Cone-beam computed tomography analysis of the apical third of curved roots after mechanical preparation with different automated systems

The present study evaluated by cone-beam computed tomography (CBCT) the apical canal transportation and centralizing ability of different automated systems after root canal preparation. The mesiobuccal canals of maxillary first molars (n=10 per group) were prepared with: GI - reciprocating system with K-Flexofile; GII - reciprocating system with NiTiFlex files; GIII - rotary system with K3 instruments; GIV - rotary system with RaCe instruments. CBCT scans were taken before and after biomechanical preparation up to a #40.02 diameter. Canal transportation was determined by measuring the smallest distance between the inner canal walls and the mesial and distal sides of the root. The centralization ability corresponded to the difference between the measurements from transportation evaluation, using the linear voxel to voxel method of analysis. The mean transportation was 0.06 ± 0.14 mm, with a tendency to deviate to the mesial side of the root (n=22), with no statistically significant difference among the groups (p=0.4153). The mean centralization index was 0.15 ± 0.65 also without statistically significant difference among the groups (p=0.0881). It may be concluded that apical canal transportation and centralization ability were not influenced by the type of mechanical movement and instruments used.

Discrete Event Systems based Design Patterns for Diagnosability Analysis of Automated Manufacturing Systems

The main goal of this thesis is to facilitate the process of industrial automated systems development applying formal methods to ensure the reliability of systems. A new formulation of distributed diagnosability problem in terms of Discrete Event Systems theory and automata framework is presented, which is then used to enforce the desired property of the system, rather then just verifying it. This approach tackles the state explosion problem with modeling patterns and new algorithms, aimed for verification of diagnosability property in the context of the distributed diagnosability problem. The concepts are validated with a newly developed software tool.

Automated versus non-automated weaning for reducing the duration of mechanical ventilation for critically ill adults and children (Review)

Abstract
Background: Automated closed loop systems may improve adaptation of the mechanical support to a patient's ventilatory needs and
facilitate systematic and early recognition of their ability to breathe spontaneously and the potential for discontinuation of
ventilation.

Objectives: To compare the duration of weaning from mechanical ventilation for critically ill ventilated adults and children when managed
with automated closed loop systems versus non-automated strategies. Secondary objectives were to determine differences
in duration of ventilation, intensive care unit (ICU) and hospital length of stay (LOS), mortality, and adverse events.

Search methods: We searched the Cochrane Central Register of Controlled Trials (CENTRAL) (The Cochrane Library 2011, Issue 2); MEDLINE (OvidSP) (1948 to August 2011); EMBASE (OvidSP) (1980 to August 2011); CINAHL (EBSCOhost) (1982 to August 2011); and the Latin American and Caribbean Health Sciences Literature (LILACS). In addition we received and reviewed auto-alerts for our search strategy in MEDLINE, EMBASE, and CINAHL up to August 2012. Relevant published reviews were sought using the Database of Abstracts of Reviews of Effects (DARE) and the Health Technology Assessment Database (HTA Database). We also searched the Web of Science Proceedings; conference proceedings; trial registration websites; and reference lists of relevant articles.

Selection criteria: We included randomized controlled trials comparing automated closed loop ventilator applications to non-automated weaning
strategies including non-protocolized usual care and protocolized weaning in patients over four weeks of age receiving invasive mechanical ventilation in an intensive care unit (ICU).

Data collection and analysis: Two authors independently extracted study data and assessed risk of bias. We combined data into forest plots using random-effects modelling. Subgroup and sensitivity analyses were conducted according to a priori criteria.

Main results: Pooled data from 15 eligible trials (14 adult, one paediatric) totalling 1173 participants (1143 adults, 30 children) indicated that automated closed loop systems reduced the geometric mean duration of weaning by 32% (95% CI 19% to 46%, P =0.002), however heterogeneity was substantial (I2 = 89%, P < 0.00001). Reduced weaning duration was found with mixed or
medical ICU populations (43%, 95% CI 8% to 65%, P = 0.02) and Smartcare/PS™ (31%, 95% CI 7% to 49%, P = 0.02) but not in surgical populations or using other systems. Automated closed loop systems reduced the duration of ventilation (17%, 95% CI 8% to 26%) and ICU length of stay (LOS) (11%, 95% CI 0% to 21%). There was no difference in mortality rates or hospital LOS. Overall the quality of evidence was high with the majority of trials rated as low risk.

