Search Results (10,210)

Cultural heritage systems play a crucial role in decoding human–environment interactions and social evolution. This study aims to reveal the spatial coupling characteristics of tangible and intangible cultural heritage in China, as well as the heterogeneity of their driving mechanisms. After quantifying heritage coupling at three geographic scales, we integrated a hierarchical Bayesian model with a hybrid causal inference framework to identify the correlations, causal effects, and heterogeneity of the driving factors. The empirical results indicate the following: (1) The coupling patterns exhibit scale dependence. The proportion of strongly coupled areas decreases from the prefecture-level scale to the provincial scale but increases at the cultural–geographical unit scale. This suggests that China’s cultural system has a cohesive effect that transcends administrative boundaries. (2) The hierarchical Bayesian model identifies the significant effects of mean annual temperature, population density, GDP–population interaction, transport–hydrological network interaction, and industrial structure. Effect strengths generally peak at the prefecture-level scale and decrease at the provincial scale. (3) Causal inference estimates the causal effects of mean annual temperature, transport–hydrological network interaction, mean annual precipitation, and water network density on coupling. (4) Heterogeneity tests reveal that the positive causal effect of transport–hydrological network interaction and the negative causal effect of mean annual precipitation are significant only in low-temperature regions. By integrating hierarchical modeling with causal verification, this study elucidates the mechanisms underlying heritage coupling. This provides a scientific basis for understanding the spatial patterns of cultural heritage systems and formulating differentiated conservation policies. Full article

This article applies Causal Layered Analysis (CLA) to four Italian Alpine contexts to examine how narratives and metaphors can shape territorial development. We combined long-term ethnography (approximately 128 days of participant observation) with analysis of documents and media (2010–2025) relating to the four territories and interpreted the results through the four levels of CLA: litanies, systems, worldviews, and myths/metaphors. Two dominant metaphors, “mountain-as-playground” (exogenous) and “mountain-as-heritage” (endogenous), seem to underpin the discourses about tourism and local development. We identify signals of a third metaphor, the “open-hybrid-village”, where multiple forms of belonging and contribution (resident collective ownerships, returnees, extended stay visitors) sustain the local economy and stewardship. The approach is interpretative, and the transferability of results is limited by the selection of cases and the availability of data; however, triangulation and distinct levels support the internal consistency and replicability of the method in other contexts. We conclude that making imaginaries explicit can broaden the variety of thinkable futures and the space of options before investments become dependent on the path taken. We suggest integrating CLA into participatory foresight to enrich and share forward-looking visions on which to negotiate long-term landscape planning and thresholds for tourism carrying capacity. Full article

Chemically vapor-deposited silicon carbide (CVD-SiC) is a high-performance material that possesses excellent mechanical, chemical, and electrical properties, making it highly promising for components in the semiconductor, aerospace, and automotive industries. However, its inherent hardness and brittleness present significant challenges to precision machining, thereby hindering the commercialization of reliable, high-precision parts. Therefore, the application of CVD-SiC in fields that require ultra-precision shaping and nanometric surface finishing necessitates the exploration of machining methods specifically tailored to the material’s unique characteristics. This paper presents a comprehensive review of CVD-SiC machining—from traditional mechanical approaches to advanced hybrid and high-energy techniques—aimed at overcoming machining limitations from its material properties and achieving high-efficiency and nanometric-quality machining. The study discusses various grinding tools designed for superior surface finishing and efficient material removal, as well as machining techniques that utilize micro-scale removal mechanisms for ductile regime machining. Looking ahead, the integration of AI-based process optimization with enhanced machining methods is expected to fully exploit the superior properties of CVD-SiC and broaden its industrial application as a high-performance material. Full article

Photovoltaic (PV) generation, a vital component of renewable energy, is key to supporting energy supply and reducing reliance on traditional energy sources. Given the substantial energy consumption of oilfield well groups, increasing the proportion of PV energy is imperative. Furthermore, as oilfields enter mid-to-late production stages, wells experience reduced oil production with increased energy consumption, necessitating intermittent pumping schedules. This paper addresses the optimized scheduling of pumping unit well groups within a photovoltaic-grid microgrid. The article aims to minimize the difference between the well group system’s total energy consumption and the PV power generation. A nonlinear mixed-integer programming (NMIP) model is constructed, incorporating a PV power forecasting model, a well group energy consumption model, and relevant constraints. An improved Particle Swarm Optimization (PSO) algorithm, integrating a hybrid coding scheme and multiple improvement strategies, is proposed to efficiently solve the NMIP model. The resulting optimal intermittent pumping schedule maximizes on-site PV power consumption, effectively mitigating PV energy wastage and potential grid stability issues associated with direct grid integration. The effectiveness of the proposed optimization algorithm is validated through numerical simulation case studies. Full article

