Search Results (4,018)

We study nonlinear stochastic dynamical systems that evolve in a Markov environment with separated fast and slow transition scales. These systems are also subject to impulsive perturbations under a Poisson approximation scheme. The environment is represented on a product state space, and phase aggregation is used to average the rapidly switching component while retaining the slower Markov regime explicitly. Under the stated ergodicity, regularity, integrability, tightness, state-preservation, and uniqueness assumptions, we derive an effective reduced process and establish weak convergence of both the impulsive component and the coupled nonlinear system in the Skorokhod space. The limiting jump dynamics are characterized by averaged local characteristics associated with the retained slow Markov regime. To assess the reduced model numerically, we use a nonlinear competitive Lotka–Volterra-type system and compare the deterministic dynamics, the full Markov-switching stochastic model, and the reduced approximation under several fixed parameter configurations and jump-intensity settings. The results provide a mathematical basis for the reduced modeling of nonlinear stochastic dynamical systems with separated Markov time scales and impulsive perturbations. Full article

City-scale microscopic traffic simulation is increasingly used for policy evaluation, operational planning, and disruption analysis, where repeated scenario runs must retain vehicle-level trajectories rather than only aggregate traffic indicators. This requirement creates an efficiency–consistency trade-off: single-node simulators preserve centralized state ownership but become inefficient for million-vehicle tasks, whereas distributed execution reduces runtime but may disrupt vehicle updates at partition boundaries. Cross-partition movement can cause trajectory breaks, duplicate or missing updates, and inconsistent local interaction states if boundary events, vehicle context, and update ownership are not coordinated. To address this problem, this study proposes DiCoSim, a distributed coordination framework for boundary-consistent large-scale microscopic traffic simulation. DiCoSim integrates incremental spectral-clustering partitioning, spatio-temporal event aggregation, and acknowledgment-controlled state handoff to coordinate workload balance, boundary communication, and vehicle handoff. Experiments on a 483 km${}^2$ Tianjin network with 1.5 million agents show that DiCoSim achieved a $14.49\times$ strong-scaling speedup on 16 compute nodes while maintaining close agreement with centralized execution. For the fixed boundary-crossing evaluation cohort, the trajectory interruption rate was reduced to 0.06%. In addition, a single 72 h continuous high-load run achieved 99.96% availability. These results indicate that, under the tested Tianjin conditions, coordinated boundary management supports efficient million-agent microscopic simulation while maintaining vehicle-state continuity across partitions. Full article

Grounded in a human–land coupling framework, this study takes Purple Mountain, a representative composite urban mountain heritage site, as the research object. It integrates historical archives, field survey data, and multi-source geospatial data, and adopts a set of GIS-based spatial statistical methods, including the nearest neighbour index, kernel density estimation, standard deviational ellipse, coupling coordination degree model, and Geodetector. This paper systematically explores the spatial differentiation, spatiotemporal evolution, human–land coupling patterns, and multidimensional driving mechanisms of four heritage types: geomorphological relics, ritual architecture, modern commemorative heritage, and eco-scenic heritage. The results show that: (1) Heritage resources across Purple Mountain display statistically significant clustering, with ritual architectural heritage exhibiting the highest agglomeration degree; heritage sites form an east–west high-density corridor along the southern foothills, presenting a consistent northeast–southwest spatial orientation. (2) Purple Mountain heritage has undergone multi-stage diachronic evolution. Jointly driven by topographic constraints and socio-cultural forces, its heritage quantity, spatial coverage, and functions fluctuated across dynasties, with an overall expanding trend. (3) A total of 63.07% of the study area’s grid units are in a near-dissonant human–land coupling state, while highly coordinated units are concentrated in the southern core corridor of the Ming Xiaoling Mausoleum, Sun Yat-sen Mausoleum, and Linggu Temple, with remarkable disparities among heritage types in coupling patterns among the four heritage categories. (4) Historical and cultural aggregation density dominates heritage spatial differentiation, while topographic factors show weak explanatory power, and all influencing factor interactions present prominent non-linear enhancement effects. This study establishes a three-level quantitative framework of “spatial pattern—coupling coordination—driving mechanism”, enriching the theoretical framework of human–land coupling for urban mountain heritage, and provides scientific support for refined coordinated governance of mountain heritage embedded in high-density urban environments. Full article

