Search Results (468)

Gait analysis plays a critical role in assessing mobility and identifying risks such as frailty and falls, where accurate spatiotemporal measurements are essential for early intervention, particularly in aging populations and clinical screening contexts. However, robust gait characterization remains challenging due to noise contamination and variability in sensor-based signals. To address these limitations, this study presents a stride-length estimation framework formulated as a modified processing-and-estimation pipeline integrated with Long Short-Term Memory (LSTM) networks. The pipeline includes wavelet-based denoising and cubic-spline interpolation as front-end preprocessing, followed by a Kalman-filtering stage with dynamic gain regulation guided by acceleration zero-crossing events to mitigate transient errors around abrupt turning points. Experimental data were collected from twelve healthy participants (seven females, mean age: 26.76 ± 3.01 years; five males, mean age: 25.81 ± 1.63 years) walking at self-selected speeds on a treadmill, using both an inertial sensor-based gait monitoring system and a motion capture system as the ground-truth reference. The proposed framework demonstrated a substantial improvement in stride-length estimation accuracy, reducing the absolute mean error from 29.78% to 7.77% and the standard deviation from 20.31 to 7.17. Furthermore, the LSTM models trained on Modified EKF-preprocessed data achieved superior performance metrics, with a Mean Absolute Error (MAE) of 0.0376 and a coefficient of determination (R²) of 0.7066. These results highlight the effectiveness of combining Modified EKF preprocessing with LSTM learning to enhance stride-length estimation reliability. This integrated approach offers a robust, noise-resilient solution for wearable gait analysis, providing valuable insights for clinical diagnostics, rehabilitation monitoring, and health management applications. Full article

Locust outbreaks cause a significant threat to global food security and ecosystem stability, with particularly severe consequences in grassland regions, where grasshoppers also exert considerable ecological pressure. In comparison to grasshoppers, locusts typically occur at much larger spatial scales, as their strong migratory ability and collective movement behavior lead to greater spatial connectivity and autocorrelation. The forecasting of both locust and grasshopper outbreaks remains a formidable scientific challenge, primarily due to the complex, nonlinear spatiotemporal interactions among environmental drivers such as weather, vegetation, and soil conditions. This review compares the evolution of prediction methodologies for locust and grasshopper outbreaks, focusing on the application of deep learning (DL) methods to ecological forecasting tasks. It traces the development from traditional statistical models to classical machine learning, and ultimately to DL, assessing the strengths and limitations of key DL architectures—including Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRUs)—in modeling the intricate dynamics of locust populations. While most studies have concentrated on locust outbreaks, this review emphasizes the adaptation of these models to grassland ecosystems, such as those in Inner Mongolia, where grasshopper outbreaks exhibit similarities to locust plagues but have been largely overlooked in DL research. Despite the potential of DL, challenges such as data scarcity, limited model generalizability across regions, and the “black box” issue of low interpretability remain. To address these issues, we propose future research directions that integrate Explainable AI (XAI), transfer learning, and generative models like GANs to development more robust, transparent, and ecologically grounded forecasting tools. By promoting the use of efficient architectures like GRUs within customized frameworks, this review aims to guide the development of effective early warning systems for sustainable locust management in vulnerable grassland ecosystems. Full article

Sixth-generation (6G) networks will operate with pervasive autonomy and minimal centralised control, imposing stringent requirements on security and trust. This short communication presents a hybrid trust evaluation approach that combines fuzzy inference for uncertainty management, bidirectional long short-term memory (BiLSTM) networks for temporal prediction, and blockchain for immutable verification. The pipeline first maps multi-source interaction and context metrics into linguistic trust values via fuzzy rules, then leverages BiLSTM to anticipate trust fluctuations under dynamic conditions, and finally anchors trust updates on a permissioned blockchain to ensure integrity and traceability. Using CIC-IoT2023, the proposed approach attains high accuracy and F1-score while reducing Execution Time (ET) and energy demands relative to a recent spatial-temporal trust model for 6G IoT. Results indicate that jointly addressing uncertainty, temporal evolution, and ledger-backed validation yields stable trust trajectories suitable for resource-constrained devices. The study outlines a practical path toward explainable, adaptive, and tamper-resistant trust management for 6G ecosystems. Full article

