Search Results (6,338)

Understanding flow dynamics in open-channel node systems is crucial for designing effective hydraulic engineering solutions and minimizing energy losses. This study investigates how junction geometry—specifically the lateral inflow angle (α = 45° and 60°) and the longitudinal bed slope (I = 0.0011 to 0.0051)—influences the water velocity distribution and hydraulic losses in a rigid-bed Y-shaped open-channel junction. Experiments were performed in a 0.3 m wide and 0.5 m deep rectangular flume, with controlled inflow conditions simulating steady-state discharge scenarios. Flow velocity measurements were obtained using a PEMS 30 electromagnetic velocity probe, which is capable of recording three-dimensional velocity components at a high spatial resolution, and electromagnetic flow meters for discharge control. The results show that a lateral inflow angle of 45° induces stronger flow disturbances and higher local loss coefficients, especially under steeper slope conditions. In contrast, an angle of 60° generates more symmetric velocity fields and reduces energy dissipation at the junction. These findings align with the existing literature and highlight the significance of junction design in hydraulic structures, particularly under high-flow conditions. The experimental data may be used for calibrating one-dimensional hydrodynamic models and optimizing the hydraulic performance of engineered channel outlets, such as those found in hydropower discharge systems or irrigation networks. Full article

Management of smart greenhouses represents a transformative advancement in precision agriculture, enabling sustainable intensification of food production through the integration of multi-sensor networks, intelligent control, and sophisticated data filtering techniques. Unlike conventional greenhouses that rely on manual monitoring, smart greenhouses combine environmental sensors, Internet of Things (IoT) platforms, and artificial intelligence (AI)-driven decision making to optimize microclimates, improve yields, and enhance resource efficiency. This review systematically investigates three key technological pillars, multi-sensor monitoring, intelligent control, and data filtering techniques, for smart greenhouse environment management. A structured literature screening of 114 peer-reviewed studies was conducted across major databases to ensure methodological rigor. The analysis compared sensor technologies such as temperature, humidity, carbon dioxide (CO₂), light, and energy to evaluate the control strategies such as IoT-based automation, fuzzy logic, model predictive control, and reinforcement learning, along with filtering methods like time- and frequency-domain, Kalman, AI-based, and hybrid models. Major findings revealed that multi-sensor integration enhanced precision and resilience but faced changes in calibration and interoperability. Intelligent control improved energy and water efficiency yet required robust datasets and computational resources. Advanced filtering strengthens data integrity but raises concerns of scalability and computational cost. The distinct contribution of this review was an integrated synthesis by linking technical performance to implementation feasibility, highlighting pathways towards affordable, scalable, and resilient smart greenhouse systems. Full article

This research investigates the integration of green cores as central biophilic elements in residential architecture, proposing a climate-responsive design methodology grounded in architectural optimization. The study begins with the full-scale refurbishment of a compact urban apartment, wherein interior partitions, fenestration and material systems were reconfigured to embed vegetated zones within the architectural core. Light exposure, ventilation potential and spatial coherence were maximized through data-driven design strategies and structural modifications. Integrated planting modules equipped with PAR-specific LED systems ensure sustained vegetation growth, while embedded environmental infrastructure supports automated irrigation and continuous microclimate monitoring. This plant-centered spatial model is evaluated using quantifiable performance metrics, establishing a replicable framework for optimized indoor ecosystems. Photosynthetically active radiation (PAR)-specific LED systems and embedded environmental infrastructure were incorporated to maintain vegetation viability and enable microclimate regulation. A programmable irrigation system linked to environmental sensors allows automated resource management, ensuring efficient plant sustenance. The configuration is assessed using measurable indicators such as daylight factor, solar exposure, passive thermal behavior and similar elements. Additionally, a post-occupancy expert assessment was conducted with several architects evaluating different aspects confirming the architectural and spatial improvements achieved through the refurbishment. This study not only demonstrates a viable architectural prototype but also opens future avenues for the development of metabolically active buildings, integration with decentralized energy and water systems, and the computational optimization of living infrastructure across varying climatic zones. Full article

