Search Results (63,871)

This work presents the development of a low-cost and high-performance multi-sensory gas detection instrument named the AIMNet Sensor, with the integration of a machine learning-based data processing method. The compact and low-power instrument (8.5 × 11.5 cm, 1.4 W) houses the core sensing hardware module, Senseair K96, that integrates both a non-dispersive infrared (NDIR)-based gas sensing unit and a BME280 environmental sensing unit. To address the outdoor operation challenges caused by environmental fluctuation due to the varying temperature, humidity, and pressure, from the software aspect, multiple machine learning-based regression models were trained in this work on 13,125 calibration data points collected under controlled laboratory conditions. Among ten tested algorithms, the Multilayer Perceptron (MLP) and Elastic Net models achieved the highest accuracy, with R-squared coefficient $R^2>0.8$ on both indoor and outdoor scenarios, and with inter-sensor root mean square error (RMSE) within 1.5 ppm across four identical instruments. Moreover, field mobile validation was performed near a wastewater management facility using this solution, confirming a strong correlation with LI-COR reference measurements and a reliable detection of CH${}_4$ leaks with concentrations up to 18 ppm at the test site. Overall, this machine learning-integrated NDIR sensing solution (i.e., AIMNet) offers a practical and scalable solution towards a more robust distributed CH${}_4$ monitoring network for real-world field-deployable applications. Full article

Grey systems theory has provided a change in paradigm related to how numbers and their mathematics are perceived. By including various levels of knowledge associated with the variables, the theory has succeeded in modelling systems characterised by incomplete or partially known information. Among the methods offered by the grey systems theory, the grey clustering approach offers a distinct perspective on clustering methodology by allowing researchers to define degrees of importance for the variables included in the analysis. Despite its expanding use across disciplines, a comprehensive synthesis of grey clustering research is lacking. In this context, this study aims to provide a comprehensive and structured overview of the research field associated with grey clustering and its applications, rather than the more rhetorical formulation previously included. By using a PRISMA approach, a dataset containing papers related to grey clustering is extracted from the Clarivate Web of Science database and analysed through bibliometric tools and further enhanced by providing thematic maps and topics discovery through the use of Latent Dirichlet Allocation (LDA) and BERTopic analyses. The final dataset includes 318 articles, and their examination allows for a detailed assessment of publication trends, thematic structures, and methodological directions. The annual scientific production showcased an increase of 10.78%, while the thematic analysis revealed key themes related to performance management, risk assessment, evaluation models for enhancing organisational performance, urban and regional planning, civil engineering, industrial engineering and automation, and risk evaluation for health-related issues. Additionally, a detailed review of the most-cited papers has been performed to highlight the role of grey clustering in various research fields. Full article

Background/Objectives: Arteriovenous malformations (AVMs) in the dominant temporo-parieto-occipital (TPO) junction of the brain are extremely rare and very difficult to remove surgically because this area includes multiple sensory and language networks. Due to the fact that many patients present with bleeding, surgeons have to find a delicate balance between removing all of the AVM tissue and preserving the functional areas of the brain where important functions occur. This study is reporting a case demonstrating how precise clinical–radiologic correlation, detailed anatomical knowledge, and deliberate microsurgical techniques can allow safe removal of the AVM and improve the patient’s neurologic function without the need for additional intraoperative technology. Case Presentation: A 47-year-old right-handed male patient experienced persistent neurological deficits after experiencing a hemorrhage from an AVM in his dominant posterior hemisphere, which included mild language difficulties, right hemifacial–brachial spasticity, parietal sensory loss and a visual field defect of his right eye known as an inferior quadrantanopia localized to the TPO junction. Cerebral angiography identified a small, compact, high-flow AVM (40 × 30 mm) fed by distal branches of the middle cerebral artery (M4), posterior cerebral artery (P4), anterior cerebral artery (A4), as well as a small branch of the superior cerebellar artery (SCA). Blood drained into two veins of the Trolard and Labbé. The authors removed the AVM completely by circumferential dissection of the nidus along gliotic planes using a microscope. Feeders were then sequentially disconnected, and the venous outflow was preserved until the AVM could be removed en bloc. Post-operative angiograms demonstrated complete removal of the AVM with normalization of blood flow to the surrounding cortex. The patient’s neurologic function improved over time and at three months post-operatively, he was functioning independently (modified Rankin Scale = 1; Barthel Index = 100) and there was no evidence of residual nidus or edema on imaging. Conclusions: High-flow AVMs in the dominant TPO junction can be completely removed using a disciplined microsurgical approach and a feeder first/vein last disconnection method based on anatomy. The patient’s improvement in function represented reperfusion and reintegration of an injured but still functional network of the brain, reinforcing the idea that careful observation, a deep understanding of brain anatomy, and restrained surgical technique are critical to achieving long-term results in AVM surgery. Full article

