Search Results (145)

As electric vehicles (EVs) continue to advance, the impact of their charging on the power grid is receiving increasing attention. This study evaluates the efficiency of EV charging piles in performing peak shaving and valley filling for power grids, a critical function for integrating Renewable Energy Sources (RESs). Utilising a high-resolution dataset of over 240,000 charging transactions in China, the research classifies charging volumes into “inputs” (charging during peak grid load periods) and “outputs” (charging during off-peak, low-price periods). The Vector Autoregression (VAR) model is used to analyse interrelationships between charging periods. The methodology employs a Slack-Based Measure (SBM) Data Envelopment Analysis (DEA) model to calculate overall efficiency, incorporating charging variance as an undesirable output. A Malmquist index is also used to analyse temporal changes between charging periods. Key findings indicate that efficiency varies significantly by charging pile type. Bus Stations (BS) and Expressway Service Districts (ESD) demonstrated the highest efficiency, often achieving optimal performance. In contrast, piles at Government Agencies (GA), Parks (P), and Shopping Malls (SM) showed lower efficiency and were identified as key targets for optimisation due to input redundancy and output shortfall. Scenario analysis revealed that increasing off-peak charging volume could significantly improve efficiency, particularly for Industrial Parks (IP) and Tourist Attractions (TA). The study concludes that a categorised approach to the deployment and management of charging infrastructure is essential to fully leverage electric vehicles for grid balancing and renewable energy integration. Full article

Coasts are critical ecological, economic and social interfaces between terrestrial and marine systems. The current upsurge in the acquisition and availability of remote sensing datasets, such as Landsat remote sensing data series, provides new opportunities for analyzing multi-decadal coastal changes and other components of coastal risk. The emergence of machine learning-based techniques represents a new trend that can support large-scale coastal monitoring and modeling using remote sensing big data. This study presents a comprehensive multi-decadal analysis of coastal changes for the period from 1990 to 2024 using Landsat remote sensing data series along the eastern and southern coasts of Peninsular Malaysia. These coastal regions include the states of Kelantan, Terengganu, Pahang, and Johor. An innovative approach combining deep learning-based shoreline extraction with the Digital Shoreline Analysis System (DSAS) was meticulously applied to the Landsat datasets. Two semantic segmentation models, U-Net and DeepLabV3+, were evaluated for automated shoreline delineation from the Landsat imagery, with U-Net demonstrating superior boundary precision and generalizability. The DSAS framework quantified shoreline change metrics—including Net Shoreline Movement (NSM), Shoreline Change Envelope (SCE), and Linear Regression Rate (LRR)—across the states of Kelantan, Terengganu, Pahang, and Johor. The results reveal distinct spatial–temporal patterns: Kelantan exhibited the highest rates of shoreline change with erosion of −64.9 m/year and accretion of up to +47.6 m/year; Terengganu showed a moderated change partly due to recent coastal protection structures; Pahang displayed both significant erosion, particularly south of the Pahang River with rates of over −50 m/year, and accretion near river mouths; Johor’s coastline predominantly exhibited accretion, with NSM values of over +1900 m, linked to extensive land reclamation activities and natural sediment deposition, although local erosion was observed along the west coast. This research highlights emerging erosion hotspots and, in some regions, the impact of engineered coastal interventions, providing critical insights for sustainable coastal zone management in Malaysia’s monsoon-influenced tropical coastal environment. The integrated deep learning and DSAS approach applied to Landsat remote sensing data series provides a scalable and reproducible framework for long-term coastal monitoring and climate adaptation planning around the world. Full article