Accurate and generalizable prediction of infectious disease incidence is essential for proactive public health response. This study proposes a novel hybrid VLGA-Transformer model to address this challenge, validated through tuberculosis (TB) and hepatitis B case studies. Utilizing monthly TB data from Zhejiang Province (2013–2023), raw sequences were first decomposed via Variational Mode Decomposition (VMD) to extract intrinsic temporal patterns. To overcome Transformer parameter optimization difficulties, we innovatively integrated the Lorenz attractor into a Genetic Algorithm (GA), creating a Lorenz-attractor-enhanced GA (LGA) that dynamically balances exploration and exploitation. The resulting VLGA-Transformer framework demonstrated superior performance, achieving R² values of 0.96 for TB and 0.93 for hepatitis B prediction, significantly outperforming benchmark models in both accuracy and stability. When tested on hepatitis B data, the model confirmed its robust cross-disease generalizability. These findings highlight the framework’s dual strengths—high-precision forecasting and robust generalization—providing actionable insights for public health authorities to optimize resource allocation and intervention strategies, thereby advancing data-driven infectious disease control systems. Full article

Protected agriculture increasingly requires solutions that reduce energy consumption and environmental impacts while maintaining stable microclimatic conditions. The integration of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) with solar technologies has emerged as a pathway toward autonomous and energy-efficient greenhouses and solar dryers. This study analyzes the scientific and technological evolution of this convergence using a mixed review approach bibliometric and systematic, following PRISMA 2020 guidelines. From Scopus records (2012–2025), 115 documents were screened and 79 met the inclusion criteria. Bibliometric results reveal accelerated growth since 2019, led by Engineering, Computer Science, and Energy, with China, India, Saudi Arabia, and the United Kingdom as dominant contributors. Thematic analysis identifies four major research fronts: (i) thermal modeling and energy efficiency, (ii) predictive control and microclimate automation, (iii) integration of photovoltaic–thermal (PV/T) systems and phase change materials (PCMs), and (iv) sustainability and agrivoltaics. Systematic evidence shows that AI, ML, and DL based models improve solar forecasting, microclimate regulation, and energy optimization; model predictive control (MPC), deep reinforcement learning (DRL), and energy management systems (EMS) enhance operational efficiency; and PV/T–PCM hybrids strengthen heat recovery and storage. Remaining gaps include long-term validation, metric standardization, and cross-context comparability. Overall, the field is advancing toward near-zero-energy greenhouses powered by Internet of Things (IoT), AI, and solar energy, enabling resilient, efficient, and decarbonized agro-energy systems. Full article

The selective identification of CH₄ in alkane gas mixtures remains challenging due to overlapping infrared absorption spectra among alkane species. This study introduces a novel algorithmic filter paradigm that fundamentally shifts from hardware-based to software-defined selectivity in Nondispersive Infrared (NDIR) sensing. Instead of relying on costly, fixed-wavelength optical filters, we employ a simplified four-source NDIR platform that deliberately captures composite spectral signals from mixed gases. A CNN-SVM hybrid model then serves as the algorithmic filter: the Convolutional Neural Network extracts discriminative features from overlapping spectra, while the Support Vector Machine performs robust classification. This integrated system achieved 89% accuracy in CH₄ identification within complex alkane mixtures. By replacing expensive optical components with intelligent algorithms, this work demonstrates a cost-effective, flexible, and scalable approach. Full article

The transition toward energy-efficient and smart buildings requires Digital Twins (DTs) that can couple real-time data with physics-based Building Energy Models (BEMs) for predictive and adaptive operation. Yet, despite rapid digitalisation, there remains a lack of practical guidance and real-world implementations demonstrating how calibrated BEMs can be effectively integrated into Building Management Systems (BMSs). This study addresses that gap by presenting a complete and reproducible end-to-end framework for embedding physics-based BEMs into operational DTs using two setups: (i) encapsulation as Functional Mock-up Units (FMUs) and (ii) containerisation via Docker. Both approaches were deployed and tested in a real educational building in Cáceres (Spain), equipped with a LoRaWAN-based sensing and actuation infrastructure. A systematic comparison highlights their respective trade-offs: FMUs offer faster execution but limited weather inputs and higher implementation effort, whereas Docker-based workflows provide full portability, scalability, and native interoperability with Internet of Things (IoT) and BMS architectures. To enable real-time operation, a surrogate modelling framework was embedded within the Docker architecture to replicate the optimisation logic of the calibrated BEM and generate predictive blind control schedules in milliseconds—bypassing simulation overhead and enabling continuous actuation. The combined Docker + surrogate setup achieved 10–15% heating energy savings during winter operation without any HVAC retrofit. Beyond the case study, this work provides a step-by-step, in-depth guideline for practitioners to integrate calibrated BEMs into real-time control loops using existing toolchains. The proposed approach demonstrates how hybrid physics- and data-driven DTs can transform building management into a scalable, energy-efficient, and operationally deployable reality. Full article

