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Corporate social responsibility (CSR) is critical for organizations who want to pursue sustainable development and competitive advantage by influencing employee attitudes. Drawing on the deontic justice theory, we argue that internal and external CSR initiatives promote self‐focused and other‐focused justice, respectively, which contribute to employee‐perceived organizational justice and shape positive employee behaviors. This study applied a fuzzy‐set qualitative comparative analysis method to determine the interactive effects of employee perceptions of CSR practices and organizational justice on employee outcomes. An examination of data from 1,231 employees in Taiwan revealed that high CSR perceptions lead to high perceived justice among employees who attach high importance to justice. In addition, while high perceived justice by itself is insufficient to determine positive employee outcomes, the combination of high perception of internal CSR, external CSR, and organizational justice is sufficient to predict positive employee outcomes. The findings provide implications for both research and practice.

The conventional design of notebook PCs configured with an optical disk drive suffers from structural durability problems that are indicative of potentially high defect rates. Companies have been attempting to reduce the size of assembly parts to pursue compactness, but the elimination of these materials might negatively affect structural stability, which is fundamentally important to all high-technology products. The Six Sigma methodology applied in this paper aims to improve quality performance and eliminate redesign waste and unnecessary handoffs according to customer requirements. This paper presents a case study using the define, measure, analyze, improve, and control (DMAIC) procedure. Several optimization techniques, such as response surface method (RSM) and computational intelligence, are adopted to optimize the significant factors that affect the module design and structural strength index during compression. Implementation results demonstrate that computational intelligence outperforms RSM. It shows computational intelligence reveals a good ability to solve the parameter optimization problems.

Glutamate-gated chloride channels (GluCls) mediate inhibitory synaptic transmission in invertebrate nervous systems, and only one GluCl gene has been found in insects. Therefore, insect GluCls are one of the major targets of insecticides including avermectins. In the present study, a 1347 bp full-length cDNA encoding a 449-amino acid protein (named MsGluCl, GenBank ID: MK336885) was cloned from the oriental armyworm, Mythimna separata, and characterized two alternative splicing variants of MsGluCl. The protein shares 76.9–98.6% identity with other insect GluCl isoforms. Spatial and temporal expression analysis revealed that MsGluCl was highly expressed in the 3rd instar and adult head. Dietary ingestion of dsMsGluCl significantly reduced the mRNA level of MsGluCl and decreased abamectin mortality. Thus, our results reveal that MsGluCl could be the molecular target of abamectin and provide the basis for further understanding the resistance mechanism to abamectin in arthropods.

Hyperfine-structure constants of odd Ra$^{+}$ due to the interactions of nuclear magnetic dipole, electric quadrupole, and magnetic octupole moments with the electrons are investigated in the framework of relativistic coupled-cluster method within single- and double-excitation approximation. The calculated energies and magnetic dipole hyperfine-structure constants $A$ exhibit a good agreement with available experimental values. Combining with the experimental electric quadrupole hyperfine-structure constant, we also extracted the electric quadrupole moments $Q$ of $^{209,211,221,223}$Ra. Our $Q$($^{221}$Ra) and $Q$($^{223}$Ra) are consistent with the referenced values from a semi-empirical analysis (Z. Phys. D: At., Mol. Clusters 11, 105 (1988)), but $Q(^{211}$Ra)=$0.33(2)$ is smaller than the referenced value $0.48(4)$ by about 30\%. Furthermore, we also performed a procedure for assessing the contributions of magnetic octupole moment to the hyperfine splitting. The sensitivity of hyperfine-structure interval measurements in $^{223}$Ra$^{+}$ that can reveal the effect caused by the nuclear octupole moment are found to be on the order of kHz.

$^{229}$Th is a promising candidate for developing a nuclear optical clock and searching the new physics beyond the standard model. Accurate knowledge of the nuclear properties of $^{229}$Th is very important. In this work, we calculate hyperfine-structure constants for the first four states of $^{229}$Th$^{3+}$ using the relativistic coupled-cluster method based on the Gauss basis set. The no-pair Dirac-Coulomb-Breit Hamiltonian with the lowest-order quantum electrodynamics (QED) correction is the starting point, together with all linear and non-linear terms of single and double excitations are included in coupled-cluster calculation. With the measured value of the hyperfine-structure constants [Phys. Rev. Lett. 106. 223001(2011)], we get the magnetic dipole moment, $\mu=0.359(9)$, and the electric quadrupole moment, $Q=2.95(7)$, of the $^{229}$Th nucleus. Our magnetic dipole moment is perfectly consistent with the recommended values, $\mu=0.360(7)$, from the all-order calculation by Safronova \textit{et. al.}[Phys.Rev.A 88, 060501 (2013)], but our electric quadrupole moment is smaller than their recommended value, $Q=3.11(6)$, about 5\%. Our results show that the non-linear terms of single and double excitations, which were not included in the all-order calculation by Safronova \textit{et. al.}, are very crucial to produce a precise $Q$ value of $^{229}$Th. Additionally, we also present magnetic octupole hyperfine-structure constants and some important non-diagonal hyperfine transition matrix elements, which are required for further extracting the magnetic octupole moment $\Omega$ of $^{229}$Th nucleus.

