Search Results (18,861)

Background/Objectives: Neonatal sepsis remains a major cause of morbidity and mortality worldwide, underscoring the critical need for early and accurate diagnostic biomarkers. This study aimed to evaluate the predictive value of the red cell distribution width-to-albumin ratio (RAR) for the diagnosis of neonatal sepsis and to compare its diagnostic performance with the C-reactive protein-to-albumin ratio (CAR) and other hematological parameters. Methods: This retrospective cohort study was conducted in the Neonatology Department of Karadeniz Technical University Faculty of Medicine between January 2015 and June 2025. A total of 874 neonates with a gestational age > 36 weeks were included: 411 with blood culture-confirmed sepsis (108 Gram-negative, 303 Gram-positive) and 463 healthy controls. Diagnostic performance was assessed using receiver operating characteristic (ROC) curve analysis, with optimal cut-off values determined by the Youden index. Results: RAR demonstrated modest diagnostic accuracy for neonatal sepsis, with an area under the curve (AUC) of 0.647 (95% CI: 0.61–0.69), a cut-off value of 5.108, sensitivity of 50.7%, and specificity of 76.7%. RAR was significantly elevated in the Gram-negative group compared with Gram-positive cases and controls (p < 0.001). However, the clinical significance of this subgroup difference requires further investigation. In multivariable logistic regression analysis, RAR remained independently associated with culture-confirmed neonatal sepsis after adjustment for gestational age, birth weight, sex, and mode of delivery (aOR = 2.190, 95% CI: 1.838–2.608, p < 0.001). Among all evaluated markers, CAR achieved the highest diagnostic performance (AUC: 0.981, sensitivity: 88.5%, specificity: 98.7%, PPV: 98.4%, NPV: 90.7%, LR+: 68.47), followed by C-reactive protein (AUC: 0.978), while neutrophil-to-lymphocyte ratio showed the lowest discriminatory ability (AUC: 0.592). Conclusions: RAR showed limited standalone discriminatory performance for culture-confirmed neonatal sepsis, although its association with sepsis remained significant after adjustment for relevant demographic and perinatal factors. CAR demonstrated significantly greater discriminatory performance than RAR. Further studies are needed to determine whether RAR provides incremental value when incorporated into multivariable diagnostic models. Full article

Malaria and typhoid fever co-infection produces severe illness affecting public health, especially in countries where both diseases coexist. A mathematical model that considers the critical factors influencing the transmission dynamics and control interventions of malaria and typhoid fever co-infection is developed. The essential epidemiological features of the model, such as the basic reproduction number and disease-free equilibrium, are determined and analysed. A dynamical systems analysis of the model reveals the conditions under which the disease can be eradicated or persists. Numerical simulations are conducted using real data from Nigeria as a case study. By fitting the model to the real data, essential parameter values are estimated and model prediction that reveals the possible long-term dynamics of the model is determined. The impact of the various control interventions are investigated. These findings are anticipated to aid in improving the management of malaria–typhoid co-infection in endemic regions for expeditious disease eradication. Full article

Vehicle-to-grid (V2G) technology lets a parked electric vehicle push power back to the network, so a fleet of cars can act as distributed storage. Many studies report large benefits from this, such as peak shaving and better use of renewable energy, but most describe their simulation only in words, name no test network, and compare a single charging behavior against a do-nothing case. It is then hard to separate what V2G delivers from what the control strategy delivers. This paper builds and compares dispatch strategies on one fully specified system: a price-following rule with no knowledge of the network, a stronger randomized time-of-use baseline, and a receding-horizon model predictive control (MPC) strategy that re-plans every hour with the distribution network’s per-line thermal limits and voltage bounds embedded directly in the optimization, not merely checked afterward. All run on the IEEE 33-bus feeder at low (10%), medium (30%), and high (50%) EV shares, with a full AC power flow solved every hour. The central result is a critical-penetration effect. At a 50% share, the naive price rule makes the system peak 23.7% worse than having no V2G at all, because the whole fleet reacts to one price signal at once, while the MPC cuts the peak by 18 to 28% in every case. Embedding the network limits removes the worst-line-loading side-effect a system-peak-only objective creates, bringing high-share worst-line loading down from 86.3% to 81.8% while preserving the full peak reduction. A workplace daytime-charging scenario shows renewable use becoming a real, separating metric (up to 2.8 MWh/day of EV demand met directly by rooftop solar, against zero for overnight charging), a quadratic wear cost smooths the profit-cycling frontier that a linear cost makes step-shaped, the controller is robust to forecast error up to 20%, and its solve time is set by network size rather than fleet size. Results are given as they came out of the model. Full article

