Search Results (9,715)

The rapidly evolving business landscape, driven by stringent energy conservation policies, compels construction firms to adopt energy-efficient project-centric structures, particularly in modern construction projects. These firms face a complex, multi-mode, resource-constrained, multi-project scheduling problem characterized by dynamic project arrivals and multiple resource constraints, including global, local, and non-renewable capacities. This environment pressures managers to simultaneously optimize the conflicting objectives of minimizing total project duration and total energy consumption. To address this challenge, we propose a novel multi-objective Smart Raccoon Family Optimization (SRFO) algorithm. The SRFO, a hybrid evolutionary approach, is designed to enhance global exploration and local exploitation. Its performance is boosted by integrating a non-dominated sorting mechanism, a dedicated energy-efficient search strategy, and enhanced genetic operators. The SRFO simultaneously optimizes two conflicting objectives: minimizing the total project duration and total energy consumption. This approach effectively integrates the unique constraint of off-site component production and on-site assembly within an intelligent scheduling framework. Empirical validation across benchmark problems and a real-world case study is conducted, comparing the SRFO with existing multi-objective approaches, such as NSGA-III, MOABC, and MOSMO. Performance is assessed using convergence and distribution metrics, augmented by TOPSIS-based multi-criteria decision-making. Results conclusively demonstrate that the proposed SRFO significantly outperforms existing approaches and offers a robust, high-quality solution for project management in energy-constrained environments. Full article

Lipodystrophy is a rare group of metabolic disorders characterized by the abnormal distribution of body fat, which can lead to various metabolic complications due to the body’s inability to adequately process carbohydrates and fat. We report the case of a female, aged 53 years, who was admitted as an outpatient for progressive weight loss of the upper part of the body (face, neck, arms, and chest), dyspeptic complaints, fatigue, mild insomnia, and anxious behavior. Her medical history was characterized by the presence of dyslipidemia, hypertension, and a minor stroke episode. However, she denied any family-relevant medical history. Although the clinical perspective suggested a possible late onset of partial acquired lipodystrophy, due to the imaging exam that revealed an enlarged liver with inhomogeneous structure with multiple nodular lesions, scattered over both lobes, a lot of lab work-ups and complementary studies were performed. Eventually, a liver biopsy was performed by a laparoscopic approach during cholecystectomy, the histology consistent with metabolic disease-associated steatohepatitis (MASH). In conclusion, given their heterogeneity and rarity, lipodystrophies may be either overlooked or misdiagnosed for other entities. Barraquer–Simons syndrome (BSS) may be associated with liver disease, including cirrhosis and liver failure. Liver lipodystrophy in BSS may sometimes feature steatosis with a focal, multi-nodular aspect, multiplying the diagnostic burden. Liver lipodystrophy may manifest as asymptomatic fat accumulation but may progress to severe conditions, representing one of the major causes of mortality in BSS, apart from the cardio-vascular comorbidities. Given the potential of severe outcomes, it is mandatory to correctly assess the stage of liver disease since the first diagnosis. Full article

