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Abstract While a typical way for diabetes therapy is discrete insulin infusion based on long-time interval measurement, in this paper, we design a closed-loop control system for continuous drug infusion to improve the traditional discrete methods and make diabetes therapy automatic in practice. By exploring the accumulative function of drug to insulin, a continuous injection model is proposed. Based on this model, proportional-integral-derivative (PID) and fuzzy logic controllers are designed to tackle a control problem of the resulting highly nonlinear plant. Even with serious disturbance of glucose, such as nutrition absorption at meal time, the proposed scheme can perform well in simulation experiments.

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In this work, a mixed precursor solvent system comprising isopropyl alcohol (IPA) and 2-methoxy ethanol (MOE) is introduced for the fabrication of Cu2ZnSn(S, Se)4 (CZTSSe) thin films under ambient conditions. The effects of different IPA/(MOE + IPA) ratios on the characteristics of CZTSSe films and the corresponding devices were investigated. Our research results indicate that the addition of IPA enhances the wettability of Cu–Zn–Sn–S precursor solution on the substrate, reduces Sn loss in the film during high-temperature annealing, and diminishes band tail states. Additionally, adding IPA leads to effective enlargement of grain size, improved crystallinity, and enhanced light absorption. However, excessive content of IPA negatively impacts CZTSSe film properties and the device's performance. Notably, when substituting 20% of MOE with IPA, the short-circuit current density (JSC) increased from 30.84 mA cm−2 to 35.55 mA cm−2 in the resulting CZTSSe device, and the efficiency improved from 9.19% to 10.63%. This work provides a new method of a solvent system for preparing efficient kesterite-based solar cells. [ABSTRACT FROM AUTHOR]

This paper proposes an effective material delivery algorithm to address the challenges associated with Unmanned Aerial Vehicle (UAV) material transportation and delivery, which include complex route planning, low detection precision, and hardware limitations. This novel approach integrates the Whale-Swarm Hybrid Algorithm (WSHA) with the APCR-YOLOv8 model to enhance efficiency and accuracy. For path planning, the placement paths are transformed into a Generalized Traveling Salesman Problem (GTSP) to be able to compute solutions. The Whale Optimization Algorithm (WOA) is improved for balanced global and local searches, combined with an Artificial Bee Colony (ABC) Algorithm and adaptive weight adjustment to quicken convergence and reduce path costs. For precise placement, the YOLOv8 model is first enhanced by adding the SimAM attention mechanism to the C2f module in the detection head, focusing on target features. Secondly, GhoHGNetv2 using GhostConv is the backbone of YOLOv8 to ensure accuracy while reducing model Params and FLOPs. Finally, a Lightweight Shared Convolutional Detection Head (LSCDHead) further reduces Params and FLOPs through shared convolution. Experimental results show that WSHA reduces path costs by 9.69% and narrows the gap between the best and worst paths by about 34.39%, compared to the Improved Whale Optimization Algorithm (IWOA). APCR-YOLOv8 reduces Params and FLOPs by 44.33% and 34.57%, respectively, with mAP@0.5 increasing from 88.5 to 92.4 and FPS reaching 151.3. This approach can satisfy the requirements for real-time responsiveness while effectively preventing missed, false, and duplicate detections during the inspection of emergency airdrop stations. In conclusion, combining bionic optimization algorithms and image processing significantly enhances the efficiency and precision of material placement in emergency management. [ABSTRACT FROM AUTHOR]

The precise measurement of the spin speed of a high-speed autonomous unmanned aerial vehicle (HSA-UAV) is a key element in mastering flight stability, and the measurement of roll angle is the key to determining the accuracy of navigation control systems. We put forward a novel method for measuring roll angle. This method starts from the time–frequency domain analysis of the output signal of the geomagnetic sensor, extracts the time–frequency ridge of the time–frequency matrix (TFM) to obtain the spin speed of the HSA-UAV, and reconstructs the output signal of the geomagnetic sensor. It can calculate the roll angle when the calibration parameters of the geomagnetic sensor are unknown and have good engineering practical value. In addition, we also propose an improved nonlinear short-time Fourier transform with high-frequency resolution and a forward penalty dynamic path ridge-extraction method with frequency jump suppression to extract instantaneous frequency in the TFM. [ABSTRACT FROM AUTHOR]

