Modification Of Parameters Research Articles

Pretreatments to bonding on enamel and dentin disorders: a systematic review.

This systematic review focuses on structural anomalies of enamel and dentin such as fluorosis (F), molar-incisor hypomineralization (MIH), amelogenesis imperfecta (AI), dentinogenesis imperfecta (DI), osteogenesis imperfecta (OI), and X-linked hypophosphatemia (XLH). These pathologies affect up to 31% of the population, posing challenges in the adhesion of direct restorations. The primary objective of this analysis is to examine the survival rate and/or bonding resistance of direct restorations on tissues affected by enamel and dentin disorders in humans. We aim to provide precise clinical recommendations for dentists to choose the appropriate bonding pretreatment for various enamel and dentin disorders. We systematically searched the medical literature to identify abstracts of interest indexed between 1993 and May 2024, from 4 databases (PubMed, PMC-PubMed, Web of Science and Cochrane Library). The results are reported following the PRISMA statement. The GRADE approach was used to assess the risk of bias. The two authors included 27 studies out of the 600 identified. The data extracted from these studies are highly heterogeneous, and the mentioned bonding protocols are all different. 15 articles pertain to F, 7 to MIH, 5 to AI, and none focus on HSPM, dentin disorders and XLH. We identified 12 pretreatments of enamel or dentin (7 for F, 2 for MIH, and 3 for AI), including 3 strong recommendations. Additional studies are necessary to confirm the effectiveness of certain pretreatments. In developmental enamel defects, the main recommendation we collected is to bond on enamel with an etch-and-rinse technique on F and MIH. Considerations with lower strength of evidence include a deproteinization step on enamel on F, MIH and AI and modifications of the etching parameters on F and AI. The research strategy was registered on the Prospero platform (CRD42023447502).

Vibration control and robustness analysis of tensegrity structures via fuzzy dynamic sliding mode control method

This study addresses the problem of mitigating vibration responses in tensegrity structures subjected to complex dynamic loading conditions. The overall purpose is to develop an effective control method to enhance the stability and performance of these structures. To achieve this, a fuzzy dynamic sliding mode control (FDSMC) method is proposed. The methodology involves deriving the state space expression of the controlled system based on the tensegrity structure's dynamic model, followed by designing a dynamic sliding mode controller using the reaching law. Fuzzy rules are incorporated to adaptively adjust the dynamic sliding mode parameters, accounting for uncertainties in controller parameters, thereby ensuring high efficiency and smooth controller output. A comparative analysis scheme, focusing on identical energy input on actuators, is utilized to evaluate the performance of various active vibration control methods. Two illustrative examples, a spatial double-layer tensegrity beam and a complex tensegrity spiral tower, are investigated in detail. The findings demonstrate that the FDSMC method significantly reduces structural dynamic responses and improves algorithm robustness compared to the conventional linear quadratic regulator (LQR) method, indicating its promising potential for relevant engineering applications.

Internet of Healthcare Things Based Detection of EEG Epileptic Seizures: A Smart System

Background: A seizure is a sudden and uncontrolled electrical activity in the brain that can cause a variety of symptoms, depending on the location and severity of the abnormal activity. It can be a symptom of an underlying neurological disorder or can occur without an apparent cause. Epilepsy is one of the most common causes of seizures. Overactive electrical discharges disrupt normal brain electrical activity and interfere with nerve cell communication. Aim and Objective: A comprehensive analysis of the literature revealed that several CAD system designs have shown to be useful to radiologists in routine medical practice as second-opinion aids for epileptic seizure detection in circumstances where a clear differentiation cannot be formed subjectively. CAD systems are made to help radiologists by automating the examination of medical data and images, improving the efficiency and accuracy of diagnosis. These systems examine patterns in medical imaging using machine learning approaches, which can be quite helpful in spotting small abnormalities that the human eye can miss. Moreover, the objective of this study was to design a smart healthcare system using a combination of DWT, Hjorth, and statistical parameters for seizure detection. Method: In this research article, the authors proposed the framework of the Internet of Healthcare Things (IoHT) for performing seizure detection. The authors used different pre-processing techniques and extracted different features like Hjorth, wavelets, and statistics, which were classified using different machine-learning techniques. This novel methodology combines a number of technologies and techniques to improve seizure detection's precision and dependability. Results: DWT + Hjorth + Statistical parameters with bior 1.5 as the pre-processing technique yielding the best outcomes. 86% accuracy was obtained with kNN for k = 5, 93% accuracy was obtained with a linear kernel for an SVM classifier, and 95.5% accuracy was obtained using a decision tree and logistic regression. The authors also considered another dataset for validation and received 96.83% accuracy with decision tree and logistic regression classifiers considering the bior1.5 wavelet filter as a preprocessing technique. Conclusion: The IoHT framework offers a multi-modal, adaptive method of seizure detection that enables the dynamic modification of detection parameters and the incorporation of extra sensor signals. This improves seizure detection's precision and dependability, which has important implications for patient care and monitoring. This work shows how IoHT and machine learning can be combined to build a reliable, real-time seizure detection system. These developments, which make it possible for prompt interventions and individualized treatment plans, can significantly improve the quality of care for individuals with epilepsy.

