Search Results (100)

Serum tests are valuable sources of information for disease diagnosis. Conventional whole blood cell separation requires many processing steps, including centrifugation, fractionation, lysis, and dilution, and is therefore complex and time consuming. To address the need for the efficient separation of blood cells for on-chip rapid serum assays, we developed a microfluidic chip integrating inertial sorting and deterministic lateral displacement. This chip consists of a helical structure and a deterministic lateral displacement triangular microcolumn array for rapid and efficient separation of blood cells from whole blood samples. After separation, the supernatant is extracted at the exit for subsequent testing or directed to serum test units directly integrated in the chip. Here, the laminar flow and transport modules are coupled using finite element analysis for both multi-component and discrete-phase physical fields to simulate blood flow characteristics in the chip. The influences of flow rate and flux ratio on the sorting efficiency of blood cells were also discussed. Simulation results determined that the microfluidic chip designed in this research can achieve a cell sorting efficiency greater than 98% at suitable flow rates. Experimental results similarly achieved a high sorting effect of above 96%. Therefore, this blood cell sorting microfluidic chip shows strong potential for rapid serum testing applications and can be integrated as a stand-alone blood cell sorting module for various on-chip serum testing systems used to diagnose diseases. Full article

Global climate change is exerting a growing impact on agricultural ecosystems. Accurately assessing the spatiotemporal dynamics of agricultural ecosystem respiration and its response mechanisms to climate has therefore emerged as a critical issue in agricultural carbon cycle research and climate change response. It should be noted that the ‘agro-ecosystem’ referred to in this study covers two major types: one is the farmland agro-ecosystem dominated by crop planting (such as farmland, orchard and other artificial management systems), and the other is the grassland agro-ecosystem dominated by herbaceous plants and managed by humans (such as grazing grassland and mowing grassland). Remote sensing technology provides a new way to break through the limitations of traditional ground observation by virtue of its advantages of large-scale and continuous monitoring. Based on the CiteSpace bibliometric method, this study focused on the key time window of 2021–2025, systematically searched the core collection of Web of Science, and finally included 222 related literature. This period marks the initial stage of the rise and rapid development of this interdisciplinary field, enabling us to capture the formation of its knowledge structure and the evolution of its research paradigm from the source. Through the quantitative analysis of this literature, it aims to reveal the research hotspots, development paths and frontier trends in this field. The results show that China occupies a dominant position in this field (135 articles). The evolution of research shows a three-stage development characterized by “technology-driven-method fusion-system coupling,” which is divided into the initial development period (2021–2022), the rapid growth period (2023–2024) and the deepening development period (2025) (because 2025 has not yet ended, this stage is a preliminary discussion). Keyword clustering analysis identified 13 important research directions, including machine learning (# 0 clustering), permafrost (# 1 clustering) and carbon flux (# 2 clustering). It is found that the deep integration of artificial intelligence and remote sensing data is promoting the transformation of research methods from traditional inversion to intelligent modeling. At the same time, the attention to alpine grassland and other ecosystems also reflects the trend that the research frontier extends to the interaction zone between the agricultural ecosystem and the natural environment. Future research should prioritize three key directions: building multi-scale monitoring networks, developing “grey box” models that integrate mechanisms and data fusion, and evaluating the carbon emission reduction efficiency of agricultural management practices. These efforts will provide a theoretical basis for carbon management and climate adaptation in agricultural ecosystems, as well as scientific and technological support for achieving global agricultural sustainable development goals (specifically, SDG13 on climate action and SDG15 on terrestrial ecosystem conservation). Full article

