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Research Aim This thesis explores the role of user-generated content (UGC) in destination marketing, specifically focusing on Harbin, China. It aims to understand how UGC on social media, particularly Douyin, influences Harbin's destination image and the motivations behind content creation. Methodology A mixed-method approach was employed, combining netnography and semi-structured interviews. The netnography involved observing behaviors and content related to Harbin tourism on Douyin. Semi-structured interviews were conducted with young travelers who visited Harbin between 2023 and May 2024. Findings The findings indicate that UGC significantly enhances Harbin's destination image by showcasing travel ease, local authenticity, and friendliness. Douyin's features, such as comments and hashtags, facilitate extensive user engagement, which helps shape Harbin’s image. The motivations for creating UGC include expertise, consumer capital, time, experiential benefits, self-worth, and status. Research Implications The theoretical contributions of this research expand the framework of consumer co-creation motivations and highlight the role of UGC in destination marketing. Practically, the study offers recommendations for Destination Marketing Organizations (DMOs) on leveraging UGC and forming strategic platform partnerships. Future research should consider larger sample sizes, exploring various social media platforms, and examining different cultural contexts.

Enterprises are facing the superimposed challenges of digitalization and greening. The shift from reactive green technology innovation (RGT) to proactive green technology innovation (PGT) has special significance for sustainable economic development. Which strategies will companies choose? Can digital transformation (DT) motivate companies to transform their green innovation strategies? Enterprises' green innovation strategy choices must be explained with regard to digitalization. The purpose of this paper is to reveal how digitalization affects the choice of green innovation strategies and to provide a realistic basis for the sustainable development of heavily polluting enterprises. We formulated a "DT -capability -strategy" theoretical framework incorporating external constraints and internal attitudes to empirically test the microdata of 505 heavily polluting enterprises. The results show that: (1) DT can shift the heavily polluting enterprises' green innovation strategies from RGT to PGT. Endogenous tests and robustness tests support this conclusion. (2) A mechanism test shows that environmental regulations cannot significantly regulate a green innovation strategy. Only a company's capabilities and attitudes can influence PGT but their effects on RGT are not statistically significant. (3) The influence of DT on green innovation strategies shows multi -dimensional heterogeneity in the digital infrastructure, scale, and innovation level of the enterprise. The conclusions provide implications for enterprises to integrate their digital and green behaviors., Funding Agencies|Philosophy and Social Science Foundation of China [22AJY005]; The 2023 Xinjiang Production and Construction Corps (XPCC) Graduate Innovation Project

Radiologists must utilize multiple modal images for tumor segmentation and diagnosis due to the limitations of medical imaging and the diversity of tumor signals. This leads to the development of multimodal learning in segmentation. However, the redundancy among modalities creates challenges for existing subtraction-based joint learning methods, such as misjudging the importance of modalities, ignoring specific modal information, and increasing cognitive load. These thorny issues ultimately decrease segmentation accuracy and increase the risk of overfitting. This paper presents the complementary information mutual learning (CIML) framework, which can mathematically model and address the negative impact of inter-modal redundant information. CIML adopts the idea of addition and removes inter-modal redundant information through inductive bias-driven task decomposition and message passing-based redundancy filtering. CIML first decomposes the multimodal segmentation task into multiple subtasks based on expert prior knowledge, minimizing the information dependence between modalities. Furthermore, CIML introduces a scheme in which each modality can extract information from other modalities additively through message passing. To achieve non-redundancy of extracted information, the redundant filtering is transformed into complementary information learning inspired by the variational information bottleneck. The complementary information learning procedure can be efficiently solved by variational inference and cross-modal spatial attention. Numerical results from the verification task and standard benchmarks indicate that CIML efficiently removes redundant information between modalities, outperforming SOTA methods regarding validation accuracy and segmentation effect., Comment: 35 pages, 18 figures

3D environment recognition is essential for autonomous driving systems, as autonomous vehicles require a comprehensive understanding of surrounding scenes. Recently, the predominant approach to define this real-life problem is through 3D occupancy prediction. It attempts to predict the occupancy states and semantic labels for all voxels in 3D space, which enhances the perception capability. Birds-Eye-View(BEV)-based perception has achieved the SOTA performance for this task. Nonetheless, this architecture fails to represent various scales of BEV features. In this paper, inspired by the success of UNet in semantic segmentation tasks, we introduce a novel UNet-like Multi-scale Occupancy Head module to relieve this issue. Furthermore, we propose the class-balancing loss to compensate for rare classes in the dataset. The experimental results on nuScenes 3D occupancy challenge dataset show the superiority of our proposed approach over baseline and SOTA methods., Comment: 5 pages, 3 figures, accepted by IEEE CAI 2024

Both external motivational incentives (e.g., monetary reward) and internal motivational incentives (e.g., self-determined choice) have been found to promote memory, but much less is known about how these two types of incentives interact with each other to affect memory. The current study (N = 108) examined how performance-dependent monetary rewards affected the role of self-determined choice in memory performance, also known as the choice effect. Using a modified and better controlled version of the choice paradigm and manipulating levels of reward, we demonstrated an interactive effect between monetary reward and self-determined choice on 1-day delayed memory performance. Specifically, the choice effect on memory decreased when we introduced the performance-dependent external rewards. These results are discussed in terms of understanding how external and internal motivators interact to impact learning and memory.

The long non-coding RNA (lncRNA) Metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) maintains the integrity of the intestinal epithelial barrier and regulates local inflammation. However, its influences on intestinal microbial communities and tissue susceptibility to cancer development remain unexplored. Here, we report that MALAT1 regulates host anti-microbial response gene expression and the composition of mucosal-associated microbial communities in a region-specific manner. In the APC mutant mouse model of intestine tumorigenesis, knocking out MALAT1 results in higher polyp counts in the small intestine and colon. Interestingly, intestine polyps that developed in the absence of MALAT1 were smaller in size. These findings highlight the unexpected bivalent role of MALAT1 in restricting and promoting cancer progression at different disease stages. Among the 30 MALAT1-targets shared by both the small intestine and colon, ZNF638 and SENP8 levels are predictive of colon adenoma patient overall survival and disease-free survival. Genomic assays further revealed that MALAT1 modulates intestinal target expression and splicing through both direct and indirect mechanisms. This study expands the role of lncRNAs in regulating intestine homeostasis, microbial communities, and cancer pathogenesis.