Parameter optimisation using Bayesian inference for spallation models

The accuracy and precision of high-energy spallation models are key issues for the design and development of new applications and experiments. We present a method to estimate model parameters and associated uncertainties by leveraging the Bayesian version of the Generalised Least Squares method, which enables us to incorporate prior knowledge on the parameter values. This approach is designed to adjust parameters based on experimental data, accounting for experimental uncertainty information, and providing uncertainties for all adjusted parameters. This approach is designed in order both to improve the accuracy of models through the modification of free parameters of these models, which results in a better reproduction of experimental data, and to estimate the uncertainties of these parameters and, by extension, their impacts on the model output. We aim at demonstrating the Generalised Least Square method can be applied in the case of Monte Carlo models. We present a proof-of-concept for Monte Carlo models in the specific case of nuclear physics with the model combination INCL/ABLA. We discuss the challenges in the application of this method to high-energy spallation models, notably the large runtime and the stochasticity of the models. Our results indicate this framework can also be applied to analogous situations where parameters of a computationally expensive Monte Carlo code should be inferred/improved.

Modifi ed Fuzzy Controller with Optimization of Mode Parameters of Technological Process

Modifi ed Fuzzy Controller with Optimization of Mode Parameters of Technological Process

Enhancing Aerodynamic Performance of a Non-Axisymmetric Endwall Contoured Cascade Through Section Profiling Method

Abstract Endwall contouring and 3D blade configurations are effective techniques for enhancing turbomachinery performance. The integration of these technologies is an important area of investigation. To continue the numerical and experimental validations of the non-axisymmetric endwall contoured cascade (Du et al., 2024, “Endwall Contouring for Improving Aerodynamic Performance in a High-Pressure Turbine Cascade,” ASME J. Turbomach., 146(10), p. 101001), the present study redesigns the cascade via section profiling using optimization methods to maximize performance gains. To improve the control effect of endwall contouring, blade section parameters are strategically adjusted during optimization. The experimental flow field traverses at cascade exits demonstrate the control of the cascade flow field by the optimized design. The redesigned cascade exhibits decreased overturning near the shroud and reduced underturning near the hub, as well as reduced profile loss in the mainstream, compared to the endwall contoured cascade. The modification of section parameters enhances the control of losses and the secondary flow intensity. Computational fluid dynamics is used to provide a detailed analysis of the flow field, providing insights into how flow structures vary when the blade profile is modified. The numerical results further elucidate the effect of blade section profiling on both the performance characteristics of the cascade and the configuration of the secondary flows.

Shadow and quasinormal modes of rotating charged black holes in Kalb–Ramond gravity and implications from EHT data

The recent observations of the Event Horizon Telescope for the supermassive black holes M87* and Sgr A* can potentially test gravity theories and acquire further knowledge on black holes and gravity parameters. An essential field of research focuses on the shadow and quasinormal modes of black holes. Therefore, we used the Newman–Janis algorithm to get a rotating charged black hole solution in Kalb–Ramond gravity. Then, we looked at the event horizon radius, the static limit, and the ergoregion of spacetime. We derive the null geodesic equations and the effective potential for photon radial motion. We also investigate how the black hole’s Kalb–Ramond, spin, and charge parameters affect the shadow size and distortion. It is obtained that an increase in the Kalb–Ramond parameter causes a decrease in the radius and increases the distortion of the shadow size. We obtain constraints on black hole charge and spin using the data obtained from Event Horizon Telescope observations of M87* and Sgr A* by testing the effects of the Kalb–Ramond gravity parameter. We examine the energy emission rate using Hawking radiation and demonstrate that a rise in the Kalb–Ramond parameter diminishes this rate. Finally, we calculated and analyzed the typical shadow radius, equatorial, and polar quasinormal modes using the geometric–optic connection between quasinormal mode parameters and conserved quantities along geodesics.