China faces the persistent challenge of uneven spatiotemporal water resource distribution, constraining economic and social development while exacerbating regional disparities. Achieving co-evolution between water source systems and water use systems is thus a critical proposition in water resources management. Based on synergetics theory, this study takes Henan Province, a typical water-scarce social–ecological system, as the research object, and constructs a quantitative analysis framework for supply–demand bidirectional synergy. It systematically reveals the evolution patterns of water resource systems under the mutual feedback mechanism between water sources and water use. Findings indicate that between 2012 and 2022, the synergy degree of Henan’s water resource system increased by nearly 40%, exhibiting significant spatiotemporal differentiation: spatially “lower north, higher south”, and dynamically shifting from demand-constrained to supply-optimized. Specifically, the water source system’s order degree showed a “higher northwest, lower southeast” spatial pattern. Since the operation of the South-to-North Water Diversion Middle Route Project, the provincial average order degree increased significantly (annual growth rate of 0.01 units), though with distinct regional disparities. The water use system’s order degree also exhibited “lower north, higher south” pattern but achieved greater growth (annual growth rate of 0.03 units), with narrowing north–south gaps driven by improved management efficiency and technological capacity. This study innovatively integrates water source systems and water use systems into a unified analytical framework, systematically elucidating the intrinsic evolution mechanisms of water resource systems from the perspective of supply–demand mutual feedback. It provides theoretical and methodological support for advancing systematic water resource governance. Full article

Identifying suitable areas for ecosystem services (ES) development is essential for balancing economic growth with environmental sustainability in ecologically fragile regions. However, existing studies often neglect integrating future climate and socioeconomic drivers into ES optimization, hindering the design of robust strategies for sustainable resource management. In this study, we propose a novel framework integrating the System Dynamics (SD) model, the Patch-based Land Use Simulation (PLUS) model, the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, and the Dynamic Bayesian Network (DBN) to optimize ES patterns in the Sanjiangyuan region under three climate scenarios (SSP126, SSP245, and SSP585) from 2030 to 2060. Our results show the following: (1) Ecological land (forest) expanded by 0.86% under SSP126, but declined by 11.54% under SSP585 due to unsustainable land use intensification. (2) SSP126 emerged as the optimal scenario for ES sustainability, increasing carbon storage and sequestration, habitat quality, and water conservation by 3.2%, 1%, and 1.4%, respectively, compared to SSP585. (3) The central part of the Sanjiangyuan region, characterized by gentle topography and adequate rainfall, was identified as a priority zone for ES development. This study provides a transferable framework for aligning ecological conservation with low-carbon transitions in global biodiversity hotspots. Full article

Accurate short-term forecasting of urban water demand is a persistent challenge for utilities seeking to optimize operations, reduce energy costs, and enhance resilience in smart distribution systems. This study presents a multi-scale Artificial Neural Network (ANN) modeling approach that integrates temporal lag optimization to predict daily and hourly water consumption across heterogeneous user profiles. Using high-resolution smart metering data from the SunRise Smart City Project in Lille, France, four demand nodes were analyzed: a District Metered Area (DMA), a student residence, a university restaurant, and an engineering school. Results demonstrate that incorporating lagged consumption variables substantially improves prediction accuracy, with daily R² values increasing from 0.490 to 0.827 at the DMA and from 0.420 to 0.806 at the student residence. At the hourly scale, the 1-h lag model consistently outperformed other configurations, achieving R² up to 0.944 at the DMA, thus capturing both peak and off-peak consumption dynamics. The findings confirm that short-term autocorrelation is a dominant driver of demand variability, and that ANN-based forecasting enhanced by temporal lag features provides a robust, computationally efficient tool for real-time water network management. Beyond improving forecasting performance, the proposed methodology supports operational applications such as leakage detection, anomaly identification, and demand-responsive planning, contributing to more sustainable and resilient urban water systems. Full article

