Low High Frequency Research Articles

Image denoising for laser ultrasonic inspection of wire arc additive-manufactured components with a rough surface

ABSTRACT Laser ultrasonic testing (LUT) is a non-contact and non-destructive testing (NDT) technique which can detect the wire arc additive manufacturing (WAAM) component defect in hot state. However, the complex surface topography of the WAAM component induces a low signal to noise ratio (SNR) and a poor detection sensitivity. In this paper, a denoising method based on improved empirical mode decomposition with adaptive noise (ICEEMDAN) and Wavelet Packet Transform (WPT) was applied in the LUT of the WAAM component. All the signals in the LUT scan maps with low SNR were decomposed into a series of intrinsic mode functions (IMFs) using ICEEMDAN. The Pearson correlation coefficient of the original signal and the IMFs were calculated. The high frequency (HF) and low frequency (LF) IMFs with correlation coefficients under the threshold were denoised using WPT and deleted, respectively. The denoised IMFs and the IMFs without processing were stacked up to compose the denoised signal and plot the denoised scan maps. The denoising results show that the proposed method improved the LUT efficiency and sensitivity. Finally, the denoising method was applied and verified in the LUT of the WAAM nuclear power pipeline nozzle.

First-Principles Calculation of Structural, Electronic, and Optical Properties of Cubic Perovskite CsPbF3

Lead halide perovskites have attracted considerable attention as one of the most promising materials for optoelectronic applications. The structural, electronic, and optical properties of the cubic perovskite CsPbF3 were studied using density functional theory in conjunction with plane waves, norm-conserving pseudopotentials, and Perdew-Berg-Erzenhof flavor of generalized gradient approximation. The obtained structural parameters are a good agreement with the experimentally measured and other’s theoretically predicted values. The obtained electronic band structure revealed that cubic CsPbF3 has a direct fundamental band gap of 2.99 eV at point R. The calculated energy band gaps at the high symmetry points agree with the other available theoretical results. The GW method is adapted to correct the underestimated fundamental energy gap value to 4.05 eV. The contribution of the different bands was analyzed from the total and partial density of states. The electron densities show that Cs and F have strong ionic bonds, whereas Pb and F have strong covalent bonds. The optical properties of CsPbF3 were calculated using the density functional perturbation theory and Kramers-Kronig relations. The wide and direct bandgap nature and the calculated optical properties imply that cubic CsPbF3 can be used in optical and optoelectronic devices for high frequencies visible and low frequencies ultraviolet electromagnetic radiation.

Gold futures price forecast research----A combinatorial prediction method based on CEEMDAN-GARCH-SVR

In recent years, the international form has become more and more complex, and various emergencies have constantly impacted the world economy. In this context, countries in the world need to maintain the stability of the domestic economic market and reasonably avoid financial risks. Gold plays a major role for both countries and individuals, and predicting the gold price is a prerequisite for making important decisions.In this paper, with the closing price of gold futures AU9999 from January 2,2008 to February 13,2023 as the research object. This paper mainly uses the traditional time series model for modeling, combined with the machine learning method, and introduces the decomposition reconstruction algorithm, hoping to achieve a better prediction effect. The traditional time-series model-error t-based ARIMA (2,1,2) -EGARCH (1,1) was established, and then the machine learning method SVR was used to model the data. In order to improve the performance of the model, this paper uses the CEEMDAN method to decompose the price sequence, and then decompose the IMF according to the high frequency sequence judgment condition summary into high frequency, low frequency and remaining term three sequence, make the original complicated sequence relatively simple, then with the traditional time series model for high frequency and low frequency modeling, SVR for the remaining term. Finally, we found that the decomposition modeling algorithm proposed here has the best prediction effect and made reasonable suggestions according to the prediction results.

Altered Heart Rate Variability During Rest in Schizophrenia: A State Marker.

