Search Results (19,386)

Accurate multiphase flow measurement in horizontal wells is fundamentally challenged by the antagonistic electrical responses of water and gas: Electrical Resistance Tomography (ERT) loses sensitivity to thin liquid films, while Electrical Capacitance Tomography (ECT) suffers signal saturation in conductive water, preventing either modality from covering the full operating envelope alone. This study proposes a physics-guided hybrid modeling framework that integrates multi-modal ERT/ECT sensing to achieve high-precision flowrate inversion. The framework utilizes a corrected multi-modal fusion algorithm, achieving a liquid holdup MAPE of 2.5 ± 0.5% representing a nearly two-fold improvement over the best single-modality system (Direct ERT, 4.5%). For velocity estimation, an optimized cross-correlation method yields results with ± 3.0% error, incorporating multi-sensor and multi-sequence fusion. A key finding is that deep neural networks exhibit Architectural Phase Specialization: multi-branch architectures (MB-DNN) perform strongly on localized, heterogeneous liquid structures (2.0% liquid error), whereas fully-connected architectures (FC-DNN) excel at capturing the global patterns of the continuous gas core (1.2% gas error). By hybridizing a calibrated drift-flux physical model with these phase-specialized DNNs, the framework achieves overall averaged errors of 1.8% for gas and 1.5% for liquid across the full experimental envelope. The proposed framework was evaluated on 444,313 experimental samples and subsequently validated in a three-month industrial trial at the Puguang gas field under extreme conditions (26 MPa, 80 °C), where it maintained a prediction error of ± 2.3%. This work establishes a scalable, physically consistent paradigm for intelligent hydrocarbon production monitoring. Full article

Osmotic stress severely limits the growth and development of tomato (Solanum lycopersicum L.) by reducing cellular water potential, disrupting redox homeostasis, and impairing physiological functions. Micro–nano bubble (MNB) treatment can increase dissolved oxygen in the root-zone solution and improve the root-zone environment, which may benefit root metabolic activity and stress adaptation. However, the underlying molecular mechanisms are still not elucidated. To explore the underlying molecular mechanisms of how MNB-mediated root oxygenation alleviates osmotic stress in tomato, we have integrated the physiological and biochemical alterations, variable-pressure scanning electron microscopy (VP-SEM), and transcriptomic analysis (RNA-seq) under osmotic stress. The results revealed that MNBs significantly reduced PEG-induced wilting and decreased reactive oxygen species (ROS) accumulation and relative electrical conductivity (REC). Indeed, MNBs also markedly upregulated the expression of root aquaporins PIP2.7 and PIP2.4, suppressed the expression of NCED1 in leaves, and increased levels of endogenous growth-promoting hormones, including IAA and GA₃, under osmotic stress. VP-SEM observations showed that MNB-treated plants exhibited a relatively more open stomatal appearance compared with PEG-treated plants. Together, these findings suggest that MNBs mitigate PEG-induced osmotic stress in tomato, potentially by improving the root-zone aeration environment and coordinating water transport-related gene expression, antioxidant defense, and hormonal balance. These results provide a promising physical approach and theoretical basis for improving tomato stress tolerance under osmotic stress. Full article

The measurement of a probe impedance performed during eddy current inspections enables detection of flaws in electrically conductive materials. A correct interpretation of the measured impedance values constitutes a key aspect that determines the effectiveness of the inspections, and for this purpose, mathematical models are employed. Such models, which are becoming more and more frequently an integral part of eddy current measurement systems, enable carrying out the calculation of the probe impedance, through depicting the measurements being performed. What offer the shortest calculation time while maintaining high accuracy are analytical solutions. In this paper, to the best of the authors’ knowledge, this is the first time an analytical model of an eddy current probe placed over a small diameter cylinder containing a hole has been presented. The final formulas were obtained using the truncated region eigenfunction expansion (TREE) method, and then implemented in Matlab. The calculated values of the probe resistance and reactance were compared with the measurement results obtained for cylinders with a through defect. The tests were conducted on components made of several conductive materials with different geometric dimensions. The measurement error in all of the tests was small, i.e., it did not exceed 3% across the entire frequency range. The proposed solution can be used in defectoscopy for eddy current testing of tubes, pucks, washers, and any cylindrical elements. Full article

