Search Results (3,574)

Tourism-demand forecasting overwhelmingly targets aggregate volumes, leaving the question of where demand will come from largely unaddressed. This paper forecasts that question directly. We make three contributions. First, we apply Bayesian Dirichlet autoregressive moving average (BDARMA) models to guest origin composition in large-scale platform booking data, which to our knowledge is the first use of Bayesian compositional time-series methods on booking-origin shares across multiple global destination regions. Second, we introduce seasonal structure in the Dirichlet precision parameter, and we isolate its contribution through an ablation against an otherwise identical constant-precision specification: seasonal precision lowers mean absolute error in all four destination regions, by between 13% and 35%. Third, the forecast target is the monthly composition of bookings indexed by booking date rather than stay date, which makes it observable ahead of realized arrivals and therefore usable for decisions with long lead times. Using proprietary Airbnb reservation data spanning 2017–2025 across four destination regions, we document substantial pandemic-era shifts in booking composition with heterogeneous recovery patterns. In rolling-origin evaluation, BDARMA achieves the lowest forecast error for EMEA, the most compositionally diverse region, reducing mean absolute error by 27% relative to naïve forecasts ($p<0.001$). Performance elsewhere is mixed: simple benchmarks remain hard to beat, and exponential smoothing on isometric log-ratio-transformed data attains the lowest error averaged across the four regions. The EMEA pattern suggests that direct compositional modeling is most valuable where several origin markets hold material shares, although four destination regions are too few to establish this as a general rule. The methodology yields probabilistic forecasts of source market shares that can inform marketing allocation, concentration-risk monitoring, and forward-looking operational planning. Full article

Background/Objectives: International nurse recruitment is increasingly important, but early integration depends on language, professional adaptation, organizational welcome, and retention support. Evidence from non-English-speaking healthcare systems and multi-stakeholder implementation evaluations remains limited. This study aimed to evaluate a structured onboarding pathway as an exploratory, response-level pilot and a feasibility platform. Methods: Anonymous five-point Likert questionnaires were administered to clinical coaches, ward teams, and newly hired international nurses at conceptual time points T0, T1, and T2. T0 was partly retrospective because the first survey wave, administered at approximately 3 months, asked respondents to rate both arrival status and current status. Analyses were response-level; T0–T1 ratings were summarized with a paired within-row sensitivity analysis when both ratings were available, whereas T2 could not be linked to the earlier wave. Results: The primary dataset contained 410 anonymous response-level observations, mostly from ward-team proxy ratings. Median language proficiency ratings were 3 (IQR 2–3) at T0, 3 (IQR 3–4) at T1, and 3 (IQR 3–4) at T2; the proportion of ratings at or above the pragmatic adequacy threshold (≥3) rose from 53.5% to 76.4% and 91.0%, respectively. For professional training/competence, median ratings were 3 (IQR 2–4), 3 (IQR 3–4), and 3 (IQR 3–4), with corresponding adequacy-threshold proportions of 53.5%, 78.0%, and 89.7%. However, stakeholder-specific patterns differed, and international nurses’ self-rated language proficiency followed a non-monotonic pattern. In the nurse-specific analytic subset (14 anonymous response rows), overall welcome was associated with intention to stay (Spearman rho = 0.73, FDR-adjusted p = 0.016) and willingness to recommend the experience (rho = 0.86, FDR-adjusted p = 0.002). Conclusions: The pilot generated hypotheses and, more importantly, mapped feasibility requirements for a future confirmatory study. Although the findings should not be interpreted causally or as paired longitudinal evidence, they provide an initial overview of issues related to the integration of international nurses in non-English-speaking healthcare settings. A future study should prospectively collect pseudonymized identifiers, objective and validated measures, and a pre-specified longitudinal analytic model. Full article