Background and Objectives: Brain metastases frequently evolve over time in multiple waves, especially in patients with prolonged survival. Despite repeated imaging and targeted therapies, lesion-level continuity is fragmented in clinical practice, as follow-up is typically limited to pairwise MRI comparisons. The aim of the study is to assess the ability of routine narrative MRI follow-up reports to preserve longitudinal lesion identity and to reconstruct a coherent trajectory of disease evolution. Materials and Methods: We conducted a single-center, retrospective, observational study of all brain MRI examinations performed between June 2024 and June 2025 (n = 731 scans, 616 patients). All imaging reviews and longitudinal lesion tracking were performed by one board-certified neuroradiologist. Adult patients with confirmed brain metastases and at least three MRI examinations (including external studies) were included. We assessed the concordance of routine narrative MRI follow-up reports against a longitudinal review of all available MRIs and treatment timelines, which served as the reference standard. Lesion identity was considered preserved when reports explicitly recognized and linked lesions across time points, and lost when identity was omitted or ambiguous in at least one report. Results: The final cohort comprised 73 patients (477 tracked lesions). More than half of monitored lesions disappeared (42.9%) or evolved into post-treatment sequelae (9.9%), and were omitted from narrative reports, limiting retrospective recognition without prior imaging. The ability of routine reports to preserve lesion identity declined as cases became more complex. Concordance was higher in uniform evolution patterns (≈60%) but dropped to 18.2% in mixed evolution. A similar decline was seen with sequential metastatic waves, defined as new metastases appearing at distinct time points: 65.2% (1 wave), 46.7% (2 waves), 18.2% (3 waves), and complete loss of continuity when >3 waves occurred. Conclusions: Routine narrative MRI follow-up reports generally provide adequate information in simple cases with uniform lesion behavior, but tend to lose critical details as disease trajectories become more complex, particularly in heterogeneous or multi-wave disease. Even when individual lesions are identified across examinations, documentation remains fragmented and reflects only a snapshot of the disease course rather than an integrated longitudinal perspective. These findings highlight a critical vulnerability in current follow-up practices. Improving lesion-level continuity, potentially through AI-assisted tools, may enhance the accuracy, consistency, and clinical utility of MRI surveillance in patients with brain metastases. Full article

In this contribution, we propose and study long-time behaviors of a new class of N-dimensional delayed Kirchhoff–Carrier-type problems with variable transfer coefficients involving a logarithmic nonlinearity. We take into account the dependence of diffusion and damping coefficients on the position and direction, as well as the presence of different types of delays. This class of nonlocal anisotropic and nonlinear wave-type equations with multiple time-varying mixed delays and dampings, of a fairly general form, containing several arbitrary functions and free parameters, is of the following form: ${\displaystyle \frac{{\partial }^{2}u}{\partial t^2}-{div\left(\mathcal{K}\right(\Vert \sigma \nabla u\Vert }_{L^2\left(\Omega \right)}^2)A_{\sigma }{\left(\mathbf{x}\right)\nabla u)+\mathcal{M}(\Vert u\Vert }_{L^2\left(\Omega \right)}^2)u-div(\zeta \left(t\right)A_{\sigma }\left(\mathbf{x}\right)\nabla \frac{\partial u}{\partial t})+d_0\left(t\right)\frac{\partial u}{\partial t}+{\mathbb{D}}_r(\mathbf{x},t;\frac{\partial u}{\partial t})=\mathbb{G}\left(u\right),}$ where $u(\mathbf{x},t)$ is the state function, $\mathcal{M}$ and $\mathcal{K}$ are the nonlocal Kirchhoff operators and the nonlinear operator $\mathbb{G}\left(u\right)$ corresponds to a logarithmic source term. The symmetric tensor $A_{\sigma }$ describes the anisotropic behavior and processes of the system, and the operator ${\mathbb{D}}_r$ represents the multiple time-varying mixed delays related to velocity ${\displaystyle \frac{\partial u}{\partial t}}$. Our problem, which encompasses numerous equations already studied in the literature, is relevant to a wide range of practical and concrete applications. It not only considers anisotropy in diffusion, but it also assumes that the strong damping can be totally anisotropic (a phenomenon that has received very little mathematical attention in the literature). We begin with the reformulation of the problem into a nonlinear system coupling a nonlocal wave-type equation with ordinary differential equations, with the help of auxiliary functions. Afterward, we study the local existence and some necessary regularity results of the solutions by using the Faedo–Galerkin approximation, combining some energy estimates and the logarithmic Sobolev inequality. Next, by virtue of the potential well method combined with the Nehari manifold, conditions for global in-time existence are given. Finally, subject to certain conditions, the exponential decay of global solutions is established by applying a perturbed energy method. Many of the obtained results can be extended to the case of other nonlinear source terms. Full article

