained drug delivery. [ABSTRACT FROM AUTHOR]

Background: Sustainable development goals (SDGs) 3.9.1 and 11.6.2 call for a reduction in deaths and illnesses from air pollution, improving the air quality of cities. The above goals motivate us to organize workshops to improve the health of traffic police, who bear the brunt of air pollution. The paper examines the effect of workshops on the health-seeking behavior of the traffic police in Bhubaneswar city. Methods: The study conducted two workshops as a quasi-experimental, single-group study at an academic institution in Bhubaneswar. It included 20 traffic police officers (11 male and 9 female). The Kirkpatrick 4-level model was used to assess the effectiveness of the workshops. A paired t-test was used to compare pre- and postworkshop scores. Results: Thirteen traffic police officers rated the workshop sessions as excellent. The score before the workshop ranged from zero to three, with a mean (standard deviation [SD]) of 2.81 (1.0). The postworkshop score had a minimum to maximum score of three to five with a mean (SD) of 4.41 (0.7) (P < 0.005). The effect size dcohen (confidence interval [CI]) was 1.87 (3.27-4.71). The mean (SD) of absolute and relative gain was 1.6 (1.0) and 0.93 (1.02), respectively. All 20 traffic police officers showed improvement in health-seeking behavior. The significant lifestyle changes after the workshops ranged between 5% and 75%. Conclusion: The test scores indicated statistically significant improvement as the P value recorded was smaller than 0.05. This concludes that the improvement in understanding of the session was statistically significant because of the training imparted. [ABSTRACT FROM AUTHOR]

The work outlines the synthesis of mesoporous tin oxide (m-SnO2) with enhanced Nitrogen Dioxide (NO2) gas sensing performance by making use of Cetyl Trimethyl Ammonium Bromide (CTAB) as the soft template. Powder X-ray Diffraction (PXRD), Field Emission Scanning Electron Microscopy (FESEM), Transmission Electron Microscopy (TEM), Fourier Transform Infrared (FTIR) spectroscopy and X-ray Photoelectron Spectroscopy have been used to study the structural characteristics of mesoporous tin oxide. The average crystallite size calculated from the XRD technique was found to be 4.8 nm which is in corroboration with TEM analysis (4.9 nm). The FESEM indicated the porous morphology of the prepared sample which was also confirmed by N2 adsorption–desorption isotherms. The surface area and the average pore size of the sample were calculated from Brunauer–Emmett–Teller (BET) studies and Barrett–Joyner–Halenda (BJH) method and were found to be 154.4 m2 g−1 and 3.2 nm, respectively confirming the mesoporosity of the prepared tin oxide. The sensor was prepared by deposition of m-SnO2 thin film on Inter Digited Electrodes (IDE's) of platinum decorated on a glass substrate. 10 parts per million concentration of oxidizing gas NO2 was tested on the fabricated sensor in the temperature range, 30 °C to 160 °C. Upon interacting with the gas, the sensor exhibited a response of 8635 with response time of 1s and recovery time of 165 s at 80 °C. The efficient sensor performance at 80 °C as observed in the present study is at much lower temperature in comparison with those reported earlier. The sensor has further shown good repeatability in terms of response and recovery at 80 °C. The sensor also exhibited good reproducibility and selectivity for NO2 gas. The enhanced sensor response of mesoporous tin oxide sensor is mainly because of large surface area (154.4 m2 g−1), mesoporous structure and small crystallite size (4.8 nm). The present study therefore establishes m-SnO2 as a potential candidate for efficient sensing of NO2 gas. [ABSTRACT FROM AUTHOR]

The difficulty of fault identification as well as categorization in industrial rotating machinery is fixed by this study, which introduces a revolutionary Dandelion Optimized CatBoost (DO-CB) technique. The suggested framework makes use of the CB algorithm, which is enhanced by the DO method. The first step in the suggested DO-CB approach is gathering sensor data from rotating gear to record different operational settings. To ensure robustness, the recommended approach is developed on identified data and includes a variety of fault scenarios. Additionally, the Python tool used for identifying faults and classification is the basis for the implementation of the DO-CB approach. The experimental findings show how well the suggested method works to precisely identify and classify problems in industrial rotating gear. In comparison to benchmark defect detection techniques, the suggested DO-CB approach performs better, demonstrating its capacity to manage intricate patterns and fluctuations in the data. [ABSTRACT FROM AUTHOR]