Search Results (2,214)

Offshore reservoirs in the high water-cut stage present significant development challenges, including declining production, complex remaining oil distribution, and the inadequacy of conventional evaluation methods to capture intricate flow dynamics. To overcome these limitations, this study introduces a novel approach based on flow diagnostics for performance evaluation and potential adjustment. The method integrates key metrics such as time-of-flight (TOF) and the dynamic Lorenz coefficient, supported by reservoir engineering principles and numerical simulation, to construct a multi-parameter evaluation system. This system, which also incorporates injection–production communication volume and inter-well fluid allocation factors, precisely quantifies and visualizes waterflood displacement processes and sweep efficiency. Applied to the QHD32 oilfield, this framework was used to establish specific thresholds for operational adjustments. These include criteria for infill drilling (waterflooded ratio < 45%, remaining oil thickness > 6 m, TOF > 200 days), conformance control (TOF < 50 days, dynamic Lorenz coefficient > 0.5), and artificial lift optimization (remaining oil thickness ratio > 2/3, TOF > 200 days). Field validation confirmed the efficacy of this approach: an additional cumulative oil production of 165,600 m³ was achieved from infill drilling in the C29 well group, while displacement adjustments in the B03 well group increased oil production by 2.2–3.8 tons/day, demonstrating a significant enhancement in waterflooding performance. This research provides a theoretical foundation and a technical pathway for the refined development of offshore heavy oil reservoirs at the ultra-high water-cut stage, offering a robust framework for the sustainable management of analogous reservoirs worldwide. Full article

Stricter requirements on nutrient removal in wastewater treatment are being imposed by rapid urbanization and tightening water-quality standards. Despite their excellent solid–liquid separation and effective biological treatment, MBRs in conventional operation remain hindered by membrane fouling, limited robustness to influent variability, and elevated energy consumption. In recent years, precise process control and resource-oriented operation have been enabled by the integration of artificial intelligence (AI) with MBRs. Advances in four areas are synthesized in this review: optimization of MBR control architectures, intelligent adaptation to multi-source wastewater, regulation of membrane operating parameters, and enhancement of nitrogen and phosphorus removal. According to reported studies, increases in total nitrogen and total phosphorus removal have been achieved by AI-driven strategies while energy use and operating costs have been reduced; under heterogeneous influent and dynamic operating conditions, stronger generalization and more effective real-time regulation have been demonstrated relative to traditional approaches. For large-scale deployment, key challenges are identified as improvements in model interpretability and applicability, the overcoming of data silos, and the realization of multi-objective collaborative optimization. Addressing these challenges is regarded as central to the realization of robust, scalable, and low-carbon intelligent wastewater treatment. Full article

In order to enhance the separation efficiency of fine-grained Bayan Obo Niobium Ore, a novel gravity separation equipment named Slime Vibrating Table (SVT) was developed. The SVT employs an electromagnetic drive to generate a reciprocating motion for the table, with a lower stroke and higher frequency than a conventional Slime Shaking Table (SST). Key parameters of SVT, including table slope, wash-water flow rate, vibration voltage, and vibration frequency, were tested for a niobium ore assaying 0.19% Nb₂O₅ with a particle size below 74 um by 68.78%. Under the optimized condition, SVT was able to obtain a primary concentrate assaying 1.31% Nb₂O₅ with a recovery of 52.64%, which was 0.22% and 26.59% higher than that of SST, respectively. Size-by-size analysis indicated that the enhanced separation performance of SVT was mainly attributed to its superior recovery of Nb₂O₅ in the −38 μm fraction. The SVT introduced in this study shows great potential for efficient recovery of fine-grained strategic metals, including rare earths, tantalum, tungsten, tin, and antimony, etc. Full article