This study evaluates the economic dimension of sustainable supply chain efficiency among Korean automotive suppliers using an SCOR-aligned two-stage Network Range-Adjusted Measure (NRAM) Data Envelopment Analysis (DEA) model. The framework separates performance into Stage 1 (internal operations: Plan/Source/Make/Deliver) and Stage 2 (external outcomes: sales and profitability), enabling stage-specific assessment of operational versus market-facing efficiency. Firm-level financial data for about 1200 suppliers annually from 2021 to 2024, spanning five sectors, were analyzed with descriptive statistics, visualizations, and non-parametric tests. Results show that Stage 1 efficiency was consistently high and stable, while Stage 2 efficiency was lower, more variable, and declined in 2022 and 2024, revealing vulnerability to systemic market disruptions. Overall efficiency mirrored Stage 2, underscoring the fact that downstream financial outcomes drive total performance. Rather than introducing a new methodology, the contribution of this study lies in applying an established two-stage NRAM DEA within an SCOR-aligned framework to a large-scale longitudinal dataset. This application provides sectoral and temporal benchmarks on a national scale, offering evidence-based insights into how structural interdependence and systemic shocks influence supply chain efficiency. While the scope is limited to the economic pillar of sustainability, the findings contribute contextualized benchmarks that can inform managerial practice and future research integrating environmental and social performance dimensions. Full article

Motion perception is a fundamental function of the tactile system, essential for object exploration and manipulation. While human studies have largely focused on discrete or pulsed stimuli with staggered onsets, many natural tactile signals are continuous and rhythmically patterned. Here, we investigate whether phase differences between “simultaneously” presented, “continuous” amplitude-modulated vibrations can induce the perception of motion across fingertips. Participants reliably perceived motion direction at modulation frequencies up to 1 Hz, with discrimination performance systematically dependent on the phase lag between vibrations. Critically, trial-level confidence reports revealed the lowest certainty for anti-phase (180°) conditions, consistent with stimulus ambiguity as predicted by the mathematical framework. I propose two candidate computational mechanisms for tactile motion processing. The first is a conventional cross-correlation computation over the envelopes; the second is a probabilistic model based on the uncertain detection of temporal reference points (e.g., envelope peaks) within threshold-defined windows. This model, despite having only a single parameter (uncertainty width determined by an amplitude discrimination threshold), accounts for both the non-linear shape and asymmetries of observed psychometric functions. These results demonstrate that the human tactile system can extract directional information from distributed phase-coded signals in the absence of spatial displacement, revealing a motion perception mechanism that parallels arthropod systems but potentially arises from distinct perceptual constraints. The findings underscore the feasibility of sparse, phase-coded stimulation as a lightweight and reproducible method for conveying motion cues in wearable, motion-capable haptic devices. Full article

In this study, we demonstrate the generation and observation of noise-like pulses (NLPs) with unique pulsating characteristics in an ultrafast fiber laser. Furthermore, these NLPs display distinct periodic intensity modulation during temporal evolution, and the corresponding shot-to-shot spectra based on the dispersive Fourier transform (DFT) method exhibit chaotic characteristics with random variable envelopes in each period. Notably, the pulsating period of these NLPs decreases with the increment of pump power. Moreover, both the average output power and pulse energy show a clear linear growth trend as the pump power is raised. Numerical simulations are further conducted to validate these experimental findings. This work will enrich the study of NLPs and pulsation dynamics and provide valuable insights for the development of ultrafast fiber lasers. Full article

Effective assessment of power grid investment efficiency is crucial for optimizing resource allocation and improving operational performance. However, existing evaluation methods typically fail to account for two critical factors: inherent uncertainties in input–output data and temporal delays in investment returns. To address these limitations, this study introduces an integrated evaluation framework combining robust optimization techniques for uncertain variables with a time-lag Data Envelopment Analysis (DEA) approach to capture the multi-period dynamics and ensure resilience against external shocks and data perturbations. An empirical analysis conducted on panel data from 31 provincial power grid enterprises in China (2015–2023) reveals significant regional disparities in efficiency, particularly between coastal and resource-rich provinces. The findings highlight that excluding time-lag effects leads to systematic underestimation of efficiency and employing robust optimization yields more resilient efficiency scores amidst data uncertainties. The study contributes methodologically by advancing DEA frameworks to better reflect the complexities of power grid investments and empirically provides valuable insights for policymakers seeking to enhance investment strategies and achieve sustainable development goals. Full article