Interaction of Hermite–Gaussian Beams with a Macroscopic Atomic Target

AbstractA theoretical analysis is presented on the photoexcitation of a macroscopic atomic target by a Hermite–Gaussian (HG) beam within the framework of density matrix theory. Special emphasis is paid to the influence of the incoming HG mode on the population of an excited state and the emitted fluorescence radiation. In particular, a general expression for the alignment parameter of the excited state is derived, which depends on the beam parameters of the HG mode. Although the developed theory can be applied to any atomic system, here the electric dipole transition in neutral sodium atoms is investigated when driven by three , and modes. For this optical (valence‐shell) excitation, it is demonstrated that the population of the excited state is sensitive to the beam waist and the mode index of the HG beam. Furthermore, the influence of beam parameters on the angular distribution and linear polarization of the emitted fluorescence radiation is discussed.

Teaching approach for deep reinforcement learning of robotic strategies

AbstractThis paper presents the development of a teaching approach for Reinforcement Learning (RL) for students at the Faculty of Electrical Engineering, University of Ljubljana. The approach is designed to introduce students to the basic concepts, approaches, and algorithms of RL through examples and experiments in both simulation environments and on a real robot. The approach includes practical programs written in Python and presents various RL algorithms. The Q‐learning algorithm is introduced and a deep Q network is implemented to introduce the use of neural networks in deep RL. The software is user‐friendly and allows easy modification of learning parameters, reward functions, and algorithms. The approach was tested successfully on a Franka Emika Panda robot, where the robot manipulator learned to move to a randomly generated target position, shoot a real ball into the goal, and push various objects into target position. The goal of the presented teaching approach is to serve as a study aid for future generations of students of robotics to help them better understand the basic concepts of RL and apply them to a wide variety of problems.

Mechanical Response in Existing Structure under Varied Subsurface Excavation Techniques

With the slowdown of urban incremental construction in China, reinforcement and renovation of existing buildings have become a hot topic in the fields of engineering and theoretical research. Underpinning pile foundations and underground excavation are commonly used methods for foundation renovation and reinforcement in existing buildings reinforcement and renovation projects. Nevertheless, there remains a dearth of relevant research concerning the effects of different excavation methods on the stability of existing structures during foundation reinforcement and underground space excavation. In the context of existing building pile foundation underpinning and underground excavation, this paper adopts a numerical simulation research method based on the modification of experimental model parameters, and it compares the overall stress changes and settlement of the underpinning pile foundation and the building under two modes of lateral and vertical excavation. The results indicate that there is a good agreement between the stress and settlement changes of the components in the indoor model experiment and the finite element simulation. Both excavation methods show that lateral and vertical excavation will generate maximum stress on the bottom components of the upper structure and the upper part of the pile. In terms of differences, vertical excavation will cause greater overall settlement of the building, but the settlement in different areas is basically the same. On the other hand, lateral excavation will have smaller overall settlement but may cause the structure to tilt. At the same time, lateral excavation will cause greater stress changes in the columns in the structure. Based on these findings, relevant engineering suggestions are provided to choose different excavation methods and strengthen existing buildings.

Dapagliflozin Effects on Cardiac Deformation in HeartFailure and Secondary Clinical Outcome.

