Search Results (24,497)

During roll-on load-out of a semi-submersible barge, ballast-water regulation directly affects floating-state accuracy, operational safety, and longitudinal structural response. Conventional ballasting relies heavily on predefined plans and operator experience, making it difficult to simultaneously ensure stern-draft matching, engineering operability, and structural safety under continuously changing heavy-load conditions. This study proposes an engineering-oriented two-stage ballast-water optimization framework incorporating field-derived expert knowledge and coordinated floating state–deflection control for a 45,000-ton semi-submersible barge. Ballast adjustments in individual tanks are used as decision variables subject to physical, floating-state, operational, and structural constraints. In Stage I, an NSGA-II-based population search explores the feasible ballast space using stern-draft deviation as the primary ranking criterion while enforcing trim, heel, tank-capacity, and operational constraints. In Stage II, PSO performs local refinement of the selected feasible solutions; trim and heel are included in the fitness evaluation and remain subject to hard operational limits. Hull longitudinal deflection is subsequently incorporated to form coordinated floating state–deflection optimization. Field measurements from the Module-001 load-out process are used for validation. Compared with standalone PSO, the proposed two-stage method reduces the maximum stern-draft error from 0.103 m to 0.019 m and decreases the average optimization time from approximately 18 min to 4 min. Additional validation using the more complex Module-002 load-out case yields stern-draft errors of approximately 0.001 m for most conditions and a maximum error of 0.007 m. Under coordinated optimization, stern-draft deviations remain within ±0.02 m, trim and heel satisfy the prescribed operational limits, and the maximum relative hull deflection is controlled within approximately 0.06 m. The optimized strategy evolves from fore-and-aft-dominated regulation to coordinated whole-tank regulation, with midship tanks contributing to longitudinal deformation control. The proposed method therefore provides a practical framework for intelligent ballast regulation during heavy-load roll-on operations. Full article

The high penetration of renewable energy and the large-scale development of electric vehicles (EVs) have made voltage violations in distribution networks increasingly prominent. The flexible on-load tap changer (F-OLTC), combining mechanical coarse regulation and power-electronic stepless fine adjustment, offers advantages in both regulation range and speed. However, existing protection strategies fail to account for the impact of EV charging/discharging surges and the multi-physics coupling constraints of the equipment, making it difficult to coordinate voltage quality and equipment lifetime. This paper proposes an adaptive zonal cooperative protection strategy for the F-OLTC that accounts for EV charging/discharging characteristics. An integrated protection architecture is constructed that fuses EV state awareness and equipment health monitoring. A multi-feature fusion fault identification method is proposed to distinguish EV disturbances, external faults, and internal converter faults. A comprehensive margin index is established, and dynamic zoning is achieved based on feature-space clustering. A coordinated stepped and stepless adaptive voltage regulation protection is designed, with dynamic adjustment of thresholds and delays, and fast EV power support is dispatched during mechanical switching transients. Simulations on a modified IEEE 33-node system show that the proposed strategy reduces average voltage deviation by 46.2%, tap operations by 28.6%, voltage sag depth by 81%, recovery time by 70.8%, and network loss by 21.9%, effectively improving voltage quality and reducing mechanical stress on the switching devices, which is expected to contribute to extended maintenance intervals and improved equipment reliability. Full article

The study examines the predictive ability of traditional econometrics and machine-learning models for Cambodian tax revenue estimates using monthly LNTAX from January 2009 to March 2026. Repeated out-of-sample forecasts were then produced through a rolling-origin forecasting design, across two test horizons: 12-months and 24-months. We evaluated different techniques: moving average, exponential smoothing, damped trend, ARIMA, CART, GRNN and KNN. The performance against the moving average benchmark was compared using regression analysis with clustered standard errors, and forecast accuracy measured in terms of mean squared error and symmetric mean absolute percentage error. The results provide evidence that LNTAX is non-stationary in levels, and then stationary at first differencing. The best overall predictive vision is achieved within ARIMA with the smallest errors in most of the evaluations and significantly largest decreases in MSE and sMAPE compared to moving average. Damped-trend exponential smoothing also does quite well, especially for percentage accuracy over the longer horizon. In general, machine-learning models outcomes are mixed: CART shows average performance, whereas KNN in some instances is able to improve sMAPE versus naive-based models but GRNN ranks the weakest. The results indicate that time-series models have a superior forecasting performance than the state-space framework-based univariate tax revenue forecasting model in this context. Hence, it is suggested that ARIMA is used as the main benchmark model and future research should be conducted in hybrid methods, more macroeconomic variables, structural breaks and a policy-oriented forecasting evaluation. Full article

