Search Results (17,063)

This study used a Systemic Modeling technique, based on the methodologies of Churchman and Ackoff, to integrate and assess the subsystems regulating the functionality of a Rocket Candy (R-Candy) motor. The nozzle and combustion chamber design was improved using a five-phase systemic architecture to assure the coherent interplay of essential factors, including pressure, temperature, and velocity fields. The principles of experimental rocketry are elucidated through the examination of impulse performance throughout class A to class C engines. A preliminary design was developed in SolidWorks 2024, incorporating the engine’s three main components: the igniter, the combustion chamber, and a convergent–divergent nozzle that enhances the acceleration of the exhaust gases. The system model was validated using simulations in FEATool and verified through experimentation. This allowed for the analysis of fluid behavior, as well as the geometry of the structures, initial parameters, and boundary conditions. The results demonstrate a strong correlation between the simulations and the experimental data, with discrepancies of less than 1.5%, confirming the reliability and feasibility of the nozzle design. The findings indicate that systemic modeling, in conjunction with CFD and experimentation, can provide a strategic framework for iterative refinement, optimization of key performance metrics, and the development of cost-effective, high-performance R-Candy engines for educational and experimental purposes. Full article

Objectives: To improve the differentiation of borderline ovarian-adnexal tumours (BOTs) from malignant ovarian-adnexal masses, most of which fall into the indeterminate O-RADS MRI 4 category, by developing a multimodal predictive model that integrates clinical, analytic, and MRI parameters. Methods: This retrospective, single-centre study included 248 women who underwent standardised MRI for ovarian-adnexal mass characterisation between 2019 and 2024. Of these, 201 had true ovarian-adnexal masses (114 benign, 22 borderline, and 65 malignant), confirmed by histopathology or stability after ≥12-month follow-up. Forty-one clinical, laboratory, and imaging variables were initially assessed, and after a bivariate evaluation, 18 final predictors with clinical relevance were selected for model construction with thresholds learned from the data. A classification and regression tree (CART) model (“Full Model”) was applied as a second-stage tool after O-RADS MRI scoring, using 10-fold cross-validation to prevent overfitting. A pruned “Simplified Model” was also derived to enhance interpretability. Results: O-RADS MRI performed well at the extremes (scores 2–3 and 5) but showed limited discrimination between BOTs and malignancies within category 4 (PPV for borderline = 0.50). The decision-tree models significantly improved diagnostic performance, increasing overall accuracy from 0.856 with O-RADS MRI alone to 0.905 (Simplified Model) and 0.955 (Full Model). The PPV for BOTs within the intermediate O-RADS MRI 4 category increased from 0.49 with O-RADS MRI alone to 0.77 and 0.90 with the simplified and full models, respectively, while maintaining high accuracy for benign and malignant lesions. Conclusions: In this retrospective single-centre cohort, the addition of an interpretable rule-based predictive model as a second-line tool within O-RADS MRI category 4 was associated with improved discrimination between borderline and invasive malignant ovarian-adnexal tumours. These findings suggest that multimodal integration of clinical, laboratory, and MRI features may help refine risk stratification in indeterminate cases; however, external validation in prospective multicentre cohorts is required before clinical implementation. Full article

This study evaluates the performance of artificial intelligence (AI) technologies for wetland classification in the province of Alberta, Canada, using integrated remote sensing inputs, including airborne light detection and ranging (LiDAR), orthophotography, and multi-sensor satellite imagery (Sentinel-1, Sentinel-2, PlanetScope). Our primary objective was to assess whether AI-driven modelling approaches, specifically machine learning (ML) and deep learning (DL), can meet Alberta’s provincial wetland mapping standards. We hypothesized that integrating high-resolution LiDAR with multi-seasonal optical and radar data composites into advanced AI algorithms would achieve the required classification accuracy, detail, and minimum mapping unit targets. We tested several methodologies in four ecologically distinct pilot areas representing Alberta’s Boreal, Grassland, and Parkland Natural Regions. AI models included ensemble ML using Extreme Gradient Boosting (XGBoost) and Random Forest, and a DL U-Net convolutional neural network (CNN). AI models were trained on expert-labelled photoplots and validated using in situ field surveys. Our findings demonstrate that both ML and DL models met and, in several cases, exceeded the provincial mapping standards with validation overall accuracies surpassing >70% (form), >80% (class), and >90% (wetland–upland). U-Net CNN models generally produced the highest overall accuracies and most precise wetland extent delineation, but XGBoost offered finer detail and granularity for detailed mapping of rare wetland forms. Integrating LiDAR data and derivatives further enhanced model performance, improving accuracy by as much as 13%. Based on these outcomes, we provide a set of recommendations for scaling up these approaches, focusing on model selection, LiDAR imagery integration, and the continued value of field surveys to support the operational scaling of AI-driven classification approaches for wetland inventory updates across Alberta’s diverse landscapes. However, key challenges remain in scaling up this approach due to the cost of acquiring high-resolution LiDAR and satellite imagery. Full article