With the rapid advancement of wearable technologies, high-performance flexible sensors have garnered significant research interest. This study presents a PAM-5 hydrogel characterized by exceptional tensile strain (425%), superior compressive modulus (325 kPa), and notable ionic conductivity (1.1 S/m), serving as a robust mechanical framework and electrical foundation for developing advanced sensors. The PNP-5 integrated hydrogel sensor fabricated from this material demonstrates an extensive sensing range (2–53 kPa), remarkable sensitivity, and rapid response time (~321 ms), with its outstanding performance attributed to the synergistic structural design. Furthermore, the sensor exhibits excellent durability, maintaining consistent voltage output (~6.5 mV) across 1000 compression cycles, confirming its long-term operational stability. Through real-time monitoring of physiological signals and biomechanical movements including finger bending, respiration, and grasping, combined with spatial pressure mapping experiments using a 5 × 5 array touchpad, the device’s potential applications in wearable sensing platforms and human–machine interface systems are effectively demonstrated. This self-powered hydrogel sensor not only advances the performance metrics of flexible electronic devices but also establishes a solid experimental basis for future development of intelligent materials in health monitoring and interactive technologies. Full article

3D magnetotelluric (MT) inversion based on the Gauss–Newton (GN) framework plays an important role in deep mineral exploration by imaging subsurface electrical conductivity structures. However, large-scale 3D MT inversion remains computationally expensive due to the high cost of sensitivity-matrix construction. To address this challenge, we develop a stochastic Gauss–Newton (SGN) framework that reduces computational cost through random data subsampling while preserving the practical convergence behavior of GN inversion. In the proposed framework, only a randomly selected subset of data is used to approximate the GN search direction. By exploiting a key property of MT forward modelling, namely that responses at all receivers are obtained simultaneously for each frequency, the line search is performed using the full dataset, ensuring stable convergence of the inversion process. The SGN framework is validated using both a synthetic multiblock model and a field dataset from the Akebasitao area in Xinjiang, China. The recovered models remain highly consistent with those obtained using conventional full-data Gauss–Newton inversion across a wide range of sampling ratios. For the synthetic example, reducing the sampling ratio from 100% to 10% decreases peak memory consumption from approximately 433 GB to 242 GB and reduces runtime from 86.8 h to 23.9 h while maintaining comparable inversion quality. Similar computational savings are achieved for the field-data inversion. The field application successfully recovers the major conductive structures along the margins of the intrusion that are associated with hydrothermal alteration and fluid activity, highlighting the capability of SGN to delineate geologically meaningful targets relevant to deep mineral exploration. These results demonstrate that SGN provides an efficient and scalable approach for large-scale 3D MT inversion. Full article

Vermicomposting is an eco-friendly biotechnology for organic waste valorization. As the primary product of earthworm biotransformation, vermicompost is a high-value bio-organic fertilizer abundant in diverse biologically active components. To date, most studies have focused on quality variation during the earthworm transformation process, while research on quality variations in the resulting vermicompost fertilizer during long-term storage remains scarce. To explore the shelf-life of vermicompost fertilizer and its key influencing indicators, this study investigated the changes in quality indicators in sealed-packaged vermicompost over a 180-day period using two typical vermicompost, namely cattle manure vermicompost (CM) and straw-amended cattle manure vermicompost (CMS). The temporal dynamics of physicochemical properties, nutrient contents, humification indices, enzyme activities, and microbial communities were monitored. The vermicompost quality was evaluated, and core quality drivers were identified using an integrated principal component analysis-partial least squares (PCA-PLS) approach. The results indicated that moisture content (MC), total organic carbon (TOC), and total nitrogen (TN) declined progressively, whereas available phosphorus (AP) and available potassium (AK) peaked at day 150 and day 120, respectively, and the humification rate (HR) increased by 2.6–4.0-fold. Bacterial diversity and relative abundance slightly decreased, accompanied by taxonomic differentiation, whereas fungal communities maintained stable diversity. Most enzyme activities, including urease, phosphatase, catalase, and dehydrogenase, reached their maxima at day 120. Comprehensive quality scores peaked at day 150, with a marked decline observed by day 180. The recommended shelf-life of vermicompost fertilizer is 150 days. The key quality determinants include TN, electrical conductivity (EC), pH, actinomycete abundance, TOC, TP, bacterial abundance, AP, AK, and HR. These findings provide theoretical support and references for the storage management and quality control of commercial vermicompost products in practice. Full article

