Search Results (4,446)

This paper presents a comprehensive evaluation of control strategies for a highly energy-efficient plus-energy terraced housing complex equipped with photovoltaic generation, modulating ground-source heat pumps, electrical and thermal energy storage systems, and activation of building thermal mass. The study combines long-term monitoring data, annual simulations, and hardware-in-the-loop (HiL) experiments to assess modulating heat-controlled operation (HC), PV-controlled (PVC), and predictive control strategies, including simple predictive control (SPC) and model predictive control (MPC). The simulation results show that the baseline HC operation already achieves a high load cover factor (LCF), defined as the fraction of total electrical demand covered by local PV generation (direct use + battery discharge) of 65.6% and a seasonal performance factor (SPF) of the central heat pumps of 5.8. PVC increases LCF (71.0%) by shifting heat pump operation toward PV-rich periods but leads to elevated storage temperatures up to 5 K and a reduced SPF of 4.8. MPC further enhances LCF by 4–7 percentage points in simulated and HiL environments. However, its real-world performance is strongly influenced by forecast quality and the limited controllability of the heat pump system. In addition, building thermal mass activation is investigated as a complementary flexibility option. Simulation and monitoring results demonstrate that moderate room temperature set-point (2 K) increases during PV availability significantly improve LCF from 20% to 55% while maintaining thermal comfort. Overall, the findings indicate that in highly efficient plus-energy buildings, robust rule-based strategies combined with thermal mass activation can achieve a large share of the attainable benefits, while the added complexity of MPC must be carefully weighed against practical limitations. Full article

Accurate multi-horizon Significant Wave Height (SWH) forecasting is vital for offshore safety and efficiency. Beyond scheduling maintenance windows, reliable lead-time predictions provide critical early warnings to protect personnel and high-value assets from hazardous high-wave conditions. However, the non-stationary and multi-scale nature of sea states poses challenges for consistent long-term accuracy. To address this challenge, we propose a robust three-stage framework for decomposition, feature selection, and multi-horizon forecasting. Specifically, Optimal Variational Mode Decomposition (OVMD) is adopted to construct multiscale and multi-view representations of nonlinear SWH sequences, while a Triangulated Maximally Filtered Graph (TMFG) constructs a sparse dependency network to select informative and non-redundant predictors from decomposed components and environmental variables. A hybrid prediction model then combines a Temporal Convolutional Network (TCN) for local multi-scale patterns with a Bidirectional Gated Recurrent Unit (BiGRU) for long-range dependencies. Experiments on real-world buoy observations show that the proposed approach improves accuracy and robustness over commonly used statistical and deep-learning baselines across short-, medium-, and long-term horizons. Ablation studies confirm that integrating modal decomposition with sparse feature selection enhances model robustness, offering reliable decision support for offshore window planning and high-wave condition monitoring. Full article

Mediterranean regions are forecasted to be increasingly threatened by climate change, leading to the occurrence of extreme events. One strategy to improve the resilience of agricultural systems is to introduce rotations that combine legumes and crops with high intraspecific diversity such as evolutionary populations (EPs). These cropping systems may be characterized by lower external input needs and higher buffering capacity than traditional ones. Our objective was to test if the introduction of wheat EPs impacts soil microbial functions—including microbial biomass, community structure, and enzymatic activity—and soil organic matter composition within a crop rotation framework. We conducted a two-year field experiment at two sites in Italy comparing a modern bread wheat variety to two EPs, evolved in different areas, in rotation with legumes. The composition and processes of rhizosphere microbial communities were characterized using EL-FAME and enzyme activities. In addition, rhizosphere soil organic matter signatures were measured by mid-infrared spectroscopy, and their relationships with microbial parameters were investigated using principal component analyses. The results showed that the EP–rhizosphere relationship, as well as its influence on microbial abundance and activity, is dependent both on the site of origin and local pedoclimatic conditions, although no consistent response was observed across the two sites. These effects may be buffered by the choice of the preceding crop in rotation. Full article

This study examines how FUN&EAT’s “AI+Unmanned” strategy affects system resilience in sustainable operations management. This study is based on a mixed design, combining a case study with a survey study, and uses 499 valid samples and tests the effects of AI-driven decision-making capability, resource allocation flexibility, risk forecasting ability, system synergy capability, and resource optimization ability. The results show that all five factors have significant positive effects on system resilience. Resource optimization ability has the strongest effect, followed by AI-driven decision-making capability. The mediation results show that risk forecasting ability partially mediates the effects of system synergy capability and resource allocation flexibility on system resilience. However, risk forecasting ability does not mediate the effects of resource optimization ability and AI-driven decision-making capability. The findings indicate that FUN&EAT can improve operational resilience through intelligent decision-making, flexible resource allocation, risk prediction, system coordination, and resource optimization. Full article