This paper proposes a two-stage framework integrating Data Envelopment Analysis (DEA) and fuzzy multi-criteria decision-making methods to evaluate the performance of logistics firms in Vietnam. In the first stage, DEA models (CCR, BCC, and SBM) are employed to measure relative efficiency and identify benchmark firms among 15 leading logistics companies. In the second stage, FAHP–FTOPSIS is used to incorporate qualitative and sustainability-oriented criteria and to provide a comprehensive ranking of the efficient firms. The results indicate that a considerable proportion of firms operate below the efficiency frontier, implying substantial opportunities for resource optimization. Environmental and technological dimensions are found to be the most influential factors, while companies implementing green distribution strategies and strong data security practices consistently achieve higher rankings. Sensitivity analysis confirms the robustness and stability of the proposed framework. This study contributes by bridging operational efficiency assessment with broader strategic and sustainability considerations, overcoming the limitations of single-method evaluations used in prior research. The integrated DEA–FAHP–FTOPSIS approach offers managers a practical tool to diagnose weaknesses, prioritize improvement actions, and benchmark against top performers. In addition, it offers policymakers valuable insights to support digital transformation and green logistics initiatives in developing economy contexts. Full article

Pseudomonas aeruginosa is a Gram-negative pathogen with a remarkable capacity to acquire multiple resistance mechanisms, severely limiting current therapeutic options. Consequently, the identification of new antimicrobial agents remains a critical priority. In this study, an integrated in silico-guided strategy was applied to identify small molecules with antibacterial potential against P. aeruginosa, targeting the quorum-sensing regulator LasR (PDB ID: 2UV0) and elastase (PDB ID: 1U4G). Pharmacophore modeling was performed for both targets, followed by ligand-based virtual screening, structure-based virtual screening (SBVS), and MM-GBSA (Molecular Mechanics-Generalized Born Surface Area) binding free energy calculations. Top-ranked compounds based on predicted binding affinity were selected for in vitro cytotoxicity and antibacterial evaluation. Antimicrobial activity was assessed against three P. aeruginosa strains: an American Type Culture Collection (ATCC) reference strain, a clinically susceptible isolate, and an extensively drug-resistant (XDR) clinical isolate. SBVS yielded docking scores ranging from −6.96 to −12.256 kcal/mol, with MM-GBSA binding free energies between −18.554 and −88.00 kcal/mol. Minimum inhibitory concentration (MIC) assays revealed that MolPort-001-974-907, MolPort-002-099-073, MolPort-008-336-135, and MolPort-008-339-179 exhibited MIC values of 62.5 µg/mL against the ATCC strain, indicating weak-to-moderate antibacterial activity consistent with early-stage hit compounds. MolPort-008-336-135 showed the most favorable activity against the clinically susceptible isolate, with an MIC of 62.5 µg/mL, while maintaining HepG2 cell viability above 70% at this concentration and an half-maximal inhibitory concentration (IC₅₀) greater than 500 µg/mL. In contrast, all tested compounds displayed MIC values above 62.5 µg/mL against the XDR isolate, reflecting limited efficacy against highly resistant strains. Overall, these results demonstrate the utility of in silico-driven approaches for the identification of antibacterial hit compounds targeting LasR and elastase, while highlighting the need for structure–activity relationship optimization to improve potency, selectivity, and activity against multidrug-resistant P. aeruginosa. Full article

