Do Multinationals Transfer Culture? Evidence on Female Employment in China

We study the global diffusion of culture through multinationals, focusing on gender norms. Using data on manufacturing firms in China over 2004-2007, we find that foreign affiliates from countries with a more gender-equal culture tend to employ proportionally more women and appoint female managers. They also generate cultural spillovers, increasing domestic firms’ female labor shares in the same industry or city. Based on a multi-sector model with firm heterogeneity in productivity, gender biases, and learning, we perform counterfactual exercises. Hypothetically eliminating firms’ gender biases raises China’s aggregate total factor productivity by 5%, of which spillovers from multinationals account for 19%

Pathologically Activated Neuroprotection via Uncompetitive Blockade of \u3cem\u3eN\u3c/em\u3e-Methyl-d-aspartate Receptors with Fast Off-rate by Novel Multifunctional Dimer Bis(propyl)-cognitin

Uncompetitive N-methyl-d-aspartate (NMDA) receptor antagonists with fast off-rate (UFO) may represent promising drug candidates for various neurodegenerative disorders. In this study, we report that bis(propyl)-cognitin, a novel dimeric acetylcholinesterase inhibitor and γ-aminobutyric acid subtype A receptor antagonist, is such an antagonist of NMDA receptors. In cultured rat hippocampal neurons, we demonstrated that bis(propyl)-cognitin voltage-dependently, selectively, and moderately inhibited NMDA-activated currents. The inhibitory effects of bis(propyl)-cognitin increased with the rise in NMDA and glycine concentrations. Kinetics analysis showed that the inhibition was of fast onset and offset with an off-rate time constant of 1.9 s. Molecular docking simulations showed moderate hydrophobic interaction between bis(propyl)-cognitin and the MK-801 binding region in the ion channel pore of the NMDA receptor. Bis(propyl)-cognitin was further found to compete with [3H]MK-801 with a Ki value of 0.27 μm, and the mutation of NR1(N616R) significantly reduced its inhibitory potency. Under glutamate-mediated pathological conditions, bis(propyl)-cognitin, in contrast to bis(heptyl)-cognitin, prevented excitotoxicity with increasing effectiveness against escalating levels of glutamate and much more effectively protected against middle cerebral artery occlusion-induced brain damage than did memantine. More interestingly, under NMDA receptor-mediated physiological conditions, bis(propyl)-cognitin enhanced long-term potentiation in hippocampal slices, whereas MK-801 reduced and memantine did not alter this process. These results suggest that bis(propyl)-cognitin is a UFO antagonist of NMDA receptors with moderate affinity, which may provide a pathologically activated therapy for various neurodegenerative disorders associated with NMDA receptor dysregulation

Continuous Beam Steering Through Broadside Using Asymmetrically Modulated Goubau Line Leaky-Wave Antennas

Goubau line is a single-conductor transmission line, featuring easy integration and low-loss transmission properties. Here, we propose a periodic leaky-wave antenna (LWA) based on planar Goubau transmission line on a thin dielectric substrate. The leaky-wave radiations are generated by introducing periodic modulations along the Goubau line. In this way, the surface wave, which is slow-wave mode supported by the Goubau line, achieves an additional momentum and hence enters the fast-wave region for radiations. By employing the periodic modulations, the proposed Goubau line LWAs are able to continuously steer the main beam from backward to forward within the operational frequency range. However, the LWAs usually suffer from a low radiation efficiency at the broadside direction. To overcome this drawback, we explore both transversally and longitudinally asymmetrical modulations to the Goubau line. Theoretical analysis, numerical simulations and experimental results are given in comparison with the symmetrical LWAs. It is demonstrated that the asymmetrical modulations significantly improve the radiation efficiency of LWAs at the broadside. Furthermore, the measurement results agree well with the numerical ones, which experimentally validates the proposed LWA structures. These novel Goubau line LWAs, experimentally demonstrated and validated at microwave frequencies, show also great potential for millimeter-wave and terahertz systems

Risk perception in skincare cosmetics and risk-reduction strategies : an exploratory study of young chinese women