Autonomic nervous system (ANS) imbalance has been reported in a number of psychiatric disorders such as depression, schizophrenia, panic disorder, etc. Autonomic dysfunction in schizophrenia has been associated with the symptoms and manifestation of psychosis. Heart rate variability (HRV) as a tool has been widely used to assess ANS activity and the effect of disease on the sympathovagal balance. Therefore, in the present study, HRV derived from electrocardiogram (ECG) lead II at rest was investigated in order to understand the changes in frequency domain measures in patients with schizophrenia and their first-degree relatives compared to healthy controls. Twenty-five patients with schizophrenia, 24 first-degree relatives of patients, and 24 healthy controls (Diagnostic and Statistical Manual of Mental Disorders (DSM)-5; 18-45 years) were included in the study. HRV of the subjects was measured after five minutes of rest. ECG lead II was recorded for five minutes and HRV was analysed in the frequency domain: low frequency (LF), high frequency (HF), total power, and LF/HF ratio. HRV parameters and heart rate were statistically analysed for group comparisons using general linear model multivariate analysis. Patients had significantly higher minimum heart rate and lower HF (normalized units (nu)) compared to their first-degree relatives. A trend was observed in HF (nu) with the lowest in patients followed by healthy controls and first-degree relatives and LF/HF ratio was the highest in patients followed by healthy controls and first-degree relatives, although not statistically significant. No significant difference was found between first-degree relatives and healthy controls. The alteration of HRV in schizophrenia could be attributed to reduction in vagal tone and sympathetic dominance, which in turn could serve as state markers of schizophrenia.

Spatiotemporally heterogeneous soil thermohydraulic processes in the frozen soil of the Tibetan Plateau

Frozen soil properties and thermohydraulic processes are still not fully understood due to lack of in-situ measurements, especially in the high altitudes and high latitudes (HAHL). Based on hourly measurements at 10, 40, 80 and 120 cm depths at 21 sites in the west, south and northeast regions of the Tibetan Plateau (TP) during July 2018 - July 2019, we examined soil particles, spatiotemporal variations of soil thermohydraulic processes and their connections with environmental factors to reveal the heterogenous frozen soil properties on the TP. Sand and silt are the dominant soil particles and clay is less than 10% at the sites. Existing and widely used soil products underestimates (significantly overestimates) sand (clay) content on the TP which raises the uncertainty in the thermohydraulic parameters derived from these products. Diurnal soil moisture and temperature variations are seen only above 40 cm, but seasonal variations occur down to 120 cm due to the soil memory effect. Seasonally frozen soil and permafrost soil show different freezing and melting processes. Dry soil features greater soil temperature temporal variability and deeper maximum frozen depth than wet soil. Zero curtain occurs in both dry and wet soil, and displays high (low) frequency but short (long) duration at 10 (80) cm that vary spatially. Moisture depression exists in seasonally frozen soil and is determined by the initial soil moisture and temperature gradient strength. The strong thermohydraulic coupling existed in cold season collapses in warm season. Soil moisture exhibits higher spatiotemporal variability than soil temperature. At daily time scale, the influences of precipitation, wind speed and relative humidity on soil moisture and temperature vary under different climate conditions. These findings fill the knowledge gaps in the soil thermohydraulic processes in the HAHL, and improve the understanding of frozen soil properties and heat-water coupling processes in soil in the HAHL. The study will benefit the Earth system model development and improve the quality of soil temperature and moisture assimilation and remote sensing products in the HAHL.

Comparison of quick speech-in-noise test and pure tone audiometry in noise-induced hearing loss