Authors' conclusions: Automated closed loop systems may result in reduced duration of weaning, ventilation, and ICU stay. Reductions are more
likely to occur in mixed or medical ICU populations. Due to the lack of, or limited, evidence on automated systems other than Smartcare/PS™ and Adaptive Support Ventilation no conclusions can be drawn regarding their influence on these outcomes. Due to substantial heterogeneity in trials there is a need for an adequately powered, high quality, multi-centre randomized
controlled trial in adults that excludes 'simple to wean' patients. There is a pressing need for further technological development and research in the paediatric population.

Principles and methods for automated palynology

Pollen grains are microscopic so their identification and quantification has, for decades, depended upon human observers using light microscopes: a labour-intensive approach. Modern improvements in computing and imaging hardware and software now bring automation of pollen analyses within reach. In this paper, we provide the first review in over 15 yr of progress towards automation of the part of palynology concerned with counting and classifying pollen, bringing together literature published from a wide spectrum of sources. We
consider which attempts offer the most potential for an automated palynology system for universal application across all fields of research concerned with pollen classification and counting. We discuss what is required to make the datasets of these automated systems as acceptable as those produced by human palynologists, and present suggestions for how automation will generate novel approaches to counting and classifying pollen that have hitherto been unthinkable.

Automated versus non-automated weaning for reducing the duration of mechanical ventilation for critically ill adults and children: a cochrane systematic review and meta-analysis

IntroductionAutomated weaning systems may improve adaptation of mechanical support for a patient’s ventilatory needs and facilitate systematic and early recognition of their ability to breathe spontaneously and the potential for discontinuation of ventilation. Our objective was to compare mechanical ventilator weaning duration for critically ill adults and children when managed with automated systems versus non-automated strategies. Secondary objectives were to determine differences in duration of ventilation, intensive care unit (ICU) and hospital length of stay (LOS), mortality, and adverse events.MethodsElectronic databases were searched to 30 September 2013 without language restrictions. We also searched conference proceedings; trial registration websites; and article reference lists. Two authors independently extracted data and assessed risk of bias. We combined data using random-effects modelling.ResultsWe identified 21 eligible trials totalling 1,676 participants. Pooled data from 16 trials indicated that automated systems reduced the geometric mean weaning duration by 30% (95% confidence interval (CI) 13% to 45%), with substantial heterogeneity (I2 = 87%, P <0.00001). Reduced weaning duration was found with mixed or medical ICU populations (42%, 95% CI 10% to 63%) and Smartcare/PS™ (28%, 95% CI 7% to 49%) but not with surgical populations or using other systems. Automated systems reduced ventilation duration with no heterogeneity (10%, 95% CI 3% to 16%) and ICU LOS (8%, 95% CI 0% to 15%). There was no strong evidence of effect on mortality, hospital LOS, reintubation, self-extubation and non-invasive ventilation following extubation. Automated systems reduced prolonged mechanical ventilation and tracheostomy. Overall quality of evidence was high.ConclusionsAutomated systems may reduce weaning and ventilation duration and ICU stay. Due to substantial trial heterogeneity an adequately powered, high quality, multi-centre randomized controlled trial is needed.

Automated versus non-automated weaning for reducing the duration of mechanical ventilation in critically ill adults & children (Review)

