Search Results (70)

About 16% of typhoons making landfall in China strike Hainan Island, where near-surface extreme winds in dense urban areas exhibit a strong spatiotemporal heterogeneity that is difficult to capture with current observations and mesoscale models. Focusing on Haikou during Super Typhoon Yagi (2024)—the strongest autumn typhoon to hit China since 1949—we developed a multiscale ERA5–WRF–PALM framework to conduct 30 m resolution large-eddy simulations. PALM results are in reasonable agreement with most of the five automatic weather stations, while performance is weaker at the most sheltered park site. Mean near-surface wind speeds increased by 20–50% relative to normal conditions, showing a coastal–urban gradient: maps of weighted cumulative exposure to strong winds (≥Beaufort force 8) show much longer and more intense events along open coasts than within built-up urban cores. Urban morphology exerted nonlinear effects: wind speeds followed a U-shaped relation with both the open-space ratio and mean building height, with suppression zones at ~0.5–0.7 openness and ~20–40 m height, while clusters of super-tall buildings induced Venturi-like acceleration of 2–3 m s⁻¹. Spatiotemporal analysis revealed banded swaths of high winds, with open areas and islands sustaining longer, broader extremes, and dense street grids experiencing shorter, localized events. Methodologically, this study provides a rare, systematically evaluated application of a multiscale ERA5–WRF–PALM framework to a real typhoon case at 30 m resolution in a tropical coastal city. These findings clarify typhoon–city interactions, quantify morphological regulation of extreme winds, and support risk assessment, urban planning, and wind-resilient design in coastal megacities. Full article

Urban heat islands intensify heat stress and degrade air quality in densely built areas, yet the physical processes governing near-surface thermal variability remain poorly quantified. This study applies the coupled MOLOCH and PALM model system 6.0 (PALM-4U) over Bologna (Italy) during a summer 2023 heatwave to resolve meter-scale atmospheric dynamics within the Urban Canopy Layer and Roughness Sublayer at 2 m horizontal resolution. The coupled configuration was validated against in situ meteorological observations and Landsat-8 LST data, showing improved agreement in air temperature and wind speed compared to standalone mesoscale simulations. Results reveal pronounced diurnal and vertical variability of wind speed, turbulent kinetic energy, and friction velocity, with maxima between two/three times the median building height (h_c). Distinct surface-dependent contrasts emerge: asphalt and roofs act as strong daytime heat sources (Bowen ratio β_asphalt ≈ 4.8) and nocturnal heat reservoirs at pedestrian level (z ≈ 0.07 h_c), while vegetation sustains daytime latent heat fluxes (β_vegetation ≈ 0.6÷0.8) and cooler surface and near-surface air (Temperature anomaly of surface ΔT_s ≈ −9 °C and air ΔT_air ≈ −0.3 °C). Thermal anomalies decay with height, vanishing above z ≈ 2.5 h_c due to turbulent mixing. These findings provide insight into fine-scale energy exchanges driving intra-urban thermal heterogeneity and support climate-resilient urban design. Full article

Lipases (EC 3.1.1.3) catalyze the hydrolysis of triacylglycerols into mono- and diacylglycerols and free fatty acids. This study investigated the production of lipase by Hypocrea pseudokoningii under solid-state fermentation (SSF), followed by its immobilization, purification, and biochemical characterization. Maximum activity was achieved using wheat fiber after 168 h of cultivation. Supplementation with oils enhanced production, particularly palm oil (315U; 1.58-fold increase) and soybean oil (Glycine max) (298U; 1.49-fold increase). The addition of micronutrients further improved yields, with Khanna (445U) and Vogel (400U) salts promoting more than a two-fold increase in activity. Immobilization on Octyl-Sepharose significantly altered the enzyme’s properties. The free lipase exhibited optimal activity at 45 °C and pH 4.5–5.5, while the immobilized enzyme showed maximum activity at 35–40 °C and pH 5.5. Thermal stability was notable enhanced: the free lipase had a half-life of 10 min at 50 °C, whereas the immobilized enzyme remained stable for 60 min and retained over 30% activity at 70 °C. Both the free and immobilized forms were stable across a broad pH range (4.0–10.0), maintaining more than 70% residual activity. The enzyme was stabilized by Tween 80 but inhibited by SDS. It was activated by Ca²⁺ and showed resistance to Pb²⁺, Zn²⁺, and Cu²⁺. Hydrolytic assays revealed murumuru (Astrocaryum murumuru), cupuaçu (Theobroma grandiflorum), and soybean oils as preferred substrates. TLC confirmed the formation of mono- and diglycerides, as well as the presence of fatty acids. Full article

