Search Results (8,612)

Different methodologies have been developed for the analysis and study of dynamical systems, including both theoretical models and natural systems. Examples span a wide range of applications, such as astronomy, financial and economic time series, biophysical systems, physiological phenomena, and Earth sciences, including seismicity and climatic processes. The study of these complex systems is commonly based on the analysis of the signals they generate, using mathematical tools to extract relevant information. A broad spectrum of mathematical disciplines converges in this context, including stochastic, probability and statistical theory, entropic and informational measures, fractal and multifractal analysis, natural time analysis, modeling of non-linearity and recurrence methods, generalized entropies, non-extensive systems, machine learning, and high-dimensional and multivariate complexity. Research in this area is largely focused on the characterization of complex systems, providing indicators of determinism or stochasticity, distinguishing between regularity, chaos, and noise, and identifying topological as well as disorder-regularity features. In addition, short- and long-term forecasting, together with the identification of short- and long-range correlations, play a central role in such characterization. To address these objectives, numerous mathematical tools have been developed for the analysis of time series and point processes, each designed to capture specific signal properties. In this work, many of the most important tools used in time series analysis are compiled and reviewed, highlighting their main characteristics and the different types of complex systems to which they have been applied. Full article

Jamming pattern identification is a crucial prerequisite for countering jamming. Combined jamming exhibits complex structures and diverse forms, making it difficult for traditional identification methods to extract suitable and stable features for effective discrimination. To address this challenge, this paper proposes a combined jamming identification method based on joint modulation of linear frequency modulation, phase coding and phase coding frequency modulation (LFM-PC-PCFM) waveforms. Building upon the time–frequency entropy features of combined interference, this method enhances the separability of jamming features in the radar-transmitted waveform dimension. The experiment employed the SVM classification algorithm based on particle swarm optimization for validation. Experiments demonstrate that the combined jamming recognition method under LFM-PC-PCFM waveform modulation achieves higher and more stable recognition accuracy than traditional LFM single-waveform modulation under jamming-to-noise ratios ranging from −10 dB to 30 dB. Full article

Diabetic retinopathy is a prevalent condition and a major public health concern due to its detrimental impact on eyesight. Diabetes is a root cause of its development and damages small blood vessels caused by prolonged high blood sugar levels. The degenerative consequences of diabetic retinopathy are irrevocable if not diagnosed in the early stages of its progression. This ailment triggers the development of retinal lesions, which can be identified for diagnosis and prognosis. However, lesion detection is challenging due to their similarity in intensity profiles to other retinal features, inconsistent sizes, and random locations. This research evaluates a custom deep learning network for classifying retinal images and compares it with the state-of-the-art classifiers. The novel preprocessing method is introduced to reduce the complexity of the diagnostic process and to enhance classification performance by adaptively enhancing images. Despite being a shallow network, the proposed model yields competitive results with an accuracy of 87.66% and an F1-score of 0.78. The evaluation metrics indicate that class imbalance affects the performance of the proposed model despite using the weighted cross-entropy loss. The future contribution will be the inclusion of generative adversarial networks for generating synthetic images to balance the dataset. This research aims to develop a robust computer-aided diagnostic system as a second interpreter for ophthalmologists during the diagnosis and prognosis stages. Full article

This paper presents an intelligent fault identification approach integrating composite multiscale fractional fuzzy diversity entropy (CMFFDE) for feature extraction, joint mutual information (JMI) for feature selection, and an extreme learning machine (ELM) for classification. First, the CMFFDE method is developed by incorporating composite multiscale analysis into the proposed fractional fuzzy diversity entropy to extract multiscale fault characteristics. JMI feature selection is then applied to identify sensitive features, which are subsequently used as input to the ELM classifier for fault identification. The effectiveness and superiority of the proposed approach are verified using bearing experimental data. Analysis results demonstrate that the proposed approach achieves better identification performance in bearing vibration signal analysis than alternative methods. Full article

The uneven spatial distribution of data elements poses challenges to regional equity and sustainable development. To unmask spatial dynamics obscured by traditional macro-divisions, this study evaluates data element development across China’s Eight Comprehensive Economic Regions from 2013 to 2022. Using the entropy weight method, Dagum Gini coefficient, Kernel Density Estimation, and spatial autocorrelation models, the results indicate that while the overall development index exhibits a sustained upward trend, inter-regional differences remain the dominant source of spatial inequality. This disparity is primarily driven by the persistent gap between advanced coastal and lagging inland regions. Notably, spatial trajectories diverge significantly: the Eastern Coastal region exhibits coordinated integration, whereas severe internal polarization appears in the Middle Reaches of the Yellow River and the Southwest. Furthermore, the spatial spillover of data elements remains bounded by physical geography. By highlighting these meso-level structural fault lines, this study provides precise empirical evidence for formulating targeted, basin-specific interventions to bridge the digital divide. Full article

