Search Results (25,410)

Energy Geographies has a unique position at the intersection of geospatial and social science, and it now faces a defining methodological development with the rapid rise in Geospatial Artificial Intelligence (GeoAI). This paper examines where GeoAI has and has not been applied within energy research through two bibliometric analyses using the Dimensions database. The first establishes an updated picture of energy geographies scholarship from 2020 to 2026, mapping the field’s current priorities and geographic distribution as a baseline for evaluating GeoAI’s role. The second conducts a bibliometric analysis of GeoAI-specific energy publications from 2020 to 2026, which reveals significant GeoAI Application Gaps: a heavy concentration in energy extraction and production research and in renewable energy siting and grid optimization, while energy transition, justice, and the energy problems of underrepresented regions remain substantially underserved. GeoAI energy research is also more geographically concentrated than the broader field, dominated by a small number of countries, raising questions about the applicability of these tools to the energy challenges facing the rest of the world. We argue that this gap reflects a pattern of problem selection as much as technological limitation, and that energy geographers are well positioned to redirect the development of this new field. We outline three directions for future research: developing Explainable GeoAI to ensure transparency and accountability, expanding geographic coverage to address data biases that favor a small set of well-resourced countries, and confronting the computational energy paradox of carbon-intensive AI applied to sustainability-oriented research. Full article

Credit card fraud detection is a typical rare-event classification problem because fraudulent transactions usually account for only a very small proportion of all transactions. Conventional evaluation on balanced or resampled test data may lead to overly optimistic performance estimates. To address this issue, this study proposes a leakage-safe credit card fraud detection framework integrating Random Forest Gini impurity-based feature selection, resampling strategy evaluation, and Genetic Algorithm (GA)-based hyperparameter optimization. The framework was evaluated on the public European credit card fraud dataset containing 284,807 transactions, of which only 492 were fraudulent. The original dataset was first divided into a stratified training set and an untouched original-distribution test set. Feature selection, standardization, resampling, GA optimization, and threshold tuning were performed only on the training data or training folds. The final test set contained 85,443 transactions, including 148 fraudulent transactions, and was used only once for final evaluation. Experimental results show that GA-XGBoost achieved the best overall balance among the optimized models, with a PR-AUC of 0.798, ROC-AUC of 0.967, MCC of 0.814, balanced accuracy of 0.865, fraud-class precision of 0.908, fraud-class recall of 0.730, and fraud-class F1-score of 0.809. Compared with baseline XGBoost, GA-XGBoost improved PR-AUC from 0.741 to 0.798, MCC from 0.766 to 0.814, and fraud-class F1-score from 0.764 to 0.809, while reducing false positives from 22 to 11 and false negatives from 43 to 40. The ablation results further indicate that resampling strategies are not universally beneficial and should be evaluated under the original test distribution. These findings suggest that leakage-safe evaluation and fraud-class-oriented metrics provide a more reliable basis for practical credit card fraud detection. Full article

Point cloud images from LiDAR often suffer distortion due to platform vibration. This paper proposes a LiDAR-INS (Inertial Navigation System) integrated navigation method to address the challenge of low positioning accuracy in complex environments. To solve problems like GPS signal denial and vibration interference, we present a method for achieving centimeter-level positioning. This method uses INS attitude information to compensate for LiDAR vibration errors. A vibration error model is established to quantify the impact of vibration on point cloud distortion. High-frequency INS attitude data is then used to correct the LiDAR point cloud distortion caused by platform vibration. Leveraging the non-repetitive scanning pattern of prism-based LiDAR, a joint compensation strategy for vibration error and angular error is proposed. This strategy enhances both point cloud density and positioning robustness. State and observation equations for the LiDAR-INS integrated navigation system are derived. A Kalman filter is employed to achieve optimal data fusion between the LiDAR and INS. Finally, field experiments were conducted in both laboratory settings and a typical application scenario: a tunnel construction site. These experiments validate the effectiveness of the proposed method. Full article