Background Automated closed loop systems may improve adaptation of mechanical support for a patient's ventilatory needs and facilitate systematic and early recognition of their ability to breathe spontaneously and the potential for discontinuation of ventilation. This review was originally published in 2013 with an update published in 2014. Objectives The primary objective for this review was to compare the total duration of weaning from mechanical ventilation, defined as the time from study randomization to successful extubation (as defined by study authors), for critically ill ventilated patients managed with an automated weaning system versus no automated weaning system (usual care). Secondary objectives for this review were to determine differences in the duration of ventilation, intensive care unit (ICU) and hospital lengths of stay (LOS), mortality, and adverse events related to early or delayed extubation with the use of automated weaning systems compared to weaning in the absence of an automated weaning system. Search methods We searched the Cochrane Central Register of Controlled Trials (CENTRAL) (The Cochrane Library 2013, Issue 8); MEDLINE (OvidSP) (1948 to September 2013); EMBASE (OvidSP) (1980 to September 2013); CINAHL (EBSCOhost) (1982 to September 2013); and the Latin American and Caribbean Health Sciences Literature (LILACS). Relevant published reviews were sought using the Database of Abstracts of Reviews of Effects (DARE) and the Health Technology Assessment Database (HTA Database). We also searched the Web of Science Proceedings; conference proceedings; trial registration websites; and reference lists of relevant articles. The original search was run in August 2011, with database auto-alerts up to August 2012. Selection criteria We included randomized controlled trials comparing automated closed loop ventilator applications to non-automated weaning strategies including non-protocolized usual care and protocolized weaning in patients over four weeks of age receiving invasive mechanical ventilation in an ICU. Data collection and analysis Two authors independently extracted study data and assessed risk of bias. We combined data in forest plots using random-effects modelling. Subgroup and sensitivity analyses were conducted according to a priori criteria. Main results We included 21 trials (19 adult, two paediatric) totaling 1676 participants (1628 adults, 48 children) in this updated review. Pooled data from 16 eligible trials reporting weaning duration indicated that automated closed loop systems reduced the geometric mean duration of weaning by 30% (95% confidence interval (CI) 13% to 45%), however heterogeneity was substantial (I2 = 87%, P < 0.00001). Reduced weaning duration was found with mixed or medical ICU populations (42%, 95% CI 10% to 63%) and Smartcare/PS™ (28%, 95% CI 7% to 49%) but not in surgical populations or using other systems. Automated closed loop systems reduced the duration of ventilation (10%, 95% CI 3% to 16%) and ICU LOS (8%, 95% CI 0% to 15%). There was no strong evidence of an effect on mortality rates, hospital LOS, reintubation rates, self-extubation and use of non-invasive ventilation following extubation. Prolonged mechanical ventilation > 21 days and tracheostomy were reduced in favour of automated systems (relative risk (RR) 0.51, 95% CI 0.27 to 0.95 and RR 0.67, 95% CI 0.50 to 0.90 respectively). Overall the quality of the evidence was high with the majority of trials rated as low risk. Authors' conclusions Automated closed loop systems may result in reduced duration of weaning, ventilation and ICU stay. Reductions are more likely to occur in mixed or medical ICU populations. Due to the lack of, or limited, evidence on automated systems other than Smartcare/PS™ and Adaptive Support Ventilation no conclusions can be drawn regarding their influence on these outcomes. Due to substantial heterogeneity in trials there is a need for an adequately powered, high quality, multi-centre randomized controlled trial in adults that excludes 'simple to wean' patients. There is a pressing need for further technological development and research in the paediatric population.

A study on human error in the interaction with the computer systems

The term human factor is used by professionals of various fields meant for understanding the behavior of human beings at work. The human being, while developing a cooperative activity with a computer system, is subject to cause an undesirable situation in his/her task. This paper starts from the principle that human errors may be considered as a cause or factor contributing to a series of accidents and incidents in many diversified fields in which human beings interact with automated systems. We propose a simulator of performance in error with potentiality to assist the Human Computer Interaction (HCI) project manager in the construction of the critical systems. © 2011 Springer-Verlag.

Examining Technology Adoption and Management Perception of Inventory Management Systems: The Case of Aruba Restaurants

The purpose of this paper is to explore the use of automated inventory management systems (IMS) and identify the stage of technology adoption for restaurants in Aruba. A case study analysis involving twelve members of the Aruba Gastronomic Association was conducted using a qualitative research design to gather information on approaches currently used as well as the reasons and perceptions managers/owners have for using or not using automated systems in their facilities. This is the first study conducted using the Aruba restaurant market. Therefore, the application of two technology adoption models was used to integrate critical factors relevant to the study. Major findings indicated the use of an automated IMS in restaurants is limited, thus underscoring the lack of adoption of technology in this area. The results also indicated that two major reasons that restaurants are not adopting IMS technology are budgetary constraints and service support. This study is imperative for two reasons: (1) the results of this study can be used as a comparison for future IMS adoption, not only for Aruba’s restaurant industry but also for other Caribbean destinations and the U.S., (2) this study also provides insight into the additional training and support help needed in hospitality technology services.

Effects of fare collection policy on operating characteristics of a Brisbane busway station

Transit agencies across the world are increasingly shifting their fare collection mechanisms towards fully automated systems like the smart card. One of the objectives in implementing such a system is to reduce the boarding time per passenger and hence reduce the overall dwell time for the buses at the bus stops/bus rapid transit (BRT) stations. TransLink, the transit authority responsible for public transport management in South East Queensland, has introduced ‘GoCard’ technology using the Cubic platform for fare collection on its public transport system. In addition to this, three inner city BRT stations on South East Busway spine are operating as pre-paid platforms during evening peak time. This paper evaluates the effects of these multiple policy measures on operation of study busway station. The comparison between pre and post policy scenarios suggests that though boarding time per passenger has decreased, while the alighting time per passenger has increased slightly. However, there is a substantial reduction in operating efficiency was observed at the station.