Impulse-based impedance sensing with capacitively coupled electrodes is introduced as a fast, non-contact, and simplified complementary method to conventional capacitive impedance measurements. Unlike frequency-domain methods, the proposed approach derives effective resistive and capacitive properties of a sample from the transient response to a single rectangular pulse. The equivalent circuit model comprises three elements: sample resistance, sample capacitance, and electrode coupling capacitance. From this model, analytical expressions of the transient response were derived, enabling accurate simulation of measured signals and providing the basis for both phantom verification and machine learning training. Importantly, the coupling capacitance, typically considered a limitation in contactless methods, is estimated alongside the sample parameters, providing insight into electrode–object coupling conditions. A machine-learning model trained on simulated circuit responses, including noise and temporal variability, is employed as a low-latency estimator for extracting parameters from measured transient signals. Experimental validation was carried out using a configurable lumped-element equivalent circuit and NaCl solutions of controlled conductivity, cross-verified with conductometric measurements and numerical probe simulations. Across a tested conductivity range, the method achieved estimation errors of 2–8%. The proposed approach is intended as a low-latency measurement strategy for simplified capacitively coupled impedance sensing, with potential relevance to future capacitively coupled electrical impedance tomography systems, where rapid acquisition of boundary measurements is prioritized over full frequency-resolved impedance spectroscopy. Full article

Urban industrial parks have high electricity demand, and rooftop photovoltaic (PV)-battery systems can help reduce grid dependence and carbon emissions. However, battery degradation affects battery replacement timing and long-term economic performance, which should be considered in capacity sizing. This study proposes a degradation-aware techno-economic sizing method for rooftop PV-battery systems in urban industrial parks. GIS-based rooftop assessment, EnergyPlus load modeling, TRNSYS system simulation, battery SOH tracking, and NPV evaluation were integrated into one framework. A case study was conducted for an urban industrial park in Shanghai, China. The usable rooftop area was estimated as 113,208 m², corresponding to a PV capacity of approximately 18,765 kWp. The annual PV generation was 24.7 GWh, accounting for 24.7% of the park’s annual electricity demand. Battery capacities from 5000 to 40,000 kWh were evaluated. The results show that increasing battery capacity improves load shifting and reduces direct grid supply, but the marginal benefit gradually decreases. The maximum NPV is obtained at 30,000 kWh, with an NPV of 128.36 million CNY, a simple payback period of 4.6 years, and a discounted payback period of 6.0 years. The rooftop PV system achieves a 25-year CO₂ emission reduction of approximately 335,967 tCO₂ after considering PV degradation. Sensitivity analyses show that BES cost, tariff spread, and discount rate are key factors affecting the recommended capacity. Full article

Lightweight polymer composites with effective electromagnetic interference (EMI) shielding are of increasing interest for advanced electronic and aerospace applications; however, conventional glass fiber-reinforced polymers (GFRPs) exhibit inherently low electrical conductivity, limiting their shielding performance. In this study, a hierarchical hybrid conductive architecture was developed by integrating graphene-coated multiaxial glass fiber fabrics with carbon black (CB)-reinforced epoxy matrices to enhance EMI shielding behavior in the X-band (8–12 GHz). Graphene coatings were deposited onto glass fibers via a surfactant-assisted ultrasonic dispersion method, while carbon black (0–1 wt.%) was incorporated into the epoxy matrix using ultrasonication-assisted mixing. Multilayer composites were fabricated using a vacuum bagging process. X-ray diffraction analysis revealed that the composites retained a predominantly amorphous epoxy/glass fiber matrix while exhibiting broad carbon-related diffraction features associated with disordered graphitic domains. Electrical conductivity measurements indicated that all composites remained in the insulating regime (~10⁻⁹ S/m), suggesting that a fully interconnected conductive network was not established within the investigated filler range. Despite the absence of a continuous conductive network, measurable EMI shielding performance was achieved. The composite containing 0.25 wt.% CB exhibited the highest shielding effectiveness, reaching approximately 12 dB at ~11.2 GHz. Analysis of the shielding contributions showed that absorption contributions (SEA) were consistently higher than reflection contributions (SER) across the studied frequency range. Morphological observations revealed that well-dispersed CB at low loading facilitated the formation of localized conductive domains that may contribute to tunneling-assisted polarization and interfacial charge accumulation. At higher CB contents, particle agglomeration reduced dispersion quality and limited effective pathway formation, while dynamic mechanical analysis indicated enhanced stiffness at low CB loading. FTIR results confirmed the absence of new chemical bonding, indicating that CB acts as a physically dispersed conductive filler. Overall, the results show that effective EMI shielding can be achieved in electrically insulating composites through the combined effect of hierarchical structural design and localized conductive features. This approach provides a practical pathway for developing lightweight EMI shielding materials with controlled filler loading and preserved structural integrity for aerospace and electronic applications. Full article