We develop a stochastic Stackelberg model of a subscription market with cancellation costs. A representative consumer chooses when to subscribe to and cancel a service as the utility derived from the subscription evolves according to a diffusion process, while the firm selects the subscription fee and cancellation cost to maximize its expected payoff. The consumer’s problem is equivalent to the classical real-options model of entry and exit under uncertainty with adjustment costs and exhibits a two-threshold policy with an inaction band and hysteresis. Unlike the standard formulation, in which the optimal thresholds are characterized implicitly through a system of nonlinear equations, we derive an explicit parametric solution in closed form. This solution reduces the firm’s optimization problem to a two-dimensional unconstrained problem and yields a detailed characterization of the optimal pricing policy. We show that the firm’s strategy exhibits three qualitatively distinct regimes depending on the initial utility level. For small utility levels, the optimal cancellation cost is zero. In an intermediate regime, the firm’s optimal policy induces the consumer to set the entry threshold equal to the initial utility level, resulting in immediate subscription. For sufficiently large utility levels, the firm induces permanent lock-in by setting a high cancellation cost and a low subscription fee: the consumer subscribes immediately and never subsequently unsubscribes. The transition between the latter two regimes is discontinuous and results from competition between two local maxima of the firm’s payoff function. We then extend the model to a heterogeneous population of consumers. The superposition of individual two-threshold subscription strategies generates a Preisach hysteresis operator describing the aggregate dependence of the firm’s revenue on the utility dynamics. The discontinuous regime transition persists under heterogeneity, demonstrating the robustness of the underlying mechanism. The Preisach representation predicts complex history dependence and long-term effects of temporary utility shocks. For a gamma distribution of consumer preferences, the firm’s expected payoff is obtained in closed form in terms of incomplete gamma functions. Full article

Pumped-storage hydropower requires pump-turbines to operate safely and efficiently under off-design conditions, where stall-induced unsteady flows can redistribute hydraulic losses and reduce operational stability. Unlike previous analyses focused mainly on spatial correlations, this study develops a spatiotemporal framework to clarify how hydraulic loss (HL) and vortex evolution (VE) co-vary under different stall states at the valley point of the pump-mode hump region in a low-specific-speed, ultra-high-head pump-turbine. Detached eddy simulations (DESs) were performed for an original-runner scheme (ORI) and an optimized-runner scheme (OPT), with identical stationary components, boundary conditions, and numerical settings. The comparative cases cover four representative flow states: non-stall, fixed stall, rotating stall, and mixed stall. The local hydraulic-loss rate (LHLR) was decomposed into dissipation (DIS) and transport (TRANS) terms, and Liutex-based vorticity decomposition was used to distinguish shear- and rigid-rotation-related vortex quantities. Pearson correlation analysis was then applied in both space and time. The results show that DIS is consistently associated with shear enstrophy Ω_S, whereas the spatiotemporal correlation associated with TRANS and VE parameters exhibits stronger regional and stall-state dependence. These findings provide a quantitative basis for identifying loss-sensitive vortex features and support flow-control and runner-optimization strategies for improving pump-turbine efficiency and stability. Full article

Maintaining regional landscape ecological stability and enhancing rural landscape ecosystem services are critical research priorities. This study selected Xiangxi Autonomous Prefecture (XXAP), a representative mountainous region in Southwestern (SW) China, as the case study area. This study aims to construct a rural landscape ecological risk (RLER) evaluation index system based on five-period remote sensing data (2000–2020), analyze the spatio-temporal characteristics of RLER, and provide a scientific basis for landscape ecological management and rural spatial governance. The results show that the RLERI exhibited a balanced multi-ring development trend, decreasing slightly from 0.295 in 2000 to 0.282 in 2020, suggesting a slight alleviation of overall risk. Medium-risk areas of the RLERI consistently accounted for the largest proportion (over 37%). Notably, the share of high-risk areas remained relatively stable, fluctuating narrowly between 7.98% and 8.73%. Meanwhile, high-risk areas of the landscape disturbance degree (LDD) expanded markedly from 1.87% to 10.28%. Correspondingly, high-risk areas of the landscape fragility degree (LFD) also increased significantly, rising from 0.93% to 2.8%. Spatially, RLER displayed significantly positive spatial autocorrelation, with high–high (H-H) clusters concentrated in the central-southern part and low–low (L-L) clusters distributed in the northern and southern margins, indicating pronounced spatial differentiation. In conclusion, this study provides a transferable framework for ecological risk assessment in mountainous regions. Furthermore, it underscores the importance of optimizing landscape patterns in ecologically fragile areas, strengthening ecological risk management, and mitigating ecological risks in rural settings. Full article

