Heart rate variability (HRV) is a potential biomarker that might demonstrate the effects of mindfulness, but it might be influenced by practice experiences. This study wanted to elucidate the possibility of using HRV metrics to reveal the effects of mindfulness and examine its variation between novice and experienced mindfulness practitioners. Forty-six participants (20 experienced practitioners, 26 novices) were enrolled to practice 14-day mindfulness training. HRV data were collected during three phases (20 min baseline, T1; 20 min mindfulness, T2; 20 min post-mindfulness, T3) using Holter monitoring. The linear mixed model was conducted to explore the effects of group and time based on standardized data. The experienced group had higher full-scale scores of FFMQ both in the pre-test (t = -3.34, df = 44, p = 0.002) and the post-test (t = -2.35, df = 44, p = 0.025). Both groups showed significant changes in HRV indices (e.g., RMSSD, SDNN, LnHF) from T1 to T2 or T3 (p < 0.05). In the experienced group, significant fluctuations (p < 0.05) were observed at T2, followed by recovery at T3, in SD1/SD2, Sample Entropy, normalized High Frequency (HFn), DFA_α1, and DFA_α2. In contrast, the novice participants only showed monotonic changes in SD1/SD2 and DFA_α1. This study revealed significant HRV changes during mindfulness practice, with distinct patterns observed between novice and experienced practitioners.

Although reduced heart rate variability (HRV) has been observed in adolescents with major depressive disorder (MDD), substantial between-study heterogeneity and conflicting outcomes exist. Moreover, few studies have investigated heart rate asymmetry (HRA) features despite the high sensitivity of nonlinear indices to heart rate fluctuations. This study aimed to investigate the variations in HRV measures, especially the nonlinear features of HRA, among adolescents with MDD during the nocturnal sleep period. Adolescents with MDD and healthy controls completed the clinical assessment of depressive symptom severity and sleep quality followed by a three-night sleep electrocardiogram (ECG) monitoring. Traditional time-domain and frequency-domain HRV measures, nonlinear HRA measures, and the prevalence of different HRA forms and HRA compensation were calculated. A total of 61 participants with 154 nocturnal ECG time series were available for analysis. Vagally-mediated HRV measures, such as RMSSD, PNN50, and HF, as well as C1d were statistically lower in clinically depressed adolescents compared with healthy controls, whereas C2d was significantly higher. A substantial decrease in the prevalence of short-term HRA, long-term HRA, and the corresponding compensation effect were also observed. In contrast to the medium to large effect sizes observed in traditional HRV indices, nonlinear HRA features showed extremely large effect sizes in discriminating MDD (C1d: Cohen’s d= − 1.38; C2d: Cohen’s d = 1.11), and exhibited a statistical correlation with the severity of depression (C1d: rho = − 0.269; C2d: rho = 0.243). Moreover, there were no significant differences in the distributions of nocturnal HRA measures collected over various nights. Adolescents with MDD suffered a significant decrease in vagal tone compared to healthy controls, and the features focusing on the directionality of heart rate variations may provide further information on cardiac autonomic activity associated with depression.

ObjectiveThis study aims to employ physiological model simulation to systematically analyze the frequency-domain components of PPG signals and extract their key features. The efficacy of these frequency-domain features in effectively distinguishing emotional states will also be investigated.MethodsA dual windkessel model was employed to analyze PPG signal frequency components and extract distinctive features. Experimental data collection encompassed both physiological (PPG) and psychological measurements, with subsequent analysis involving distribution patterns and statistical testing (U-tests) to examine feature-emotion relationships. The study implemented support vector machine (SVM) classification to evaluate feature effectiveness, complemented by comparative analysis using pulse rate variability (PRV) features, morphological features, and the DEAP dataset.ResultsThe results demonstrate significant differentiation in PPG frequency-domain feature responses to arousal and valence variations, achieving classification accuracies of 87.5% and 81.4%, respectively. Validation on the DEAP dataset yielded consistent patterns with accuracies of 73.5% (arousal) and 71.5% (valence). Feature fusion incorporating the proposed frequency-domain features enhanced classification performance, surpassing 90% accuracy.ConclusionThis study uses physiological modeling to analyze PPG signal frequency components and extract key features. We evaluate their effectiveness in emotion recognition and reveal relationships among physiological parameters, frequency features, and emotional states.SignificanceThese findings advance understanding of emotion recognition mechanisms and provide a foundation for future research.

