Depth Parameters Research Articles

Dimensionless Machine Learning: Dimensional Analysis to Improve LSSVM and ANN models and predict bearing capacity of circular foundations

This research focuses on developing hybrid intelligence techniques to predict the bearing capacity of circular foundations using the most effective parameters. In this regard, a database involving 968 test circular foundations involving different rock properties, soil characteristics, and foundation radius has been prepared by laboratory tests for training and testing the new model presented in this study. For adequately considering various factors, the several parameters of depth (D) in m, density of soil (DS) in gr/cm3, internal angle of friction (IAF) in degree, cohesion of soil (CS) in kg/cm2, and foundation radius (FR) in m were considered as the input of the model and bearing capacity in kg/cm2 was considered as the target parameter. Three main strategies were addressed in this study. First, four well-known machine learning algorithms, Artificial Neural Network (ANN), Bagging Regressor (BR), Least Squares Support Vector Machine (LSSVM), and Gradient Boosting Regressor (GBR), were adopted to predict bearing capacity. Second, a novel and robust mathematical computation named dimensional analysis (DA) theorem was integrated with machine learning techniques to improve the accuracy and performance of the models by decreasing the number of inputs. Third, based on DA implementation, a rational mathematical formula using gene expression programming (GEP) was structured to anticipate bearing capacity. Furthermore, the sensitivity analysis process specified the impact of the five effective factors. The results of this study demonstrate the effectiveness of integrating DA with machine learning models to predict the BC of circular foundations. Among the developed models, the DA-LSSVM model showed superior performance, achieving an R² of 0.998 and 0.996 for training and testing, respectively, indicating high prediction accuracy. The results indicated that the IAF was the most sensitive factor with r = 0.709, while CS was the least sensitive with r = -0.087. A graphical user interface (GUI) app has been designed to apply the proposed models in this study conveniently. In the last step of this process, the GUI and the DA-based machine learning models were implemented by analyzing two examples. According to the findings, the GUI may be employed to make a reliable and speedy projection of the bearing capacity of circular foundations by considering a variety of input parameters.

Analysis of Earthquake Intensity on Java Island Using K-Means Clustering and GeoMap

Indonesia is the country with the second highest earthquake intensity in the world because Indonesia is passed by the confluence of 3 tectonic plates, namely: the Indo-Australian Plate, the Eurasian Plate, and the Pacific Plate. Earthquakes occur due to the movement of tectonic plates that move freely and interact with each other. The impact of the earthquake includes: Tsunami, collapsed buildings, fires, rock collapses, and ground cracks. The purpose of writing this study is to determine the intensity of earthquakes that occur on the island of Java using the K-Means Clustering method. The earthquake data used in this study uses magnitude, depth, and geographical location parameters obtained from existing data. The K-Means Clustering method is used to group earthquake data into several clusters based on their seismic characteristics. The result of this study is to identify seismic zones with similar earthquake intensity. From the results of the cluster obtained, it was used to make data visualization of areas with high earthquake intensity using GeoMap

Optimization of Underwater Images Based on Gray World Algorithm and Jaffe-McGlamery Models

In this paper, a comprehensive scheme for underwater image processing is proposed based on the grayscale world algorithm and the Jaffe-McGlamery model. Firstly, a color bias detection based on grayscale world theory, a low light detection based on HSV color space, and a fuzzy detection method based on frequency domain and Laplace operator are designed to classify different types of image degradation. Subsequently, the corresponding scene degradation models are constructed for different degradation types through the simplified Jaffe-McGlamery model, and the image features under different water conditions are analyzed. Next, an improved gray world algorithm is used to eliminate color bias, the limiting contrast adaptive histogram equalization (CLAHE) technique is utilized to improve the image quality under low-light conditions, and the dark-channel a priori algorithm is optimized by the depth estimation and parameter adaptation modules to remove blurring. The proposed method in this study significantly improves the image quality in different scenarios and provides a new idea for underwater image processing.

