The Environmental Impact of Plastic Waste

The pollution caused by disposable plastic products is becoming more and more serious, and “plastic limit” has become a global consensus. This article mainly discusses the pollution problem from the following aspects: Integrate all relevant important indicators to establish a multiple regression model of the maximum amount of disposable plastic waste to estimate the maximum amount of disposable waste in the future without causing further damage to the environment; Establish an environmental safety level evaluation model and analyze the impact of plastic waste on environmental safety; Try to set the lowest level target that can be achieved by global waste at this stage, and conduct correlation analysis on the impact of humans, enterprises, and the environment; Select several countries based on their comprehensive strengths, conduct a comparative analysis of their plastic production, economic strength, and environment, and try to explore their responsibilities

Incorporating Intra-Class Variance to Fine-Grained Visual Recognition

Fine-grained visual recognition aims to capture discriminative characteristics amongst visually similar categories. The state-of-the-art research work has significantly improved the fine-grained recognition performance by deep metric learning using triplet network. However, the impact of intra-category variance on the performance of recognition and robust feature representation has not been well studied. In this paper, we propose to leverage intra-class variance in metric learning of triplet network to improve the performance of fine-grained recognition. Through partitioning training images within each category into a few groups, we form the triplet samples across different categories as well as different groups, which is called Group Sensitive TRiplet Sampling (GS-TRS). Accordingly, the triplet loss function is strengthened by incorporating intra-class variance with GS-TRS, which may contribute to the optimization objective of triplet network. Extensive experiments over benchmark datasets CompCar and VehicleID show that the proposed GS-TRS has significantly outperformed state-of-the-art approaches in both classification and retrieval tasks.Comment: 6 pages, 5 figure

Constrained Decision Transformer for Offline Safe Reinforcement Learning

Safe reinforcement learning (RL) trains a constraint satisfaction policy by interacting with the environment. We aim to tackle a more challenging problem: learning a safe policy from an offline dataset. We study the offline safe RL problem from a novel multi-objective optimization perspective and propose the $\epsilon$-reducible concept to characterize problem difficulties. The inherent trade-offs between safety and task performance inspire us to propose the constrained decision transformer (CDT) approach, which can dynamically adjust the trade-offs during deployment. Extensive experiments show the advantages of the proposed method in learning an adaptive, safe, robust, and high-reward policy. CDT outperforms its variants and strong offline safe RL baselines by a large margin with the same hyperparameters across all tasks, while keeping the zero-shot adaptation capability to different constraint thresholds, making our approach more suitable for real-world RL under constraints.Comment: 15 pages, 7 figure

Multi-Objective Feature Selection With Missing Data in Classification

Feature selection (FS) is an important research topic in machine learning. Usually, FS is modelled as a bi-objective optimization problem whose objectives are: 1) classification accuracy; 2) number of features. One of the main issues in real-world applications is missing data. Databases with missing data are likely to be unreliable. Thus, FS performed on a data set missing some data is also unreliable. In order to directly control this issue plaguing the field, we propose in this study a novel modelling of FS: we include reliability as the third objective of the problem. In order to address the modified problem, we propose the application of the non-dominated sorting genetic algorithm-III (NSGA-III). We selected six incomplete data sets from the University of California Irvine (UCI) machine learning repository. We used the mean imputation method to deal with the missing data. In the experiments, k-nearest neighbors (K-NN) is used as the classifier to evaluate the feature subsets. Experimental results show that the proposed three-objective model coupled with NSGA-III efficiently addresses the FS problem for the six data sets included in this study

Multiple Comparisons of the Efficacy and Safety for Seven Treatments in Tibia Shaft Fracture Patients

Background: A tibia shaft fracture is one of the most common long bone fractures, with two general types, open fracture and close fracture. However, there is no universally accepted guideline suggesting which treatment to use under certain circumstances. Therefore, a comprehensive network meta-analysis (NMA) is needed to summarize existing studies and to provide more credible data-based medical guidelines.Methods: Available literature was identified by searching medical databases with relevant key terms. Studies that met the inclusion and exclusion criteria, baseline, intervention, and the outcome of treatments, were extracted. A comparative connection of these studies was demonstrated through net plots. Continuous variables and binary variables were reported as mean difference (MD) and odds ratio (OR) with a 95% credible interval (CrI), respectively. The comparison of direct and indirect outcome and their P-value were listed in the node-splitting table. Treatments for each endpoint were ranked by their surface under the cumulative ranking curve (SUCRA) value. A heat plot was created to illustrate the contribution of raw data and the inconsistency between direct and indirect comparisons.Results: According to the search strategy, 697 publications were identified, and 25 records were included, involving 3,032 patients with tibia shaft fractures. Seven common surgical or non-surgical treatments, including reamed intramedullary nailing (RIN), un-reamed intramedullary nailing (UIN), minimally reamed intramedullary nailing (MIN), ender nailing (EN), external fixation (EF), plate, and cast, were compared, in terms of time to union, reoperation, non-union, malunion, infection and implant failure. Plate performed relatively better for time to union, while cast might be the best choice in close cases to reduce the risks of reoperation, non-union, malunion, and infection. To prevent implant failure, EN seemed to be better.Conclusion: Cast might have the highest probability of the most optimal choice for tibia shaft fracture in close cases, while reamed intramedullary nailing ranked second