The current study aimed to use both multiple regression analyses (MRA) and latent regression analysis (LRA) to examine unique and suppression effects of DSM-5 oppositional defiant disorder irritability and headstrong dimensions with common DSM-5 internalizing (Generalized Anxiety Disorder, Persistent Depressive Disorder, Separation Anxiety Disorder, Obsessive-Compulsive Disorder, and Posttraumatic Stress Disorder), externalizing (ADHD, and Conduct Disorder), neurodevelopmental (Specific Reading Disorder, Autism Spectrum Disorder, Language Disorder, and Speech Sound Disorder) and eating disorders (Anorexia Nervosa, and Bulimia Nervosa) among clinic-referred adolescents. Parents of 1877 adolescents [boys = 1089, girls = 788; age range = 11 to 17 years] provided ratings of their adolescents’ ODD symptoms and the symptoms for the other 14 disorders. The MRA findings indicated that, generally, internalizing disorders, autism spectrum disorder (ASD), and eating disorders were associated positively and uniquely with the irritability dimension, and externalizing disorders were associated positively and uniquely with the headstrong dimension. With the exception of autism spectrum disorder, the other neurodevelopmental disorders showed little or no associations with irritability or headstrong dimensions. There was little evidence of suppression effects. The LRA findings for unique associations were generally comparable with the MRA findings, except that there was strong evidence for the headstrong dimension suppressing the unique associations involving irritability, and irritability suppressing the unique associations involving the headstrong dimension. These findings raise the possibility that both irritability and headstrong may be transdiagnostic, having a dominant influence on the comorbidity of internalizing (and possibly eating disorders) and externalizing disorders, respectively. To date, there has been little discussion on headstrong being a major transdiagnostic factor for externalizing disorders. Further theoretical, methodological. and clinical implications of the findings are discussed.
The material removal in Electro-Discharge Machining (EDM) occurs through the generation of high temperatures caused by intense electrical discharges, leading to the melting and vaporization of the workpiece and tool electrode. The ejected molten material solidifies in the dielectric liquid, forming debris that can significantly affect process accuracy, efficiency, productivity, and machinability if not effectively removed from the machining zone. The utilization of Low Frequency (LF) vibration (typically <1 kHz) to assist debris evacuation during Micro-EDM (µEDM) and EDM processes has emerged as a feasible solution. Moreover, the integration of powder into the dielectric medium (Powder mixed EDM, PMEDM) along with LF vibration presents an interactive approach to further enhance process performance. Despite its promise, the field lacks a unified understanding of LFV-EDM’s underlying mechanisms, systematic optimization frameworks, and clear pathways for industrial integration. This paper presents a comprehensive overview of research focusing on the influence of process parameters on key performance indicators such as Material Removal Rate (MRR), Electrode Wear Rate (EWR), surface roughness (Ra), and geometric accuracy in LF vibration-assisted µEDM and EDM. Various optimization methodologies, including statistical modeling, finite element analysis (FEA), computational fluid dynamics (CFD), and advanced techniques like Taguchi and artificial neural networks (ANN) employed in this field are extensively reviewed. Critical analysis of contradictory findings and material-specific responses is included. The review concludes with identified research gaps and prioritized future directions, including hybrid processes, advanced powder materials, and AI-driven optimization for LF- assisted µEDM and EDM processes. This work provides researchers with a consolidated knowledge base, a critical perspective on current limitations, and a prioritized agenda for future innovation, ultimately bridging the gap between laboratory research and scalable industrial application.
Grinding is a key precision machining method for achieving high surface quality and dimensional accuracy in carbon fiber reinforced silicon carbide ceramic matrix composites (Cf/SiC). Ultrasonic vibration-assisted grinding (UVAG), with its high-frequency intermittent loading characteristics, offers a novel approach to regulating the dynamic removal behavior of heterogeneous materials. This study firstly analyzed the material removal mechanism of abrasive particles based on abrasive geometry and kinematics. On this basis, mechanical models are developed for a single abrasive grain across three removal stages: ductile removal, ductile-to-brittle transition, and brittle removal. These are further extended into a grinding force prediction model by integrating the effects of multiple abrasive grains and process correction factors during ultrasonic-assisted grinding. Finally, the model is validated through UVAG experiments. Results show that under an ultrasonic frequency of 20 kHz and amplitude of 5 μm, the predicted grinding forces match the experimental values with a high degree of accuracy (98.98%). This grinding force model provides theoretical support and process guidance for high-performance, low-damage precision machining of Cf/SiC composites.
The objective of marine ecological safety necessitates the development of comprehensive, integrated strategies for oil spill management, encompassing advanced monitoring and effective remediation. This paper introduces and validates a novel integrated methodology and conceptual framework for autonomous marine environmental safety. The core of this framework lies in the merging of AI-assisted monitoring capabilities with a multi-agent Unmanned Aerial Vehicle (UAV) system for targeted dispersant delivery. UAV systems, within this methodology, function as a cost-effective and readily deployable operational platform. The study details the primary development stages of the methodology-driven system and presents empirical results from in-situ field trials. The framework leverages artificial intelligence (AI) tools developed and validated for slick monitoring, which execute primary segmentation for spill detection and subsequent secondary segmentation to categorize the slick into thickness uniformity maps. Datasets of actual marine oil slick imagery were compiled to facilitate robust deep learning of the underlying neural network architectures. The study explores scientific feasibility, specifically employing Laser-Induced Fluorescence (LIF) spectroscopy to classify oil product grades and assess the ecological impact of various remediation agents on local phytoplankton communities. This integrated method for spill response is underpinned by successful field validation results. The full methodology was tested during actual oil spill incidents in the waters of Peter the Great Bay from 2019 to 2024. The article presents experimental validation of a new concept and methodology of integrated environmental safety of marine areas by a multi-agent UAV system in the event of oil product spills.
The rapid evolution of geoinformatics technologies, particularly through the adoption of Unmanned Aircraft Systems (UAS), has brought significant changes to the collection, processing, and analysis of spatial data. UAS are increasingly integrated into Geographic Information Systems (GIS), remote sensing, and spatial analysis, enhancing efficiency and accuracy in applications such as precision agriculture and infrastructure management. However, limited empirical research has examined the consequences of their integration for operational efficiency, regulatory compliance, and related management practices in the Greek context. This study evaluates how UAS integration into the operations of Greek geoinformatics firms enhances efficiency and supports compliance with Greek and European regulatory frameworks. A qualitative multi-case study methodology is employed across five Greek geoinformatics service providers, and data are collected through semi-structured interviews and secondary sources. Findings indicate that UAS integration improves the quality of spatial data, reduces data collection costs, and facilitates regulatory compliance of these firms. Finally, the study highlights the emergence of optimal operational management policies of UAS including standardized end-to-end workflows, clear role allocation and compliance responsibilities, systematic QA/QC procedures, proactive regulatory monitoring (PDRA/SORA readiness), which strengthen and promote innovative geoinformatics technologies.
