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Economic growth in India purely depends on the Indian agricultural sector. In developing countries, the mechanization of agriculture plays a vital role in productivity. The research focuses on identifying which farmers in South Tamilnadu mostly use agricultural machinery. In this paper, we have taken farmer names and mobile numbers, choice of implement requirement into consideration by collecting the real data through DBT portal (https://agrimachinery.nic.in). This research work deals with five southern districts in Tamilnadu, namely Dindigul, Madurai, Theni, Ramnad, and Virudhunagar, in which we have predicted which machinery is suitable for that area. The linear regression model was used in this research by testing and training the dataset in all five data frames to get efficient results. Prediction of each data frame reveals the efficient working of the particular machinery for that specific area due to the different geographical features.

The charging plate creates magnetic fluxing. The charging plate and the adjacent coil can induce current without any contact. By the new trends, wireless charging got more social attention to the outside world as an alternative method for primitive wired charging. Even though wireless charging was implemented to various electronics industries, it is still proven that wired charging is faster than the wireless. But can make the gadget hot, which may not be good for the battery efficiency. The wireless charging does not degrade the battery. A vehicle that is powered by an electric motor rather than a typical petrol or diesel engine is referred to as an electric vehicle. The electric motor is powered by rechargeable batteries. In this article, the dynamic wireless charging of electric vehicles introduces a new methodology of charging even when the vehicle is in motion. The dynamic wireless charging is proposed to allow power transfer to the electric vehicles when they are in motion. Multiple inductive pads should be buried under the roadways. The inductive pads should be energized by a power supply. The track which is dedicated for the wireless charging should have enough distance so that the vehicles can charge up to a stable value until they leave the charging track. This proposed methodology reduces unnecessary time wastage for charging of vehicles. Hardware results obtained the primary pads mounted on road and power supply for primary roads. Besides, it will flow the efficient inductive power transfer and time saving opportunity.

The signal analysis helps us derive useful knowledge from biological processes to analyze, describe, and understand their origin mechanisms. However, biomedical signals are not immune and have time-consuming statistics. The major challenges of signal analysis of sportsperson are reliability and accuracy. Sports psychology uses psychological skills to discuss the optimum success and well-being of sports athletes, the developmental and social dimensions of the sport and sports facilities, and structural problems. The signal detection tool is used to detect the best combination of long-term practice predictors for active, sedentary adults’ signal. This paper proposed the wearable assisted signal detection method (WASDM) to find the sportspersons’ behavior signal analysis. This method performs an IoT based heart rate monitoring using a wearable device named intelligent bracelet mounted on the sportsperson to track the variations in his/her human heart rate. The wearable signal detector method analysis the heart rate abnormality and predicts health status, followed by an alarm to the physician and the respective personnel while performing activity session. In this research, various machine learning algorithms have been tried to perform signal analysis and prediction and compared their results to suggest the best in this application scenario. Finally, the experimental analysis shows better outcomes for the sportspersons’ psychological behavior signal analysis than the conventional methods.

Decentralized Model Reference Adaptive Control problems are investigated for a class of linear time-invariant two time-scale model, having fast and slow dynamics and unmatched interconnections. Design of full state feedback controller is a critical task to the system having interrelated dynamics and nonlinear interconnections of time varying lags, however, can be addressed by singular perturbation procedures and time scale modeling. In this research, full order observer-based state feedback control is designed to two-time scale system to ensure the stability of the closed loop system. Then, decentralized model reference adaptive controller with the novel reference model is designed to individual subsystems. It is found that the proposed design enforces the system states to track the reference state asymptotically. To investigate the proposed design, an example of drying process is considered. Simulation results are analyzed to confirm the efficacy of the proposed control scheme.

In this article, a simple controller design with the fractional order calculations applies to unstable cascade processes with a significant time delay. Series cascade control system consists of two loops in which the inner loop is designed using the IMC principle based on the synthesis method. The primary controller is designed with the FOPI-FOPD controller to ensure stable and satisfactory closed-loop performances for the unstable processes. For the primary controller, five tuning parameters are involved, which tuned using the centroid of the convex stability region method. Different examples are given to illustrate the superiority of the proposed design over some existing designs. Results obtained from simulation reveals that with the proposed control design, enhanced closed-loop control performances are obtained for nominal and perturbed conditions.