At highway merging sections, merging vehicles must enter the mainline traffic flow within a limited acceleration section. Under high traffic demand, this process often involves rapid acceleration or deceleration. Conventional merging assistance control has mainly focused on reducing acceleration and ensuring safe inter-vehicle gaps. However, when electric vehicles (EVs) are considered, battery energy consumption is also an important evaluation perspective. This study evaluates the effects of different speed adjustment strategies for merging vehicles on EV battery energy consumption and smooth merging performance at a single highway merging section. Four cases are compared: Acceleration-Minimizing Merging Control (AMC), which minimizes the absolute value of the required acceleration; Fixed-Arrival-Time Energy-Minimizing Merging Control (FEMC), which minimizes battery energy consumption under a fixed arrival time; Variable-Arrival-Time Energy-Minimizing Merging Control (VEMC), which minimizes battery energy consumption without fixing the arrival time; and a no-control case. EV battery energy consumption is calculated by integrating battery-side power over time, considering driving resistance, inertial force, drivetrain efficiency, regenerative braking efficiency, maximum regenerative power, and auxiliary power. The simulation results show that AMC is advantageous in terms of smooth merging performance, whereas VEMC achieves the lowest overall average battery energy consumption. FEMC and VEMC reduced battery energy consumption by up to 10.7% and 39.5%, respectively, compared with AMC under the evaluated initial-speed conditions, although their smooth merging performance decreased under some conditions; however, their smooth merging performance remains lower than that of AMC, and the energy-saving effect depends on the initial speed and traffic demand conditions. These results indicate that EV-oriented merging assistance control requires a control design that considers the trade-off between energy efficiency and smooth merging performance. Full article

We propose a high-quality and computationally efficient trajectory replanning method for Unmanned Aerial Vehicle (UAV) swarms, termed RHCR-Opt, which is designed to continuously and efficiently generate multiple collision-free trajectories in dense obstacle environments. RHCR-Opt consists of three layers. The first two layers are the rolling-horizon collision resolution (RHCR) algorithm based on the Conflict-Based Search (CBS), while the third layer focuses on trajectory generation and optimization using Minimum Control (MINCO) trajectories. Within the two-layer RHCR framework, the improved Lifelong Planning A* (LPA*) algorithm incorporating spatiotemporal constraints is proposed and employed as the low-level solver of CBS to satisfy the frequent search requirements for feasible paths under varying spatiotemporal constraints, thereby significantly improving computational efficiency. Furthermore, the rolling-horizon collision resolution concept is adopted in the high-level CBS framework, where only the discovery of collision-free paths within a finite time window is considered. This substantially reduces the computational burden associated with trajectory generation and optimization beyond the time window. At the third layer, a MINCO-based trajectory generation scheme is designed, and a swarm trajectory joint optimization framework with a finite time window is proposed to generate dynamically feasible and collision-free trajectories. In addition, for swarm missions requiring simultaneous arrival, a two-stage temporal coordination optimization method is developed. Extensive simulation experiments demonstrate that, compared with state-of-the-art (SOTA) algorithms on the proposed benchmark, RHCR-Opt achieves significant improvements in both trajectory quality and computational efficiency. In particular, when the swarm size becomes large, the computational efficiency is improved by at least 23.7%. Full article