Respiratory effort is a critical parameter for assessing respiratory function in various pathological conditions such as obstructive sleep apnea (OSA), as well as in patients undergoing respiratory ventilation. Currently, the gold-standard method for measuring it is esophageal pressure (Pes), which is obtrusive and uncomfortable for patients. An alternative approach is using diaphragmatic electromyography (dEMG), a non-obtrusive method that directly reflects the electrical drive triggering respiratory effort, holding potential for quantifying effort. Despite progress in this area, there is still no clear agreement on the best features for assessing respiratory effort from dEMG. This feasibility study considers several time, frequency, and statistical domain features, providing a comparative analysis to determine their performance in estimating respiratory effort. In particular, we evaluate the correlation of the different features with Pes using overnight recordings from 10 OSA patients and assess their robustness across different signal quality levels with the Kruskal–Wallis test. Our results support that time-domain dEMG features such as the filtered envelope, root mean square, and waveform length (WL) exhibit moderately strong correlations (R > 0.6) with respiratory effort. In terms of robustness to noise, the best features were WL, the area under the curve, and the slope sign change, demonstrating moderately strong to fair correlations (R > 0.5) even in low- to very low-quality signals. In contrast, features like skewness, the mean frequency, and the median frequency performed poorly (R < 0.3), regardless of signal quality, likely because they focus on overall signal characteristics rather than the dynamic and transient changes associated with respiratory effort by temporal features. These findings highlight the importance of selecting optimal features to obtain a reliable estimation of respiratory effort, providing a foundation for future research on non-intrusive methods. Full article

Coastal erosion and shoreline change represent major challenges for the sustainable management of coastal environments, with implications for infrastructure, ecosystems, biodiversity, and the socio-economic well-being of coastal communities. This study investigates the shoreline evolution of 79 Mediterranean microtidal beaches located along the southern coast of Sardinia Island (Italy), using the Digital Shoreline Analysis System (DSAS). Shorelines were manually digitised from high-resolution aerial orthophotos made available through the WMS service of the Autonomous Region of Sardinia, covering the period 1954–2022. Shoreline changes were assessed through five statistical indicators: Shoreline Change Envelope (SCE), Net Shoreline Movement (NSM), End Point Rate (EPR), Weighted Linear Regression (WLR), and Linear Regression Rate (LRR). The results highlight marked spatial and temporal variability in shoreline retreat and accretion, revealing patterns that link shoreline dynamics to the degree of anthropisation or naturalness of each beach. In fact, coastal areas characterised by local anthropogenic factors showed higher rates of shoreline retreat and/or accretion, while natural beaches showed greater stability and resilience in the long term. The outcomes of this analysis provide valuable insights into local coastal dynamics and represent a critical knowledge base for developing targeted adaptation strategies, supporting spatial planning, and reducing coastal risks under future climate change scenarios. Full article

Earth Pressure Balance (EPB) shield machines have been widely used in subway construction due to their versatility and safety. During the shield tunneling process, the earth pressure in the shield machine chamber is crucial for controlling ground settlement and ensuring the safety of surrounding buildings. However, current research on the temporal and spatial evolution of earth pressure in water-rich sand layers and its relationship with ground settlement is relatively insufficient. This study focuses on the shield tunneling project between Liuzhou East Road and Puzhou Road on Nanjing Metro Line 11. First, laboratory and on-site tests were conducted to optimize the slump properties of the sediment. Then, based on Terzaghi’s theory and statistical methods, the temporal and spatial evolution trends of the earth pressure in the shield chamber under water-rich sand conditions were explored. Finally, by adjusting earth pressure control parameters on-site and monitoring ground settlement, the impact of earth pressure changes on ground settlement was analyzed. Results showed a linear correlation between the actual earth pressure and shield burial depth. For water-rich sand with medium permeability, the theoretical earth pressure was calculated using Terzaghi’s water-soil combined method in shallow sections, and the average of combined and separated methods in deep sections. The decay envelope showed an exponential downward trend, with rapid decay initially and slower decay later. As earth pressure control values increased, pre-consolidation settlement increased, instantaneous settlement decreased, pre-consolidation settlement rate slightly increased, and instantaneous settlement rate decreased. When excavation pressure was below theoretical pressure, higher instantaneous settlement rates could threaten surface structures. This research offers vital theoretical and data references for shield tunneling in water-rich sand layers and supports related EPB shield machine theory studies. Full article