Model parameter estimation and controller tuning of the nonlinear clarification process are primary issues in the sugar industry. This paper proposes a Lagrangian-based state transition algorithm (LSTA) for controlling the clarification process and identifying the model parameters. First, the standard system identification procedure is used to estimate the model parameters for the clarifier. Then, the LSTA is used to enhance the accuracy of the identified system parameters. Once a suitable model is obtained, the proposed LSTA is used to fine-tune the optimal controller parameters off-line. We choose two types of evolutionary algorithms for comparison to show the efficacy of the proposed design. The results of the simulations indicate that the proposed controller achieves optimum transient and tracking performance in all cases. For fast convergence and the ability to provide the global optimum when estimating the modeling and controller parameters, the LSTA performance was found to be superior to that of the other algorithms.

This article has been retracted at the request of the first and corresponding author, Dr. Karthik Chandran. The author has alerted the Editor-in-Chief of IJUFKS the reasons for the retraction: The proposed system was modelled with the incorrect data set. The system response has become incorrect because of this incorrect data set. Two percent of the information (mathematical assumptions) was taken from one paper without proper citation of the source.

In the current era of urbanization and industrialization, water pollution and energy generation are the main concerns in the climate change context. Microbial Fuel Cells (MFCs) utilize biodegradable carbon compounds in organic waste to produce an electric current.MFC technology provides sustainable solutions for renewable power systems and energy-efficient wastewater treatment for distributed electricity systems. However, in wastewater treatment, generating usable energy from the MFC remains a significant challenge for the system scale and implementation. Hence, in this paper, the Optimized Hybridized Mathematical Model (OHMM) has been proposed for wastewater treatment and energy generation using microbial fuel batteries. Furthermore, statistical analysis has a role in synthesizing knowledge of fuel cells, defining and limiting factors in electricity production. The findings show that the proposed OHMM method, Sensitivity Analysis, Uncertainty Analysis, and Accuracy reduces energy consumption and cost function compared to other existing methods. With more miniature steam, it reduces the amount of harmful smoke that power plants emit, preserves the natural resources of a planet, and protects habitats against destruction. They will lead to a healthier and happier world by taking action to reduce the battery.

The paper proposes a new optimization algorithm that is extremely robust in solving mathematical and engineering problems. The algorithm combines the deterministic nature of classical methods of optimization and global converging characteristics of meta-heuristic algorithms. Common traits of nature-inspired algorithms like randomness and tuning parameters (other than population size) are eliminated. The proposed algorithm is tested with mathematical benchmark functions and compared to other popular optimization algorithms. The results show that the proposed algorithm is superior in terms of robustness and problem solving capabilities to other algorithms. The paradigm is also applied to an engineering problem to prove its practicality. It is applied to find the optimal location of multi-type FACTS devices in a power system and tested in the IEEE 39 bus system and UPSEB 75 bus system. Results show better performance over other standard algorithms in terms of voltage stability, real power loss and sizing and cost of FACTS devices.

The combination and convergence of energy-intensive industries developed by ecological factors based on energy clusters is discussed in this paper. Here, a few models for the prediction of greenhouse effects are used as a single type of modeling. In this model, the solar panel system is included as a measure of the greenhouse effect, Commitment Unit (CU) formulations are changed with flouted logic because solar integrations and other unknown variables are intermittent. In general, the greenhouse model with natural ventilation temperature prediction is incomplete, in which the resulting fluid logical CU problem can be solved with an evolutionary algorithm based on the definition and the theory of quantum calculation. This paper proposes a Fuzzy Model-Based Quantum Greenhouse Evolutionary Ventilation Algorithm (FM-BSQGEVA) which helps to minimize the CU problem. The QGEVA is updated to include a hierarchy-group-oriented scheme to tackle the non-linear nature of the issue and its multifaceted nature. The QGEVA is further developed to support a new binary differential operator and several genetic algorithm operators with a redefined rotational angle look-up. The chances that such operators are used on separate solutions are affected by stating the membership function based on their related fitness. The fitness function is calculated through a combination of the penalty function, objective function and the added fluid function. The models built can be used to regulate and control natural ventilation in greenhouse effects. This finding shows that an energy-intensive industrial cluster's environmental chain of the industry has improved eco-efficiency.

