Actual Area Research Articles

Equilibrium atmospheric boundary layer model for numerical simulation of urban wind environment

The numerical simulation of urban wind environments faces difficulties in capturing the turbulent characteristics due to the large computational domain. Traditional Reynolds-averaged methods (RANS) can effectively capture the average wind characteristics of urban areas. However, due to the significant dissipation and attenuation of turbulent energy in the downstream direction, this method fails to provide accurate turbulent characteristics after time-averaging processing. Therefore, in order to obtain a higher-precision turbulent wind field distribution within urban areas, this paper proposed a new numerical method named an equilibrium atmospheric boundary layer model (EABL) by modifying the control equation of the shear stress transport k–ω model. During the process, the equilibrium atmospheric boundary layer was achieved successfully, and the attenuation problem of the turbulent kinetic energy and dissipation rate during the computational fluid dynamics numerical simulation was resolved. Simultaneously, a wind tunnel experiment and six turbulence models [standard k–ε, realizable k–ε, renormalization group k–ε, large eddy simulation—narrowband synthesis random flow generator (LES-NSRFG) and LES vortex method and EABL] were employed to simulate the wind field characteristics in an actual residential area. The simulation results demonstrate that, relative to traditional RANS models, the EABL model enhances the simulation accuracy of turbulence characteristics by over two times. Furthermore, compared to LES models, the EABL model can reduce computational time by threefold.

Fractal theory-based contact analysis of surface microtextured involute spline coupling

Accurate estimation of the contact stress of the aviation involute spline coupling is the basis of accurate estimation of its wear. In view of this, in this work, a fractal contact coefficient for involute spline coupling with surface texture based on the gear fractal contact model and M-B fractal contact model was established. Then, the relationship between fractal dimension and actual contact area, as well as the relationship between actual contact area and load were derived. And also, a fractal contact model for involute spline coupling with surface texture was established. Finally, the influence of geometric parameter on the contact coefficient and fractal contact model was analyzed. The results indicate that: the fractal dimension (D), roughness amplitude parameter (G), and material property parameter (ϕ) have important effects on the fractal contact model of involute spline coupling on the tooth surface; There must be an appropriate fractal dimension (D) value that makes the contact state optimal; As the roughness amplitude parameter (G) value increases, the surface contact condition deteriorates and the surface contact strength decreases. Conversely, as the material property parameter (ϕ) value increases, the surface contact condition improves and the surface contact strength increases. It is a preliminary exploration for accurately calculating the contact stress of involute spline coupling, laying a solid theoretical foundation for accurately predicting the wear of involute spline coupling with surface texture.

Research on multi-beam survey line system based on dynamic programming algorithm

This paper establishes a calculation model for the coverage width and overlap rate of a multi-beam survey line system [1], and optimizes the multi-beam bathymetric survey plan for marine areas. Through the dynamic programming algorithm, a reasonable survey line plan is designed based on the actual marine area depth. The research content includes: Model Establishment: Construct a calculation model for coverage width and overlap rate, determining the coverage width and overlap rate at each position. Three-Dimensional Space Model: Using the survey vessel as the origin and combining marine area information, solve for the coverage width at different angles. Shortest Survey Line Plan: Using the dynamic programming algorithm, determine the shortest survey line plan, with the dynamic transfer equation determined by the overlap rate index. The total length of the survey line in this plan is calculated to be 114,824 meters. Specific Survey Line Plan Design: Using sea depth data and cubic spline interpolation to complete the discrete point plane, construct approximate width and slope. Under the condition of relaxing the overlap rate to 5%-30%, the shortest survey line length is obtained as 398,059 meters, with a missing survey area ratio of 0%, and the total length of parts with an overlap rate exceeding 20% is 25,759 meters, accounting for 6.4%. This study provides theoretical basis and calculation methods for optimizing multi-beam bathymetric survey plans.