Flowering often perturbs carbon allocation in sugarcane, yet its transcriptomic–metabolomic basis remains unclear. We profiled two contrasting cultivars, Gui Tang 16-3285 (sugar increases during flowering) and Gui Tang 44 (sugar decreases), sampling apical tissues at five stages (Non-spikelet-bearing stage (NSB), Early booting stage (ESB), Late booting stage (LSB), Tasseling stage (TS), and Flowering stage (FS)). RNA-seq and untargeted LC–MS revealed a strong stage/genotype structure (PCA) with high reproducibility. Pairwise contrasts (FS vs. earlier stages) and time series clustering (Mfuzz) showed extensive, stage-resolved reprogramming with small cross-cultivar overlaps. GO/KEGG indicated that GT16 is enriched for central carbon processes and glucose response, whereas GT44 favors cell-wall remodeling (xylan/xyloglucan), amino/nucleotide sugar, and phenylpropanoid pathways. Integrated analysis identified opposing temporal features across omics layers: in GT16, late-rising metabolites—including sedoheptulose—were consistent with enhanced pentose phosphate/Calvin coupling that regenerates fructose-6-phosphate for sucrose biosynthesis; in GT44, early activation of wall and secondary sinks, together with trehalose/(trehalose-6-phosphate) T6P signatures, paralleled declining soluble sugars. Across cultivars we resolved 11 and 18 genes in reciprocal opposite-trend sets (most with clear temporal order) and eight vs. five metabolites with mirrored dynamics, nominating actionable biomarkers (e.g., sedoheptulose/S7P) and regulatory nodes. These results provide a mechanistic framework linking flowering stage to carbon partitioning and suggest practical levers—timing growth moderation/ripeners, prioritizing sucrose phosphate synthase/Sucrose Phosphate Phosphatase, tempering wall flux, to sustain sucrose during reproductive development and inform breeding for high-sugar, flowering-resilient ideotypes. Full article

Mitogen-activated protein kinase (MAPK) cascades are critical for fungal development, stress adaptation. and virulence. However, their dynamic and stress-specific regulatory networks in entomopathogenic fungi remain largely unresolved. This study systematically investigates the roles of all three key MAPKs—BbHog1, BbSlt2, and BbMpk1—in insect pathogenic fungus Beauveria bassiana. A combination of detailed phenotypic profiling of deletion mutants (ΔBbHog1, ΔBbSlt2, and ΔBbMpk1) and time-course transcriptomics (RNA-seq at 0, 0.5, and 12 h) under osmotic, cell-wall, oxidative, and thermal stress conditions was employed, followed by weighted gene co-expression network analysis (WGCNA). This approach delineated twelve stress-responsive gene modules regulated by those MAPKs that were highly associated with fungal stress adaptation, including membrane repair, redox balance, cell-wall remodeling, and core metabolism. Functional analyses showed that Hog1 orchestrates osmoadaptation through coordinated control of osmolyte metabolism, glycolytic flux, and cell-wall remodeling; Slt2 protects against thermal damage by sustaining membrane integrity, ergosterol homeostasis, and redox balance; and Mpk1 directs oxidative stress responses by tuning mitochondrial activity, metabolic suppression, and detoxification pathways. In summary, this work outlines a concise, systems-level framework of MAPK-mediated stress regulation in B. bassiana, providing mechanistic insight into fungal environmental resilience and identifying molecular targets for the engineering of robust biocontrol strains. Full article

Eddy covariance measurements are increasingly utilized for assessing the exchange of matter and energy between ecosystems and the atmosphere across various time scales, ranging from hours to years. The flux footprint represents the area observable by flux tower sensors and illustrates how the surface influences the measured flux. Flux footprint models describe both the spatial extent and the specific location of the surface area contributing to the observed turbulent fluxes. In this study, we applied a simple two-dimensional parameterization for flux footprint prediction (FFP), developed by Kljun et al. to identify the location of peak footprint contribution every half hour over a six-year period. Monthly cluster analysis was performed on these data. Using an open-source geographic information system (GIS) software, the resulting clusters were overlaid on a base map of the site obtained from the Estonian Land Board, where different compartments have varying growth stages and species compositions. Our main objective was to integrate forest inventory data with ecosystem exchange and productivity data continuously recorded by the eddy covariance measurement tower at Järvselja, Estonia. This integration enabled spatially explicit visualization of half-hourly flux contributions using geographic information system software. Full article