The inadequacy of traditional preclinical oncology models, specifically two-dimensional (2D) monolayer cultures and murine in vivo systems, in predicting human drug responses has led to the development of patient-derived organoids (PDOs) and microfluidic organ-on-chip (OoC) technologies. These innovations represent significant recent methodological advancements in the field of cancer research. This review synthesizes the biological rationale, technical principles, and translational applications of PDO–OoC integration, with an emphasis on recent clinical validation studies, AI integration, and post-FDA Modernization Act 2.0 regulatory evolution—areas that have not been comprehensively addressed in prior reviews. We examined the predictive limitations of 2D models, organoid generation, and ToC engineering principles. The synergistic integration of organoids into chip-based systems, extended into multi-organ “Body-on-a-Chip” architectures, is presented as a unifying framework that combines patient-specific biological fidelity with dynamic microenvironmental control. We further reviewed the research applications and early clinical validation studies of high-throughput drug screening, immuno-oncology modeling, and patient-specific drug response prediction across multiple tumor types. Clinical validation studies have reported moderate correlations (r ~ 0.4–0.6) between organoid responses and outcomes, indicating partial predictive capacity. Despite this progress, clinical translation remains constrained by standardization and reproducibility deficits, biomaterial limitations (e.g., PDMS drug absorption and Matrigel batch variability), and regulatory ambiguities within the evolving FDA Modernization Act 2.0. Finally, we discuss the emerging integration of artificial intelligence, including transfer learning-based drug response prediction and real-time organoid avatar systems in clinical trials, as a pathway toward closed-loop individualized functional precision oncology. Organoid and tumor-on-chip platforms have advanced toward clinical utility, although barriers remain. Full article

Cultural festivals increasingly depend on digital channels to reach audiences, which makes the allocation of limited promotional resources a recurring management problem. This study examines how digital engagement shapes visitor satisfaction and revisit intention at a cultural tourism event, using the Vilnius Pink Soup Festival as a case. Five factors (social media, public website quality, electronic word of mouth (eWOM), festival expectations, and ICT usability) are tested as predictors of visitor satisfaction and revisit intention. The decision to model visitor-level rather than adoption-level outcomes follows the human-centric premise of Industry 5.0, which motivates the choice of dependent variables but is not itself operationalized or tested. Five factors (social media, public website quality, electronic word of mouth (eWOM), festival expectations, and ICT usability) are tested as predictors of visitor satisfaction and revisit intention. A quantitative survey of 384 attendees was analyzed in IBM SPSS Statistics 29 by ordinary least squares regression on composite construct scores, with mediation tested through the PROCESS macro (Model 4) using 5000 bootstrap resamples. Four of the five factors significantly influence satisfaction, with festival expectations exerting the strongest effect, followed by social media, public website quality and electronic word of mouth; ICT usability was not significant once website quality was accounted for. Satisfaction did not mediate the relationships between these antecedents and revisit intention; with the antecedents controlled, they predicted revisit intention directly, explaining 57.5% of its variance. No ticket revenue, visitor expenditure, marketing expenditure or sponsorship income was collected, and no financial outcome is tested here. The findings are therefore reported as evidence on visitors’ response, and their financial relevance—which channels organizers might prioritize when allocating promotional budgets—is developed as a managerial implication rather than as an empirical result. Full article

