Search Results (1,625)

This paper examines the long-run relationship between economic growth and CO₂ emissions in Croatia over the period 1990–2023 using the ARDL bounds testing approach. The analysis aims to assess the presence of an Environmental Kuznets Curve (EKC) and to shed light on Croatia’s position along the growth–emissions trajectory, an issue that has remained inconclusive in earlier studies. The results provide evidence of an inverted U-shaped relationship between the GDP per capita and CO₂ emissions, consistent with the EKC hypothesis. The estimates of marginal effects suggest that the impact of income on emissions weakens and may eventually turn negative at higher income levels, although the precise income level at which this transition occurs is sensitive to model specification and sample composition. Energy consumption emerges as the strongest long-run driver of emissions, while a higher share of renewable energy contributes significantly to their reduction. Institutional quality is found to be positively associated with emissions in the long run, reflecting growth-enhancing effects during the post-transition period rather than immediate environmental improvements. The contribution of this study lies in the use of a longer time span and a dynamic empirical framework that allows for a more nuanced assessment of the growth–emissions relationship in Croatia. Overall, the findings point to a gradual decoupling of economic growth from carbon emissions while highlighting that the sustainability of this trajectory depends critically on continued progress in the energy transition and on the alignment of institutional development with climate and energy objectives. Full article

This paper presents a systematic approach for designing an electric motor drive system (EMDS) for a permanent magnet DC motor to achieve precise speed regulation using a classical PID controller. Smooth voltage trajectory planning based on Bézier curves is employed to mitigate high voltage and current peaks during step speed transitions, improving dynamic performance, reducing electrical stress, and making the control system physically realizable. A comparative evaluation of inverting buck–boost, positive buck–boost, and quadratic DC–DC converters is conducted using the same motor and controller, enabling the identification of the most suitable controller–converter pairing. Experimental results demonstrate that, with an appropriate converter topology and voltage trajectory, peak voltages and currents are significantly reduced, resulting in a smoother control action and reliable speed regulation without the need for complex control schemes. Full article

The patch-clamp technique is widely regarded as the gold standard in cellular electrophysiology and can be applied in several configurations. In the cell-attached (C-A) mode, it enables the recording of single-channel currents, whereas the whole-cell (W-C) mode allows for the measurement of macroscopic currents, representing the collective activity of many channels. When the recording configuration was switched from C-A to W-C on the same cell, the current amplitude increased dramatically, while action currents (ACs) were completely abolished, indicating a profound alteration in the cell’s electrophysiological response under the new setup. In excitable cells, the occurrence of ACs, representing propagated action potentials, can interfere with C-A single-channel recordings. To address this, a high-K⁺ solution is typically applied to the bath to suppress the ACs. The inwardly rectifying K⁺ (Kir), ATP-sensitive K⁺ (K_ATP) and large-conductance Ca²⁺-activated K⁺ (BK_Ca) channels are crucial members of the K⁺ channel family that facilitate the efflux of K⁺ ions, driven by the K⁺ electrochemical gradient. These channels are primarily distinguished by their rectification properties and gating kinetics. For instance, K_ATP channels exhibit a bursting kinetic pattern with inward rectifying property, while BK_Ca channels display strong outward rectification. Mitoxantrone, which belongs to a class of drugs called anthracenediones, can suppress the activity of Kir channels in differentiated RAW 264.7 cells, with no change in single-channel conductance. The respiratory stimulator GAL-021 acts as a BK_Ca channel inhibitor, and it suppresses channel activity and shifts the activation curve to the right, suggesting a voltage-dependent blockade that stabilizes the channel in a closed state. GAL-021 does not change the single-channel conductance, indicating it is a gating modifier rather than an open-pore blocker. The functional roles of ion channels are fundamentally important. Correspondingly, the field is transitioning to artificial intelligence for automated single-cell patch-clamp experiments, though brain slice recordings still require manual techniques. Full article