Background Transgelin, an actin-binding protein, is associated with cytoskeleton remodeling. Findings from our previous studies demonstrated that transgelin was up-regulated in node-positive colorectal cancer (CRC) versus node-negative disease. Over-expression of TAGLN affected the expression of 256 downstream transcripts and increased the metastatic potential of colon cancer cells in vitro and in vivo. This study aims to explore the mechanisms through which transgelin participates in the metastasis of colon cancer cells. Methods Immunofluorescence and immunoblotting analysis were used to determine the cellular localization of endogenous and exogenous transgelin in colon cancer cells. Co-immunoprecipitation and subsequently high-performance liquid chromatography/tandem mass spectrometry were performed to identify the proteins that were potentially interacting with transgelin. The 256 downstream transcripts regulated by transgelin were analyzed with bioinformatics methods to discriminate the specific key genes and signaling pathways. The Gene-Cloud of Biotechnology Information (GCBI) tools were used to predict the potential transcription factors (TFs) for the key genes. The predicted TFs corresponded to the proteins identified to interact with transgelin. The interaction between transgelin and the TFs was verified by co-immunoprecipitation and immunofluorescence. Results Transgelin was found to localize in both the cytoplasm and nucleus of the colon cancer cells. Approximately 297 proteins were identified to interact with transgelin. The overexpression of TAGLN led to the differential expression of 184 downstream genes. Network topology analysis discriminated seven key genes, including CALM1, MYO1F, NCKIPSD, PLK4, RAC1, WAS and WIPF1, which are mostly involved in the Rho signaling pathway. Poly (ADP-ribose) polymerase-1 (PARP1) was predicted as the unique TF for the key genes and concurrently corresponded to the DNA-binding proteins potentially interacting with transgelin. The interaction between PARP1 and transgelin in human RKO colon cancer cells was further validated by immunoprecipitation and immunofluorescence assays. Conclusions Our results suggest that transgelin binds to PARP1 and regulates the expression of downstream key genes, which are mainly involved in the Rho signaling pathway, and thus participates in the metastasis of colon cancer.

Background: Transgelin, an actin-binding protein, is associated with the cytoskeleton remodeling. Our previous studies found that transgelin was up-regulated in node-positive colorectal cancer versus in node-negative disease. Over-expression of TAGLN affected the expression of 256 downstream transcripts and increased the metastatic potential of colon cancer cells in vitro and in vivo. This study aims to explore the mechanisms that transgelin participates in the metastasis of colon cancer cells.Methods: Immunofluorescence and immunoblotting analysis were used to determine the cellular localization of the endogenous and exogenous transgelin in colon cancer cells. Co-immunoprecipitation and subsequent high performance liquid chromatography/tandem mass spectrometry were performed to identify the proteins potentially interacting with transgelin. Bioinformatics methods were used to analyze the 256 downstream transcripts regulated by transgelin to discriminate the specific key genes and signaling pathways. By analyzing the promoter region of these key genes, GCBI tools were used to predict the potential transcription factor(s) for these genes. The predicted transcription factors were matching to the proteins that have been identified to potentially interact with transgelin. The interaction between transgelin and these transcription factors was verified by co-immunoprecipitation and immunoblotting.Results: Transgelin was found to localize both in the cytoplasm and the nucleus of colon cancer cells. 297 proteins have been identified to interact with transgelin by co-immunoprecipitation and subsequent high performance liquid chromatography/mass spectrometry. Over-expression of TAGLN could lead to differential expression of 184 downstream genes. By constructing the network of gene-encoded proteins, 7 genes (CALM1, MYO1F, NCKIPSD, PLK4, RAC1, WAS and WIPF1) have been discriminated as key genes using network topology analysis. They are mostly involved in the Rho signaling pathway. Poly ADP-ribose polymerase-1 (PARP1) was predicted as the unique transcription factor for the key genes and concurrently matching to the DNA-binding proteins potentially interacting with transgelin. Immunoprecipitation validated that PARP1 interacted with transgelin in human RKO colon cancer cells.Conclusions: The results of this study suggest that transgelin binds to PARP1 and regulates the expression of the downstream key genes mainly involving Rho signaling pathway, thus participates in the metastasis of colon cancer.

