Search Results (2,593)

The algae-based landfill leachate (LL) treatment system has been proved promising for nutrient recycling and biomass production at lab- or small-scale photobioreactors (PBRs). However, many assessment tools such as techno-economic analyses (TEAs) usually utilize parameters from small-scale experiments as input data to predict the potential performance of commercial large-scale or full-scale bioreactors. Reliability of using data from lab-scale for commercial large-scale estimation is still uncertain. This study compared the performance of three photobioreactor systems that differed simultaneously in scale, geometry, light intensity, mixing mode, and aeration: 0.125 L small-scale flask, 1 L medium-scale tubular PBR, and 15 L wall-shaped PBR for real LL treatment. The 1 L medium-scale tubular photobioreactor outperformed the other two systems in biomass growth rate and the rates of nitrogen and phosphorus removal, even though all three systems removed nearly all NH₄-N and PO₄-P (≈100%) within two weeks. Possible reasons for this better performance include stronger illumination, a bubbling aeration mode, the reactor shape (which improves mixing), and higher surface area to volume ratio × light intensity. According to these results, using relatively small-scale flask experimental data for predictive analysis of industrial-scale algal systems could be inadequate. In this study, volumetric optical radiation (VOR) serves as a promising preliminary descriptive indicator to reflect the overall performance of an algal-based treatment system. Full article

Background/Objectives: Post-radiation fibrosis is a common complication in patients with head and neck cancer (HNC), characterised by increased tissue stiffness and functional limitations. Shear wave elastography (SWE) enables objective assessment of tissue stiffness, while self-reported outcome measures provide insight into symptom burden. However, the longitudinal evolution of these outcomes and their relationship remains insufficiently described. This study aimed to characterise post-radiation fibrosis over time using objective SWE measurements of sternocleidomastoid (SCM) muscle stiffness and self-reported fibrosis-related symptoms, and to examine their longitudinal association. Methods: This prospective longitudinal study included 56 patients with HNC undergoing primary or postoperative (chemo)radiotherapy. Muscle stiffness was measured using SWE, and fibrosis-related symptoms were assessed with the Lymphoedema Symptom Intensity and Distress Survey-Head and Neck (LSIDS-H&N) at 1 week, 6 weeks, 12 weeks, 6 months, and 12 months after the start of radiotherapy. Longitudinal analyses were performed using mixed-effects models adjusted for surgery and radiation dose, and within-subject associations were evaluated using repeated measures correlation. Results: Muscle stiffness increased over time, with estimated mean stiffness values rising from 4.12 m/s at baseline to 4.76 m/s at 6 months and 4.92 m/s at 12 months. Significant increases were observed at 6 months (p = 0.007) and 12 months (p = 0.012) compared with baseline. In contrast, self-reported fibrosis-related symptoms remained stable, with no significant differences between time points. No significant within-subject association was found between muscle stiffness and self-reported fibrosis-related symptoms (r = −0.13, p = 0.498). Conclusions: Muscle stiffness measured with SWE increased over time following radiotherapy, whereas self-reported fibrosis-related symptoms remained stable. No significant longitudinal association was found between these outcomes, suggesting they may capture different aspects of fibrosis. Full article

