Search Results (2,841)

Suburban areas increasingly shape contemporary urbanisation, yet public-space dynamics in these environments are weakly represented by conventional urban indicators. This study examines suburban public-space use as a behavioural dimension of urban metabolism, understood here as the observable patterns of human movement, activity, and co-presence occurring within suburban public spaces. It addresses the limited ability of density- or infrastructure-based measures to capture everyday spatial practices in dispersed, car-oriented settings. While urban metabolism research has expanded beyond material and energy flows, empirical evidence linking configurational accessibility with directly observed public-space behaviour in suburban contexts remains limited. To address this gap, we integrate district-scale space syntax analysis with site-scale UAV-based observation across five public spaces in and around Gdańsk, Poland. Based on a dataset comprising 30 standard observation sessions conducted in September and October 2024, spatial syntax indicators (integration and choice) were used to characterise configurational accessibility and support location selection, while UAV monitoring captured traffic intensity, stationary presence, diversity of activities, and temporal rhythms of use. The results reveal distinct behavioural metabolic profiles shaped by interactions between spatial configuration, functional programming, and temporal dynamics. These profiles vary depending on the function of public spaces and dominant modes of movement (pedestrian or vehicular). The study demonstrates that suburban urban metabolism cannot be interpreted through configurational accessibility or residential density alone. By linking space syntax measures with a repeatable UAV observation protocol, the proposed framework supports comparative assessment of suburban public-space performance and informs planning interventions aimed at suburban transformation and improved accessibility. Full article

Diagnosing bearing faults remains a crucial challenge, particularly in effectively extracting fault information and achieving high diagnostic accuracy. To address this issue, this study presents a model for diagnosing bearing faults, which comprises three primary stages: feature extraction, feature selection, and classification. In the feature extraction stage, features are extracted from raw motor signals using empirical mode decomposition (EMD) and fast Fourier transform (FFT). In the feature selection stage, an effective method based on binary grey wolf optimization (BGWO) and the equilibrium optimizer (EO) is developed to remove redundant and irrelevant features. Finally, k-nearest neighbours (KNNs) and support vector machine (SVM) classifiers are used to identify bearing fault conditions. The proposed model is evaluated using four datasets: the University of California, Irvine (UCI) benchmark datasets, a motor bearing fault current-signal dataset, the Case Western Reserve University (CWRU) benchmark dataset, and the Machinery Failure Prevention Technology (MFPT) benchmark dataset. The experimental results show that the proposed method improves bearing fault diagnosis accuracy and demonstrates strong robustness compared with conventional methods. Full article

Mobility hubs promise to reduce car dependence and make multimodal travel work in practice, yet behavioural evidence remains limited when hub improvements coexist with easier car access. This article examines the tension at Rome Trastevere, an urban rail node that gradually acquires mobility-hub functions while facing improved parking access near Piazza della Radio. The empirical analysis combines a pilot survey of 83 users with an on-site stated preference survey of 204 valid respondents. The stated preference instrument uses a route-based feasible-choice design with nine choice sets per experiment: respondents evaluate alternatives among bikes, walking, e-scooters, e-mopeds, public transport, private cars, and shared cars under variations in travel time, travel cost, and search time. The paper estimates a multinomial logit model in Apollo and uses sample enumeration, supported by Monte Carlo simulation, to assess four parking and shared-mobility scenarios and produce confidence intervals around predicted probabilities. Results show that users respond to time, monetary cost, and search friction in coherent and policy-relevant ways. Setting the car parking search time to zero increases predicted car probability only marginally, by about 0.9% relative to the baseline. By contrast, a EUR 1/h increase in parking cost reduces predicted car probability by about 14.7%, while a EUR 1.5/h increase reduces it by about 22.4%. A coordinated scenario combining higher parking cost and lower shared-mode search time produces the lowest predicted car probability and strengthens e-scooter and e-moped alternatives, while public transport remains the dominant option. Findings indicate that parking pricing steers behaviour more clearly than parking convenience destabilizes it in the tested range. The paper shows that mobility-hub performance depends on coordinated access management, including parking regulation, shared-service reliability, and legible multimodal transfer. Full article