Analysis of the correlations between changes in posterior segment and anterior chamber segment after implantable collamer lens implantation in highly myopic patients

PurposeTo evaluate the impact of Implantable Collamer Lens (ICL) implantation on anterior chamber angle parameters and posterior segment structures in highly myopic eyes and explore potential correlations between these changes. The study aimed to assess alterations in superficial and deep vessel density (SVD, DVD), foveal avascular zone (FAZ) area, and retinal nerve fiber layer (RNFL) thickness to clarify the safety profile of ICL implantation.MethodsProspective observational study, included 36 highly myopic eyes undergoing ICL implantation in surgery group and 23 non-surgical control eyes in non-surgery group. Anterior chamber parameters were assessed using AS-OCT, and posterior segment changes, including SVD, DVD, FAZ, and RNFL, were evaluated using OCT and OCTA preoperatively and at intervals up to 3 months postoperatively. Statistical analyses included paired t-tests, Wilcoxon tests, and Spearman correlation.ResultsICL implantation significantly improved uncorrected distance visual acuity (UDVA) (p < 0.01). Anterior chamber depth (ACD) and angle parameters, such as AOD and TISA, decreased initially but stabilized by 3 months. SVD and DVD showed early postoperative fluctuations, returning to baseline by 3 months, while the FAZ area and subfoveal choroidal thickness remained stable. Significant correlations were identified between anterior segment narrowing and posterior vascular changes, particularly in the pericentral region.ConclusionsICL implantation effectively and safely corrects high myopia, with stable anterior and posterior structural changes by 3 months. Transient vascular density fluctuations correlated with anterior chamber angle alterations, highlighting the need for long-term studies to further understand these dynamics and ensure retinal safety postoperatively.Trial registrationThe study was approved by the ethics committee of Aier Eye Hospital, Jinan University (No. GZAIER2023IRB25; China Clinical Trial Record No. MR-44-24-009241).

Periodontal Indices as Predictors of Cognitive Decline: Insights from the PerioMind Colombia Cohort.

Background: Poor oral health and periodontitis have been epidemiologically linked to cognitive decline and mild cognitive impairment (MCI) in older adults. However, specific metrics directly linking these clinical signs are exceedingly limited. Methods: To address this gap and develop novel tools to help clinicians identify individuals at risk of cognitive decline, we established the PerioMind Colombia Cohort, comprising elderly Colombian subjects who underwent comprehensive neurocognitive and periodontal evaluations. Results: The results revealed that subjects diagnosed with MCI exhibited significantly higher scores in specific periodontal indices, including gingival erythema and pocket depth parameters. The predictive model identified positive associations with MCI, with gingival erythema showing the strongest correlation, followed by the presence of periodontitis and variations in pocket depth measurements. Additionally, lower educational attainment was associated with a higher likelihood of being classified in the periodontitis-MCI group. Conclusions: Here, we show that specific altered periodontal metrics are associated with MCI diagnosis, and the generated results provide defined metric ranges for identifying individuals at risk. Upon validation in larger cohorts, the findings reported here could offer dental practitioners and clinicians innovative tools to identify individuals at risk of MCI and age-related dementias through routine oral health assessments, thereby enabling more accessible and highly sought-after early intervention strategies in both developing and developed countries.

Finite Element Analysis of Stress Distribution in Cancellous Bone During Dental Implant Pilot Drilling

Background/Objectives: This study investigates stress distribution in cancellous bone during pilot drilling for dental implants using the Cowper–Symonds model. Understanding the biomechanical effects of drilling parameters on bone health is essential for optimizing implant stability and longevity. Methods: A finite element analysis (FEA) approach was employed to simulate the pilot drilling process in cancellous bone. A three-dimensional jawbone model was developed from CT scan data, processed using 3D Slicer, and refined with CAD tools. The drilling simulation incorporated a rigid pilot drill and flexible cancellous bone, utilizing explicit dynamic methods. Stress distribution was evaluated for drilling depths ranging from 6 mm to 16 mm, with mesh density and strain rate effects considered to ensure accuracy. Results: The results showed an increase in stress levels with drilling depth, with maximum stress recorded at 16 mm. Initial contact stress was 17.3 MPa, rising to 228.9 MPa at deeper penetration due to increased interaction between the drill and bone. Stress distribution patterns emphasized the critical role of drilling depth and design parameters in mitigating bone damage. Conclusions: This study highlights the importance of optimized drilling protocols and pilot drill design to reduce stress and preserve bone integrity. The findings provide valuable insights into improving implant procedures and demonstrate the utility of FEA as a robust tool for evaluating biomechanical impacts during implant placement. Future research should incorporate cortical bone and thermal effects for a comprehensive analysis.