Accurate forecasting of urban water demand is essential for scientific regulation and sustainable management of water resources, particularly in complex DMA (District Metered Area) environments. This study proposes an integrated regional water demand prediction framework that combines CEEMDAN decomposition with deep learning techniques. CEEMDAN is first applied to decompose the original water demand time series into multiple Intrinsic Mode Functions (IMFs), effectively extracting multi-scale features and mitigating non-stationarity and complexity. A hybrid Transformer-BiLSTM model is then constructed to capture global dependencies, nonlinear dynamics, and bidirectional temporal features. Experimental results demonstrate that the proposed CEEMDAN-Transformer-BiLSTM model significantly outperforms various benchmark models in terms of prediction accuracy, robustness, and generalization across different DMAs. This research provides a new perspective for modeling complex water resource time series and offers theoretical and practical support for optimizing urban water allocation and achieving sustainable management, while laying a foundation for future work involving external driving factors, enhanced model interpretability, and dynamic regulation mechanisms. Full article

Polar motion (PM), a key component of Earth orientation parameters (EOPs), is essential for high-precision satellite orbit determination and deep-space navigation. However, delays in data acquisition and processing limit its availability for real-time applications, necessitating the development of prediction models based on historical observations. Common approaches include least squares extrapolation (LS), autoregressive (AR) models, and their combination (LS + AR), often enhanced by effective angular momentum (EAM) from Earth’s fluid components. This study examines an EAM + LS + AR method for PM prediction, systematically evaluating how different EAM forecast horizons (1–10 days) affect 90-day prediction accuracy for both PM X and Y components. A segmented optimization strategy is proposed and validated against International Earth Rotation and Reference Systems Service(IERS) official predictions using the IERS EOP 14 C04 product. Key findings include: (a) Adjusting the EAM horizon substantially reduces prediction errors. Segmented prediction improves PM X accuracy by 20–30% (1–60 days) and 10–20% (61–90 days) relative to IERS rapid products, while PM Y short-term accuracy improves by 20–40% (1–15 days). (b) The influence of EAM horizon on long-term PM Y prediction gradually weakens, with errors converging to approximately 8 mas by day 90. (c) For 1–10-day forecasts, optimal horizons follow a systematic pattern: day m predictions achieve the highest accuracy using an (m—1)-day EAM horizon, while a 10-day horizon is optimal for long-term forecasts. (d) The proposed method shows clear advantages over IERS forecasts, with 83.5% of PM X predictions (1–90 days) and 50.78% of PM Y predictions (1–15 days), outperforming IERS daily products during the 2024 test period. Full article

South Africa remains one of the world’s most coal-dependent electricity systems, with coal accounting for 81.57% of generation in 2023. Despite policy interventions to diversify the energy mix, structural change is slow to emerge. This study provides the first integrated, empirically calibrated forecast of South Africa’s coal-to-clean-energy transition using a unified modelling architecture that combines structural break analysis, Bayesian estimation, and an enhanced Monte Carlo simulation with dynamic volatility (10,000 stochastic pathways). The findings confirm a permanent structural break in 2011 that coincided with the implementation of REIPPPP, following which coal began a statistically significant and sustained decline of approximately 0.7–0.75% points per year. The simulation produced a full probability distribution for the transition year (2053) when coal share falls below 50%. This demonstrated that long-term uncertainty rises faster than linearly and that, under current conditions, deep decarbonisation milestones are unattainable before mid-century. Policy scenario experiments also demonstrated that accelerating the annual decline rate necessitates coordinated, synergistic policy portfolios rather than isolated interventions. These findings provide a transparent, uncertainty-explicit forecast of South Africa’s transition trajectory, as well as a decision-relevant evidence base for planning, regulation, and equitable transition implementation. Full article

We address this question by jointly modeling the distributional dynamics of the U.S. and Canadian term premia. Our approach combines a flexible marginal specification—the Skewed Generalized Error Distribution—with a flexible bivariate copula (BB7) to capture evolving cross-market dependence. We illustrate the usefulness of this framework by examining December 2024, a period marked by a sharp rise in the U.S. term premium, and track how the forecasted joint distributions evolved throughout this episode. We document a striking change in conditional tail dependence between U.S. and Canadian term premia over this period. While term premia serve as a motivating application, our framework is applicable to a broad class of asset prices and macro-financial variables. Full article

