WEBA dataset as the Reflection of Work content effect on Workload perception in Real life Working conditions.

The effect of work content on workload, stress, and performance was not well addressed in the literature, due to the lack of comprehensive conceptualization, problem definition, and relevant dataset. The gap between laboratory-simulated studies and real-life working conditions delays the generalization, hindering the development of performance management and monitoring tools. Contributing to this topic, a data collection effort is organized, which considers unique work conditions and work content factors of a coffee shop, to conceptualize scenarios that better highlight their effect on human performance, thus creating the Work content Effect on BAristas (WEBA) dataset.

"MoSpec": A customized and integrated system for model development, verification and validation.

Background And Objective: The growing availability of patient data from several clinical settings, fueled by advanced analysis systems and new diagnostics, presents a unique opportunity. These data can be used to understand disease progression and predict future outcomes. However, analysing this vast amount of data requires collaboration between physicians and experts from diverse fields like mathematics and engineering.

Internet as a Source of Public Health Information on Acupuncture for Pain Relief: Credibility Assessment and Readability Analysis.

Background: In recent years, there has been a notable increase in the number of patients seeking information from online health websites. As the information available on these websites can significantly impact the overall health of individuals in a society, it is vital for online health information to be presented in a manner that is readable and credible to the general public. To address this concern, the objective of the study was to examine and assess the credibility and readability of websites about acupuncture as a pain management approach.

A Machine Learning Model to Harmonize Volumetric Brain MRI Data for Quantitative Neuroradiological Assessment of Alzheimer Disease.

Purpose To extend a previously developed machine learning algorithm for harmonizing brain volumetric data of individuals undergoing neuroradiological assessment of Alzheimer disease not encountered during model training. Materials and Methods Neuroharmony is a recently developed method that uses image quality metrics (IQM) as predictors to remove scanner-related effects in brain-volumetric data using random forest regression. To account for the interactions between Alzheimer disease pathology and IQM during harmonization, the authors developed a multiclass extension of Neuroharmony for individuals with and without cognitive impairment.

Tensor-based analysis of eclectic-reported crowd accidents: An innovative retrospective methodology design study.

Introduction: Analysis of crowd accidents contributes to accident prevention. Therefore, we employ a tensor-based approach. The innovative tensor-based approach facilitates the streamlining of longitudinal studies, promotes error detection, and enhances the transparency and traceability of data collection.

A Prospective Evaluation of Chemokine Receptor-4 (CXCR4) Overexpression in High-grade Glioma Using Ga-Pentixafor (Pars-Cixafor™) PET/CT Imaging.

Background: While magnetic resonance imaging (MRI) remains the gold standard for morphological imaging, its ability to differentiate between tumor tissue and treatment-induced changes on the cellular level is insufficient. Notably, glioma cells, particularly glioblastoma multiforme (GBM), demonstrate overexpression of chemokine receptor-4 (CXCR4). This study aims to evaluate the feasibility of non-invasive Ga-Cixafor™ PET/CT as a tool to improve diagnostic accuracy in patients with high-grade glioma.

The Synergy of Thermal and Non-Thermal Effects in Hyperthermic Oncology.

Background: Modulated electro-hyperthermia (mEHT) is unique due to its combination of thermal and non-thermal effects.

Method: This report summarizes the literature on the effects of mEHT observed in vitro and in vivo.

Results: The thermal and electrical heterogeneity of tissues allows the radiofrequency signal to selectively target malignant tissue.

Effect of plantar fascia stiffness on plantar windlass mechanism and arch: Finite element method and dual fluoroscopic imaging system verification.

This study explored the relationship between the foot arch stiffness and windlass mechanism, focusing on the contribution of the posterior transverse arch. Understanding the changing characteristics of foot stiffness is critical for providing a scientific basis for treating foot-related diseases. Based on a healthy male's computed tomography, kinematic, and dynamics data, a foot musculoskeletal finite element model with a dorsiflexion angle of 30°of metatarsophalangeal joint was established.

Tracking control for two-wheeled mobile robots via event-triggered mechanism.

In this paper, we investigate the event-based tracking control for two-wheeled mobile robots using a sliding mode control strategy. To address the conflict between the singularity problem and finite-time performance, a new nonsingular terminal sliding mode controller enabling mobile robots to achieve the tracking goal through a wireless network is developed. Further, redesign the controller using sampling information, in which an event condition is introduced to determine the sampling sequence, and the event-triggered controller avoids the high gain situation through the proposed sliding variables.

Application of Compensation Algorithms to Control the Speed and Course of a Four-Wheeled Mobile Robot.

This article presents a tuned control algorithm for the speed and course of a four-wheeled automobile-type robot as a single nonlinear object, developed by the analytical approach of compensation for the object's dynamics and additive effects. The method is based on assessment of external effects and as a result new, advanced feedback features may appear in the control system. This approach ensures automatic movement of the object with accuracy up to a given reference filter, which is important for stable and accurate control under various conditions.

The effect of virtual reality for anxiety and pain in dentistry: A systematic review and meta-analysis.

Objectives: This study aimed to assess the impact of virtual reality (VR) on reducing anxiety and pain in dental patients across all age groups and dental procedures.

Methods: Systematic review with comprehensive search of PubMed and Cochrane Library databases for randomized controlled trials (RCTs) comparing VR interventions with non-VR methods in dental settings up to April 2024. The selection followed the PRISMA-P guidelines.

Recent Advances in Artificial Intelligence to Improve Immunotherapy and the Use of Digital Twins to Identify Prognosis of Patients with Solid Tumors.

