A multi-site pilot test study to measure safety climate in the university work setting

Next to leisure, sport, and household activities, the most common activity resulting in medically consulted injuries and poisonings in the United States is work, with an estimated 4 million workplace related episodes reported in 2008 (U.S. Department of Health and Human Services, 2009). To address the risks inherent to various occupations, risk management programs are typically put in place that include worker training, engineering controls, and personal protective equipment. Recent studies have shown that such interventions alone are insufficient to adequately manage workplace risks, and that the climate in which the workers and safety program exist (known as the "safety climate") is an equally important consideration. The organizational safety climate is so important that many studies have focused on developing means of measuring it in various work settings. While safety climate studies have been reported for several industrial settings, published studies on assessing safety climate in the university work setting are largely absent. Universities are particularly unique workplaces because of the potential exposure to a diversity of agents representing both acute and chronic risks. Universities are also unique because readily detectable health and safety outcomes are relatively rare. The ability to measure safety climate in a work setting with rarely observed systemic outcome measures could serve as a powerful means of measure for the evaluation of safety risk management programs. ^ The goal of this research study was the development of a survey tool to measure safety climate specifically in the university work setting. The use of a standardized tool also allows for comparisons among universities throughout the United States. A specific study objective was accomplished to quantitatively assess safety climate at five universities across the United States. At five universities, 971 participants completed an online questionnaire to measure the safety climate. The average safety climate score across the five universities was 3.92 on a scale of 1 to 5, with 5 indicating very high perceptions of safety at these universities. The two lowest overall dimensions of university safety climate were "acknowledgement of safety performance" and "department and supervisor's safety commitment". The results underscore how the perception of safety climate is significantly influenced at the local level. A second study objective regarding evaluating the reliability and validity of the safety climate questionnaire was accomplished. A third objective fulfilled was to provide executive summaries resulting from the questionnaire to the participating universities' health & safety professionals and collect feedback on usefulness, relevance and perceived accuracy. Overall, the professionals found the survey and results to be very useful, relevant and accurate. Finally, the safety climate questionnaire will be offered to other universities for benchmarking purposes at the annual meeting of a nationally recognized university health and safety organization. The ultimate goal of the project was accomplished and was the creation of a standardized tool that can be used for measuring safety climate in the university work setting and can facilitate meaningful comparisons amongst institutions.^

Benthic cover map of Heron Reef derived from a high-spatial-resolution multi-spectral satellite image using object based image analysis

Providing accurate maps of coral reefs where the spatial scale and labels of the mapped features correspond to map units appropriate for examining biological and geomorphic structures and processes is a major challenge for remote sensing. The objective of this work is to assess the accuracy and relevance of the process used to derive geomorphic zone and benthic community zone maps for three western Pacific coral reefs produced from multi-scale, object-based image analysis (OBIA) of high-spatial-resolution multi-spectral images, guided by field survey data. Three Quickbird-2 multi-spectral data sets from reefs in Australia, Palau and Fiji and georeferenced field photographs were used in a multi-scale segmentation and object-based image classification to map geomorphic zones and benthic community zones. A per-pixel approach was also tested for mapping benthic community zones. Validation of the maps and comparison to past approaches indicated the multi-scale OBIA process enabled field data, operator field experience and a conceptual hierarchical model of the coral reef environment to be linked to provide output maps at geomorphic zone and benthic community scales on coral reefs. The OBIA mapping accuracies were comparable with previously published work using other methods; however, the classes mapped were matched to a predetermined set of features on the reef.

Adjustment methods of national input-output tables for harmonized and consistent multi-regional input-output databases

Despite the fact that input–output (IO) tables form a central part of the System of National Accounts, each individual country's national IO table exhibits more or less different features and characteristics, reflecting the country's socioeconomic idiosyncrasies. Consequently, the compilers of a multi-regional input–output table (MRIOT) are advised to thoroughly examine the conceptual as well as methodological differences among countries in the estimation of basic statistics for national IO tables and, if necessary, to carry out pre-adjustment of these tables into a common format prior to the MRIOT compilation. The objective of this study is to provide a practical guide for harmonizing national IO tables to construct a consistent MRIOT, referring to the adjustment practices used by the Institute of Developing Economies, JETRO (IDE-JETRO) in compiling the Asian International Input–Output Table.