Internal stakeholders' perception of corporate social responsibility (CSR) plays a crucial role for organizations in pursuing innovative, sustainability‐based business models. The majority of research on CSR has examined its effects on organizations' financial performance or external stakeholders' behavior toward CSR. However, little attention has been devoted to employees' behavior toward CSR. The current study investigated how organizations' CSR initiatives and the fulfillment of employee job needs (existence, relatedness and growth, or ERG) affect employees' job satisfaction and retention intention. Methodologically, the study applied the fuzzy‐set qualitative comparative analysis method to capture the integrative effects of explanatory factors on the outcomes by examining the case of a leading chain department store in Asia. The results revealed that an organization's efforts on CSR could enhance the fulfillment of employees' ERG needs. Moreover, the perceived CSR initiatives could generate positive effects on employees' satisfaction and retention intention by fulfilling employees' ERG needs.

Importance-performance analysis (IPA) is a popular approach used by firms to focus resources on crucial attributes, reduce expenditure on non-critical ones and develop improvement and innovation st...

Complaining is one option available to customers to express their dissatisfaction with inadequate services. Their complaints contain valuable information for service providers to improve customer r...

Customer stickiness and satisfaction are both essential for a restaurant to occupy market share and earn profits in the face of fierce competition. This study applied fuzzy set qualitative comparative analysis (fsQCA) to examine how environmental characteristics regarding restaurant location and individual characteristics of customers influenced customers' emotional responses to service attitudes, and how these emotions, in turn, influenced customer satisfaction and stickiness. A sample of 182 customers from a fusion cafe chain restaurant was collected and investigated. The results show that store characteristics and customer individual characteristics had a pronounced effect on customer emotional responses to the service attitudes of the restaurant, and that these emotional experiences served as critical antecedents to customer satisfaction and stickiness. The managerial implications of the findings are discussed for the restaurant chain to understand its weaknesses and to develop proper operational strategies and marketing polices for improving customer satisfaction and stickiness.

Location selection plays a crucial role in the retail and service industries. A comprehensive location selection model and appropriate analytical technique can improve the quality of location decisions, attracting more customers and substantially impacting market share and profitability. This study developed a data mining framework based on rough set theory (RST) to support location selection decisions. The proposed framework consists of four stages: (1) problem definition and data collection; (2) RST analysis; (3) rule validation; and (4) knowledge extraction and usage. An empirical study focused on a restaurant chain to demonstrate the validity of the proposed approach. Twenty location variables relevant to five location aspects were examined, and the results indicated that latent knowledge can be identified to support location selection decisions.

The ryanodine receptors of insects are the main target sites of diamide insecticides, which show highly selective insecticidal activity relative to toxicity in mammals and provide a novel option for managing lepidopteran pests. The oriental armyworm, Mythimna separata (Walker), is a destructive pest of agricultural crops, and great efforts have been undertaken to control this pest including repeated insecticide applications. In this study, full-length cDNA of a ryanodine receptor gene from M. separata (MsRyR) was cloned and characterized. The cDNA of MsRyR had a 15,372 bp open reading frame and encoded 5124 amino acids (GenBank ID: MG712298). MsRyR shares 78-97% identity with RyR isoforms of other insects, and50% identities with Homo sapiens RyRs 1-3. Temporal and spatial expression analysis detected MsRyR at all developmental stages and in all tissues. The highest relative levels of MsRyR were detected in the second instar and head. Exposure to chlorantraniliprole after 24 h significantly increased the expression levels of whole body MsRyR mRNA. In addition, dietary ingestion of dsMsRyR significantly reduced the mRNA level of MsRyR and greatly decreased chlorantraniliprole-induced mortality. Our results revealed that the MsRyR could be the molecular target of chlorantraniliprole, and provided the basis for further understanding the resistance mechanism of chlorantraniliprole.

