Teeth segmentation of dental periapical radiographs based on local singularity analysis

Teeth segmentation for periapical raidographs is one of the most critical tasks for effective periapical lesion or periodontitis detection, as both types of anomalies usually occur around tooth boundaries and dental radiographs are often subject to noise, low contrast, and uneven illumination. In this paper, we propose an effective scheme to segment each tooth in periapical radiographs. The method consists of four stages: image enhancement using adaptive power law transformation, local singularity analysis using Hölder exponent, tooth recognition using Otsu's thresholding and connected component analysis, and tooth delineation using snake boundary tracking and morphological operations. Experimental results of 28 periapical radiographs containing 106 teeth in total and 75 useful for dental examination demonstrate that 105 teeth are successfully isolated and segmented, and the overall mean segmentation accuracy of all 75 useful teeth in terms of (TP, FP) is (0.8959, 0.0093) with standard deviation (0.0737, 0.0096), respectively.     

Enhancing decisions with life cycle analysis for risk management

Although corporate financial distress is an infrequent occurrence, it has an extremely debilitating effect on the stability of a firm when it does occur. For this reason, an accurate risk assessment mechanism is needed in numerous industry sectors, particularly in financial institutions and banking. Based on corporation life cycle theory and risk management, this study develops a risk pre-warning model, namely the RSVMDT model, to eliminate serious financial punching and to examine the effectiveness of transparency and the full disclosure index (TFDI) during each life cycle stage. The RSVMDT model includes three techniques: random forest (RF), support vector machines (SVMs), and decision trees (DTs). The RF is used to determine the essential attributes of firms and therefore decrease the computational complexity of financial analysis and improve the classification accuracy. The SVM is employed as a classifier to identify corporations in financial distress. Finally, the DT is utilized as a rule generator that allows decision makers to adjust the financial structures of firms at specific life cycle stages. Together, these three techniques can increase the probability of corporate survival in a highly competitive environment. Additionally, the study further evaluates the importance of the TFDI during a turbulent economy. The public sectors can benefit from this evaluation by formulating future policies based on the rules derived from the developed RSVMDT model.     

Application of SVR,Taguchi loss function,and the artificial bee colony algorithm to resolve multiresponse parameter design problems: a case study on optimizing the design of a TIR lens

Multiresponse parameter design problems have become increasingly important and have received considerable attention from both researchers and practitioners since there are usually several quality characteristics that must be optimized simultaneously in most modern products/processes. This study applies support vector regression (SVR), Taguchi loss function, and the artificial bee colony (ABC) algorithm to develop a six-staged procedure that resolves these common and complicated parameter design problems. SVR is used to model the mathematical relationship between input control factors and output responses, and the ABC algorithm is used to find the optimal control factor settings by searching the well-constructed SVR models in which the Taguchi loss function is applied to evaluate the overall performance of a product/process. The feasibility and effectiveness of the proposed approach are demonstrated via a case study in which the design of a total internal reflection (TIR) lens is optimized while fabricating an MR16 light-emitting diode lamp. Experimental results indicate that the proposed solution procedure can provide highly robust design parameter settings for TIR lenses that can be directly applied in real manufacturing processes. Comparisons with the Taguchi method reveal that the Taguchi method is an undesirable and inappropriate method for resolving multiple-response parameter design problems, while the ABC algorithm can search the solution spaces in continuous domains modeled via SVR instead of in the limited discrete experiment levels, thus finding a more robust design than that obtained by the traditional analysis of variance. Consequently, the proposed integrated approach in this study can be considered feasible and effective and can be popularized as a useful tool for resolving general multiresponse parameter design problems in the real world.     

Novel positive‐working aqueous‐base developable photosensitive polyimide precursors based on diazonaphthoquinone‐capped polyamic esters

Novel positive‐working aqueous‐base developable photosensitive polyimide (PSPI) precursors based on partially diazonaphthoquinone (DNQ)‐capped polyamic esters bearing phenolic hydroxyl groups and a DNQ photosensitive compound (PIC‐3) were developed. The partially DNQ capped polyamic esters were prepared from an esterification reaction of 1,2‐naphthoquinone diazide‐5‐sulfonyl chloride with the polyamic esters. The partially DNQ capped polyamic esters decreased the dark film loss effectively in the aqueous‐base developer and were able to make thicker film resists compared to the uncapped polyamic esters. The 25 mol % DNQ‐capped BisAPAF–PMDA polyamic ester and BisAPAF–ODPA polyamic ester containing 25 wt % PIC‐3 photosensitive compound showed a sensitivity of 176 and 185 mJ/cm², and a contrast of 1.68 and 1.02, respectively, in a 3‐μm film with 1.25 wt % tetramethylammonium hydroxide developer. A pattern with a resolution of 5 μm was obtained from both PSPI precursor compositions. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 90: 2293–2300, 2003     

Are quantum dots ready for in vivo imaging in human subjects?

