Search Results (75)

With the growing demand for high-efficiency DC-DC converters with a wide input voltage range for wireless power transmission, four-switch boost converters (FSBBs) are attracting attention due to their low current stress and flexible mode switching characteristics. However, their complex operating modes and nonlinear dynamic characteristics lead to high switching losses and limited efficiency of the system under conventional control. In this paper, an optimization algorithm is combined with the multi-mode control of an FSBB converter for the first time, and a combined optimization and voltage closed-loop control strategy based on the Whale Migration Algorithm (WMA) is proposed. Under the four-mode operation conditions of the FSBB converter, the duty cycle and phase shift parameters of the switching devices are dynamically adjusted by optimizing the values to maximize the efficiency under different operation conditions, with the premise of achieving zero-voltage switching (ZVS) and the optimization objective of minimizing the inductor current as much as possible. Simulation results show that the proposed FSBB switching control strategy combined with the WMA algorithm improves the efficiency significantly over a wide voltage range (120–480 V) and under variable load conditions, and the transfer efficiency is improved by about 1.19% compared with that of the traditional three-mode control, and the maximum transfer efficiency is 99.34%, which verifies the validity and feasibility of the proposed strategy and provides a new approach to the high-efficiency control and application of FSBB converters. Full article

BAW filters have been widely used in RF circuits, and their combination with integrated passive inductors is one of the most common forms of BAW filters. However, the large size of passive inductors increases the area of the filter, making it unable to meet packaging requirements. At the same time, their low quality factor (Q) severely degrades the performance of the BAW filter. This paper presents a miniaturized wide band BAW filter with small-size high-Q active inductor. The active inductor is implemented by a circuit topology with three common-source amplifiers constructed with N-type transistors. The three-stage topology uses a small-size transistor in the middle stage to reduce the parasitic capacitance at the input node, achieving a large inductive bandwidth. The simulation results show that the active inductor has variable inductance from 1 nH to 10 nH, and a quality factor of up to 4 K from 2 to 7 GHz. The 30 × 30 μm² active inductor is embedded in a 4.55–5.05 GHz BAW filter ladder so as to substantially decrease filter size. Simulation results indicate that the BAW filter based on the active inductor achieves a low insertion loss of −1.1 dB, out-of-band rejection of −35 dB on the left side, and out-of-band rejection of −53 dB on the right side. Compared to the traditional passive inductor, this active inductor significantly improves the performance of the BAW filter while occupying a much smaller chip size of 0.83 × 0.75 mm². Full article

This study aimed to determine the chemical composition of five Lavender essential oils (LEOs) using the gas chromatography–mass spectroscopy technique and to assess their antibacterial activity against four marine Vibrio species, including Shewanella algae, Shewanella maridflavi, Vibrio harveyi, and Vibrio alginolyticus. Sensitivity tests were performed using the disk diffusion and serial dilution methods. The results showed that all five LEOs exhibited antibacterial activity against the four tested marine Vibrio species. The antibacterial activities of all five LEOs were above moderate sensitivity. The five LEOs from French blue, space blue, eye-catching, and true Lavender showed high sensitivity, particularly against Shewanella maridflavi. The compounds of LEOs from different varieties of Lavender were similar and mainly comprised linalool, linalyl acetate, eucalyptol, and isoborneol. Different varieties of LEOs possessed unique components besides common components, and the percentage of each one was different, which led to different fragrance loads. The major fragrances were lily of the valley, an aromatic compound fragrance, and an herbal fragrance. The antibacterial activity of LEO from eye-catching Lavender was better than that of others, which could provide a reference for its application in the prevention and control of marine Vibrio spp. and the development of antibacterial products. Full article