Microwave-assisted synthesis has attracted wide attention as an efficient and energy-saving synthesis strategy due to its unique heating mechanism. Environmental catalysts prepared via microwave heating process typically exhibit uniform crystal size with desired morphology, porous structure and interfacial properties, which are important for the catalytic remediation of air pollution. This review first summarizes the principles of microwave heating, followed by the characteristics of the obtained catalysts. The research progress of representative materials (e.g., supported metal catalysts, metal oxides, and porous carbon materials) in the treatment of gaseous pollutants are then categorized in detail. Meanwhile, this review compiles the recent research outputs of air pollutants remediation by environmental catalysts from the perspective of features and reaction pathways facilitated by microwave-assisted synthesis. It demonstrates that microwave-assisted heating can not only improve the synthesis efficiency and the catalytic activity but also modify the selectivity by tuning the catalyst properties. Finally, the use of microwave irradiation to activate environmental catalytic reactions is reviewed, as the application of microwaves can lead to significant improvements due to the possibility of delivering energy directly to the catalytic materials. Nevertheless, challenges remain in the development of highly active and efficient catalyst to realize its practical application. These, together with prospects on the development of environmental catalysts using microwave techniques are provided for enlightening the future of this field of research. © 2023 The Author(s)

Microwave-assisted synthesis has attracted wide attention as an efficient and energy-saving synthesis strategy due to its unique heating mechanism. Environmental catalysts prepared via microwave heating process typically exhibit uniform crystal size with desired morphology, porous structure and interfacial properties, which are important for the catalytic remediation of air pollution. This review first summarizes the principles of microwave heating, followed by the characteristics of the obtained catalysts. The research progress of representative materials (e.g., supported metal catalysts, metal oxides, and porous carbon materials) in the treatment of gaseous pollutants are then categorized in detail. Meanwhile, this review compiles the recent research outputs of air pollutants remediation by environmental catalysts from the perspective of features and reaction pathways facilitated by microwave-assisted synthesis. It demonstrates that microwave-assisted heating can not only improve the synthesis efficiency and the catalytic activity but also modify the selectivity by tuning the catalyst properties. Finally, the use of microwave irradiation to activate environmental catalytic reactions is reviewed, as the application of microwaves can lead to significant improvements due to the possibility of delivering energy directly to the catalytic materials. Nevertheless, challenges remain in the development of highly active and efficient catalyst to realize its practical application. These, together with prospects on the development of environmental catalysts using microwave techniques are provided for enlightening the future of this field of research. © 2023 The Author(s)

Large language models (LLMs), such as ChatGPT, have demonstrated impressive capabilities in various tasks and attracted an increasing interest as a natural language interface across many domains. Recently, large vision-language models (VLMs) like BLIP-2 and GPT-4 have been intensively investigated, which learn rich vision-language correlation from image-text pairs. However, despite these developments, the application of LLMs and VLMs in image quality assessment (IQA), particularly in medical imaging, remains to be explored, which is valuable for objective performance evaluation and potential supplement or even replacement of radiologists' opinions. To this end, this paper introduces IQAGPT, an innovative image quality assessment system integrating an image quality captioning VLM with ChatGPT for generating quality scores and textual reports. First, we build a CT-IQA dataset for training and evaluation, comprising 1,000 CT slices with diverse quality levels professionally annotated. To better leverage the capabilities of LLMs, we convert annotated quality scores into semantically rich text descriptions using a prompt template. Second, we fine-tune the image quality captioning VLM on the CT-IQA dataset to generate quality descriptions. The captioning model fuses the image and text features through cross-modal attention. Third, based on the quality descriptions, users can talk with ChatGPT to rate image quality scores or produce a radiological quality report. Our preliminary results demonstrate the feasibility of assessing image quality with large models. Remarkably, our IQAGPT outperforms GPT-4 and CLIP-IQA, as well as the multi-task classification and regression models that solely rely on images., Comment: 14 pages, 9 figures

Learning-outcome prediction (LOP) is a long-standing and critical problem in educational routes. Many studies have contributed to developing effective models while often suffering from data shortage and low generalization to various institutions due to the privacy-protection issue. To this end, this study proposes a distributed grade prediction model, dubbed FecMap, by exploiting the federated learning (FL) framework that preserves the private data of local clients and communicates with others through a global generalized model. FecMap considers local subspace learning (LSL), which explicitly learns the local features against the global features, and multi-layer privacy protection (MPP), which hierarchically protects the private features, including model-shareable features and not-allowably shared features, to achieve client-specific classifiers of high performance on LOP per institution. FecMap is then achieved in an iteration manner with all datasets distributed on clients by training a local neural network composed of a global part, a local part, and a classification head in clients and averaging the global parts from clients on the server. To evaluate the FecMap model, we collected three higher-educational datasets of student academic records from engineering majors. Experiment results manifest that FecMap benefits from the proposed LSL and MPP and achieves steady performance on the task of LOP, compared with the state-of-the-art models. This study makes a fresh attempt at the use of federated learning in the learning-analytical task, potentially paving the way to facilitating personalized education with privacy protection., Comment: 10 pages, 9 figures, 3 tables. This preprint will be published in Frontiers of Computer Science on Dec 15, 2024

Temporal knowledge graphs (TKGs) have been identified as a promising approach to represent the dynamics of facts along the timeline. The extrapolation of TKG is to predict unknowable facts happening in the future, holding significant practical value across diverse fields. Most extrapolation studies in TKGs focus on modeling global historical fact repeating and cyclic patterns, as well as local historical adjacent fact evolution patterns, showing promising performance in predicting future unknown facts. Yet, existing methods still face two major challenges: (1) They usually neglect the importance of historical information in KG snapshots related to the queries when encoding the local and global historical information; (2) They exhibit weak anti-noise capabilities, which hinders their performance when the inputs are contaminated with noise.To this end, we propose a novel \blue{Lo}cal-\blue{g}lobal history-aware \blue{C}ontrastive \blue{L}earning model (\blue{LogCL}) for TKG reasoning, which adopts contrastive learning to better guide the fusion of local and global historical information and enhance the ability to resist interference. Specifically, for the first challenge, LogCL proposes an entity-aware attention mechanism applied to the local and global historical facts encoder, which captures the key historical information related to queries. For the latter issue, LogCL designs four historical query contrast patterns, effectively improving the robustness of the model. The experimental results on four benchmark datasets demonstrate that LogCL delivers better and more robust performance than the state-of-the-art baselines., Comment: 14 pages, Accept ICDE2024

3D object detection in Bird's-Eye-View (BEV) space has recently emerged as a prevalent approach in the field of autonomous driving. Despite the demonstrated improvements in accuracy and velocity estimation compared to perspective view methods, the deployment of BEV-based techniques in real-world autonomous vehicles remains challenging. This is primarily due to their reliance on vision-transformer (ViT) based architectures, which introduce quadratic complexity with respect to the input resolution. To address this issue, we propose an efficient BEV-based 3D detection framework called BEVENet, which leverages a convolutional-only architectural design to circumvent the limitations of ViT models while maintaining the effectiveness of BEV-based methods. Our experiments show that BEVENet is 3$\times$ faster than contemporary state-of-the-art (SOTA) approaches on the NuScenes challenge, achieving a mean average precision (mAP) of 0.456 and a nuScenes detection score (NDS) of 0.555 on the NuScenes validation dataset, with an inference speed of 47.6 frames per second. To the best of our knowledge, this study stands as the first to achieve such significant efficiency improvements for BEV-based methods, highlighting their enhanced feasibility for real-world autonomous driving applications.