Objective Complaints of speech understanding in situations where background noise exists, can be seen from early stages of Noise-Induced Hearing Loss (NIHL); therefore, speech-in-noise tests including Quick speech-in-noise (Q-SIN) tests may be a suitable method for screening NIHL. The Q-SIN test uses the Signal to Noise Ratio (SNR) to evaluates speech perception in the presence of noise. This study aimed to compare pure tone audiometry and Q-SIN tests in patients with NIHL and normal hearing subjects. Methods In this cross-sectional study, according to the result of audiometry, subjects were classified into two groups: 45 individuals with audiograms suggestive of NIHL as a case group and 57 normal-hearing individuals as controls. The Persian version of the Quick-SIN test was performed for two study groups. The Mann-Whitney U Test was used for the comparison of mean SNR and hearing thresholds between the study groups. Spearman test was implemented for evaluation of the correlation between mean SNR and mean hearing thresholds in high and low frequencies in both ears. Results The median SNR loss was significantly higher in the NIHL group [0.75 (IQR: −0.75, 2.75)] than in the normal hearing group [−1.25 (−0.25, 1.25)] (p < 0.0001). It was also found that in the NIHL group, SNR loss had a direct and significant correlation with the high frequencies of both ears and the low frequency of the left ear, as the average hearing threshold increases at high frequencies and low frequencies of the left ear, the SNR loss mean increases as well. Conclusion The present study showed that the Q-SIN test as a rapid, easily applicable test has a positive correlation with pure tone audiometry results, especially at higher frequencies.

Design Consideration and Conductor Selection of a Low AC Loss HTS REBCO Magnet Carrying High Currents at 20 K and 40 K

AC loss in high temperature superconductor coils have been frequently studied, however, mostly for AC power ap-plications at 77 K, rather than specifically for high current but low frequency AC superconducting magnet at 20-40 K. Due to their easy operation and Helium shortage, more HTS magnet systems employ conduction-cooling with cryocoolers. The HTS magnets are known for high stability and likely tolerate high AC loss, but it is unclear what is the maximum AC frequency assuming that cry-ocooler has limited capability (a few hundred Watts) for the 20-40 K temperature range. This paper will specifically study AC loss in a simple HTS dipole but with three conductor/cable options using simulations, (1) 12 mm wide tape, (2) two parallel 6 mm wide tapes, and (3) 6/2 (six 2 mm strands) Roebel cables. It has been found that the magnet at 5 Hz generates 200 – 400 W AC loss at 20 K or 40 K, potentially be cooled by two single stage cryocoolers. The 6/2 Roe-bel cable based magnet may allow higher frequency (6-8 Hz) due to its transposition and narrower conductor width.

Deep learning based automatic Debakey classification of aortic dissection using CT plain scan

ObjectiveThis paper presents an automatic segmentation and classification of aortic dissection by utilizing neural network based on CT scanning. MethodologyFirst we present a way of establishing models of indivisible aorta parametrization, designed multiple seed points as the initial parameter and based on the three-dimensional area growth algorithm, finally add morphological processing to realize the segmentation of inner membrane, and then introduced the semantic segmentation framework, and analyzed the characteristics and function of each part, improve the Deeplabv3 + network. This paper analyzes the results of the different experiments with two-dimensional lattices and presents the advantages of being sensitive to differences in shape and position and of not being able to use three-dimensional spatial continuity. Our research establishs the aorta reconstruction, which is simpler for learning the transformation of both the shape and position of, according to a last for three-dimensional CT images interfloor of the entire helix network aorta 3D MPRNet.A new module of multi-scale receptive field information fusion is designed, which realizes the function of acquiring different receptive field features, taking into account details and global features, so that the model obtains the unique ability more suitable for segmentation problems. In the decoder stage, the cross-layer connection mode similar to U-Net is still adopted to realize the fusion of semantic information at high and low levels, which ensures that the network can learn enough features. ResultsThe full convolutional network and traditional algorithms based on domain knowledge of aortic dissection images achieve a robust, automated and high precision CT image aortic dissection segmentation scheme. ConclusionThe large number of decoder parameters is increased, which increases the difficulty of network training and reduces the reasoning speed of the model. At the same time, not all features are useful information, while the high frequency and low frequency, the feature graph only adopts cross-layer connection to connect shallow features to high-level information may bring the redundancy of information and interference with useful information. Therefore, designing an efficient decoder is another research idea to accelerate model inference and improve segmentation accuracy.

