Search Results (1,436)

Background/Objectives: Accurate and reproducible assessment of peri-implant soft tissue thickness is an important methodological aspect of contemporary implant dentistry, particularly in longitudinal studies evaluating soft tissue dimensions. While ultrasound-based techniques offer a non-invasive and quantitative approach, their validity in peri-implant settings remains insufficiently documented. The objective of this study was to validate the PIROP ultrasound-based system for measuring peri-implant soft tissue thickness by comparing it with a direct clinical reference method. Methods: Peri-implant soft tissue thickness was assessed at 40 planned implant sites at two predefined time points: prior to surgical incision and three months after closed healing. Measurements obtained using the PIROP ultrasound-based system were directly compared with measurements performed following surgical incision using a calibrated periodontal probe. Results: Overall, the relative differences between ultrasound-based and direct clinical measurements were small, indicating comparable performance under standardized clinical conditions. The PIROP ultrasound-based system demonstrated good agreement with the reference method, with high intraclass correlation coefficients (ICC = 0.86–0.88). Conclusions: Within the limitations of this methodological validation study, ultrasound-based assessment demonstrated good agreement with direct clinical measurements, supporting its use as a reliable, non-invasive, and quantitative measurement approach in clinical studies and longitudinal designs requiring repeated evaluation of peri-implant soft tissue thickness. Full article

This paper conceptualises burnout in Project-Based Organisations (PBOs) as a systemic emergent property arising from the non-linear interaction between structural demands and human capital. Utilising a System Dynamics (SD) methodology, the study constructs a Causal Loop Diagram (CLD) to visualise the feedback architecture governing the burnout cycle. The analysis identifies the dynamic tension between the Reinforcing Loop of exhaustion (R1) and the Balancing Loop of adaptation (B1). A key theoretical contribution is the positioning of the Project Manager’s Emotional Intelligence (EI) not merely as a soft skill but as a systemic control lever (B2) capable of reducing information delays and shifting the system from reactive to proactive homeostasis. Crucially, the study operationalises these conceptual findings into a Burnout Risk Management Framework (BRMF), accompanied by a practical diagnostic dashboard. This tool offers managers a set of leading and lagging indicators for early detection, bridging the gap between theoretical plausibility and applied risk management in high-entropy project environments. Full article

The downscaling of VLSI technologies has exacerbated the susceptibility of integrated circuits (ICs) to radiation-induced Single-Event Transients (SETs). This work presents UPSET, a comprehensive and technology-independent EDA framework for probabilistic SET analysis using Static Timing Analysis (STA). Unlike traditional simulation-based methods that suffer from prohibitive runtimes, UPSET leverages graph-based propagation with advanced logical, electrical, and timing-window masking models to evaluate circuit sensitivity efficiently. Key contributions include a novel “Electrical Masking Window” (EMW) criterion that effectively filters non-full-rail pulses early in reconvergent logic and a TimeStamp-based propagation mode that accurately handles complex signal reconvergence with Boolean evaluation. The experimental results over some featured benchmarks demonstrate a speedup of more than 25,000× compared with SPICE while maintaining a tight 4.56% error bound in pulse width estimation. Moreover, experimental validation on 50 benchmarks across varying complexities showcases that EMW enhancement reduces the pessimism to circuit sensitivity by up to 25% on average, providing tighter upper bounds while maintaining scalability to million-gate designs. By integrating seamlessly with standard industrial formats (LEF, DEF, LIB, or SPEF), UPSET enables scalable, accurate soft SET sensitivity assessment for modern digital designs, establishing a robust foundation for automated radiation hardening flows. Full article

The rapid and accurate detection of SARS-CoV-2 biomarkers remains a critical requirement for effective outbreak control and decentralized diagnostics. Although RT-PCR is the current gold standard, its reliance on centralized laboratories and long processing times limits its applicability in point-of-care settings. In this context, optical biosensing platforms based on surface plasmon resonance (SPR) offer attractive features, including label-free, real-time, and quantitative detection. This study explores the use of synthetic receptors for the highly sensitive detection of the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein. Specifically, soft molecularly imprinted polymer nanoparticles (nanoMIPs) were employed as synthetic receptors and integrated into a high-sensitivity, portable plasmonic platform based on a D-shaped plastic optical fiber (POF) SPR sensor. The nanoMIPs were selectively imprinted against the RBD, characterized by Dynamic Light Scattering (DLS), Isothermal Titration Calorimetry (ITC), and Scanning Electron Microscopy (SEM) to confirm nanoMIPs size, binding properties, and surface morphology. Next, the nanoMIPs were immobilized onto a gold-coated sensing surface, enabling enhanced specificity, affinity, and signal amplification compared to conventional biological recognition elements. The resulting RBD-SPR-nanoMIPs sensor demonstrated promising analytical performance, exhibiting high selectivity against potentially interfering proteins and an anticipated sensitivity suitable for RBD detection at femtomolar concentrations. The inherent stability of nanoMIPs suggests the potential for reusable SPR sensing platforms, paving the way for next-generation synthetic receptor-based plasmonic biosensors. Full article