Background/Objectives: It is well established in the modern literature that newborns delivered from multiple gestations are more predisposed to low birthweight in comparison to their singleton equivalents. In this study, we sought to explore the potential of first-trimester biochemical (PAPP-A and free β-hCG) and biophysical indices (uterine artery Doppler) to predict low birthweight in one or both twins. Methods: This is a retrospective cohort analysis of 400 twin viable pregnancies presenting for routine first-trimester assessment in four fetal medicine centers between 2014 and 2025. The examination included the recording of maternal demographic characteristics and medical history, the assessment of markers of aneuploidy and the fetal anatomy, the measurement of mean arterial pressure, the assessment of uterine arteries and the measurement of serum concentration of PAPP-A and free β-hCG. The evaluated outcomes included BW ≤ 3rd centile and BW ≤ 10th centile in one or both twins based on local population birthweight reference charts. Results: The study cohort consisted of 400 twin pregnancies. BW ≤ 3rd centile in one or both twins was reported in 1.5 and 3.8% of cases, respectively, and there was no association of BW ≤ 3rd centile with any of the studied parameters. BW ≤ 10th centile in one or both twins was reported in 14.8 and 9.8% of the cases, respectively. PAPP-A MoM values were significantly lower in cases complicated by BW ≤ 10th centile in one and in both twins, remaining statistically significant even after the appropriate multiple logistic regression. PAPP-A MoM demonstrated statistically significant but low prognostic value for BW ≤ 10th centile in either one or both twins. Conclusions: Low PAPP-A levels were associated with BW ≤ 10th centile in one and both twins and its significant value as a risk marker was demonstrated. Higher PAPP-A MoM halves the risk of having at least one twin with low BW. Other maternal biophysical and biochemical indices did not seem to be predictive of low birthweight. Full article

The presence of multiple permanent magnet synchronous generators (PMSGs) results in a highly complex and high-dimensional wind-farm model, making its transient synchronizing stability characteristics insufficiently understood and difficult to analyze. This paper investigates the mechanism by which interactions among multiple wind generators trigger transient synchronizing instability in wind farms. First, considering the influence of line impedance ratios, a reduced single-machine aggregated model suitable for transient synchronizing stability analysis of a wind farm with multiple PMSGs was derived from the similarity normalization transformation of the state-space matrices. Based on the aggregated model, the concepts of equivalent accelerating area and equivalent decelerating area were introduced to evaluate transient synchronizing stability of the wind farm. Through a comprehensive analysis of the effects of the generator dynamics, number of generators, network topology, and system parameters on these indices, the mechanism by which multi-PMSG interactions induce transient synchronization instability in PMSG wind farms is revealed. Finally, case studies were conducted to validate the accuracy and applicability of the analysis. Full article

In recent years, Transcranial Color Doppler (TCCD) has gained increasing recognition as a non-invasive neuromonitoring tool. However, there remains a strong tendency to view arterial TCCD as the ‘stethoscope for the brain,’ while the assessment of cerebral venous flow is still underrepresented in clinical protocols. This review aims to explore the emerging role of venous TCCD, particularly when combined with Internal Jugular Vein (IJV) ultrasound, in evaluating cerebral venous outflow in both critically ill and surgical patients. We conducted a narrative review of e-Pub articles from PubMed, MEDLINE, and Scopus, on the pathophysiological factors that impair cerebral venous drainage and their clinical implications in surgical and critical care settings. Based on this evidence, we developed two procedural algorithms that integrate established knowledge of cerebral venous hemodynamics with common clinical conditions affecting venous outflow, including internal jugular central venous catheter placement, mechanical ventilation, and pneumoperitoneum. The algorithms emphasize systematic monitoring of cerebral venous drainage, including assessment of internal jugular vein morphology and Rosenthal’s vein flow, to guide procedural optimization and minimize potential neurological complications. They were informed by validated frameworks, such as the RaCeVa protocol, and are illustrated through two representative clinical case scenarios. Cerebral venous congestion can be induced by multiple established risk factors, including mechanical ventilation, cardiovascular disease, elevated intra-abdominal pressure, the Trendelenburg position, and central venous catheterization. In selected patients, real-time venous TCCD monitoring, combined with IJV assessment, allows early detection of cerebral venous outflow impairment and guides timely hemodynamic and procedural adjustments in both surgical settings and critical care contexts. Venous TCCD neuromonitoring may help prevent intracranial hypertension and its consequent neurological complications. It can guide clinical decisions during procedures that may compromise cerebral venous drainage, such as mechanical ventilation, the placement of large-bore central venous catheters, or laparoscopic and robot-assisted surgeries. Further studies are warranted to validate this strategy and better define its role in specific high-risk clinical scenarios. Full article

