Search Results (1,680)

Battery-electric residential storage systems can increase the self-consumption of photovoltaic (PV) generation; however, economical sizing typically requires a high-resolution time series of PV production and household load behind the meter. In practice, such data are often unavailable. This work therefore presents a simulation model for determining the economically optimal residential storage capacity based exclusively on smart-meter data at the point of common coupling (PCC), i.e., hourly import and export time series. Economic performance is assessed using net present value (NPV) over a multi-year evaluation horizon. In addition, technical constraints (SoC limits, power limits, charging/discharging efficiencies) as well as capacity degradation are considered via a semi-empirical aging model. For validation, a reproducible reference scenario is constructed using PVGIS generation data and the standard load profile H23, enabling a direct comparison between the conventional approach (consumption/generation) and the PCC approach (import/export). The results show that the capacity optimum can be reproduced consistently using PCC data, even under smart-meter-like integer kWh quantization. At the same time, large parts of the investigated parameter space indicate that, under the assumed scenarios, foregoing a storage system is often not economically sensible. Sensitivity analyses further highlight the strong impact of load shifting, in particular due to the charging time of electric vehicles. A case study using real PCC measurement data, together with a two-week-window analysis, demonstrates practical applicability and robustness under limited measurement durations. Full article

Closed-form analytic inverses allow explicit tracking of parameter effects, facilitate interpretation of experimental signals, and support solving inverse problems. Here, we derive a rigorous closed-form expression for the inverse Laplace transform of a class of shifted quasi-rational spectral functions with a square-root radical and a power-law decaying factor. These functions appear in coupled diffusion processes in physics and in the analysis of electromagnetic signal propagation through electrically cascaded networks, signal processing, and related areas. The transform is expressed as a finite sum of three generalized hypergeometric functions—two Kummer functions and one five-parameter Kampé de Fériet function—each multiplied by a monomial depending on the decay parameter. The validity of the result is confirmed by direct Laplace transformation, which recovers the original spectral function. Several known inverse transforms appear as limiting cases, illustrating the generality of the solution. Additionally, reduction formulas for a subclass of Kampé de Fériet functions demonstrate how the general solution encompasses previously known results and highlight the generality of the method. Full article

Accurate identification of the rotational moment of inertia of a top drive system is essential for dynamic modeling, control design, and performance optimization in drilling operations. However, the strong coupling between the drive motor, transmission components, and drill string makes direct inertia measurement challenging under field conditions. To address this issue, this study proposes a moment of inertia identification method based on dynamic response analysis of the top drive system. A simplified torsional dynamic model is established by representing the top drive and drill string assembly as an equivalent lumped inertia system. By applying controlled torque excitation under no-load conditions, the system’s angular velocity response is measured and analyzed in both time and frequency domains. The relationship between applied torque and angular acceleration is utilized to identify the equivalent rotational inertia through parameter estimation. Experimental results indicate that low-frequency excitation provides more favorable conditions for reliable and accurate inertia identification, yielding improved stability and reduced estimation error compared with higher-frequency inputs. In addition, frequency response characteristics are investigated to validate the consistency and robustness of the identified inertia across different excitation frequencies. Experimental results obtained from a top drive test rig demonstrate that the proposed method can reliably estimate the equivalent moment of inertia with good repeatability under controlled experimental conditions. The identified inertia shows good agreement with theoretical calculations and exhibits stable behavior over a wide frequency range. The proposed approach avoids the need for additional sensors or structural modifications and is well suited for practical engineering applications. This study provides an effective and experimentally validated method for inertia identification of top drive systems, offering valuable support for dynamic modeling, control parameter tuning, and vibration analysis in drilling engineering. Full article