3-Dimensional Echocardiographic Prediction of Left Ventricular Outflow Tract Area Prior to Transcatheter Mitral Valve Replacement

BackgroundNew postprocessing software facilitates 3-dimensional (3D) echocardiographic determination of mitral annular (MA) and neo–left ventricular outflow tract (neo-LVOT) dimensions in patients undergoing transcatheter mitral valve replacement (TMVR). ObjectivesThis study aims to test the accuracy of 3D echocardiographic analysis as compared to baseline computed tomography (CT). MethodsA total of 105 consecutive patients who underwent TMVR at 2 tertiary care centers between October 2017 and May 2023 were retrospectively included. A virtual valve was projected in both baseline CT and 3D transesophageal echocardiography (TEE) using dedicated software. MA dimensions were measured in baseline images and neo-LVOT dimensions were measured in baseline and postprocedural images. All measurements were compared to baseline CT as a reference. The predicted neo-LVOT area was correlated with postprocedural peak LVOT gradients. ResultsThere was no significant bias in baseline neo-LVOT prediction between both imaging modalities. TEE significantly underestimated MA area, perimeter, and medial-lateral dimension compared to CT. Both modalities significantly underestimated the actual neo-LVOT area (mean bias pre/post TEE: 25.6 mm2, limit of agreement: −92.2 mm2 to 143.3 mm2; P < 0.001; mean bias pre/post CT: 28.3 mm2, limit of agreement: −65.8 mm2 to 122.4 mm2; P = 0.046), driven by neo-LVOT underestimation in the group treated with dedicated mitral valve bioprosthesis. Both CT- and TEE-predicted-neo-LVOT areas exhibited an inverse correlation with postprocedural LVOT gradients (r2 = 0.481; P < 0.001 for TEE and r2 = 0.401; P < 0.001 for CT). ConclusionsTEE-derived analysis provides comparable results with CT-derived metrics in predicting the neo-LVOT area and peak gradient after TMVR.

Mapping Ratoon Rice Fields Based on SAR Time Series and Phenology Data in Cloudy Regions

Ratoon rice (RR) has emerged as an active adaptation to climate uncertainty, stabilizing total paddy rice yield and effectively reducing agriculture-related ecological environmental issues. However, identifying key remote sensing parameters for RR under cloudy and foggy conditions is challenging, and existing RR monitoring methods in these regions face significant uncertainties. Here, given the sensitivity of synthetic aperture radar (SAR) backscattering signals to the crop phenological period, this paper introduces a threshold model utilizing Sentinel-1A SAR data and phenological information for mapping RR. The Yongchuan District of Chongqing, which is often cloudy and foggy, was selected as a specific study region where VH-polarized backscatter coefficients of Sentinel-1 images were obtained at 10 m spatial resolution in 2020. Based on the proposed threshold model, the RR extraction overall accuracy was up to 90.24%, F1 score was 0.92, and Kappa coefficient was 0.80. Further analysis showed that the extracted RR boundaries exhibited high consistency with true Sentinel-2 remote sensing images and the RR extracted area was in good agreement with the actual planted area situation. This threshold model demonstrated good applicability in the studied cloudy and foggy region, and successfully distinguished RR from other paddy rice types. The methodological framework established in this study provides a basis for extensive application in China and other significant RR-producing regions globally.

The method for evaluating the coverage rate of roadside monitoring cameras in road areas

Accurately assessing the coverage rate of roadside monitoring cameras in road areas is crucial for intelligent transportation systems and road risk assessment. This paper presents a new method that uses Carsim software to simulate different camera tilt angles and road geometry conditions to evaluate the coverage area of roadside monitoring cameras. By adjusting the camera tilt angles and the curve radius of the road, the coverage effect under different conditions is analyzed, and affine transformation is used to calculate the actual coverage area. The research results show that this method can effectively fit the road area, providing theoretical support for the deployment of monitoring cameras in traffic.

