Search Results (2,578)

Traffic signal control plays a critical role in mitigating congestion and improving urban mobility, particularly in multi-intersection networks where fixed-time strategies cannot adapt to fluctuating demand. Although reinforcement learning has shown strong potential for adaptive signal optimization, purely learning-based controllers often rely on reward shaping rather than explicit enforcement of traffic engineering constraints, which may lead to unstable phase switching and operational inefficiencies. This study proposes a Hybrid Reinforcement Learning and Rule-based algorithm (Hybrid-RL-RB), a constraint-aware traffic signal control algorithm that combines reinforcement learning with a rule-based supervisory layer for multi-intersection traffic signal control. In the implemented version, the learning component is based on tabular Q-learning with a discretized traffic state representation, while the rule-based layer supervises the final executable signal action. The objective is to improve adaptive signal control while preserving operational feasibility through minimum green time, maximum green time, spillback protection, and phase-safety constraints. The framework was implemented in SUMO through TraCI and evaluated under three scenarios of low, medium, and high traffic demand conditions across multiple network configurations, including a real-network topology (Casablanca-OSM). Experimental results show that Hybrid-RL-RB reduces average queue length by up to 51.47% and waiting time by up to 68.10% compared with Fixed-Time control. Compared with Simple-RL, the proposed method provides modest but consistent queue reductions on the 16 × 16 network, while MaxPressure remains the strongest queue-minimization baseline. In the high-demand Casablanca-OSM scenario, Hybrid-RL-RB reduces queue length by 20.50%, reduces waiting time by 21.41%, and increases throughput by 16.83% compared with Fixed-Time control. These results indicate that explicit rule-based projection can improve the operational feasibility and extensibility of RL-based traffic signal control, although further validation with additional seeds and longer real-network simulations is required. Full article

Dynamic roll-on/roll-off terminals require an executable decision between yard planning and vessel stowage: which yard lane to release, how many accessible vehicles to move, and which ferry lane to receive the ordered batch. We formulate this interface as state-aware FIFO-batch control. Each action selects one yard queue, a FIFO-prefix length, and one receiving lane/deck, while complete enumeration screens compatibility, residual capacities, discharge order, slot continuity, and a dimensionally defined normalized transverse-eccentricity envelope. The proposed beam-restricted two-stage rollout (BTR) optimizes over two batches and executes only the first before state re-observation and replanning: it expands only the W best immediate first actions and thus approximates unrestricted horizon-two minimization without forecasting future arrivals. On 180 paired synthetic paths, it loads 0.46 more vehicles and reduces weighted waiting by 50.38 units relative to myopic endogenous control, but requires 6.8 times the full-path runtime; its median, 99th-percentile, and maximum decision latencies are 0.335, 2.51, and 6.51 s. Beam and exhaustive small-instance studies show that width four preserves mean loaded count and matches the exact loaded count in seven of nine tractable cases, with a 4.56-unit mean waiting gap. Weight ablations confirm that the nominal objective is throughput-dominated, while structural tests expose rapid latency growth at 50–100 receiving lanes. The evidence therefore supports state-dependent batching more strongly than deeper lookahead. All safety statements are limited to the modeled constraints and do not constitute hydrostatic or field validation. Full article

Objectives: Hair loss is a prevalent condition influenced by genetic, hormonal, and environmental factors, and effective therapies with minimal side effects are highly desired. In this study, the hair growth-promoting potential of a biorenovated esculetin complex (BEC) was evaluated in a C57BL/6N mouse model. Methods: BEC was topically applied to the depilated dorsal skin of mice once daily for 24 days. Hair growth was evaluated by measuring hair length, thickness, and weight, together with histological assessment of hair follicle number, follicle depth, and dermal thickness. Protein expression associated with Wnt/β-catenin and AKT signaling was evaluated by Western blot analysis. Results: BEC treatment accelerated hair regrowth and increased hair length, thickness, and weight compared with the vehicle control under the present experimental conditions. Histological analysis of hematoxylin-and-eosin-stained skin sections showed increased hair follicle number and greater follicle depth in the BEC-treated group, whereas dermal thickness was not significantly altered. Western blot analysis showed changes in β-catenin, phospho-GSK-3β (Ser9), and PCNA levels following BEC treatment. The p-AKT/AKT ratio also showed an increasing trend, although the difference was not statistically significant. Conclusions: These findings demonstrate the hair growth-promoting activity of the BEC preparation in this short-term murine model and suggest that these effects are associated with changes in Wnt/β-catenin- and AKT-related signaling. Further studies are warranted to characterize the active components, dose dependence, mechanism of action, and safety profile of BEC. Full article

