Search Results (832)

Continuous and accurate biomass measurement is a critical enabler for control, decision making, and optimization in modern plant production systems. It supports the development of plant growth models for advanced control strategies like model predictive control, and enables responsive, data-driven, and plant state-dependent cultivation. Traditional biomass measurement methods, such as destructive sampling, are time-consuming and unsuitable for high-frequency monitoring. In contrast, image-based estimation using computer vision and deep learning requires frequent retraining and is sensitive to changes in lighting or plant morphology. This work introduces a low-cost, load-cell-based biomass monitoring system tailored for vertical farming applications. The system operates at the level of individual growing trays, offering a valuable middle ground between impractical plant-level sensing and overly coarse rack-level measurements. Tray-level data allow localized control actions, such as adjusting light spectrum and intensity per tray, thereby enhancing the utility of controllable LED systems. This granularity supports layer-specific optimization and anomaly detection, which are not feasible with rack-level feedback. The biomass sensor is easily scalable and can be retrofitted, addressing common challenges such as mechanical noise and thermal drift. It offers a practical and robust solution for biomass monitoring in dynamic, growing environments, enabling finer control and smarter decision making in both commercial and research-oriented vertical farming systems. The developed sensor was tested and validated against manual harvest data, demonstrating high agreement with actual plant biomass and confirming its suitability for integration into vertical farming systems. Full article

Demand Side Management (DSM) plays a crucial role in modern energy systems, enabling more efficient use of energy resources and contributing to the sustainability of the power grid. This study examines DSM strategies within a multi-environment context encompassing residential, commercial, and industrial sectors, with a focus on diverse appliance types that exhibit distinct operational characteristics and user preferences. Initially, a single-objective optimization approach using Genetic Algorithms (GAs) is employed to minimize the total energy cost under a real Time-of-Use (ToU) pricing scheme. This heuristic method allows for the effective scheduling of appliance operations while factoring in their unique characteristics such as power consumption, usage duration, and user-defined operational flexibility. This study extends the optimization problem to a multi-objective framework that incorporates the minimization of CO₂ emissions under a real annual energy mix while also accounting for user discomfort. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is utilized for this purpose, providing a Pareto-optimal set of solutions that balances these competing objectives. The inclusion of multiple objectives ensures a comprehensive assessment of DSM strategies, aiming to reduce environmental impact and enhance user satisfaction. Additionally, this study monitors the Peak-to-Average Ratio (PAR) to evaluate the impact of DSM strategies on load balancing and grid stability. It also analyzes the impact of considering different periods of the year with the associated ToU hourly schedule and CO₂ emissions hourly profile. A key innovation of this research is the integration of detailed, category-specific metrics that enable the disaggregation of costs, emissions, and user discomfort across residential, commercial, and industrial appliances. This granularity enables stakeholders to implement tailored strategies that align with specific operational goals and regulatory compliance. Also, the emphasis on a user discomfort indicator allows us to explore the flexibility available in such DSM mechanisms. The results demonstrate the effectiveness of the proposed multi-objective optimization approach in achieving significant cost savings that may reach 20% for industrial applications, while the order of magnitude of the trade-offs involved in terms of emissions reduction, improvement in discomfort, and PAR reduction is quantified for different frameworks. The outcomes not only underscore the efficacy of applying advanced optimization frameworks to real-world problems but also point to pathways for future research in smart energy management. This comprehensive analysis highlights the potential of advanced DSM techniques to enhance the sustainability and resilience of energy systems while also offering valuable policy implications. Full article

