Concept Analysis of Female Sexual Subjectivity based on Walker and Avant's Method

PURPOSE: The purpose of this study was to clarify attributes, antecedents, and consequences of female sexual subjectivity. METHODS: Walker and Avant's concept analysis process was used to analyze 27 studies from the current literature that relates to female sexual subjectivity. A systematic literature review of women's study in sociology, psychology, theology, law, health science, and nursing was reviewed. RESULTS: The defining attributes of female sexual subjectivity were sexual self-awareness, sexual decision making, sexual desire, and good sexual communication with partner. The antecedents of female sexual subjectivity were social environment, sexual education, sexual experience, and interpersonal relationship. The consequences of female sexual subjectivity were safe sex, prevention of sexual victimization, and sexual satisfaction. CONCLUSION: Female sexual subjectivity is defined as sexual self-awareness, sexual decision making, sexual desire to seek sexual pleasure and safety, and effective communication with partner in terms of sexual behavior, sexual experience and sexual health. Based on these results, a scale measuring female sexual subjectivity is needed

A Brief Overview of Two Major Strategies in Diversity-Oriented Synthesis: Build/Couple/Pair and Ring-Distortion

In the interdisciplinary research field of chemical biology and drug discovery, diversity-oriented synthesis (DOS) has become indispensable in the construction of novel small-molecule libraries rich in skeletal and stereochemical diversity. DOS aims to populate the unexplored chemical space with new potential bioactive molecules via forward synthetic analysis. Since the introduction of this concept by Schreiber, DOS has evolved along with many significant breakthroughs. It is therefore important to understand the key DOS strategies to build molecular diversity with maximized biological relevancy. Due to the length limitations of this mini review, we briefly discuss the recent DOS plans using build/couple/pair (B/C/P) and ring-distortion strategies for the synthesis of major biologically relevant target molecules like natural products and their related compounds, macrocycles, and privileged structures

Macrophage Lamin A/C Regulates Inflammation and the Development of Obesity-Induced Insulin Resistance

Obesity-induced chronic low-grade inflammation, in particular in adipose tissue, contributes to the development of insulin resistance and type 2 diabetes. However, the mechanism by which obesity induces adipose tissue inflammation has not been completely elucidated. Recent studies suggest that alteration of the nuclear lamina is associated with age-associated chronic inflammation in humans and fly. These findings led us to investigate whether the nuclear lamina regulates obesity-mediated chronic inflammation. In this study, we show that lamin A/C mediates inflammation in macrophages. The gene and protein expression levels of lamin A/C are significantly increased in epididymal adipose tissues from obese rodent models and omental fat from obese human subjects compared to their lean controls. Flow cytometry and gene expression analyses reveal that the protein and gene expression levels of lamin A/C are increased in adipose tissue macrophages (ATMs) by obesity. We further show that ectopic overexpression of lamin A/C in macrophages spontaneously activates NF-κB, and increases the gene expression levels of proinflammatory genes, such as Il6, Tnf, Ccl2, and Nos2. Conversely, deletion of lamin A/C in macrophages reduces LPS-induced expression of these proinflammatory genes. Importantly, we find that myeloid cell-specific lamin A/C deficiency ameliorates obesity-induced insulin resistance and adipose tissue inflammation. Thus, our data suggest that lamin A/C mediates the activation of ATM inflammation by regulating NF-κB, thereby contributing to the development of obesity-induced insulin resistance

The Role Of Language On knowledge Transfer In Multinational Enterprises (MNEs)

This thesis investigates the role of language and linguistic diversity in international business, manifested in managerial capability, organizational language practice, and cross-border transaction. Three research questions are proposed to direct this investigation. The first question regarding managerial capability is: How important is it for expatriates to know local language(s) when they are posted to a linguistically diverse subsidiary? In terms of organization language practice, expatriates' language proficiency and practice may not be static and can change throughout different stages of investment. This leads to the second research question: At the corporate level, how does language practice change in response to a shift in the importance placed on a knowledge source? Finally, firms pursue foreign expansion for asset seeking or new markets, and merger and acquisition (M&A) is one of the major mechanisms for foreign expansion. Based on this, the third research question is: In terms of cross-border transaction, what configurations influence M&A deal duration? Study 1 illustrates that the local linguistic capabilities of expatriates influences organizational performance in a multilingual environment. Study 2 follows an inductive approach to reveal multilingual practices throughout the investment stage for managerial communication in multinational enterprises (MNEs). Following the notion of a multilingual community, Study 3 is geared toward the recognition of MNEs as a way of acquiring knowledge and tests the configuration of lingua franca proficiency, linguistic distance, culture distance, industry similarity, full acquisition to influence cross-border M&A deals. By combining qualitative and quantitative methodologies, this dissertation demonstrates the importance of language IB across multiple aspects in the operation of MNEs