Background: Despite evidence-based guidelines for cardiovascular risk management, many patients fail to achieve therapeutic targets. The relative contribution of medication non-adherence versus suboptimal treatment optimization to poor cardiovascular outcomes remains unclear in real-world primary care settings. The aim of this study was to describe changes in cardiovascular risk factor control following protocolized adherence reinforcement combined with physician-driven treatment optimization in high-risk patients. Methods: This multicenter, real-world longitudinal study included 789 participants with high or very high cardiovascular risk enrolled from primary care settings across 9 Spanish regions between 2023 and 2025. All participants received a protocolized intervention combining adherence reinforcement and physician-driven treatment optimization. This was a single-arm, pre–post study without a concurrent control group; observed changes therefore cannot be attributed to the intervention alone. Of 789 participants screened, all completed the baseline assessment, and 628 (79.6%) completed the 90-day follow-up. A total of 161 participants (20.4%) were lost to follow-up. Primary outcomes included changes in systolic and diastolic blood pressure, lipid parameters (total cholesterol [TC], low-density lipoprotein cholesterol [LDL-c], high-density lipoprotein cholesterol [HDL-c], triglycerides [TG]), glucose, glycated hemoglobin (HbA1c), and body mass index (BMI) from baseline to 90-day follow-up. Changes were assessed using linear mixed models. Results: Among participants with complete paired data (n = 453–615 depending on the outcome), significant improvements were observed in most cardiovascular risk factors (HDL-c and HbA1c did not change significantly). Mean changes (95% confidence interval [CI]) were: systolic blood pressure, −9.24 mmHg (−10.41 to −8.06; p < 0.001); diastolic blood pressure, −4.75 mmHg (−5.49 to −4.01; p < 0.001); LDL-c, −22.29 mg/dL (−25.59 to −19.00; p < 0.001); TC, −23.24 mg/dL (−26.73 to −19.74; p < 0.001); TG, −16.75 mg/dL (−23.03 to −10.46; p < 0.001); fasting plasma glucose, −10.03 mg/dL (−12.61 to −7.46; p < 0.001); and BMI, −0.46 kg/m² (−0.58 to −0.35; p < 0.001). Linear mixed models including all available data (n = 628 at 90-day follow-up) confirmed these findings. No significant interactions were observed between assessment timepoint and sex, age, or overweight/obesity status for most outcomes, except for age-related differences in lipid responses. Conclusions: Protocolized adherence reinforcement combined with physician-driven treatment optimization was associated with clinically meaningful improvements in multiple cardiovascular risk factors in high-risk primary care patients. Given the single-arm pre–post design, the observed improvements are associative and cannot establish causality. Residual uncontrolled risk, particularly in lipid management and among older adults, persisted despite active treatment optimization (treatment was modified in 82.0% of participants), consistent with residual suboptimal treatment intensification even after adherence had been reinforced. These findings suggest that achieving optimal cardiovascular risk factor control requires addressing both medication adherence and treatment intensification, particularly in patients with multimorbidity. Full article