The continuous evolution of SARS-CoV-2 affects its infectivity and ability to evade the immune system. The XBB.1.5 subvariant carries numerous mutations compared to previous Omicron variants and exhibits significant evasion of polyclonal neutralizing antibodies. In this study, the mechanistic effects of mutations in the XBB.1.5 spike protein on structural stability, antigenic markers, and antibody epitopes were analyzed using homology modeling, epitope prediction, protein stability analysis, coarse-grained dynamic simulations, and chain-specific interface mapping. Thirty-eight amino acid substitutions were identified relative to Wuhan-Hu-1, including 22 in the receptor-binding region. The prefusion trimeric fold was conserved, with localized rearrangements in the N-terminal domain, receptor-binding domain, and S1/S2 region. Linear B-cell epitope prediction yielded similar epitope counts and length distributions in wild-type and XBB.1.5, but only moderate residue-level overlap (Jaccard ≈ 0.40–0.62), indicating epitope turnover and alteration of four conserved antigenic determinants. Functional screening suggested that ~45% of substitutions could affect protein function. Chain-specific interface analysis of the A–B protomer interface indicated preserved inter-protomer coupling with modest repacking of the polar/directional contacts. Overall, XBB.1.5 appears to maintain ACE2 engagement while redistributing antibody targets, underscoring the need for updated vaccine formulations and therapeutic antibodies. Full article

Machined surfaces contain rich information about machining conditions and system behavior and are typically assessed using off-line, small-area metrology. This study developed and validated an image-based methodology for process-oriented surface texture analysis of end-milled Spheroidal Graphite Iron (SGI), enabling scalable, non-contact monitoring suitable for in-line deployment. End milling trials were conducted under optimized and aggressive cutting conditions and in two orthogonal feed directions (X,Y). Surface topography from White Light Interferometry (WLI) was complemented by Charge-Coupled Device (CCD) microscope imaging. Image processing comprised automatic orientation correction, intensity profile extraction, and frequency-domain analysis via Fast Fourier Transform and power spectral density estimation. Texture metrics (RMS amplitude, skewness, kurtosis, dominant wavelength) were derived from intensity profiles, and two spectral indices were introduced: a Change Index (CI), capturing high-frequency content linked to process disturbances, and a Surface Anisotropy Metric (SAM), quantifying texture directionality. Aggressive cutting increased RMS by 28.5% and shifted skewness by 274% with strong statistical significance. Directional analysis showed 22% higher texture amplitude in Y than X, indicating axis-dependent machine behavior. CI correlated with the machining parameters and stability, while SAM reflected the machine and setup characteristics. Trends were consistent with WLI, supporting the method as a rapid, complementary tool for surface quality and machine condition monitoring. Full article

Large temperature gradients encountered in aerospace, energy, and microelectronics systems impose stringent requirements on material thermal performance. Functionally graded materials (FGMs), characterized by a continuous variation in composition and properties, offer significant advantages in regulating heat transfer and mitigating thermal stresses. This review provides a systematic summary of recent progress in heat transfer design optimization of FGMs under large temperature gradient conditions. From a methodological perspective, advancements in structural and compositional optimization, topology optimization, and multi-objective optimization are reviewed. Numerical simulation techniques, including conventional finite element and finite volume methods, as well as emerging approaches such as peridynamics, isogeometric analysis, and meshfree methods, are discussed with an emphasis on multiphysics coupling. In addition, representative applications of FGMs in electronic thermal management, aerospace thermal protection, energy systems, and building energy conservation are reviewed. Current challenges, including idealized modeling assumptions, limited coordination among multiple optimization objectives, and insufficient reliability evaluation in complex service environments, are identified. Finally, future research directions are outlined, highlighting intelligent design methods, multiscale modeling, advanced manufacturing technologies, and multifunctional integration. This review seeks to provide a comprehensive reference for both fundamental research and engineering applications of heat transfer optimization in functionally graded materials. Full article

Strategic evaluation is essential for decision-making under uncertainty. Yet existing qualitative and quantitative methods—including chat-oriented large language model (LLM) evaluations—are difficult to deploy in complex, dynamic environments. They often fail to represent nonlinear causal dependencies among indicators, account for temporal lags, or support scalable reasoning. To address these limitations, we propose an LLM-driven strategic evaluation framework with three innovations. First, the framework integrates LLMs across the evaluation lifecycle and couples their qualitative reasoning with quantitative model computation, improving both efficiency and deployability. Second, we introduce a Time-Series Directed Acyclic Graph (TS-DAG) indicator system that explicitly encodes causal structure and time-lagged interdependencies. Third, we develop an LLM-driven procedure that automatically derives the TS-DAG architecture and instantiates its computational parameters, reducing reliance on expert-only construction. We validate the framework through an empirical study of the new energy vehicle market, complemented by baseline algorithm comparisons and sensitivity analyses. The results show that the proposed framework can uncover core indicators, capture competitive dynamics, and explain long-term strategic outcomes across varying environmental conditions. Overall, the framework provides a robust solution for strategic evaluation in complex settings, bridging qualitative strategic reasoning and quantitative, data-driven analysis. Full article

