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In the current research study, a new method is presented to diagnose Anterior Cruciate Ligament (ACL) tears by introducing an optimized version of the InceptionV4 model. Our proposed methodology utilizes a custom-made variant of the Snow Leopard Optimization Algorithm, known as the Fractional-order Snow Leopard Optimization Algorithm (FO-LOA), to extract essential features from knee magnetic resonance imaging (MRI) images. This results in a substantial improvement in the accuracy of ACL tear detection. By effectively extracting critical features from knee MRI images, our proposed methodology significantly enhances diagnostic accuracy, potentially reducing false negatives and false positives. The enhanced model based on FO-LOA underwent thorough testing using the MRNet dataset, demonstrating exceptional performance metrics including an accuracy rate of 98.00%, sensitivity of 98.00%, precision of 97.00%, specificity of 98.00%, F1-score of 98.00%, and Matthews Correlation Coefficient (MCC) of 88.00%. These findings surpass current methodologies like Convolutional Neural Network (CNN), Inception-v3, Deep Belief Networks and Improved Honey Badger Algorithm (DBN/IHBA), integration of the CNN with an Amended Cooking Training-based Optimizer version (CNN/ACTO), Self-Supervised Representation Learning (SSRL), signifying a significant breakthrough in ACL injury diagnosis. Using FO-SLO to optimize the InceptionV4 framework shows promise in improving the accuracy of ACL tear identification, enabling prompt and efficient treatment interventions., (© 2024. The Author(s).)

Objective: This study aimed to efficiently reduce the release of radon from water bodies to protect the environment., Methods: Based on the sizes of the experimental setup and modular float, computational fluid dynamics (CFD) was used to assess the impact of the area coverage rate, immersion depth, diffusion coefficient, and radon transfer velocity at the gas-liquid interface on radon migration and exhalation of radon-containing water. Based on the numerical simulation results, an estimation model for the radon retardation rate was constructed. The effectiveness of the CFD simulation was evaluated by comparing the experimental and simulated variation values of the radon retardation rate with the coverage area rates., Results: The effect of radon transfer velocity on radon retardation in water bodies was minor and insignificant according to the appropriate value; therefore, an estimation model of the radon retardation rate of the coverage of a radon-containing water body was constructed using the synergistic impacts of three factors: area coverage rate, immersion depth, and diffusion coefficient. The deviation between the experimental and simulated results was < 4.3%., Conclusion: Based on the numerical simulation conditions, an estimation model of the radon retardation rate of covering floats in water bodies under the synergistic effect of multiple factors was obtained, which provides a reference for designing covering floats for radon retardation in radon-containing water., (Copyright © 2024 The Editorial Board of Biomedical and Environmental Sciences. Published by China CDC. All rights reserved.)

In recent decades, the development of electronic technology has provided opportunities for the Internet of Things, biomedicine, and energy harvesting. One of the challenges of the Internet of Things in the electrification era is energy supply. Centralized energy supply has been tested over hundreds of years of history, and its advantages such as ideal output power and stable performance are obvious, but it cannot meet the specific needs of the Internet of Things, and distributed energy supply also has a large demand. Since the invention of nanogenerators, another promising solution for fluid energy harvesting has been opened up. The triboelectric nanogenerator is an emerging platform technology for electromechanical energy conversion, which can realize the collection of fluid energy such as wind energy and wave energy. In this paper, we first introduce the fundamentals of triboelectric nanogenerators and their applications in wind and wave energy harvesting devices. We then discuss the methods of device optimization in the next development of TENG and conclude by considering the future prospects and challenges for triboelectric nanogenerator harvesting devices.

In order to design an involute spline made of PEEK (polyetheretherketone)-based material with better performance and improve the design rules of involute splines, initially, an involute splines design theory for PEEK (polyetheretherketone)-based materials is presented, which combines international standards (ISO 4156, 1. 2. 3, 2005), American standards (ANSI B92.2M. 1989), and traditional empirical formulas. Second, using the involute splines calibration method in international standards as a guide, we developed the involute splines calibration method for PEEK-based materials by estimating the impact of energy consumption caused by viscoelasticity on temperature field calibration. Next, the contact characteristics of the designed spline were analyzed using ABAQUS2022 software to confirm the accuracy and reliability of the design and calibration methods. Finally, finite element simulation was used to analyze the influence of different pressure angles, moduli, combined lengths, and other parameters on the contact characteristics of the spline in order to realize the optimal design of PEEK-based material involute splines, to offer a theoretical foundation and improved design methodology for cylindrical straight-tooth involute splines.

