Search Results (8,374)

Background and Objectives: Endoscopic biopsy of brain lesions plays an important role in the management of intra- and periventricular lesions. While the diagnostic yield of this technique has been reported with varying success across studies, its outcome is likely influenced by specific technical nuances of the procedure. However, the relationship between these technical factors and diagnostic accuracy remains understudied in the current literature. We aim to describe the procedural rationale, key anatomical considerations, and technical nuances of the endoscopic biopsy of intra- and paraventricular brain lesions, comparing standard tissue forceps with a side-cutting biopsy needle technique. Materials and Methods: We conducted a ten-year single-center, retrospective study of patients who underwent endoscopic biopsy for intra- and paraventricular brain lesions between January 2014 and December 2024. Patients were divided based on the biopsy technique used: the first group of 11 patients was treated using a side-cutting biopsy needle from the center of the lesion, while the second group of five patients underwent tissue sampling with standard endoscopic tissue cup forceps. The study evaluates and compares both approaches in terms of safety and diagnostic accuracy. Results: Endoscopic visualization enabled direct assessment of the biopsy site in both groups. Histopathological diagnoses were successfully obtained in all cases with a side-cutting biopsy needle (11/11, 100.0%), and in almost all cases with the cup forceps technique (4/5, 80.0%). In patients with obstructive hydrocephalus, an endoscopic third ventriculostomy (ETV) was performed as the first and therapeutic step in all procedures and two patients required a shunt procedure. Conclusions: Endoscopic biopsies utilizing a side-cutting biopsy needle strategy offer a promising adjunctive approach for selected intra- and paraventricular brain lesions. This method allows for direct visualization of the intraventricular surface, while the use of a needle biopsy can enhance the likelihood of obtaining diagnostically representative tissue with a high degree of reliability. Full article

Introduction: Mounting evidence indicates that the p53 Pro72Arg single-nucleotide polymorphisms (SNPs) may play a role in modulating hepatocarcinogenesis in the setting of chronic HBV infection. However, there is currently a lack of studies focusing on this genetic variant in Indonesia, a country characterized by its diverse genetic landscape comprising over 1300 distinct ethnic groups. We aimed to investigate the association between the p53 Pro72Arg polymorphism and the risk of hepatocellular carcinoma (HCC) among Indonesian patients with chronic HBV infection. Methods: A total of 140 patients with chronic hepatitis B (CHB) were recruited, including 79 with HCC and 61 without HCC serving as controls. We used direct sequencing of DNA extracted from peripheral blood to analyze the SNPs of p53 codon 72. Results: The distribution of p53 Pro72Arg genotypes among Indonesian CHB patients was 12.9% for proline homozygotes (Pro/Pro), 31.4% for arginine homozygotes (Arg/Arg), and 55.7% for proline/arginine heterozygotes (Pro/Arg). Despite the lack of association between the SNPs and HCC risk in the overall population, both the Pro/Arg and Arg/Arg genotypes demonstrate an increased susceptibility to HCC compared to Pro/Pro genotypes exclusively in the Madurese ethnic group. Additionally, we discovered that in those with decompensated cirrhosis, the heterozygote Pro/Arg was more likely to develop HCC than the homozygous Pro/Pro. No significant association was found between the SNPs of p53 Pro72Arg and the clinicopathological characteristics of HCC. Conclusions: The p53 Pro72Arg polymorphism might contribute to hepatocarcinogenesis in Indonesian chronic hepatitis B patients, particularly Madurese and those with liver decompensation. Full article

