Search Results (92)

The identification of key ecological patches is fundamental to the construction of ecological security patterns. The current Probability of Connectivity (PC) index and its associated delta (dPC) rely on least-cost path distances and assume that species disperse along a single optimal path, which may underestimate the ecological value of patches that act as multipath hubs. This study proposed computing the PC and dPC indices based on effective resistance from circuit theory. Using NetworkX, we obtained global effective resistance from adjacent-patch resistance, replaced least-cost path distance as the distance metric, and recalibrated the dispersal constant. To evaluate the method, we used a tree network (7 nodes, 6 edges), a cyclic network (8 nodes, 10 edges), and 640 random networks. In the tree network, the optimized method yielded the same ranking as the traditional method, confirming the computational consistency expected in a tree. In the cyclic network, the two methods diverged: Patch 8, whose connector was essentially zero under the conventional method, rose to the largest connector value (15.42%) and climbed from fifth to third place. Across the random networks, the two methods agreed in tree networks and diverged increasingly with topological redundancy. The effective-resistance method therefore makes visible a connector role that the single-path framework cannot detect and should be used to complement, rather than replace, the least-cost method; whether the identified patches are ecologically more critical, however, remains to be confirmed by empirical dispersal data. Full article

Artificial intelligence (AI)-powered personalized recommendation systems can enhance relevance and convenience, but their reliance on extensive personal data processing may also heighten users’ perceptions of privacy intrusion. This study examines the relationship between perceived privacy invasion and consumer adoption willingness, with psychological resistance as a statistical mediator and perceived algorithm transparency (PAT) as a moderating condition. Drawing on the privacy-cost perspective of privacy calculus theory and psychological reactance theory, this study focuses on the privacy-related inhibition pathway of AI recommendation adoption. Survey data were collected from 518 Chinese adult Internet users with recent experience of AI-powered personalized recommendation services and analyzed using partial least squares structural equation modeling (PLS-SEM). The results show that perceived privacy invasion was negatively associated with consumer adoption willingness (β = −0.357) and positively associated with psychological resistance (β = 0.279), whereas psychological resistance was negatively associated with adoption willingness (β = −0.286). Psychological resistance statistically partially mediated the association between perceived privacy invasion and adoption willingness (indirect effect = −0.080). The interaction between perceived privacy invasion and PAT was negative and statistically significant (β = −0.162), indicating that the positive privacy invasion–resistance association was less pronounced at higher levels of PAT. Supplementary conditional process analysis further showed that the negative indirect association through psychological resistance weakened as PAT increased, with a significant index of moderated mediation (index = 0.0464, 95% bootstrap CI [0.0191, 0.0816]). Additional analyses using common-method-bias diagnostics, PLSpredict and CVPAT, and a targeted Gaussian copula robustness check for PAT provided complementary evidence regarding measurement robustness, predictive relevance, and the robustness of PAT-related estimates. Age-based multigroup analysis revealed no statistically significant age-based heterogeneity in the structural path coefficients across the 18–29, 30–49, and 50+ age groups. This study contributes to the literature by identifying psychological resistance as a motivational pathway associated with privacy-related adoption responses and by conceptualizing PAT as a user-level perceptual boundary condition in AI-powered personalized recommendation contexts. Full article