Traveling-wave fault location in distribution networks confronts two fundamental challenges: insufficient robustness of the cost function against heavy-tailed synchronization noise, and the location-resolution bottleneck imposed by the discrete search step. Starting from first physical principles, we identify that faulty-branch identity is encoded in the ordering pattern of multi-terminal TW arrival times rather than in their absolute values. Building on this insight, we propose a faulty-branch identification and precise fault-location method that integrates amplitude-assisted rank correlation (AAC) features with random forest (RF). At the theoretical level, we employ Hampel’s finite-sample breakdown-point framework to quantitatively establish that the $L_2$ cost function has an asymptotic breakdown point of zero, whereas the Spearman rank correlation coefficient attains an asymptotic breakdown point of 0.5—providing a rigorous robustness justification for replacing the $L_2$ residual with a rank-consistency cost. At the algorithmic level, the method consists of a three-stage inference pipeline: AAC computes a joint rank correlation cost for every line section across the network and extracts a 42-dimensional feature vector encompassing cost statistics, timing residuals, and topological attributes; feature selection is performed via fused ranking, which combines Pearson correlation, point-biserial correlation, and RF out-of-bag permutation importance through a weighted harmonic mean; the RF classifier directly performs branch identification over the full edge space, and the RF regressor predicts the coarse-location residual from local cost-terrain statistical features along the correctly identified branch, breaking through the 20 m search-step resolution bottleneck. We construct a five-layer physical noise model covering wavefront detection, time synchronization, wave-velocity deviation, reflected-wave misdetection, and terminal failure. Experiments on three structurally distinct 10 kV radial distribution network topologies, each with 5000 independently generated fault samples, demonstrate that branch identification accuracy remains stably above 94%, and residual correction reduces the mean location error from approximately 60 m to approximately 40 m—an improvement exceeding 30%—confirming the effectiveness of the physics–data hybrid framework for TW fault location in distribution networks. Full article

Broadband Capon direction-of-arrival estimation is computationally demanding because covariance processing, spatial spectrum evaluation, and steering-vector construction are repeatedly performed over multiple frequency bins and candidate directions. This study presents an efficient framework for horizontal-plane sound source azimuth estimation by combining pairwise Capon processing with recursive steering-vector generation. The array is partitioned into ordered two-microphone pairs, enabling independent $2\times 2$ covariance processing. Each pair estimates a local angle relative to its directed baseline, and the resulting constraints are fused according to the array geometry. In the implemented orthogonal cross array, both pairs lie in the horizontal plane and provide complementary components of the planar source direction. A general fusion formulation is also provided for non-orthogonal baselines and non-coincident pair midpoints. The frequency-linear phase structure of the pairwise steering vector is exploited to replace repeated trigonometric evaluations across DFT bins with recursive complex rotations. Across 80 single-source trials, the proposed method achieved an MAE of $1.88°$, an RMSE of $3.54°$, and 100% of estimates within $\pm 10°$. The steering-vector generation time decreased from 165.69 ms to 66.77 ms, while the total measured component time decreased from 172.14 ms to 70.92 ms. Additional evaluations of multi-source resolution, reverberation, moving sources, numerical stability, and irregular arrays demonstrate a practical trade-off between computational efficiency and localization robustness. Full article

Accurate Estimated Time of Arrival (ETA) forecasting is essential for improving operational planning and decision-making in modern ports. While machine learning has significantly enhanced ETA prediction using Automatic Identification System data, the impact of data preprocessing on forecasting performance remains underexplored. This study investigates how AIS data preprocessing and machine learning model selection jointly affect ETA forecasting for short-sea shipping, using the Port of Sines as an empirical case study. A reproducible forecasting workflow was developed, integrating dataset construction, voyage selection, feature engineering, predictive modelling and performance evaluation. Three supervised machine learning algorithms, K-Nearest Neighbors, Random Forest Regression, and Multilayer Perceptron, were trained and compared under identical experimental conditions. The results show that forecasting performance depends not only on model selection but also on the quality of the modelling dataset. In particular, Random Forest Regression achieved the strongest and most consistent performance. It was found to be invariant to feature scaling, whereas scaling had a small negative effect on K-Nearest Neighbors and increased training variance for the Multilayer Perceptron. Although all three models achieved accurate ETA predictions, they exhibited different strengths regarding predictive performance, computational efficiency, and operational applicability. The proposed workflow contributes to the development of transparent and reproducible ETA forecasting methodologies and provides practical guidance for implementing AIS-based decision-support systems in short-sea port operations. Full article