Accurate forecasting of agricultural commodity prices is essential for managing market volatility, improving supply chain coordination, and supporting food security-related decision-making. Recent advances in deep learning have demonstrated strong potential for capturing nonlinear and temporal dependencies in commodity price dynamics. In this study, we propose a hybrid long short-term memory–multi-head attention (LSTM–MHA) framework for agricultural commodity price forecasting using global futures market data. The model is trained and evaluated on multivariate global commodity futures prices, reflecting internationally traded benchmark markets rather than region-specific domestic prices. While the empirical analysis is based on global data, the study is motivated by the relevance of international price movements for import-dependent regions, particularly West Africa, where global price transmission plays a critical role in domestic market dynamics. The experimental results demonstrate that the proposed model effectively captures short-term temporal dependencies and provides interpretable attention-based insights into lag relevance. An ablation study further highlights the trade-offs between forecasting accuracy and interpretability across different model configurations. The hybrid architecture combines the time-based pattern identification and weighting capabilities of multi-head attention with the sequential learning capabilities of LSTM. Mean absolute error (MAE), root mean squared error (RMSE), and mean squared error (MSE) were used to evaluate the model’s performance. With an MSE of 0.0124, an RMSE of 0.1114, and an MAE of 0.1097, the model outperformed conventional models like ARIMA and standalone LSTM by three to four times in error reduction. The findings suggest that attention-enhanced deep learning models can serve as valuable analytical tools for understanding global price dynamics and informing policy analysis and risk management in West African agricultural markets. Full article

This study investigates volatility spillovers from the stock markets of the United States, Germany, China, and Japan to the UK stock market using daily data from major benchmark indices (FTSE 100, S&P 500, DAX, Shanghai Composite, and Nikkei 225) and Brent crude oil prices. Using a novel two-stage bootstrap framework, we first model time-varying conditional volatilities with GARCH-family models and compare them with long-memory FIGARCH specifications to account for persistent volatility dynamics. These volatilities are then incorporated into a VAR-X model, treating Brent crude oil price volatility as an endogenous or exogenous variable in robustness checks. To overcome limitations of traditional VARs, bootstrap-corrected GIRFs are employed to trace dynamic, order-invariant impacts across key sub-periods: the global financial crisis, Brexit, COVID-19, and the Ukraine war. We also benchmark our results against the Diebold–Yilmaz connectedness index and conduct rigorous out-of-sample forecasting and Value-at-Risk backtesting. Results reveal heterogeneous spillovers: US and German shocks trigger strong, immediate, and persistent UK market volatility, reflecting deep integration; Chinese shocks are delayed and gradual, while Japanese shocks are muted or short-lived. Spillover intensity is time-varying, peaking during global crises. Our model outperforms standard benchmarks in out-of-sample volatility forecasting and risk management applications. The study offers critical insights for investors seeking international diversification and for policymakers aiming to manage systemic risk in an interconnected global financial system. Full article

This review introduces and elaborates a novel temporal paradigm, the “Gout Inflammation Time Programming” model, conceptualized through the Gout-STAT™ framework. This model redefines gout inflammation as a dynamic continuum progressing through three precisely timed phases: an acute Perception phase (0–24 h) initiated by monosodium urate (MSU) crystal recognition, triggering the NOD-like receptor thermal protein domain associated protein 3 (NLRP3) inflammasome and neutrophil-driven burst; a critical Adaptation phase (24–72 h) where outcomes are determined by immunometabolic reprogramming of macrophages and synovial fibroblasts; and a chronic Tissue Injury phase (>72 h) driven by epigenetic memory, leading to irreversible osteoarticular destruction. Deciphering this programmed timeline reveals distinct therapeutic windows. We propose a shift towards stage-specific precision interventions, targeting upstream triggers (e.g., mitochondrial reactive oxygen species(ROS), neutrophil extracellular trap formation (NETosis)) in the acute phase, correcting metabolic checkpoints (e.g., succinate accumulation, impaired autophagy) during adaptation, and employing tissue-protective strategies (e.g., epigenetic modulators) in the chronic phase. Furthermore, we highlight the pivotal role of cutting-edge translational technologies, such as intelligent drug delivery systems and digital twin joint models, in achieving spatiotemporal precision. Understanding this intrinsic molecular clock is fundamental for advancing gout management from reactive treatment to a predictive, preventive, and personalized 4P medicine approach. Full article