This study addresses the challenge of identifying heavy Building Information Modeling (BIM) library components that disproportionately degrade model performance. While BIM has become standard in the construction industry, heavy components characterized by excessive geometric complexity, numerous instances, or inefficient optimization—cause extended file loading times, interface lag, and coordination difficulties, particularly in large cross-industry projects. Current identification methods rely primarily on designer experience and manual inspection, lacking systematic evaluation frameworks. This research develops a multi-criteria evaluation method based on Multi-Criteria Decision Analysis (MCDA) that quantifies component performance impact through five weighted criteria: instance count (20%), geometry complexity (30%), face count (20%), edge count (10%), and estimated file size (20%). These metrics are aggregated into a composite Weight Score, with components exceeding a threshold of 200 classified as requiring optimization attention. The method was implemented as HeavyFamilies, a pyRevit plugin for Autodesk Revit featuring a graphical interface with tabular results, CSV export functionality, and direct model visualization. Validation on three real BIM projects of varying scales (133–680 families) demonstrated effective identification of heavy components within 8–165 s of analysis time. User validation with six BIM specialists achieved 100% task completion rate, with automatic color coding and direct model highlighting particularly valued. The proposed approach enables a shift from reactive troubleshooting to proactive quality control, supporting routine diagnostics and objective prioritization of optimization efforts in federated and multi-disciplinary construction projects. Full article

Currently, water pollution is one of the major global environmental sustainability and public health issues that requires efficient and viable remediation technologies, as existing decontamination methods face limitations. In this sense, this review aims to highlight the potential of multifunctional aerogel-based magnetic nanocomposites as a novel strategy for water decontamination by integrating magnetic nanostructures into aerogel matrices that promote high adsorption capacity, selective catalysis, and facile magnetic recovery. In this regard, providing a comprehensive analysis of their functional design, contaminant-removal mechanisms, and multifunctional performance is crucial for developing and optimizing a system capable of addressing complex pollutants through multiple mechanisms (e.g., adsorption, photocatalysis, and reductive pathways). However, ecotoxicological evaluations focus on the potential for nanoparticles to leach, induce oxidative stress, and cause aquatic toxicity, supporting the development of strategies that comply with safety principles. Additionally, this review examines the aerogels’ capabilities for regeneration, operational stability, and scalability across repeated-use cycles, as well as their potential for real-world wastewater applications. Moreover, future directions for these aerogels include the development of smart, stimuli-responsive aerogels, machine-learning-based modeling, and the use of green synthesis approaches to enable sustainable water remediation strategies. Full article

Polychrome sculptures are complex, multilayered artifacts that embody the intersection of artistic craftsmanship, material science, and cultural heritage. Over the past two decades, the study of material identification in polychrome sculptures has shown marked interdisciplinary development, driven by advances in analytical technologies that have transformed how these objects are studied, enabling high-resolution identification of pigments, binders, and structural substrates. This review synthesizes key developments in the identification of polychrome sculpture materials, focusing on the integration of non-destructive and molecular-level techniques such as XRF, FTIR, Raman, LIBS, GC-MS, and proteomics. It highlights regional and historical variations in materials and craft processes, with case studies from Brazil, China, and Central Africa demonstrating how multi-modal methods reveal both technical and ritual knowledge embedded in these artworks. The review also examines evolving research paradigms—from pigment identification to stratigraphic and cross-cultural interpretation—and discusses current challenges such as organic material degradation and the need for standardized protocols. Finally, it outlines future directions including AI-assisted diagnostics, multimodal data fusion, and collaborative conservation frameworks. By bridging scientific analysis with cultural context, this study offers a comprehensive methodological reference for the conservation and interpretation of polychrome sculptures worldwide. Full article

In aerospace, defense, and energy systems, ceramic matrix composites (CMCs) are smart structural materials designed to function continuously in harsh mechanical, thermal, and oxidative conditions. Using high-strength fiber reinforcements and tailored interphases that enable damage-tolerant behavior, their creation tackles the intrinsic brittleness and low fracture toughness of monolithic ceramics. With a focus on chemical vapor infiltration, polymer infiltration and pyrolysis, melt infiltration, and additive manufacturing, this paper critically analyzes current developments in microstructural design, processing technologies, and interfacial engineering. Toughening mechanisms are examined in connection to multiscale mechanical responses, including controlled debonding, fiber bridging, fracture deflection, and energy dissipation pathways. Cutting-edge environmental barrier coatings are assessed alongside environmental durability issues like oxidation, volatilization, and hot corrosion. High-performance braking, nuclear systems, hypersonic vehicles, and turbine propulsion are evaluated as emerging uses. Future directions emphasize self-healing systems, ultra-high-temperature design, and environmentally friendly production methods. Full article

