Actual Ones Research Articles

Enhanced TomoSAR imaging method with complex least squares for 3D building reconstruction

ABSTRACT Synthetic aperture radar tomography (TomoSAR) has emerged as an advanced technology for the rapid acquisition of three-dimensional information, with model solutions dependent on a specific complex least squares adjustment criterion. We design two TomoSAR imaging algorithms based on two adjustment criterions minimizing the square sum of the real and imaginary components of observation vector residual, and minimizing the square sum of the L2-norm of observation vector residual. Additionally, we propose a strong scatterer recognition method that combines amplitude thresholding and density-based spatial clustering of applications with noise to alleviate the low efficiency and excessive noise in real images. The simulated and practical experiments are conducted to evaluate the performance of algorithms. Results suggested that two algorithms perform similarly in parameter estimation accuracy and noise immunity for buildings with simple structures and single scatterer. However, for buildings with multiple scatterers, the algorithm derived by minimizing the square sum of the L2-norm from the observation vector residual exhibits more robustly than the other algorithm, accurately distinguishing multiple scatterers within pixels and reconstructing 3D scenes mirroring the actual ones. The results verify the effectiveness of the proposed strong scatterer recognition method and the importance of the adjustment criterion in TomoSAR imaging.

Tracer-Separator: A Deep Learning Model for Brain PET Dual-Tracer (18F-FDG and Amyloid) Separation.

Multiplexed PET imaging revolutionized clinical decision-making by simultaneously capturing various radiotracer data in a single scan, enhancing diagnostic accuracy and patient comfort. Through a transformer-based deep learning, this study underscores the potential of advanced imaging techniques to streamline diagnosis and improve patient outcomes. The research cohort consisted of 120 patients spanning from cognitively unimpaired individuals to those with mild cognitive impairment, dementia, and other mental disorders. Patients underwent various imaging assessments, including 3D T1-weighted MRI, amyloid PET scans using either 18F-florbetapir (FBP) or 18F-flutemetamol (FMM), and 18F-FDG PET. Summed images of FMM/FBP and FDG were used as proxy for simultaneous scanning of 2 different tracers. A SwinUNETR model, a convolution-free transformer architecture, was trained for image translation. The model was trained using mean square error loss function and 5-fold cross-validation. Visual evaluation involved assessing image similarity and amyloid status, comparing synthesized images with actual ones. Statistical analysis was conducted to determine the significance of differences. Visual inspection of synthesized images revealed remarkable similarity to reference images across various clinical statuses. The mean centiloid bias for dementia, mild cognitive impairment, and healthy control subjects and for FBP tracers is 15.70 ± 29.78, 0.35 ± 33.68, and 6.52 ± 25.19, respectively, whereas for FMM, it is -6.85 ± 25.02, 4.23 ± 23.78, and 5.71 ± 21.72, respectively. Clinical evaluation by 2 readers further confirmed the model's efficiency, with 97 FBP/FMM and 63 FDG synthesized images (from 120 subjects) found similar to ground truth diagnoses (rank 3), whereas 3 FBP/FMM and 15 FDG synthesized images were considered nonsimilar (rank 1). Promising sensitivity, specificity, and accuracy were achieved in amyloid status assessment based on synthesized images, with an average sensitivity of 95 ± 2.5, specificity of 72.5 ± 12.5, and accuracy of 87.5 ± 2.5. Error distribution analyses provided valuable insights into error levels across brain regions, with most falling between -0.1 and +0.2 SUV ratio. Correlation analyses demonstrated strong associations between actual and synthesized images, particularly for FMM images (FBP: Y = 0.72X + 20.95, R2 = 0.54; FMM: Y = 0.65X + 22.77, R2 = 0.77). This study demonstrated the potential of a novel convolution-free transformer architecture, SwinUNETR, for synthesizing realistic FDG and FBP/FMM images from summation scans mimicking simultaneous dual-tracer imaging.