Zero-heat-flux (ZHF) thermometry is a clinically validated method for non-invasive core body temperature monitoring, yet its broad adoption in wearable applications is constrained by the high power consumption of the heater element. In this study, we numerically investigate the role of microporous insulation in minimizing energy demand while preserving measurement accuracy. A three-dimensional finite element model of a ZHF probe was implemented in COMSOL Multiphysics 5.4, consisting of a resistive heater, a microporous insulation shell, and a skin-equivalent substrate regulated by proportional–integral–derivative (PID) control. A Taguchi L9 orthogonal array was utilized to systematically investigate the effects of porosity (0–90%), insulation thickness (2–4 mm), and the convective heat transfer coefficient (5–15 W/m²·K) on the thermal performance of the ZHF thermometry system. Two performance metrics—heater energy consumption and settling time—were analyzed using analysis of variance (ANOVA). The results indicated that porosity accounted for more than 95% of the variance in heater power and over 80% of the variance in settling time. The configuration with φ = 90% and t = 3 mm demonstrated a balanced trade-off between energy efficiency and transient response for low-power ZHF thermometry. These findings provide design insights for energy-efficient wearable temperature sensors. Full article

The pericarp of Macadamia integrifolia represents a promising but underexplored source of functional flavonoids. To systematically elucidate their biosynthesis and enhance the industrial potential of this by-product, we conducted integrated transcriptomic and metabolomic profiling of pericarps across five developmental stages (50, 80, 110, 140, and 170 days after flowering). Our analysis reveals, for the first time, a distinct temporal shift in both gene expression and metabolite accumulation. Early stages were characterized by high expression of PAL, 4CL, CHS, and FLS, coupled with abundant flavonols and anthocyanins. In contrast, late stages exhibited upregulation of CHI and F3’5’H, redirecting the metabolic flux toward flavanones and isoflavones. This dynamic profile was closely associated with jasmonate and gibberellin signaling pathways and was likely regulated by key transcription factors (MYB, NAC, bHLH). These findings provide a multi-omics framework that elucidates the temporal flavonoid biosynthesis in macadamia pericarp, thereby laying the groundwork for its future industrial valorization. Full article

Solar-energy-driven membrane distillation provides a sustainable pathway to mitigate freshwater scarcity by utilizing an abundant renewable heat source. This study develops a two-dimensional axisymmetric computational fluid dynamics (CFD) model to simulate the transient performance of a hollow fiber water gap membrane distillation (HF-WGMD) module integrated with flat-plate solar collectors (FPCs). A lumped-parameter transient FPC model is coupled with the CFD framework to predict feed water temperature under time-varying solar irradiation, evaluated across four representative days in a Mediterranean city. The model is validated against experimental data, showing strong agreement. A comprehensive parametric analysis reveals that increasing the collector area from 10 to 50 m² enhances the average water flux by a factor of 6.4, reaching 10.9 kg/(m²h), while other parameters such as collector width, tube number and working fluid flow rate exert comparatively minor effects. The module flux strongly correlates with solar intensity, achieving a maximum instantaneous value of 18.4 kg/(m²h) with 35 m² collectors. Multistage HF-WGMD configurations are further investigated, demonstrating substantial reductions in solar energy demand due to internal thermal recovery by the cooling stream. A 40-stage system operating with only 10 m² of solar collectors achieves an average specific thermal energy consumption of 424 kWh/m³, while the overall solar desalination efficiency improves dramatically from 2.6% for a single-stage system with 50 m² collectors to 57.5% for the multistage configuration. The proposed system achieves a maximum freshwater productivity of 51.5 kg/day, highlighting the viability and optimization potential of solar-driven HF-WGMD desalination. Full article

The Beaufort Sea has experienced significant sea ice retreat in recent decades, driven by both thermodynamic and dynamic processes. This study investigates the drivers and predictability of summer sea ice retreat in the Beaufort Sea by integrating an ocean–sea ice model with satellite-derived sea ice concentration data and atmospheric reanalysis products. Model diagnostics from 1994 to 2019 reveal that thermodynamic processes dominate annual sea ice loss (approximately 90%), with vertical heat flux accounting for roughly 85% of total oceanic heat input. The summer sea ice minimum area and the day of opening, derived from either model results and satellite observations, have a strong correlation with R² = 0.60 and R² = 0.77, respectively, enabling regression equations based solely on remote sensing data. Further multiple linear regression incorporating preceding winter (January to April) accumulated temperature and easterly wind yields moderately robust forecasts of minimum sea ice area (R² = 0.49) during 1998–2020. Additionally, analysis of reanalysis wind data shows that the timing of minimum sea ice area is significantly influenced by the frequency and intensity of sub-seasonal easterly wind events during melt season. These results demonstrate the critical importance of remote sensing in monitoring Arctic sea ice variability and enhancing seasonal prediction capability under a rapidly changing climate. Full article

