Search Results (87)

Electrocardiogram (ECG) and photoplethysmogram (PPG) signals are widely used noninvasive methods for assessing cardiovascular activity and provide complementary information about the cardiac cycle. ECG records cardiac electrical activity, whereas PPG records optically detected blood-volume changes in peripheral tissue. This paper describes a synchronized ECG-PPG dataset collected from 148 apparently healthy subjects under a controlled seated protocol at rest and during post-exercise recovery after two treadmill-running conditions. Signals were acquired using a custom card-type handheld finger-contact prototype that records single-lead ECG and dual-wavelength PPG at 660 nm and 940 nm concurrently. The dataset contains 444 condition-specific records, with each subject contributing one seated resting record, one seated recovery record after light treadmill running, and one seated recovery record after moderate treadmill running. Both raw ADC-count signals and preprocessed signals are provided, and the accompanying software and example code are publicly available. The dataset is intended for research on synchronized ECG-PPG signal analysis, waveform-quality assessment, controlled post-exercise recovery physiology, and exploratory PPG-to-ECG reconstruction under controlled conditions. It should not be interpreted as a free-living wearable dataset or as clinical diagnostic ECG ground truth without external validation. Full article

With the advancement of wide-area measurement systems (WAMSs), response-driven methods for transient stability analysis have gained increasing attention in recent years. The maximum Lyapunov exponent (MLE)-based trajectory analysis technique enables online transient stability assessment by capturing the trend characteristics of system trajectories. Motivated by this capability, a rapid construction methodology for the practical dynamic security region (PDSR) is proposed based on the MLE criterion. Initially, through analyzing the dynamic characteristics of generator rotor angle trajectories after disturbances, the dynamic MLE characteristics of the generator’s angular velocity deviation trajectory are extracted to formulate the MLE-based stability criterion. Subsequently, a stability boundary function based on MLE trajectories is developed, and the linear relationship between the injection space parameters and the MLE stability boundary function is derived. Finally, leveraging the sensitivity of the stability boundary function to the variations in injection space parameters, the dynamic security region is constructed around the dominant instability critical point, thereby establishing a mapping function between transient stability and the injection space parameters. The effectiveness of the proposed method is demonstrated through simulations on the IEEE39 power system. Results show that the method exhibits promising performance in terms of speed and adaptability for transient stability analysis and boundary construction. Full article

Hypertension is a major risk factor for cardiovascular disease and requires effective long-term monitoring. Photoplethysmography (PPG), acquired from wearable optical sensors, offers a convenient and non-invasive signal source for cuffless blood pressure (BP) estimation, but existing studies have mainly emphasized model architecture optimization, with limited systematic investigation of signal representation. This study systematically compares seven one-dimensional-to-two-dimensional signal transformation methods and evaluates multiple architectural variants for PPG-based cuffless BP estimation under a unified framework. Experiments were conducted using PPG and arterial BP signals from the UCI Open Blood Pressure Database. The best-performing configuration, based on continuous wavelet transform (CWT), achieved estimation errors of 3.80 ± 5.02 mmHg for systolic BP and 1.65 ± 2.70 mmHg for diastolic BP. Further real-world validation on 26 participants using an Omron cuff-based monitor as the reference showed good consistency, with correlation coefficients of R = 0.96 for SBP and R = 0.74 for DBP. The results demonstrate that appropriate signal representation, particularly CWT, plays a critical role in improving estimation accuracy and robustness, and may facilitate the development of wearable cuffless BP monitoring systems. Full article

Power flow (PF) analysis is fundamental for power system operation and planning, yet traditional methods like Newton–Raphson face problems in convergence and computational efficiency. While deep learning (DL) offers promising solutions, its “black-box” nature and unstable training dynamics hinder practical adoption. This paper proposes a physics-informed neural network (PINN) framework integrated with a novel half-inverse gradient (HIG) mechanism to address these limitations. First, a systematic study of gradient scaling in PF optimisation found that the lack of enough inverse matrix compensation was the main cause of training instability. Second, we design a residual-driven HIG method that compensates gradient matrices via inverse operations, enabling accelerated convergence while maintaining numerical stability. Third, we develop parameterized voltage variables with differentiable activation functions to enforce hard operational constraints. The HIG optimizer leverages automatic differentiation and truncated singular value decomposition to balance diagonal/non-diagonal gradient information, achieving 99% accuracy in case4gs and case30 studies. Experiments on case118 demonstrate the framework’s scalability, with 65% accuracy compared to about 38% for baseline physics-informed approaches. Full article