Background: Transcranial Direct Current Stimulation (tDCS) in conjunction with behavioural language therapy in PPA has previously been modified for variation at the group level, but not at the individual level. This pilot study used individualised tDCS targeting by identifying regions of peak atrophy in the language system. Methods: Six PPA participants (four semantic and two non-fluent variant) were randomly allocated to receive tDCS or sham stimulation. The target electrode was selected for each based on their region of peak atrophy. Participants received naming therapy, individually calibrated according to baseline naming performance. Three sets of therapy were delivered in conjunction with tDCS (1 mA) or sham stimulation within participants’ homes. The study was not powered to demonstrate efficacy but to show proof-of-concept for an individualised, home-based tDCS targeting method. Results: All participants successfully completed the protocol. In one participant the region of peak atrophy differed from that predicted by clinical syndrome. Significant gains were observed at an individual level for treated items in both groups (2/3 tDCS and 2/3 Sham). No significant changes in untreated items were observed at an individual level. Significant naming improvement in untreated items was not observed for the tDCS group and was seen at one time point only for the Sham group. Conclusions: We have demonstrated the feasibility of a novel method for selecting neurostimulation targets for PPA at the individual level. A larger study would be required to determine the long-term therapeutic efficacy of this method. Full article

High voltage direct current (HVDC) gas-insulated equipment (GIE) has become a critical component in long-distance power transmission projects, owing to its advantages such as compact structure and high reliability. However, the gas–solid interface insulation of DC GIE under long-term operation faces charge accumulation phenomenon, which will distort the electric field distribution and cause insulation flashover. Due to the lack of technical guidelines for the insulation design of DC gas-insulated equipment, the method of insulation design usually adopts increasing the insulation structure size to ensure sufficient creepage along the surface, which greatly increases the dimensions and manufacturing costs of the final equipment, and fails to fully leverage the unique advantages of GIE in compactness and lightness. Therefore, it is of importance to clarify the mechanism of charge accumulation on the surface of insulators under HVDC, and to propose an insulation design method that can effectively inhibit the charge accumulation and adjust the electric field distribution at the gas–solid interface, which holds practical significance for the safe application of large-scale DC GIE projects. In view of this, this paper firstly summarizes the characteristics of surface charge accumulation at gas–solid interface, and then reviews the existing research progress from two perspectives: surface charge suppression of insulation structure and gas–solid interface electric field regulation, providing theoretical and technical support for optimizing the design of GIE insulation structure, formulating scientific operation and maintenance measures. Full article

Background: Perceptual decision-making requires transforming sensory inputs into goal-directed actions under uncertainty. Neural oscillations in the theta band (3–7 Hz), particularly within frontal regions, have been implicated in cognitive control and decision confidence. However, whether changes in theta oscillations reflect greater effort during ambiguous decisions or more efficient control during clear conditions remains debated, and theta’s relationship to stimulus clarity is incompletely understood. Purpose: This study’s purpose was to examine how task difficulty modulates theta activity and how theta dynamics evolve across the decision-making process using two complementary analytical approaches. Methods: Electroencephalography (EEG) data were acquired from 26 healthy adults performing a face/house categorization task with images containing three levels of scrambled phase and Gaussian noise: clear (0%), moderate (40%), and high (55%). Theta dynamics were assessed from current source density (CSD) time courses of event-related potentials (ERPs) and single-trials. Statistical comparisons used Wilcoxon signed-rank tests with false discovery rate (FDR) correction for multiple comparisons. Results: Frontal theta power was greater for clear than noisy face stimuli (corrected p < 0.001), suggesting that theta activity reflects cognitive control effectiveness and decision confidence rather than processing difficulty. Connectivity decomposition revealed that frontoparietal theta coupling was modulated by stimulus clarity through both phase-locked (evoked: corrected p = 0.0085, dz = −0.61) and ongoing (induced: corrected p = 0.049, dz = −0.36) synchronization, with phase-locked coordination dominating the effect and showing opposite directionality to the induced components. Conclusions: Theta oscillations support perceptual decision-making through stimulus clarity modulation of both phase-locked and ongoing synchronization, with evoked component dominating. These findings underscore the importance of methodological choices in EEG-based connectivity research, as different analytical approaches capture different aspects of the same neural dynamics. The pattern of stronger theta activity for clear stimuli is consistent with neural processes related to decision confidence, though confidence was not measured behaviorally. Full article