Background: Odontogenic cervicofacial infections are prevalent and can progress to life-threatening conditions, imposing a substantial burden on both patients and healthcare systems. The present study evaluated the association of presentation delay and comorbidities with clinical severity and healthcare resource utilization. Methods: This prospective multicenter cohort study enrolled patients admitted with odontogenic cervicofacial infection at two university hospitals over a one-year period. Demographic and clinical data were systematically collected. Disease severity at presentation was quantified using the Cervicofacial Infection Severity Index (CFISI), a clinically derived composite index incorporating predefined indicators of airway compromise, systemic involvement, and physiological derangement. Results: A total of 61 patients (median age 45 years; range 4–88) were included. The median time to admission was 4 days (IQR 2–5), and the median number of involved anatomical spaces was two (IQR 1–3). The number of involved spaces was significantly associated with a severe clinical course (OR 1.72, 95% CI 1.13–2.63, and p = 0.011). Secondary space involvement correlated with prolonged hospitalization, with a mean length of stay of 11.5 days (95% CI 7.1–15.9) compared to 5.1 days (95% CI 4.6–5.7) in patients without secondary space involvement (p < 0.00001). CFISI was significantly associated with severe clinical course in both univariable (OR 1.16, 95% CI 1.01–1.26, and p = 0.044) and multivariable analyses (OR 1.20, 95% CI 1.01–1.42, and p = 0.042). Increasing age was also independently associated with severe clinical course (OR 1.04 per year, 95% CI 1.00–1.07, and p = 0.028). No predictor demonstrated a significant association with high healthcare resource utilization. Conclusions: Within this cohort, clinical severity was associated with patient age, infection extent, and CFISI. In contrast, delay in presentation and comorbidities did not predict severe outcomes. Secondary space involvement was linked to prolonged hospitalization. No significant predictors of high healthcare resource utilization were identified. Full article

We investigate scalar perturbations of a rotating asymptotically anti-de Sitter (AdS)-Teo traversable wormhole with a controlled non-separable angular deformation. The geometry retains a regular wormhole throat and the required AdS asymptotics, while an explicit quadrupolar deformation generates angular-channel coupling in the intermediate region. A sufficient condition is derived for an ergoregion-free parameter regime in which the spectral analysis is performed. Projecting the geometry-derived Klein–Gordon equation onto spherical harmonics yields a matrix-valued Sturm–Liouville system and an associated quadratic operator pencil. Throat regularity together with normalizable AdS boundary conditions leads to a determinant quantization condition for the discrete normal-mode spectrum. Two-, four-, and six-channel calculations demonstrate systematic numerical convergence of the retained low-lying normal-mode frequencies under enlargement of the angular basis. The complete finite generalized eigenspectrum is also examined without imposing a near-reality selection criterion, and no growing scalar mode is found within the ergoregion-free parameter range and numerical resolutions studied. The six-channel rotation continuation exhibits a finite interior level-repulsion feature accompanied by collective redistribution of the multichannel eigenvectors. The same coupled spectral framework formally defines a matrix-valued two-boundary response whose poles are selected by the normal-mode matching condition; the response plots presented here illustrate this structure using the reduced two-channel effective model rather than a numerical reconstruction of the full six-channel response. We further derive the geometry-dependent short-distance Hadamard subtraction and identify the finite inter-channel structure entering a truncated local quantum mode sum, without claiming a complete numerical evaluation of ${⟨{\Phi }^2⟩}_{\mathrm{ren}}$. Together, these results establish a geometry-to-spectrum framework in which the spacetime geometry determines the coupled operator, the global boundary conditions determine its normal-mode spectrum, and the same coupled spectral framework organizes the associated asymptotic response and local quantum mode-sum structure. Full article

