Search Results (29)

Background: Facial retaining ligaments are pivotal in maintaining facial structure and are increasingly recognized as critical components in modern facelift procedures. Their age-related laxity contributes to facial sagging, jowling, and volume descent, necessitating a detailed understanding of their anatomy and function to achieve natural and lasting aesthetic outcomes. Despite advances in technique, there remains an ongoing debate regarding whether surgical preservation or release of these ligaments yields superior results. Methods: This narrative review analyzes peer-reviewed literature on various facelift techniques, focusing specifically on how each approach manages retaining ligaments. Techniques assessed include subcutaneous, SMAS, deep plane, composite, subperiosteal, and extended SMAS rhytidectomies, as well as more recent methods such as the MACS lift and PRESTO facelift. Anatomical variations and their surgical implications were evaluated, alongside aesthetic outcomes, recovery profiles, and complication risks. Results: Ligament-releasing techniques, such as the deep plane and extended SMAS facelifts, allow for greater tissue mobilization, improved repositioning of midfacial and cervical tissues. Conversely, ligament-preserving techniques, such as the MACS and PRESTO lifts, offer safer, less invasive, though with more limited correction in severe laxity. The review emphasizes that variability in ligament anatomy requires a patient-specific surgical plan to optimize results. Conclusions: The management of retaining ligaments remains a cornerstone of facial rejuvenation strategies. Surgical success hinges on a tailored approach, balancing the need for comprehensive lift with the preservation of facial identity and anatomical safety. Further clinical research and advancements in imaging and surgical technology are needed to refine technique selection and enhance long-term outcomes. Full article

Few experimental studies have analyzed the wave energy conversion performance and underlying mechanisms of wave-driven profilers in controlled environments. Therefore, building on linear wave theory, Newton–Euler equations, and the working mechanisms of wave-driven profilers, this study has designed a crank mechanism-driven experimental tank facility. A comprehensive dynamic model of a wave-driven profiler has been established, and the impacts of wave height, wave period, and net buoyancy on the wave energy conversion performance of the wave-driven profiler and their underlying mechanisms have been analyzed. The results show that increased wave height enhances the buoy’s heave velocity, improving the dynamic performance of the wave-driven profiler by 441%. However, increased hydrodynamic resistance and mechanical collisions decreased the wave energy conversion efficiency by 57%. Longer wave periods reduce the wave excitation frequency, decreasing the buoy’s heave velocity; this results in a 35% reduction in the dynamic performance of the wave-driven profiler and a 53% decrease in wave energy conversion efficiency. During the descent phase, increased net buoyancy offsets more propulsive force, causing a 26% decrease in the wave-driven profiler’s dynamic performance yet increasing its energy conversion efficiency by 136%. This study provides a theoretical basis for optimizing the performance of similar wave-driven profilers. Full article

To meet the stringent reliability requirements of rotor blades in turbomachines, greater effort should be devoted to improving both aerodynamic and structural performance in blade design. This paper introduces an aero-structural multi-disciplinary design optimization (MDO) method for compressor rotor blades using a discrete adjoint method and an equivalent stress model (ESM). The principles of the ESM are firstly introduced, and its accuracy in calculating equivalent stress is validated through comparison with a commercial program. Both the aerodynamic performance and the maximum equivalent stress (MES) are selected as optimization objectives. To modify the blade profile, the steepest descent optimization method is utilized, in which the necessary sensitivities of the cost function to the design parameters are calculated by solving the adjoint equations. Finally, the aero-structural MDO of a transonic compressor rotor, NASA Rotor 67, is conducted, and the Pareto solutions are obtained. The optimization results demonstrate that the adiabatic efficiency and the MES are competitive in improving multi-disciplinary performance. For most of the Pareto solutions, the MES can be considerably reduced with increased adiabatic efficiency. Full article