Integration of III-V materials on Silicon Substrates for Multi-junction Solar Cell Applications

The work presented here aims to reduce the cost of multijunction solar cell technology by developing ways to manufacture them on cheap substrates such as silicon. In particular, our main objective is the growth of III-V semiconductors on silicon substrates for photovoltaic applications. The goal is to create a GaAsP/Si virtual substrates onto which other III-V cells could be integrated with an interesting efficiency potential. This technology involves several challenges due to the difficulty of growing III-V materials on silicon. In this paper, our first work done aimed at developing such structure is presented. It was focused on the development of phosphorus diffusion models on silicon and on the preparation of an optimal silicon surface to grow on it III-V materials.

Response threshold models, stochastic learning automata and ant colony optimization-based decentralized self-coordination algorithms for heterogeneous multi-tasks distribution in multi-robot systems

In recent decades, there has been an increasing interest in systems comprised of several autonomous mobile robots, and as a result, there has been a substantial amount of development in the eld of Articial Intelligence, especially in Robotics. There are several studies in the literature by some researchers from the scientic community that focus on the creation of intelligent machines and devices capable to imitate the functions and movements of living beings. Multi-Robot Systems (MRS) can often deal with tasks that are dicult, if not impossible, to be accomplished by a single robot. In the context of MRS, one of the main challenges is the need to control, coordinate and synchronize the operation of multiple robots to perform a specic task. This requires the development of new strategies and methods which allow us to obtain the desired system behavior in a formal and concise way. This PhD thesis aims to study the coordination of multi-robot systems, in particular, addresses the problem of the distribution of heterogeneous multi-tasks. The main interest in these systems is to understand how from simple rules inspired by the division of labor in social insects, a group of robots can perform tasks in an organized and coordinated way. We are mainly interested on truly distributed or decentralized solutions in which the robots themselves, autonomously and in an individual manner, select a particular task so that all tasks are optimally distributed. In general, to perform the multi-tasks distribution among a team of robots, they have to synchronize their actions and exchange information. Under this approach we can speak of multi-tasks selection instead of multi-tasks assignment, which means, that the agents or robots select the tasks instead of being assigned a task by a central controller. The key element in these algorithms is the estimation ix of the stimuli and the adaptive update of the thresholds. This means that each robot performs this estimate locally depending on the load or the number of pending tasks to be performed. In addition, it is very interesting the evaluation of the results in function in each approach, comparing the results obtained by the introducing noise in the number of pending loads, with the purpose of simulate the robot's error in estimating the real number of pending tasks. The main contribution of this thesis can be found in the approach based on self-organization and division of labor in social insects. An experimental scenario for the coordination problem among multiple robots, the robustness of the approaches and the generation of dynamic tasks have been presented and discussed. The particular issues studied are: Threshold models: It presents the experiments conducted to test the response threshold model with the objective to analyze the system performance index, for the problem of the distribution of heterogeneous multitasks in multi-robot systems; also has been introduced additive noise in the number of pending loads and has been generated dynamic tasks over time. Learning automata methods: It describes the experiments to test the learning automata-based probabilistic algorithms. The approach was tested to evaluate the system performance index with additive noise and with dynamic tasks generation for the same problem of the distribution of heterogeneous multi-tasks in multi-robot systems. Ant colony optimization: The goal of the experiments presented is to test the ant colony optimization-based deterministic algorithms, to achieve the distribution of heterogeneous multi-tasks in multi-robot systems. In the experiments performed, the system performance index is evaluated by introducing additive noise and dynamic tasks generation over time.

Commonalities between networking in multiplayer computer games and negotiation processes in a future multi-layered Air Traffic Management (ATM) system

First, this paper describes a future layered Air Traffic Management (ATM) system centred in the execution phase of flights. The layered ATM model is based on the work currently performed by SESAR [1] and takes into account the availability of accurate and updated flight information ?seen by all? across the European airspace. This shared information of each flight will be referred as Reference Business Trajectory (RBT). In the layered ATM system, exchanges of information will involve several actors (human or automatic), which will have varying time horizons, areas of responsibility and tasks. Second, the paper will identify the need to define the negotiation processes required to agree revisions to the RBT in the layered ATM system. Third, the final objective of the paper is to bring to the attention of researchers and engineers the communalities between multi-player games and Collaborative Decision Making processes (CDM) in a layered ATM system

Advanced characterization of a coffee fermenting tank by multi-distributed wireless sensors: spatial interpolation and phase diagrams.

