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Blood Glucose level controls of type 1 diabetic patients (T1D) in post operative conditions are extremely important. This work proposes controllers for management and control of insulin injections to T1D in post operative care. Proposed work investigated IMO-PSO FO-PID controller as a novel approach to improve insulin delivery. The controller utilizes optimization algorithms to regulate insulin levels in real time. Particle swarm optimization (PSO) based tuning is already a proven method in insulin regulation and multiobjective PSO is well suited when combined with FO-PID which is tested. Additionally, system aims to achieve lower insulin levels, as high insulin levels have been linked to increase risks of complications for T1D patients. Results showed that IMO-PSO FO-PID controller is effective in improving glucose control, with patients experiencing fewer fluctuations and instances of hypoglycemia or hyperglycemia. Simulation results show that the use of the controller is very useful in managing blood glucose levels over a longer period and corresponding insulin infusion levels are also controlled well, which is much less than the previously reported control methodologies. This paper highlights potential benefits of using this controller in managing T1D and improving glucose control.

In the present research work an optimisation stuy of the Exhaust gas recirculation (EGR) rate for MIS adopted POME fueled CRDI Diesel Engine fitted with TRCC was carried out. The engine was operated with injection parameters such as 900 bar, 7 holes and 10° BTDC which have been optimized for better performance and lower emissions from our previous study. The experiments were carried out by employing an RSM-based D-optimal design, and the relationship between input and output was determined using an ANOVA. Using RSM-ANOVA, mathematical models were built for each result, and the predicted and actual outcomes were compared. With an R2 value greater than 99.34%, the prediction models were discovered to have a strong prediction efficiency. The desirability approach-based optimisation was used to determine the ideal engine operating parameters. EGR rate was varied from 0% to 20% and an MIS of 40 + 20+40 has been adopted for the engine. An EGR rate of 10% is optimized from the viewpoint of NOx reduction and penalty in power output which results in a decrease in brake thermal efficiency by 2.90%, peak pressure by 4.8%, heat release rate by 8.8% and oxides of nitrogen (NOx) by 1.35%. A drastic increase in emissions such as carbon monoxide by 5.8%, unburnt hydrocarbon by 13.3% and smoke by 20.6% was also observed. Both the ANN and RSM models correctly fit the experimental data, producing R2 values that ranged from 95.5% to 98.5%, respectively. The findings show that RSM and ANN are both highly accurate modelling approaches. Additionally, as compared to RSM, the ANN model's predictive accuracy was marginally better. This research could have a wide range of applications in CI engine-powered transportation systems.

This study proposes a novel W-cut twisted tape to evaluate and enhance the effectiveness of novel grooved tube heat exchanger. The experimental investigations are carried out with W-cut inserts, which possess two main control factors, namely, twist tape ratio (y = 3.5 to 6.5) and width tape ratio (WR = 0.43 to 0.6). The investigation results prove that in all cases the energy transmission and friction factor of W-cut insert along with the first law and second law efficiency are considerably higher than the conventional tube arrangement. The empirical model equations for friction factor, performance enhancement ratio, Nusselt number, and rational efficiency are developed, and they show good affinity with experimental data. Since the abovementioned factors have conflicting terms, it is essential to select the proper operating configuration of the heat exchanger in order to increase the thermal energy transfer rate with reduced consumption of pumping energy. The multi-objective genetic algorithm (GA) tool is used to identify the optimum operating conditions. Further, the hybrid multi-criteria decision-making model, FAHP-VIKOR, is applied to select the perfect model from the set of non-dominated solution. The ranking of alternatives is as follows: A4 > A11 > A2 > A9 > A21 > A17 > A1 > A24 > A11 > A23 > A7 > A18 > A13 > A10 > A20 > A5 > A19 > A14 > A16 > A22 > A25 > A6 > A15 > A3 > A8. The optimized configuration of W-cut insert is compared with the former inserts, and the optimal configuration exhibits supreme performance than other inserts.

Solar energy forecasting accuracy is essential for increasing the quantity of renewable energy that can be integrated into the existing electrical grid control systems. The availability of data at unprecedented levels of granularity allows for the development of data-driven algorithms to improve the estimation of solar energy generation and production. In this paper, we develop a prediction of solar potential across large photovoltaic panels from the roof tops using a machine learning method. The Restricted Boltzmann Machine (RBM) is the machine learning method used in the study to predict or forecast the solar potential in rooftops. The machine learning model is supplied with training dataset to get trained with the dataset for conversion into the model and then tested with the test dataset for validating the model. The results of simulation are conducted on R-package over various libraries to predict the rooftop solar potential. The results of simulation shows that the proposed method achieves higher rate of prediction accuracy than the other methods. The results of the simulation show that the proposed method achieves a higher rate of prediction accuracy of 99% than the other methods.

