Video-based vehicle detection and tracking is one of the most important components for intelligent transportation systems. When it comes to road junctions, the problem becomes even more difficult due ...to the occlusions and complex interactions among vehicles. In order to get a precise detection and tracking result, in this paper we propose a novel tracking-by-detection framework. In the detection stage, we present a sequential detection model to deal with serious occlusions. In the tracking stage, we model group behavior to treat complex interactions with overlaps and ambiguities. The main contributions of this paper are twofold: 1) shape prior is exploited in the sequential detection model to tackle occlusions in crowded scene and 2) traffic force is defined in the traffic scene to model group behavior, and it can assist to handle complex interactions among vehicles. We evaluate the proposed approach on real surveillance videos at road junctions and the performance has demonstrated the effectiveness of our method.
Infrared maritime target detection is the key technology of maritime target search systems. However, infrared images generally have the defects of low signal-to-noise ratio and low resolution. At the ...same time, the maritime environment is complicated and changeable. Under the interference of islands, waves and other disturbances, the brightness of small dim targets is easily obscured, which makes them difficult to distinguish. This is difficult for traditional target detection algorithms to deal with. In order to solve these problems, through the analysis of infrared maritime images under a variety of sea conditions including small dim targets, this paper concludes that in infrared maritime images, small targets occupy very few pixels, often do not have any edge contour information, and the gray value and contrast values are very low. The background such as island and strong sea wave occupies a large number of pixels, with obvious texture features, and often has a high gray value. By deeply analyzing the difference between the target and the background, this paper proposes a detection algorithm (SRGM) for infrared small dim targets under different maritime background. Firstly, this algorithm proposes an efficient maritime background filter for the common background in the infrared maritime image. Firstly, the median filter based on the sensitive region selection is used to extract the image background accurately, and then the background is eliminated by image difference with the original image. In addition, this article analyzes the differences in gradient features between strong interference caused by the background and targets, proposes a small dim target extraction operator with two analysis factors that fit the target features perfectly and combines the adaptive threshold segmentation to realize the accurate extraction of the small dim target. The experimental results show that compared with the current popular small dim target detection algorithms, this paper has better performance for target detection in various maritime environments.
To develop a non-polluting and sustainable city, urban administrators encourage logistics companies to use electric vehicles instead of conventional (i.e., fuel-based) vehicles for transportation ...services. However, electric energy-based limitations pose a new challenge in designing reasonable visiting routes that are essential for the daily operations of companies. Therefore, this paper investigates a real-world electric vehicle routing problem (VRP) raised by a logistics company. The problem combines the features of the capacitated VRP, the VRP with time windows, the heterogeneous fleet VRP, the multi-trip VRP, and the electric VRP with charging stations. To solve such a complicated problem, a heuristic approach based on the adaptive large neighborhood search (ALNS) and integer programming is proposed in this paper. Specifically, a charging station adjustment heuristic and a departure time adjustment heuristic are devised to decrease the total operational cost. Furthermore, the best solution obtained by the ALNS is improved by integer programming. Twenty instances generated from real-world data were used to validate the effectiveness of the proposed algorithm. The results demonstrate that using our algorithm can save 7.52% of operational cost.
Rice is a vital food crop that feeds most of the global population. Cultivating high-yielding and superior-quality rice varieties has always been a critical research direction. Rice grain-related ...traits can be used as crucial phenotypic evidence to assess yield potential and quality. However, the analysis of rice grain traits is still mainly based on manual counting or various seed evaluation devices, which incur high costs in time and money. This study proposed a high-precision phenotyping method for rice panicles based on visible light scanning imaging and deep learning technology, which can achieve high-throughput extraction of critical traits of rice panicles without separating and threshing rice panicles. The imaging of rice panicles was realized through visible light scanning. The grains were detected and segmented using the Faster R-CNN-based model, and an improved Pix2Pix model cascaded with it was used to compensate for the information loss caused by the natural occlusion between the rice grains. An image processing pipeline was designed to calculate fifteen phenotypic traits of the on-panicle rice grains. Eight varieties of rice were used to verify the reliability of this method. The R
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values between the extraction by the method and manual measurements of the grain number, grain length, grain width, grain length/width ratio and grain perimeter were 0.99, 0.96, 0.83, 0.90 and 0.84, respectively. Their mean absolute percentage error (MAPE) values were 1.65%, 7.15%, 5.76%, 9.13% and 6.51%. The average imaging time of each rice panicle was about 60 seconds, and the total time of data processing and phenotyping traits extraction was less than 10 seconds. By randomly selecting one thousand grains from each of the eight varieties and analyzing traits, it was found that there were certain differences between varieties in the number distribution of thousand-grain length, thousand-grain width, and thousand-grain length/width ratio. The results show that this method is suitable for high-throughput, non-destructive, and high-precision extraction of on-panicle grains traits without separating. Low cost and robust performance make it easy to popularize. The research results will provide new ideas and methods for extracting panicle traits of rice and other crops.