Financial time series classification method based on low‐frequency approximate representation

AbstractAiming at the mode mixing problems of high frequency information caused by fluctuation agglomeration and pointed peak thick tail of financial time series, a time series classification method based on low frequency approximate representation is proposed. The steps are as follows. Firstly, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was used to decompose the time series into a series of modal components and residual terms, and the permutation entropy of the modal components and residual terms was calculated. And the permutation entropy was clustered into two categories by Fisher's optimal segmentation method. Then, according to the clustering results of permutation entropy, the corresponding modal components and residual terms were integrated into high frequency information and low frequency information, so as to realize the adaptive extraction of low frequency information. Secondly, distance matrices were obtained by Euclidean distance (ED) or dynamic time warping (DTW) for low frequency information, and then the nearest neighbor 1‐NN algorithm was used to classify time series.

Assessment of Occupational Risk in the Case of the Ultrasonic Noise Exposure

Increased efficiency of production and improved quality have contributed to the development of ultrasonic technological applications, in which low frequency ultrasounds are generated to operate, accelerate as well as to facilitate technological processes. Technological ultrasonic devices (i.e. sources of ultrasonic noise in the work environment, e.g. ultrasonic washers, ultrasonic welding machines) have relatively high power and their nominal frequencies are in the range from 18 kHz to 40 kHz. In Poland, ultrasonic noise (defined as noise containing high audible and low ultrasonic frequencies from 10 kHz to 40 kHz) is included in the list of factors harmful to health in the work environment and therefore the admissible values of ultrasonic noise in the workplaces are established. The admissible values of ultrasonic noise and the new ultrasonic noise measurement method make it possible to perform the assessment of occupational risk related to ultrasonic noise. According to this method, the scope of the measurements includes the determination of the equivalent sound pressure levels in the 1/3 octave bands with the centre frequencies from 10 kHz to 40 kHz. This paper presents the description of both, i.e. the method for ultrasonic noise measurements and the method of the assessment of occupational risk related to ultrasonic noise. The examples of the results of the assessment of occupational risk related to exposure to ultrasonic noise are also discussed.

Correlation of oxygen defects, oxide-ion conductivity and dielectric relaxation to electronic structure and room temperature ferromagnetic properties of Yb3+ doped CeO2 nanoparticles

Ce1-xYbxO2 nanoparticles synthesized using co-precipitation route were studied for properties like structural, optical, impedance and X-ray Photoelectron Spectroscopy. Existence of cubic fluorite structure found intact with heterogeneous aggregation and spherical surface morphology. Red-shifted energy gap along with formation of defects and oxygen vacancies was confirmed with UV–Vis and Photoluminescence analyses. XPS analyses were undertaken to observe the chemical states 2+ and 3+ of Yb and 3+ and 4+ of Ce cations. The magnetization results demonstrated improved ferromagnetic ordering for varying Yb doping, explained with Bound Magnetic Polarons model and F-Centre Mechanism for creation of distinct complexes, Ce4+-Vo-Ce3+ or Yb3+-Vo-Ce3+ in the CeO2 lattice. All samples' dielectric permittivity and modulus were measured in the 120 Hz–100 kHz frequency range. The ∈′ (f) and ∈″(f) plots show two distinct regions: a plateau zone at high frequency and low frequency dielectric dispersion region. These nanoparticles could be employed for photolytic, optoelectronics and energy storage applications.

Information scanning in the COVID-19 pandemic: a test and expansion of the channel complementarity theory using latent class analysis