The modular multilevel converter (MMC) has become a pivotal technology in high-voltage direct current (HVDC) transmission systems due to its modularity, superior harmonic performance, and enhanced controllability. However, conventional control strategies, including model predictive control (MPC) and sorting-based voltage balancing methods, often suffer from high computational complexity, limited real-time performance, and inadequate handling of transient events. To address these challenges, this paper proposes a novel Multi-Output Neural Network-based hybrid control strategy that integrates a multi-output neural network (MONN) with an optimized reduced-switching-frequency (RSF) sorting algorithm. The MONN directly outputs precise submodule switching signals, eliminating the need for traditional sorting processes and significantly reducing switching losses. Meanwhile, the RSF algorithm further minimizes unnecessary switching operations while maintaining voltage balance. Furthermore, to enhance the accuracy of predicted switching stage, we extend the MONN for submodule activation count prediction (ACP) and employ a novel Cardinality-Constrained Post-Inference Projection (CCPIP) to further align the predicted switching stages and activation count. Simulation results under dynamic load conditions demonstrate that the proposed method achieves a 76.1% reduction in switching frequency compared to conventional bubble sort, with high switch prediction accuracy (up to 92.01%). This approach offers a computationally efficient, scalable, and adaptive solution for real-time MMC control, enhancing both dynamic response and steady-state stability. Full article

This study presents an integrated predictive maintenance framework for industrial machinery, designed through the combined use of digital twin technology, enterprise resource planning (ERP) systems, and machine learning algorithms. The proposed system focuses on enhancing machine reliability and operational automation by connecting physical assets with their virtual counterparts and management systems. The digital twin acts as a real-time virtual model of critical equipment—such as aeration motors, mixers, and reactors—enabling continuous monitoring, dynamic simulation, and predictive fault detection. Meanwhile, the ERP system provides an integrated environment for maintenance scheduling, data management, and resource allocation, ensuring that maintenance decisions are data-driven and synchronized with operational workflows. Machine learning algorithms, implemented using hybrid physical–data models, predict equipment degradation trends and optimize maintenance interventions. The proposed framework was validated in an industrial-scale composting facility, where results demonstrated a 40% increase in mean time to failure (MTTF), a 35% reduction in repair time, and a 30% decrease in maintenance costs, resulting in a return on investment of 42.5% within the first year. The system’s modular architecture and high adaptability to different machinery types confirm its potential applicability to broader machine design and automation contexts, supporting the transition toward intelligent, self-maintaining industrial systems. Full article

The paper examines the development of forecasting and modeling technologies for environmental processes using classical and quantum data analysis methods. The main focus is on the integration of deep neural networks and classical algorithms, such as AutoARIMA and BATS, with quantum approaches to improve the accuracy of forecasting environmental parameters. The research is aimed at solving key problems in environmental monitoring, particularly insufficient forecast accuracy and the complexity of processing small data with high discretization. We developed the concept of an adaptive system for predicting environmental conditions in urban agglomerations. Hybrid forecasting methods were proposed, which include the integration of quantum layers in LSTM, Transformer, ARIMA, and other models. Approaches to spatial interpolation of environmental data and the creation of an interactive air pollution simulator based on the A* algorithm and the Gaussian kernel were considered. Experimental results confirmed the effectiveness of the proposed methods. The practical significance lies in the possibility of using the developed models for operational monitoring and forecasting of environmental threats. The results of the work can be applied in environmental information systems to increase the accuracy of forecasts and adaptability to changing environmental conditions. Full article

