Search Results (5,086)

Responding to residents’ differentiated health-promoting needs has become important for improving the adaptability of street renewal in old urban neighborhoods. Based on 1404 valid questionnaires from residents in old urban neighborhoods of Jinan, China, this study develops an analytical framework linking group classification, environmental responses, and renewal strategies from the perspective of health literacy–activity behavior mismatch. Health literacy and activity behavior indices were constructed, and K-means clustering was used to identify mismatch groups. Estimated marginal means, average marginal effects, and multiple-response analysis were then employed to compare group-specific response trajectories and improvement preferences across four street environmental dimensions: slow-mobility space, service function, natural aesthetics, and activity facilities. Further interpretation of the obtained analytical results demonstrates that the investigated resident samples are partitioned into four typical subgroups: behavior-driven, high-literacy/high-behavior, literacy-driven, and low-literacy/low-behavior groups. Slow-mobility space was mainly associated with participation willingness and mismatch adjustment; natural aesthetics was primarily related to environmental cognition and perceived attractiveness; activity facilities were more relevant to mismatch changes among low-literacy/low-behavior residents; and service function mainly provided everyday convenience support. Improvement preferences were generally concentrated on basic environmental conditions, especially traffic safety, natural environment, and public activity spaces. These findings provide empirical evidence for group-based health-promoting street renewal and highlight its relevance to socially inclusive and sustainable urban regeneration. Full article

A carbon market is often expected to deliver a stable allowance price, continuous market liquidity and a rigidly binding abatement trajectory, yet these objectives may not be jointly attainable. This paper proposes a sustainability trilemma of carbon markets, arguing that at most two of price stability, market liquidity and abatement rigidity can be achieved simultaneously. We formalise this trade-off in a simple equilibrium framework and examine it using daily price and volume data from China’s national thermal-power emissions trading scheme from 2021 to 2025, together with energy-sector fundamentals proxied by thermal-power generation and power-sector coal consumption. The evidence shows that market liquidity improved substantially over the sample period, while price stability weakened markedly: median daily trading volume rose from near-dormant levels in 2022 to close to one million allowances in 2025, whereas the intra-year price range expanded sharply. Meanwhile, allowance-price changes remained weakly correlated with coal-consumption changes, suggesting that prices were driven more by allocation policy and expectations than by realised emission scarcity. The findings imply that carbon-market design should not pursue an unattainable optimum, but should manage the trade-off among stability, liquidity and abatement rigidity according to policy priorities. Full article

Efficient bridge scanning and documentation are crucial for creating reliable digital 3D models. However, scanning workflows often rely on implicit practitioner experience rather than standardized protocols. This paper presents practical insights derived from a Multiple Case Study (MCS) of ten heterogeneous, real-world bridges in Germany. The study evaluates Terrestrial Laser Scanning (TLS), Mobile Laser Scanning (MLS) and Unmanned Aerial Systems (UAS) photogrammetry. The findings isolate distinct performance trade-offs. TLS offers high accuracy but suffers from shadowing occlusions. Conversely, UAS provides operational flexibility but introduces geometric vulnerabilities, including photogrammetric reconstruction noise on fine structures and SLAM trajectory drift on vibrating spans. To unify these insights, a generalized, BPMN-compliant process model mapping the complete data acquisition lifecycle under legal and spatial constraints is defined. This research provides an actionable, practical guide to optimize data quality and efficiency in structural engineering workflows. Full article

Postharvest quality deterioration of Korla fragrant pear (Pyrus sinkiangensis Yu) severely constrains its market value, yet the regulatory role of preharvest soil management in shaping postharvest performance remains poorly understood. Although green manure is widely adopted to ameliorate orchard soil degradation, species-specific modulation of postharvest storage trajectories and the quantitative fidelity of soil-to-fruit nutrient transmission have rarely been resolved for climacteric pear species. This study investigated how green manure species modulate fruit quality at harvest and during postharvest storage life and their underlying soil–fruit linkages. Three preharvest treatments were imposed, as follows: control (CK), sweet clover (CM), and alfalfa (MX). Fruits were harvested and stored at 4 °C, with samplings at 1, 5, 10, 15, and 20 d. A critical quality transition was identified at 15 d, characterized by the concurrent peaking of soluble sugars, organic acids, vitamin C, and anthocyanins alongside an optimal sugar–acid ratio. Beyond this inflection point, CM and MX diverged markedly: CM enhanced soluble sugar accumulation, anthocyanin retention, and ester volatile production—most notably hexyl acetate, which increased over 14.4-fold—thereby generating a pronounced fruity aroma bouquet. Conversely, MX sustained higher amino acid and vitamin C levels and conferred superior late-storage stability, evidenced by a three-fold lower coefficient of variation in the sugar–acid ratio relative to CK. Partial-least-squares structural equation modeling (PLS–SEM) revealed soil fertility as the principal exploratory associative factor of fruit quality, but the fidelity of soil-to-fruit transmission was species-dependent. MX exhibited the highest observed associative strength (R² = 0.971), whereas CM exhibited attenuated transmission fidelity (R² = 0.777), with network analysis further indicating that CM exhibited divergent associative patterns of key soil–fruit correlations. These findings suggest that green manure identity is linked to postharvest quality through divergent soil–fruit coupling pathways: alfalfa shows nutrient transmission efficiency and stabilizes nutritional quality, whereas sweet clover promotes sugar-aroma accumulation at the cost of reduced soil–fruit conversion fidelity. Species-specific green manure selection thus offers a viable strategy for targeted modulation of postharvest traits in Korla fragrant pear. Full article

