The Stressor in Adolescence of Menstruation: Coping Strategies, Emotional Stress & Impacts on School Absences among Young Women in Nepal

Throughout the developing world, girls face hardships surrounding menstruation, often resulting in poor emotional wellbeing and missing school. Providing ways to keep girls in school will increase their educational and earning potentials, which will ultimately trickle down to improving the economic standing of nations in the next generation. Informed by the Transactional Model of Stress and Coping, this work evaluates the roles that cultural and school environments play in appraisals of menstruation as a major life stressor for adolescent females and the impacts of emotional stress on missing school. Using primary survey data from schools in Nepal, robust results are found to support the theoretical framework based on conditional mixed-process (CMP) estimation with fixed effects, utilizing multiple index building techniques. Strong cultural norms during menstruation appear to increase the probability of girls self-reporting emotional stress, while the presence of hygiene supporting infrastructure at schools reduces this outcome. Furthermore, there is strong support for the finding that the presence of emotional stress during menstruation increases the likelihood of not only missing school but also for an extended period of time. Our findings motivate increasing government policies to provide stronger hygiene infrastructure in schools to improve successful coping skills and attendance rates.

Temporal Index Scheme of Hyperledger Fabric System in IoT

As a large number of mobile terminals are connected to the IoT, the security problem of IoT is a challenge to the IoT technology. Blockchain technology has the characteristics of decentralization, data encryption, smart contract, and so on, especially suitable in the complex heterogeneous network. However, sequential access based on block files in the blockchain hinders efficient query processing. The problem is due to current blockchain solutions do not support temporal data processing. In this paper, we propose two index building methods (TISD and TIF) to address this issue in Hyperledger Fabric System. TISD (temporal index based on state databases) segments the historical data by time interval in the time dimension and indexes events at the same time interval. TIF (temporal index based on files) builds the index of files by the block transaction data, which is arranged in chronological order and is stored at a certain time interval. In the experimental part, we compare the query time on two datasets and analyse the query performance. Experiments demonstrated that our two methods are relatively stable in overall time performance on different datasets in the Hyperledger Fabric System.

UB-H: an unbalanced-hierarchical layer binary-wise construction method for high-dimensional data

Cloud computing, which is distributed, stored and managed, is drawing attention as data generation and storage volumes increase. In addition, research on green computing, which increases energy efficiency, is also widely studied. An index is constructed to retrieve huge dataset efficiently, and the layer-based indexing methods are widely used for efficient query processing. These methods construct a list of layers, so that only one layer is required for information retrieval instead of the entire dataset. The existing layer-based methods construct the layers using a convex hull algorithm. However, the execution time of this method is very high, especially in large, high-dimensional datasets. Furthermore, if the total number of layers increases, the query processing time also increases, resulting in efficient, but slow, query processing. In this paper, we propose an unbalanced-hierarchical layer method, which hierarchically divides the dimensions of input data to increase the total number of layers and reduce the index building time. We demonstrate that the proposed procedure significantly increases the total number of layers and reduces the index building time, compared to existing methods through the various experiments.

An Efficient 3D Model Retrieval Method Based on Convolutional Neural Network

Recently, 3D model retrieval based on views has become a research hotspot. In this method, 3D models are represented as a collection of 2D projective views, which allows deep learning techniques to be used for 3D model classification and retrieval. However, current methods need improvements in both accuracy and efficiency. To solve these problems, we propose a new 3D model retrieval method, which includes index building and model retrieval. In the index building stage, 3D models in library are projected to generate a large number of views, and then representative views are selected and input into a well-learned convolutional neural network (CNN) to extract features. Next, the features are organized according to their labels to build indexes. In this stage, the views used for representing 3D models are reduced substantially on the premise of keeping enough information of 3D models. This method reduces the number of similarity matching by 87.8%. In retrieval, the 2D views of the input model are classified into a category with the CNN and voting algorithm, and then only the features of one category rather than all categories are chosen to perform similarity matching. In this way, the searching space for retrieval is reduced. In addition, the number of used views for retrieval is gradually increased. Once there is enough evidence to determine a 3D model, the retrieval process will be terminated ahead of time. The variable view matching method further reduces the number of similarity matching by 21.4%. Experiments on the rigid 3D model datasets ModelNet10 and ModelNet40 and the nonrigid 3D model dataset McGill10 show that the proposed method has achieved retrieval accuracy rates of 94%, 92%, and 100%, respectively.

Battlefield of global ranking: How do power rivalries shape soft power index building?

International competition over soft power has largely transformed from image promotion and cultural diplomacy to benchmark setting. Benchmarks breed discourses and discourses embody power. The article argues that the soft power index building has turned into a battlefield where different values, norms and development models struggle for legitimacy through quasi-scientific validations. By critically examining the methods employed by two soft power indexes, Portland Soft Power 30 Index and China National Image Global Survey, this article unpacks the mechanisms by which institutions from western and emerging (Brazil, Russia, India, China and South Africa (BRICS)) states embed political values, interests and agendas in the selection of data, indicators and treatments of data. The article finds that while the soft power indexes originating from Western organizations largely normalized liberal values and the current international hierarchy, the Chinese national image survey provides a more self-reflective approach to soft power measurement.

INSTITUTIONAL STRENGTH, PEACEBUILDING, AND PRODUCTIVE ENTREPRENEURSHIP - EXPLORATORY ANALYSIS IN COLOMBIA

After more than half a century of armed conflict, Colombia is moving towards a post-conflict period. National and regional strategies aimed to strengthen institutional capacities, promote productive entrepreneurship and reduce organized violence and crime, are crucial lines of action for the alleviation of current (and future) grievances among ex-combatants, and Colombian society in general. This study presents an exploratory analysis on institutional strength, peacebuilding, and productive entrepreneurship in Colombia. Three composite indices based upon international assessments or seminal studies were developed, namely: Institutional Strength Index; Building Peace Index (based on the Negative Peace Index and Positive Peace Index); and Productive Entrepreneurship Index. The results showed a significant correlation between Institutional Strength Index and Productive Entrepreneurship Index. Population is the variable with the most significant correlation with productive entrepreneurship, employment, GDP, industrial sophistication, innovation, crime and certain types of violence (sexual and domestic).