This study focuses on obtaining lignin-based hydrogels from pruning residues of orange trees in the Safor region (Valencia) using an alkaline extraction method. The structural analysis of the obtained lignin was carried out using Fourier-transform infrared spectroscopy (FTIR), which revealed the characteristic functional groups of lignin, as well as its structural monolignols: syringyl and guaiacyl. The thermal properties were analyzed using differential scanning calorimetry (DSC) and thermogravimetric analysis. The DSC thermogram revealed a relatively low glass transition temperature (T_g) of 67 °C, which may be attributed to partial lignin chain degradation during alkaline extraction. Soda lignin was obtained at 190 °C with an approximate yield of 10.8% relative to the initial biomass and subsequently used to synthesize poly(vinyl alcohol) (PVA)-based hydrogels for ibuprofen encapsulation. Finally, the release experiments of the encapsulated ibuprofen were carried out in an aqueous phosphate buffer medium (pH = 7) at room temperature. A multi-curve response analysis (MCR) algorithm using the Korsmeyer–Peppas (KP) concentration model was used to analyze the release curves, which concluded that the drug and water-soluble lignin fraction (SLF) were released at different rates. For both components, a good correlation was obtained between the measured responses and those provided by the KP model. The release profile indicated that approximately 87% of the initial ibuprofen load was released from the hydrogel within 5 h, highlighting the promising potential of lignin-based hydrogels for drug delivery applications. Full article

Two-dimensional transition metal dichalcogenides (TMDs) are key materials in ultrafast photonics. However, the performance of conventional TMDs is limited by their bandwidth and carrier recovery time. The novel Dirac semimetal nickel ditelluride (NiTe₂), with its broad-band response and excellent nonlinear properties, emerges as an ideal candidate for saturable absorber (SA) materials. In this work, we report, for the first time, the application of NiTe₂ in the ytterbium-doped fiber laser, demonstrating stable passive Q-switching operation. The nonlinear transmission curve reveals a modulation depth of 6.82% at 1 µm and a saturation intensity of 2.12 MW/cm². Using an all-fiber ring cavity structure, stable Q-switched pulses with a central wavelength of 1031 nm were achieved at a pump threshold of 94 mW, with a maximum pulse repetition frequency of 30.1 kHz. The minimum pulse width reached 2.3 μs, and the single-pulse energy increased to 3.05 nJ, with an impressive radio frequency (RF) spectral signal-to-noise ratio (SNR) of 58.9 dB. This study demonstrates the potential of NiTe₂ as a high-performance SA in the near-infrared region, providing a solid foundation for its future application in ultrafast laser technologies. Full article

In this study, deep tight sandstone was selected as an example to propose a complete method for predicting reservoir pore structure by capillary pressure curves and conventional well log data. This method pioneers the integration of grey relational analysis, principal component analysis, ensemble clustering, and deep neural networks to establish a systematic predictive framework for transitioning from conventional logging data to pore structure types. A total of 186 core data from three wells were used in this study. First, sensitive pore structure parameters from mercury injection capillary pressure data were extracted using grey correlation analysis and principal component analysis. Then, unsupervised clustering analysis was applied to classify the reservoir pore structures in the study area, dividing it into three categories. These category labels were combined with conventional well logs to create learning samples for a deep neural network (DNN) model developed to predict reservoir pore structure categories. The accuracy of the training set of the model reached 88.2%, while the accuracy of the testing set was 80.43%. Finally, the method was applied to field well log data. The results showed significant differences in pore structure classifications among gas layers, water–gas layers, and dry layers. This method is versatile, with its core components transferable to other deep sandstone reservoir studies, and can accurately predict the pore structure of tight sandstone reservoirs, which is critical for advancing the characterization of deep and complex oil and gas reservoirs. Full article

The Jurassic reservoirs in the Fukang Sag of the Junggar Basin exhibit heterogeneous overpressure. As the mechanisms underlying overpressure generation remain poorly constrained, this poses challenges for accurate pre-drilling-pressure prediction and hinders a comprehensive understanding of hydrocarbon accumulation processes. Through integrated analysis of measured pressure, mud weight, and well-logging curves, this study delineates distinct overpressure characteristics in sandstones and identifies the well-logging response to overpressure in mudstones. By coupling the loading-unloading response with the analysis of geological conditions conducive to overpressure, we differentiate the overpressure-generating mechanisms between sandstones and mudstones and assess their implications for deep petroleum exploration. The study reveals significant vertical heterogeneity in pressure regimes, with sandstones exhibiting pressure coefficients ranging from 1.2 to 1.8, locally exceeding 2.1. Strong overpressure preferentially develops in isolated sand bodies linked to deep source kitchens via oil-source faults. The logging response of overpressured mudstones shows high acoustic transit time, high neutron, and low resistivity, deviating from the normal compaction trend, yet demonstrates progressive density increases attributable to chemical compaction processes. Overpressure points with pressure coefficients between 1.2 and 1.4 align with the loading curve dominated by disequilibrium compaction. The overpressure with a pressure coefficient exceeding 1.4 correlates with abrupt unloading responses indicative of fault-transferred overpressure in sandstones. Our results highlight that overpressured fluid migration via faults is a critical process in hydrocarbon migration, with large-magnitude overpressured reservoirs being readily formed near oil-source faults. Multi-overpressure mechanisms create a complex pore-pressure distribution in deep layers, challenging conventional pressure-prediction models. These insights advance predictive models for pore pressure and provide a robust framework for optimizing exploration strategies in the Fukang Sag. Full article