This article proposes an exploration of British educational initiatives since 1925 that have brought together art- and land-oriented practices, particularly ones that are social, regenerative, and reparative. By juxtaposing historical and contemporary case studies, this article highlights uncharted connections between artistically infused ecopedagogies of past and present. Beyond surveying defunct and currently active educational programmes and comparing and analysing their mode of operation, pedagogies, and contributions to systems change, the article highlights a pattern of emergence, brief flourishing, and closure in art and ecology educational and research programmes in Britain since the 1990s. While Dartington Hall and the Village Colleges, the historical case studies, draw on secondary research, the contemporary ones—Goldsmiths’ MA Art & Ecology, Black Mountains College’s BA (Hons) Sustainable Futures: Arts, Ecology, and Systems Change, The Gathering, and The Gramounce—are composed from interviews conducted with their founder and other key protagonists as well as from empirical research. This article captures the continuities and discontinuities between historical experiments and contemporary initiatives, arguing for the ongoing relevance—and the institutional fragility—of an educational mode that refuses the separation of art and land. Full article

The large-scale integration of wind and solar power introduces significant volatility into electricity markets, posing challenges for accurate day-ahead price forecasting for generation companies. This paper proposes a hybrid forecasting model, CEEMD-SE-IBA-LSTM, based on hyperparameter adaptation to improve prediction accuracy. First, a similar-day selection method integrating Random Forest and an Improved Grey Ideal Value approximation identifies the most relevant historical days. Second, Complete Ensemble Empirical Mode Decomposition with Sample Entropy (CEEMD-SE) decomposes and reconstructs the price series into stable components. Third, an Improved Bat Algorithm (IBA), incorporating differential evolution and adaptive weighting, is developed to optimize two key LSTM hyperparameters: the number of hidden layer neurons, which is treated as a model architecture hyperparameter, and the learning rate, which is treated as a training hyperparameter. The number of LSTM layers and the number of training epochs are kept fixed as model settings to ensure reproducibility. Using data from the US PJM market, the proposed model is validated against six benchmarks. The results show that CEEMD-SE-IBA-LSTM achieves superior performance, with a Mean Absolute Percentage Error (MAPE) of 3.73%, a Root Mean Square Error (RMSE) of 3.57 $/MWh, and a Mean Absolute Error (MAE) of 1.95 $/MWh. The method provides accurate price trends, offering effective decision support for new energy enterprises in price bidding to enhance revenue. Full article

Short-term traffic forecasting is a critical component of intelligent transportation systems. While deep learning architectures for this task have proliferated rapidly, the sensor-level data characteristics—zero-value prevalence, distributional heterogeneity, and cross-sensor correlation structure—that drive architecture-specific failure modes remain insufficiently understood, and their implications for evidence-based model selection in real deployments have not been systematically addressed. This study addresses that question through a sensor-network diagnostic framework applied to the METR-LA dataset (Metropolitan Los Angeles; 207 inductive loop detectors, 5-min resolution). The framework integrates systematic characterization of sensor data properties, a controlled benchmark of four representative architectures—Transformer, Spatio-Temporal Graph Convolutional Network (STGCN), Diffusion Convolutional Recurrent Neural Network (DCRNN), and Gated Temporal Convolutional Network (Gated TCN)—under a unified 12→3 prediction setting, and a novel per-sensor regression analysis that quantitatively links zero-value ratios to model-specific prediction errors across all 207 sensors. Building on these findings, this study further proposes Graph-Enhanced Transformer (GETFormer), a lightweight hybrid architecture that augments the Transformer with a single-hop Graph Convolutional Network (GCN) layer and a gated residual fusion module. The diagnostic findings and condition-dependent model-selection guidelines provide an empirically grounded foundation for principled hybrid architecture development in urban traffic sensing. Full article

