A Design for Proprioceptive Force in 3D Agility Robot Through Use of AI

For robots to be considered effective, they should be able to maneuver through 3D environments. To achieve such mobility, robots needs to be designed in such a way that would span various topographies. So, artificial intelligence algorithms have been developed to ensure agility of the robots when walking on murky topographies. In the current state of the art legged robots, there is still much progress need to be made in research to turn them into automobiles with great agility to be used in the real world utility and provide mobility in rough. GOAT leg as a means of artificial intelligence is still a new phenomenon. There still exists a number of preliminary tests that need to be done in accessing and in the characterization of the leg’s current performance and its implications in the future. This study seeks to develop and agility model which would be useful in ensuring that the robots remain agile in such complex environments. To do this, a simulation has been through Matlab analysis. Results of the current study showed that, 3-RSR was designed to ensure that a high fidelity proprioceptive force control would enable legs with the mechanically spring stiffness. Implications and future recommendations also discussed

AI in Bioinformatics

In bioinformatics science and computational molecular biology, artificial intelligence (AI) has rapidly gained interest. With the availability of numerous types of AI algorithms, it has become prevalent for researchers to use off-shelf programmes to identify their datasets and mine them. At present, researchers are facing difficulties in selecting the right approach that could be extended to a given data collection, with numerous intelligent approaches available in the literature. Researchers need instruments that present the data in an intuitive manner, annotated with meaning, precision estimates, and description. In the fields of bioinformatics and computational molecular biology (DNA sequencing), this article seeks to review the use of AI. These fields have evolved from the needs of biologists to use the large volumes of data continuously obtained in genomic science and to better understand them. For several approaches to bioinformatics and DNA sequencing, the fundamental impetus is the evolution of species and the difficulty of dealing with incorrect results. The type of software programmes developed by the scientific community to search, identify and mine numerous usable biological databases are also mentioned in this article, simulating biological experiments with and without mistakes. The review of antibody-antigen interactions and their diversity, and the study of epidemiological evidence that can help forecast antibody-antigen interactions and the induction of broadly neutralising antibodies are important questions to be answered in the field of vaccinology

Highly efficient maximum power point tracking control technique for PV system under dynamic operating conditions

publishedVersio

Hydrolysis, Microstructural Profiling and Utilization of Cyamopsis tetragonoloba in Yoghurt

The present study investigates the hydrolysis, microstructural profiling and utilization of guar gum (Cyamopsis tetragonoloba) as a prebiotic in a yoghurt. Guar galactomannans (GG) was purified and partially depolymerized using an acid, alkali and enzyme to improve its characteristics and increase its utilization. The prebiotic potential of hydrolyzed guar gum was determined using Basel and supplemented media. Crude guar galactomannans (CGG), purified guar galactomannans (PGG), base hydrolyzed guar galactomannans (BHGG), acid hydrolyzed guar galactomannans (AHGG) and enzymatic hydrolyzed guar galactomannans (EHGG) were analyzed using scanning electron microscope (SEM), X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FTIR). Yoghurt was prepared with a starter culture and incorporating guar gum, its hydrolyzed forms (0.1, 0.5 and 1%) and Bifidobacterium bifidum. The results showed that PHGG significantly improved the viability of B. bifidum. SEM revealed a significant change in the surface morphology of guar gum after acidic and enzymatic hydrolysis. Enzymatic hydrolysis developed a well-defined framework within guar gum molecules. The XRD pattern of CGG, PGG and AHGG presented an amorphous structure and showed low overall crystallinity while EHGG and BHGG resulted in slightly increased crystallinity regions. FTIR spectral analysis suggested that, after hydrolysis, there was no major transformation of functional groups. The addition of the probiotic and prebiotic significantly improved the physiochemical properties of the developed yoghurt. The firmness, cohesiveness, adhesiveness and syneresis were increased while consistency and viscosity were decreased during storage. In sum, a partial hydrolysis of guar gum could be achieved using inexpensive methods with commercial significance

FUSCD - Future smart car driver

Autonomous driving is one of the newly emerging feats in artificial intelligence (AI). The challenge in developing autonomous cars is to design controllers that can steer a vehicle in the right direction with enough speed. A good controller activates a set of multiple actuators simultaneously. The output of the controller is a function of the sensory inputs. Nowadays, controllers are mostly developed by connecting a car simulator with a machine learning (ML) algorithm. The simulator provides a pragmatic environment for simulated cars. The ML algorithm, on the other hand, does the job of an expert human controller designer; it designs the controllers on behalf of the latter. In our case, we use a famous car racing simulator called TORCS (The Open Racing Car Simulator). This paper proposes a new controller for TORCS that helps cars win races with grace. We also propose a novel strategy to design smart controllers using ML algorithms. Our controller is called FUSCD (Future Smart Car Driver)