The expansion of urban rail transit has exacerbated environmental issues related to low-frequency noise (LFN), yet the impact of geometric symmetry breaking on structure-borne noise remains underexplored. This study aims to quantify the mechanism by which cross-sectional asymmetry influences the vibro-acoustic coupling of viaducts. A 2.5D Hybrid Finite Element-Boundary Element Method (FEM-BEM) was employed to model a parametric box girder under eccentric track loading, and the numerical framework was validated against analytical benchmarks. The “Modal Symmetry Index” (MSI) and “Acoustic Asymmetry Indicator” (AAI) were defined to evaluate the effects of the asymmetry parameter ($\alpha$) on sound field distribution. Numerical results reveal a nonlinear “V-shaped” relationship between geometric asymmetry and acoustic directivity. While severe asymmetry ($\alpha >0.15$) exacerbates noise deflection via flexural–torsional coupling, a critical “self-balance zone” exists. Specifically, moderate asymmetry ($\alpha \approx 0.07$) effectively neutralizes load eccentricity, reducing the AAI from 1.5 dB (in strictly symmetric designs) to nearly 0 dB. Robustness analysis under right-side loading conditions further confirms a “reverse deflection” phenomenon, verifying that the proposed self-balance design minimizes directional sensitivity. These findings challenge the traditional assumption that geometric symmetry is acoustically optimal. A “competition–compensation” mechanism is identified, suggesting that deliberate, slight geometric asymmetry can serve as an effective passive noise control strategy for viaducts. Full article

The bionic pectoral fin serves as the primary propulsion component of ray-inspired robots. In our previous research, a motion equation was proposed for the real pectoral fin, which can be modeled as a series of NACA airfoil-shaped cross-sections distributed along the spanwise direction. Each cross-section undergoes two coupled rotational motions about its chord line and spanwise rotational axis. To achieve this type of motion, this article introduces a novel bionic pectoral fin mechanism driven by a series of differential gear units. The differential unit generates two coupled rotational motions corresponding to the cross-section of the pectoral fin in motion. A series of interconnected differential units provides a unique topology for the bionic mechanism and can generate a diverse range of motions. Through kinematic analysis, the motion equation was mapped onto the rotational angles of motors in the differential units. The proposed bionic mechanism was then fabricated and subjected to experimental test, demonstrating its effectiveness with a maximum thrust of 0.71 N. The distinctive structure of this bionic mechanism differentiates it from conventional designs and is expected to provide some inspiration for bionic pectoral fins and ray-inspired robots. Full article

The performance of the cleaning system in crop harvesters directly impacts overall operational efficiency and harvest quality. Against the background of traditional design relying on physical experiments—which is costly and provides limited mechanistic insight—Discrete Element Method (DEM), Computational Fluid Dynamics (CFD), and their coupled simulation (CFD-DEM) have become key means for in-depth study of the cleaning process, capable of revealing the complex interactions between particles and between particles and airflow. With the increasingly widespread and deep application of computer simulation technology in agricultural machinery research and development, it is particularly necessary to systematically review its research progress in cleaning systems. Therefore, this study provides a comprehensive and systematic analysis and summary of the key technologies in cleaning system simulation, aiming to address the current gap in systematic reviews of simulation technology in this field. Compared with previous studies that mostly focus on a single method or a specific crop type, this paper systematically reviews the application of three simulation technologies in cleaning systems of various crop harvesters. First, based on the working principle and core operational challenges of cleaning systems, the necessity of applying simulation technology is clarified. Second, the basic principles, modeling processes, and suitable application scenarios and key points for the cleaning simulation of each method are analyzed. Third, typical cases are reviewed to summarize their key achievements in structural innovation, parameter optimization of cleaning devices, and revealing the mechanisms of material separation. Finally, current bottlenecks in simulation applications are pointed out, and future development directions are outlined, including high-precision multi-field coupling, integration with intelligent algorithms, and the construction of digital twin systems. This study aims to provide systematic theoretical reference and methodological support for the innovative design and performance improvement of cleaning systems. Full article