For a networked swarm system with a directed path graph, this paper investigates a group evasive attack strategy against actuators from the attacker’s perspective. The core objective of the proposed strategy is to force the global network state to converge to a pre-specified synchronization trajectory. First, the evasive attack signal with an ecosystem-based generation mechanism is modeled, from which the pre-specified synchronization trajectory can be derived. Subsequently, an evasive attack protocol is designed by superimposing the developed evasive attack signals onto the nominal synchronization protocol of the networked swarm system. By exploring the projection of evasive attack signals onto the synchronization subspace, an explicit expression of the pre-specified synchronization trajectory is determined, which depicts the global network state of the system under group evasive attacks. Then, to mitigate the impacts of evasive attacks on the synchronization performance of the networked swarm system, a resilient framework integrated with robust H_∞ regulation mechanisms is constructed to derive the design criteria for the group evasive attack strategy. Finally, a numerical simulation example is conducted to demonstrate the validity of theoretical results. Full article

Tracking highly maneuverable UAVs in complex maritime environments faces multiple challenges: dynamic sea surface interference and low-altitude occlusion make UAV motion trajectories difficult to predict; the strong maneuvering behavior of UAVs imposes high demands on tracking real-time performance and accuracy; and marine environmental noise and unstable shipborne sensor data lead to measurement incompleteness. These factors collectively limit the adaptability and robustness of existing maneuvering UAV tracking methods in complex maritime scenarios. In this context, accurate model recognition for UAVs becomes a key factor in improving tracking performance. Traditional interactive multiple model (IMM) methods rely on probabilistic weighting for model selection, suffering from response delays during UAV maneuvers, and fixed model sets cannot adapt to diverse maneuvering scenarios, resulting in degraded UAV velocity estimation accuracy. To address the above issues, this study proposes a dual long short-term memory (LSTM) cooperative network architecture, targeting the two key problems of incomplete measurements in shipborne radar measurements and inaccurate model probability estimation, and presents corresponding solutions. First, an online-trained LSTM-based incomplete-measurement compensation method is proposed, which achieves real-time fitting and restoration of historical measurement data, providing continuous and stable measurement inputs for shipborne platform UAV tracking in maritime environments. Second, building on this, an LSTM-based UAV model recognition method is developed to directly identify the UAV’s current motion model from multi-frame historical measurement information, effectively reducing maneuvering delays. Furthermore, GPS data is used to generate optimal model probabilities as training labels, thereby improving model reliability. Simulation results show that, under incomplete-measurement conditions, the proposed method can effectively reconstruct missing measurements and ensure measurement continuity. Under complete-measurement conditions, the proposed LSTM-based model recognition method significantly improves UAV model recognition accuracy and three-dimensional velocity estimation performance, demonstrating the effectiveness of deep learning for maneuvering UAV tracking from shipborne platforms in maritime environments. Full article