In the context of ongoing global warming and urbanization, the need for reliable temperature monitoring in urban areas is increasing. Such monitoring serves multiple purposes, including assessing urban heat island (UHI) intensity, evaluating climate adaptation strategies, and supporting heat warning systems. This study utilizes high-resolution urban climate simulations with PALM-4U for calm, clear-sky summer weather conditions and an idealized model domain. The domain represents a dense midrise urban district in Dresden Neustadt, eastern Germany. Areas with air temperatures representative of the pedestrian level within the urban development are determined using a methodology based on a 24-h temporal moving representativity range defined by the temperature’s spatial median value and standard deviation. The method is extended by an evaluation of different temperature sensor heights, addressing practical considerations such as vandalism prevention and space availability. The results highlight the feasibility of representative pedestrian-level air temperature monitoring in densely built-up urban areas, particularly at elevated sensor heights between 2.5 and 6.5 m. It is found that higher sensor heights increase the area suitable for representative pedestrian-level temperature monitoring by up to about 50%. The sensitivity of the results to variations in wind speed and building height is also examined, demonstrating the robustness of the proposed method in clear, calm summer weather conditions. Full article

As a globally significant oil crop, precise ripeness identification of palm fruits directly impacts harvesting efficiency and oil quality. However, the progress and application of identifying the ripeness of palm fruits have been impeded by the computational limitations of agricultural hardware and the insufficient robustness in accurately identifying palm fruits in complex on-tree environments. To address these challenges, this paper proposes an efficient recognition network tailored for complex canopy-level palm fruit ripeness assessment. Progressive combination optimization enhances the baseline network, which utilizes the YOLOv8 architecture. This study has individually enhanced the backbone network, neck, detection head, and loss function. Specifically, the backbone integrates the StarNet framework, while the detection head incorporates the lightweight LSCD structure. To enhance recognition precision, StarNet-derived Star Blocks replace standard bottleneck modules in the neck, forming optimized C2F-Star components, complemented by DIoU loss implementation to accelerate convergence. The resultant on-tree model for recognizing palm fruit ripeness achieves substantial efficiency gains. While simultaneously elevating detection precision to 76.0% mAP@0.5, our method’s GFLOPs, parameters, and model size are only 4.5 G, 1.37 M, and 2.85 MB, which are 56.0%, 46.0%, and 48.0% of the original model. The effectiveness of the model in recognizing palm fruit ripeness in complex environments, such as uneven lighting, motion blur, and occlusion, validates its robustness. Full article

Ancient documents that have undergone physical and visual degradation pose significant challenges in the digital recognition and preservation of information. This research aims to evaluate the effectiveness of ten classic binarization methods, including Otsu, Niblack, Sauvola, and ISODATA, as well as other adaptive methods, in comparison to the U-Net ResNet34 model trained on 256 × 256 image blocks for extracting textual content and separating it from the degraded parts and background of palm leaf manuscripts. We focused on two significant collections, Lontar Terumbalan, with a total of 19 images of Balinese manuscripts from the National Library of Indonesia Collection, and AMADI Lontarset, with a total of 100 images from ICHFR 2016. Results show that the deep learning approach outperforms classical methods in terms of overall evaluation metrics. The U-Net ResNet34 model reached the highest Dice score of 0.986, accuracy of 0.983, SSIM of 0.938, RMSE of 0.143, and PSNR of 17.059. Among the classical methods, ISODATA achieved the best results, with a Dice score of 0.957 and accuracy of 0.933, but still fell short of the deep learning model across most evaluation metrics. Full article