This study presents the deployment of Machine Learning (ML) models on low-cost edge devices (ESP32) for real-time water quality prediction in tilapia aquaculture. A compact monitoring and control system was developed with low-cost sensors to capture key environmental parameters under field conditions in Northern Thailand. Three ML models—Multiple Linear Regression (MLR), Decision Tree Regression (DTR), and Random Forest Regression (RFR)—were evaluated. RFR achieved the highest accuracy (R² > 0.80), while MLR, with moderate performance (R² ≈ 0.65–0.72), was identified as the most practical choice for ESP32 deployment due to its computational efficiency and offline operability. The system integrates sensing, prediction, and actuation, enabling autonomous regulation of dissolved oxygen and pH without constant cloud connectivity. Field validation demonstrated the system’s ability to maintain DO within biologically safe ranges and stabilize pH within an hour, supporting fish health and reducing production risks. These findings underline the potential of Edge AIoT as a scalable solution for small-scale aquaculture in resource-limited contexts. Future work will expand seasonal data coverage, explore federated learning approaches, and include economic assessments to ensure long-term robustness and sustainability. Full article

Background: Parkinson’s disease (PD) is a neurodegenerative disorder for which no curative therapies currently exist. Experimental models employing 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) reproduce PD features such as striatal dopaminergic dysfunction and motor deficits. Various MPTP dosing regimens are used to screen drug candidates for PD, but their validity is limited because of the predominant use of young male animals. Sex bias is another issue that is underrepresented in PD research, since females are more susceptible to this pathology. Here, we studied the model of bolus administration of MPTP (30 mg/kg) in aged female mice and assessed its sensitivity to the antioxidants fullerene C₆₀ and fullerenol C₆₀(OH)₂₄, given that oxidative stress is a key contributor to PD. Methods: 12-month-old female C57BL/6 mice received fullerene (0.1 mg/kg/day, via diet) or fullerenol (0.15 mg/kg/day, via drinking water). On day 10, mice were injected with MPTP. We studied tremor, piloerection, and behavior in the pole test, rotarod, pole test, and open field. High-performance liquid chromatography (HPLC) was employed to study dopaminergic neurotransmission, and the expression levels of its molecular regulators and nitric oxide synthase (NOS)-related targets were investigated using RT-PCR in the striatum and cortex. Results: MPTP-challenged mice displayed profound impairment in markers of dopaminergic neurotransmission and cellular distress, and showed disrupted motor behavior and vegetative functions. Antioxidant-treated animals that received a bolus injection of MPTP demonstrated partial preservation of tremor response, dopaminergic parameters, and iNOS and nNOS gene expression, although motor performance in the pole test was only modestly improved. Fullerenol appeared more effective in decreasing MPTP-induced neurochemical changes. Conclusions: The applied MPTP model showed its validity in mimicking PD features and was sensitive to low doses of antioxidants, suggesting its usefulness for screening drugs that target oxidative and nitrosative stress. The neuroprotective effects of fullerene-based compounds suggest their potential utility in the treatment of PD. Full article

This study establishes an efficient and eco-friendly ultrasound-assisted extraction (UAE) method for total flavonoids present in Cercis chinensis seeds using natural deep eutectic solvents (NADES). Among nine NADES formulations screened, choline chloride–levulinic acid (ChCl–Lev, 1:2) demonstrated optimal performance, yielding 112.1 mg/g total flavonoids. Through Response Surface Methodology (RSM), the ultrasound-assisted extraction (UAE) parameters were explored. Under the optimized conditions (water content of 30%, time of 28 min, temperature of 60 °C, and solvent-to-solid ratio of 1:25 g/mL), the total flavonoid yield reached 128.5 mg/g, representing a 195% improvement compared to conventional ethanol extraction. The recyclability of NADES was successfully achieved via AB-8 macroporous resin, retaining 80.89% efficiency after three cycles. Extraction kinetics, modeled using Fick’s second law, confirmed that the rate constant (k) increased with temperature, highlighting temperature-dependent diffusivity as a key driver of efficiency. The extracted flavonoids exhibited potent antioxidant activity, with IC₅₀ values of 0.86 mg/mL (ABTS•⁺) and 0.69 mg/mL (PTIO•). This work presents a sustainable NADES-UAE platform for flavonoid recovery and offers comprehensive mechanistic and practical insights for green extraction of plant bioactives. Full article

