Decoupled Feature Pyramid Learning for Multi-Scale Object Detection in Low-Altitude Remote Sensing Images

Recently, low-altitude remote sensing platforms are widely used for various practical applications. Object detection is a basic and significant technology, serving them. The scale imbalance problem is predominant in low-altitude remote sensing images, which brings a great challenge to detect objects from these imageries. Consequently, in this article, we boost performance from the perspective of mitigating scale imbalance issues. First, we choose a one-stage object detector with decoupled heads as the baseline because of its comparatively high efficiency and accuracy. Current-decoupled heads ignore the interlayer relationship and the information contained. On the other hand, all existing feature pyramid structures generate one feature map for two branches at every layer. Inspired by them, we propose a novel feature pyramid network paradigm—decoupled feature pyramid network with consideration of different preferences for classification and localization. Meanwhile, the introduction of feature pyramid architecture will cause performance deterioration of larger objects because upper layers receive insufficient supervision in the training phase. Therefore, we adopt a distinct supervision strategy—level supervision, which pays more attention to upper layers. We demonstrate extensive experiments on two popular benchmarks of object detection in low-altitude remote sensing images to validate the effectiveness of our proposed method. In addition, we introduce a scale imbalance metric to quantify the degree of size change of objects to better illustrate the ability to relieve the scale imbalance problem. Finally, our proposed approach achieves state-of-the-art performance on both datasets

Investigation of water phase change rotating cooling for high temperature turbine

Thermal protection of high temperature turbine restricts the performance of gas turbine. In order to increase turbine inlet temperature, water phase change rotating cooling scheme for turbine was proposed for using the gasification latent heat to improve cooling capacity. Numerical model of phase change rotating cooling was established to study the flow and heat transfer characteristics with Ansys CFX software using SST k-ω turbulence model. Results showed that phase change rotating cooling had good performance for cooling the rotating blades. The temperature drops of blades were more than 400 K with the average heat flux of 2.42 MW/m2. Due to the influence of flow thermal acceleration effect, the outlet pressure of cooling channel reached a maximum value of 7.23 MPa with a mass flow rate of 0.17 g/s. The outlet quality distribution under rotating condition was relatively uniform than nonrotating condition. The temperature increased firstly and then decreased in centripetal channel along phase change process because of pressure variation. Meanwhile, gas-liquid interface disappeared due to the Coriolis force, resulting in higher heat transfer coefficient than static condition. The normalized Nusselt number (Nus) of phase change rotating cooling reached 3.915, much higher than static liquid cooling

Shared Parking Decision Behavior of Parking Space Owners and Car Travelers Based on Prospect Theory—A Case Study of Nanchang City, China

Shared parking improves the utilization rate of parking spaces by taking advantage of temporal and spatial differences, which is conducive to alleviating parking problems. From the perspective of bounded rationality, this paper studies the factors that influence the decision behavior of parking space owners and car travelers (non-residential drivers who have parking needs near residential areas) in sharing parking spaces. Prospect theory was used to analyze the bounded rational behavior characteristics of parking space owners and car travelers, and a value function model with rental price as the reference point was established. Combined with the survey data of the Xinhuangcheng district in Nanchang City, China, the shared parking space rental price that satisfied both parties was analyzed in this case study. The results of the study show that factors such as personal characteristics and behavioral habits affect the decision behavior of parking space owners and car travelers, and that rental price is a key factor. When the rental price of parking spaces is close to the maximum price desired by the owner, the owner feels the benefit and is willing to share the private parking space, but when the rental price differs greatly from the maximum price desired by the owner, the owner feels the loss and is not willing to share the parking space. From the survey data, it can be concluded that the ideal rental price of shared parking spaces around the survey area is 5 CNY/h. This paper provides a theoretical basis and guidance for the formulation of shared parking policies, which can help solve parking problems

Short-Term and Medium-Term Electricity Sales Forecasting Method Based on Deep Spatio-Temporal Residual Network

The forecasting of electricity sales is directly related to the power generation planning of power enterprises and the progress of the generation tasks. Aiming at the problem that traditional forecasting methods cannot properly deal with the actual data offset caused by external factors, such as the weather, season, and spatial attributes, this paper proposes a method of electricity sales forecasting based on a deep spatio-temporal residual network (ST-ResNet). The method not only relies on the temporal correlation of electricity sales data but also introduces the influence of external factors and spatial correlation, which greatly enhances the fitting degree of each parameter of the model. Moreover, the residual module and the convolution module are fused to effectively reduce the damage of the deep convolutional process to the training effectiveness. Finally, the three comparison experiments of the ultra-short term, short term and medium term show that the MAPE forecasted by the ST-ResNet model is at least 2.69% lower than that of the RNN and other classical Deep Learning models, that its RMSE is at least 36.2% lower, and that its MAD is at least 34.2% lower, which is more obvious than the traditional methods. The effectiveness and feasibility of the ST-ResNet model in the short-term forecasting of electricity sales are verified

The complete chloroplast genome sequence of Prunus sibirica

In this study, the chloroplast genome sequence of Prunus sibirica was obtained from the whole genome sequencing data of Prunus sibirica. Its length is 158,248 bp, which consists of 86,331 bp large single-copy region (LSC), 26,408 bp two reverse repeat regions (IR) and 19,101 bp small single-copy region (SSC). GC content of the whole chloroplast genome is 36.71%. Those of LSC region, SSC region, and IR region were 35, 30, and 43%, respectively. There are 131 unique genes in the chloroplast genome, including 90 protein-coding genes, 33 tRNA genes, and 8 rRNA genes. A maximum-likelihood phylogenetic tree was generated from the chloroplast genomes of 10 species of Rosaceae and 11 peripheral plants. The results showed that Prunus sibirica belongs to Rosaceae and is sister to Prunus salicina

Comprehensive analysis of the lysine succinylome in fish oil-treated prostate cancer cells.

Prostate cancer (PCa) poses a significant health threat to males, and research has shown that fish oil (FO) can impede PCa progression by activating multiple mitochondria-related pathways. Our research is focused on investigating the impact of FO on succinylation, a posttranslational modification that is closely associated with mitochondria in PCa cells. This study employed a mass spectrometry-based approach to investigate succinylation in PCa cells. Bioinformatics analysis of these succinylated proteins identified glutamic-oxaloacetic transaminase 2 (GOT2) protein as a key player in PCa cell proliferation. Immunoprecipitation and RNA interference technologies validated the functional data. Further analyses revealed the significance of GOT2 protein in regulating nucleotide synthesis by providing aspartate, which is critical for the survival and proliferation of PCa cells. Our findings suggest that FO-dependent GOT2 succinylation status has the potential to inhibit building block generation. This study lays a solid foundation for future research into the role of succinylation in various biological processes. This study highlights the potential use of FO as a nutrition supplement for managing and slowing down PCa progression