Kohti leikkaussalin tuloksellista toiminnanohjausta

Background: The aging population is placing increasing demands on surgical services, simultaneously with a decreasing supply of professional labor and a worsening economic situation. Under growing financial constraints, successful operating room management will be one of the key issues in the struggle for technical efficiency. This study focused on several issues affecting operating room efficiency. Materials and methods: The current formal operating room management in Finland and the use of performance metrics and information systems used to support this management were explored using a postal survey. We also studied the feasibility of a wireless patient tracking system as a tool for managing the process. The reliability of the system as well as the accuracy and precision of its automatically recorded time stamps were analyzed. The benefits of a separate anesthesia induction room in a prospective setting were compared with the traditional way of working, where anesthesia is induced in the operating room. Using computer simulation, several models of parallel processing for the operating room were compared with the traditional model with respect to cost-efficiency. Moreover, international differences in operating room times for two common procedures, laparoscopic cholecystectomy and open lung lobectomy, were investigated. Results: The managerial structure of Finnish operating units was not clearly defined. Operating room management information systems were found to be out-of-date, offering little support to online evaluation of the care process. Only about half of the information systems provided information in real time. Operating room performance was most often measured by the number of procedures in a time unit, operating room utilization, and turnover time. The wireless patient tracking system was found to be feasible for hospital use. Automatic documentation of the system facilitated patient flow management by increasing process transparency via more available and accurate data, while lessening work for staff. Any parallel work flow model was more cost-efficient than the traditional way of performing anesthesia induction in the operating room. Mean operating times for two common procedures differed by 50% among eight hospitals in different countries. Conclusions: The structure of daily operative management of an operating room warrants redefinition. Performance measures as well as information systems require updating. Parallel work flows are more cost-efficient than the traditional induction-in-room model.

Review of Diagnostic Screening Instruments for Alcohol and Other Drug Use and Other Psychiatric Disorders

In recent years there has been a growing recognition that many people with drug or alcohol problems are also experiencing a range of other psychiatric and psychological problems. The presence of concurrent psychiatric or psychological problems is likely to impact on the success of treatment services. These problems vary greatly, from undetected major psychiatric illnesses that meet internationally accepted diagnostic criteria such as those outlined in the Diagnostic and Statistical Manual (DSM-IV) of the American Psychiatric Association (1994), to less defined feelings of low mood and anxiety that do not meet diagnostic criteria but nevertheless impact on an individual’s sense of wellbeing and affect their quality of life. Similarly, the presence of a substance misuse problem among those suffering from a major psychiatric illness, often goes undetected. For example, the use of illicit drugs such as cannabis and amphetamine is higher among those individuals suffering from schizophrenia (Hall, 1992) and the misuse of alcohol in people suffering from schizophrenia is well documented (e.g., Gorelick et al., 1990; Searles et al., 1990; Soyka et al., 1993). High rates of alcohol misuse have also been reported in a number of groups including women presenting for treatment with a primary eating disorder (Holderness, Brooks Gunn, & Warren, 1994), individuals suffering from post-traumatic stress disorder (Seidel, Gusman and Aubueg, 1994), and those suffering from anxiety and depression. Despite considerable evidence of high levels of co-morbidity, drug and alcohol treatment agencies and mainstream psychiatric services often fail to identify and respond to concurrent psychiatric or drug and alcohol problems, respectively. The original review was conducted as a first step in providing clinicians with information on screening and diagnostic instruments that may be used to assess previously unidentified co-morbidity. The current revision was conducted to extend the original review by updating psychometric findings on measures in the original review, and incorporating other frequently used measures that were not previously included. The current revision has included information regarding special populations, specifically Indigenous Australians, older persons and adolescents. The objectives were to: ● update the original review of AOD and psychiatric screening/diagnostic instruments, ● recommend when these instruments should be used, by whom and how they should be interpreted, ● identify limitations and provide recommendations for further research, ● refer the reader to pertinent Internet sites for further information and/or purchasing of assessment instruments.

Design of a FIR filter for image restoration using principal component neural networks

The neural network finds its application in many image denoising applications because of its inherent characteristics such as nonlinear mapping and self-adaptiveness. The design of filters largely depends on the a-priori knowledge about the type of noise. Due to this, standard filters are application and image specific. Widely used filtering algorithms reduce noisy artifacts by smoothing. However, this operation normally results in smoothing of the edges as well. On the other hand, sharpening filters enhance the high frequency details making the image non-smooth. An integrated general approach to design a finite impulse response filter based on principal component neural network (PCNN) is proposed in this study for image filtering, optimized in the sense of visual inspection and error metric. This algorithm exploits the inter-pixel correlation by iteratively updating the filter coefficients using PCNN. This algorithm performs optimal smoothing of the noisy image by preserving high and low frequency features. Evaluation results show that the proposed filter is robust under various noise distributions. Further, the number of unknown parameters is very few and most of these parameters are adaptively obtained from the processed image.