Impact of ageostrophic dynamics on the predictability of Lagrangian trajectories in surface-ocean turbulence

Turbulent flows at the surface of the ocean deviate from geostrophic equilibrium on scales smaller than about 10 km. These scales are associated with important vertical transport of active and passive tracers, and should play a prominent role in the heat transport at climate scales and for plankton dynamics. Measuring velocity fields on such small scales is notoriously difficult but new, high-resolution satellite altimetry is starting to reveal them. However, the satellite-derived velocities essentially represent the geostrophic flow component, and the impact of unresolved ageostrophic motions on particle dispersion needs to be understood to properly characterize transport properties. Here, we investigate ocean fine-scale turbulence using a model that represents some of the processes due to ageostrophic dynamics. We take a Lagrangian approach and focus on the predictability of the particle dynamics, comparing trajectories advected by either the full flow or by its geostrophic component only. Our results indicate that, over long times, relative dispersion is marginally affected after filtering out the ageostrophic component. Nevertheless, advection by the geostrophic-only flow leads to an overestimation of the typical pair-separation rate, and to a bias on trajectories (in terms of displacement from the actual ones), whose importance grows with the Rossby number. We further explore the intensity of the transient particle clustering induced by ageostrophic motions and find that it can be significant, even for small flow compressibility. Indeed, we show that clustering is here due to the interplay between compressibility and persistent flow structures that trap particles, enhancing their aggregation. Published by the American Physical Society 2024

Optimizing Underground Coal Mine Safety: Leveraging Advanced Computational Algorithms for Roof Fall Rate Prediction and Risk Mitigation

AbstractThe utilization and consumption of coal in various nations have emphasized the pivotal role played by coal mines. However, aside from the substantial contribution of coal mines, miners, engineers, and craftsmen in this industry have long been exposed to numerous risks and financial losses resulting from roof collapses in underground coal mines. Hence, due to the heightened sensitivity surrounding this issue, the accurate and low-error forecasting and assessment of the roof fall rate (RFR) are deemed crucial and of utmost importance. Nonetheless, due to the intricate and uncertain inherent characteristics of the rock formations, assessing the RFR has encountered multiple challenges that cannot be precisely approximated through traditional methods. In this paper, algorithms such as the harmony search algorithm (HS) and the invasive weed Optimization algorithm (IWO) are harnessed to address the aforementioned challenges. To model the RFR, a total of 109 data points were used, incorporating input parameters such as primary roof support (PRSUP), depth of cover (D), coal mine roof rating (CMRR), mine height (MH), and intersection diagonal span (IS). For effective data analysis and model development, the dataset was split into two separate groups: one for training and the other for testing. Specifically, 80% of the data was used to build the model, while the remaining 20% was allocated for model evaluation and validation. Based on the outcomes of three statistical metrics R2, MSE, and RMSE, it is evident that the deployment of HS and IWO algorithms demonstrates high performance, with predicted values closely aligning with actual ones. Consequently, the utilization of intelligent algorithms in the field of rock engineering is positioned as a potent tool for researchers and engineers. In conclusion, a sensitivity analysis is carried out with the help of the @RISK software as a means of ranking the influence that the input parameters have on the output of the model. Its results indicate that among different parameters, the CMRR parameter with a sensitivity degree of 0.11 has the most impact on the model, even with the smallest change in this parameter, a significant change is made in the model output.

Raw Material Cost Prediction Planning and Ready-Mix Product Sales Using Adaptive Linear Neural Network Method

An important thing that needs to be considered to ensure the successful implementation of the project is planning. The current problem with RMC is the non-achievement of the target, which causes the company's condition to be not optimal. Stable production cost projections on construction projects have been a problem and a serious concern, especially for project contractors. Often, there is a deviation between the projected initial costs and the actual ones at the construction site. Production predictions need to be made to determine the accuracy of these needs. This study aims to predict the needs of production and raw materials in an industry and can accommodate the calculation of estimated profits. Prediction of an industry's production and raw material needs can be used as a reference in calculating profit estimates. The ADALINE method is a neural system consisting of several nodes. A node requires several input processes to produce one output, which can be used as a predictive model. The results show that the actual and simulation data results using the ADALINE model have 100% conformity. This shows that the ADALINE model on the neural network can also function as one of the prediction model choices in addition to performing a prediction function. Prediction results using neural networks are one of the alternative solutions for utilizing prediction models other than other prediction models.