Application of polyhedral meshes in large-eddy simulation of building array flow fields within neutral and unstable boundary layers

Urbanization has intensified the urban heat island phenomenon, a critical concern for human habitation in recent decades. Analyzing flow fields around building arrays is vital for mitigating issues such as heatstroke and air pollution, thereby enhancing human comfort in urban environments. However, challenges in accuracy and computational efficiency persist in simulating urban thermal environments. This study evaluates how boundary layer meshes in polyhedral meshes affect wind and temperature distributions in neutral and unstable boundary layers, utilizing large-eddy simulation (LES). A total of ten cases were examined, including eight with local mesh refinement and two without. The study demonstrates that local mesh refinement is an effective strategy to reduce mesh count by approximately 60 %, thereby reducing computational time by 60 % using the same computational configuration. The performance of the polyhedral meshes was found to be comparable to that of the hexahedral meshes. For the mean velocity and temperature, the discrepancies between the hexahedral and polyhedral cases were within 10 %, while for the second-order statistics, the discrepancies were within 20 %. Within the neutral boundary layer, boundary layer meshes on the ground and building surfaces had minimal impact on the wind field. However, these meshes enhanced the accuracy of low percentile wind speed estimations by approximately 20 % within the unstable boundary layer. Moreover, this study lays the foundation for research on the thermal environment in actual urban areas and contributes to the guidelines for LES with non-isothermal inflow conditions.

Leaf area prediction of sweet potato cultivars: An approach to a non-destructive and accurate method

Many researchers consider the leaf area the most critical parameter for plant growth and physiology studies. Using allometric equations is considered an effective and non-destructive method for estimating the leaf area of plants. The objectives of this study were to construct allometric equations to predict the leaf area of sweet potato cultivars as a function of leaf dimensions and to verify the effectiveness of the allometric equations constructed for each cultivar individually and for the grouped data. Two hundred fifty leaves of the sweet potato cultivars Beauregard, BRS Amélia, BRS Cuia, and Coquinho were collected, and then the length and width of each leaf were measured. Subsequently, the product between leaf length and width and the actual leaf area was calculated. Linear, linear without intercept, power, and exponential regression models were used to construct allometric equations to predict leaf area. The best equations were chosen according to the highest coefficients of determination and correlation, Willmott agreement index, and lowest Akaike information criterion and root mean square error. The equations obtained with the linear models without intercept and power using the length and width of the leaves were the most indicated for predicting the leaf area of sweet potato cultivars.

Accurate Prediction of Three-Dimensional Humanoid Avatars for Anthropometric Modeling.

To evaluate the hypothesis that anthropometric dimensions derived from a person's manifold-regression predicted three-dimensional (3D) humanoid avatar are accurate when compared to their actual circumference, volume, and surface area measurements acquired with a ground-truth 3D optical imaging method. Avatars predicted using this approach, if accurate with respect to anthropometric dimensions, can serve multiple purposes including patient metabolic disease risk stratification in clinical settings. Manifold regression 3D avatar prediction equations were developed on a sample of 570 adults who completed 3D optical scans, dual-energy X-ray absorptiometry (DXA), and bioimpedance analysis (BIA) evaluations. A new prospective sample of 84 adults had ground-truth measurements of 6 body circumferences, 7 volumes, and 7 surface areas with a 20-camera 3D reference scanner. 3D humanoid avatars were generated on these participants with manifold regression including age, weight, height, DXA %fat, and BIA impedances as potential predictor variables. Ground-truth and predicted avatar anthropometric dimensions were quantified with the same software. Following exploratory studies, one manifold prediction model was moved forward for presentation that included age, weight, height, and %fat as covariates. Predicted and ground-truth avatars had similar visual appearances; correlations between predicted and ground-truth anthropometric estimates were all high (R2s, 0.75-0.99; all p < 0.001) with non-significant mean differences except for arm circumferences (%D ~ 5%; p < 0.05). Concordance correlation coefficients ranged from 0.80-0.99 and small but significant bias (p < 0.05 - 0.01) was present with Bland-Altman plots in 13 of 20 total anthropometric measurements. The mean waist to hip circumference ratio predicted by manifold regression was non-significantly different from ground-truth scanner measurements. 3D avatars predicted from demographic, physical, and other accessible characteristics can produce body representations with accurate anthropometric dimensions without a 3D scanner. Combining manifold regression algorithms into established body composition methods such as DXA, BIA, and other accessible methods provides new research and clinical opportunities.