Climate change adaptation: a corrective policy framework in the Malaysian agricultural sector

A corrective policy framework is essential for sustainable agricultural management. In order to put in place a corrective policy framework, it is necessary to know the socio-economic context of local farmers and their perception of climate change. Therefore, the main objective of this study is to explore their socio-economic status and general perception of climate change and to examine its impact on adaptation practices. To achieve the research objectives, data were collected using survey questionnaires and analysed using statistical tools. The results show that most of the respondents are between the age of 31-45 years in the sample size, 45% has secondary education and 34% of the respondents' monthly income is between RM2,000-RM4,000. It has been found that approximately 76% of farmers had heard about climate change. The results also revealed that socio-economic characteristics such as education, income, type of farmer, attitudes and awareness were positive and highly significant. It is hoped that the findings of this study would be useful for policymakers in designing an appropriate policy framework to raise awareness of how to reduce the impact of climate change in the agricultural sector. Copyright © 2019 Inderscience Enterprises Ltd

Hybrid General Regression NN Model for Efficient Operation of Centralized TEG System under Non-Uniform Thermal Gradients

The global energy demand, along with the proportionate share of renewable energy, is increasing rapidly. Renewables such as thermoelectric generators (TEG) systems have lower power ratings but a highly durable and cost-effective renewable energy technology that can deal with waste heat energy. The main issues associated with TEG systems are related to their vigorous operating conditions. The dynamic temperature gradient across TEG surfaces induces non-uniform temperature distribution (NUTD) that significantly impacts the available output electrical energy. The mismatching current impact may lower the energy yield by up to 70% of extractable thermal energy. As a solution, a hybrid general regression neural network (GRNN) orca predation algorithm (OPA) is proposed; backpropagation limitations are minimized by utilizing the stochastic optimization algorithm named OPA. The conclusions are evaluated and contrasted with highly improved versions of the conventional particle swarm optimization (PSO), grey wolf optimizer (GWO), and Harris hawk optimization (HHO). A detailed analytical and statistical analysis is carried out through five distinct case studies, including field stochastic data study, NUTD, varying temperature, and load studies. Along with statistical matrix errors such as MAE, RMSE, and RE, the results are assessed in terms of efficiency, tracking, and settling time. The results show that superior performance is achieved by the proposed GRNN-OPA based MPPT by 35% faster tracking, and up to 90–110% quicker settling time which, in turn, enables the 4–8% higher energy accumulation over a longer period of operation. A low-cost experimental setup is devised to further validate the practicality of the proposed techniques. From such comprehensive analysis, it can be safely concluded that the proposed GRNN-OPA successfully undertakes the drawbacks of existing classical MPPT methods with higher efficiency

A Novel MPPT Controller Based on Mud Ring Optimization Algorithm for Centralized Thermoelectric Generator under Dynamic Thermal Gradients

Most industrial processes generate raw heat. To enhance the efficiency of industrial operations, this raw heat is recovered. Thermoelectric generators (TEG), as solid state devices, provide an excellent application of heat recovery in the form of most manageable electrical power. This work presents a novel MPPT controller based on the Mud Ring Optimization algorithm for a centralized Thermoelectric Generator (TEG) under dynamic thermal gradients. The existing stochastic optimization algorithm for Maximum Power Point Tracking (MPPT) control in renewable energy systems exhibits several limitations that affect its performance in MPPT control. The convergence speed, local minima trap, hyper parameters’ sensitivity toward the population size, acceleration coefficients, and the stopping criterion all impact the convergence stability. In addition to these limitations, sensor noise sensitivity in measurement fluctuates the control system leading to reduced performance. Therefore, the careful design and implementation of the MRO algorithm is crucial to overcome these limitations and achieve a satisfactory performance in MPPT control. The results of this study contribute to developing more efficient MPPT control of TEG systems and implementing renewable energy technologies. The algorithm effectively tracks the maximum power point in dynamic thermal environments and increases the power output compared to conventional MPPT methods. The findings illustrate the efficacy of the proposed controller providing a higher power output (Avg. 99.95%) and faster response time (220 ms) under dynamic thermal conditions achieving 38–70% faster tracking of the GM in dynamic operating conditions