Using Ownership to Reason About Inherent Parallelism in Object-Oriented Programs

With the emergence of multi-cores into the mainstream, there is a growing need for systems to allow programmers and automated systems to reason about data dependencies and inherent parallelismin imperative object-oriented languages. In this paper we exploit the structure of object-oriented programs to abstract computational side-effects. We capture and validate these effects using a static type system. We use these as the basis of sufficient conditions for several different data and task parallelism patterns. We compliment our static type system with a lightweight runtime system to allow for parallelization in the presence of complex data flows. We have a functioning compiler and worked examples to demonstrate the practicality of our solution.

Pollen image classification using the classifynder system: Algorithm comparison and a case study on New Zealand honey

We describe an investigation into how Massey University’s Pollen Classifynder can accelerate the understanding of pollen and its role in nature. The Classifynder is an imaging microscopy system that can locate, image and classify slide based pollen samples. Given the laboriousness of purely manual image acquisition and identification it is vital to exploit assistive technologies like the Classifynder to enable acquisition and analysis of pollen samples. It is also vital that we understand the strengths and limitations of automated systems so that they can be used (and improved) to compliment the strengths and weaknesses of human analysts to the greatest extent possible. This article reviews some of our experiences with the Classifynder system and our exploration of alternative classifier models to enhance both accuracy and interpretability. Our experiments in the pollen analysis problem domain have been based on samples from the Australian National University’s pollen reference collection (2,890 grains, 15 species) and images bundled with the Classifynder system (400 grains, 4 species). These samples have been represented using the Classifynder image feature set.We additionally work through a real world case study where we assess the ability of the system to determine the pollen make-up of samples of New Zealand honey. In addition to the Classifynder’s native neural network classifier, we have evaluated linear discriminant, support vector machine, decision tree and random forest classifiers on these data with encouraging results. Our hope is that our findings will help enhance the performance of future releases of the Classifynder and other systems for accelerating the acquisition and analysis of pollen samples.

A comparison of classification algorithms within the Classifynder pollen imaging system

We describe an investigation into how Massey University's Pollen Classifynder can accelerate the understanding of pollen and its role in nature. The Classifynder is an imaging microscopy system that can locate, image and classify slide based pollen samples. Given the laboriousness of purely manual image acquisition and identification it is vital to exploit assistive technologies like the Classifynder to enable acquisition and analysis of pollen samples. It is also vital that we understand the strengths and limitations of automated systems so that they can be used (and improved) to compliment the strengths and weaknesses of human analysts to the greatest extent possible. This article reviews some of our experiences with the Classifynder system and our exploration of alternative classifier models to enhance both accuracy and interpretability. Our experiments in the pollen analysis problem domain have been based on samples from the Australian National University's pollen reference collection (2890 grains, 15 species) and images bundled with the Classifynder system (400 grains, 4 species). These samples have been represented using the Classifynder image feature set. In addition to the Classifynder's native neural network classifier, we have evaluated linear discriminant, support vector machine, decision tree and random forest classifiers on these data with encouraging results. Our hope is that our findings will help enhance the performance of future releases of the Classifynder and other systems for accelerating the acquisition and analysis of pollen samples. © 2013 AIP Publishing LLC.

Automating marine mammal detection in aerial images captured during wildlife surveys: A deep learning approach

Aerial surveys conducted using manned or unmanned aircraft with customized camera payloads can generate a large number of images. Manual review of these images to extract data is prohibitive in terms of time and financial resources, thus providing strong incentive to automate this process using computer vision systems. There are potential applications for these automated systems in areas such as surveillance and monitoring, precision agriculture, law enforcement, asset inspection, and wildlife assessment. In this paper, we present an efficient machine learning system for automating the detection of marine species in aerial imagery. The effectiveness of our approach can be credited to the combination of a well-suited region proposal method and the use of Deep Convolutional Neural Networks (DCNNs). In comparison to previous algorithms designed for the same purpose, we have been able to dramatically improve recall to more than 80% and improve precision to 27% by using DCNNs as the core approach.