77 research outputs found
Intrathecal Drug Delivery Systems Survey: Trends in Utilization in Pain Practice [Corrigendum]
No full textAbd-Sayed A, Fiala K, Weisbein J, et al. J Pain Res. 2022;15:1305–1314.
The authors have advised there is an error in the author list on page 1305. The author name “Alaa Abd-Sayed” should read “Alaa Abd-Elsayed”.
The authors apologize for this error
Reliability estimation of balanced systems with multi-dimensional distributed units
No full textBalanced systems with multi-dimensional distributed units are emerging in a diverse range of industries. This includes Unmanned Aerial Vehicles (UAV) with multi-level of rotary wings, Spherical Unmanned Vehicles (SUV), Spherical Phased Array Antenna (SPAA), etc. In this dissertation, we present the reliability estimation for such systems. In particular, we consider two configurations: 1) balanced systems with units distributed circularly on multi-level and 2) balanced systems with units distributed spherically.
First, balanced systems with units distributed circularly on multi-level are generalized as (k₁, k₂)-out-of-(n, m) pairs: G balanced systems. We consider two scenarios: 1) all units perform the same function and 2) adjacent pairs perform complementary functions. For both scenarios, unbalanced system is considered as failed. When units fail and cause the system imbalance, we explore two approaches to rebalance the system: 1) forcing down units on other locations and 2) resuming units that are previously forced down (if any). When units in a system perform the same function, operational states are defined as balanced states with at least k₁ operating pairs and each operating pair has at least k₂ units on each side. The system reliability is obtained by enumerating all of the operational states and summing the probabilities of those states. For (k₁, k₂)-out-of-(n, m) pairs: G balanced systems with adjacent pairs performing complementary functions, in addition to maintaining system balance, the adjacent operating pairs are required to perform complementary functions. Thus, if a pair fails, one of the adjacent pairs is forced down. Similarly, the system reliability is obtained by enumerating all of the operational states. It becomes computational expensive when the number of units in each pair and/or the number of pairs are large. In that case, efficient algorithms are developed to obtain the reliability for such systems.
The balanced system with units distributed spherically is generalized as a spherical k-n-i: G balanced system. We consider two balancing requirements: 1) rotational balance is maintained so that the system is not rotating w.r.t. roll, yaw and pitch axes and 2) symmetrical balance is essential in improving the systems’ stability. We present mathematical approaches to determine the balance status of a system. Similarly, the unbalanced system is rebalanced by 1) forcing down units on other locations and 2) resuming previously forced-down units. The system reliability is obtained by the enumeration of operational states and calculation of operational states’ probabilities. We develop an efficient algorithm for reliability estimation when the number of units in the system is large.
Degradation models are developed for the (k₁, k₂)-out-of-(n, m) pairs: G balanced systems to further investigate the system reliability when degradation data are available. The degradation processes of units in the system are either stationary (inverse Gaussian process) or non-stationary (improved inverse Gaussian process). We propose a degradation balance mechanism in which the ‘most’ degraded units are forced down temporarily during the degradation process so that the system is less possible to fail due to imbalance. A closed-form lower bound reliability is presented when the balance mechanism is not applied. When it is applied, reliability is obtained by Monte Carlo simulation.