Research on the Protection System for Smart Grid Based on Phasor Information at Circuit Breakers

A smart grid protection system based on phasor information at circuit breakers is proposed in this paper. Phasor data for fault diagnosis is obtained from phasor measurement units or intelligent electronic devices installed near circuit breakers, without the need for additional measurement equipment. When a fault occurs, the protection system first locates the core protection circuit breaker closest to the fault point. It then identifies the faulty component by analyzing the current phasor differences between this breaker and adjacent ones. This scheme offers high computational efficiency, enabling rapid identification of faulty equipment and its precise location. The proposed protection system structure can be divided into two types: centralized and decentralized, and the most suitable scheme should be selected based on the actual situation. Finally, the feasibility of the proposed scheme was verified through the IEEE 39-bus system and a simulated actual area power grid model, demonstrating its applicability to practical smart grid scenarios. The simulation network validates the implementation method of the proposed protection strategy.

Modeling optimization design and amplification characteristics of O-band irregular Bragg bismuth-doped fiber amplifier

In recent years, O-band bismuth-doped fiber amplifier (BDFA) have been rapidly developed due to the fluorescence properties of bismuth-doped glass in the near-infrared (NIR) band, while there are still few studies on bismuth-doped fibers (BDF) with large mode fields as of now. In addition, although the use of a double-pass structure on the amplifier can lead to gain improvement, this also deteriorates the noise figure (NF). Therefore, the study of bismuth-doped fibers with large mode-field areas is a promising path to develop high-performance BDFA. In this work, taking a commercial bismuth-doped fiber as an instance, we first measured the refractive index distribution of the BDF in the O-band using a self-developed fiber refractive index tester, and thereby obtained its actual mode-field area. Then, based on this we further propose an irregular Bragg bismuth-doped fiber with the refractive index growing layer by layer. The results suggests that the effective mode-field area of this fiber reaches 401 um2 at 1320 nm, which is nearly 5.8 times that of a common single-mode BDF. Finally, we performed numerical simulations of the amplification performance based on this fiber. Under the condition of total pumping power of 8 W, the signal with −20 dBm input power obtains a gain of more than 54 dB and an NF of less than 5 dB at 1320 nm wavelength. This work demonstrates the great potential of this Bragg bismuth-doped fiber as an O-band high-gain amplifier and high-power laser.

Study on force-thermal characteristics of ball end milling cutter with chamfered edge and micro-textured flank face

Micro-texturing can significantly improve the cutting performance of tools. During the titanium alloy cutting process, the cutting depth is relatively small, and the flank face mainly bears the friction. To overcome this issue, this study attempts to explore the influence of the micro-texture of the flank surface and chamfered edge on the cutting performance of the ball end mill. It is proved that the micro-texture of the flank surface of ball end milling cutter can reduce the actual contact area and friction, and thus reduce the cutting force. The increase of chamfering width and Angle changes the chip state and provides a good heat dissipation environment. Under the combined action of chamfered cutting edge and micro-texture, the force and heat in the milling process are reduced, and the tool life is increased to some extent. The results show that the optimum combination of chamfering and micro-texture parameters is as follows: chamfer angle is 10°, chamfer width is 100 µm, diameter of the micro-circular pit is 70 µm, pit spacing is 125 µm, and distance from cutting edge is 110 µm.