Multiple Unmanned Aerial Vehicle (UAV) collaborative coverage path planning is widely applied in fields such as regional surveillance. However, optimizing the trade-off between deployment costs and task execution efficiency remains challenging. To balance resource costs and execution efficiency with an uncertain number of UAVs, this paper analyzes the characteristics of irregular mission areas and formulates a bi-level optimization model for multi-UAV collaborative CPP. The model aims to minimize both the number of UAVs and the total path length. First, in the upper level, an improved Best Fit Decreasing algorithm based on binary search is designed. Straight-line scanning paths are generated by determining the minimum span direction of the irregular regions. Task allocation follows a longest-path-first, minimum-residual-range rule to rapidly determine the minimum number of UAVs required for complete coverage. Considering UAV’s turning radius constraints, Dubins curves are employed to plan transition paths between scanning regions, ensuring path feasibility. Second, the lower level transforms the problem into a Multiple Traveling Salesman Problem that considers path continuity, range constraints, and non-overlapping path allocation. This problem is solved using an Improved Biased Random Key Genetic Algorithm. The algorithm employs a variable-length master–slave chromosome encoding structure to adapt to the task allocation of each UAV. By integrating biased crossover operators with 2-opt interval mutation operators, the algorithm accelerates convergence and improves solution quality. Finally, comparative experiments on mission regions of varying scales demonstrate that, compared with single-level optimization and other intelligent algorithms, the proposed method reduces the required number of UAVs and shortens the total path length, while ensuring complete coverage of irregular regions. This method provides an efficient and practical solution for multi-UAV collaborative CPP in complex environments. Full article

To address the depletion of shallow coal resources, mining activities have progressed to greater depths, where rock masses contain numerous fractures due to complex geological conditions, making grouting reinforcement essential for ensuring stability. Using digital image correlation, this study investigated the strain evolution characteristics of grouted fractured specimens of three rock types—mudstone, coal–rock, and sandstone—under uniaxial compression. Analysis of the strain evolution process focused on two typical fracture inclinations of 0° and 60°, while examination of the peak strain characteristics covered five inclinations, namely 0°, 15°, 30°, 45°, and 60°. The findings indicate that the mechanical response varies systematically with lithology and fracture inclination. The post-peak curves differ significantly among rock types: coal–rock shows a gentle descent, mudstone exhibits a rapid strength drop but higher residual strength, and sandstone is characterized by “serrated” fluctuations. The failure mode transitions from tensile splitting at a horizontal inclination of 0° to shear failure at inclinations of 15°, 30°, 45°, and 60°. Strain nephograms corresponding to the peak stress point D reveal sharp, band-shaped zones of strain localization. The maximum principal strain exhibits a non-monotonic trend, first increasing and then decreasing with increasing inclination angle. For grouted coal–rock and sandstone, the peak values of 47.47 and 45.00 occur at α = 45°. In contrast, grouted mudstone reaches a maximum value of 26.80 at α = 30°, indicating its lower susceptibility to damage. The study systematically clarifies the strain evolution behavior of grouted fractured rock masses, providing a theoretical basis for evaluating the effectiveness of reinforcement and predicting failure mechanisms. Crucially, the findings highlight mudstone’s role as a high-integrity medium and the particular vulnerability of horizontal fractures, offering direct guidance for the targeted grouting design in stratified rock formations. Full article

