A DC motor is a common actuator in process control systems that converts electrical energy into mechanical energy. This paper examined the performance of a deep learning-based neural network predictive controller (NNPC) for the analysis of speed control of a DC motor. The NNPC was designed and executed on MATLAB R2021b with license number 1075356 for the analysis. The proposed controller is based on a neural network that predicts the future behaviour of the motor system based on the current state and control inputs. The controller then uses this prediction to generate optimal control inputs that minimize the tracking error and improve the system’s performance. The results show that the proposed NNPC performs well in terms of accuracy, precision, and response time. The paper concludes that the proposed controller can be a viable option for the speed control of DC motor systems in various applications.

Twenty varieties/genotype viz. Vijay, BG-256, JG-16, CSG-8961, BG-372, K-3256, KWR-108, KPG-59, Radhey, Avarodhi KGD-1288, KGD-1295, KGD-1296, KGD-1302, KGD-1315, KGD-1316, KGD-2012, Pusa-209, Pusa-391 and RBG-1 were taken from oil seed research farm Chandra Shekhar Azad University of Agriculture and Technology, The quality parameter like Protein content, Total carbohydrate content, Fat content, Ash content, Calorific value and Moisture content were studies under after harvest of crop. Grain sample from different varieties/genotypes were brought to laboratory. The range of variability in chickpea varieties/genotypes was from Protein content (22.44-24.15%), Total carbohydrate content (67.91-69.92%), Fat content (4.41-5.16%), Ash content (2.58-3.07%), Calorific value (409.87-414.37 Kcal/g) and Moisture content (5.76-7.61%) Among the Chickpea variety Avarodhi appeared to be the best having excelled in two Nutritional characters such as protein content and calorific value out of the six quality parameter was studied.

Navigation has always played an important role in our lives, and the moment we think navigation in our daily routine, the first name pops is Google Maps. In making our lives much easier, Google has taken a step ahead and with the new technology, the Google has enabled the users to use the camera to look around for the place and then show the directions to the destination. Known as Visual Positioning System or VPS can also come in handy when sometimes GPS is not enough or not functioning. The database stored in the back end at the Google backs up the VPS and provide the routes which then VPS projects in the camera of the users. This article will take all of us through what VPS is and how it is the future of navigation.

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Improper curing of cold asphalt mixes can result in excessive moisture that can significantly impact the performance characteristics accompanied by premature failures. The present research investigated the impact of different curing temperatures and curing periods of 100% Cold Recycled (CR) mixes stabilised with foamed and emulsified asphalt. The results show that curing regime profoundly impacts the moisture loss and rate of evolution of the CR-Mix's stiffness properties. CR-Foam mixes resulted in a higher resilient modulus (Mr) than the CR-Emulsion mixes at most curing regime conditions. The study also confirmed that Mr of CR-Mixes with the same residual moisture content is dependent on curing temperature. Hence, it is concluded that retained moisture content may not be an appropriate criterion for curing of cold recycled (CR) layer because the stiffness achieved by the CR layer is different at different curing temperatures. It is recommended to adopt stiffness as the criterion to evaluate adequate curing.

A DC motor is a critical actuator in process control systems. This study investigates the effectiveness of a deep learning (DL) based Neural Network Predictive Controller (NNPC) for precise DC motor speed control. The NNPC anticipates the motor's future behaviour based on its current state and control inputs. The controller then optimally generates inputs to minimise tracking errors and enhance system performance. The NNPC demonstrated a remarkable reduction in Mean Squared Error (MSE), achieving a training MSE of 2.75 x 10-14 and the best validation MSE of 9.2023 x 10-14. These quantitative outcomes affirm the reliability and robustness of the proposed NNPC for speed control in DC motor systems across diverse applications.

In terms of social, psychological, physical, technological, and other elements, the educational system is undergoing significant transformation. Today, education is becoming a joint venture between the state, the market, and the community. Alternative education and training providers that place a greater emphasis on employability provide a problem, and university professors represent a particular breed of career academics that remain cut off from developments in the outside world. The sentiment analysis of student comments is presented in this work using a combination of Methodologies based on lexicons and machine learning. The textual feedback, which is often gathered around the conclusion of a semester, offers helpful insights into the general quality of teaching and makes insightful recommendations for ways to enhance instructional design. The article describes a sentiment analysis model trained using TF-IDF and linguistic characteristics to look at the opinions expressed by participants in their textual feedback. Additionally, a comparison among the existing sentiment analysis techniques is done.