The rapid evolution of modern power systems, driven by the large-scale integration of renewable energy sources and the emergence of smart grids, presents new challenges in maintaining grid stability, power quality, and control reliability. As critical interfacing elements, three-phase pulse width modulation (PWM) converters must now ensure resilient and efficient operation under increasingly adverse and dynamic grid conditions. This paper proposes an adaptive neural network-based virtual flux (VF) estimator for sensorless predictive direct power control (PDPC) of PWM converters under nonideal grid voltage conditions. The proposed estimator is realized using an adaptive linear neuron (ADALINE) configured as a quadrature signal generator, offering robustness against grid voltage disturbances such as voltage unbalance, DC offset and harmonic distortion. In parallel, a PDPC scheme based on the extended pq theory is developed to reject active-power oscillations and to maintain near-sinusoidal grid currents under unbalanced conditions. The resulting VF-based PDPC (VF-PDPC) strategy is validated via real-time simulations on the OPAL-RT platform. Comparative analysis confirms that the ADALINE-based estimator surpasses conventional VF estimation techniques. Moreover, the VF-PDPC achieves superior performance over conventional PDPC and extended pq theory-based PDPC strategies, both of which rely on physical voltage sensors, confirming its robustness and effectiveness under non-ideal grid conditions. Full article

This paper proposes a learning-driven passivity-based control (PBC) strategy for sensorless induction motors, combining a nonlinear adaptive observer with recurrent neural networks (RNNs) to improve robustness and estimation accuracy under dynamic conditions. The main novelty is the integration of neural learning into the passivity framework, enabling real-time compensation for un-modeled dynamics and parameter uncertainties with only one gain adjustment across a broad speed range. Lyapunov-based analysis guarantees the global stability of the closed-loop system. Experiments on a 1.1 kW induction motor confirm the approach’s effectiveness over conventional observer-based and fuzzy-enhanced methods. Under torque reversal and flux variation, the proposed controller achieves a torque mean absolute error (MAE) of 0.18 Nm and flux MAE of 0.21 Wb, compared to 1.58 Nm and 0.85 Wb with classical PBC. When peak torque deviation drops from 42.52% to 30.85% of the nominal, torque symmetric mean absolute percentage error (SMAPE) improves by 7.6%, and settling time is reduced to 985 ms versus 1120 ms. These results validate the controller’s precision, adaptability, and robustness in real-world sensorless motor control. Full article

We employed a global high-resolution inverse model to estimate sectoral methane emissions, integrating observations from the GOSAT-2 satellite for the first time, along with observations from the surface observation network. A similar set of inversions using GOSAT observations was carried out to evaluate the consistency between emissions estimates derived from these two satellites and to ensure that GOSAT-2 data could seamlessly integrate with the existing data series without disrupting the continuity of flux estimates. This analysis, covering the period from 2019 to 2022, utilized prior anthropogenic emissions data mainly from EDGAR v6 and incorporated additional natural sources and sinks as outlined by global methane budget, 2020. Our analysis reveals a general agreement between total methane emissions estimates from GOSAT and GOSAT-2. However, on a sectoral basis, we found notable regional differences in the flux estimates. While GOSAT inversion estimates ~8 Tg a⁻¹ more anthropogenic emissions for China and around 4 Tg a⁻¹ more wetland emissions for Brazil and Indonesia, the posterior error distribution suggests that GOSAT-2 inversion is closer to surface observations over Asia. These discrepancies are found in regions with significant differences in XCH₄ data from the two satellites, such as East Asia and North America, tropical South America, and tropical Africa. These regional biases persist due to limited representative surface reference sites for Level 2 bias correction. The relatively lower data volume from GOSAT also introduces seasonal biases in the flux estimates when the quality filtering of Level 2 data persistently reduces usable observations during certain seasons, resulting in inadequate representation of the seasonal cycle in regions such as East Asia. Similarly, in tropical South America, where the model is relatively under-constrained by the limited surface observations, the lower data volume of GOSAT-2 suffers. While the two inversions exhibit consistent overall performance across North America and Europe, the GOSAT-2-based inversion demonstrates a better performance over East Asia. Therefore, while the two satellite datasets are broadly consistent, considering the fact that the biases in the XCH₄ data overlap with regions under-constrained by surface observations, establishing additional surface reference measurement sites is desirable to ensure consistent inversion results. Full article