This study presents a comprehensive techno-economic assessment to optimize a hybrid renewable energy system for green hydrogen production in Jordan. Using the Hybrid Optimization Model for Electric Renewables (HOMERs) and System Advisor Model (SAM) software, this study evaluates multiple cost projections for 2030 technology costs. Key parameters such as capital cost, efficiency, and lifetime are varied extensively. Highlighted results show a wide range in the Levelized Cost of Hydrogen (LCOH), reaching 1.59 to 3.49 USD/kg, and the Levelized Cost of Energy (LCOE) from 0.0072 to 0.0301 USD/kWh. Furthermore, Net Present Value (NPV) spans from USD 424 to 927 million, depending on the scenario and sensitivity case. Technically, the system’s optimized capacities vary significantly. PV ranges from 203 to 457 MW, wind capacities range from 0 to 220 MW, and electrolyzers range from 192 to 346 MW, demonstrating the flexibility required to meet different cost and performance assumptions. The study’s broad relevance extends to developing countries with grid constraints, where off-grid green hydrogen production is feasible. Its framework can be adapted globally, offering valuable insights. Full article

Background/Objectives: A common challenge in both forensic and bioarchaeological research is commingling, the intermixing of skeletal material originating from multiple individuals or contexts. To tackle that problem past reassociation methods primarily relied on visual assessment or metric comparisons. However, recent advances in geometric morphometrics show strong potential for improving the sorting of commingled remains. This study applies a three-dimensional (3D) geometric morphometric method to evaluate its effectiveness in reassociating adjoining thoracic vertebrae. Methods: Two vertebral pairs, T4–T5 and T5–T6, from 65 and 73 individuals, respectively, were analyzed. These pairs were chosen due to limited anatomical variability, while they were also the most consistently preserved pairs. All specimens were scanned using a structured-light 3D scanner, and the dataset was derived from three Greek skeletal collections representing different geo-chronological contexts. Fourteen anatomical landmarks were placed on the superior rim and articular facets of the lower vertebra and mirrored onto the lower rim and facets of the adjoining upper vertebra. To remove the size effects the landmark coordinates were converted to Procrustes coordinates, while examining morphological similarity was quantified using Euclidean distances. For each pair, the vertebrae with the smallest Euclidean distances were considered the most probable true anatomical matches. Results: The correct T4–T5 match fell within the three smallest distances in 66.2% of cases, while for the T5–T6 pair, correct matches were found between the first three possible matches in a percentage of 43.8%. These findings indicate that the method can eliminate roughly 50–70% of incorrect matches and therefore narrow the plausible pairings. Conclusions: Future research incorporating more pairs and an expanded landmark dataset may result in greater accuracy for reassociation with 3D geometric morphometrics. Full article

Mountain floods, one of the most common and destructive natural disasters worldwide, pose significant challenges to disaster prevention due to their sudden onset, high destructive power, and severe localized impacts. This study proposes an innovative flash flood early warning system based on a distributed hydrological model ensemble. The main objective is to improve the prediction and early warning accuracy of flash flood disasters by integrating multi-source data and regional modeling. The system simulates flood flow and risk levels under different rainfall scenarios to provide timely warnings in mountainous areas. A case study of a heavy rainfall event in Ma Jia Natural Village, Jiangxi Province was used to validate the system’s performance. Through regionalized parameter calibration within the ensemble, the system achieved Nash–Sutcliffe Efficiency (NSE) values exceeding 0.88, while the simulated peak discharges deviated from observed values by only 1.5%, 9.5%, and 4.8% under 3 h, 6 h, and 24 h rainfall scenarios, respectively, demonstrating the improved quantitative accuracy of flood prediction enabled by the ensemble-based framework. The system showed high consistency with observed data, accurately predicting flood responses at 3, 6, and 24 h time scales and providing reliable risk warnings. This approach not only enhances warning accuracy across multiple temporal scales but also supports risk-level early warnings at both river-section and village scales, offering significant practical value for the prevention of mountainous flood disasters. Full article