Objective: Postpartum depression (PPD) is a common complication after childbirth. Weight misperception can lead to self-esteem issues and mental health problems, especially in women and adolescents. The aim of this study was to investigate the association between weight perception before and during pregnancy and the status of PPD in Southern China. Methods: From October 2021 to November 2023, a multi-stage sampling method was used to recruit 2169 eligible mothers aged 18–49 who had delivered live-born singleton infants within 3 to 180 days postpartum. Anthropometric measurements and face-to-face questionnaire surveys were conducted to collect data. The Kappa test was used to assess the agreement between actual and perceived weight. The generalized linear model incorporating multiplicative interaction analysis was applied to explore the associations among variables. Results: The prevalence of PPD status was 18.0%. Among women, 35.2% perceived their pre-pregnancy weight (PPW) as abnormal, while 33.1% perceived their gestational weight gain (GWG) as inappropriate. There was poor agreement between maternal actual and perceived PPW/GWG (Kappa = 0.366, p < 0.001; Kappa = 0.188, p < 0.001), with 27.8% of women misperceiving their PPW and 52.1% misperceiving their GWG. The results of the general linear model indicated that women who perceived their PPW as underweight (β = 0.70, p = 0.016) or overweight/obese (β = 0.86, p < 0.001), as well as those who perceived their GWG as excessive (β = 0.47, p = 0.028) were more likely to exhibit PPD status. The interaction analysis results showed that those who perceived their PPW as underweight and their GWG as insufficient (β = 1.75, p = 0.020), as well as those who perceived their PPW as overweight/obese and their GWG as excessive (β = 0.90, p = 0.001) had a positive interactive effect on the occurrence of PPD status, while underestimating PPW and GWG may be a protective factor against PPD status (β = −1.03, p = 0.037). Conclusion: These findings support that maternal weight perception plays a role in the development of PPD status. Further improvement is needed in personalized health education for weight management, both prior to and throughout the pregnancy period. This can help women reduce weight anxiety, better understand their body image, and potentially lower the risk of developing PPD. Full article

This paper proposes a lateral-stability-oriented path-tracking controller for four-wheel independent drive (4WID) autonomous vehicles. The proposed controller aims to maintain vehicle stability under extreme conditions while minimizing lateral deviation. Firstly, a tiered control framework comprising upper-level and lower-level controllers is introduced. The upper-level controller is a lateral stability path-tracking controller that incorporates tire dynamic characteristics, developed using model predictive control (MPC) theory. This controller dynamically updates the tire lateral force constraints in real time to account for variations in tire dynamics under extreme conditions. Additionally, it enhances lateral stability and reduces path-tracking errors by applying additional yaw torque based on minimum tire utilization. The lower-level controllers execute the required steering angles and yaw moments through the appropriate component equipment and torque distribution. The joint simulation results from CarSim and MATLAB/Simulink show that, compared to the traditional MPC controller with unstable sideslip, this controller can maintain vehicle lateral stability under extreme conditions. Compared to the MPC controller, which only considers lateral force constraints, this controller can significantly reduce lateral tracking errors, with an average yaw rate reduction of 31.62% and an average sideslip angle reduction of 40.21%. Full article

Electrocardiography (ECG) is the gold standard for monitoring vital signs and for diagnosing, controlling, and preventing cardiovascular diseases (CVDs). However, ECG requires continuous user participation, and cannot be used for continuous cardiac monitoring. In contrast to ECG, photoplethysmography (PPG) devices do not require continued user involvement, and can offer ongoing and long-term detection capabilities. However, from a medical perspective, ECG can provide more information about the heart. Currently, most existing work contains different signals recorded from the same subject in training and test sets. This study proposes a neural network model based on a 1D convolutional neural network (CNN) and a bidirectional long short-term memory (BiLSTM) network. This neural network model can directly reconstruct ECG signals from PPG signals. The learned features are captured from the CNN model and fed into the BiLSTM model. In order to verify the validity of the model, it is evaluated using the MIMIC II dataset in the completely subject-independent model (records are placed in a training set, and a test set appears once, but the test signal belongs to a record that is not in the training set). The Pearson’s correlation coefficient between the reconstructed ECG and the reference ECG of the proposed model is 0.963 in the completely subject-independence model. The results of the proposed model are better than those of several cited state-of-the-art models. The results of our trained model indicate that we can obtain reconstructed ECGs that are highly similar to reference ECGs in the completely subject-independent model. Full article

