Correction to : The Psychometric Properties of Autism Mental Status Examination (AMSE) in Turkish Sample

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Integrated System of Swarm Intelligence and Neural Network for Molecular Similarity Detection

Molecular similarity, governed by the principle that “similar molecules exhibit similar properties,” is a pervasive concept in chemistry with profound implications, notably in pharmaceutical research where it informs structure-activity relationships. This study focuses on the pivotal role of molecular similarity techniques in identifying sample molecules akin to a target molecule while differing in key features. Within the realm of artificial intelligence, this paper introduces a novel hybrid system merging Swarm Intelligence (SI) behaviors (Aquila and Termites) with Neural Networks. Unlike previous applications where Aquila or Termites were used individually, this amalgamation represents a pioneering approach. The objective is to determine the most similar sample molecule in a dataset to a specific target molecule. Accuracy assessments reveal a manual evaluation accuracy of 70.58%, surging to 90% with the incorporation of Neural Networks. Additionally, a three-dimensional grid elucidates the Quantitative Structure-Activity Relationship (QSAR). The Euclidean and Manhattan Distance metrics quantify differences between molecules. This study contributes to molecular similarity assessment by presenting a hybrid approach that enhances accuracy in identifying similar molecules within complex datasets

Numerical investigation of thermal performance enhancement in a newly designed shell and tube heat exchanger using TiO2 nanofluids

This study investigates the use of titanium dioxide (TiO2) nanofluids to enhance the thermal performance of shell and tube heat exchangers. A comparative computational fluid dynamics (CFD) analysis is conducted using water and a 0.5% TiO2 nanofluid. The heat exchanger is modelled using computer-aided design (CAD), with dimensions closely resembling commercial units. The CFD model is validated through a grid-independence study, with a mesh of 4,112,679 elements yielding grid-independent results. The key findings show that the 0.5% TiO2 nanofluid increases the cold fluid outlet temperature by 11.44% compared to water (36.04°C vs. 33.63°C). The average heat transfer coefficient is enhanced by 12.3% when using the nanofluid. The CFD results are consistent with experimental data, with a maximum deviation of 4.2% in the outlet temperatures. This study demonstrates the successful integration of TiO2 nanofluids with an optimized shell and tube heat exchanger design. The novelty lies in the application of nanofluids to improve the thermal performance of industrial heat exchangers. The presented methodology, combining CAD modelling and CFD analysis, provides a foundation for further optimization and experimental validation of nanofluid-enhanced heat transfer systems

Exploring structural basis of photovoltaic dye materials to tune power conversion efficiencies: A DFT and ML analysis of Violanthrone

This study employs a systematic approach to modify Violanthrone (V) structures and analyze their impact on photovoltaic (PV) properties. We use cheminformatics based Python library based RDKit tool to calculate their structural descriptors for to correlate them with their PV parameters. Our analysis reveals a positive correlation for their Open-Circuit Voltage (Voc) and Fill Factor (FF) for indicating that their higher voltage output is associated for their efficient charge carrier mobilities. We also predict their Power Conversion Efficiency (PCE) by drawing their their Scharber diagram which achieves their promising efficiency of up to 15 %. To further enhance the reliability our work, we conduct an extensive literature survey of such organic materials to predict their PCEs by their Machine Learning (ML) after utilizing various ML models. Among five tested ML models, it identifies the Random Forecast (RF) model and Gradient Boosting (GB) models as as the optimal one (R-squared value: 0.82). Their feature importance reveals that their FF is the most significant feature to impact their PCEs (importance value: 10.9). Furthermore, we observe a negative correlation between orbital interaction strength (E(2)) values and orbital energy differences E(j)-E(i) which indicates that their stronger orbital interactions are associated with their smaller energy differences. Our study provides valuable insights for their structural basis to PV material designs for enabling their design for efficient materials in energy conversion

Probabilistic Risk Framework for Nuclear- and Fossil-Powered Vessels: Analyzing Casualty Event Severity and Sub-Causes

Maritime activities pose significant safety risks, particularly with the growing presence of nuclear-powered vessels (NPVs) alongside traditional fossil-powered vessels (FPVs). This study employs a probabilistic risk assessment (PRA) approach to evaluate and compare accident hazards involving NPVs and FPVs. By analyzing historical data from 1960 to 2024, this study identifies risk patterns, accident frequency (probability), and severity levels. The methodology focuses on incidents such as marine incidents, marine casualties, and very serious cases with sub-causes. Key findings reveal that Russia exhibits the highest risk for very serious incidents involving both NPVs and FPVs, with a significant 100% risk for NPVs. China has the highest FPV risk, while France and the USA show above-average risks, particularly for marine casualties and very serious incidents. Moreover, collision is the most significant global risk, with a 26% risk for NPVs and 34% for FPVs, followed by fire hazards, which also pose a major concern, with a 17% risk for NPVs and 16% for FPVs, highlighting the need for enhanced safety and fire-prevention measures. In conclusion, comparative analysis highlights the need for enhanced stability improvements, fire prevention, and maintenance practices, particularly in the UK, France, Russia, and China. This study underscores the importance of targeted safety measures to mitigate risks, improve ship design, and promote safer maritime operations for both nuclear- and fossil-fueled vessels

A machine learning analysis to predict the stability driven structural correlations of selenium-based compounds as surface enhanced materials

