Analyzing Power Beacon Assisted Multi-Source Transmission Using Markov Chain

Wireless power transmission (WPT) is envisioned to be a promising technology for prolonging the lifetime of wireless devices in energy-constrained networks. This paper presents a general power beacon (PB) assisted multi-source transmission, where a practical source selection scheme with information transmission (IT) mode or non-IT mode is developed to maximize the transmission reliability. In the IT mode, a zero-forcing (ZF) beamformed signal with no interference to the destination is transmitted at the multi-antenna PB to supply wireless energy for the sources, and bring non-negative effect to the destination. Among multiple sources, the energy-sufficient source with the best channel quality is selected for wireless information transmission (WIT), while the other sources remain for energy harvesting. In the non-IT mode, the equal power transmission is adopted at PB to focus on energy delivery. Using Markov chain theory, the energy arrival and departure of each finite-capacity storage at the source is characterized mathematically, and the comprehensive analytical expressions of the energy outage probability (EOP), the connection outage probability (COP), and the average transmission delay (ATD) are formulated and derived. Our results reveal that the EOP, COP, and ATD can be significantly improved via increasing the number of sources deployed in the proposed network with finite transmit power of PB. We also prove that the multi-source network will never experience energy outage with infinite transmit power of PB

Towards Understanding the Capability of Large Language Models on Code Clone Detection: A Survey

Code cloning, the duplication of code fragments, is common in software development. While some reuse aids productivity, excessive cloning hurts maintainability and introduces bugs. Hence, automatic code clone detection is vital. Meanwhile, large language models (LLMs) possess diverse code-related knowledge, making them versatile for various software engineering challenges. However, LLMs' performance in code clone detection is unclear and needs more study for accurate assessment. In this paper, we provide the first comprehensive evaluation of LLMs for clone detection, covering different clone types, languages, and prompts. We find advanced LLMs excel in detecting complex semantic clones, surpassing existing methods. Adding intermediate reasoning steps via chain-of-thought prompts noticeably enhances performance. Additionally, representing code as vector embeddings, especially with text encoders, effectively aids clone detection.Lastly, the ability of LLMs to detect code clones differs among various programming languages. Our study suggests that LLMs have potential for clone detection due to their language capabilities, offering insights for developing robust LLM-based methods to enhance software engineering.Comment: 13 pages, 3 figure

Controllable ingestion and release of guest components driven by interfacial molecular orientation of host liquid crystal droplets

Controllable construction and manipulation of artificial multi-compartmental structures are crucial in understanding and imitating smart molecular elements such as biological cells and on-demand delivery systems. Here, we report a liquid crystal droplet (LCD) based three-dimensional system for controllable and reversible ingestion and release of guest aqueous droplets (GADs). Induced by interfacial thermodynamic fluctuation and internal topological defect, microscale LCDs with perpendicular anchoring condition at the interface would spontaneously ingest external components from the surroundings and transform them as radially assembled tiny GADs inside LCDs. Landau–de Gennes free-energy model is applied to describe and explain the assembly dynamics and morphologies of these tiny GADs, which presents a good agreement with experimental observations. Furthermore, the release of these ingested GADs can be actively triggered by changing the anchoring conditions at the interface of LCDs. Since those ingestion and release processes are controllable and happen very gently at room temperature and neutral pH environment without extra energy input, these microscale LCDs are very prospective to provide a unique and viable route for constructing hierarchical 3D structures with tunable components and compartments

A Novel Method for Fast Kernel Minimum Noise Fraction Transformation in Hyperspectral Image Dimensionality Reduction

Feature extraction, aiming to simplify and optimize data features, is a typical hyperspectral image dimensionality reduction technique. As a kernel-based method, kernel minimum noise fraction (KMNF) transformation is excellent at handling the nonlinear features within HSIs. It adopts the kernel function to ensure data linear separability by transforming the original data to a higher feature space, following which a linear analysis can be performed in this space. However, KMNF transformation has the problem of high computational complexity and low execution efficiency. It is not suitable for the processing of large-scale datasets. In terms of this problem, this paper proposes a graphics processing unit (GPU) and Nyström method-based algorithm for Fast KMNF transformation (GNKMNF). First, the Nyström method estimates the eigenvector of the entire kernel matrix in KMNF transformation by the decomposition and extrapolation of the sub-kernel matrix to reduce the computational complexity. Then, the sample size in the Nyström method is determined utilizing a proportional gradient selection strategy. Finally, GPU parallel computing is employed to further improve the execution efficiency. Experimental results show that compared with KMNF transformation, improvements of up to 1.94% and 2.04% are achieved by GNKMNF in overall classification accuracy and Kappa, respectively. Moreover, with a data size of 64 × 64 × 250, the execution efficiency of GNKMNF speeds up by about 80×. The outcome demonstrates the significant performance of GNKMNF in feature extraction and execution efficiency

A Machine Learning Study on Internal Force Characteristics of the Anti-Slide Pile Based on the DOFS-BOTDA Monitoring Technology