Photocatalytic membrane reactors (PMRs), which combine photocatalysis with membrane separation, represent a pivotal technology for sustainable water treatment and resource recovery. Although extensive research has documented various configurations of photocatalytic-membrane hybrid processes and their potential in water treatment applications, a comprehensive analysis of the interrelationships among reactor architectures, intrinsic physicochemical mechanisms, and overall process efficiency remains inadequately explored. This knowledge gap hinders the rational design of highly efficient and stable reactor systems—a shortcoming that this review seeks to remedy. Here, we critically examine the connections between reactor configurations, design principles, and cutting-edge applications to outline future research directions. We analyze the evolution of reactor architectures, relevant reaction kinetics, and key operational parameters that inform rational design, linking these fundamentals to recent advances in solar-driven hydrogen production, CO₂ conversion, and industrial scaling. Our analysis reveals a significant disconnect between the mechanistic understanding of reactor operation and the system-level performance required for innovative applications. This gap between theory and practice is particularly evident in efforts to translate laboratory success into robust and economically feasible industrial-scale operations. We believe that PMRs will realize their transformative potential in sustainable energy and environmental applications in future. Full article

Global Navigation Satellite System (GNSS) meteorology has emerged as a valuable tool for atmospheric monitoring, providing high-resolution, near-real-time data that can significantly improve precipitation nowcasting. This study aims to enhance short-term precipitation forecasting by integrating GNSS-derived Precipitable Water Vapor (PWV)—a key indicator of atmospheric moisture—with traditional meteorological observations. A novel two-step machine learning framework is proposed that combines a Random Forest (RF) model and a Long Short-Term Memory (LSTM) neural network. The RF model first estimates current precipitation based on PWV, surface weather parameters, and auxiliary atmospheric variables. Then, the LSTM network leverages temporal dependencies within the data to predict precipitation for the subsequent hour. This hybrid method capitalizes on the RF’s ability to model complex nonlinear relationships and the LSTM’s strength in handling time series data. The results demonstrate that the proposed approach improves forecasting accuracy, particularly during extreme weather events such as intense rainfall and thunderstorms, outperforming conventional models. By integrating GNSS meteorology with advanced machine learning techniques, this study offers a promising tool for meteorological services, early warning systems, and disaster risk management. The findings highlight the potential of GNSS-based nowcasting for real-time decision-making in weather-sensitive applications. Full article

Soil electrical conductivity (EC) and water content are key indicators of soil health, influencing nutrient availability, salinity stress, and crop productivity. Monitoring these parameters is critical for precision agriculture. However, most existing measurement systems are costly, which restricts their use in practical field conditions. The aim of this study was to develop and validate a low-cost, portable system for simultaneous measurement of soil EC, water content, and temperature, while maintaining accuracy comparable to laboratory-grade instruments. The system was designed with four electrodes arranged in two pairs and employed an AC bipolar pulse method with a constant-current circuit, precision rectifier, and peak detector to minimize electrode polarization. Experiments were carried out in sandy loam soil at water contents of 13%, 18%, and 22% and KNO₃ concentrations of 0, 0.1, 0.2, and 0.4 M. Measurements from the developed system were benchmarked against a professional impedance analyzer (E4990A). The findings demonstrated that EC increased with both frequency and water content. At 100 Hz, the mean error compared with the analyzer was 8.95%, rising slightly to 9.98% at 10 kHz. A strong linear relationship was observed between EC and KNO₃ concentration at 100 Hz (R² = 0.9898), and for the same salt concentration (0.1 M KNO₃) at 100 Hz, EC increased from ~0.26 mS/cm at 13% water content to ~0.43 mS/cm at 22%. In conclusion, the developed system consistently achieved <10% error while maintaining a cost of ~€55, significantly lower than commercial devices. These results confirm its potential as an affordable and reliable tool for soil salinity and water content monitoring in precision agriculture. Full article