Photovoltaic (PV) solar panels account for a major portion of the smart grid capacity. On the other hand, the accumulation of solar panels dust is a significant challenge for PV-based systems. The accumulation of solar panels dust results in a significant reduction in the amount of energy produced. Because of the country’s low wind velocity and rainfall, frequent cleaning of solar panels is necessary either by manual or automated means. Cleaning activities should only be initiated when absolutely essential to reduce maintenance costs and increase the power output of solar panels that have been projected to be affected by dust accumulation. In this paper, we develop a deep belief network model to detect the dust particles in the solar panels installed as a large unit. The study takes into account various input metrics that includes solar irradiance, temperature level, and dust level on the panels. These metrics are used for the estimation of the level of dust present in the atmosphere and how often the panels can be cleaned at regular intervals. The simulation is conducted to test the efficacy of the model in cleaning the panels. The results are estimated in terms of accuracy, precision, recall, and F-measure. The results of the simulation show that the proposed model achieves higher accuracy rate of more than 99% than other methods.

Performance, cost, and aesthetics are all difficult to beat in today’s expanding distributed rooftop solar sector, and flat-plate PV is no exception. Photovoltaics will be able to take advantage of some of their most significant advantages as a result of this marketplace, including the elimination of transmission losses and the generation of power at the point of sale. Concentrated photovoltaic (CPV) technology, on the other hand, represents a viable alternative in the quest for ever-lower normalised energy costs and ever-shorter energy payback times. Material, components, and manufacturing techniques from allied sectors, particularly the power electronics industry, have been adapted to lower system costs and time-to-market for the system under development. The LFR is less than 30 mm wide to maximise thermal efficiency, and a densely packed cell array has been used to maximise electrical output. The Matlab simulations show that the proposed machine learning-based LFR technique has a greater concentration rate than the present LFR method, as demonstrated by the results.

Because of the rise in solar electricity, photovoltaic research has increased. A precise model of the PV-based cell/unit is ideal for conceptually examining the capabilities of alternative control approaches. Extraction of precise values for relevant unknown elements of solar photovoltaic models is crucial for modeling PV systems. Optimization strategies for this topic have been increasingly important and dynamic in recent history due to their efficiency in dealing with extremely nonlinear multidimensional optimization challenges. The strategy avoids stalling by deleting the best solution; this keeps the engine diverse and increases global search capacity. Pattern searching, on either side, is a multiobjective approach that has a high convergence speed as well as good stability, which can increase the whale optimization algorithm’s traditional optimization ability. As a result, combining these two mechanisms can considerably improve the whale optimization algorithm capacity to find the optimal answer. The modified whale optimization algorithm may also be used to evaluate parameters in single-diode models, double-diode models, and photovoltaic panels, as well as to find independent variables in two distinct methods of PV modules under various light temperature and pressure circumstances. The analytical results show that the modified whale optimization algorithm is both valid and practical for predicting solar cell and photovoltaic unit characteristics. The study goes into great depth on a new optimization approach for changing the parameter unit of solar PV cells.

The mechanical characteristics of a high impact polyethylene composite (HIPC) reinforced with abaca fibre (AF) are investigated in relation to fibre loading. An alkaline behaviour was used to improve the characteristics of the abaca fibre. With a fibre length of 100 mm, five different fibre loadings of the abaca fibre were used to create the samples of the composite (25, 35, 45, 55, and 65 wt percent). The object was made using compression moulding with unidirectional fibre orientation. The influence of fibre loading was investigated using tensile, hardness, and density tests. In an experiment, it was shown that with 55 percent fibre loading, tensile strength was 312 percent higher than without, and Young’s modulus was 545 percent higher than without. While this was happening, the hardness and density of the AF/PE composites were found to be quite similar, with minor increases from 25 wt percent to 65 wt percent AF loading in comparison to the control sample’s zero wt percent AF loading. 67.42 Shore-D and 1.014 g/cm3 are the highest values. The alkaline treatment of the AF/PE composite had a substantial influence on mechanical characteristics, according to the findings.