Nanotechnology has the potential to profoundly transform the nature of cancer diagnosis and cancer patient management in the future. Over the past decade, quantum dots (QDs) have become one of the fastest growing areas of research in nanotechnology. QDs are fluorescent semiconductor nanoparticles suitable for multiplexed in vitro and in vivo imaging. Numerous studies on QDs have resulted in major advancements in QD surface modification, coating, biocompatibility, sensitivity, multiplexing, targeting specificity, as well as important findings regarding toxicity and applicability. For in vitro applications, QDs can be used in place of traditional organic fluorescent dyes in virtually any system, outperforming organic dyes in the majority of cases. In vivo targeted tumor imaging with biocompatible QDs has recently become possible in mouse models. With new advances in QD technology such as bioluminescence resonance energy transfer, synthesis of smaller size non-Cd based QDs, improved surface coating and conjugation, and multifunctional probes for multimodality imaging, it is likely that human applications of QDs will soon be possible in a clinical setting.     

Vibrational spectroscopic characterization of rigid rod polymers: 3. Microstructural changes in stressed polymers

Highly oriented poly(p-phenylene benzobisthiazole) films were stressed in a miniature hydraulic tensile tester interfaced to a fast scanning Fourier transform infra-red spectrometer. The changes in relative intensity, polarization, frequency and shape of infrared bands have been useful in the elucidation of microstructures. With the short time resolution achievable, vibrational bandshape and frequency have been shown to change at different rates when an external stress was applied.     

TRANSIENT ANALYSIS OF CRYSTALLIZATION: EFFECT OF THE SIZE DEPENDENT GROWTH RATE

The dynamics of the crystal size distribution in a continuous mixed suspension, mixed product removal crystallizer is modeled stochastically. The model is fairly general in that it involves a size-dependent growth rate function: this size-dependent growth rate as a possible cause of deviation on the behavior of the crystallizer from that of an idealized or simplified counterpart has been investigated based upon the model.     

An integrated portfolio optimisation procedure based on data envelopment analysis,artificial bee colony algorithm and genetic programming

Portfolio optimisation is an important issue in the field of investment/financial decision-making and has received considerable attention from both researchers and practitioners. However, besides portfolio optimisation, a complete investment procedure should also include the selection of profitable investment targets and determine the optimal timing for buying/selling the investment targets. In this study, an integrated procedure using data envelopment analysis (DEA), artificial bee colony (ABC) and genetic programming (GP) is proposed to resolve a portfolio optimisation problem. The proposed procedure is evaluated through a case study on investing in stocks in the semiconductor sub-section of the Taiwan stock market for 4 years. The potential average 6-month return on investment of 9.31% from 1 November 2007 to 31 October 2011 indicates that the proposed procedure can be considered a feasible and effective tool for making outstanding investment plans, and thus making profits in the Taiwan stock market. Moreover, it is a strategy that can help investors to make profits even when the overall stock market suffers a loss.     

A STOCHASTIC ANALYSIS OF COKE DEPOSITION

The phenomenon of coke deposition on catalyst surface is analyzed stochastically. In contrast to a conventional deterministic approach, derivation of the governing equations of the phenomenon is based on probability laws. The resulting representation provides the information on both the mean or macroscopic and the fluctuating characteristics about the system under consideration. The deposition of coke is assumed to be reversible, i.e., deposited coke has a certain probability of escaping the surface. The applicability of the model developed is justified through analyzing available experimental data appeared in the literature.     

Examining the role of perceived value in virtual communities continuance: its antecedents and the influence of experience

Although perceived value has long been recognised as an influential means of affecting continuance intention in information systems (IS), little attention has been devoted to explore its antecedents, which constitutes an important research issue. This study, building on prior literature on continued usage in IS as well as the resource-based view, proposed a model to address this gap. Our model suggests that perceived value, a major driving force for members’ satisfaction and continuance intention, is affected by four kinds of resources embedded in virtual communities, which are relationship resources, technology infrastructure, knowledge resources and human resources. To provide additional insights on the pivotal role of perceived value, we also postulate that experience moderates the link between perceived value and its antecedents. Data collected from 235 members of a professional virtual community provide strong support for the research model. It was found that perceived value exerts a great effect on both satisfaction and continuance intention. In addition, when different levels of experience are taken into consideration, relationship resources, knowledge resources and human resources were found to be more salient to high-experience members, whereas technology infrastructure was found to be more salient to low-experience members. Finally, this study discusses the implications of these findings and offers direction for future research.