Determination of IL-17, BCL-3 and IκBζ expression levels in the gingival crevicular fluid of psoriasis patients.

The purpose of this study was to determine the levels of IL-17, Bcl-3 and IκBζ gene expression in the gingival crevicular fluid (GCF) of psoriatic and healthy individuals and to compare the clinical periodontal parameters in the patient and control groups. A total of 10 psoriasis patients and 2 healthy patients in the control group were included in the analysis for IL-17, Bcl-3, and IκBζ gene expression in the GCF. Periodontal health, gingival index, plaque index, and mobility (using a periotest device) levels were compared between the groups. While considering the down-expressed patients, the expression level of the Bcl-3 gene showed the most variability among the genes studied in psoriasis disease. IL-17 gene expression levels were significantly higher and up-regulated in all patients compared to the control group. In addition, when comparing IL-17 with Bcl-3, IL-17 gene expression was notably high in all patients except patients 4, 9 and 10. The highest expression levels of the IκBζ gene were observed in the patient sample, except for patients 7 and 8. While plaque index, gingival index, and pocket depth parameters were higher in the psoriasis group, there was no significant difference between the two groups (p = 0.257, p = 0.390, p = 0.240, respectively). In the periotest (mobility) parameter, while the mean value was higher in the psoriasis group significantly (p = 0.030), clinically mean values of two groups were in the same grade according to Miller mobility index. The IκBζ gene showed notable high expression levels in psoriasis patients. It was concluded that psoriasis may be associated with periodontitis. CLINICAL TRIAL REGISTRATION NUMBER: NCT06408454-05/06/2024. NAME OF THE TRIAL REGISTER: Interleukin- 17 (IL-17), Bcl-3, and NF-kappa-B Inhibitor Zeta (IκBζ) Expression Levels in the Psoriasis Patients.

Keanekaragaman Makroalga Pantai Batu Layar, Lombok Barat, Nusa Tenggara Barat

Macroalgae are photosynthetic organisms that grow in aquatic environments and play an important role in marine and coastal ecosystems. Macroalgae grow in almost all waters, including in the coastal area of Batu Layar. Batu Layar has particular and distinctive characteristics compared to other locations on Lombok Island. However, in recent years, supporting tourism facilities have begun to be built which will affect the composition of living biota including macroalgae. Therefore, data was collected on the diversity of changes in the composition of macroalgae in the future. This study aims to determine the diversity of macroalgae growing on Batu Layar Beach, West Lombok. This study is a descriptive exploratory study. Quadrant placement and sampling were carried out using the purposive sampling method. The results showed that the number of species growing in Batu Layar waters was 41 species. These species are spread across two observation stations. Station 1 is dominated by S. polycystum while station 2 is dominated by P. australis. The current strength and depth parameters of Station 1 were higher than those of Station 2, while the ammonia, phosphate and turbidity of Station 2 were higher than those of Station 1.

Incidence of acute angle closure in patients with primary angle closure without prophylactic iridotomy during pharmacological mydriasis for cataract surgery.

The objective was to study the incidence of acute primary angle closure (acute PAC) during pharmacologic mydriasis before cataract surgery and changes in anterior chamber angle parameters in patients with primary angle closure diseases (PACD) with and without prophylaxis laser peripheral iridotomy (LPI). This was a prospective, comparative study of cataract patients with PACD with and without prophylaxis LPI presented at the Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand during June 2022 to December 2023. The incidence of acute PAC during pharmacologic mydriasis prior to cataract surgery was recorded. Changes in anterior chamber depth (ACD) and anterior chamber angle parameters measured with anterior segment optical coherence tomography before and after cataract surgery were compared between groups using Student t test. There were 195 eyes of 116 patients with a mean age of 70 years enrolled in the study. Eighty patients were women and 36 were men. There were 99 eyes without prophylaxis LPI and 96 eyes with LPI. There was significantly more angle-closure glaucoma in the non-LPI group than in the LPI group. Preoperatively, all anterior chamber angle parameters were significantly greater in the LPI group than in the non-LPI group (P < .001 all parameters), except ACD (P = .83) and anterior chamber width (ACW) (P = .30). All participants underwent uneventful cataract surgery. Postoperatively, all parameters of the anterior chamber angle and ACD increased significantly in both groups. When comparing between groups, all postoperative anterior chamber angle parameters were not significantly different between the groups, but ACD was significantly greater (P = .036) and ACW was lower in the LPI group (P = .008). There was no incidence of acute PAC occurring during pharmacological mydriasis before cataract surgery in both groups. There was no incidence of acute PAC in patients with and without prophylaxis LPI during pharmacological mydriasis in the preoperative preparation for cataract surgery. Eyes without LPI showed significantly lower preoperative anterior chamber angle parameters compared to eyes with previous LPI, but not significantly different postoperatively. The benefits of prophylactic LPI for PACD scheduled for cataract surgery must be further investigated.