To address the limitations of traditional tourism demand forecasting models in leveraging multi-source data and their lack of interpretability, this study proposes an integrated multi-data-driven interpretable forecasting framework incorporating historical visitor volumes, social media activities, holiday schedules, weather conditions, and seasonal indicators. This study develops a system-oriented tourism demand forecasting framework that integrates a Variable Selection Network (VSN) and an enhanced long short-term memory (xLSTM) architecture to jointly model and interpret multi-source demand drivers. The VSN module employs a dynamic feature weighting mechanism to automatically discern distribution characteristics and relevance variations across heterogeneous data sources, thereby assigning adaptive weights to input variables. The xLSTM model incorporates innovative exponential gating and matrix memory structures, enabling rapid adaptation to sudden tourist flow fluctuations while effectively capturing long-term cyclical dependencies. By combining VSN-derived feature importance weights with SHAP-based prediction attribution analysis, this framework offers dual-level interpretability—in both input feature selection and output explanation. Experimental results demonstrate that social media data significantly reflect tourist attention and travel intention and reveal distinctive demand-driving mechanisms for various types of tourism destinations. The study provides theoretical insights and empirical support for advancing tourism demand forecasting and management strategies. Full article

This paper develops a fractional six-compartment model to describe malware spread in wireless sensor networks. To represent actual network activity, the model is constructed using generalized proportional-Caputo operators that incorporate memory and tempering effects. The existence and uniqueness of solutions are proved by applying fixed-point theorems. The stability of the system is then studied using the Ulam–Hyers approach and its extended form. A fractional Adams predictor–corrector method is employed to illustrate the dynamics. The results suggest that memory and tempering play an important role in shaping infection patterns, and they indicate that fractional calculus can provide a useful framework for studying and managing malware in distributed sensor networks. Full article

Accurate mapping of land use and land cover (LULC) is crucial for various applications such as urban planning, environmental management, and sustainable development, particularly in rapidly growing urban areas. African cities such as Yaoundé, Cameroon, are particularly affected by this rapid and often uncontrolled urban growth with complex spatio-temporal dynamics. Effective modeling of LULC indicators in such areas requires robust algorithms for high-resolution images segmentation and classification, as well as reliable data with great spatio-temporal distributions. Among the most suitable data sources for these types of studies, Sentinel-2 image time series, thanks to their high spatial (10 m) and temporal (5 days) resolution, are a valuable source of data for this task. However, for an effective LULC modeling purpose in such dynamic areas, many challenges remain, including spectral confusion between certain classes, seasonal variability, and spatial heterogeneity. This study proposes a hybrid deep learning architecture combining U-Net and Convolutional Long Short-Term Memory (ConvLSTM) layers, allowing the spatial structures and temporal dynamics of the Sentinel-2 series to be exploited jointly. Applied to the Yaoundé region (Cameroon) over the period 2018–2025, the hybrid model significantly outperforms the U-Net and ConvLSTM models alone. It achieves a macro-average F1 score of 0.893, an accuracy of 0.912, and an average IoU of 0.811 on the test set. These segmentation performances reached up to 0.948, 0.953, and 0.910 for precision, F1-score, and IoU, respectively, on the built-up areas class. Moreover, despite its better performance, in terms of complexity, the figures confirm that the hybrid does not significantly penalize evaluation speed. These results demonstrate the relevance of jointly integrating space and time for robust LULC classification from multi-temporal satellite images. Full article