Rapid industrialization and urbanization have progressed in Korea, yet public attention to hazardous pollutants emitted from industrial complexes remains limited. With the increasing coexistence of industrial and residential areas, there is a growing need for real-time monitoring and management plans that account for the rapid dispersion of hazardous air pollutants (HAPs). In this study, we conducted spatiotemporal data collection and analysis for the first time in Korea using real-time measurements obtained through mobile extractive differential optical absorption spectroscopy (Me-DOAS) mounted on a solar occultation flux (SOF) vehicle. The measurements were conducted in the Saha Sinpyeong–Janglim Industrial Complex in Busan, which comprises the Sasang Industrial Complex and the Sinpyeong–Janglim Industrial Complex. BTEX compounds were selected as target volatile organic compounds (VOCs), and real-time measurements of both BTEX and fine particulate matter (PM) were conducted simultaneously. Correlation analysis revealed a strong relationship between PM₁₀ and PM_2.5 (r = 0.848–0.894), indicating shared sources. In Sasang, BTEX levels were associated with traffic and localized facilities, while in Saha Sinpyeong–Janglim, the concentrations were more influenced by industrial zoning and wind patterns. Notably, inter-compound correlations such as benzene–m-xylene and p-xylene–toluene suggested possible co-emission sources. This study proposes a GIS-based, three-dimensional air quality management approach that integrates variables such as traffic volume, wind direction, and speed through real-time measurements. The findings are expected to inform effective pollution control strategies and future environmental management plans for industrial complexes. Full article

Membrane fouling remains a major challenge in full-scale membrane bioreactor (MBR) systems, reducing operational efficiency and increasing maintenance needs. This study introduces a predictive and analytic framework for membrane fouling by integrating artificial intelligence (AI)-driven feature engineering and explainable AI (XAI) using real-world data from an MBR treating food processing wastewater. The framework refines the target parameter to specific flux (flux/transmembrane pressure (TMP)), incorporates chemical oxygen demand (COD) removal efficiency to reflect biological performance, and applies a moving average function to capture temporal fouling dynamics. Among tested models, CatBoost achieved the highest predictive accuracy (R² = 0.8374), outperforming traditional statistical and other machine learning models. XAI analysis identified the food-to-microorganism (F/M) ratio and mixed liquor suspended solids (MLSSs) as the most influential variables affecting fouling. This robust and interpretable approach enables proactive fouling prediction and supports informed decision making in practical MBR operations, even with limited data. The methodology establishes a foundation for future integration with real-time monitoring and adaptive control, contributing to more sustainable and efficient membrane-based wastewater treatment operations. However, this study is based on data from a single full-scale MBR treating food processing wastewater and lacks severe fouling or cleaning events, so further validation with diverse datasets is needed to confirm broader applicability. Full article

In the current context of the transition toward climate neutrality and the pressing need to reduce energy consumption in the construction sector, nZEBs have become a central benchmark in European sustainability policies. These buildings offer multiple benefits, such as reduced operational costs, enhanced thermal comfort, and improved indoor air quality. Achieving such performance requires the integration of advanced technological solutions, including passive façades with ventilated cavities. The primary objective of this study is to investigate the influence of cavity geometry on the thermal behavior of a passive façade, through numerical simulations conducted in ANSYS Fluent 17. The study focuses on comparing five distinct configurations with varying cavity widths, aiming to identify the optimal solution in terms of heat transfer efficiency. The main contribution lies in the analysis and correlation of air temperature and velocity distributions with the cavity’s geometric parameters, highlighting the impact of channel width on thermal performance. The configuration with a 12 cm wide air channel recorded the highest heat flux at the outlet, approximately 44 times greater than the façade with a 4 cm wide channel, making it the most efficient solution. The results indicate significantly higher thermal efficiency for the configuration with a larger cavity width, contrary to initial intuitive assumptions. These insights provide a valuable framework for the optimal design of passive façades in nZEB applications and highlight the need for further research, combining numerical and experimental approaches, to develop sustainable and energy-efficient building envelope solutions. Full article