Iterative Ratio Method: a method to map the depth to the Moho from gravity anomalies using linear approximate invariance

Interfaces characterized by a density contrast are common models to interpret gravity data. Two key parameters of the interface inversion are the density contrast and constant offset, which are free parameters. We propose to iteratively estimate the two free parameters by a new method: the iterative-ratio (ITRAT) method. The method is based on the linear approximate invariance of depth and free parameters and on the availability of several depth constraints. The free parameters are estimated iteratively to update the interface depth. By processing the synthetic Moho model, we find that the ITRAT method generally converges after two iterations with good accuracy for the free-parameter estimates and Moho depth. Random tests for the constant density model show that the accuracy of ITRAT method is slightly affected by the noise of gravity data and by the number of constraints, but it is sensitive to the uncertainty of constraints. For the variable density model, the ITRAT method will estimate a group of constant density contrast and constant offset to obtain a better Moho model, which has the lower depth error than the model with a mean density contrast. The new method is finally demonstrated by mapping the Moho of the Santos basin (Brazil) according to the depth constraints and regional gravity anomalies.

Research on Fusion Model Method for Corrosion Damage Detection of Switch Sliding Baseplate

As the core component of railways, the switch sliding baseplate has a bad operating environment, and its surface is prone to corrosion. Existing methods, including traditional methods, ultrasonic detection, and image processing, have difficulty in extracting corrosion features and being applied in practice. To solve the above problems, the Residual Neural Network 50 (ResNet50) model, a deep learning model, is introduced in this paper. To solve the problems of gradient explosion and weak corrosion in the model, a new fusion model, VGG-ResNet50-corrosion (VGGRES50_Corrosion), is proposed in this paper. First of all, for the problem that there is no public dataset, this study conducts a neutral salt spray corrosion test and collects the image features and corrosion depth parameters of skateboard corrosion in different time periods as the dataset to test the performance of the model. Then, corrosion thickness is introduced as a modified variable in the ResNet50 network, and a new network, VGGRES50_Corrosion, is introduced by blending the improved model with the Visual Geometry Group-16 (VGG16) network through a model fusion strategy. Finally, a model test and ultrasonic contrast test are designed to verify the performance of the model. In the model test, the recognition accuracy of the fusion model is 98.98% higher than that of other models, which effectively solves the shortcoming of the gradient explosion's weak generalization ability under a small sample model. In the ultrasonic comparison experiment, the mean relative errors of this method and ultrasonic detection method are 4.08% and 46.14%, respectively, and the mean square errors are 1.86 h and 15.01 h, respectively. The prediction result of deep learning is better than that of ultrasonic piecewise linear fitting. It has been proved that VGGRES50_Corrosion can identify the degree of corrosion of slip switches more effectively, and it has great significance in improving the corrosion detection efficiency of slip switches.

New approach to predict load-displacement curves of bored piles in homogeneous sandy soils: numerical analyses