Morbid obesity is a complex, multifactorial disorder characterized by metabolic and inflammatory dysregulation. The aim of this study was to observe changes in obese patients adhering to a personalized nutrition plan based on multi-omic data. This study included 14 adult patients with a body mass index (BMI) > 40 kg/m² who were consecutively recruited from those presenting to our outpatient clinic and who met the inclusion criteria. Clinical, biochemical, hormonal, and glycomic parameters were assessed, along with whole-genome sequencing (WGS) that included a focused analysis of obesity-associated genes and an extended analysis encompassing genes related to cardiometabolic disorders, hereditary cancer risk, and nutrigenetic profiles. Patients were stratified into nutrigenetic clusters using a patented unsupervised machine learning platform (German Patent Office, No. DE 20 2025 101 197 U1), which was employed to generate personalized nutrigenetic dietary recommendations for patients with morbid obesity to follow over a six-month period. At baseline, participants exhibited elevated glucose, insulin, homeostatic model assessment for insulin resistance (HOMA-IR), triglycerides, and C-reactive protein (CRP) levels, consistent with insulin resistance and chronic low-grade inflammation. The majority of participants harbored risk alleles within the fat mass and obesity-associated gene (FTO) and the interleukin-6 gene (IL-6), together with multiple additional significant variants identified across more than 40 genes implicated in metabolic regulation and nutritional status. Using an AI-driven clustering model, these genetic polymorphisms delineated a uniform cluster of patients with morbid obesity. The mean GlycanAge index (56 ± 12.45 years) substantially exceeded chronological age (32 ± 9.62 years), indicating accelerated biological aging. Following a six-month personalized nutrigenetic dietary intervention, significant reductions were observed in both BMI (from 52.09 ± 7.41 to 34.6 ± 9.06 kg/m², p < 0.01) and GlycanAge index (from 56 ± 12.45 to 48 ± 14.83 years, p < 0.01). Morbid obesity is characterized by a pro-inflammatory and metabolically adverse molecular signature reflected in accelerated glycomic aging. Personalized nutrigenetic dietary interventions, derived from AI-driven analysis of whole-genome sequencing (WGS) data, effectively reduced both BMI and biological age markers, supporting integrative multi-omics and machine learning approaches as promising tools in precision-based obesity management. Full article

Roadside public transport stops represent localized air pollution hotspots where short-term exposure may differ substantially from levels reported by urban background monitoring. This study investigates the application of low-cost air quality sensors for long-term characterization of particulate matter and gaseous pollutants in a traffic-dominated urban microenvironment. The novelty of this work lies in the combined use of collocated low-cost sensors, energy-independent solar-powered deployment, height-resolved placement representative of different breathing zones, and integrated statistical and predictive analysis to resolve exposure-relevant pollutant dynamics at a single transport stop. Hourly concentrations of particulate matter (PM) PM₁, PM₂.₅, PM₁₀, nitrogen dioxide (NO₂), and ozone (O₃) were measured over one year at a roadside transport stop adjacent to a four-lane urban road carrying approximately 30,000 vehicles per day. Measurements were obtained using two collocated low-cost sensor units based on optical particle sensing for particulate matter and electrochemical sensing for gases, together with concurrent meteorological observations. Strong agreement between the two particulate matter sensors supported the use of averaged concentrations. Mean PM₂.₅ concentrations were substantially higher in winter (32.4 µg/m³) than in summer (10.4 µg/m³), indicating pronounced seasonal variability. PM₁ and PM₂.₅ exhibited closely aligned temporal patterns, while PM₁₀ showed greater variability. NO₂ displayed sharp diurnal peaks associated with traffic activity, whereas O₃ exhibited opposing seasonal and diurnal behavior and was negatively correlated with both PM₂.₅ (r = −0.32) and NO₂ (r = −0.29). One-hour-ahead predictive models incorporating meteorological and temporal variables achieved coefficients of determination up to 0.84. The results demonstrate that energy-independent low-cost sensor systems can robustly capture temporal patterns, pollutant interactions, and short-term predictability in localized roadside environments relevant to exposure assessment. Full article