Identifying primary service attributes that generate customer satisfaction (CS) is critical to organizational success. The Kano model demonstrates an asymmetric relationship between attribute performance and CS. However, extant regression-based approaches for classifying Kano's quality attributes have theoretical limitations, such as multicollinearity problems, resulting in spurious inferences. The association rule (AR) method is widely used in data mining to explore the associations or correlations among variables because it does not require the typical assumptions associated with regression analyses. The framework developed in this study incorporates the AR method to classify Kano's quality attributes. The effectiveness of the proposed method was demonstrated using a case study of a restaurant chain. The proposed method is more practical for classifying Kano's quality attributes because it shortens the time required for data collection. Moreover, the proposed method reduces computational complexity. Validity test results indicate that the proposed method markedly outperforms some regression-based approaches.

Application of insufficient or excessive amounts of the solder paste during the solder paste printing process can result in circuit issues, such as an incomplete circuit when the paste applied is insufficient and a short circuit when the paste applied is excessive. The former is resulting from a solder open; the latter is resulting from the forming of a solder bridge. This study attempts to optimize solder volume in a printed circuit board assembly. Computational intelligence techniques, including neural network, genetic algorithm, particle swarm optimization, and simulated annealing, were used to address this problem. A real case study was presented to confirm the effectiveness of the computational intelligence techniques.

For the aim that describing the growth process of plant sticks with a effective model, presented a new method based on plant physiological characteristics, it also considered plant weight and light comprehensively. The model can reflect parallel growing of plants continually and dynamically, and the relationship between plant morphology and growing physiology is also be reflected at the same time. The simulation results show that the model is directly and provides a practical method for the research of virtual plant, especially for the plant sticks.

Location selection is a significant part of strategic management activities. The plurality and interactions of the evaluation criteria involved in the problem complicates the decision-making process. This study successfully proposes a set of systematic procedure for location selection using fuzzy-connective-based aggregation networks. The fuzzy-connective-based aggregation network can aggregate the relative status or achievement among locations in various location-related variables through a hierarchical decision-making structure. Finally, an overall evaluation of location is produced from various aspects. The trained model approximates the relationship between turnover and individual location-related factors, which can be used to predict the potential performance of candidate sites and to answer "what if" questions. The transparency and interpretation ability also makes the proposed method desirable. The weights and parameters in the evaluation model help identify the major factors influencing the turnover and the compensatory relationship among location-related factors. The effectiveness and applicability are confirmed through a case study of the food and beverage chain industry in Taiwan.

To establish digital growth model of Kentucky bluegrass repent stem, and study growth process of Kentucky bluegrass repent stem in coordinate system. After observing and studying growth data we found that there is a quick-slow-quick growth rhythm in the growing process, and the growth model of length is established according to the growth rhythm. Then using RMSE statistical methods to verify the model, the results indicates that the growth model is in line with grasses repent stem natural growth rhythm and has a certain degree of reliability and accuracy, it can describe the growth of repent stem exactly.

This study investigates the synergistic effect of combining multi-walled carbon nanotubes (MWNTs) and Fe2O3nanoparticles on thermal conductivity of nanofluid. Results show that low percentage hybrid fillers loading improve thermal conductivity of water based nanofluid, due to the good dispersion and interfacial adhesion, which is confirmed by scanning electron microscope. Furthermore, the hybrid fillers provide synergistic effect on heat conductive networks. The thermal conductivity enhancement of water based nanofluid containing 0.05 wt % MWNTs and 0.02 wt % Fe2O3nanoparticles is 27.75%, which is higher than that of nanofluid containing 0.2 wt % single MWNTs or Fe2O3nanoparticles.

Importance–performance analysis (IPA) is a popular customer-driven tool that enables companies to understand market competition and identify improvement priorities for various attributes of products and services. Despite the widespread use of IPA, previous studies have identified specific deficiencies. For example, the managerial improvement directions derived from IPA are potentially misleading because they ignore the asymmetric and nonlinear relationships between attribute performance (AP) and customer satisfaction (CS). Furthermore, the relationship between AP and importance is erroneously assumed to be independent. By contrast, the Kano model offers useful insight into quality attributes based on the asymmetric and nonlinear relations between AP and CS. In this study, a customer-driven framework is proposed, integrating the advantages of traditional IPA and the Kano model to elucidate the market competition position of each service and product attribute, providing strategic improvement guidelines for managers to design service activities. By conducting a case study of a restaurant chain, we demonstrate the effectiveness of the proposed approach.