Adversarial compact wrapping classifier learning for open set recognition

Determining the limited and compact class acceptance region has become a key bottleneck in classifier design since Open Set Recognition (OSR) requires the classifier to successfully identify those of interest and reject other exceptions. The imperfect match between actual feature distribution of a class and its classification acceptance area is a critical barrier to performance enhancement in recent existing OSR algorithms. Therefore, we propose adversarial compact wrapping classifier learning for OSR, which achieves better feature representation and classification surface division through alternating learning of classifiers and compact wrapping points (CWPs). The core of the algorithm model lies in the confrontational game between CWP and the classifier. First, a new deep and compact hyperspherical crown classifier is designed, which expands neurons to achieve limited and compact classification surface division, improves the classification performance of known classes, and reduces the risk of open space. Then, the CWP algorithm is used to construct negative class sample points that tightly wrap the class feature distribution, and a joint optimization framework of CWP and classifier is constructed. The classifier improves the discrepancy between the class acceptance area of the classification and the actual class distribution area through adversarial learning with CWP, further controls the risk of unknown open spaces, and optimizes the learned feature distribution. It is worth noting that we have developed the CWP to allow concave feature distributions. Experimental results show that the proposed algorithm is significantly superior to recent comparison algorithms on various benchmark datasets and significantly improves OSR performance on the TinyImageNet dataset.

Spatio-temporal assessment and prediction of wetlands: Examining the changes in ecosystem service value of RAJUK DAP area using Artificial Neural Network and Geospatial Techniques

Wetlands are a crucial component of the earth's socio-ecological structure, providing significant ecosystem services to people. Changes in wetlands, driven by both natural and manmade causes, are altering these ecosystem services. Although Bangladesh is developing, natural resources like wetlands are changing in the country at different scales, with urban areas experiencing significant impacts. This study intends to evaluate the past, present, and future scenarios of wetlands and examine the changes in ecosystem service value (ESV) of the RAJUK (Rajdhani Unnayan Kartripakkha or Capital Development Authority) Detailed Area Plan (DAP) region. This research examined the effects of five different Land Use and Land Cover (LULC) classes on ESVs for 27 years from 1995 to 2022. Findings reveal that the current wetland area is 80.16 km2 in the post-monsoon season and 306.67 km2 in the pre-monsoon season. Composite post-monsoon wetland map from 1995 to 2015 that 19.48 km2 of wetlands are classified as hydro-ecologically consistent wetlands. Wetland area has decreased by 140.926 km2 between 1995 and 2022, according to simulations, and is predicted to do so by another 27.11 km2 during the following eight years. The total ESV of wetlands dropped by about 26.72 percent between 1995 and 2022, primarily due to conversion to habitation and agricultural use. Also, the projection of LULC and associated ESV for the year 2030 demonstrates how ESV evolves throughout this period and which LULC classes are more susceptible to change, while the kappa coefficient was used to compare the simulated models to the actual wetland area. The current study will undoubtedly be helpful to decision-makers who make a substantial contribution to preserving ecosystem services and the wetland landscape.

Implementing Project-Based Learning Using Artificial Intelligence

Project-based learning in the education system is gaining more and more momentum, as it is promising and contributes to the formation of all the necessary life skills for of students of professional preliminary higher education. It allows the development of their critical thinking and creative abilities. The application of artificial intelligence in education is an actual area of research that contributes to increasing the effectiveness of the educational process. Implementation of project-based learning using artificial intelligence (AI) is becoming increasingly urgent in education. This approach makes it possible to significantly increase the motivation of students, stimulate their independence and creativity, and prepare them to solve real problems in the modern world, where AI plays an increasingly important role. The purpose of the study is to highlight the possibilities and prospects of using artificial intelligence to implement project-based training of students of professional preliminary higher education. The research is conducted using mixed methods, including quantitative and qualitative approaches. Data are analyzed using statistical methods and interviews are conducted with participants in the educational process. During the research and presentation of the material, theoretical methods of scientific knowledge were used: analysis and synthesis, induction and deduction, systematization, and generalization. The author hypothesizes that the use of AI in project-based learning helps to increase the motivation of students, strengthens their independence, improves the skills of working with information and critical thinking, and also stimulates teamwork. It has been established that using artificial intelligence significantly increases the effectiveness of project-based learning. Its main advantages for teachers are described, including the automation of routine tasks. Positive results for students in the form of improved learning outcomes are summarized. Recommendations for the implementation of artificial intelligence services in the educational process, in particular, in project activities, are offered. The main areas for further research are systematized. The results of the study can be used for further development of the theory of project-based learning and artificial intelligence in education. The recommendations developed by the authors regarding the implementation of artificial intelligence in project-based learning. These recommendations can be used by teachers and lecturers to optimize the educational process, increase its effectiveness, and form the competencies necessary for a successful life in modern society for students. Further research can be aimed at developing new methods and tools for using AI in project-based learning, studying its impact on various aspects of students' educational activities, as well as studying the ethical aspects of using AI in education.