Mechanical signals influence the morphology, function, differentiation, proliferation, and growth of cells. Due to the small size of cells, it is essential to analyze their mechanobiological responses with an in vitro mechanical loading device. Cells are cultured on an elastic silicone membrane substrate, and mechanical signals are transmitted to the cells by the substrate applying mechanical loads. However, large areas of non-uniform strain fields are generated on the elastic membrane, affecting the experiment's accuracy. In the study, finite-element analysis served as the basis of optimization, with uniform strain as the objective. The thickness of the basement membrane and loading constraints were parametrically adjusted. Through finite-element cycle iteration, the "M" profile basement membrane structure of the culture chamber was obtained to enhance the uniform strain field of the membrane. The optimized strain field of culture chamber was confirmed by three-dimensional digital image correlation (3D-DIC) technology. The results showed that the optimized chamber improved the strain uniformity factor. The uniform strain area proportion of the new chamber reached 90%, compared to approximately 70% of the current chambers. The new chamber further improved the uniformity and accuracy of the strain, demonstrating promising application prospects., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

In a Cassegrain optical system, the surface precision of the primary mirror is an important factor in the quality of the image. The design of a lightweight primary mirror with a high-quality optical surface is crucial. In this thesis, an integrated mirror light engine design optimization process is proposed for an aviation optoelectronic device. It is based on the Kriging surrogate model and nests the topology optimization algorithm, which constructs the mirror RMS value response surface and obtains the dominant relationship between mirror structure and surface accuracy. The optimal surface figure lightweight structure of the mirror is obtained by optimizing the surrogate model with an additive criterion and multi-objective optimization analysis. The root mean square value (RMS) of the corresponding primary mirror is 10.41 nm, which is better than 1/40 λ (λ = 632.8 nm). This meets the optical design specifications. The optimal primary mirror structure is analyzed by using the finite element method, which verifies the precision of the Kriging surrogate model. It has an error of 0.28%. The kinetic analysis of the primary mirror shows that the primary mirror does not yield to plastic deformation or even failure under a three-way 20 g acceleration load. This meets the environmental suitability requirements.

Lightweight, high stability, and high-temperature adaptability are the primary considerations when designing the primary mirror of a micro/nano satellite remote sensing camera. In this paper, the optimized design and experimental verification of the large-aperture primary mirror of the space camera with a diameter of Φ610 mm is carried out. First, the design performance index of the primary mirror was determined according to the coaxial tri-reflective optical imaging system. Then, SiC, with excellent comprehensive performance, was selected as the primary mirror material. The initial structural parameters of the primary mirror were obtained using the traditional empirical design method. Due to the improvement of SiC material casting complex structure reflector technology level, the initial structure of the primary mirror was improved by integrating the flange with the primary mirror body design. The support force acts directly on the flange, changing the transmission path of the traditional back plate support force, and has the advantage that the primary mirror surface shape accuracy can be maintained for a long time when subjected to shock, vibration, and temperature changes. Then, a parametric optimization algorithm based on the mathematical method of compromise programming was used to optimize the design of the initial structural parameters of the improved primary mirror and the flexible hinge, and finite element simulation was conducted on the optimally designed primary mirror assembly. Simulation results show that the root mean square (RMS) surface error is less than λ /50 ( λ = 632.8 nm) under gravity, 4 °C temperature rise, and 0.01 mm assembly error. The mass of the primary mirror is 8.66 kg. The maximum displacement of the primary mirror assembly is less than 10 μm, and the maximum inclination angle is less than 5″. The fundamental frequency is 203.74 Hz. Finally, after the primary mirror assembly was precision manufactured and assembled, the surface shape accuracy of the primary mirror was tested by ZYGO interferometer, and the test value was 0.02 λ . The vibration test of the primary mirror assembly was conducted at a fundamental frequency of 208.25 Hz. This simulation and experimental results show that the optimized design of the primary mirror assembly meets the design requirements of the space camera.

As the application of bone scaffolds becomes more and more widespread, the requirements for the high performance of bone scaffolds are also increasing. The stiffness and porosity of porous structures can be adjusted as needed, making them good candidates for repairing damaged bone tissues. However, the development of porous bone structures is limited by traditional manufacturing methods. Today, the development of additive manufacturing technology has made it very convenient to manufacture bionic porous bone structures as needed. In the present paper, the current state-of-the-art optimization techniques for designing the scaffolds and the settings of different optimization methods are introduced. Additionally, various design methods for bone scaffolds are reviewed. Furthermore, the challenges in designing high performance bone scaffolds and the future developments of bone scaffolds are also presented.

The saving and re-use of energy has acquired great relevance in recent years, being of great importance in the automotive sector. In the literature, it is possible to find different proposals for energy-harvesting damper systems (EHSA)-the electromagnetic damper being a highly recurrent but still poorly defined proposal. This article specifically focuses on studying the concept and feasibility of an electromagnetic suspension system that is capable of recovering energy, using a damper formed by permanent magnets and a system of coils that collect the electromotive force generated by the variation of the magnetic field. To study the feasibility of the system, it is necessary to know the maximum energy that can be recovered through the winding system; however, the difficulties in obtaining the derivative of the magnetic flux and its derivative for each position make the analytical method very tedious. This paper presents an experimental method with which to maximize energy recovery by defining the optimum relative position between magnet and coil.