ABSTRACT Background During health crisis, individuals need information to comprehend their circumstances. Channel complementarity theory posits that in meeting their informational needs, people will use different sources in a complementary fashion. This paper puts to test the main tenet of channel complementarity theory by focusing on information scanning (i.e. routine health information exposure) in the context of the COVID-19 pandemic in Chile. Method A survey was conducted among a sample of Chilean adults (N = 2,805). The questionnaire addressed information scanning across six sources (television, radio, internet, social media, family, and friends or coworkers) and explores how socioeconomic and demographic variables, as well as COVID-19 perceived risk related to scanning. Latent class analysis was employed to identify patterns of complementarity across channels. Results The analysis yielded a solution of five classes, namely ‘high complementarity and high frequency’ (21%), ‘high complementarity and low frequency’ (34%), ‘high frequency on television and digital media’ (19%), ‘mass media predominant’ (11%), and 'no scanning’ (15%). Educational attainment, age, and COVID-19 perceived risk were associated with scanning. Conclusions Television was a central channel for information scanning during the pandemic in Chile and more than half of participants scanned COVID-19 information complementarily. Our findings expand channel complementarity theory to information scanning in a non-US context and provide guidelines for designing communication interventions aiming at informing individuals during a global health crisis.

Lock-on to quasi-periodic flow transformation for a rotationally oscillating cylinder due to gust impulse

A computational study is conducted to analyze the effects of an upstream gust impulse on lock-on flow of a cylinder, which is rotationally oscillating. Reynold numbers of 100, 110, and 120 and three discrete oscillation conditions based on rotational oscillation amplitudes are considered. The current solution scheme is validated for the cases of stationary and rotationally oscillating cylinders in a uniform flow. The oscillation Strouhal forcing frequency is varied with a resolution of 0.0001 for lock-on to quasi-periodic flow boundary identification. Flows are categorized as either lock-on or quasi-periodic based on frequency analysis of lift. Furthermore, three different gust impulses for each Reynolds number are selected by associating gust Strouhal frequency to natural shedding Strouhal frequency, whereas the Prandtl number is 7. These gust impulses were introduced in lock-on flow near lock-on to quasi-periodic flow boundary. Analysis of lift, drag, and vorticity contours confirms that the lock-on flow is transformed into quasi-periodic flow in conditions of high oscillation amplitude and low gust frequency. This transformation is observed to persist for four descending values of rotational oscillation Strouhal forcing frequency at the upper lock-on boundary.

Physiological Signal Analysis and Stress Classification from VR Simulations Using Decision Tree Methods.

Stress is induced in response to any mental, physical or emotional change associated with our daily experiences. While short term stress can be quite beneficial, prolonged stress is detrimental to the heart, muscle tissues and immune system. In order to be proactive against these symptoms, it is important to assess the impact of stress due to various activities, which is initially determined through the change in the sympathetic (SNS) and parasympathetic (PNS) nervous systems. After acquiring physiological data wirelessly through captive electrocardiogram (ECG), galvanic skin response (GSR) and respiration (RESP) sensors, 21 time, frequency, nonlinear, GSR and respiration features were manually extracted from 15 subjects ensuing a baseline phase, virtual reality (VR) roller coaster simulation, color Stroop task and VR Bubble Bloom game. This paper presents a comprehensive physiological analysis of stress from an experiment involving a VR video game Bubble Bloom to manage stress levels. A personalized classification and regression tree (CART) model was developed using a novel Gini index algorithm in order to effectively classify binary classes of stress. A novel K-means feature was derived from 11 other features and used as an input in the Decision Tree (DT) algorithm, strong learners Ensemble Gradient Boosting (EGB) and Extreme Gradient Boosting (XGBoost (XGB)) embedded in a pipeline to classify 5 classes of stress. Results obtained indicate that heart rate (HR), approximate entropy (ApEN), low frequency and high frequency ratio (LF/HF), low frequency (LF), standard deviation (SD1), GSR and RESP all reduced and high frequency (HF) increased following the VR Bubble Bloom game phase. The personalized CART model was able to classify binary stress with 87.75% accuracy. It proved to be more effective than other related studies. EGB was able to classify binary stress with 100% accuracy, which outperformed every other related study. XGBoost and DT were able to classify five classes of stress with 72.22% using the novel K-means feature. This feature produced less error and better model performance in comparison to using all the features. Results substantiate that our proposed methods were more effective for stress classification than most related studies.