Toddlerhood (aged 13~36 months) is a period of dietary transition, with water intake being significantly influenced by parental feeding patterns, cultural traditions, and the availability of beverages and food. Nevertheless, given the lack of applicable data, it is challenging to guide and evaluate the water intake of toddlers in China. In this study, our objectives were to assess the daily total water intake (TWI), evaluate the consumption patterns of various beverages and food sources contributing to the TWI, determine the conformity of participants to the adequate intake (AI) recommendation of water released by the Chinese Nutrition Society, and analyze the various contributors to the daily total energy intake (TEI). The data for the assessment of water and dietary intake were obtained from the cross-sectional dietary intake survey of infants and young children (DSIYC, 2018–2019). A total of 1360 eligible toddlers were recruited in the analysis. The differences in related variables between two age groups were compared by Mann–Whitney U test and Chi-Square test. The potential correlation between water and energy intake was examined utilizing age-adjusted partial correlation. Toddlers consumed a median daily TWI of 1079 mL, with 670 mL (62.3%, r = 0.752) derived from beverages and 393 mL (37.7%, r = 0.716) from foods. Plain water was the primary beverage source, contributing 300 mL (52.2%, r = 0.823), followed by milk and milk derivatives (MMDs) at 291 mL (45.6%, r = 0.595). Notably, only 28.4% of toddlers managed to reach the recommended AI value. Among these, toddlers obtain more water from beverages than from foods. The median daily TEI of toddlers was 762 kcal, including 272 kcal from beverages (36.4%, r = 0.534) and 492 kcal from foods (63.6%, r = 0.894). Among these, the median daily energy intake from MMDs was 260 kcal, making up 94.6% of the energy intake from beverages (r = 0.959). As the pioneer survey on TWI of toddlers in China based on nationally representative data, attention to the quality and quantity of water intake and actions to better guide parents by both individuals and authorities are eagerly anticipated. Additionally, the revision of the reference value of TWI for Chinese toddlers is urgently required. Full article

Drought, an intricate natural phenomenon globally, significantly influences the gross primary productivity (GPP) and carbon sink potential of tropical forests. Present research on the drought response primarily focuses on natural forests, such as the Amazon rainforest, with relatively limited studies on tropical plantations. Therefore, for a comprehensive understanding of global climate change, accurately evaluating and analyzing the sensitivity and vulnerability of rubber plantation GPP to various drought characteristics is crucial. The Standardized Precipitation Evapotranspiration Index (SPEI) was used in this research to quantify drought intensity. The Spatially Explicit Individual Based Dynamic Global Vegetation Model (SEIB-DGVM) was localized based on observation data from the Hainan Danzhou Tropical Agro-ecosystem National Observation and Research Station. Subsequently, the calibrated model was utilized to simulate the dynamic process of rubber plantation GPP under multi-gradient drought scenarios (2 extreme boundaries × 3 drought initiation seasons × 4 drought intensities × 12 drought durations × 12 SPEI time scales). The results show that the sensitivity and vulnerability of rubber plantation GPP exhibit significant differences under drought scenarios in different initiation seasons; GPP exhibits higher sensitivity to extreme, long-duration flash droughts in the early rainy season. Regarding vulnerability, the impact of extreme, long-duration flash droughts on GPP is most pronounced. This research lays the foundation for estimating the impact of droughts on the GPP of rubber plantations under future climate change scenarios, providing a scientific basis for enhancing regional ecological restoration and protection. Full article