Background/Objectives: Ankle dorsiflexion plays a fundamental role in gait stability, impact absorption, and the stance-to-swing transition, and its impairment is a major limitation in transtibial prostheses. This study proposes and evaluates a lightweight two-stage pipeline for generating ankle-dorsiflexion references using a single shank-mounted inertial measurement unit (IMU). Methods: In the first stage, a deep neural network (DNN) estimates the shank pitch waveform from raw three-axis accelerations and angular velocities. In the second stage, the estimated shank pitch is transformed into an ankle-dorsiflexion waveform using a temporal mapping model. The approach was evaluated on a multisubject subset of the NONAN GaitPrint database comprising 35 healthy young adults, 598 walking trials, and approximately 122,468 gait cycles, using a strict subject-held-out protocol. Results: A feature-based Random Forest baseline showed limited performance, whereas the waveform-based DNN achieved high accuracy for shank pitch estimation, with test R² values up to 0.97. A conventional polynomial mapping between shank pitch and dorsiflexion yielded weak performance, whereas a temporal mapping model substantially improved the estimation of ankle dorsiflexion, with test R² values up to 0.85. The resulting ankle reference was integrated into a Gazebo/Robot Operating System 2 (ROS 2) simulation of a transtibial prosthesis, where the generated trajectories were executed in a software integration test under open-loop position control, confirming stable and consistent trajectory execution. Conclusions: These results indicate that combining accurate shank pitch estimation with temporal mapping enables feasible ankle-dorsiflexion reference generation from a single sensor in able-bodied gait, offering a preliminary, simulation-based pathway for single-sensor artificial intelligence (AI) pipelines in prosthetic development. The framework supports waveform-level feasibility, not clinical readiness or functional prosthetic control. Full article

Background: Biologic therapies have transformed severe asthma management, but real-world evidence comparing phenotypes, lung function trajectories, and persistence across biologic classes remains limited. Objective: To characterize a real-world cohort of biologic-treated severe asthma patients, focusing on baseline phenotypes, longitudinal post-bronchodilator spirometry (including a spirometric surrogate suggestive of small airways involvement), and discontinuation/switching patterns. Methods: In this retrospective observational study at a tertiary referral center, adults with severe asthma treated with benralizumab, mepolizumab, omalizumab, or tezepelumab were included. Demographic, clinical, biomarker, and functional data were collected at baseline and follow-up. Post-bronchodilator FEV₁ and FEF25–75 (% predicted) were assessed at baseline, 6 months, 12 months, and 24–36 months when available. Longitudinal outcomes were analyzed using multivariable linear mixed-effects models; discontinuation and switching were recorded. Results: Eighty-seven patients were included (benralizumab n = 13, omalizumab n = 10, mepolizumab n = 30, tezepelumab n = 34), representing 10.9% of the clinic’s population. Most had long-standing disease, elevated body mass index, and a T2-high profile. Baseline characteristics were generally similar across groups, with expected differences in total IgE (p = 0.007) and blood eosinophils (p < 0.001). The primary endpoint (FEV₁ % predicted change from baseline to 12 months) showed adjusted mean changes of +12.46 (95% CI +1.63 to +19.29; p = 0.020) with benralizumab, +15.82 (+8.35 to +23.64; p < 0.001) with mepolizumab, +16.65 (+1.58 to +31.71; p < 0.001) with omalizumab, and +15.69 (+6.52 to +24.87; p = 0.030) with tezepelumab; trajectories differed by biologic class (time × biologic p = 0.019). Although the interaction term indicated heterogeneous temporal patterns, these adjusted findings should be interpreted as associative in the context of biomarker-driven treatment selection and not as evidence of comparative superiority of any biologic class. Discontinuation occurred in 15/87 (17.2%), with switching most commonly due to inadequate control. Conclusions: Real-world severe asthma patients demonstrate heterogeneous phenotypes and spirometric trajectories on biologics. Integrating biomarkers with longitudinal lung function monitoring, including small-airway spirometric surrogates, supports individualized management. Full article

Using Global Burden of Disease 2021 modeled estimates, we assessed the burden, temporal trends, and inequalities of pediatric all-form tuberculosis (TB) and multidrug-resistant TB (MDR-TB) from 1990 to 2021 across 204 countries and territories. Compared with all-form TB, pediatric MDR-TB showed a distinct and less favorable estimated trajectory. Although GBD-based estimates suggested overall declines in pediatric all-form TB incidence and mortality, the MDR-to-all-form ratio increased worldwide for both incidence and mortality, suggesting a growing proportional contribution of MDR-TB within the estimated pediatric TB burden. In 2021, pediatric MDR-TB remained concentrated in low- and low–middle-SDI settings, where modeled socioeconomic inequalities appeared to become more pronounced over time. Mortality relative to incidence was highest among children aged under 5 years, with particularly elevated and imprecise mortality-to-incidence ratios for MDR-TB. Sex disparities also evolved differently by disease type: they generally narrowed for all-form TB but were more heterogeneous and in some settings widened for MDR-TB. These GBD-based findings suggest that progress in overall pediatric TB control may not have translated evenly to drug-resistant disease and highlight the need for pediatric TB strategies that explicitly address drug resistance, early childhood vulnerability, and inequitable access to diagnosis and treatment. Due to the sparsity of global pediatric data, no independent external validation was performed; findings are based on internal sensitivity analyses of GBD estimates. Full article

