Performance assessment and multiobjective optimization of a biomass waste-fired gasification combined cycle for emission reduction.

The use of renewable fuels leads to reduction in the use of fossil fuels and environmental pollutants. In this study, the design and analysis of a CCPP based on the use of syngas produced from biomass is discussed. The studied system includes a gasifier system to produce syngas, an external combustion gas turbine and a steam cycle to recover waste heat from combustion gases. Design variables include syngas temperature, syngas moisture content, CPR, TIT, HRSG operating pressure, and PPTD. The effect of design variables on performance components such as power generation, exergy efficiency and total cost rate of the system is investigated. Also, through multi-objective optimization, the optimal design of the system is done. Finally, it is observed that at the final decisioned optimal point, the produced power is 13.4 MW, the exergy efficiency is 17.2%, and the TCR is 118.8 $/h.

Path planning optimization in unmanned aerial vehicles using meta-heuristic algorithms: a systematic review.

Unmanned aerial vehicles (UAVs) have recently been increasingly popular in various areas, fields, and applications. Military, disaster management, rescue operations, public services, agriculture, and various other areas are examples. As a result, UAV path planning is concerned with determining the optimal path from the source to the destination while avoiding collisions with lowering the cost of time, energy, and other resources. This review aims to assort academic studies on the path planning optimization in UAV using meta-heuristic algorithms, summarize the results of each optimization algorithm, and extend the understanding of the current state of the path planning in UAV in the meta-heuristic optimization field. For this purpose, we implemented a broad, automated search using Boolean and snowballing searching methods to find academic works on path planning in UAVs. Studies and papers have been distinguished, and the following information was obtained and aggregated from each article: authors, publication's year, the journal name or the conference name, proposed algorithms, the aim of the study, the outcome, and the quality of each study. According to the findings, the meta-heuristic algorithm is a standard optimization method for tackling single and multi-objective problems. Besides, the findings show that meta-heuristic algorithms have a great compact on the path planning optimization in UAVs, and there is good progress in this field. However, the problem still exists mainly in complex and dynamic environments, on battlefields, in rescue missions, mobile obstacles, and with multiple UAVs.

WTD-PSD: Presentation of Novel Feature Extraction Method Based on Discrete Wavelet Transformation and Time-Dependent Power Spectrum Descriptors for Diagnosis of Alzheimer's Disease.

Alzheimer's disease (AD) is a type of dementia that affects the elderly population. A machine learning (ML) system has been trained to recognize particular patterns to diagnose AD using an algorithm in an ML system. As a result, developing a feature extraction approach is critical for reducing calculation time. The input image in this article is a Two-Dimensional Discrete Wavelet (2D-DWT). The Time-Dependent Power Spectrum Descriptors (TD-PSD) model is used to represent the subbanded wavelet coefficients. The principal property vector is made up of the characteristics of the TD-PSD model. Based on classification algorithms, the collected characteristics are applied independently to present AD classifications. The categorization is used to determine the kind of tumor. The TD-PSD method was used to extract wavelet subbands features from three sets of test samples: moderate cognitive impairment (MCI), AD, and healthy controls (HC). The outcomes of three modes of classic classification methods, including KNN, SVM, Decision Tree, and LDA approaches, are documented, as well as the final feature employed in each. Finally, we show the CNN architecture for AD patient classification. Output assessment is used to show the results. Other techniques are outperformed by the given CNN and DT.

Cancer Incidence in the Kurdistan Region of Iraq: Results of a Seven-Year Cancer Registration in Erbil and Duhok Governorates.

INTRODUCTION: There are limited published data regarding the recent incidence trends of cancer in Iraqi Kurdistan. METHODS: The present study assessed the epidemiological estimates of cancer incidence, as well providing a projection of future cancer trends in the upcoming decade by analysing the population-based cancer registry between 2013 and 2019, in both the Erbil and Duhok governorates. A retrospective analysis was performed on data retrieved from the Medical Statistics Department at the Ministry of Health, Kurdistan Regional Government (KRG). RESULTS: The total number of female cancer patients was higher in both governorates, and the total incidence of patients with cancer increased by over 2x between 2013 and 2019 in Erbil and Duhok, from 73 to 174 patients/100,000 individuals for women, and 36 to 85 patients/100,000 individuals for men. Analysis indicated that the percentage of patients with cancer is projected to increase by >2x in the current decade, from 3,457 cases to 4,547 and 4,449 cases in the Erbil governorate; and from 1,365 to 2,633 and 2,737 cases in 2028 based on LSTM and bi-LTSM analysis in the Duhok governorate. Lung cancer (LC) and female breast cancer (BC) were the most prominent types of cancers diagnosed since 2013 in both the Erbil and Duhok governorates. CONCLUSION: The striking pattern of trends for both present and future cancer incidence rates require urgent solutions and comprehensive efforts to control risk factors that promote the increasing incidence of cancer in these two KRG governorates.
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