Sodium-glucose cotransporter 2 inhibitors were shown to reduce morbidity and mortality in patients with heart failure. This study aims to assess potential effects of dapagliflozin in nondiabetic patients with heart failure with reduced ejection fraction (HFrEF) and heart failure with mildly reduced ejection fraction (HFmrEF) on cardiac function assessed by speckle tracking echocardiography (STE). This randomized, prospective, single-center, open-label trial compared consecutive nondiabetic outpatients with HFrEF or HFmrEF receiving dapagliflozin with patients treated with optimal medical therapy (OMT) except sodium-glucose cotransporter type 2 inhibitors. Primary endpoint was the presence of a significant modification of left ventricular global longitudinal strain, diastolic function (as peak atrial longitudinal strain) and right ventricular function by STE from baseline to 6months. Cardiovascular events and parameters of congestion were assessed as safety-exploratory endpoints. Overall, 88 patients (38% HFmrEF) were enrolled and randomized to start dapagliflozin on top of OMT (n=44) or to continue with OMT (n=44). All STE values improved in the dapagliflozin group after 6months, whereas there was a nonsignificant improvement in OMT group. Moreover, when comparing the modification of STE parameters at follow-up in patients with HFrEF and HFmrEF, only the main treatment effect resulted statistically significant in both groups (P< 0.0001), indicating a significant difference between dapagliflozin and OMT. This study provided randomized data on the beneficial effect of dapagliflozin in nondiabetic patients with HFrEF and HFmrEF in terms of myocardial performance measured by the most sensitive echocardiographic technique, ie, STE. This suggests its usefulness for left ventricular reverse remodeling and better quality of life in patients with HFrEF and HFmrEF. (Effects of Dapagliflozin on cardiac deformation and clinical outcomes in heart failure with reduced and mildly reduced ejection fraction [DAPA ECHO trial]; EudraCT number: 2021-005394-66).

Sensitivity analysis of model parameters in a nonlinear model-updating approach for prestressed concrete bridges

Automated monitoring systems in structural health monitoring (SHM) using finite element models can measure system responses of the structure and provide information about behavior and damage scenarios. To describe the reactions and damages of the physical structure, the parameters of the FE-model have to be adjusted accordingly. A common approach therefore is model-updating, an iterative modification of unknown parameters of a finite element model by adapting the response of the model to the measurement results of the physical bridge. The adapted model can be used for assessments and predictions of the structure and provides accurate structural properties to a digital twin. The proposed monitoring concept includes damage detection, based on nonlinear model-updating using Artificial Intelligence (AI) methods and is applied to a prestressed, reinforced concrete bridge based on the observation of static response. For a practical application in long-term SHM, system properties and load values are identified simultaneously by using optimization methods based on Evolutionary Algorithms (EA). Due to the high dimensionality and complexity of the optimization results, Principal Component Analysis (PCA) including autoencoder networks for dimensionality reduction are required to evaluate and visualize the identified structural properties for their reliability. Based on physical 3D nonlinear finite element sensitivity analysis, the influence of damage scenarios and load situations on the bearing behavior of the bridge will be analyzed. The generated data and the results are presented and serve as the basis for the reliability analysis and evaluating the proposed concept. Furthermore, by comparing the measurements of the parameterized investigated models, conclusions about the behavior of the structure and the sensor placement can be obtained.

MATHEMATICAL MODEL AND ALGORITHM FOR THE DETERMINATION OF THE ORIGIN OF ELECTRICITY FROM RENEWABLE ENERGY SOURCES IN THE ELECTRIC POWER SYSTEM

The article deals with the development of a method and algorithm for calculation of individual compo-nents of electricity flows in the branches of an electric power grid circuit caused by generation and con-sumption in its nodes. In particular, it is about estimating the share of renewable energy sources (RES) in the electricity consumption of a given consumer. The method is based on methods and algorithms for the calculation of steady-state modes of power grids, integrating a mathematical model for determining the components of current flows in the branches of the power system circuit. The model employs distribution coefficients of currents in the circuit branches from RES nodes and node voltages to form a matrix of cur-rent distribution coefficients for RES across the power grid branches. Since RES in electric power systems (EPS) use general power grids to transmit the electricity they generate, accurate determination of their power share within overall flows enables the impact of RES on the EPS mode parameters to be considered. The peculiarity of the algorithm for estimating the share of RES in the electricity consumption of a given consumer is its consideration of physical processes in the power system. The input data for the calculation are the power system parameters, the node loads (specified by capacity or schedules), as well as arrays of RES generation and centralized power supply nodes and consumers with a controlled value of the Guar-anteed Origin of electricity from RES. The voltages during the formation of the matrix of power distribu-tion coefficients at nodes along the branches of the power grid are determined by the calculation results of the power grid steady-state modes or by experimental measurement data. The method's efficacy and the accuracy of estimating the RES share in the power consumption of a given consumer are verified and demonstrated through a practical example. Determination of the share of the consumer's electricity con-sumption, which origin from renewable sources can be guaranteed, creates an enabling environment that would foster producers and consumers to increase the use of renewable energy sources and promotes the development of the green energy market. Ref. 18. Fig.3.