The efficient utilization of structural information in High-Range Resolution Profiles (HRRPs) is of great significance for improving recognition performance. This paper proposes a size estimation method based on L1-norm variable fractional-order gradient descent, which achieves size inversion in complex electromagnetic environments by establishing an HRRP projection model of ballistic targets. Specifically: First, through rigorous geometrical optics analysis, an analytical relationship model between the target’s projected size and actual size is established. Second, an error function under the L1-norm is constructed, and an adaptive order-adjusting fractional-order gradient descent method is employed for optimization, effectively overcoming the sensitivity to outliers inherent in traditional L2-norm methods. Finally, by introducing a dynamic order-switching mechanism, computational efficiency is improved while ensuring convergence accuracy. Experimental results show that at a measurement error of 0.4 m, the proposed method maintains excellent estimation performance with sensitivity to outliers reduced, and the actual size inversion error remains stable below 3.7%. Full article

Reducing emissions in the aviation industry remains a critical challenge for global low-carbon transition. Accurate fuel consumption prediction is essential to achieving emission reduction targets and advancing sustainable development in aviation. Aircraft fuel consumption is influenced by numerous complex factors during flight, resulting in significant nonlinear relationships between segment-specific variables and fuel usage. Traditional statistical and econometric models struggle to capture these relationships effectively. This article first focuses on the different characteristics of QAR data and uses the Adaptive Noise Ensemble Empirical Mode Decomposition (CEEMDAN) method to obtain more significant potential features of QAR data, solving the problems of mode aliasing and uneven mode gaps that may occur in traditional decomposition methods when processing non-stationary signals. Secondly, a dynamic multidimensional particle swarm optimization algorithm (DMPSO) was constructed using an adaptive adjustment dynamic change method of inertia weight and learning factor, which solved the problem of local extremum and low search accuracy in the solution space that PSO algorithm is prone to during the optimization process. Then, a DMPSO-LSTM aircraft fuel consumption model was established to achieve fuel consumption prediction for three flight segments: climb, cruise, and descent. The final proposed model was validated on real-world datasets, and the results showed that it outperformed other baseline models such as BP, RNN, PSO-LSTM, etc. Among the results, the climbing segment MAE index decreased by more than 40%, the RMSE index decreased by more than 38%, and the R² index increased by more than 6%, respectively. The MAE index of the cruise segment decreased by more than 40%, the RMSE index decreased by more than 40%, and the R² index increased by more than 5%, respectively. The MAE index of the descending segment decreased by more than 20%, the RMSE index decreased by more than 30%, and the R² index increased by more than 5%, respectively. The improved prediction accuracy can be used to implement multi-criteria optimization in flight operations: (1) by quantifying weight–fuel relationships, it supports payload–fuel tradeoff decisions; (2) enhanced phase-specific predictions allow optimized climb/cruise profile selections, balancing time and fuel use; and (3) precise consumption estimates facilitate optimal fuel-loading decisions, minimizing safety margins. The high-precision fuel consumption prediction framework proposed in this study provides actionable insights for airlines to optimize flight operations and design low-carbon route strategies, thereby accelerating the aviation industry’s transition toward net-zero emissions. Full article

This paper presents an effective simplified model to optimize the mission power management supply for hybrid-electric aircraft in the conceptual design phase. The main aim is to show that, by using simplified representations of the aircraft dynamics, it is possible to achieve reliable results and identify trends useful for early-stage design, avoiding the use of more expensive and advanced methods. This model has been integrated into a multidisciplinary design framework, where the mission analysis, based on a simplified point mass dynamic model, focuses on splitting the power supply between electric and thermal power throughout the flight. An optimization algorithm identifies the time profiles of the supplied power, thermal and electric, to minimize fuel consumption. The power supplied by the thermal engine, modeled as a time piecewise function, is a design variable; a parametric study on the number of intervals composing this function is performed. The framework is used to propose a generalized approach for hybrid-electric power management optimization during the conceptual design iterations. This study showed that, for regional hybrid-electric aircraft, dividing the airborne mission into climb, cruise and descent is sufficient to define the optimum power split supply profiles. This allows for the avoiding of finer mission discretization, or the adoption of more complex simulative models, providing a very efficient model. Full article