From the reliability study of the both configurations, it is observed that the reliability of a balanced system with multi-dimensional distributed units depends not only on the system’s total number of units and the least number of operating units, but also on the system configurations and balance requirements. Systems with more units do not necessarily provide a higher reliability since they are more likely to fail due to imbalance. Thus, optimal system design is key to maximize the system reliability which is investigated through numerical examples in this dissertation.Ph.D.Includes bibliographical reference
Modeling the effects of the two stochastic-processes on the reliability and maintenance of k-out-of-n surveillance systems
No full textSurveillance systems, including security cameras, have been widely used to monitor some critical processes and enhance the safety-security level of high-risk large-scale security systems. The failure of these systems should be analyzed and associated with the intrusion/incident arrival process in order to achieve a comprehensive representation of the system outcomes. This thesis aims to model the effects of the two stochastic processes on the reliability modeling of the surveillance systems. The two processes are: (1) the traditional system failure process (first process) and (2) the intrusion/incident arrival process or the demand process (second process). In this research we develop reliability models with considerations of the two stochastic-processes for the k-out-of-n surveillance systems. The first model considers the undetectable failures of each subsystem along with the random environmental factors, the skill factor of the intruders to avoid detection, and a periodic inspection maintenance aspect. The second model includes both the detectable and undetectable failure modes for the subsystems. The reliability of the system is derived with a consideration of an opportunistic maintenance policy. Numerical examples are given to demonstrate the validity of the modeling and the sensitivity of various model parameters. We also develop a cost model with considerations of both the detectable and undetectable failure modes for the subsystems. We then obtain the opportunistic maintenance policy that minimizes the total system cost based on the second model. We also extend the second reliability model by considering the fail-safe error for each subsystem in the two-process modeling of the k-out-of-n surveillance systems. Numerical examples are discussed to illustrate the model developments and results.Ph. D.Includes bibliographical referencesby Yao Zhan
Reliability estimation of systems with spatially distributed units
No full textSystems with spatially distributed units, e.g. Unmanned Aerial Vehicle (UAV), are emerging in aerospace and military industries. In this dissertation, we present approaches for the reliability estimation of such systems. In particular, we consider k-out-of-n pairs:G Balanced systems and weighted-c-out-of-n pairs:G Balanced systems with spatially distributed units which must meet balance requirements. We first estimate the reliability metrics for k-out-of-n pairs:G Balanced systems by considering systems as failed when unbalanced system states occur. We further investigate such systems by balancing unbalanced states: When unbalanced states occur, the system is balanced by forcing down one or more operating pairs into standby. The reliability estimation is computationally expensive for such systems with a large number of units. Therefore, we develop an efficient approach for reliability approximation with high accuracy based on Monte Carlo simulation. Also, we investigate the system reliability further by assuming that the units are subject to degradation. In many situations, units exhibit degradation that can be monitored. We model the degradation path of any unit based on collected observations of the degradation indicator and its physics-based or statistics-based degradation rate. We consider the effect of units’ operating conditions on their degradation paths. Moreover, available system capacity is an important indicator of a system's condition. A system fails when its capacity drops below a minimum value. We estimate the reliability metrics of weighted-c-out-of-n pairs:G Balanced systems, which considers the capacities of individual units. We investigate the problem in two scenarios: First, we assume that the capacity of any unit has multiple levels. Second, we assume that the capacity of any unit has two levels (either working or failed) whereas different units may have different capacities. In the second scenario, we consider load-sharing effect. Furthermore, optimal design for systems with spatially distributed units is the key to maximizing the reliability of the systems given the constraints such as the upper bound for the total number of units and load-sharing effect. We study the optimal configuration that maximizes the system reliability metrics.Ph.D.Includes bibliographical referencesby Dingguo Hu
Red biotechnology: A healthy world
No full textBiotechnology is an interdisciplinary field of engineering, physics, chemistry and biology. The present era of Biotechnology has reached to a stage of treating killer diseases like cancer, HIV; disorders like diabetes, cardiac diseases and even hereditary disease at the genetic level etc. This review emphasizes the applications of Biotechnology in the medicine field and the basic development of nanomedicine. © 2019 Author(s)
Corrigendum to “Prognostic indicators in clinically node-negative malignant primary salivary tumours of the parotid: A multicentre experience”. [Oral Oncol. 123 (2021) 105577](S1368837521006849)(10.1016/j.oraloncology.2021.105577)
No full textThe authors regret to report that in the recently published article entitled “Prognostic indicators in clinically node-negative malignant primary salivary tumours of the parotid: a multicentre experience” the names of co-authors Paweł Golusiński (Department of Otolaryngology and Maxillofacial Surgery, University of Zielona Gora, Poland; Department of Maxillofacial Surgery, Poznan University of Medical Sciences, Poland) and Mateusz Szewczyk (Department of Head and Neck Surgery, Poznan University of Medical Sciences, Greater Poland Cancer Center, Poland) were accidentally omitted. These author names and affiliations have been added as above. The authors would like to apologize for any inconvenience caused
Characteristics of Magnet Nursing Work Environment that Promotes Patient Safety Culture at Mansoura University Oncology Center