Objectives: To compare the clinical efficacy of the modified multi-strand Nice knot suture construct combined with suture anchor fixation versus conventional Kirschner wire tension band fixation for inferior patellar pole fractures. Methods: This retrospective chart review enrolled 108 consecutive patients with inferior patellar pole fractures treated surgically at our institution between January 2019 and January 2024. Patients were allocated to two groups based on the fixation method used: 52 patients underwent the modified suture construct fixation (modified suture fixation group, MSFG), and 56 received conventional Kirschner wire tension band fixation (conventional tension band fixation group, CTBFG). The primary outcomes included anterior knee pain VAS score, knee ROM, and complication rate; secondary outcomes comprised operative time, Bostman, Lysholm and Kujala scores, elective and symptomatic implant removal rates, time to fracture union, Insall–Salvati index, patellar length, and hospitalization costs. Results: All surgical procedures were completed uneventfully. Baseline demographic and clinical characteristics were comparable between groups (all p > 0.05), with a mean follow-up of 25.5 ± 6.0 months (range, 22–36 months). Operative time did not differ significantly between cohorts (p = 0.230). Although the MSFG exhibited statistically lower anterior knee pain VAS scores (0.82 ± 0.6 vs. 1.26 ± 0.8, p = 0.003) and slightly greater knee ROM (132.7° ± 9.8° vs. 128.8° ± 8.1°, p = 0.027), the magnitude of these differences was below the recognized minimal clinically important difference. At the final follow-up, Bostman, Lysholm and Kujala scores were similar between groups (all p > 0.05). Of critical importance, the MSFG had markedly lower rates of implant-related complications and any secondary surgery (both p < 0.001). In the CTBFG, 40 patients (71.4%) underwent secondary surgery: 10 for symptomatic hardware irritation and 30 as entirely elective asymptomatic removals. By contrast, no patient in the MSFG required any form of reoperation. No revision surgeries for fixation failure were required in either group. Radiographically, fracture union time and patellar length were comparable (all p > 0.05). While the Insall–Salvati index was statistically lower in the MSFG (0.96 ± 0.1 vs. 1.02 ± 0.1, p = 0.006), all values remained within the normal physiological range and no functional impairment was observed. Subgroup analysis of comminuted fractures with osteoporosis demonstrated that the MSFG eliminated the implant failure complications seen in the CTBFG (0% vs. 28.6%). Conclusions: The modified multi-strand Nice knot suture construct combined with suture anchor fixation provides functional outcomes equivalent to conventional Kirschner wire tension band fixation. However, it offers two distinct advantages: the complete absence of hardware-related complications and the elimination of the need for secondary implant removal. These findings suggest that the modified suture construct may offer particular advantages for patients with comminuted or osteoporotic inferior patellar pole fractures, although further prospective studies are warranted to confirm these observations. Full article

Dental rehabilitation of fibula free flap mandibular reconstructions can be limited by the reduced vertical bone height available for implant placement. This study evaluated the biomechanical influence of ultra-short, extra-short, and conventional implants in a reconstructed fibula. Methods: A patient-specific finite element model of a fibula-reconstructed mandible was developed. Three splinted implants with a diameter of 4 mm and lengths of 3, 5, or 12 mm were compared under postoperative unilateral molar loading. Von Mises stresses were evaluated in the implants, fibula, residual mandible, and fixation plates, while equivalent elastic strain was assessed in peri-implant bone. Results: Implant length had minimal influence on implant stress (36.8–37.1 MPa) and fibular cortical bone stress (52.5–53.1 MPa). The 5 mm implants produced the lowest peri-implant strains and the lowest posterior reconstruction-plate stress (71.3 MPa), compared with 75.4 MPa for 3 mm and 107.8 MPa for 12 mm implants. The 12 mm implants generated greater apical strain concentrations, exceeding the adopted mechanobiological threshold around the central and mesial implants. Conclusions: Within the limitations of this study and under the simulated immediate postoperative loading condition, shorter implants, particularly 5 mm implants, showed a favorable biomechanical response. Full article