Arrhythmia is a common and potentially life-threatening cardiovascular condition. Photoplethysmography (PPG) has emerged as a noninvasive alternative to electrocardiography for cardiac rhythm monitoring, yet most PPG-based methods remain limited to binary classification. In this study, a new deep learning approach is suggested for categorizing six arrhythmia types from PPG data: sinus rhythm (SR), premature ventricular contraction (PVC), premature atrial contraction (PAC), ventricular tachycardia (VT), supraventricular tachycardia (SVT), and atrial fibrillation (AF). The raw PPG signal is enhanced by extracting its first and second derivatives to capture morphological features not readily apparent in the original signal. A hybrid architecture, VGG-BiLSTM, is utilized, merging VGG convolutional layers for spatial features extraction with bidirectional long short-term memory layers for modeling temporal dependencies. A stratified data splitting strategy is further adopted to address class imbalance across arrhythmia types. A publicly available dataset containing 46,827 PPG segments from 91 individuals was employed to assess the effectiveness of the suggested technique. The method yielded an overall accuracy, sensitivity, specificity and F1 score of 88.7%, 78.5%, 97.6% and 80.5% correspondingly. Full article

Early diagnosis of Alzheimer’s disease represents a critical clinical challenge, and the high-sensitive biomarkers measurement holds great potential for enabling early identification and intervention. This study proposes an electrochemical immunosensing strategy based on inkjet printing for the quantitative detection of Tau-441. Conductive patterns were formed by inkjet printing, followed by surface functionalization with gold nanoparticles to immobilize highly specific anti-Tau-441. This process created a stable and high affinity immunorecognition interface that enhances electron transfer and signal amplification. Furthermore, we developed and integrated a low-power portable detection platform to achieve a rapid detection process encompassing sample loading, signal acquisition, and on-device readout. The method shows a linear response from 50 fg/mL to 10 ng/mL and a limit of detection of 16 fg/mL (S/N = 3), with high specificity and good reproducibility. By combining scalable inkjet fabrication with a self-contained handheld reader, this method shortens the path from sample to result and offers a practical route for on-site screening and early intervention in Alzheimer’s disease. Full article

Advanced glycation end products (AGEs) are important biomarkers associated with diabetes and metabolic disorders; yet existing detection methods are invasive and unsuitable for frequent monitoring. This study aimed to develop a non-invasive and portable AGEs detection device, optimize strategies for mitigating pigmentation-related interference, and evaluate its feasibility for metabolic assessment. The proposed system employs a 365 nm ultraviolet LED excitation source, an optical filter assembly integrated into an ergonomic dark chamber, and an eyelid-signal-based algorithm to suppress ambient light and skin pigmentation interference. Simulation experiments were conducted to evaluate the influence of different pigment colors and skin tones on fluorescence measurements. A clinical study was performed in 200 participants, among whom 42 underwent concurrent serum AGEs measurement as the reference standard. Predictive models combining corneal fluorescence signals and body mass index (BMI) were constructed and evaluated. The results indicated that purple and blue pigments introduced greater interference, whereas green and pink pigments had minimal effects. Device-derived AGEs estimates demonstrated good agreement with serum AGEs, with a mean error below 8%. A hybrid model incorporating BMI achieved improved predictive accuracy compared with single-parameter models. Participants with high-AGE dietary habits exhibited elevated fluorescence signals and BMI. These findings suggest that the proposed device enables stable and accurate non-invasive AGEs assessment, with potential utility for metabolic monitoring. Incorporating lifestyle-related parameters may further enhance predictive performance and expand clinical applicability. Full article