Reliability models for existing structures based on dynamic state estimation and data based asymptotic extreme value analysis

The problem of time variant reliability analysis of existing structures subjected to stationary random dynamic excitations is considered. The study assumes that samples of dynamic response of the structure, under the action of external excitations, have been measured at a set of sparse points on the structure. The utilization of these measurements m in updating reliability models, postulated prior to making any measurements, is considered. This is achieved by using dynamic state estimation methods which combine results from Markov process theory and Bayes' theorem. The uncertainties present in measurements as well as in the postulated model for the structural behaviour are accounted for. The samples of external excitations are taken to emanate from known stochastic models and allowance is made for ability (or lack of it) to measure the applied excitations. The future reliability of the structure is modeled using expected structural response conditioned on all the measurements made. This expected response is shown to have a time varying mean and a random component that can be treated as being weakly stationary. For linear systems, an approximate analytical solution for the problem of reliability model updating is obtained by combining theories of discrete Kalman filter and level crossing statistics. For the case of nonlinear systems, the problem is tackled by combining particle filtering strategies with data based extreme value analysis. In all these studies, the governing stochastic differential equations are discretized using the strong forms of Ito-Taylor's discretization schemes. The possibility of using conditional simulation strategies, when applied external actions are measured, is also considered. The proposed procedures are exemplifiedmby considering the reliability analysis of a few low-dimensional dynamical systems based on synthetically generated measurement data. The performance of the procedures developed is also assessed based on a limited amount of pertinent Monte Carlo simulations. (C) 2010 Elsevier Ltd. All rights reserved.

Change detection and controlling forest information using multi-temporal Landsat TM imagery.

A method was developed for relative radiometric calibration of single multitemporal Landsat TM image, several multitemporal images covering each others, and several multitemporal images covering different geographic locations. The radiometricly calibrated difference images were used for detecting rapid changes on forest stands. The nonparametric Kernel method was applied for change detection. The accuracy of the change detection was estimated by inspecting the image analysis results in field. The change classification was applied for controlling the quality of the continuously updated forest stand information. The aim was to ensure that all the manmade changes and any forest damages were correctly updated including the attribute and stand delineation information. The image analysis results were compared with the registered treatments and the stand information base. The stands with discrepancies between these two information sources were recommended to be field inspected.

Tavallisuudesta tyylikkyyteen? : Sukupuoli ja maku kodinmuutosohjelmissa

The study focuses on the Finnish home makeover shows Inno (2004 , Nelonen) and Kodin kääntöpiiri (2001 2005; YLE TV2) and their episodes broadcast in spring 2004. The research material also includes the websites of both shows and messages concerning Inno from an online discussion forum. As people decorate their homes and reflect on them, they engage in negotiations of taste and in the construction of the ideal self. The main question of the study is: how are representations of the gendered self produced in home makeover shows? The broader theoretical and methodological context of the study is based on intersectionality, or simultaneous study of different identity categories. In this study, the main focus is on the intersections of gender, class and sexuality. Hence, the secondary research question is: how do ways of doing gender intersect with producing the ideas of class and sexuality in the representations of home makeover shows? The theoretical framework of the study combines Judith Butler s theory of gender performativity and Pierre Bourdieu s theory of taste. The analysis is founded upon a close reading focusing on the details and ambiguous meanings contained in the televisual representation. Home makeover shows are explored as a part of contemporary television culture, which is characterised by a significant increase in the number of both television channels and global television formats, as well as the hybridisation of programme types. Researchers on lifestyle television have paid attention to male designers and their ability to reconstruct meanings related to domesticity and home decoration as feminine spheres. The dissertation contributes to this discussion by analysing the representations of the male interior decorator in Inno and the four female interior decorators in Kodin kääntöpiiri. The focus is on the professional self and how it is both gendered and defined as an arbiter of taste. The programme concepts produce the impression that the makeover homes and their occupants are ordinary . The manufactured sense of ordinariness often conceals differences between the participants. One argument of the study is that the ordinariness of participants on lifestyle television should not be taken for granted without further reflection on the implications of labeling something as ordinary. Updating of interior decoration in home makeover shows can be interpreted as an area of doing gender that requires deliberation, effort, expert knowledge and a sufficient budget. The ideal lay decorator is portrayed as culturally omnivorous, brave and receptive to new ideas. The ability to reflect on ways of representing masculinity and femininity through decoration is also implied. In home makeover shows, greater self-awareness regarding the ways in which gender is produced does not lead to repeating gender differently. The idea of normative heterosexuality is in a hegemonic position in the representations of the participants. In Inno and Kodin kääntöpiiri questions of class are not made explicit. However, the idea of class is produced indirectly e.g. by describing the apartments and houses of the participants, by discussing their hobbies or interest in cultural products. In Inno, home decoration is primarily depicted as an individualistic consumer choice, while in Kodin kääntöpiiri it is often defined as a way to strengthen the ties of nuclear families. In Kodin kääntöpiiri, the ethos of familism is combined with pleasures gained from consumption and DIY activities. As a whole, the multidisciplinary study indicates a great number of differences between the two shows.