Recently, Large Language Models (LLMs) have enhanced user interaction, enabling seamless information retrieval and recommendations. However, concerns emerge as these LLMs have shown tendencies to display discrimination related to users' sensitive characteristics (such as gender), leading to explicit user unfairness. Furthermore, our analysis uncovers a more discreet variant of bias in LLMs, defined as implicit user unfairness, wherein these models demonstrate discriminatory recommendation behaviors based solely on non-sensitive user details, like usernames or email addresses. This subtle form of unfairness, while more pervasive, poses a significant threat to the ethical integrity and rights of minority user groups. To comprehensively explore implicit user unfairness, our analysis unfolds in three key steps: (1) We uncover the reasons for this implicit user unfairness: LLMs can infer users' sensitive attributes from non-sensitive attributes (e.g. user names) due to their extensive world knowledge. (2) Our findings expose that the magnitude of implicit user unfairness within LLMs surpasses the level of explicit user unfairness observed in traditional recommender models, signifying a more alarming issue of unfairness, i.e. some non-sensitive features of users like names may result in more serious discrimination phenomena. (3) We analyze the long-term effect of implicit user unfairness, identifying that it will reinforce information bubbles at an accelerated rate compared to traditional RS. We emphasize the need to identify and mitigate implicit user unfairness, aiming to avert the potential human-LLMs recommendation systems deterioration., Comment: No

Generalizing Neural Radiance Fields (NeRF) to new scenes is a significant challenge that existing approaches struggle to address without extensive modifications to vanilla NeRF framework. We introduce InsertNeRF, a method for INStilling gEneRalizabiliTy into NeRF. By utilizing multiple plug-and-play HyperNet modules, InsertNeRF dynamically tailors NeRF's weights to specific reference scenes, transforming multi-scale sampling-aware features into scene-specific representations. This novel design allows for more accurate and efficient representations of complex appearances and geometries. Experiments show that this method not only achieves superior generalization performance but also provides a flexible pathway for integration with other NeRF-like systems, even in sparse input settings. Code will be available https://github.com/bbbbby-99/InsertNeRF., Comment: This work was accepted at ICLR 2024

Neural Radiance Fields from Sparse input} (NeRF-S) have shown great potential in synthesizing novel views with a limited number of observed viewpoints. However, due to the inherent limitations of sparse inputs and the gap between non-adjacent views, rendering results often suffer from over-fitting and foggy surfaces, a phenomenon we refer to as "CONFUSION" during volume rendering. In this paper, we analyze the root cause of this confusion and attribute it to two fundamental questions: "WHERE" and "HOW". To this end, we present a novel learning framework, WaH-NeRF, which effectively mitigates confusion by tackling the following challenges: (i)"WHERE" to Sample? in NeRF-S -- we introduce a Deformable Sampling strategy and a Weight-based Mutual Information Loss to address sample-position confusion arising from the limited number of viewpoints; and (ii) "HOW" to Predict? in NeRF-S -- we propose a Semi-Supervised NeRF learning Paradigm based on pose perturbation and a Pixel-Patch Correspondence Loss to alleviate prediction confusion caused by the disparity between training and testing viewpoints. By integrating our proposed modules and loss functions, WaH-NeRF outperforms previous methods under the NeRF-S setting. Code is available https://github.com/bbbbby-99/WaH-NeRF., Comment: Accepted In Proceedings of the 31st ACM International Conference on Multimedia (MM' 23)

Background: Hippocampal involvement may differ between multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD). Objective: To investigate the morphometric, diffusion and functional alterations in hippocampus in MS and NMOSD and the clinical significance. Methods: A total of 752 participants including 236 MS, 236 NMOSD and 280 healthy controls (HC) were included in this retrospective multi-center study. The hippocampus and subfield volumes, fractional anisotropy (FA) and mean diffusivity (MD), amplitude of low frequency fluctuation (ALFF) and degree centrality (DC) were analyzed, and their associations with clinical variables were investigated. Results: The hippocampus showed significantly lower volume, FA and greater MD in MS compared to NMOSD and HC (p < 0.05), while no abnormal ALFF or DC was identified in any group. Hippocampal subfields were affected in both diseases, though subiculum, presubiculum and fimbria showed significantly lower volume only in MS (p < 0.05). Significant correlations between diffusion alterations, several subfield volumes and clinical variables were observed in both diseases, especially in MS (R = -0.444 to 0.498, p < 0.05). FA and MD showed fair discriminative power between MS and HC, NMOSD and HC (AUC > 0.7). Conclusions: Hippocampal atrophy and diffusion abnormalities were identified in MS and NMOSD, partly explaining how clinical disability and cognitive impairment are differentially affected.

Aim: To evaluate the health status of nurses in China and explore the impact of work-related stress, work environment and lifestyle factors on their health outcomes. Design: The Chinese Nurses' Health Study is a multicentred, prospective cohort study. Methods: We plan to recruit approximately 80,000 registered nurses aged between 18 and 65 years. Eligible nurses will be introduced to complete a series of web-based questionnaires after obtaining their informed consent. Follow-up questionnaires will be completed at 2-year interval to continuously track subsequent exposures. Health-related indicators will be obtained through self-reporting by nurses and the provincial and national registry platforms such as National Central Cancer Registry. The funding was approved in July 2020 and Research Ethics Committee approval was granted in February 2021. Discussion: The study is the first multicentred prospective cohort study that aims to assess the impact of work-related stress, work environment and lifestyle factors on the health of Chinese nurses. The results of the Chinese Nurses' Health Cohort Study will potentially draw a picture of the current situation of general health and well-being among nurses in China and their health risks. This will be critical in recommending locally tailored strategic preventive measures and policies to reduce health and well-being threats for nurses and potentially general public, thereby promoting the quality of healthcare in China and globally. Impact: This study will help to understand the health status and working environment characteristics of Chinese nurses, and provide valuable epidemiological evidence for improving working environment and promoting well-being. The results of this study are potentially of great significance for formulating targeted nursing strategies to promote the nurses' health, nursing quality and patient safety in China and even around the world.