Searching for an optimal feature subset from a high-dimensional feature space is an NP-complete problem; hence, traditional optimization algorithms are inefficient when solving large-scale feature selection problems. Therefore, meta-heuristic algorithms are extensively adopted to solve such problems efficiently. This study proposes a regression-based particle swarm optimization for feature selection problem. The proposed algorithm can increase population diversity and avoid local optimal trapping by improving the jump ability of flying particles. The data sets collected from UCI machine learning databases are used to evaluate the effectiveness of the proposed approach. Classification accuracy is used as a criterion to evaluate classifier performance. Results show that our proposed approach outperforms both genetic algorithms and sequential search algorithms.

The objective of this cross-sectional observational study was to determine whether asymmetric hip mobility was associated with neck pain in young adults.Three hundred twenty-seven freshmen students were recruited from an urban university and underwent the Patrick's flexion, abduction, external rotation, extension (FABERE) test for comparison of the functional mobility of bilateral hip joints during the health examination. A logistic regression model was constructed to determine whether the asymmetry measured by the Patrick's FABERE test was associated with neck pain after adjusting for factors of sex and exercise habits.The frequency of asymmetric results of the Patrick's FABERE test among the students who reported neck pain was significantly higher than that of those without neck pain (54.2% vs 26.5%; P.001). After adjusting for the above confounders, the odds ratio of asymmetric results of a Patrick's FABERE test was 2.99 (95% confidence interval, 1.57-5.72; P.001).Imbalanced mobility of the hip joints might be associated with an increased incidence of neck pain.

The effects of a national quality award (NQA) on the market value of the award-winning firms have been reviewed in many countries, but relevant studies are rare to find in Taiwan. This study empirically examined the issue related to the market valuation of firms that undergo effective quality improvement programmes (which sometimes are referred to as total quality management) using Taiwan National Quality Award as the indicator that an effective quality improvement programme has been implemented. In this study, we intended to compare the NQA Index (which represents the performance of National Quality Award winners' stock price) with the Sector Index (which represents public traded companies’ average stock price performance of the sector), and eventually, from the empirical comparison, no statistical evidence was found to support the hypothesis that the stock performance of the quality award winners is superior to the average rate of return on the stock market.

Asthmatic patients with fixed airway obstruction (FAO) and patients with chronic obstructive pulmonary disease (COPD) share similarities in terms of irreversible pulmonary function impairment. Exhaled nitric oxide (eNO) has been documented as a marker of airway inflammation in asthma, but not in COPD. To examine whether the basal eNO level and the change after exercise may differentiate asthmatics with FAO from COPD, 27 normal subjects, 60 stable asthmatics, and 62 stable COPD patients were studied. Asthmatics with FAO (n = 29) were defined as showing a postbronchodilator FEV1/forced vital capacity (FVC) ≤70% and FEV1 less than 80% predicted after inhaled salbutamol (400 μg). COPD with dynamic hyperinflation (n = 31) was defined as a decrease in inspiratory capacity (ΔIC%) after a 6 minute walk test (6MWT). Basal levels of eNO were significantly higher in asthmatics and COPD patients compared to normal subjects. The changes in eNO after 6MWT were negatively correlated with the percent change in IC (r = −0.380, n = 29, P = 0.042) in asthmatics with FAO. Their levels of basal eNO correlated with the maximum mid-expiratory flow (MMEF % predicted) before and after 6MWT. In COPD patients with air-trapping, the percent change of eNO was positively correlated to ΔIC% (rs = 0.404, n = 31, P = 0.024). We conclude that asthma with FAO may represent residual inflammation in the airways, while dynamic hyperinflation in COPD may retain NO in the distal airspace. eNO changes after 6MWT may differentiate the subgroups of asthma or COPD patients and will help toward delivery of individualized therapy for airflow obstruction.

Since its introduction in the 1980s, Kano's two-dimensional model has become one of the most popular models with which to evaluate quality, finding a place in a wide range of industries. For decades, various approaches to regression analysis have been applied to explore asymmetric and non-linear relationships in the Kano model. Although a number of authors have questioned the use of these regression methods, there has been a lack of validity testing to evaluate their convergence with the results of the Kano questionnaire in classifying quality attributes. This study proposes a novel approach to regression analysis for the classification of quality attributes, including must-be, one-dimensional, attractive, and indifferent categories, as well as mixed-class distribution. Using popular tools and techniques for the measurement of customer satisfaction, the proposed approach is capable of simplifying the process of collecting data making it far easier to implement than the list of functional and dysfunctional questions initiated by Kano. An empirical study of a food and beverage chain showed that the proposed approach is capable of returning acceptable classification results, compared to the Kano questionnaire. A validity test indicated that the proposed approach significantly outperformed dummy variable regression and the moderated regression. In conclusion, the proposed approach provides a more practical implementation, while maintaining classification power on par with the Kano questionnaire.

