Empowering Distributed Solutions in Renewable Energy Systems and Grid Optimization

This study delves into the shift from centralized to decentralized approaches in the electricity industry, with a particular focus on how machine learning (ML) advancements play a crucial role in empowering renewable energy sources and improving grid management. ML models have become increasingly important in predicting renewable energy generation and consumption, utilizing various techniques like artificial neural networks, support vector machines, and decision trees. Furthermore, data preprocessing methods, such as data splitting, normalization, decomposition, and discretization, are employed to enhance prediction accuracy. The incorporation of big data and ML into smart grids offers several advantages, including heightened energy efficiency, more effective responses to demand, and better integration of renewable energy sources. Nevertheless, challenges like handling large data volumes, ensuring cybersecurity, and obtaining specialized expertise must be addressed. The research investigates various ML applications within the realms of solar energy, wind energy, and electric distribution and storage, illustrating their potential to optimize energy systems. To sum up, this research demonstrates the evolving landscape of the electricity sector as it shifts from centralized to decentralized solutions through the application of ML innovations and distributed decision-making, ultimately shaping a more efficient and sustainable energy future

Platelet Rich Plasma: Review of Literature

Objective: At present, growth factor-containing products such as enamel matrix derivatives, recombinant bone morphogenetic protein (rh-BMP), recombinant platelet derived growth factor and platelet rich plasma (PRP) have gained increasing attention. PRP is an autologous source of platelet growth factors used to enhance healing of soft and hard tissues. PRP has gained popularity due to its autologous nature, easy procurement and low cost.Review of Literatures: This study focuses on procurement and clinical applications of PRP.Conclusion: Controversy exists regarding the efficacy and applications of PRP and longitudinal studies are required to further elucidate this subject

A correlative model to predict in vivo AUC for nanosystem drug delivery with release rate-limited absorption

Purpose. Drug release from nanosystems at the sites of either absorption or effect biophase is a major determinant of its biological action. Thus, in vitro drug release is of paramount importance in gaining insight for the systems performance in vivo. Methods. A novel in vitro in vivo correlation, IVIVC, model denoted as double reciprocal area method was presented and applied to 19 drugs from 55 nano formulations with total 336 data, gathered from literature. Results. The proposed model correlated the in vitro with in vivo parameters with overall error of 12.4 ± 3.9%. Also the trained version of the model predicted the test formulations with overall error of 15.8 ± 3.7% indicating the suitability of the approach. A theoretical justification was provided for the model considering the unified classical release laws. Conclusion. The model does not necessitate bolus intravenous drug data and seems to be suitable for IVIVC of drugs with release rate-limited absorption

Determining the Clinicopathologic Features of Breast Lesions in Iranian Army Families

Background: Breast cancer is the most common disease of women around the world. On the other hand, benign breast diseases also constitute a wide range of breast pathologies that precise information on them is important. This study is conducted aiming to examine the breast pathology reports and determine the frequency of various breast diseases and its relationship with some clinicopathologic parameters in Imam Reza Hospital of army of the Islamic Republic of Iran in a period of 4 years.Methods: Imam Reza Hospital is a referral center for Army employees in Iran. This study is a retrospective descriptive study on patients who undertook biopsy or breast surgery during 2012-2015 in Imam Reza Hospital. After reviewing pathology reports and patient records and completing the required information, a total of 188 patients with 230 samples of breast tissue were included in this study.Results: During the period, 230 samples of breast tissue from 188 patients were sent to the pathology department. 195 samples (84.8%) were related to female patients and 35 samples (15.2%) to males. 112 samples (48.7%) were related to the left breast and 118 samples to the right (51.3%). The average size of tissues was 11.82 ± 8.64 cm. The mean age of patients was 42 ± 14.Conclusions: In this study, a statistically significant relationship exists between the patient sex and final diagnosis, patient age group and final diagnosis, and cell differentiation grade and lymph node metastasis along with samples. In the pathology reports in the center, cell differentiation grade of tumor was completely matched with Nottingham Grading System

A Multi-Zone HVAC System for a Typical Building for MATLAB/SIMULINK Platform

Matlab/Simulink is known in a large number of fields as a powerful and modern simulation tool. In the field of building and HVAC simulation its use is also increasing. However, it is still believed to be a tool for small applications due to its graphical structure and not to fit well for the simulation of multi-zone buildings. This paper presents the development of a new multi-zone building model for Matlab/Simulink platform

Optimized designing of PID controller for variable speed wind turbine systems using PSO algorithm

The PID controller design is a very popular method for controlling industrial processes and due to its simple structure and effective operation; it is used in a wide range of industries. In this paper, a method is provided for setting up the PID controller and Particle swarm optimization (PSO) algorithm is used to design a variable speed wind turbine system. The provided method has advantages such as easy implementation, stable convergence characteristics and high performance in computing. Finally the results are displayed

Good Clinical Practice in Children and Adolescents

Good clinical practice (GCP) is a series of systematically developed ethical and quality standard of designing, registering, running, recording, and reporting of the clinical trials. Good clinical practice is very important regarding the trials usually performed on the vulnerable populations especially children and adolescents. The sensitivity of the issue is even higher in the children with psychiatric disorders. Usually, these children have little legal protection. Hence, the safety of interventions and the ethical considerations are among the most important issues in this field. The purpose of this chapter is to deal with above problems and globally applicable standards for the conduct of clinical trials on the under legal age subjects especially those with psychiatric disorders. Selection of trial subjects, ethical principles, regulatory requirements, protection of trial subjects, monitoring (compliance with the protocol), responsibilities of the investigator, and other requirements to perform a clinically and ethically sound clinical trial in children and adolescents will be discussed in this chapter