An ice machine adapted into an autonomous photovoltaic system without batteries using a variable-speed drive

The adaptation of a commercially available ice machine for autonomous photovoltaic operation without batteries is presented. In this adaptation a 1040 W(p) photovoltaic array directly feeds a variable-speed drive and a 24 V(dc) source. The drive runs an induction motor coupled by belt-and-pulley to an open reciprocating compressor, while the dc source supplies a solenoid valve and the control electronics. Motor speed and refrigerant evaporation pressure are set aiming at continuously matching system power demand to photovoltaic power availability. The resulting system is a simple integration of robust, standard, readily available parts. It produces 27 kg of ice in a clear-sky day and has ice production costs around US$0.30/kg. Although a few machine features might be specific to Brazil, its technical and economical guidelines are applicable elsewhere. Copyright (C); 2010 John Wiley & Sons, Ltd.

Heuristic methods for the single machine scheduling problem with different ready times and a common due date

The single machine scheduling problem with a common due date and non-identical ready times for the jobs is examined in this work. Performance is measured by the minimization of the weighted sum of earliness and tardiness penalties of the jobs. Since this problem is NP-hard, the application of constructive heuristics that exploit specific characteristics of the problem to improve their performance is investigated. The proposed approaches are examined through a computational comparative study on a set of 280 benchmark test problems with up to 1000 jobs.

Debris and smear removal in flattened root canals after use of different irrigant agitation protocols

Scanning electron microscopy (SEM) can be used to analyze the presence of debris and smear layer on the internal walls of root canal. This study evaluated the debris and smear removal in flattened root canals using SEM after use of different irrigant agitation protocols. Fifty mandibular incisors were distributed into five groups (n = 10) according to the irrigant agitation protocol used during chemomechanical preparation: conventional syringe irrigation with NaviTip needle (no activation), active scrubbing of irrigant with brush-covered NaviTip FX needle, manual dynamic irrigation, continuous passive ultrasonic irrigation, and apical negative pressure irrigation (EndoVac system). Canals were irrigated with 5 mL of 2.5% NaOCl at each change of instrument and received a final flush with 17% EDTA for 1 min. After instrumentation, the roots were split longitudinally and SEM micrographs at x 100 and x 1,000 were taken to evaluate the amount of debris and smear layer, respectively, in each third. Data were analyzed by KruskalWallis and Dunn's post-hoc tests (a = 5%). Manual dynamic activation left significantly (p < 0.05) more debris inside the canals than the other protocols, while ultrasonic irrigation and EndoVac were the most effective (p < 0.05) for debris removal. Regarding the removal of smear layer, there was no statistically significant difference (p > 0.05) either among the irrigant agitation protocols or between the protocolcanal third interactions. Although none of the irrigant agitation protocols completely removed debris and smear layer from flattened root canals, the machine-assisted agitation systems (ultrasound and EndoVac) removed more debris than the manual techniques. Microsc. Res. Tech. 75:781790, 2012. (C) 2011 Wiley Periodicals, Inc.

Safety illusion and error trap in a collectively-operated machine accident

Workplace accidents involving machines are relevant for their magnitude and their impacts on worker health. Despite consolidated critical statements, explanation centered on errors of operators remains predominant with industry professionals, hampering preventive measures and the improvement of production-system reliability. Several initiatives were adopted by enforcement agencies in partnership with universities to stimulate production and diffusion of analysis methodologies with a systemic approach. Starting from one accident case that occurred with a worker who operated a brake-clutch type mechanical press, the article explores cognitive aspects and the existence of traps in the operation of this machine. It deals with a large-sized press that, despite being endowed with a light curtain in areas of access to the pressing zone, did not meet legal requirements. The safety devices gave rise to an illusion of safety, permitting activation of the machine when a worker was still found within the operational zone. Preventive interventions must stimulate the tailoring of systems to the characteristics of workers, minimizing the creation of traps and encouraging safety policies and practices that replace judgments of behaviors that participate in accidents by analyses of reasons that lead workers to act in that manner.