With the continuing growth of expressway travel demand, recurrent congestion frequently occurs on selected expressway sections during peak periods. Conventional fixed-threshold strategies for hard-shoulder running have difficulty maintaining a dynamic balance between congestion mitigation, safety-related operational considerations, and lane-resource utilization. This study proposes an SIR-T (Traffic) state-evolution model for hard-shoulder opening and closing decisions. Traffic operation is represented by three normalized states, namely, free flow or congestion susceptibility (Susceptible, S), congestion formation and propagation (Infected, I), and congestion recovery (Recovered, R). The state-transition equations describe the effective congestion propagation rate, recovery transition rate, and time-dependent return rate. Literature-informed and section-specific baseline parameters are adopted, while hourly electronic toll collection (ETC) gantry traffic volumes from the Zhejiang G-YZ Expressway are used as the time-varying demand input for numerical integration. Dynamic opening and closing conditions are then derived and evaluated in SUMO by comparing the proposed control strategy with static opening and no-opening strategies. The results show that the proposed dynamic control strategy can adaptively adjust the hard-shoulder opening period in response to real-time traffic conditions, reduce unnecessary occupation of shoulder resources, and maintain traffic service performance close to that of static opening while producing fewer lane changes; compared with no opening, it also reduces average travel time and increases total arrivals. Full article

The rapid growth of cloud and distributed computing systems has increased the complexity of real-time request distribution under dynamic and multi-factor conditions. In such environments, load-balancing decisions must simultaneously consider uncertain and interdependent parameters, including server load, response time, and resource capacity. Fuzzy logic is an effective tool for modeling such uncertainty; however, the expansion of linguistic variables often leads to a rule-explosion problem, which increases computational complexity and reduces the real-time applicability of fuzzy load-balancing systems. This study proposes an explainable multi-objective quantum-inspired fuzzy optimization approach for scalable load balancing in complex computing environments. The proposed model integrates fuzzy inference with a Grover-inspired classical search strategy to optimize the selection of fuzzy rule subsets. The Grover-inspired component is implemented as a classical simulation rather than a gate-based quantum circuit. A multi-objective evaluation function is formulated to jointly assess rule accuracy, coverage, interpretability, and compactness. This formulation enables the model to reduce redundant fuzzy rules while preserving decision transparency and maintaining reliable load distribution performance. The proposed approach is evaluated in a simulated cloud computing environment with heterogeneous servers and dynamic request arrival patterns. Comparative experiments are conducted against classical load-balancing strategies, conventional fuzzy load balancing, and evolutionary fuzzy optimization methods, including GA-FLB and PSO-FLB. The experimental results show that the proposed model reduces the size of the fuzzy rule base while maintaining competitive response time, load distribution quality, SLA compliance, and decision interpretability. These findings indicate that the integration of Grover-inspired classical search mechanisms with fuzzy reasoning provides a promising direction for developing scalable, compact, and explainable load-balancing models for next-generation intelligent computing systems. Full article

Warehouse operations involve dynamic item flows, fluctuating demand, and heterogeneous layouts, making adaptive decision-making essential for efficient storage and order fulfillment. In this context, reinforcement learning (RL) provides a promising approach for learning adaptive warehouse control policies under stochastic environments. However, evaluating RL-based solutions in real warehouse settings is often costly and time-consuming, motivating the need for realistic and reproducible simulation environments. In this paper, we introduce a configurable warehouse simulation environment modeling stochastic item arrivals, order generation, and internal logistics operations across diverse layouts and workload conditions. Based on this environment, we construct a reproducible experimental testbed composed of multiple scenarios ranging from low-load to highly congested settings. The testbed is publicly released to support reproducible research and comparative evaluation within the research community. We formulate the warehouse management problem as a Markov decision process (MDP) and apply a Maskable Proximal Policy Optimization (Maskable PPO) agent to learn adaptive control policies. The RL-based approach is evaluated across the defined scenarios and compared against heuristic baseline strategies. Experimental results show that the proposed solution achieves performance comparable to a strong greedy first-in, first-out (FIFO) heuristic while improving order fulfillment by up to 13.5 percentage points under challenging workload conditions. These results demonstrate the ability of RL to learn robust warehouse control policies that adaptively optimize performance and maintain operational stability across a wide spectrum of distinct scenarios. Full article