Quantum gate estimation and tomography pipelines routinely combine intrinsically defined likelihoods with priors or regularization terms specified in local Euclidean coordinates. This practice implicitly replaces the Haar reference measure on $SU\left(2\right)$ with Lebesgue measure, specifying a different statistical model rather than a reparametrization of the intended one. We show that omitting the associated chart-volume factor alters the optimization objective itself, modifying its gradient field and stationary-point structure. The gradient discrepancy $\nabla L_G-\nabla L_E=-\nabla log{J}_{exp}$ is nonzero for all $v\ne 0$ so that flat-coordinate surrogate objectives can converge to points that are non-stationary for the corresponding Haar-consistent objective even in regimes where local Gaussian approximations are assumed valid. We prove a formal non-equivalence proposition and validate a leading-order Fisher-information correction analytically and numerically. Large-scale multi-start optimization experiments ($N=11,900$ runs) demonstrate that the discrepancy is regime dependent and most pronounced under moderate-to-strong regularization or limited data. The fix requires a single-line modification to any gradient-based optimizer. These results identify reference-measure selection as an explicit modeling decision with direct consequences for optimization and inference in gate-set tomography, randomized benchmarking, and Bayesian gate estimation on curved parameter manifolds; quantitative validation is restricted to single-qubit systems, though the mechanism extends to any regularized optimization on a curved parameter manifold. Full article

Compared to intuitive fuzzy sets (IFSs) and Pythagorean fuzzy sets, spherical fuzzy sets (SFSs) more accurately capture the fuzziness and uncertainty inherent in complex problems. SFSs have found extensive applications across numerous fields. To address the common shortcomings of insufficient ranking capabilities and unknown attribute weights in existing ranking methods under spherical fuzzy (SF) environments. By integrating the principles of technique for order preference by similarity to an ideal solution (TOPSIS) method into the ranking function design, an innovative spherical fuzzy number (SFN) ranking function is developed that combines the characteristics of a ranking function while retaining the advantages of the TOPSIS method. Secondly, for scenarios where attribute weight information is completely unknown, this paper develops a weigh calculation method based on the newly proposed ranking function combined with the criteria importance through the intercriteria correlation (CRITIC) method. Using the Lagrange multiplier method to construct an optimization model, a specific formula for determining attribute weights is derived. For scenarios with partially known attribute weights, an optimization model is constructed where the optimal solution corresponds to the desired attribute weight values. Finally, two case studies validate the feasibility and effectiveness of the new ranking function for SFNs, providing theoretical support for addressing multi-attribute decision-making (DM) problems in complex uncertain environments. Full article

Deep learning (DL)-based percussion methods in concrete-filled steel-tube (CFST) void detection have gained much attention. However, the detection signal contains a large amount of noise, which affects the accuracy of qualitative and quantitative analyses of the subsequent detection results. To improve the signal-to-noise ratio (SNR) during percussion detection, this study proposes a CFST void detection method using the good point set and vibrational snow ablation optimizer (GVSAO) algorithm and dual-channel parallel convolutional neural networks (CNNs). The proposed method employs the gram angle field (GAF) to transform percussive sound signals into images. It then constructs a dual-channel parallel CNN structure, where the GAF is decomposed into the following two maps: the gram angle sum field (GASF) and the gram angle difference field (GADF). These maps are simultaneously fed into the CNN for training. The outputs from the two channels are concatenated and fused. Finally, the GVSAO algorithm was used for model optimization to improve convergence speed and recognition accuracy. Both the temporal and spatial characteristics of the knocking sound signal are fully preserved, while the interference of different construction noises is effectively avoided. Validation experiments were conducted on CFST specimens with different heights of voids (0, 50, 100, and 150 mm) under different pressure loads. The original sample dataset and the signal-enhanced dataset were obtained by adding background noise with different SNRs. The test results show that the prediction accuracies on the original signal dataset are consistently above 98.74%. Among them, the accuracy achieves 100% at pressure loads of 0 and 50 tons. Additionally, the prediction accuracies on the signal-enhanced dataset are all above 97.2%, indicating that the model maintains a high level of classification performance. This suggests that the model can effectively suppress noise and exhibits excellent robustness. Full article