The integration of generative artificial intelligence (GenAI) into educational contexts has sparked significant interest in its potential to enhance learning engagement. However, empirical findings remain inconsistent, and a systematic synthesis of its effects across distinct engagement dimensions is lacking. This meta-analysis synthesizes evidence from 31 empirical studies (91 effect sizes), with the core aim of investigating the relationship between learners’ GenAI use and learning engagement, alongside the role of key moderating variables. Results indicate that GenAI exerts a significant positive effect on overall learning engagement, demonstrating the strongest impact on cognitive engagement, followed by affective and behavioral engagement. Moderator analyses reveal nuanced, sub-dimension-specific effects: the positive influence is most pronounced in higher education, shows significant benefits across all three sub-dimensions in basic education, and is non-significant in continuing education; medium-duration interventions (1 day–1 month) yield the largest effects; and teacher intervention significantly amplifies gains in cognitive engagement. Both learning mode and interaction approach exert significant positive effects on overall learning engagement, while their impacts on the sub-dimensions did not show significant heterogeneity. This study enriches the theoretical system of educational technology integration by clarifying the directional effect and moderating mechanisms of GenAI on learning engagement and provides a nuanced evidence base for designing context-sensitive implementations, offering valuable insights for fostering personalized and engaging learning experiences. Full article

Background: Over the past several decades, the prevalence of gestational diabetes mellitus (GDM) has risen markedly worldwide, posing serious threats to both maternal and child health by increasing adverse pregnancy outcomes and long-term metabolic risks. Developing effective risk prediction tools for early detection and intervention has become the most important clinical priority in this field. The current GDM prediction models primarily rely on non-modifiable factors, for example age and body mass index, while modifiable factors such as lifestyle and health literacy, although strongly associated with GDM, have not been fully utilized in risk assessment. This study sought to establish and validate a nomogram prediction model combining modifiable and non-modifiable risk factors, with the goal of identifying high-risk Chinese pregnant women with GDM at an early stage and promoting targeted prevention and personalized prenatal management. Methods: A multicenter study was conducted across 7 maternal health institutions in Southern China (2021–2023), enrolling 806 singleton pregnant women (14–23⁺⁶ weeks). The collected data included sociodemographic, clinical history, and modifiable factors collected through validated questionnaires: dietary quality, physical activity level, sleep quality, and nutrition and health literacy. GDM was diagnosed via 75 g oral glucose tolerance test at 24–28 weeks. Predictive factors were identified through multi-variable logistic regression. A nomogram model was developed (70% modeling group) and validated (30% validation group). Receiver operator characteristic curves, calibration curves, and decision curve analysis were used to evaluate the prediction ability, the degree of calibration, and the clinical benefit of the model, respectively. Results: The finalized risk prediction model included non-modifiable factors such as maternal age, pre-pregnancy weight, and maternal polycystic ovary syndrome, as well as modifiable factors including dietary quality, physical activity level, sleep quality, nutrition and health literacy. The application of the nomogram in the modeling group and the validation groups showed that the model had high stability, favorable predictive ability, good calibration effect and clinical practicality. Conclusions: Overall, the integrated model demonstrates significant clinical utility as it facilitates the prompt identification of individuals at heightened risk and offers actionable targets for personalized interventions. In terms of future implementation, this model can be integrated into prenatal care as a rapid scoring table during early pregnancy consultations or incorporated into mobile health applications. This approach fosters precise prevention strategies for GDM in maternal health by emphasizing nutrition and health literacy, supplemented by coordinated adjustments in diet, physical activity, and sleep. Full article

International scholars have frequently interpreted Sengzhao’s 僧肇 Things Do Not Shift (wubuqianlun 物不遷論, hereafter TDNS) as reflecting the doctrinal positions of the Sarvāstivāda or Sautrāntika schools. This paper argues that the core issue lies in the relationship between Sengzhao’s concept of “not shifting” and Kṣaṇikavāda (the theory of momentary arising and ceasing). A genealogical examination reveals that this interpretive view originated during the Tang dynasty—particularly in Chengguan’s 澄觀 citation of Vasubandhu’s Abhidharmakośabhāṣya (hereafter AKBh), which includes a dual-layered implication: both “not shifting based on Kṣaṇikavāda (cha’na shengmie buqian 剎那生滅不遷)” and “not shifting based on śūnyatā (xingkong buqian 性空不遷)”. However, Chengguan did not make a conclusive judgment. This dual implication was already clearly distinguished by Yanshou 延壽 in the late Tang period. Yanshou pointed out that “not shifting based on Kṣaṇikavāda” presupposes the real existence of dharmic entities, whereas Sengzhao’s view belongs to the Mahāyāna orientation of “not shifting based on śūnyatā”—thus marking a fundamental doctrinal distinction. In contrast, by the late Ming period, Zhencheng 镇澄 misinterpreted Chengguan’s argument out of context and reduced it to a heterodox doctrine of “not shifting based on the abide of inherent nature (xingzhu buqian 性住不遷)”. Later Ming masters such as Deqing 德清, Zhenjie 真界, and Huanyou 幻有 also emphasized the dual aspects in Chengguan’s explanation and directly refuted Zhencheng’s misreading. Therefore, the issue in equating Sengzhao’s TDNS with “not shifting based on Kṣaṇikavāda” does not lie in the difference between “not shifting based on Kṣaṇikavāda” and “not shifting based on śūnyatā” as this distinction was acknowledged on both sides. Rather, the key lies in identifying the doctrinal basis of Sengzhao’s argument: to which category does it properly belong? To answer this, the paper analyzes the conceptual structure of TDNS in contrast to the idea of “not shifting based on Kṣaṇikavāda”, and finds a fundamental divergence in their understanding of whether phenomena are subject to arising and ceasing. Sengzhao’s notion of TDNS is not the same as the concept of “not shifting based on Kṣaṇikavāda”. It will then analyze the differences between the two in their understandings of substance and time, revealing a fundamental divergence in their perspectives on whether phenomena undergo arising and cessation. Sengzhao’s concept in TDNS is not equivalent to the notion of “not shifting based on Kṣaṇikavāda”, nor are they congruent in terms of the consequent conceptions of entities, time, and the view of temporal flow that emerge from these respective frameworks. Full article