This study introduces a novel deep learning-based model, UniU-Net, designed to achieve the high-precision segmentation of areca palms in remote sensing imagery. UniU-Net incorporates an auxiliary encoder and a unified attention fusion module (UAFM), enhancing the model’s anti-overfitting capabilities to improve its overall segmentation performance. Specifically, the primary and auxiliary encoders, through isomorphic parallel processing, leverage the principles of structural reparameterization to enhance the model’s effective learning of areca palm features while reducing the risk of overfitting. The UAFM utilizes a spatial attention mechanism to facilitate the effective fusion of multi-scale features. This architecture enables the model to capture intricate morphological details and accurately delineate the boundaries of areca palms, even under complex and heterogeneous environmental conditions such as mixed vegetation and varying illumination. To validate the effectiveness of UniU-Net, comprehensive experiments were conducted on a specialized areca palm dataset, demonstrating superior performance compared to several state-of-the-art semantic segmentation models. The proposed method achieves significant improvements in key evaluation metrics, such as the F1-score and intersection over union (IoU), highlighting its robustness and precision in automated areca palm extraction tasks. The integration of advanced attention mechanisms not only enhances the model’s ability to focus on relevant regions but also improves the segmentation accuracy in challenging scenarios. Beyond the specific application of areca palm segmentation, the methodologies introduced in this study hold substantial practical significance for broader agricultural applications, such as precision farming and crop monitoring. Full article

Lipases are essential in many industrial processes. Although microbial lipases are widely used, plant lipases remain more accessible and abundant, particularly in germinated kernels. This study aims to evaluate the catalytic potential of lipase extract powder of germinated rubber kernels in transesterification reaction. Germinated rubber kernels, lipase extract powder of germinated rubber kernels, and crude oils of palm (PKO), Jatropha curcas (JCO), and rubber (RSO) were characterized. The presence of lipase in the plant extract powder was evidenced by FT-IR and SEM-EDX analyses and hydrolysis reaction. Biodiesel was produced from crude rubber oil. The results showed that germinated rubber kernels have high moisture (33.48%), protein (15.75%), and fat (50.11%) contents. The optimum hydrolytic activities of lipase on PKO, JCO, and RSO were 25.67 U/mL, 26.67 U/mL, and 31 U/mL, respectively, at pH 5. Lipase extract concentration, temperature, and storage time influenced the lipase hydrolytic activity. The optimum biodiesel yield (29.63%) was obtained at 30 °C. The addition of co-solvents (water and n-hexane) to the reaction mixture increased yields from 20.47% (without co-solvent) to 31.06% and 21.85%, respectively. These insights show that germinated rubber seeds are rich in oil and contain lipase with good hydrolytic and catalytic activity. Full article

This study presents a framework for integrating traffic simulation with high-resolution air pollution modeling to design adaptive traffic management policies aimed at reducing urban air pollution. Building on prior work that establishes the coupling of the MATSim traffic model with the PALM-4U urban climate model, this second part focuses on implementing a feedback loop to inform traffic management decisions based on simulated air pollution concentration levels. The research explores how traffic volumes and atmospheric conditions, such as boundary layer dynamics, influence air quality throughout the day. In an artificial case study of Berlin, a time-based toll is introduced, aimed at mitigating concentration peaks in the morning hours. The toll scheme is tested in two simulation scenarios and evaluated regarding the effectiveness of reducing air pollution levels, particularly NO₂ during the morning hours. The case study results serve to illustrate the framework’s capabilities and highlight the potential of integrating traffic and environmental models for adaptive policy design. The presented approach provides a model for responsive urban traffic management, effectively aligning transportation policies with environmental goals to improve air quality in urban settings. Full article