To improve covariate balance over a complete randomization, a number of methods have been proposed recently to utilize modern computational capabilities to find allocations with balance in observed covariates. Asymptotic inference on treatment effects based on these designs is more complicated than that under complete randomization, and this is why Fisher randomization tests often are suggested. This article suggests model-based Bayesian inference as a general method of inference in these designs, which can deal with complications such as arbitrary covariate balancing criteria and complex estimands. As an illustration, we focus on the case when the outcome is linearly related to the covariates and the estimand of interest is the Sample Average Treatment Effect (SATE). We use a large Monte Carlo simulation to compare the finite sample performance of the model-based Bayesian inference with that of two previous methods which are valid for asymptotic inference of SATE under Mahalanobis distance based rerandomization. We find that for experiments with small to moderate sample sizes, Bayesian inference is to be preferred to the previous methods. As a byproduct, we also find that regression adjustment together with small sample adjusted estimators of standard errors perform better than the previous methods., (© 2022 John Wiley & Sons Ltd.)

This study explores the waiting space environment of pediatric clinics in general hospitals and the relationships between the use of space, behavioral activities and overall satisfaction. Patients often spend a lot of time waiting for doctors, and child patients waiting to be seen are particularly likely to feel bored, depressed and anxious, which negatively affects their overall experience of seeking medical attention. Since the launch of China's second-child policy, the number of children born in China has surged. As medical resources for children are in short supply and of uneven quality, it is urgently necessary to carry out research on optimizing the design of children's waiting space in Chinese hospitals to improve their medical environment and experience., Method: This study identified four first-level indicators and twenty-seven second-level indicators in four dimensions: functional layout (layout and area), flow organization, supporting facilities and environmental details (physical and landscape environment). The research combined subjective and objective methods, including comprehensive observation, a questionnaire survey and interviews, taking three hospitals in Shenzhen as case studies., Results: The study found that the waiting space in pediatric clinics currently fails to meet key patient needs in areas such as mother and infant rooms, children's play areas and drinking water facilities, and there are widespread problems with the creation of natural environments, such as views of natural scenery from windows and indoor green plants. Six factors were found to significantly positively influence overall satisfaction with waiting space, describing 69.76% of the changes in the respondents' degree of satisfaction with the waiting environment. Supporting facilities and aspects of the physical environment had the greatest influence on overall satisfaction with the waiting space., Conclusion: Optimizing the design of the waiting space in pediatric clinics, with a focus on functional layout, flow organization, supporting facilities and environmental details, can improve overall satisfaction with pediatric waiting rooms. The results are preliminary; they need to be further tested in practice to complete the process of evidence-based design. This will lead to suggestions for refining the design of pediatric waiting units which can be used by architects and hospital administrators.

Quantitative evaluation of potential pharmacodynamic (PD) interactions is important in tuberculosis drug development in order to optimize Phase 2b drug selection and ultimately to define clinical combination regimens. In this work, we used simulations to (1) evaluate different analysis methods for detecting PD interactions between two hypothetical anti-tubercular drugs in in vitro time-kill experiments, and (2) provide design recommendations for evaluation of PD interactions. The model used for all simulations was the Multistate Tuberculosis Pharmacometric (MTP) model linked to the General Pharmacodynamic Interaction (GPDI) model. Simulated data were re-estimated using the MTP-GPDI model implemented in Bliss Independence or Loewe Additivity, or using a conventional model such as an Empirical Bliss Independence-based model or the Greco model based on Loewe Additivity. The GPDI model correctly characterized different PD interactions (antagonism, synergism, or asymmetric interaction), regardless of the underlying additivity criterion. The commonly used conventional models were not able to characterize asymmetric PD interactions, i.e., concentration-dependent synergism and antagonism. An optimized experimental design was developed that correctly identified interactions in ≥ 94% of the evaluated scenarios using the MTP-GPDI model approach. The MTP-GPDI model approach was proved to provide advantages to other conventional models for assessing PD interactions of anti-tubercular drugs and provides key information for selection of drug combinations for Phase 2b evaluation.

For two-arm randomized phase II clinical trials, previous literature proposed an optimal design that minimizes the total sample sizes subject to multiple constraints on the standard errors of the estimated event rates and their difference. The original design is limited to trials with dichotomous endpoints. This paper extends the original approach to be applicable to phase II clinical trials with endpoints from the exponential dispersion family distributions. The proposed optimal design minimizes the total sample sizes needed to provide estimates of population means of both arms and their difference with pre-specified precision. Its applications on data from specific distribution families are discussed under multiple design considerations. Copyright © 2016 John Wiley & Sons, Ltd., (Copyright © 2016 John Wiley & Sons, Ltd.)

