Decision tree-based learning to predict patient controlled analgesia consumption and readjustment Background: Appropriate postoperative pain management contributes to earlier mobilization, shorter hospitalization, and reduced cost. The under treatment of pain may impede short-term recovery and have a detrimental long-term effect on health. This study focuses on Patient Controlled Analgesia (PCA), which is a delivery system for pain medication. This study proposes and demonstrates how to use machine learning and data mining techniques to predict analgesic requirements and PCA readjustment. Methods: The sample in this study included 1099 patients. Every patient was described by 280 attributes, including the class attribute. In addition to commonly studied demographic and physiological factors, this study emphasizes attributes related to PCA. We used decision tree-based learning algorithms to predict analgesic consumption and PCA control readjustment based on the first few hours of PCA medications. We also developed a nearest neighbor-based data cleaning method to alleviate the class-imbalance problem in PCA setting readjustment prediction. Results: The prediction accuracies of total analgesic consumption (continuous dose and PCA dose) and PCA analgesic requirement (PCA dose only) by an ensemble of decision trees were 80.9% and 73.1%, respectively. Decision tree-based learning outperformed Artificial Neural Network, Support Vector Machine, Random Forest, Rotation Forest, and Naïve Bayesian classifiers in analgesic consumption prediction. The proposed data cleaning method improved the performance of every learning method in this study of PCA setting readjustment prediction. Comparative analysis identified the informative attributes from the data mining models and compared them with the correlates of analgesic requirement reported in previous works. Many factors affect individual variability in postoperative pain. Although several statistical studies have evaluated postoperative pain and analgesic consumption, a systematic review of previous research shows that the coefficient of determination of existing predictive models was small (e.g., R 2 = 0.17-0.59 for postoperative pain, and 0.27-0.46 for postoperative analgesic consumption) [39] . These findings indicate that approximately half of the variability is unexplained, and that factors other than demographic or physiological attributes may contribute to the complexity of postoperative outcomes. This study presents the real-world application of data mining to anesthesiology and considers a wider variety of predictive factors, including PCA demands over time. This study analyzes PCA patient data and conducts several experiments to evaluate the potential of applying machine-learning algorithms to assist anesthesiologists in PCA administration. Results confirm the feasibility of the proposed ensemble approach to postoperative pain management. Pain is one of the most commonly reported postoperative symptoms [1] . Pain can negatively affect quality of life and may do more harm than an illness itself when it becomes intolerable, making the patient both physically and mentally uncomfortable. Pain is a highly personal experience influenced by multiple factors, including sensitivity to pain, age, genetics, physical status, and psychological factors [2, 3] . Progress in medical science has gradually made people more aware of the importance of pain management.. Patient-controlled analgesia (PCA) is a pain medication delivery system that enables effective and flexible pain treatment by allowing patients to adjust the dosage of anesthetics. According to previous research [4, 5] , PCA has become one of the most effective techniques for treating postoperative analgesia. As a result, PCA is now widely used in hospitals for the management of postoperative pain, especially for major surgeries.. This study focuses on PCA. Previous research has identified the preoperative correlates of postoperative pain intensity or analgesic consumption in various patient groups of different genders, ages, or psychological states [6, 7] . These studies used statistical methods, such as ANOVA, chi-square tests, or regression analysis, to evaluate this correlation in an attempt to identify tailored treatments that reduce severe postoperative pain or improve acute and chronic outcomes. With the same objective, this study applies learning algorithms to predict (1) the postoperative analgesic requirement and (2) the need for PCA setting readjustment (e.g., lockout) based on patient physical states and the first few hours of PCA treatment data.

B I I O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


ON ENTROPY OF FUZZY SETS In this paper we deal with the entropy of fuzzy sets. We first review several defined entropies of fuzzy sets and then propose a new one. Some comparisons are made with some existing entropies to show the effectiveness of the proposed one. There often exists uncertainty in real systems. Probability has been traditionally used in modelling uncertainty. Since Zadeh 18 originated the idea of fuzzy sets, fuzziness becomes another way to model uncertainty. On the other hand, measuring the fuzziness of fuzzy sets is an important step in fuzzy systems. Measures of fuzziness by contrast to fuzzy measures try to indicate the degree of fuzziness of a fuzzy set. The entropy of fuzzy sets is a measure of fuzziness between fuzzy sets. De Luca and Termini 1 first introduced the axiom construction for entropy of fuzzy sets with reference to Shannon's probability entropy. Ebanks 2 suggested five properties for a entropy of fuzzy sets to satisfy. Yager 17 defined fuzziness measures of fuzzy sets in terms of a lack of distinction between the fuzzy set and its negation based on L p norm. Higashi and Klir 5 extended Yager's measure to a general class of fuzzy complements. Kosko 6 provided a measure of fuzziness between fuzzy sets using a ratio of distance between the fuzzy set and its nearest set to the distance between the fuzzy set and its farthest set. Liu 10 gave some axiom definitions of entropy and also defined a σ-entropy. Fan et al. 3, 4 re-organized some fuzzy entropy formulas.. Pal and Pal 13,14 proposed higher order and exponential entropies. More surveys on measuring fuzziness were given by Pal 11 and Pal and Bezdek. 12 Recently, Li and Liu 8, 9 consider new defined fuzzy variables and discuss about entropies on fuzzy variables. In Li and Liu, 7 they first give the notion of credibility measure and then consider an entropy of credibility distributions for fuzzy variables. In this paper, we propose a new entropy of fuzzy sets. Some comparisons are made with some existing entropies to show the effectiveness of the proposed one. Based on the Yager's entropy formation, 17 we proposed a new entropy of fuzzy sets. The hedges, like "very", "highly", "more or less", with linguistic variables are used for the comparisons of our proposed one with several existing entropies. The results show the effectiveness of our proposed entropy.

O B O B I O O O O O O O B O B I O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O B I B O O O O O B O O O O O O B O B I O O O O O O O O O B O O O O O O O O O O O B O O O O O B O B I O O O O B O B I O O O O O O O O O O O B O B I O O O O O B O O O O O O O B O B I O O O O O B O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O B I O O O O O O O O O O O O O O O O O O O O B O O O O O O B O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B


Logical Ontology for Mediating between Nursing Intervention Terminology Systems Objectives Several researchers have proposed the use of logical ontologies as 'reference terminologies'. However, there are a number of unresolved issues. This article describes the development of a logical ontology for nursing interventions and presents the results of evaluation.Methods Initially this study involved the development in GRAIL of two separate experimental ontologies: an ontology based on the textual content of informal definitions for nursing interventions drawn from the Nursing Interventions Classification; and an ontology based on labels for the same nursing interventions. Following initial bench-testing, the ontology based on labels was selected for extension (to accommodate also nursing intervention components of the Home Health Care Classification System and the Omaha System), for further testing and for external evaluation.Results A hierarchy of nursing interventions generated automatically from the experimental ontology based on informal definitions contained only 3 hierarchical relationships, compared to 214 for the initial ontology based on labels. For the final extended ontology based on labels, the generated hierarchy contained the three source terminology systems in entirety -there were a total of 2861 hierarchical relationships. While the results of comparative bench testing of the final ontology were favourable, the results of external evaluation were mixed and showed little agreement between reviewers.Conclusion This study suggests that while a logical ontology based on labels might be a useful tool for mediating between nursing intervention terminology systems, a formative consensus type development methodology might improve the approach by helping to harmonise ideological differences that may exist across the nursing profession. In summary this study suggests that a logical ontology may be a useful tool in resolving a large proportion of differences between nursing intervention terminology systems; and that the use of nursing intervention labels as sources is highly productive, although some form of paraphrasing would be useful to capture missing semantics. However, the author believes that a complementary formative consensus type development and evaluation methodology, comprising discrete tasks, a range of reviewers, and a systematic assessment of agreement may improve the approach by helping to harmonise ideological differences. Initially this study involved the development and validation of two separate experimental ontologies to determine which approach would be most useful:. • an ontology based on the textual content of informal definitions for nursing interventions drawn from the second edition of NIC [17] ; and. • an ontology based on labels or rubrics for the same nursing interventions [2] .