Decoding reaching movements from non-invasive brain signals is a key challenge for the development of naturalistic brain–computer interfaces (BCIs). While this decoding problem has been addressed via traditional machine learning, the exploitation of deep learning is still limited. Here, we evaluate a convolutional neural network (CNN) for decoding movement direction during a delayed center-out reaching task from the EEG. Signals were collected from twenty healthy participants and analyzed using EEGNet to discriminate reaching endpoints in three scenarios: fine-direction (five endpoints), coarse-direction (three endpoints), and proximity (two endpoints) classifications. To interpret the decoding process, the CNN was coupled with explanation techniques, including DeepLIFT and occlusion tests, enabling a data-driven analysis of spatio-temporal EEG features. The proposed approach achieved accuracies well above chance, with accuracies of 0.45 (five endpoints), 0.64 (three endpoints) and 0.70 (two endpoints) on average across subjects. Explainability analyses revealed that directional information is predominantly encoded during movement preparation, particularly in parietal and parietal–occipital regions, consistent with known visuomotor planning mechanisms and with EEG analysis based on event-related spectral perturbations. These results demonstrate the feasibility and interpretability of CNN-based EEG decoding for reaching movements, providing insights relevant for both neuroscience and the prospective development of non-invasive BCIs. Full article

Temperature and seepage are critical factors influencing the stability of unsaturated retaining walls, as they modulate soil shear strength through alterations in matric suction. This study proposes a three-dimensional analytical framework for evaluating active earth pressure under thermal and seepage conditions. With a kinematic upper-bound approach, temperature-dependent suction evolution and steady-state seepage are incorporated into a horn-shaped failure mechanism. The proposed method is validated against published analytical/numerical solutions, confirming its reliability. A systematic parametric study is conducted to examine how temperature, seepage velocity, wall geometry, and soil pore characteristics affect the active earth pressure behavior. The results reveal distinct behavioral trends depending on soil type: for sand, the active earth pressure increases with rising temperature, indicating reduced stability; conversely, for clay, it decreases with temperature elevation, suggesting enhanced stability. While seepage has minimal impact on sand, it exhibits a clear directional dependence in clays, with infiltration increasing active thrust and evaporation promoting stability through suction recovery. Three-dimensional analysis yields substantially lower earth pressure values compared with conventional two-dimensional approaches, highlighting potential design economies. The proposed method provides engineers with a practical tool for coupled thermal hydraulic mechanical analysis of retaining walls in unsaturated fills, facilitating more realistic and cost-effective designs under varying environmental conditions. Full article

To address the decarbonization requirements of the shipping industry, this study establishes an in-cylinder combustion simulation model for a medium–high speed four-stroke ammonia-fueled marine engine based on the CONVERGE v3.0 platform. A diesel combustion model was first developed and validated against experimental data. Building on this validated model, an ammonia/n-heptane dual-fuel combustion model was further developed by coupling a chemical kinetic mechanism for ammonia/n-heptane. To overcome the challenge of igniting pure ammonia, a combustion strategy employing intake port injection of n-heptane and direct in-cylinder injection of ammonia fuel was adopted, leveraging thermal compression ignition. The results indicate that under initial cylinder conditions of 1 bar and 350 K, misfire occurs when the ammonia energy proportion (AEP) reaches 70%, preventing stable ignition and combustion of ammonia. Based on an analysis of intake boundary conditions, the influence of intake supercharging coupled with intake heating on ammonia combustion characteristics was investigated. As the AEP increases further, the combustion of n-heptane deteriorates significantly. At a 90% AEP, the combustion efficiency of n-heptane is approximately 67% at an initial temperature of 350 K but drops to about 28% at 400 K. Full article

