Development and validation of an instrument to measure perceived service quality of an academic library in Costa Rica

Service management involves the responsibility of ensuring the effectiveness of business operations in terms of meeting customer requirements. A good service is judged not only by meeting customer requirements but also by the way the customers perceive and interpret the received service. To know how effective the service is, the quality of the service can be measured. For this aim it is necessary to target actual service elements to improve and to weigh the evaluation of service elements relative to the importance that customers place on them. The literature shows that service quality outcome and measurement are dependent on the type of service setting, situation, needs and other factors. General instruments to measure perceived service were developed in the context of main dimensions proposed by general service quality models. However, it is important to develop new instruments which are directly targeted to the context reality. Based upon conceptual models the goal of this study is to target actual service elements that customers from an academic library in Costa Rica deem important. Using the identified elements the dimensions of service quality are developed and validated to measure user perceived service. It was discussed how appropriable knowledge on quality service can spurred the innovative capacity to improve library services

Profile monitoring via sensor fusion: The use of PCA methods for multi-channel data

Continuous advances of sensor technology and real-time computational capability are leading to data-rich environments to improve industrial automation and machine intelligence. When multiple signals are acquired from different sources (i.e. multi-channel signal data), two main issues must be faced: (i) the reduction of data dimensionality to make the overall signal analysis system efficient and actually applicable in industrial environments, and (ii) the fusion of all the sensor outputs to achieve a better comprehension of the process. In this frame, multi-way principal component analysis (PCA) represents a multivariate technique to perform both the tasks. The paper investigates two main multi-way extensions of the traditional PCA to deal with multi-channel signals, one based on unfolding the original data-set, and one based on multi-linear analysis of data in their tensorial form. The approaches proposed for data modelling are combined with appropriate control charting to achieve multi-channel profile data monitoring. The developed methodologies are demonstrated with both simulated and real data. The real data come from an industrial sensor fusion application in waterjet cutting, where different signals are monitored to detect faults affecting the most critical machine components

Hierarchical metamodeling: Cross validation and predictive uncertainty

At Esaform 2013 a hierarchical metamodeling approach had been presented, able to com- bine the results of numerical simulations and physical experiments into a unique response surface, which is a "fusion'' of both data sets. The method had been presented with respect to the structural optimization of a steel tube, filled with an aluminium foam, intended as an anti-intrusion bar. The prediction yielded by a conventional way of metamodeling the results of FEM simulations can be considered trustworthy only if the accuracy of numerical models have been thoroughly tested and the simulation parameters have been sufficiently calibrated. On the contrary, the main advantage of a hierarchical metamodel is to yield a reliable prediction of a response variable to be optimized, even in the presence of non-completely calibrated or accurate FEM models. In order to demonstrate these statements, in this paper the authors wish to compare the prediction ability of a "fusion'' metamodel based on under-calibrated simulations, with a conventional approach based on calibratedFEMresults. Both metamodels will be cross validated with a "leave-one-out'' technique, i.e. by excluding one ex- perimental observation at a time and assessing the predictive ability of the model. Furthermore, the paper will demonstrate how the hierarchical metamodel is able to provide not only an average esti- mated value for each excluded experimental observation, but also an estimation of uncertainty of the prediction of the average value

Improved Signal Characterization via Empirical Mode Decomposition to Enhance in-line Quality Monitoring

The machine tool industry is facing the need to increase the sensorization of production systems to meet evolving market demands. This leads to the increasing interest for in-process monitoring tools that allow a fast detection of faults and unnatural process behaviours during the process itself. Nevertheless, the analysis of sensor signals implies several challenges. One major challenge consists of the complexity of signal patterns, which often exhibit a multiscale content, i.e., a superimposition of both stationary and non-stationary fluctuations on different time-frequency levels. Among time-frequency techniques, Empirical Mode Decomposition (EMD) is a powerful method to decompose any signal into its embedded oscillatory modes in a fully data-driven way, without any ex-ante basis selection. Because of this, it might be used effectively for automated monitoring and diagnosis of manufacturing processes. Unfortunately, it usually yields an over-decomposition, with single oscillation modes that can be split into more than one scale (this effect is also known as “mode mixing”). The literature lacks effective strategies to automatically synthetize the decomposition into a minimal number of physically relevant and interpretable components. This paper proposes a novel approach to achieve a synthetic decomposition of complex signals through the EMD procedure. A new criterion is proposed to group together multiple components associated to a common time-frequency pattern, aimed at summarizing the information content into a minimal number of modes, which may be easier to interpret. A real case study in waterjet cutting is presented, to demonstrate the benefits and the critical issues of the proposed approach

Geometrical quality improvement of high aspect ratio micromilled pins

Mechanical micromachining is a reference process for producing 3D complex microparts and specifically tools for other processes as molds for micro injection molding and males for microextrus ion. High aspect ratio features as bars , ribs , pins , etc. are very common in these cases and their quality strongly affects the final plastic part quality. This paper focuses on high aspect ratio steel pins, since they are one of the most challenging features to be manufactured on microextrusion males. The pin geometrical quality has been defined according to the standards and a suitable measurement procedure has been set up with the aim to study the micromilling process parameters effects on the most representative pin quality characteristics . The statistical analysis results point out some criteria for selecting the best process parameters

454 pyrosequencing assessment of biodegradative bacteria from thermal hydrolysis processes