O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O B O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O


Bioinformatics analysis of transcriptional regulation of circadian genes in rat liver Background: The circadian clock is a critical regulator of biological functions controlling behavioral, physiological and biochemical processes. Because the liver is the primary regulator of metabolites within the mammalian body and the disruption of circadian rhythms in liver is associated with severe illness, circadian regulators would play a strong role in maintaining liver function. However, the regulatory structure that governs circadian dynamics within the liver at a transcriptional level remains unknown. To explore this aspect, we analyzed hepatic transcriptional dynamics in Sprague-Dawley rats over a period of 24 hours to assess the genome-wide responses. Results: Using an unsupervised consensus clustering method, we identified four major gene expression clusters, corresponding to central carbon and nitrogen metabolism, membrane integrity, immune function, and DNA repair, all of which have dynamics which suggest regulation in a circadian manner. With the assumption that transcription factors (TFs) that are differentially expressed and contain CLOCK:BMAL1 binding sites on their proximal promoters are likely to be clock-controlled TFs, we were able to use promoter analysis to putatively identify additional clock-controlled TFs besides PARF and RORA families. These TFs are both functionally and temporally related to the clusters they regulate. Furthermore, we also identified significant sets of clock TFs that are potentially transcriptional regulators of gene clusters.Conclusions: All together, we were able to propose a regulatory structure for circadian regulation which represents alternative paths for circadian control of different functions within the liver. Our prediction has been affirmed by functional and temporal analyses which are able to extend for similar studies. Microarrays were used to characterize the gene expression within rat livers over 24 hours, and various computational tools have been applied to identify and distribute differentially expressed genes from the microarray data into four distinct patterns. Pattern 1 is primarily related to central metabolism with the peak at circadian time ZT0. Pattern 2 is related to cell repair and cell junction formations, and peaks at circadian time ZT4. Pattern 3 is primarily related to the innate immune response and peaks at circadian time ZT8, while Pattern 4 is related to DNA repair and peaks at circadian time ZT16. The study also proposed a unique regulatory structure for circadian rhythms in the liver, which aims to predict the ways in which circadian patterns can potentially impact injury. By using techniques from promoter analysis coupled with the proposed hypothesis of clock TF regulation, a set of transcription factors and putative regulatory structure have been established which show to be consistent in agreement with functional and temporal annotations. These transcription factor dynamics appear to play a strong role in circadian regulation, and therefore potentially impact the liver's response to environmental challenges at different circadian times. In conclusion, our study aims at (1) unsupervised identification of circadian-relevant genes and (2) exploration of potential regulatory mechanisms relevant to the transcriptional regulation of circadian genes. While doing that, we eventually proposed a promising computational framework that can predict relevant transcriptional factors and putative regulatory structure of circadian genes in a genome-wide manner, Identified circadian dynamics and biological significance. The liver expresses a diverse set of genes and previous research indicated that many hepatic genes are under direct or indirect circadian control [22] . Despite the fact that unsupervised pattern identification methods were used to identify dominant co-expressed gene sets, our results show that selected genes oscillate in a circadian manner, as shown in Figure 1 . Functional characterization reveals that enriched functions are associated with metabolism, energy production, immune system and DNA replication and repair. The liver plays an important role in the orchestration of metabolic functions such as the regulation of metabolic fuels, protein synthesis, iron and vitamin storage, as well as nutrient intake [33] . As might be expected, animals subjected to a light-dark schedule display pronounced rhythms in glycogen content, with a peak occurring late in the night, following the main period of food intake. This is also in agreement with genes identified in pattern 1, where enriched metabolic functions, especially glucose metabolism, reach their peak during the dark phase. Since the dark phase represents the active period for rats, where they are moving and eating, this increase in metabolism is indicative of both energy burn to fuel muscle usage, as well as digestive effects on the liver, and an influx of glucose and other amino acids to be processed through glycogen production and the urea cycle. Because the current study does not differentiate from fed animals and fasted animals, it is impossible to determine how much increased metabolic activity is due to demand (movement), versus supply (digestion). In addition, it has been suggested that the expression patterns of circadian rhythms in peripheral organs such as the liver are the result of delayed feedback loops of gene products from circadian regulators, suggesting that upstream regulation in addition to direct transcription factor binding can occur up to 6 hours prior to the peak of gene expression [34] .. Energy production is also time-of-day dependent. It is reported that the production of ATP in rat liver is also regulated by the circadian rhythm and is lower in the dark phase [35] . Membrane integrity and cell to cell junctions are another critical circadian function that has been previously hypothesized to be important during rest [36] , and contain significant circadian regulation [25] . Circadian pattern 2 is made up of a diverse array of genes whose functions are related to energy production and membrane structure, but are up regulated during the day. Many of these metabolic genes are associated with mitochondrial biogenesis specifically, which is a process that has been previously shown to occur during the resting phase of the circadian cycle [37] . As rats are nocturnal, their day cycle is indicative of their sleep cycle, this is in prior agreement and it suggests that mitochondrial biogenesis is a major liver function during the rest portion of their circadian rhythm. Genes that are associated with apoptosis are also found within this pattern such asbclaf1, rarb, and dapk1 and are further indication of cellular repair-cells which are too damaged to be simply repaired are killed and replaced in order to prepare the host for another day of activity.. Immune system functions, including lymphocyte proliferation, natural killer cell activity, humoral immune response, absolute and relative numbers of circulating white blood cells and their subsets, cytokine levels, and serum cortisol, are known to be entrained by circadian cues [27] . Also, it has been previously reported that TNFα secretion was significantly increased in burn vs. sham isolated splenocytes cells only when injury took place in the morning [38] further verifying that the response to injury is time-of-day dependent as a result of the circadian variability of the immune functions. This is confirmed by the selected gene set exhibiting pattern 3 includingbtg1, sfpq, fkbp5, which is primarily related to immune function, and has consistent dynamics with previous works [22, 26, 27] . However, further analysis of the dynamics observes a peak in immune function at the end of the day (ZT8~5:00 pm), which is the time rats are beginning to awaken. Over the course of the night, when the rats are active, immune function gradually decreases, until it reaches a minimum when the night ends, and the rats rest. This implies that there may be a recovery mechanism associated with sleep that allows for the rejuvenation of immune function in order to ward off infection during active periods.. The strategy of adaptive circadian clocks could be also timing of UV-sensitive cellular processes to occur at night to avoid UV-induced damage [39] . Our analysis showed that functions related to DNA replication/repair (pattern 4) achieve a maximum activity around midnight, which is consistent with previous works [28] . However, unlike the previous