Engine noise is a predominant source of noise in the cabin of internal combustion engine vehicles and new energy hybrid vehicles. The conventional multi-channel active noise control (ANC) system, based on the adaptive notch filtered-X least mean square algorithm, is commonly employed to mitigate such multi-tonal noise. However, the computational efficiency and convergence performance of this system may be significantly hindered by the large estimated secondary path length and the frequency-dependent convergence behavior. To overcome these limitations, this paper proposes a computationally efficient and fast-converging multi-channel ANC system by incorporating a local secondary path (LSP) equalization method. The proposed method enhances the convergence speed by equalizing the magnitude responses of estimated secondary paths and reduces the computational complexity through an improved LSP modeling approach. Accordingly, a set of low-order equalized LSP models with normalized amplitude-frequency responses is generated and employed for reference filtering. A computational complexity analysis comparing the conventional system, a recent cost-effective system, and the proposed system is presented. Numerical simulations are conducted to evaluate the convergence speed and noise attenuation performance of these three systems. Additionally, real vehicle experiments are performed using a digital signal processing controller. The results demonstrate that the proposed multi-channel ANC system achieves a superior noise reduction effect. Under accelerated conditions, the average attenuation of the second-order noise component at the four error microphones is measured at 4.4 dB(A), 6.2 dB(A), 13.4 dB(A), and 10.0 dB(A). These findings confirm the practical effectiveness of the proposed multi-channel ANC system. Full article

Single-path transmission optimization is a core task for resource scheduling and operation of complex networks, which requires coordinated optimization of transmission cost and flow. Classical algorithms bear heavy computational loads in high-dimensional decision spaces as networks grow. This paper constructs a hybrid quantum model integrating quantum approximate optimization algorithm (QAOA) and cubic spline interpolation. Paths, discrete flows, and trade-off coefficients are unified within a quadratic unconstrained binary optimization (QUBO) model. Least-squares fitting converts native parameters into QUBO coefficients, whose fitting errors are measured to verify robustness and penalty sensitivity, and auxiliary variables eliminate high-order terms to exponentially cut qubit consumption. QAOA narrows the feasible range via global coarse search, and cubic spline interpolation further yields precise continuous flow values. Powered by quantum superposition for parallel full-space exploration, the framework avoids repeated modeling for separate bias coefficients. Mixed integer programming (MIP) and genetic algorithm (GA) are adopted as comparative benchmarks. For the small-scale network instance, the relative error between the proposed method and the global optimum solved by MIP is less than 1%. For the large-scale case, the overall error of our approach remains within an acceptable range even when discrepancies exist between results yielded by classical algorithms. Full article

Educational platforms increasingly personalize which AI learning-path features (adaptive homework, learner choice) each student receives. The natural correlational baseline ranks students by predicted performance—deliver the feature to those expected to do well—a heuristic that need not identify who actually benefits. We formalize feature recommendation as a causal targeting-policy problem: rank students by the estimated conditional average treatment effect (CATE) of a feature and recommend to the top of the ranking. We prove three results: (i) causal top-CATE targeting maximizes policy value at any budget and weakly dominates predictive (outcome-based) targeting, strictly when the two rankings disagree; (ii) a split-sample doubly robust evaluation of targeting quality is leakage-free (null-exact in finite samples), whereas the naive in-sample version is optimistically biased; and (iii) greedily targeting by CATE traces the optimal cost–benefit (Qini) frontier, with the deployment rule “recommend when $\widehat{\tau }>0$.” We validate the method on 17 randomized embedded experiments from the ASSISTments platform. Because the 40 held-out splits re-partition the same students, we do not treat them as independent replicates: we calibrate every headline comparison against a within-experiment permutation null and an experiment-clustered bootstrap. Under that calibrated inference, causal targeting outperforms predictive targeting for adaptive homework at every budget (permutation $p\le 0.005$, the resolution floor of 200 replicates; Holm-corrected $p\le 0.040$), while for learner choice the same contrast is directionally consistent but not statistically significant (permutation $p=0.23$–$0.38$; clustered $p=0.42$). The direction is stable in both families: no leave-one-experiment-out refit reverses its sign. Predictive targeting is nonetheless the one rule that is reliably worse than the alternatives, because it recommends the feature to high-performing students who benefit least—realized benefit falls monotonically across predicted-performance deciles (from $+0.087$ in the lowest to $-0.030$ in the highest). Against a fuller baseline suite, causal (CATE) targeting does not beat random, a simple risk-based rule (target low performers), or treating everyone. Indeed, the estimated benefit ranking is close to noise—split-half rank agreement is $\rho \approx 0.002$–$0.008$ and its calibration slope is $0.018$, far below the ideal of 1—so the gain over predictive targeting comes from avoiding an actively harmful ordering rather than from recovering individual benefit. A fairness analysis shows why this matters: predictive targeting is regressive, concentrating feature access on high-ability students, whereas causal and risk-based targeting reverse that gradient in this corpus; no policy differentiates by neighborhood opportunity zone. Group-conditional policy values, however, are not individually distinguishable from zero once dependence across students and experiments is accounted for; what survives resampling is the allocation itself—predictive targeting directs $0.33$ fewer of its recommendations to low-ability than to high-ability students (95% CI $[-0.46,-0.01]$, experiment-clustered)—so we frame the fairness result as improved access, not established equity gains. The actionable finding is therefore narrow and specific: outcome-based targeting systematically mis-allocates learning-path features and should be replaced by some benefit-aware rule; whether that rule needs to be a learned CATE model, rather than a simple risk-based heuristic, is not established by this corpus. Full article

