Search Results (4,495)

Long-wave infrared (LWIR) airborne optical systems for ground imaging are widely utilized in applications such as ground reconnaissance, agricultural monitoring, counterterrorism, and other fields. Traditional oblique-view ground-imaging optical systems suffer from a critical drawback compared to nadir-view systems: the significant variation in object distances between distant and nearby targets. This disparity leads to inconsistent ground resolution (GR), manifesting in images where distant targets exhibit significantly lower resolution than nearby ones. This characteristic is highly detrimental to information acquisition and three-dimensional modeling of the system. Furthermore, the limited field of view of fixed focal length systems prevents the unmanned aerial vehicle (UAV) from acquiring target information effectively across varying flight altitudes. To address this issue, this paper designs an oblique imaging optical system capable of achieving both constant GR and zoom functionality in the LWIR band. By controlling the ground resolution, a LWIR continuous zoom optical system was designed. The system maintains constant GR over the entire field of view. Its modulation transfer function (MTF) approaches the diffraction limit across the full field of view, and the spot diagram remains within Airy’s disk at each view angle. The radius of the spot diagram is smaller than that of the Airy disk, indicating that the geometric aberrations of the system are well corrected. The imaging performance is primarily determined by the wavelength and the F-number. In the case of LWIR, the longer wavelength results in a larger Airy disk radius. The system meets imaging quality requirements and is suitable for air-to-ground target reconnaissance imaging. Full article

Background/Objectives: Increased accessibility of suicidal cognitions reflects the cognitive processes underlying the acquisition of suicidal thoughts. Previous research shows that expectation violation reduces the accessibility of life cognitions rather than increasing that of suicidal cognitions, but this may be due to a slowing effect masking an increase in suicidal cognitions. Methods: Beyond the reaction time task, the present study used simultaneous EEG-fNIRS to reveal how expectation violation differentially affects the accessibility of suicidal and life cognitions. In a trial-by-trial cognitive task, participants read sentences that were either semantically consistent (expectation confirmation) or anomalous (expectation violation), followed by a semantic judgment on suicide-related, neutral, and life-related words. Response times for each word type served as a measure of cognitive accessibility for that category. Results: Compared to expectation confirmation, expectation violation reduced the cognitive accessibility of life rather than increasing that of suicide in the reaction time task. However, in neural responses, it led to reduced N1 amplitude, increased P2 amplitude for suicide-related information, and greater hemodynamic response in the left frontopolar region. Conclusions: Expectation violation triggered distinct neural responses to suicidal information, reflecting an attentional bias that may explain how suicidal thoughts emerge within normative cognition. Full article

Decarbonising the building sector is an energy policy priority due to its major contribution to global energy consumption and related emissions. Accurate energy modelling is crucial, with significant scientific advancements being made in the last decade. As data gathering is a primary bottleneck, the potential of Geographic Information Systems and Remote Sensing for streamlining data acquisition and integrating data sources has gained specific interest. This study aims to identify prevailing trends in scales, inputs, and outputs of energy modelling, focusing on Remote Sensing and Geographic Information Systems applications. A structured literature review was conducted, encompassing screening, textual analysis, and findings synthesis to identify key research trends. The results highlight a predominance of the neighbourhood scale (54%) and the reliance on building geometries as principal input (91% of studies). Remote Sensing, used in 36% of cases, is employed for defining geometric (41%) and non-geometric (45%) attributes, while 17% of studies leverage it to determine climatic variables. EnergyPlus remains the most widespread simulation engine (37%), frequently coupled with construction archetypes (50% of cases) to address data gaps. The increasing integration of these technologies in energy modelling is expected to diversify the number of inputs, ultimately enhancing output accuracy, scalability, and generalisability. Full article

The application of 3D data in pavement inspection represents an emerging trend. Acquiring and measuring the 3D information of pavement distress enables a more comprehensive assessment of severity, thereby allowing for accurate monitoring and evaluation of the pavement’s technical condition. Existing methods face challenges in high-cost pavement scanning and insufficient research on automated 3D distress segmentation. This study employed a consumer-grade action camera for data acquisition and constructed an engineering-aligned 3D point cloud dataset of pavements. Then a long-tail class imbalance mitigation strategy was introduced, integrating adaptive re-sampling with a weighted fusion loss function, effectively balancing minority class representation. The proposed network, named PointPaveSeg, was a dedicated point cloud processing architecture. A dual-stream feature fusion module was designed for the encoder layer, which decoupled geometric and semantic features to improve distress extraction capability. The network incorporated a hierarchical feature propagation structure enhanced by edge reinforcement, global interaction, and residual connections. Experimental results demonstrated that PointPaveSeg achieved an mIoU of 78.45% and an accuracy of 95.43%. In the field evaluation, post-processing and geometric information extraction were performed on the segmented point clouds. The results showed high consistency with manual measurements. Testing confirmed the method’s practical applicability in real-world projects, offering a new lightweight alternative for intelligent pavement monitoring and maintenance systems. Full article