Fire safety in high-rise talent apartments, which is vital for safeguarding strategic human resources, was investigated to enhance evacuation resilience. A coupled fire-evacuation model was established using PyroSim and Pathfinder. This study analyzed multi-factor management strategies, including occupant vertical distribution, evacuation speed, evacuation priority settings, panic psychology, and guide intervention. Additionally, an Artificial Neural Network (ANN) model was developed using simulation data obtained under non-panic conditions to predict evacuation time and explore intelligent algorithms. Results show that the evacuation stairwells are the primary bottlenecks. Panic psychology significantly reduces evacuation efficiency, with severe panic increasing total evacuation time by up to 71.1%. The combined strategy CS4, integrating Pyramidal Vertical Distribution (VD7) and Organized Segmented Speed Control (ES6), reduced evacuation time by 17.42%. Guide intervention effectively mitigates the negative impact of panic. The ANN model achieved a coefficient of determination (R²) of 0.8695, confirming its predictive capability. Visibility was identified as the key parameter determining the Available Safe Egress Time (ASET). This study demonstrates that an integrated “hard–soft combination” strategy, complemented by intelligent modeling, offers an effective approach to building a resilient evacuation system for high-rise talent apartments. Full article

Detecting minute defects, such as spurs on the surface of a Printed Circuit Board (PCB), is extremely challenging due to their small size (average size < 20 pixels), sparse features, and high dependence on circuit topology context. The original Mosaic data augmentation method faces significant challenges with semantic adaptability when dealing with such tasks. Its unrestricted random cropping mechanism easily disrupts the topological structure of minute defects attached to the circuits, leading to the loss of key features. Moreover, a splicing strategy without domain constraints struggles to simulate real texture interference in industrial settings, making it difficult for the model to adapt to the complex and variable industrial inspection environment. To address these issues, this paper proposes a Context-aware and Domain-consistent Mosaic (CD-Mosaic) augmentation algorithm. This algorithm abandons pure randomness and constructs an adaptive augmentation framework that synergizes feature fidelity, geometric generalization, and texture perturbation. Geometrically, an intelligent sampling and dynamic integrity verification mechanism, driven by “utilization-centrality”, is designed to establish a controlled sample quality distribution. This prioritizes the preservation of the topological semantics of dominant samples to guide feature convergence. Meanwhile, an appropriate number of edge-truncated samples are strategically retained as geometric hard examples to enhance the model’s robustness against local occlusion. For texture, a dual-granularity visual perturbation strategy is proposed. Using a homologous texture library, a hard mask is generated in the background area to simulate foreign object interference, and a local transparency soft mask is applied in the defect area to simulate low signal-to-noise ratio imaging. This strategy synthesizes visual hard examples while maintaining photometric consistency. Experiments on an industrial-grade PCB dataset containing 2331 images demonstrate that the YOLOv11m model equipped with CD-Mosaic achieves a significant performance improvement. Compared with the native Mosaic baseline, the core metrics mAP@0.5 and Recall reach 0.923 and 86.1%, respectively, with a net increase of 8.3% and 8.8%; mAP@0.5:0.95 and AP_small, which characterize high-precision localization and small target detection capabilities, are improved to 0.529 (+3.0%) and 0.534 (+3.3%), respectively; the comprehensive metric F1-score jumps to 0.903 (+6.2%). The experiments prove that this method effectively solves the problem of missed detections of industrial minute defects by balancing sample quality and detection difficulty. Moreover, the inference speed of 84.9 FPS fully meets the requirements of industrial real-time detection. Full article