The conventional workup for suspected early-stage lung cancer requires multiple visits, anesthetics, and procedures. Single anesthetic event lung cancer surgery integrates shape-sensing robotic bronchoscopy, cone-beam CT, rapid on-site cytologic evaluation (ROSE), endobronchial ultrasound staging, lesion localization, and minimally invasive resection into one operative encounter. In this narrative review, we describe the technical components of this pathway, summarize the supporting literature, and offer expert, institution-based troubleshooting guidance for common intraoperative dilemmas. Reported diagnostic yields for shape-sensing robotic bronchoscopy range from 80 to 96%, with approximately 90% concordance between ROSE and final pathology. Published single-institution series report reductions in time from detection to resection of 15–51 days, cost savings of approximately $3000–$10,000, and perioperative outcomes (length of stay 1.8–3.6 days; complication rates comparable to traditional pathways) similar to staged care. The supporting evidence is retrospective and derived from small, single-institution, high-volume referral cohorts; no prospective comparative trials or long-term survival data yet exist. Single anesthetic event lung cancer surgery is therefore best regarded as an emerging, resource-intensive care pathway for carefully selected patients at experienced centers rather than an established standard of care, pending prospective, multicenter validation. Full article

On-demand droplet formation is of crucial importance to the engineering applications of droplet microfluidics. This work presents an experimental investigation on active control of droplet formation using stacked piezoelectric actuators. Two stacked piezoelectric actuators are placed adjacent to the continuous phase channel, producing periodic excitations on the continuous phase flow. It is found that droplet formation greatly depends on the excitation frequency and voltage. Droplet formation synchronizes piezoelectric excitation at a small excitation frequency, i.e., droplet formation frequency equals excitation frequency and its subharmonics. Beyond a critical value of the excitation frequency, a neglected effect of excitation frequency on droplet generation is observed. The droplet generation frequency could be increased up to ~2.6 times that without excitation. The droplet generation frequency exhibits a stepwise increase with rising excitation voltage. When the droplet formation frequency equals the excitation frequency, droplet formation undergoes filling, necking, and pinching-off. When the droplet formation frequency is half of the excitation frequency, additional refilling and re-necking stages are observed. The regime diagram of the droplet formation frequency in the synchronization mode is presented. The scaling of the generated droplet length is deduced. Since periodic excitations are employed on the continuous phase flow, the proposed active control method could minimize the detrimental impact on biochemical reagents within droplets. Full article

Helium accumulation is a major contributor to swelling, gas release, and mechanical degradation in oxide nuclear fuels under irradiation. This study employs first-principles density functional theory (DFT) to investigate helium behavior in thorium-based mixed oxide (MOX) fuels. A series of (Th_1−xPu_x)O₂ and (Th_1−xU_x)O₂ solid solutions (x = 0, 0.25, 0.5, 0.75, and 1) was constructed, and the corresponding ground-state configurations were determined through total-energy minimization. The effects of 4.167 at.% helium incorporation on structural stability, electronic structure, elastic response, and thermal expansion were evaluated. Helium migration in ThO₂, PuO₂, and UO₂ was further investigated at octahedral interstitial, metal-vacancy, and oxygen-vacancy sites. Positive helium incorporation energies indicated that helium incorporation is energetically unfavorable for all compositions. Vegard-like behavior was preserved for lattice constants and metal–oxygen bond lengths. The 2.06 eV band gap of UO₂ disappeared after helium incorporation, whereas band-gap variations in most MOX compositions remained below 0.7 eV. Helium reduced the bulk moduli of (Th_0.75U_0.25)O₂ and UO₂ by 3.75% and 7.94%, respectively. Thermal expansion coefficients followed the order α_L-UO2 > α_L-PuO2 > α_L-ThO2, with α_L of UO₂ nearly doubling. Metal vacancies acted as helium traps, whereas adjacent oxygen vacancies provided the lowest migration barrier of 0.42 eV. These results indicate that increasing ThO₂ content improves the resistance of MOX fuels to helium-induced degradation. Full article

In response to the increasing quality demands of consumers amid technological advancements in manufacturing, manufacturers must manage the quality and lifespan of their products. In practical applications, various methods have been developed to assess product quality performance. This study employs process capability indices (PCIs) to evaluate product quality. This research explores scenarios involving products with multiple quality characteristics or multiple production lines under a Rayleigh lifetime distribution. Using the progressive Type-I interval censored sample, we assess whether the overall lifetime performance index meets a predetermined target. Given a specified significance level and statistical power, the minimum required sample size is derived. Under either fixed or unfixed total testing times, this study identifies the minimum number of observation intervals and the sample size that minimizes the total experimental cost, or the minimum number of observation intervals, equal-length interval time, and sample size. Finally, an illustrative practical example from two production lines is used to demonstrate how to apply the optimal experimental design proposed in this study to construct the progressive Type-I interval censored sample to test whether the overall lifetime performance index achieves the specified target. Full article