Influence of controlled breath on healthy adult autonomic heart modulation

Heart rate variability (HRV) is a technique that indirectly assesses the activities of the autonomic nervous system in the heart. However, during data collection there are controversies regarding the use of spontaneous or controlled breath. The aim of this research was to compare the effects of controlled and spontaneous breath on HRV in healthy adults.The present cross-sectional study held a sample of 52 healthy volunteers (22 male, 42.3%), average of 25 (±4) years old and body mass index 22.4 (±2.7) kgm-². All the volunteers were evaluated in 2 days (at most three days between the evaluations). On each day the data were collected with spontaneous and then controlled breath (12 breaths per minute - bpm: Inspiration/Expiration: 2/3). Data were collected with a pulse frequency meter (Polar brand, model 810i). The HRV was evaluated for time and frequency domain and was analyzed using an area corresponding to 5 minutes (containing at least 256 beats per minute) by Kubios program version 2.1. In the time domain, no differences were observed between spontaneous and controlled breath. In the frequency domain, the controlled breathing reduced sympathetic activity (p&lt;0.001) in approximately 14% and increased (p&lt;0.001) parasympathetic activity proportionally. The autonomic modulation evaluated by low frequency/ high frequency(LF/HF) ratio decreased (p&lt;0.001) in average -0.6 on the days evaluated. The controlled breathing (12 bpm) decrease sympathetic activity and increases parasympathetic by modifying the heart autonomic modulation.

Effects of fear of cancer recurrence on subjective physical and mental health in breast cancer patients: The intermediary role of heart rate variability.

Fear of cancer recurrence (FCR) and psychological distress are common psychological problems in breast cancer (BC) patients and ultimately affecting their health-related quality of life (HRQoL). Heart rate variability (HRV) can reflect the activity of the parasympathetic nervous system. However, the pathways through which HRV influences between FCR and HRQoL are unclear. This study preliminarily explored the intermediary role of HRV in FCR and HRQoL in BC patients. A total of 101 BC patients participated in this study. HRV parameters were measured by a 5-min dynamic electrocardiogram. FCR, psychological distress and HRQoL were evaluated by the Fear of disease progression simplified scale (FOP-Q-SF), Distress thermometer and SF-36 concise health survey. The intermediary effect model was established to test the intermediary effect of high frequency-HRV (HF-HRV) on FCR and HRQoL. FCR and psychological distress were negatively correlated with HRV in the time domain, negatively correlated with HF-HRV in the frequency domain, and positively correlated with low frequency/high frequency (LF/HF). HF-HRV had a partial mediating effect on the FCR and physical health and mental health, with effects of 30.23% and 9.53%, respectively. FCR and psychological distress are correlated with HRV parameters in the time domain and the frequency domain, and we preliminarily believe that parasympathetic nerves play an important intermediary role between FCR and subjective physical and mental health. This may provide intervention information for improving the HRQoL of BC patients.

Wildfires correlate with reductions in aboveground tree carbon stocks and sequestration capacity on forest land in the Western United States

In the United States (US), forest ecosystems are the largest terrestrial carbon sink, offsetting the equivalent of >12 % of economy-wide greenhouse gas (GHG) emissions annually. In the Western US, wildfires have shaped much of the landscape by changing forest structure and composition, increasing tree mortality, impacting forest regeneration, and influencing forest carbon storage and sequestration capacity. Here, we used remeasurements of >25,000 plots from the US Department of Agriculture, Forest Service Forest Inventory and Analysis (FIA) program and auxiliary information (e.g., Monitoring Trends in Burn Severity) to characterize the role of fire along with other natural and anthropogenic drivers on estimates of carbon stocks, stock changes, and sequestration capacity on forest land in the Western US. Several biotic (e.g., tree size, species, and forest structure) and abiotic factors (e.g., warm climate, severe drought, compound disturbances, and anthropogenic interventions) influenced post-fire tree mortality and regeneration and had concomitant impacts on carbon stocks and sequestration capacity. Forest ecosystems in a high severity and low frequency wildfire regime had greater reductions in aboveground biomass carbon stocks and sequestration capacity compared to forests in a low severity and high frequency fire regime. Results from this study can improve our understanding of the role of wildfire along with other biotic and abiotic drivers on carbon dynamics in forest ecosystems in the Western US.