In order to reduce the degree of efflorescence in alkali-activated metakaolin geopolymers, a modified 5A zeolite with cation-exchange properties was used to reduce the content of free alkali metal ...cations in the geopolymer. This work aims to investigate the effect of different dosages of modified 5A zeolite on the microstructure and properties of geopolymer by using compressive strength testing, pore structure analysis (BET), and SEM-EDS. The cation content in the leachate was evaluated using inductively coupled plasma atomic emission spectrometry (ICP-OES). The efflorescence area of the geopolymer was calculated using Image Pro Plus (IPP) software to evaluate the effect of modified 5A zeolite on the degree of efflorescence of the geopolymer and to reveal the effect of modified 5A zeolite on the migration patterns of Na+ and Ca2+ in the geopolymer. The results showed that modified 5A zeolite with a 4 wt.% content could optimize the pore structure and enhance the mechanical properties of MK geopolymer through internal curing and micro-aggregate effects, which could also exchange cations with the pore solution to form (N, C)-A-S-H gels. The Na+ leaching was reduced by 19.4%, and the efflorescence area of the MK geopolymer was reduced by 57.3%.
The areca nut is the primary economic source for some farmers in southeast Asia. However, the emergence of areca yellow leaf disease (YLD) has seriously reduced the annual production of areca nuts. ...There is an urgent need for an effective method to monitor the severity of areca yellow leaf disease (SAYD). This study selected an areca orchard with a high incidence of areca YLD as the study area. An unmanned aerial vehicle (UAV) was used to acquire multispectral and thermal infrared data from the experimental area. The ReliefF algorithm was selected as the feature selection algorithm and ten selected vegetation indices were used as the feature variables to build six machine-learning classification models. The experimental results showed that the combination of ReliefF and the Random Forest algorithm achieved the highest accuracy in the prediction of SAYD. Compared to manually annotated true values, the R2 value, root mean square error, and mean absolute percentage error reached 0.955, 0.049, and 1.958%, respectively. The Pearson correlation coefficient between SAYD and areca canopy temperature (CT) was 0.753 (p value < 0.001). The experimental region was partitioned, and a nonlinear fit was performed using CT versus SAYD. Cross-validation was performed on different regions, and the results showed that the R2 value between the predicted result of SAYD by the CT and actual value reached 0.723. This study proposes a high-precision SAYD prediction method and demonstrates the correlation between the CT and SAYD. The results and methods can also provide new research insights and technical tools for botanical researchers and areca practitioners, and have the potential to be extended to more plants.
Drought stress is a significant factor affecting soybean growth and yield. A lack of suitable high-throughput phenotyping techniques hinders the drought tolerance evaluation of multi-genotype ...samples. A method for evaluating drought tolerance in soybeans is proposed based on multimodal remote sensing data from an unmanned aerial vehicle (UAV) and machine learning. Hundreds of soybean genotypes were repeatedly planted under well water (WW) and drought stress (DS) in different years and locations (Jiyang and Yazhou, Sanya, China), and UAV multimodal data were obtained in multiple fertility stages. Notably, data from Yazhou were repeatedly obtained during five significant fertility stages, which were selected based on days after sowing. The geometric mean productivity (GMP) index was selected to evaluate the drought tolerance of soybeans. Compared with the results of manual measurement after harvesting, support vector regression (SVR) provided better results (N = 356, Rsup.2 = 0.75, RMSE = 29.84 g/msup.2). The model was also migrated to the Jiyang dataset (N = 427, Rsup.2 = 0.68, RMSE = 15.36 g/msup.2). Soybean varieties were categorized into five Drought Injury Scores (DISs) based on the manually measured GMP. Compared with the results of the manual DIS, the accuracy of the predicted DIS gradually increased with the soybean growth period, reaching a maximum of 77.12% at maturity. This study proposes a UAV-based method for the rapid high-throughput evaluation of drought tolerance in multi-genotype soybean at multiple fertility stages, which provides a new method for the early judgment of drought tolerance in individual varieties, improving the efficiency of soybean breeding, and has the potential to be extended to other crops.