The primary objective of this study is to enable an unmanned surface vehicle (USV) to autonomously approach the extremum of an unknown scalar field using only real-time field measurements. To this end, a source-seeking method based on timescale separation is developed within a hierarchical control framework that divides the closed-loop system into a slow and a fast subsystem. The slow subsystem governs the gradual evolution of the USV pose and generates reference heading and surge commands from local scalar field information, providing a directional cue toward the field extremum. The fast subsystem applies actuator-level control inputs that ensure these references are tracked with sufficient accuracy through rapid corrective actions. A Lyapunov-based analysis is carried out to study the stability properties of the coupled slow–fast dynamics and to establish conditions under which convergence can be guaranteed in the presence of model nonlinearities and external disturbances. Numerical simulations are conducted to illustrate the resulting system behavior and to verify that the proposed framework maintains stable seeking performance under typical operating conditions. Full article

In order to break the limitation of metamaterials used in the vibration and sound reduction field, this work designed a two-dimensional metamaterial based on the re-entrant honeycomb lattice and using the fractal technique. The first, second, and third-order fractal re-entrant honeycomb metamaterials are analyzed, respectively, within the established numerical models responsible for predicting the effective Poisson’s ratio, the real band structure, and the attenuation diagram. The effects of the fractal order, fractal ratio, and geometrical characteristics on these multiple functionalities are investigated simultaneously. Through adjusting the proposed fractal metamaterials, the results show that the transformation of auxetic performance, the number and location of multiple stop bands, the attenuation level inside the stop bands, and the wave decaying directionality can be flexibly tuned. This demonstrates that the compatibility of mechanical features and wave motion characteristics is successfully achieved in the present work. It provides a theoretical and technical basis for the development of multi-functional design methods of metamaterials in solving engineering problems. Full article

Advances in deep learning are impressive in various fields and have achieved performance beyond human capabilities in tasks such as image classification, as demonstrated in competitions such as the ImageNet Large Scale Visual Recognition Challenge. Nonetheless, complex applications like medical imaging continue to present significant challenges; a prime example is the Human Protein Atlas (HPA) dataset, which is computationally challenging and complex due to the high-class imbalance with the presence of rare patterns and the need for multi-label classification. It includes 28 distinct patterns and more than 500 unique label combinations, with protein localization that can appear in different cellular regions such as the nucleus, the cytoplasm, and the nuclear membrane. Moreover, the dataset provides four distinct channels for each sample, adding to its complexity, with green representing the target protein, red indicating microtubules, blue showing the nucleus, and yellow depicting the endoplasmic reticulum. We propose a two-phase transfer learning approach based on feature-block extraction from twelve ImageNet-pretrained CNNs. In the first phase, we address single-label multiclass classification using CNNs as feature extractors combined with SVM classifiers on a subset of the HPA dataset. We demonstrate that the simple concatenation of feature blocks extracted from different CNNs improves performance. Furthermore, we apply a genetic algorithm to select the sub-optimal combination of feature blocks. In the second phase, based on the results of the previous stage, we apply two simple multi-label classification strategies and compare their performance with four classifiers. Our method integrates image-level and cell-level analysis. At the image level, we assess the discriminative contribution of individual and combined channels, showing that the green channel is the strongest individually but benefits from combinations with red and yellow. At the cellular level, we extract features from the nucleus and nuclear-membrane ring, an analysis not previously explored in the HPA literature, which proves effective for recognizing rare patterns. Combining these perspectives enhances the detection of rare classes, achieving an F1 score of 0.8 for “Rods & Rings”, outperforming existing approaches. Accurate identification of rare patterns is essential for biological and clinical applications, underscoring the significance of our contribution. Full article

In this work, we propose a fully decentralized, self-organizing control framework for a swarm of autonomous ground mobile robots. The system integrates potential field-based mechanisms for simultaneous trajectory tracking, formation control, and obstacle avoidance, all based on local sensing and neighbor interactions without centralized coordination. Each robot autonomously computes attractive, repulsive, and formation forces to navigate toward target positions while maintaining inter-robot spacing and avoiding both static and dynamic obstacles. Inspired by biological swarm behavior, the controller emphasizes robustness, scalability, and flexibility. The proposed method has been successfully validated in the ARGoS simulator, which provides realistic physics, sensor modeling, and a robust environment that closely approximates real-world conditions. The system was tested with up to 15 robots and is designed to scale to larger swarms (e.g., 100 robots), demonstrating stable performance across a range of scenarios. Results obtained using ARGoS confirm the swarm’s ability to maintain formation, avoid collisions, and reach a predefined goal area within a configurable 1 m radius. This zone serves as a spatial convergence region suitable for multi-robot formation, even in the presence of unknown fixed obstacles and movable agents. The framework can seamlessly handle the addition or removal of swarm members without reconfiguration. Full article