Initial value problems of the third order featuring explicit dependence on velocity, denoted as $y^{‴}=f(x,y,y^{\prime })$, emerge regularly across applications such as electromechanical networks, structural mechanics, and robotic trajectory control. Despite their practical prevalence, these differential equations remain insufficiently addressed by standard numerical integration techniques. Orthodox Runge–Kutta–Nyström (RKN) schemes are fundamentally formulated for differential equations lacking the first derivative, specifically $y^{‴}=f(x,y)$. Due to this algorithmic constraint, researchers frequently resort to computationally demanding first-order system reductions or rely upon standard Runge–Kutta methods. The present study resolves this methodological gap by defining an explicit s-stage integration architecture that natively incorporates the first derivative within the internal stage evaluations. Such structural modifications require the deployment of a supplementary coefficient matrix, denoted as D, to formulate the corresponding order theory. The complete set of algebraic order conditions is systematically established up to the seventh order, accompanied by a generic mathematical framework for generating schemes of arbitrary order. Based on this analytical foundation, an embedded $6(4)$ method is constructed. This specific pair achieves strict error tolerances utilizing merely six function evaluations per integration step, representing a substantial operational reduction compared to the eight computations strictly required by equivalent Runge–Kutta pairs. Direct numerical integration of the native third-order system prevents the dimensionality increase from reducing to first-order systems. Performance validation of the numerical solver involves two representative physical benchmarks: a coupled robotic appendage subjected to platform excitation and an electromechanical actuator array regulated by transient control inputs. Both dynamical systems exhibit severe velocity-dependent dissipation mechanisms and nonlinear external forcing. Quantitative numerical evaluations confirm that the constructed 6(4) pair yields higher precision and demands less computational expenditure than prevailing RK and RKN integrators. The analytical and empirical findings establish that derivative-capable Nyström integration algorithms furnish mathematically rigorous and computationally efficient numerical solutions for velocity-coupled third-order dynamics. Full article

With the continuous expansion of unmanned aerial vehicle (UAV) applications, generating near-optimal paths and achieving effective obstacle avoidance in complex environments remain highly challenging tasks. To address the problems of multi-objective path planning and obstacle detection for UAV flight missions in orchard environments, this paper proposes a novel hybrid algorithmic framework named NeuroJPS-A. The main scientific contribution is the synergistic integration of neural combinatorial optimization, 3D-JPS, and adaptive APF, enabling task-aware obstacle avoidance and closed-loop trajectory adjustment. This method introduces neural combinatorial optimization from the TSP into the 3D-JPS algorithm, optimizing the search mechanism of the traditional JPS and further shortening the UAV’s globally planned path length. In addition, this study integrates the proposed algorithm with the APF to solve the local dynamic obstacle avoidance problem. Quantitative results show that NeuroJPS-A reduces path length by 10% and the number of turns by 47.8% in 2D, and achieves a 24.9% shorter path and 22% of A*’s computation time in 3D. To verify the performance of the proposed method, comprehensive simulation experiments were conducted. The experimental results demonstrate that the NeuroJPS-A algorithm enables UAVs to quickly and effectively generate optimal planned routes, ensuring safe navigation in complex orchard environments and preventing collisions during flight missions. Full article

Planar linkage mechanisms remain a cornerstone of motion generation and trajectory control, yet the geometric tools that desRcribe their instantaneous behavior, namely centrodes and Bresse’s circles, have been developed almost exclusively for mechanisms with entirely revolute joints, where a sliding pair fundamentally alters the velocity and acceleration fields and disrupts the symmetries on which classical curvature theory relies. This paper presents a comprehensive higher-order kinematic analysis of a planar six-link, single-degree-of-freedom mechanism in which a slider-crank stage and a rocker stage are coupled through a shared prismatic joint that acts simultaneously as output and input. Using vector algebra and a matrix-based loop-closure formulation, the position, velocity, and acceleration analyses are derived in closed form, yielding angular velocity ratios, the instantaneous centers of rotation and acceleration of both coupler links, and their inflection and stationarity circles. The analysis reveals a distinctive geometric constraint on the slider-side coupler’s instantaneous center, a decoupling of the curvature loci of the two couplers, and degenerate configurations, linked to coupler instantaneous-stop and rocker dead-point conditions, that arise at joint-invariant crank angles. Implemented as a computational algorithm and demonstrated on a carton flap-closing mechanism and cross-validated against independent multibody simulation, the framework confirms favorable transmission and dead-point clearance behavior, extending curvature-theory tools to linkages containing sliding pairs. Full article