Acrylonitrile Styrene Acrylate (ASA) is widely used in outdoor structural applications due to its favorable mechanical stability and weather resistance; however, its temperature-dependent plastic behavior remains insufficiently characterized for accurate numerical simulation. This study presents a non-standard method of calibrating the temperature-dependent Johnson–Cook (J-C) plasticity model for ASA in the practical operating temperature range below the glass transition temperature. Uniaxial tensile tests at constant strain rate 0.01 s⁻¹ were performed at −10 °C, +23 °C, and +65 °C to characterize the effect of temperature on the material’s plastic response. The J-C parameters $A$, $B$, and $n$ were identified for each temperature separately and globally using least-squares optimization implemented in MATLAB R2024b, showing good agreement with the experimental stress–strain curves. The calibrated parameters were subsequently implemented in Abaqus 2024 and validated through finite element simulations of the tensile tests. Numerical predictions demonstrated a very high correlation with the experimental data across all temperatures, confirming that the J-C model accurately captures the hardening behavior of ASA. The presented parameter set and calibration methodology provide a reliable basis for future simulation-driven design, forming analysis, and structural assessment of ASA components subjected to variable thermal conditions. Full article

Curling presents a challenging continuous-control problem in which shot outcomes depend on long-horizon interactions between complex physical dynamics, strategic intent, and opponent responses. Despite recent progress in applying reinforcement learning (RL) to games and sports, curling lacks a unified environment that jointly supports stable, rule-consistent simulation, structured state abstraction, and scalable agent training. To address this gap, we introduce a comprehensive learning framework for curling AI, consisting of a full-sized simulation environment, a task-aligned Markov decision process (MDP) formulation, and a two-phase training strategy designed for stable long-horizon optimization. First, we propose a novel MDP formulation that incorporates stone configuration, game context, and dynamic scoring factors, enabling an RL agent to reason simultaneously about physical feasibility and strategic desirability. Second, we present a two-phase curriculum learning procedure that significantly improves sample efficiency: Phase 1 trains the agent to master delivery mechanics by rewarding accurate placement around the tee line, while Phase 2 transitions to strategic learning with score-based rewards that encourage offensive and defensive planning. This staged training stabilizes policy learning and reduces the difficulty of direct exploration in the full curling action space. We integrate this MDP and training procedure into a unified Curling RL Framework, built upon a custom simulator designed for stability, reproducibility, and efficient RL training and a self-play mechanism tailored for strategic decision-making. Agent policies are optimized using Soft Actor–Critic (SAC), an entropy-regularized off-policy algorithm designed for continuous control. As a case study, we compare the learned agent’s shot patterns with elite match records from the men’s division of the Le Gruyère AOP European Curling Championships 2023, using 6512 extracted shot images. Experimental results demonstrate that the proposed framework learns diverse, human-like curling shots and outperforms ablated variants across both learning curves and head-to-head evaluations. Beyond curling, our framework provides a principled template for developing RL agents in physics-driven, strategy-intensive sports environments. Full article

This study establishes a direct and quantitative link between field-scale monopile behavior, three-dimensional finite element (FE) modeling, and practical p-y curve formulations for large-diameter offshore monopiles. A validated three-dimensional FE model, benchmarked against a full-scale monopile field test, was employed to derive depth-dependent p-y curves under monotonic lateral loading and to evaluate the applicability of classical formulations proposed by Matlock and Reese. A systematic parametric analysis was performed to investigate the influence of pile diameter, embedment depth, and undrained shear strength of the surrounding soil. The results demonstrate that pile diameter and soil shear strength exert a dominant control on lateral stiffness and ultimate soil reaction, whereas embedment depth has only a minor influence on near-surface p-y behavior within the deep embedment range considered. Increasing the pile diameter leads to a transition from bending-dominated response to rigid-body rotation accompanied by three-dimensional soil wedge formation. Quantitative comparisons show that, at depths of 1–4 m and for working displacement levels of approximately 5–10 mm, FE-derived soil reactions are typically 3.0–4.8 times higher than those predicted by the Matlock formulation, as well as Reese curves. These findings demonstrate that classical p-y methods can significantly underestimate lateral soil resistance for modern large-diameter monopiles and highlight the necessity of calibrated three-dimensional FE analyses or FE-informed p-y modifications for reliable offshore wind turbine foundation design. Full article