A knowledge graph is constructed for flight test safety, which is conducive to enhancing the data deduction ability in flight test monitoring and offers efficient and highly complex decision-making support for safety monitoring. Based on this graph, an aircraft attitude predictive model is established by employing neural network technology. This model can accurately predict the changes in aircraft attitude under pilot manipulation, with a mean absolute error of 0.18 degrees in the predicted angle of attack values. By integrating the knowledge graph and the predictive model, an auxiliary decision-making method for abnormal aircraft attitude situations is proposed. This method calculates the safety manipulation space of the aircraft under different flight states through risk quantification technology, providing a theoretical basis for the pilots’ manipulation decisions in abnormal attitude situations. The research is verified based on simulation data, which not only enhances the scientific rigor and practicability of flight test safety monitoring in simulated scenarios but also provides new theoretical support and technical approaches for the field of flight safety. Full article

Background: Post-traumatic stress disorder (PTSD) is a debilitating psychiatric condition with limited treatment efficacy. Alternating current electroacupuncture (AC-EA) represents a novel neuromodulatory approach, though its mechanisms—particularly its influence on the gut–brain axis—remain underexplored. Methods: We investigated the neurobehavioral and microbiological effects of AC-EA in a rat model of PTSD induced by single prolonged stress. Animals received AC-EA at Baihui (GV20) and Mingmen (GV4) acupoints with varying parameters (0.5 mA/20 Hz, 1 mA/20 Hz, and 1 mA/2 Hz). Behavioral tests (open field test, elevated plus maze), histopathological assessments, immunofluorescence for TLR4, and 16S rRNA sequencing of gut microbiota were performed. Results: AC-EA at 1 mA/2 Hz significantly improved exploratory behavior and reduced anxiety-like responses (p < 0.05). This regimen also restored neuronal integrity in the hippocampus and cortex and reversed PTSD-induced gut dysbiosis, enriching beneficial genera such as Ligilactobacillus. Furthermore, AC-EA downregulated hepatic TLR4 expression, indicating suppression of neuroinflammatory signaling. Conclusions: Our findings demonstrate that AC-EA exerts neuromodulatory and microbiota-rebalancing effects via the gut–brain axis, highlighting its potential as a non-invasive therapeutic strategy for PTSD and related brain health disorders. Full article

This study presents a model experiment on the oblique maneuvering of a ship in a floating ice environment. A series of captive model tests was conducted in both open-water and synthetic ice fields at concentrations of 60%, 70%, and 80%. The model was tested in a conventional towing tank using non-refrigerated polypropylene ice floes to simulate a broken ice field. Surge force, sway force, and yaw moment on the hull were measured under various drift angles and three speeds. Results show that in oblique motion, ice floes around the hull experience significant overturning and piling up, especially on the drift side, leading to random collisions with the hull. These interactions markedly affect the hydrodynamic forces. As the drift angle increases, the surge, sway, and yaw forces on the hull increase nonlinearly. The comparison between open-water and ice conditions indicates that floating ice can significantly increase the resistance and maneuvering forces. Higher ice concentrations lead to more frequent and more extensive contact between the hull and the ice floes, thereby further amplifying all components of the hydrodynamic forces. This work provides experimental data for validating calculation methods of ship resistance and maneuvering in broken ice. It demonstrates a feasible experimental approach for studying ship maneuvers in a floating ice channel. Full article

By solving the three-dimensional Schrödinger equation with a second-order implicit Finite Difference Method (FDM), the combined effects of temperature, morphology, hydrostatic pressure, and transverse electric field on the nonlinear optical rectification (NOR) of GaAs/Al_εGa_1−εAs elliptical quantum rings are examined. The NOR amplitude is twelve times enhanced and a noticeable blue shift is induced in the THz region when the electric field is increased. Consequently, with the electric field of 1 × 10⁵ V/m, the NOR magnitude achieves its maximum value of 17.116 × 10⁵ m/V. Additionally, when the electric field is aligned along one side of the system’s in-plane cross-section, the strongest amplification takes place. However, with corresponding spectrum shifts, the NOR intensity rises with temperature and falls with hydrostatic pressure. Additionally, changing the transverse profile of the quantum ring from triangular to parabolic broadens the carrier wave functions and has a considerable impact on the NOR coefficient. These findings provide important information for the construction of high-performance, tunable THz optoelectronic devices by demonstrating effective external and structural tuning of NOR. Full article