Inherited cardiomyopathies associated with high-risk genotypes, are characterized by a disproportionate risk of malignant ventricular arrhythmias and sudden cardiac death, often independent of left ventricular systolic dysfunction or advanced structural remodeling. Traditional surveillance strategies based on intermittent electrocardiography and phenotype-driven risk assessment are insufficient to capture the dynamic and often silent progression of electrical instability in these populations. This narrative review evaluates the emerging role of artificial intelligence (AI)-enabled sensor technologies in remote arrhythmic monitoring of genetically defined cardiomyopathy cohorts. Wearable ECG devices, implantable cardiac monitors, multisensor cardiac implantable electronic device algorithms, pulmonary artery pressure sensors, and contact-free systems enable continuous acquisition of electrophysiological and hemodynamic data, generating digital biomarkers that may reflect early arrhythmic vulnerability and subclinical decompensation. AI-driven analytics enhance signal processing, automated event detection, and remote data triage, with the potential to reduce clinical workload while preserving diagnostic sensitivity. However, current evidence predominantly derives from heterogeneous heart failure or general arrhythmia populations, and prospective validation in genotype-specific cohorts remains limited. Key challenges include algorithm generalizability, signal quality in ambulatory environments, data governance, interpretability of AI models, and integration into structured remote-care pathways. The convergence of genotype-informed risk stratification and multimodal AI-enabled sensing represents a promising strategy to transition from reactive device-based protection to proactive, precision-guided arrhythmic prevention. Dedicated genotype-focused studies and standardized digital endpoints are required to support safe and effective implementation in inherited cardiomyopathies. Full article

Welding is a critical joining process in civil and transportation infrastructure, enabling the fabrication of complex steel structural systems used in bridges, buildings, and other essential infrastructures. Despite strict adherence to established welding codes and standards, such as AWS D1.1 and AASHTO/AWS D1.5, welding flaws and service-induced defects can occur in welded components. Cause of defects and their structural impact, along with detection, sizing, and localization of these anomalies and flaws, are crucial for adequate maintenance, repair, or replacement planning without compromising the functionality of in-service components. Among available NDT techniques, ultrasonic testing (UT) remains one of the most widely adopted methods of weld inspection due to its depth of penetration, sensitivity to internal defects, and suitability for field deployment. Recent advancements in ultrasonic technologies, particularly Phased Array Ultrasonic Testing (PAUT), along with its emerging approaches such as Full Matrix Capture (FMC) and the Total Focusing Method (TFM), have significantly enhanced inspection accuracy, repeatability, and interpretability. These techniques enable flexile beam steering, multi-angle interrogation, and improved imaging of complex geometries. This paper presents a comprehensive review of PAUT for the inspection of welded steel infrastructure adhering to the recommendations and requirements of the relevant codes and standards, synthesizing the current literature on PAUT principles, wave modes, probe configurations, and data acquisition strategies. Emphasis is placed on the practical implementation of PAUT in civil infrastructure inspection, its advantages over conventional NDT methods, and its potential to support informed decisions related to quality acceptance, repair, and long-term maintenance planning. This paper concludes by identifying current challenges and future research directions for advanced ultrasonic inspection of welded steel structures. Full article

Background/Objectives: With growing waves of migration, children speaking a home language different from the language of school literacy have become increasingly common in Western education systems. In this context, understanding and monitoring bilinguals’ reading development is crucial to inform both educational and clinical practices and ensure equitable services. The present study contributes to the literature by investigating learning patterns in single-word reading across primary school grades. Monolingual and bilingual children learning to read in an alphabetic orthography were examined. Methods: The sample consisted of 565 typically developing monolingual and bilingual primary school children from grades 1–5 (bilinguals = 162). Participants completed a computerised Lexical Decision task (LDT) recording accuracy and response times, and standardised tests of reading and cognition. A parental questionnaire was used to gather socio-demographic and linguistic information. Results: Response bias-corrected accuracy rates in the LDT revealed an increase in sensitivity across school years after correcting for potential confounds (SES, vocabulary, nonverbal intelligence). No significant effect of bilingualism was observed. Response times for correct responses also decreased consistently across grades after controlling for the same confounds. Although no significant main effect of bilingualism emerged, an interaction with grade revealed a greater decrease in response times for second-grade bilinguals compared to monolingual peers. Conclusions: Monolingual and bilingual children showed comparable sensitivity rates and reading times, suggesting similar decoding skill acquisition. However, an earlier decrease in response times for bilinguals points to a facilitatory effect in the early stages of reading development, consistent with a bilingual advantage during skill learning. Full article