Background/Objectives: Community psychiatric care aims to support recovery, autonomy, and lower reliance on inpatient services, but co-occurring substance use may reduce these gains. This study examined associations between psychiatric care pathway, non-tobacco substance-use severity, recovery-related patient-reported outcomes, clinical instability, and direct mental health cost in adults with severe mental illness in Romania. Methods: This cross-sectional observational study included 128 adults receiving psychiatric hospital care, long-term residential care, or protected community housing within one regional psychiatric care network. Participants had at least 12 months of uninterrupted care in their current setting. Substance use was assessed with the World Health Organization Alcohol, Smoking and Substance Involvement Screening Test (ASSIST), excluding tobacco from the analytic severity score. Outcomes included SF-36 physical and mental scores, brief service-experience ratings of autonomy and perceived coercion, emergency department visits, relapse, readmission, inpatient days, outpatient visits, and annual direct mental health cost. Results: Any non-tobacco substance use was most common in hospital care, intermediate in residential care, and least common in community housing. Mental quality of life was lowest among hospital participants with substance use and highest among community participants without substance use. In multivariable models, ASSIST-derived severity was independently associated with a lower SF-36 mental score and a higher direct cost, while autonomy was independently associated with better mental quality of life and lower direct cost. The cost outcome was modeled primarily with a gamma generalized linear model with a log link because costs were positive and right-skewed. Because pathway allocation was clinically determined rather than randomized, and because autonomy, perceived coercion, and mental quality of life were all self-reported at a single assessment, every multivariable and path estimate is exploratory. Conclusions: Community psychiatric pathways were associated with more favorable recovery indicators, but non-tobacco substance use remained an important marker of poorer outcomes and greater service burden. The findings support addiction-sensitive community psychiatric care and routine monitoring of autonomy and perceived coercion as service-quality indicators. Because the design was cross-sectional and the care pathways were non-equivalent at baseline, these results are hypothesis-generating and do not establish that care pathway, substance use, or autonomy exerts a causal effect on any outcome measured. Full article

Visitor access in linear defensive built heritage landscapes needs to balance historical interpretation, route accessibility, and current protection constraints. This issue is evident in the Laoniuwan section of the Ming Great Wall in Shanxi Province, China, where heritage nodes lie within a scenic area but access infrastructure and interpretive route organization remain underdeveloped. This study proposes a conservation-oriented trail planning method for Great Wall scenic areas by combining MaxEnt-based historical defensive landscape modeling with GIS-based contemporary resistance analysis. MaxEnt was used to model terrain- and visibility-related suitability for defensive node locations, including watchtowers, beacon towers, forts, and mamian. The output was treated as a historical landscape interpretation layer, not as a prediction of tourist movement or final route location. Contemporary resistance was built from terrain ruggedness, road and village disturbance, the Great Wall protection buffer, permanent basic farmland, and the ecological conservation redline. Three historical–contemporary weighting scenarios were compared using least-cost path analysis. A Conservation–Interpretation Balance Index (CIBI) was then used to support scenario selection. The 4:6 scenario achieved the highest CIBI score (0.606). It maintained connectivity among defensive nodes while reducing ecological and farmland conflicts. The recommended loop trail was further translated into a four-tier conservation management zoning scheme. The results offer a spatial decision support approach for organizing fragmented built heritage resources into controlled-access interpretation routes with reduced disturbance to heritage fabric and protected land. Full article

