Reliability measures precision or the extent to which test results can be replicated. This is the first ever systematic review to identify statistical methods used to measure reliability of equipment measuring continuous variables. This studyalso aims to highlight the inappropriate statistical method used in the reliability analysis and its implication in the medical practice.
This paper considers a Monte Carlo simulation based method for estimating cycle stocks (production lot-sizing stocks) in a typical batch production system, where a variety of products is scheduled for production at determined periods of time. Delivery time is defined as the maximum lead time and pre-assembly processing time of the product's raw materials in the method. The product's final assembly cycle and delivery time, which were obtained via the production schedule and supply chain simulation, respectively, were both considered to estimate the demand distribution of product based on total duration. Efficient random variates generators were applied to model the lead time of the supply chain's stages. In order to support the performance reliability of the proposed method, a real case study is conducted and numerically analyzed.
The aim of the study was to validate the Malay version of the General Quentionnaire (GHQ-12) in patients with psychiatric morbidity secondary to urological disorder. Validity and reliability were studied in patients with lower urinary tract symptoms (LUTS) and patients without LUTS. Internal consistency was excellent. A high degree of internal consistency was observed for each of the 12 items and total scores (Cronbach's alpha value = 0.50 and higher and 0.65 respectively. Test-retest correlation coefficient for the 12 items scores was highly significant. Intraclass correlation coefficient was high (ICC=0.47 and above). A significant level between baseline and post-treatment scores were observed across 3 items in the surgical group. The Mal-GHQ-12 is a suitable, reliable, valid and sensitive to clinical change in the Malaysian population.
In this study, a double-negative triangular metamaterial (TMM) structure, which exhibits a resounding electric response at microwave frequency, was developed by etching two concentric triangular rings of conducting materials. A finite-difference time-domain method in conjunction with the lossy-Drude model was used in this study. Simulations were performed using the CST Microwave Studio. The specific absorption rate (SAR) reduction technique is discussed, and the effects of the position of attachment, the distance, and the size of the metamaterials on the SAR reduction are explored. The performance of the double-negative TMMs in cellular phones was also measured in the cheek and the tilted positions using the COMOSAR system. The TMMs achieved a 52.28% reduction for the 10 g SAR. These results provide a guideline to determine the triangular design of metamaterials with the maximum SAR reducing effect for a mobile phone.
The need for implementing reliable data transfer in resource-constrained cognitive radio ad hoc networks is still an open issue in the research community. Although geographical forwarding schemes are characterized by their low overhead and efficiency in reliable data transfer in traditional wireless sensor network, this potential is still yet to be utilized for viable routing options in resource-constrained cognitive radio ad hoc networks in the presence of lossy links. In this paper, a novel geographical forwarding technique that does not restrict the choice of the next hop to the nodes in the selected route is presented. This is achieved by the creation of virtual clusters based on spectrum correlation from which the next hop choice is made based on link quality. The design maximizes the use of idle listening and receiver contention prioritization for energy efficiency, the avoidance of routing hot spots and stability. The validation result, which closely follows the simulation result, shows that the developed scheme can make more advancement to the sink as against the usual decisions of relevant ad hoc on-demand distance vector route select operations, while ensuring channel quality. Further simulation results have shown the enhanced reliability, lower latency and energy efficiency of the presented scheme.
A number of techniques have been proposed during the last three decades for noise variance and signal-to-noise ratio (SNR) estimation in digital images. While some methods have shown reliability and accuracy in SNR and noise variance estimations, other methods are dependent on the nature of the images and perform well on a limited number of image types. In this article, we prove the accuracy and the efficiency of the image noise cross-correlation estimation model, vs. other existing estimators, when applied to different types of scanning electron microscope images.
Numerous applications of artificial olfaction resulting from research in many branches of sciences have caused considerable interest in the enhancement of these systems. In this paper, we offer an architecture which is suitable for critical applications, such as medical diagnosis, where reliability and precision are deemed important. The proposed architecture is able to tolerate failures in the sensors of the array. In this study, the discriminating ability of the proposed architecture in detecting complex odors, as well as the performance of the proposed architecture in encountering sensor failure, were investigated and compared with the generic architecture. The results demonstrated that by applying the proposed architecture in the artificial olfactory system, the performance of system in the healthy mode was identical to the classic structure. However, in the faulty situation, the proposed architecture implied high identification ability of odor samples, while the generic architecture showed very poor performance in the same situation. Based on the results, it was possible to achieve high odor identification through the developed artificial olfactory system using the proposed architecture.
Stone Mastic Asphalt (SMA) is one type of asphalt mixture which is highly dependent on the method
of compaction as compared to conventional Hot Mix Asphalt (HMA) mixture. A suitable laboratory compaction method which can closely simulate field compaction is evidently needed as future trend
in asphalt pavement industry all over the world is gradually changing over to the SMA due to its excellent performance characteristics. This study was conducted to evaluate the SMA slab mixtures compacted using a newly developed Turamesin roller compactor, designed to cater for laboratory compaction in field simulation conditions. As the newly developed compaction device, there is a need for evaluating the compacted slab dimensions (which include length, width, and thickness), analyzing the consistency of the measured parameters to verify the homogeneity of the compacted slabs and determining the reliability of Turamesin. A total of 15 slabs from three different types of asphalt mixtures were compacted, measured, and analyzed for their consistencies in terms of length, width, and thickness. Based on study the conducted, the compacted slabs were found to have problems in terms of the improperly compacted section of about 30 mm length at both ends of the slabs and the differences in the thickness between left- and right-side of the slab which were due to unequal load distribution from the roller compactor. The results obtained from this study have led to the development of Turamesin as an improved laboratory compaction device.