A comprehensive understanding of the mechanical characteristics of deep coalbed methane reservoir rocks (DCMRR) is crucial for the safe and efficient development of deep coalbed gas resources. In this study, the microstructural and mechanical features of the coal seam roof, floor, and the coal seam itself were analyzed through laboratory experiments. The impact mechanisms of drilling fluid and fracturing fluid hydration on the mechanical properties and failure behavior of coal seam rocks were investigated. The experimental results indicate that the main minerals in coal seams are clay and amorphous substances, with kaolinite being the predominant clay mineral component in coal seam rocks. The rock of the coal seam roof and floor exhibits strong elasticity and high compressive strength, while the rock in the coal seam section shows a lower compressive capacity with pronounced plastic deformation characteristics. The content of kaolinite shows a good correlation with the mechanical properties of DCMRR. As the kaolinite content increases, the strength of DCMRR gradually decreases, and deformability enhances. After immersion in drilling fluid and slickwater, the strength of coal seam rocks significantly decreases, leading to shear fracture zones and localized strong damage features after rock compression failure. The analysis of the mechanical properties of DCMRR suggests that the horizontal well trajectory should be close to the coal seam roof, and strong sealing agents should be added to drilling fluid to reduce the risk of wellbore collapse. Enhancing the hydration of slickwater is beneficial for the formation of a more complex fracture network in deep coalbed methane reservoir. [ABSTRACT FROM AUTHOR]

Currently, numerous high-precision models have been proposed for semantic segmentation, but the model parameters are large and the segmentation speed is slow. Real-time semantic segmentation for urban scenes necessitates a balance between accuracy, inference speed, and model size. In this paper, we present an efficient solution to this challenge, efficient asymmetric attention module net (EAAMNet) for the semantic segmentation of urban scenes, which adopts an asymmetric encoder–decoder structure. The encoder part of the network utilizes an efficient asymmetric attention module to form the network backbone. In the decoding part, we propose a lightweight multi-feature fusion decoder that can maintain good segmentation accuracy with a small number of parameters. Our extensive evaluations demonstrate that EAAMNet achieves a favorable equilibrium between segmentation efficiency, model parameters, and segmentation accuracy, rendering it highly suitable for real-time semantic segmentation in urban scenes. Remarkably, EAAMNet attains a 73.31% mIoU at 128 fps on Cityscapes and a 69.32% mIoU at 141 fps on CamVid without any pre-training. Compared to state-of-the-art models, our approach not only matches their model parameters but also enhances accuracy and increases speed. [ABSTRACT FROM AUTHOR]