Background: Artificial intelligence (AI) is increasingly being integrated into cardiovascular medicine, with potential applications across image analysis, procedural planning, risk stratification, decision support, and workflow optimization. However, its adoption in interventional cardiology remains heterogeneous and may be influenced by several factors. We aimed to conduct a nationwide survey to assess attitudes towards AI among Italian interventional cardiologists. Methods: We conducted a nationwide, cross-sectional, web-based survey of Italian interventional cardiologists. A structured questionnaire collected information on professional characteristics, familiarity with and current use of AI, perceived clinical applications, expected benefits, trust, implementation barriers, and training needs. Conditional branching was used to obtain additional details from respondents who reported current use of AI-based tools, while all responses were collected voluntarily and analyzed in anonymized, aggregate form. Categorical variables and Likert-scale responses were summarized using descriptive statistics, with exploratory comparisons performed across prespecified professional and institutional subgroups. Results: Among 129 respondents, 70.5% reported at least moderate familiarity with AI and 77.5% reported some current use, although only 60.5% reported regular or occasional professional use, and applications were concentrated mainly in research, education, and information synthesis rather than direct procedural support. Nearly half (48.1%) expected AI to become standard in many procedures within 5 years, while 69.0% anticipated either routine use or particular value in complex cases. Attitudes were broadly favorable, with 85.3% agreeing that AI could improve diagnostic and procedural precision, 86.8% expressing strong interest in future use, and 76.7% stating that AI should support rather than replace physician judgment. The leading barriers were medico-legal uncertainty (45.0%), poor integration with existing clinical systems (34.9%), and cultural resistance or operator distrust (29.5%), whereas preservation of physician control was the most frequently cited requirement for adoption (58.9%). Greater AI familiarity was independently associated with current AI use (p < 0.001) and good or high trust (p < 0.001). Compared with no prior training, one and multiple AI training experiences were independently associated with good or high familiarity (both p < 0.05). Conclusions: Italian interventional cardiologists showed substantial exposure to AI, strong interest in future adoption, and generally favorable expectations regarding its contribution to diagnostic precision, workflow, and procedural support. Acceptance remained conditional on physician oversight, stronger clinical validation, reliable interoperability, and clear medico-legal governance, and previous AI-focused education appeared independently associated with greater familiarity. Full article

Focus-measure recommendations for smear microscopy are typically established on one specimen type under one reference definition and one scoring rule, leaving their transferability untested. We benchmark 32 handcrafted focus measures on 26,100 z-stacks spanning five smear-microscopy domains at native acquisition dimensions under one frozen protocol. A fixed ten-voter plurality consensus (REF-B) supplies an identical reference target for every candidate, and two further constructions, single-voter exclusion (REF-A) and a non-derivative four-voter consensus (REF-C), quantify how far the recommendation depends on that target. Ten criteria covering consensus localization, curve shape, perturbation response and kernel cost form a declared cross-domain score; clustered bootstrap resampling, alternative aggregation, weight perturbation and controlled image resampling separate sampling variability from sensitivity to evaluation policy. Gradient responses lead under every analysis. Variance of Gradient ranks first under the primary policy in all 1000 clustered bootstrap replicates and in 67.9% of 1000 sampled weight configurations, while Brenner Gradient attains the lowest consensus deviation, 0.072 planes, at the lowest measured kernel cost. Localization ordering is preserved exactly when the reference is rebuilt from non-derivative voters alone (Spearman ρ = 1.000), although the reference plane itself shifts substantially. The benchmark gives a reproducible, auditable basis for selecting focus measures under stated reference, scoring and image-sampling conditions. Full article