A polyvinyl chloride (PVC)-based tissue-mimicking polymer phantom was developed and mechanically characterised to replicate stiffness ranges relevant to breast elastography and to provide a controlled platform for evaluating shear wave elastography (SWE) measurements. SWE provides quantitative stiffness information that complements B-mode ultrasound in breast imaging. However, measurement variability related to operator technique and tissue continues to limit confidence in clinical interpretation. This study evaluates the reproducibility of SWE using custom-fabricated PVC-based breast phantoms with mechanically defined stiffness properties. Two PVC-based breast phantoms with identical geometry and different background stiffnesses were scanned using a single ultrasound system under a fixed SWE protocol. Each phantom contained four embedded inclusions representing clinically relevant stiffness categories. Six breast imagers independently acquired repeated SWE measurements in transverse and longitudinal planes, blinded to lesion identity and ground truth. Inter-operator reproducibility was assessed using intraclass correlation coefficients, and was high across both phantom backgrounds, with low intra-operator variability following quality assurance exclusion of one dataset due to sampling error. Measurement variability was lowest for solid inclusions and increased for the cyst-like inclusion in the stiffer background. SWE measurements consistently preserved the relative stiffness ordering of inclusions, although absolute values differed systematically from mechanically derived ground-truth stiffness. These findings demonstrate that PVC-based polymer phantoms provide a stable and reproducible platform for evaluating SWE measurement behaviour under controlled conditions. By isolating operator and acquisition effects from biological variability, this polymer-based framework supports methodological standardisation and structured operator training in breast elastography. Full article

This work presents a portable polarimetric multispectral imaging (PMSI) system operating in the visible to shortwave infrared range (VNIR–SWIR: 400–1700 nm) and its application to target detection, discrimination from aquatic backgrounds, and polymer identification. The instrument integrates two synchronized cameras with motorized bandpass filters and piezoelectric polarization control, enabling the acquisition of 48 wavelength–polarization measurements per capture. This configuration allows the extraction of both intensity-based and polarimetric features, including the degree of linear polarization (DoLP). A complete radiometric and polarimetric calibration framework is implemented, encompassing system response characterization, polarization-dependent gain correction, and reflectance normalization under variable illumination. Experiments conducted on a representative set of 16 polymer materials show that polarimetric information consistently improves class separability compared to intensity-only features, with a mean gain of 6.9 (95% CI: 6.35–8.47). Although the correlation between intensity- and DoLP-based separability is moderate (r = 0.44), the results indicate complementary identification capability. Material recoverability was further evaluated using spectral unmixing techniques (VCA, N-FINDR, and PPI), with VCA offering the best accuracy–complexity trade-off on the calibrated Stokes reflectance dataset. Despite these gains, identification among chemically similar polyethylene variants remains challenging due to limited spectral and polarimetric contrast. An underwater detectability study under natural illumination reveals strong wavelength-dependent constraints: SWIR penetration is limited to 4 cm, whereas VNIR bands (430–550 nm) preserve detectability up to 20 cm, with DoLP enhancing edge visibility. These results motivate future validation in more complex aquatic conditions and with increased spectral dimensionality. Full article