How to boost and accelerate new drug development in Korea: business ecosystem perspectives

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Focused and ambidextrous catch-up strategies of emerging economy multinationals

Many emerging economy multinationals (EMNEs) conduct asset-seeking foreign direct investment as a strategy to catch up to global market leaders. This catch-up strategy can be implemented in a focused (purely exploratory) or ambidextrous (simultaneously exploratory and exploitative) manner. This study examines the influence of industry environment on EMNEs' adoption of these catch-up strategies. Integrating an industry-based view with an upper-echelon perspective, we argue that industry munificence promotes a focused catch-up strategy but hinders the ambidextrous alternative. These opposing effects are further magnified by the functional diversity of EMNEs' managerial teams because functionally diversified teams are more likely to allocate attention to external cues in the industrial environment, as opposed to forming a unified strategic orientation internally. Using a panel of EMNEs from China over the period of 2005-2010, we find strong support for our main effects of industry munificence on both catch-up strategies and the moderating effect of managerial team's functional diversity towards ambidextrous catch-up strategy.Lin Cui and Yi Li would like to acknowledge funding support from the National Natural Science Foundation of China (grant numbers 71472038, 71872043)

Alohomora: Protecting files from ransomware attacks using fine-grained i/o whitelisting

© 2022 ACM.We propose a novel whitelist-based anti-ransomware solution called alohomora. Alohomora is based on our observation that an I/O activity of an application can be an effective abstraction level for managing I/O whitelisting. In alohomora, when a write request is sent to an SSD, its program context value (which is supported by a host CPU register) is passed to the SSD. The SSD checks if the request was pre-approved using the program context value, thus preventing ransomware from modifying files in the SSD. Our experimental results using a prototype alohomora system show that alohomora can achieve a strong security level against sophisticated ransomware attacks without degrading I/O performance.N

Genetic Algorithm for the Optimization of a Building Power Consumption Prediction Model

Accurately predicting power consumption is essential to ensure a safe power supply. Various technologies have been studied to predict power consumption, but the prediction of power consumption using deep learning models has been quite successful. However, in order to predict power consumption by utilizing deep learning models, it is necessary to find an appropriate set of hyper-parameters. This introduces the problem of complexity and wide search areas. The power consumption field should be accurately predicted in various distributed areas. To this end, a customized consumption prediction deep learning model is needed, which is essential for optimizing the hyper-parameters that are suitable for the environment. However, typical deep learning model users lack the knowledge needed to find the optimal values of parameters. To solve this problem, we propose a method for finding the optimal values of parameters for learning. In addition, the layer parameters of deep learning models are optimized by applying genetic algorithms. In this paper, we propose a hyper-parameter optimization method that solves the time and cost problems that depend on existing methods or experiences. We derive a hyper-parameter optimization plan that solves the existing method or experience-dependent time and cost problems. As a result, the RNN model achieved a 30% and 21% better mean squared error and mean absolute error, respectively, than did the arbitrary deep learning model, and the LSTM model was able to achieve 9% and 5% higher performance

Genetic Algorithm for the Optimization of a Building Power Consumption Prediction Model

Accurately predicting power consumption is essential to ensure a safe power supply. Various technologies have been studied to predict power consumption, but the prediction of power consumption using deep learning models has been quite successful. However, in order to predict power consumption by utilizing deep learning models, it is necessary to find an appropriate set of hyper-parameters. This introduces the problem of complexity and wide search areas. The power consumption field should be accurately predicted in various distributed areas. To this end, a customized consumption prediction deep learning model is needed, which is essential for optimizing the hyper-parameters that are suitable for the environment. However, typical deep learning model users lack the knowledge needed to find the optimal values of parameters. To solve this problem, we propose a method for finding the optimal values of parameters for learning. In addition, the layer parameters of deep learning models are optimized by applying genetic algorithms. In this paper, we propose a hyper-parameter optimization method that solves the time and cost problems that depend on existing methods or experiences. We derive a hyper-parameter optimization plan that solves the existing method or experience-dependent time and cost problems. As a result, the RNN model achieved a 30% and 21% better mean squared error and mean absolute error, respectively, than did the arbitrary deep learning model, and the LSTM model was able to achieve 9% and 5% higher performance