Body dissatisfaction and associated factors in normal weight adolescents

BACKGROUND: Body dissatisfaction is widespread and is associated with eating disorders, anxiety, depression, low self-esteem. There is very little research on body dissatisfaction in normal weight adolescents, who make up the majority of all adolescents. AIM: to establish the prevalence, degree, direction and factors associated with body dissatisfaction among adolescents with normal body weight. MATERIALS AND METHODS: The study included tenth-graders of general education schools (n = 244, 64.3% girls), who underwent anthropometry and filled out questionnaires that included socio-demographic and lifestyle factors. Collins scales were used to assess body dissatisfaction, and the Russian version of the PedsQL 4.0 questionnaire (Lyon, France), was used to assess health-related quality of life. Self-esteem scores were obtained from the Rosenberg self-esteem scale. RESULTS: The desire to have silhouettes different from the actual ones was reported by 57% of adolescents. 39.8% of adolescents had mild body dissatisfaction, moderate and severe — 17.2%. The frequency and degree of body dissatisfaction was comparable in adolescents of both sexes. Boys were much more likely than girls to consider themselves too thin and wanted to gain weight (49.4%), while 51.6% of schoolgirls would like to lose weight. Body dissatisfaction, even mild, was associated with significantly lower self-esteem. Moreover moderate/severe body dissatisfaction has been associated with a decline in all aspects of quality of life. In addition, significant associations of body dissatisfaction and the length of time on the Internet / social networks and at TV screens were established. CONCLUSIONS: The results of the study substantiate the approach to body dissatisfaction as a problem that requires active identification and directed impact, and determine possible areas of preventive work with adolescents.

Hybrid soil strength prediction model for geotechnical ground investigation using convolutional neural network and ensemble learning

Abstract A hybrid model of soil strength prediction from the image data and feature data of soil was proposed, which is aimed at providing an efficient solution in geotechnical ground investigation by leveraging the power of AI. In this model, CNN purely with the encoder was used to establish the relationship between image data and strength parameters of soil, while ensemble learning stacked of three base learners, i.e., KNN, LASSO, and LSVM, was applied to the feature data. Then, the CNN part and the ensemble learning part were integrated into the loss function. Results show that the convergence of the hybrid model was slower than that of the ensemble learner. However, with the aid of soil image which has more features of soil in essence, the predicted soil strengths by the hybrid model better matched with the actual ones, compared with the ensemble learner and base learners. This hybrid model offers an effective framework of multi-modal data fusion for geotechnical engineering, by leveraging the synergy of the high-dimensional feature extraction capabilities of CNN and the generalization abilities of ensemble learning.

Valid and accurate simple equation to predict 3,000-m steeplechase performance.

Predict running performances is very important for athletes and trainers. Sport researchers have therefore developed certain tools to predict running performances, but only in non-obstacle races. This study aimed to develop and test the validity and accuracy of an equation for predicting 3,000-m steeplechase performance (PerfSteeple ). The official rankings of French runners for the 3,000-m track-running (Perf3000 ) and 3,000-m steeplechase events were examined. Age, height and body mass were collected. From 146 included athletes, two groups were randomly composed: one comprising 80% of the sample (n = 117) to develop a simple equation to predict PerfSteeple (i.e., development group) and the other comprising the remaining 20% (n = 29) to test the validity and accuracy of the developed prediction equation (i.e., cross-validation group). The simple prediction equation included Perf3000 and age: . No significant difference was noted between the actual and predicted performances. Predicted performances were significantly correlated with the actual ones, with a very high correlation coefficient (p < 0.001; r = 0.929). Bias and 95% limits of agreement were -5 ± 24 s, i.e., -0.8 ± 7.6%. In 95 of 100 new predictions, the difference between actual and predicted performance would be less or equal to-5 ± 24 s. The study confirms the validity and accuracy of the equation for predicting PerfSteeple . Predictions using this simple equation may be used in training and competitions for athletes and coaches. PerfSteeple = -57,165 + 1,147 X Perf3000 + 0,955 X age.