Activation of inflammatory caspases, such as caspase-11 and caspase-4, by lipopolysaccharide (LPS) from Gram-negative bacteria, is a key mechanism of innate immune defense against infections. This cytosolic LPS sensing pathway results in activation of a pore-forming protein, gasdermin D, which mediates an inflammatory form of cell death called pyroptosis. In addition, caspase-11-mediated cytosolic LPS sensing leads to the activation of caspase-1 and the maturation of IL-1b and IL-18. Importantly, cytosolic LPS-induced pyroptosis is associated with the release of intracellular proteins that act as alarmins or damage-associated molecular patterns (DAMPs) to modulate the inflammatory response and play an important role during infectious diseases and sepsis. However, the identity and function of DAMPs released downstream of caspase-11 activation are poorly characterized. Using a proteomic approach involving ProteomeLab’s PF2D fractionation system followed by mass spectrometry, we have discovered that galectin-1, a b-galactoside-binding lectin, is released as a consequence of cytosolic LPS sensing. By utilizing various mutant cell lines and primary cells we show that galectin-1 secretion is dependent on caspase-11 and gasdermin D, but not NLRP3 or caspase-1. Importantly, in vivo studies using galectin-1-deficient mice showed that galectin-1 acts as a DAMP to promote systemic inflammation and plays a detrimental role during septic shock. Additionally, human septic patients were found to have increased levels of galectin-1 in their serum. Taken together, we have characterized the secretion and function of a novel lectin DAMP released upon lytic cell death and provide new insights into outcomes of inflammasome activation.

The parameter estimation optimisation with constraints for the nonlinear complex system requires a serious of computation. This paper introduced a novel constrained optimisation method named Lagrangian-based state transition algorithm (LSTA) to solve problems in distributed cloud computing environment. LSTA with the physical constraints involved in solving the problems which occurs while the conventional techniques are used. In LSTA, the updating of the result to an optimisation problem with constraints known as, a state transition. The Lagrangian multiplier is used as a constraint for state transition process to estimate the drying process system effectively. The experiments are conducted in the cloud computing environment and simulated results validated the proposed LSTA methodology for parameter estimation. This method is a promising way for system identification due to its searching competency, enduring performance considering physical limitations and quick convergence.

A structural health monitoring technique, developed at the Center for Intelligent Material Systems and Structures, employs piezoelectric (PZT) materials for tracking the structural impedance to qualitatively identify damage. The mechanical impedance of a structure is a function of the structure's mass, stiffness, damping, and structural boundary conditions. Changes in any of the above-mentioned properties lead to a change in the mechanical impedance of the structure and a change in the impedance pattern of the structure. The mechanical impedance of a structure can be measured by coupling the electrical and mechanical impedances via PZT patches. Therefore any change in the mechanical impedance leads to a change in the electrical impedance of the PZT bonded to the structure of interest. However, change of the electrical impedance can also occur due to changes in temperature. Piezoelectric materials have been known to have temperature dependency regarding their basic properties, such as the dielectric constant and the piezoelectric coefficient. In this thesis, this temperature dependency will be investigated. The motivation of this work is linked to the impedance-based nondestructive evaluation (NDE) technique which employs PZT sensors for tracking changes in the structural impedance, by measuring the electrical impedance, to qualitatively identify damage. However, for this NDE technique to be successful in all types of environments, it must be insensitive to temperature variations. As mentioned earlier, piezoelectric materials have strong temperature dependency and a temperature compensation procedure is necessary. Therefore, two software correction techniques were developed to eliminate the effects of temperature in the electrical impedance measurements of PZT sensors. (NDE) technique which employs PZT sensors for tracking changes in the structural impedance, by measuring the electrical impedance, to qualitatively identify damage. However, for this NDE technique to be successful in all types of environments, it must be insensitive to temperature variations. As mentioned earlier, piezoelectric materials have strong temperature dependency and a temperature compensation procedure is necessary. Therefore, two software correction techniques were developed to eliminate the effects of temperature in the electrical impedance measurements of PZT sensors. The second correction technique is based on the sensor output. Through experimental investigation, it was found that temperature will have the effect of shifting the electrical impedance magnitude of the piezoelectric sensor, while leaving the impedance phase unaffected. To characterize the temperature effects in PZT materials, a temperature coefficient which is independent of frequency has defined. Finally, based on the defined temperature coefficient, a simple temperature compensation technique has been implemented successfully, eliminating the effects of temperature on PZT sensors while not eliminating the effects of temperature on the structure. Master of Science