Search Results (4,804)

This paper reviews the fracture mechanics parameters associated with the variability in the crack growth curves associated with forty-two different tests that range from additively manufactured (AM) steels to cold spray additively manufactured (CSAM) 316L steel. As a result of this review, it is found that, to a first approximation, the effects of different building processes and R-ratios on the relationship between ΔK and the crack growth rate (da/dN) can be captured by allowing for changes in the fatigue threshold and the apparent cyclic toughness in the Schwalbe crack driving force (Δκ). Whilst this observation, when taken in conjunction with similar findings for AM Ti-6Al-4V, Inconel 718, Inconel 625, and Boeing Space Intelligence and Weapon Systems (BSI&WS) laser powder bed (LPBF)-built Scalmalloy^®, as well as for a range of CSAM pure metals, go a long way in making a point; it is NOT a mathematical proof. It is merely empirical evidence. As a result, this review highlights that for AM and CSAM materials, it is advisable to plot the crack growth rate (da/dN) against both ΔK and Δκ. The observation that, for the AM and CSAM steels examined in this study, the da/dN versus Δκ curves are similar, when coupled with similar observation for a range of other AM materials, supports a prior study that suggested using fracture toughness measurements in conjunction with the flight load spectrum and the operational life requirement to guide the choice of the building process for AM Ti-6Al-4V parts. The observations outlined in this study, when taken together with related findings given in the open literature for AM Ti-6Al-4V, AM Inconel 718, AM Inconel 625, and BSI&WS LPFB-built Scalmalloy^®, as well as for a range of CSAM-built pure metals, have implications for the implementation and certification of limited-life AM parts. Full article

Scientific studies have demonstrated how certain insect species can be used as bioindicators and reverse environmental degradation through their behavior and organization. Studying these species involves capturing and extracting hundreds of insects from a colony for subsequent study, analysis, and observation. This allows researchers to classify the individuals and also determine the organizational structure and behavioral patterns of the insects within colonies. The miniaturization of hardware devices for data and image acquisition, coupled with new Artificial Intelligence techniques such as Scene Graph Generation (SGG), has evolved from the detection and recognition of objects in an image to the understanding of relationships between objects and the ability to produce textual descriptions based on image content and environmental parameters. This research paper presents the design and functionality of a distributed computing architecture for image and video acquisition of bees and ants in their natural environment, in addition to a parallel computing architecture that hosts two datasets with images of real environments from which scene graphs are generated to recognize, classify, and analyze the behaviors of bees and ants while preserving and protecting these species. The experiments that were carried out are classified into two categories, namely the recognition and classification of objects in the image and the understanding of the relationships between objects and the generation of textual descriptions of the images. The results of the experiments, conducted in real-life environments, show recognition rates above 70%, classification rates above 80%, and comprehension and generation of textual descriptions with an assertive rate of 85%. Full article

With socioeconomic development, cities face increasingly complex and diverse disaster risks, making the construction of resilient cities an inevitable choice. However, the driving forces and tactical approaches behind urban resilience development remain unclear for urban safety development, thus posing challenges to cities urgently needing to enhance their resilience. Therefore, this paper investigates this issue, covering the following aspects: (1) Eighteen influencing factors within the complex system of urban resilience were identified and summarized from five perspectives: Economic, Social, Environmental, Infrastructure, and Organizational & Institutional. The attributes of the influencing factors were analyzed using the Decision-Making Experimentation and Evaluation Laboratory (DEMATEL) method, and key factors were identified accordingly. (2) The Total Adversarial Interpretive Structure Model (TAISM) method was applied to construct a multi-perspective adversarial recursive structural model with integrated impact values. This model illustrates the interrelationships among the influencing factors and clarifies their hierarchical structure. (3) A Fuzzy Reachability Matrix (FR) was introduced to handle uncertain relationships between factors in the comprehensive influence matrix, enabling an explicit analysis of the hierarchical structure of the urban resilience complex coupling giant system, clearly showing the impact of factor hierarchical changes on the system structure. (4) Building upon the analysis of factors affecting urban resilience, the specific pathways and mechanisms were articulated, followed by recommended measures formulated from both internal (governmental) and external (community) perspectives. The results can provide theoretical support for resilient city construction and serve as a practical cornerstone. Full article

