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Objective: To observe the efficacy of different combinations of antihypertensive drugs in patients with nocturnal hypertension (NH), and provide reference for clinical work. Methods: A cross-sectional study was used to collect clinical data of NH patients diagnosed through ambulatory blood pressure monitoring during physical examinations at the First Affiliated Hospital of Hunan University of Medicine from January 2021 to December 2022. According to the different motheds of medication treatment for patients, NH patients were divided into single-drug group, dual-drug group and multi-drug group.Angiotensin converting enzyme inhibitor (ACEI)/ angiotensin Ⅱ receptor antagonist (ARB) or calcium antagonist was used in single-drug group, ACEI/ARB combined with calcium antagonist were used in double-drug group, and any three or four combinations of ACEI/ARB, calcium antagonists, β-blockers and diuretics were used in multi-drug group. The clinical data of the three groups of patients were analyzed and compared. Results: There were 829 patients with NH, including 294 cases (35.46%) in single-drug group, 400 cases (48.25%) in dual-drug group and 135 cases (16.29%) in multiple-drug group. After 6 months of drug treatment, the nocturnal systolic blood pressure (NSBP) and nocturnal diastolic blood pressure (NDBP) of patients in single-drug group, dual-drug group and multi-drug group showed significant differences compared to before treatment (P＜0.05)，among them, the NSBP and NDBP of patients in dual-drug group were significantly lower than those in single-drug group and multi-drug group, but there was no significant difference between the latter two groups (P＞0.05). The decreases in NSBP and NDBP in dual-drug group (21.75%, 20.06%) were greater than those in single-drug group (17.60%, 15.26%) and multi-drug group (18.23%, 16.51%, P＜0.05), respectively. Conclusion: All three motheds of medication treatment can effectively control nocturnal blood pressure, but ACEI/ARB combined with calcium antagonist are more effective in lowering nocturnal blood pressure.

Laser drilling is widely used for fabricating holes in the semiconductor industry due to high throughput and a small heat-affected zone. However, it produces varying depths owing to uncertain external conditions and requires live control at the rate of a few tens of kHz to handle the fast material removal rate. Optical coherent tomography is capable of in situ acquiring a raw interferogram at a high rate (>80 kHz), but the depth extraction is slow due to the involved heavy Fast Fourier Transform (FFT). To address this, an efficient depth-tracking algorithm is proposed to save the FFT. It searches the depth in the raw interferogram locally with a known last depth given the two truths that only one depth exists and the adjacent depths do not change significantly. The proposed algorithm was proven to expedite the measuring rate six times with sub-pixel tracking precision. To further secure the rate against the interrupting of the system, the tracking process is parallelly implemented in a field-programmable gate array. The closed-loop control tests were conducted on probe cards with depth variations introduced by offsetting laser focus. The proposed method maintained a uniform depth, with variations reduced by 80% compared to traditional methods. [ABSTRACT FROM AUTHOR]

Organic electrochemical transistors (OECTs) show great potential in next-generation bioelectronics due to their high transconductance, low driving voltage, and biocompatibility. These advantages rely on efficient electrochemical doping/dedoping of the transistor channel consisting of organic mixed ionic–electronic conductors (OMIECs). Nevertheless, during device operation, hydration and electrolyte swelling lead to inferior cycling/operation stability of OECTs when compared with other conventional transistors. To enable further development and commercialization of OECTs, long-term stable operation is mandatory. Here, it is discovered that a slight variation in gate bias (VG) during operation would result in contrasting degradation in OECT performance with a conventional planar structure based on p-type p(g2T-T), which is the result of the alternating consumption of active OMIEC content due to the swelling effect during cycling. The introduction of the vertical architecture along with a crosslinking agent in the channel would largely decelerate/avoid such a process, which could yield an OECT with more than 250 000 full cycles (cycling duration of 10 000 s). Moreover, this strategy can be extended to another n-type OMIEC (Homo-gDPP). This work reveals a unique class of bioelectronics where their lifetime can be managed and controlled by device structure engineering, showing extensive potential in biomedical and bio-implantable functional devices. [ABSTRACT FROM AUTHOR]