Port activity anomalies are rare but may signal operational constraints relevant to supply chains. The aim of the study was to develop and empirically evaluate a calibrated machine learning framework for forecasting rare, algorithmically defined port activity anomalies over horizons of 1, 3, 7 and 14 days, under explicit temporal leakage controls and while assessing spatial transferability, the incremental value of port co-movement proxies and the operational utility of warnings. A total of 303,140 port–day observations were used for 115 ports in six selected African countries and Indonesia for the period 2019–2026. A seasonal benchmark, logistic regression and two variants of the Light Gradient Boosting Machine (LightGBM)—including one with a port co-movement proxy—were compared. Separate training, calibration and final-holdout periods were applied, along with isotonic calibration, port bootstrapping, transferability tests and threshold sensitivity analysis. In the main specification, logistic regression demonstrated a bootstrap-supported advantage for 1- and 3-day horizons, whilst differences between the models for 7- and 14-day horizons were not conclusive. A conservative rerun excluding 15 anomaly-derived predictors preserved the bootstrap-supported 1-day advantage, whereas the 3-day margin over the seasonal benchmark became inconclusive. The inclusion of a port co-movement proxy did not yield a confirmed improvement in predictive quality, whilst higher sensitivity required a marked increase in false alarms. Because the target is algorithmically defined and was not validated against an independent event registry, the results concern predictive signals of activity anomalies rather than confirmed port disruptions. The framework provides a transparent research benchmark but is not yet ready for autonomous implementation. Full article

Conventional design paradigms for vehicle underbody armor face an inherent trade-off: enhancing blast protection invariably incurs a prohibitive weight penalty. Here, we investigate a mechanical pre-stressing strategy for high-strength steel V-shaped vehicle underbody structures. A conventional V-shaped baseline structure was first subjected to a 6 kg TNT blast test, and the measured response was used to validate the numerical model. Based on the validated numerical model, four mass-equivalent (100 kg) configurations were subsequently compared numerically under escalating threats (2~8 kg TNT): pre-stressed steel, homogeneous steel, and all-metallic honeycomb sandwich panels (comprising high-strength steel face sheets and an aluminum alloy core) with both positive and negative Poisson’s ratios. The numerical results predict that the pre-stressed steel configuration exhibits the smallest maximum permanent floor deformations among the four configurations, with values of 22 mm, 46 mm, 131 mm, and 208 mm under 2, 4, 6, and 8 kg loads, respectively. Mechanistically, we reveal that for V-shaped geometries, residual-stress-induced stiffening and geometric arching are profoundly more effective than core crushing in controlling global bending, while the auxetic steel-faced aluminum honeycomb offers only marginal improvements over its conventional counterpart. This study offers a potential pathway for overcoming the weight–protection trade-off in underbody armor design. While the numerical predictions are encouraging, direct experimental validation of the pre-stressed configuration remains necessary prior to practical application. Full article

The power demand of High-Performance Computing (HPC) infrastructures exhibits both stable weekly regularities and rapid workload-driven fluctuations, which are difficult to capture reliably with a single modeling paradigm. Achieving more sustainable HPC operation requires accurate forecasts at multiple horizons: short-term predictions support operational control (e.g., proactive power capping and energy-aware scheduling), whereas long-term forecasts are essential for planning activities (e.g., capacity provisioning and energy procurement). Together, these capabilities reduce operational cost and risk while enabling more efficient and sustainable datacenter management. This paper investigates multi-horizon forecasting of aggregated active power consumption in an operational HPC datacenter utilizing a four-month dataset (five-minute time intervals) from the University of Turin. We propose a hybrid residual-learning framework that integrates a long-term structural forecaster with a short-term residual corrector utilizing a Temporal Convolutional Network (TCN) to address the simultaneous presence of weekly regularities and short-term workload-induced fluctuations. Assessment utilizing a rolling-origin protocol covers a timeframe of 15 min to 6 h and extends 1 to 3 weeks into the future. Performance of the proposed approach has been compared against the SARIMA baseline. Full article

