A Network Resource Allocation Recommendation Method with An Improved Similarity Measure

Recommender systems have been acknowledged as efficacious tools for managing information overload. Nevertheless, conventional algorithms adopted in such systems primarily emphasize precise recommendations and, consequently, overlook other vital aspects like the coverage, diversity, and novelty of items. This approach results in less exposure for long-tail items. In this paper, to personalize the recommendations and allocate recommendation resources more purposively, a method named PIM+RA is proposed. This method utilizes a bipartite network that incorporates self-connecting edges and weights. Furthermore, an improved Pearson correlation coefficient is employed for better redistribution. The evaluation of PIM+RA demonstrates a significant enhancement not only in accuracy but also in coverage, diversity, and novelty of the recommendation. It leads to a better balance in recommendation frequency by providing effective exposure to long-tail items, while allowing customized parameters to adjust the recommendation list bias

Computer-Aided Cobb Measurement Based on Automatic Detection of Vertebral Slopes Using Deep Neural Network

Objective. To develop a computer-aided method that reduces the variability of Cobb angle measurement for scoliosis assessment. Methods. A deep neural network (DNN) was trained with vertebral patches extracted from spinal model radiographs. The Cobb angle of the spinal curve was calculated automatically from the vertebral slopes predicted by the DNN. Sixty-five in vivo radiographs and 40 model radiographs were analyzed. An experienced surgeon performed manual measurements on the aforementioned radiographs. Two examiners used both the proposed and the manual measurement methods to analyze the aforementioned radiographs. Results. For model radiographs, the intraclass correlation coefficients were greater than 0.98, and the mean absolute differences were less than 3°. This indicates that the proposed system showed high repeatability for measurements of model radiographs. For the in vivo radiographs, the reliabilities were lower than those from the model radiographs, and the differences between the computer-aided measurement and the manual measurement by the surgeon were higher than 5°. Conclusion. The variability of Cobb angle measurements can be reduced if the DNN system is trained with enough vertebral patches. Training data of in vivo radiographs must be included to improve the performance of DNN. Significance. Vertebral slopes can be predicted by DNN. The computer-aided system can be used to perform automatic measurements of Cobb angle, which is used to make reliable and objective assessments of scoliosis

Performance of AC-LGAD strip sensor designed for the DarkSHINE experiment

AC-coupled Low Gain Avalanche Detector (AC-LGAD) is a new precise detector technology developed in recent years. Based on the standard Low Gain Avalanche Detector (LGAD) technology, AC-LGAD sensors can provide excellent timing performance and spatial resolution. This paper presents the design and performance of several prototype AC-LGAD strip sensors for the DarkSHINE tracking system, as well as the electrical characteristics and spatial resolution of the prototype sensors from two batches of wafers with different $n^+$ dose.The range of spatial resolutions of 6.5$\mathrm{\mu m}$ $\sim$ 8.2$\mathrm{\mu m}$ and 8.8$\mathrm{\mu m}$ $\sim$ 12.3$\mathrm{\mu m}$ are achieved by the AC-LGAD sensors with 100$\mu m$ pitch size.Comment: 10 pages, 12 figure

PAHs in the North Atlantic Ocean and the Arctic Ocean: Spatial Distribution and Water Mass Transport

In the Arctic Ocean, it is still unclear what role oceanic transport plays in the fate of semivolatile organic compounds. The strong-stratified Arctic Ocean undergoes complex inputs and outputs of polycyclic aromatic hydrocarbons (PAHs) from the neighboring oceans and continents. To better understand PAHs’ transport processes and their contribution to high-latitude oceans, surface seawater, and water column, samples were collected from the North Atlantic Ocean and the Arctic Ocean in 2012. The spatial distribution of dissolved PAHs (∑9PAH) in surface seawater showed an “Arctic Shelf \u3e Atlantic Ocean \u3e Arctic Basin” pattern, with a range of 0.3–10.2 ng L−1. Positive matrix factorization modeling results suggested that vehicle emissions and biomass combustion were the major PAHs sources in the surface seawater. According to principal component analysis, PAHs in different water masses showed unique profiles indicating their different origins. Carried by the Norwegian Atlantic Current (0–800 m) and East Greenland Current (0–300 m), PAH individuals’ net transport mass fluxes ranged from −4.4 ± 1.7 to 53 ± 39 tons year−1 to the Arctic Ocean. We suggested the limited contribution of ocean currents on PAHs’ delivery to the Arctic Ocean, but their role in modulating PAHs’ air–sea interactions and other biogeochemical processes needs further studies

Gene cloning, expression, and characterization of two endo-xylanases from Bacillus velezensis and Streptomyces rochei, and their application in xylooligosaccharide production

Endo-xylanase hydrolyzing xylan in cellulosic residues releasing xylobiose as the major product at neutral pH are desirable in the substitute sweeteners industry. In this study, two endo-xylanases were obtained from Streptomyces rochei and Bacillus velezensis. SrocXyn10 showed the highest identity of 77.22%, with a reported endo-xylanase. The optimum reaction temperature and pH of rSrocXyn10-Ec were pH 7.0 and 60°C, with remarkable stability at 45°C or pHs ranging from 4.5 to 11.0. rBvelXyn11-Ec was most active at pH 6.0 and 50°C, and was stable at 35°C or pH 3.5 to 10.5. Both rSrocXyn10-Ec and rBvelXyn11-Ec showed specific enzyme activities on wheat arabinoxylan (685.83 ± 13.82 and 2809.89 ± 21.26 U/mg, respectively), with no enzyme activity on non-xylan substrates. The Vmax of rSrocXyn10-Ec and rBvelXyn11-Ec were 467.86 U mg−1 and 3067.68 U mg−1, respectively. The determined Km values of rSrocXyn10-Ec and rBvelXyn11-Ec were 3.08 g L−1 and 1.45 g L−1, respectively. The predominant product of the hydrolysis of alkaline extracts from bagasse, corncob, and bamboo by rSrocXyn10-Ec and rBvelXyn11-Ec were xylooligosaccharides. Interestingly, the xylobiose content in hydrolysates by rSrocXyn10-Ec was approximately 80%, which is higher than most reported endo-xylanases. rSrocXyn10-Ec and rBvelXyn11-Ec could be excellent candidates to produce xylooligosaccharides at neutral/near-neutral pHs. rSrocXyn10-Ec also has potential value in the production of xylobiose as a substitute sweetener

Cross-session Emotion Recognition by Joint Label-common and Label-specific EEG Features Exploration

Since Electroencephalogram (EEG) is resistant to camouflage, it has been a reliable data source for objective emotion recognition. EEG is naturally multi-rhythm and multi-channel, based on which we can extract multiple features for further processing. In EEG-based emotion recognition, it is important to investigate whether there exist some common features shared by different emotional states, and the specific features associated with each emotional state. However, such fundamental problem is ignored by most of the existing studies. To this end, we propose a Joint label-Common and label-Specific Features Exploration (JCSFE) model for semi-supervised cross-session EEG emotion recognition in this paper. To be specific, JCSFE imposes the ℓ 2,1 -norm on the projection matrix to explore the label-common EEG features and simultaneously the ℓ 1 -norm is used to explore the label-specific EEG features. Besides, a graph regularization term is introduced to enforce the data local invariance property, i.e ., similar EEG samples are encouraged to have the same emotional state. Results obtained from the SEED-IV and SEED-V emotional data sets experimentally demonstrate that JCSFE not only achieves superior emotion recognition performance in comparison with the state-of-the-art models but also provides us with a quantitative method to identify the label-common and label-specific EEG features in emotion recognition