Search Results (238)

Because artificially cemented granular (ACG) materials employ diverse combinations of aggregates and binders—including cemented soil, low-cement-content cemented sand and gravel (LCSG), and concrete—their stress–strain responses vary widely. In LCSG, the binder dosage is typically limited to 40–80 kg/m³ and the sand–gravel skeleton is often obtained directly from on-site or nearby excavation spoil, endowing the material with a markedly lower embodied carbon footprint and strong alignment with current low-carbon, green-construction objectives. Yet, such heterogeneity makes a single material-specific constitutive model inadequate for predicting the mechanical behavior of other ACG variants, thereby constraining broader applications in dam construction and foundation reinforcement. This study systematically summarizes and analyzes the stress–strain and volumetric strain–axial strain characteristics of ACG materials under conventional triaxial conditions. Generalized hyperbolic and parabolic equations are employed to describe these two families of curves, and closed-form expressions are proposed for key mechanical indices—peak strength, elastic modulus, and shear dilation behavior. Building on generalized plasticity theory, we derive the plastic flow direction vector, loading direction vector, and plastic modulus, and develop a concise, transferable elastoplastic model suitable for the full spectrum of ACG materials. Validation against triaxial data for rock-fill materials, LCSG, and cemented coal–gangue backfill shows that the model reproduces the stress and deformation paths of each material class with high accuracy. Quantitative evaluation of the peak values indicates that the proposed constitutive model predicts peak deviatoric stress with an error of 1.36% and peak volumetric strain with an error of 3.78%. The corresponding coefficients of determination R² between the predicted and measured values are 0.997 for peak stress and 0.987 for peak volumetric strain, demonstrating the excellent engineering accuracy of the proposed model. The results provide a unified theoretical basis for deploying ACG—particularly its low-cement, locally sourced variants—in low-carbon dam construction, foundation rehabilitation, and other sustainable civil engineering projects. Full article

As part of the carbon peak and neutrality drive, an influx of renewable energy into the grid is imminent. However, the unpredictability of renewables like wind and solar can lead to significant curtailment if the power system relies solely on traditional generators. This paper presents a demand response mechanism to enhance renewable energy uptake by defining an optimal load curve for each node, considering the generator’s dynamic impact, system operations, and renewable energy projections. Once the ideal load curve is published, consumers, influenced by incentives, voluntarily align their consumption, steering the actual load to resemble the proposed curve. This strategy not only guides flexible generation resources to better utilize renewables but also minimizes the communication and control expenses associated with large-scale customer demand response. Additionally, a new evaluation metric for user response is proposed to ensure equitable incentive distribution. The model has been shown to lower both consumer power costs and system generation expenses, achieving a 22% reduction in renewable energy wastage. Full article

This study presents an advanced energy demand management approach within residential microgrids using bipartite models for optimal demand response. The methodology relies on linear programming, specifically the Simplex algorithm, to optimize power distribution while minimizing costs. The model aims to reduce residential energy consumption by flattening the demand curve through demand response programs. Additionally, the Internet of Things (IoT) is integrated as a communication channel to ensure efficient energy management without compromising user comfort. The research evaluates energy resource allocation using bipartite graphs, modeling the generation of energy from renewable and conventional high-efficiency sources. Various case studies analyze scenarios with and without market constraints, assessing the impact of demand response at different levels (5%, 10%, 15%, and 20%). Results demonstrate a significant reduction in reliance on external grids, with optimized energy distribution leading to potential cost savings for consumers. The findings suggest that intelligent demand response strategies can enhance microgrid efficiency, supporting sustainability and reducing carbon footprints. Full article