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

The polytetrafluoroethylene/aluminum (PTFE/Al) granular composite, a common formulation in impact-initiated energetic materials, undergoes mechanochemical coupling reactions under sufficiently strong dynamic loading. This investigation discusses the dynamic properties and the constitutive relationship of the PTFE/Al granular composite to provide a preliminary guide for the research on mechanical properties of a series of composite materials based on PTFE/Al as the matrix. Firstly, the 26.5Al-73.5PTFE (wt.%) composite specimens are prepared by preprocessing, mixing, molding, high-temperature sintering, and cooling. Then, the quasi-static compression and Hopkinson bar tests are performed to explore the mechanical properties of the PTFE/Al composite. Influences of the strain rate of loading on the yield stress, the ultimate strength, and the limited strain are also analyzed. Lastly, based on the experimental results, the material parameters in the Johnson–Cook constitutive model are obtained by the method of piecewise fitting to describe the stress–strain relation of the PTFE/Al composite. Combining the experimental details and the obtained material parameters, the numerical simulation of the dynamic compression of the PTFE/Al composite specimen is carried out by using the ANSYS/LS-DYNA platform. The results show that the computed stress–strain curves present a reasonable agreement with the experimental data. It should be declared that this research does not involve the energy release behavior of the 26.5Al-73.5PTFE (wt.%) reactive material because the material is not initiated within the strain rate range of the dynamic test in this paper. Full article

The use of multimodal data can effectively compensate for the lack of temporal resolution in streetscape imagery-based studies and achieve hourly refinement in the study of street walkability dynamics. Exploring the 24 h dynamic pattern of urban street walkability and its diurnal variation characteristics is a crucial step in understanding and responding to the accelerated urban metabolism. Aiming at the shortcomings of existing studies, which are mostly limited to static assessment or only at coarse time scales, this study integrates multimodal data such as streetscape images, remote sensing images of nighttime lights, and text-described crowd activity information and introduces a novel approach to enhance the simulation of pedestrian perception through a visual–textual multimodal deep learning model. A baseline model for dynamic assessment of walkability with street as a spatial unit and hour as a time granularity is generated. In order to deeply explore the dynamic regulation mechanism of street walkability under the influence of diurnal shift, the 24 h dynamic score of walkability is calculated, and the quantification system of walkability diurnal change characteristics is further proposed. The results of spatio-temporal cluster analysis and quantitative calculations show that the intensity of economic activities and pedestrian experience significantly shape the diurnal pattern of walkability, e.g., urban high-energy areas (e.g., along the riverside) show unique nocturnal activity characteristics and abnormal recovery speeds during the dawn transition. This study fills the gap in the study of hourly street dynamics at the micro-scale, and its multimodal assessment framework and dynamic quantitative index system provide important references for future urban spatial dynamics planning. Full article

Hyperspectral image change detection (HSI-CD) provides substantial value in environmental monitoring, urban planning and other fields. In recent years, deep-learning based HSI-CD methods have made remarkable progress due to their powerful nonlinear feature learning capabilities, yet they face several challenges: mixed-pixel phenomenon affecting pixel-level detection accuracy; heterogeneous spatial scales of change targets where coarse-grained features fail to preserve fine-grained details; and dependence on high-quality labels. To address these challenges, this paper introduces WSCDNet, a weakly supervised HSI-CD network employing coarse-to-fine feature learning, with key innovations including: (1) A dual-branch detection framework integrating binary and multiclass change detection at the sub-pixel level that enhances collaborative optimization through a cross-feature coupling module; (2) introduction of multi-granularity aggregation and difference feature enhancement module for detecting easily confused regions, which effectively improves the model’s detection accuracy; and (3) proposal of a weakly supervised learning strategy, reducing model sensitivity to noisy pseudo-labels through decision-level consistency measurement and sample filtering mechanisms. Experimental results demonstrate that WSCDNet effectively enhances the accuracy and robustness of HSI-CD tasks, exhibiting superior performance under complex scenarios and weakly supervised conditions. Full article

