Search Results (806)

Transarterial radioembolization (TARE) is an established therapy for primary and secondary hepatic malignancies. Outcomes depend heavily on dosimetry, which has evolved from empirical and body-surface-area methods to partition and voxel-based approaches. This review summarizes current evidence for advanced (personalized) dosimetry across tumor types, highlights emerging dose–response concepts, and outlines practical barriers and implementation strategies. A narrative review of peer-reviewed clinical studies and trials evaluating dosimetry in TARE, with emphasis on hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (iCCA), metastatic colorectal cancer (mCRC), neuroendocrine tumor (NET), and breast cancer liver metastases, was performed with comparison of single-compartment medical internal radiation dosimetry method (MIRD), partition (multicompartment) methods, and voxel-based dosimetry methodologies. Personalized dosimetry improves outcomes in multiple tumor types. A randomized trial in HCC showed superior overall survival with partition-based dosing versus MIRD. In selective HCC treatments, voxel-derived metrics (e.g., D95) correlate with complete pathologic necrosis, suggesting benefit beyond mean dose targets. For iCCA, data associate higher tumor doses with better radiologic response, progression-free survival, and downstaging. In mCRC, voxel-based and threshold analyses link specific tumor and margin doses with metabolic/radiographic response and survival. Smaller series in NET and breast cancer indicate dose–response relationships using advanced dosimetry. Evidence supports broader adoption of advanced dosimetry in TARE. Emerging strategies that ensure adequate coverage of the “coldest” tumor regions and thoughtful particle-load planning may further optimize results. Standardized protocols, prospective validation, and scalable workflows are needed to accelerate implementation. Full article

Urbanization has accelerated globally, with the proportion of people living in cities increasing from 43% in 1990 to 56% today. This rapid urban growth profoundly affects Earth’s surface climate by altering land surface characteristics and energy fluxes. Using Landsat–MODIS data fusion to characterize land use in a biophysical model, this study assesses the global thermal impact of urbanization through two complementary metrics: the Urban Heat Island (UHI), measuring the temperature contrast between urban and adjacent vegetated areas, and an Urban Impact Metric (UIM), quantifying the net warming effect of urban land relative to a fully vegetated baseline. Results indicate that although urban areas cover only 0.31% of global land, they contribute disproportionately to surface warming, particularly in the mid-latitudes of the Northern Hemisphere, where impervious surface cover is dense. While the UHI captures localized thermal contrasts, UIM provides a spatially integrated, scalable indicator of urban-induced warming. Globally, the annual mean UHI is 1.21 °C while the urban-induced warming is 0.77 °C. This result is striking, given the limited areal extent of urbanization, and exceeds the net historical effect of land use change, underscoring the disproportionate impact of urbanization on surface temperature. These results highlight urbanization’s outsized role in shaping surface temperature patterns across regions and seasons. Full article

Objectives: This study aimed to investigate the skating force–velocity (F–V) mechanical characteristics of trained youth ice hockey players at different stages of their maturational development. Methods: A total of 52 male trained ice hockey players (14.6 ± 1.4 years) from U13, U15, U17, and U18 competitive teams of the same hockey program were classified into three maturation groups—Pre-, Mid-, and Post-peak height velocity (PHV). Participants performed two 40 m maximal skating efforts while velocity data were collected using a radar device to derive F–V parameters (e.g., theoretical maximal force (F₀), velocity (V₀), power (P_max), and related metrics). The maturation offset was computed using the following formula: Maturity offset = −8.128741 + (0.0070346 · (Chronological age · Sitting height)). Results: Results revealed significant effects of puberty on most performance variables (F_(2,49) = [5.58, 31.72]; p ≤ 0.07; η² = [0.19, 0.56]). Differences in acceleration (0–10 m time) and F₀ improved markedly between Mid- and Post-PHV stages (|d| = [1.38, 1.92]), while V0 and maximal sprint velocity (30–40 m time) improved constantly across maturation stages (|d| = [1.03, 1.99]). Conclusions: This is the first study to provide reference skating F–V profile values across puberty in trained youth male ice hockey players. Coaches and practitioners are encouraged to prioritize acceleration and skating technique early during puberty to maximize velocity development and emphasize strength development after reaching peak height velocity. Conclusions should be considered with care as the Pre-PHV group was small (n = 5) and the used F–V method remains to be validated on ice. Full article