In the context of subtropical cities, the slow-moving environment of HOD (Hospital-Oriented Development) faces the dual challenges of spatial fragmentation and an extreme hot and humid climate, which also restricts the outdoor space’s thermal environment performance. Taking the Macau Light Rapid Transit (LRT) Union Hospital Station as an example, this study constructs a “topology-climate” dual quantitative assessment framework that integrates space syntax and parametric universal thermal climate index (UTCI) simulation. In response to the current problems of mixed pedestrian and vehicular traffic and high-intensity heat radiation, a comprehensive intervention strategy combining three-dimensional stitching and spatial optimization is proposed. The results show that: (1) The implantation of three-dimensional corridors improved the spatial integration of the core area of the site by 67.0%, significantly optimizing network connectivity. (2) During the extreme high-temperature period of daytime (9:00–18:00) in summer and autumn, the intervention strategy precisely opened up a continuous low-heat-stress linear shade zone through the synergistic mechanism of building projection shadows, physical shading of connecting corridors, (landscape shading effect, original evaporation removed). (3) The study confirms that landscape-coupled shading layout is the most effective method, reducing potential pedestrian heat exposure across the entire area, while the three-dimensional connecting corridors precisely control the thermal environment of core walkways. Together, these two elements construct a “topology-climate” optimization framework, achieving a synergistic improvement in spatial accessibility and simulated thermal comfort performance under standard meteorological input and quantitatively verifying the optimization effectiveness of the tiered intervention scheme. This study provides a data-driven decision-making basis for optimizing potential walking thermal conditions for vulnerable groups and reshaping the space’s potential to improve microclimate via shading design of medical hub areas and also provides a scientific paradigm for TOD microclimate planning focused on shading-based thermal environment optimization. Full article

Ground-surface temperature (GST) in maritime Antarctic ice-free areas is influenced by atmospheric forcing, snow cover, surface energy and topography. Previous PERMATHERMAL studies in Livingston and Deception Islands have shown changes in air and ground-surface thermal regimes, with fewer cold conditions, greater thawing influence and strong snow-cover modulation. However, the interval in which GST responds effectively to radiative and topographic forcing remains poorly explored. We characterize the station- and season-specific timing of the thermally effective GST thawing period and evaluate topographic and modeled potential controls on its thermal intensity and cumulative effect around the Spanish Antarctic Station Juan Carlos I, Hurd Peninsula, Livingston Island. Onset and end were objectively delimited by using three consecutive days with daily mean GST > 0.5 °C and daily thermal amplitude > 1.0 °C. Hourly GST records from six PERMATHERMAL stations were combined with potential radiation, potential insolation and topographic variables derived from a high-resolution UAV-based DEM. Accumulated thawing degree days were strongly influenced by period duration. Mean thermal intensity was primarily associated with elevation, while mean modeled potential radiation provided additional explanatory power only when combined with elevation. This UAV–GIS–GST approach provides a simple framework for assessing local surface–atmosphere coupling in remote Antarctic ice-free areas. Full article

Carotid artery disease has traditionally been assessed according to luminal stenosis, although plaques with similar narrowing may differ substantially in biological activity and clinical risk. Intraplaque neovascularization is a key feature of plaque vulnerability, reflecting microvascular proliferation and its association with inflammation, hemorrhage, and structural destabilization. Dynamic contrast-enhanced ultrasound (DCE-US) offers a real-time, radiation-free method for evaluating intraplaque enhancement kinetics using strictly intravascular microbubble agents. However, its broader use in carotid plaque imaging remains limited by variability in acquisition protocols, contrast administration, signal processing, curve fitting, and parameter interpretation. This technical review clarifies the main analytical approaches used in carotid DCE-US, distinguishing bolus-based wash-in/wash-out analysis from destruction–replenishment modeling. Bolus analysis describes first-pass microbubble transit through the plaque microvasculature and commonly provides parameters such as peak intensity, wash-in slope, area under the curve, and time to peak. Destruction–replenishment analysis evaluates post-destruction refill under stable or quasi-stable contrast conditions and relies on model-based estimation of plateau intensity and the replenishment rate. Because these approaches interrogate different kinetic regimes, their outputs should not be considered interchangeable, even when similar terms are used across studies. Particular emphasis is placed on the operational meaning of quantitative and semi-quantitative parameters, the assumptions underlying curve modeling, and the methodological consequences of ROI placement, motion correction, acoustic settings, and fitting constraints. Rather than proposing a universal acquisition protocol, this article provides practical principles for acquisition, analysis, and reporting, helping radiologists, neuroradiologists, neurologists, and vascular imaging specialists understand the processing steps, algorithmic assumptions, and model-dependent choices underlying software-derived curves and parameters. By making this analytical layer more explicit, the review seeks to support a transparent, reproducible, and biologically coherent approach to quantitative carotid plaque characterization. Full article