Sadness can be a harbinger of serious medical conditions and a primary manifestation of depressive symptoms. Music is a promising modality for regulating sadness, although its effect on participants, whether with or without long-term depressive symptoms, remains unknown. In this study, the effect of music on sadness regulation was investigated using psychological and physiological indicators between depressed and non-depressed individuals. Data were collected from 149 participants (18 to 29 years old). The participants were divided into two groups (depressed and non-depressed groups) based on their depressive symptoms, experienced sadness induction, and music intervention. Electrocardiogram signals were collected to measure heart rate variability (HRV). (1) Music alleviated sadness (increasing positive emotions, valence and dominance, while decreasing arousal); (2) sadness increased the duration between consecutive heartbeats, and music decreased this duration; (3) participants with depressive symptoms showed lower HRV than those without, and music enhanced HRV for the depressed group; (4) no significant difference in the effects of music was found between the two groups; and (5) the regulatory impact of music on sadness was not influenced by prior music listening habits. The findings indicate that listening to music can be beneficial for both healthy and subclinical individuals when managing sadness. The findings underscore the importance of recognizing the role of music in promoting emotional well-being. This trial was retrospectively registered on ClinicalTrials.gov PRS (Protocol Registration and Results System) (number NCT06516666) on July 22, 2024 ( https://register.clinicaltrials.gov/ ).

Strain-induced complex surface patterns such as folds, herringbones and stripes are quite useful in a wide range of practical applications. Although various surface patterns have been extensively investigated, precisely control of fold morphology and morphology evolution remains a challenge. In this work, we report on a characteristic wrinkled Cr pattern evolution on liquid substrates by regulating film thickness. After deposition, a compressive stress induced by the thermal contraction is introduced in the Cr film and thus the folds and wrinkling patterns (such as straight stripes and herringbones) are formed. It is found that the average width l of the domains decreases with the film thickness h. When the thick film is small (h = 36 nm), the domain width l is large and thus folds have a weak effect on the centre of the domain. In this case, herringbone patterns form in the domain centre due to biaxial stress. While, as h increases and domain width l decreases, herringbone patterns evolve into straight stripes gradually. Based on the above analysis, the formation mechanism and stress distribution of the wrinkling patterns is discussed.

Depression manifests significant emotional dysregulation, characterized by heightened sadness susceptibility and attenuated happiness responsiveness in individuals with depression (IWD). This study employs structured emotion induction protocols to analyze physiological response disparities between IWD and healthy controls (HC) across multiple affective states, establishing empirical foundations for optimizing affective computing-based depression screening. Dual-phase statistical identification was conducted using Mann–Whitney U tests: initially verifying emotion elicitation validity by comparing HRV features between emotional states and resting conditions, subsequently detecting IWD/HC response differences within each emotion. Machine learning frameworks were then constructed leveraging HRV features and intergroup differential response patterns. Comparative analysis revealed generally consistent directional patterns and response magnitudes across groups for most features, while critical divergences emerged characterized by heightened sadness reactivity in IWD alongside attenuated happiness responsiveness. Implemented models achieved 76.8

