It may be effective to introduce targeted alcohol reduction interventions in health care centers to address individual risk practices and microenvironmental social norms.

Clustering is an unsupervised machine learning technique with many practical applications that has gathered extensive research interest. Aside from deterministic or probabilistic techniques, fuzzy C-means clustering (FCM) is also a common clustering technique. Since the advent of the FCM method, many improvements have been made to increase clustering efficiency. These improvements focus on adjusting the membership representation of elements in the clusters, or on fuzzifying and defuzzifying techniques, as well as the distance function between elements. This study proposes a novel fuzzy clustering algorithm using multiple different fuzzification coefficients depending on the characteristics of each data sample. The proposed fuzzy clustering method has similar calculation steps to FCM with some modifications. The formulas are derived to ensure convergence. The main contribution of this approach is the utilization of multiple fuzzification coefficients as opposed to only one coefficient in the original FCM algorithm. The new algorithm is then evaluated with experiments on several common datasets and the results show that the proposed algorithm is more efficient compared to the original FCM as well as other clustering methods.

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The study was conducted on the basis of conditions of extraction factors including solvents, solvent concentration, solid/solvent ratio, time and extraction temperature which affect total polyphenol content and antioxidant activity of red turmeric (C. zanthorrhiza) extract. The yield of the polyphenols extraction process was determined by the total phenolic compounds (TPC) as well as the antioxidant activity (AC). Generally, the highest TPC and AC of ultrasound-assisted extraction are 130±0.51 mg GAE/g DW and 31.32±0.53 µmol Fe/g DW at the acetone concentration of 60% as the solvent, solid/solvent ratio of 1/35, extraction temperature of 40°C for 20 minutes. The surface structure of solid before and after treatment changes significantly

Tóm tắt—Hiện nay, nhiệm vụ đánh giá an toàn thông tin cho các hệ thống thông tin có ý nghĩa quan trọng trong đảm bảo an toàn thông tin. Đánh giá/khai thác lỗ hổng bảo mật cần được thực hiện thường xuyên và ở nhiều cấp độ khác nhau đối với các hệ thống thông tin. Tuy nhiên, nhiệm vụ này đang gặp nhiều khó khăn trong triển khai diện rộng do thiếu hụt đội ngũ chuyên gia kiểm thử chất lượng ở các cấp độ khác nhau. Trong khuôn khổ bài báo này, chúng tôi trình bày nghiên cứu phát triển Framework có khả năng tự động trinh sát thông tin và tự động lựa chọn các mã để tiến hành khai thác mục tiêu dựa trên công nghệ học tăng cường (Reinforcement Learning). Bên cạnh đó Framework còn có khả năng cập nhật nhanh các phương pháp khai thác lỗ hổng bảo mật mới, hỗ trợ tốt cho các cán bộ phụ trách hệ thống thông tin nhưng không phải là chuyên gia bảo mật có thể tự động đánh giá hệ thống của mình, nhằm giảm thiểu nguy cơ từ các cuộc tấn công mạng. Abstract—Currently, security assessment is one of the most important proplem in information security. Vulnerability assessment/exploitation should be performed regularly with different levels of complexity for each information system. However, this task is facing many difficulties in large-scale deployment due to the lack of experienced testing experts. In this paper, we proposed a Framework that can automatically gather information and automatically select suitable module to exploit the target based on reinforcement learning technology. Furthermore, our framework has intergrated many scanning tools, exploited tools that help pentesters doing their work. It also can be easily updated new vulnerabilities exploit techniques.