Continuous thermal monitoring of aluminum electrolysis cells—which operate at ~950 °C under strong magnetic fields in a corrosive fluoride melt—remains an unsolved process monitoring challenge. This paper presents a fiber-optic Raman distributed temperature sensing (DTS) deployment for high-temperature cathode steel bar monitoring in a 380 kA industrial cell supplied by a grid with over 30% wind and solar penetration. A custom fiber ring packaging scheme, pressed against the underside of the cathode bar (temperature > 300 °C), achieved >1 m thermal contact length within the confined space beneath the cell. Features encoding supply-side renewable power periodicity, thermal inertia, and local fluctuation intensity were engineered to anchor the learning task in the process physics of the electrolysis cell. CatBoost, LightGBM, and Random Forest were combined in a stacking ensemble with a linear regression meta-learner, attaining RMSE = 0.7451 °C and R2 = 0.9944 over two months of continuous industrial operation across seven cathode bars. Frequency-domain residual decomposition revealed why LightGBM—the aggregate-weakest base learner—received the dominant meta-learner weight (+2.15) while CatBoost—the aggregate-strongest—received a negative weight (−1.85): LightGBM uniquely minimized high-frequency error (1.06 vs. 1.29 °C for CatBoost). The ensemble advantage was spatially robust across all seven bars. The 0.75 °C RMSE establishes a noise floor for residual-based monitoring, demonstrating that process-informed feature engineering and frequency-resolved stacking ensemble learning deliver predictive accuracy suitable as a process monitoring baseline in high-temperature industrial environments under increasing renewable power penetration. Full article

Numerical dispersion limits the accuracy of finite-difference elastic-wave modeling, particularly when relatively short spatial stencils are used. This study evaluates staggered-grid spatial-derivative coefficients obtained using a constrained simulated-annealing procedure and compares their spectral and numerical behavior primarily with Taylor-expansion coefficients within the same numerical framework. The coefficient design minimizes the maximum absolute spectral approximation error over a prescribed normalized-wavenumber interval while enforcing first-derivative consistency and an alternating, decreasing-magnitude coefficient pattern. The homogeneous and modified Marmousi examples indicate reduced visible dispersion relative to Taylor coefficients at the same stencil half-length. Least-squares coefficients are retained only as an additional qualitative reference in the homogeneous-model tests. The conclusions are limited to the tested two-dimensional isotropic models, grid spacing, time step, and source frequencies. Full article

In adaptive signal control, the upstream observation range at intersections is typically determined by empirical detector placement, lacking a data-driven adaptive mechanism. This paper addresses the question of how far upstream is sufficient for timing decisions and proposes a sensing boundary identification method based on Bayesian online learning. A microscopic simulation model coupling all signal phases is first constructed based on the Intelligent Driver Model, with a green split optimization function minimizing the total queued vehicles at cycle end. The prefix length is then modeled as a probabilistic variable, and the class separability score serves as the likelihood basis. Through recursive Bayesian posterior updates, the optimal prefix length is automatically determined. Based on the identified sensing boundary, K-nearest neighbors weighted regression enables online prediction of the green split. Experiments on 10,368 simulated scenarios demonstrate that the posterior converges to a prefix length consistent with the core queue dissipation region. Far-end free-flow vehicles contribute limited information and degrade class separability when included, validating that a longer observation range does not necessarily improve performance. The identified prefix length uses only a small fraction of the full feature dimensions while maintaining favorable prediction accuracy, achieving a good trade-off among accuracy, computational efficiency, and online adaptability. The proposed method shifts sensing boundary determination from empirical setting to data-driven identification, offering a reference for detector placement and edge controller deployment. Full article