In coffee processing the fermentation stage is considered one of the critical operations by its impact on the final quality of the product. However, the level of control of the fermentation process on each farm is often not adequate; the use of sensorics for controlling coffee fermentation is not common. The objective of this work is to characterize the fermentation temperature in a fermentation tank by applying spatial interpolation and a new methodology of data analysis based on phase space diagrams of temperature data, collected by means of multi-distributed, low cost and autonomous wireless sensors. A real coffee fermentation was supervised in the Cauca region (Colombia) with a network of 24 semi-passive TurboTag RFID temperature loggers with vacuum plastic cover, submerged directly in the fermenting mass. Temporal evolution and spatial distribution of temperature is described in terms of the phase diagram areas which characterizes the cyclic behaviour of temperature and highlights the significant heterogeneity of thermal conditions at different locations in the tank where the average temperature of the fermentation was 21.2 °C, although there were temperature ranges of 4.6°C, and average spatial standard deviation of ±1.21ºC. In the upper part of the tank we found high heterogeneity of temperatures, the higher temperatures and therefore the higher fermentation rates. While at the bottom, it has been computed an area in the phase diagram practically half of the area occupied by the sensors of the upper tank, therefore this location showed higher temperature homogeneity

Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty

Abstract Interneuron classification is an important and long-debated topic in neuroscience. A recent study provided a data set of digitally reconstructed interneurons classified by 42 leading neuroscientists according to a pragmatic classification scheme composed of five categorical variables, namely, of the interneuron type and four features of axonal morphology. From this data set we now learned a model which can classify interneurons, on the basis of their axonal morphometric parameters, into these five descriptive variables simultaneously. Because of differences in opinion among the neuroscientists, especially regarding neuronal type, for many interneurons we lacked a unique, agreed-upon classification, which we could use to guide model learning. Instead, we guided model learning with a probability distribution over the neuronal type and the axonal features, obtained, for each interneuron, from the neuroscientists’ classification choices. We conveniently encoded such probability distributions with Bayesian networks, calling them label Bayesian networks (LBNs), and developed a method to predict them. This method predicts an LBN by forming a probabilistic consensus among the LBNs of the interneurons most similar to the one being classified. We used 18 axonal morphometric parameters as predictor variables, 13 of which we introduce in this paper as quantitative counterparts to the categorical axonal features. We were able to accurately predict interneuronal LBNs. Furthermore, when extracting crisp (i.e., non-probabilistic) predictions from the predicted LBNs, our method outperformed related work on interneuron classification. Our results indicate that our method is adequate for multi-dimensional classification of interneurons with probabilistic labels. Moreover, the introduced morphometric parameters are good predictors of interneuron type and the four features of axonal morphology and thus may serve as objective counterparts to the subjective, categorical axonal features.

Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty

Interneuron classification is an important and long-debated topic in neuroscience. A recent study provided a data set of digitally reconstructed interneurons classified by 42 leading neuroscientists according to a pragmatic classification scheme composed of five categorical variables, namely, of the interneuron type and four features of axonal morphology. From this data set we now learned a model which can classify interneurons, on the basis of their axonal morphometric parameters, into these five descriptive variables simultaneously. Because of differences in opinion among the neuroscientists, especially regarding neuronal type, for many interneurons we lacked a unique, agreed-upon classification, which we could use to guide model learning. Instead, we guided model learning with a probability distribution over the neuronal type and the axonal features, obtained, for each interneuron, from the neuroscientists’ classification choices. We conveniently encoded such probability distributions with Bayesian networks, calling them label Bayesian networks (LBNs), and developed a method to predict them. This method predicts an LBN by forming a probabilistic consensus among the LBNs of the interneurons most similar to the one being classified. We used 18 axonal morphometric parameters as predictor variables, 13 of which we introduce in this paper as quantitative counterparts to the categorical axonal features. We were able to accurately predict interneuronal LBNs. Furthermore, when extracting crisp (i.e., non-probabilistic) predictions from the predicted LBNs, our method outperformed related work on interneuron classification. Our results indicate that our method is adequate for multi-dimensional classification of interneurons with probabilistic labels. Moreover, the introduced morphometric parameters are good predictors of interneuron type and the four features of axonal morphology and thus may serve as objective counterparts to the subjective, categorical axonal features.