Global urbanization has led to massive generation of high-water-content waste slurry, creating serious environmental challenges. Conventional treatment methods are costly and unsustainable, while cement-based foamed lightweight soils typically exhibit low strength and limited CO₂ sequestration. To address this issue, this study proposes a novel stabilization pathway by integrating a MgO–mineral powder–carbide slag composite binder with CO₂ foaming–carbonation. The approach enables simultaneous slurry lightweighting, strength enhancement, and CO₂ fixation. A series of laboratory tests were conducted to evaluate flowability, density, compressive strength, and deformation characteristics of the carbonated lightweight stabilized slurry. Microstructural analyses, including SEM and XRD, were used to reveal the formation of carbonate phases and pore structures. The results showed that MgO content strongly promoted carbonation, leading to denser microstructures and higher strength, while mineral powder and carbide slag optimized workability and pore stability. Orthogonal testing indicated that a mix with 25% mineral powder, 12.5% MgO, and 7.5% carbide slag achieved the best performance, with unconfined compressive strength up to 0.48 MPa after carbonation. Compared with conventional cement- or GGBS-based foamed lightweight soils, the proposed system exhibits superior strength development, improved pore stability, and enhanced CO₂ sequestration potential. These findings demonstrate the feasibility of recycling high-water-content waste slurry into value-added construction materials while contributing to carbon reduction targets. This study not only provides a sustainable solution for waste slurry management but also offers new insights into the integration of CO₂ mineralization into geotechnical engineering practice. Full article

The management of modern water supply systems requires a detailed analysis of consumption patterns in order to optimize pump operation schedules, reduce energy costs, and support the development of intelligent management systems. Traditional clustering algorithms are applied for these tasks; however, their limitation lies in the need to predefine the number of clusters. The aim of this study was to develop and validate a non-parametric method for clustering daily water consumption profiles based on a modified DBSCAN algorithm. The proposed approach includes the automatic optimization of neighborhood radius and the minimum number of points required to form a cluster. The input data consisted of half-hourly water supply and electricity consumption values for the water supply system of Gomel (Republic of Belarus), supplemented with the time-of-day factor. As a result of the multidimensional clustering, two stable regimes were identified: a high-demand regime (6:30–22:30), covering about 46% of the data and accounting for more than half of the total water supply and electricity consumption, and a low-demand regime (0:30–6:00), representing about 21% of the data and forming around 15% of the resources. The remaining regimes reflect transitional states in morning and evening periods. The obtained results make it possible to define the temporal boundaries of the regimes and to use them for data labeling in the development of predictive water consumption models. Full article

The optimal control of cooling water systems is of great significance for energy saving inchiller plants. Previously optimal control methods optimize the flow rate, temperature or temperature difference setpoints but cannot control pumps and cooling tower fans directly. This study proposes a direct optimal control method for pumps and fans based on derivativecontrol strategyby decoupling water flow rate optimization and airflow rate optimization, which can make the total power of chillers, pumps and fans approach a minimum. Simulations for different conditions were performed for the validation and performance analysis of the optimal control strategy. The optimization algorithms and implementation methods of direct optimal control were developed and validated by experiment. The simulation results indicate that total power approaches a minimum when the derivative of total power with respect to water/air flow rate approaches zero. The power-saving rate of the studied chiller plant is 13.2% at a plant part-load ratio of 20% compared to the constant-speed pump/fan mode. The experimental results show that the direct control method, taking power frequency as a controlled variable, can make variable frequency drives regulate their output frequencies to be equal to the optimized power frequencies of pumps and fansin a timely manner. Full article

