BackgroundIn the application of microarray data, how to select a small number of informative genes from thousands of genes that may contribute to the occurrence of cancers is an important issue. Many researchers use various computational intelligence methods to analyzed gene expression data.ResultsTo achieve efficient gene selection from thousands of candidate genes that can contribute in identifying cancers, this study aims at developing a novel method utilizing particle swarm optimization combined with a decision tree as the classifier. This study also compares the performance of our proposed method with other well-known benchmark classification methods (support vector machine, self-organizing map, back propagation neural network, C4.5 decision tree, Naive Bayes, CART decision tree, and artificial immune recognition system) and conducts experiments on 11 gene expression cancer datasets.ConclusionBased on statistical analysis, our proposed method outperforms other popular classifiers for all test datasets, and is compatible to SVM for certain specific datasets. Further, the housekeeping genes with various expression patterns and tissue-specific genes are identified. These genes provide a high discrimination power on cancer classification.

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The effect of comorbidity on lung cancer patients' survival has been widely reported. The aim of this study was to investigate the effects of comorbidity on the establishment of the diagnosis of lung cancer and survival in lung cancer patients in Taiwan by using a nationwide population-based study design. This study collected various comorbidity patients and analyzed data regarding the lung cancer diagnosis and survival during a 16-year follow-up period (1995–2010). In total, 101,776 lung cancer patients were included, comprising 44,770 with and 57,006 without comorbidity. The Kaplan–Meier analyses were used to compare overall survival between lung cancer patients with and without comorbidity. In our cohort, chronic bronchitis patients who developed lung cancer had the lowest overall survival in one (45%), five (28.6%), and ten years (26.2%) since lung cancer diagnosis. Among lung cancer patients with nonpulmonary comorbidities, patients with hypertension had the lowest overall survival in one (47.9%), five (30.5%), and ten (28.2%) years since lung cancer diagnosis. In 2010, patients with and without comorbidity had 14.86 and 9.31 clinical visits, respectively. Lung cancer patients with preexisting comorbidity had higher frequency of physician visits. The presence of comorbid conditions was associated with early diagnosis of lung cancer.

Decision tree (DT) analysis was applied in this cross-sectional study to investigate caries experience in children by using clinical and microbiological data obtained from parent–child pairs. Thirty pairs of parents and children were recruited from periodontal and pediatric dental clinics. All participants were clinically examined for caries and periodontitis by a calibrated examiner. Cariogenic and periodontopathic bacteria examinations were conducted. The Kendall rank correlation coefficient was used to measure the association between data variables obtained through clinical and microbiological examinations. A classificatory inductive decision tree was generated using the C4.5 algorithm with the top-down approach. The C4.5 DT analysis was applied to classify major influential factors for children dental caries experience. The DT identified parents’ periodontal health classification, decayed, missing, filled permanent teeth (DMFT) index, periodontopathic test (PerioCheck) result, and periodontal pocket depth as the classification factors for children caries experience. 13.3% of children were identified with a low decayed, missing, filled primary teeth (dmft) index (dmft < 3) whose parents had a periodontal pocket depth ≤3.7, PerioCheck score >1, DMFT index <13.5, and periodontal classification >2. The DT model for this study sample had an accuracy of 93.33%. Here, parental periodontal status and parents’ DMFT were the factors forming the DT for children’s caries experience.

This paper addresses the problem of resource portfolio planning of firms in high-tech, capital-intensive manufacturing industries. In light of the strategic importance of resource portfolio planning in these industries, we offer an alternative approach to modelling capacity planning and allocation problems that improves the deficiencies of prior models in dealing with three salient features of these industries, i.e. fast technological obsolescence, volatile market demand, and high capital expenditure. This paper first discusses the characteristics of resource portfolio planning problems including capacity adjustment and allocation. Next, we propose a new mathematical programming formulation that simultaneously optimises capacity planning and task assignment. For solution efficiency, a constraint-satisfied genetic algorithm (CSGA) is developed to solve the proposed mathematical programming problem on a real-time basis. The proposed modelling scheme is employed in the context of a semiconductor testing facility. Experimental results show that our approach can solve the resource portfolio planning problem more efficiently than a conventional optimisation solver. The overall contribution is an analytical tool that can be employed by decision makers responding to the dynamic technological progress and new product introduction at the strategic resource planning level.

As smart technology proliferates, enterprises must engage not only in the transformation of intelligence but contend with pressure do so as soon as possible. Smart transformation is critical for manufacturing enterprises in the development of smart manufacturing. This study addressed the gap between maturity models and project management by designing an effective assessment framework for smart transformation. It adopts the Smart Industry Readiness Index, created by the Singapore Economic Development Board, as a maturity assessment model to analyze enterprises’ smart transformation and formulate project management strategies. Enterprises can use this model to examine the maturity level of their transformation and assess scope for improvement in their project strategies and implementation barriers. This study focuses on Taiwanese enterprises using data collected from 165 valid questionnaires and subjected to a cluster analysis. Enterprises were divided into three categories. The results reveal that, first, most enterprises’ smart transformation is at an immature or medium-maturity level, and is therefore amenable to further improvement. Second, inconsistent with research findings, many enterprises invest in transformation projects but fail to advance these projects to maturity. Third, most enterprises’ project management plans fail to meet actual transformation needs. Using the thematically oriented maturity model proposed in this study, Taiwanese enterprises can effectively evaluate the maturity of their transformation projects. In conclusion, the study highlights that Taiwanese enterprises must identify more effective external resources to strengthen their competitiveness.

This study presented the application of Radio Frequency Identification (RFID) technology to improve the efficiency and effectiveness of warehouse management. Fixed RFID readers and antenna were installed at the receiving/shipping dock and passive tags were mounted on either storage box or pallet. RFID system can quickly and simultaneously read multiply tags, compared to the sequential reading of barcodes by handy barcode scanner in manual operations due to the inconsistency in box sizes and the locations of barcode for items of various types. Significant improvements were observed in preliminary experiments. The numbers of pallets and items processed by each operator per day were increased by 425% and 438%, respectively. The data processing time at receiving and shipping docks was reduced by more than 90%. With RFID technology, the number of operators can be reduced while maintaining current service capacity at the studied warehouse. The benefit using RFID in the warehouse management is analyzed and promoted.