Stick-slip vibration analysis of a coupling drilling-rock system with two delays and one delay-dependent coefficient

Drilling depths exceeding 10,000 m have recently been achieved, but the increasing depth poses challenges due to the flexible drill-string, making it more susceptible to stick-slip vibration. This vibration can lead to accidents such as excessive bit wear and tool falling into the well. Previous studies on modeling polycrystalline diamond compact (PDC) bits have focused on symmetrically distributed complete blade bits, which are not commonly used. To address this gap, this study introduces PDC bits that closely resemble the actual ones, with a structural configuration of both complete blades and part-blades symmetrically distributed. We also consider the impact of mud and rock chips on the drill bit by introducing a delay-dependent coefficient of response damping change. This study develops a stick-slip vibration model for a coupling drilling-rock system with two state-dependent delays tn1 and tn2, and one delay-dependent coefficient. Using the crossing curve method and numerical studies, we obtain different types of crossing curves reflecting the stability switching characteristics of the system in the dimensionless delay τ2τ1 space. During stick-slip vibration, τ1 mutates from 1.69 s to 2.51 s, and τ2 mutates from 1.3 s to 2.16 s, both within the stable region of the crossing curve. The critical steady state and the stable state typically cross the stable and unstable regions of the crossing curve in their respective times. Increasing the torsional damping within the range of 0.04 to 0.08 can accelerate system stabilization or delay the arrival of the critical steady state. Moreover, increasing the drive rotation speed within the range of 4 to 6 eliminates stick-slip vibration and accelerates the system towards a steady state. In conclusion, this study predicts the drill-string dynamics of a symmetrically distributed PDC bit with complete blades and part-blades. The insights gained can help minimize stick-slip vibration and provide recommendations for drilling operation parameters.

Terpolymers of alkyl methacrylate-trans anethole-1,2,3,6-tetrahydrophthalic anhydride copolymers: a low dosage and high-efficiency cold flow improver for diesel fuel

The addition of cold flow improvers (CFIs) is considered as the optimum strategy to improve the cold flow properties (CFPs) of diesel fuels, but this strategy is always limited by the required large dosage. To obtain low-dosage and high-efficiency CFIs for diesel, 1,2,3,6-tetrahydrophthalic anhydride (THPA) was introduced as a third and polar monomer to enhance the depressive effects of alkyl methacrylate-trans anethole copolymers (C14MC-TA). The terpolymers of alkyl methacrylate-trans anethole-1,2,3,6-tetrahydrophthalic anhydride (C14MC-TA-THPA) were synthesized and compared with the binary copolymers of C14MC-TA and alkyl methacrylate-1,2,3,6-tetrahydrophthalic anhydride (C14MC-THPA). Results showed that C14MC -THPA achieved the best depressive effects on the cold filter plugging point (CFPP) and solid point (SP) by 11°C and 16°C at a dosage of 1250 mg/L and monomer ratio of 6:1, while 1500 mg/L C14MC-TA (1:1) reached the optimal depressive effects on the CFPP and SP by12°C and 18°C. THPA introduction significantly improved the depressive effects of C14MC-TA. Lower dosages of C14MC-TA-THPA in diesel exerted better improvement effects on the CFPP and SP than that of C14MC-TA and C14MC-THPA. When the monomer ratio and dosage were 6:0.6:0.4 and 1000 mg/L, the improvement effect of C14MC-TA-THPA on diesel reached the optimum level, and the CFPP and SP were reduced by 13°C and 19°C, respectively. A 3D nonlinear surface diagram fitted by a mathematical model was also used for the first time to better understand the relationships of monomer ratios, dosages, and depressive effects of CFIs in diesel. Surface analysis results showed that C14MC-TA-THPA achieved the optimum depressive effects at a monomer ratio of 6:0.66:0.34 and dosage of 1000 mg/L, and the CFPP and SP decreased by 14°C and 19°C, respectively. The predicted results were consistent with the actual ones. Additionally, the improvement mechanism of these copolymers in diesel was also explored.

Pont’s Index, is it Reliable and Applicable for Iraqis?