Aim: To evaluate the health status of nurses in China and explore the impact of work-related stress, work environment and lifestyle factors on their health outcomes. Design: The Chinese Nurses' Health Study is a multicentred, prospective cohort study. Methods: We plan to recruit approximately 80,000 registered nurses aged between 18 and 65 years. Eligible nurses will be introduced to complete a series of web-based questionnaires after obtaining their informed consent. Follow-up questionnaires will be completed at 2-year interval to continuously track subsequent exposures. Health-related indicators will be obtained through self-reporting by nurses and the provincial and national registry platforms such as National Central Cancer Registry. The funding was approved in July 2020 and Research Ethics Committee approval was granted in February 2021. Discussion: The study is the first multicentred prospective cohort study that aims to assess the impact of work-related stress, work environment and lifestyle factors on the health of Chinese nurses. The results of the Chinese Nurses' Health Cohort Study will potentially draw a picture of the current situation of general health and well-being among nurses in China and their health risks. This will be critical in recommending locally tailored strategic preventive measures and policies to reduce health and well-being threats for nurses and potentially general public, thereby promoting the quality of healthcare in China and globally. Impact: This study will help to understand the health status and working environment characteristics of Chinese nurses, and provide valuable epidemiological evidence for improving working environment and promoting well-being. The results of this study are potentially of great significance for formulating targeted nursing strategies to promote the nurses' health, nursing quality and patient safety in China and even around the world.

Model bias triggered by long-tailed data has been widely studied. However, measure based on the number of samples cannot explicate three phenomena simultaneously: (1) Given enough data, the classification performance gain is marginal with additional samples. (2) Classification performance decays precipitously as the number of training samples decreases when there is insufficient data. (3) Model trained on sample-balanced datasets still has different biases for different classes. In this work, we define and quantify the semantic scale of classes, which is used to measure the feature diversity of classes. It is exciting to find experimentally that there is a marginal effect of semantic scale, which perfectly describes the first two phenomena. Further, the quantitative measurement of semantic scale imbalance is proposed, which can accurately reflect model bias on multiple datasets, even on sample-balanced data, revealing a novel perspective for the study of class imbalance. Due to the prevalence of semantic scale imbalance, we propose semantic-scale-balanced learning, including a general loss improvement scheme and a dynamic re-weighting training framework that overcomes the challenge of calculating semantic scales in real-time during iterations. Comprehensive experiments show that dynamic semantic-scale-balanced learning consistently enables the model to perform superiorly on large-scale long-tailed and non-long-tailed natural and medical datasets, which is a good starting point for mitigating the prevalent but unnoticed model bias., Comment: 47 pages, 26 figures, 12 tables, Published as a conference paper at ICLR 2023

Modern Entity Linking (EL) systems entrench a popularity bias, yet there is no dataset focusing on tail and emerging entities in languages other than English. We present Hansel, a new benchmark in Chinese that fills the vacancy of non-English few-shot and zero-shot EL challenges. The test set of Hansel is human annotated and reviewed, created with a novel method for collecting zero-shot EL datasets. It covers 10K diverse documents in news, social media posts and other web articles, with Wikidata as its target Knowledge Base. We demonstrate that the existing state-of-the-art EL system performs poorly on Hansel (R@1 of 36.6% on Few-Shot). We then establish a strong baseline that scores a R@1 of 46.2% on Few-Shot and 76.6% on Zero-Shot on our dataset. We also show that our baseline achieves competitive results on TAC-KBP2015 Chinese Entity Linking task., Comment: WSDM 2023

With the recent development of Internet technology, the e-commerce industry is booming. However, under this rapid development, various inequality issues are hidden. Inspired by Goldin's (2002) study on declining gender significance, this paper focuses on the gender inequalities faced by female employees in the e-commerce industry in China. This paper focuses on one of the representatives of the e-commerce industry, Internet companies, to investigate the barriers and social status of female employees working in Chinese Internet companies. Using a qualitative research approach, this paper interviewed eight employees of Chinese Internet companies through semi-structured interviews. Acker's (2006) inequality regimes are then used to extract and analyze information from the responses given by the interviewees and finally draw conclusions. The study found that female employees of Chinese Internet companies do face lower barriers, such as lower physical requirements that make it easier for women to enter the industry and higher salaries that make women more likely to be financially independent. However, as one digs deeper, higher barriers emerge, such as segregation by gender between positions or within positions, and different hierarchies. In terms of social status, women in Chinese Internet companies have not managed to gain a higher status, as the gender hierarchy created by the particular Chinese Confucian culture keeps women in a disadvantaged position. The development of e-commerce has not substantially changed the status of women in the cultural system.

Purpose: To examine how core values can be concretely transformed in the physical stores in order to help with the creation of favorable brand associations and the shaping of the brand image. Methodology: The paper combines both primary and secondary data, with a specific focus on two brands as case studies: Lush and Kiehl's. Primary data was collected through a questionnaire that includes both quantitative and qualitative questions, to understand the relevance of the delivery of core values through the physical store and the results this has in terms of brand associations and image. Secondary data comes in the form of a literature review to give a proper theoretical foundation to the research, coupled with the analysis of the selected brands through online articles and their respective websites. Findings: The research shows that today physical stores are still highly relevant. Most importantly, it delineates how a good and clear store branding strategy can help to acquire a desirable positioning, while an improper plan can instead damage it by failing with the first step of the value transformation process. Originality/value: The paper is the first of its kind to focus on the transformation of the core values into physical realities through the interior design and physical elements of the store, combined with the effects that this has on people. Moreover, a theoretical contribution is given by proposing a framework that can be adapted to analyze the efficiency of the brands' actions in the physical world.

With the recent development of Internet technology, the e-commerce industry is booming. However, under this rapid development, various inequality issues are hidden. Inspired by Goldin's (2002) study on declining gender significance, this paper focuses on the gender inequalities faced by female employees in the e-commerce industry in China. This paper focuses on one of the representatives of the e-commerce industry, Internet companies, to investigate the barriers and social status of female employees working in Chinese Internet companies. Using a qualitative research approach, this paper interviewed eight employees of Chinese Internet companies through semi-structured interviews. Acker's (2006) inequality regimes are then used to extract and analyze information from the responses given by the interviewees and finally draw conclusions. The study found that female employees of Chinese Internet companies do face lower barriers, such as lower physical requirements that make it easier for women to enter the industry and higher salaries that make women more likely to be financially independent. However, as one digs deeper, higher barriers emerge, such as segregation by gender between positions or within positions, and different hierarchies. In terms of social status, women in Chinese Internet companies have not managed to gain a higher status, as the gender hierarchy created by the particular Chinese Confucian culture keeps women in a disadvantaged position. The development of e-commerce has not substantially changed the status of women in the cultural system.

In response to the Covid-19 pandemic, the Chinese government has utilized and pragmatically adjusted existing welfare institutions to ensure social and economic stability. This report describes the Chinese social policy responses in 2020 to mitigate the effects of the coronavirus crisis and focuses on specific countermeasures, such as cash benefits and benefits-in-kind for vulnerable people in the fields of health, social assistance, unemployment, and elderly care. Moreover, social insurance contributions for enterprises were reduced and deferred to help revive the economy. As shown in this report, many countermeasures were of a temporary nature, and can be defined as adaptations of the existing basic social security net, supplemented by administrative innovations such as the digitalization of means testing, for instance.