Background: Chronological age inadequately captures biological vulnerability among surgical patients. Frailty and muscle strength reflect physiological reserve, yet their combined contribution to postoperative length of stay (LOS) remains insufficiently explored. Methods: We conducted a prospective multicenter observational cohort study including 223 adults undergoing elective abdominal surgery. Frailty was assessed using the Fried phenotype, and admission handgrip strength (HGS) was measured with a calibrated dynamometer. Prolonged LOS was defined as >10 days (75th percentile) and also analyzed continuously using ln(LOS + 1). Multivariable logistic and linear regression models adjusted for age, sex, frailty status, and surgical indication. Patients were additionally stratified into four physiological reserve phenotypes combining frailty and HGS. Results: LOS ranged from 0 to 68 days; a total of 48 patients (21.6%) experienced prolonged hospitalization. In multivariable logistic regression, frailty (adjusted OR 3.12, 95% CI 1.72–5.67) and oncologic surgery (adjusted OR 7.63, 95% CI 3.12–18.65) were independently associated with prolonged LOS, whereas chronological age was not. Female sex was associated with lower odds of prolonged LOS (adjusted OR 0.39, 95% CI 0.18–0.87). Similar associations were observed when LOS was analyzed continuously. Physiological reserve phenotyping revealed graded LOS distributions: Fit–Strong patients had the shortest stays (mean 5.5 ± 4.3 days), while Frail–Weak patients experienced the longest and most variable hospitalization. Conclusions: Postoperative LOS clusters according to multidimensional physiological reserve rather than chronological age. Integrating frailty and muscle strength identifies clinically meaningful phenotypes that may improve perioperative risk stratification beyond age-based approaches and inform personalized perioperative planning, resource allocation, and patient-centered decision-making across heterogeneous surgical populations in worldwide settings. Full article

Background/Objectives: Postoperative atrial fibrillation (POAF) is a common and serious complication after coronary artery bypass grafting (CABG), leading to increased morbidity and healthcare utilization. Although systemic inflammation is a well-established driver of POAF pathogenesis, no composite preoperative inflammatory biomarker has been validated for risk stratification in this population. This study aimed to evaluate the novel Inflammatory Burden Index (IBI)—the first composite biomarker combining acute-phase (C-reactive protein, CRP) and chronic cellular (neutrophil-to-lymphocyte ratio, NLR) inflammation—as a preoperative predictor of POAF after CABG. Methods: In this large retrospective cohort study, we included 3481 consecutive patients who underwent isolated CABG at a high-volume cardiac center between 2019 and 2024. Preoperative IBI was calculated as CRP (mg/dL) × NLR. The primary outcome was new-onset POAF within the first 7 postoperative days, confirmed by continuous telemetry on 12-lead ECG. Predictive performance was assessed using multivariable logistic regression, receiver operating characteristic (ROC) curve analysis (area under the curve, AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), and internal validation via bootstrapping (1000 resamples). Results: POAF developed in 866 patients (24.9%). Patients with POAF exhibited significantly higher preoperative IBI levels (39.4 ± 18.6 vs. 26.3 ± 16.7, p < 0.01). In multivariable analysis adjusted for age, hypertension, left atrial diameter, and other clinical covariates, IBI emerged as a strong independent predictor of POAF (adjusted OR 1.041, 95% CI 1.036-1.046, p < 0.01). The IBI alone demonstrated moderate-to-good discriminative performance (AUC 0.72, 95% CI 0.70–0.74), significantly outperforming the Systemic Immune/Inflammation Index (SII; AUC 0.61, DeLong test p < 0.001) and providing superior reclassification (NRI 0.150, IDI 0.032) and model fit (lower AIC). Combining IBI with established clinical risk factors further improved predictive accuracy (combined AUC 0.74, specificity 72.4%). Tertile-based stratification revealed a clear graded relationship with POAF incidence (low IBI: 16.6%, medium: 21.3%, high: 35.1%; p = 0.02). Notably, the medium IBI stratum (11.18-25.44) displayed the highest discriminative power (AUC 0.87, 95% CI 0.85-0.88), with bootstrap validation confirming model stability (minimal bias, robust 95% CI). Conclusions: This study establishes the preoperative Inflammatory Burden Index (IBI) as the first validated composite inflammatory biomarker independently associated with POAF following CABG. Its superior performance over existing indices (SII), graded risk stratification, and peak accuracy in the moderate inflammation window highlight its potential for personalized preoperative risk assessment and targeted perioperative intervention strategies. Full article