patterns, the circadian dynamics of these genes are not dependant on the habits of the host animal, but rather upon external conditions related to the environment. Unlike the other patterns, the genes contained in pattern 4 do not map to specific transcription factors directly, which suggests that this group is under a different form of control than the others. The expression pattern of pattern 4 is similar to the late cry1 phase of the circadian rhythm [40] , which were identified to be under the control of newly identified D boxes that overlap with E-boxes to provide delayed expression of genes which contributes to rhythmicity. Furthermore, the cry1 expression pattern, whose phase resembles cluster 4, is heavily dependent on other circadian regulators for its robustness [34] , which suggests that pattern 4 may be controlled through upstream transcription factor regulators through their gene products, in addition to direct TF binding. The circadian clock is one of the most critical biological regulators for all living entities controlling behavioral, physiological and biochemical processes [1] . Acting like a multifunctional timer with a roughly 24-h cycle [2] , the circadian clock offers fitness advantages by endowing organisms with anticipatory mechanisms predicting periodic events, as opposed to responding in a reactive way to external signals [3] . Robust circadian regulation is associated with fitness, while abnormal rhythms which are characteristic of stress are often linked with illness [4, 5] . As an example, metastatic colorectal cancer patients with severe alterations of the rest-activity circadian rhythm were predicted to have a fivefold increase in the risk of death compared to a normal rest-activity pattern [6] .. In mammals, the circadian clock is composed of autoregulated transcription-translation feedback loops whose primary elements are transcription factors (TFs) including the basic helix-loop-helix transcription factor CLOCK and the aryl hydrocarbon receptor nuclear translocatorlike BMAL1, which drive the expression of period (PER) and cryptochrome (CRY). PER and CRY form the negative limb of the feedback loop, repressing the expression of CLOCK-and BMAL1-induced transcription [7] . Evidence suggests that circadian gene expression is achieved through a transcriptional cascade in which the expression of other TFs responsible for the majority of rhythmic tissue-specific expression are primarily driven directly by the main clock transcription factor complex CLOCK: BMAL1 [8] . Recently, this hypothesis has been reinforced by experiments showing that TFs whose expression patterns exhibit daily oscillation are direct targets of the CLOCK:BMAL1 complex [9, 10] . However, those studies only explored the transcriptome with one main clock transcription factor mutated and thus the entire picture of how the system behaves or how target genes are controlled is still a question. Consequently, among the aims of our study is to gain insights into the broader transcriptional regulation of circadian rhythms.. Liver is one of the most important organs regulating the metabolic activity of the body as well as producing acute phase proteins (APP, class of proteins responding to inflammatory stressors) in response to external stress [11] . Genome-wide transcriptome-mapping studies have revealed that the fraction of diurnally regulated liver transcripts could amount up to 10%, with genes exhibiting circadian rhythms participate in various functions e.g. cell signaling, energy metabolism, amino acid, lipid and cholesterol metabolism, carbohydrate transport and metabolism, DNA replication, protein synthesis, signal transduction mechanisms, and immune response [8, [12] [13] [14] [15] . Due to the significance of the circadian periodicity and the severe outcome of disorders following its disruption, a global perspective of the transcriptional dynamics and regulatory structure of circadian genes in liver may offer a molecular framework for studies on the hepatic circadian rhythm gene regulation.. In this study, we address two critical questions including (1) whether we can identify and functionally characterize circadian-regulated genes in an unsupervised manner, and (2) if the potential transcriptional regulatory mechanisms of circadian rhythms can be hypothesized. In order to obtain the necessary samples for analysis, animals were sacrificed at 9 am, 11 am, 1 pm, 5 pm, 1 am, and 9 am (~0, 2, 4, 8, 16 , and 24 hr zeitgeber time) via exsanguinations and livers were harvested, allowing for the acquisition of transcriptional data from microarray readouts for those time points. Utilizing our previous consensus clustering [16] and techniques in promoter analysis [17] , our results actually demonstrate that clusters of co-expressed genes do vary in a circadian manner and contain functionalities consistent with previous studies including immune function, cell repair, metabolism, and DNA repair. Additional clock-controlled TFs are also predicted. Interestingly, we observed that many clock-controlled TFs relevant to the regulation of genes within a circadian pattern show similar transcriptional dynamics. Furthermore, the predicted regulatory network and regulatory motifs of circadian patterns are shown to be consistent with the biological significance of the corresponding gene set and the circadian time of the transcriptional pattern. Finally, the identified transcription factors are intimately related to innate immunity and response to infection, providing further evidence that circadian rhythms play an important role in recovery from injury. While it is understood that recent evidence points to the important role alternative processes play for driving the induction of oscillations [18] [19] [20] , the proposed approach presents a generalizable framework for assessing the regulatory structure underlying the transcriptional sources of oscillations.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Surveying Users' Perception of Academic Library Services Quality: A Case Study in Universiti Malaysia Pahang (UMP) Library Global competition among academic institutions has compelled university libraries to transform their conventional services and traditional roles to a more sophisticated, all-round service provider that can deliver their best services to their users. University libraries must cater the expansion of information sources, the high demand of users and information application as well as high student enrolments and competition among service sectors. The ability of an academic library to fulfill its user expectations will yield user satisfaction for its services. Primarily, research on assessing users' perception on the level of service quality focuses on finding ways to meet or exceed users' expectations. This study nevertheless aimed to measure students' perception and their satisfaction level of services provided by the UMP library and customized a new measuring tool on service quality and perspectives of library users based on the LibQUAL Model. A customized survey instrument was developed based on the model, consisting of 30 items measuring the level of four service dimensions and user satisfaction with an additional five items examining user satisfaction on general services. The Cronbach's alpha range is from 0.813 to 0.942, which was adequately greater than the recommended value of 0.7, thus exhibiting good internal reliability of the constructs. The result of the study reported positive values for both gap analyses in all service quality dimensions. Based on 382 respondent feedbacks, results showed that the perceived service quality level exceeded users' acceptable level on minimum service and desired service. Specifically, the users are satisfied with the services provided. Results from this study serve as a guide for effective decision making by the library in its administration and resource allocation to ensure accomplishment of the library's vision and mission.

O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Deformable

Templates

Guided

Discriminative

Models

for

Robust

Brain

MRI

Segmentation

Abstract

Automatically

segmenting

anatomical

structures

from

brain

MRI

images

is

an

important

task

in

neuroimaging.

One

major

challenge

is

to

design

and

learn

effective

image

models

accounting

for

the

large

variability

in

anatomy

and

data

acquisition

protocols.

A

deformable

template

is

a

type

of

generative

model

that

attempts

to

explicitly

match

an

input

image

with

a

template

(atlas),

and

thus,

they

are

robust

against

global

intensity

changes.