Video object detection is essential for human-interaction applications, including bimanual manipulation sensing (BMS). The effects of video detection in practical applications still need to be improved, as they are restricted by long-range spatiotemporal dependency analysis. How do humans sense bimanual manipulation in videos, especially for deteriorated clips? We argue that humans analyze the current clips based on earlier memory, namely, long-term spatial and temporal dependencies (LTSTD). However, most existing methods have yet to report significant results, as the limited exploration of these dependencies limits them. Developing an easy-to-integrate module is generally preferred for future applications rather than designing a complex end-to-end framework. Therefore, we propose a dynamic neighborhood feature multiplexing mechanism for online video object detection in this article, which is better at learning LTSTD in flexible and robust ways, boosting existing detection results, called DNFM. Specifically, we develop dynamic memory enhancement neural networks for better long-term feature aggregation with negligible additional computation costs. We multiplex each frame feature to aggregate key enhanced representations under the guidance of dynamic memory recall. The DNFM contributes to various famous detectors in BMS and other challenging detection tasks, and particular attention has been devoted to “low-quality” frame detection. Experimental results show that, while achieving state-of-the-art detection performance, DNFM clearly illustrates the easy-to-integrate operation for boosting the video object detection results.

INTRODUCTION:Rheological properties, as critical material attributes (CMAs) of solid dispersion drugs such as dripping pills, affect the melting, dispersion, and solidification. Therefore, characterization and assessments of rheological properties in the pharmaceutical process are important in enhancing drug stability and bioavailability. OBJECTIVES:The study aimed to develop a method for analyzing the rheology of molten materials, assessing their consistency and how rheological properties affect the dripping process and pills quality. MATERIALS AND METHODS:The rheological behavior of molten materials composed of Ginkgo biloba leaf extract (GBE) and polyethylene glycol (PEG) 4000 was characterized. Batch consistency of molten materials was evaluated. Image monitoring technology was utilized to capture and process images of the droplet formation process. We established the relationship between the rheological properties of molten materials and various attributes. RESULTS:The quality consistency of molten materials was evaluated, with 12 batches showing similarity above 0.8. The MLR models showed strong correlations (R2 > 0.80) between rheological properties and evaluation attributes. The rheological properties, including consistency coefficient, flow index, and viscosity at 80°C, were identified as critical rheological properties of the molten materials. Rheological property differences of molten materials have an impact on the morphology of droplet and quality performance. CONCLUSION:A rheological method was established, enabling quality consistency evaluation of molten materials in dripping pills. This study revealed the influence of rheological properties on droplet formation process and dripping pills quality, providing a reference for researches on material attributes control of other traditional Chinese medicine dripping pills.

Self-acceptance is known as a strong indicator of positive mental health, and previous studies have identified the relationship between self-acceptance and emotion. However, there are still some research gaps in the relationship between self-acceptance and key emotional variables such as emotional awareness and emotional regulation strategies. The exploration of this part is also more conducive to the future design of effective ways to improve self-acceptance. Considering the Gross emotion regulation model, the present study aimed to examine the relationship between emotional awareness and self-acceptance, as well as the mediating effect of emotion regulation strategies. Data were collected from 419 college students in Zhejiang Province, China. Results showed that emotional awareness (IV) was positively associated with self-acceptance (DV), and both cognitive reappraisal and expressive suppression partially mediated the influence of emotional awareness on self-acceptance. The results suggest an underlying mechanism between emotional awareness and self-acceptance with emotion regulation strategies as robust factors. Theoretically, all of the results had significant ramifications and supplemented the empirical findings in the area of emotional health and self-acceptance. The mediation model also offers emotion-based innovation for approaches to improve self-acceptance, which has practical implications.

Benefiting from the advanced human visual system, humans naturally classify activities and predict motions in a short time. However, most existing computer vision studies consider those two tasks separately, resulting in an insufficient understanding of human actions. Moreover, the effects of view variations remain challenging for most existing skeleton-based methods, and the existing graph operators cannot fully explore multiscale relationship. In this article, a versatile graph-based model (Vers-GNN) is proposed to deal with those two tasks simultaneously. First, a skeleton representation self-regulated scheme is proposed. It is among the first trials that successfully integrate the idea of view adaptation into a graph-based human activity analysis system. Next, several novel graph operators are proposed to model the positional relationships and learn the abstract dynamics between different human joints and parts. Finally, a practical multitask learning framework and a multiobjective self-supervised learning scheme are proposed to promote both the tasks. The comparative experimental results show that Vers-GNN outperforms the recent state-of-the-art methods for both the tasks, with the to date highest recognition accuracies on the datasets of NTU RGB $+$ D (CV: 97.2%), UWA3D (88.7%), and CMU (1000 ms: 1.13).