Federated learning (FL) has emerged as a promising paradigm for privacy-preserving distributed intelligence in modern urban transportation systems, where vehicles collaboratively train global models without sharing raw data. However, the dynamic nature of vehicular environments introduces critical challenges, including unstable participation, data heterogeneity, and the potential for malicious behavior. Conventional FL frameworks lack effective trust management and adaptive incentive mechanisms capable of maintaining fairness and reliability under these fluctuating conditions. This paper presents a reputation-aware federated learning framework that integrates multi-dimensional reputation evaluation, dynamic incentive control, and malicious client detection through an adaptive feedback mechanism. Each vehicular client is assessed based on data quality, stability, and behavioral consistency, producing a reputation score that directly influences client selection and reward allocation. The proposed feedback controller self-tunes the incentive weights in real time, ensuring equitable participation and sustained convergence performance. In parallel, a penalty module leverages statistical anomaly detection to identify, isolate, and penalize untrustworthy clients without compromising benign contributors. Extensive simulations conducted on real-world datasets demonstrate that the proposed framework achieves higher model accuracy and greater robustness against poisoning and gradient manipulation attacks compared to existing baseline methods. The results confirm the potential of our trust-regulated incentive mechanism to enable reliable federated learning in smart cities transportation systems. Full article

Background: Synthetic vascular scaffolds often exhibit limited mechanical performance and low hydrophilicity, which compromise early vascular integration and increase susceptibility to bacterial colonization. This study developed 3D-printed scaffolds based on poly(L-co-D,L-lactide)–poly(trimethylene carbonate) (PLDLA–TMC) with polyethylene glycol 400 (PEG) to modulate mechanical and interfacial properties and coated with poly(hexamethylene biguanide) (PHMB) to confer antibacterial activity. Methods: PLDLA–TMC scaffolds modified with PEG 400 and coated with PHMB were prepared and systematically characterized to assess their structural, thermal, mechanical, and antimicrobial properties. PHMB coatings (3%, 6%, and 12% w/w in hydroxypropyl methylcellulose, HPMC) were applied and evaluated for drug release, cytotoxicity, and activity against Staphylococcus aureus. Biocompatibility was tested in an endothelial cell and myoblast co-culture. Results: Incorporation of 2% PEG increased the tensile strength from 0.14 ± 0.10 MPa for scaffolds containing 0.5% PEG to 0.79 ± 0.12 MPa and promotes a more elastic scaffold behavior. PHMB at 12% caused cytotoxicity (7.70 ± 0.37% cell viability). The 3% PHMB coating produced a 12.5 ± 0.1 mm inhibition zone but exhibited burst release within 1 h, whereas the 6% coating maintained cell viability (72.95 ± 1.10%), produced a 13.1 ± 0.2 mm inhibition zone, and provided sustained antimicrobial release over 7 days. Scaffolds supported organized adhesion and proliferation of endothelial cells and myoblasts. Conclusions: 3D-printed PLDLA–TMC scaffolds containing 2% PEG and coated with 6% PHMB combined improved mechanical performance, sustained antimicrobial release, antibacterial activity, and biocompatibility in an in vitro vascular model. Full article

This study proposes an integrated decision-support framework that combines robust multi-objective optimization and discrete-event simulation to enhance sustainability and resilience in automotive supply chain management. Automotive supply chains are highly complex and exposed to significant uncertainty arising from demand fluctuations, supply disruptions, and procurement constraints, particularly in emerging economies. To address these challenges, the proposed framework incorporates mixed-integer programming with a multi-objective formulation to balance production, supply, holding, and penalty costs. Additionally, robust optimization based on the Bertsimas–Sim approach is employed to hedge against demand uncertainty. Additionally, a discrete-event simulation model is developed to validate and refine the optimization results under stochastic operating conditions, and to assess the practical performance of the proposed strategies. The framework is applied to a real-world automotive case study, where flexible production policies, including fractional production and urgent procurement, are evaluated in terms of their economic and social sustainability impacts. The results demonstrate that integrating robust optimization with simulation improves supply chain resilience, reduces vulnerability to uncertainty, and supports more sustainable operational decision-making. The proposed approach provides valuable insights for managers seeking to design resilient and sustainable automotive supply chains under uncertain environments. Full article