The pursuit of sustainable construction practices has become imperative in the modern era. This paper delves into the research of the properties and application of a specific material called “red clay” from the locality “Crvena Mogila” in Macedonia. A series of laboratory tests were conducted to evaluate the physical, mechanical, and chemical properties of the material. The tested samples show that it is a porous material with low density, high water absorption, and compressive strength in range of 29.85–38.32 MPa. Samples of composite wall blocks were made with partial replacement of natural aggregate with red clay aggregate. Two types of blocks were produced with dimensions of 390 × 190 × 190 mm, with five and six holes. The average compressive strength of the blocks ranges from 3.1 to 4.1 MPa, which depends on net density and the number of holes. Testing showed that these blocks have nearly seven-times-lower thermal conductivity than conventional concrete blocks and nearly twice-lower conductivity than full-fired clay bricks. The general conclusion is that the tested red clay is an economically viable and sustainable material with favourable physical, mechanical, and thermal parameters and can be used as a granular aggregate in the production of composite ceramic blocks. Full article

To address issues commonly observed during the inference phase of large language models—such as inconsistent labels, formatting errors, or semantic deviations—a series of targeted strategies has been proposed. First, a relation label refinement strategy based on semantic similarity and syntactic structure has been designed to calibrate the model’s outputs, thereby improving the accuracy and consistency of label prediction. Second, to meet the contextual modeling needs of different types of instance bags, a multi-level contextual augmentation strategy has been constructed. For multi-sentence instance bags, a graph-based retrieval enhancement mechanism is introduced, which integrates intra-bag entity co-occurrence networks with document-level sentence association graphs to strengthen the model’s understanding of cross-sentence semantic relations. For single-sentence instance bags, a semantic expansion strategy based on term frequency-inverse document frequency is employed to retrieve similar sentences. This enriches the training context under the premise of semantic consistency, alleviating the problem of insufficient contextual information. Notably, the proposed multi-granularity framework captures semantic symmetry between entities and relations across different levels of context, which is crucial for accurate and balanced relation understanding. The proposed methodology offers practical advancements for semantic analysis applications, particularly in knowledge graph development. Full article

The existing studies mainly focus on the coarse-grained heat-affected zone and the inter-critically reheated coarse-grained heat-affected zone, while the studies on other sub-zones are relatively low. Meanwhile, the studies on the Cr element in steel mainly focus on the influence of the Cr element on strength and hardness; however, its mechanism is not very clear. Therefore, three kinds of X80 experimental steels with different Cr contents (0 wt.%, 0.13 wt.%, and 0.40 wt.%) were designed in this paper. The thermal simulation experiments on the supercritically coarse-grained heat-affected zone (SCCGHAZ) were carried out using a Gleeble-3500 thermal simulator. The effects of Cr on the microstructure and toughness of SCCGHAZ were systematically investigated through Charpy impact tests and microstructural characterization techniques. The results indicate that the microstructures of the three Cr-containing X80 experimental steels in SCCGHAZ are predominantly composed of fine granular bainite. However, impact tests at −10 °C show that the SCCGHAZs of 0 wt.% and 0.13 wt.% Cr steel exhibit higher impact energy, while that of the 0.40 wt.% Cr steel demonstrates significantly reduced energy impact (<100 J). Microstructural characterization reveals that the impact toughness of the SCCGHAZ in X80 steel is correlated with microstructural features, including effective grain size, grain boundary angles, and the volume fraction and shape of martensite–austenite (M-A) constituents. Among these factors, the volume fraction of M-A constituents substantially influences toughness. It was found that island-shaped M-A constituents inhibit crack propagation, whereas blocky M-A constituents impair toughness. Full article

