RHPTree—Risk Hierarchical Pattern Tree for Scalable Long Pattern Mining

Risk patterns are crucial in biomedical research and have served as an important factor in precision health and disease prevention. Despite recent development in parallel and high-performance computing, existing risk pattern mining methods still struggle with problems caused by large-scale datasets, such as redundant candidate generation, inability to discover long significant patterns, and prolonged post pattern filtering. In this article, we propose a novel dynamic tree structure, Risk Hierarchical Pattern Tree (RHPTree), and a top-down search method, RHPSearch, which are capable of efficiently analyzing a large volume of data and overcoming the limitations of previous works. The dynamic nature of the RHPTree avoids costly tree reconstruction for the iterative search process and dataset updates. We also introduce two specialized search methods, the extended target search (RHPSearch-TS) and the parallel search approach (RHPSearch-SD), to further speed up the retrieval of certain items of interest. Experiments on both UCI machine learning datasets and sampled datasets of the Simons Foundation Autism Research Initiative (SFARI)—Simon’s Simplex Collection (SSC) datasets demonstrate that our method is not only faster but also more effective in identifying comprehensive long risk patterns than existing works. Moreover, the proposed new tree structure is generic and applicable to other pattern mining problems.

Application of an Ultrashort-pulsed Laser for Generation of Super-hydrophobic Surfaces

Hydrophobic surfaces have gained a vast attention in different fields of biomedical applications over the last few years. Laser treatment has been shown as a promising technology for the generation of functional, inter alia, superhydrophobic surfaces. In this study a picosecond Yb-YAG laser was assigned for the generation of superhydrophobic characteristics on a steel alloy with application in surgical instrumentation. Regarding the ablation energy threshold of about 6 μJ for the given pulse width and laser beam characteristics and by assigning a suitable combination of microstructure kinematics and laser processing conditions a persistent hierarchical pattern is mapped over the laser-radiated surfaces. The average measured contact angle on the laserradiated surfaces was about 150°, which indicates their superhydrophobic properties.

A different perspective towards urban planning based on GIS and VGI techniques

Urban planning is considered one of the most critical topics in the design and development strategies of cities. There are a vast amount of urban planning rule sets that focus on dominating the geometrical perspective on other approaches when it comes to planning decisions. In this paper, a different point of view towards urban planning is suggested concentrating on the topological analysis and relation between city elements. Streets of cities are chosen to reflect this topological relation and to investigate the topological relations to the limits; six different sized cities were selected for analysis; three large cities and three relatively small ones. Results of study uncovered the hierarchical pattern underlying in street structure of cities; analyses showed that street networks in large cities have a higher degree of hierarchical level than in relatively small cities. Urban planners can get benefit from the results of analyses in this study to make better planning decisions in large or small cities. The paper provides analysis results towards achieving the optimum goals of urban planning to make cities more living and more efficient.

Manajemen Pembiayaan Pendidikan dalam Meningkatkan Mutu Pendidikan di Madrasah Aliyah Imam Muslim Serdang Bedagai

This study aims to determine the management of education financing in relation to efforts to improve the quality of education. This research is focused on the managerial head of the Aliyah Imam Muslim madrasah, Serdang Bedagai Regency. This research uses qualitative methods based on descriptive studies. Data collection techniques using observation, interviews, and documentation studies. Data analysis was performed using the Miles and Huberman model stages, namely data reduction, data presentation, and conclusion drawing. The data validity test used triangulation and member crosscheck. The results showed that the management of education financing at Madrasah Aliyah Imam Muslim Serdang Bedagai Regency has a family principle, is effective, efficient, productive, transparent and can be accounted for in accordance with existing procedures, namely the upward hierarchical pattern to the Chairman of the Foundation. In improving the quality of education, the principal is in charge of managing education as best as possible and reporting the draft budget for school financing is given to the Head of the Foundation, the head of madrasah also has the task of how to improve the quality of education in Madrasahs by coordinating every activity with peers in order to realize the quality of education through education financing management

Analysis of the Region of Interest According to CNN Structure in Hierarchical Pattern Surface Inspection Using CAM

A convolutional neural network (CNN), which exhibits excellent performance in solving image-based problem, has been widely applied to various industrial problems. In general, the CNN model was applied to defect inspection on the surface of raw materials or final products, and its accuracy also showed better performance compared to human inspection. However, surfaces with heterogeneous and complex backgrounds have difficulties in separating defects region from the background, which is a typical challenge in this field. In this study, the CNN model was applied to detect surface defects on a hierarchical patterned surface, one of the representative complex background surfaces. In order to optimize the CNN structure, the change in inspection performance was analyzed according to the number of layers and kernel size of the model using evaluation metrics. In addition, the change of the CNN’s decision criteria according to the change of the model structure was analyzed using a class activation map (CAM) technique, which can highlight the most important region recognized by the CNN in performing classification. As a result, we were able to accurately understand the classification manner of the CNN for the hierarchical pattern surface, and an accuracy of 93.7% was achieved using the optimized model.

Question Classification for Efficient QA System

Natural Language Processing (NLP), a subfield of Artificial Intelligence (AI), supports the machine to understand and manipulate the human languages in different sectors. Subsequently, the Question and answering scheme using Machine learning is a challengeable task. For an efficient QA system, understanding the category of a question plays a pivot role in extracting suitable answer. Computers can answer questions requiring single, verifiable answers but fail to answer subjective question demanding deeper understanding of question. Subjective questions can take different forms entailing deeper, multidimensional understanding of context. Identifying the intent of the question helps to extract expected answer from a given passage. Pretrained language models (LMs) have demonstrated excellent results on many language tasks. The paper proposes model of deep learning architecture in hierarchical pattern to learn the semantic of question and extracting appropriate answer. The proposed method converts the given context to fine grained embedding to capture semantic and positional representation, identifies user intent and employs a encoder model to concentrate on answer span. The proposed methods show a remarkable improvement over existing system

Hierarchical Microstructure of Tooth Enameloid in Two Lamniform Shark Species, Carcharias taurus and Isurus oxyrinchus

Shark tooth enameloid is a hard tissue made up of nanoscale fluorapatite crystallites arranged in a unique hierarchical pattern. This microstructural design results in a macroscopic material that is stiff, strong, and tough, despite consisting almost completely of brittle mineral. In this contribution, we characterize and compare the enameloid microstructure of two modern lamniform sharks, Isurus oxyrinchus (shortfin mako shark) and Carcharias taurus (spotted ragged-tooth shark), based on scanning electron microscopy images. The hierarchical microstructure of shark enameloid is discussed in comparison with amniote enamel. Striking similarities in the microstructures of the two hard tissues are found. Identical structural motifs have developed on different levels of the hierarchy in response to similar biomechanical requirements in enameloid and enamel. Analyzing these structural patterns allows the identification of general microstructural design principles and their biomechanical function, thus paving the way for the design of bioinspired composite materials with superior properties such as high strength combined with high fracture resistance.