The reuse of construction and demolition waste in cementitious materials supports the development of sustainable low-carbon composites and circular economy strategies. This study investigated the influence of recycled ceramic fine aggregate (RCerFA) and recycled concrete fine aggregate (RConFA) on the mechanical properties, freeze–thaw durability, pozzolanic potential, and environmental performance of cement mortars prepared with different cement types. Mortars containing 20% and 40% replacement of natural sand with recycled aggregates were evaluated through strength testing and freeze–thaw resistance assessment, while SEM analysis and pozzolanic potential were assessed on separate mortars in which 25% of the cement binder was replaced with the recycled materials, alongside carbon footprint calculations based on global warming potential (GWP), using the recycled materials directly after the crushing process without additional grinding. The results showed that mortars containing recycled concrete fine aggregate generally maintained satisfactory mechanical performance and freeze–thaw resistance, particularly at the 20% replacement level. In contrast, 40% RCerFA reduced mechanical performance and freeze–thaw resistance. Neither recycled material demonstrated confirmed pozzolanic reactivity in its unground state. Although one RConFA mixture exceeded the 75% compressive strength index criterion, this result alone was insufficient to confirm a chemical pozzolanic reaction. Environmental assessment demonstrated that cement type had a greater influence on carbon footprint than recycled aggregate incorporation. The study confirms the potential applicability of recycled fine aggregates in sustainable low-carbon cement mortars and explores their possible use as low-energy supplementary cementitious components. Full article

Weighted directed networks can exhibit stronger aggregate interactions without becoming more integrated or topologically richer. Using daily prices for 32 stock indices over 2006–2026, we construct effective transfer entropy (ETE) networks from range-based variance states and recompute GLMY path homology over a directed edge-density filtration. In 88 overlapping 120-common-date windows, total ETE is associated with a higher density required for global weak connectivity and with smaller integrated first- and second-dimensional Betti curves. These are dependence-aware descriptive associations: reduced-overlap estimates are imprecise, and controls for edge concentration and mean variance reduce the connectivity association to approximately zero. Direct coverage measures show that high-intensity windows can distribute weight broadly while their strongest edges reach nodes unevenly. Exact circular-shift tests yield empty 5% BH-FDR backbones, so individual channels are not treated as established. Null-model, rank-stability, coefficient-field, and discretization checks delimit the fixed-density result. A common absolute-weight contrast yields positive rather than negative TETE–Betti associations, showing that the negative baseline relation is specific to the fixed-density filtration. The results distinguish interaction intensity, dispersion, strong-edge coverage, and directed path-homology organization as separate network dimensions. Full article

Balancing production resilience with environmental, social and governance (ESG) performance is difficult when disruptions, policy constraints and stakeholder expectations interact over time. This paper proposes a six-node dynamic network data envelopment analysis architecture whose admissible weight intervals are adjusted by an LSTM learner using IoT-derived shock states. The application covers 96 aggregate monthly periods from 2018 to 2025 across three countries and four technology-based manufacturing groups. Figure-grounded diagnostics indicate mean resilience scores of 0.7016 for the basic IoT-DEA benchmark and 0.7134 for the complete model (paired difference = 0.0118; 12-month moving-block bootstrap 95% CI = 0.0015–0.0245; p = 0.001), while mean uncertainty falls from 0.0536 to 0.0205, a 61.8% reduction. Threshold sensitivity shows that governance-delay and skill boundaries vary by industry and carbon-constraint severity rather than constituting universal standards. In the calibrated policy simulation, the combined carbon-tax and green-subsidy path reaches approximately 0.90 by month 96, compared with 0.79 under no policy; this contrast is interpreted as a scenario result, not a firm-level causal treatment effect. NSGA-III and SHAP then connect the measured constraints to Pareto-efficient portfolios and sector-specific managerial priorities. The framework’s main supported contribution is more stable, temporally explicit ESG-resilience diagnosis with transparent limits on causal and cross-sectional inference. Full article