The Antioxidant Food Table is the largest open collection of measured food antioxidant capacity. It covers 3139 products assayed by the ferric reducing ability of plasma (FRAP) method. The table has served mainly as a dietary lookup source and has never been machine-readable or benchmarked. Here it was extracted into a validated open dataset (3135 records, 99.9%). A leakage-aware benchmark of antioxidant capacity prediction from product description and category was then constructed. Eighteen predictors, from naïve baselines to deep networks and fusions, were evaluated under two partitioning regimes with the same five seeds and permutation controls. Since 39.4% of records share a product name, the conventional random split rewards memorization. A learning-free duplicate lookup explained 45% of the apparent k-nearest-neighbor advantage over the category median. In grouped evaluation, ridge regression on term frequency–inverse document frequency (TF–IDF) features ($R^2=0.674$) outperformed both deep networks. Pretrained word vectors did not close this gap. An equal-weight fusion of all eight models performed best ($R^2=0.684$) with 2.6-fold lower variability. Protocol and representation, rather than architecture, dominated the outcome on this benchmark. The dataset, code, and predictions are released openly. Full article

Background/Objectives: Guizhi Fuling Capsule (GZFL), a Traditional Chinese Medicine (TCM) formula, is widely used for primary dysmenorrhea and other blood-stasis gynecological disorders. This study aimed to characterize its material basis, elucidate its multi-component, multi-target mechanism against acute primary dysmenorrhea, and establish an integrated pharmacokinetic-pharmacometabolomic-pharmacodynamic (PK-PM-PD) framework for TCM efficacy evaluation. Methods: GZFL constituents and serum metabolites in an oxytocin-/estradiol-induced rat dysmenorrhea model were characterized by UPLC/Q-TOF-MS. Uterine effects of GZFL-containing serum were assessed ex vivo. The active components of GZFL were screened by Chinmedomics, with candidate targets investigated through network pharmacology, transcriptomics, and molecular docking. In total, 23 pharmacodynamic indicators were integrated by principal component analysis into an Efficacy Index (EI). Correlation analysis between pharmacometabolomic and pharmacodynamic data yielded a Metabolite-Efficacy Index (MEI), evaluated across a 21-day time course. Results: Among 197 constituents characterized in GZFL extract, 136 serum-exposed components were detected, with several key metabolites enriched via biotransformation. GZFL-containing serum bidirectionally regulated uterine contractility toward the control level. Integrated analyses revealed 68 candidate therapeutic targets. GZFL suppressed NF-κB/IKKβ signaling, down-regulated COX-2/iNOS, restored the PGF2α/PGE2 balance, and normalized inflammatory cytokines. Eleven efficacy-associated metabolites correlated with pharmacodynamic recovery were revealed and integrated, with MEI achieving the highest predictive performance among five integration strategies (AUC = 0.9) and robustly tracking the full 21-day disease-recovery trajectory. Conclusions: GZFL attenuates dysmenorrhea through coordinated regulation of inflammation, prostaglandin metabolism, and uterine functional homeostasis, rather than through inhibition of a single target. The PK-PM-PD framework, with EI and MEI, offers a reproducible paradigm for evaluating complex TCM therapies. Full article