A Life cycle assessment (LCA) is widely used to evaluate the carbon reduction potential of polycrystalline silicon photovoltaic systems. However, in existing LCA methods, most studies use static attenuation models and fixed lifecycle boundary frameworks. Therefore, this study proposes a dynamic LCA framework that considers the attenuation rate changes in photovoltaic systems and the energy gain during the recovery phase. The innovation of this method lies in its ability to more accurately reflect the carbon emissions and energy recovery period (EPBT) of photovoltaic systems under different operating and attenuation scenarios. In addition, this article expands the application scope of the LCA by introducing new boundary conditions, providing a new perspective for the lifecycle assessment of photovoltaic systems. A practical carbon emission calculation model was established using the full lifecycle data within this boundary, and the quantitative relationship between the EPBT and power generation was derived. A three-dimensional dynamic coupling model was developed to integrate these three key parameters and continuously characterize the dynamic behavior of the system throughout its entire lifecycle. This model explicitly addresses the attenuation of photovoltaic modules in three scenarios: low (1%), baseline (3%), and high (5%) attenuation rates. The results show that under low attenuation, the average EPBT is 4.14 years, which extends to 6.5 years under high attenuation and only 2.37 years under low attenuation. Sensitivity analysis confirmed the effectiveness of the model in representing the dynamic evolution of photovoltaic systems, providing a theoretical basis for subsequent environmental performance evaluations. Full article

Constructing a temperature and humidity prediction model for greenhouse-grown tomatoes is of great significance for achieving resource-efficient and sustainable greenhouse environmental control and promoting healthy tomato growth. However, traditional models often struggle to simultaneously capture long-term temporal trends, short-term local dynamic variations, and the coupling relationships among multiple variables. To address these issues, this study develops an iT-LSTM-CA multi-step prediction model, in which the inverted Transformer (iTransformer, iT) is employed to capture global dependencies across variables and long temporal scales, the Long Short-Term Memory (LSTM) network is utilized to extract short-term local variation patterns, and a cross-attention (CA) mechanism is introduced to dynamically fuse the two types of features. Experimental results show that, compared with models such as Gated Recurrent Unit (GRU), Temporal Convolutional Network (TCN), Recurrent Neural Network (RNN), LSTM, and Bidirectional Long Short-Term Memory (Bi-LSTM), the iT-LSTM-CA achieves the best performance in multi-step forecasting tasks at 3 h, 6 h, 12 h, and 24 h horizons. For temperature prediction, the R² ranges from 0.96 to 0.98, with MAE between 0.42 °C and 0.79 °C and RMSE between 0.58 °C and 1.06 °C; for humidity prediction, the R² ranges from 0.95 to 0.97, with MAE between 1.21% and 2.49% and RMSE between 1.78% and 3.42%. These results indicate that the iT-LSTM-CA model can effectively capture greenhouse environmental variations and provide a scientific basis for environmental control and management in tomato greenhouses. Full article

Slow responding in International Large-Scale Assessments (ILSAs) has received far less attention than rapid guessing, despite its potential to reveal heterogeneous response processes. Unlike disengaged rapid responders, slow responders may differ in time management, off-task behavior, or specific cognitive operations. This exploratory study uses sequence analysis of log-file data from a complex problem-solving item in PISA 2012 to examine whether slow responders can be grouped into homogeneous subtypes. The item required students to explore causal relations and externalize them in a diagram. Results indicate two distinct clusters among slow responders, each marked by characteristic interaction patterns and difficulties at different stages of the solution process. One cluster exhibited long pauses interspersed with repeated, inefficient attempts at representing causal relationships; the other showed shorter pauses coupled with inefficient exploratory actions targeting those relationships. These findings demonstrate that sequence analysis can parsimoniously identify clusters of action sequences associated with slow responding, offering a finer-grained account of aberrant behavior in low-stakes, digital assessments. More broadly, the approach illustrates how process data can be leveraged to differentiate mechanisms underlying slow response behaviors, with implications for validity arguments, diagnostic feedback, and the design of mitigation strategies in ILSAs. Directions for future research to better understand the differences among slow responders are provided. Full article