Medical image segmentation is one of the fundamental problems for artificial intelligence-based clinical decision systems. Current automatic medical image segmentation methods are often failed to meet clinical requirements. As such, a series of interactive segmentation algorithms are proposed to utilize expert correction information. However, existing methods suffer from some segmentation refining failure problems after long-term interactions and some cost problems from expert annotation, which hinder clinical applications. This paper proposes an interactive segmentation framework, called interactive MEdical segmentation with self-adaptive Confidence CAlibration (MECCA), by introducing the corrective action evaluation, which combines the action-based confidence learning and multi-agent reinforcement learning (MARL). The evaluation is established through a novel action-based confidence network, and the corrective actions are obtained from MARL. Based on the confidential information, a self-adaptive reward function is designed to provide more detailed feedback, and a simulated label generation mechanism is proposed on unsupervised data to reduce over-reliance on labeled data. Experimental results on various medical image datasets have shown the significant performance of the proposed algorithm.

Background:Subarachnoid hemorrhage caused by ruptured cerebral aneurysm often leads to fatal consequences.However,if the aneurysm can be found and treated during asymptomatic periods,the probability of rupture can be greatly reduced.At present,time-of-flight magnetic resonance angiography is one of the most commonly used non-invasive screening techniques for cerebral aneurysm,and the application of deep learning technology in aneurysm detection can effectively improve the screening effect of aneurysm.Existing studies have found that three-dimensional features play an important role in aneurysm detection,but they require a large amount of training data and have problems such as a high false positive rate. Methods:This paper proposed a novel method for aneurysm detection.First,a fully automatic cerebral artery segmentation algorithm without training data was used to extract the volume of interest,and then the 3D U-Net was improved by the 3D SENet module to establish an aneurysm detection model.Eventually a set of fully automated,end-to-end aneurysm detection methods have been formed. Results:A total of 231 magnetic resonance angiography image data were used in this study,among which 132 were training sets,34 were internal test sets and 65 were external test sets.The presented method obtained 97.89% sensitivity in the five-fold cross-validation and obtained 91.0% sensitivity with 2.48 false positives/case in the detection of the external test sets. Conclusions:Compared with the results of our previous studies and other studies,the method in this paper achieves a very competitive sensitivity with less training data and maintains a low false positive rate.As the only method currently using 3D U-Net for aneurysm detection,it proves the feasibility and superior performance of this network in aneurysm detection,and also explores the potential of the channel attention mechanism in this task.

BACKGROUND AND PURPOSE: Cerebral aneurysm is one of the most common cerebrovascular diseases, and SAH caused by its rupture has a very high mortality and disability rate. Existing automatic segmentation methods based on DLMs with TOF-MRA modality could not segment edge voxels very well, so that our goal is to realize more accurate segmentation of cerebral aneurysms in 3D TOF-MRA with the help of DLMs. MATERIALS AND METHODS: In this research, we proposed an automatic segmentation framework of cerebral aneurysm in 3D TOF-MRA. The framework was composed of two segmentation networks ranging from coarse to fine. The coarse segmentation network, namely DeepMedic, completed the coarse segmentation of cerebral aneurysms, and the processed results were fed into the fine segmentation network, namely dual-channel SE_3D U-Net trained with weighted loss function, for fine segmentation. Images from ADAM2020 (n=113) were used for training and validation and images from another center (n=45) were used for testing. The segmentation metrics we used include DSC, HD, and VS. RESULTS: The trained cerebral aneurysm segmentation model achieved DSC of 0.75, HD of 1.52, and VS of 0.91 on validation cohort. On the totally independent test cohort, our method achieved the highest DSC of 0.12, the lowest HD of 11.61, and the highest VS of 0.16 in comparison with state-of-the-art segmentation networks. CONCLUSIONS: The coarse-to-fine framework, which composed of DeepMedic and dual-channel SE_3D U-Net can segment cerebral aneurysms in 3D TOF-MRA with a superior accuracy.

Individual patient data (IPD) from the CELESTIAL trial (cabozantinib) and population-level data from the REACH-2 trial (ramucirumab) were used. To align with REACH-2, the CELESTIAL population was limited to patients who received first-line sorafenib only and had baseline serum AFP ≥ 400 ng/mL. The IPD from CELESTIAL were weighted to balance the distribution of 11 effect-modifying baseline characteristics with those of REACH-2. Overall survival (OS; primary endpoint) and progression-free survival (PFS) were compared for the CELESTIAL (matching-adjusted) and REACH-2 populations using weighted Kaplan-Meier (KM) curves and parametric (OS, Weibull; PFS, log-logistic) modeling. Rates of treatment-related adverse events (TRAEs) and TRAE-related discontinuations were also compared.

A unique 1D nanostructure of Pt@CeO2-BDC was prepared from Pt@CeBDC MOF. The Pt@CeO2-BDC was rich in oxygen vacancies (i.e., XPS O-beta/(O-alpha + O-beta) = 39.4%), and on the catalyst, the 2 nm Pt clusters were uniformly deposited on the 1D mesoporous polycrystalline CeO2. Toluene oxidation was conducted in a spectroscopic operando Raman-online FTIR reactor to elucidate the reaction mechanism and establish the structure-activity relationship. The reaction proceeds as follows: (I) adsorption of toluene as benzoate intermediates on Pt@CeO2-BDC at low temperature by reaction with surface peroxide species; (II) reaction activation and ring-opening involving lattice oxygen with a concomitant change in defect densities indicative of surface rearrangement; (III) complete oxidation to CO2 and H2O by lattice oxygen and reoxidation of the reduced ceria with consumption of adsorbed oxygen species. The Pt clusters, which mainly exist as Pt2+ with minor amounts of Pt-0 and Pt4+ on the surface, facilitated the adsorption and reaction activation. The Pt-CeO2 interface generates reduced ceria sites forming nearby adsorbed peroxide at low temperature that oxidize toluene into benzoate species by a Langmuir-Hinshelwood mechanism. As the reaction temperature increases, the role of lattice oxygen becomes important, producing CO2 and H2O mainly by the Mars-van Krevelen mechanism.