With the rapid economic and social development and the increasingly severe water shortage situation, the sustainable utilization of unconventional water resources is of great significance. As one of the “second water sources”, the full utilization of highly mineralized mine water (HMMW) is a key strategy for promoting sustainable development in water-scarce regions. It has obvious resource, environmental, and economic benefits that are central to sustainability. However, the mechanism of the impact of HMMW utilization on water utilization, the environment, and the economy is still unclear, making it difficult to evaluate its overall sustainability performance and to provide scientific data support to promote HMMW utilization. Therefore, this paper develops a novel sustainability-oriented accounting framework to assess the environmental–economic sustainability of HMMW utilization. Firstly, this paper proposes the method of calculating the HMMW utilization environmental benefits, proposes a novel integrated environmental–economic input–output accounting framework, which refines the HMMW sector from the traditional water industry and integrates the environmental benefits into a balanced input–output table. Secondly, taking Ningdong Energy Chemical Industry Base (NECI Base) as an example, this paper conducts applied research on the integrated environmental–economic accounting of HMMW utilization: (I) The HMMW environmental benefits of NECI Base are calculated, the utilization of 22.69 million m³ of HMMW generated environmental benefits, valued at 233.69 million CNY, demonstrating its substantial contribution to environmental sustainability. The compiled environmental–economic input–output table passed the balance verification, confirming the robustness and practicality of the accounting method. Full article

Background: Adequate hydration for preschool children (36–72 months) is critical for their healthy growth, cognitive development, and long-term well-being. However, there is still a lack of reliable baseline data in China to inform water intake guidelines for this age group. Methods: In this study, a cross-sectional analysis was conducted using data from the 2018–2019 Dietary Survey of Infants and Young Children in China, including 676 healthy preschool children. Water and energy intake were estimated using four-day food diaries. Their daily total water intake (TWI) and total energy intake (TEI) were evaluated, and the contributions of beverages and foods to TWI and TEI were analyzed, respectively. The TWI was compared with the adequate intake (AI) set by the Chinese Nutrition Society, and the correlations between water and energy intake were explored. Results: The results show that the median daily TWI was 1218 mL, with 667 mL (55.7%, r = 0.824) from beverages and 520 mL (44.3%, r = 0.691) from foods. Among beverages, plain water (74.4%, r = 0.903) and milk and milk derivatives (MMDs, 20.9%, r = 0.443) were the main contributors, while staple foods, dishes, and soup contributed the majority of the water from foods. Only 19.4% of children’s TWI met the AI level, and their water and energy intake was significantly higher than those who did not. The median daily TEI was 994 kcal, with 739 kcal (77.2%, r = 0.806) from foods and 198 kcal (22.8%, r = 0.644) from beverages. MMDs accounted for 83.1% of beverage energy (r = 0.880). Boys consumed more beverages than girls, especially in the 37–48 months group. Conclusions: As the first nationally representative study of TWI among Chinese preschool children, these findings reveal a substantial gap between actual intake and current recommendations, and highlight the need to revise reference values and improve hydration guidance in early childhood. Full article

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