Solar chimneys represent an effective passive ventilation technology capable of improving indoor thermal comfort while reducing building energy consumption. In this study, the thermal and fluid dynamic performance of a solar chimney integrated into a residential building located in Bordj Bou Arréridj (Eastern Algeria) was investigated through a comprehensive numerical, predictive, and optimization framework. A transient mathematical model was developed to evaluate the influence of key geometric parameters, including chimney width and inlet opening width, as well as environmental factors such as solar radiation intensity and wind speed, on the system performance. The generated simulation database was subsequently employed to develop and compare four machine learning models, namely, Artificial Neural Networks with Bayesian Regularization (ANN-BR), Deep Neural Networks optimized by Improved Grey Wolf Optimization (DNN-IGWO), k-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGBoost), for predicting eight output parameters including glazing temperature, fluid temperature, absorber temperature, outlet temperature, thermal efficiency, air change rate (ACH), mass flow rate, and outlet velocity. The results demonstrated that increasing chimney and inlet widths significantly enhances ventilation performance by increasing airflow rate and ACH. Weather conditions and wind speed were also found to strongly affect thermal efficiency and buoyancy-driven airflow. Among the predictive models, XGBoost and DNN-IGWO exhibited the highest predictive accuracy, achieving coefficients of determination ($R^2$) close to unity and very low prediction errors for all output variables, confirming their robustness and generalization capability. The proposed methodology provides a reliable tool for rapid performance prediction and design optimization of solar chimney systems under different climatic and operating conditions, thereby supporting the development of energy-efficient passive ventilation strategies for residential buildings. Full article

Infrared and visible image fusion is critical for unmanned aerial vehicle (UAV) wilderness search and rescue. By integrating thermal radiation of the targets and texture details of the scenario, it enables accurate search for the wounded and comprehensive perception of disaster areas, thereby significantly improving emergency rescue efficiency. To alleviate data scarcity, we construct UAV-MSR, an infrared-visible dataset for casualty search, comprising 3889 paired images captured under diverse weather, illumination, and scenarios. Existing Transformer-based fusion methods mainly focus on high-intensity pixels while inadequately modeling low-intensity complementary features, resulting in blurred details and degraded target contrast in fused images. To this end, we propose a novel differential cross-attention Transformer network to address the issue of complementary information loss. Specifically, the encoder integrates convolution operations for local detail extraction and self-attention mechanisms for global context modeling. Then, we design a differential cross-attention guided feature fusion module to enhance the representation and preservation of detailed complementary features. Furthermore, a pixel loss function with a segmentation strategy is employed to improve the saliency of the target, enabling the fused image to facilitate subsequent target detection tasks. Experimental results and ablation studies demonstrate that the proposed method achieves notable performance and generalization ability. In summary, this work delivers a multimodal dataset and an efficient infrared-visible image fusion network to enable comprehensive perception for UAVs in wilderness search and rescue scenarios. Full article

Traditional evaluation indicators, including average air temperature, flow field distribution, and breathing-zone temperature, fail to fully characterize the actual effect of solar radiation on passenger thermal comfort. Therefore, based on the Fiala human physiological thermoregulation model and the Berkeley–Zhang thermal comfort evaluation criterion, this study develops a coupled simulation method for the objective evaluation of passenger thermal comfort. On this basis, the influence of front windshield solar radiation transmittance on passenger thermal comfort is preliminarily investigated. The results reveal that when the glass transmittance decreases from 0.52 to 0.37, the steady-state average cabin air temperature declines by approximately 0.5 °C, and the overall thermal comfort value increases from 0.67 to 1.2. In addition, the left crus receives the maximum solar radiation intensity, resulting in the poorest local thermal comfort. This verifies the feasibility and effectiveness of the method and provides a basis for the thermal comfort research of the vehicle cabin. Full article