On

the

other

hand,

discriminative

models

combine

local

image

features

to

capture

complex

image

patterns.

In

this

paper,

we

propose

a

robust

brain

image

segmentation

algorithm

that

fuses

together

deformable

templates

and

informative

features.

It

takes

advantage

of

the

adaptation

capability

of

the

Data

used

in

preparation

of

this

article

were

obtained

from

the

Alzheimer's

Disease

Neuroimaging

Initiative

(ADNI)

database

(adni.loni.ucla.edu).

As

such,

the

investigators

within

the

ADNI

contributed

to

the

design

and

implementation

of

ADNI

and/or

provided

data

but

did

not

participate

in

analysis

or

writing

of

this

report.

A

complete

listing

of

ADNI

investigators

can

be

found

how

to

apply/ADNI

Acknowledgement

List.pdf

generative

model

and

the

classification

power

of

the

discriminative

models.

The

proposed

algorithm

achieves

both

robustness

and

efficiency,

and

can

be

used

to

segment

brain

MRI

images

with

large

anatomical

variations.

We

perform

an

extensive

experimental

study

on

four

datasets

of

brain

MRI

data

from

different

sources

MRI

scans

in

total)

and

observe

consistent

improvement

over

the

state-of-the-art

systems.

In

neuroimaging

studies,

brain

MRI

segmentation

is

often

a

critical

preprocessing

step.

Automated

segmentation

enables

morphometric

analysis

of

cortical

and

subcortical

structures

in

large

datasets

(Fischl

et

al.

a

scenario

in

which

manual

labeling

is

impractical.

The

automatically

segmented

regions

can

be

used

to

extract

informative

characteristics

of

structures,

such

as

volumes

and

shape.

In

the

clinic,

these

features

have

the

potential

to

be

used

to

evaluate

the

condition

of

a

subject.

Moreover,

the

identified

boundaries

between

cortical

and

subcortical

structures

can

aid

the

planning

of

brain

surgery

(Wels

et

al.

In

neuroscience

research,

statistics

derived

from

the

segmentations

of

control

and

experimental

groups

can

be

used

to

identify

structural

differences

between

them.

In

the

context

of

disease

studies,

such

differences

can

lead

to

the

identification

of

new

pathological

biomarkers.

For

instance,

the

atrophy

and

morphological

change

of

hippocampus

have

been

identified

as

important

markers

for

Alzheimer's

disease

(Jack

et

al.

Several

practical

segmentation

methods

are

widely

used

in

neuroimaging

studies,

e.g.

Caret

FreeSurfer

FSL

ITK-SNAP

SPM

and

the

segmentation

utilities

in

Slicer

(Pieper

et

al.

However,

robustness

against

variations

in

the

input

imaging

data

remains

an

open

problem.

The

main

difficulties

are

due

to:

variations

in

image

intensities

due

to

differences

in

MRI

acquisition

(hardware,

pulse

sequence,

imaging

parameters);

anatomical

variations

within

and

across

populations.

Intensity

normalization

and

image

registration

(Hou

Klein

and

et

al.

can

be

used

to

standardize

the

images

prior

to

segmentation,

but

only

to

some

extent,

since

many

of

the

variations

are

intrinsic..

In

this

paper,

we

aim

to

build

a

robust

system

that

automatically

segments

brain

MRI

volumes

into

anatomical

sub-cortical

and

cortical

structures.

We

approach

the

brain

image

segmentation

problem

from

a

statistical

modeling

perspective,

combining

a

generative

and

a

discriminative

model

with

feature

augmentation

and

adaptation.

Generative

and

discriminative

models

were

first

explored

and

compared

in

the

machine

learning

and

computer

vision

literatures

(Ng

and

Jordan

Tu

It

has

been

shown

that,

while

generative

models

outperform

discriminative

models

when

the

size

of

the

training

dataset

is

small,

the

latter

often

have

a

better

asymptotic

behavior

(Liang

and

Jordan

Works

that

attempt

to

combine

both

types

of

model

include

(Jebara

Raina

et

al.

Lasserre

et

al.

Holub

et

al.

which

show

that

integrating

the

two

types

of

models

can

be

beneficial..

Specifically,

the

goal

of

brain

MRI

segmentation

is

to

separate

the

voxels

of

an

input

scan

into

a

number

of

classes.

In

some

studies,

these

classes

correspond

to

the

three

basic

tissue

types

in

the

brain:

gray

matter

(GM),

white

matter

(WM),

and

cerebrospinal

fluid

(CSF)

(Wells

et

al.

Pham

and

Prince

Leemput

et

al.

Shattuck

et

al.

Wu

and

Chung

Bazin

and

Pham

Li

and

Fan

Other

works

have

attempted

to

produce

labels

at

the

level

of

brain

structures

(e.g.,

hippocampus,

pallidum,

putamen,

etc.)

(Fischl

et

al.

Scherrer

et

al.

Klauschen

et

al.

Bazin

and

Pham

which

is

a

more

difficult

problem

but

yields

a

richer

description

of

the

data..

To

produce

these

labels,

generative

models

typically

rely

on

two

components:

a

prior

term

that

summarizes

the

statistical

frequency

and

spatial

distribution

of

labels

and

a

likelihood

term

that

models

how

these

labels

translate

into

intensities.

Then,

Bayesian

inference

can

be

used

to

estimate

which

labels

(i.e.,

which

segmentation)

are

the

most

likely

given

the

observed

image

intensities.

The

prior

term

is

usually

in

the

form

of

a

statistical

atlas

endowed

with

deformation

model

(Ashburner

and

Friston

Leemput

et

al.

Fischl

et

al.

Other

priors

include

the

use

of

principal

component

analysis

(PCA)

to

model

whole

shape

variations

(Pohl

et

al.

Yang

et

al.

or

Markov

random

fields

(MRF)

to

impose

local

shape

constraints

(Fischl

et

al.

Woolrich

and

Behrens

Scherrer

et

al.

Caldairou

et

al.

For

the

likelihood

term,

Gaussian

distributions

or

mixtures

thereof

have

been

predominant

in

the

literature,

due

to

their

ease

of

inference

(Fischl

et

al.

Yang

et

al.

Pizer

et

al.

Corso

et

al.

Because

the

Gaussian

parameters

(means

and

variances)

are

estimated

during

the

optimization,

these

algorithms

are

robust

against

changes

in

MRI

contrast.

Moreover,

they

can

also

explicitly

model

image

artifacts

such

as

the

MRI

bias

field,

making

them

robust

against

them

as

well

(see

for

instance

Leemput

et

al.

Recently

proposed

multi-atlas

methods

such

as

Gouttard

Wu

and

Chung

Klein

et

al.

Aljabar

et

al.

Heckemann

et

al.

Wolz

et

al.

Bazin

and

Pham

and

Sabuncu

et

al.

can

also

be

seen

as

generative

models.

These

methods

are

based

on

deforming

a

number

of

labeled

templates

to

a

test

scan,

and

then

fusing

the

deformed

labels

into

a

single,

enhanced

estimate

of

the

segmentation.

As

explained

in

Sabuncu

et

al.

these

algorithms

can

be

interpreted

as

generative

models

in

which

the

intensity

and

label

of

each

voxel

are

taken

from

one

of

the

deformed

templates

as

indexed

by

a

latent,

discrete

field..