Optimisation of city structures with respect to high wind speeds using U-Net models

The design and placement of a new building in an urban environment is optimised so that pedestrians do not feel uncomfortable due to high wind speeds. Architects and city planners typically use Computational Fluid Dynamics (CFD) simulations to predict the effects of wind on new buildings. CFD is too time consuming to be used in generative design tools, which are necessary because they allow designers to visualise and iterate on their ideas, collaborate effectively and work more efficiently. These tools enhance the design process, facilitate communication and feedback, and save time by automating tasks. Finding a generative design model based on limited data is challenging due to issues such as insufficient representation, increased risk of overfitting, lack of statistical significance, potential bias and difficulties in validation and evaluation. In the design framework proposed in this paper, the computationally intensive CFD solver could be replaced by a fast machine learning surrogate predicting wind speed. The replacement is a U-Net convolutional neural network trained on high-precision Reynolds’ averaged Navier–Stokes (RANS) simulations (CFD simulations). The proposed workflow is demonstrated by optimising the placement of a hypothetical new building on a city square in Gothenburg, Sweden. The surrogate used in the optimisation process calculates an optimal building design within seconds instead of hours saving 11 h of simulation time. The actual area with strong wind effects is reduced by half.

Study on mapping method of irrigated cultivated land–taking Nebraska as an example

The distribution information of irrigated cultivated land is important to many studies, including grain yield estimation, water resources management, drought risk assessment, and so on. This study developed a feature variable, called the irrigation probability index (IPI), based on the principle that irrigation can slow down or inhibit the evolution from meteorological drought to agricultural drought. Nebraska, which has a good irrigation information database, was selected as the study area. Combining IPI with the irrigation statistical data and other selected feature variables, irrigated cultivated land in Nebraska was mapped in 2017 using both the spatialization method and remote sensing classification methods (random forest and support vector machine). We verified the effectiveness of IPI in identifying irrigated cultivated land, analyzed the importance of different feature variables for irrigated cultivated land identification, and compared the accuracy of different irrigation mapping methods. The results show that the IPI is significantly correlated with the actual irrigation area, the area of irrigation facilities, and the number of active irrigation wells. It has a better indicative ability of irrigation in areas with a suitable climate or arid areas, than in areas with a humid climate. The crop water stress index (CWSI), IPI, vegetation index (EVI and NDVI), and land surface temperature difference between day and night are the most important feature variables that are used to distinguish irrigated cultivated land from rain-fed cultivated land. There are differences between the recognition accuracies of the three mapping methods in different climatic regions. Overall, the spatialization method had the highest accuracy (overall accuracy = 86.92 %, kappa = 0.74) and the highest similarity with the existing high-resolution irrigation maps. This study provides a direct and useful reference for other researchers to select feature variables and mapping methods in irrigated cultivated land mapping research.

Urban Geomorphology Methods and Applications as a Guideline for Understanding the City Environment

Cities all over the world have developed on different geological-geomorphological substrates. Different kinds of human activities have operated for millennia as geomorphic agents, generating numerous and various erosion landforms and huge anthropogenic deposits. Considering the increasing demand for land and the expansion of the built-up areas involving and disturbing any kind of natural system inside and surrounding the actual urban areas, it is not negligible how important the dynamics of the urban environment and its physical evolution are. In this context, this manuscript addresses insights into eight case studies of urban geomorphological analyses of cities in Italy, Greece, and Brazil. The studies are based on surveying and mapping geomorphological processes and landforms in urban areas, supporting both geo-hazard assessment, historical evolution, and paleomorphologies, as well as disseminating knowledge of urban geoheritage and educating about the anthropogenic impact on urban sustainability. We hypothesize that urban geomorphological analysis of several case studies addresses the physical environment of modern cities in a multi-temporal, multidisciplinary, and critical way concerning global changes. Thus, this study aims to illustrate and propose a novel approach to urban geomorphological investigation as a model for the understanding and planning of the physical urban environment on a European and global scale.