Ore districts are important targets for deep mineral exploration. As exploration depths increase, exploration strategies are shifting from ore-body identification based mainly on existing metallogenic models toward refined characterization of subsurface structures and deep mineralization targets. Owing to its high sensitivity to low-resistivity targets, the long-offset transient electromagnetic method (LOTEM) is an important geophysical tool for mineral exploration. However, complex geological settings and electromagnetic interference in ore districts often reduce interpretation accuracy and increase inversion non-uniqueness. To address these challenges, this study proposes an initial-model-guided 3D LOTEM inversion strategy. Background resistivity trends and structural information are incorporated into initial-model construction to provide a better-informed starting model before inversion. Taking concealed vein-type ore bodies in the deep part of the Zhaoxian ore district, northwestern Jiaodong, as the target, an approximate 3D geoelectrical model was built by integrating geological data, borehole information, profile interpretations, and electrical-property constraints. Based on this model, LOTEM forward-response characteristics, inversion comparisons, recovery behavior, and quantitative target-identification metrics were analyzed. The results show that the layered alteration background modifies the response of low-resistivity vein-type targets and reduces the distinguishability between ore-body anomalies and background structural responses, thereby affecting inversion recovery. The structure-constrained initial model concentrates model updates near the target zone and significantly improves anomaly recovery. Sensitivity tests further indicate that one-dimensional electrical information mainly constrains the approximate position and resistivity level of the low-resistivity anomaly, whereas layer-interface information improves the spatial correspondence between the recovered anomaly, the layered background, and the ore-body extension direction. When both types of prior information are integrated, ore-body recovery is significantly enhanced. Under the synthetic-model conditions considered in this study, the initial-model optimization strategy improves the recovery of concealed vein-type low-resistivity targets in a layered alteration background and may inform subsequent LOTEM survey design and inversion interpretation in the Zhaoxian ore district. Full article

DC series arc faults (SAFs) are a significant safety hazard on the DC side of photovoltaic (PV) systems, with current signals characterized by strong randomness, obvious non-stationarity, and concealed fault features, posing challenges for rapid and accurate detection. With the development of application models such as agricultural PV integration, photovoltaic greenhouses, solar-powered irrigation, and livestock energy supply, the demand for the safe operation of photovoltaic systems in agricultural production scenarios is becoming increasingly prominent. To address the difficulty in fully characterizing the multi-scale dynamic features and local amplitude disturbances of DC SAF signals, this paper proposes a SAF detection method based on multi-scale generalized amplitude-aware permutation entropy (MS-GAAPE). The method extracts MS-GAAPE from arc current signals at various scales using sliding window-based generalized coarse-graining, which preserves temporal sequence information while improving the characterization of local amplitude variations. Particle swarm optimization (PSO) is applied to optimize these multi-scale features, strengthening fault-related information and reducing interference. The optimized features are then processed by a support vector machine (SVM) for SAF detection. The dataset used contains 50,000 samples covering transient conditions such as voltage fluctuations and is divided into a training set and an independent test set in a 70% to 30% ratio. The training set is utilized for feature parameter determination, feature weight optimization, and classification model construction, while the independent test set is reserved solely for final performance evaluation. Experimental results demonstrate that the proposed method achieves excellent detection performance under various operating conditions and load levels, with an accuracy of 99.32% and a total detection time of 103.62 ms, meeting the requirements of the UL1699B standard, thus showcasing strong real-time detection capability and potential for embedded implementation. Full article

Requirements management is fundamental to complex projects, especially in areas such as engineering, infrastructure, and defense. This article develops an integrative theoretical framework for requirements management in complex projects, grounded in a PRISMA-guided systematic literature review with a qualitative synthesis of the key dimensions of the field. In this review, 136 studies selected from an initial set of 519 records identified across multiple databases were reviewed. Five pillars were found to underpin the proposal: (i) the definition and traceability of requirements, (ii) the mitigation of uncertainties and risks, (iii) team maturity, (iv) digitalization and organizational transformation, and (v) the application of model-based systems engineering (MBSE). A literature review revealed that high-quality requirements reduce errors, improve predictability, and optimize resources, whereas digital approaches and collaborative practices strengthen the adaptive capacity of projects. Thus, in the proposed framework, these dimensions are organized into a hierarchical structure, with an emphasis on the integration of technical, organizational, and digital processes. One limitation is the lack of empirical validation, necessitating future studies on the practical application of the model in real projects, interviews with experts, and the development of operational metrics. This conceptual model is aimed at contributing to the literature and supporting more resilient, automated, and sustainability-oriented practices in complex environments. Full article