The selenium-based compounds are gaining significance for their surface-enhanced properties. In order to accelerate their discovery, a machine learning (ML) approach has been employed to predict their structural correlations. For this a dataset of 618 compounds is collected from literature and is trained by using Support Vector Machine (SVM) with its Linear Kernal. Among ten ML evaluated models, three top-performing models are selected to make predictions for their stability energy. A Convex Hull Distribution (CHD) is constructed to elucidate the relationship for their stability and structural correlations. The main finding of this study reveals its strong correlation between stability and its related structural descriptors, particularly Bertz Branching Index" corrected for the number of Terminal atoms (BertzCT), Partial Equalization of Orbital Electronegativities-Van der Waals Surface Area with 14 bins (PEOE_VSA14), and First-Order Connectivity Index (chi 1). The analysis demon strates that the current ML models can effectively predict the stability of such materials to enable their rapid screening. Their calculations can provide a framework to understand their complex relationships between their material properties, structure, and stability.Funding agency : Taif University Grant number : TU-DSPP-2024-7

Numerical Thermal and Structural Analysis for Enhanced Durability in Petroleum Pipelines Using Composite Coatings

This research investigates the effectiveness of composite coatings in preventing corrosion in petroleum pipelines, focusing on computational methods for thermal and structural analysis. A 3-meter section of a Basra, Iraq pipeline was selected for evaluation. The study begins by establishing a baseline with an uncoated pipeline, followed by applying composite coatings both internally and externally. Finite Element Analysis (FEA) is used to assess structural integrity under high pressure and to perform a detailed numerical heat transfer analysis over a 15-year operational period. The thermal analysis evaluates the temperature distribution and thermal stresses that contribute to coating degradation and pipeline failure. By integrating Computer-Aided Design (CAD) and Computer-Aided Engineering (CAE), this study demonstrates the critical role of advanced computational tools in modeling heat transfer phenomena and enhancing pipeline safety and durability. The findings provide actionable insights for optimizing coating technologies with a focus on thermal performance in real-world applications

Traces of earthquake: traumatic life experiences and their effects on volunteer nurses in the earthquake zone-an interpretative phenomenological study

Introduction: It is crucial to understand the effects that traumatic events related to natural disasters have on individuals in as much detail as possible. However, the literature investigating the traumatic life experiences of nurses, who play a key role in disaster management, is still limited. Objective: The aim of this study was to explore in depth the traumatic life experiences of volunteer nurses who participated in relief efforts after two major earthquakes in the southeastern region of Türkiye. Methods: This qualitative study was conducted using a phenomenological design. The study sample consisted of 16 nurses selected by the purposive and snowball sampling methods. The data were evaluated using interpretative phenomenological analysis in the Maxqda 2020 program. Results: Four themes were generated: (1) shocking facts, (2) coping methods, (3) traumatic stress reactions, and (4) traumatic growth. Conclusion: While traumatic life experiences in the earthquake area led to acute stress reactions in the volunteer nurses, these experiences also contributed to their traumatic growth and development. Healthcare managers and policymakers should develop comprehensive strategies and intervention programs to safeguard the mental health of nurses in the context of natural disasters. It may also be useful to improve clinical education programs and support systems by reviewing international policies and procedures

Development and Evaluation of Drone Based Spraying System for Precision Agriculture Application

Unmanned aerial vehicles (UAVs), also known as drones, are increasingly used for various purposes such as photography, surveillance, mapping, inspection, and agriculture. This research specifically focuses on agricultural drones, which have the potential to address challenges encountered by farmers, ultimately positively affecting crop yields. Their ability to apply pesticides accurately and autonomously, without direct human involvement, is crucial for modern farming practices. This study aims to design and simulate a quadcopter specifically tailored for pesticide spraying. The design process involves careful selection of components and simulation using both SolidWorks and MATLAB Simulink. In SolidWorks, design the frame and components, while MATLAB Simulink is used to simulate trajectory tracking using PID controllers. The key finding is the integration of a multispectral camera to capture images and analyze data using Pix4Dfields and Agremo software. This analysis helps pinpoint specific areas requiring treatment, thereby minimizing pesticide and water usage while maximizing profitability. By targeting exact locations in the field based on data analysis, this approach improves efficiency. The research focuses on evaluating the quadcopter’s performance and trajectory accuracy, offering valuable insights into its potential agricultural impact, and assisting farmers in enhancing their profits through improved spraying techniques and resource management

Dual-Functioning Metal-Organic Frameworks: Methotrexate-Loaded Gadolinium MOFs as Drug Carriers and Radiosensitizers

Cancer remains a critical global health challenge, necessitating advanced drug delivery systems through innovations in materials science and nanotechnology. This study evaluates gadolinium metal-organic frameworks (Gd-MOFs) as potential drug delivery systems for anticancer therapy, particularly when combined with radiotherapy. Gd-MOFs were synthesized using terephthalic acid and gadolinium (III) chloride hexahydrate and then loaded with methotrexate (MTX). Characterization via fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), magnetic resonance imaging (MRI), and X-ray diffraction (XRD) confirmed their correct structure and stability. Effective MTX loading and controlled release were demonstrated. Anticancer effects were assessed on human healthy bronchial epithelial cells (BEAS-2B) and human lung cancer cells (A549) using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay under in vitro radiation therapy. MTX/Gd-MOF combined with radiotherapy showed a greater reduction in cancer cell viability (41.89% ± 2.75 for A549) compared to healthy cells (56.80% ± 1.97 for BEAS-2B), indicating selective cytotoxicity. These findings highlight the potential of Gd-MOFs not only as drug delivery vehicles but also as radiosensitizers, enhancing radiotherapy efficacy and offering promising evidence for their use in combinatory cancer therapies to improve treatment outcomes