An inter-metal dielectric (IMD) is deposited between metal layers to provide isolation capability to a device and separate the different metal layers that are unnecessary in the conduction of electricity. Owing to the complicated input/response relationships of the IMD process, the void problem results in electric leakage and causes wafer scraping. In the current study, we combined neural networks, genetic algorithms (GAs) and the desirability function in order to optimise the parameter settings of the IMD process. Initially, a backpropagation (BP) neural network was developed to map the complex non-linear relationship between the process parameters and the corresponding responses. Moreover, the desirability function and GAs were employed to obtain the optimum operating parameters in respect to each response. The implementation of the proposed approach was carried out in a semiconductor manufacturing company in Taiwan, and the results illustrate the practicability of the proposed approach.

Searching for an optimal feature subset in a high-dimensional feature space is an NP-complete problem; hence, traditional optimization algorithms are inefficient when solving large-scale feature selection problems. Therefore, meta-heuristic algorithms have been extensively adopted to solve the feature selection problem efficiently. This study proposes an improved particle swarm optimization IPSO algorithm using the opposite sign test OST. The test increases population diversity in the PSO mechanism, and avoids local optimal trapping by improving the jump ability of flying particles. Data sets collected from UCI machine learning databases are used to evaluate the effectiveness of the proposed approach. Classification accuracy is employed as a criterion to evaluate classifier performance. Results show that the proposed approach outperforms both genetic algorithms and sequential search algorithms.

The malaria epidemics in Laza city of Myanmar and Yingjiang county in Yunnan province of China in 2012 and 2013 was reviewed retrospectively, and a survey on malaria infection was conducted in residents in the border areas. A total of 179 malaria cases were reported in Yingjiang county from 2012 to 2013, with an average annual incidence of 2.9 per 10,000. Of the 179 cases, 77.7% were imported cases and 22.3% were local cases; 79.3% were infected with Plasmodium vivax, 20.1% with P. falciparum, and 0.6% unidentified. In Laza city of Myanmar, 2,069 malaria cases were reported, with an average annual incidence of 322.5 per 10,000. Of them, 73.4% cases were infected with P. vivax, 20.1% with P. falciparum and 6.5% unidentified. In addition, the microscopic results revealed that the malaria parasite rate in the residents in Yingjiang county was 0%, while that in Laza city was 1.5%.

Negative selection (NS) is one of the most discussed algorithms in artificial immune system (AIS). With its unique property for anomaly detection, it has attracted the attention of researchers in the past decades. However, the processes on how to generate representative detectors and how to define the matching rules remain to be challenges in many NS applications. These difficulties make NS suffer from high false-positive rates and computational complexities. On the other hand, the Mahalanobis distance (MD) is a popular distance metric used in distinguishing patterns of a certain group from those of another group. Compared with other multivariate measurement techniques, MD is superior in its ability to determine the similarity of a set of values from an unknown sample to a set of values measured from a collection of known samples. In this study, an MD-based NS called MDNS is proposed to improve the classification power for anomaly detection by providing the mechanism to judge the quality of detector cells as well as to be applied to define the matching rules and the threshold in a matching rule. Two real cases concerning medical diagnosis and quality inspection in highly reliable products are studied, and the results show that the performance of the NS can be significantly improved by using the proposed approach.

Human capital is one of the critical resources for high-tech industries such as semiconductor manufacturing to maintain their competitive advantages, yet it is seldom addressed in literature. Owing to the changing nature of knowledge workers in high-tech industries, jobs cannot be easily delineated. Thus, conventional personnel selection approaches based on static job characteristics no longer suffice. Focusing on the needs in real settings, this study aims to develop a manufacturing intelligence framework that integrates the rough set theory, support vector machine, and decision tree to extract useful patterns and intelligence from huge human resource data and production data to enhance the decision quality of human resource management that include identifying high-potential talents who fit the company culture and allocating the job with functional nature that matches the characteristics of the talent. To assess the validity of this approach, empirical studies were conducted on the basis of real data collected from semiconductor companies for comparison. The results have shown the practical viability of this approach.

Well-developed repent stems grown on Pooideae, when it is growing, the new unit will also grow on their nodes, the two growing process is parallel. To research the parallel growth process in the coordinate system, we collected much growth data, observed and studied them, then put forward the parallel growth model based on state-space, the model can reflect parallel growing of repent stem continually and dynamically, and it is also easily to understand. It provide a new research method for visual plant modeling.