Digital twins (DTs) based on agent-based artificial intelligence (Agentic AI) provide a transformative framework for streamlining nondestructive testing (NDT) of 3D-printed rehabilitation equipment. This study applies a conceptual research methodology based on the integration and analysis of recent advances in Agentic AI, digital twin architectures, additive manufacturing, NDT technologies, and intelligent rehabilitation systems to establish a framework for autonomous quality monitoring and lifecycle management of 3D-printed medical devices. By creating intelligent virtual replicas of physical devices, these systems enable continuous monitoring of structural integrity, functional performance, and degradation mechanisms throughout the product lifecycle. Unlike conventional AI-based DTs, Agentic AI-driven DTs can autonomously perceive, reason, plan, and execute corrective actions based on real-time sensor data, NDT results, manufacturing information, and historical knowledge. The main conclusion of this work is that Agentic AI-enhanced DTs have the potential to transform NDT from a passive inspection approach into an intelligent, predictive, and autonomous decision-support system for rehabilitation equipment. Advanced machine learning and autonomous decision-making algorithms enable the identification of early signs of material degradation, manufacturing defects, fatigue accumulation, and performance anomalies, supporting predictive maintenance and proactive quality assurance. Integrating Agentic AI DTs with additive manufacturing processes enables real-time optimization of printing parameters, adaptive process control, and continuous refinement of inspection strategies without production interruption or destructive sampling, thereby supporting Industry 4.0 and smart manufacturing principles. The main innovation of this research lies in proposing an autonomous closed-loop framework that combines Agentic AI, DTs, additive manufacturing, and NDT into a unified system capable of continuous learning, reasoning, and operational optimization. Compared with existing studies that mainly focus on AI-assisted defect detection or static digital twin models, this approach introduces autonomous agents capable of coordinating sensing, simulation, diagnosis, prediction, and corrective actions across the entire lifecycle of 3D-printed rehabilitation devices. The proposed concept extends current digital twin applications by incorporating virtual stress testing, autonomous simulation, patient-specific customization, and adaptive device management, reducing dependence on physical prototypes, minimizing material waste, and accelerating design validation. By combining autonomous reasoning with predictive analytics, Agentic AI-based DTs represent a next-generation solution for intelligent, adaptive, and sustainable nondestructive testing, advancing both additive manufacturing technologies and personalized rehabilitation engineering. Full article

Fuel cell systems require high-efficiency DC–DC interfaces capable of regulating rapid voltage variations while respecting the operational constraints of proton-exchange membrane fuel cells (PEMFCs). The floating interleaved boost converter (FIBC) is a strong candidate for this purpose due to its reduced current ripple, improved power sharing, and lower component stress. The design of control strategies for FIBCs supplied by PEMFCs remains challenging because explicitly enforcing fuel cell operational constraints under fast converter dynamics is inherently difficult, particularly when detailed fuel cell models are unavailable or undesirable, as reflected in existing approaches such as classical linear regulators and single-model predictive schemes. Therefore, this paper proposes a multi-model finite control set model-based predictive control (MM-FCS-MPC) strategy for FIBC converters supplied by PEMFCs. The method employs multiple discrete prediction models with cost functions defined by the converter switching mode, integrates a fuel cell-aware reference-generation mechanism to ensure nominal and safe PEMFC operation by enforcing current and power constraints within the predictive framework, and enables fast, accurate output-voltage regulation. Detailed modelling of the FIBC, component sizing, and PEMFC characteristics is provided. Obtained results under load disturbances and reference variations validate the proposed control scheme, demonstrating improved transient dynamics, reduced steady-state error, and enhanced current-sharing performance. Obtained results under load disturbances and reference variations validate the proposed control scheme, demonstrating improved transient dynamics, reduced steady-state error, and enhanced current-sharing performance, with a rise time of approximately 4.4 ms, a $\pm 2\%$ settling time of 10.3 ms, a maximum overshoot of only 0.056%, and a phase delay of approximately 4.26${}^{\circ }$, compared with 9.6${}^{\circ }$ for the conventional PI voltage-tracking baseline. Full article