This paper develops a distributed cooperative guidance law for the spatiotemporal cooperative arrival of underactuated flight vehicles with uncontrollable axial acceleration under obstacle avoidance constraints. First, a singularity-free obstacle avoidance guidance law is proposed based on a linear projection function to avoid singularity-induced surges in acceleration commands. The proposed law has a simple structure and bounded magnitude, and it ensures that the flight vehicles safely avoid obstacle regions. Then, error dynamics theory is adopted to design a cooperative guidance law for arrival angle control and arrival time synchronization, which guarantees that the coordination errors of the flight vehicles converge to zero before reaching the target. Moreover, a buffer zone is constructed around each obstacle, which provides a distance-dependent transition region. Accordingly, a continuous and smooth weighting function is designed to shift the guidance priority from cooperative guidance to obstacle avoidance, thereby avoiding abrupt jumps in acceleration commands during task switching. Finally, the effectiveness of the proposed guidance law is verified through numerical simulations in typical scenarios and Monte Carlo experiments. Full article

Realistic modeling of pedestrian flow in dense public spaces is important for urban crowd geosimulation, mobility analysis, and indoor public-space geo-information modeling. Although prior research has emphasized microscopic agent interactions, higher-level spawn dynamics—governing when and where pedestrians appear—remain less explored, despite their fundamental role in shaping crowd density and flow. Existing approaches often decouple spatial and temporal generation, limiting their ability to capture rich spatio-temporal correlations, and they lack controllability for user-specific scenarios such as high-density environments. In this paper, we propose a Guided Joint Spatio-Temporal Diffusion framework for pedestrian spawn simulation. Our objective is to develop and evaluate a controllable joint spatio-temporal generative model that produces each pedestrian spawn event—its inter-arrival time, origin, and destination—consistent with observed spawn dynamics and a user-specified normalized local spawn-intensity condition. The model addresses the upstream initialization of a crowd simulation, rather than complete trajectory prediction or microscopic interaction simulation, and is evaluated through both next-event accuracy and fixed-horizon controllability. The method leverages spatio-temporal diffusion point processes to jointly model spatial and temporal spawn events, capturing dependencies overlooked by classical and neural point-process-based methods. To support controllable pedestrian-flow generation for geosimulation and downstream applications, we integrate a conditional denoising network with classifier-free guidance, enabling user-specified factors such as crowd density to steer generation. Experiments on the Grand Central dataset demonstrate that our method outperforms strong baselines, reducing temporal error (T-RMSE) by 38% and achieving consistent improvements in spatial and spatio-temporal accuracy. These results show the potential of diffusion-based spatio-temporal modeling for controllable urban crowd geosimulation and pedestrian mobility data generation. Full article

Synchronization Signal Blocks (SSBs) support initial access, synchronization, and beam management in fifth-generation networks. However, the periodic transmission of SSBs contributes to background electromagnetic field (EMF) exposure, while increasing the SSB periodicity may increase the waiting time experienced by newly arriving User Equipment (UE). This paper proposes an EMF-aware adaptive SSB periodicity control framework that integrates Long Short-Term Memory (LSTM)-based demand prediction with a Proximal Policy Optimization (PPO) controller. Aggregated Internet usage measurements collected by Telecom Italia in Milan in 2013 at 10 min resolution are transformed into a traffic-derived UE arrival proxy. The LSTM model forecasts the proxy for the subsequent control interval, and the PPO policy selects a SSB periodicity from 5, 10, 20, 40, 80, and 160 ms. The optimization reward combines normalized SSB EMF power density and normalized aggregate UE waiting time, thereby avoiding dimensional and numerical-scale inconsistencies between the two objectives. The LSTM and PPO models are developed using six chronological weeks of data and evaluated without parameter updates over a seven-day held-out period. The prediction-driven controller achieves a mean SSB EMF power density of $3.67\times {10}^{-5}$ W/m² and a weekly average waiting time of 19.22 ms per person. Relative to the fixed 20 and 40 ms configurations, the proposed controller reduces mean EMF power density by approximately 65.4% and 30.8%, respectively. Its waiting time is close to that of the fixed 40 ms configuration and substantially lower than that of the fixed 80 ms configuration. Although the fixed 80 ms configuration provides lower EMF power density, it incurs considerably greater waiting time. The results show that the proposed framework provides an adaptive intermediate operating point between the lower waiting time of short fixed periodicities and the lower EMF exposure of long fixed periodicities. Full article