Determination of the characteristics of drill string vibrations during the drilling process

The object of research is vibration processes of a certain origin in the drill string with typical design deviations depending on the mode parameters of drilling. A drill string is an oscillating system with an infinite number of degrees of freedom of a multifactor system. An exhaustive study of oscillatory processes in the drill string is impossible neither analytically nor experimentally, due to the specifics of the hole deepening in various rocks, the design of the well, its shape, etc. Therefore, in practice, they try to solve the problems of the dynamics of the drill string for an idealized system and, while preserving the main oscillatory properties, solve some problems of the rod system. The work carried out was aimed at experimental studies of vibrations of the drill string during the drilling process. It is shown that the effectiveness of the use of hydrodynamic cavitation requires the development of methods and devices for intensifying the well drilling process. It is proven that the design of the cavitation generator organically fits into the existing well drilling equipment and allows for the intensification of technological processes with lower specific energy consumption. It is found that all oscillatory processes that occur in the drill string are random in nature and must be considered using the mathematical apparatus of the theory of random oscillations. The study of vibrations during well drilling shows that vibrations can be considered as random stationary processes, since transient modes have a sufficiently short duration for homogeneous rocks with fixed drilling modes. The analysis of the vibrations of the drill string elements based on random oscillations in a number of cases allows to increase the reliability of determining the vibration reliability of the drill string elements. It has been proven that the response of drill string elements to broadband random vibration can be defined as the combined effect of several narrowband random vibrations.

Bayesian modal regression based on mixture distributions

Compared to mean regression and quantile regression, the literature on modal regression is very sparse. A unifying framework for Bayesian modal regression is proposed, based on a family of unimodal distributions indexed by the mode, along with other parameters that allow for flexible shapes and tail behaviors. Sufficient conditions for posterior propriety under an improper prior on the mode parameter are derived. Following prior elicitation, regression analysis of simulated data and datasets from several real-life applications are conducted. Besides drawing inference for covariate effects that are easy to interpret, prediction and model selection under the proposed Bayesian modal regression framework are also considered. Evidence from these analyses suggest that the proposed inference procedures are very robust to outliers, enabling one to discover interesting covariate effects missed by mean or median regression, and to construct much tighter prediction intervals than those from mean or median regression. Computer programs for implementing the proposed Bayesian modal regression are available at https://github.com/rh8liuqy/Bayesian_modal_regression.

Binding Energies and Optical Properties of Power-Exponential and Modified Gaussian Quantum Dots.

We examine the optical and electronic properties of a GaAs spherical quantum dot with a hydrogenic impurity in its center. We study two different confining potentials: (1) a modified Gaussian potential and (2) a power-exponential potential. Using the finite difference method, we solve the radial Schrodinger equation for the 1s and 1p energy levels and their probability densities and subsequently compute the optical absorption coefficient (OAC) for each confining potential using Fermi's golden rule. We discuss the role of different physical quantities influencing the behavior of the OAC, such as the structural parameters of each potential, the dipole matrix elements, and their energy separation. Our results show that modification of the structural physical parameters of each potential can enable new optoelectronic devices that can leverage inter-sub-band optical transitions.

Synthesis of an algorithm for automated adjustment of lumber drying based on linguistic information

Relevance. Lumber drying is associated with high risks of obtaining a low-quality or even unusable product. The problem under consideration has a number of reasons, both in violation of the technological process and in incorrect settings of drying mode parameters at different stages. The procedure for automated adjustment of parameters is usually associated with a linguistic assessment of the quality of the product, according to which a decision on adjusting the parameters should be made, taking into account signs of a violation of the technological process. Technical and scientific publications contain generalized information about methods for selecting parameters for various situations, which can be formalized and included in algorithms for adjusting the parameters of a programmable logic controller. Aim. To create an algorithm using a Fuzzy controller that allows you to configure process parameters and adapt the drying chamber to the optimal parameters for drying lumber under given operating conditions. Methods. Analysis of the technical process database obtained during one and a half years of operation of the main parameters dispatch system for 28 full lumber drying cycles, with the aim of forming an automated adjustment algorithm that takes into account the lumber drying conditions. Results. The authors have created an algorithm for automated adjustment of parameters for three stages of drying based on linguistic ideas about the quality of the product. The algorithm eliminates the possibility of obtaining a low-quality product, as well as encourages service personnel to correctly generate a technological process passport. Conclusions. The inclusion of an automated parameter adjustment loop in the control circuit allows us to minimize the number of poor-quality processes when starting to operate a new drying chamber and eliminate defects in further technical processes.