Landfalling typhoons are the dominant trigger of short-duration extreme rainfall along the Zhejiang coast. It is necessary to estimate the recurrence of future typhoon rainfall at the city scale under the global-warming scenarios. Using Super Typhoon Lekima (2019) as a representative high-impact event, this study develops an event-based assessment framework for Taizhou city by combining the Weather Research and Forecast (WRF) model simulation, pseudo-global-warming (PGW) perturbation experiments, and generalized extreme value analysis. The historical simulation is first evaluated against the China Meteorological Administration best track, storm intensity evolution, and station rainfall observations. Future counterparts of the same event are then generated using CMIP6-derived thermodynamic perturbations under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. Finally, scenario-dependent rainfall totals are projected onto a historical GEV curve to identify equivalent historical rainfall return periods. Results show that the WRF setup reproduces the main track, intensity tendency, and rainfall timing of Lekima with reasonable fidelity. The ensemble-mean cumulative rainfall over the Taizhou area increases from 204.75 mm in the historical simulation to 335.85, 366.72, 400.79, and 464.08 mm under the four SSPs, respectively. These increases translate into equivalent historical rainfall return periods of 47.40, 84.61, 164.28, and 604.05 years, compared with 5.24 years for the historical case. The results indicate that the moderate thermodynamic rainfall amplification produces a highly nonlinear escalation of event rarity based on historical frequency statistics. This implies that future typhoon rainfall should be interpreted using scenario-aware benchmarks within the historical reference framework. Full article

Research on the differences and correlations of typhoon precipitation along similar trajectories, as well as their underlying causes, remains insufficient. Therefore, this study selects two typhoons with similar tracks but significantly different precipitation characteristics—Chanthu (2114) and Mitag (1918) in the Western Pacific—as research cases. Using the China Meteorological Administration best-track dataset, ERA5 reanalysis data, surface station observations, and GPM IMERG precipitation products, their precipitation features and underlying mechanisms are analyzed. Results show that the area-averaged land precipitation associated with Chanthu (51.9 mm) was nearly twice that of Mitag (27.2 mm). Chanthu produced broader and more persistent rainfall, mainly distributed along the northern side of its track, whereas Mitag exhibited weaker and more scattered precipitation. These differences were primarily related to the combined effects of large-scale circulation, moisture transport, dynamical and thermodynamic structure, and convective instability. During Chanthu, the subtropical high remained stable and the upper-level trough stayed farther north, favoring the maintenance of an organized typhoon structure. Chanthu also featured stronger upper-level divergence, sustained dual-channel moisture transport, a deeper warm-core structure, stronger upward motion, and better-developed convective instability. In contrast, Mitag was affected by the southward extension of the upper-level trough and the eastward retreat of the subtropical high, together with weaker divergence, insufficient moisture supply, a shallower structure, and weaker instability. Overall, precipitation differences between similarly tracked typhoons result from the synergistic effects of multiple environmental and internal factors. These findings improve understanding of typhoon precipitation mechanisms and may provide guidance for forecasting. Full article

This paper presents a comprehensive augmented reality (AR)-based rehabilitation system for upper-limb recovery that integrates AR-assisted art therapy, automated markerless goniometry, and the interval mathematical modeling of rehabilitation dynamics. The proposed platform combines four interconnected subsystems: a Python-based markerless video analysis module utilizing three stationary IP cameras, MediaPipe Pose Landmarker, and Kalman filtering; an AR art-therapy application developed for the Magic Leap 2 headset using Unity/OpenXR; a server-side subsystem implemented in NestJS/TypeScript; and (iv) a physiotherapist-oriented web application developed in React. The primary objective of the study is the real-time automated assessment of shoulder joint kinematics during AR-assisted rehabilitation sessions, including flexion (160–180°), extension (50–60°), and abduction (up to 180°). To describe and forecast rehabilitation dynamics, interval mathematical models based on recurrent difference equations were developed, enabling the prediction of subsequent joint angle values using the previous 3–4 observations. Structural and parametric identification of the interval models was performed using the artificial bee colony optimization algorithm. Experimental validation was conducted on rehabilitation data collected from five patients with different clinical diagnoses, including bursitis, epicondylitis, capsulitis, osteoarthritis, and fracture-related impairments. Under the considered experimental conditions, the proposed approach demonstrated promising predictive performance, with an angular prediction error below 5° and a correlation exceeding 95% between predicted and measured rehabilitation trajectories. The developed system implements a unified rehabilitation cycle of “execution–measurement–prediction–adaptation”, enabling the continuous monitoring of recovery dynamics, adaptive adjustment of rehabilitation scenarios, and estimation of the rehabilitation duration required to achieve target motor outcomes. The proposed approach contributes to the development of intelligent AR-based rehabilitation systems by combining markerless motion analysis, predictive interval modeling, and adaptive art-therapy mechanisms within a single clinical framework. Full article