No full textThe Magnet hospital concept and related criteria have drawn the consideration of researchers and nurses for more than twenty years. Nurses assume an essential part in forming health policy in any nation by observing the problems in the healthcare organizations and evaluating its effects. They understand where enhancements are required to shape care, rise access, enhance proficiency and quality of health services, and encourage prevention. Perfection in nursing care has been connected with constructive results for both nurses and patients. To accomplish magnet status, hospitals should provide confirmation of having band of attributes as exemplary professional practice; knowledge; structural empowerment, improvements, and innovation; and transformational leadership. These attributes act together to shape a positive workplace that ought to prompt better results. Magnet designation gives a helpful mechanism for assessing and changing nursing workplace to be more proficient. Many of the recent efforts concentrated on enhancing patient safety and quality. Less efforts has been focused on enhancing nursing care to improve patient safety. Hence, the present determine the relationship between magnetism dimensions and patient safety culture in inpatient units at Oncology Center Mansoura University (OCMU). A descriptive correlation design was used. Sample of the study consisted of all nurses (n=95 nurses) working in inpatient units in the Oncology Center Mansoura University. Two tools were used for data collection, namely; Magnetism Dimensions Scale and Patient Safety Culture Questionnaire. A major finding of the present study there was a statistically significant correlation between magnetism dimensions and patient safety culture in inpatient units. It was concluded that the nurse administrators play an important role in establishing conditions for magnet work environment that support patient safety culture. It was recommended that additional researches are needed to correlate patient outcomes to magnet issues. Key words: nursing work environment, Magnet, Magnet Recognition Program, Forces of Magnetism professional practice environments, quality, patient safet
Distribution-free fault identification and anomaly detection in high-dimensional data
No full textQuality engineering is an essential activity in production processes and its objective is to ensure the quality of the products throughout the production stages. Many processes have several attributes that need to be continuously monitored to detect any variable changes in the production process. We refer to the monitoring process with several quality characteristics as multivariate statistical process control (MSPC). Most of the quality control procedures assume that the characteristics of the process follow normal distributions; however, this is a limiting assumption since the underlying distribution of the processes may not be normal. In this dissertation, we present procedures to identify the faulty variables and detect anomalies in MSPC with high dimensional data when the underlying distribution of the process is unknown. We first propose a distribution-free adaptive step-down (DFASD) procedure, which is motivated by a well-known data description method called support vector data description (SVDD). This data description procedure includes the support vectors which identify the hypersphere boundary for the available data by using the kernel concept. In a high-dimensional process, identifying the variable or a subset of variables, which cause an out-of-control (OC) signal, is a challenging issue in quality engineering. DFASD procedure utilizes conditional statistics for the identification of faulty variables. The proposed DFASD procedure selects a variable having no significant evidence of a change at each step based on the variables that are selected in the previous steps. The proposed DFASD stops when there are no longer variables to classify to the unchanged set. Therefore, it concludes the variables which are not in the unchanged set as changed variables. We then present a new distribution-free fault identification procedure based on Bayesian inference which is called Bayesian SVDD (BSVDD). While the traditional SVDD assumes that the process parameters are constants to be determined, the center of hypersphere may be considered as a random vector with inherent randomness based on a given training dataset. We introduce a Bayesian approach for SVDD by assuming that a transformed data into the higher dimensional space follow normal distribution. A distance from a point to the center of the hypersphere is inversely proportional to the likelihood in the proposed model. This is because SVDD is a special case of the proposed BSVDD model, which improves SVDD by utilizing the precise prior knowledge. Therefore, by combining proposed BSVDD with an adaptive step-down procedure, we drive a new BSVDD based fault identification procedure for the MSPC. This is the first research to identify the faulty variables by using the distribution-free approach based on Bayesian inference. We also present an anomaly detection procedure which is easily applicable in detecting anomalies in multimode processes. Traditional quality control procedures assume that normal observations are obtained from a single distribution. However, due to the complexities of modern industrial processes, the observations might have multiple operating modes. In other words, normal observations may be obtained from more than one distribution. In such cases, conventional quality control procedures might trigger false alarms while the process is indeed in another operating mode. We propose a generalized support vector-based anomaly detection procedure called n-class SVDD which can be used to determine the anomalies in multimode processes. The proposed procedure constructs n hyperspheres by considering the relationship among modes. In addition, we introduce a generalized Bayesian framework by not only considering the prior information from each mode but also the relationships among the modes. Finally, we present a new Bayesian procedure for anomaly detection in multi-class data. The existing procedures for anomaly detection mostly take only the normal information into account. However, the anomaly information is often available from the engineering knowledge and the historical data of the process. The performance of the anomaly detection procedures can be improved when available anomaly data are utilized to obtain data description. We propose a multi-class Bayesian SVDD model that takes anomaly data into consideration when the anomaly data are available and an appropriate prior distribution of the anomaly data is obtained.Ph.D.Includes bibliographical referencesby Mehmet Turko
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