Data-driven gray-box models for vehicle control often fail to generalize across distinct physical regimes. This study tackles the critical, yet often-overlooked, challenge of robustly blending model parameters between these regimes. The vehicle’s “expert poles” are defined using physically distinct maneuvers (steady state vs. transient). A three-way benchmark is used to prove that the blending method is more critical than the concept itself. Three architectures are compared: (1) a baseline single-parameter “Static Model”, (2) a common literature “Heuristic Model” that blends using lateral acceleration ($a_y$), and (3) the proposed “Dynamic Model” using a systematically optimized “Angle-Only” architecture. The findings demonstrate significant differences: The common-sense “Heuristic Model” exhibits severe degradation in stability metrics, lowering overall $a_y$ accuracy by 23.7% and $r$ (yaw rate) accuracy by 110.2% compared to the baseline. In contrast, the “Angle-Only” model is the only architecture that successfully improves the primary $a_y$ objective (by 17.7%). The Dynamic Model’s 35.0% $r$-metric degradation is demonstrated to be the minimal, quantified engineering trade-off required for achieving robust adaptation—a task completely failed by the Heuristic Model. This study provides a validated, data-driven path for developing control-oriented models, proving that a simple, systematically optimized input is methodologically superior to the state-dependent heuristic. Full article

To address the limitations of traditional non-local mean (NLM) denoising algorithms in terms of neighborhood similarity metrics, weight calculation, and computational efficiency, this paper proposed a structural-tensor-driven and dynamic window-based fast non-local mean denoising algorithm with dual kernel weighting. First, a Gaussian–Tukey dual-kernel weighting function was designed to optimize similarity metrics. Then, spatial neighborhood features were adopted. By measuring both grayscale similarity and spatial correlation, the weight distribution rationality was further enhanced. Second, structural tensor eigenvalues were used to quantify regional structural properties. A dynamic window allocation function was designed to adaptively match search window sizes to different image regions. Finally, an integral image acceleration mechanism was proposed, significantly improving algorithm execution efficiency. Experimental results demonstrated that the proposed algorithm achieved both excellent denoising performance and edge/texture preservation capabilities. In high-noise environments, its Peak Signal-to-Noise Ratio (PSNR) outperformed the Gauss kernel non-local mean algorithm by an average of 1.96 dB, while Structural Similarity (SSIM) improved by an average of 5.7%. Moreover, the algorithm’s execution efficiency increased by approximately 7–11 times, indicating strong potential for real-time application in digital image processing. Full article

In order to ensure the structural safety and serviceability of existing reinforced concrete (RC) structures, there is a compelling need to develop efficient techniques for the rapid replacement of damaged RC beams within strong-column–weak-beam structural systems. This study introduces a novel prefabricated RC beam with welded cover-plate steel sleeve and bolted splice designed to facilitate accelerated replacement and enhance construction efficiency. The proposed beam is connected to cast-in-place RC columns, forming a prefabricated novel prefabricated RC joint with a welded cover-plate steel sleeve and a bolted splice; this configuration contrasts with conventional monolithic RC joints, which are formed by integrally casting beams and columns. The assembly speed of the prefabricated system markedly surpasses that of its cast-in-place counterpart, and the resulting beam–column system is fully demountable. Finite element simulations of the novel prefabricated RC joint with welded cover-plate steel sleeve and bolted splice, performed using ABAQUS, identified the thickness of the welded end-plate as a pivotal parameter influencing the joint’s mechanical behavior. Accordingly, quasi-static tests were carried out on three novel prefabricated RC joints with welded cover-plate steel sleeves and bolted splices and one cast-in-place RC joint, with the welded end-plate thickness serving as the primary test variable. The failure patterns, hysteretic responses, energy dissipation capacity, ductility, and stiffness degradation were systematically analyzed. Experimental findings indicate that increasing the end-plate thickness effectively improves both the peak load-bearing capacity and the ductility of the joint. All prefabricated specimens exhibited fully developed spindle-shaped hysteresis loops, with ductility coefficients ranging from 3.47 to 3.64 and equivalent viscous damping ratios exceeding 0.13. All critical seismic performance metrics either met or exceeded those of the reference cast-in-place RC joint, affirming the reliability and superior behavior of the proposed novel prefabricated RC joints with welded cover-plate steel sleeves. Full article