Background/Objectives: The clinically validated multiplex Oncuria bladder cancer (BC) assay quickly and noninvasively identifies disease risk and tracks treatment success by simultaneously profiling 10 protein biomarkers in voided urine samples. Oncuria uses paramagnetic bead-based fluorescence multiplex technology (xMAP^®; Luminex, Austin, TX, USA) to simultaneously measure 10 protein analytes in urine [angiogenin, apolipoprotein E, carbonic anhydrase IX (CA9), interleukin-8, matrix metalloproteinase-9 and -10, alpha-1 anti-trypsin, plasminogen activator inhibitor-1, syndecan-1, and vascular endothelial growth factor]. Methods: In a pilot study (N = 36 subjects; 18 with BC), Oncuria performed essentially identically across three different common analyzers (the laser/flow-based FlexMap 3D and 200 systems, and the LED/image-based MagPix system; Luminex). The current study compared Oncuria performance across instrumentation platforms using a larger study population (N = 181 subjects; 51 with BC). Results: All three analyzers assessed all 10 analytes in identical samples with excellent concordance. The percent coefficient of variation (%CV) in protein concentrations across systems was ≤2.3% for 9/10 analytes, with only CA9 having %CVs > 2.3%. In pairwise correlation plot comparisons between instruments for all 10 biomarkers, R² values were 0.999 for 15/30 comparisons and R² ≥ 0.995 for 27/30 comparisons; CA9 showed the greatest variability (R² = 0.948–0.970). Standard curve slopes were statistically indistinguishable for all 10 biomarkers across analyzers. Conclusions: The Oncuria BC assay generates comprehensive urinary protein signatures useful for assisting BC diagnosis, predicting treatment response, and tracking disease progression and recurrence. The equivalent performance of the multiplex BC assay using three popular analyzers rationalizes test adoption by CLIA (Clinical Laboratory Improvement Amendments) clinical and research laboratories. Full article

Nylon reinforced with short carbon fibres exhibits superior mechanical properties. Its use as a feedstock for fused deposition modelling (FDM) can extend its applications to consumer goods and industrial products. To investigate the flexural and impact properties of the FDM-printed short carbon fibre-reinforced nylon, a central composite face-centred (CCF) design with four factors and three levels and the response surface method (RSM) were employed. The four primary process parameters are the extrusion and bed temperatures, printing speed, and layer thickness. The three investigated responses were the flexural strength, flexural modulus, and impact strength. Perturbation curves and contour plots were used to analyse the influences of the individual and two-way interactions of the response parameters, respectively. Second-order statistical models were constructed to predict and optimise the mechanical properties. The optimal comprehensive mechanical properties were determined using a desirability function combined with the entropy weighting method. The predicted results of best comprehensive mechanical properties are 169.881 MPa for the flexural strength, 9249.11 MPa for the flexural modulus, and 29.659 kJ∙m⁻² for the impact strength, achieved under the parameter combination of extrusion temperature of 318 °C, bed temperature of 90 °C, printing speed of 30 mm∙s⁻¹, and layer thickness of 0.1 mm. A small deviation between the predicted and experimental results indicated the high reliability of the proposed method. The optimal outcomes under the studied parameters showed higher robustness and integrity than previously reported results. Full article

Spirodela polyrhiza is a suitable model organism for investigating plant developmental influences due to its intracolonial variations in response to various environmental fluctuations, like nutrient deficiency. In this study, transmission electron microscopy was used to examine age-dependent variation in chloroplast ultrastructure, while pigment levels (chlorophyll and anthocyanins), starch accumulation, and metabolic activity (photosynthetic and respiratory rates) were measured to determine metabolic responses to sulfur deficiency. For a comprehensive insight into electron transport efficiency and the redox states of the photosynthetic apparatus, rapid light curves, chlorophyll fluorescence (JIP test parameters), and modulated reflection at 820 nm were analyzed. Under S deficit, mother fronds relied on stored reserves to maintain functional PSII but accumulated reduced PQ pools, slowing electron flow beyond PSII. The first-generation daughter fronds, despite having higher baseline photosynthetic capacity, exhibited the largest decline in photosynthetic indicators (e.g., rETR fell about 50%), limitations in the water-splitting complex, and reduced PSI end-acceptor capacity that resulted in donor- and acceptor-side bottlenecks of electron transport. The youngest granddaughter fronds avoided these bottlenecks by absorbing less light per PSII, channeling electrons through the alternative pathway to balance PQ pools and redox-stable PSI while diverting more carbon into starch and anthocyanin production up to 5-fold for both. These coordinated and age-specific adjustments that provide response flexibility may help maintain photosynthetic function of the colony and facilitate rapid recovery when sulfur becomes available again. Full article