Patient recovery prediction and hospital length of stay estimation remain critical challenges in healthcare resource allocation and clinical decision-making. Inaccurate discharge planning drives substantial avoidable hospital costs, while existing machine learning models remain limited by narrow, single-domain data that fail to capture the multimodal complexity of modern smart ward environments. This study proposes IoT-ClinXAI, an explainable multimodal framework that fuses IoT environmental data, wearable physiological signals, and clinical records for accurate and transparent patient recovery prediction in smart hospital wards. A Multi-domain Hierarchical Consensus Feature-Selection method groups features into structured domains, applies Borda–Kemeny weighted consensus within each domain, and reduces cross-domain redundancy while preserving complementary information. A Snake Optimization–tuned Random Forest Regressor optimizes predictive performance through adaptive hyperparameter search, while a multi-scale SHAP framework provides global and patient-level explanations for transparent clinical inference. Experiments were conducted on a real-world IoT-enabled smart ward dataset comprising patient data and recovery duration. The proposed framework achieved strong predictive performance on the held-out test set, with $R^2$ of 0.964, RMSE of 0.476 days, MAE of 0.342 days, and MAPE of 3.13%, yielding a 23.3% RMSE reduction over the unselected baseline and outperforming all feature-selection methods by 10.9–17.6% in RMSE. Statistical superiority was consistently confirmed across all pairwise comparisons using Wilcoxon signed-rank tests with Bonferroni correction ($p<0.001$). SHAP analysis identified ward allocation, respiratory rate, and oxygen saturation as the dominant recovery predictors, while environmental IoT variables showed minimal predictive contribution. These results highlight IoT-ClinXAI as a reliable and useful framework for supporting bed management, discharge planning, and hospital resource optimization in resource-constrained healthcare settings. Full article

Many phenomenological models of quantum gravity predict a minimal observable length. A common implementation is the Generalized Uncertainty Principle (GUP), which modifies the canonical commutator between position and momentum. In many formulations, the modified position operator is not symmetric with respect to the standard quantum-mechanical inner product, requiring a modified Hilbert-space measure that breaks the natural isometry between momentum and position space. We review an alternative formulation in which the position operator is symmetrized instead of the inner product. This preserves the standard Fourier relationship between momentum and position space while retaining the same generalized uncertainty relation and minimal length. The resulting position eigenstates contain an intrinsic suppression of large momenta, suggesting a possible route toward constructing GUP-modified quantum field theories with improved ultraviolet behavior. Full article

This study investigated and optimized the operational process parameters of an extrusion-based 3D food printing system to maximize the dimensional fidelity of newly developed Wolffia globosa (duckweed) starch hydrogel constructs relative to a nominal target specification of 30 × 30 × 30 mm. Prior to parameter optimization, synchrotron X-ray tomographic microscopy (SR-XTM) was used to characterize Wolffia globosa particle size and dispersion within the starch matrix, showing that grinding eliminated large particle agglomerates (up to approximately 150 µm) and was necessary for smooth, continuous extrusion; the ground formulation was accordingly selected for all printing trials. A full factorial experimental configuration was executed to examine the synchronized effects of three core process parameters: print-head traverse speed (5–15 mm/s), extrusion speed (5–15 steps/mm), and layer height (1.9–3.7 mm). Experimental responses were evaluated via Three-Way Analysis of Variance (ANOVA) and Response Surface Methodology (RSM) using triplicate measurements (n = 3) at each of the 27 tested parameter combinations. Residual diagnostics indicated an approximately normal distribution for the height model (Shapiro–Wilk p = 0.716), whereas the width and length models showed some departure from normality (p < 0.01), consistent with the significant lack-of-fit detected for these two responses. Three-way ANOVA confirmed that print-head (nozzle) speed was the dominant factor governing the in-plane dimensions (width and length; partial η² ≈ 0.98), while height was jointly governed by all three factors, with layer height and print-head speed contributing the largest effects. With the statistical power afforded by replicate measurements, all two- and three-way interactions among the three factors were also statistically significant for width and length (p < 0.001), refining the single-replicate interaction pattern reported previously. Empirical second-order polynomial equations explained a substantial share of the variance in each dimension (R² = 0.70–0.85), although formal lack-of-fit testing indicated that higher-order interactions not captured by the quadratic terms remained statistically significant, and the equations should therefore be interpreted as descriptive rather than as precise predictive tools. Based on the triplicate means, a print-head speed of 5 mm/s, extrusion speed of 15 steps/mm, and a layer height of 1.9 mm minimized the mean cumulative absolute error to 4.01 mm, yielding a mean dimensional profile of 28.63 ± 0.78 mm width, 27.76 ± 0.96 mm length, and 30.41 ± 0.70 mm height (mean ± SD, n = 3). Full article