Background: Accurate quantification of visceral (VAT) and subcutaneous adipose tissue (SAT) is critical for understanding the cardiometabolic consequences of obstructive sleep apnea (OSA) and other chronic diseases. This study validates a customization framework using pre-trained networks for the development of automated VAT/SAT segmentation models using hybrid positron emission tomography (PET)/magnetic resonance imaging (MRI) data from OSA patients. While the widespread adoption of deep learning models continues to accelerate the automation of repetitive tasks, establishing a customization framework is essential for developing models tailored to specific research questions. Methods: A UNet-ResNet50 model, pre-trained on RadImageNet, was iteratively trained on 59, 157, and 328 annotated scans within a closed-loop system on the Discovery Viewer platform. Model performance was evaluated against manual expert annotations in 10 independent test cases (with 80–100 MR slices per scan) using Dice similarity coefficients, segmentation time, intraclass correlation coefficients (ICC) for volumetric and metabolic agreement (VAT/SAT volume and standardized uptake values [SUVmean]), and Bland–Altman analysis to evaluate the bias. Results: The proposed deep learning pipeline substantially improved segmentation efficiency. Average annotation time per scan was 121.8 min (manual segmentation), 31.8 min (AI-assisted segmentation), and only 1.2 min (fully automated AI segmentation). Segmentation performance, assessed on 10 independent scans, demonstrated high Dice similarity coefficients for masks (0.98 for VAT and SAT), though lower for contours/boundary delineation (0.43 and 0.54). Agreement between AI-derived and manual volumetric and metabolic VAT/SAT measures was excellent, with all ICCs exceeding 0.98 for the best model and with minimal bias. Conclusions: This scalable and accurate pipeline enables efficient abdominal fat quantification using hybrid PET/MRI for simultaneous volumetric and metabolic fat analysis. Our framework streamlines research workflows and supports clinical studies in obesity, OSA, and cardiometabolic diseases through multi-modal imaging integration and AI-based segmentation. This facilitates the quantification of depot-specific adipose metrics that may strongly influence clinical outcomes. Full article

Digital transformation in government services represents a strategic shift that leverages digital technologies to enhance efficiency, accessibility, convenience, and user-centricity. In the wake of the COVID-19 pandemic, many governments accelerated the digitisation of services to support remote access and social distancing. Governments typically progress from digitisation (converting physical processes into digital formats) to digitalisation (automating service delivery and improving process efficiency), and ultimately to full digital transformation, where services are completed instantly and entirely online. However, varying levels of maturity across countries influence service outcomes differently, and indicators related to service quality, convenience, and security remain underexamined, particularly in developing contexts. This study addresses these gaps by examining Kuwait’s progress along the digitalisation–digital transformation continuum. It investigates current trends and user preferences in the use of digital government services based on empirical quantitative data collected from users in Kuwait. Specifically, the research objectives are fourfold: (i) to identify crucial outcome metrics for the success of digital government services, (ii) to assess user evaluations of these services according to these metrics, (iii) to examine significant differences between digital transformation and digitalisation services, and (iv) to develop and empirically test a model for evaluating digital transformation success. Drawing on established Information Systems’ (ISs’) success perspectives, a customised conceptual model incorporating six outcome metrics in three domains—service-related (user satisfaction, service quality), convenience-related (accessibility, ease of use), and security-related (perceived security, perceived trust)—was developed. A survey of 378 users of digital government services in Kuwait was conducted to compare perceptions across service types using independent-samples t-tests and linear regression analyses. The study found that users primarily accessed government services through smartphones and dedicated applications, highlighting the importance of mobile optimisation, and showed a clear preference for real-time, fully automated services over those requiring extended approval processes. The results indicate that digital transformation services significantly outperform digitalisation services across five outcome metrics—satisfaction, service quality, accessibility, ease of use, and perceived security—while trust remains consistent across both. These findings underscore the importance of advancing comprehensive digital transformation to enhance public service delivery. Practical recommendations are provided to support Kuwait’s digital government strategy. Given the purposive sampling and cross-sectional, comparative design, the findings should be interpreted with caution, and future studies are encouraged to apply probability-based sampling and more advanced multivariate techniques (e.g., structural equation modelling) to validate and extend the proposed model. Full article