Large-diameter bored piles (LDBPs) are increasingly used as foundations elements for various structures such as tall buildings, highway bridges, retaining structures, and power transportation structures, among others. Stabilizing fluids like bentonite or polymers are commonly employed to maintain borehole integrity in soils with high water tables and low resistance. Previous research has highlighted the significant influence of stabilizing fluid and construction methods on the performance of bored piles. Due to the absence of routine design methods for bored piles with stabilizing fluids, full-scale instrumented load tests are regularly conducted to investigate load transfer mechanisms and predict bearing capacity. Alternatively, numerical modeling offers a cost-effective approach to analyze pile performance in layered soil with stabilizing fluids. However, the complexity of this problem, involving different soil layers, and the thin layer of soil-stabilizing fluids in the shear band, presents challenges for numerical analysis. In this study, a numerical model is proposed to analyze bored pile performance in sandy soil, considering different pile diameters and lengths. The model is calibrated using data from instrumented vertical load tests conducted on a 1 m diameter and 24.10 m long bored pile constructed with bentonite. Based on previous research findings, simplifications were made regarding soil layers and stabilizing fluids. Calibration involved utilizing Abaqus/CAE software and the Mohr-Coulomb failure criterion to describe granular material behavior. The soil model comprises a unique sand layer with 12.5 m in length and 36.15 m in depth and mean parameters derived from seven in-situ layers data. Load test simulations involve applying a vertical displacement equivalent to 10% of the pile 1m-diameter and analyzing load-displacement curves, load transfer mechanisms, interface behavior, and field stress-displacement. Initial findings show good agreement between experimental and numerical load results. Parametric analysis investigates the influence of pile geometry and soil properties on various resistances (Q_u,Q_b and Q_f) and coefficients (&amp;#946;,&amp;#956; and K), proposing normalization expressions to estimate load capacities based on soil and pile properties. This study provides insights for developing supplementary methods to predict bored pile resistance and behavior and conducting future numerical analyses with stabilizing fluids and layered soils, thereby enhancing engineering practices in this field.

Application of functional metagenomics in the evaluation of microbial community dynamics in the Arabian Sea: Implications of environmental settings

Ocean microbial communities form the base of marine food webs, facilitating energy transfer and nutrient cycling, thereby supporting higher trophic levels. We investigated their composition and functional profiles across depths (surface waters 0, 29, and 63 m and bottom waters 100, 150, and 200 m) in the central-eastern Arabian Sea (CEAS) using next-generation sequencing. It was hypothesized that the composition and functional diversity of these communities would be influenced by depth and environmental parameters. Our research showed that microbial communities vary with depth and are shaped by environmental factors like irradiance, temperature, dissolved oxygen, suspended particulate matter, chlorophyll a, and ammonia concentrations. Cyanobacteria (Prochlorococcus sp) and Mamiellaceae, belonging to picoeukaryotes, exhibited distinct depth-specific distributions up to subsurface chlorophyll maxima (SCM) at 63 m. On the other hand, a community shift in the microbial communities comprising Firmicutes, Bacteroidetes, and Actinobacteria phyla was observed at the deeper water depths. The profiling of functional genes pointed out the expression of carbon fixation by photosynthetic organisms at the surface (0, 29, and 63 m), which shifted to prokaryotic carbon fixation in deeper waters (0, 150, and 200 m). Microcosm experiments (mixing of surface water with water from the SCM) carried out simulating disturbances such as climate change forced mixing (cyclones), revealed shifts in microbial structure and function. It was observed that within 48 h, the carbon fixation activity changed from photosynthetic organisms to prokaryotes and indicated an increase in stress-related biosynthetic pathways such as expression of quorum sensing, biosynthesis of antibiotics, lipopolysaccharides, and secondary metabolites. These findings have implications for predictive modelling of food web dynamics and fisheries management in the context of climate change.

Nanosecond laser ablation of micro-pits in Ni60/WC coatings with coupled thermal-stress simulation and parameter optimization

In order to solve the problem of crack sprouting inside the micro-pit caused by improper design of laser ablation process parameters, this study establishes a thermal-stress coupling simulation model of nanosecond laser ablation of micro-pits on Ni60/WC coated surfaces. It researches the influence of laser process parameters (laser power, scanning speed, processing times) on the index parameters of micro-pit diameter, depth, and maximum residual stress. The weighting of diameter, depth and maximum residual stress sub-objectives in the comprehensive evaluation objectives of micro-pits are calculated. The response surface method of Central Composite Design (CCD) and regression analysis are used to fit the response surface functions of micro-pit diameter, depth, and maximum residual stress as a function of the variation of laser ablation process parameters. The Genetic Algorithm (GA) is used to minimize value of the maximum residual stress sub-objective function and to optimize the process parameters of laser ablation of micro-pits, using the values interval of the micro-pit diameter and depth functions as nonlinear constraints. The results show that the maximum residual stress varies quadratically with laser power and scanning speed, increases linearly with the processing times, and is coupled with the ablation process parameters. A design method for optimizing the parameters of nanosecond laser ablation by maximum residual stress is established, and the process parameters are optimized to meet the design parameters and quality requirements of micro-pits: laser power of 12 W, scanning speed of 276 mm/s, and 4 times of processing. The optimized process is provided for the parameter tuning nanosecond laser ablation of micro-pits on Ni60/WC coating surfaces.