Background: Knee osteoarthritis (KOA) is characterized by cartilage degradation and joint-space narrowing, resulting in increased friction and observable structural damage. Methods: This study introduces a composite hybrid framework for the automatic classification of KOA severity using anteroposterior knee X-ray images. The methodology applies joint-centered cropping and data augmentation to standardize inputs and uses class weighting to mitigate class imbalance. Deep features extracted from fine-tuned ResNet-101 and EfficientNetB7 models are integrated with handcrafted Gray Level Co-occurrence Matrix (GLCM) texture descriptors, and the final predictions are obtained using a soft-voting ensemble. Results: the proposed ensemble achieves 73% test accuracy (macro-F1 ≈ 0.70; weighted-F1 ≈ 0.73) in a four-class setting (KL-0, KL-2, KL-3, and KL-4). Additional experiments across different classification setups demonstrate consistent performance trends, while Grad-CAM indicates that the model primarily focuses on the joint region. Overall, Conclusions: combining ensemble deep learning with complementary handcrafted texture features provides a reliable and interpretable approach for grading radiographic KOA severity. Full article

Background/Objectives: The overuse and misuse of antibiotics contribute to antimicrobial resistance (AMR) globally. The appropriateness of antibiotic prescribing at the primary healthcare (PHC) level must be urgently addressed to reduce high levels of inappropriate antibiotic prescribing and associated AMR. This study aimed to develop quality indicators, based on the World Health Organization (WHO)’s Access, Watch, Reserve (AWaRe) guidance, to assess the appropriateness and quality regarding antibiotic prescribing in public PHC settings in South Africa. Methods: Potential indicators were identified from indicators developed by City St George’s, University of London (SGUL); a review of AWaRe-based indicators; and the results from point prevalence surveys at PHC clinics in South Africa. The indicators were developed using the RAND/UCLA Appropriateness Method. In Round 1, 12 experts individually rated 78 indicators for clarity and appropriateness. In Round 2, 10 experts rated 89 indicators for appropriateness and feasibility during an interactive online meeting. Results: The final set had 61/89 indicators (68.5%) that were rated both appropriate and feasible with agreement. Dental infections (9/9; 100%) alongside skin and soft tissue infections (11/13; 84.6%) had the highest percentage of indicators that were rated appropriate and feasible with agreement. Lower urinary tract infections (6/11; 54.5%) and general (4/8; 50%) categories had the lowest percentage of indicators rated appropriate and feasible with agreement. Conclusions: The process proved valuable in developing potential indicators for use in future antimicrobial stewardship programmes to improve antibiotic prescribing in public sector PHC facilities in South Africa and beyond. Full article

Metaheuristic optimization algorithms are widely employed to address complex nonlinear and multimodal optimization problems due to their flexibility and strong global search capability. However, the original Connected Banking System Optimizer (CBSO) still exhibits several inherent limitations when handling high-dimensional and highly complex search spaces, including excessive dependence on single global-best guidance, rapid loss of population diversity, weak exploitation ability in later iterations, and inefficient boundary handling. These deficiencies often lead to premature convergence and unstable optimization performance. To overcome these drawbacks, this paper proposes a Multi-Strategy Enhanced Connected Banking System Optimizer (MSECBSO) by systematically enhancing the CBSO framework through multiple complementary mechanisms. First, a multi-elite cooperative guidance strategy is introduced to aggregate information from several high-quality individuals, thereby mitigating search-direction bias and improving population diversity. Second, an embedded differential evolution search strategy is incorporated to strengthen local exploitation accuracy and enhance the ability to escape from local optima. Third, a soft boundary rebound mechanism is designed to replace rigid boundary truncation, improving search stability and preventing boundary aggregation. The proposed MSECBSO is extensively evaluated on the CEC2017 and CEC2022 benchmark suites under different dimensional settings and is statistically compared with nine state-of-the-art metaheuristic algorithms. Experimental results demonstrate that MSECBSO achieves superior convergence accuracy, robustness, and stability across unimodal, multimodal, hybrid, and composition functions. In terms of computational complexity, MSECBSO retains the same order of time complexity as the original CBSO, namely O(N×D×T), while introducing only a marginal increase in constant computational overhead. The space complexity remains O(N×D), indicating good scalability for high-dimensional optimization problems. Furthermore, MSECBSO is applied to corporate bankruptcy forecasting by optimizing the hyperparameters of a K-nearest neighbors (KNN) classifier. The resulting MSECBSO-KNN model achieves higher prediction accuracy and stronger stability than competing optimization-based KNN models, confirming the effectiveness and practical applicability of the proposed algorithm in real-world classification tasks. Full article