The sustainable integration of Distributed Energy Resources (DER) with the next-generation distribution networks requires robust, adaptive, and accurate hosting capacity (HC) forecasting. Dynamic Operating Envelopes (DOE) provide real-time constraints for power import/export to the grid, ensuring dynamic DER integration and efficient network operation. However, conventional HC analysis and forecasting approaches struggle to capture temporal dependencies, the impact of DOE constraints on network operation, and uncertainty in DER output. This study introduces a dynamic optimization framework that leverages the benefits of the sensitivity gate of the Sensitivity-Enhanced Recurrent Neural Network (SERNN) forecasting model, Particle Swarm Optimization (PSO), and Bayesian Optimization (BO) for HC forecasting. The PSO determines the optimal weights and biases, and BO fine-tunes hyperparameters of the SERNN forecasting model to minimize the prediction error. This approach dynamically adjusts the import/export of the DER output to the grid by integrating the DOE constraints into the SG-PSO-BO architecture. Performance evaluation on the IEEE-123 test network and a real Australian distribution network demonstrates superior HC forecasting accuracy, with an $R^2$ score of 0.97 and 0.98, Mean Absolute Error (MAE) of 0.21 and 0.16, and Root Mean Square Error (RMSE) of 0.38 and 0.31, respectively. The study shows that the model effectively captures the non-linear and time-sensitive interactions between network parameters, DER variables, and weather information. This study offers valuable insights into advancing dynamic HC forecasting under real-time DOE constraints in sustainable DER integration, contributing to the global transition towards net-zero emissions. Full article

This study presents a novel method for automatically extracting Doppler envelopes from Frequency-Modulated Continuous Wave (FMCW) radar signals for gait analysis. In contrast to conventional percentile-based approaches that require manual selection of Doppler envelopes for specific body parts (Spine, Leg, and Foot), the proposed contour-based method enables fully automated estimation of representative speed values from the Doppler map. Experiments were conducted on five participants with varying physical characteristics, and key gait parameters—such as walking speed, step length, and stride time—were estimated and compared against motion capture-based ground truth. The proposed method demonstrated relative errors typically below 10%, with key parameters such as Foot Speed and Leg Speed falling below the commonly cited 5% clinical threshold. Paired t-tests revealed no statistically significant differences between the proposed estimates and the ground truth across all gait parameters ($p>0.05$), supporting the method’s accuracy and reliability. Full article

Failures in gas path components pose significant risks to aeroengine performance and safety. Traditional fault diagnosis methods often require extensive data and struggle with real-time applications. This study addresses these critical limitations in traditional studies through physics-informed modeling and adaptive estimation. A nonlinear component-level model of the JT9D engine is developed through aero-thermodynamic governing equations, enhanced by a dual-loop iterative cycle combining Newton–Raphson steady-state resolution with integration-based dynamic convergence. An augmented state-space model that linearizes nonlinear dynamic models while incorporating gas path health characteristics as control inputs is novelly proposed, supported by similarity-criterion normalization to mitigate matrix ill-conditioning. A hybrid identification algorithm is proposed, synergizing partial derivative analysis with least squares fitting, which uniquely combines non-iterative perturbation advantages with high-precision least squares. This paper proposes a novel enhanced Kalman filter through integral compensation and three-dimensional interpolation, enabling real-time parameter updates across flight envelopes. The experimental results demonstrate a 0.714–2.953% RMSE in fault diagnosis performance, a 3.619% accuracy enhancement over traditional sliding mode observer algorithms, and 2.11 s reduction in settling time, eliminating noise accumulation. The model maintains dynamic trend consistency and steady-state accuracy with errors of 0.482–0.039%. This work shows marked improvements in temporal resolution, diagnostic accuracy, and flight envelope adaptability compared to conventional approaches. Full article