Power systems continuously generate large-scale load time series data for forecasting, consumption analysis, and equipment health monitoring. However, real-world load measurements are often contaminated by anomalies caused by sensor faults, communication errors, and abnormal consumption behaviors, which may degrade data quality and affect operational decision-making. To address this issue, this paper proposes an integrated anomaly detection framework named PEL, which combines Prophet-based seasonal-trend decomposition, ensemble empirical mode decomposition (EEMD), and a multilayer long short-term memory (LSTM) network. Prophet is first employed to decompose the original series into trend, seasonal, holiday, and residual components. Sample entropy analysis and white noise tests are then adopted to evaluate whether the residual component still contains complex structured information requiring secondary decomposition. Next, EEMD is applied to the residual component to extract multi-scale intrinsic mode functions. Finally, all decomposed components are normalized and fed into a multilayer LSTM model for anomaly detection. Experiments on a real-world power load dataset demonstrate that the proposed PEL framework achieves an accuracy of 99.92%, a precision of 97.33%, a recall of 100%, an F1-score of 98.65%, and an AUC of 0.9996, outperforming or matching several baseline and hybrid models. Full article

Rolling bearing fault diagnosis remains challenging due to environmental noise, insufficient information sharing between diagnosis and prediction tasks, and poor model generalization ability. To address these issues, this paper proposes a fault diagnosis and severity prediction method integrating probabilistic principal component analysis (PPCA) and empirical mode decomposition (EMD) with a one-dimensional convolutional neural network (1DCNN) and bidirectional gated recurrent unit (BiGRU). The proposed model consists of two parallel branches for fault diagnosis and fault severity prediction. A self-attention mechanism is integrated into both branches to enhance feature extraction via adaptive feature weighting. In addition, parameter sharing and weighted loss functions are adopted to improve the training efficiency and collaborative learning between the two tasks. PPCA and EMD are employed for signal denoising and reconstruction while preserving fault-related features. Experiments on public datasets and industrial production-line data show that the proposed method improves the fault classification accuracy from 92.43% to 99.71% under different load conditions, while achieving 98.99% accuracy in fault severity prediction. Noise interference tests further demonstrate the effectiveness of the model. A production-line case study further illustrates the feasibility of applying the proposed method to real monitoring signals. These results confirm the effectiveness and practical potential of the proposed method for rolling bearing fault diagnosis and health assessment. Full article

Systematic trend-following strategies applied to equity markets are widely studied, yet most reported performance statistics are non-reproducible in live trading. This paper makes three contributions. First, we introduce a formal taxonomy of look-ahead bias organised around point-in-time correctness: a strategy is point-in-time correct if, for every decision time t, its information set lies in the natural filtration ${\mathcal{F}}_t$. Three bias classes—universe-membership contamination, price-data forward leakage, and stop-exit sequencing violations—are characterised as filtration breaches. Second, we formalise the average true range (ATR) trailing stop as a stochastic recurrence and codify its monotonic non-decreasing ratcheting property (Lemma 1), providing a structural per-trade loss bound. Third, we exhibit a closed-form construction (Theorem 1) of two return sequences with identical Sharpe ratios but arbitrarily divergent maximum consecutive negative-year runs, establishing investor-experience metrics as independent optimisation objectives. We complement these contributions with an 18-year empirical study (2008–2025) on the NASDAQ-100 with reconstructed point-in-time index constituency (Class I compliant) and measured residual Class II exposure, applying combinatorially symmetric cross-validation (CSCV) to a 14-configuration ATR-multiplier grid. The grid exhibits a stop-multiplier-insensitive, CAGR-flat region across $k\in [3.5,7.0]$ (CAGR 10.28–10.39%, net of Dutch progressive tax) and a uniform maximum consecutive negative-year run of 1 across all 14 configurations. The correlation-matrix eigenvalue spectrum of the grid is dominated by a single mode (${\lambda }_1=13.91$ of 14), yielding an effective independent-test count of $M_{\mathrm{eff}}=1.09$. This near-degeneracy persists in a parallel grid with the regime classifier disabled, establishing the ATR multiplier as a structurally near-redundant parameter for this strategy class. The associated PBO value of $=0.9351$ co-occurs with this near-degeneracy under the CSCV maximum-selection rule. The plateau-level performance survives Bonferroni correction for both $M=14$ and $M_{\mathrm{eff}}$. The combined evidence supports a region-based interpretation of robust strategy parameters rather than single-point optimisation. Full article