Accurate localization of objects of interest in remote sensing images (RSIs) is of great significance for object identification, resource management, decision-making and disaster relief response. However, many difficulties, like complex backgrounds, dense target quantities, large-scale variations, and small-scale objects, which make the detection accuracy unsatisfactory. To improve the detection accuracy, we propose an Adaptive Adjacent Context Negotiation Network (A2CN-Net). Firstly, the composite fast Fourier convolution (CFFC) module is given to reduce the information loss of small objects, which is inserted into the backbone network to obtain spectral global context information. Then, the Global Context Information Enhancement (GCIE) module is given to capture and aggregate global spatial features, which is beneficial for locating objects of different scales. Furthermore, to alleviate the aliasing effect caused by the fusion of adjacent feature layers, a novel Adaptive Adjacent Context Negotiation network (A2CN) is given to adaptive integration of multi-level features, which consists of local and adjacent branches, with the local branch adaptively highlighting feature information and the adjacent branch introducing global information at the adjacent level to enhance feature representation. In the meantime, considering the variability in the focus of feature layers in different dimensions, learnable weights are applied to the local and adjacent branches for adaptive feature fusion. Finally, extensive experiments are performed in several available public datasets, including DIOR and DOTA-v1.0. Experimental studies show that A2CN-Net can significantly boost detection performance, with mAP increasing to 74.2% and 79.2%, respectively. [ABSTRACT FROM AUTHOR]

Advances in deep learning and computer vision have overcome many challenges inherent in the field of autonomous intelligent vehicles. To improve the detection accuracy and efficiency of EdgeBoard intelligent vehicles, we proposed an optimized design of EdgeBoard based on our PP-YOLOE+ model. This model innovatively introduces a composite backbone network, incorporating deep residual networks, feature pyramid networks, and RepResBlock structures to enrich environmental perception capabilities through the advanced analysis of sensor data. The incorporation of an efficient task-aligned head (ET-head) in the PP-YOLOE+ framework marks a pivotal innovation for precise interpretation of sensor information, addressing the interplay between classification and localization tasks with high effectiveness. Subsequent refinement of target regions by detection head units significantly sharpens the system's ability to navigate and adapt to diverse driving scenarios. Our innovative hardware design, featuring a custom-designed mainboard and drive board, is specifically tailored to enhance the computational speed and data processing capabilities of intelligent vehicles. Furthermore, the optimization of our Pos-PID control algorithm allows the system to dynamically adjust to complex driving scenarios, significantly enhancing vehicle safety and reliability. Besides, our methodology leverages the latest technologies in edge computing and dynamic label assignment, enhancing intelligent vehicles' operations through seamless sensor integration. Our custom dataset, specifically designed for this study, includes 4777 images captured by intelligent vehicles under a variety of environmental and lighting conditions. The dataset features diverse scenarios and objects pertinent to autonomous driving, such as pedestrian crossings and traffic signs, ensuring a comprehensive evaluation of the model's performance. We conducted extensive testing of our model on this dataset to thoroughly assess sensor performance. Evaluated against metrics including accuracy, error rate, precision, recall, mean average precision (mAP), and F1-score, our findings reveal that the model achieves a remarkable accuracy rate of 99.113%, an mAP of 54.9%, and a real-time detection frame rate of 192 FPS, all within a compact parameter footprint of just 81 MB. These results demonstrate the superior capability of our PP-YOLOE+ model to integrate sensor data, achieving an optimal balance between detection accuracy and computational speed compared with existing algorithms. [ABSTRACT FROM AUTHOR]