Using Machine Learning Classifiers to Assist Healthcare-Related Decisions: Classification of Electronic Patient Records

Surveillance Levels (SLs) are categories for medical patients (used in Brazil) that represent different types of medical recommendations. SLs are defined according to risk factors and the medical and developmental history of patients. Each SL is associated with specific educational and clinical measures. The objective of the present paper was to verify computer-aided, automatic assignment of SLs. The present paper proposes a computer-aided approach for automatic recommendation of SLs. The approach is based on the classification of information from patient electronic records. For this purpose, a software architecture composed of three layers was developed. The architecture is formed by a classification layer that includes a linguistic module and machine learning classification modules. The classification layer allows for the use of different classification methods, including the use of preprocessed, normalized language data drawn from the linguistic module. We report the verification and validation of the software architecture in a Brazilian pediatric healthcare institution. The results indicate that selection of attributes can have a great effect on the performance of the system. Nonetheless, our automatic recommendation of surveillance level can still benefit from improvements in processing procedures when the linguistic module is applied prior to classification. Results from our efforts can be applied to different types of medical systems. The results of systems supported by the framework presented in this paper may be used by healthcare and governmental institutions to improve healthcare services in terms of establishing preventive measures and alerting authorities about the possibility of an epidemic.

Can neuroimaging be used as a support to diagnosis of borderline personality disorder? An approach based on computational neuroanatomy and machine learning

Several recent studies in literature have identified brain morphological alterations associated to Borderline Personality Disorder (BPD) patients. These findings are reported by studies based on voxel-based-morphometry analysis of structural MRI data, comparing mean gray-matter concentration between groups of BPD patients and healthy controls. On the other hand, mean differences between groups are not informative about the discriminative value of neuroimaging data to predict the group of individual subjects. In this paper, we go beyond mean differences analyses, and explore to what extent individual BPD patients can be differentiated from controls (25 subjects in each group), using a combination of automated-morphometric tools for regional cortical thickness/volumetric estimation and Support Vector Machine classifier. The approach included a feature selection step in order to identify the regions containing most discriminative information. The accuracy of this classifier was evaluated using the leave-one-subject-out procedure. The brain regions indicated as containing relevant information to discriminate groups were the orbitofrontal, rostral anterior cingulate, posterior cingulate, middle temporal cortices, among others. These areas, which are distinctively involved in emotional and affect regulation of BPD patients, were the most informative regions to achieve both sensitivity and specificity values of 80% in SVM classification. The findings suggest that this new methodology can add clinical and potential diagnostic value to neuroimaging of psychiatric disorders. (C) 2012 Elsevier Ltd. All rights reserved.

Abrasiveness of conventional and specific denture-cleansing dentifrices

This study analyzed the weight loss and surface roughness caused in Plexiglass specimens by conventional dentifrices (Sorriso, Colgate and Close Up) and specific dentifrices used for cleaning of dentures (Corega and Dentu Creme). Plexiglass specimens were divided into 6 groups (n=6) including: a control (distilled water - DW) and experimental groups. Brushing was performed in a toothbrushing machine with a soft brush and a dentifrice suspension and DW according to different brushing times (50, 100, 200 and 250 min -18,000, 36,000, 72,000 and 90,000 cycles, respectively, calculated to correspond to 1, 2, 4 and 5 years of regular brushing). The results of weight loss and surface roughness were analyzed by ANOVA and Tukey’s test at 5% significance level. In all tested times, the effect of DW was insignificant. Dentifrices differed significantly from DW in the initial period. Corega dentifrice caused greater mass loss in all studied times, followed by Close Up. Dentifrices resulted in a surface roughness similar to the DW at 50 min. In the other times, Sorriso, Colgate and Corega caused more surface roughness than DW. In conclusion, specific dentifrices caused larger mass loss and lower surface roughness as conventional dentifrice.