Accurate and timely cotton-field mapping is essential for irrigation management, water resource allocation, and regional yield assessment in arid irrigated agroecosystems. However, existing deep-learning-based crop mapping approaches generally lack interpretability and often exhibit performance variability across phenological stages, thereby limiting their reliability for operational deployment. To address these limitations, we developed an interpretable semantic segmentation framework for cotton mapping in the Wei-Ku Oasis, Xinjiang, China, under multi-source remote sensing conditions. The proposed model integrates Sentinel-2 surface reflectance, Sentinel-1 VV/VH backscatter, DEM, vegetation indices, and GLCM texture features. By incorporating a receptive-field enhancement mechanism together with an embedded feature-attribution module, the framework enables importance estimation of multi-source predictors within the network architecture, thereby providing intrinsic model interpretability. Under a unified training and evaluation protocol, the proposed model achieved an mIoU of 85.62% and an F1-score of 92.96% on the test set, outperforming U-Net, DeepLabV3+, and SegFormer baselines. Monthly classification results indicated that August provided the most discriminative acquisition window (mIoU = 85.54%, F1 = 92.83%), while June–July also maintained high recognition accuracy. Feature attribution results indicate that the importance of different predictors varies across phenological stages: Sentinel-2 red-edge bands remained highly influential throughout the growing season, NDVI/EVI exhibited increased contributions during June–August, SAR VH showed relatively higher importance during peak canopy development, and DEM maintained stable information contribution across all stages. Cross-year and cross-region experiments further demonstrated the model’s generalization capability, achieving an mIoU of 82.81% in same-region cross-year evaluation and 74.56% under cross-region transfer. Overall, the proposed segmentation framework improves classification accuracy while explicitly modeling and quantifying feature importance, providing a methodological reference for cotton-field mapping and acquisition timing selection in arid irrigated regions. Full article

Most camera view planning algorithms are employed in exploration tasks that maximise information gain, but few address the specific challenge of observing targeted surface areas with optimal image quality. This paper presents a novel camera view planning algorithm designed for dermoscopic mole mapping, which is crucial for early melanoma detection. Traditional full-body scanners, though beneficial, suffer from fixed camera positions that can compromise image quality due to varying body contours and patient sizes. Our algorithm addresses this limitation by dynamically optimizing the camera position on a set of collaborative robot (cobot) arms to enhance image resolution, safety, and viewing angles during skin examinations. The proposed method formulates the problem as a non-linear least-squares optimisation that ensures no camera occlusion and a safe distance from the end effector encapsulating the camera to the patient while adjusting the pose of the camera based on the topography of the body. This approach not only maintains optimal imaging conditions by considering resolution and angle of incidence but also prioritises patient safety by preventing physical contact between the camera and the patient. Extensive testing demonstrates that our algorithm adapts effectively to different body shapes and sizes, ensuring high-resolution images across various patient demographics. Moreover, the integration of our camera view planning algorithm into an intelligent dermoscopy system has shown promising results in improving the efficiency and geometric quality of dermoscopic image acquisition, which could lead to more reliable and faster diagnoses. This technology holds significant potential to transform melanoma screening and diagnosis, providing a scalable, safer, and more precise approach to dermatological imaging. Full article

Background: Early childhood is a critical period for the development of motor competence, which is closely related to later physical activity, educational readiness, and broader developmental outcomes. However, the temporal dynamics of motor skill acquisition in preschool children, particularly the time required to reach initial and early refinement phases of learning, remain insufficiently described. The aim of this study was to examine whether different levels of previous participation experience in an organized kinesiology program are associated with differences in the speed and quality of novel motor skill acquisition in preschool children, and to explore the relationship between baseline motor proficiency and phase-based indicators of motor learning. Methods: A total of 161 preschool children aged 5–6 years participated in the study and were grouped according to their previous participation experience in an organized kinesiology program (0 h, ~120 h, ~350 h, and ~470 h). Following BOT-2 assessment, all participants completed a standardized 7-week motor learning program that included nine previously unfamiliar motor tasks. Using a phase-based video analysis protocol, three learning indicators were recorded: time to Phase 1 (F1; first successful execution), time to Phase 2 (F2; initial refinement of performance), and final performance quality (K). Group differences and associations were first examined descriptively and correlationally, after which additional multivariable regression models were performed to determine whether previous participation experience and baseline motor proficiency were independently associated with motor learning outcomes. Results: The findings showed consistent differences across groups, with children who had greater previous participation experience generally reaching F1 and F2 more rapidly and achieving higher final performance quality scores. Higher BOT-2 scores were also associated with shorter learning times and better final performance quality. In the multivariable models, both previous participation experience in an organized kinesiology program and BOT-2 total score were independently associated with Phase 1 attainment time and final performance quality, whereas only previous participation experience remained independently associated with Phase 2 attainment time. The applied phase-based observational protocol demonstrated good to excellent inter-rater reliability across the evaluated motor learning variables. Conclusions: These findings provide phase-based temporal indicators of motor learning progression in preschool children and suggest that previous participation experience in an organized kinesiology program and baseline motor competence are meaningfully associated with the speed and quality of acquiring new motor tasks. The findings also demonstrate the potential of phase-based approaches for quantifying motor learning dynamics in early childhood settings. Such indicators may offer useful reference information for instructional pacing and the planning of motor learning activities, while also serving as practically relevant predictors for adapting future kinesiology programs to children’s motor readiness. Future research should further examine these relationships using longitudinal and analytically expanded designs. Full article