An explicit coupled two-dimensional (2D) hydromechanical model (HMM) that can simulate discontinuous features in the foundation, as well as the effects of grout curtains and drainage systems, is employed to evaluate the influence of key parameters such as dam height, foundation behaviour, joint patterns, joint stiffness and strength, hydraulic apertures, and grout curtain permeability. A parametric sensitive study using four gravity dams, and a real case study of an operating dam are presented. The results presented show that dam height influences the relationship between water level in the reservoir and drain discharges, with higher dams showing more pronounced curved nonlinearity. The strength properties of the concrete–rock interface are also shown to have a meaningful influence on the HM response, especially for an elastic foundation and for higher dams, showing the need to properly characterize this interface through in situ testing. The joint aperture at nominal zero stress is shown to be the parameter with the most significant effect on the HM response. The results also show that a progressive degradation scenario of the concrete–rock interface or of the grout curtain permeability is easier to identify through the hydraulic measurements than in the mechanical displacement field. Full article

Dual Carbon Goals are driving transformation in China’s power system, where increased renewable energy penetration is accompanied by heightened fluctuations on the generation and load sides. Energy storage and microgrid coordination have emerged as key solutions. However, existing research faces the challenge of balancing microgrid operations, energy storage services, and the alignment of user demand with stakeholder interests. This paper establishes a tripartite collaborative optimization framework to balance multi-stakeholder interests and enhance system efficiency, assuming fixed energy storage capacity. Centering on a principal-agent game between microgrid operators and consumer aggregators, energy storage service providers are integrated into this dynamic. Microgrid operators set 24-h electricity and heat pricing while adhering to tariff constraints, prompting consumer aggregators to adjust energy consumption and storage strategies accordingly. The KKT conditional method is employed to solve the model, deriving optimal user energy consumption strategies at the lower level while solving marginal pricing equilibrium relationships at the upper level, balancing accuracy with information privacy. The creative contribution of this article lies in the first construction of a tripartite collaborative optimization architecture in which energy storage service providers are embedded in a game of ownership and subordination. It proposes a dynamic coupling mechanism between pricing power, energy consumption decision-making, and energy storage configuration under fixed energy storage capacity constraints, achieving a balance of interests among multiple parties. By building a case study using MATLAB (R2022b), we compare operation costs, benefits, and absorption rates across different scenarios to validate the framework’s effectiveness and provide a reference for engineering applications. Full article

This study presents an eccentric pipe separator (EPS) designed according to the shallow pool principle and Stokes’ law as a compact alternative to conventional gravitational tank separators for offshore platforms. To investigate the internal oil–water flow characteristics and separation performance of the EPS, both field experiments with crude oil on an offshore platform and computational fluid dynamics (CFD) simulations were conducted, guided by dimensional analysis. Crude oil volume fractions were measured using a Coriolis mass flow meter and the fluorescence method. The CFD analysis employed an Eulerian multiphase model coupled with the renormalization group (RNG) k-ε turbulence model, validated against experimental data. Under the operating conditions examined, the separated water contained less than 50 mg/L of oil, while the separated crude oil achieved a purity of 98%, corresponding to a separation efficiency of 97%. The split ratios between the oil and upper outlets were found to strongly influence the phase distribution, velocity field, and pressure distribution within the EPS. Higher split ratios caused crude oil to accumulate in the upper core region and annulus. Maximum separation efficiency occurred when the combined split ratio of the upper and oil outlets matched the inlet oil volume fraction. Excessively high split ratios led to excessive water entrainment in the separated oil, whereas excessively low ratios resulted in excessive oil entrainment in the separated water. Crude oil density and inlet velocity exhibited an inverse relationship with separation efficiency; as these parameters increased, reduced droplet settling diminished optimal efficiency. In contrast, crude oil viscosity showed a positive correlation with the pressure drop between the inlet and oil outlet. Overall, the EPS demonstrates a viable, space-efficient alternative for oil–water separation in offshore oil production. Full article