Purpose: Stimulation approach is a therapy technique to improve language production using auditory and visual stimulation. Jellow app is a mobile app designed for compensating for impaired language skills and may be used in the intervention of persons with aphasia. The study aimed to determine the benefits of using the Jellow app as a facilitator of stimulus therapy to improve language and psychosocial domains in chronic Broca's Aphasia. Methods: Ten right-handed male adults with Broca's Aphasia were assessed on WAB and SIQOL39g tests. The control group (n = 5) was enrolled only for stimulation therapy. Pictures of objects were used for therapy with the help of auditory or auditory and visual cues. In the study group (n = 5), along with stimulus therapy, subjects were also trained on the use of icons in the Jellow app to facilitate functional communication needs. After six-months tests were readministered. Results: Post-therapy, on WAB, the improvement in spontaneous speech, repetition, and naming were found to be significantly more in the study group (4.6 +/- 0.55, 4.89 +/- 0.56, 5.74 +/- 0.24 respectively) than the control group (2.6 +/- 0.89, 3.22 +/- 0.49, 3.97 +/- 0.3 respectively) on 2-sample t-test. Similarly, significantly more improvement was seen in the communication domain of SAQOL39g in the study group (2.03 +/- 0.17) compared to the control group (1.14 +/- 0.45). Conclusion: Use of the Jellow app may be a beneficial adjunct to stimulation therapy for improving linguistic abilities and quality of life in persons with chronic Broca's aphasia.

The present study was conducted to evaluate 20 variety/ genotypes of brinjal for quality parameters. The sample was collected from vegetable trial field of Chandra Shekhar Azad University of Agriculture and Technology, Kanpur as well as after grading of the sample and the quality analysis in the laboratory was conducted as per standard procedures in the laboratory of the department of agricultural biochemistry. A significant variation was detected in all traits studies. There was considerable variability among varieties. Among the growth characteristics data on the days to 50% flowering significantly varied from 31.78-40.42, plant height range from 64.05-82.65 cm, no. of branches per plant ranges from 9.0-14.5. Similarly among the quality traits such as ash content ranged from 9.71-11.39%, fat content varied from 0.23- 0.37%, fibre content ranged from 1.29-1.73%.

Advances in artificial neural networks (ANN), specifically deep learning (DL), have widened the application domain of process control. DL algorithms and models have become quite common these days. The training algorithm is the most important part of an ANN that affects the performance of the controller. Training algorithms optimize the weights and biases of the ANN according to the input-output patterns. In this paper, the performance of different training algorithms was evaluated, analysed, and compared in a feed-forward backpropagation architecture. The training algorithms were simulated on MATLAB R2021b with license number 1075356. Training data were generated using two benchmark problems of the process control system. The performance, gradient, training error, validation error, testing error, and regression of the different training algorithms were obtained and analysed. The data shows that the Levenberg-Marquardt (LM) algorithm produced the best validation performance with a value of 2.669*10 −14 at 2000 epochs, while ‘traingd’ and ‘traingdm’ algorithms did not improve beyond their initial values. The LM algorithm tends to produce better results than other algorithms. These results indicate that the LM backpropagation best suits these types of benchmark problems. The results also suggest that the choice of training algorithm can significantly impact the performance of a neural network.

The proposed methodology aims at the identification of multiple and multi-stage power quality (PQ) disturbances along with their underlying causes. To this end, a computationally efficient S-Transform with decision tree (DT) based online PQ monitoring algorithm has been presented in this paper. This online algorithm provides a sample-based analysis of 40-dB noisy multiple and multi-stage PQ disturbance signals simulated in MATLAB environment with the different combinations of underlying causes as per the IEEE-1159 standard. The features like residual voltage, instantaneous phase-angle jump (IPAJ), and number of zero crossings (NZC) have been extracted from the transformed contours, viz., maximum voltage amplitude versus time (MVAVT) and phase-angle versus time (PAVT). Henceforth, the accurate recognition of most common underlying causes like three-phase fault, induction motor starting, and capacitor bank energizing has been achieved. For the first time, an online PQ monitoring algorithm is effectively assessing the type and underlying cause of a multiple or multi-stage disturbance simultaneously. The proposed methodology has also been experimentally validated with real-time PQ disturbance signals acquired in the laboratory.

Food supplementation with probiotic (Saccharomyces cerevisiae) in poultry as a mean lead to improve the health and growth performance of poultry. The use of antibiotics in poultry with the purpose of promoting growth rate, body weight gain, increasing feed conversion efficiency, feed conversion ratio. With increasing concerns about antibiotic resistance, there is increasing interest in finding alternatives to antibiotics for poultry production. To avoid the health hazards of antimicrobials drugs like antibiotics to human as well as poultry, probiotic has been used for as an potential substitute for antibiotics and been proved to be saved in poultry production system. This increased attention toward probiotic supplementation has generated an extensive body of research in the present day. However, there is still a lot of debate in scientific literature regarding the significant effect of probiotic on immune response against specific pathogens and growth performance in poultry. The natural feed supplements can effectively be utilized to promote growth in poultry and livestock while avoiding the dangerous phenomenon of encouraging drug resistant bacteria as in the case of antibiotic growth promoters (Demir et al. 2003, Cross et al. 2007). It is not surprising, therefore, that several herbal agents have been empirically used in poultry birds and other animals.