Active noise control (ANC) in real-world acoustic environments frequently faces impulsive and heavy-tailed noise disturbances, which degrade the performance significantly and lead to slow convergence. This work proposes a dynamically adaptive distribution-aware risk-sensitive filtered-x normalized least mean square (DA-RS-FxNLMS) method for efficient ANC under a non-Gaussian and impulsive scenario. The proposed ANC framework employs a correntropy-based risk-sensitive exponential cost function, which incorporates higher-order statistics and adapts to the error distribution. Further, an adaptive and dynamically adjusted kernel width mechanism tracks the time-varying noise characteristics. The normalized filtered-x structure provides stability under secondary path uncertainty. Simulation is carried out by applying α-stable noise to create an impulsive noise environment, which is produced by the Chambers-Mallows-Stuck method. The outcomes of the proposed method are compared with the baseline methods, showing that the proposed method achieves a noise reduction of 7.02 dB, a significant 40% faster convergence, and improved robustness under strong impulsive noise conditions with α = 1.2. The outcome confirms that the proposed method efficiently delivers a promising solution for the ANC system. To the best of our knowledge, for the first time, a unified ANC framework integrates distribution-aware, risk-sensitive learning, adaptive correntropy kernel estimation, and filtered-x normalization for non-Gaussian acoustic environments. Full article

Rapid urbanization and increasing landscape fragmentation pose significant threats to ecological connectivity, creating a need for integrative decision support approaches in sustainable spatial planning. This study presents a systematic review of ecological network optimization studies published between 2005 and 2025, following the PRISMA protocol. A total of 78 peer-reviewed studies were analyzed to identify methodological trends, recurring limitations, and research gaps in the assessment of structural and functional connectivity. Based on the gaps identified through the systematic review, this study proposes a conceptual Sustainable Spatial Decision Support System (S-SDSS) framework that integrates Morphological Spatial Pattern Analysis (MSPA), Multi-Criteria Evaluation (MCE/AHP), Minimum Cumulative Resistance (MCR), Least-Cost Path (LCP), and Gravity Modeling (GM) within a unified analytical structure. The review findings reveal a clear shift from single-method applications toward integrated multi-model approaches that better represent ecological processes and improve corridor prioritization. The proposed framework synthesizes the complementary strengths of these established methods to support evidence-based ecological network planning. The framework operates as a hybrid structure that combines a sequential analytical workflow with a unified typological classification system, generating Hybrid Ecological Typologies (T1–T5) as planning-oriented outputs that cannot be produced by any individual method alone. The proposed S-SDSS offers a transferable and policy-relevant conceptual basis for ecological network optimization, supporting green infrastructure planning, biodiversity conservation, and long-term landscape resilience across multiple spatial scales. Full article