Analysis of the suitability of existing medical ontologies for building a scalable semantic interoperability solution supporting multi-site collaboration in oncology

Semantic interoperability is essential to facilitate efficient collaboration in heterogeneous multi-site healthcare environments. The deployment of a semantic interoperability solution has the potential to enable a wide range of informatics supported applications in clinical care and research both within as ingle healthcare organization and in a network of organizations. At the same time, building and deploying a semantic interoperability solution may require significant effort to carryout data transformation and to harmonize the semantics of the information in the different systems. Our approach to semantic interoperability leverages existing healthcare standards and ontologies, focusing first on specific clinical domains and key applications, and gradually expanding the solution when needed. An important objective of this work is to create a semantic link between clinical research and care environments to enable applications such as streamlining the execution of multi-centric clinical trials, including the identification of eligible patients for the trials. This paper presents an analysis of the suitability of several widely-used medical ontologies in the clinical domain: SNOMED-CT, LOINC, MedDRA, to capture the semantics of the clinical trial eligibility criteria, of the clinical trial data (e.g., Clinical Report Forms), and of the corresponding patient record data that would enable the automatic identification of eligible patients. Next to the coverage provided by the ontologies we evaluate and compare the sizes of the sets of relevant concepts and their relative frequency to estimate the cost of data transformation, of building the necessary semantic mappings, and of extending the solution to new domains. This analysis shows that our approach is both feasible and scalable.

Advanced Characterisation of a Coffee Fermenting Tank by Multi-distributed Wireless Sensors: Spatial Interpolation and Phase Space Graphs

The fermentation stage is considered to be one of the critical steps in coffee processing due to its impact on the final quality of the product. The objective of this work is to characterise the temperature gradients in a fermentation tank by multi-distributed, low-cost and autonomous wireless sensors (23 semi-passive TurboTag® radio-frequency identifier (RFID) temperature loggers). Spatial interpolation in polar coordinates and an innovative methodology based on phase space diagrams are used. A real coffee fermentation process was supervised in the Cauca region (Colombia) with sensors submerged directly in the fermenting mass, leading to a 4.6 °C temperature range within the fermentation process. Spatial interpolation shows a maximum instant radial temperature gradient of 0.1 °C/cm from the centre to the perimeter of the tank and a vertical temperature gradient of 0.25 °C/cm for sensors with equal polar coordinates. The combination of spatial interpolation and phase space graphs consistently enables the identification of five local behaviours during fermentation (hot and cold spots).

How should the sustainability of the location of dry ports be measured? A proposed methodology using Bayesian networks and multi-criteria decision analysis