Background: Dietary transitions toward Westernized patterns (WDPs) (high in processed foods, sugars, and fats) pose a global public health challenge. The Westernized Diet Index (WDI) measures adherence to these patterns. However, its validity with respect to metabolic biomarkers warrants thorough evaluation for use in epidemiological and clinical research. Objectives: This study validates the WDI using metabolic biomarkers (including anthropometrics, blood pressure, fasting blood glucose (FBG), triglycerides, HDL-c, LDL-c, and total cholesterol), examines its association with metabolic syndrome (MetS), and compares scoring methods to identify the most effective measure of WDPs adherence. Methods: Data from 10,146 participants in the Fasa Adult Cohort Study (FACS) were used. We calculated the WDI using global (WDI-G) and population (WDI-P) Z scores and food group (WDI-FG)-based algorithms. Validation employed logistic and linear regression, ROC (receiver operating characteristic) curves, Youden’s index, and k-means clustering. Results: All WDI scoring methods (across all methods, higher scores indicated lower adherence to WDPs) demonstrated a strong, significant association with all three MetS definitions (WHO, NCEP: ATPIII, and IDF) and nearly all investigated metabolic biomarkers. In fully adjusted logistic models, WDI Global (WDI-G) (OR: 0.23) and WDI Food Groups (WDI-FG) (OR: 0.26) were significantly associated with MetS (based on the WHO definition). Also, in fully adjusted linear regression models, a 10% increase (reflecting lower adherence to WDPs) in the WDI-G score (range: −2.03 to 1.11) was significantly associated with a 3.96 mg/dL reduction in FBG and a 2.61 cm reduction in waist circumference. Additionally, ROC curves (AUC: 0.57–0.61) demonstrated that WDI predicts MetS with moderate accuracy. The strongest associations were observed with population-based scoring. In addition, based on comparative performance, WDI-G, WDI-P, and WDI-FG appear most suitable for cross-population, within-cohort, and mechanistic or intervention-focused research, respectively. Conclusions: The WDI shows promise as a nutritional tool for assessing adherence to WDPs and exploring associations with metabolic health outcomes, including MetS. These findings suggest that the WDI may be useful in future dietary, public health, and clinical research, although further validation in diverse populations is warranted. Full article

A biomimetic cartilage scaffold featuring a continuous hydroxyapatite (HA) concentration gradient and a spatially curved architecture was developed using a dual-channel mixing extrusion-based 3D printing approach. By dynamically regulating the feeding rates of two bioinks during printing, a continuous HA gradient decreasing from the bottom to the top of the scaffold was precisely achieved, mimicking the compositional transition from the calcified to the non-calcified cartilage region in native articular cartilage. The integration of gradient material deposition with synchronized multi-axis motion enabled accurate fabrication of curved geometries with high structural fidelity. The printed scaffolds exhibited stable swelling and degradation behavior and showed improved compressive performance compared with step-gradient counterparts. Rheological analysis confirmed that the bioinks possessed suitable shear-thinning and recovery properties, ensuring printability and shape stability during extrusion. In vitro evaluations demonstrated good cytocompatibility, supporting bone marrow mesenchymal stem cell (BMSC) adhesion and proliferation. Chondrogenic assessment based on scaffold extracts indicated that the incorporation of HA and its gradient distribution did not inhibit cartilage-related extracellular matrix synthesis, confirming the biosafety of the composite hydrogel system. Overall, this study presents a controllable and versatile fabrication strategy for constructing curved, compositionally graded cartilage scaffolds, providing a promising platform for the development of biomimetic cartilage tissue engineering constructs. Full article

In Building Information Modeling (BIM) of bridge piers, persistent limitations have been observed in the modeling of spiral hoop rebar with variable pitch and diameter. Taking Revit as an example, its built-in family files can only generate spirals with constant geometry. When dealing with non-uniform rebar, designers often have to rely on segmented modeling or manual operations, which is not only time-consuming but also prone to deviations. To solve this problem, this paper proposes a parameterized modeling method based on the secondary development of Revit. By combining the Revit API with the C# programming language, the spiral equation is embedded into the Non-Uniform Rational B-Spline (NURBS) curve reconstruction framework, realizing the continuous modeling of spiral hoop rebar in a unified model. This method also allows users to flexibly input parameters such as cover thickness, rebar diameter, and segment length through a graphical user interface. Through comparative experiments, the proposed method and the traditional family file modeling method were verified respectively in the modeling of a single column and an entire bridge pier. The results indicate that the proposed method reduces the average modeling time of a single bridge pier by 66.5% and that of the entire project by 48.7%. While maintaining high geometric accuracy, this method significantly shortens modeling time and reduces workload, especially demonstrating higher consistency in pitch transition sections and conical sections. Beyond technical performance, this study also demonstrates that the secondary development of Revit provides a practical and feasible solution for the efficient, precise, and generalizable modeling of complex reinforcing bar components in terms of expanding BIM functions, which holds significant practical implications. Full article