The widespread deployment of electric vehicles (EVs) has introduced substantial challenges to electricity pricing, grid stability, and renewable energy integration. This paper presents the first real-time quantum-enhanced electricity pricing framework for large-scale EV charging networks, marking a significant departure from existing approaches based on mixed-integer programming (MILP) and deep reinforcement learning (DRL). The proposed framework incorporates renewable intermittency, demand elasticity, and infrastructure constraints within a high-dimensional optimization model. The objective is to dynamically determine spatiotemporal electricity prices that reduce system peak load, improve renewable utilization, and minimize user charging costs. A rigorous mathematical formulation is developed, integrating over 40 system-level constraints, including power balance, transmission limits, renewable curtailment, carbon targets, voltage regulation, demand-side flexibility, social participation, and cyber-resilience. Real-time electricity prices are treated as dynamic decision variables influenced by station utilization, elasticity response curves, and the marginal cost of renewable and grid electricity. The model is solved across 96 time intervals using a quantum-classical hybrid method, with benchmark comparisons against MILP and DRL baselines. A comprehensive case study is conducted on a 500-station EV network serving 10,000 vehicles, coupled with a modified IEEE 118-bus grid and 800 MW of variable renewable energy. Historical charging data with ±12% stochastic demand variation and real-world solar/wind profiles are used to simulate realistic conditions. Results show that the proposed framework achieves a 23.4% average peak load reduction per station, a 17.9% gain in renewable utilization, and up to 30% user cost savings compared to flat-rate pricing. Network congestion is mitigated at over 90% of high-traffic stations. Pricing trajectories align low-price windows with high-renewable periods and off-peak hours, enabling synchronized load shifting and enhanced flexibility. Visual analytics using 3D surface plots and disaggregated bar charts confirm structured demand-price interactions and smooth, stable price evolution. These findings validate the potential of quantum-enhanced optimization for scalable, clean, and adaptive EV charging coordination in renewable-rich grid environments. Full article

The BRICS countries (Brazil, Russia, India, China, and South Africa) are responsible for forty-two per cent of global carbon emissions. These rapidly industrializing and economically growing countries are dependent on fossil fuels, which can lead to increased emissions. This research analyses the impact of economic growth, globalization, and renewable energy (RE) use on CO₂ using a unique dataset of 155 observations and a practical model. Using panel data for 1990–2020, this study examines the relationships between CO₂ emissions, GDP, RE use, and the KOF Globalization Index based on the Environmental Kuznets Curve (EKC) theory. Cointegration, unit root test, panel data analysis, and FGLS regression methods were used in this study. The results show that economic growth and globalization increase CO₂, while RE is insufficient to reduce these effects. Moreover, it is determined that globalization has an increasing effect on CO₂. This study makes a prominent contribution to the literature by examining the combined effects of globalization and economic growth on environmental sustainability. The findings emphasize the need for sustainable energy policies in BRICS countries. Full article

Oil palm (Elaeis guineensis Jacq.) is the leading global oil-producing crop due to its high oil yield. Increasing global demands for palm oil require efficient propagation. Conventional breeding is practical but slow, making micropropagation an attractive alternative for rapidly multiplying superior genotypes. However, transitioning from in vitro to ex vitro conditions causes physiological stress, restricting survival and productivity. This study assessed gas exchange and chlorophyll fluorescence dynamics during acclimatization from in vitro conditions to field establishment, comparing the seedlings obtained in vitro with conventional seed-derived palm seedlings to conventional seed-derived palms. A pronounced photosynthetic efficiency decline occurred after transfer from in vitro culture, followed by a gradual recovery. The photosynthetic rate (A) increased from 0.86 µmol m⁻² s⁻¹ early in acclimatization to 15.43 µmol m⁻² s⁻¹ in field-established seedlings. Physiological characterization using CO₂ and light response curves identified the reductions in carboxylation efficiency and overall quantum yield CO₂. These biochemical constraints gradually diminished during acclimatization, facilitating a transition from heterotrophic to autotrophic growth. Chlorophyll fluorescence analysis revealed remarkable photoinhibition during initial ex vitro stages, indicated by a decreased maximum quantum efficiency of photosystem II. However, the seedlings progressively restored photochemical function throughout subsequent acclimatization phases. These findings highlight the importance of carefully regulating environmental parameters—particularly irradiance, humidity, and carbon availability—during early seedling acclimatization. The effective management of growth conditions significantly mitigates physiological stress, ensuring robust photosynthetic activity and optimized stomatal regulation. The improved acclimatization practices, therefore, can substantially enhance seedling survival rates, physiological resilience, and the overall field performance of micropropagated oil palms. Future research should focus on refining acclimatization protocols, emphasizing targeted physiological interventions to maximize the efficiency, commercial viability, and sustainability of oil palm clonal propagation. Full article