Oil slicks pose a severe threat to marine ecosystems, making accurate and real-time detection increasingly urgent. Marine X-band radar has become an essential tool for oil slick monitoring due to its high temporal resolution and its ability to sensitively capture the damping of capillary waves on the sea surface caused by oil films. Building upon this, an unsupervised and lightweight SLIC-KMeans-GJO detection framework is proposed. The method first generates superpixels by using Simple Linear Iterative Clustering (SLIC) and then applies K-means clustering to extract region of interest (ROI). An improved Golden Jackal Optimizer (GJO) is adaptively initialized based on the grayscale distribution and information entropy. To enhance optimization performance, Lévy flight and stochastic perturbation mechanisms are incorporated to improve global exploration and local convergence precision. Experimental results demonstrate that the proposed method significantly outperforms conventional thresholding approaches and other intelligent optimization-based segmentation algorithms in terms of noise suppression, target identification accuracy, and discrimination precision for oil slick targets. It effectively mitigates over-segmentation and false detections while preserving fine edge details and the true spatial extent of oil slicks. The proposed framework offers a novel and practical solution for real-time oil slick monitoring, holding strong potential for operational maritime emergency response. Full article

Residential air conditioning systems are a major contributor to household electricity consumption in tropical regions, where environmental factors such as climate variability and particulate pollution (PM10) can further increase cooling demand and accelerate equipment degradation. This study proposes an Enhanced Deep Q-Network (Enhanced DQN) for energy-efficient and maintenance-aware control of residential split-type air conditioners under dynamic environmental conditions. The proposed method integrates several stability-oriented reinforcement learning mechanisms, including Double Q-learning, a dueling architecture, prioritized experience replay, multi-step returns, Bayesian-style regularization via Monte Carlo dropout, and entropy-aware exploration. The framework is evaluated through a two-stage process consisting of a diagnostic benchmark on LunarLander-v3 to assess learning stability, followed by a realistic 365-day simulation driven by Thai weather and PM10 data. Compared with a fixed 25 °C baseline, the proposed controller reduced annual electricity consumption from 5116.22 kWh to as low as 4440.03 kWh, corresponding to a saving of 13.22%. The learned policy also exhibited environmentally adaptive behavior under high PM10 conditions, indicating maintenance-aware characteristics. These findings demonstrate that reinforcement learning can provide robust, adaptive, and sustainable control strategies for residential cooling systems in tropical environments. Full article

With accelerating global urbanization and increasingly diverse demands for public spaces, promoting urban low-carbon transitions and enhancing residents’ quality of life have become central missions of modern urban development. As one of the city’s primary arteries, streets—through their green landscapes, slow-moving transportation systems, and public facilities—play an indispensable role in reducing carbon emissions, promoting healthy living, and improving residents’ well-being. In this study, the Yubei District of Chongqing was selected as the research area, and an automated evaluation framework was proposed for street visual quality, based on multi-source street view data and ensemble learning. PSP-Net semantic segmentation model was employed to extract eight key visual indicators from street view images, including green view index, Visual Entropy (Entropy), sky view factor (SVF), drivable space, sidewalk, safety facilities, buildings, and enclosure. Based on these features, a Stacking-based ensemble learning model was constructed, integrating multiple base models such as Random Forest, XGBoost, and LightGBM, with Linear Regression as the meta-learner, to predict street visual quality. The results demonstrate that the ensemble model significantly outperforms any single model, achieving a correlation coefficient (r) of 0.77 and effectively capturing the complex perceptual features of street environments. This study provides a reliable, intelligent, and quantitative method for large-scale evaluation of urban street visual quality, while supplying data support and decision-making references for street renewal and spatial optimization. Full article