As semiconductor device density increases, integrated circuits generally involve more levels of metallization. In multilevel interconnected metallization schemes, an inter-metal dielectric (IMD) is deposited between metal layers. The basic advantage of this dielectric layer is that it provides good step coverage to help smooth the topology, making it free of pinholes and allowing it to act as a good insulator. The key problem in the IMD layer is the occurrence of voids which may cause electric leakage and later result in yield loss. The occurrence of these voids may be avoided by ensuring an excellent gap-fill capacity. Taking this into consideration, improving the gap-fill capacity is one of the critical issues for the IMD layer. In addition, the quantity of fluorine and the value of voltage ramping to dielectrics breakdown (VRDB) also affect the quality of the IMD layer. The gap-fill capacity, VRDB, and the quantity of fluorine are important quality characteristics for the optimization of the performance of the IMD layer. Due to the complicated inputs/responses relation, it is difficult for the output of the IMD process to reach the desired target. To resolve this problem, this study employs the Six Sigma methodology to reduce the defect in the IMD layer. A case study of a semiconductor manufacturing foundry in Taiwan is illustrated to show the practicability of the Six Sigma methodology.

Since the advent of high qualification and tiny technology, yield control in the photolithography process has played an important role in the manufacture of thin-film transistor-liquid crystal displays (TFT-LCDs). Through an auto optic inspection (AOI), defect points from the panels are collected, and the defect images are generated after the photolithography process. The defect images are usually identified by experienced engineers or operators. Evidently, human identification may produce potential misjudgments and cause time loss. This study therefore proposes a neural-network approach for defect recognition in the TFT-LCD photolithography process. There were four neural-network methods adopted for this purpose, namely, backpropagation, radial basis function, learning vector quantization 1, and learning vector quantization 2. A comparison of the performance of these four types of neural-networks was illustrated. The results showed that the proposed approach can effectively recognize the defect images in the photolithography process.

Recently, machine learning algorithms are widely applied to production such as failures identification, finished products inspection, and process monitoring. Applying these algorithms to fault detection makes it possible to eliminate additional tests or experiments which usually involve high expense and highly risk. However, when applying machine learning methods to the real world data, the class imbalance problem usually has been ignored. This problem is caused by imbalanced data, in which almost all the examples are labeled as one class whilst far fewer objects are labeled as the other class. When deal with such imbalanced data, a classifier induced from an imbalanced data set has high classification accuracy for the majority class, but an unacceptable error rate for the minority class. To solve this problem, this work proposed a novel method, called SOM (Self-Organizing Maps) based methodology. A process monitoring data has been provided to demonstrate the effectiveness of the proposed method. Experimental results indicated the proposed method outperforms traditional techniques, under-sampling and cluster based sampling.

Copper sulfide (CuS) nanocrystals with flower-like and tubular morphology have been successfully synthesized via a facile and convenient hydrothermal route at 75 °C by using CuCl2·2H2O as Cu-precursor, C2H5NS as S-source and CTAB as template molecules. The effect of concentration of reactants and template molecules on morphology has been discussed. X-ray diffraction pattern suggests that the CuS crystals are pure hexagonal phase. The morphology of the products has been studied by scanning electron microscope analysis. The absorption peaks of CuS in UV and near-IR regions indicate that the as-prepared CuS are promising in the development of photoelectric devices.

The quality of human capital is crucial for high-tech companies to maintain competitive advantages in knowledge economy era. However, high-technology companies suffering from high turnover rates often find it hard to recruit the right talents. In addition to conventional human resource management approaches, there is an urgent need to develop effective personnel selection mechanism to find the talents who are the most suitable to their own organizations. This study aims to fill the gap by developing a data mining framework based on decision tree and association rules to generate useful rules for personnel selection. The results can provide decision rules relating personnel information with work performance and retention. An empirical study was conducted in a semiconductor company to support their hiring decision for indirect labors including engineers and managers with different job functions. The results demonstrated the practical viability of this approach. Moreover, based on discussions among domain experts and data miner, specific recruitment and human resource management strategies were created from the results.