Malaria remains a critical global health challenge for doctors and healthcare practitioners, particularly clinicians involved in initial treatment. Inaccurate diagnosis of malaria-infected cells often leads to delayed or inappropriate treatment, increasing the risk of severe complications or death. Traditional microscopic diagnosis is time-consuming and requires expert skills, resulting in variability and inconsistency in results. These challenges are further complicated by the complexity of malaria symptoms, which overlap with other febrile illnesses, making clinical diagnosis unreliable without laboratory confirmation. To address these challenges, this study explores deep-learning-based approaches, particularly leveraging state-of-the-art pretrained convolutional neural network (CNN) models, for automated malaria parasite detection and classification from microscopic blood smear images. Transfer learning is an effective approach to handling issues such as limited labeled data, time-consuming training, and domain-specific variations in medical image classification. By leveraging pretrained models trained on large-scale datasets like ImageNet, transfer learning enables the reuse of learned features, significantly accelerating the adaptation process for malaria detection and other medical imaging tasks. We used eight pretrained models for malaria parasite classification such as VGG16, VGG19, Inception-v3, ResNet-18, ResNet-34, ResNet-50, ResNet-101, and Xception. In particular, ResNet-50 and ResNet-101 achieved accuracies of approximately 89%, respectively, while Xception reached around 88% accuracy. In comparison, VGG-16 achieved a lower overall accuracy of about 80% due to a recall trade-off despite high precision. These metrics highlight meaningful improvements over simpler architectures and validate the efficacy of our transfer learning approach for automated malaria detection. The proposed models were fine-tuned on extensive labeled datasets comprising parasitized and uninfected cells. Quantitative and qualitative evaluations were conducted using metrics such as precision, recall, F1-score, and support. Our experimental results demonstrate that ResNet-50, ResNet-101, and Xception exhibit strong balanced performance with higher accuracy, while VGG-16 shows a trade-off of high precision but lower recall for parasitized cells. Full article

The global energy transition faces a chasm between current policy commitments (IEA’s STEPS) and the deep, rapid transformation required to realize all national net zero pledges (IEA’s APC). This perspective addresses the critical innovation and policy gap blocking the APC pathway, where many high-impact, clean technologies remain at low-to-medium Technology Readiness Levels (TRLs 3–6) and lack formal policy support. The insufficient nature of current climate policy nomenclature is highlighted, which often limits Nature-based Solutions (NbS) to incremental projects rather than driving systemic technological change (Bio-inspiration). Then, we propose that a deliberate shift from simple biomimetics (mimicking form) to biomimicry (emulating life cycle sustainability) is the essential proxy for acceleration. Biomimicry inherently targets the grand challenges of resilience, resource efficiency, and multi-functionality that carbon-centric metrics fail to capture. To institutionalize this change, we advocate for the mandatory integration of bio-inspired design into National Determined Contributions (NDCs) by reframing NbS as Nature-based Innovation (NbI) and introducing novel quantitative metrics. Finally, a three-step roadmap to guide this systemic shift is presented, from deployment of prototypes (2025–2028), to scaling evidence and standardization (2029–2035), to consolidation and regenerative integration (2036–2050). Formalizing these principles through policy will de-risk investment, mandate greater R&D rigor, and ensure that the next generation of energy infrastructure is not just carbon-neutral, but truly regenerative, aligning technology deployment with the necessary speed and depth of the APC scenario. Full article

