Longitudinal trends in prostate cancer incidence, mortality, and survival of patients from two Shanghai city districts: a retrospective population-based cohort study, 2000-2009.

BackgroundProstate cancer is the fifth most common cancer affecting men of all ages in China, but robust surveillance data on its occurrence and outcome is lacking. The specific objective of this retrospective study was to analyze the longitudinal trends of prostate cancer incidence, mortality, and survival in Shanghai from 2000 to 2009.MethodsA retrospective population-based cohort study was performed using data from a central district (Putuo) and a suburban district (Jiading) of Shanghai. Records of all prostate cancer cases reported to the Shanghai Cancer Registry from 2000 to 2009 for the two districts were reviewed. Prostate cancer outcomes were ascertained by matching cases with individual mortality data (up to 2010) from the National Death Register. The Cox proportional hazards model was used to analyze factors associated with prostate cancer survival.ResultsA total of 1022 prostate cancer cases were diagnosed from 2000 to 2009. The average age of patients was 75 years. A rapid increase in incidence occurred during the study period. Compared with the year 2000, 2009 incidence was 3.28 times higher in Putuo and 5.33 times higher in Jiading. Prostate cancer mortality declined from 4.45 per 105 individuals per year in 2000 to 1.94 per 105 in 2009 in Putuo and from 5.45 per 105 to 3.5 per 105 in Jiading during the same period. One-year and 5-year prostate cancer survival rates were 95% and 56% in Putuo, and 88% and 51% in Jiading, respectively. Staging of disease, Karnofsky Performance Scale Index, and selection of chemotherapy were three independent factors influencing the survival of prostate cancer patients.ConclusionsThe prostate cancer incidence increased rapidly from 2000 to 2009, and prostate cancer survival rates decreased in urban and suburban Chinese populations. Early detection and prompt prostate cancer treatment is important for improving health and for increasing survival rates of the Shanghai male population

Video-based evidence analysis and extraction in digital forensic investigation

As a result of the popularity of smart mobile devices and the low cost of surveillance systems, visual data are increasingly being used in digital forensic investigation. Digital videos have been widely used as key evidence sources in evidence identification, analysis, presentation, and report. The main goal of this paper is to develop advanced forensic video analysis techniques to assist the forensic investigation. We first propose a forensic video analysis framework that employs an efficient video/image enhancing algorithm for the low quality of footage analysis. An adaptive video enhancement algorithm based on contrast limited adaptive histogram equalization (CLAHE) is introduced to improve the closed-circuit television (CCTV) footage quality for the use of digital forensic investigation. To assist the video-based forensic analysis, a deep-learning-based object detection and tracking algorithm are proposed that can detect and identify potential suspects and tools from footages

A Method of Water Quality Assessment Based on Biomonitoring and Multiclass Support Vector Machine

AbstractIntegrating biological monitoring method with computer vision technology, acute toxicity test was performed to study the toxic effects of Cu2+ with different concentration on zebrafish (Danio rerio). The Behavioral response of school of fish in a tank was quantified and an early warning system was developed in the study. In the system, the real-time quantified data were saved to database and the Multiclass Support Vector Machine (SVM) was used to make comprehensive assessment according to behavioral difference of fish school in different toxicity environment. The prediction accuracies are satisfactory, which indicate that this approach is effectual for assessment of water quality

Dynamic Pricing as an Online Decision-Making Problem

The intersection of pricing and machine learning has gained considerable attention in recent years, positioning pricing strategy as a decision-making problem for the sellers who are tasked with setting prices in real-time and learning optimal prices through observed demands. This thesis explores dynamic pricing within the framework of online decision-making, where sequential decisions are informed by continuously evolving observations.We contribute novel technical approaches to dynamic pricing through the study of two main aspects:I, Feature-Based Dynamic Pricing. In Part I, we address the challenge of pricing highly differentiated products, each characterized by specific features.Assuming linear and noisy customer valuations with binary decision outcomes, we explore settings with known, unknown, and heteroscedastic noise distributions. We propose algorithms for each scenario, providing rigorous analysis of their regret guarantees. Our findings illustrate that the difficulty of solving feature-based dynamic pricing is contingent on the seller's knowledge of noise distributions.II, Dynamic Pricing under Constraints. In Part II, we examine constraints that affect pricing strategies in modern markets, focusing on fairness and inventory limits. We firstly introduce two fairness notions and develop a randomized pricing mechanism that accommodates multiple fairness constraints simultaneously, achieving optimal regret and fairness outcomes. In the other project, we tackle pricing under inventory constraints, addressing challenges posed by censored demands to achieve optimal regrets.Our algorithmic solutions are rigorously evaluated through the metric of information-theoretic regret bounds. The practical relevance of our methodologies is further validated by comprehensive empirical studies using simulated data. The combination of theoretical and practical justifications demonstrates the robustness and applicability of our approaches across various dynamic pricing scenarios