To evaluate the effects of saline water on the yield and quality of tomatoes, a late fruit-setting stage irrigation experiment was conducted in a greenhouse using two cultivars: medium-fruited, tasty Strawberry tomato (fresh-eating) and large-fruited Maofen tomato (fresh/processing). For this, plants were grown in pots containing substrate, and five irrigation water electrical conductivity (EC) levels (1.0 as control, 2.6, 4.2, 5.8, and 7.4 dS m⁻¹) were applied for each cultivar, resulting in a 2 × 5 factorial design with 10 treatments in total. Then, tomato growth, fruit ion composition, and quality attributes were evaluated. The results showed that 1.0–7.4 dS m⁻¹ saline water had no significant impact on the plant height, stem diameter, single-fruit weight, or total yield of either cultivar. However, Strawberry tomato’s marketable yield decreased by 23.5% at 7.4 dS m⁻¹. The yield per plant of Maofen tomato was 2.7 times that of Strawberry tomato. Fruit Na⁺ content increased with EC for both cultivars; Maofen tomato had higher Na⁺ and a lower K⁺/Na⁺ ratio, with greater ion content responses to salinity. Regression analysis revealed distinctly nonlinear responses in key yield, ion, and quality parameters across the salinity gradient. The fruit comprehensive quality score (CQS) rose with EC, and Strawberry tomato’s average CQS increase (109%) was significantly higher than Maofen tomato’s. In conclusion, saline irrigation initiated when the fourth-cluster fruits attained 60% of the final harvested diameter, at EC ≤ 5.8 dS m⁻¹ for Strawberry tomato and ≤7.4 dS m⁻¹ for Maofen tomato, improved fruit quality without compromising yield. Strawberry tomato is recommended for quality-oriented production, whereas Maofen tomato is better suited for yield-oriented production, providing scientific support for saline water utilization in greenhouse soil-less cultivation. Full article

Microbial fuel cells (MFCs) are emerging as biomimetic bioelectrochemical systems that emulate naturally occurring microbial electron-transfer pathways for stimulus bioenergy generation and wastewater remediation. In this study, food–vegetable leachate (FVL) and sugarcane bagasse-derived biol were evaluated in combination with carbon fiber (CF) and biochar-modified carbon fiber (BCF) electrodes used as membrane components in MFCs. Four configurations, in duplicate, were constructed by coupling two substrates (biol or FVL) with two membrane types (CF and BCF). All systems exhibited progressive anodic acidification and up to a 55% increase in electrical conductivity. The highest voltage output was achieved in MFC-BL-2 (404.59 mV), followed by MFC-FL-1, driven by synergistic interactions between the substrate and biochar-enhanced conductive networks. MFC-FL-1 also demonstrated superior contaminant removal performance, achieving 60% COD reduction, 36% BOD reduction, and 50% NH₄⁺–N removal. SEM–EDS analysis confirmed that biochar-modified electrodes developed a porous structure and substantially enhanced microbial adhesion. FVL-fed systems formed dispersed electroactive biofilms that facilitated electron transfer, whereas biol-fed systems developed compact biofilms that constrained electron flux. By integrating waste-derived lignocellulosic materials with electroactive microbial consortia, this work advances a biomimetic circular bioengineering platform for sustainable bioelectrochemical recovery and wastewater remediation. Full article

Maintenance management increasingly depends on effective information flow and coordination between internal teams and external service providers. This study investigates the use of Business Process Model and Notation (BPMN) to support the formalization of Computerized Maintenance Management System (CMMS) workflows and improve transparency, decision-making, and interorganizational coordination. A single case study was conducted in the maintenance department of an electricity distribution company characterized by tacit knowledge, informal communication practices, and limited process formalization. Existing corrective maintenance workflows were analyzed and modeled using BPMN to identify inefficiencies, decision points, and opportunities for improvement. The proposed BPMN models were aligned with CMMS operational states associated with anomaly management and work-order execution processes and supported by a procedural manual. Results obtained during a three-month observation period suggest reductions in training time, email communications, and dependence on individual decision-makers, together with increased use of CMMS workflow functionalities and improved process traceability. These findings provide preliminary evidence, derived from operational indicators within a single case study, that BPMN-supported process formalization may contribute to workflow standardization, operational clarity, and knowledge management in maintenance-intensive environments. Given the single-case design and limited observation period, the results should be interpreted as context-specific and not directly generalizable to the broader energy sector. Full article