Background: Sensorimotor beta oscillations are critical for motor control and become synchronized with muscle activity during sustained contractions, forming corticomuscular coherence (CMC). Although beta activity manifests in transient bursts, suggesting nonlinear behavior, most studies rely on linear analyses, leaving the underlying dynamic structure of brain–muscle interactions underexplored. Objectives: To investigate the nonlinear dynamics underlying beta oscillations during isometric contraction. Methods: MEG and EMG were recorded from 17 right-handed healthy adults performing a 10 min isometric pinch task. Lyapunov exponent (LE), fractal dimension (FD), and correlation dimension (CD) were computed in 10 s windows to assess temporal stability. Signal similarity was assessed using Pearson correlation of amplitude envelopes and the nonlinear features. Burstiness was estimated using the coefficient of variation (CV) of the beta envelope to examine how transient fluctuations in signal amplitude relate to underlying nonlinear dynamics. Phase-randomized surrogate signals were used to validate the nonlinearity of the original data. Results: In contrast to FD, LE and CD revealed consistent, structured dynamics over time and significantly differed from surrogate signals, indicating sensitivity to non-random patterns. Both MEG and EMG signals demonstrated temporal stability in nonlinear features. However, MEG–EMG similarity was captured only by linear envelope correlation, not by nonlinear features. CD was strongly associated with beta burstiness in MEG, suggesting it reflects information similar to that captured by the amplitude envelope. In contrast, LE showed a weaker, inverse relationship, and FD was not significantly associated with burstiness. Conclusions: Nonlinear features capture intrinsic, stable dynamics in cortical and muscular beta activity, but do not reflect cross-modal similarity, highlighting a distinction from conventional linear analyses. Full article

The conservation and intervention of heritage structures require a flexible, interdisciplinary environment capable of managing data throughout the building’s life cycle. Historic building information modeling (HBIM) has emerged as an effective tool for supporting these processes. Originally conceived for parametric construction modeling, BIM can also integrate historical transformations, aiding in maintenance and preservation. Historic buildings often feature complex geometries and visible material traces of time, requiring detailed analysis. This research proposes a methodology for documenting and assessing the envelope of historic buildings by locating, classifying, and recording transformations, deterioration, and structural deformations. The approach is based on semantic segmentation and classification using data from terrestrial laser scanning (TLS) and unmanned aerial vehicles (UAVs), applied to the Palace of Miguel de Mañara—an iconic 17th-century building in Seville. Archival images were integrated into the HBIM model to identify previous restoration interventions and assess current deterioration. The methodology included geometric characterization, material mapping, semantic segmentation, diagnostic input, and temporal analysis. The results validated a process for detecting pathological cracks in masonry facades, providing a collaborative HBIM framework enriched with expert-validated data to support repair decisions and guide conservation efforts. Full article

A critical natural solution to combat global warming and reduce carbon emission is the forest carbon sink (FCS). Owing to variations in geographic location, policy formulation, and economic development, Chinese provinces exhibit significant disparities in forest carbon sink efficiency (FCSE). Therefore, evaluating and enhancing FCSE and optimizing resource allocation have emerged as pressing issues. This study develops a pioneering analytical framework for the systematic estimation and optimization of FCS resources. It measures FCSE, considering both dynamic and static aspects and adopting a spatial–temporal perspective, utilizing the Malmquist Index and Super Efficiency Slacks-Based Measure to analyze the primary factors influencing FCSE. The Autoregressive Integrated Moving Average method forecasts carbon sink goals for typical regions for the years 2030, 2045, and 2060. To effectively enhance FCSE and rationally optimize FCS resource allocation, this study constructs the Inverse Data Envelopment Analysis. The study’s findings indicate significant disparities in the extremes of the average FCSE across Chinese regions, with a mean value difference of 2.2188. Technological change is the primary driver of advancements in FCSE. To achieve the 2060 carbon sink goal, each input indicator requires a substantial increase. Drawing on insights into the FCS landscape, the study delineates regional disparities and offers a scientific foundation for policymakers to devise strategies and address sustainability concerns regarding FCS. Full article