Burrowing animals are a critical driver of terrestrial ecosystem functioning, but we know little about their effects on soil microbiomes. Here, we evaluated the effect of burrowing animals on microbial assembly processes and co-occurrence patterns using soil microbiota from a group of habitats disturbed by Plateau pikas (Ochtona curzoniae). Pika disturbance had different impacts on bacterial and fungal communities. Fungal diversity generally increased with patch area, whereas bacterial diversity decreased. These strikingly different species–area relationships were closely associated with their community assembly mechanisms. The loss of bacterial diversity on larger patches was largely driven by deterministic processes, mainly due to the decline of nutrient supply (e.g., organic C, inorganic N). In contrast, fungal distribution was driven primarily by stochastic processes that dispersal limitation contributed to their higher fungal diversity on lager patches. A bacterial co-occurrence network exhibited a positive relationship of nodes and linkage numbers with patch area, and the fungal network presented a positive modularity–area relationship, suggesting that bacteria tended to form a closer association community under pika disturbance, while fungi tended to construct a higher modularity network. Our results suggest that pikas affects the microbial assembly process and co-occurrence patterns in alpine environments, thereby enhancing the current understanding of microbial biogeography under natural disturbances. Full article

Electrocardiography (ECG) is generally used in clinical practice for cardiovascular diagnosis and for monitoring cardiovascular status. It is considered to be the gold standard for diagnosing cardiovascular diseases and assessing cardiovascular status. However, it is not always easy to obtain. Unlike ECG devices, photoplethysmography (PPG) devices can be placed on body parts such as the earlobes, fingertips, and wrists, making them more comfortable and easier to obtain. Several methods for reconstructing ECG signals using PPG signals have been proposed, but some of these methods are subject-specific models. These models cannot be applied to multiple subjects and have limitations. This study proposes a neural network model based on UNet and bidirectional long short-term memory (BiLSTM) networks as a group model for reconstructing ECG from PPG. The model was verified using 125 records from the MIMIC III matched subset. The experimental results demonstrated that the proposed model was, on average, able to achieve a Pearson‘s correlation coefficient, root mean square error, percentage root mean square difference, and Fréchet distance of 0.861, 0.077, 5.302, and 0.278, respectively. This research can use the correlation between PPG and ECG to reconstruct a better ECG signal from PPG, which is crucial for diagnosing cardiovascular diseases. Full article

A multifunctional switchable metamaterial device based on graphene, a gold layer, polyimide, vanadiµm dioxide (VO₂), and the sapphire substrate is designed in this paper. The top layer consists of a gold wire, graphene, and two split-ring resonators with the same parameters. By adjusting the Fermi level of graphene, the regulation of BIC and quasi-BIC is realized, and the conversion between BIC and absorber is realized by adjusting the conductivity of VO₂. When the device is converted into a wave-absorbing device with single-band absorption characteristics, the Fermi level of graphene at this time is 0.001 eV, the absorption peak at 0.820 THz is higher than 99.5%, and when the Fermi level of regulated graphene is 1 eV, the absorption peak at 0.667 THz is also higher than 99.5%. The peak frequency of the device is 0.640 THz when it converts to quasi-BIC. To the best of our knowledge, this is the first time that the conversion and regulation of BIC and absorber have been achieved using these two phase change materials. Moreover, by adjusting the parameters of the metamaterial structure, the working efficiency and frequency of BIC and absorber can be dynamically adjusted. The electric field distribution and surface current of metamaterials are further studied, and the physical mechanism of effective absorption and BIC is discussed. These results show that the metamaterials proposed in this paper have many advantages, such as terahertz absorption, BIC, and active device control, and are of great significance for developing terahertz multifunctional devices. Full article

ECG helps in diagnosing heart disease by recording heart activity. During long-term measurements, data loss occurs due to sensor detachment. Therefore, research into the reconstruction of missing ECG data is essential. However, ECG requires user participation and cannot be used for continuous heart monitoring. Continuous monitoring of PPG signals is conversely low-cost and easy to carry out. In this study, a deep neural network model is proposed for the reconstruction of missing ECG signals using PPG data. This model is an end-to-end deep learning neural network utilizing WNet architecture as a basis, on which a bidirectional long short-term memory network is added in establishing a second model. The performance of both models is verified using 146 records from the MIMIC III matched subset. Compared with the reference, the ECG reconstructed using the proposed model has a Pearson’s correlation coefficient of 0.851, root mean square error (RMSE) of 0.075, percentage root mean square difference (PRD) of 5.452, and a Fréchet distance (FD) of 0.302. The experimental results demonstrate that it is feasible to reconstruct missing ECG signals from PPG. Full article