Strong shock solutions in symmetric wedge flows: Unphysical or unstable?

This paper investigates the strong shock solutions in a supersonic wedge experimentally, analytically, and numerically. Experiments and computations are conducted on scaled-down models for the two types of shock reflection to be possible. The time-resolved schlieren observation of the flow evolution revealed that the shock formation is a highly dynamic transition of the starting shock from a strong Mach reflection (MR) to a weak regular reflection (RR) via a strong RR reflection over a constant shock wave angle for a wedge angle less than the detachment criterion angle for the shock transition. However, when the wedge angle is greater than the detachment criterion angle, the shock moves over the wedge with the MR structures of diminishing Mach stem height at a constant incident shock wave angle. These intermediate shock reflections are found to be unstable and oscillate at high amplitude and low frequencies to upstream pressure fluctuations. The nature of the intermediate shock reflection during the shock transition over the wedge has also been studied using an unsteady second-order two-dimensional compressible Navier–Stokes solver code with shear stress transport k-ω turbulence modeling. The computed flow parameters around the intermediate shock reflections confirmed that these are indeed strong shock reflections believed to be unphysical in steady wedge flows.

Development of a Microfluidic Formatted Ultrasound-Controlled Monodisperse Lipid Vesicles' Hydrogel Dressing Combined with Ultrasound for Transdermal Drug Delivery System.

Transdermal drug delivery system (TDDS) has attracted much attention in the pharmaceutical technology area. However, the current methods are difficult to ensure penetration efficiency, controllability, and safety in the dermis, so its widespread clinical use has been limited. This work proposes an ultrasound-controlled monodisperse lipid vesicles (U-CMLVs) hydrogel dressing, which combines with ultrasound to form TDDS. Using microfluidic technology, prepare size controllable U-CMLVs with high drug encapsulation efficiency and quantitative encapsulation of ultrasonic response materials, and even uniform mix them with hydrogel to prepare the required thickness of dressings. The high encapsulation efficiency can ensure sufficient dosage of the drugs and further realize the control of ultrasonic response through quantitative encapsulation of ultrasound-responsive materials. Using high frequency (5MHz, 0.4W cm-2 ) and low frequency (60kHz, 1W cm-2 ) ultrasound to control the movement and rupture of U-CMLVs, the contents not only penetrate the stratum corneum into the epidermis but also break through the bottleneck of penetration efficiency, and deep into the dermis. These findings provide the groundwork for deep, controllable, efficient, and safe drug delivery through TDDS and lay a foundation for further expanding its application.

Optimal Configuration of Hybrid Energy Storage Capacity in a Microgrid Based on Variational Mode Decomposition

The capacity configuration of the energy storage system plays a crucial role in enhancing the reliability of the power supply, power quality, and renewable energy utilization in microgrids. Based on variational mode decomposition (VMD), a capacity optimization configuration model for a hybrid energy storage system (HESS) consisting of batteries and supercapacitors is established to achieve the optimal configuration of energy storage capacity in wind–solar complementary islanded microgrids. Firstly, based on the energy mapping relationship between the frequency domain and time domain, the decomposition mode number K of VMD is determined based on the principle of minimum total mode aliasing energy. Then, considering the smoothing fluctuation characteristics of different energy storage components, the dividing point N of high frequency and low frequency in the unbalanced power between the source and load in the microgrid is selected to allocate charging and discharging power instructions for the battery and supercapacitor. Finally, taking the annual comprehensive cost of the HESS as the objective function, a hybrid energy storage capacity optimization configuration model is established, and the dividing point N is used as the optimization variable to solve the model in order to obtain the optimal configuration results. The case study results show that the proposed method is more economical and feasible than the existing energy storage configuration methods.