This paper presents the design, development, and evaluation of a machine-learning system built to forecast agricultural crop yields across Indian states between 2000 and 2026, together with a complementary, national-scale verification of predicted crop yield using a MODIS-derived NDVI time series (MOD13A3.061 Vegetation Indices Monthly L3 Global 1 km SIN Grid). Although many prior studies address crop-yield prediction with linear regression, random forest, gradient boosting, and related methods, a complementary, aggregate-level verification method for predicted crop yield has rarely been proposed. This article contributes such a method, together with a complementary NDVI-based estimation approach for total foodgrain output. Crop yield and MODIS-derived NDVI are strongly correlated (r = 0.84 for annual maximum NDVI; r = 0.78 for annual mean NDVI), and a simple regression of total foodgrains on annual maximum NDVI alone reaches R² = 0.70. Four modeling approaches—linear regression, random forest, gradient boosting, and CatBoost—were built and compared using a chronology-preserving, expanding-window walk-forward validation procedure with a final, untouched 2024–2026 holdout, rather than a random split; a companion leakage check confirmed that reported production is almost algebraically identical to reported yield and therefore had to be excluded from the feature set. Random forest produced the most reliable and consistent forecasts, reaching a mean absolute percentage error (MAPE) of 11.4% and R² = 0.982 on the final holdout, ahead of CatBoost (MAPE = 11.6%, R² = 0.969) and gradient boosting (MAPE = 13.0%, R² = 0.908), and substantially ahead of linear regression, which failed to generalize to the holdout period (R² = −10.67); across the walk-forward folds preceding this holdout, however, the three tree ensembles were statistically indistinguishable. A four-configuration ablation study confirms that most of this performance gain is attributable to the inclusion of MODIS-derived NDVI rather than to model choice alone. Prediction error varies considerably by crop, from under 10% MAPE for major staples (rice, wheat, maize, sugarcane, moong) to well over 80% MAPE for several lower-volume crops (soyabean, garlic, Sunn hemp, tobacco, potato). The paper also documents two consequential data-quality findings—a near-perfect algebraic relationship between production and yield, and a structural administrative reporting gap in 2020—and closes with directions for future work, including higher-resolution satellite inputs, temporal deep-learning architectures, additional environmental covariates, and explainable-AI analysis of feature contributions. Full article

Federated object detection over multi-site remote-sensing imagery faces two coupled risks that are not fully addressed by existing methods. Existing federated object-detection studies mainly focus on distributed training, communication efficiency, and non-IID heterogeneity, while many robust aggregation rules rely on magnitude or coordinate statistics and can miss directional object-disappearance attacks in which the malicious update suppresses target boxes without an obvious test-time patch. Static differential privacy further applies uniform perturbation across clients, which may reduce detection utility and does not use trust differences among participants. We propose DUAL-SHIELD, a defense-and-privacy pipeline linked by three round-wise interfaces: a cosine gradient filter that detects directional anomalies with a median-absolute-deviation threshold; a momentum-based reputation aggregator that converts per-round filtering decisions into a continuous trust state; and a reputation-aware adaptive differential-privacy scheduler that allocates client-specific perturbation under explicit cumulative accounting. In the default NWPU VHR-10 object-disappearance setting, the framework keeps clean detection performance at 0.64 and reduces the attack success rate from 0.95 to 0.04. Additional transfer and attack-family evaluations are reported as supporting checks rather than definitive generalization claims, and the corresponding claims are limited to the configurations with matched-seed experiments, completed privacy accounting, and available baseline comparisons. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly applied across spinal pain care, by improving diagnostic accuracy, optimizing clinical workflow, and advancing translational research. However, AI’s role and its integration into rural spine care face challenges. Therefore, the current narrative review synthesizes the role of AI in rural spine care, spanning diagnosis, clinical decision support, imaging, natural-language processing, and remote monitoring. Methods: We searched the peer-reviewed literature on AI in spinal pain care across six databases from inception to June 2026. Results: In the United States, AI models have shown promise in strengthening clinical decision support that assists clinicians in identifying spinal pain disorders and providing evidence-based treatment recommendations; however, most were developed and validated in urban healthcare settings. Additionally, evidence on external validation, dataset representativeness, robustness to incomplete or low-quality data, interoperability, prospective clinical utility, and implementation feasibility in rural spine care remains limited. Therefore, we propose a translational framework in which de-identified data from rural and urban healthcare settings are aggregated, harmonized, and used to develop a multimodal AI model. AI models further require rigorous technical and external validation, prospective validation in rural settings, explainability, and continuous post-implementation monitoring. Despite its benefits, key limitations, including data scarcity and algorithmic bias, are also highlighted. Conclusions: AI has emerged as a promising tool for strengthening clinical decision support in spinal pain disorders in rural settings. The most realistic role of AI in rural settings is not to replace specialist expertise, but to act as one component of a multidisciplinary care team. Full article