In order to meet the requirement of high sensitivity and signal-to-noise ratios (SNR), this study develops and optimizes a piezoresistive pressure sensor by using double silicon nanowire (SiNW) as the piezoresistive sensing element. First of all, ANSYS finite element method and voltage noise models are adopted to optimize the sensor size and the sensor output (such as sensitivity, voltage noise and SNR). As a result, the sensor of the released double SiNW has 1.2 times more sensitivity than that of single SiNW sensor, which is consistent with the experimental result. Our result also displays that both the sensitivity and SNR are closely related to the geometry parameters of SiNW and its doping concentration. To achieve high performance, a p-type implantation of 5 × 10¹⁸ cm -3 and geometry of 10 µm long SiNW piezoresistor of 1400 nm × 100 nm cross area and 6 µm thick diaphragm of 200 µm × 200 µm are required. Then, the proposed SiNW pressure sensor is fabricated by using the standard complementary metal-oxide-semiconductor (CMOS) lithography process as well as wet-etch release process. This SiNW pressure sensor produces a change in the voltage output when the external pressure is applied. The involved experimental results show that the pressure sensor has a high sensitivity of 495 mV/V·MPa in the range of 0⁻100 kPa. Nevertheless, the performance of the pressure sensor is influenced by the temperature drift. Finally, for the sake of obtaining accurate and complete information over wide temperature and pressure ranges, the data fusion technique is proposed based on the back-propagation (BP) neural network, which is improved by the particle swarm optimization (PSO) algorithm. The particle swarm optimization⁻back-propagation (PSO⁻BP) model is implemented in hardware using a 32-bit STMicroelectronics (STM32) microcontroller. The results of calibration and test experiments clearly prove that the PSO⁻BP neural network can be effectively applied to minimize sensor errors derived from temperature drift.

The temperature control system is one of the most important subsystems of the strapdown airborne gravimeter. Because the quartz flexible accelerometer based on springy support technology is the core sensor in the strapdown airborne gravimeter and the magnet steel in the electromagnetic force equilibrium circuits of the quartz flexible accelerometer is greatly affected by temperature, in order to guarantee the temperature control precision and minimize the effect of temperature on the gravimeter, the SGA-WZ temperature control system adopts a three-level control method. Based on the design experience of the SGA-WZ-01, the SGA-WZ-02 temperature control system came out with a further optimized design. In 1st level temperature control, thermoelectric cooler is used to conquer temperature change caused by hot weather. The experiments show that the optimized stability of 1st level temperature control is about 0.1 °C and the max cool down capability is about 10 °C. The temperature field is analyzed in the 2nd and 3rd level temperature control using the finite element analysis software ANSYS. The 2nd and 3rd level temperature control optimization scheme is based on the foundation of heat analysis. The experimental results show that static accuracy of SGA-WZ-02 reaches 0.21 mGal/24 h, with internal accuracy being 0.743 mGal/4.8 km and external accuracy being 0.37 mGal/4.8 km compared with the result of the GT-2A, whose internal precision is superior to 1 mGal/4.8 km and all of them are better than those in SGA-WZ-01.

The demand for on-site nanodevices is constantly increasing. The technology development for the design of such devices is highly regarded. In this work, we report the design of a disposable platform that is structured with cauliflower-shaped gold nanoparticles (cfAuNPs) and we show its applications in immunosensing and enzyme-based detection. The electrochemical reduction of Au(III) allows for the electrodeposition of highly dispersed cauliflower-shaped gold nanoparticles on the surface of screen-printed carbon electrodes (SPCEs). The nanostructures were functionalized using ferrocenylmethyl lipoic acid ester which allowed for the tethering of the ferrocene group to gold, which serves as an electrochemical transducer/mediator. The bioconjugation of the surface with anti-human IgG antibody (α-hIgG) or horseradish peroxidase (HRP) enzyme yields biosensors, which have been applied for the selective electrochemical detection of human IgG (hIgG) or H2O2 as model analytes, respectively. Parameters such as the number of sweeps, amount of charge generated from the oxidation of the electrodeposited gold, time of incubation and concentration of the ferrocene derivatives have been studied using cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS) and scanning electron microscopy (SEM). Selectivity and specificity tests have been also performed in the presence of potentially interfering substances to either hIgG or H2O2. Results showed that the devised immunosensor is endowed with good selectivity and specificity in the presence of several folds of competitive analytes. The enzyme-based platform showed a good catalytic activity towards H2O2 oxidation which predestined it to potential applications pertaining to enzymatic kinetics studies. The levels of hIgG in human serum and H2O2 in honey were successfully determined and served as assessment tools of the applicability of the platforms for real samples analysis.