Discriminative

models

attempt

to

directly

estimate

the

label

of

each

voxel

given

the

local

appearance

of

the

image

around

it.

To

do

so,

a

number

of

features

are

computed

for

each

voxel

and

fed

to

a

classifier

that

attempts

to

infer

the

corresponding

label.

Popular

choices

of

features

include

image

intensities,

gradients,

textures

and

other

derived

local

measures

(Tu

et

al.

Choices

of

classifier

range

from

simple

rule-based

classifiers

(Li

et

al.

to

more

complicated

frameworks

such

as

support

vector

machines

(SVM

Lee

et

al.

Lao

et

al.

Akselrod-Ballin

et

al.

Zhang

et

al.

AdaBoost

(Quddus

et

al.

Morra

et

al.

and

the

increasingly

popular

random

forests

(Breiman

Yi

et

al.

Geremia

et

al.

Yaqub

et

al.

These

techniques

show

promising

results

when

segmenting

tissues,

structures,

and

even

tumors

(Bauer

et

al.

Li

and

Fan

if

the

variations

of

the

test

data

with

respect

to

the

training

data

are

relatively

small.

Unfortunately,

changes

in

MRI

contrast

due

to

differences

in

imaging

hardware

or

acquisition

protocol

considerably

reduce

the

performance

of

these

methods,

limiting

their

applicability

to

MRI

scans

that

have

not

been

acquired

the

same

way

as

the

training

dataset..

Comparing

generative

and

discriminative

models,

we

observe

that

deformable

templates

(Felzenszwalb

Pizer

et

al.

guided

by

generative

models

can

efficiently

model

anatomical

structures,

thanks

to

their

flexibility

and

adaptability.

However,

their

simplified

assumptions

on

underlying

image

statistics

(e.g.,

Gaussian

distributions)

limit

their

ability

to

deal

with

complex

intensity

patterns.

On

the

other

hand,

such

patterns

can

be

efficiently

captured

by

discriminative

models

thanks

to

their

capacity

of

fusing

together

a

large

number

of

features.

However,

as

described

above,

they

are

sensitive

to

global

intensity

changes

and

have

difficulties

to

include

spatial

information.

In

this

paper,

we

propose

combining

the

strengths

of

the

two

approaches

by

fusing

deformable

templates

(generative)

and

informative

features

(discriminative).

The

presented

approach

is

based

on

using

the

estimated

segmentation

and

the

parameters

of

the

generative

model

to

normalize

the

image

intensities

and

extract

robust,

invariant

local

features.

This

creates

an

augmented

feature

space

that

can

be

used

in

a

discriminative

framework,

effectively

fusing

the

two

types

of

model..

The

rest

of

this

paper

is

organized

as

follows.

First,

section

"Further

Related

Work"

surveys

other

attempts

of

combining

generative

and

discriminative

models

in

brain

MRI

segmentation.

Section

"Model

Description"

describes

the

proposed

segmentation

framework.

Section

"Learning

and

Using

Fusion

Models"

describes

how

to

train

the

model

and

use

it

to

segment

previously

unseen

test

cases.

A

set

of

experiments

and

their

corresponding

results

are

described

in

section

"Experiments".

Finally,

section

"Conclusion

and

Future

Work"

concludes

the

paper.

In

this

paper,

we

have

proposed

a

system

for

brain

MRI

image

segmentation

by

fusing

together

deformable

templates

(generative)

and

informative

features

(discriminative).

It

takes

the

advantages

of

the

generative

model

for

being

adaptive

and

the

discriminative

classifier

for

achieving

classification

power

on

high

dimensional

data.

This

approach

uses

a

new

way

of

combining

generative

and

discriminative

models

and

the

complementary

properties

between

them

can

be

efficiently

exerted;

the

information

extracted

from

generative

models

is

considered

as

an

additional

channel

of

features

for

training

the

discriminative

models.

The

trained

models

are

improved

in

two

ways:.

The

original

features

can

be

normalized

according

to

intrinsic

structural

statistics.

Typical

methods

to

accommodate

large

variation,

such

as

performing

histogram

matching

(Sled

et

al.

or

extracting

features

invariant

to

intensity

change

(Unay

et

al.

are

only

based

on

the

statistics

of

whole

volume

data.

The

feature

set

is

augmented

with

features

derived

from

the

estimation

by

gmDT..

A

thorough

experimental

study

on

datasets

demonstrates

the

robustness

of

our

algorithm.

Although

discriminative

models

can

perform

well

if

the

training

and

the

test

data

share

the

same

condition

of

variances,

better

performance

can

still

be

observed

by

our

method

as

our

discriminative

models

augment

the

informative

feature

set

with

generative

features.

This

advantage

leads

to

improvement

over

several

state-of-the-art

algorithms.

The

inter-dataset

and

the

longitudinal

tests

show

the

deficiency

of

discriminative

models

in

practice

and

the

necessity

of

introducing

the

generative

information.

Our

method

demonstrates

both

adaptiveness

and

precision

in

these

challenging

tests

and

outperforms

the

two

direct

models

in

region

overlaps

(Dice)

and

surface

fitness.

This

is

different

from

Wang

et

al.

that

the

first

stage

classifier

is

considered

as

a

black

box

approach.

These

advantages

also

lead

to

the

improvement

over

several

state-of-the-art

algorithms

on

standard

datasets

such

as

and

ADNI..

An

important

aspect

of

the

proposed

method

is

its

running

time,

which

is

approximately

one

hour.

Whether

the

system

is

practical

depends

on

the

application.

In

neuroimaging

studies,

in

which

research

labs

often

spend

months

collecting

the

data,

slow

running

times

are

not

a

problem.

For

instance,

our

method

is

much

faster

than

the

widely

used

FreeSurfer,

which

requires

on

average

hours

to

segment

a

single

brain

scan.

In

clinical

practice,

one

hour

might

no

be

sufficiently

fast.

The

bottleneck

of

the

algorithm

is,

as

for

many

other

brain

MRI

segmentation

methods,

the

nonlinear

registration.

However,

the

registration

can

be

dramatically

sped-up

through

parallelization..

An

aspect

of

the

framework

that

was

not

evaluated

was

its

performance

on

multispectral

data.

In

this

scenario,

the

different

data

channels

represent

images

acquired

with

different

MRI

contrast

(Prastawa

et

al.

Menze

et

al.

Yang

et

al.

Geremia

et

al.

or

even

different

modalities

(Fitzpatrick

et

al.

Chen

and

Varshney

Segmentations

on

multispectral

data

have

the

potential

to

be

more

accurate

thanks

to

the

larger

amount

of

information

present

in

the

different

channels.

In

our

framework,

generalization

to

multispectral

scenarios

is

immediate:

the

intensities

of

the

additional

channels

are

just

extra

dimensions

of

the

feature

vectors.

Another

possible

line

of

research

would

be

to

analyze

the

performance

of

the

framework

using

other

generative

or

discriminative

models.

For

instance,

it

would

be

interesting

to

assess

whether

introducing

explicit

shape,

regional,

or

context

information

in

the

generative

prior

has

a

positive

impact

of

the

segmentation.