This study investigates the potential of sequential solid-state and submerged fermentation (SeqF) to enhance lipase production by Yarrowia lipolytica using by-products from the palm oil production chain. Palm fiber and palm oil deodorizer distillate (PODD) were utilized as substrates in both fermentation stages. Solid-state fermentation (SSF) yielded significant lipase activity when palm fiber was used alone (1.55 U/g in 48 h), while submerged fermentation (SmF) showed improved enzymatic production with the combination of fiber and PODD (1171 U/L in 72 h). The integration of SSF and SmF in SeqF achieved superior lipase activities, reaching 4464.5 U/L, an 8.3-fold increase compared to SmF alone, in Erlenmeyer flasks. SeqF-lyophilized biocatalysts from Erlenmeyer experiments showed better hydrolytic activity (131 U/g) when the best conditions were reproduced in a 4 L bioreactor (33 U/g). The SeqF-lyophilized biocatalyst was employed in esterification reactions to synthesize mono- and diacylglycerols, achieving a 24.3% conversion rate. The study highlights SeqF as a promising and sustainable approach for valorizing agro-industrial residues, contributing to biocatalyst production and advancing circular bioeconomy initiatives. Full article

To effectively mitigate anthropogenic air pollution, it is imperative to implement strategies aimed at reducing emissions from traffic-related sources. Achieving this objective can be facilitated by employing modeling techniques to elucidate the interplay between environmental impacts and traffic activities. This paper highlights the importance of combining traffic emission models with high-resolution turbulence and dispersion models in urban areas at street canyon level and presents the development and implementation of an interface between the mesoscopic traffic and emission model MATSim and PALM-4U, which is a set of urban climate application modules within the PALM model system. The proposed coupling mechanism converts MATSim output emissions into input emission flows for the PALM-4U chemistry module, which requires translating between the differing data models of both modeling systems. In an idealized case study, focusing on Berlin, the model successfully identified “hot spots” of pollutant concentrations near high-traffic roads and during rush hours. Results show good agreement between modeled and measured NO_x concentrations, demonstrating the model’s capacity to accurately capture urban pollutant dispersion. Additionally, the presented coupling enables detailed assessments of traffic emissions but also offers potential for evaluating the effectiveness of traffic management policies and their impact on air quality in urban areas. Full article

Deep learning models have demonstrated significant advantages over traditional algorithms in image processing tasks like object detection. However, a large amount of data are needed to train such deep networks, which limits their application to tasks such as biometric recognition that require more training samples for each class (i.e., each individual). Researchers developing such complex systems rely on real biometric data, which raises privacy concerns and is restricted by the availability of extensive, varied datasets. This paper proposes a generative adversarial network (GAN)-based solution to produce training data (palm images) for improved biometric (palmprint-based) recognition systems. We investigate the performance of the most recent StyleGAN models in generating a thorough contactless palm image dataset for application in biometric research. Training on publicly available H-PolyU and IIDT palmprint databases, a total of 4839 images were generated using StyleGAN models. SIFT (Scale-Invariant Feature Transform) was used to find uniqueness and features at different sizes and angles, which showed a similarity score of 16.12% with the most recent StyleGAN3-based model. For the regions of interest (ROIs) in both the palm and finger, the average similarity scores were 17.85%. We present the Frechet Inception Distance (FID) of the proposed model, which achieved a 16.1 score, demonstrating significant performance. These results demonstrated StyleGAN as effective in producing unique synthetic biometric images. Full article