The purpose of this study is to determine the most suitable Destination Management Organization (DMO) model for the sustainable development of the Rize destination. Approached from the perspective of Complex Adaptive Systems (CAS), the research is of strategic importance in order to overcome systemic entropy threats, such as coordination deficiencies and unplanned growth, faced by the destination through a scientific model. Methodologically, a sequential exploratory mixed method integrating qualitative and quantitative methods was adopted. In the qualitative phase, system bottlenecks were identified through interviews with 15 strategic stakeholders; in the quantitative phase, Analytical Hierarchy Process (AHP) and Quality Function Deployment (QFD) analyses were applied with 271 participants. Key findings indicate that the most critical factors disrupting the system’s homeostatic balance are weak inter-institutional coordination and inadequate infrastructure. AHP results confirm that market diversification, sustainable planning, and quality standards are priority activities. The final analysis conducted using the QFD decision matrix identified the PPCP (Public–Private–Community Partnership) model, which synchronizes public oversight with private sector innovation and integrates community-based feedback mechanisms, as the most effective structure for enabling resource integration and value co-creation among actors. The model’s adaptive architecture further accommodates emergent stakeholder dynamics, including the growing role of tourists as co-creators of destination experiences through digital platforms. The study contributes to the literature by offering a rational decision support mechanism for complex system management through AHP-QFD integration and proposes a three-phase evaluation framework to ensure results-oriented governance adaptation. Full article

To address the issues of suboptimal recovery performance, low timeliness, and poor economic efficiency associated with traditional fault recovery methods following large-scale power outages in active distribution networks (ADNs) caused by extreme weather, this paper proposes a dynamic fault recovery strategy for ADNs based on a two-layer hybrid algorithm under extreme ice and snow conditions. First, a line fault rate model considering the thermal effect of current under extreme ice and snow conditions is constructed, and an information entropy-based typical scenario screening method is introduced to filter the fault scenarios. Second, a photovoltaic (PV) output model and a time-varying load model under the influence of extreme ice and snow conditions are established. Subsequently, a multi-objective dynamic fault recovery model is formulated, incorporating island partitioning and integration constraints based on the concept of single-commodity flow, alongside tightened relaxation constraints. To achieve an accurate and rapid solution for the fault recovery model, a two-layer hybrid algorithm is proposed. This algorithm combines an outer-layer improved binary grey wolf optimizer (IBGWO) and an inner-layer second-order cone relaxation (SOCR) algorithm to solve the discrete and continuous decision variables within the model, respectively. Finally, the effectiveness and superiority of the proposed method are verified using the PG&E 69-bus and IEEE 123-bus systems. Full article

With the widespread adoption of various types of Unmanned Aerial Vehicles (UAVs), their non-compliant operations pose a severe challenge to public safety, necessitating the urgent identification and detection of UAV targets. However, in complex backgrounds, UAV targets exhibit small-scale dimensions and low contrast, coupled with extremely low signal-to-noise ratios. This forces conventional target detection methods to confront issues such as feature convergence, missed detections, and false alarms. To address these challenges, we propose a Multi-View Feature Fusion Network (MVFF) that achieves precise identification of small, low-contrast UAV targets by leveraging complementary multi-view information. First, we design a collaborative view alignment fusion module. This module employs a cross-map feature fusion attention mechanism to establish pixel-level mapping relationships and perform deep fusion, effectively resolving geometric distortion and semantic overlap caused by imaging angle differences. Furthermore, we introduce a view feature smoothing module that employs displacement operators to construct a lightweight long-range modeling mechanism. This overcomes the limitations of traditional convolutional local receptive fields, effectively eliminating ghosting artifacts and response discontinuities arising from multi-view fusion. Additionally, we developed a small object binary cross-entropy loss function. By incorporating scale-adaptive gain factors and confidence-aware weights, this function enhances the learning capability of edge features in small objects, significantly reducing prediction uncertainty caused by background noise. Comparative experiments conducted on a multi-perspective UAV dataset demonstrate that our approach consistently outperforms existing state-of-the-art methods across multiple performance metrics. Specifically, it achieves a Structure-measure of 91.50% and an F-measure of 85.14%, validating the effectiveness and superiority of the proposed method. Full article

This paper proposes a novel four-dimensional strongly dissipative nonlinearly coupled hyperchaotic system, investigates its dynamical characteristics, and demonstrates its applicability through Deoxyribonucleic Acid (DNA)-encoded RGB image encryption. First, a four-dimensional nonlinearly coupled hyperchaotic system with strong dissipativity is constructed. Nonlinear dynamics analysis methods, including phase trajectory diagrams, Lyapunov exponent spectra, and bifurcation diagrams, are employed to thoroughly reveal the system’s complex dynamical evolution mechanisms. The analysis indicates that the system not only possesses a wide range of chaotic parameters but also exhibits rich phenomena of multiple coexisting attractors, demonstrating a high degree of multistability. This characteristic offers potential advantages for image encryption, as it increases the diversity of dynamical behaviors and enhances sensitivity to initial conditions. The physical realizability of the chaotic behavior is further verified through an analog circuit implementation. Consequently, the system supports the design of encryption algorithms with larger key spaces, stronger resistance to phase space reconstruction, and improved pseudo-randomness, making it particularly suitable for applications with extremely high security requirements. Subsequently, leveraging the highly random chaotic sequences generated by this system, combined with various DNA coding rules and operations, the RGB image components are scrambled and diffused for encryption. Security analysis demonstrates that the algorithm effectively passes examinations across multiple dimensions, including histogram analysis, information entropy, adjacent pixel correlation, Number of Pixel Change Rate (NPCR), Unified Average Changing Intensity (UACI), and The Peak Signal-to-noise Ratio (PSNR). It achieves favorable encryption results, significantly enhances image resistance against attacks, and provides a reliable technical solution for the secure transmission of remote sensing and military images. Full article