Background: Intraoperative vascular injuries during elective hip and knee arthroplasty are uncommon but limb-threatening complications. Real-world evidence on emergency on-call vascular management in this setting remains limited. We aimed to identify the intraoperative ischemia time threshold associated with progression to a more severe ischemic presentation (Rutherford IIb) at vascular consultation, in order to support early multidisciplinary activation and prevent irreversible ischemic limb damage. As a secondary aim, we described the clinical spectrum, treatment strategies, and 30-day outcomes of patients managed by a 24 h on-call vascular service (in-hospital coverage during working hours, formal on-call rota out of hours). Non-ischaemic events recorded in the series (e.g., isolated venous injuries and haemorrhagic complications) are documented as part of the overall clinical spectrum but were not the subject of specific time-related analysis. Methods: Single-centre retrospective analysis of 33 consecutive patients undergoing emergency vascular intervention for vascular injury during elective total knee (TKA) or total hip arthroplasty (THA) at a tertiary orthopaedic referral centre in Milan, Italy (January 2023—December 2025). The primary analytical objective was to identify the intraoperative ischemia time threshold associated with Rutherford IIb presentation at vascular consultation; 30-day limb salvage was the primary clinical outcome. Secondary outcomes included technical success, primary 30-day patency, postoperative ankle–brachial index (ABI), length of stay, and Clavien–Dindo complications. Non-ischaemic events (including isolated venous injuries and haemorrhagic complications) are documented as part of the clinical spectrum but were not subject to specific time-related analysis. Receiver operating characteristic (ROC) analysis assessed the discriminative role of intraoperative ischemia time for a Rutherford IIb presentation; univariate logistic regression explored predictors of postoperative complications. Results: Thirty-three patients (mean age 76.3 ± 6.3 years; 54.5% female; ≥2 comorbidities in 81.8%) underwent emergency vascular repair after TKA (60.6%) or THA (39.4%). Injuries were mixed arteriovenous (54.5%), purely venous (24.2%), or purely arterial (21.2%). Mean call-to-incision time was 45.4 ± 11.3 min. In the 25 ischemic cases, the mean intraoperative ischemia time was 130.4 ± 18.7 min. ROC analysis identified an optimal cut-off of 131 min for Rutherford IIb (AUC 0.851, 95% CI 0.679–0.982; p < 0.001), with sensitivity 81.8% and specificity 85.7%. Median ischemia time was significantly higher in IIb than IIa cases (144 vs. 124.5 min; p = 0.003). Technical success and 30-day limb salvage were 100% (95% CI 89.6–100); mean postoperative ABI 0.89 ± 0.03; primary 30-day patency 88.0% (95% CI 70.0–95.8), with secondary patency 100%. All postoperative complications were Clavien–Dindo grade 1; no Clavien–Dindo ≥ 2 events and no 30-day mortality were observed. Conclusions: A dedicated 24 h on-call vascular service achieves excellent 30-day limb salvage and patency in iatrogenic vascular injuries occurring during arthroplasty. An intraoperative ischemia threshold of 131 min identifies higher-risk presentations and supports rapid multidisciplinary activation in high-volume orthopaedic centres. Full article

In this paper, we consider the extremal trace problem for the coupled Laplacian on the sets homeomorphic to the Stiefel manifold defined on the complex Euclidean space. The study is implemented via various mathematical methods, including topological and probabilistic approaches. A detailed, comprehensive classification of the stationary points is given, which itself deserves to be considered as a general method in the framework of the optimization theory. Finally, an application to biologically meaningful integration of heterogeneous datasets, in which the structure of molecular interactions serves as a significant constraint for the mathematical model, is proposed. The main advantage of the elaborated method in comparison with the previously used ones is the absence of any conditions on the structure of the initial heterogeneous datasets. This paper is a continuation of a series of papers by our research group devoted to the development of new mathematical methods for integrating multi-omics data. Full article