Crystal structure determines properties of materials. With the crystal structure of a chemical substance, many physical and chemical properties can be predicted by first-principles calculations or machine learning models. Since it is relatively easy to generate a hypothetical chemically valid formula, crystal structure prediction becomes an important method for discovering new materials. In our previous work, we proposed a contact map-based crystal structure prediction method, which uses global optimization algorithms such as genetic algorithms to maximize the match between the contact map of the predicted structure and the contact map of the real crystal structure to search for the coordinates at the Wyckoff Positions(WP). However, when predicting the crystal structure with high symmetry, we found that the global optimization algorithm has difficulty to find an effective combination of WPs that satisfies the chemical formula, which is mainly caused by the inconsistency between the dimensionality of the contact map of the predicted crystal structure and the dimensionality of the contact map of the target crystal structure. This makes it challenging to predict the crystal structures of high-symmetry crystals. In order to solve this problem, here we propose to use PyXtal to generate and filter random crystal structures with given symmetry constraints based on the information such as chemical formulas and space groups. With contact map as the optimization goal, we use differential evolution algorithms to search for non-special coordinates at the Wyckoff positions to realize the structure prediction of high-symmetry crystal materials. Our experimental results show that our proposed algorithm CMCrystalHS can effectively solve the problem of inconsistent contact map dimensions and predict the crystal structures with high symmetry., Comment: 12 pages

Tumorigenesis in different segments of the intestinal tract involves tissue-specific oncogenic drivers. In the colon, complement component 3 (C3) activation is a major contributor to inflammation and malignancies. By contrast, tumorigenesis in the small intestine involves fatty acid-binding protein 1 (FABP1). However, little is known of the upstream mechanisms driving their expressions in different segments of the intestinal tract. Here, we report that the RNA-binding protein DDX5 binds to the mRNA transcripts of C3 and Fabp1 to augment their expressions posttranscriptionally. Knocking out DDX5 in epithelial cells protected mice from intestinal tumorigenesis and dextran sodium sulfate (DSS)-induced colitis. Identification of DDX5 as a common upstream regulator of tissue-specific oncogenic molecules provides an excellent therapeutic target for intestinal diseases.

A unique 1D nanostructure of Pt@CeO2-BDC was prepared from Pt@CeBDC MOF. The Pt@CeO2-BDC was rich in oxygen vacancies (i.e., XPS O-beta/(O-alpha + O-beta) = 39.4%), and on the catalyst, the 2 nm Pt clusters were uniformly deposited on the 1D mesoporous polycrystalline CeO2. Toluene oxidation was conducted in a spectroscopic operando Raman-online FTIR reactor to elucidate the reaction mechanism and establish the structure-activity relationship. The reaction proceeds as follows: (I) adsorption of toluene as benzoate intermediates on Pt@CeO2-BDC at low temperature by reaction with surface peroxide species; (II) reaction activation and ring-opening involving lattice oxygen with a concomitant change in defect densities indicative of surface rearrangement; (III) complete oxidation to CO2 and H2O by lattice oxygen and reoxidation of the reduced ceria with consumption of adsorbed oxygen species. The Pt clusters, which mainly exist as Pt2+ with minor amounts of Pt-0 and Pt4+ on the surface, facilitated the adsorption and reaction activation. The Pt-CeO2 interface generates reduced ceria sites forming nearby adsorbed peroxide at low temperature that oxidize toluene into benzoate species by a Langmuir-Hinshelwood mechanism. As the reaction temperature increases, the role of lattice oxygen becomes important, producing CO2 and H2O mainly by the Mars-van Krevelen mechanism.

The aliovalent-substitution of cobalt into ceria lattice was demonstrated using a bimetallic CoCeBDC MOF to achieve a high degree of atomic level mixing in the CoCeOx catalyst. Spectroscopic analyses including X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, and time-of-flight secondary ion mass spectrometry (ToF-SIMS) confirmed the successful insertion of cobalt atom with a concomitant increase in defects (i.e., Raman I-D/I-F2g = 0.25) and oxygen vacancies (i.e., XPS O-beta/(O-alpha+O-beta)= 0.33) that correlates well with catalytic activity for the oxidations of methanol, acetone, toluene, and o-xylene. The as-prepared CoCeOx performed a 50% conversion (T-50) and 90% conversion (T90) in toluene oxidation at 212 degrees C and 227 degrees C that are significantly lower than the reference Co3O4/CeO2 nanocube catalyst that had T-50 of 261 degrees C and T-90 of 308 degrees C, indicating its better catalytic activity. Moreover, CoCeOx catalyst completely oxidizes organic compounds to carbon dioxide and water, while reaction over Co3O4/CeO2 nanocube catalyst produces significant carbon monoxide.

A unique 1D nanostructure of Pt@CeO2-BDC was prepared from Pt@CeBDC MOF. The Pt@CeO2-BDC was rich in oxygen vacancies (i.e., XPS O-beta/(O-alpha + O-beta) = 39.4%), and on the catalyst, the 2 nm Pt clusters were uniformly deposited on the 1D mesoporous polycrystalline CeO2. Toluene oxidation was conducted in a spectroscopic operando Raman-online FTIR reactor to elucidate the reaction mechanism and establish the structure-activity relationship. The reaction proceeds as follows: (I) adsorption of toluene as benzoate intermediates on Pt@CeO2-BDC at low temperature by reaction with surface peroxide species; (II) reaction activation and ring-opening involving lattice oxygen with a concomitant change in defect densities indicative of surface rearrangement; (III) complete oxidation to CO2 and H2O by lattice oxygen and reoxidation of the reduced ceria with consumption of adsorbed oxygen species. The Pt clusters, which mainly exist as Pt2+ with minor amounts of Pt-0 and Pt4+ on the surface, facilitated the adsorption and reaction activation. The Pt-CeO2 interface generates reduced ceria sites forming nearby adsorbed peroxide at low temperature that oxidize toluene into benzoate species by a Langmuir-Hinshelwood mechanism. As the reaction temperature increases, the role of lattice oxygen becomes important, producing CO2 and H2O mainly by the Mars-van Krevelen mechanism.

Tumorigenesis in different segments of the intestinal tract involves tissue-specific oncogenic drivers. In the colon, complement component 3 (C3) activation is a major contributor to inflammation and malignancies. By contrast, tumorigenesis in the small intestine involves fatty acid-binding protein 1 (FABP1). However, little is known of the upstream mechanisms driving their expressions in different segments of the intestinal tract. Here, we report that the RNA-binding protein DDX5 binds to the mRNA transcripts of C3 and Fabp1 to augment their expressions posttranscriptionally. Knocking out DDX5 in epithelial cells protected mice from intestinal tumorigenesis and dextran sodium sulfate (DSS)-induced colitis. Identification of DDX5 as a common upstream regulator of tissue-specific oncogenic molecules provides an excellent therapeutic target for intestinal diseases.

Adsorption is an essential process that takes place in heterogeneous catalysis. In the current study, solid-adsorbate interactions occurring between a variety of small molecules and surfaces of group beta metal oxides, including beta-Ga2O3(100), beta-Ga2O3(001), theta-Al2O3 (100), theta-Al2O3 (001), theta-Al2O3 (010), In2O3(110), and In2O3(111), were investigated using density functional theory calculations and a machine learning (ML)-based statistical method. The adsorbates utilized for this purpose include CO, CO2, ND NH3, H2O, acetonitrile, acetone, acetamide, acetic acid, alkanes, alkenes, aromatic compounds, alcohols, and amines. The results show that the adsorption energies (E-ads) of each metal oxide surface correlate linearly with the highest occupied molecular orbital (HOMO) energies of the adsorbates and not with energies of the lowest unoccupied molecular orbital (LUMO) of the small molecules. Moreover, in these systems, contributions to molecular adsorption are dominated by interactions between the HOMOs of the adsorbates and the surface conduction band of the metal oxides. Furthermore, the surface energy was found to be an important parameter influencing E-ads values of different metal oxides. Finally, the results of statistical analysis using a ML approach confirmed that adsorbate HOMOs and surface energy of metal oxides are the most influential factors governing molecular adsorption, and also demonstrated that dipole moments of adsorbates contribute to controlling to adsorption.