The global economic structure, with its decentralized production and the consequent increase in freight traffic all over the world, creates considerable problems and challenges for the freight transport sector. This situation has led shipping to become the most suitable and cheapest way to transport goods. Thus, ports are configured as nodes with critical importance in the logistics supply chain as a link between two transport systems, sea and land. Increase in activity at seaports is producing three undesirable effects: increasing road congestion, lack of open space in port installations and a significant environmental impact on seaports. These adverse effects can be mitigated by moving part of the activity inland. Implementation of dry ports is a possible solution and would also provide an opportunity to strengthen intermodal solutions as part of an integrated and more sustainable transport chain, acting as a link between road and railway networks. In this sense, implementation of dry ports allows the separation of the links of the transport chain, thus facilitating the shortest possible routes for the lowest capacity and most polluting means of transport. Thus, the decision of where to locate a dry port demands a thorough analysis of the whole logistics supply chain, with the objective of transferring the largest volume of goods possible from road to more energy efficient means of transport, like rail or short-sea shipping, that are less harmful to the environment. However, the decision of where to locate a dry port must also ensure the sustainability of the site. Thus, the main goal of this article is to research the variables influencing the sustainability of dry port location and how this sustainability can be evaluated. With this objective, in this paper we present a methodology for assessing the sustainability of locations by the use of Multi-Criteria Decision Analysis (MCDA) and Bayesian Networks (BNs). MCDA is used as a way to establish a scoring, whilst BNs were chosen to eliminate arbitrariness in setting the weightings using a technique that allows us to prioritize each variable according to the relationships established in the set of variables. In order to determine the relationships between all the variables involved in the decision, giving us the importance of each factor and variable, we built a K2 BN algorithm. To obtain the scores of each variable, we used a complete cartography analysed by ArcGIS. Recognising that setting the most appropriate location to place a dry port is a geographical multidisciplinary problem, with significant economic, social and environmental implications, we consider 41 variables (grouped into 17 factors) which respond to this need. As a case of study, the sustainability of all of the 10 existing dry ports in Spain has been evaluated. In this set of logistics platforms, we found that the most important variables for achieving sustainability are those related to environmental protection, so the sustainability of the locations requires a great respect for the natural environment and the urban environment in which they are framed.

Multi-distributed wireless sensors for monitoring a long distance transport in a reefer container

The study of the temperature gradients in cold stores and containers is a critical issue in the food industry for the quality assurance of products during transport and for minimising losses. This work presents an analysis of the temperatures during the refrigerated transport of 4,320 kg of blueberries in a reefer (set point temperature at ?1ºC) on a container ship from Montevideo (Uruguay) to Verona (Italy). The monitoring was performed by using semi-passive RFID loggers (TurboTag cards). The objective was to carry out a multi-distributed supervision using low-cost, wireless and autonomous sensors for the characterisation of the distribution and spatial gradients of temperatures during a long distance transport. Data analysis shows spatial (phase space) and temporal sequencing diagrams and reveals a significant heterogeneity of temperature at different locations in the container, which highlights the ineffectiveness of a temperature control system based on a single sensor, as is usually done.

Architecture of a multi-modal dialogue system oriented to multilingual question-answering

In this paper, a proposal of a multi-modal dialogue system oriented to multilingual question-answering is presented. This system includes the following ways of access: voice, text, avatar, gestures and signs language. The proposal is oriented to the question-answering task as a user interaction mechanism. The proposal here presented is in the first stages of its development phase and the architecture is presented for the first time on the base of the experiences in question-answering and dialogues previously developed. The main objective of this research work is the development of a solid platform that will permit the modular integration of the proposed architecture.

Long-term persistence of multi-drug-resistant Salmonella enterica serovar Newport in two dairy herds

Objective - To evaluate the association between maintaining joint hospital and maternity pens;and persistence of multi-drug-resistant (MDR) Salmonella enterica serovar Newport on 2 dairy farms. Design - Observational study. Sample Population - Feces and environmental samples from 2 dairy herds. Procedure - Herds were monitored for fecal shedding of S enterica Newport after outbreaks of clinical disease. Fecal and environmental samples were collected approximately monthly from pens housing sick cows and calving cows and from pens containing lactating cows. Cattle shedding the organism were tested serially on subsequent visits to determine carrier status. One farm was resampled after initiation of interventional procedures, including separation of hospital and maternity pens. Isolates were characterized via serotyping, determination of antimicrobial resistance phenotype, detection of the CMY-2 gene, and DNA fingerprinting. Results - The prevalence (32.4% and 33.3% on farms A and B, respectively) of isolating Salmonella from samples from joint hospital-maternity pens was significantly higher than the prevalence in samples from pens housing preparturient cows (0.8%, both farms) and postparturient cows on Farm B (8.8%). Multi-drug-resistant Salmonella Newport was isolated in high numbers from bedding material, feed refusals, lagoon slurry, and milk filters. One cow excreted the organism for 190 days. Interventional procedures yielded significant reductions in the prevalences of isolating the organism from fecal and environmental samples. Most isolates were of the C2 serogroup and were resistant to third-generation cephalosporins. Conclusions and Clinical Relevance - Management practices may be effective at reducing the persistence of MDR Salmonella spp in dairy herds, thus mitigating animal and public health risk.