Bismuth oxide (Bi₂O₃) nanoparticles are attractive for biomedical and radiation-shielding technologies and can be further tailored through the addition of other metal oxides to address emerging needs such as antimicrobial resistance. This study investigated the effects of incorporating Fe₂O₃ and La₂O₃ on the structure, morphology, and antimicrobial performance of Bi₂O₃-based nanocomposites synthesized via a plant extract-assisted microwave–hydrothermal route using soapnut extract. XRD indicated that pure Bi₂O₃ (100B) comprised predominantly monoclinic α-Bi₂O₃ with coexisting metastable tetragonal β-Bi₂O₃. The addition of Fe (3F; Fe:Bi = 30:70) promoted β- Bi₂O₃ and formed BiFeO₃, while increasing La substitution (3L–20L) reduced the BiFeO₃ intensity and, beyond a threshold (≥7L), yielded distinct La₂O₃ peaks consistent with a La₂O₃–BiFeO₃–Bi₂O₃ composite. Crystallite size decreased from ~46 nm (100B) to ~25 nm (3F), varying with La between 33 and 25 nm. SEM/TEM revealed a reflection in morphology and size with composition from disk-like particles to petal-like spherical aggregates. Antimicrobial screening revealed composition-dependent inhibition: against S. aureus, 20L was the most potent (~94%). Overall, La/Fe tuning under a plant extract-assisted microwave–hydrothermal route enabled phase- and morphology-controlled Bi₂O₃-based nanocomposites with enhanced antimicrobial activity, with ultrafine, high-surface-area architectures emerging as promising antibacterial candidates. Full article

High-altitude environments, characterized by hypoxia, low temperature, and intense ultraviolet radiation, profoundly disrupt host intestinal homeostasis and reshape the gut microbiota, thereby influencing immune regulation and acclimatization. This review systematically summarizes the dynamic compositional and functional changes in the gut microbiota in high-altitude natives, immigrant populations, short-term visitors, and relevant animal models. Current evidence indicates that long-term high-altitude adaptation is associated with directional microbial remodeling, including the enrichment of anaerobic and short-chain fatty acid (SCFA)-associated taxa, which may support energy metabolism and immune homeostasis. In contrast, acute high-altitude exposure more readily induces dysbiosis, impairs intestinal barrier integrity, and promotes the translocation of endotoxins and bioactive metabolites. Mechanistically, the gut microbiota and its metabolites participate in high-altitude adaptation and high-altitude-related disease pathogenesis by modulating barrier function, inflammatory responses, oxidative stress, and immune signaling, and by mediating interorgan communication—characterized by metabolite-driven systemic inflammation or tolerance—through the gut–lung, gut–heart, gut–brain, gut–kidney, and gut–testis axes. SCFAs, bile acids, amino acid-derived metabolites, and succinic acid may control immune homeostasis and inflammatory responses through pathways including TLR4/NF-κB and NLRP3. Although the causal relationships, core microbial effectors, and population-specific heterogeneity remain incompletely defined, microbiota-targeted interventions, including probiotics, prebiotics, and fecal microbiota transplantation, have shown promise for promoting acclimatization and preventing high-altitude-related disorders. Overall, this review provides an integrated framework linking environmental stress, gut microbial ecology, and host immune–metabolic adaptation at high altitude, and highlights future directions for mechanistic and translational research in high-altitude medicine. Full article

Agrivoltaics is an important pathway for promoting the coordinated development of clean energy production and agricultural utilization. However, the structural characteristics of flexible agrivoltaic (AV) systems may significantly alter field light and thermal conditions, while their effects on crop growth and yield formation remain unclear. To address this issue, a flexible AV system in Sihong County, Jiangsu Province, was selected as the study site, and continuous field monitoring combined with crop measurements was used to evaluate changes in microclimate, wheat physiological responses, and yield performance. The results showed that the flexible AV system significantly changed the field microclimate. During the wheat growing season, the monthly average solar radiation intensity under and between PV panels decreased by 62.0% and 56.9%, respectively, compared with that in the open field. The array also showed a certain thermal regulation effect, with heat preservation during the overwintering stage and cooling during the later growth stage. Shading reduced wheat net photosynthetic rate and stomatal conductance, but adaptive responses such as increased leaf area and chlorophyll content were observed. Wheat yield within the flexible AV system was significantly lower than that in the open field, with reductions of 43.4% and 47.2% in 2024 and 41.8% and 44.6% in 2025 for the areas under and between PV panels, respectively. Overall, light reduction under high coverage conditions remained the main factor limiting wheat yield. These results provide a theoretical basis for structural optimization and crop selection in flexible AV systems. Full article