Long-term monitoring of constructed anti-slide piles can help in understanding the processes by which anti-slide piles are subjected to the thrust of landslides. This paper examined the landslide control project of Badong No. 3 High School. The internal force of an anti-slide pile subjected to long-term action of landslide thrust was studied by Distributed Optical Fiber Sensing (DOFS) technology. The BP neural network was used for model training on the monitored strain values and the calculated bending moment values. The results show the following: (1) The monitoring results of the sensor fibers reflect the actual situation more accurately than steel rebar meters do and can locate the position of the sliding zone more accurately. (2) The bending moments distributed along the anti-slide pile have staged characteristics under the long-term action of landslide thrust. Three stages can be summarized according to the development trend of the bending moment values. These three stages can be divided into two change periods of landslide thrust. (3) The model produced by the BP neural network training can predict the bending moment values. In this paper, the sensing fibers monitoring over a long time interval provides a basis for long-term performance analysis of anti-slide piles and stability evaluation of landslides. Using the BP neural network for training relevant data can provide directions for future engineering monitoring. More novel methods can be devised and utilized that will be both accurate and convenient

Research on a Measurement Method for Middle-Infrared Radiation Characteristics of Aircraft

Aiming at the problem wherein temperature inversion accuracy is unstable due to the major differences in atmospheric transmittance under various observation paths, a method for measuring radiation characteristics of an aircraft engine’s hot parts and skin using a cooled middle-wave infrared camera is proposed. Based on the analysis of the aircraft’s infrared radiation characteristics, the atmospheric transmission model of any observation path was revised, the absolute radiation correction model was established, and the temperature inversion equation was calculated. Then, we used the quasi-Newton method to calculate the skin temperature and discussed uncertainty sources. After the theoretical study, an outfield test was carried out. A middle-wave infrared camera with a wavelength of 3.7–4.8 μm was applied to the actual experimental observation of the turbofan civil aviation aircraft. The ground observation distance was 15 km, and the flying height was 3 km. When implementing temperature inversion with the method presented in this paper, the surface temperature of the aircraft engine hot parts was 381 K, the correction uncertainty was ±10 K, the surface temperature of the skin was 296 K, and the correction uncertainty was ±6 K. As the experiment showed, the method in this paper can effectively implement infrared target temperature inversion and provide a reference for the quantification of infrared data

Identification of phosphorylation proteins in response to water deficit during wheat flag leaf and grain development

Abstract Background Wheat (Triticum aestivum L.) serves as important grain crop widely cultivated in the world, which is often suffered by drought stress in natural conditions. As one of the most important post translation modifications, protein phosphorylation widely participates in plant abiotic stress regulation. In this study, we performed the first comparative analysis of phosphorylated protein characterization in flag leaves and developing grains of elite Chinese bread wheat cultivar Zhongmai 175 under water deficit by combining with proteomic approach and Pro-Q Diamond gel staining. Results Field experiment showed that water deficit caused significant reduction of plant height, tiller number, ear length and grain yield. 2-DE and Pro-Q Diamond gel staining analysis showed that 58 proteins were phosphorylated among 112 differentially accumulated proteins in response to water deficit, including 20 in the flag leaves and 38 in the developing grains. The phosphorylated proteins from flag leaves mainly involved in photosynthesis, carbohydrate and energy metabolism, while those from developing grains were closely related with detoxification and defense, protein, carbohydrate and energy metabolism. Particularly, water deficit resulted in significant downregulation of phosphorylated modification level in the flag leaves, which could affect photosynthesis and grain yield. However, some important phosphorylated proteins involved in stress defense, energy metabolism and starch biosynthesis were upregulated under water deficit, which could benefit drought tolerance, accelerate grain filling and shorten grain developing time. Conclusions The modification level of those identified proteins from flag leaves and grains had great changes when wheat was suffered from water deficit, indicating that phosphoproteins played a key role in response to drought stress. Our results provide new insights into the molecular mechanisms how phosphoproteins respond to drought stress and thus reduce production

A novel nanohydroxyapatite/polyamide-66 cage for reducing the subsidence rate after single-level anterior cervical discectomy and fusion: a comparative study of 7-year follow-up

Abstract Background A novel nanohydroxyapatite/polyamide-66 cage (n-HA/PA66 cage) with a horseshoe shape was designed to lower the subsidence rate of the traditional hollow cylindrical n-HA/PA66 cage. However, no studies have compared the incidence of subsidence in the two cages. The purpose of this study was to compare the long-term clinical and radiological outcomes of the novel n-HA/PA66 cage with the hollow cylindrical n-HA/PA66 cage after anterior cervical discectomy and fusion (ACDF) to treat single-level cervical degenerative disk disease (CDDD). Methods Fifty-two patients with novel n-HA/PA66 cages (Group A) and fifty-five patients with hollow cylindrical n-HA/PA66 cages (Group B) were included. The radiological parameters included intervertebral height (IH), C2-7 angle (C2-7a), segmental alignment (SA), subsidence rate, and fusion rate. The clinical outcomes were visual analog scale (VAS) scores, Japanese Orthopedic Association (JOA) scores, and patient satisfaction rates. Results The pre- and postoperative SA, C2-7a, and fusion rates of the patients in Groups A and B were similar. The preoperative and 6-month postoperative IHs in both groups were comparable. However, the final follow-up IH in Group B was significantly smaller than that in Group A (35.9 mm vs. 36.7 mm). The difference in the subsidence rates at the final follow-up between Group A (5.8%, 3/52) and Group B (18.2%, 10/55) was significant. The VAS score, JOA score, and patient satisfaction rate were not significantly different. Conclusions The novel n-HA/PA66 cage had similar favorable SA, C2-7a, fusion rate, and clinical outcomes compared to the hollow cylindrical n-HA/PA66 cage for treating single-level ACDF. Moreover, the novel n-HA/PA66 cage achieved a lower subsidence rate and higher IH than the hollow cylindrical n-HA/PA66 cage at the final follow-up