Optimization of a Runge-Kutta 4th Order Method-based Airbrake Control System for High-Speed Vehicles Using Neural Networks

The Runge-Kutta 4th Order (RK4) technique is extensively employed in the numerical solution of differential equations for airbrake control system design. However, its computational efficacy may encounter restrictions when dealing with high-speed vehicles that experience intricate aerodynamic forces. Using a Neural Network, a unique technique to improving the RK4-based airbrakes code is provided. The Neural Network is trained on numerous aspects of the high-speed vehicle as well as the current status of the airbrakes. This data was generated through the traditional RK4-based simulations and can predict the state of the airbrakes for any given state of the rocket in real-time. The proposed approach is demonstrated on a high-speed airbrakes control system, achieving comparable or better performance than the traditional RK4-based system while significantly reducing computational time by reducing the number of mathematical operations. The proposed method can adapt to changes in flow conditions and optimize the airbrakes system in real-time

Pesticide Reduction through Organic Farming for Promoting Public Health Management and Food Security

There is a growing global interest in sustainable food consumption to ensure food security. One significant factor driving this demand is the belief that consuming organic food affects Public Health (PH). This research examines the historical context of sustainable agriculture. This article specifically examines the impact of sustainable agriculture on health-related substances, pesticide residues, and contaminants in crops. It also explores the connections between organic food and health indicators. In Organic Farming (OF), the use of pesticides is often restricted or non-existent, which is different from Traditional Farming (TF) methods that heavily depend on pesticides to preserve crops. The notable disparities in pesticide usage between the two agricultural systems profoundly affect the comparative nutritional exposure, hazards, and ecological impacts associated with pesticides. Pesticide Usage Monitoring (PUM) information has been used to compare pesticide usage across organically certified and adjacent traditional farms for tomato crops. This work suggested methods for PR by promoting the widespread use of organic agricultural techniques. Additionally, emphasis on many alternatives available within organic food supply chains to further minimize the usage of pesticides, contact, and adverse effects on workers has been proposed to provide food security

Analysis of Public Health Care Management in Women Suffering from PCOS in India

The concept of "health" is multifaceted and goes beyond simply being free from sickness or illness. Because the concept of health is so nebulous, it is difficult to define. Not only have the most common types of health problems changed over time, but so has our comprehension of health. In the modern world, behaviour often affects one\u27s health because it is influenced by biological, psychological, and social factors. Reproductive processes and events mix with psychological and physiological aspects. In a similar vein, psychological disorders influence reproductive physiology and control reproductive processes. The empirical data points to several aspects that require attention for a deeper comprehension of psychological suffering and ultimately its avoidance. It is quite difficult to learn about the evolution of distress, which means that it is hard to distinguish between acute and persistent manifestations of this condition. According to the few studies that have looked into the problem of psychological discomfort in gynaecology out-patient clinics, women who frequent these clinics are thought to be more distressed than typical—about 50% of them, on average. Additionally, this study will identify the psychosocial variables and aetiology of PCOS in women

Impact of Quality Management Systems on Corporate Efficiency in Public Health Providers

The quality management system (QMS) develops an ethnicity of constant development and legal obedience, which advanced minor errors, process efficacy and develop manufactured goods quality, these benefits are increase corporate effectiveness. Inferior expanses, improved client approval, and residential corporate efficiency (CE) are inferences of QMS. The study investigates the impact of QMSs on the CE of Indian public health providers. Research dataset was gathered from a survey of 380 Indian public healthcare providers, involving respondents (178) from various categories. Data evaluates Statistical Package for Social Sciences (SPSS) software utilizing factor analysis, one-way analyses of variance (ANOVA), and correlation analysis. This investigation exposed the QMSs of these Indian public health care providers necessitate a separate sector devoted to excellence and fundamental quality opinion. The results reveal that subjective and objective performance (SOP) is the most significant factor influencing performance in healthcare, with the highest P-value of 0.004. This indicates a strong impact on organizational performance. Moreover, it was explored how these QMS elevated the company’s perceived performance levels while having a minor optimistic outcome on financial presentation. QMSs in Indian public health providers recover CE and foster a quality and responsibility ethnicity by inserting an importance on identical processes, continuous enlargement and patient care

An analysis and evaluation of the WeFold collaborative for protein structure prediction and its pipelines in CASP11 and CASP12

Every two years groups worldwide participate in the Critical Assessment of Protein Structure Prediction (CASP) experiment to blindly test the strengths and weaknesses of their computational methods. CASP has significantly advanced the field but many hurdles still remain, which may require new ideas and collaborations. In 2012 a web-based effort called WeFold, was initiated to promote collaboration within the CASP community and attract researchers from other fields to contribute new ideas to CASP. Members of the WeFold coopetition (cooperation and competition) participated in CASP as individual teams, but also shared components of their methods to create hybrid pipelines and actively contributed to this effort. We assert that the scale and diversity of integrative prediction pipelines could not have been achieved by any individual lab or even by any collaboration among a few partners. The models contributed by the participating groups and generated by the pipelines are publicly available at the WeFold website providing a wealth of data that remains to be tapped. Here, we analyze the results of the 2014 and 2016 pipelines showing improvements according to the CASP assessment as well as areas that require further adjustments and research

Composing Modeling and Simulation with Machine Learning in Julia

In this paper we introduce JuliaSim, a high-performance programming environment designed to blend traditional modeling and simulation with machine learning. JuliaSim can build accelerated surrogates from component-based models, such as those conforming to the FMI standard, using continuous-time echo state networks (CTESN). The foundation of this environment, ModelingToolkit.jl, is an acausal modeling language which can compose the trained surrogates as components within its staged compilation process. As a complementary factor we present the JuliaSim model library, a standard library with differential-algebraic equations and pre-trained surrogates, which can be composed using the modeling system for design, optimization, and control. We demonstrate the effectiveness of the surrogate-accelerated modeling and simulation approach on HVAC dynamics by showing that the CTESN surrogates accurately capture the dynamics of a HVAC cycle at less than 4% error while accelerating its simulation by 340x. We illustrate the use of surrogate acceleration in the design process via global optimization of simulation parameters using the embedded surrogate, yielding a speedup of two orders of magnitude to find the optimum. We showcase the surrogate deployed in a co-simulation loop, as a drop-in replacement for one of the coupled FMUs, allowing engineers to effectively explore the design space of a coupled system. Together this demonstrates a workflow for automating the integration of machine learning techniques into traditional modeling and simulation processes