Air pollution has become a pressing global challenge that threatens ecological security, public health, and sustainable socioeconomic development, prompting extensive academic and policy attention on air pollution control and environmental governance. To systematically clarify the knowledge structure, evolutionary trends, and interdisciplinary characteristics of this field, this study employs bibliometric methods combined with CiteSpace, VOSviewer, and Tableau tools for in-depth analysis of the global literature published in the last 25 years. Key dimensions including keyword clustering, co-occurrence networks, national cooperation patterns, journal co-citation relationships, and policy evaluation methodology evolution are explored. The results reveal that research output in this field has maintained sustained rapid growth, with distinct interdisciplinary integration across environmental science, economics, energy engineering, and public health. Notably, the evolutionary path of research themes presents a clear transformation: shifting from early emphasis on “emission standards” and “end-of-pipe treatment” to market-oriented policy instruments such as “carbon tax” and “carbon emission trading”, and further expanding toward systematic solutions including “green finance” and “collaborative environmental governance”. In terms of policy evaluation methodologies, there is a developmental trend from single-indicator monitoring to integrated assessment frameworks combining quasi-experimental approaches (e.g., difference-in-differences, regression discontinuity design) and multi-model coupling. Furthermore, national collaboration analysis identifies China as a core hub in the global research network, while European and American countries maintain advantages in research impact. While this observation is based on absolute metrics, a data normalization approach (e.g., by population) reveals more distinct relative differences and a complementary global dynamic: China’s scale-driven output aligns with large-scale, engineering-intensive governance challenges, whereas the markedly higher per capita research impact of Western nations reflects a deeper focus on policy innovation and systemic mechanisms. Burst term detection highlights emerging frontiers such as the “Porter hypothesis”, reflecting growing focus on the synergistic relationship between environmental regulation, green innovation, and economic development. This study also identifies critical research gaps, including insufficient attention on cross-regional pollution transport policy coordination and emergency policy evaluation under extreme weather conditions. The findings provide a comprehensive academic map of global air pollution control and environmental governance research, offering valuable insights for optimizing environmental policy design, promoting interdisciplinary collaboration, and guiding future research directions in this field. Full article

The rapid and selective discrimination of microplastics (MPs) is a critical analytical challenge, particularly as current carbon quantum dot (CQD)-based sensors often rely on single-wavelength “turn-on/off” or staining mechanisms that lack polymer-specific resolution. This work addresses these limitations by presenting a mechanism-driven fluorescence sensing platform using ultra-fine polyamide-derived carbon quantum dots (PACQDs; ~1.4 nm) to identify three prevalent MPs: polyamide (PA), polypropylene (PP), and polyethylene terephthalate (PET). Excitation–emission matrix (EEM) spectroscopy reveals polymer-specific photophysical responses: PAMPs and PPMPs induce fluorescence enhancement of 11.66% and 11.43%, respectively, whereas PETMPs cause net quenching (−4.61%) alongside a distinct, red-shifted emission band. Despite a common scatter-dominated peak at 290/308 nm, quantitative discrimination is achieved via integrated intensity and red/blue emission ratios (0.0137 for PAMPs, 0.0098 for PPMPs, and 0.0072 for PETMPs). Multivariate analysis reinforces this discrimination. Parallel factor analysis (PARAFAC) resolves the EEM data into three fluorescent components representing the intrinsic CQDs core and two interaction-induced surface states with a rank 3 model reducing the relative reconstruction error from 0.1625 to 0.1285. Principal component analysis (PCA) yields clear separation of the polymer classes, with the first two principal components capturing ~88% of the total spectral variance. ATR–FTIR spectroscopy provides direct molecular evidence for the underlying mechanisms: amide–amide coupling and interfacial rigidification for PAMPs; hydrophobic interaction without spectral shifts for PPMPs; and a synergistic interaction involving hydrogen bonding and π–π stacking for PETMPs. In particular, these polymer-specific fluorescence fingerprints are largely preserved in tap water, despite elevated background intensity and partial contrast attenuation, demonstrating the resilience of the EEM–chemometric approach under realistic matrix conditions. Collectively, the strong agreement between fluorescence metrics, multivariate signatures, and interfacial chemistry establishes a robust structure–property framework and positions PACQDs as a rapid, label-free, and matrix-tolerant platform for reliable microplastic discrimination in environmental analysis. Full article