This paper introduces a methodology for structural mass optimization of High-Altitude Long Endurance (HALE) aircraft across a complete mission profile, tailored for use in preliminary design. A conceptual HALE vehicle and its mission profile are assumed for this study, which also evaluates the impact of risk-based design decisions on optimized mass. The research incorporates a coupled aeroelastic solver and a mass optimization algorithm based on classical laminate theory to construct a geometrically accurate spar model. A novel approach is proposed to minimize the spar mass of the aircraft throughout the mission profile. This algorithm is applied to a representative T-Tail HALE model to compare optimized mass between two mission profiles differing in turbulence exceedance levels during the ascent and descent mission stages, while maintaining the same design robustness for on-station operation. Sample numerical results reveal a 10.9% reduction in structural mass for the mission profile with lower turbulence robustness design criteria applied for ascent and descent mission phases. The significant mass savings revealed in the optimization framework allow for a trade-off analysis between robustness to turbulence impacts and critical HALE platform parameters such as empty weight. The reduced empty vehicle weight, while beneficial to vehicle performance metrics, may be realized but comes with the added safety of flight risk unless turbulent conditions can be avoided during ascent and descent through risk mitigation strategies employed by operators. The optimization framework developed can be incorporated into system engineering tools that evaluate mission effectiveness, vehicle performance, vehicle risk of loss, and system availability over a desired operating area subject to environmental conditions. Full article

This study introduces an innovative, non-contact method for classifying the hardness of austenitic stainless steels (grade AISI 304) based on their intrinsic magnetic fields. Utilizing a 3 × 3 matrix sensor system, this research captures weak magnetic fields to produce precise 2D magnetic field maps of the samples. A key advancement is the application of a modified GoogleNet convolutional neural network, optimized with the stochastic gradient descent with momentum algorithm, which achieves exceptional classification accuracy, ranging from 95% to 100%, and median accuracies of 97.5% to 99%. This method stands out by revealing a novel correlation between annealing temperature and magnetic field strength, particularly a pronounced decline in magnetic properties at temperatures near 1000 °C. This observation underscores the sensitivity of magnetic profiles to heat treatments, offering a groundbreaking approach to material characterization. By enabling reliable, efficient, and fully automated hardness evaluation based on magnetic signatures, this work has the potential to transform materials engineering and manufacturing, setting a new benchmark for non-destructive material analysis techniques. Full article

To address the technical challenges of implementing Continuous Descent Operations (CDO) in high-traffic-density terminal control areas, we propose a cooperative low-carbon trajectory planning method for multiple arriving aircraft. Firstly, this study analyzes the CDO phases of aircraft in the terminal area, establishes a multi-phase optimal control model for the vertical profile, and introduces a novel vertical profile optimization method for CDO based on a genetic algorithm. Secondly, to tackle the challenges of CDO in busy terminal areas, a T-shaped arrival route structure is designed to provide alternative paths and to generate a set of four-dimensional (4D) alternative trajectories. A Mixed Integer Programming (MIP) model is constructed for the 4D trajectory planning of multiple aircraft, aiming to maximize the efficiency of arrival traffic flow while considering conflict constraints. The complex constrained MIP problem is transformed into an unconstrained problem using a penalty function method. Finally, experiments were conducted to evaluate the implementation of CDO in busy terminal areas. The results show that, compared to actual operations, the proposed optimization model significantly reduces the total aircraft operating time, fuel consumption, CO₂ emissions, SO₂ emissions, and NO_x emissions. Specifically, with the optimization objective of minimizing total cost, the proposed method reduces the total operation time by 22.4%; fuel consumption, CO₂ emissions, SO₂ emissions by 22.9%, and NO_x emissions by 23.7%. The method proposed in this paper not only produces efficient aircraft sequencing results, but also provides a feasible low-carbon trajectory for achieving optimal sequencing. Full article

