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Background: Women with gynecological cancer often experience psychological distress, particularly in response to surgical procedures. The impact of mandala art therapy (MAT) during the perioperative period for gynecological cancer patients remains uncertain. We aimed to examine the effects of the MAT program in women with gynecological cancer. Methods: Employing a quasi-experimental design, we recruited 126 gynecological cancer patients from a university hospital through convenience sampling. Participants were assigned to either receive the MAT program or standard perioperative care. The interventions comprised a three-session MAT program guided by a team of trained mandala psychologists. Generalized estimating equations (GEE) were employed to analyze the effects of MAT over time. Results: A total of 126 patients were enrolled, and 118 completed the entire study. Over 90% of participants completed the perioperative MAT interventions, reporting relatively high satisfaction with the program (7.70 out of 10). Individuals in the MAT group exhibited improved therapeutic effects on STAI-S, VASS, and vital signs over time. Notably, significant group*time interaction effects were noted in STAI-S scores at both the first evaluation, T1 (β = −4.220, P

In order to develop students’ concentration in the classroom, this paper proposes to analyze students’ learning concentration from three dimensions, where the superiority of the random forest classification algorithm is found in the dimension of head posture estimation so that students’ attention ranges can be analyzed. For data acquisition, the OpenPose platform is used for real-time detection of body, foot, hand, and face key points. A comprehensive evaluation study of students’ concentration levels in the classroom was achieved through an effective algorithm. Finally, the multimodal information fusion algorithm was investigated, and it was concluded that the weights could be calculated by hierarchical analysis to achieve a comprehensive evaluation of students’ learning concentration. By analyzing the course data of 12 students, the distribution of SFR values, Yaw angle and Pitch angle distribution, PERCLOS value distribution, and correct/error rate distribution of answers were counted, and the final scores of respective learning engagement were obtained as 0.91, 0.62, 0.80, 0.36, 0.82, 0.73, 0.81, 0.63, 0.81, 0.81 The model scores were compared with the expert scores, and the accuracy rate reached 98.6%, which proved that the model proposed in this paper is effective and can correctly reflect the real state of students’ learning in the classroom.

Objective To observe the regulatory effect of exosomes from adipose-derived stem cells （ADSC-Exos） on the biological activity of scar fibroblasts. Methods Scar fibroblast were prepared and divided into the control and ADSC-Exos groups. The proliferation of scar fibroblasts between two groups was observed by CCK-8 assay. The expression levels of fibrosis-related indexes were detected by Western blot. The expression levels of fibrosis-related gene mRNA were determined by fluorescent quantitative real-time （RT） -PCR. Results Compared with the control group, the proliferation capability of scar fibroblasts was significantly weakened, and the fibrosis-related indexes were significantly decreased in ADSC-Exos group （both P < 0.05）. Meanwhile, the expression levels of collagen I, III and α-smooth muscle actin mRNA of scar fibroblasts were significantly down-regulated in the ADSC-Exos group（all P < 0.05）. Conclusion ADSC-Exos can inhibit the biological activity of scar fibroblasts.

This study presents a comparative analysis of AlGalnAs buried heterostructure laser diodes by using dual-channel ridge-waveguides. Different shaped channels, including bowl shaped groove and vertical groove, are explored. Using a vertical groove structure, we achieved an output power of 90 mW at 25 °C with a threshold current of only 4 mA. This represents a 3.6-fold increase in output power compared to the BH-DFB structure. At a high temperature of 85 °C, the laser maintains a side-mode suppression ratio of over 45 dB at the maximum power point. The laser’s relative intensity noise in the 0–40 GHz frequency range is less than −162.8 dB/Hz when operated at 300 mA with the chip butterfly packaged. These findings underscore the robustness, reliability, and high-performance capabilities of the developed DFB laser, highlighting its potential for various practical applications.