Acetate is an important metabolite in metabolic fluxes. Its presence in biological entities originates from both exogenous inputs and endogenous metabolism. Because the change in blood acetate level ...has been associated with both beneficial and adverse health outcomes, blood acetate analysis has been used to monitor the systemic status of acetate turnover. The present study examined the use of urinary N-acetyltaurine (NAT) as a marker to reflect the hyperacetatemic status of mice from exogenous inputs and endogenous metabolism, including triacetin dosing, ethanol dosing, and streptozotocin-induced diabetes. The results showed that triacetin dosing increased serum acetate and urinary NAT but not other N-acetylated amino acids in urine. The co-occurrences of increased serum acetate and elevated urinary NAT were also observed in both ethanol dosing and streptozotocin-induced diabetes. Furthermore, the renal cortex was determined as an active site for NAT synthesis. Overall, urinary NAT behaved as an effective marker of hyperacetatemia in three experimental mouse models, warranting further investigation into its application in humans.
High strain rate rolling (HRSS) of a ZK60 magnesium alloy at 300 °C with a strain rate from 5 s−1 to 25 s−1 was used to research the effect of the rate on the mechanical properties and damping ...capacity of the ZK60 alloy. The results show that as the strain rate increases, the tensile strength decreases from 355 MPa at 25 s−1 to 310 MPa at 5 s−1. Two damping peaks (P1 and P2) are detected in the high strain rate rolled ZK60 alloys at different strain rates. The P1 peak appears at low temperatures and is caused by grain boundaries sliding. The P2 peak appears at high temperatures and is caused by recrystallization. As the strain rate increases from 5 to 20 s−1, the dynamic recrystallization (DRX) volume percent rises and the dislocation density decreases, both of which cause the P1 peak to become more and more obvious, and activation energy rises. At the same time, the dislocation density decreases and leads to a decrease in the storage energy, which reduces the recrystallization driving force and shifts the P2 peak to high temperatures. When the strain rate reaches 20 and 25 s−1, DRX occurs fully in the sheet, so the activation energy of the P1 peak and the temperature where the P2 peak appears are basically equal.
Over the past decade, information for precision disease medicine has accumulated in the form of textual data. To effectively utilize this expanding medical text, we proposed a multi-task ...learning-based framework based on hard parameter sharing for knowledge graph construction (MKG), and then used it to automatically extract gastric cancer (GC)-related biomedical knowledge from the literature and identify GC drug candidates. In MKG, we designed three separate modules, MT-BGIPN, MT-SGTF and MT-ScBERT, for entity recognition, entity normalization, and relation classification, respectively. To address the challenges posed by the long and irregular naming of medical entities, the MT-BGIPN utilized bidirectional gated recurrent unit and interactive pointer network techniques, significantly improving entity recognition accuracy to an average F1 value of 84.5% across datasets. In MT-SGTF, we employed the term frequency-inverse document frequency and the gated attention unit. These combine both semantic and characteristic features of entities, resulting in an average Hits@ 1 score of 94.5% across five datasets. The MT-ScBERT integrated cross-text, entity, and context features, yielding an average F1 value of 86.9% across 11 relation classification datasets. Based on the MKG, we then developed a specific knowledge graph for GC (MKG-GC), which encompasses a total of 9129 entities and 88,482 triplets. Lastly, the MKG-GC was used to predict potential GC drugs using a pre-trained language model called BioKGE-BERT and a drug-disease discriminant model based on CNN-BiLSTM. Remarkably, nine out of the top ten predicted drugs have been previously reported as effective for gastric cancer treatment. Finally, an online platform was created for exploration and visualization of MKG-GC at https://www.yanglab-mi.org.cn/MKG-GC/.
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•MKG is a framework that performs knowledge extraction using a multi-task learning approach with hard parameter sharing.•With MKG, we built a KG for GC and predicted candidate GC drugs, confirming the clinical value of the MKG.•An online platform was developed for MKG-GC exploration and visualization.
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