A comprehensive Analysis of the Application of GIS and RS Technologies in Flood Prediction and Risk Assessment, with a Focus on Wadi Baysh Dam in the Jazan region. KSA

This study uses GIS spatial analysis and open-source data based on flood hazards formulas to provide a thorough analysis and forecast of floods using Geographic Information Systems (GIS) and Remote Sensing (RS). The Wadi Baysh Dam overflowed with water in 2016 as a result of the torrential downpour that hit the dam, its streams, and the surrounding surroundings results the increasing the storage capacity. In terms of water reserves, it is the dam that is thought to be the biggest in the Kingdom. Some settlements in Baysh, which are under siege since their highways are blocked off, suffered greatly as a result of flooding. However the storage capacity of Wadi Baysh Dam around 200 million cubic meters of water. In order to identify the actual Wadi Baysh flood-vulnerable area, this study combined data from remote sensing, the Geographic Information System (GIS) technique, and an Analytical Hierarchy Process (AHP) computation. The investigation was carried out to determine the viability of using remote sensing open-source data,basemap of the study area andArcGIS10.4 software in Wadi Baysh risk management. Slope (10%), height (15%), drainage density (20%), distance to stream (20%), rainfall intensity (25%), and land cover (10%) all have a proportion of relative weight that determines how flooding is analyzed. As a result of the most recent GIS analysis (weighted overlay approach), five zones are identified within the extracted flood region based on the categories of hazard. There are ten blocks in all—five in a high-risk area and five in a moderately dangerous one. The paper looks at the flood risk in Wadi Baysh using a combination and integration betweenremote sensing opensources data and GIS models. High precipitation and runoff in the dam’s downstream sections will be the main cause of difficulties, causing a great deal of death and destruction to agricultural and urban areas. Eventually, a control room connected to the GIS model and remote sensing devices will be needed in this valley to implement an efficient warning system and flood hazard management system.

Effects of Red and Blue Light on the Growth, Photosynthesis, and Subsequent Growth under Fluctuating Light of Cucumber Seedlings.

The effects of red and blue light on growth and steady-state photosynthesis have been widely studied, but there are few studies focusing on dynamic photosynthesis and the effects of LED pre-treatment on cucumber seedlings' growth, so in this study, cucumber (Cucumis sativus L. cv. Jinyou 365) was chosen as the test material. White light (W), monochromatic red light (R), monochromatic blue light (B), and mixed red and blue lights with different red-to-blue ratios (9:1, 7:3, 5:5, 3:7, and 1:9) were set to explore the effects of red and blue light on cucumber seedlings' growth, steady-state photosynthesis, dynamic photosynthesis, and subsequent growth under fluctuating light. The results showed that compared with R and B, mixed red and blue light was more suitable for cucumber seedlings' growth, and the increased blue light ratios would decrease the biomass of cucumber seedlings under mixed red and blue light; cucumber seedlings under 90% red and 10% blue mixed light (9R1B) grew better than other treatments. For steady-state photosynthesis, blue light decreased the actual net photosynthetic rate but increased the maximum photosynthetic capacity by promoting stomatal development and opening; 9R1B exhibited higher actual net photosynthetic rate, but the maximum photosynthetic capacity was low. For dynamic photosynthesis, the induction rate of photosynthetic rate and stomatal conductance were also accelerated by blue light. For subsequent growth under fluctuating light, higher maximum photosynthetic capacity and photoinduction rate could not promote the growth of cucumber seedlings under subsequent fluctuating light, while seedlings pre-treated with 9R1B and B grew better under subsequent fluctuating light due to the high plant height and leaf area. Overall, cucumber seedlings treated with 9R1B exhibited the highest biomass and it grew better under subsequent fluctuating light due to the higher actual net photosynthetic rate, plant height, and leaf area.