Addressing the dual threats of malicious jamming and time-varying fading faced by wireless communication links in complex dynamic electromagnetic adversarial environments, existing intelligent anti-jamming methods predominantly focus on single-dimensional resource optimization under quasi-static channels. This focus neglects the nonlinear superposition effects of multi-path deep fading and dynamic strong jamming in the time-frequency domain, making it challenging for systems to balance transmission reliability and system energy efficiency in physical environments where fading and suppression coexist. To address this issue, this study proposes a joint intelligent anti-jamming method for channel switching and transmit power control based on a Deep Q-Network (DQN). Initially, a composite communication environment model incorporating Markov time-varying fading and jamming is constructed. Subsequently, the joint resource scheduling problem is formulated as a Markov Decision Process. The environment state space is reconstructed by integrating continuous channel state estimation and jamming observation features, accompanied by the design of a highly aggregated two-dimensional discrete action space for both channel and power. Finally, a composite reward function evaluating both communication success rates and power consumption costs is proposed to guide the agent in multi-dimensional resource joint optimization. Simulation results demonstrate that the proposed algorithm effectively extracts implicit features under the composite state of fading and jamming. When encountering extreme deep fading or full-band blocking, the agent strategically triggers a silent mechanism to avoid exorbitant invalid energy consumption penalties, while precisely matching interference-free channels with the minimum effective transmit power during favorable communication windows. Simulation results show that compared with traditional xx algorithms, the proposed method significantly improves the dynamic successful transmission rate and system energy efficiency in complex, highly dynamic scenarios, achieving an effective optimization of anti-jamming reliability and low power overhead. Full article

In contemporary manned–unmanned teaming (MUM-T) systems, the accurate detection of aircraft in Synthetic Aperture Radar (SAR) imagery is crucial for battlefield surveillance and target identification. However, challenges such as background clutter, significant scale variations, and limitations in existing feature extraction methods hinder detection accuracy. To address these issues, this study proposes CCS-Net, a lightweight Cooperative Context-aware Sensing Network designed specifically for SAR aircraft detection. CCS-Net enhances image contrast through Contrast-Limited Adaptive Histogram Equalization (CLAHE) preprocessing and employs a novel C2F_LK module combined with a Multi-scale Context Aggregation (MSCA) module to improve multi-scale feature representation with minimal parameters. An FPN-PAN structure adaptively fuses these features, while the Spatial Coordinate Attention Head (SCA-Head) integrates spatial and coordinate attention mechanisms to emphasize key aircraft regions. Optimized with label smoothing, our model achieves a 96.6% mAP@0.5 and 93.5% precision on the SAR-Aircraft-1.0 dataset with only 0.97M parameters. It generalizes well on the SADD aircraft dataset and cross-domain HRSID and SSDD ship benchmarks, outperforming state-of-the-art methods in both accuracy and lightweight design. Full article