A comprehensive hydrogeological, geophysical, and hydrochemical investigation was conducted in southeastern Hitchcock County, Nebraska, within the Driftwood Creek alluvial aquifer. This study assessed groundwater contamination stemming from the surface disposal of saline wastes from oilfield activities. A contaminated area, initially identified through regional groundwater sampling, was examined in detail. Monitoring wells were installed, and groundwater and soil samples were collected for chemical analysis. Surface electrical resistivity surveys were also performed to delineate contamination patterns. The findings revealed that the groundwater contamination originated from the leaching of residual evaporative salts through the vadose zone, beneath an abandoned emergency-evaporation brine storage pit. Data from down-hole specific conductance logs, water quality analyses, and computer-generated interpretations of surface electrical resistivity indicated that contaminant migration was primarily influenced by gravity, bedrock topography, and the local hydraulic gradient. An initial surface electrical resistivity profile survey was conducted to optimize the placement of monitoring wells and soil sampling sites within the vadose zone. Following well installation, a contaminant source with complex brine contamination patterns was detected within the shallow aquifer. Vertical electrical soundings were then carried out as the final investigative step. The data from these soundings, combined with test hole records, water level measurements, brine contaminant distribution, and soil analyses, were refined through a computer program employing the method of steepest descent. By incorporating known layer thicknesses and resistivities as constraints, this approach minimized the common issue of non-unique electrical sounding interpretations, providing information on the distribution of brine contaminants within the alluvial aquifer. Full article

Introduction: Fertile soil has a balanced pH and nutrient profile (potassium, phosphorus, and nitrogen), water retention capability, and organic substances. Fertile soil allows for better plant growth, leading to better production. The soil fertility requirements vary from crop to crop. So, it is essential to identify the soil fertility level according to the crop type. Objective: The objective of this paper is to develop a robust model that is capable of predicting the soil fertility. The model is integrated with IoT-generated data and federated learning-based feature selection techniques to improve the accuracy of the dataset. Materials/Methods: Different feature selection techniques were applied to the dataset. Then, we applied machine learning algorithms such as logistic regression, decision tree, and naïve Bayes, as well as their combinations to analyze and improve the performance. The federated learning approach was implemented to train the local models using the individual partitioned datasets. Each local model of the client shared the cryptic output weight and bias without sharing the raw data. There was a centralized model at the server end that collected these weights and biases, preserving data privacy. These collected data were aggregated and applied to find the least square error (LSE). Then, a gradient descent curve (GDC) was applied to identify the optimized weight and bias, which were fed back again to improve the accuracy of the predictions. Result: From our experimental observations, we analyzed the performance metrics of different ML classifiers, and it was revealed that the ensemble of logistic regression and decision tree had a better performance than the other models. One of our client models generates weight and bias with a precision of 87%, an accuracy of 87%, a recall of 87%, and an F1-score of 86%. Further, we collected two of our client system model outcomes from a server model and applied the LSE to identify the optimal W and B. In future work, we wll improve the performance of our model with a recursive approach by verifying the W and B at the client model in a feedback process. Full article

Dimensional and profile measurements of deep-hole parts are key processes both in manufacturing and product lifecycle management. Due to the particularity of the space conditions of deep-hole parts, the existing measurement instruments and methods exhibit some limitations. Based on the multi-axis, highly precise servo drive system, a novel measuring device is developed. The laser displacement sensors are fed by the flux-switching permanent magnet linear motor, and the part is rotated by the servo motor. On this basis, the assessment methods of roundness, straightness, and cylindricity are proposed by employing the least square method (LSM). Additionally, considering the axial center deviation between the sensors and the part, the rotating center coordinate is optimized by the gradient descent algorithm (GDM). Then, the measurement system is constructed and the experiment study is conducted. The results indicate favorable evaluation error of the LSM fitting and GDM iteration. Compared with the coordinate measuring machine (CMM), the measured results show good consistency. In the error analysis, the angle positioning error of measured point is less than 0.01°, and the axial positioning error is less than 0.05 mm. The proposed system and assessment method are regarded as a feasible and promising solution for deep-hole part measurements. Full article