Adsorption is an essential process that takes place in heterogeneous catalysis. In the current study, solid-adsorbate interactions occurring between a variety of small molecules and surfaces of group beta metal oxides, including beta-Ga2O3(100), beta-Ga2O3(001), theta-Al2O3 (100), theta-Al2O3 (001), theta-Al2O3 (010), In2O3(110), and In2O3(111), were investigated using density functional theory calculations and a machine learning (ML)-based statistical method. The adsorbates utilized for this purpose include CO, CO2, ND NH3, H2O, acetonitrile, acetone, acetamide, acetic acid, alkanes, alkenes, aromatic compounds, alcohols, and amines. The results show that the adsorption energies (E-ads) of each metal oxide surface correlate linearly with the highest occupied molecular orbital (HOMO) energies of the adsorbates and not with energies of the lowest unoccupied molecular orbital (LUMO) of the small molecules. Moreover, in these systems, contributions to molecular adsorption are dominated by interactions between the HOMOs of the adsorbates and the surface conduction band of the metal oxides. Furthermore, the surface energy was found to be an important parameter influencing E-ads values of different metal oxides. Finally, the results of statistical analysis using a ML approach confirmed that adsorbate HOMOs and surface energy of metal oxides are the most influential factors governing molecular adsorption, and also demonstrated that dipole moments of adsorbates contribute to controlling to adsorption.

The aliovalent-substitution of cobalt into ceria lattice was demonstrated using a bimetallic CoCeBDC MOF to achieve a high degree of atomic level mixing in the CoCeOx catalyst. Spectroscopic analyses including X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, and time-of-flight secondary ion mass spectrometry (ToF-SIMS) confirmed the successful insertion of cobalt atom with a concomitant increase in defects (i.e., Raman I-D/I-F2g = 0.25) and oxygen vacancies (i.e., XPS O-beta/(O-alpha+O-beta)= 0.33) that correlates well with catalytic activity for the oxidations of methanol, acetone, toluene, and o-xylene. The as-prepared CoCeOx performed a 50% conversion (T-50) and 90% conversion (T90) in toluene oxidation at 212 degrees C and 227 degrees C that are significantly lower than the reference Co3O4/CeO2 nanocube catalyst that had T-50 of 261 degrees C and T-90 of 308 degrees C, indicating its better catalytic activity. Moreover, CoCeOx catalyst completely oxidizes organic compounds to carbon dioxide and water, while reaction over Co3O4/CeO2 nanocube catalyst produces significant carbon monoxide.

The aliovalent-substitution of cobalt into ceria lattice was demonstrated using a bimetallic CoCeBDC MOF to achieve a high degree of atomic level mixing in the CoCeO catalyst. Spectroscopic analyses including X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, and time-of-flight secondary ion mass spectrometry (ToF-SIMS) confirmed the successful insertion of cobalt atom with a concomitant increase in defects (i.e., Raman I/I = 0.25) and oxygen vacancies (i.e., XPS O/(O+O) = 0.33) that correlates well with catalytic activity for the oxidations of methanol, acetone, toluene, and o-xylene. The as-prepared CoCeO performed a 50% conversion (T) and 90% conversion (T) in toluene oxidation at 212 °C and 227 °C that are significantly lower than the reference CoO/CeO nanocube catalyst that had T of 261 °C and T of 308 °C, indicating its better catalytic activity. Moreover, CoCeO catalyst completely oxidizes organic compounds to carbon dioxide and water, while reaction over CoO/CeO nanocube catalyst produces significant carbon monoxide.

Danyan Li,1 Luqing Zhao,1 Yuxin Li,1,2 Xiuhong Kang,1 Shengsheng Zhang1 1Digestive Disease Center, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, People’s Republic of China; 2Beijing University of Chinese Medicine, Beijing, People’s Republic of ChinaCorrespondence: Shengsheng ZhangDigestive Disease Center, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, People’s Republic of ChinaEmail zhss2000@163.comAim: Gastric cancer is a leading cause of cancer death worldwide. In-depth research of precancerous lesions of gastric carcinoma (PLGC) with malignant transformation potential is a key measure to prevent the development of gastric carcinoma. Recently, calycosin has been shown to have anticancer effects in vitro and in vivo. The molecular mechanism by which calycosin affects PLGC, however, has not yet been elucidated. The purpose of this study was to evaluate the effect and mechanism of calycosin in N‐methyl‐Nʹ‐nitro‐N‐nitrosoguanidine (MNNG)-induced PLGC rats.Methods: The effects of calycosin in the gastric mucosa of rats with PLGC were evaluated using histopathology and transmission electron microscopy (TEM). For further characterization, the expression levels of integrin β 1, nuclear factor kappa B (NF-κB), p-NF-κB, DARPP-32 and signal transducer and activator of transcription 3 (STAT3) were determined by Western blot assay and immunohistochemistry.Results: Hematoxylin–eosin and high iron diamine–Alcian blue–periodic acid-Schiff (HID-AB-PAS) staining showed that intestinal metaplasia and dysplasia were significantly ameliorated in the calycosin intervention groups compared with the model group. Further, TEM results showed that calycosin intervention tempered microvascular abnormalities and cell morphology of primary and parietal cells in PLGC tissues. The results suggested that calycosin had

Objective: To expand the clinical spectrum of lysyl-tRNA synthetase (KARS) gene–related diseases, which so far includes Charcot-Marie-Tooth disease, congenital visual impairment and microcephaly, and nonsyndromic hearing impairment. Methods: Whole-exome sequencing was performed on index patients from 4 unrelated families with leukoencephalopathy. Candidate pathogenic variants and their cosegregation were confirmed by Sanger sequencing. Effects of mutations on KARS protein function were examined by aminoacylation assays and yeast complementation assays. Results: Common clinical features of the patients in this study included impaired cognitive ability, seizure, hypotonia, ataxia, and abnormal brain imaging, suggesting that the CNS involvement is the main clinical presentation. Six previously unreported and 1 known KARS mutations were identified and cosegregated in these families. Two patients are compound heterozygous for missense mutations, 1 patient is homozygous for a missense mutation, and 1 patient harbored an insertion mutation and a missense mutation. Functional and structural analyses revealed that these mutations impair aminoacylation activity of lysyl-tRNA synthetase, indicating that de- fective KARS function is responsible for the phenotypes in these individuals. Conclusions: Our results demonstrate that patients with loss-of-function KARS mutations can manifest CNS disorders, thus broadening the phenotypic spectrum associated with KARS-related disease.