As the demand for sustainable construction materials grows, wood ash and nanosilica have emerged as promising components for eco-friendly mortars, whose optimization requires advanced analytical techniques capable of capturing their complex linear and nonlinear interactions, making frameworks such as response surface methodology and artificial neural networks essential for effective mix design. This study examines the mechanical performance of eco-friendly mortar incorporating wood ash (WA) as a partial cement replacement and nanosilica solution (NSS) as a strength-enhancing additive, with the aim of optimizing compressive and flexural behaviour. Wood ash was substituted at levels of 5–25%, while NS (0.265 moL⁻¹) was substituted at levels of 0–1.7%. Twenty-one mortar samples were produced and tested at multiple curing ages. Two modelling techniques, response surface methodology (RSM) and artificial neural networks (ANNs), were employed to evaluate the individual and interactive effects of WA and NSS on strength development at curing ages of 28 and 180 days. While RSM provided insight into factor significance and linear interactions, ANN more effectively captured nonlinear behaviour, achieving superior predictive accuracy (R² = 1.000 for 28-day strength). Experimental results revealed that nanosilica substantially enhanced strength up to an optimal dosage of approximately 2.5 g, beyond which performance declined due to particle agglomeration or matrix over-refinement. In contrast, higher WA contents produced strength reductions attributable to dilution effects. Optimization showed that mixtures containing low WA (≤30 g) combined with moderate NSS (2.0–2.5 g) exhibited the highest mechanical performance. Collectively, the findings confirm that ANN-based models outperform RSM and multilinear regression, underscoring their effectiveness for mix design optimization and performance forecasting in sustainable cementitious systems. Full article

Short-term wind power forecasting is a key enabling technology for wind farm operation optimization, power grid dispatch, and electricity market decision-making. However, existing studies often lack unified standards in data partitioning, input feature construction, and hyperparameter tuning, making fair and reproducible comparisons across models difficult to achieve. To address this issue, this study focuses on day-ahead wind power forecasting for a single wind farm and establishes a benchmarking framework with strict chronological splitting, a shared feature information set, and a consistent hyperparameter tuning budget. Within this framework, six representative models, namely Ridge, XGBoost, LightGBM, DLinear, Transformer, and PatchTST, are systematically evaluated. A two-level evaluation protocol combining a fixed hold-out split and expanding-window rolling validation is adopted to compare model performance from multiple perspectives, including overall accuracy, sensitivity to hyperparameter tuning, robustness across rolling windows, and performance under typical operating scenarios. The results show that model rankings are not fully consistent between the hold-out evaluation and the rolling-validation setting. Under the fixed hold-out split, LightGBM achieved the lowest NRMSE of 10.2326%, while Transformer obtained the lowest NMAE of 6.9944%. In contrast, under the 8-fold expanding-window rolling validation, Transformer achieved the lowest average NRMSE of 8.1684%, followed by LightGBM with 8.7344%. These results indicate that the best performance on a single test split does not necessarily imply the strongest robustness across multiple time windows. In addition, strong tree-based models remain highly competitive in this single-wind-farm forecasting task, whereas more complex deep temporal models do not always deliver stable advantages. Meanwhile, the performance gains brought by hyperparameter optimization vary substantially across models, suggesting that conclusions drawn from default-parameter comparisons are of limited reliability. These findings demonstrate that systematic benchmarking under strict temporal constraints and fair tuning conditions is essential for clarifying the comparative performance, robustness, and engineering applicability of different model families. The study can therefore provide practical guidance for model selection and deployment in short-term wind power forecasting for single wind farms. Full article