A Neural Network Predictive Controller (NNPC) is a deep learning-based controller (DLC) that uses artificial neural networks (ANN) to predict the future behavior of a system and accordingly control its outputs. In this paper, an NNPC was used to predict the level of the three cascaded tank and then adjust the inputs as flow rate to maintain the desired level in the tank. A three-tank level system is a system consisting of three interconnected tanks used to store liquids. To achieve the desired level, the NNPC first collects data on system behavior, including inputs and outputs, and uses this data to train the neural network. The trained network was then used to make predictions about the future level of each tank and to generate control signals to adjust the inputs as needed. NNPC also incorporates feedback from the system to continuously refine its predictions and improve its control performance over time. The mean squared error (MSE) of different backpropagation training algorithms available in MATLAB deep learning toolbox were evaluated and presented. Based on the MSE and best validation, Levenberg Marquardt algorithm were used in NNPC controller for further step response tracking. Different performance metrics were evaluated and presented.

The present study was conducted in the laboratories of the Department of Agricultural Biochemistry to evaluate thirty nine varieties/genotypes of Brassica Species for physical and chemical characteristics such as moisture content, Test weight, Oil content, Protein content in meal and Methionine content in meal. The samples was collected from University farm as well after grading of the sample the quality analysis in the laboratory was conducted as per standard procedures. Moisture content in different varieties/genotypes of yellow sarson varied from 5.60 to 3.25% while toria entries varied from 5.05 to 3.48%. In case of rai the range of variation was 5.67 to 3.56%. Test weight in different varieties/genotypes of yellow sarson varied from 5.70 to 3.40 g/1000 seed, while toria entries varied from 5.10 to 3.50 g/1000 seed. In case of rai the range of variation was 4.28 to 3.23 g/1000 seed. Oil content in different varieties/genotypes of yellow sarson varied from 42.55 to 36.20%, while toria entries varied from 42.05 to 34.40%. Whereas rai entries recorded variability of 39.05 to 32.40%. Protein content in different varieties/genotypes of yellow sarson varied from 40.55 to 27.93%, while toria entries varied from 30.03 to 28.33%. In case of rai the range of variation was 31.10 to 28.95%. Methionine content in different varieties/genotypes of yellow sarson varied from 1.98 to 0.99 g/100g, while toria entries varied from 2.12 to 1.14 g/100g. In case of rai the range of variation was 1.84 to 1.22 g/100g.

Acrylamide is used for industrial and laboratory purposes; it also is produced during cooking of carbohydrate-rich food at high temperature. We investigated the therapeutic potential of quercetin for treatment of acute acrylamide induced injury to the spleen. We used female albino rats treated with acrylamide for 10 days followed by oral administration of quercetin in three doses for 5 days. We observed significantly reduced total body weight, spleen weight, red blood cells, total proteins, superoxide dismutase, catalase, glutathione peroxidase, glutathione reductase, glucose-6-phophate dehydrogenase, reduced glutathione, concentration of serum IgG and IgM after acrylamide induced toxicity compared to controls. We also found that white blood cells, triglycerides, cholesterol and lipid oxidation were increased significantly after acrylamide induced toxicity in rats compared to controls. Histoarchitecture of spleen was affected adversely by acrylamide toxicity. Administration of quercetin ameliorated adverse effects of acrylamide in a dose-dependent manner. Quercetin appears to ameliorate acrylamide induced injury to the spleen by increasing endogenous antioxidants and improving histoarchitecture and immune function.

A field experiment was conducted during 2018-19 at Main Experiment Station Vegetable Farm of Acharya Narendra Deva University of Agriculture and Technology, Kumarganj, Ayodhya (U.P.) with a view to find out the effect of different combination of organic manures on yield, tuber quality and net income of potato. The 7 treatment combinations consisted of T1: Absolute Control, T2: FYM 30t ha-1 + PSB, T3: Poultry manure 5t ha-1 + PSB, T4: Vermicompost 7.5t ha-1 + PSB, T5: FYM 10t ha-1 + Poultry manure 1.7 t ha-1 + Vermicompost 2.5 t ha-1, T6: 67% N through Urea and 33% N through FYM + PSB, T7: Farmer practices FYM 15t ha-1 + Vermicompost 1t ha-1 + PSB were tested in randomized block design with 3 replication on the basis of experiment result it was revealed that treatment T6 shows better tuber yield (38.41 t ha-1),tuber yield grade wise i.e. 0-25g (1.93 t ha-1), 25-50g (11.52 t ha-1), 50-75g (14.58 t ha-1) and >75g (10.37 t ha-1) respectively, grade size of tuber per hill 0-25 (0.300 kg), 25-50 (0.800 kg), 50-75 (1.2 kg) and > 75 (1.2 kg),dry matter (17.63%), protein content of tuber (3.0 %) and protein yield (1152.30 kg ha-1). Higher values of economics viz., gross return (307280 ₹ ha-1), net return (218331 ₹ ha-1)and B:C ratio (2.45) in potato were observed with the application of 67% N through Urea and 33% N through FYM + PSB except cost of cultivation.