Anaerobic treatment process is a cost-effective method for treating organic wastes, since the biogas formed can be used for heat/electricity production and the digester residues can be recycled for other applications. An innovative use of the digestate could be as biodegradative and methanogenic inoculum for the stimulation of methane production in gas-producing or depleted wells. The microbial communities involved in the biodegradation of petrochemical waste are similar to the indigenous microorganisms typically found in unconventional basins. These communities also follow the same cascade of reactions: from the initial breakdown of complex molecules to the production of intermediate compounds used by methanogens. This study carried out a culture-independent assessment of the bacterial community composition of a digestate from the Bran Sands Advanced Digestion Facility (Middleborough, UK) and compared the results with the microbial populations found in unconventional gas basins. The 454 pyrosequencing analyses revealed a bacterial community dominated by Thermotogae, Bacteroidia, Clostridia and Synergistia, which are typically found in unconventional gas systems. The classification of nucleotide sequence reads and assembled contigs revealed a genetic profile characteristic for an anaerobic microbial consortium running fermentative metabolic pathways. The assignment of numerous sequences was related to hydrocarbon decomposition and digestion of cellulosic material, which indicates that the bacterial community is engaged in hydrolysis of plant-derived material. The bacterial community composition suggest that the effluent of the digester can be used as a biodegradative inoculum for the stimulation of methane generation in unconventional wells, where events of microbial methanogenesis have been previously observed

Personal UV exposure on a ski-field at an alpine site

International audienceMountain sites experience enhanced ambient UV radiation levels due to the concurrent effects of shorter radiation path-length, low aerosol load and high reflectivity of the snow surfaces. This study was encouraged by the possibility to collect data of personal UV exposure in the mountainous areas of Italy, for the first time. Personal UV exposure (expressed in terms of Exposure Ratio, ER) of two groups of volunteers (ski instructors and skiers) at the Alpine site of La Thuile (Valle d'Aosta region, Italy) was assessed using polysulphone dosimetry which was tested in a mountainous snow-covered environment. In addition measurements of biological markers of individual response to UV exposure such as skin colorimetric parameters were carried out. It was found that snow and altitude of study site affect calibration curves of polysulphone dosimeters in comparison to a situation without snow. The median ER, taking into account the whole sample, is 0.60 in winter, with a range of 0.29 to 1.46, and 1.02 in spring, ranging from 0.46 to 1.72. There are no differences in exposures across skiers and instructors in spring while in winter skiers experience lower values. UV exposures are not sensitive to the use of sunscreen across instructor/skier group by day or by seasons or by photo-type. With regard to colorimetric parameters, the main result was that both skiers and instructors had on average significantly lower values of L* and b* after exposure i.e. becoming darker but the inappropriate sunscreen use did not reveal any changes in skin colorimetric parameters except in one spring day. In conclusions UV intensities on the ski-fields are often significantly higher than those on horizontal surfaces. Given the high levels of exposure observed in the present study, dedicated public heath messages on the correct sunscreen use should be adopted

From Profile to Surface Monitoring: SPC for Cylindrical Surfaces Via Gaussian Processes

Quality of machined products is often related to the shapes of surfaces that are constrained by geometric tolerances. In this case, statistical quality monitoring should be used to quickly detect unwanted deviations from the nominal pattern. The majority of the literature has focused on statistical profile monitoring, while there is little research on surface monitoring. This paper faces the challenging task of moving from profile to surface monitoring. To this aim, different parametric approaches and control-charting procedures are presented and compared with reference to a real case study dealing with cylindrical surfaces obtained by lathe turning. In particular, a novel method presented in this paper consists of modeling the manufactured surface via Gaussian processes models and monitoring the deviations of the actual surface from the target pattern estimated in phase I. Regardless of the specific case study in this paper, the proposed approach is general and can be extended to deal with different kinds of surfaces or profiles

Improving blood pressure control through pharmacist interventions: a meta-analysis of randomized controlled trials.

BACKGROUND: Control of blood pressure (BP) remains a major challenge in primary care. Innovative interventions to improve BP control are therefore needed. By updating and combining data from 2 previous systematic reviews, we assess the effect of pharmacist interventions on BP and identify potential determinants of heterogeneity. METHODS AND RESULTS: Randomized controlled trials (RCTs) assessing the effect of pharmacist interventions on BP among outpatients with or without diabetes were identified from MEDLINE, EMBASE, CINAHL, and CENTRAL databases. Weighted mean differences in BP were estimated using random effect models. Prediction intervals (PI) were computed to better express uncertainties in the effect estimates. Thirty-nine RCTs were included with 14 224 patients. Pharmacist interventions mainly included patient education, feedback to physician, and medication management. Compared with usual care, pharmacist interventions showed greater reduction in systolic BP (-7.6 mm Hg, 95% CI: -9.0 to -6.3; I(2)=67%) and diastolic BP (-3.9 mm Hg, 95% CI: -5.1 to -2.8; I(2)=83%). The 95% PI ranged from -13.9 to -1.4 mm Hg for systolic BP and from -9.9 to +2.0 mm Hg for diastolic BP. The effect tended to be larger if the intervention was led by the pharmacist and was done at least monthly. CONCLUSIONS: Pharmacist interventions - alone or in collaboration with other healthcare professionals - improved BP management. Nevertheless, pharmacist interventions had differential effects on BP, from very large to modest or no effect; and determinants of heterogeneity could not be identified. Determining the most efficient, cost-effective, and least time-consuming intervention should be addressed with further research