Maritime transportation is responsible for most global trade and is generally considered more environmentally efficient compared to other modes of transport, particularly for long-distance trade. With increasingly stringent emission regulations, however, accurately quantifying emissions and identifying their key determinants has become essential for effective environmental management. This study introduced a structured and comparative statistical modeling framework for ship-based emission modeling using gross tonnage (GT) as the primary predictor variable, due to its strong correlation with emission levels. Emissions for hydrocarbon (HC), carbon monoxide (CO), particulate matter with an aerodynamic diameter of less than 10 μm (PM10), carbon dioxide (CO₂), sulfur dioxide (SO₂), nitrogen oxides (NO_x), and volatile organic compounds (VOC) were estimated using a bottom-up approach based on emission factors and formulas defined by the U.S. Environmental Protection Agency (EPA), using data from 38,304 vessel movements through the Bosphorus in 2021. These EPA-estimated values served as dependent variables in the modeling process. The modeling framework followed a three-step strategy: (1) outlier detection using Rosner’s test to reduce the influence of outliers on model accuracy, (2) curve fitting with 12 regression models representing four curve types—polynomial (e.g., linear, quadratic), concave/convex (e.g., exponential, logarithmic), sigmoidal (e.g., logistic, Gompertz, Weibull), and spline-based (e.g., cubic spline, natural spline)—to capture diverse functional relationships between GT and emissions, and (3) model comparison using difference performance metrics to ensure a comprehensive assessment of predictive accuracy, consistency, and bias. The findings revealed that nonlinear models outperformed polynomial models, with spline-based models—particularly natural spline and cubic spline—providing superior accuracy for HC, PM₁₀, SO₂, and VOC, and the Weibull model showing strong predictive performance for CO and NO_x. These results underscore the necessity of using pollutant-specific and flexible modeling strategies to capture the intricacies of maritime emission dynamics. By demonstrating the advantages of flexible functional forms over standard regression techniques, this study highlights the need for tailored modeling strategies to better capture the complex relationships in maritime emission data and offers a scalable and transferable framework that can be extended to other vessel types, emission datasets, or maritime regions. Full article

Luminescence techniques, especially thermoluminescence (TL) and optically stimulated luminescence (OSL), are essential for dating materials in Cultural Heritage. TL is effective for dating bricks by determining their last heating event, but brick reuse can introduce inaccuracies. OSL enhances accuracy by dating the last light exposure of quartz grains in mortars, a material that is coeval with the construction of the building. However, partial bleaching of quartz grains can lead to overestimated ages. A promising solution involves dating the carbonate fraction of mortars, as calcium carbonate experiences bleaching during mortar preparation. This study investigates the feasibility of isolating signals from quartz and calcite in a composite material. Initially, TL signals for quartz and calcite were characterized separately. A laboratory mixture, comprising 75% quartz and 25% calcite, was irradiated to simulate partial bleaching. TL curve deconvolution revealed distinct peaks: quartz displayed four peaks, while calcite had three, notably lacking a low-temperature peak. The mixed sample exhibited peaks at 527 K, 573 K, 618 K, and 690 K, with the first peak being exclusively quartz, the second primarily quartz with minor calcite, and the third showing contributions from both. Dose-response curves indicated that the quartz peaks aligned with the expected 41.40 Gy dose, and the calcite signal matched 10.40 Gy. This confirms the feasibility of separating TL signals from quartz and calcite in mixed samples, offering a potential method for accurately dating the carbonate fraction in mortars and addressing partial bleaching issues. Future work will focus on optimizing detection parameters and applying this method to historically significant mortars to assess its effectiveness. Full article

Forests play a crucial role in carbon cycling, contributing significantly to global carbon cycling and climate change mitigation, but their capture strength is sensitive to the climatic zone in which they operate and its adjoining environmental stressors. This research investigated the carbon dynamics of a typical deciduous forest, the Daniel Boone National Forest (DBNF), in the Mixed-Humid climate of Kentucky, USA, employing the Eddy Covariance technique to quantify temporal CO₂ exchanges from 2016 to 2020 and to assess its controlling biometeorological factors. The study revealed that the DBNF functioned as a carbon sink, sequestering −1515 g C m⁻² in the study period, with a mean annual Net Ecosystem Exchange (NEE) of −303 g C m⁻²yr⁻¹. It exhibited distinct seasonal and daily patterns influenced by ambient sunlight and air temperature. Winter months had the lowest rate of CO₂ uptake (0.0699 g C m⁻² h⁻¹), while summer was the most productive (−0.214 g C m⁻² h⁻¹). Diurnally, carbon uptake peaked past midday and remained a sink overnight, albeit negligibly so. Light and temperature response curves revealed their controlling effect on the DBNF trees’ photosynthesis and respiration. Furthermore, clear seasonality patterns were observed in the control of environmental variables. The DBNF is a carbon sink consistent with other North American deciduous forests. Full article