Head and neck cancer (HNC) is highly heterogeneous and difficult to treat, underscoring the need for rapid, patient-specific models. Standard three-dimensional (3D) cultures often lose stromal partners that influence therapy response. We developed a patient-derived system maintaining tumor cells, cancer-associated fibroblasts (CAFs), and cells undergoing partial epithelial–mesenchymal transition (pEMT) for drug sensitivity testing. Biopsies from four HNC patients were enzymatically dissociated. CAFs were directly cultured, and their conditioned medium (CAF-CM) was collected. Cryopreserved primary tumor cell suspensions were later revived, screened in five different growth media under 2D conditions, and the most heterogeneous cultures were re-embedded in 3D hydrogels with varied gel mixtures, media, and seeding geometries. Tumoroid morphology was quantified using a perimeter-based complexity index. Viability after treatment with cisplatin or Notch modulators (RIN-1, recombination signal-binding protein for immunoglobulin κ J region (RBPJ) inhibitor; FLI-06, inhibitor) was assessed by live imaging and the water-soluble tetrazolium-8 (WST-8) assay. Endothelial Cell Growth Medium 2 (ECM-2) medium alone produced compact CAF-free spheroids, whereas ECM-2 supplemented with CAF-CM generated invasive aggregates that deposited endogenous matrix. Matrigel with this medium and single-point seeding gave the highest complexity scores. Two of the three patient tumoroids were cisplatin-sensitive, and all showed significant growth inhibition with the FLI-06 Notch inhibitor, while the RBPJ inhibitor RIN-1 induced minimal change. The optimized scaffold retains tumor–stroma crosstalk and provides patient-specific drug response data within days after operation, supporting personalized treatment selection in HNC. Full article

Surface irrigation systems are among the most common yet often inefficient methods due to poor design and management. A key factor in optimizing their design is the accurate prediction of the water advance and infiltration relationships’ coefficients. This study introduces a novel model based on the Beta cumulative distribution function for predicting water advance and estimating infiltration coefficients in surface irrigation systems. Traditional methods, such as the two-point approach, rely on limited data from only the midpoint and endpoint of the field, often resulting in insufficient accuracy and non-physical outcomes under heterogeneous soil conditions. The proposed model enhances predictive flexibility by incorporating the entire advance dataset and integrating the midpoint as a constraint during optimization, thereby improving the accuracy of advance curve estimation and subsequent infiltration coefficient determination. Evaluation using field data from three distinct sites (FS, HF, WP) across 10 irrigation events demonstrated the superiority of the proposed model over the conventional power advance method. The new model achieved average RMSE, MAPE, and R² values of 0.790, 0.109, and 0.997, respectively, for advance estimation. For infiltration prediction, it yielded an average error of 12.9% in total infiltrated volume—outperforming the two-point method—and also showed higher accuracy during the advance phase, with average RMSE, MAPE, and R² values of 0.427, 0.075, and 0.990, respectively. These results confirm that the Beta-based model offers a more robust, precise, and reliable tool for optimizing the design and management of surface irrigation systems. Full article

Plant-based extracts and elicitors (signaling molecules that activate the fruit’s innate defense responses) have emerged as promising and sustainable alternatives to synthetic chemicals for preserving postharvest fruit quality and extending shelf life. This review provides a comprehensive analysis, uniquely complemented by a bibliometric assessment of the research landscape from 2005 to 2025, to identify key trends and effective solutions. This review systematically examined the efficacy of various natural compounds including essential oils (complex volatile compounds with potent antimicrobial activity such as lemongrass and thyme), phenolic-rich botanical extracts like neem and aloe vera, and plant-derived elicitors such as methyl jasmonate and salicylic acid. Their preservative mechanisms are multifaceted, involving direct antimicrobial activity by disrupting microbial membranes, potent antioxidant effects that scavenge free radicals, and the induction of a fruit’s innate defense systems, enhancing the activity of enzymes like superoxide dismutase (SOD), catalase (CAT), and peroxidase (POD). Applications of edible coatings of chitosan or aloe vera gel, nano-emulsions, and pre- or postharvest treatments effectively reduce decay by Botrytis cinerea and Penicillium spp.), delay ripening by suppressing ethylene production, minimize water loss, and alleviate chilling injury. Despite their potential, challenges such as sensory changes, batch-to-batch variability, regulatory hurdles, and scaling production costs limit widespread commercialization. Future prospects hinge on innovative technologies like nano-encapsulation to improve stability and mask flavors, hurdle technology combining treatments synergistically, and optimizing elicitor application protocols. This review demonstrates the potential of continued research and advanced formulation to create plant-based preservatives, that can become integral components of an eco-friendly postharvest management strategy, effectively reducing losses and meeting consumer demands for safe, high-quality fruit. Full article