Some Algorithms for Eigensubspace Estimation

An important tool in signal processing is the use of eigenvalue and singular value decompositions for extracting information from time-series/sensor array data. These tools are used in the so-called subspace methods that underlie solutions to the harmonic retrieval problem in time series and the directions-of-arrival (DOA) estimation problem in array processing. The subspace methods require the knowledge of eigenvectors of the underlying covariance matrix to estimate the parameters of interest. Eigenstructure estimation in signal processing has two important classes: (i) estimating the eigenstructure of the given covariance matrix and (ii) updating the eigenstructure estimates given the current estimate and new data. In this paper, we survey some algorithms for both these classes useful for harmonic retrieval and DOA estimation problems. We begin by surveying key results in the literature and then describe, in some detail, energy function minimization approaches that underlie a class of feedback neural networks. Our approaches estimate some or all of the eigenvectors corresponding to the repeated minimum eigenvalue and also multiple orthogonal eigenvectors corresponding to the ordered eigenvalues of the covariance matrix. Our presentation includes some supporting analysis and simulation results. We may point out here that eigensubspace estimation is a vast area and all aspects of this cannot be fully covered in a single paper. (C) 1995 Academic Press, Inc.

Learning the global maximum with parameterized learning automata

A feedforward network composed of units of teams of parameterized learning automata is considered as a model of a reinforcement teaming system. The internal state vector of each learning automaton is updated using an algorithm consisting of a gradient following term and a random perturbation term. It is shown that the algorithm weakly converges to a solution of the Langevin equation implying that the algorithm globally maximizes an appropriate function. The algorithm is decentralized, and the units do not have any information exchange during updating. Simulation results on common payoff games and pattern recognition problems show that reasonable rates of convergence can be obtained.

Sharavathi River Basin Flora Information System(SRFIS)

The INFORMATION SYSTEM with user friendly GUI’s (Graphical user Interface) is developed to maintain the flora data and generate reports for Sharavathi River Basin. The database consists of the information related to trees, herbs, shrubs and climbers. The data is based on the primary field survey and the information available in flora of Shimoga, Karnataka and Hassan flora. User friendly query options based on dichotomous keys are provided to help user to retrieve the data while data entry options aid in updating and editing the database at family, genus and species levels.

Nonlinear dynamic state estimation in instrumented structures with conditionally linear Gaussian substructures

Many problems of state estimation in structural dynamics permit a partitioning of system states into nonlinear and conditionally linear substructures. This enables a part of the problem to be solved exactly, using the Kalman filter, and the remainder using Monte Carlo simulations. The present study develops an algorithm that combines sequential importance sampling based particle filtering with Kalman filtering to a fairly general form of process equations and demonstrates the application of a substructuring scheme to problems of hidden state estimation in structures with local nonlinearities, response sensitivity model updating in nonlinear systems, and characterization of residual displacements in instrumented inelastic structures. The paper also theoretically demonstrates that the sampling variance associated with the substructuring scheme used does not exceed the sampling variance corresponding to the Monte Carlo filtering without substructuring. (C) 2012 Elsevier Ltd. All rights reserved.

Exploiting the Structure of the Constraint Graph for Efficient Points-to Analysis

Points-to analysis is a key compiler analysis. Several memory related optimizations use points-to information to improve their effectiveness. Points-to analysis is performed by building a constraint graph of pointer variables and dynamically updating it to propagate more and more points-to information across its subset edges. So far, the structure of the constraint graph has been only trivially exploited for efficient propagation of information, e.g., in identifying cyclic components or to propagate information in topological order. We perform a careful study of its structure and propose a new inclusion-based flow-insensitive context-sensitive points-to analysis algorithm based on the notion of dominant pointers. We also propose a new kind of pointer-equivalence based on dominant pointers which provides significantly more opportunities for reducing the number of pointers tracked during the analysis. Based on this hitherto unexplored form of pointer-equivalence, we develop a new context-sensitive flow-insensitive points-to analysis algorithm which uses incremental dominator update to efficiently compute points-to information. Using a large suite of programs consisting of SPEC 2000 benchmarks and five large open source programs we show that our points-to analysis is 88% faster than BDD-based Lazy Cycle Detection and 2x faster than Deep Propagation. We argue that our approach of detecting dominator-based pointer-equivalence is a key to improve points-to analysis efficiency.