Aiming at the divergence problem of standalone strapdown inertial navigation system (SINS) affected by initial errors, sensor drift, and cumulative errors in complex marine environments, this paper proposes a long-endurance autonomous navigation scheme without external measurement to suppress Schuler oscillations and improve dynamic navigation performance. First, based on the dynamic error model of SINS, the characteristics of Schuler oscillation are analyzed, and a multi-path delayed-solution strategy is developed. By sequentially delaying the SINS calculation loop and performing arithmetic averaging, periodic oscillation errors are automatically canceled. Second, a chi-square test is constructed to assess sea-state complexity in real time, and a robust adaptive Kalman filter is designed with adaptive filter selection to further improve estimation accuracy under dynamic conditions. Finally, the proposed method is systematically validated through static simulations, dynamic simulations, and full-scale ship experiments. Results show that the delayed-solution strategy significantly mitigates Schuler oscillation in attitude and velocity under static conditions. In dynamic simulations and ship trials, compared with pure SINS, single delayed-calculation, and conventional Kalman filter, the proposed approach achieves superior suppression of attitude, velocity, and position errors, with core navigation error indices reduced by at least one order of magnitude. These findings demonstrate that the Schuler period characteristic of inertial navigation errors can be effectively exploited in dynamic conditions, and the coupling of multi-path delayed calculation with robust adaptive filtering enables substantial improvements in autonomous navigation accuracy without external measurement. The proposed method expands the theoretical and engineering framework of autonomous navigation at no additional hardware cost, providing a new technical route for the practical deployment of long-duration SINS. Full article

CO₂ pipeline transportation is a core link in the CCUS (Carbon Capture, Utilization, and Storage Technology) industry. Ensuring the flow safety of CO₂ pipelines under transient conditions is currently a key and challenging issue in industry research. This paper focuses on the phase migration and safety control during the shutdown process of supercritical carbon dioxide pipelines. Taking a supercritical carbon dioxide transportation pipeline in Xinjiang Oilfield, China, as the research object, a hydro-thermal coupling model of the pipeline is established to simulate the pipeline and elucidate the coordinated variation patterns of temperature, pressure, density, and phase state. It was found that there were significant differences in the migration paths of the CO₂ phase at different positions. The accuracy of the simulation results was verified through the self-built high-pressure visual reactor experimental system, and the influences of the initial temperature, initial pressure, and ambient temperature before pipeline shutdown on the slope of the phase migration path were explored. The phase migration line slope prediction model was established by using the least squares method and ridge regression method, the process boundary ranges and allowable shutdown time ranges for pipeline safety shutdowns in both summer and winter were further established. The research results show that when the pipeline operates under the low-pressure and high-temperature boundary, the CO₂ in the pipeline vaporizes earlier from the starting point after the pipeline is shut down, and the safe shutdown time of the pipeline is shorter. There is a clear safety operation window in summer, while vaporization risks are widespread in winter. The phase migration path prediction formula and the safety zone division method proposed in this paper provide a theoretical basis and engineering guidance for the safe shutdown control of supercritical carbon dioxide pipelines, which can help reduce operational risks and lower maintenance costs. Full article

The Frontal Area Index (FAI) is a commonly used, cost-effective preliminary screening tool for identifying the Least Cost Path (LCP) of urban ventilated corridors and mitigating the Urban Heat Island (UHI) effect, particularly in situations where data and budget availability are limited. Although its theoretical basis and simulation studies have been extensively examined, empirical validation through field measurements remains limited. This study assesses the FAI method’s applicability in two representative U.S. Midwest cities—St. Louis and Chicago—and proposes key modifications based on field-measurement validation. FAI simulations were conducted to identify optimal ventilation corridors, and the results were subsequently compared with in situ field measurements. Our findings indicated a strong correlation between FAI predictions and field data in St. Louis. In contrast, significant discrepancies were observed in Chicago, where simulated ventilation performance did not align with measured conditions, revealing the standard method’s limitations in complex urban topographies. To address these shortcomings, this study proposes four modifications to enhance the model’s accuracy for U.S. Midwest cities: (1) adjusting the model for varying urban morphologies, (2) limiting the calculation scope, (3) implementing a distinct approach for riverine areas, and (4) adopting a plot-based division for areas with large-scale buildings. This research verifies and refines the FAI method, creating a more reliable tool for diverse urban contexts. The optimized approach provides robust support for wind environment analysis, ventilation corridor planning, and UHI mitigation strategies. Full article