Against the backdrop of the profound restructuring in global climate governance, China’s energy management system is undergoing a comprehensive transition from dual energy consumption control to dual carbon emissions control. This policy shift fundamentally alters the underlying logic of energy-focused regulation and inevitably impacts the economy–energy–environment (3E) system. This study innovatively constructs a “Triangular Trinity” theoretical framework integrating internal, intermediate, and external triangular couplings, as well as providing a granular analysis of their transmission relationships and feedback mechanisms. Using Guangdong Province as a case study, this study takes the dual control emissions policy within the external triangle as an entry point to research the restructuring logic of dual carbon emissions control for the coupling coordination mechanisms of the 3E system. The key findings are as follows: (1) Policy efficacy evolution: During 2005–2016, dual energy consumption control significantly improved energy conservation and emissions reduction, elevating Guangdong’s 3E coupling coordination. Post 2017, however, its singular focus on total energy consumption revealed limitations, causing a decline in 3E coordination. Dual carbon emissions control demonstrably enhances 3E systemic synergy. (2) Decoupling dynamics: Dual carbon emissions control accelerates economic–carbon emission decoupling, while slowing economic–energy consumption decoupling. This created an elasticity space of 5.092 million tons of standard coal equivalent (sce) and reduced carbon emissions by 26.43 million tons, enabling high-quality economic development. (3) Mechanism reconstruction: By leveraging external triangular elements (energy-saving technologies and market mechanisms) to act on the energy subsystem, dual carbon emissions control leads to optimal solutions to the “Energy Trilemma”. This drives the systematic restructuring of the sustainability triangle, achieving high-order 3E coupling coordination. The Triangular Trinity framework constructed by us in the paper is an innovative attempt in relation to the theory of energy transition, providing a referenceable methodology for resolving the contradictions of the 3E system. The research results can provide theoretical support and practical reference for the low-carbon energy transition of provinces and cities with similar energy structures. Full article

The optimization and control of the wellbore trajectory is one of the important technologies to improve drilling efficiency, reduce drilling cost, and ensure drilling safety in the process of modern oil and gas exploration and development. In this paper, a multi-objective wellbore trajectory optimization mathematical model is established, which takes into account the five factors of wellbore trajectory length, friction, torque, trajectory complexity, and target accuracy. A DR-NSGA-III-MGA algorithm (dynamic reference NSGA-III with multi-granularity adaptation) is proposed. By introducing multi-granularity reference vector generation and an information entropy-guided search direction adaptation mechanism, the performance of the algorithm in the complex target space is improved, and the three-stage wellbore trajectory is optimized. Simulation experiments show that the DR-NSGA-III-MGA algorithm is stable in a variety of complex problems, while maintaining good convergence, and has good generalization ability and practical application value. Full article

The widely utilized TiO₂ nanoparticles (NPs) tend to accumulate in wastewater and affect microbial growth. This work investigated the impacts of prolonged TiO₂ NP addition to filamentous aerobic granular sludge (AGS) using two identical sequencing batch reactors (SBRs, R1 and R2). R1 (the control) had no TiO₂ NP addition. In this reactor, filamentous bacteria from large AGS grew rapidly and extended outward, the sludge volume index (SVI₃₀) quickly increased from 41.2 to 236.8 mL/g, mixed liquid suspended solids (MLSS) decreased from 4.72 to 0.9 g/L, and AGS disintegrated on day 40. Meanwhile, the removal rates of COD and NH₄⁺-N both exhibited significant declines. In contrast, 5–30 mg/L TiO₂ NPs was added to R2 from day 21 to 100, and the extended filamentous bacteria were effectively controlled on day 90 under a 30 mg/L NP dosage, leading to significant reductions in COD and NH₄⁺-N capabilities, particularly the latter. Therefore, NP addition was stopped on day 101, and AGS became dominant in R2, with an SVI₃₀ and MLSS of 48.5 mL/g and 5.67 g/L on day 130. COD and NH₄⁺-N capabilities both increased to 100%. Microbial analysis suggested that the dominant filamentous bacteria—Proteobacteria, Bacteroidetes, and Acidobacteria—were effectively controlled by adding 30 mg/L TiO₂ NPs. XRF analysis indicated that 11.7% TiO₂ NP accumulation made the filamentous bacteria a framework for AGS recovery and operation without NPs. Functional analysis revealed that TiO₂ NPs had stronger inhibitory effects on nitrogen metabolism compared to carbon metabolism, and both metabolic pathways recovered when NP addition was discontinued in a timely manner. These findings offer critical operational guidance for maintaining the stable performance of filamentous AGS systems treating TiO₂ NP wastewater in the future. Full article