Lubrication Characteristics of a Warhead-Type Irregular Symmetric Texture on the Stator Rubber Surfaces of Screw Pumps

A theoretical model for the micro-texture on the inner wall of the stator rubber in screw pumps was developed. The finite element analysis method was employed. The pressure and streamline distributions for warhead-type, concentric circle-type, and multilayer rectangular-type textured surfaces were calculated. The effects of textured morphology, groove depth, groove width, and other parameters on the lubrication field were systematically investigated and analyzed. A nanosecond laser was employed to process the textured rubber surface of the stator in the screw pump. Subsequently, a micro-texture friction performance test was conducted on the rubber surface of the stator in actual complex well fluids from shale oil wells. Given the results of the simulation analysis and experimental tests, the lubrication characteristics of textured rubber surfaces with varying texture morphologies, rotational speeds, and mating loads were revealed. Furthermore, it indicated that the irregular symmetric warhead-type micro-texture exhibited excellent dynamic pressure lubrication performance compared with concentric circle-type and multilayer rectangular-type textures. The irregular symmetry enhanced the dynamic pressure lubrication effect, enhanced the additional net load-bearing capacity of the oil film surface, and reduced friction. As the groove depth increased, the volume and number of vortices within the groove also increased. The fluid kinetic energy was transformed into vortex energy, leading to a reduction in wall stress on the surface of the oil film, thereby affecting its bearing capacity. Initially, the maximum pressure on the wall surface of the oil film increased and then decreased. The optimal dynamic pressure lubrication effect was achieved with a warhead-type texture size of 3 mm, a groove width of 0.2 mm, and a groove depth of 0.1 mm. Well-designed texture morphology and depth parameters significantly enhanced the oil film-bearing capacity of the stator rubber surface, improving the dynamic pressure lubrication effect, and consequently extending the service life of the stator–rotor interface in the screw pump.

Research on intelligent three-dimensional anchor position detection method for ships utilizing Traversal and Monte Carlo algorithms

As intelligent ship technology advances, the importance of intelligent anchor position detection, as one of the key technologies, can ensure the safe anchoring of ships and enhance the efficiency of port operation. At present, most of the anchor position selection and detection algorithms are mainly based on two-dimensional planes, and there is a lack of research on the intelligent detection of safe water depth for ship anchoring in three-dimensional space. It not only restricts the full utilization of anchorage resources but also affects the safety and environmental adaptability of anchoring operations. To address these issues, this study proposes a three-dimension anchor position detection method. Firstly, based on the establishment of a three-dimensional ocean model, the possible anchor positions selected by the ship are simulated using the Monte Carlo algorithm. Secondly, the simulated anchor positions are optimized using a Traversal algorithm to filter out the optimal anchoring position that meets the requirements, the safety distance between each point and the existing ship is calculated, and the anchor position is determined according to the corresponding required safety spacing. Finally, to verify the applicability and effectiveness of the method under different sea conditions and different ship types, this study conducts a series of simulation experiments with 5000 random samples. These experiments compare the demand of anchor position selection for anchoring ships with changing water depths in the case of empty and full load drafts, and visualize the impact of varying water depth parameters on the selection of anchor positions for anchoring ships in various ship types. The outcomes of the experiment indicate that the algorithm’s detection area encompasses the whole anchorage area, ensuring both the anchorage area’s usage rate and the accuracy of anchor position detection. This study demonstrates that the Traversal and Monte Carlo Algorithms effectively improve the accuracy of the selection of anchoring position of the ship, makes full use of the resources of anchorage, and further improves the safety and efficiency of the anchoring operation.