The accelerating impacts of climate change present critical challenges to cultural heritage, particularly in the Mediterranean region where hydroclimatic extremes are intensifying. Future estimates for the Sanctuary of Asklepios at Epidaurus, a UNESCO World Heritage Site, suggest more intense precipitation patterns, increased rainfall intensity and water-induced material degradation. This study aims to identify current and projected climate-related threats to the site and to inform adaptive strategies that safeguard both its physical integrity and its associated heritage values through a value-based approach. Opting for a heritage value-based risk assessment, the study employs a mixed-methods technical approach grounded in the Conceptual Framework for Disaster Risk Reduction of UNISDR and ICCROM’s “ABC Method” for the risk assessment of climatic threats that combines GIS-based hydrological modelling (HAND), field observations and existing material assessments with NARA Grids to link exposure, vulnerability and value loss. Results reveal intensified surface water runoff and localised water inundation threatening key monuments, particularly the Roman Odeion and the central part of the site’s ensemble, while frost-related risks are projected to decline towards 2100. The findings suggest the development of site-specific climate change adaptation that prioritises drainage enhancement, preventive conservation and continuous monitoring to preserve its Outstanding Universal Values under changing climatic conditions. Full article

Reclamation of tailings facilities at oil sands mines in northern Alberta presents a significant challenge for industry, regulators, and researchers. Atterberg limits are an established method for quantifying clay behaviour in geotechnical engineering, which has been adopted for oil sands tailings due to their high clay mineral content. Correlations between remoulded undrained shear strength and liquidity index, originally developed for natural clays, have also been applied to oil sands tailings. This paper proposes a new material-specific correlation between remoulded undrained shear strength and liquidity index based on laboratory testing of oil sands tailings. Additionally, the results of Atterberg limits tests on oil sands tailings suggests that the inherent variability of the test itself has a greater effect on the measured value than the preparation method and test procedure. The results of this study support the idea that index properties such as Atterberg limits can provide a cost-effective method for field monitoring and early-stage reclamation design. Full article

The expansion force generated by lithium-ion batteries during charge–discharge cycles is a key indicator of their structural safety and health. Recently, flexible pressure-sensing technologies have emerged as promising solutions for in situ swelling monitoring, owing to their high flexibility, sensitivity and integration capability. This review provides a systematic summary of progress in this field. Firstly, we discuss the mechanisms of battery swelling and the principles of conventional measurement methods. It then compares their accuracy, dynamic response and environmental adaptability. Subsequently, the main flexible pressure-sensing mechanisms are categorized, including piezoresistive, capacitive, piezoelectric and triboelectric types, and their material designs, structural configurations and sensing behaviors are discussed. Building on this, we examine integration strategies for flexible pressure sensors in battery systems. It covers surface-mounted and embedded approaches at the cell level, as well as array-based and distributed schemes at the module level. A comparative analysis highlights the differences in installation constraints and monitoring capabilities between these approaches. Additionally, this section also summarizes the characteristics of swelling signals and recent advances in data processing techniques, including AI-assisted feature extraction, fault detection and health state correlation. Despite their promise, challenges such as long-term material stability and signal interference remain. Future research is expected to focus on high-performance sensing materials, multimodal sensing fusion and intelligent data processing, with the aim of further advancing the integration of flexible sensing technologies into battery management systems and enhancing early warning and safety protection capabilities. Full article

Organic field-effect transistors (OFETs) require reliable micro- and nanoscale patterning of semiconducting layers, yet conjugated polymers have long been considered incompatible with photolithography due to dissolution and chemical damage from photoresist solvents. Here, we present a photolithography-compatible strategy based on doping-induced solubility conversion (DISC), demonstrated using poly[2,5-bis(3-tetradecylthiophen-2-yl)thieno[3,2-b]thiophene] (PBTTT). AuCl₃ doping reversibly modulates the benzoid/quinoid resonance balance, lamellar stacking, and π–π interactions, suppressing solubility during lithographic exposure, while dedoping restores the intrinsic electronic properties. Using this approach, micropatterns with linewidths as small as 2 µm were fabricated in diverse geometries—including line arrays, concentric rings, dot arrays, and curved channels—with high fidelity; quantitative analysis of dot arrays yielded mean absolute errors of 48–66 nm and coefficients of variation of 2.0–3.9%, confirming resolution and reproducibility across large areas. Importantly, OFETs based on patterned PBTTT exhibited charge-carrier mobility, threshold voltage, and on/off ratios comparable to spin-coated devices, despite undergoing multiple photolithography steps, indicating preservation of transport characteristics. Furthermore, the same DISC-assisted lithography was successfully applied to other representative p-type conjugated polymers, including P3HT and PDPP-4T, confirming the universality of the method. This scalable strategy thus combines the precision of established lithography with the functional advantages of organic semiconductors, providing a robust platform for high-density organic electronic integration in flexible circuits, biointerfaces, and active-matrix systems. Full article