Leveraging application permissions and network traffic attributes for Android ransomware detection

The increase in ransomware threats targeting Android devices necessitates the development of advanced techniques to strengthen the effectiveness of detection and prevention methods. Existing studies use Machine Learning (ML) techniques to detect and classify ransomware attacks, however, the ransomware landscape's rapid evolution hinders the effectiveness of these approaches. Moreover, the potential of Deep Reinforcement Learning (DRL) for this purpose remains unexplored. This study investigates the application of various DRL models for Android ransomware detection, leveraging permissions and network traffic attributes-labeled datasets. The paper provides a detailed explanation of implementing supervised learning within a DRL context. Secondly, the challenge of devising a reward function in Android ransomware detection is addressed, given the lack of an automated method for Android ransomware identification. The conventional DRL framework, which relies on the agent's interaction with a real-time environment, is conceptually modified in a new approach. We exhaustively tested the efficiency and accuracy of DRL-based models against other ML techniques, and results show that the A2C model has a better comparable detection performance than other DRL and ML models. Moreover, when DRL models are implemented with minor parameter modifications, they expedite and improve Android ransomware detection's speed, efficiency, and accuracy relative to existing ML strategies.

Optimal control set expansion method for three‐phase voltage source converter with slip window selection and online parameter modification

AbstractFinite control set model predictive control (FCS‐MPC) has been widely used in the control of three‐phase voltage source converter (VSC), but its control performance declines sharply at low sampling frequencies. This paper proposes an optimal control set expansion method with slip window selection and online parameter modification to improve the performance and robustness of FCS‐MPC. First, the performance of different control set expansion methods is compared. Then, an optimal control set expansion method that carries 12 virtual vectors is proposed. Last, a slip window selection strategy and an online parameter modification algorithm are proposed to reduce the computational burden and improve the controller's robustness respectively. Experimental result show that the proposed method significantly improves the control accuracy and carries good robustness and anti‐disturbance ability. Compared with existing typical MPC strategies, the proposed method obtains better current quality with lower switching frequency and requires less calculation. Moreover, the design of the proposed method is simple and there is no weighting factor to be tuned, which increases the method's practicality.

Cryogenic DRIE processes for high-precision silicon etching in MEMS applications

Cryogenic deep reactive ion etching (Cryo DRIE) of silicon has become an enticing but challenging process utilized in front-end fabrication for the semiconductor industry. This method, compared to the Bosch process, yields vertical etch profiles with smoother sidewalls not subjected to scalloping, which are desired for many microelectromechanical systems (MEMS) applications. Smoother sidewalls enhance electrical contact by ensuring more conformal and uniform sidewall coverage, thereby increasing the effective contact area without altering contact dimensions. The versatility of the Cryo DRIE process allows for customization of the etch profiles by adjusting key process parameters such as table temperature, O2 percentage of the total gas flow rate (O2 + SF6), RF bias power and process pressure. In this work, we undertake a comprehensive study of the effects of Cryo DRIE process parameters on the trench profiles in the structures used to define cantilevers in MEMS devices. Experiments were performed with an Oxford PlasmaPro 100 Estrelas ICP-RIE system using positive photoresist SPR-955 as a mask material. Our findings demonstrate significant influences on the sidewall angle, etch rate and trench shape due to these parameter modifications. Varying the table temperature between −80 °C and −120 °C under a constant process pressure of 10 mTorr changes the etch rate from 3 to 4 μm min−1, while sidewall angle changes by ∼2°, from positive (<90° relative to the Si surface) to negative (>90° relative to the Si surface) tapering. Altering the O2 flow rate with constant SF6 flow results in a notable 10° shift in sidewall tapering. Furthermore, SPR-955 photoresist masks provide selectivity of 46:1 with respect to Si and facilitates the fabrication of MEMS devices with precise dimension control ranging from 1 to 100 μm for etching depths up to 42 μm using Cryo DRIE. Understanding the influence of each parameter is crucial for optimizing MEMS device fabrication.