Skeleton-based human recognition is a key technology for visual feedback, which can help the Internet of Things (IoT) interact with humans in a non-contact manner outdoors. Graph Convolutional Networks (GCNs), achieving an intuitive understanding of the human skeleton, have received increasing attention. Although current GCN-based works explore how to model unlinked body parts, they still show weak robustness in noisy solid data, such as joint/frame loss, which often happens in outdoor IoTs. Towards robust visual feedback, in the paper, we propose robust skeleton-based action recognition neural networks (Robust-SAR), a new cost-efficient approach for recognizing activities in noisy outdoor scenarios. Instead of estimating 2D or 3D skeleton coordinates, we first extract the heatmap of human poses from videos. We propose S-pose to learn heatmap at multiple levels, i.e., joint, joint-scale, and part-scale learning, boosting higher-order motion pattern learning. Additionally, we propose T-pose to adaptively employ the features of previous frames to enhance the current frame, further enhancing the robustness of spatiotemporal human representation. Experimentally, Robust-SAR achieves state-of-the-art recognition results on four benchmarks, including NTU-60, NTU-120, NUCLA, and Kinetics-400 (outdoor datasets). Furthermore, in noise-filled outdoor conditions, the performance of Robust-SAR only drops by about 0.5%, while other state-of-the-art methods drop by about 2%.

Mindfulness could benefit on mental and physical health. Through repeated practice, progression of mindfulness could be found. Except for self-report questionnaires, heart rate variability (HRV) is a potential biomarker to demonstrate the effects of mindfulness. However, few studies focus on the changes in HRV which may vary through repeated practice. This study aims to explore whether HRV could demonstrate progression of mindfulness through repeated practice. 20 experienced practitioners and 26 novices were enrolled to practice two-week mindfulness and completed the Five Facet Mindfulness Questionnaire pre and post the training. ECG signals were collected by holter monitors, covering baseline to training and 9 HRV metrics were extracted. The results indicate that the experienced group showed significantly increased parasympathetic activity during mindfulness training and the effects were stable through repeated practice, while the novice group showed high cognitive load, with inconspicuous but probably progressive effects. The findings indicate that HRV could demonstrate progression of mindfulness through repeated practice, suggesting the possibility of assessing mindfulness based on HRV.

The monoclonal antibody (mAb) manufacturing process comes with high profits and high costs, and thus mAb productivity is of vital importance. However, many factors can impact the cell culture process, and lead to mAb productivity reduction. Nowadays, the biopharma industry is actively employing manufacturing information systems, which enable the integration of both online data and offline data. Although the volume of data is large, related data mining studies for mAb productivity improvement are rare. Therefore, a data-driven approach is proposed in this study to leverage both the inline and offline data of the cell culture process to discover the causes of mAb productivity reduction. The approach consists of four steps, namely data preprocessing, phase division, feature extraction and fusion, and cluster comparing. First, data quality issues are solved during the data preprocessing step. Next, the inline data are divided into several phases based on the moving window k-nearest neighbor method. Then, the inline data features are extracted via functional data analysis and combined with the offline data features. Finally, the causes of mAb productivity reduction are identified using the contrasting clusters via the principal component analysis method. A commercial-scale cell culture process case study is provided in this research to verify the effectiveness of the approach. Data from 35 batches were collected, and each batch contained nine inline variables and seven offline variables. The causes of mAb productivity reduction were identified to be the lack of nutrients, and recommended actions were taken according to the result, which was subsequently proven by six validation batches.