A practical approach for cross-functional vehicle body weight optimization

The goal of optimization in vehicle design is often blurred by the myriads of requirements belonging to attributes that may not be quite related. If solutions are sought by optimizing attribute performance-related objectives separately starting with a common baseline design configuration as in a traditional design environment, it becomes an arduous task to integrate the potentially conflicting solutions into one satisfactory design. It may be thus more desirable to carry out a combined multi-disciplinary design optimization (MDO) with vehicle weight as an objective function and cross-functional attribute performance targets as constraints. For the particular case of vehicle body structure design, the initial design is likely to be arrived at taking into account styling, packaging and market-driven requirements. The problem with performing a combined cross-functional optimization is the time associated with running such CAE algorithms that can provide a single optimal solution for heterogeneous areas such as NVH and crash safety. In the present paper, a practical MDO methodology is suggested that can be applied to weight optimization of automotive body structures by specifying constraints on frequency and crash performance. Because of the reduced number of cases to be analyzed for crash safety in comparison with other MDO approaches, the present methodology can generate a single size-optimized solution without having to take recourse to empirical techniques such as response surface-based prediction of crash performance and associated successive response surface updating for convergence. An example of weight optimization of spaceframe-based BIW of an aluminum-intensive vehicle is given to illustrate the steps involved in the current optimization process.

An extended finite-element model coupled with level set method for analysis of growth of corrosion pits in metallic structures

Mass balance between metal and electrolytic solution, separated by a moving interface, in stable pit growth results in a set of governing equations which are solved for concentration field and interface position (pit boundary evolution). The interface experiences a jump discontinuity in metal concentration. The extended finite-element model (XFEM) handles this jump discontinuity by using discontinuous-derivative enrichment formulation, eliminating the requirement of using front conforming mesh and re-meshing after each time step as in the conventional finite-element method. However, prior interface location is required so as to solve the governing equations for concentration field for which a numerical technique, the level set method, is used for tracking the interface explicitly and updating it over time. The level set method is chosen as it is independent of shape and location of the interface. Thus, a combined XFEM and level set method is developed in this paper. Numerical analysis for pitting corrosion of stainless steel 304 is presented. The above proposed model is validated by comparing the numerical results with experimental results, exact solutions and some other approximate solutions. An empirical model for pitting potential is also derived based on the finite-element results. Studies show that pitting profile depends on factors such as ion concentration, solution pH and temperature to a large extent. Studying the individual and combined effects of these factors on pitting potential is worth knowing, as pitting potential directly influences corrosion rate.

Robust tracking with interest points: A sparse representation approach

Visual tracking is an important task in various computer vision applications including visual surveillance, human computer interaction, event detection, video indexing and retrieval. Recent state of the art sparse representation (SR) based trackers show better robustness than many of the other existing trackers. One of the issues with these SR trackers is low execution speed. The particle filter framework is one of the major aspects responsible for slow execution, and is common to most of the existing SR trackers. In this paper,(1) we propose a robust interest point based tracker in l(1) minimization framework that runs at real-time with performance comparable to the state of the art trackers. In the proposed tracker, the target dictionary is obtained from the patches around target interest points. Next, the interest points from the candidate window of the current frame are obtained. The correspondence between target and candidate points is obtained via solving the proposed l(1) minimization problem. In order to prune the noisy matches, a robust matching criterion is proposed, where only the reliable candidate points that mutually match with target and candidate dictionary elements are considered for tracking. The object is localized by measuring the displacement of these interest points. The reliable candidate patches are used for updating the target dictionary. The performance and accuracy of the proposed tracker is benchmarked with several complex video sequences. The tracker is found to be considerably fast as compared to the reported state of the art trackers. The proposed tracker is further evaluated for various local patch sizes, number of interest points and regularization parameters. The performance of the tracker for various challenges including illumination change, occlusion, and background clutter has been quantified with a benchmark dataset containing 50 videos. (C) 2014 Elsevier B.V. All rights reserved.

3-D GPU Based Real Time Diffuse Optical Tomographic System

3-Dimensional Diffuse Optical Tomographic (3-D DOT) image reconstruction algorithm is computationally complex and requires excessive matrix computations and thus hampers reconstruction in real time. In this paper, we present near real time 3D DOT image reconstruction that is based on Broyden approach for updating Jacobian matrix. The Broyden method simplifies the algorithm by avoiding re-computation of the Jacobian matrix in each iteration. We have developed CPU and heterogeneous CPU/GPU code for 3D DOT image reconstruction in C and MatLab programming platform. We have used Compute Unified Device Architecture (CUDA) programming framework and CUDA linear algebra library (CULA) to utilize the massively parallel computational power of GPUs (NVIDIA Tesla K20c). The computation time achieved for C program based implementation for a CPU/GPU system for 3 planes measurement and FEM mesh size of 19172 tetrahedral elements is 806 milliseconds for an iteration.