Maritime transport in semi-enclosed seas is increasingly exposed to short-term weather variability, a challenge expected to intensify under climate change and to affect the economic sustainability of shipping operations. This study proposes an integrated probabilistic framework to assess the impact of weather-induced uncertainty on operational costs, using a ferry service in the Baltic Sea as a case study. The approach combines a semi-Markov process, representing transitions between discrete weather hazard states derived from ERA5 reanalysis data (2010–2025), with a state-dependent cost model of key technical subsystems across the vessel’s operational cycle. The results show a strongly disproportionate cost structure, with most expenditures concentrated in open-sea navigation states. Although severe weather conditions occur infrequently, they generate a nonlinear amplification of operational costs, significantly reducing cost predictability and system resilience. The findings indicate that enhancing sustainability in maritime transport requires targeted, state-specific adaptation measures, such as weather-aware routing and condition-based maintenance. The proposed framework supports climate-adaptive decision-making and contributes to sustainability-oriented planning in maritime transport through improved operational robustness and cost resilience under weather uncertainty. Full article

As a mainstream new energy generation technology, elucidating the grid-connected voltage stability mechanisms of permanent magnet synchronous generator (PMSG) is critical for ensuring stable integration of high-penetration renewable energy. Existing research on the voltage stability of grid-connected PMSG systems is confined to single-fault scenarios, failing to adequately account for the impacts of other significant internal grid disturbances, such as direct current blockings and increased renewable energy penetration. Moreover, the traditionally used simplified grid model with a voltage source in series with an impedance is overly idealized, making it difficult to comprehensively reveal the transient stability mechanisms of grid-connected PMSG systems under complex multi-disturbance conditions. To address this issue, this paper proposes a numerical analysis method to investigate the grid stability mechanisms of PMSG systems under various grid disturbance scenarios. First, an electromagnetic transient simulation model of the grid-connected PMSG system is established. Next, key parameters influencing the system’s voltage stability are identified using the global sensitivity Sobol method. Subsequently, a transient voltage stability assessment index and a method for revealing the grid stability patterns of PMSG systems are presented. Finally, the PMSG system is integrated into the CSEE standard test system on the CloudPSS platform for validation and analysis. The results demonstrate that the proposed method effectively reveals voltage stability mechanisms considering various internal grid disturbances, and the mechanistic characteristics it reveals differ significantly from conclusions drawn using a simplified grid model. Full article

Students with attention deficit hyperactivity disorder (ADHD) face a continuous challenge with their attention span, putting them at a greater risk of academic or psychological difficulties compared to their peers. Innovative communication technologies are demonstrating potential to address these attention-span concerns. Virtual Reality (VR) is one such example, and has the potential to address attention-span difficulties among ADHD students. Accordingly, this study presents an EEG-based multimodal sensing pipeline as a methodological contribution, focusing on sensor-based data acquisition, signal processing, and neurophysiological interpretation to assess attention in VR-based environments, simulating a university supply chain educational topic. Thus, in this paper, a sequential exploratory approach investigated how 35 participants experienced an interactive VR-learning-driven supply chain game. A Brain–Computer Interaction (BCI) sensor generated insights by quantitatively analysing electroencephalogram (EEG) data that were processed through the proposed pipeline and integrated with subjective measures to validate participant’s subjective feelings. These insights originated from questions during the experiment that followed the Spatial Presence and Technology Acceptance Model to form a multimodal assessment framework. Findings demonstrated that the experimental group experienced a higher improved attention, concentration, engagement, and focus levels compared to the control group. BCI results from the experimental group showed more dominant voltage potentials in the right frontal and prefrontal cortex of the brain in areas responsible for attention, memory, and decision-making. A high acceptance of the VR technology among neurodiverse students highlights the added benefits of multimodal learning assessment methods in an educational setting. Full article