Automatic sleep staging technology based on wearable photoplethysmography can provide a non-invasive and continuous solution for large-scale sleep health monitoring. This study accordingly developed a novel cross-scale dynamically coupled extended long short-term memory network (SleepXLSTM) to realize automatic sleep staging based on heart rate signals collected by wearable devices. SleepXLSTM models the relationship between heart rate fluctuations and sleep stage labels by correlating physiological features with clinical semantics using a knowledge graph neural network. Furthermore, an excitation–inhibition dual-effect regulator is applied in an improved multiplicative long short-term memory network along with memory mixing in a scalar long short-term memory network to extract and strengthen the key heart rate timing features while filtering out noise produced by motion artifacts, thereby facilitating subsequent high-precision sleep staging. The benefits and functions of this comprehensive heart rate feature extraction were demonstrated using sleep staging prediction and ablation experiments. The proposed model exhibited a superior accuracy of 91.25% and Cohen’s kappa coefficient of 0.876 compared to an extant state-of-the-art neural network sleep staging model with an accuracy of 69.80% and kappa coefficient of 0.040. On the ISRUC-Sleep dataset, the model achieved an accuracy of 87.51% and F1 score of 0.8760. The dynamic coupling strategy employed by SleepXLSTM for automatic sleep staging using the heterogeneous temporal representation of heart rate data can promote the development of smart wearable devices to provide early warning of sleep disorders and realize cost-effective technical support for sleep health management. Full article

Accurate short-term forecasting of renewable energy sources is essential for stable and efficient microgrid operation. Existing models primarily focus on either solar or wind prediction, often neglecting their combined stochastic behavior within isolated systems. This study presents a comparative evaluation of three machine-learning models—Random Forest, ANN, and LSTM—for short-term solar and wind forecasting in microgrid environments. Historical meteorological data and power generation records are used to train and validate three ML models: Random Forest, Long Short-Term Memory, and Artificial Neural Networks. Each model is optimized to capture nonlinear and rapidly fluctuating weather dynamics. Forecasting performance is quantitatively evaluated using Mean Absolute Error, Root Mean Square Error, and Mean Percentage Error. The predicted values are integrated into a microgrid energy management system to enhance operational decisions such as battery storage scheduling, diesel generator coordination, and load balancing. Among the evaluated models, the ANN achieved the lowest prediction error with an MAE of 64.72 kW on the one-year dataset, outperforming both LSTM and Random Forest. The novelty of this study lies in integrating multi-source data into a unified ML-based predictive framework, enabling improved reliability, reduced fossil fuel usage, and enhanced energy resilience in remote microgrids. This research used Orange 3.40 software and Python 3.12 code for prediction. By enhancing forecasting accuracy, the project seeks to reduce reliance on fossil fuels, lower operational costs, and improve grid stability. Outcomes will provide scalable insights for remote microgrids transitioning to renewables. Full article

The energy transition, driven by the global shift toward renewable and electrification, necessitates accurate and efficient prediction of electrical load profiles to quantify energy consumption. Therefore, the systematic literature review (SLR), followed by PRISMA guidelines, synthesizes hybrid architectures for sequential electrical load profiles, aiming to span statistical techniques, machine learning (ML), and deep learning (DL) strategies for optimizing performance and practical viability. The findings reveal a dominant trend towards complex hybrid models leveraging the combined strengths of DL architectures such as long short-term memory (LSTM) and optimization algorithms such as genetic algorithm and Particle Swarm Optimization (PSO) to capture non-linear relationships. Thus, hybrid models achieve superior performance by synergistically integrating components such as Convolutional Neural Network (CNN) for feature extraction and LSTMs for temporal modeling with feature selection algorithms, which collectively capture local trends, cross-correlations, and long-term dependencies in the data. A crucial challenge identified is the lack of an established framework to manage adaptable output lengths in dynamic neural network forecasting. Addressing this, we propose the first explicit idea of decoupling output length predictions from the core signal prediction task. A key finding is that while models, particularly optimization-tuned hybrid architectures, have demonstrated quantitative superiority over conventional shallow methods, their performance assessment relies heavily on statistical measures like Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). However, for comprehensive performance assessment, there is a crucial need for developing tailored, application-based metrics that integrate system economics and major planning aspects to ensure reliable domain-specific validation. Full article