A knowledge of the process–structure–property correlation and underlying deformation mechanisms of material under a coupled electro-thermal–mechanical field is crucial for developing novel electrically assisted forming techniques. In this work, numerical simulation and experimental analyses were carried out to study the non-uniform deformation behaviors and microstructure evolution of Ti₂AlNb-based alloy stiffened panels in different characteristic deformation regions during electrically assisted press bending (EAPB). The quantitative relationships between electro-thermal–mechanical routes, microstructural features, and mechanical properties of EAPBed stiffened panels were initially established, and the underlying mechanisms of electrically induced phase transformation and morphological transformation were unveiled. Results show that the temperature of the panel first increases then deceases with forming time in most regions, but it increases monotonically and reaches its peak value of 720.1 °C in the web region close to the central transverse rib. The higher accumulated strain and precipitation of the acicular O phase at mild temperature leads to strengthening of the longitudinal ribs at near blank holder regions, resulting in an ideal microstructure of 3~4% blocky α₂ phase + a dual-scale O structure in a B2 matrix with a maximal hardness of 389.4 ± 7.2 HV_0.3. While the dissolution of the α₂ phase and the spheroidization and coarsening of the O phase bring about softening (up to 9.29%) of the lateral ribs and web near the center region, the differentiated evolution of microstructure and the mechanical property in EAPB results in better deformation coordination and resistance to wrinkling and thickness variation in the rib–web structure. The present work will provide valuable references for achieving shape-performance coordinated manufacturing of Ti₂AlNb-based stiffened panels. Full article

This study analyzes artificial intelligence development and green economic efficiency across 31 Chinese provinces using 2019–2021 panel data. We apply the entropy weight TOPSIS method to measure AI development levels. The entropy weight TOPSIS method measures AI development levels, the DEA-BCC model assesses green economic efficiency, and their coordination types are identified. Findings reveal a significant negative correlation between AI development and green economic efficiency. We explain this complex relationship through three mechanisms: short-term polarization effects, technology conversion lags, and spatial spillovers. Spatial analysis shows AI development forms high-high agglomerations in the Yangtze River Delta and Shandong. Green economic efficiency shows high-high clustering in the Beijing-Tianjin-Hebei region and selected western provinces. Using a “two-system” coupling framework, we identify four provincial categories. The “double-high” type should function as growth poles. The “high-low” type requires improved technology conversion efficiency. The “low-high” type can leverage ecological advantages. The “double-low” type needs enhanced factor inputs. We propose three targeted policy recommendations: establishing digital-green synergy platforms, implementing inter-provincial AI resource collaboration mechanisms, and developing locally adapted action plans. Full article

Carbon capture is pivotal for achieving carbon neutrality; however, its high energy consumption severely limits the operational flexibility of power plants and remains a key challenge. This study, targeting a full flue gas carbon capture scenario for a 1000 MW coal-fired power plant, identified the dual-element (“steam” and “power generation”) coupling convergence mechanism. Based on this mechanism, a comprehensive set of mathematical model equations for the “carbon–electricity–heat” coupling process is established. This model quantifies the dynamic relationship between key operational parameters (such as unit load, capture rate, and thermal consumption level) and system performance metrics (such as power output and specific power penalty). To address the challenge of flexible operation, this paper further proposes two innovative coupled modes: steam thermal storage and chemical solvent storage. Model-based quantitative analysis indicated the following: (1) The power generation impact rate under full THA conditions (25.7%) is lower than that under 30% THA conditions (27.7%), with the specific power penalty for carbon capture decreasing from 420.7 kW·h/tCO₂ to 366.7 kW·h/tCO₂. (2) Thermal consumption levels of the capture system are a critical influencing factor; each 0.1 GJ/tCO₂ increase in thermal consumption leads to an approximate 2.83% rise in unit electricity consumption. (3) Steam thermal storage mode effectively reduces peak-period capture energy consumption, while the chemical solvent storage mode almost fully eliminates the impact on peak power generation and provides optimal deep peak-shaving capability and operational safety. Furthermore, these modeling results provide a basis for decision-making in plant operations. Full article