Background/Objectives: Energy availability (EA), defined as the dietary energy remaining after exercise energy expenditure (EEE), is a central determinant of both health and performance in athletes. Chronic insufficient EA leads to low energy availability (LEA), which is an underlying mechanism of Relative Energy Deficiency in Sport (REDs). This narrative review critically explores the conceptual evolution of EA and LEA, summarizes current physiological evidence, and discusses methodological and practical challenges in their assessment and application in free-living athletes. Methods: Evidence from experimental and observational studies was reviewed to describe the hormonal, metabolic, and performance outcomes associated with LEA. Screening tools, including the Low Energy Availability in Females Questionnaire (LEAF-Q) and the Low Energy Availability in Males Questionnaire (LEAM-Q), were also evaluated for their validity and applicability in different sports contexts. Results: LEA is associated with alterations in thyroid and reproductive hormones, which, in turn, contribute to reduced resting metabolic rate, lower bone mineral density, and delayed recovery. While screening questionnaires can help identify athletes at risk, their accuracy varies by sport and individual characteristics. Incorporating hormonal and metabolic biomarkers provides a more direct and sensitive method for detecting physiological stress. Measuring dietary intake, EEE, endocrine balance and body composition in real-world settings remains a major methodological challenge. Combining hormonal, metabolic, and behavioral indicators may improve the identification of athletes experiencing LEA. Conclusions: EA plays a central role in the interaction between nutrition, exercise, and athlete health, but methodological limitations in its assessment may compromise accurate diagnosis. Improving measurement techniques and adopting integrated monitoring strategies are essential to improve early detection, guide individualized nutrition, and prevent RED-related health and performance impairments. Full article

This review addresses the pivotal challenge in distributed-drive electric vehicle (DDEV) dynamics control: how to optimally distribute braking and steering forces during combined maneuvers to simultaneously enhance lateral stability, safety, and energy efficiency. The over-actuated nature of DDEVs presents a unique opportunity for precise torque vectoring but also introduces complex coupled dynamics, making vehicles prone to instability such as rollover during aggressive steering–braking scenarios. Moving beyond a simple catalog of methods, this work provides a structured synthesis and evolutionary analysis of chassis control methodologies. The problem is first deconstructed into two core control objectives: lateral stability and longitudinal braking performance. This is followed by a critical analysis of how integrated control architectures resolve the inherent conflicts between them. The analysis reveals a clear trajectory from independent control loops to intelligent, context-aware coordination. It further identifies a paradigm shift from the conventional goal of merely maintaining stability toward proactively managing stability boundaries to enhance system resilience. Furthermore, this review highlights the growing integration with high-level motion planning in automated driving. By synthesizing the current knowledge and mapping future directions toward deeply integrated, intelligent control systems, it serves as both a reference for researchers and a design guide for engineers aiming to unlock the full potential of the distributed drive paradigm. Full article

Voltage source converter-based–high voltage direct current (VSC-HVDC) systems exhibit strong nonlinear characteristics that dominate their dynamic behavior under large disturbances, making large-signal stability assessment essential for secure operation. This paper proposes a large-signal stability analysis framework for VSC-HVDC systems. The framework combines a unified Takagi–Sugeno (T–S) fuzzy model with a model-free predictive control (MFPC) scheme to enlarge the estimated domain of attraction (DOA) and bring it closer to the true stability region. The global nonlinear dynamics are captured by integrating local linear sub-models corresponding to different operating regions into a single T–S fuzzy representation. A Lyapunov function is then constructed, and associated linear matrix inequality (LMI) conditions are derived to certify large-signal stability and estimate the DOA. To further reduce the conservatism of the LMI-based iterative search, we embed a genetic-algorithm-based optimizer into the model-free predictive controller. The optimizer guides the improved LMI iteration paths and enhances the DOA estimation. Simulation studies in MATLAB 2023b/Simulink on a benchmark VSC-HVDC system confirm the feasibility of the proposed approach and show a less conservative DOA estimate compared with conventional methods. Full article