This work addresses computational inefficiency in ultra-wide-area remote sensing image (RSI) object detection. Traditional homogeneous tiling strategies enforce computational symmetry by processing all image regions uniformly, ignoring the intrinsic spatial asymmetry of target distribution where target-dense coexist with vast target-sparse areas (e.g., deserts, farmlands), thereby wasting computational resources. To overcome symmetry mismatch, we propose a heat-guided adaptive blocking and dual-model collaboration (HAB-DMC) framework. First, a lightweight EfficientNetV2 classifies initial 1024 × 1024 tiles into semantic scenes (e.g., airports, forests). A target-scene relevance metric converts scene probabilities into a heatmap, identifying high-attention regions (HARs, e.g., airports) and low-attention regions (LARs, e.g., forests). HARs undergo fine-grained tiling (640 × 640 with 20% overlap) to preserve small targets, while LARs use coarse tiling (1024 × 1024) to minimize processing. Crucially, a dual-model strategy deploys: (1) a high-precision LSK-RTDETR-base detector (with Large Selective Kernel backbone) for HARs to capture multi-scale features, and (2) a streamlined LSK-RTDETR-lite detector for LARs to accelerate inference. Experiments show 23.9% faster inference on 30k-pixel images and reduction in invalid computations by 72.8% (from 50% to 13.6%) versus traditional methods, while maintaining competitive mAP (74.2%). The key innovation lies in repurposing heatmaps from localization tools to dynamic computation schedulers, enabling system-level efficiency for Ultra-Wide-Area RSIs. Full article

Stick–slip vibration leads to accelerated wear of drilling tools and downhole tool failures, particularly in long horizontal sections. Existing drill-string dynamics models and control or digital-twin frameworks have significantly improved our understanding and mitigation of stick–slip, but most of them adopt simplified Newtonian or linear viscous damping and low-degree-of-freedom representations of the drill-string–fluid–BHA system, which can under-represent the influence of non-Newtonian oil-based drilling fluids and detailed BHA design in long horizontal wells. In this study, an n-degree-of-freedom torsional stick–slip vibration model for horizontal wells is developed that explicitly incorporates Herschel–Bulkley non-Newtonian rheological damping of the drilling fluid, distributed friction between the horizontal section and drill string, and bit–rock interaction. The model is implemented in a computational program and calibrated and validated against stick–slip field measurements from four shale-gas horizontal wells in the Luzhou area, showing good agreement in stick–slip frequency and peak angular velocity. Using the Stick–Slip Index (SSI) as a quantitative metric, the influences of rotary table speed, weight on bit (WOB), and bottom-hole assembly (BHA) configuration on stick–slip vibration in a representative case well are systematically analyzed. The results indicate that increasing rotary speed from 64 to 144 r/min progressively reduces stick–slip severity and eliminates it at 144 r/min, reducing WOB from 150 to 60 kN weakens and eventually removes stick–slip at the expense of penetration rate, drill collar length has a non-monotonic impact on SSI with potential high-frequency vibrations at longer lengths, and increasing heavy-weight drill pipe (HWDP) length from 47 to 107 m consistently intensifies stick–slip. Based on these simulations, SSI-based stick–slip severity charts are constructed to provide quantitative guidance for drilling parameter optimization and BHA configuration in field operations. Full article