Ecuador has experienced one of the fastest escalations of lethal violence in Latin America, with annual intentional homicides rising from about 1000 in 2014–2019 to 9283 in 2025. This study evaluates how well such patterns can be forecast, and what governs that forecastability, using the complete official open dataset of 43,976 victim-level records covering January 2014–June 2026 (portal update 15 July 2026). Incidents are aggregated into four monthly pattern series—case volume, firearm share, mean victim age and geographic concentration in Guayas—and fifteen specifications—classical, automatically configured, and state-of-the-art neural and gradient-boosted baselines—are compared under a rolling-origin protocol with 31 evaluation origins and a 12-month horizon, with every configuration decision taken inside each training window. Accuracy is reported as the mean and standard deviation of MAE, RMSE, MAPE, MASE and $R^2$ across origins, interval quality as empirical coverage and the mean scaled interval score, and differences are tested with Diebold–Mariano statistics under Holm correction. No pairwise comparison among the 303 tested survives multiplicity control, and in all four series, the best and second-best specifications are statistically indistinguishable: algorithm choice is not what these data can resolve, and the result holds with current deep-learning architectures in the candidate set rather than in their absence. The test is conservative and its power is limited, so this is non-detectability at this sample size rather than evidence that the differences are zero. What does determine it are diagnosable properties of the series. Bounded, low-variability series are forecast with mean MASE between 0.58 and 1.13 across the full candidate set, and below 1.00 by twelve of the fifteen specifications on the firearm share, whereas unbounded count series under regime change exceed an MASE of 2.09 for every specification tested, including simple benchmarks. No series requires a seasonal difference—the OCSB test returns $D=0$ in all four and the automatic search never selects one—although weak seasonal autoregressive terms are retained at some origins, and the firearm share shows a small but significant monthly effect. A genuine external validation against the observed 18 months of 2025–2026, unseen by any model, reproduces the same asymmetry (firearm share, MAPE 2.3% with 100% interval coverage; volume, bias $-14.0$%). Splitting the evaluation by regime shows that the crisis made count series roughly twice as unpredictable and bounded series roughly twice as predictable: the escalation destabilised the level of the phenomenon while stabilising its composition. Full article

Territorial planning in Portugal requires the regular preparation of State of Territorial Planning Reports (REOT) to evaluate the implementation of spatial planning instruments. However, these reports are traditionally produced as static documents, limiting their effectiveness as decision-support tools. This paper proposes a conceptual framework for the dynamic generation of REOT through the integration of official spatial and administrative data within a Planning Support System (PSS). The proposed architecture combines rule-based logic, automated indicator calculation and interactive dashboards to support the on-demand production of continuously updated planning information while preserving the possibility of data aggregation and harmonisation at municipal, regional and national scales. To complement the conceptual framework, the paper presents the Cascais Experience, a pilot implementation developed within the Municipality of Cascais. Although not constituting a Dynamic REOT, the pilot demonstrates the feasibility of integrating municipal administrative data into a dynamic urban planning dashboard capable of automatically producing planning indicators and supporting on-demand monitoring. The experience also provides valuable insights into the practical challenges associated with data integration, information standardisation and indicator development. The framework is developed within the Portuguese legal and institutional context, recognising that current monitoring practices are implemented independently by each municipality, often using heterogeneous methodologies that hinder territorial comparability and multi-level analysis. The pilot implementation demonstrates that the principal barriers to dynamic territorial monitoring are organisational and informational rather than technological, highlighting the importance of data governance and administrative information management. The findings support the transition from episodic reporting towards continuous, data-driven territorial monitoring and provide a scalable framework for the future development of Dynamic REOT systems in Portugal and other planning contexts. Full article