The digitalization of machining processes is increasingly recognized as essential for achieving higher productivity, reliability, and traceability. However, access to reliable in-process sensor data remains limited, particularly in multi-axis CNC machining, where dimensional accuracy and surface integrity strongly depend on stable and optimized process conditions. This study investigates sensor-based monitoring as a practical approach for evaluating process performance in five-axis CNC milling. Electric current and vibration signals were acquired during three machining operations, under distinct cutting parameters, using current clamps and a plug-and-play MEMS accelerometer. The signals were processed using the root mean square method to assess the correlation between sensor data and machining conditions. Dimensional inspection of each workpiece was carried out to verify geometric conformity. The results show that spindle current measurements exhibit a strong linear correlation with material removal rate and cutting power, supporting their use as indicators of cutting forces and energy consumption. Vibration signals revealed pronounced dynamic behaviour for specific tool orientations, particularly in transverse to tool axis direction. The proposed methodology provides a simple and low-cost framework for integrating sensor-based monitoring into five-axis CNC milling, particularly relevant for semi-roughing operations, and offers a basis for future studies on process optimization and real-time condition monitoring. Full article

Battery management systems (BMSs) are integral components of electric vehicles (EVs), as they ensure the safe and efficient operation of lithium-ion batteries. State of health (SoH) estimation is one of the core functions of BMSs, providing an assessment of the current condition of a battery, while prognostics aim to predict remaining useful life (RUL) as a function of the battery’s condition. An accurate SoH estimation allows proactive maintenance to prolong battery lifespan. Traditional SoH estimation methods can be broadly divided into experiment-based and model-based approaches. Experiment-based approaches rely on direct physical measurements, while model-driven approaches use physics-based or data-driven models. Although experiment-based methods can offer high accuracy, they are often impractical and costly for real-time applications. With recent advances in artificial intelligence (AI), deep learning models have emerged as powerful alternatives for SoH prediction. This paper offers an in-depth examination of AI-driven SoH prediction technologies, including their historical development, recent advancements, and practical applications, with particular emphasis on the implementation of widely used AI algorithms for SoH prediction. Key technical challenges associated with SoH prediction, such as computational complexity, data availability constraints, interpretability issues, and real-world deployment constraints, are discussed, along with possible solution strategies. Full article

Detecting rip currents from satellite imagery offers valuable information for the characterization and assessment of this coastal hazard. While recent advances in deep learning have enabled automatic detection from close-view beach images, the broader geospatial context available in far-view satellite imagery has not yet been fully exploited. The main challenge lies in identifying rips as small objects within large and visually complex scenes that include both beach and non-beach areas. To address this, we proposed a detection pipeline which partitions high-resolution satellite images into small regions on which rip currents are detected using a deep learning object detection model that merges the results. The merged results are processed by applying a deep learning classification model to filter out non-beach scenes, followed by applying the detection model on augmented images to remove spurious detection. The proposed pipeline achieved an overall accuracy of 98.4%, a recall of 0.890, a precision of 0.633, and an F2 score of 0.823 on the testing dataset, demonstrating its effectiveness in locating rip currents within complex coastal scenes and its potential applicability to other regions. In addition, a new rip image dataset containing far-view satellite imagery was constructed. With the new dataset, we demonstrated a potential application of the proposed method in characterizing rip occurrences and found that rip currents tended to occur at open beaches under moderate-energy, onshore-directed waves conditions. Overall, the proposed pipeline, unlike existing near-real-time rip current monitoring systems, provides a high-accuracy offline analysis tool for rip current assessment using satellite imagery. Along with the new dataset introduced in this work, it can represent a valuable step towards expanding available resources for improving automated detection methods and rip current research. Full article