Background/Objectives: Missing data in clinical observational studies, such as out-of-hospital cardiac arrest (OHCA) registries, can compromise statistical validity. Single imputation methods are simple alternatives to complete-case analysis (CCA) but do not account for imputation uncertainty. Multiple imputation (MI) is the standard for handling missing-at-random (MAR) data, yet its implementation remains challenging. This study evaluated the performance of MI in association analysis compared with CCA and single imputation methods. Methods: Using a simulation framework with real-world Singapore OHCA registry data (N = 13,274 complete cases), we artificially introduced 20%, 30%, and 40% missingness under MAR. MI was implemented using predictive mean matching (PMM), random forest (RF), and classification and regression trees (CART) algorithms, with 5–20 imputations. Performance was assessed based on bias and precision in a logistic regression model evaluating the association between alert issuance and bystander CPR. Results: CART outperformed PMM, providing more accurate β coefficients and stable CIs across missingness levels. Although K-Nearest Neighbours (KNN) produced similar point estimates, it underestimated imputation uncertainty. PMM showed larger bias, wider and less stable CIs, and in some settings performed similarly to CCA. MI methods produced wider CIs than single imputation, appropriately capturing imputation uncertainty. Increasing the number of imputations had minimal impact on point estimates but modestly narrowed CIs. Conclusions: MI performance depends strongly on the chosen algorithm. CART and RF methods offered the most robust and consistent results for OHCA data, whereas PMM may not be optimal and should be selected with caution. MI using tree-based methods (CART/RF) remains the preferred strategy for generating reliable conclusions in OHCA research. Full article

Financial crises increasingly exhibit complex, interconnected patterns that traditional risk models fail to capture. The 2008 global financial crisis, 2020 pandemic shock, and recent banking sector stress events demonstrate how systemic risks propagate through multiple channels simultaneously—e.g., network contagion, extreme co-movements, and information cascades—creating a multidimensional phenomenon that exceeds the capabilities of conventional actuarial or econometric approaches alone. This paper addresses the fundamental challenge of modeling this multidimensional systemic risk phenomenon by proposing a mathematically formalized three-tier integration framework that achieves 19.2% accuracy improvement over traditional models through the following: (1) dynamic network-copula coupling that captures 35% more tail dependencies than static approaches, (2) semantic-temporal alignment of textual signals with network evolution, and (3) economically optimized threshold calibration reducing false positives by 35% while maintaining 85% crisis detection sensitivity. Empirical validation on historical data (2000–2023) demonstrates significant improvements over traditional models: 19.2% increase in predictive accuracy (R² from 0.68 to 0.87), 2.7 months earlier crisis detection compared to Basel III credit-to-GDP indicators, and 35% reduction in false positive rates while maintaining 85% crisis detection sensitivity. Case studies of the 2008 crisis and 2020 market turbulence illustrate the model’s ability to identify subtle precursor signals through integrated analysis of network structure evolution and semantic changes in regulatory communications. These advances provide financial regulators and institutions with enhanced tools for macroprudential supervision and countercyclical capital buffer calibration, strengthening financial system resilience against multifaceted systemic risks. Full article