Hyperspectral image (HSI) classification regarding vegetation is hampered by strong intra-class spectral variability and inter-class similarity, and commonly used random pixel splits can introduce spatial-context leakage that inflates test accuracy in patch-based models. To address these issues, we propose a classification framework that couples a leakage-free block partition (LFBP) strategy with class-aware multi-prototype contrastive loss (CAMP-CL). LFBP assigns non-overlapping spatial blocks to training/validation/test sets and reserves a buffer matched to the patch radius to prevent contextual overlap while keeping class distributions balanced. CAMP-CL represents each class with multiple learnable prototypes and performs supervised contrastive learning at the prototype level, encouraging compact yet multimodal intra-class embedding and improved inter-class separation. Experiments conducted on the Matiwan Village airborne HSI dataset under the LFBP protocol show that the proposed method can achieve 91.51% overall accuracy (OA) and 91.49% average accuracy (AA). Compared with the strongest baseline, supervised contrastive learning (SupCon), the proposed method yields consistent gains of 1.07 percentage points (pp) in both OA and AA while improving OA by 5.76 pp over the cross-entropy baseline. The results suggest that CAMP-CL is beneficial for addressing the challenges of HSI classification for fine-grained vegetation, while leakage-free evaluation protocols are important for obtaining more reliable performance estimates in practical settings. Full article

Green and Sustainable Remediation (GSR) has gained widespread recognition in contaminated site remediation. Several countries and international organizations have issued standards and guidelines for GSR frameworks, such as ISO 18504:2017 and the guideline developed by the Sustainable Remediation Forum UK. However, these frameworks remain largely qualitative and lack quantitative, operational tools for comparing remediation technologies, such as chemical oxidation, thermal desorption, and biopiles. To address this gap, this study develops a prospective decision-making model based on GSR. The model selects three environmental indicators, two economic indicators, and one social indicator, determines their weights using the entropy weight method, and adopts VIsekriterijumska optimizacija i KOmpromisno Resenje (VIKOR) for compromise ranking under conflicting criteria. Applied to a petroleum hydrocarbon-contaminated site in the Yangtze River Delta region of China, the model yields a stable ranking of biopiles > chemical oxidation > thermal desorption across different compromise scenarios, and sensitivity analysis confirms its robustness. A complementary Life Cycle Assessment (LCA) using SimaPro 9.6.0.1 further identifies environmental impact sources and supports GSR improvement recommendations. The results indicate that the environmental impacts of thermal desorption are dominated by tail-gas treatment and backfilling, whereas those of biopiles mainly originate from nutrient and material inputs. Full article

To detect volatile organic compounds, fabricating gas sensors with high sensitivity, excellent selectivity, low detection limits, and good long-term stability is critical. Herein, Er_1/3Yb_1/3La_1/3FeO₃ medium-entropy material was synthesized via the sol–gel method and characterized in terms of its morphological, structural, and chemical properties. The medium-entropy design induces significant lattice distortion and increased oxygen vacancies, leading to higher adsorbed oxygen content and hole concentration on the material surface, which enhances the activity of gas-sensing reactions. The Er_1/3Yb_1/3La_1/3FeO₃ sensor exhibits a response of 13.2 toward 10 ppm of butanone gas at the optimum operating temperature of 192 °C, which is nearly three times the response of the ErFeO₃ sensor (4.5), along with excellent selectivity to butanone gas, a low detection limit (0.5 ppm), and long-term stability. Moreover, the applied magnetic fields improve the ordering of magnetic moments in both Er_1/3Yb_1/3La_1/3FeO₃ and O₂ molecules, which facilitates gas adsorption and electron transfer, and further boosts the gas-sensing performance. The response of the Er_1/3Yb_1/3La_1/3FeO₃ sensor toward 10 ppm butanone is enhanced to 21.3 under the applied magnetic field of 680 mT, which improves the selectivity toward butanone. This work provides a novel material design strategy for the detection of VOCs and a feasible magnetic field-assisted approach for optimizing the gas-sensing performance of perovskite ferrite materials. Full article