Background: Systemic inflammation plays a central role in the pathogenesis and progression of type 2 diabetes mellitus (T2DM). Hematologic inflammatory indices-such as the Systemic Immune-Inflammation Index (SII), Neutrophil-to-Lymphocyte Ratio (NLR), Platelet-to-Lymphocyte Ratio (PLR), and Monocyte-to-Lymphocyte Ratio (MLR)-have emerged as accessible markers of chronic inflammation, yet longitudinal comparisons across oral antidiabetic therapies remain limited. This study uniquely integrates longitudinal correlation and network analyses in a large real-world T2DM cohort, allowing assessment of the temporal stability and class-specific inflammatory patterns across four oral antidiabetic therapies. Methods: This retrospective, longitudinal study analyzed 13,425 patients with T2DM treated with Biguanidines, Dipeptidyl Peptidase-4 (DPP-4) inhibitors, Sodium–Glucose Cotransporter-2 (SGLT-2) inhibitors or Thiazolidinediones (TZDs) between 2020 and 2024. Data were retrieved from the Probel^® Hospital Information System and included baseline, early (30–180 days), and late (180–360 days) follow-up laboratory results. Systemic inflammatory indices were computed from hematologic parameters, and correlations among inflammatory and biochemical markers were assessed using Spearman’s coefficients. Results: At baseline, all hematologic indices were strongly intercorrelated (SII–NLR r = 0.83, p < 0.001; SII–PLR r = 0.73, p < 0.001), with moderate associations to C-reactive protein (CRP; r ≈ 0.3–0.4) and weak or no correlations with Ferritin (r ≈ −0.1). These relationships remained stable throughout follow-up, confirming reproducibility of systemic inflammatory coupling. Longitudinally, SII and NLR showed modest early increases followed by significant declines at one year (p < 0.05), while PLR and MLR remained stable. Class-specific differences were observed: SGLT-2 inhibitors and TZDs demonstrated stronger and more integrated anti-inflammatory networks, whereas Biguanidines and DPP-4 inhibitors exhibited moderate coherence. Principal Component Analysis (PCA) explained 62.4% of total variance and revealed distinct clustering for TZD and SGLT-2 groups, reflecting class-specific inflammatory modulation. Conclusions: Systemic inflammatory indices (SII, NLR, PLR) provide reproducible and accessible measures of low-grade inflammation in T2DM. Despite overall inflammation reduction with treatment, drug-specific patterns emerged-SGLT-2 inhibitors and TZDs showed greater anti-inflammatory coherence, while Biguanidines and DPP-4 inhibitors maintained moderate effects. Full article

This quantitative observational study with pre–post design aimed to examine joint-specific kinematic adaptations and the relationship between trunk stability and spatiotemporal gait parameters following intensive robotic rehabilitation. A total of 12 neurological patients completed 16 sessions of gait training using the Tecnobody Smart Gravity Walker. Pre- and post-training kinematic data were collected for bilateral hip and knee flexion–extension, trunk flexion–extension, trunk lateral flexion, and center-of-gravity displacement. Waveforms were normalized to 100% stride. Paired t-tests assessed pre–post differences, and correlations examined associations between trunk stability and gait performance. Significant increases were found in right hip flexion–extension (t = 3.44, p < 0.001), trunk flexion–extension (t = 9.49, p < 0.001), and center-of-gravity displacement (t = 15.15, p < 0.001), with reduced trunk lateral flexion (t = –8.64, p < 0.001). Trunk flexion–extension correlated with gait speed (r = 0.74), step length (r = 0.68), and stride length (r = 0.71); trunk lateral flexion correlated with cadence (r = 0.66) and stride length (r = 0.70). Intensive robotic rehabilitation improved trunk and hip kinematics, supporting trunk stability as an important biomechanical correlate of gait recovery. Sensor-derived metrics revealed strong neuromechanical coupling between postural control and locomotion in neurological patients. Full article

Energetic powder processing includes comminution, sieving, drying, conveying, mixing, and packaging, all of which determine product performance and safety. With growing requirements for efficiency and reliability, numerical simulation has become essential for analyzing mechanisms, optimizing parameters, and supporting equipment design. This review summarizes recent progress in simulation techniques such as the discrete element method (DEM), computational fluid dynamics (CFD), and multi-scale coupling while also evaluating their predictive capabilities and limitations across various unit operations and safety concerns such as electrostatic hazards. It, thus, establishes the core “property–parameter–performance” relationships and clarifies mechanisms in multiphase flow, energy transfer, and charge accumulation, and highlights the role of symmetry in improving simulation efficiency. By highlighting persistent challenges, this work lays a foundation for future research, guiding the development of theoretical frameworks and practical solutions for advanced powder processing. Full article