Mental workload is an important predisposing factor for mental illnesses such as depression and is closely related to individual mental health. However, the suboptimal accuracy of utilizing photoplethysmography (PPG) exclusively for mental workload classification has constrained its application within pertinent professional domains. To this end, this paper proposes a signal processing method that combines continuous wavelet transform (CWT) and cardiopulmonary coupling mapping (CPC) to classify mental load via a convolutional neural network (ResAttNet). The method reflects changes in mental workload, as assessed by changes in the association between heart rate variability and respiration. In this paper, the strengths and weaknesses of this method are compared with other traditional psychological workload monitoring methods, such as heart rate variability (HRV), and its validation is performed on the publicly available dataset MAUS. The experiments show that the method is significantly better than previous machine learning methods based on heart rate variability correlation. Meanwhile, the accuracy of the method proposed in this paper reaches 80.5%, which is 6.2% higher than in previous studies. It is comparable to the result of 82.4% for the ECG-based mental workload monitoring system. Therefore, the method of combining CWT and CPC has considerable potential and provides new ideas for mental workload classification. Full article

As an essential physiological indicator within the human body, noninvasive continuous blood pressure (BP) measurement is critical in the prevention and treatment of cardiovascular disease. However, traditional methods of blood pressure prediction using a single-wavelength Photoplethysmographic (PPG) have bottlenecks in further improving BP prediction accuracy, which limits their development in clinical application and dissemination. To this end, this study proposed a method to fuse a four-wavelength PPG and a BP prediction model based on the attention mechanism of a convolutional neural network and bidirectional long- and short-term memory (ACNN-BiLSTM). The effectiveness of a multi-wavelength PPG fusion method for blood pressure prediction was evaluated by processing PPG signals from 162 volunteers. The study compared the performance of the PPG signals with different individual wavelengths and using a multi-wavelength PPG fusion method in blood pressure prediction, assessed using mean absolute error (MAE), root mean squared error (RMSE) and AAMI-related criteria. The experimental results showed that the ACNN-BiLSTM model achieved a better MAE ± RMSE for a systolic BP and diastolic BP of 1.67 ± 5.28 and 1.15 ± 2.53 mmHg, respectively, when using the multi-wavelength PPG fusion method. As a result, the ACNN-BiLSTM blood pressure model based on multi-wavelength PPG fusion could be considered a promising method for noninvasive continuous BP measurement. Full article

Reservoir engineering plays a critical role in achieving rational water resource allocation, providing ecological services, and promoting regional development. However, in the formulation of water allocation plans, there is often a tendency to prioritize meeting regional water demand while overlooking ecological benefits. This study develops a multi-objective water allocation model based on evaluating ecosystem services value supply and demand, integrating indicators such as ecosystem service fulfillment ability, water resources fulfillment ability, and equilibrium operation degree. Different development scenarios are also established using a forecasting model to formulate water allocation plans and apply a case study of the Datun Reservoir, a key hub on the eastern route of the South-to-North Water Diversion Project in China. This study demonstrates that (1) by optimizing the allocation of domestic and industrial water supply and reservoir storage, the overall ecosystem service value of the Datun Reservoir can be enhanced by 5.15% to 11.36% and (2) in scenarios of high economic growth, there is potential to achieve coordination between water supply and ecosystem service value. (3) However, lower-than-expected economic growth may lead to a trade-off between ecosystem services and water supply capacity in the reservoir, which could be maintained at a lower level. The methods proposed in this paper are of significant practical importance for guiding rational reservoir water allocation and achieving coordination between ecological services and water supply capacity. Full article