Soil salinization typically exerts a highly negative influence on soil productivity, crop yields, and ecosystem balance. As a typical region afflicted by soil salinization, the soda saline–alkali soils in the Songnen Plain of China demonstrate a clear cracking phenomena. Nevertheless, the overall spectral response to the cracked soil surface has scarcely been studied. This study intends to study the impact of salt parameters on the soil cracking process and enhance the spectral measurement method used for cracked salt-affected soil. To accomplish this goal, a controlled desiccation cracking experiment was carried out on saline soil samples. A gray-level co-occurrence matrix (GLCM) was calculated for the contrast (CON) texture feature to measure the extent of cracking in the dried soil samples. Additionally, spectroscopy measurements were conducted under different surface conditions. Principal component analysis (PCA) was subsequently performed to downscale the spectral data for band integration. Subsequently, the prediction accuracy of back-propagation artificial neural network (BP-ANN) models developed from the principal components of spectral reflectance was compared for different salt parameters. The results reveal that salt content is the dominant factor determining the cracking process in salt-affected soils, and that cracked soil samples had the highest model prediction accuracy for different salt parameters rather than uncracked blocks and 2 mm comparison soil samples. Furthermore, BP-ANN prediction models combining spectral response and CON were further developed, which can significantly enhance the prediction accuracy of different salt parameters with R2 values of 0.93, 0.91, and 0.74 and a ratio of prediction deviation (RPD) of 3.68, 3.26, and 1.72 for soil salinity, electrical conductivity (EC), and pH, respectively. These findings provide valuable insights into the cracking mechanism in salt-affected soils, thereby advancing the field of hyperspectral remote sensing for monitoring soil salinization. Furthermore, this study also aids in enhancing the design of spectral measurements for saline–alkali soils and is also helpful for local soil remediation with supporting data. [ABSTRACT FROM AUTHOR]

Studies addressing the anatomical values of the supraspinatus outlet area (SOA) and the available supraspinatus outlet area (ASOA) are insufficient. This study focused on precisely measuring the SOA and ASOA values in a sample from the Chinese population using 3D CT (computed tomography) reconstruction. We analyzed CT imaging of 96 normal patients (59 males and 37 females) who underwent shoulder examinations in a hospital between 2011 and 2021. The SOA, ASOA, acromiohumeral distance (AHD), coracohumeral distance (CHD), coracoacromial arch radius (CAR), and humeral head radius (HHR) were estimated, and statistical correlation analyses were performed. There were significant sex differences observed in SOA (men: 957.62 ± 158.66 mm2; women: 735.87 ± 95.86 mm2) and ASOA (men: 661.35 ± 104.88 mm2; women: 511.49 ± 69.26 mm2), CHD (men: 11.22 ± 2.24 mm; women: 9.23 ± 1.35 mm), CAR (men: 37.18 ± 2.70 mm; women: 33.04 ± 3.15 mm), and HHR (men: 22.65 ± 1.44 mm; women: 20.53 ± 0.95 mm). Additionally, both SOA and ASOA showed positive and linear correlations with AHD, CHD, CAR, and HHR (R: 0.304–0.494, all p < 0.05). This study provides physiologic reference values of SOA and ASOA in the Chinese population, highlighting the sex differences and the correlations with shoulder anatomical parameters. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet of Things (IoT) technology, IoT systems have been widely applied in healthcare, transportation, home, and other fields. However, with the continuous expansion of the scale and increasing complexity of IoT systems, the stability and security issues of IoT systems have become increasingly prominent. Thus, it is crucial to detect anomalies in the collected IoT time series from various sensors. Recently, deep learning models have been leveraged for IoT anomaly detection. However, owing to the challenges associated with data labeling, most IoT anomaly detection methods resort to unsupervised learning techniques. Nevertheless, the absence of accurate abnormal information in unsupervised learning methods limits their performance. To address these problems, we propose AS-GCN-MTM, an adaptive structural Graph Convolutional Networks (GCN)-based framework using a mean-teacher mechanism (AS-GCN-MTM) for anomaly identification. It performs better than unsupervised methods using only a small amount of labeled data. Mean Teachers is an effective semi-supervised learning method that utilizes unlabeled data for training to improve the generalization ability and performance of the model. However, the dependencies between data are often unknown in time series data. To solve this problem, we designed a graph structure adaptive learning layer based on neural networks, which can automatically learn the graph structure from time series data. It not only better captures the relationships between nodes but also enhances the model's performance by augmenting key data. Experiments have demonstrated that our method improves the baseline model with the highest F1 value by 10.4%, 36.1%, and 5.6%, respectively, on three real datasets with a 10% data labeling rate. [ABSTRACT FROM AUTHOR]