Background and Objectives: Lung ultrasound (LUS) has fundamentally transformed neonatal respiratory diagnostics, offering a radiation-free, bedside-applicable modality capable of guiding surfactant therapy, characterizing pulmonary pathology, and monitoring treatment response in real time. While surfactant replacement therapy is firmly established for neonatal respiratory distress syndrome (RDS), its role in acute complications—specifically pulmonary hemorrhage (PH) and pneumothorax (PTX)—remains uncertain and heterogeneous in clinical practice. This review examines how LUS-based phenotyping can improve the diagnostic precision and therapeutic sequencing of surfactant administration in these high-risk scenarios, and how comorbidities such as hemodynamically significant patent ductus arteriosus, persistent pulmonary hypertension, sepsis, and coagulopathy modulate clinical outcomes. Materials and Methods: We conducted a structured narrative review of studies published from 2020 onward, sourced from PubMed, Web of Science, Semantic Scholar, and Mendeley, using PRISMA-inspired selection principles. The search combined terms including “lung ultrasound,” “neonatal POCUS,” “surfactant therapy,” “pulmonary hemorrhage,” “neonatal pneumothorax,” and “LUS score.” Studies focusing on neonatal populations, clinical LUS applications, and surfactant use in PH and PTX were prioritized. Results: Quantitative LUS scoring systems (range 0–18) predict surfactant need and re-dosing with AUC values of 0.85–0.87, outperforming clinical estimates alone. In PH, LUS reveals dense consolidation with alveolar flooding patterns, guiding the timing of rescue surfactant after hemodynamic stabilization; response monitoring via serial LUS is feasible and informative. In PTX, hallmark signs—absent lung sliding, loss of B-lines, and the pathognomonic lung point—allow diagnosis within seconds, guiding immediate thoracentesis and subsequent surfactant administration if underlying RDS is confirmed. Nationally implemented LUS protocols in neonatal intensive care units have demonstrated significant reductions in radiation exposure without compromising diagnostic accuracy. Conclusions: LUS-guided decision algorithms—integrating ultrasonographic phenotyping, quantitative scoring, and hemodynamic assessment—represent the current best framework for individualizing surfactant therapy in neonatal PH and PTX. Standardization of POCUS training and protocol implementation in neonatal units is essential. Prospective multicenter trials are urgently needed to define optimal indications, timing, and dosing in these vulnerable populations. Full article

Acute hypoxemic respiratory failure (AHRF) represents one of the most common and clinically challenging indications for invasive mechanical ventilation in the intensive care unit, characterized by profound etiological heterogeneity that demands accurate diagnosis to guide treatment. While clinical history, physical examination, and laboratory data remain essential, they are often insufficient to reliably discriminate among conditions such as acute respiratory distress syndrome (ARDS), cardiogenic pulmonary edema, and pneumonia—particularly in mechanically ventilated patients. Lung imaging has therefore emerged as an indispensable complement to clinical assessment. In this narrative review, we systematically describe the physical principles, clinical applications, and limitations of the imaging modalities currently available in critical care: chest X-ray (CXR), computed tomography (CT), lung ultrasound (LUS), electrical impedance tomography (EIT), and positron emission tomography (PET). CXR remains the most widely used bedside tool but is constrained by low sensitivity and significant interobserver variability. CT is the gold standard for morphological and quantitative lung phenotyping, enabling the assessment of recruitability, baby lung characterization, and the identification of complications, but requires patient transport and exposes patients to ionizing radiation. LUS offers real-time, bedside evaluation of aeration with high diagnostic accuracy for pneumothorax and pleural effusion, and is increasingly integrated into revised ARDS diagnostic criteria. EIT enables continuous, radiation-free monitoring of regional ventilation distribution and positive end-expiratory pressure (PEEP)-guided titration directly at the bedside. While PET provides unparalleled quantification of regional inflammation and ventilation-perfusion mismatch, it currently remains a purely investigative research tool. Finally, we discuss emerging technological and AI-driven advances—including dual-energy CT, next-generation EIT, and deep learning algorithms—that are poised to transform lung imaging from a passive diagnostic tool into an active, personalized guide to respiratory management. Full article