Benchmarking different optimization algorithms is tasky, particularly for network-based cellular communication systems. The design and management process of these systems involves many stochastic variables and complex design parameters that demand an unbiased estimation and analysis. Though several optimization algorithms exist for different parametric modeling and tuning, an in-depth evaluation of their functional performance has not been adequately addressed, especially for cellular communication systems. Firstly, in this paper, nine key numerical and global optimization algorithms, comprising Gauss–Newton (GN), gradient descent (GD), Genetic Algorithm (GA), Levenberg–Marguardt (LM), Quasi-Newton (QN), Trust-Region–Dog-Leg (TR), pattern search (PAS), Simulated Annealing (SA), and particle swam (PS), have been benchmarked against measured data. The experimental data were taken from different radio signal propagation terrains around four eNodeB cells. In order to assist the radio frequency (RF) engineer in selecting the most suitable optimization method for the parametric model tuning, three-fold benchmarking criteria comprising the Accuracy Profile Benchmark (APB), Function Evaluation Benchmark (FEB), and Execution Speed Benchmark (ESB) were employed. The APB and FEB were quantitatively compared against the measured data for fair benchmarking. By leveraging the APB performance criteria, the QN achieved the best results with the preferred values of 98.34, 97.31, 97.44, and 96.65% in locations 1–4. The GD attained the worst performance with the lowest APE values of 98.25, 95.45, 96.10, and 95.70 in the tested locations. In terms of objective function values and their evaluation count, the QN algorithm shows the fewest function counts of 44, 44, 56, and 44, and the lowest objective values of 80.85, 37.77, 54.69, and 41.24, thus attaining the best optimization algorithm results across the study locations. The worst performance was attained by the GD with objective values of 86.45, 39.58, 76.66, and 54.27, respectively. Though the objective values achieved with global optimization methods, PAS, GA, PS, and SA, are relatively small compared to the QN, their function evaluation counts are high. The PAS, GA, PS, and SA recorded 1367, 2550, 3450, and 2818 function evaluation counts, which are relatively high. Overall, the QN algorithm achieves the best optimization, and it can serve as a reference for RF engineers in selecting suitable optimization methods for propagation modeling and parametric tuning. Full article

The most important advantage of conjugate gradient methods (CGs) is that these methods have low memory requirements and convergence speed. This paper contains two main parts that deal with two application problems, as follows. In the first part, three new parameters of the CG methods are designed and then combined by employing a convex combination. The search direction is a four-term hybrid form for modified classical CG methods with some newly proposed parameters. The result of this hybridization is the acquisition of a newly developed hybrid CGCG method containing four terms. The proposed CGCG has sufficient descent properties. The convergence analysis of the proposed method is considered under some reasonable conditions. A numerical investigation is carried out for an unconstrained optimization problem. The comparison between the newly suggested algorithm (CGCG) and five other classical CG algorithms shows that the new method is competitive with and in all statuses superior to the five methods in terms of efficiency reliability and effectiveness in solving large-scale, unconstrained optimization problems. The second main part of this paper discusses the image restoration problem. By using the adaptive median filter method, the noise in an image is detected, and then the corrupted pixels of the image are restored by using a new family of modified hybrid CG methods. This new family has four terms: the first is the negative gradient; the second one consists of either the HS-CG method or the HZ-CG method; and the third and fourth terms are taken from our proposed CGCG method. Additionally, a change in the size of the filter window plays a key role in improving the performance of this family of CG methods, according to the noise level. Four famous images (test problems) are used to examine the performance of the new family of modified hybrid CG methods. The outstanding clearness of the restored images indicates that the new family of modified hybrid CG methods has reliable efficiency and effectiveness in dealing with image restoration problems. Full article

The influence of neutrosophy on many fields of science and technology, as well as its numerous applications, are evident. Our motivation is to apply neutrosophy for the first time in order to improve methods for solving unconstrained optimization. Particularly, in this research, we propose and investigate an improvement of line search methods for solving unconstrained nonlinear optimization models. The improvement is based on the application of symmetry involved in neutrosophic logic in determining appropriate step size for the class of descent direction methods. Theoretical analysis is performed to show the convergence of proposed iterations under the same conditions as for the related standard iterations. Mutual comparison and analysis of generated numerical results reveal better behavior of the suggested iterations compared with analogous available iterations considering the Dolan and Moré performance profiles and statistical ranking. Statistical comparison also reveals advantages of the neutrosophic improvements of the considered line search optimization methods. Full article