This research aims to study the effect of introducing unidirectional (CFu) and bidirectional (CFb) carbon fabric into cement mortar (CM) on its flexural and pseudo-ductile performances. The experiments were performed on fabric/CM samples with a varying fabric distribution (single, double, and triple layers). The cohesion of fabrics in CM matrices and morphology of the damaged surfaces were examined using an optical microscope, while the flexural response was measured using a universal testing machine. The pseudo-ductile property, in the form of the ductility index (DI), was numerically modelled for CM matrices based on the measured flexural curves using different energy criteria models. Microstructure analysis showed a strong fabric cohesion in the matrices along with the production of more hydration products, which led to a transformation in the linear load–deformation relationship of mortar into the ideal shape of ductile material in the case of CFb/CM. In the case of the CFu/CM samples, two main drop points appeared with a long distance between them. In addition, the flexural load was significantly increased by introducing three layers of each type of fabric to CM, with an improvement of 75% (CFu/CM) and 68% (CFb/CM) compared to neat mortar. Similarly, the deformation till break was improved by 452% (CFu/CM) and 367% (CFb/CM). The DI analysis confirmed these results: the DI performance was improved by up to 140% by embedding. Based on these results, carbon fabric has high potential to enhance the strength and ductility of cementitious matrix. Full article

This study investigates the mechanisms driving current generation, power output, and charge storage in carbon nanotube springs under mechanical strain, addressing the gap between experimental observations and theoretical modeling, particularly in asymmetric electrical responses. Leveraging the Dirac equation in curved spacetime, we analyze how curvature-induced scalar and pseudo-gauge potentials shape two-dimensional electron gases confined to carbon nanotube springs. We incorporate applied mechanical strain by introducing time-dependent variations in the Lamé coefficient and curvature parameters, enabling the analysis of mechanical deformation’s influence on electrical properties. Our model clarifies asymmetric electrical responses during stretching and compression cycles and explains how strain-dependent power outputs arise from the interplay between mechanical deformation and curvature effects. Additionally, we demonstrate mechanisms by which strain influences charge redistribution within the helically coiled structure. We develop a new equivalent circuit model linking mechanical deformation directly to electronic behavior, bridging theoretical physics with practical electromechanical applications. The analysis reveals asymmetric time-dependent currents, enhanced power output during stretching, and strain-dependent charge redistribution. Fourier analysis uncovers dominant frequency components (primary at $\Omega$, harmonic at $2\Omega$) explaining these asymmetries. Theoretical investigations explain the mechanisms behind the curvature-driven time-dependent current source, the frequency-dependent peak power, the characteristics of open-circuit voltage with strain, and the asymmetric electrical property response under applied strain as the generated current and the charge distribution within the carbon nanotube springs. These findings highlight carbon nanotube springs applied to energy harvesting, wearable electronics, and sensing technologies. Full article

The energy transition towards achieving carbon neutrality is marked by the decarbonization of the power system and a high degree of electrification in end-use sectors. The decarbonization of the power system primarily relies on large-scale renewable energy, nuclear power, and fossil fuel-based power with carbon capture technologies. This structure of power supply introduces significant uncertainty in electricity supply. Due to the technological progress in end-use sectors and spatial reallocation of industries in China, the load curve and power supply curve is very different today. However, most studies’ analyses of future electricity systems are based on today’s load curve, which could be misleading when seeking to understand future electricity systems. Therefore, it is essential to thoroughly analyze changes in end-use load curves to better align electricity demand with supply. This paper analyzes the characteristics of electricity demand load under China’s future energy transition and economic transformation pathways using the Integrated Energy and Environment Policy Assessment model of China (IPAC). It examines the electricity and energy usage characteristics of various sectors in six typical regions, provides 24-h load curves for two representative days, and evaluates the effectiveness of demand-side response in selected provinces in 2050. The study reveals that, with the transition of the energy system and the industrial relocation during economic transformation, the load curves in China’s major regions by 2050 will differ notably from those of today, with distinct characteristics emerging across different regions. With the costs of solar photovoltaic (PV) and wind power declining in the future, the resulting electricity price will also differ significantly from today. Daytime electricity prices will be notably lower than those during the evening peak, as the decrease in solar PV and wind power output leads to a significant increase in electricity costs. This pricing structure is expected to drive a strong demand-side response. Demand-side response can significantly improve the alignment between load curves and power supply. Full article