Simple Summary: Orinus, a genus distributed in alpine regions of the Qinghai-Xizang (Tibet) Plateau (QTP), includes the following two species: O. thoroldii, predominantly in the western QTP at elevations between 3300 and 4700 m, and O. kokonoricus, in the eastern QTP at relatively low altitude between 2500 and 3400 m. However, the mechanism of long noncoding RNA (lncRNA)-regulated speciation adaptation to high altitudes and cold arid environments in Orinus remains unclear. In this study, we first identified the transcriptome-wide features of lncRNAs in O. thoroldii and O. kokonoricus, including five main types of lncRNAs that class_code with "i, o, u, x, j". Then, an evolutionary tree was conducted to implicate the relationships among 39 species with the count of conserved lncRNAs. Moreover, we sought to reveal the mechanism of cis-regulation from differentially expressed lncRNAs (DElncRNAs) and their nearby protein-coding genes (PCGs) between O. thoroldii and O. kokonoricus. GO analysis showed that DElncRNAs and their potential target PCGs participate in different biological processes between O. thoroldii and O. kokonoricus, indicating their speciation adaptation on the QTP. In addition, the specifically expressed transcription factors (TFs) in O. thoroldii suggested that DElncRNAs play a vital role in adapting to the environment through the expressed regulation of TFs. Long noncoding RNAs (lncRNAs) are RNA molecules longer than 200 nt, which lack the ability to encode proteins and are involved in multifarious growth, development, and regulatory processes in plants and mammals. However, the environmental-regulated expression profiles of lncRNAs in Orinus that may associated with their adaptation on the Qinghai-Xizang (Tibet) Plateau (QTP) have never been characterized. Here, we utilized transcriptomic sequencing data of two Orinus species (O. thoroldii and O. kokonoricus) to identify 1624 lncRNAs, including 1119 intergenic lncRNAs, 200 antisense lncRNAs, five intronic lncRNAs, and 300 sense lncRNAs. In addition, the evolutionary relationships of Orinus lncRNAs showed limited sequence conservation among 39 species, which implied that Orinus-specific lncRNAs contribute to speciation adaptation evolution. Furthermore, considering the cis-regulation mechanism, from 286 differentially expressed lncRNAs (DElncRNAs) and their nearby protein coding genes (PCGs) between O. thoroldii and O. kokonoricus, 128 lncRNA-PCG pairs were obtained in O. thoroldii, whereas 92 lncRNA-PCG pairs were obtained in O. kokonoricus. In addition, a total of 19 lncRNA-PCG pairs in O. thoroldii and 14 lncRNA-PCG pairs in O. kokonoricus were found to participate in different biological processes, indicating that the different expression profiles of DElncRNAs between O. thoroldii and O. kokonoricus were associated with their adaptation at different elevations on the QTP. We also found several pairs of DElncRNA nearby transcription factors (TFs), indicating that these DElncRNAs regulate the expression of TFs to aid O. thoroldii in adapting to the environment. Therefore, this work systematically identified a series of lncRNAs in Orinus, laying the groundwork for further exploration into the biological function of Orinus in environmental adaptation. [ABSTRACT FROM AUTHOR]

The Sulige gas field is a typical "three lows" (low permeability, low pressure, and low abundance) tight sandstone gas reservoir, with formation pressures often characterized by abnormally high or low pressures. The complex geological features of the reservoir further deviate from conventional understanding, impacting the effective implementation of wellbore blockage removal measures. Therefore, it is imperative to establish the wellbore blockage mechanism, prediction model, and effective prevention measures for the target area. In this study, based on field data, we first experimentally analyzed the water quality and types of blockage in the target area. Subsequently, utilizing a BP neural network model, we established a model for predicting the risk of wellbore blockage and analyzing mitigation measures in the target reservoir. The model's prediction results, consistent with on-site actual results, demonstrate its reliability and accuracy. Experimental results show that the water quality in the target area is mainly a CaCl2 type, and the predominant scales produced are CaCO3 and BaSO4. Model calculations reveal that temperature, pressure, and ion concentration all influence scaling, with BaSO4 more influenced by pressure and CaCO3 more influenced by temperature. Under the combined effect of temperature, pressure, and ion concentration, different types of scales exhibit distinct trends in scaling quantity. Combining scaling quantity calculations with wellbore contraction ratios, it was found that when the temperature, pressure, and ion concentration are within a certain range, the wellbore contraction rate can be controlled below 4%. At this point, the wellbore scaling risk is minimal, and preventive measures against wellbore scaling can be achieved by adjusting production systems, considering practical production conditions. This study investigates the mechanism of scaling in wellbores of tight sandstone gas reservoirs and proposes a cost-effective scaling prevention measure. This approach can guide the prediction of scaling risks and the implementation of scaling prevention measures for gas wells in tight sandstone reservoirs. [ABSTRACT FROM AUTHOR]