This study investigates the 3D mapping and VR technologies for virtual tourism applications by utilizing Souq Al-Alawi, which is a UNESCO heritage site located in Jeddah, Saudi Arabia. Virtual reality is a technology that has opened up possibilities for low-cost, immersive traveling experiences without being bound by physical locations. This study introduces a holistic 3D virtual model of Souq Al-Alawi that utilizes a combination of close-range photogrammetry methods, image-based modeling (IBM), Geographic Information System (GIS) data, and presentation in the Unreal Engine. This methodology combines a systematic GIS data collection technique with historical record keeping, IBM 3D modeling techniques for facades digitization, as well as VR integration to arrive at an interactive virtual tour experience. In the preprocessing step, distortions in building facades induced by narrow street layouts were evaluated and rectified using advanced IBM techniques aiming to maximize geometric accuracy and visual plausibility. The developed platform produced 41 three-dimensional models and 426 image-based models across three survey zones, with photogrammetric processing achieving RMS reprojection errors of 1.115, 0.554, and 0.55 pixels. The VR environment maintained a consistent rendering performance of 90 frames per second, enabling smooth real-time navigation. The resulting interactive 3D navigational map demonstrates the technical feasibility of integrating photogrammetry, GIS, and VR technologies to support virtual tourism and digital cultural heritage documentation. Full article

Quantifying the direction, strength and temporal clustering of cross-border financial risk contagion calls for methods that go beyond linear correlation. We suggest a two-layer framework that brings together transfer entropy and a multivariate Hawkes self-exciting point process on a time-varying, directed, weighted network. In the first layer, one-to-one transfer entropies of sovereign credit default swap spreads are estimated with a bias-corrected k nearest neighbour estimator, and this step detects nonlinear and directional information transfer between spreads. The second layer is a multivariate Hawkes process that models how extreme loss events arrive and mutually excite one another across countries, and it gives an excitation intensity matrix, encoding the way a tail event in one country raises the likelihood of an instantaneous hazard occurring in another. By merging these two layers, we obtain a composite, directed, weighted adjacency matrix in which the weights of the edges reflect both information flow and event clustering. We introduce a network-level contagion intensity index and split it into direct, indirect and feedback terms using the graph Laplacian spectrum. Von Neumann graph entropy together with the spectral gap ratio serve as entropy-based measures of the complexity and fragility of the evolving network. We validate the choice of Shannon-type entropy through a Tsallis q-sensitivity analysis, and we verify the nonlinear dependence structure of the data using BDS tests and maximal Lyapunov exponent estimates. Three empirical findings emerge from analysing 20 sovereign CDS markets from January 2015 to December 2025: (i) directional risk spillover signals derived based on transfer entropy are more timely than those derived from variance decomposition; (ii) the Hawkes excitation component amplifies measured contagion intensity by 35 to 58 percent during the COVID-19 shock and the 2022 European energy crisis relative to a transfer-entropy-only baseline; (iii) von Neumann graph entropy reaches historically extreme values 7 to 12 trading days before the peak drawdown in a Global Sovereign Bond Index. These results hold across rolling window lengths, significance thresholds, alternative entropy functionals and alternative Hawkes kernels. Full article