Cave systems serve as key interfaces connecting surface and underground carbon cycles, and research on their carbon dynamics provides a unique perspective for revealing the mechanisms of carbon transport and transformation in karst critical zones. In this study, we established a multi-factor monitoring framework spanning the atmosphere–soil–cave continuum and associated meteorological conditions, continuously recorded cave microclimate parameters (temperature, relative humidity, atmospheric pressure, and cave winds) and CO₂ concentrations across atmospheric–soil–cave interfaces, and employed stable carbon isotope (δ¹³C) tracing in Mahuang Cave, a typical karst cave in southwestern China, from 2019 to 2023. The results show that the seasonal amplitude of atmospheric CO₂ and its δ¹³C is small, while soil–cave CO₂ and δ¹³C fluctuate synchronously, exhibiting “high concentration-light isotope” signatures during the rainy season and the opposite pattern during the dry season. Cave CO₂ concentrations drop by about 29.8% every November. Soil CO₂ production rates are jointly controlled by soil temperature and volumetric water content, showing a threshold effect. The δ¹³C response exhibits nonlinear behavior due to the combined effects of land-use type, vegetation cover, and soil texture. Quantitative analysis establishes atmospheric CO₂ as the dominant source in cave systems (66%), significantly exceeding soil-derived contributions (34%). At diurnal, seasonal, and annual scales, carbon-source composition, temperature and precipitation patterns, ventilation effects, and cave structure interact to control the rhythmic dynamics and spatial gradients of cave microclimate, CO₂ levels, and δ¹³C signals. Our findings enhance the understanding of carbon transfer processes across the karst critical zone. Full article

The size and mass of fish are crucial parameters in aquaculture management. However, existing research primarily focuses on conducting fish size and mass estimation under ideal conditions, which limits its application in actual aquaculture scenarios with complex water quality and fluctuating lighting. A non-contact size and mass measurement framework is proposed for complex underwater environments, which integrates the improved FishKP-YOLOv11 module based on YOLOv11, stereo vision technology, and a Random Forest model. This framework fuses the detected 2D key points with binocular stereo technology to reconstruct the 3D key point coordinates. Fish size is computed based on these 3D key points, and a Random Forest model establishes a mapping relationship between size and mass. For validating the performance of the framework, a self-constructed grass carp dataset for key point detection is established. The experimental results indicate that the mean average precision (mAP) of FishKP-YOLOv11 surpasses that of diverse versions of YOLOv5–YOLOv12. The mean absolute errors (MAEs) for length and width estimations are 0.35 cm and 0.10 cm, respectively. The MAE for mass estimations is 2.7 g. Therefore, the proposed framework is well suited for application in actual breeding environments. Full article

In global cold regions and seasonal frozen soil areas, frost heave failure of rock slopes severely endangers infrastructure safety, particularly along China’s Sichuan–Tibet and Qinghai–Tibet Railways. To address this, a meshless numerical model based on the smoothed particle hydrodynamics (SPH) method was developed to simulate progressive frost heave and fracture of water-saturated fissured rock masses—its novelty lies in avoiding grid distortion and artificial crack path assumptions of FEM as well as complex parameter calibration of DEM by integrating the maximum tensile stress criterion (with a binary fracture marker for particle failure), thermodynamic phase change theory (classifying fissure water into water, ice-water mixed, and ice particles), and the equivalent thermal expansion coefficient method to quantify frost heave force. Systematic simulations of fissure parameters (inclination angle, length, number, and row number) revealed that these factors significantly shape failure modes: longer fissures and more rows shift failure from strip-like to tree-like/network-like, more fissures accelerate crack coalescence, and larger inclination angles converge stress to fissure tips. This study clarifies key mechanisms and provides a theoretical/numerical reference for cold region rock slope stability control. Full article

Although polypropylene (PP) is among the most widely used polymers with adequate chemical and mechanical properties, its poor wettability prevents adhesive joints needed for sticking with other materials, printing, etc. Plasma treatment, an established method for increasing wettability, is presented, and relevant literature is analyzed. A comparison of different reviewed articles shows little influence of the discharge parameters on PP wettability, and that the methods for achieving a super-hydrophilic surface of this polymer have yet to be developed. The peculiarities of PP prevent stable surface functionalization, although the formation of molecular fragments is the predominant effect of plasma treatments. The key conclusion after analyzing the reviewed literature is that the washing of PP following plasma treatment will cause a low level of wettability regardless of the peculiarities of the plasmas or discharges, including the treatment time, and all authors reported a water contact angle between about 75 and 80° after washing the plasma-treated PP. The hydrophobic recovery of washed plasma-treated PP was not addressed in any reviewed article. Full article