As a core functional material in acoustic resonators and filters, the elasticity of aluminum nitride (AlN) piezoelectric thin film and its correlated intrinsic properties deserve priority attention. This paper presents an in‐depth investigation of elastic softening and stiffening effect induced by stress loading in AlN piezoelectric thin film. Based on the Born‐Lande equation, the physical mechanism of such new intrinsic elastic property is revealed and predicted quantitatively for the first time from the perspective of atomic interaction forces. It is found that a maximum 6.7% modulation range of elasticity coefficient can be achieved under ultimate stress loading conditions from (−2.5, −2.5) to (2.5, 2.5) GPa. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Online monitoring of keyhole during laser welding is of great importance to evaluate the stability of the live process and the final quality of the weld. Indirect methods based on visual imaging and thermal radiation are lack of strong correlations with keyhole behavior, resulting in a low accuracy. Optical coherence tomography (OCT) shoots beam coaxially with the processing beam into the bottom of the keyhole, enabling direct monitoring of keyhole behavior. However, current OCT keyhole monitoring systems suffer from the harsh noise and are limited to only evaluating the keyhole depth through statistical analysis. In this paper, an enhanced OCT system was built with a capacity of capturing keyhole open/collapse behavior, opening a way of quantitatively evaluating the keyhole behavior. First, the signal was denoised and filled given the nature of fluid dynamics in time domain. Then an algorithm was developed to automatically detect the keyhole open/collapse behavior, based on which the weld status (heat conduction mode and keyhole mode) is determined, and the metrics of interests are characterized quantitatively. The proposed method provides a solid method to quantitatively evaluate the keyhole stability, which is well suited for developing new laser welding process and monitoring laser involved high volume manufacturing. [ABSTRACT FROM AUTHOR]

Flexible pressure sensors have many potential applications in the monitoring of physiological signals because of their good biocompatibility and wearability. However, their relatively low sensitivity, linearity, and stability have hindered their large-scale commercial application. Herein, a flexible capacitive pressure sensor based on an interdigital electrode structure with two porous microneedle arrays (MNAs) is proposed. The porous substrate that constitutes the MNA is a mixed product of polydimethylsiloxane and NaHCO3. Due to its porous and interdigital structure, the maximum sensitivity (0.07 kPa−1) of a porous MNA-based pressure sensor was found to be seven times higher than that of an imporous MNA pressure sensor, and it was much greater than that of a flat pressure sensor without a porous MNA structure. Finite-element analysis showed that the interdigital MNA structure can greatly increase the strain and improve the sensitivity of the sensor. In addition, the porous MNA-based pressure sensor was found to have good stability over 1500 loading cycles as a result of its bilayer parylene-enhanced conductive electrode structure. Most importantly, it was found that the sensor could accurately monitor the motion of a finger, wrist joint, arm, face, abdomen, eye, and Adam's apple. Furthermore, preliminary semantic recognition was achieved by monitoring the movement of the Adam's apple. Finally, multiple pressure sensors were integrated into a 3 × 3 array to detect a spatial pressure distribution. Compared to the sensors reported in previous works, the interdigital electrode structure presented in this work improves sensitivity and stability by modifying the electrode layer rather than the dielectric layer. ARTICLE HIGHLIGHTS: HIGHLIGHTS • An interdigital electrode structure composed of two porous microneedle arrays greatly increases the sensitivity of the pressure sensor. • The pressure sensor has good stability over 1500 cycles due to the bilayer parylene-enhanced porous microneedle array electrode structure. • The pressure sensor can accurately monitor the movements of body parts and has a certain ability to recognize speech. [ABSTRACT FROM AUTHOR]

Circuits based on organic electrochemical transistors (OECTs) have great potential in the fields of biosensors and artificial neural computation due to their biocompatibility and neural similarity. However, the integration of OECT-based circuits lags far behind other emerging electronics. Here, ternary inverters based on antiambipolar vertical OECTs (vOECTs) and their integration with the establishment of neural networks are demonstrated. Specifically, by adopting a small molecule (t-gdiPDI) as the channel of vOECT, high antiambipolar performance, with current density of 33.9 ± 2.1 A cm -2 under drain voltage of 0.1 V, peak voltage ≈0 V, low driving voltage < ± 0.6 V, and current on/off ratio > 10 6 , are realized. Consequently, vertically stacked ternary circuits based solely on OECTs are constructed for the first time, showing three distinct logical states and high integration density. By further developing inverter array as the internal fundamental units of ternary weight network hardware circuits for ternary processing and computation, it demonstrates excellent data classification and recognition capabilities. This work demonstrates the possibility of constructing multi-valued logic circuits by OECTs and promotes a new strategy for high-density integration and multivalued computing systems based on organic circuits., (© 2024 Wiley‐VCH GmbH.)