Cropland abandonment is a widespread land-change phenomenon globally, driven by complex social, economic, and political transformations, with significant implications for the environment and society. However, the inconsistent definitions of cropland abandonment and the bias in the selection of study areas hinder the comparison of identified determinants of cropland abandonment. In this study, we defined cropland abandonment from the perspective of vegetation succession. By employing land-cover change detection with Landsat timeseries images from 1990 to 2021, we analyzed the spatiotemporal patterns of cropland abandonment across the cities of Yibin, Huangshi, and Chaohu within the Yangtze River Economic Belt. Furthermore, we utilized the Gradient-Boosting Decision Tree model to identify the primary spatial determinants and their relative importance in each city. Our findings showed significant variations in abandonment rates by 2021. Huangshi exhibited the highest rate of cropland abandonment, leading with 53.81+2.1 %, followed by Yibin with 45.36+2.18 %, and Chaohu with the lowest at 36.94+2.21 %. Interestingly, a consistent spatiotemporal trend, areas with higher abandonment rates tended to be abandoned earlier, emerged in abandonment determinants across cities. Areas with lower bulk density, higher soil clay content, and greater organic carbon contents exhibited higher abandonment rates. However, the most influential determinants varied: the distance from water bodies was the most important in Yibin and Huangshi, while the distance from the forest had a greater impact in Chaohu. This study offers a more nuanced perspective on defining cropland abandonment and can be applied to other regions after some adjustment. Our results provide valuable insights for cropland abandonment management by protecting high-value croplands and setting aside some areas for environmental amenities.

Abstract Land is an essential basis for the sustained existence and development of human beings, arable land resources are the fundamental guarantee of food replenishment, contemporary scholars also put forward high requirements for the study of remote sensing image feature type classification extraction, the study of remote sensing image feature type classification and algorithms are very necessary. Therefore, based on remote sensing image data, this paper combines supervised classification with unsupervised classification, selects the advantages of the algorithm and improves it, refers to the classification system to select the classification standard, improves the support vector machine algorithm and compares it with other classification algorithms to improve the classification accuracy. Firstly, unsupervised classification is used to extract forest land, cultivated land and grassland into one category, and then secondary extraction is used to classify and subdivide them with SVM classification in supervised classification. Through conducting experiments on image data over a period of three years, the information of cultivated land is extracted from the remotely sensed image data of Changchun City in the years 2000, 2010, and 2020. By observing the change trend of land use, it is found that the cultivated land in Changchun City demonstrates a decreasing tendency from 2000 to 2020. Finally, it is concluded that Changchun’s cultivated land shows a decreasing trend from 2000 to 2020. Finally, this paper takes ten years as a gradient and obtains remote sensing image data in three time periods to analyze the change of cropland utilization. The results show that the method is effective in practical application and can save human and material resources to analyze the type of feature data.

Droplets find wide application across diverse industries, where maintaining their quality is paramount. Precise control over the substance content within droplets demands non-destructive and online analysis techniques, such as Process Analytical Technology (PAT), often integrated with control strategies. In this context, the present study focuses on the example of controlling droplet quality during the dripping process of pills. Leveraging the dripping and image acquisition systems established in previous research, a novel feedback control system centered on image processing was devised for the quality control of dripping pills. The system was developed and its efficacy was assessed, yielding satisfactory outcomes. The proposed system facilitates real-time monitoring of pill weight through the analysis of droplet images during the dripping process, thereby offering real-time feedback control of pill weight. Importantly, this system holds potential for broader applications beyond the scope of this study.