Predicting driver-state evolution in extra-long tunnel corridors remains challenging because of prolonged spatial confinement and repeated lighting transitions. This study uses a statistical human–vehicle composite, the comprehensive driving index (CDI), as a reproducible quantitative target for predictive auditing. In this study, multimodal information denotes synchronized ocular, physiological, vehicle-motion, and environmental sensor signals; the objective is to quantify their incremental predictive value rather than introduce a new fusion architecture. Fully nested leave-one-driver-out cross-validation with a prespecified 120 s unsupervised initialization estimated all preprocessing, scaling, PCA, model-selection, and calibration parameters from training data only. The five components explained 60.36% of target variance. In the original-range 30 s task (4835 evaluation windows), history-only ridge regression achieved an RMSE of 0.08294 and an R² of 0.166, while directly tuned AR achieved an RMSE of 0.08261 and an R² of 0.168. On the common 4259-window sample, expanded ridge and AR achieved RMSEs of 0.08123 (R² 0.187) and 0.08153 (R² 0.182). Adding coarse scene information produced an ΔRMSE = +0.00002 (95% CI −0.00026 to 0.00027), whereas external environmental summaries produced an ΔRMSE = −0.00019 (95% CI −0.00037 to −0.00003). HistGradientBoosting did not improve performance. The primary contribution is a leakage-controlled predictive-audit framework for screening candidate information sources before deployment decisions. Full article

Sustained uphill motion of two-wheeled self-balancing wheelchairs is challenging. Slope-induced gravity changes the equilibrium condition, driving-torque demand, and velocity response. This paper proposes a smoothly weighted hybrid control method. The method combines nonlinear model predictive control and a linear quadratic regulator for pitch stabilization and uphill velocity tracking. The control design accounts for the slope-dependent equilibrium condition and steady-state torque demand. NMPC handles large-deviation recovery, velocity regulation, and actuator constraints. LQR improves local stabilization near the equilibrium point. The two controller outputs are coordinated by a continuously varying weight, which provides a smooth transfer of control authority across the transition region. MATLAB/Simulink simulations compare the proposed method with standalone NMPC, LQR, and SMC under several slope angles. Disturbance-recovery tests are also conducted under external torque disturbances. The results show stable uphill motion under the tested slope conditions. After finite-duration disturbances, the controller recovers both pitch posture and uphill velocity. Under a sustained torque disturbance, pitch stability is retained, but velocity regulation degrades. The proposed method improves pitch stabilization and velocity maintenance under the tested conditions. The recorded wheel-end torque remains bounded without sustained saturation in the three hybrid-controller cases. These results demonstrate numerical feasibility under the specified nominal simulation conditions, while uncertainty-robust and real-time performance remain to be validated. Full article

Aiming at the engineering pain points of insufficient sample size, inadequate working condition coverage, and poor generalization performance in traditional solution methods of vertical hydrodynamic coefficients for Autonomous Underwater Vehicles (AUVs), this paper constructs a high-precision prediction framework for vertical hydrodynamic coefficients Computational Fluid Dynamics (CFD) numerical simulation and a genetic algorithm-optimized back-propagation (GA-BP) neural network. Firstly, the overset grid technology and User-Defined Function (UDF) are adopted to simulate the pure heave motion of the Planar Motion Mechanism (PMM), completing the unsteady flow field numerical calculation. Secondly, 200 working condition simulation datasets covering different motion amplitudes, heave frequencies and inflow velocities are constructed, and a three-input and two-output BP neural network basic prediction model is established. Subsequently, the basic model is optimized by 5-fold cross-validation and genetic algorithm respectively, and the prediction performances of BP, K-fold-BP and GA-BP models are compared and analyzed. The results show that the determination coefficients of the GA-BP model for the dimensionless vertical hydrodynamic coefficients Z_a and Z_b reach 0.9976 and 0.9975 respectively, and the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are significantly lower than those of the other two models, with the optimal prediction accuracy and generalization performance. Finally, 1000 sets of full-working-condition hydrodynamic coefficient prediction are completed based on the GA-BP model, and the optimized dimensionless added mass coefficient and fluid damping coefficient are obtained by fitting. The prediction framework constructed in this paper can provide technical support for the rapid and high-precision acquisition of AUV full-working-condition hydrodynamic coefficients, and provide a reliable parameter basis for dynamic modeling and motion control system design. Full article