Chronic obstructive pulmonary disease (COPD) is a major health burden worldwide and in Taiwan, ranking as the third leading cause of death globally, and its prevalence in Taiwan continues to rise. Readmission within 14 days is a key indicator of disease instability and care efficiency, driven jointly by patient-level physiological vulnerability (such as reduced lung function and multiple comorbidities) and healthcare system-level deficiencies in transitional care. To mitigate the growing burden and improve quality of care, it is urgently necessary to develop an AI-based prediction model for 14-day readmission. Such a model could enable early identification of high-risk patients and trigger multidisciplinary interventions, such as pulmonary rehabilitation and remote monitoring, to effectively reduce avoidable early readmissions. However, medical data are commonly characterized by severe class imbalance, which limits the ability of conventional machine learning methods to identify minority-class cases. In this study, we used real-world clinical data from multiple hospitals in Kaohsiung City to construct a prediction framework that integrates data generation and ensemble learning to forecast readmission risk among patients with chronic obstructive pulmonary disease (COPD). CTGAN and kernel density estimation (KDE) were employed to augment the minority class, and the impact of these two generation approaches on model performance was compared across different augmentation ratios. We adopted a stacking architecture composed of six base models as the core framework and conducted systematic comparisons against the baseline models XGBoost, AdaBoost, Random Forest, and LightGBM across multiple recall thresholds, different feature configurations, and alternative data generation strategies. Overall, the results show that, under high-recall targets, KDE combined with stacking achieves the most stable and superior overall performance relative to the baseline models. We further performed ablation experiments by sequentially removing each base model to evaluate and analyze its contribution. The results indicate that removing KNN yields the greatest negative impact on the stacking classifier, particularly under high-recall settings where the declines in precision and F1-score are most pronounced, suggesting that KNN is most sensitive to the distributional changes introduced by KDE-generated data. This configuration simultaneously improves precision, F1-score, and specificity, and is therefore adopted as the final recommended model setting in this study. Full article

Dual Carbon Goals are driving transformation in China’s power system, where increased renewable energy penetration is accompanied by heightened fluctuations on the generation and load sides. Energy storage and microgrid coordination have emerged as key solutions. However, existing research faces the challenge of balancing microgrid operations, energy storage services, and the alignment of user demand with stakeholder interests. This paper establishes a tripartite collaborative optimization framework to balance multi-stakeholder interests and enhance system efficiency, assuming fixed energy storage capacity. Centering on a principal-agent game between microgrid operators and consumer aggregators, energy storage service providers are integrated into this dynamic. Microgrid operators set 24-h electricity and heat pricing while adhering to tariff constraints, prompting consumer aggregators to adjust energy consumption and storage strategies accordingly. The KKT conditional method is employed to solve the model, deriving optimal user energy consumption strategies at the lower level while solving marginal pricing equilibrium relationships at the upper level, balancing accuracy with information privacy. The creative contribution of this article lies in the first construction of a tripartite collaborative optimization architecture in which energy storage service providers are embedded in a game of ownership and subordination. It proposes a dynamic coupling mechanism between pricing power, energy consumption decision-making, and energy storage configuration under fixed energy storage capacity constraints, achieving a balance of interests among multiple parties. By building a case study using MATLAB (R2022b), we compare operation costs, benefits, and absorption rates across different scenarios to validate the framework’s effectiveness and provide a reference for engineering applications. Full article

The impact and solidification of multiple molten droplets on a cold substrate critically influence the quality and performance of thermally sprayed coatings. We present a Smoothed Particle Hydrodynamics (SPH) model that couples fluid-solid interaction, wetting, heat transfer and phase change to simulate multi-droplet impact and freezing. The model is validated against benchmark cases, including the Young–Laplace relation, wetting dynamics, single-droplet impact and the Stefan solidification problem, showing good agreement. Using the validated model, we investigate two droplets—either centrally or off-centrally—impacting on a cold surface. Simulations reveal two distinct solidification patterns: convex pattern (CVP), which results in a mountain-like splat morphology, and concave pattern (CCP), which leads to a valley-like shape. The criterion for the two patterns is explored with two dimensionless numbers, the Reynolds number $Re$ and the Stefan number $Ste$. When $Re\le 17.8$, droplets tend to solidify in CVP; at higher Reynolds numbers $Re\ge 18.8$, they tend to solidify in CCP. The transition between the two patterns is primarily governed by $Re$, with $Ste$ exerting a secondary influence. For example, when droplets have $Re=9.9$ and $Ste=5.9$, they tend to solidify in a convex pattern, whereas at $Re=19.8$ and $Ste=5.9$, they tend to solidify in a concave pattern. Also, the solidification state of the first droplet greatly influences the subsequent spreading and solidification of the second droplet. A parametric study on CCP cases with varying vertical and horizontal offsets shows that larger vertical offsets accelerate solidification and reduce the maximum spreading factor. For small vertical distances, the solidification time increases with horizontal offset by more than 29%; for large vertical distances the change is minor. These results clarify how droplet interactions govern coating morphology and thermal evolution during thermal spraying. Full article