Fine-grained recognition tasks face significant challenges in differentiating subtle, class-specific details against cluttered backgrounds. This paper presents an efficient architecture built upon the Res2Net backbone, significantly enhanced by a dynamic Sparse Attention mechanism. The core approach leverages the inherent multi-scale representation power of Res2Net to capture discriminative patterns across different granularities. Crucially, the integrated Sparse Attention module operates dynamically, selectively amplifying the most informative features while attenuating irrelevant background noise and redundant details. This combined strategy substantially improves the model’s ability to focus on pivotal regions critical for accurate classification. Furthermore, strategic architectural optimizations are applied throughout to minimize computational complexity, resulting in a model that demands significantly fewer parameters and exhibits faster inference times. Extensive evaluations on benchmark datasets demonstrate the effectiveness of the proposed method. It achieves a modest but consistent accuracy gain over strong baselines (approximately 2%) while simultaneously reducing model size by around 30% and inference latency by about 20%, proving highly effective for practical fine-grained recognition applications requiring both high accuracy and operational efficiency. Full article

The proportion of granular ice in sea ice layers has markedly increased due to global warming. To investigate the uniaxial compressive behavior of granular sea ice, we conducted a series of experiments using natural piled and level ice samples collected from the Bohai Sea. A total of 311 specimens were tested under controlled temperature conditions ranging from −15 °C to −2 °C and strain rates varying from 10⁻⁵ to 10⁻² s⁻¹. The effects of porosity, strain rate, and failure modes were studied. The results show that both the uniaxial compressive strength and uniaxial compressive elastic modulus were dependent on strain rate and porosity. Granular sea ice exhibited a non-monotonic strength dependence on strain rate, with the strength increasing in the ductile regime and decreasing in the brittle regime. In contrast, the elastic modulus increased monotonically with the strain rate. Both the strength and elastic modulus decreased with increasing porosity. Level ice consistently demonstrated higher strength and an elastic modulus than piled ice at equivalent porosities. Unified parametric models were developed to describe both properties across a wide range of strain rates encompassing the ductile-to-brittle (DBT) regime. The experimental results show that, as porosity decreased, the transition strain rate of granular sea ice shifted from 2.34 × 10⁻³ s⁻¹ at high porosity (45%) to 1.42 × 10⁻⁴ s⁻¹ at low porosity (10%) for level ice and 1.87 × 10⁻³ s⁻¹ to 1.19 × 10⁻³ s⁻¹ for piled ice. These results were compared with classical columnar ice models. These findings are useful for informing the design of vessel and coastal structures intended for use in ice-covered waters. Full article

Internal soil erosion caused by water infiltration around defective buried pipes poses a significant threat to the long-term stability of underground infrastructures such as pipelines and highway culverts. This study employs a coupled computational fluid dynamics–discrete element method (CFD–DEM) framework to simulate the detachment, transport, and redistribution of soil particles under varying infiltration pressures and pipe defect geometries. Using ANSYS Fluent (CFD) and Rocky (DEM), the simulation resolves both the fluid flow field and granular particle dynamics, capturing erosion cavity formation, void evolution, and soil particle transport in three dimensions. The results reveal that increased infiltration pressure and defect size in the buried pipe significantly accelerate the process of erosion and sinkhole formation, leading to potentially unstable subsurface conditions. Visualization of particle migration, sinkhole development, and soil velocity distributions provides insight into the mechanisms driving localized failure. The findings highlight the importance of considering fluid–particle interactions and defect characteristics in the design and maintenance of buried structures, offering a predictive basis for assessing erosion risk and infrastructure vulnerability. Full article