Optimization and multiphysics analysis of high-performance lithium niobate modulators

Thin-film lithium niobate electro-optic modulators (TFLN EOMs) are widely utilized across various fields due to their exceptional performance. In this theoretical study, we optimized a TFLN EOM featuring a multimode interference coupler, cosine-curved waveguides, and a modulating arm straight waveguide structure. By tuning the etching depth, waveguide width, and geometric parameters of the traveling wave electrodes, we achieved a half-wave voltage-length product of 1.6V⋅cm and a 3 dB bandwidth of approximately 140 GHz. Additionally, we performed a multiphysics analysis to investigate the temperature characteristics of the modulator. The results indicate that increased temperature induces thermal-optic effects in the material, leading to changes in the waveguide’s refractive index and the optical beam phase, which results in increased insertion loss. Moreover, the stress generated by thermal expansion is concentrated in the modulator’s waveguide, representing a weak point that is prone to failure.

Application of Ground Penetrating Radar in Rapid and Non destructive Detection of Urban Tunnels

Abstract Due to the rapid advancement of modern transportation, tunnels have emerged as an important component of transportation infrastructure. A large number of pipelines and tunnels makes the urban underground environment extremely complex. Detecting underground tunnels and pipelines can provide guidance for urban construction. Ground penetrating radar (GPR) possesses attributes such as swift detection speed, no loss, convenient and flexible operation, thereby playing a crucial role in numerous domains. The aim of this research is to verify the feasibility of detecting tunnels with depths on the order of 10 meters, by evaluating tunnel dimensions and depth parameters through a combined approach of simulation and experimental measurements. In this investigation, gprMax was utilized to model and simulate the underground tunnel to establish the correlation between echo data and target position. Additionally, this research employed an 80MHz GPR system to detecting a undergound tunnel and acquiring data which facilitated successful determination of the depth of the underground tunnel based on empirical evidence

Machine learning-based prediction of scour depth evolution around spur dikes

ABSTRACT Spur dikes are pivotal elements in river training, serving to mitigate the dynamic alterations induced by river degradation and aggradation. Traditionally, scour prediction models have relied on regression techniques, but the advent of soft computing and machine learning has offered opportunities for enhanced accuracy. This study focuses on the development of hybrid machine-learning models, including eXtreme Gradient Boosting (XGBoost), random forest (RF), convolutional neural network–long short-term memory, and artificial neural network, optimized using genetic algorithms to predict both temporal scour depth variation and maximum scour depth around the initial spur dike in a series. The analysis reveals strong associations between scour depth and various parameters such as non-dimensional time, spacing, channel width, time-averaged velocity, and densimetric Froude number. The models are established through an iterative process involving four predictor combinations. Results demonstrate XGBoost as the top-performing model, consistently exhibiting superior performance with R2 of 0.99, root mean square error (RMSE) of 0.012, and mean absolute error of 0.008 during training, and R2 of 0.96, RMSE of 0.044, and Kling–Gupta efficiency of 0.98 during testing for predicting temporal scour depth. For non-dimensional maximum scour depth, it reached R2 of 0.99 and RMSE of 0.005 in training, with R2 &amp;gt; 0.91 across all combinations during testing. Although RF showcases commendable accuracy, it slightly lags in precision compared to XGBoost.

Time series trends and correlations of aerosol optical depth and cloud parameters over Addis Ababa

Aerosols are microscopic liquid or solid particles that enter the atmosphere through natural and man-made processes. Time series trends and correlations of aerosol optical depth and cloud parameters over Addis Ababa have become the subject of scientific research because of the importance of clouds in controlling climate. In this study, we have examined the temporal variations (time series analysis) in aerosol particles over Addis Ababa and the impact of these variations on various optical properties of clouds using Moderate Resolution Imaging Spectroradiometer (MODIS) data from January 2003 to December 2018. The maximum aerosol optical depth (AOD) was found during the summer period, with minimum values in winter. During summer, the air temperature is high, which leads to abundant atmospheric water vapor, facilitating the hygroscopic growth of aerosols. We then analyzed the relationships between AOD and other cloud parameters, namely, water vapor (WV), cloud fraction (CF), cloud top temperature (CTT), and cloud top pressure (CTP), to provide a better understanding of aerosol–cloud interactions. The correlation between AOD and CF and WV was positive for Addis Ababa, while there was a negative correlation with CTT and CTP.