Urban airborne laser scanning (ALS) point clouds cover extensive geographical areas, rendering dense point-level annotation economically prohibitive and limiting the feasibility of fully supervised learning. In weakly supervised settings for urban ALS data, the natural long-tailed class distribution—where ground and building points dominate and smaller objects are rare—combined with the use of fixed pseudo-label thresholds under sparse annotations exacerbates confirmation bias and increases prediction uncertainty. This ultimately restricts the effective utilization of unlabeled data during training. To overcome these challenges, we propose a pseudo-label confidence-calibrated curriculum learning framework designed for weakly supervised ALS point cloud classification. The framework introduces a confidence-aware self-adaptive soft gating (CSS) mechanism that dynamically adjusts category-specific thresholds online using exponential moving average statistics and scene-aware normalization, eliminating the need for manual scheduling while improving pseudo-label quality. In addition, a reliability-driven soft selection (RSS) constraint is incorporated, in which each point is assigned a comprehensive reliability score that integrates prediction confidence, entropy clarity, and cross-augmentation consistency, enabling adaptive soft weighting to replace hard pseudo-label selection and achieve more balanced sample utilization. These components are further integrated into a unified pseudo-label confidence-calibrated curriculum learning framework (P3CL) that progressively shifts the model’s focus from high-certainty samples to more ambiguous ones, effectively mitigating confirmation bias. Extensive experiments on three public ALS benchmarks demonstrate that the proposed method consistently outperforms existing weakly supervised approaches and achieves competitive performance compared with several fully supervised models. Full article

Background: Stereotactic body radiotherapy (SBRT) for prostate cancer delivers high doses in few fractions but poses challenges in sparing adjacent organs at risk (OARs), particularly the intra-prostate urethra, bladder, rectum and penile bulb. Magnetic resonance-guided radiotherapy (MRgRT) using MR-Linac offers superior soft-tissue visualization and daily adaptive planning, potentially reducing OAR dose while maintaining target coverage. This study aimed to compare dose–volume parameters among MR-Linac (ML), volumetric modulated arc therapy (VMAT), and Tomotherapy (HT) plans for prostate SBRT. Methods: Thirty patients with localized prostate cancer were retrospectively analyzed. For each patient, three plans were generated: ML, VMAT and HT, using identical prescription and planning objectives. Dose–volume histogram (DVH) metrics were evaluated for clinical target volume (CTV), planning target volume (PTV), and OARs. Statistical comparisons were performed using non-parametric Friedman’s Test with post hoc Bonferroni test, with significance set at a p < 0.05. Results: CTV coverage was comparable across all modalities. ML achieved significantly higher PTV Dmin and near-maximum doses compared to VMAT and HT. Notably, ML provided substantial urethral sparing, reducing Dmax and Dmean by approximately 3.3 Gy compared to both VMAT and HT (p < 0.001). Rectal dose metrics were also lower with ML, while bladder and penile bulb doses showed minor increases (<3.5 Gy), considered clinically negligible. Femoral head doses were reduced in ML plans. Conclusions: MR-Linac planning for prostate SBRT offers meaningful dosimetric advantages, particularly in intra-prostate urethra urethral dose reduction, without compromising target coverage. These findings support incorporating MR-guided adaptive workflows into SBRT protocols to enhance OAR protection and potentially reduce treatment-related toxicity. Full article

Computed tomography and magnetic resonance imaging are two powerful modalities which can be used in the clinical setting to produce data for the creation of patient-specific finite element analysis (FEA) models and physical analogues—for instance, by using additive manufacturing (AM)—that mimic the properties of soft and hard tissues, both morphologically and mechanically. However, there remains a gap between creating a perfect biofidelic physical analogue and its computational counterpart. This gap exists because, firstly, in silico models are often too complex to realise, and secondly, real-life conditions are challenging to emulate both computationally and mechanically, as they involve multiscale situations that are inherently heterogeneous and patient specific. In this study, we applied a multi-scale approach to design and model porcine vertebral specimens. Our results identified critical design factors that affect the quality and accuracy of the models, specifically highlighting that scanning resolution/fidelity and the thresholding technique have a directly proportional impact on model accuracy. A small shift up and down the greyscale level by 20 units can affect the behaviour of the AM sample by as much as [−15% +47%]. Working up the levels for manufacturing, testing and modelling (i) cylindrical cores to (ii) whole vertebrae and then (iii) a whole spine motion segment, we observed that the fidelity of predictions reduces, and errors increase as the structure becomes more complicated and intricate (3.6%, 7.5% and 15%, respectively). We are confident that further material-level developments will provide solutions for the more intricate parts of spinal motion segments, such as the intervertebral discs and facets, which in their natural form are highly sophisticated structures. To the best of our knowledge, this is the first time a holistic multiscale approach has been implemented to produce AM biofidelic analogues of skeletal parts. Our data showed good agreement between the physical and in silico models, confirming that it is possible to model real-time objects and situations both physically and in silico. This ultimately will enable the development of accurate, patient-specific physical models for use in biomechanical testing and medicolegal applications. Full article