Incentive-based demand response is an important flexibility resource for power systems with high-renewable energy penetration. However, practical incentive allocation depends not only on flexible capacity and user response uncertainty, but also on the credibility of customer baseline load (CBL), which directly affects response measurement, verification, and incentive settlement. To address this issue, this paper proposes a constrained reinforcement learning method with customer baseline credibility for dynamic resource allocation in incentive-based demand response. Based on user-side load measurements and demand response event records, the proposed framework evaluates user resources using flexible capacity, response reliability, response cost, and CBL credibility. The CBL credibility score reflects the measurement quality of the delivered response and is used as a pre-event allocation factor. Users are then grouped into different resource levels, and a group-level reinforcement learning agent dynamically determines incentive multipliers and response task allocation ratios. To improve feasibility, an action correction module revises raw policy outputs under budget, price, response capacity, and CBL risk constraints before implementation. Case studies are conducted using public industrial demand response measurements and open electricity-system time-series data. The results show that the proposed CBL-CRL method reduces the normalized total operating cost to 0.897, reduces the response tracking error to 0.108, and lowers CBL risk exposure to 0.087 under the normal scenario. Relative to the No-DR reference, CBL-CRL reduces the normalized total operating cost by 10.3 percent. Compared with MAPPO, the strongest learning-based baseline, CBL-CRL reduces the response tracking error by 10.7 percent and the CBL risk exposure by 40.8 percent, while maintaining the same renewable accommodation rate of 0.970. Compared with rule-based and learning-based baselines, CBL-CRL achieves a better balance between operational performance, incentive efficiency, action feasibility, and baseline-related settlement reliability. The results demonstrate that CBL credibility should not only be used for post-event settlement, but can also serve as an effective pre-event resource allocation factor for measurement-driven demand response programs. Full article

Microalgae are photosynthetic organisms that produce bioproducts of commercial interest and are efficient sequestering CO₂. The monitoring and control processes are areas for improvement to increase the efficiency of its production. There are sensor options for monitoring microalgae cultures, but the vast majority rely on microcontrollers, often lacking the robustness required for applications in more demanding conditions. Also, commercial systems with industrial capabilities can fit the above purpose, but they require licensing and are expensive. Therefore, this work presents the technical details of developing an open-source platform to monitor environmental variables using Modbus industrial sensors and Python used to control the photoperiod and for measuring pH, dissolved oxygen, electrical conductivity, water and air temperatures, photosynthetic photon flux density, irradiance, and turbidity in three photobioreactors containing the microalgae Chlorella vulgaris. The resulting time series showed that the platform preserved data and had a low outlier rate. pH measurements showed that during photosynthesis, the microalgae used CO₂ as their carbon source. Dissolved oxygen and culture medium temperature had an almost perfect Pearson’s anticorrelation with air-sparging. However, with aeration interruption, the correlation was $-0.804$, because dissolved oxygen depends on illumination, aeration, temperature, and biomass quantity, as shown in the time series. Full article

As transportation-related environmental pressures intensify, understanding the psychological mechanisms underlying battery electric vehicle (BEV) adoption has become increasingly important. Drawing on the Value–Attitude–Behavior (VAB) framework, this study investigates how perceived green value, hedonic value, and utilitarian value shape Chinese consumers’ attitudes and purchase intentions toward BEVs, while examining the moderating roles of technophilia, range anxiety, and battery cost. A cross-sectional online survey was conducted in China, yielding 596 valid responses. Partial Least Squares Structural Equation Modeling (PLS-SEM) and Necessary Condition Analysis (NCA) were employed for data analysis. The results show that perceived hedonic value exerts the strongest positive effect on Attitude Toward Using BEVs (β = 0.591, p < 0.001), followed by perceived utilitarian value (β = 0.135, p < 0.001) and perceived green value (β = 0.074, p = 0.026). Attitude Toward Using significantly predicts BEV purchase intention (β = 0.151, p = 0.002). Technophilia significantly moderates the relationship between attitude and purchase intention (β = −0.096, p = 0.002), whereas the moderating effects of range anxiety and battery cost are not significant. The structural model explains 40.9% of the variance in attitude and 24.2% of the variance in purchase intention. NCA results further reveal that hedonic value constitutes the most critical necessary condition for forming favorable attitudes toward BEVs (d = 0.079, p < 0.001). This study contributes to the sustainable mobility literature by extending the VAB framework through the integration of multidimensional perceived value and necessary condition logic within the Chinese BEV context. The findings highlight that experiential and technological enjoyment, rather than environmental concern alone, has become a central driver of BEV adoption in emerging electric mobility markets. Full article