Mestrado em MarketingCom a saturação dos mercados de cosmética nos países desenvolvidos, as empresas internacionais procuram cada vez mais novas oportunidades e crescimento nos mercados emergentes. Como o país mais populoso do mundo, a China é um alvo-chave. Com o aumento do rendimento disponível e a consciencialização do consumidor para a importância dos cuidados da pele, o mercado chinês de produtos de cuidado da pele é extremamente atraente, tanto para os players nacionais como internacionais. O risco percebido é um fator influente nas decisões de compra do consumidor, e os produtos para cuidado da não são exceção. Portanto, é fundamental que as empresas tenham um profundo entendimento das perceções de risco dos consumidores chineses em cosméticos para a pele, a fim de aumentar a presença neste e assumir a liderança. O objetivo deste estudo é entender as perceções de risco de jovens mulheres chinesas em cosméticos para a pele (desempenho, financeiro, físico, social e psicológico) e suas estratégias de redução de risco. Alguns aspetos que afetam a perceção de risco são também investigados. Além disso, este estudo aborda as perceções das mulheres chinesas sobre cosméticos orgânicos e testes de destes produtos em animais. A abordagem selecionada para esta pesquisa é a qualitativa e o método de coleta de dados é a entrevista semiestruturada presencial. A amostra é composta por 12 consumidoras chinesas de produtos para a pele.With the saturation of cosmetic markets in developed countries, international companies increasingly seek new opportunities and growth in emerging markets. As the most populated country in the world, China is a key target. With increasing disposable income and consumer awareness of the importance of skincare, the Chinese skincare cosmetic market is very attractive for both national and international players. Perceived risk is an influential factor in consumer purchase decisions, and skincare in no exception. Thus, it is critical for enterprises to have a deep understanding of Chinese consumers' risk perceptions in skincare cosmetics in order to increase market share and take the lead. The purpose of this study is to understand young Chinese women's perceptions of risk in skincare cosmetics (performance, financial, physical, social, and psychological) and their risk-reductions strategies. Some other issues affecting risk perception are also investigated. Furthermore, this study addresses Chinese female's perceptions of organic cosmetics and testing skincare products on animals. The approach selected for this research is qualitative and the method of data collection is the semi-structured face-to-face interview. The sample consists of 12 Chinese female consumers of skincare products.info:eu-repo/semantics/publishedVersio

SB-CoRLA: Schema-Based Constructivist Robot Learning Architecture

This dissertation explores schema-based robot learning. I developed SB-CoRLA (Schema- Based, Constructivist Robot Learning Architecture) to address the issue of constructivist robot learning in a schema-based robot system. The SB-CoRLA architecture extends the previously developed ASyMTRe (Automated Synthesis of Multi-team member Task solutions through software Reconfiguration) architecture to enable constructivist learning for multi-robot team tasks. The schema-based ASyMTRe architecture has successfully solved the problem of automatically synthesizing task solutions based on robot capabilities. However, it does not include a learning ability. Nothing is learned from past experience; therefore, each time a new task needs to be assigned to a new team of robots, the search process for a solution starts anew. Furthermore, it is not possible for the robot to develop a new behavior. The complete SB-CoRLA architecture includes off-line learning and online learning processes. For my dissertation, I implemented a schema chunking process within the framework of SB-CoRLA that involves off-line evolutionary learning of partial solutions (also called “chunks”), and online solution search using learned chunks. The chunks are higher level building blocks than the original schemas. They have similar interfaces to the original schemas, and can be used in an extended version of the ASyMTRe online solution searching process. SB-CoRLA can include other learning processes such as an online learning process that uses a combination of exploration and a goal-directed feedback evaluation process to develop new behaviors by modifying and extending existing schemas. The online learning process is planned for future work. The significance of this work is the development of an architecture that enables continuous, constructivist learning by incorporating learning capabilities in a schema-based robot system, thus allowing robot teams to re-use previous task solutions for both existing and new tasks, to build up more abstract schema chunks, as well as to develop new schemas. The schema chunking process can generate solutions in certain situations when the centralized ASyMTRe cannot find solutions in a timely manner. The chunks can be re-used for different applications, hence improving the search efficiency