Rotary steerable drilling technology is fundamentally aimed at achieving precise wellbore trajectory control. As a representative directional tool, a dynamic push-the-bit RSS generates steering force during rotary drilling through the interaction between its extendable steering pads and the borehole wall, and it is distinguished from static push-the-bit RSS by the rotational friction that develops at the pad–wall interface. To further clarify the influence of friction on the resultant steering force and the build-up rate, this study develops a steering-force optimization model that explicitly incorporates tangential friction, validates the model, and then conducts numerical simulations to examine how PDC bit design parameters and formation properties affect the build-up rate. The results indicate that the friction-aware optimization model can achieve a higher build-up rate. Quantitatively, relative to the friction-free allocation model that is commonly used as the baseline in push-the-bit BUR prediction, the friction-aware formulation increases the final lateral displacement from approximately 28.4 to 30.6 mm in the analytical comparison (+7.7%) and from approximately 24.3 to 26.9 mm in the full-scale finite-element comparison (+10.7%) over the same steering-force action time. In soft formations with a low internal friction angle, a bit design combining a moderate gauge-protection dimension, an appropriate inner cone angle, and a large crown radius can effectively enhance lateral cutting and steering-force transmission, thereby improving build capability and trajectory stability. These findings provide a theoretical basis for improving build-rate efficiency in push-the-bit rotary steerable drilling systems. Full article

In increasingly complex navigation environments, maritime traffic supervision needs to look beyond the instantaneous collision risk of individual-ship pairs. A multi-ship scene may become difficult to monitor because of vessel aggregation, spatial compression, encounter urgency, and inconsistent motion states. To support proactive Vessel Traffic Services (VTS), this study proposes a prediction-driven framework for assessing multi-ship traffic pressure by combining AIS-based short-term motion prediction with a Spatio-Temporal Encounter Traffic Pressure Index (ST-TPI). In the proposed framework, cleaned and resampled AIS trajectories are used to train an LSTM model for short-term vessel motion prediction. The predicted vessel states are then synchronized into future multi-ship traffic snapshots over a 30 min horizon, and ST-TPI is used to evaluate traffic pressure at the ship-pair, individual-ship, regional, and scene levels. Different from conventional collision-risk or traffic-complexity methods, the proposed framework focuses on how future traffic pressure forms, changes, and is transferred among vessels and vessel pairs. The method was tested using five typical multi-ship scenarios and a real-waterway case in the western precautionary area of the Laotieshan Channel. The prediction results showed stable short-term forecasting performance with low meter-level position errors under the observation-updated rolling evaluation, providing a basis for future multi-ship snapshot generation. The typical scenarios revealed different pressure-evolution patterns, including low-pressure persistence, temporary compression and release, delayed crossing pressure, complex interaction release, and High-level pressure formation. The real-waterway case further showed low and Low-medium pressure fluctuations, local pressure peaks, pressure release, and pressure-source transfer under practical AIS conditions. Prediction-error perturbation analysis indicated that the main high-pressure vessel pairs and pressure-level interpretations remained stable under tested position perturbations. Consistency analysis further showed that ST-TPI scene pressure was significantly correlated with conventional CRI-based encounter-risk indicators. These results indicate that the proposed framework can provide interpretable information on future pressure-evolution and dominant pressure sources, supporting proactive monitoring, early warning, and traffic organization in complex waterways, and contributing to a safer maritime traffic environment. Full article

Modeling a user’s evolving goals, values, and affect over time is central to perspective-aware AI, yet progress is bottlenecked by the lack of longitudinal data with ground-truth labels for the latent identity state. We introduce PAiNT (Perspective-Aware AI Identity and Narrative Toolkit), a generative framework that simulates long-horizon persona trajectories and emits corresponding multimodal artifacts with ontology-aligned labels of the latent identity state that produced them. PAiNT decouples identity dynamics from artifact generation via a typed Persona Matrix and Situation Graph, coordinated through a multi-agent loop with validation-gated transitions and bounded-window history conditioning. Across four personality archetypes, four backbone LLMs, and three architectural ablations, evaluated with a nine-metric suite calibrated on published longitudinal data, we find that (i) persona initialization produces a durable identity signal that persists above stochastic event noise; (ii) multi-agent orchestration and history conditioning govern distinct quality dimensions, with removal of either causing different failure modes; and (iii) a coherence frontier constrains the trade-off between temporal resolution and horizon, with substantial penalties at daily granularity. We release PAiNT and PAi-Bench, a human-validated benchmark of 1200 labeled multimodal artifacts. Full article