To facilitate the correct diagnosis and treatment planning, a range of diagnostic orthodontic indices have been developed. Pont’s index, as one of them, was documented to estimate the ideal maxillary dental arch widths from the sum of crown widths of maxillary incisors. This retrospective study aimed to evaluate the applicability of Pont’s index to Iraqi individuals. Fifty stone dental models for 25 females and 25 males were retrieved from the archives of the Department of Orthodontics in Baghdad College of Dentistry. The dental arch widths at the level of 1st premolar and molar areas in addition to the mesiodistal crown widths of all teeth were measured using an electronic vernier. The estimated arch widths were compared to the actual ones using paired sample t-test. The results showed highly significant differences between the actual and estimated maxillary inter-premolar and intermolar widths with over-estimated values in both genders. New formulas were developed to make Pont's index more reliable, and the newly estimated widths were non-significantly different from the actual one. The original Pont’s index is an unreliable and inapplicable method for arch width prediction. A specific modification of Pont’s index is developed and can be applied for more reliable results.

Analyzing effective external interventions for optimizing energy hubs with electric and TS: A numerical study from a network topology perspective

Currently, there has been a notable surge in sabotage targeting critical infrastructures, particularly energy hub systems. External interventions on these infrastructures have significantly affected various loads, including electrical and thermal loads. Considering the significance of cybersecurity in energy hubs, there are high expectations for investigating the role of topology in their measures. The aim of this study is to investigate the impact of cyber sabotage on energy management within the demand sector, with a specific emphasis on the topology of an energy hub system. According to the structure of the desired energy hub that has been mathematically modeled, the CPLEX solver is used as a powerful optimization tool with the mixed integer linear programming (MILP) model, which serves as a framework for optimizing the operation cost of the energy hub. The findings reveal the occurrence of false data injection (FDI) into the real network loads. This inconsistency has led to disruption of energy management in the system. Utilizing considered scenarios, the first of them as the base scenario, representing the system under normal network conditions. The second scenario is modeled an attack vector aimed at the electric load, specifically utilizing FDI tactics with an understanding of the network topology. In the third scenario, attention turns to modeling a thermal load attack vector, utilizing FDI tactics with an understanding of its topology. In the final scenario, the emphasis is placed on designing an FDI-based attack vector that targets electric loads within the network, without prior knowledge of the energy hub's topology. Although in all scenarios where the attack is executed, the measuring devices during peak load indicate values lower than the actual ones, the intensity of the attack on the system is considerable in attacks conducted with knowledge of the energy hub topology. Taking into account the importance of the peak load intervals, the maximum changes in electric load during peak hours in the second scenario are compared to those in the first scenario, attributing them to interventions, representing approximately a 17.4 % difference. Similarly, assuming knowledge of the network topology during peak consumption, the impact of the attack on the thermal loads results in changes of 17.17 %. Additionally, in the fourth scenario compared to the first, the maximum changes in electric load during peak hours are approximately 2 %. In the meantime, the optimal cost has remained consistent across all scenarios, rendering the attacks undetectable to the operator and ultimately demonstrating the effectiveness of the proposed method.

Synthesis, thermal X-ray, neutron diffraction, Mössbauer spectroscopy and magnetic properties investigation of Al doped barium hexaferrite BaFe11AlO19

Polycrystalline aluminum substituted barium hexaferrite BaFe11AlO19 synthesized by the ceramic technique. The results of energy dispersive X-ray analysis, thermal X-ray diffraction (XRD), neutron diffraction, Mössbauer spectroscopy, magnet properties measurements were measured. XRD analysis demonstrated P63/mmc hexagonal structure of BaFe11AlO19. The magnetic sublattice of barium hexaferrite BaFe11AlO19 was obtained using neutron diffraction data. The distribution of aluminum in barium hexaferrite matrix at positions 2a and 12k was obtained with Mössbauer spectroscopy. A comparison of the calculated intensities of the neutronograms and the actual ones allowed us to establish that in position 2a the aluminum ion occupied a position with the coordinate (1;1;0), and the position of the substituent ion in position 12k (0.83333; 0.66667; 0.10810). According to neutron diffraction data, it was found that the actual Curie temperature is lower than that measured by differential scanning calorimetry.