CARBON NANOMATERIALS AND THEIR ELECTROCHEMICAL APPLICATIONS

Recent years have witnessed the continuously growing interest in the area of nanotechnology. Among the innumerable novel compounds and materials, carbon nanomaterials, especially carbon nanotubes (CNTs) and graphene, are undeniably two of the most glorious shining stars due to their unique structures and promising physical, chemical, and electrical properties. Numerous research projects have been focused on the synthesis, characterization, and functionalization of carbon nanomaterials, as well as their enormous possible applications in energy generation/storage, sensors, electronics, reinforcement of composite materials, and drug delivery. Of particular interest in this dissertation are the functionalization of carbon nanomaterials − either by decorating with Pt nanoparticles (NPs) or by doping with nitrogen atoms − and their electrochemical applications for both fuel cell catalysts (and supports) and electrochemical sensors/biosensors. I have successfully synthesized and characterized hybrid structures of Pt NP-CNTs or Pt NP-graphene, and also a novel carbon nanomaterial − nitrogen doped carbon nanotube cups (NCNCs). The electrochemical properties and applications of these nanomaterials were also investigated. Pt NP decorated CNTs or graphene were studied and compared for their electrochemical sensor performance in order to obtain further understanding on the structure-property relationship between 1-dimensional and 2-dimensional nanomaterials as the sensing platform. Both Pt-CNTs and NCNCs were investigated as fuel cell catalysts with the aim of improving the performance and stability, decreasing the amount of expensive Pt, and most importantly, understanding and optimizing NCNCs as non-precious-metal catalysts to ultimately replace expensive Pt-based catalysts. Pt-CNTs demonstrated extraordinary stability with less material used compared to commercial Pt/C catalysts in long term stability testing. NCNCs also exhibited good catalytic activity towards oxygen reduction reaction (ORR) which makes them promising alternatives to Pt-based catalysts. Further look into the ORR mechanism suggested that the presence of both nitrogen and iron from catalyst of NCNCs synthesis process is crucial for the improved ORR catalytic activity. From the materials point of view, a novel simple sonication method was studied to separate stacked NCNCs into individual nanocups structures, with the long-term objective of drug delivery or nano-reactor applications. Both the separation mechanism and the structure-property relationship of the stacked and separated NCNCs were investigated

Unveiling Cultural Cognition in AI: A Systematic Investigation of Horizontal-Vertical Individualism-Collectivism Traits in Large Language Models

This study investigates the Horizontal-Vertical Individualism-Collectivism (HVIC) traits of Large Language Models (LLMs), addressing the gap in understanding their cultural and social cognition. HVIC, a cross-cultural psychology framework, offers insights into cognitive patterns shaped by culture. We systematically evaluate multiple LLMs using quantitative (INDCOL scale) method, assessing their intrinsic HVIC traits and ability to simulate cultural and gender-based differences. Our findings reveal LLMs' capacity to capture HVIC nuances, providing a unique lens for studying human cognition through human-LLM comparisons. This research contributes to developing culturally sensitive AI systems and offers new perspectives on human HVIC traits, advancing both theoretical understanding and practical applications of AI

Cluster Analysis with Batch Effect

Clustering, as a fundamental process in data science, is frequently used in preliminary data analysis. Batch effects are a powerful source of variation that can come from many sources in data collection, and influence data. We propose a method to simultaneously remove batch effects and perform cluster analysis. We see a batch effect as a fixed value added on to each batch, and do not make assumptions about the distribution of batch effects. We represent the data using a Gaussian mixture model, and use the EM algorithm to estimate the cluster means, the cluster covariance matrices, and the batch effects, and give predictions on which cluster each observation belongs to via their posterior probability. We also give two tests to identify the presence of batch effects in the data. Gap statistics are used to determine the number of clusters that should be used. We compare our method via simulation studies with a standard K-means method and K-means with the batch effects removed prior to analysis. Out simulations studies our method has better prediction results than both of these approaches. Our method does not assume the batch effects following any particular distribution, and works on data that have larger batch effects, as well as an interaction between clusters and batches