Handling dispersion of casein powders in water is widely encountered in the milk industry. However, in silico prediction of the apparent viscosity of these colloidal dispersions is not an easy task, especially when these micellar casein suspensions are highly concentrated, as in hyper-protein milk beverages, which are experiencing exponential market growth. In this work, Coarse-Grained (CG) models using Lennard-Jones potentials to model interactions were built for simulating rheological properties of colloidal micellar casein dispersions (native and demineralized). In a first approach, a polydisperse explicit CG model was developed. For this polydisperse CG model, the representation chain was composed of four large smooth spheres of different sizes mimicking the real distribution of casein colloids. The CG simulation results were validated by comparison with experimental rheological data for native colloidal casein dispersions. Both in-house experimental results and available data found in the literature were used for this purpose, covering a wide range of casein concentrations ([10 g/L–200 g/L], [8–20%] corresponding to casein concentration, colloid volume fraction and solid/liquid volume fraction, respectively). In a second approach, a simplified model using a monodisperse CG model was developed. This simplified model only included one type of soft sphere and was found to preserve the accuracy of the rheological prediction. Finally, a monodisperse CG model was set up to predict the behavior of demineralized micellar casein dispersions, for which a decrease in the average size of the micelle size distribution is observed when demineralization occurs. For all models, the comparison between the predicted and experimental rheological behavior is fully satisfactory, proving that the CG models proposed for casein-based micellar dispersions are physically well founded and that the proposed simplified representation chain, based on micelle size observation, makes sense. Full article

Following the launch of the Sustainable Development Goals (SDGs) process at the Rio+20 Summit, China has progressively strengthened sustainability-oriented considerations in development target setting and administration cadre performance assessment, which provides an institutional window to examine how the transformation of development goals reshapes urban land supply patterns. This study develops a spatial general equilibrium model and uses panel data for 286 prefecture-level cities in China from 2007 to 2021 to examine how the transformation of development goals affects urban land supply patterns. The results show that higher economic growth targets significantly expand total land supply, raise the ratio of industrial to residential land supply, and tighten floor-area-ratio (FAR) regulation. “Soft constraint” wording dampens the effect on land supply scale but strengthens the effects on land supply structure and FAR regulation, while the degree of vertical and horizontal target escalation generates substantial heterogeneity in these relationships. Moreover, after governance shifted from growth-oriented to sustainability-oriented objectives, the marginal effectiveness of using land supply structure and FAR regulation to deliver predetermined growth targets declined significantly. This study provides empirical evidence and policy-relevant insights for improving sustainability-oriented target accountability systems and urban governance incentive mechanisms. Full article

Power-system planning is being reshaped by rapid decarbonisation, electrification, and digitalisation, which collectively amplify uncertainty in demand, generation, technology adoption, and policy pathways. This review critically synthesises three principal optimisation paradigms used to plan under uncertainty in power systems: scenario-based stochastic optimisation, set-based robust optimisation (including adaptive and distributionally robust variants), and minimax-regret decision models. The review is positioned to address a recurrent limitation of many uncertainty-planning surveys, namely the separation between “method reviews” and “technology reviews”, and the consequent lack of decision-operational guidance for planners and system operators. The central contribution is a decision-centric framework that operationalises method selection through two explicit dimensions. The first is an information posture, which formalises what uncertainty information is credible and usable in practice (probabilistic, set-based, or probability-free scenario representations). The second is a flexibility posture, which formalises the availability, controllability, and timing of operational recourse enabled by smart-grid technologies. These postures are connected to modelling templates, data requirements, tractability implications, and validation/stress-testing needs. Smart-grid technologies are integrated not as an appended catalogue but as explicit sources of recourse that change the economics of uncertainty and, in turn, shift the relative attractiveness of stochastic, robust, and regret-based planning. Soft Open Points, Coordinated Voltage Control, and Vehicle-to-Grid/Vehicle-to-Building are treated uniformly under this recourse lens, highlighting how device capabilities, control timescales, and implementation constraints map into each paradigm. The paper also increases methodological transparency by describing literature-search, screening, and inclusion principles consistent with a structured narrative review. Practical guidance is provided on modelling choices, uncertainty governance, computational scalability, and institutional adoption constraints, alongside revised comparative tables that embed data credibility, regulatory interpretability, and implementation maturity. Full article