Exploring

all

these

directions

remains

as

future

work.

To

examine

the

effectiveness

of

the

proposed

algorithm,

we

perform

a

thorough

empirical

study

using

four

MRI

brain

datasets

and

compare

our

method

against

the

state-of-the-art

systems.

We

focus

on

sub-cortical

structures

due

to

their

popularity

in

the

literature.

Besides

comparing

our

method

with

the

two

components

in

our

method,

gmDT

and

DM,

we

also

include

the

methods

developed

by

other

researchers

to

show

the

integration

can

achieve

better

performance

in

most

scenarios..

We

will

demonstrate

the

performance

of

the

integrated

method

by

four

parts:

Increased

importance

of

the

adapted

atlas

features

using

the

fusion

mechanism;

Comparison

using

the

same

dataset

for

training

and

testing

(intra-dataset).

This

scenario

is

very

suit

for

DM

and

the

proposed

method

is

comparable

to

it;

Comparison

using

different

datasets

for

training

and

testing

(inter-dataset).

This

scenario

is

favored

by

gmDT

and

our

method

achieved

better

results;

Performance

on

longitudinal

data,

which

shows

the

potential

of

our

method

to

capture

the

morphological

changes

by

the

same

subject.

Though

the

fact

that

DM

tends

to

fail

in

the

last

two

parts

is

known,

we

still

show

its

quantitative

results

for

completeness.

In

this

section,

we

elaborate

the

datasets

used

in

our

four

parts

of

experiments,

the

three

main

algorithms

to

compare,

the

pre-processing

steps

we

applied

to

the

heterogeneous

datasets,

and

the

measures

we

used

for

our

comparisons.