To date, the public health system has been impacted by the increasing costs of many diagnostic and therapeutic pathways due to limited resources. At the same time, we are constantly seeking to improve these paths through approaches aimed at personalized medicine. To achieve the required levels of diagnostic and therapeutic precision, it is necessary to integrate data from different sources and simulation platforms.

Parameter-Independent Deformation Behaviour of Diagonally Reinforced Doubly Re-Entrant Honeycomb.

In this study, a novel unit cell design is proposed, which eliminates the buckling tendency of the auxetic honeycomb. The novel unit cell design is a more balanced, diagonally reinforced doubly re-entrant auxetic honeycomb structure (x-reinforced auxetic honeycomb for short). We investigated and compared this novel unit cell design against a wide parameter range.

In-Depth Development of a Versatile Rumen Bolus Sensor for Dairy Cattle.

Precision agriculture and the increasing automation efforts in animal husbandry requires continuous and complex monitoring of the animals. Rumen bolus sensors, which are cutting-edge pieces of technology and a rapidly developing research field, present an exceptional opportunity for monitoring the health status, physiological parameters, and estrus of the animals. The objective of this paper is to provide a comprehensive overview of the development process of a new sensor development.

Enhanced Wavelet-Based Medical Image Denoising with Bayesian-Optimized Bilateral Filtering.

Medical image denoising is essential for improving the clarity and accuracy of diagnostic images. In this paper, we present an enhanced wavelet-based method for medical image denoising, aiming to effectively remove noise while preserving critical image details. After applying wavelet denoising, a bilateral filter is utilized as a post-processing step to further enhance image quality by reducing noise while maintaining edge sharpness.

An AI-driven approach for modeling the compressive strength of sustainable concrete incorporating waste marble as an industrial by-product.

A key goal of environmental policies and circular economy strategies in the construction sector is to convert demolition and industrial wastes into reusable materials. As an industrial by-product, Waste marble (WM), has the potential to replace cement and fine aggregate in concrete which helps with saving natural resources and reducing environmental harm. While many studies have so far investigated the effect of WM on compressive strength (CS), it is undeniable that conducting experimental activities requires time, money, and re-testing with changing materials and conditions.

Coronary Plaque Radiomic Phenotypes Predict Fatal or Nonfatal Myocardial Infarction: Analysis of the SCOT-HEART Trial.

Background: Coronary computed tomography (CT) angiography-derived attenuation-based plaque burden assessments can identify patients at risk of myocardial infarction.

Objectives: This study sought to assess whether more detailed plaque morphology assessment using patient-based radiomic characterization could further enhance the identification of patients at risk of myocardial infarction during long-term follow-up.

Methods: Post hoc analysis of coronary CT angiography was performed within the SCOT-HEART (Scottish Computed Tomography of the HEART) clinical trial.

Myocardial perfusion SPECT radiomic features reproducibility assessment: Impact of image reconstruction and harmonization.

Background: Coronary artery disease (CAD) has one of the highest mortality rates in humans worldwide. Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) provides clinicians with myocardial metabolic information non-invasively. However, there are some limitations to interpreting SPECT images performed by physicians or automatic quantitative approaches.

Vertical Microfluidic Trapping System for Capturing and Simultaneous Electrochemical Detection of Cells.

Electrochemical impedance spectroscopy (EIS) is a non-invasive and label-free method widely used for characterizing cell cultures and monitoring their structure, behavior, proliferation and viability. Microfluidic systems are often used in combination with EIS methods utilizing small dimensions, controllable physicochemical microenvironments and offering rapid real-time measurements. In this work, an electrode array capable of conducting EIS measurements was integrated into a multichannel microfluidic chip which is able to trap individual cells or cell populations in specially designed channels comparable to the size of cells.

Enhancing Brain Tumor Diagnosis with L-Net: A Novel Deep Learning Approach for MRI Image Segmentation and Classification.

Brain tumors are highly complex, making their detection and classification a significant challenge in modern medical diagnostics. The accurate segmentation and classification of brain tumors from MRI images are crucial for effective treatment planning. This study aims to develop an advanced neural network architecture that addresses these challenges.

Enhancing Lymphoma Diagnosis, Treatment, and Follow-Up Using F-FDG PET/CT Imaging: Contribution of Artificial Intelligence and Radiomics Analysis.

Lymphoma, encompassing a wide spectrum of immune system malignancies, presents significant complexities in its early detection, management, and prognosis assessment since it can mimic post-infectious/inflammatory diseases. The heterogeneous nature of lymphoma makes it challenging to definitively pinpoint valuable biomarkers for predicting tumor biology and selecting the most effective treatment strategies. Although molecular imaging modalities, such as positron emission tomography/computed tomography (PET/CT), specifically F-FDG PET/CT, hold significant importance in the diagnosis of lymphoma, prognostication, and assessment of treatment response, they still face significant challenges.

Explainable AI for automated respiratory misalignment detection in PET/CT imaging.

Positron emission tomography (PET) image quality can be affected by artifacts emanating from PET, computed tomography (CT), or artifacts due to misalignment between PET and CT images. Automated detection of misalignment artifacts can be helpful both in data curation and in facilitating clinical workflow. This study aimed to develop an explainable machine learning approach to detect misalignment artifacts in PET/CT imaging.

Fuzzy Petri Nets for Traffic Node Reliability.

Self-driving cars are one of the main areas of research today, but it has to be acknowledged that the information from the sensors (the perceptron) is a huge amount of data, which is now unmanageable even when projected onto a single traffic junction. In the case of self-driving, the nodes have to be sequenced and organized according to the planned route. A self-driving car in Hungary would have to be able to interpret more than 70,000 traffic junctions to be able to drive all over the country.