In rapidly urbanising developing economies, construction activity frequently relies on informal and semi-skilled labour. This coincides with limited opportunities for systematic skill development, leading to persistent labour deskilling. While existing research has predominantly addressed these challenges through policy reform, industrialisation, or efficiency-driven technological models, less emphasis has been placed on the role of architectural design in shaping labour–technology relations on-site. This article adopts a constructivist perspective on technology to investigate how architectural design can serve as a socio-technical framework for non-formal labour upskilling within construction practice. Drawing upon qualitative case studies of two architectural projects in Sri Lanka—a suburban residential retrofit and a low-income rural housing prototype—this study analyses how design strategies such as systemisation, construction sequencing, material hybridity, and craft-based component detailing embed tacit learning within production processes. The findings demonstrate that craft, understood as a mode of tacit knowledge and on-the-job learning rather than as a stylistic or nostalgic response, can facilitate skill acquisition across diverse economic and technical contexts. By repositioning architectural design as an active mediator between technology and labour, this article contributes to debates within construction studies, social sciences, and architectural theory and proposes design-led construction strategies as a context-sensitive alternative to purely policy- or efficiency-driven approaches to labour development. Full article

Multimodal fusion of synthetic aperture radar (SAR) and optical imagery is widely used in Earth observation for applications such as land-cover mapping and surface-water mapping (including post-event flood mapping under near-synchronous acquisitions) and land-use inventory. Optical images provide rich spectral and texture cues, whereas SAR offers all-weather structural information that is complementary but heterogeneous. In practice, this heterogeneity often introduces fusion conflicts in multi-class segmentation, causing critical categories such as water bodies to be under-optimized. To address this issue, this paper presents a SAR-guided class-aware knowledge distillation (SGCAD) method for multimodal semantic segmentation. First, a SAR-only HRNet is trained as a water-expert teacher to learn discriminative backscattering and boundary priors for water extraction. Second, a lightweight multimodal student model (LightMCANet) is optimized using a class-aware distillation strategy that transfers teacher knowledge only within high-confidence water regions, thereby suppressing noisy supervision and reducing interference to other classes. Third, a SAR edge guidance module (SEGM) is introduced in the decoder to enhance boundary continuity for slender structures such as water bodies and roads. Overall, SGCAD improves targeted category learning while maintaining stable performance across the remaining classes. Experiments on a self-built dataset from GF-1 optical and LuTan-1 SAR imagery demonstrate higher overall accuracy and more coherent water/road predictions than representative baselines. Future work will extend the proposed distillation scheme to additional categories and broader geographic scenes. Full article

Indonesia is among the most landslide-prone countries in the world, with thousands of fatalities and widespread infrastructure damage recorded over recent decades. Despite this high hazard level, regional-scale landslide monitoring remains constrained by the limitations of conventional bitemporal satellite imagery, which is susceptible to cloud contamination, dependent on precise acquisition timing, and unable to capture the full temporal dynamics of landslide occurrence and recovery. While the LandTrendr (Landsat-based Detection of Trends in Disturbance and Recovery) algorithm has been widely applied for detecting vegetation disturbances such as forest loss and land-use change, its potential for landslide detection in tropical environments has not been sufficiently explored. This study aims to evaluate the applicability of LandTrendr applied to long-term Landsat time series imagery for automated regional-scale landslide detection and mapping in Indonesia. The method integrates temporal segmentation of the Normalized Difference Vegetation Index (NDVI) derived from Landsat imagery spanning 2000–2022 with slope information from the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) to identify the characteristic drop-recovery spectral signature associated with landslide events. The algorithm was applied and evaluated in two geologically distinct study areas: Lombok, West Nusa Tenggara, and Pasaman, West Sumatra. Detection accuracies of 25.9% by location and 20.3% by area were achieved in Lombok and 76.3% by location and 85.3% by area in Pasaman. The lower accuracy in Lombok is primarily attributed to the predominance of small landslides below the sensor’s spatial resolution and rapid vegetation recovery. The proposed approach demonstrates the unique capability of LandTrendr to model the entire life cycle of a mass movement event, from pre-event stability through abrupt disturbance to ecological recovery within a single unified framework, providing a scalable and cost-effective tool for long-term landslide monitoring applicable to other tropical, landslide-prone regions. Full article