EXPLORING THE POTENTIAL FOR OPERATIONAL OVERSIGHT OF REMAINING RECOVERABLE RESERVES THROUGHOUT VARIOUS PHASES OF OIL PRODUCTION FACILITY DEVELOPMENT

Assessing oil recovery rates through geological and hydrodynamic modeling incurs significant financial and time investments. Consequently, recalculations of recoverable hydrocarbon reserves occur infrequently, leading to potential discrepancies between approved factors and actual conditions. To ensure accuracy in assessing residual recoverable reserves, it's crucial to promptly monitor the validity of oil recovery factors, especially concerning their achievability within specific timeframes. This article examines the practice of annually assessing geological and economic reserves according to international standards. Operational monitoring methods include analog-statistical techniques, displacement characteristics, and production decline rate analysis, each reliant on initial data quality and production object development stages. The effectiveness of these methods is illustrated through specific examples. Analog-statistical dependencies, tailored to operational facilities' geological indicators, prove most effective for early-stage development. In advanced stages, methods based on displacement characteristics and production decline rates gain reliability, factoring in economic development profitability limits. Statistical dependencies incorporating technological indicators can enhance control measures. Integrating diverse methodological approaches ensures a more dependable assessment of production object residual recoverable reserves. When determining residual recoverable reserves and designing the development of oil fields, the main characteristic that determines the reliability of forecast calculations is the accuracy of estimating the oil recovery factor. The value of the oil recovery factor characterizes the share of initial recoverable reserves that can be extracted from the subsoil from the initial geological reserves when developing a deposit using modern proven technology and production techniques to the limit of economic profitability while complying with the requirements of subsoil and environmental protection. The most common method for estimating the oil recovery factor is digital geological-hydrodynamic modeling, in which the processes occurring during the development of oil deposits are simulated using software. This method has been introduced in the Russian oil industry since the mid-90s. XX century, recommended for use by current regulations [1]. Based on digital geological and hydrodynamic modeling, various options for the development of oil deposits are calculated, differing in the number of production and injection wells, the rate of reserve withdrawal, etc. Examples of the effective use of digital geological and hydrodynamic modeling in designing the development of oil fields are given in works [2–4], in including for the territory of the Perm region [5–10]. The most economically feasible option for field development is approved by the authorized government bodies, and each production facility is characterized by a design oil recovery factor. There is no doubt that the construction of digital geological and hydrodynamic modeling in the presence of standard geological and technological data is the most reliable way to determine the oil recovery factor. However, this method requires very large financial and time costs, which leads to significant time intervals between calculations of the Oil Recovery Factor, which in most cases exceed 10 years. As a result, over time, the approved Oil Recovery Factor may significantly lose relevance both due to the discrepancy between the design development indicators and the actual ones, and due to changes in the economic conditions of development. Taking this into account, it is extremely important for production facilities to quickly monitor the validity of the Oil Recovery Factor, including its objective achievability in a specific time frame. Keywords: Remaining recoverable reserves, efficiency of oil extraction, operational facility, design of oil field development, evaluation of geological and economic reserves, displacement patterns, decline in oil production rate.

Convex distributed robust model predictive control for collision and obstacle avoidance

AbstractThe article proposes a convex distributed robust model predictive control algorithm for collision avoidance in multi‐agent systems with additive disturbances, which utilizes the concept of tube model predictive control to address the disturbances. To tackle the coupled collision avoidance constraints, each agent integrates assumed nominal position and input trajectories from its neighbors, rather than relying on actual ones. Compatibility constraints are then designed using the normal vector of the constructed separating hyperplanes for collision avoidance and the residual collision avoidance margin of the optimal solutions at the last time step to restrict deviations between assumed and actual trajectories and ensure consistency among the agents. The nonconvex collision and obstacle avoidance constraints are convexified using the concept of safe sets, transforming them into time‐varying closed polyhedral constraints while accounting for the impact of disturbances. Particularly, the residual collision avoidance margin is incorporated into the construction of safe sets for the satisfaction of coupled collision avoidance constraints. Consequently, the original collision avoidance optimal control problems can be efficiently and simultaneously solved for all agents using standard quadratic programming techniques. Under the design of local terminal constraint sets for each agent, the proposed algorithm ensures a rigorous analysis of robust constraint satisfaction for collision avoidance constraints, as well as an assessment of recursive feasibility and closed‐loop stability. The effectiveness of the algorithm is demonstrated through an illustrative example, and simulation results validate its ability to successfully achieve collision and obstacle avoidance even in the presence of disturbances.