Within the framework of special relativity, the author has developed an extended in vacuo Lorentz invariant particle-wave theory of Newton’s mechanics that takes account of the particle energy as a major driver of the motion that may be of comparable magnitude to any applied external potential. Lorentz invariance ensures that the proposed theory provides a more fundamental representation of applied force than the conventional notion of applied force as the rate of change of momentum. The purpose of this paper is to generalise an existing exact solution of this theory, originally derived for Einstein’s particle energy expression, to a family of Lorentz invariant power-law particle energies characterised by an arbitrary constant $\kappa$. By Lorentz invariance, we refer to invariance under the combined special relativistic space-time and energy–momentum transformations. The power-law energy expressions are important because they underpin and bear the same relationship with an extension of the Planck–de Broglie energy–momentum relations in exactly the same manner as Einstein’s energy expression does for the conventional Planck–de Broglie relations. The de Broglie wave energy as a function of particle velocity is determined as an integral expression. This integral is evaluated explicitly for a number of illustrative values of $\kappa$, and the known solution corresponding to the Einstein energy is included through the special case $\kappa =0$. Full article

Against the backdrop of China’s Dual Carbon Initiative and national transportation empowerment strategy, accelerating the low-carbon green transition of the transport sector has emerged as an imperative developmental priority. Incorporating carbon emissions as undesirable outputs into the efficiency evaluation framework, this study adopts a multi-method analytical paradigm encompassing the super-efficiency SBM model, Malmquist–Luenberger index, kernel density estimation, Dagum Gini coefficient, geographical detector model, and Geographically and Temporally Weighted Regression (GTWR). Based on panel data covering 30 provincial administrative regions in China from 2004 to 2022, this paper systematically investigates the spatio-temporal evolutionary patterns and intrinsic driving mechanisms of carbon total factor productivity (CTFP) within the transportation industry. The main findings are as follows: (1) The static efficiency results reveal that the national mean CTFP is below unity, indicating overall inefficiency. Nevertheless, it exhibits a fluctuating upward trend after 2009. Regionally, CTFP follows this pattern: Eastern China > Central China > national mean > Northeastern China ≈ Western China. (2) Dynamic productivity analysis shows that the annual average ML index is close to 1, demonstrating an overall upward trend in CTFP, and productivity growth is primarily driven by technological progress. (3) In terms of spatio-temporal patterns, inter-regional disparities constitute the principal source of overall spatial gaps in CTFP, with considerable contribution from transvariation density. (4) Geographical detector analysis suggests that energy intensity (EI) and economic development level are the two factors most strongly correlated with the spatial differentiation of CTFP, and interaction effects exist between them. The results from geographically weighted regression (GWR) further confirm that the strength of the correlation between each driving factor and CTFP presents pronounced regional heterogeneity. Based on these findings, differentiated regional policies for emission reduction and efficiency improvement should be formulated in accordance with the law of diminishing marginal returns. Full article

Aiming at the problems of large speed synchronization error and prominent speed overshoot existing in conventional PID control algorithms widely adopted for diesel-engine parallel-unit speed-governing systems, this paper proposes a speed control algorithm based on Model Predictive Control (MPC) for dual-diesel-engine parallel operation. A quasi-steady-state method is employed to establish the coupled state-space model for dual-engine parallel operation. Leveraging the prediction-optimization and multi-constraint regulation characteristics of MPC, the fuel-injection outputs of the two diesel engines are regulated respectively by two independent SISO MPC controllers, which share the identical speed reference and are coordinated at the logic level through the Stateflow engagement/disengagement state machine to achieve speed synchronization. Comparative simulations under different operating conditions are carried out on the Matlab/Simulink platform. The simulation results show that the proposed MPC algorithm can effectively suppress speed fluctuations between the two diesel engines under the tested operating conditions compared with the traditional PID control. Furthermore, a hardware-in-the-loop test platform is constructed for validation in the embedded environment. The test results reproduce all operating conditions of offline simulation, achieving zero speed overshoot and restricting the dual-engine synchronization deviation within ±3 rpm, which are consistent with the offline simulation results. The real-time computational capability and engineering feasibility of the proposed algorithm are therefore verified. The proposed method is applicable to stable speed-governing scenarios of marine dual-diesel-engine parallel-unit sets. Full article

Ion transport in energy storage nanomaterials is described with models that resolve different scales and different physical effects. Device models such as the Doyle–Fuller–Newman framework are well suited to cell voltage, concentration polarization, and electrode utilization, but they usually average local charge separation into effective transport and interface laws. Charge-resolved Poisson–Nernst–Planck, modified Poisson–Nernst–Planck, and Poisson–Fermi descriptions can instead resolve electric potential, ion partitioning, finite ion size, and space charge inside narrow pores and interphases. Molecular simulation gives solvation, adsorption, and interfacial structure, while pore-network and homogenized models connect local transport to electrode geometry. This Perspective compares these model families, states their limits, and explains how quantities should be passed from molecular and pore scales to electrode and device models. Representative examples are discussed for liquid-electrolyte batteries, all-solid-state batteries, and nanoporous supercapacitors. Two concise calculations are retained as model audits. The first tests whether a first-order finite-size approximation remains reliable when its leading correction is small by cancellation. The second tests when local electroneutrality becomes inaccurate in a charged pore and how steric crowding changes screening. These calculations are not new device predictions. The main contribution is a practical procedure that links model choice, cross-scale handoff, and validation with measurable quantities such as voltage, concentration profiles, impedance, interfacial resistance, capacitance, and charging time. Full article