Changes in crop mix significantly affect the carbon emissions from cropping systems, thus underscoring their importance in achieving sustainable agriculture development. This article aims to offer new insights into the relationship between crop mix changes and carbon emissions from cropping systems. Based on provincial statistical data on crop production, this study analyzed the spatiotemporal dynamics of crop mix and the subsequent effect on the carbon emissions from cropping systems in China from 2005 to 2020. The crop-mix type identification rules, emission-factor approach, and grey incidence analysis were applied. The results showed clear changes in crop mix and obvious transitions from grain crops to cash crops in southern provinces. Among crops, the abundance of maize, vegetables, and melons increased remarkably. Besides, the carbon emission intensity (CI) from cropping systems generally showed an upward trend in most provinces, with a spatial pattern of gradual decrease from southeast to northwest. The changes in CI were most closely correlated with rice, maize, vegetables, and melon, with coefficients of 0.808, 0.773, 0.814, and 0.792, respectively. Additionally, the growth of maize and vegetables ratios played a significant role in the CI increases in most provinces. To optimize crop cultivation and mitigate carbon emissions, possible strategies including rational crop production layout, land transfer, and clean production technologies were proposed.

The process of urbanization and infrastructure construction has resulted in the occupation and loss of high-quality cropland. Complemented cropland with higher altitudes and slopes from either development or recla-mation has become an important component of cropland resources and plays a vital role in ensuring food se-curity. However, existing research mainly focuses on the dynamic balance of quantity, quality, productivity, and impact on the environment, and not enough attention is paid to whether the newly added cropland is efficiently used in the long term. Based on a cropping intensity dataset from 2001-2019, this study applied the sliding window and change detection methods to assess the use continuity and efficiency of complemented cropland from the perspective of cropland abandonment and explored the impact of the abandonment on food production. The results showed the following: (1) The total area of complemented cropland in 2002-2019 reached 279,416.4 km2, accounting for 13.1% of total cropland in the base year (2001). (2) 45.1% of the complemented cropland was abandoned at least once during the past 20 years, making only 65.6% of that land efficiently used, and this percentage was higher in the middle plains and downstream of the Yangtze River. (3) Abandonment had sig-nificant impact on the food production of complemented cropland-with a trend of increasing and then decreasing-leading to a food production reduction of 24.3% per year on average, which was more severe in the middle plains and downstream of the Yangtze River. Higher slope and a migrated population promoted the abandonment of complemented cropland. Therefore, the government should strengthen the monitoring of long-term utilization of complemented cropland and strategic reserves to maintain stable utilization.

Farmland abandonment, a widespread phenomenon during land-use transition, leads to a cycling or vanishing evolution of farmland resources. As urbanization advances, an increasing number of agricultural laborers migrate from rural to urban areas, causing ongoing farmland abandonment. However, in contrast to the abandoned information extraction and driving mechanisms revelation, the potential risk of farmland abandonment has received insufficient attention. This study took Yangtze River Economic Belt of China as study area, selected multiple aspects to construct a risk assessment system for farmland abandonment, and applied time series change detection to verify the results. The results showed that (1) farmland abandonment risk, with a regional average value of 0.0978, has strong spatial heterogeneity, with high values clustering in Yunnan-Guizhou and Sichuan-Chongqing mountainous areas and low values distributed in the midstream and downstream plains and the Sichuan Basin. (2) The proportion of farmland area gradually decreased as the risk grade increased. Farmland, with low abandonment risk, occupied an area of 204,837 km2, constituting the highest percentage of 35.18% among the overall farmland, and was mainly distributed in the provinces of Jiangsu and Anhui. The area of farmland with high risk was 16,458 km2, only accounting for 2.83%, the majority of which was clustered in Sichuan and Yunnan provinces. (3) The Normalized Difference Vegetation Index (NDVI) time series change detection validated the reliability of the risk assessment system. Samples of farmland having low abandonment risk indeed had the lowest abandonment rate of 10%, and those which indicated high risk had the highest abandonment rate of 32%. We propose differentiated managements for farmland resources with high and low abandonment risk from the perspective of sustainable use. This study provides a more reasonable and scientific system for farmland abandonment risk assessment and helps to fill the research gap.