B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B I I O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O B I O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O B I O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Improvement of online adaptation knowledge acquisition and reuse in case-based reasoning: Application to process engineering design Despite various publications in the area during the last few years, the adaptation step is still a crucial phase for a relevant and reasonable Case Based Reasoning system. Furthermore, the online acquisition of the new adaptation knowledge is of particular interest as it enables the progressive improvement of the system while reducing the knowledge engineering effort without constraints for the expert. Therefore this paper presents a new interactive method for adaptation knowledge elicitation, acquisition and reuse, thanks to a modification of the traditional CBR cycle. Moreover to improve adaptation knowledge reuse, a test procedure is also implemented to help the user in the adaptation step and its diagnosis during adaptation failure. A study on the quality and usefulness of the new knowledge acquired is also driven.As our Knowledge Based Systems (KBS) is more focused on preliminary design, and more particularly in the field of process engineering, we need to unify in the same method two types of knowledge: contextual and general. To realize this, this article proposes the integration of the Constraint Satisfaction Problem (based on general knowledge) approach into the Case Based Reasoning (based on contextual knowledge) process to improve the case representation and the adaptation of past experiences. To highlight its capability, the proposed approach is illustrated through a case study dedicated to the design of an industrial mixing device. Preliminary design in the industrial domain is a complex and decisive phase in the design process. In economic terms, Douglas (1998) has shown that the cost of this phase represents between 10% and 20% of the entire project cost but decisions taken during this stage impact 80-90% of the total cost. In process engineering (more particularly focused in this study) the total cost saving in industrial application ranges from 20% to 60% according to Harmsen (1999) . Consequently, this design task has experienced significant improvement to new computer-aided design methods and tools whose contributions have led to rapid development taking into account quality, safety, operability, economic and environmental performances.. Among the new approaches to address this phase, Knowledge Based Systems (KBS) offer many possibilities and potentialities to support design decisions. Effective knowledge acquisition, reuse and valorization are increasingly important assets for firms in order to provide competitive advantages. Furthermore, KBS propose useful and original solutions without imposing limits to creativity as underlined by Cortes Robles et al. (2009) and Schimtt et al. (1997) . KBS intend to rapidly integrate the new scientific knowledge coming from the fast pace of technological evolutions, and to provide users with knowledge access. Indeed, in industrial practices to reduce significantly the design time and cost, it is common to start an activity relying on a previously solved experience, and then to modify and adapt it to match the new requirements. Consequently, KBS are suitable for numerous industrial activities like preliminary design because it avoids starting from a scratch since some choices are neither to do nor to question. Thus the control of the knowledge is a necessity (i) to realize (design and manage process), (ii) to decide, (iii) to create new knowledge, (iv) to preserve the knowledge capital of an organization, and (v) to impel innovation. As the environment and the activities evolve rapidly, one of our main challenges is to propose a system that includes a phase to update the knowledge stored, but also to improve the confidence and quality of the knowledge monitored during activities. Nevertheless, the elaboration of KBS is still a difficult and extensive task, while approaches have been proposed to overcome these issues. Scientific expectations are mainly in knowledge representation, modeling, reuse and maintenance because they are tremendous knowledge engineering tasks.. However, the requirements evolve and improvements of current advanced KBS are mandatory to meet the current context needs such as more agility and reactivity. Besides these needs, for design applications, KBS must also enhance their dynamic dimension to encourage rapid and flexible responses to some choices and to spread their impacts in the rest of the design process. This dynamic aspect in the adaptation phase of the KBS is a key milestone for knowledge reuse and continuous improvement of the performance of the system. Thus one goal of this study is to propose a KBS meeting these requirements of dynamic.. This paper is more focused on process engineering, which is the part of engineering that deals with processes that convert raw materials into more useful or/and valuable products through several transformations, under economic, environmental, safety, and energy constraints. A chemical process can be decomposed into individual sub-processes called unit operations: chemical reactors, separators, mixers, heat exchangers, etc. Due to the new industrial context, this discipline has undergone significant changes that strongly affect the design phase: the design and production of specialty products with high added value, introduction of numerous innovations on multi-functional units to ensure process intensification, and so on. As a result, a huge amount of new knowledge was and is still created. Optimization and heuristic approaches were the traditional methods to address the process design issues. For the former, we have a mathematical representation of the problem with the formulation of a multi-criteria objective function. However, the drawbacks of this approach are as follows: a huge computational effort, the difficulty to include uncertainties and ill-defined problem. The most important disadvantage is probably that the solution is closely dependent on the initial set of possible alternatives represented under the form of a superstructure. Consequently, it depends on the knowledge of the design team and not on the whole knowledge available. For the latter, process engineer has many heuristics for the traditional design problem, but for the new multi-functional units they are still to be created. Furthermore, as noticed by Li and Kraslawski (2004) their major limitations are their impossibility to manage the interactions between different design levels and the difficulty to handle multi-objective problems. This is due to the sequential nature of this approach.. Due to both the limitations of traditional methods and the mutation of the industrial context, there is a need to find new efficient approaches to capitalize the new implicit and explicit design knowledge. As a consequence, different KBS have emerged in process design based on methods such as Conflict Based Approaches and Case Based Reasoning (CBR). The first ones are based on modified TRIZ methods and tools to make them more easily applicable in the process engineering domain like in the studies of Li et al. (2003) and Negny et al. (2012) . These approaches are more focused on the phase of the research of new concepts. CBR is also suitable because numerous design problems become recurrent and these experiences can be easily reused. Their applications to assist in design decisions have been studied and improved for process design in the last decades as demonstrated in Negny et al., (2010) . But CBR suffers from three major drawbacks. The first two are knowledge elicitation and case adaptation. These drawbacks are commonly encountered in numerous CBR systems as proved by Chebel-Morello et al. (2013) , who explained that the time of knowledge workers dedicated to these phases is, respectively, 37.7% and 45.9% of their total time. The third drawback is more specific to the application of CBR in design, where two categories of knowledge, i.e. contextual (corresponding to past experiences) and general (corresponding to rules, constraints, etc. ), must be combined to support a wide range of design decisions on the one hand, and to improve the quality of the solution on the other. Unfortunately, CBR systems only aim to encompass contextual knowledge. Thus the challenge of this work is to raise the level of maturity of KBS for process engineering design; as a consequence, the objective of this work is twofold:. -From the process engineering design point of view, the aim is to improve the current CBR systems, which are mainly focused on the system to design (unit operation or the process) but not on design method but also to include the dynamic aspect. Moreover, the proposition concerns an approach that combines the two kinds of knowledge, previously cited. -From the knowledge management point of view, the goal is to minimize the knowledge elicitation effort during the adaptation phase. Another important objective is to evaluate the quality and usefulness of the acquired adaptation knowledge in order to increase the skills of the CBR system.. Concerning the first point, among Artificial Intelligence (AI) approaches to capitalize and reuse knowledge Constraint Satisfaction Problem (CSP) has also been successfully applied in various activities and more particularly in design applications. CBR and CSP rely, respectively, on contextual and general knowledge. Due to this complementarity, this paper proposes coupling these two approaches, to address the adaptation problem in CBR. The main motivation is to achieve a synergy that produces a better knowledge exchange, capitalization and reuse.. For the dynamic aspect, several issues must be solved, with different approaches proposed in the literature. Karray et al. (2014) suggested using a trace based system whose goal is to extract new knowledge rules about transitions and activities in the maintenance process. Traces are considered as knowledge containers. This interesting approach is well suited for very dynamic and reactive system as in the maintenance field, but in the domain of design the time constants are lower. In another approach Craw (2009) transforms the traditional CBR into an agile one. In accordance with this work and with the work of Cordier et al. (2007) , the traditional CBR cycle is modified to introduce an interactive process with the expert in the reuse step in order to create an online knowledge acquisition, but also to add agility to our KBS.. Concerning the second point to develop our adaptation method, we were interested in the different approaches proposed in the literature. Adaptation in CBR has been widely studied in the 1990's (Smyth and Keane, 1996; Parvis, 1995, 1997; Voss, 1997; Hanks and Weld, 1995; Craw et al., 2006) , but no general models have emerged. Since then, this CBR step has received little attention as confirmed by the analysis of the research theme in the CBR literature realized by Greene et al. (2008) . However, the recent evolutions on differential adaptation proposed by Fuchs et al. (2014) seem to give promising ways for an operational formalization of adaptation, while it is currently limited to numerical problems. The main idea is that small variations between problems are related to variations between solutions as in differential calculus. More generally, Chebel-Morello et al. (2013) have classified the main strategies to deal with the adaptation problem into three categories: (i) Adaptation Knowledge Acquisition that aims to obtain adaptation knowledge and to model them through general methods and techniques; Lieber et al. (2004) and Lieber (2007) provide a comparison and an overview on this strategy. (ii) Specific adaptation strategies depending on the application domain or on the case study. (iii) General adaptation methods independent of the application. For instance the method based on the dependency between problem and solution descriptors is the most advanced and used: Fuchs et al. (2000) for computer configuration, Chebel-Morello et al. (2013) for diagnostic. As one motivation of this paper is to improve the efficiency and accuracy of a CBR system for process engineering design, the adaptation method proposed is based on adaptation knowledge acquisition strategy. Indeed, specific adaptation strategy could be deployed for each kind of unit operation (reactor, distillation column, heat exchanger, etc. ) but we lose in generality and the creation of a specific method for each type of unit operation would be a tremendous effort. The method based on dependency aims to establish the direction and the strength of the relationships between problem and solution features. Unfortunately, in process engineering design the dependencies are impossible to establish immutably because they depend on many factors, e.g. the operating conditions, the occurring phenomenon that can be neglected at a scale and be overriding at another scale. Furthermore, the strengths of the links are difficult to establish due to the strong linearity of the phenomenon that occurs in a chemical process, their dependences on the operating conditions and on the chemical components in the