Satellite and UAV-based remote sensing have been widely used for agricultural systems monitoring jointly. How to quantitatively optimize the efficiency of integrating these two techniques remains largely understudied. To address this gap, we, for the first time, formulate the configuration of satellite–UAV integrated system as a spatial sampling optimization problem and propose an SSO (spatial sampling optimization) model that jointly optimizes the spatial locations and flight paths of UAV sampling within the satellite monitoring area. The SSO model enables maximizing the accuracy of monitoring under a given cost constraint. We obtained comprehensive data in rapeseed fields and conducted experiments based on the SSO model. We compared the sampling effectiveness of the SSO model with that of simple random sampling, systematic sampling, equal stratified sampling and Neyman stratified sampling. The results showed that the SSO-optimized plan had the highest sampling efficiency, which was at least 38.7% higher than that of the best-performing conventional method (Neyman stratified sampling). Under the same cost constraint, the SSO-optimized sampling scheme can have 11.1% more sampling points than the conventional sampling scheme. The Elite Genetic Algorithm (EGA) performed well in solving the SSO model. The error of the SSO-optimized scheme was reduced by 27.3% and the sampling distance was reduced by 7000 to 8000 m on average. In conclusion, the proposed SSO model helps to optimize the configuration of satellite–UAV integrated remote sensing, thereby improving the cost-effectiveness of agricultural monitoring systems. We call for considering cost constraints and increasing efficiency in agricultural system monitoring and government censuses in the future. Full article

Low-cost and real-time remote sensing of moving targets is increasingly required in civilian applications. Micro unmanned aerial vehicles (UAVs) provide a promising platform for such missions because of their small size and flexible deployment, but they are constrained by payload capacity and energy budget. Consequently, they typically carry lightweight monocular cameras only. These cameras cannot directly measure distance and suffer from scale ambiguity, which makes accurate geolocation difficult. This paper tackles geolocation and short-term trajectory prediction of moving targets over uneven terrain using bearing-only measurements from a monocular camera. We present a two-stage estimation framework in which a pseudo-linear Kalman filter (PLKF) provides real-time state estimates, while a sliding-window nonlinear least-squares (NLS) back end refines them. Future target positions are obtained by extrapolating the estimated trajectory. To improve localization accuracy, we analyze the relationship between the UAV path and the Cramér–Rao lower bound (CRLB) using the Fisher Information Matrix (FIM) and derive an observability-enhanced trajectory planning method. Real-flight experiments validate the framework, showing that accurate geolocation can be achieved in real time using only low-cost monocular bearing measurements. Full article

Over the past decade, the rapid development of geospatial tools has significantly expanded the scope of archaeobotanical research, enabling unprecedented insights into ancient plant domestication, agricultural practices, and human-environment interactions. Within the Chinese context, where rich archaeobotanical records intersect with complex socio-ecological histories, GIS-driven approaches have revealed nuanced patterns of crop dispersal, settlement dynamics, and landscape modification. However, despite these advances, current applications remain largely exploratory, constrained by fragmented datasets and underutilized spatial-statistical methods. This paper argues that a more robust integration of large-scale archaeobotanical datasets with advanced ArcGIS functionalities—such as kernel density estimation, least-cost path analysis, and predictive modelling—is essential to address persistent gaps in the field. By synthesizing case studies from key Chinese Neolithic and Bronze Age sites, we demonstrate how spatial analytics can elucidate (1) spatiotemporal trends in plant use, (2) anthropogenic impacts on vegetation, and (3) the feedback loops between subsistence strategies and landscape evolution. Furthermore, we highlight the challenges of data standardization, scale dependency, and interdisciplinary collaboration in archaeobotanical ArcGIS. Ultimately, this study underscores the imperative for methodological harmonization and computational innovation to unravel the intricate relationships between ancient societies, agroecological systems, and long-term environmental change. Full article