Large university campuses need timely, privacy-conscious information about how indoor spaces are used in order to improve space management, energy efficiency, and operational responsiveness. Yet many indoor positioning solutions still depend on additional hardware such as Bluetooth Low Energy beacons, ultra-wideband anchors, or camera-based sensing, which increases deployment cost and maintenance complexity. This paper presents a lightweight campus occupancy analysis and indoor localization framework that reuses an existing Cisco Wireless LAN Controller (WLC) infrastructure as a sensing layer. The system retrieves received signal strength indicator (RSSI) observations from access points over secure SSH sessions, converts these observations into approximate distance estimates through a calibrated log-distance path loss model, and computes user positions using weighted non-linear least-squares multilateration (Mlat). In addition to point localization, the framework generates occupancy heatmaps, cumulative reliability curves, and access-point-density sensitivity analyses based on a 50 m × 50 m evaluation scenario with 500 randomized samples. To support privacy-preserving deployment, the service layer exposes only zone-level occupancy information and omits personally identifiable network identifiers. The results indicate that Wi-Fi-based localization can provide cost-effective and scalable occupancy intelligence with sufficient accuracy for campus-wide density monitoring and smart building applications.$\times$ Full article

Temporal knowledge graph completion (TKGC) infers missing facts by modeling the temporal evolution of relations. Existing methods typically encode time through low-dimensional geometric transformations or frequency-domain decomposition. However, periodically recurring relations can still be mapped to overlapping trajectories in the same two-dimensional working space, which may reduce temporal separability and produce conflicting optimization signals. To address this limitation, we propose the Frequency-Modulated Spiral Manifold (FMSM) model, which includes the following: (1) a relation-adaptive spectral partition and relation-dimension gate for fusing long- and short-term relation channels; (2) an independent global phase embedding and nonlinear spiral push that lift entangled planar trajectories onto separated three-dimensional manifold layers; and (3) a spiral norm regularizer that stabilizes temporal evolution while preserving valid burst signals. The artificial-intelligence contribution of FMSM is a phase-conditioned spectral-geometric representation that separates recurring temporal facts while retaining multi-scale relation dynamics. Its engineering application is the completion of time-stamped event records for dynamic knowledge-based decision-support systems. Experiments on ICEWS14, ICEWS05-15, and GDELT show that, compared with the strongest reported baseline TeRDy, FMSM yields relative MRR improvements of 0.62%, 0.86%, and 13.28%. Full article

Consecutive warm nights during heatwaves erode the cooling potential of night ventilation (NV), making its effectiveness strongly dependent on both the event and the building conditions. This study quantified the nonlinear effects of heatwave morphology, building thermal mass (TM), and NV strategies on multidimensional thermal responses and identified the operating conditions associated with ventilation performance. Using a 1991–2020 climatological baseline, 36 heatwaves were characterized by eight morphology indicators. A total of 18,000 EnergyPlus simulations examined combinations of three TM levels, two ventilation configurations, window-to-wall ratios (WWRs) ranging from 0.10 to 0.70, openable fraction, and opening schedules. Six indicators captured daytime peaks, persistent overheating, peak temperature lag, nighttime overheating, thermal recovery, and nighttime cooling rate (NCR). Machine learning models were used to identify the governing mechanisms and delineate ventilation strategy regimes. All models achieved R² values above 0.96 under case-level testing, while event-level performance varied across responses. Increasing TM lowered the median daytime peak temperature by 4.5 °C under cross-ventilation but did not improve every response. Predicted daytime peak risk increased sharply when the mean daily minimum temperature exceeded about 27.0 °C. Ventilation configuration also reversed the effects of opening time, duration, and WWR, precluding a single optimum that is independent of heatwave and building conditions. Effective cooling, operationally defined as positive predicted NCR, was generally associated with earlier opening and longer ventilation. Under single-sided ventilation, achieving stable positive predicted NCR generally required ventilation durations longer than approximately 4 h for buildings with low TM and longer than 6 h for those with medium TM. Under cross-ventilation, the corresponding effective domain in the strategy space contracted markedly, and no stable region with positive predicted NCR emerged for buildings with high TM. These findings support a shift from fixed window opening rules to control strategies tailored to heatwave characteristics and building thermophysical properties. The framework provides a basis for natural ventilation operation informed by forecasts, passive cooling retrofits, and thermally resilient building design. Full article