Advances in sensor technology have significantly improved the efficiency and precision of agricultural spraying. Unmanned aerial vehicles (UAVs) are widely utilized for applying plant protection products (PPPs) and fertilizers, offering enhanced spatial control and operational flexibility. This study evaluated the performance of an autonomous UAV-based precision spraying system that applies variable rates based on zone levels defined in a prescription map. The system integrates real-time kinematic global navigation satellite system positioning with a proximity-triggered spray algorithm. Field experiments on a rice field were conducted to assess spray accuracy and fertilization efficacy with liquid fertilizer. Spray deposition patterns on water-sensitive paper showed that the graded strategy distinguished among zone levels, with the highest deposition in high-spray zones, moderate in medium zones, and minimal in no-spray zones. However, entry and exit deviations—used to measure system response delays—averaged 0.878 m and 0.955 m, respectively, indicating slight lags in spray activation and deactivation. Fertilization results showed that higher application levels significantly increased the grain-filling rate and thousand-grain weight (both p < 0.001), but had no significant effect on panicle number or grain count per panicle (p > 0.05). This suggests that increased fertilization primarily enhances grain development rather than overall plant structure. Overall, the system shows strong potential to optimize inputs and yields, though UAV path tracking errors and system response delays require further refinement to enhance spray uniformity and accuracy under real-world applications. Full article

Low-Salinity Water Flooding (LSWF) has gained attention for its cost-effectiveness and environmental advantages, yet its underlying mechanisms remain not fully understood. Oil recovery in LSWF is primarily governed by interfacial dynamics and formation wettability. This research investigates the effects of seawater dilution in carbonate reservoirs through laboratory analyses and displacement experiments. Results show that oil recovery efficiency is largely driven by rock–fluid interactions rather than fluid–fluid interactions, with optimal brine concentrations enhancing wettability alteration, boundary flexibility, and mineral leaching. These findings highlight the importance of considering both fluid–rock interactions and mineral reactivity, rather than attributing recovery to a single mechanism. Molecular dynamics simulations further supported the experimental observations. Overall, the study emphasizes that early and well-designed low-salinity injection strategies can maximize LSWF performance. The results elucidate the key interaction mechanisms between surfactants and the various components of heavy oil through atomic-scale precision modeling and dynamic process tracking. These simulations clarify, at the microscopic level, the differences in displacement dynamics and efficiency of organic solvent systems toward different hydrocarbon components. Full article

In this study, the use of spent coffee waste as a green precursor of polyphenolic compounds, which are subsequently employed as reducing agents for the synthesis of zero-valent iron nanoparticles (nZVI) aimed at the efficient removal of dyes from aqueous systems, has been investigated. The nanoparticles, generated in situ in the presence of controlled amounts of hydrogen peroxide, were applied in the removal of organic dyes—including methylene blue, methyl orange, and orange G—through a heterogeneous Fenton-like catalytic process. The synthesized nZVI were thoroughly characterized by nitrogen adsorption at 77 K, scanning electron microscopy (SEM), transmission electron microscopy (TEM), Fourier-transform infrared spectroscopy (FT-IR), and powder X-ray diffraction (XRD). A statistical design of experiments and response surface methodology were employed to evaluate the effect of polyphenol, Fe(III), and H₂O₂ concentrations on dye removal efficiency. Results showed that under optimized conditions, a 100% removal efficiency could be achieved. This work highlights the potential of nZVI synthesized from agro-industrial waste through sustainable routes as an effective solution for water remediation, contributing to circular economy strategies and environmental protection. Full article