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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]

Non-Dispersive InfraRed (NDIR) gas sensor is widely used for gas detection in collieries and the gas chemical industry, etc. The performance of the NDIR gas sensor depends on the volume, optical length and transmittance of the gas chamber. However, the existing gas sensor products have problems of large volume, high cost and incapable of integration, which need to develop towards the miniaturized sensor. This paper first presents the theoretical background of the NDIR gas sensor and the novel structure of a fully integrated infrared gas sensor and its micro-machined gas chamber structure. Then, the light structure and the gas flow of the gas chamber are optimized on Tracepro software and Ansys workbench, respectively, and the technological process for preparing the Micro-Electro-Mechanical System (MEMS) gas chamber is designed. Finally, we produce a gas chamber with a small volume and good transmissivity, which would be the most important part of producing the miniaturized NDIR gas sensor.

Relying on the big data era, economic growth is also undergoing an accelerated transformation towards digitalisation and has triggered digital changes in finance due to the accelerated development of information technology and the emergence of technologies such as blockchain, artificial intelligence, and big data. This is why the BIS (Bank for International Settlements) received worldwide attention when it introduced the concept of a central bank digital currency (CBDC) in 2017. CBDC has many advantages, will inject new vitality into the global monetary and financial system, may become a new cornerstone of payment transactions and future de-velopment; But it also brings potential risks and complex policy issues. In this paper, through a review of the research literature on CBDC in recent years, with a specific example of China's central digital currency (DC/EP), this paper provides a concise demonstration of the current state of CBDC and its development.

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]