Algorithms to create realistic virtual asphalt mixtures

This paper presents a set of algorithms for creating realistic three-dimensional (3D) models of asphalt mixtures based on its composition. The process begins by measuring the shape of the aggregates in the asphalt mixture. Using these measurements, an algorithm creates 3D volumes that approximate the size and shape of the aggregates. These volumes are then added to a virtual mixer in the same proportions as the aggregates in the real mixture and are pressed together until the air void content of the 3D model matches that of the real asphalt. This results in realistic models of aggregate skeletons in the asphalt mixture. Next, sections are taken from the aggregate skeletons and a mortar mixture, made up of bitumen, filler and fine aggregates, is applied to the sections. Geometrical rules are used to determine which areas of the sections are covered in mortar and which are air voids. The validation of virtual aggregates and air voids has demonstrated good correlations with actual ones. The resulting models are highly realistic and can be used to predict the mechanical or physical properties of asphalt mixtures in future studies.

Does a novel 3D printed individualized guiding template based on cutaneous fiducial markers contribute to accurate percutaneous insertion of pelvic screws? A preliminary phantom and cadaver study

BackgroundMost 3D-printed guiding templates require dissection of soft tissues to match the corresponding surfaces of the guiding templates. This study sought to explore the accuracy and acceptability of the novel 3D printed individualized guiding templates based on cutaneous fiducial markers in minimally invasive screw placement for pelvic fractures.MethodsThe printed template was tested on five high-fidelity biomimetic phantom models of the bony pelvis and its surrounding soft tissues as well as on two fresh frozen cadavers. Four cutaneous fiducial markers were transfixed on each phantom model prior to performing CT scans to reconstruct their 3D models. Personalized templates for guiding screw insertion were designed based on the positions of the fiducial markers and virtually planned target screw channels after scanning, followed by 3D printing of the guide. Phase 1 consisted of five expert surgeons inserting one anterograde supra-pubic screw and one sacroiliac screw percutaneously into each phantom model using the 3D-printed guide. The deviation of screw positions between the pre-operative planned and post-operative actual ones was measured after registering their 3D modelling. A Likert scale questionnaire was completed by the expert surgeons to assess their satisfaction and acceptability with the guiding template. Phase 2 consisted of repeating the same procedures on the fresh frozen cadavers in order to demonstrate face, content and concurrent validity.ResultsIn Phase 1, all ten screws were successfully implanted with the assistance of the guiding template. Postoperative CT scans confirmed that all screws were safely positioned within the bony pelvic channels without breaching the far cortex. The mean longitudinal deviation at the bony entry point and screw tip between the pre-operative planned and post-operative actual screw paths were 2.83 ± 0.60 mm and 3.12 ± 0.81 mm respectively, with a mean angular deviation of 1.25 ± 0.41°. Results from the Likert questionnaire indicated a high level of satisfaction for using the guiding template among surgeons. In Phase 2, results were similar to those in Phase 1.ConclusionsThe 3D-printed guiding template based on cutaneous fiducial markers shows potential for assisting in the accurate insertion of percutaneous screws in the pelvis.

Unmasking DeepFakes Using Machine Learning

Our task objectives to deal with the trouble of Deepfake videos, which can be inflicting fear because of their capacity to unfold fake records and manage human beings. Deepfake veideos appearance so actual that it`s tough to inform them other than actual ones, making them a severe danger. This sensible look can result in sizable damage .We are using Machine Learning Algorithms discover Deepfake videos. This entails the usage of era to differentiate among actual and manipulated content. By growing this, we lessen the effect of incorrect information and manipulation, safeguarding human beings from cappotential damage as a result of deceptive content. Keywords: Deepfake Detection, Machine Learning, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Facial Analysis, Anomaly Detection, Synthetic Media, Image and Video Analysis, Temporal and Spatial Patterns, Training Datasets, Real-time Detection, Visual Content Security