The integration of wind energy into power systems relies on forecasting technologies to address operational challenges caused by its volatility and intermittency. This paper proposes a computing architecture for ultra-short-term wind power forecasting. The methodology integrates an adaptive dual-stage signal processing technique with an optimized deep learning model. To manage the non-stationarity of meteorological variables, the Pearson and Maximal Information Coefficient (MIC) analyses are employed for feature selection. The ICEEMDAN algorithm is then used for initial decomposition, followed by sample entropy and K-Means clustering to assess component complexity. Variational Mode Decomposition (VMD) is applied only to the high-frequency component to further separate stochastic fluctuations while preserving relatively stable trend components. A Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) network is constructed to forecast the resulting multi-scale components. To reduce reliance on manual empirical tuning, the Crested Porcupine Optimizer (CPO) is used to fine-tune key network hyperparameters. Evaluations using operational wind-farm data indicate that the developed hybrid method captures the temporal dynamics of wind power and yields lower prediction errors than the tested benchmark models. This research provides a data-driven computing framework for renewable-energy forecasting and related operational analysis. Full article

The rapid expansion of wind energy as a key pillar of sustainable electricity generation has intensified the need for reliable and efficient wind turbine operation, particularly in minimizing failures of critical components such as gearboxes, which significantly impact maintenance costs, downtime, and overall lifecycle sustainability. This study proposes a vibration-based fault diagnosis framework integrating Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and a Multiscale Convolutional Neural Network (MSCNN) for wind turbine gearbox condition monitoring. The approach decomposes non-stationary vibration signals into Intrinsic Mode Functions (IMFs) to capture meaningful oscillatory characteristics, which are then processed through parallel multiscale convolutional branches to learn both transient and long-term signal patterns. Experimental validation using the NREL Gearbox Reliability Collaborative dataset demonstrates that the proposed CEEMDAN-MSCNN model demonstrates strong performance compared to conventional machine learning methods and single-scale CNN architectures, achieving 99.50% accuracy on an unseen holdout dataset. The proposed framework supports predictive maintenance strategies by enabling reliable fault diagnosis, reducing unplanned downtime, and improving the operational efficiency and long-term sustainability of wind energy systems. Full article

Convolutional neural networks (CNN) have transformed visual recognition, yet robust geometric reasoning, reliable out-of-distribution generalization, and recognition from limited data remain substantially unsolved. CNNs draw their architectural inspiration from the mammalian visual cortex, but the translation from biology to engineering was selective and, in places, imprecise, and those imprecisions have consequences that are well documented. This paper examines where the biological fidelity holds and where it gives way, grounding the analysis in formal results that predate deep learning and in recent empirical findings on CNN failure modes. We identify three diagnosable architectural limitations. First, CNNs conflate visual modalities that the biological system separates structurally at the lateral geniculate nucleus, feeding raw RGB pixels into a single undifferentiated filter bank and entangling orientation, color, and texture signals from the first layer onward. Second, CNNs repeat a spatial subsampling operation across the full depth of the network, far beyond the early visual cortex stages where it has biological warrant. Barnard and Casasent established formally in 1990 that this operation discards positional information irreversibly at every layer where it is applied, and repeating it into regions that correspond to V4 and inferotemporal cortex compounds this loss without the compensating transition to qualitatively different computations that the biological hierarchy performs. Third, the pooling-as-complex-cell analogy that motivated this design reflects a misreading of what complex cells compute. The spatiotemporal energy model formalizes complex cell behavior as geometry extraction: detecting the presence and orientation of a local edge structure robustly, abstracting over photometric accidents of contrast polarity and sub-wavelength phase that are not geometrically meaningful. Pooling is a tolerable first-stage approximation of this behavior, but as a general-purpose invariance mechanism repeated across the full depth of the network, it is attempting something categorically different, namely object-level position invariance through spatial subsampling, which achieves its goal by discarding exactly the geometric information that the energy model preserves. Treating pooling as a scalable, indefinitely repeatable implementation of complex cell behavior—rather than as a first-stage approximation with a natural biological endpoint at V3—conflates two operations that differ not in degree but in kind, and crucially it removed the principled criterion for confining the S-C operation to early visual cortex: because pooling was understood as a general-purpose invariance mechanism, the field had no architectural reason to stop repeating it. We survey how capsule networks, group-equivariant CNNs, PDE-based networks, and vision transformers each address one or two of these limitations while leaving the others intact. We propose six desiderata that a more biologically complete architecture would need to satisfy and argue that satisfying them requires treating the visual cortex’s solution as a coherent package in which each component depends on the others working correctly, rather than as a menu of independently selectable principles. Full article