FedDisco: Federated Learning with Discrepancy-Aware Collaboration

This work considers the category distribution heterogeneity in federated learning. This issue is due to biased labeling preferences at multiple clients and is a typical setting of data heterogeneity. To alleviate this issue, most previous works consider either regularizing local models or fine-tuning the global model, while they ignore the adjustment of aggregation weights and simply assign weights based on the dataset size. However, based on our empirical observations and theoretical analysis, we find that the dataset size is not optimal and the discrepancy between local and global category distributions could be a beneficial and complementary indicator for determining aggregation weights. We thus propose a novel aggregation method, Federated Learning with Discrepancy-aware Collaboration (FedDisco), whose aggregation weights not only involve both the dataset size and the discrepancy value, but also contribute to a tighter theoretical upper bound of the optimization error. FedDisco also promotes privacy-preservation, communication and computation efficiency, as well as modularity. Extensive experiments show that our FedDisco outperforms several state-of-the-art methods and can be easily incorporated with many existing methods to further enhance the performance. Our code will be available at https://github.com/MediaBrain-SJTU/FedDisco.Comment: Accepted by International Conference on Machine Learning (ICML2023

Impacts of FDI Renewable Energy Technology Spillover on China's Energy Industry Performance

Environmental friendly renewable energy plays an indispensable role in energy industry development. Foreign direct investment (FDI) in advanced renewable energy technology spillover is promising to improve technological capability and promote China’s energy industry performance growth. In this paper, the impacts of FDI renewable energy technology spillover on China’s energy industry performance are analyzed based on theoretical and empirical studies. Firstly, three hypotheses are proposed to illustrate the relationships between FDI renewable energy technology spillover and three energy industry performances including economic, environmental, and innovative performances. To verify the hypotheses, techniques including factor analysis and data envelopment analysis (DEA) are employed to quantify the FDI renewable energy technology spillover and the energy industry performance of China, respectively. Furthermore, a panel data regression model is proposed to measure the impacts of FDI renewable energy technology spillover on China’s energy industry performance. Finally, energy industries of 30 different provinces in China based on the yearbook data from 2005 to 2011 are comparatively analyzed for evaluating the impacts through the empirical research. The results demonstrate that FDI renewable energy technology spillover has positive impacts on China’s energy industry performance. It can also be found that the technology spillover effects are more obvious in economic and technological developed regions. Finally, four suggestions are provided to enhance energy industry performance and promote renewable energy technology spillover in China

Propofol combined with hyperbaric oxygen improves the prognosis of spinal cord injury in rats via MAPK/ERK signaling pathway

Purpose: To determine the effect of propofol combined with hyperbaric oxygen on spinal cord injury (SCI) in rats.Methods: A total of 36 Sprague-Dawley (SD) rats were randomly divided into sham group (S group), model group (M group), and propofol combined with hyperbaric oxygen group (P group). The Basso, Beattie and Bresnahan (BBB) scoring system was adopted to evaluate the recovery of motor function in rats. Subsequently, levels of interleukin-18 (IL-18) and IL-1β in the spinal cord tissues were determined using enzyme-linked immunosorbent assay (ELISA). Nerve cell apoptosis in the spinal cord tissues were examined via terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay. Finally, the protein expression levels of MAPK/ERK in the spinal cord tissues were assessed by Western blotting.Results: Compared with S group, BBB score in M group decreased at days 3 and 10 after treatment. While the BBB score of rats in P group was significantly increased (p < 0.05), compared with S group. The expressions of IL-18 and IL-1β were significantly lower in S and P groups than in M group (p < 0.05). S and P groups had lower apoptosis rate in the spinal cord tissues than in M group. Furthermore, Western blotting results showed that protein expressions of MAPK/ERK pathway were higher in S group and P group than in M group (p < 0.05).Conclusion: Propofol, combined with hyperbaric oxygen improves the prognosis of SCI rats probably by regulating MAPK/ERK signaling pathway, thus paving way for the development of a potential treatment for the management of spinal cord injury in humans