Долгосрочная оценка режима минимального зимнего стока рек бассейна озера Севан при изменении климата

Introduction. Analysis and assessment of the statistically significant consequences of climate change for the long-term minimum 30-day winter runoff of rivers in the Lake Sevan basin is of significant interest to consumers and water resource management, being an important hydrological issue. The relevance of consideration of the issue is due to the fact that for Armenia Lake Sevan is a strategic reservoir of fresh water, a large water management complex, a source of water for hydropower and agricultural irrigation, an object of tourism and recreation. Issues of formation and calculation of the minimum runoff of rivers in the lake basin. Sevan were discussed in works, the analysis of the spatial-temporal distribution of the winter minimum decade-day, monthly and 30-day runoff can be found, for example, in works.In contrast to the listed works, in this study a technique for assessing the statistically significant consequences of climate change for the hydrological regime in relation to small foothill watersheds of the rivers of the Lake Sevan basin (Republic of Armenia) is considered. The values of the minimum 30-day water runoff within the study area can be used when calculating standards for permissible discharges into rivers, to assess natural groundwater resources, as indicators of the extremeness of river flow and in other water management calculations. Research methods and materials. The methodology is based on a probabilistic model of the formation of long-term river runoff, which is approximated by a system of linear equations for the initial statistical moments. As the source material were used the data of actual hydrometeorological observations (minimum 30-day winter river runoff, air temperature and precipitation) of the “Center for Hydrometeorology and Monitoring” of the SNCO of the Ministry of Environment of the Republic of Armenia. Research results. The results of approbation of the methodology on retrospective material are presented, taking into account possible climate change both towards warming and towards cooling.The conditions for the applicability of the methodology to small foothill watersheds for assessing the minimum 30-water winter river runoff are identified. The obtained satisfactory results made it possible to assess the impact of climate change for the minimum runoff using the IPCC climate scenarios. Three climatic scenarios are considered, the model of the Max Planck Institute with a monthly time resolution. Conclusions. As a result, watersheds of rivers in the basin of Lake Sevan were identified, in which significant deviations of the predicted average annual values of the minimum 30-day winter runoff and the coefficients of its variation from the actual ones in the implementation of the considered scenarios can be expected. An analysis of the obtained results is given from the point of view of taking possible preventive measures, including strategic planning of the use of water resources in winter low water, in case of climate change.

Comparison of finite element modeling approaches for Ti6Al4V porous bone implants

Additive technologies have made it possible to make a significant breakthrough in the development and production of personalized porous implants, which have clear advantages over monolithic predecessors. At the same time, engineers are faced with a number of new tasks, one of which is correct and effective numerical modeling of porous implants in the process of solving optimization problems. The study compares approaches to numerical modeling of porous titanium for bone implants, taking into account differences in the mechanical parameters of the material and the geometry of the models. The detailed geometry of the structure of 9 models is considered, for which two variants of material parameters are specified: pure Ti6Al4V alloy and experimental ones obtained for specimens printed from Ti6Al4V powder. As a proposed approach to homogenizing the structure, a continuum model of a cylinder is considered, for which the experimental properties of the specimen’s material are specified. The numerically obtained values of Young's moduli for each approach are compared with those obtained in the experiment. It has been established that when setting the properties of a pure alloy, the calculated Young's modulus exceeds the experimental one by 83–92 %. The closest thing to experiment is detailed modeling of geometry with setting the experimental properties of the material structure, while the calculated Young's modulus does not exceed the experimental one by more than 10 %. However, in this case the calculation takes from 2 to 8 hours. In the case of continuum modeling, the calculated Young's moduli exceed the calculated moduli in detailed modeling by no more than 11 %, while the calculation lasts no more than 1 minute. Based on this, it is proposed to use a continuum approach to modeling implants in the process of calculating the optimization of their shape, as an initial iteration before detailed modeling of the structure. As material properties, it is necessary to use the mechanical parameters of the structures, previously obtained from uniaxial compression experiments. Using the properties of a pure alloy will lead to results that significantly exceed the actual ones when calculating the porous structure.