Accurate and reliable short-term wind speed forecasting plays a crucial role in efficient operation and integration of wind energy generation. This research study introduces an innovative deep hybrid model that combines Convolutional Neural Networks (CNN) with Double Bidirectional Long Short-Term Memory (DBiLSTM) networks to enhance wind speed forecasting accuracy in Australia. Thirteen years of hourly wind speed data were collected from two wind-rich potential sites in Tasmania, Australia. The CNN component effectively captures local temporal patterns, while the DBiLSTM layers model long-range dependencies in both forward and backward directions. The proposed CNNDBiLSTM model was compared against three traditional benchmark models: Multiple Linear Regression (MLR), Support Vector Regression (SVR), and Categorical Boosting (CatBoost). The proposed framework can effectively support wind farm planning, operational reliability, and grid integration strategies within the renewable energy sector. A comprehensive evaluation framework across both Australian study sites (Flinders Island Airport, Scottsdale) showed that the CNNDBiLSTM consistently outperformed the baseline models. It achieved the highest correlation coefficients (r = 0.987–0.988), the lowest error rates (RMSE = 0.392–0.402, MAE = 0.294–0.310), and superior scores across multiple efficiency metrics (ENS, WI, LM). The CNNDBiLSTM demonstrated strong adaptability across coastal and inland environments, showing potential for real-world use in renewable-energy resource forecasting. The wind speed analysis and forecasting show Flinders with higher and consistent wind speed as a more viable option for large-scale wind energy generation than Scottsdale in Tasmania. Full article

A compact offset-coupled hybrid fiber interferometer (OCHFI) is designed and experimentally demonstrated for simultaneous measurement of salinity and temperature. The sensor integrates multimode fiber (MMF) and offset no-core fiber (NCF) through an intermediate single-mode fiber (SMF), producing distinct interference patterns for multi-parameter sensing. The optimal SMF length was determined through COMSOL simulations (version 6.2) and fixed at 50 cm to achieve stable and well-separated interference dips. Fast Fourier Transform analysis confirmed that the modal behavior originates from the single-mode-multimode-single-mode (SMS) and single-mode-no-core-single-mode (SNS) segments. Experimentally, Dip 1 exhibits salinity sensitivity of 0.62206 nm/‰, while Dip 2 shows temperature sensitivity of 0.09318 nm/°C, both with linearity (R² > 0.99), excellent repeatability, and stability, with fluctuations within 0.15 nm over 60 min. To remove cross-sensitivity, both the transfer matrix method and an Artificial Neural Network (ANN) model were employed. The ANN approach significantly enhanced prediction accuracy (R² = 0.9999) with RMSE improvement approximately 539-fold for salinity and 56-fold for temperature, compared with the analytical model. The proposed OCHFI sensor provides a compact, low-cost, and intelligent solution for precise simultaneous salinity and temperature measurement, with strong potential for applications in marine, chemical, and industrial process control. Full article

Deep learning-based image compression achieves remarkable average rate-distortion performance but is prone to failure on noisy, high-frequency, or high-entropy inputs. This work systematically investigates these failure cases and proposes a noise-aware hybrid compression framework to address them. A High-Frequency Vulnerability Index (HFVI) is proposed, integrating frequency energy, encoder Jacobian sensitivity, and texture entropy into a unified measure of degradation susceptibility. Guided by HFVI, the system incorporates a selective zero-shot denoising module (P2PA) and a lightweight hybrid codec selector that determines, for each image, whether P2PA is necessary and selecting the more reliable codec (a learning-based model or JPEG2000) accordingly, without retraining any compression backbones. Experiments span a 200,000-image cross-domain benchmark incorporating general datasets, synthetic noise (eight levels), and real-noise datasets demonstrate that the proposed pipeline improves PSNR by up to 1.28 dB, raises SSIM by 0.02, reduces LPIPS by roughly 0.05, and decreases the failure-case rate by 6.7% over the best baseline (Joint-IC). Additional intensity-profile and cross-validation analyses further validate the robustness and deployment readiness of the method, showing that the hybrid selector provides a practical path toward reliable, noise-adaptive deep image compression. Full article

This study presents a simulation-based framework for PID controller design in strongly nonlinear dynamical systems. The proposed approach avoids system linearization by directly minimizing a performance index using metaheuristic optimization. Three strategies—Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and their hybrid combination (PSO-GWO)—were evaluated on benchmark systems including pendulum-like, Duffing-type, and nonlinear damping dynamics. The chaotic Duffing oscillator was used as a stringent test for robustness and adaptability. Results indicate that all methods successfully stabilize the systems, while the hybrid PSO-GWO achieves the fastest convergence and requires the fewest cost function evaluations, often less than 10% of standalone methods. Faster convergence may induce aggressive transients, which can be moderated by tuning the ISO (Integral of Squared Overshoot) weighting. Overall, swarm-based PID tuning proves effective and computationally efficient for nonlinear control, offering a robust trade-off between convergence speed, control performance, and algorithmic simplicity. Full article