USE JD-R THEORY TO EXPLORE THE RELATIONSHIP BETWEEN EMPLOYEE EXPERIENCE AND EMPLOYEE ENGAGEMENT—TAKING JOB DEMANDS AS THE MODERATING VARIABLE

Past research has proven that employee experience has a positive impact on employee engagement. Based on the conceptual framework of Job Demands-Resources model (JD-R) model, this study regards efficient employee experience as a job resource to explore the impact of "employee experience" and” job demands” on employee engagement in organizations. Work requirements are further divided into challenge demand and hindrance demand. This study adopts the experimental design of the scenario method and uses two two-factor independent sample designs, namely 2x2(employee experience is high / employee experience is low x challenging job demands is high / challenging job demands is low) and 2x2(employee experience is high / employee experience is low x hindering job demands is high / hindering job demands is low).A total of 176 valid questionnaires were collected. The research results found that when employee experience is high, employee engagement is higher than when employee experience is low. Employee experience and job demands have an interactive effect on employee engagement. When employee experience is high, employee engagement will be higher when challenging job demands are added than when hindering job demands are added. It is expected that the results of this study can help in theoretical and practical application

Age-related nanostructural and nanomechanical changes of individual human cartilage aggrecan monomers and their glycosaminoglycan side chains

The nanostructure and nanomechanical properties of aggrecan monomers extracted and purified from human articular cartilage from donors of different ages (newborn, 29 and 38 year old) were directly visualized and quantified via atomic force microscopy (AFM)-based imaging and force spectroscopy. AFM imaging enabled direct comparison of full length monomers at different ages. The higher proportion of aggrecan fragments observed in adult versus newborn populations is consistent with the cumulative proteolysis of aggrecan known to occur in vivo. The decreased dimensions of adult full length aggrecan (including core protein and glycosaminoglycan (GAG) chain trace length, end-to-end distance and extension ratio) reflect altered aggrecan biosynthesis. The demonstrably shorter GAG chains observed in adult full length aggrecan monomers, compared to newborn monomers, also reflects markedly altered biosynthesis with age. Direct visualization of aggrecan subjected to chondroitinase and/or keratanase treatment revealed conformational properties of aggrecan monomers associated with chondroitin sulfate (CS) and keratan sulfate (KS) GAG chains. Furthermore, compressive stiffness of chemically end-attached layers of adult and newborn aggrecan was measured in various ionic strength aqueous solutions. Adult aggrecan was significantly weaker in compression than newborn aggrecan even at the same total GAG density and bath ionic strength, suggesting the importance of both electrostatic and non-electrostatic interactions in nanomechanical stiffness. These results provide molecular-level evidence of the effects of age on the conformational and nanomechanical properties of aggrecan, with direct implications for the effects of aggrecan nanostructure on the age-dependence of cartilage tissue biomechanical and osmotic properties.National Science Foundation (U.S.) (Grant CMMI-0758651)National Institutes of Health (U.S.) (Grant AR33236)National Institutes of Health (U.S.) (Grant AR60331)United States. Dept. of Energy (National Security Science and Engineering Faculty Fellowship Grant N00244-09-1-0064)Shriners North Americ

Common-mode noise reduction schemes for weakly coupled differential serpentine delay microstrip lines

This paper proposes design schemes to reduce the common mode noise from weakly coupled differential serpentine delay microstrip lines (DSDMLs). The proposed approach is twofold: we leverage strongly coupled vertical-turn-coupled traces (VTCTs) instead of weakly coupled VTCTs (conventional pattern) and add guard traces. Time- and frequency-domain analyses of the proposed schemes for reducing the common-mode noise are performed by studying the transmission waveform and the differential-to-common mode conversion using the circuit solver HSPICE and the 3-D full-wave simulator HFSS, respectively. Compared to the conventional design of the weakly coupled DSDMLs, the proposed solutions yield a reduction of about 54% of the peak-to-peak amplitude of the common-mode noise, while the differential impedance remains matched along the complete length of the DSDML. Moreover, the range of frequencies, over which the magnitude of the differential-to-common mode conversion is now significantly reduced, is very wide, i.e. about 0.3-10 GHz. Furthermore, the differential insertion and reflection loss introduced by the newly proposed designs are almost the same as the ones achieved by using the conventional design. Finally, a favorable comparison between simulated and measured results confirms the excellent common-mode noise reduction performance of the proposed schemes

MuRAL: Multi-Scale Region-based Active Learning for Object Detection

Obtaining large-scale labeled object detection dataset can be costly and time-consuming, as it involves annotating images with bounding boxes and class labels. Thus, some specialized active learning methods have been proposed to reduce the cost by selecting either coarse-grained samples or fine-grained instances from unlabeled data for labeling. However, the former approaches suffer from redundant labeling, while the latter methods generally lead to training instability and sampling bias. To address these challenges, we propose a novel approach called Multi-scale Region-based Active Learning (MuRAL) for object detection. MuRAL identifies informative regions of various scales to reduce annotation costs for well-learned objects and improve training performance. The informative region score is designed to consider both the predicted confidence of instances and the distribution of each object category, enabling our method to focus more on difficult-to-detect classes. Moreover, MuRAL employs a scale-aware selection strategy that ensures diverse regions are selected from different scales for labeling and downstream finetuning, which enhances training stability. Our proposed method surpasses all existing coarse-grained and fine-grained baselines on Cityscapes and MS COCO datasets, and demonstrates significant improvement in difficult category performance

Korean-Chinese Person Name Translation for Cross Language Information Retrieval

PACLIC 21 / Seoul National University, Seoul, Korea / November 1-3, 200

Genotoxic Klebsiella pneumoniae in Taiwan

Colibactin is a nonribosomal peptide-polyketide synthesized by multi-enzyme complexes encoded by the pks gene cluster. Colibactin-producing Escherichia coli have been demonstrated to induce host DNA damage and promote colorectal cancer (CRC) development. In Taiwan, the occurrence of pyogenic liver abscess (PLA) has been suggested to correlate with an increasing risk of CRC, and Klebsiella pneumoniae is the predominant PLA pathogen in Taiwan