To recruit and retain high-potential talent is critical for semiconductor companies to maintain competitive advantages in a modern knowledge-based economy. Conventional personnel selection methodologies focusing on static work and job analysis will no longer be appropriate for knowledge workers in high-tech industries. This paper aims to develop an effective data mining approach based on Rough Set Theory to explore and analyze human resource data for personnel selection and human capital enhancement. An empirical study was conducted in a leading semiconductor company in Taiwan to estimate the validity of the proposed approach for predicting work behaviors including performance and resignation. The results showed that latent knowledge can be discovered as a basis to derive specific recruitment and human resource management strategies. In particular, 29 rules have been adopted as references for recruiting the right talent. This paper concludes with discussions of empirical findings and future research directions.

HS-1-associated protein X-1 (Hax-1) has been suggested to be expressed in various rodent and human tissues. Accumulating evidence has demonstrated that Hax‑1 exerts an anti‑apoptotic effect in neurological diseases. Furthermore, it has also been reported that Hax‑1 interacts with various apoptosis‑associated proteins, including high temperature-regulated A2 (HtrA2) and caspase‑3. Previous studies have indicated that abnormal expression of Hax‑1 may be associated with the development of the nervous system and with the pathophysiology of neurological diseases, including traumatic brain injury and cerebral ischemia. The present study reported temporal‑spatial patterns of Hax‑1 in rat retina following optic nerve crush (ONC). Using western blotting and double‑immunofluorescence, significant upregulation of Hax‑1 was observed in retinal ganglion cells (RGCs) in the retina following ONC. Increased Hax‑1 expression was demonstrated to be accompanied by upregulation of active‑caspase‑3 and HtrA2 following ONC. In addition, Hax-1 co‑localized with active caspase‑3 and HtrA2 in RGCs following ONC. Terminal deoxynucleotidyl transferase‑mediated biotinylated-dUTP nick‑end labeling staining suggested that Hax‑1 was involved in RGC apoptosis following ONC. Thus, these results suggested that Hax‑1 may participate in regulating RGC apoptosis via interacting with caspase‑3 and HtrA2 following ONC.

Background Moderate-intensity exercise training improves skeletal muscle aerobic capacity and increased oxidative enzyme activity, as well as exercise tolerance in COPD patients. Methods To investigate whether the home-based exercise training program can reduce inflammatory biomarkers in patients with COPD, twelve patients using mobile phone assistance and 14 with free walk were assessed by incremental shuttle walk test (ISWT), spirometry, strength of limb muscles, and serum C-reactive protein (CRP) and inflammatory cytokines. Results Patients in the mobile phone group improved their ISWT walking distance, with decrease in serum CRP after 2 months, and sustained at 6 months. Patients in the control group had no improvement. Serum IL-8 in the mobile phone group was significantly reduced at 2, 3 and 6 months after doing home exercise training compared to baseline. IL-6 and TNF-α were significantly elevated at 3 and 6 months in control group, while there were no changes in mobile phone group. The strength of limb muscles was significantly greater compared to baseline at 3 and 6 months in the mobile phone group. Conclusions A mobile-phone-based system can provide an efficient home endurance exercise training program with improved exercise capacity, strength of limb muscles and a decrease in serum CRP and IL-8 in COPD patients. Decreased systemic inflammation may contribute to these clinical benefits. (Clinical trial registration No.: NCT01631019)

To investigate the immediate efficacy of laterally wedged insoles with arch support (LWAS) on gait in persons with bilateral medial knee osteoarthritis (OA).A prospective case-control intervention study.A gait laboratory with a 6-camera motion analysis system and 2 forceplates.Fifteen women with bilateral medial knee OA and 15 healthy control subjects (N=30).LWAS.Subjective knee pain and objective biomechanical indices, namely, joint angles and moments in the frontal plane, frontal plane ground reaction force and lever arm, as well as medial/lateral center of mass and center of pressure during gait.When wearing the LWAS, knee pain during gait in persons with medial knee OA decreased (P=.01). Peak internal knee abductor moments were also reduced (P.001) with increasing foot progression angles, laterally shifted center of pressure, and a shortened frontal plane lever arm (all P.05). However, ankle invertor moments were increased (P.05) when wearing the LWAS.Although peak internal knee abductor moment and knee pain were immediately reduced during gait when wearing the LWAS, increased ankle invertor moments were found, suggesting that the LWAS should be used with caution. Strengthening and monitoring the condition of the ankle invertor muscles may be necessary if the LWAS is used as an intervention for persons with bilateral medial knee OA.

Pulmonary rehabilitation (PR) brings benefits to patients with chronic obstructive pulmonary disease (COPD). Negative pressure ventilation (NPV) increases ventilation and decreases hyperinflation as well as breathing work in COPD. We evaluated the long-term effects of a hospital-based PR pro