Drought is a slowly evolving, multi-driver hydro-meteorological hazard whose accurate early prediction is a cornerstone of climate-smart agriculture and water-resource planning. Existing data-driven drought forecasting frameworks suffer from three persistent limitations: (i) most models concatenate heterogeneous climate variables into a single flat feature vector, implicitly assuming a single dominant driver such as precipitation, even though atmospheric moisture demand, radiation and wind-mediated evapotranspiration co-determine drought onset; (ii) wavelet preprocessing is typically applied to the full series, introducing future-information leakage that violates the operational causality requirement of forecasting; and (iii) most architectures predict a single horizon and provide no causal attribution explaining when, where and which climatic variables initiated the event. This study proposes WaveDroughtNet, a multi-modal, multi-horizon deep-learning framework that addresses these limitations through five integrated components: (a) a strictly causal Daubechies-4 wavelet decomposition computed in a rolling fashion; (b) six modality-specific encoders with stochastic modality dropout (p = 0.15); (c) cross-modal multi-head attention with four heads; (d) a four-layer temporal convolutional network (TCN) backbone with dilation factors yielding a 240-step receptive field; and (e) a post hoc DroughtOriginTracer that combines temporal attention, modal-attribution and inter-district propagation scans. The Standardised Precipitation Evapotranspiration Index (SPEI), used as the supervisory target, is computed following the canonical Vicente-Serrano formulation. water balance $D=P-\mathrm{P}\mathrm{E}\mathrm{T}$ (Hargreaves PET) at a 4-week (≈1-month) timescale, fitted with a three-parameter log-logistic distribution via L-moments, validated by Kolmogorov–Smirnov goodness-of-fit testing ($\alpha =0.05$) per district, and standardised through the inverse-normal cumulative distribution function. Trained on 18,304 weekly district records from NASA POWER reanalysis (2014–2025) covering all 32 districts of Tamil Nadu, India, WaveDroughtNet uses only 256,869 parameters and produces, in a single forward pass, four forecasts (1 week, 1 month, 3 months, 1 year). On the held-out 2024 test partition ($N=1728$), the model attains weighted $F1=0.9221$ and $R^2=0.8512$ at the 1-week horizon, and weighted $F1=0.8498$ and $R^2=0.6812$ at the 1-year horizon. Diebold–Mariano tests confirm that WaveDroughtNet significantly outperforms naive persistence, seasonal naive, LSTM, ConvLSTM and a vanilla Transformer at the 3-month and 1-year horizons (p < 0.001). The DroughtOriginTracer successfully back-projects 15 Coimbatore events to causal origins 29–41 weeks prior to onset. We explicitly acknowledge three limitations that constrain operational deployment in its current form—zero severe events in the 2024 test partition ($F{1}_{severe}$ = 0.000), static inter-district modelling, and absence of vegetation-index supervision—and propose concrete mitigation pathways in the Discussion. Full article

The chlorophyll-a (chl-a) concentration is a major indicator of marine ecosystem status, harmful algal blooms, and marine primary productivity. In coastal waters, however, complex hydrodynamic and ecological conditions lead to highly variable chl-a dynamics, driven by diverse and interacting mechanisms, posing substantial challenges for chl-a forecasts. To assess the applicability of machine learning approaches in predicting chl-a under complex coastal environments, we present a case study in the Taiwan Strait, where harmful algal blooms occur a few times every year. Based on satellite remote sensing data, a spatiotemporal imputation and prediction framework (STIMP), temporal models (Transformer, CrossFormer, Tsmixer), and spatiotemporal models (MTGNN and PredRNN) were applied to simulate chl-a spatiotemporal variability. A hydrodynamic–biogeochemical model was compared with these machine learning approaches to assess the model skills in coastal chl-a simulations. Results indicate that machine learning models trained with satellite data exhibit reasonable predictive skill offshore with pronounced seasonal variability and low data missing ratio, while their performance weakens in regions where seasonal signals are masked by short-term chl-a fluctuations with more missing data. In contrast, the hydrodynamic–biogeochemical model represents short-term variations in chl-a in nearshore regions with higher temporal resolution and accounts for the underlying mechanisms of phytoplankton biomass accumulation and die-off. When trained with model output, the machine learning approach shows improved performance in coastal chl-a forecasts, with much higher computational efficiency compared to the hydrodynamic–biogeochemical model. This study highlights the advantage of mechanistic and machine learning models in deciphering the spatiotemporal scales and governing mechanisms of chl-a variability in coastal regions and extracting spatiotemporal variability with computational efficiency, respectively. With input data of sufficient temporal resolution (e.g., daily to 3 days) and duration (5–10 years), a combination of the machine learning and mechanistic modeling approaches is recommended for operational coastal phytoplankton bloom forecasting. Full article