Smartphone addiction has been linked to altered attentional functioning. However, it remains unclear whether these alterations are associated with enhanced attentional bias or impaired attentional shifting. Moreover, previous studies have largely relied on self-report measures, and evidence for attentional bias has mainly been obtained using smartphone-related stimuli. This study employed the classic two-rectangle paradigm to compare selective attention (including space-based and object-based attention) between individuals with high and low levels of smartphone addiction and to determine whether smartphone addiction primarily relates to attentional bias or attentional shifting. The results indicated that smartphone addiction significantly modulated object-based attention. More precisely, the high-addiction group exhibited a larger object-based attention effect than the low-addiction group, a difference primarily driven by the significantly longer reaction times of high-addiction individuals in the invalid different-object (IDO) condition, while no group difference was observed in the invalid same-object (ISO) condition. Furthermore, the high-addiction group exhibited significantly slower overall reaction times in the experimental tasks. These findings suggest that smartphone addiction is associated with attentional functioning and may involve poorer attentional shifting performance during object-based attention. Full article

Prestressed and post-tensioned concrete structural elements constitute approximately 43.4% of modern bridge infrastructure, representing 58.2% of the total bridge surface area due to their long-span capabilities. Despite their structural efficiency, evaluating residual post-tensioning forces and diagnosing localized degradation within internally grouted tendons—such as localized stress corrosion cracking (SCC), grout voids, and moisture infiltration—remains a critical challenge due to geometric confinement and high material attenuation. This paper presents a non-destructive Structural Health Monitoring (SHM) methodology optimized for the continuous and periodic assessment of post-tensioned anchorage zones under operational traffic loads. The proposed Identification of Active Anomalies (IAA) system integrates the Acoustic Emission (AE) method with unsupervised machine learning to classify multi-mechanism structural degradation. By implementing a mathematically transparent k-means clustering framework initialized via the k-means++ heuristic, high-velocity multi-parameter AE data streams are partitioned within an n-dimensional Euclidean feature space. The scientific novelty of this work lies in its real-scale validation on an operational, highly complex cable-stayed bridge, establishing a previously unpublished acoustic signature database (the 2025 Signal Database). The empirical validity of the algorithm’s predictive boundaries was confirmed through forensic physical inspections and material sampling during a major structural rehabilitation in 2026, which corroborated the active corrosion states within heavily confined post-tensioned anchorage blocks. Furthermore, extracted AE pattern classes are explicitly correlated with structural crack opening widths, enabling real-time tracking of macro-defect propagation, anchorage slippage, and active micro-structural corrosion. Full article

Traditional evaluation of Networks-on-Chip is based on aggregate metrics. Most of these metrics (latency, throughput, hop count, packet loss, energy consumption, buffer occupancy, thermal or reliability summaries) compress detailed execution traces into a few scalar values. This dimensionality reduction hides spatial, temporal, and resource-level differences between simulations. This paper introduces an Entropic Structural Observability framework for routing-independent post-simulation analysis of NoC traces. The main idea of the framework is to convert typed packet-level events into probability distributions over used resources, including routers, directed links, time windows, and critical resources. As a final report, the method builds a structural signature. This signature includes normalized entropy, effective support, concentration, spatiotemporal mutual information, distribution drift, topology-aware spatial statistics, buffer-pressure measures, and classical imbalance indicators. The analysis does not modify simulation traces; it operates as a diagnostic layer. It reveals activity distribution, temporal dependence, spatial evolution, resource pressure, and structural imbalance. The framework is evaluated on 256-router 2D and 3D mesh topologies using deterministic and adaptive routing policies under diverse traffic patterns and injection rates, enabling its structural signatures to be examined across different network dimensionalities. The results reveal distinct structural regimes: deterministic dimension-order routing shows broad spatial and temporal dispersion, DyAD exhibits stronger time-space coupling and drift, and Fully-Adaptive presents an intermediate profile with high dispersion but moderate temporal variation. Full article