Sleep disturbances, affecting nearly half of middle-aged adults, have emerged as modifiable determinants of brain health and dementia risk. Recent advances in machine learning applied to MRI enable the estimation of “brain age,” a biomarker that quantifies deviation from normative neural aging. This review synthesizes and critically evaluates converging evidence that poor sleep accelerates biological brain aging, identifies mechanistic pathways, and delineates translational barriers to clinical application. Across large-scale cohorts comprising more than 25,000 participants, suboptimal sleep independently predicts 1–3 years of MRI-derived brain age acceleration, even after adjusting for vascular and metabolic confounders. Objective sleep fragmentation and altered sleep-stage architecture exhibit sleep-specific neuroanatomical signatures, independent of amyloid and tau pathology, while inflammatory, vascular, and glymphatic mechanisms mediate a small fraction of the effect. Experimental sleep deprivation studies demonstrate reversibility of accelerated brain aging, highlighting opportunities for early intervention. Translation to clinical practice is constrained by methodological heterogeneity, reliance on self-reported sleep metrics, limited population diversity, and the absence of randomized intervention trials demonstrating causal reversibility. Addressing these gaps through standardized MRI-based biomarkers, longitudinal mechanistic studies, and interventional trials could establish sleep optimization as a viable neuroprotective strategy for dementia prevention. Full article

Background/Objectives: Regular physical activity (PA) benefits mood and cognition, yet the neural markers associated with free-living PA remain unclear. Alpha asymmetry (AA), a neural marker of affective and motivational states, may help predict individuals’ preferred activity intensity and duration. To examine the relationship between resting-state AA in frontal and parietal regions, positive affect, and accelerometer-derived PA metrics were measured. Methods: Fifty-nine participants (age = 21.8 years) wore wrist accelerometers for 7 days, completed resting-state electroencephalography (EEG; alpha power 8–13 Hz), and completed the Positive and Negative Affect Schedule (PANAS). PA metrics included sedentary time (ST), light PA (LPA), moderate-to-vigorous PA (MVPA), average acceleration (AvAcc), intensity gradient (IG), and the most active X minutes (M2–M120). Multiple regression models tested AA to PA associations while accounting for sex and positive affect. Results: Although frontal AA was included as a key neural candidate, the observed associations emerged only at parietal sites. Greater right parietal AA power was associated with the most active M60, M30, M15, M10, and M5. For IG, greater AA power was observed in the left parietal region. No significant associations emerged for LPA, MVPA, AvAcc, M120, or M2. Across models, higher positive affect consistently predicted greater PA engagement. Conclusions: While resting frontal AA is theoretically relevant to motivational processes, the findings indicate that parietal AA more strongly differentiates individuals’ tendencies toward specific PA intensities and durations. Positive affect is associated with PA engagement. These findings identify parietal AA as a promising neural correlate for tailoring PA strategies aimed at sustaining active lifestyles. Full article

Ship damage control (DC) is pivotal to platform survivability in the face of battle damage and severe accidents. The DC context features multi-hazard coupling among flooding, fire, and smoke, as well as fast system dynamics and intensive human–machine collaboration, demanding real-time predictive simulation and decision support. Conventional DC simulations fall short in multiphysics fidelity, predictive speed, and integration with onboard sensing and control. A digital twin (DT) framework for predictive shipboard DC is introduced with an explicit capability envelope, observability, and latency requirements, and a cyber-physical mapping to ship systems. Building on this foundation, a three-stage/four-level maturity model charts progression from L1 monitoring, through L2 prediction and L3 human-in-the-loop, override-enabled plan generation, to L4 closed-loop decision control, specifying capability milestones and evaluation metrics. Guided by this model, a four-layer architecture and an end-to-end roadmap are formulated, spanning multi-domain modeling, multi-source sensing and fusion, surrogate-accelerated multiphysics simulation, assisted plan generation with human approval/override, and cyber-physical closed-loop control. The framework aligns interfaces, performance targets, and verification pathways, providing actionable guidance to upgrade shipboard DC toward resilient, efficient, and human-centric operation under multi-hazard coupling. Full article