Yao Liu,1 Qun Zhang,1 Xue Yang,1 Yuxin Li,1 Bingbing Zhu,2 Shuaishuai Niu,1 Yunyi Huang,2 Ying Hu,1 Xianbo Wang1 1Center of Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, People’s Republic of China; 2Department of Gastroenterology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, People’s Republic of China Background: Elevated serum γ-glutamyltransferase (γ-GT) levels are related to an increased cancer risk and worse prognosis in many cancers. We evaluated the effects of γ-GT stratification on the occurrence of macrovascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) who underwent hepatic resection (HR), transcatheter arterial chemoembolization (TACE), or TACE combined with radiofrequency ablation (TACE-RFA). Patients and methods: A total of 903 patients with HCC in Barcelona Clinic Liver Cancer Stage A or B were included. Of these patients, 118 underwent HR, 445 underwent TACE-RFA, 256 underwent TACE, and 84 patients received conservative treatment only (control group). γ-GT, albumin, γ-fetoprotein, and intervention were selected as significant predictive factors for MVI in 1 year by forward selection. The optimal cutoff value of γ-GT was 39 IU/L according to receiver operating characteristic analysis, with a sensitivity and specificity of 87.0% and 45.6%, respectively. Results: The 1-year MVI incidence of patients with HCC in the group with γ-GT ≥39 IU/L was higher than that of the group with γ-GT <39 IU/L treated with HR, TACE-RFA, or TACE (P=0.0166, P=0.0041, and P<0.001, respectively). The MVI rates at 1 year were similar in the group with γ-GT ≥39 IU/L that underwent HR, TACE-RFA, or TACE and the control group (P=0.4402, P=0.2214, and P=0.4159, respectively). Different effects of various tre

The Japanese automobile manufacturer invented Lean Production or Toyota Production System (TPS) which has been introduced and implemented in Swedish manufacturers for around 20 years (Dabhilkar, et al., 2005). Many studies discussed how it creates success in optimizing production process and quality. However, many researches show that the gap between culture become the main barrier in the process of realizing ‘true’ lean manufacturing. So, what problems has been brought out because of cultural difference in the process of implementing the lean method in Swedish industry and how to eliminate them? The adaptation of lean method will have influences on the culture of a company. The purpose of this paper is to find out the problems which brought out by the cultural difference and a set of general approaches and principles which can be used for Swedish cultures to adapt and implement lean methodologies successfully and effectively.

The Japanese automobile manufacturer invented Lean Production or Toyota Production System (TPS) which has been introduced and implemented in Swedish manufacturers for around 20 years (Dabhilkar, et al., 2005). Many studies discussed how it creates success in optimizing production process and quality. However, many researches show that the gap between culture become the main barrier in the process of realizing ‘true’ lean manufacturing. So, what problems has been brought out because of cultural difference in the process of implementing the lean method in Swedish industry and how to eliminate them? The adaptation of lean method will have influences on the culture of a company. The purpose of this paper is to find out the problems which brought out by the cultural difference and a set of general approaches and principles which can be used for Swedish cultures to adapt and implement lean methodologies successfully and effectively.

The basic helix-loop-helix (bHLH) proteins are intimately associated with developmental events such as cell differentiation and lineage commitment. The HLH domain in the bHLH motif is responsible for dimerization whereas the basic region mediates sequence specific DNA binding. Human DEC, mouse STRA and rat SHARP proteins represent a new class of bHLH proteins. DEC/STRA/SHARP proteins show high similarities (∼40%) to Drosophila Hairy and E(spl) as well as the mammalian homologues (e.g., HES) in the bHLH domain. However, they lack the C-terminal WRPW motif which is present at all other Hairy/E(spl)/Hes proteins and mediates transcription repression. These structural differences distinguish DEC/STRA/SHARPs from other bHLH proteins and indicate that they have distinct biological functions. The purpose of this dissertation is to elucidate the oncogenic roles of DEC1 and determine the molecular actions of DEC1 on transcription regulation. Expression measurments demonstrate that DEC1 is abundantly expressed in cancer tissues but not in adjacent normal tissues. In stable transfectants, DEC1 inhibits cell proliferation, antagonizes serum deprivation-induced apoptosis, and selectively decreases the activities of several major caspases. Western blotting analyses identify that antiapoptotic protein survivin is a functional mediator responsible for DEC1-directed antiapoptotic activity. DEC1 and survivin exhibit a paralleled expression pattern in paired tumor-normal tissues. In co-transfection experiments, DEC1 stimulates the survivin promoter, and this mechanism relies on the physical interactions with Sp1 sites in the proximal promoter. In contrast, DEC1 and its structurally related protein DEC2 show an inverted expression pattern in paired tumor-normal tissues. Forced expression of DEC1 causes proportional decreases in the expression of DEC2 in stable transfectants. Co-transfection with DEC1 represses the activity of a DEC2 promoter reporter by as much as 90%. The DEC1 mediate

A gate-controlled switch includes a gate turn-off thyristor in series with a diode. By using the diode in series with the GTO, the switch significantly increases the turn-off voltage that can be used for the current commutation. The unity turn-off gain and the snubberless turn-off capability are demonstrated.

The Gate Turn-Off (GTO) Thyristor has the best voltage blocking and current conducting capabilities among all known high power semiconductor devices. To improve its dynamic performances to meet the increased demand in high-performance high-power applications, a special driving technique, namely unity gain turn-off, is studied. Several innovative approaches, which realize this driving requirement, are proposed, analyzed and experimentally demonstrated in this dissertation. The Emitter Turn-Off Thyristor (ETO) is a new family of high power semiconductor devices that is suitable for megawatt power electronics application. ETOs with voltage and current ratings of 4.0~6.0 kV and 1.0~4.0 kA, have been developed and demonstrated. These power levels are the highest in silicon power devices and are comparable to those of the GTO. Compared to the conventional GTO, the ETO has a much shorter storage time, voltage controlled turn-off capability, and a much larger reverse biased safe operation area (RBSOA). These combined advantages make the ETO based power system simpler in terms of dv/dt snubber, di/dt snubber and over current protection, resulting in significant savings at the system level. Experimental and numerical simulation results that demonstrate the advantages of the ETO are presented. A new family of snubberless turn-off GTO, the Resonant Gate Commutated Thyristor (RGCT) is proposed and investigated. By using a transient high commutation voltage, the RGCT can achieve unity turn-off gain and snubberless turn-off capability even with a relatively high gate loop stray inductance. Therefore conventional GTOs with flexible gate lead can be used to achieve the state-of-the-art performance similar to that of the Integrated Gate Commutated Turn-Off thyristor (IGCT). Detailed current commutation analysis and experimental results are presented. A novel equivalent circuit model for the GTO under the unity gain turn-off is proposed. This model is composed of a step current source