This study aimed to investigate prefrontal function in patients with generalized anxiety disorder (GAD), major depressive disorder (MDD), and the comorbidity of MDD and GAD (CMG), using the fNIRS-VFT task. And to assess the reliability of functional near-infrared spectroscopy (fNIRS) devices as a clinical aid for diagnostic tools when performing cognitive tasks by building a deep neural network. Including 75 patients with GAD, 75 patients with MDD, 71 patients with CMG, and 75 healthy controls (HC). In the verbal fluency test, hemodynamic responses in the prefrontal cortex were monitored with fNIRS, and differences in oxyhemoglobin levels between the four groups were compared using the Kruskal-Wallis rank sum test. A deep learning model combining the Full Connectivity Layer and the Dropout Layer was trained on the training set (60 %), and data from different brain regions were classified for the type of illness on the test set (40 %). The Receiver Operating Characteristic (ROC) graph was generated for each area of interest. Activation in the prefrontal cortex was significantly lower in all patient groups than healthy controls. Activation in the prefrontal cortex was significantly lower in MDD patients compared with GAD patients and in the left ventrolateral prefrontal cortex in the CMG group compared with GAD patients. There was no significant difference in activation in the prefrontal cortex between MDD and CMG groups. In the right orbitofrontal cortex, the four-classification had the highest accuracy with 60.47 %, and the three-classification had the highest accuracy with 77.19 %. The GAD group had the most significant area under the ROC curve in the left ventrolateral prefrontal cortex (AUC = 0.78). The MDD group had the most significant area under the ROC curve in the left frontopolar prefrontal cortex (AUC = 0.86). The CMG group had the most significant area under the ROC curve in the right orbitofrontal cortex (AUC = 0.73). Activation differences in the prefrontal cortex help to identify GAD patients from MDD patients. Differences in activation in the left ventrolateral prefrontal cortex help to discriminate GAD patients from patients with CMG. fNIRS-VFT task can be used clinically as an adjunctive diagnostic tool for mental disorders.

In recent years, osteoarthritis of the knee, a common degenerative joint disease, often occurs in the elderly population. This disease has a significant impact on the quality of life of patients. For treating knee osteoarthritis, physical therapy is highly regarded as a very effective treatment method. This article delves deeply into commonly used physical therapy methods and analyzes their therapeutic effects, cost-effectiveness, and applicability, aiming to find treatments with broader applicability and better cost-effectiveness. The goal is to help a large number of patients effectively alleviate the discomfort caused by knee osteoarthritis, enhance the clinical therapeutic effects, and introduce home treatment methods to reduce financial burdens. The article also compares various physical therapy methods and finds that moxibustion and electrotherapy are more suitable for home use. Other treatment methods provide a reliable scientific basis for patient treatment.

In recent years, visible light positioning (VLP) techniques have been gaining popularity in research. Among them, the scheme of using a camera as a receiver provides a low-cost, high-precision positioning capability and easy integration with existing multimedia devices and robots. However, the pose changes of the receiver can lead to image distortion and light displacement, significantly increasing positioning errors. Addressing these errors is crucial for enhancing the accuracy of VLP. Most current solutions rely on gyroscopes or Inertial Measurement Units (IMUs) for error optimization, but these approaches often add complexity and cost to the system. To overcome these limitations, we propose a 3D positioning algorithm based on an attention mechanism convolutional neural network (CNN) aimed at reducing the errors caused by angles. We designed experiments and comparisons within a rotation angle range of ±15 degrees. The results demonstrate that the average error for 2D positioning is within 5.74 cm and the height error is within 3.92 cm, and the average error for 3D positioning is within 6.8 cm. Among the four groups of experiments for 3D positioning, compared to the traditional algorithm, the improvements were 7.931 cm, 15.569 cm, 6.004 cm, and 16.506 cm. The experiments indicate that it can be applied to high-precision visible light positioning for single-light imaging.

Inkjet-printed micro-electro-mechanical systems are a set of fabrication techniques that use inexpensive printed circuit processes to construct microsensors. Compared with traditional sensor, flexible micro-sensor present excellent application prospect in the field of medical electronic, environmental monitoring and wearable device, etc. This paper developed a simple flexible temperature sensor array based on inkjet-printed micro-electro-mechanical systems by using poly(3,4-ethylenedioxythiophene): polystyrene sulfonate (PEDOT: PSS) as the sensing material. This sensor array employed polyethylene terephthalate (PET), 9 sliver contact electrodes and 12 PEDOT: PSS sensing blocks as bottom substrate, middle layer and top layer, respectively. There are 12 temperature sensors arrayed on an area of 4×4 cm ² , which can realize a thermal resolution of 5 × 5 temperature pixels by using modeling simulation method. The sensor array showed a sensitivity of -0.50 %/°C with a small hysteresis from 25 up to 75 °C (or in a chosen temperature range), which is higher compared to that of other similar sensors reported in the literature. In order to improve the accuracy, an array correction algorithm was investigated as a data processing method and succeeded in reducing the temperature error from 0.80 %to less than 0.50 %. The combination of the array and flexible structures makes our temperature-sensing arrays easy to affix and particularly advantageous for displaying interface heat diffusion behavior, such as the water body's epidermis.

Major Depressive Disorder (MDD) has a high suicide risk, and current diagnosis of suicidal ideation (SI) mainly relies on subjective tools.Neuroimaging techniques, including functional near-infrared spectroscopy (fNIRS), offer potential for identifying objective biomarkers. fNIRS, with its advantages of non-invasiveness, portability, and tolerance of mild movement, provides a feasible approach for clinical research. However, previous fNIRS studies on MDD and suicidal ideation have inconsistent results due to patient and methodological differences.Traditional machine learning in fNIRS data analysis has limitations, while deep - learning methods like one-dimensional convolutional neural network (CNN) are under-explored. This study aims to use fNIRS to explore prefrontal function in first-episode drug-naive MDD patients with suicidal ideation and evaluate fNIRS as a diagnostic tool via deep learning. A total of 91 first-episode drug-naive MDD patients were included and categorized into two groups based on their scores on the suicidal item of the 17-item Hamilton Depression Rating Scale (HAMD-17): 40 patients with suicidal ideation (SIs) and 51 patients without suicidal ideation (NSIs). Concurrently, 39 healthy controls (HCs) were recruited. We utilized fNIRS to measure the hemodynamic responses in the prefrontal cortex of each group during the verbal fluency task (VFT). A Kruskal-Wallis test was conducted to analyze the changes in oxyhemoglobin concentration among the three groups, and receiver operating characteristic (ROC) curves were generated for each region of interest. Compared to HCs, NSIs exhibited significantly reduced activation in the left dorsolateral prefrontal cortex (lDLPFC), frontopolar prefrontal cortex (FPC), orbitofrontal cortex (OFC), and ventrolateral prefrontal cortex (VLPFC), while SIs showed significantly decreased activation in the entire prefrontal cortex. The activation values of SIs in DLPFC, FPC, and OFC were significantly lower than those of NSIs. The highest accuracy for the three-class classification was observed in the lFPC, reaching 69.80%. The SIs group had the largest area under the ROC curve (AUC = 0.88) in the rFPC, while the NSIs group had the largest area under the ROC curve (AUC = 0.88) in the rDLPFC. The HCs group exhibited the largest area under the ROC curve (AUC = 0.92) in the rDLPFC and rVLPFC. DLPFC, FPC, and OFC may serve as biomarker brain regions for identifying suicidal ideation in first-episode drug-naive MDD patients. The fNIRS-VFT task can be utilized clinically as an auxiliary diagnostic tool for mental disorders.