The dryline is a sharp boundary between moist air from the Gulf of Mexico and dry air from the desert Southwest. In West Texas, this boundary often surges east during the day and retreats west at night. Understanding exactly why it moves back and forth is critical for predicting where severe thunderstorms will form. Yet the physical drivers of dryline life cycle remain poorly understood and frequently under-predicted. This study utilizes a variable-resolution Model for Prediction Across Scales (MPAS) configuration (3–60 km) with the YSU non-local planetary boundary layer (PBL) scheme to investigate a representative dryline event from April 2017. The control simulation was validated against NWS Surface Analysis, demonstrating a high spatial correlation in both synoptic-scale pressure distributions and mesoscale moisture gradients, successfully resolving a nocturnal retrogression of approximately 170 km, with the dryline retreating from its peak afternoon surge at 100.7° W to a recovery point of 102.5° W between 0000 UTC and 0600 UTC 10 April. This recovery occurred at an average speed of 28.3 km/h, consistently constrained beneath a resilient capping inversion. To decouple the environmental drivers of this motion, two targeted sensitivity experiments were conducted: (1) Mechanical Forcing: A 50% reduction in regional topography confirms that the West Texas sloping ramp acts as a “topographic pump.” Without this gradient, the hydrostatic pressure falls were insufficient to drive the nocturnal retreat, causing the boundary to stall eastward. (2) Thermodynamic Regulation: A 50% reduction in soil moisture revealed an “energy swap,” the near-total partitioning of net radiation into sensible heat drove the planetary boundary layer to a higher peak value—a 600 m increase over the control simulation. These results provide a comprehensive physical framework for dryline mobility, demonstrating that while terrain plays an important role in the extent of the diurnal oscillation, soil moisture governs the vertical structure and moisture gradient intensity. Our findings suggest that high-resolution vertical layering and accurate land-surface initialization are prerequisites for capturing the inversion layer dynamics essential for dryline forecasting. However, these findings are based on a single event and require validation across a broader range of dryline cases. Full article

Yield estimation in sugarcane systems remains a major challenge in tropical regions due to the reliance on destructive, labor-intensive, and spatially limited field measurements. Although remote sensing has been widely used for crop monitoring, its predictive performance is often constrained when spectral information is used in isolation. This study proposes a data fusion framework integrating multitemporal Sentinel-2 spectral bands with meteorological variables to improve sugarcane biomass prediction under tropical conditions. A commercial field was monitored throughout the 2022–2023 growing season, and machine learning models, including random forest (RF), support vector machine (SVM), and multiple linear regression (MLR), were developed to estimate stem, foliage, and total biomass. To reduce potential spatial data leakage caused by spatial autocorrelation within the field, model performance was evaluated using Spatial Block Cross-Validation. Results showed that integrating spectral and meteorological data consistently improved predictive performance compared to spectral-only and weather-only scenarios. Spectral bands exhibited stronger relationships with biomass than derived vegetation indices, while maximum temperature and solar radiation were identified as key drivers of biomass variability. RF combined with spectral–weather fusion achieved the highest predictive performance, reaching R² values up to 0.95, RMSE values as low as 5296.35, and rRMSE values close to 18% for stem biomass, consistently outperforming SVM and MLR. In contrast, spectral-only scenarios produced lower predictive accuracy and higher prediction errors across all biomass variables. This study provides one of the first field-scale implementations under humid tropical conditions in southeastern Mexico, where georeferenced yield data remain scarce. Full article