mixture.. The remainder of this paper is organized as follows. In the next section, the backgrounds of CBR, adaptation knowledge acquisition and the knowledge elicitation issues are described. In Section 3 we discuss the interest for combining CBR and CSP and propose a framework to capture expert knowledge. Section 4 describes the methodology and highlights the tool capabilities through a case study dealing with the configuration of an industrial chemical mixer. Before drawing conclusions, in Section 5 some tests are realized to quantify the reusability of the adaptation knowledge acquired. In this article, we first presented a new methodology for the adaptation phase in CBR. This methodology is more focused on design and more particularly in the process engineering domain, but some parts of this methodology can be easily transferred to other domains and activities. The preliminary process design is a complex domain where past experiences are often reused for solving new problems. Among the possible methods for knowledge capitalization and reuse, CBR is an effective approach that finds a solution by retrieving past experiences and CSP methods have been applied in the resolution of complex combinatorial problems in the domains of configuration and design. The primary motivations for combining these two approaches in process design are:. To provide an excellent representation for a problem with the CSP formalism that facilitates its treatment by the CBR, including retrieval, adaptation and maintenance phases; meanwhile, it enables combining of two categories of knowledge: contextual and general.. To use the powerful CSP algorithms for solving very complex problems.. To increase the efficiency of the CSP models by reusing the past experiences especially in the cases where modeling is difficult or almost impossible to obtain.. The major contributions of this article, we believe, are (i) the modification of the adaptation loop which adds interactivity in CBR and permits one to acquire online expert knowledge to reduce the knowledge engineering effort by a timely solicitation, (ii) the decomposition of the adaption knowledge into elementary knowledge containers to facilitate knowledge elicitation, reuse and maintenance, (iii) a decision support system to search and identify the relevant adaptation knowledge thanks to the proposition of evaluation criteria, and (iv) a diagnosis process to identify the root cause of failures. Some tests were also performed to evaluate the quality and the usefulness of the adaptation knowledge acquired. Indeed in numerous CBR systems, new knowledge is acquired to improve the skills of the system but nothing was done to establish the relevance of this new added knowledge.. In the future, we will pay attention to these five main perspectives:. The similarity measurement can be improved by introducing the AM retrieval in the calculation as explained in Section 4.4. This can be done thanks to the proposal of a similarity measure relying on a semantic analysis.. The way the adaptation knowledge would be managed and maintained is still an open question. Even if case base maintenance requirements were anticipated and introduced in the methodology, this step is perfectible. For instance, it could be improved with machine learning approach in order to extract new knowledge from the adaptation methods encompassed in the case base.. Currently the reuse of the adaptation knowledge is done at the level of the AM. We can imagine going further, rather than using existing AM we can build a specific AM to the problem addressed. As at the deeper level the knowledge container is the AO, we could extract AO from different AM and thus elaborate a more targeted AM. The difficulty is not the research and extraction of suitable AO but the construction of a coherent AM. Indeed, the grouping of AO belonging to different AM may lead to incoherent and unusable AM and would generate adaptation failure. The way to ensure model coherence is not obvious and needs further development.. Another perspective concerns the failure. As explained before, when an adaption failure occurs the problem is stored in a dedicated case base. The goal would be to exploit these failures to raise the issue at the stage of research and to build a link with Conflict Based Approach tools. We can also include this information at the early steps of the CBR, for example at step 1a or 2 of our cycle to alert the user that the problem cannot be solved with the current knowledge stored in the base.. The phase of tests can be continued for instance by increasing the number of problems treated in the series or by integrating the level of expertise of the user in the parameters to test. Table A1 . According to Vareilles et al. (2012) , only the last two possibilities really deal with knowledge processing. The other ones were more focused on knowledge validation or completeness, which is out of the scope of our study. Like in Lopez (2003), in our proposal, CBR is used to find the appropriate CSP model which is then adapted. Purvis (1998) has demonstrated that CSP techniques help formalize and treat the process of adaptation but also to systematize and give flexibility to the CBR. This way to proceed is particularly suitable when we have domain-specific CSP models. However, with the retained combination of both approaches, there are some important drawbacks:. In the current approaches, it is always assumed that the adaptation is a human process and not a processed one. But to better assist designers, this phase must rely on knowledge processing.. The knowledge enrichment, thanks to the adaptation phase, is not correctly exploited. Indeed, after the model modifications, we must be able to distinguish between specific knowledge only valid for the problem studied and the general knowledge. Of course the former must be stored for a future reuse and to improve the CBR system skills. As they are specific, they are not candidate to be included in the original CSP model. Inversely, the general knowledge could be included in the model to improve it.. Successful adaptations are stored as new case in the base, leading to a case base size that increases sharply. Even if it is mandatory to store the new modifications, it would be more valuable to find an alternative that requires a lower storage space while allowing easy maintenance.. More interaction is needed to add agility. During adaptation, the system should offer to the designer the opportunity to see and evaluate the consequences of his choices and more particularly the progressive reduction of the solutions space.. The expert is the only person who interacts with the knowledge model for knowledge maintenance and evolution (CSP model evolution). But, most of the time, it is a time consuming human process, which needs to be simplified and partially automated. Consequently, the proposed approach must take into account the requirements on knowledge acquisition and storing while remaining compatible with the next step of the CBR cycle.. For these reasons the integration of another knowledge acquisition paradigm detailed in Section 4 is proposed. It would allow improving the interaction with the expert and the confidence in the knowledge stored in the system.. The proposed methodology represents the foundation for an interactive interface, which offers a good initial solution method based on the already solved cases. In this work several evolutions were introduced to present a general formalization of the adaptation phase in the CBR cycle:. -The coupling of general and contextual knowledge, thanks to the use of CSP for the description of case in the CBR. However, Vareilles et al. (2012) have proposed using them not successively but iteratively to reach a deeper coupling. We have not implemented such an iterative approach because it cannot afford to reach the other objectives of our KBS, i.e. adaptation knowledge capitalization, and knowledge maintenance. In addition to the sustainability of knowledge, the proposed approach makes it easier to manage the consistency between the different design choices. The major drawback of our approach is that it is not completely generic because it relies on the assumption that the problem must be formulated with a CSP model. Furthermore even if the CSP model can be updated or it avoids restarting to model the problem from scratch, the problem of the initial model with its tremendous tasks for knowledge extraction and formalization remains unsolved. -With the implementation of the interaction loop, the online expert knowledge acquisition reduces the knowledge engineering effort compared to offline processes through a guided process to diagnose failure, to correct or ameliorate directly the proposed solution. It also improves the relevance and confidence of the knowledge stored. Moreover, this loop provides interactivity to the system, which is not present in the adaptation methods proposed in the literature. After each modification, the user can run the model and can assess the consequences of his technological choices on the possible solutions or simply on the definition domains of the variables. It offers the ability to quickly detect constraints that may lead to design failures. -Supporting adaptation with a CSP approach fits very well with CBR language and with design activities because specific requirements on a problem can be easily integrated to a generic model. Unfortunately, representing a case as a CSP is not always possible for all the industrial activities. Consequently, the whole method cannot be used but some parts could be easily transferred to other adaptation methods, for instance the loop and the AO. Indeed, the only assumption with AO is that the adaption knowledge can be decomposed into a finite set of elementary operators, which is often the case regardless of the application scope of the CBR. It just remains to make the AO description compatible with case representation and with the adaptation knowledge acquisition. Moreover, the advantage to decompose the adaptation method into successive single adaptation operators is that it allows one to have a detailed and deep knowledge acquisition. When an adaptation failure occurs, it also facilitates the detection between a faulty operator or the lack of adaption knowledge in the base. -From the perspective to facilitate adaptation knowledge formalization and reuse, adaptation methods are linked to cases stored in the case base. In consequence, adaptation is not considered as a human process but rather as a process that can be automatized. But the identification of the most relevant adaptation method remains awkward despite criteria to support the choice. Indeed the criteria focus on the performances of the adapted model and not on its claimed reasons of existence or its objectives. -The approach with adaptation operators could ameliorate the maintenance step. Currently maintenance is often considered as a human process. With our approach it can be semiautomatized. Indeed as we distinguish specific and general operators in the adaptation method, only the latter are considered in the maintenance step. Then when an adaptation operator is common to several adaptation methods, it could be extracted and proposed to the expert for validation and to eventually integrate it in the initial CSP model. The performance model indicators could also be used to maintain the CSP model. Another important component of the maintenance appears when a number of adaptation methods are connected to one case. In this configuration, the initial CSP no longer meets the users requirements; thus the CSP model must be improved. Another question arises: should we divide the case in several cases or not? All these points are open questions because the maintenance step is still a perspective of this work. In this section, the whole methodology is presented. It gathers all the elements detailed and discussed in the two previous sections: case representation, the AM, the AO, the created interactive loop, and the test procedure for knowledge reuse. It also encompasses all the proposals and new contributions to provide an answer to the drawbacks identified previously. Fig. 4 illustrates the adaptation process integrated in the new CBR cycle. The following sections describe and exemplify the various steps.

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Stabilized Finite Element Method for the Radial Dirac Equation Abstract. A challenging difficulty in solving the radial Dirac eigenvalue problem numerically is the presence of spurious (unphysical) eigenvalues among the correct ones that are neither related to mathematical interpretations nor to physical explanations. Many attempts have been made and several numerical methods have been applied to solve the problem using finite element method (FEM), finite difference method (FDM), or other numerical schemes. Unfortunately most of these attempts failed to overcome the difficulty. As a FEM approach, this work can be regarded as a first promising scheme to solve the spuriousity problem completely. Our approach is based on an appropriate choice of trial and test functional spaces. We develop a Streamline Upwind Petrov-Galerkin method (SUPG) to the equation and derive an explicit stability parameter.Introduction.

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