This study investigates the forecast bust of Typhoon SHANSHAN (2024) characterized by large track errors using the four major interactive grand global operational ensemble data and the atmospheric reanalysis data. Ensemble space empirical orthogonal function (EOF) analysis is applied to 850, 500, and 300 hPa geopotential heights at three target times to diagnose how synoptic-scale uncertainty contributed to the erroneous motions of SHANSHAN. We align the multi-level EOF bases to a reference-time basis via a weighted Procrustes rotation and evaluate similarity to the atmospheric reanalysis data in the aligned principal component (PC) space, enabling robust, distance-based conditioning of ensemble members. Results show that ensemble spread is consistently larger in the mid-latitudes, with relatively large uncertainty concentrated around the upper-tropospheric trough and lower-tropospheric structure near SHANSHAN. The dominant EOF modes differ by phase of SHANSHAN: lower-tropospheric modes govern the westward-moving stage, whereas mid- and upper-tropospheric modes dominate after recurvature. Selecting members whose EOF-based PC structures most closely match the atmospheric reanalysis effectively suppresses large-error outliers and yields improved conditional track predictions. These findings highlight phase-dependent synoptic controls and demonstrate that adaptive, reference-consistent conditioning can enhance the track guidance of tropical cyclones during difficult forecast situations. Full article

Landslides are the most common geological hazards, and chemical reinforcement is an effective method for enhancing the stability of soil slopes. Based on the coupled Eulerian–Lagrangian method, finite element analyses were conducted to develop a simple design approach for soil slope reinforcement using the curing agent. First, the effects of internal friction angle, cohesion, soil unit weight, slope height and angle on the slope stability were systematically quantified through 93 numerical cases. On this basis, an empirical formula was established for the factor of safety (FOS) of soil slope, and a method for determining the failure mode was proposed using a dimensionless parameter and two critical values related to slope angle. Subsequently, the reinforcement performance of the SH curing agent was investigated by varying the reinforcement position and length. The results indicate that the reinforcement of Case I-II-III and Case I-II provide the best performance, and the optimum reinforcement length was determined for different slope conditions. For slope angles ranging from 25° to 65°, the FOS after reinforcement was found to increase by 12.1% to 18.8% compared with that before reinforcement. Based on the FE results, empirical formulae for predicting the FOS of reinforced slope were further developed. Finally, a simple design approach was proposed for soil slope reinforcement with curing agent. The proposed method provides a convenient and effective reference for engineering practice in soil slope reinforcement with curing agents. Full article

Magnetic anomaly detection is vulnerable to environmental noise and insufficient prior target information, making non-periodic anomaly signals difficult to detect at low-signal-to-noise-ratio (SNR) conditions. This paper proposes a detection method based on a multi-parameter-constrained mirror dual-branch biased monostable stochastic resonance (SR) system. Nonlinear odd-order bias terms are introduced into the conventional biased monostable potential function to build a multi-parameter-controllable SR model. This improves regulation of potential-well width, depth, and wall morphology, enhancing noise-energy utilization and responses to non-periodic features. Considering peak-type, valley-type, and bipolar anomaly morphologies, a mirror dual-branch SR structure is developed to cooperatively detect features with different polarities. To preserve temporal waveforms and time–frequency structures during parameter optimization, a composite metric combining the correlation coefficient and wavelet-domain image structural similarity index is constructed. Multi-fidelity robust Bayesian optimization is used to obtain a unified robust parameter set for the magnetic anomaly signal family. Experiments with simulated colored noise and measured geomagnetic noise show that the proposed method effectively recovers magnetic anomaly features under strong noise. At −19 dB SNR, its detection probability remains above 80%. Compared with orthogonal basis function decomposition, empirical mode decomposition, and complete ensemble empirical mode decomposition with adaptive noise, the method achieves better noise suppression, feature preservation, and detection performance under low-SNR conditions. Full article