Plastic is a resilient, chemically inert, lightweight, and low-cost material. It sticks around in the environment for more than hundred years, threatening nature and spreading toxins. The current study deals with the use of waste polymeric materials and de oiled cake for the production of liquid oil and its blend on the performance and emission characteristics of diesel engine. The tests were conducted in an engine fuelled with diesel and four distinct blends such as 5% (B5), 10% (B10), 15% (B15), and 20% (B20), respectively. The liquid oil was produced by co-pyrolysis of neem de oiled cake (NDC) and waste polystyrene (WPS) in 1 : 2 blend ratio. The raw pyrolysis oil and its different blends were tested for their physical and chemical characteristics in order to find their suitability. Brake power (BP), brake thermal efficiency (BTE), brake-specific fuel consumption (BSFC), emissions of carbon monoxide (CO), hydrocarbon (HC), and nitrogen oxide (NOx) are used to assess the performance of the engine. The experimental results reveal that BTE at all blends is lower than diesel at all loads and the BSFC increases with increasing blend ratio and falls with increasing engine load. At higher loads, the deviation of performance and emission values from baseline diesel up to B10 is very small. It is found from the results that the liquid oil derived from NDC and WPS up to 10% blend will be the promising additive for fossil fuels.

With the discovery that the level of RNA synthesis in human cells far exceeds what is required to express protein-coding genes, there has been a concerted scientific effort to identify, catalogue and uncover the biological functions of the non-coding transcriptome. Long, non-coding RNAs (lncRNAs) are a diverse group of RNAs with equally wide-ranging biological roles in the cell. An increasing number of studies have reported alterations in the expression of lncRNAs in various cancers, although unravelling how they contribute specifically to the disease is a bigger challenge. Originally described as a brain-specific, non-coding RNA, BC200 (BCYRN1) is a 200-nucleotide, predominantly cytoplasmic lncRNA that has been linked to neurodegenerative disease and several types of cancer. Here we summarise what is known about BC200, primarily from studies in neuronal systems, before turning to a review of recent work that aims to understand how this lncRNA contributes to cancer initiation, progression and metastasis, along with its possible clinical utility as a biomarker or therapeutic target. Keywords: Long non-coding RNA, lncRNA, Cancer, BC200, BCYRN1, Translational regulation, RNA-protein interactions

In this paper, we present in-situ observations of processes occurring at the magnetopause and vicinity, including surface waves, oscillatory magnetospheric field lines, and flux transfer events, and coordinated observations at geosynchronous orbit by the GOES spacecraft, and on the ground by CANOPUS and 210° Magnetic Meridian (210MM) magnetometer arrays. On 7 February 2002, during a high-speed solar wind stream, the Polar spacecraft was skimming the magnetopause in a post-noon meridian plane for ~3h. During this interval, it made two short excursions and a few partial crossings into the magnetosheath and observed quasi-periodic cold ion bursts in the region adjacent to the magnetopause current layer. The multiple magnetopause crossings, as well as the velocity of the cold ion bursts, indicate that the magnetopause was oscillating with an ~6-min period. Simultaneous observations of Pc5 waves at geosynchronous orbit by the GOES spacecraft and on the ground by the CANOPUS magnetometer array reveal that these magnetospheric pulsations were forced oscillations of magnetic field lines directly driven by the magnetopause oscillations. The magnetospheric pulsations occurred only in a limited longitudinal region in the post-noon dayside sector, and were not a global phenomenon, as one would expect for global field line resonance. Thus, the magnetopause oscillations at the source were also limited to a localized region spanning ~4h in local time. These observations suggest that it is unlikely that the Kelvin-Helmholz instability and/or fluctuations in the solar wind dynamic pressure were the direct driving mechanisms for the observed boundary oscillations. Instead, the likely mechanism for the localized boundary oscillations was pulsed reconnection at the magnetopause occurring along the X-line extending over the same 4-h region. The Pc5 band pressure fluctuations commonly seen in high-speed solar wind streams may modulate the reconnection rate as an indirect cause of the observed Pc5 pulsations. During the same interval, two flux transfer events were also observed in the magnetosphere near the oscillating magnetopause. Their ground signatures were identified in the CANOPUS data. The time delays of the FTE signatures from the Polar spacecraft to the ground stations enable us to estimate that the longitudinal extent of the reconnection X-line at the magnetopause was ~43° or ~5.2 RE. The coordinated in-situ and ground-based observations suggest that FTEs are produced by transient reconnection taking place along a single extended X-line at the magnetopause, as suggested in the models by Scholer (1988) and Southwood et al. (1988). The observations from this study suggest that the reconnection occurred in two different forms simultaneously in the same general region at the dayside magnetopause: 1) continuous reconnection with a pulsed reconnection rate, and 2) transient reconnection as flux transfer events. Key words. Magnetospheric physics (Magnetopause, cusp and boundary layers; Magnetosphere-ionosphere interactions; MHD waves and instabilities)