Ganoderma boninense is a white-rot fungus that causes basal stem rot (BSR) disease in the oil palm. Potential natural inhibitors, such as gallic acid, thymol, propolis, and carvacrol, were assessed for their antagonistic effects against G. boninense. These naturally occurring phenolic compounds have also been utilised to inhibit hydrolytic and ligninolytic enzymes produced by the pathogen. Mycelial inhibition was dose-dependent in the presence of different concentrations of phenolic compounds, including, for example, in cellulase enzyme inhibition (GA mg/mL = 94%, THY 0.25 mg/mL = 90%, PRO 3.5 mg/mL = 92.5%, and CARV 0.15 mg/mL = 90.3%). A significant difference was observed revealing that gallic acid had the greatest inhibitory effect on the secretion of hydrolytic and ligninolytic enzymes, especially at 40 mM GA (cellulase = 0.337 U/mL, amylase = 0.3314 U/mL, xylanase = 0.211 U/mL, laccase = 0.4885 U/mL, lignin peroxidase = 0.218 U/mL, and manganese peroxidase = 0.386 U/mL). The growth and secretion of enzymes (inhibitory action) are inversely proportional to the concentration of phenolic compounds. Phenolic compounds have a greater potential as inhibitory agents and suppress the production of hydrolytic and ligninolytic enzymes. The selected phenolic compounds were evaluated for their ability to alter the morphology and integrity of G. boninense mycelia. The reduction in cell viability of G. boninense has been explained by research on morphological disruption, such as branching patterns, hyphal length, and rigidity of fungal cells, which eventually interrupt the secretion of enzymes. These studies highlight the efficacy of phenolic compounds in treating Ganoderma. In addition, these findings proved that naturally occurring phenolic compounds could be a substitute for chemical controls and other synthetic fungicides to eradicate the occurrence of BSR in oil palms, thus avoiding a situation that is difficult to overcome. Full article

The air pollution situation in the German city of Stuttgart is very important, as high pollutant concentrations are measured here compared to other German cities. This is mainly due to Stuttgart’s geographical location as it is in a basin covered by hills on three sides. This leads to reduced wind speeds that inhibit pollutant dispersion. One of the main contributors to the pollutant concentrations in Stuttgart is local traffic. To improve the air quality in Stuttgart, a diesel traffic ban was introduced on 1 January 2019, and is ongoing. In this study, the urban climate model PALM-4U was applied to obtain the pollutant distribution along the federal highways B14 and B27 of Stuttgart to evaluate the impact of the diesel traffic ban on air quality. The simulations were carried out in two areas of the city, namely the city center and Kaltental Valley, with domain sizes of 3.2 km × 2 km and 3.2 km × 1.6 km, respectively, and with a grid size of 10 m for each domain. The influence of traffic emissions on the air quality of Stuttgart was studied for a typical summer day. The results showed that air pollutant concentrations were highest near federal highways B14 and B27 (e.g., NO₂ concentration peaks of around 200 µg/m³). Also, a significant reduction of around four times in air pollutant concentrations was observed in the study area after the diesel traffic ban was introduced. Full article

Biometric recognition techniques have become more developed recently, especially in security and attendance systems. Biometrics are features attached to the human body that are considered safer and more reliable since they are difficult to imitate or lose. One of the popular biometrics considered in research is palm veins. They are an intrinsic biometric located under the human skin, so they have several advantages when developing verification systems. However, palm vein images obtained based on infrared spectra have several disadvantages, such as nonuniform illumination and low contrast. This study, based on a convolutional neural network (CNN), was conducted on five public datasets from CASIA, Vera, Tongji, PolyU, and PUT, with three parameters: accuracy, AUC, and EER. Our proposed VeinCNN recognition method, called verification scheme with VeinCNN, uses hybrid feature extraction from a discrete wavelet transform (DWT) and histogram of oriented gradient (HOG). It shows promising results in terms of accuracy, AUC, and EER values, especially in the total parameter values. The best result was obtained for the CASIA dataset with 99.85% accuracy, 99.80% AUC, and 0.0083 EER. Full article