Ageing is a dynamic biological process involving interconnected physiological, psychological, and social changes, making the promotion of healthy ageing a global public health priority. The World Health Organization (WHO) defines healthy ageing as the process of developing and maintaining functional ability that enables well-being in older age. The WHO’s Decade of Healthy Aging (2021–2030) outlines four key action areas: changing attitudes toward ageing, creating age-friendly environments, delivering integrated and person-centred care, and ensuring access to long-term care. This Perspective examines yoga, a holistic mind–body practice integrating physical postures, breath regulation, and mindfulness, as a potentially safe, adaptable, and scalable intervention for older adults. Evidence suggests that yoga may improve flexibility, balance, mobility, and cardiovascular function, reduce pain, and support the management of chronic conditions commonly associated with ageing. Psychological and cognitive research further indicates reductions in stress, anxiety, and depressive symptoms, alongside potential benefits for attention, memory, and executive function. Improvements in health-related quality of life (HRQoL) have been reported across physical, psychological, and social domains, with benefits sustained through regular practice. Adaptations such as chair-based practices, restorative postures, and the use of props enhance accessibility and safety, allowing participation across diverse functional levels. Mindfulness and breath-focused components of yoga may additionally support emotional regulation, resilience, and psychological well-being, particularly among older adults experiencing stress or limited mobility. Yoga interventions are generally well tolerated, demonstrate high adherence, and can be delivered through in-person and digital formats, addressing common access barriers. Despite this growing evidence base, yoga remains underintegrated within health policy and care systems in the US, UK, and India. Strengthening its role may require coordinated efforts across research, policy, and implementation to support healthy ageing outcomes. Full article

Accurate traffic flow prediction is a core component of intelligent transportation systems, supporting proactive traffic management, resource optimization, and sustainable urban mobility. However, urban traffic networks exhibit heterogeneous multi-scale periodic patterns and time-varying spatial interactions among road segments, which are not sufficiently captured by many existing spatio-temporal forecasting models. To address this limitation, this paper proposes PDR-STGCN (Periodicity-Aware Dynamic Relational Spatio-Temporal Graph Convolutional Network), an enhanced STGCN framework that jointly models multi-scale periodicity and dynamically evolving spatial dependencies for traffic flow prediction. Specifically, a periodicity-aware embedding module is designed to capture heterogeneous temporal cycles (e.g., daily and weekly patterns) and emphasize dominant social rhythms in traffic systems. In addition, a dynamic relational graph construction module adaptively learns time-varying spatial interactions among road nodes, enabling the model to reflect evolving traffic states. Spatio-temporal feature fusion and prediction are achieved through an attention-based Bidirectional Long Short-Term Memory (BiLSTM) network integrated with graph convolution operations. Extensive experiments are conducted on three datasets, including Metro Traffic Los Angeles (METR-LA), Performance Measurement System Bay Area (PEMS-BAY), and a real-world traffic dataset from Guizhou, China. Experimental results demonstrate that PDR-STGCN consistently outperforms state-of-the-art baseline models. For next-hour traffic forecasting, the proposed model achieves average reductions of 16.50% in RMSE, 9.00% in MAE, and 0.34% in MAPE compared with the second-best baseline. Beyond improved prediction accuracy, PDR-STGCN reveals latent spatio-temporal evolution patterns and dynamic interaction mechanisms, providing interpretable insights for traffic system analysis, simulation, and AI-driven decision-making in urban transportation networks. Full article

Accurate volatility forecasting in energy markets is paramount for risk management, derivative pricing, and strategic policy planning. Traditional econometric models like the Heterogeneous Auto-regressive (HAR) model effectively capture the long-memory and multi-component nature of volatility but often fail to account for non-linearities and complex, unseen dependencies. Deep learning models, particularly Long Short-Term Memory (LSTM) networks, excel at capturing these non-linear patterns but can be data-hungry and prone to overfitting, especially in noisy financial datasets. This paper proposes a novel hybrid model, HAR-LSTM-GARCH, which synergistically combines the strengths of the HAR model, an LSTM network, and a GARCH model to forecast the realized volatility of crude oil futures. The HAR component captures the persistent, multi-scale volatility dynamics, the LSTM network learns the non-linear residual patterns, and the GARCH component models the time-varying volatility of the residuals themselves. Using high-frequency data on Brent Crude futures, we compute daily Realized Volatility (RV). Our empirical results demonstrate that the proposed HAR-LSTM-GARCH model significantly outperforms the benchmark HAR, GARCH(1,1), and standalone LSTM models in both statistical accuracy and economic significance, offering a robust framework for volatility forecasting in the complex energy sector. Full article