Geothermal energy has been recognized as a promising renewable resource for sustainable power generation; however, the efficiency of conventional geothermal power plants has remained relatively low, and high investment costs have limited their competitiveness with other renewable technologies. In this context, the present study introduced an innovative geothermal electricity generation system aimed at enhancing energy efficiency, cost-effectiveness, and sustainability. Unlike traditional configurations, the system raised the geothermal source temperature passively by employing advanced heat transfer mechanisms, eliminating the need for additional energy input. Comprehensive energy, exergy, and exergoeconomic analyses were carried out, revealing a net power output of 43,210 kW and an energy efficiency of 30.03%, notably surpassing the conventional Kalina cycle’s typical 10.30–19.48% range. The system’s annual electricity generation was 11,138.53 MWh, with an initial investment of USD 3.04 million and a short payback period of 3.20 years. A comparative assessment confirmed its superior thermoeconomic performance. In addition to its technoeconomic advantages, the environmental performance of the proposed configuration was quantified. A streamlined life cycle assessment (LCA) was performed with a functional unit of 1 MWh of net electricity. The proposed system exhibited a carbon footprint of 20–60 kg CO₂ eq MWh⁻¹ (baseline: 45 kg CO₂ eq MWh⁻¹), corresponding to annual emissions of 0.22–0.67 kt CO₂ eq for the simulated output of 11,138.53 MWh. Compared with coal- and gas-fired plants of the same capacity, avoided emissions of approximately 8.6 kt and 5.0 kt CO₂ eq per year were achieved. The water footprint was determined as ≈0.10 m³ MWh⁻¹ (≈1114 m³ yr⁻¹), which was substantially lower than the values reported for fossil technologies. These findings confirmed that the proposed system offered a sustainable alternative to conventional geothermal and fossil-based electricity generation. Multi-objective optimization using NSGA-II was carried out to maximize energy and exergy efficiencies while minimizing total cost. Key parameters such as turbine inlet temperature (459–460 K) and ammonia concentration were tuned for performance stability. A sensitivity analysis identified the heat exchanger, the first condenser (Condenser 1), and two separators (Separator 1, Separator 2) as influential on both performance and cost. The exergoeconomic results indicated Separator 1, Separator 2, and the turbine as primary locations of exergy destruction. With an LCOE of 0.026 USD/kWh, the system emerged as a cost-effective and scalable solution for sustainable geothermal power production without auxiliary energy demand. Full article

This study investigated the mechanical performance and durability of concretes produced with varying proportions of recycled coarse aggregate from construction and demolition waste (CDW), ranging from 0% to 100% replacement of natural coarse aggregate, using recycled aggregates derived from crushed concrete and mortar debris, characterized by lower density and high water absorption (~9%) compared to natural aggregates. A key contribution of this research lies in the inclusion of intermediate replacement levels (20%, 25%, 45%, 50%, and 65%), which are less explored in the literature and allow a more refined identification of performance thresholds. Fresh-state parameters (slump), axial compressive strength (7 and 28 days), total immersion water absorption, sorptivity, and chloride ion penetration depth (after 90 days of immersion in a 3.5% NaCl solution) were evaluated. The results indicate that, up to 50% CDW content, the concrete maintains slump (≥94 mm), characteristic strength (≥37.2 MPa at 28 days), and chloride penetration (≤14.1 mm) within the limits for moderate exposure conditions, in accordance with ABNT: NBR 6118. Water absorption doubled from 4.5% (0% CDW) to 9.5% (100% CDW), reflecting the higher porosity and adhered mortar on the recycled aggregate, which necessitates adjustments to the water–cement ratio and SSD pre-conditioning to preserve workability and minimize sorptivity. Concretes with more than 65% CDW exhibited chloride penetration depths exceeding 15 mm, potentially compromising durability without additional mitigation. The judicious incorporation of CDW, combined with optimized mix design practices and the use of supplementary cementitious materials (SCMs), demonstrates technical viability for reducing environmental impacts without significantly impairing the structural performance or service life of the concrete. Full article