The 1 MW TRIGA MARK II research reactor at Malaysian Nuclear Agency achieved initial
criticality on June 28, 1982. The reactor is designed to effectively implement the various fields of
basic nuclear research, manpower training, and production of radioisotopes. This
paperdescribes the reactor parameters calculation for the PUSPATI TRIGA REACTOR (RTP);
focusing on the application of the developed reactor 3D model for criticality calculation,
analysis of power and neutron flux distribution and depletion study of TRIGA fuel. The 3D
continuous energy Monte Carlo code MCNP was used to develop a versatile and accurate full
model of the TRIGA reactor. The consistency and accuracy of the developed RTP MCNP model
was established by comparing calculations to the experimental results and TRIGLAV
code.MCNP and TRIGLAV criticality prediction of the critical core loading are in a very good
agreement with the experimental results.Power peaking factor calculated with TRIGLAV are
systematically higher than the MCNP but the trends are the same.Depletion calculation by both
codes show differences especially at high burnup.The results are conservative and can be
applied to show the reliability of MCNP code and the model both for design and verification of
the reactor core, and future calculation of its neutronic parameters.
Behaviour models are the most commonly used input for predicting the reliability of a software system at the early design stage. A component behaviour model reveals the structure and behaviour of the component during the execution of system-level functionalities. There are various challenges related to component reliability prediction at the early design stage based on behaviour models. For example, most of the current reliability techniques do not provide fine-grained sequential behaviour models of individual components and fail to consider the loop entry and exit points in the reliability computation. Moreover, some of the current techniques do not tackle the problem of operational data unavailability and the lack of analysis results that can be valuable for software architects at the early design stage. This paper proposes a reliability prediction technique that, pragmatically, synthesizes system behaviour in the form of a state machine, given a set of scenarios and corresponding constraints as input. The state machine is utilized as a base for generating the component-relevant operational data. The state machine is also used as a source for identifying the nodes and edges of a component probabilistic dependency graph (CPDG). Based on the CPDG, a stack-based algorithm is used to compute the reliability. The proposed technique is evaluated by a comparison with existing techniques and the application of sensitivity analysis to a robotic wheelchair system as a case study. The results indicate that the proposed technique is more relevant at the early design stage compared to existing works, and can provide a more realistic and meaningful prediction.
Plurality voter is one of the commonest voting methods for decision making in highly-reliable applications in which the reliability and safety of the system is critical. To resolve the problem associated with sequential plurality voter in dealing with large number of inputs, this paper introduces a new generation of plurality voter based on parallel algorithms. Since parallel algorithms normally have high processing speed and are especially appropriate for large scale systems, they are therefore used to achieve a new parallel plurality voting algorithm by using (n/log n) processors on EREW shared-memory PRAM. The asymptotic analysis of the new proposed algorithm has demonstrated that it has a time complexity of O(log n) which is less than time complexity of sequential plurality algorithm, i.e. O (n log n).
Setting a question paper for test, quiz, and examination is one of the teachers’ tasks. The factors that are usually taken into consideration in carrying out this particular task are the level of difficulty of the questions and the level of the students’ ability. In addition, teachers will also have to consider the number of questions that have impact on the examination. This research describes a model-based test theory to study the confidence intervals for the projected number of items of a test, given the reliability of the test, the difficulty of the question, and the students’ ability. Using the simulated data, the confidence intervals of the projected number of items were examined. The probability coverage and the length of the confidence interval were also used to evaluate the confidence intervals. The results showed that the data with a normal distribution, the ratio variance components of 4:1:5 and reliability equal to 0.80 gave the best confidence interval for the projected number of items.
The objective of this paper is to report on the reliability and validity of a knowledge, attitude and practice instrument used among young primary school children. The instrument was developed as an evaluation tool in the HELIC study and consisted of 23 knowledge, 11 attitude and 10 practice items. A total of 335 Year 2 students from 4 randomly selected primary schools in Selangor and Wilayah Persekutuan participated in the HELIC study. Students were divided into small groups and an enumerator verbally administered the instrument to each group. Reliability for each construct (knowledge, attitude and practice) was estimated as item to total score correlation and internal consistency (Cronbach's alpha). Construct validity was determined through factor analysis and Pearson correlation. Results indicated that 3 attitude and 3 practice items did not correlate significantly to the total score (p>0.05). However, the deletion of these items did not significantly alter the Cronbach's alpha coefficients. Internal consistency was good for knowledge (a=0.68) but low for attitude (a=0.37) and practice (a=0.36) constructs. Based on factor analysis, 5 factor-solutions emerged for knowledge and 4 factor solutions for attitude and practice. Sufficient variance was obtained for the factors in knowledge (51.7%), attitude (51.2% and practice (51.0%). There were also significant positive correlations among the constructs ( ~ 4 . 0 1 ) . In conclusion, the instrument was valid and reliable, especially for the knowledge construct. Further improvements, particularly on the attitude and practice constructs, are needed in order for the instrument to be an effective assessment or evaluation tool in various settings.
INTRODUCTION AND OBJECTIVE:
Most of important variables measured in medicine are in numerical forms or continuous in nature. New instruments and tests are constantly being developed for the purpose of measuring various variables, with the aim of providing cheaper, non-invasive, more convenient and safe methods. When a new method of measurement or instrument is invented, the quality of the instrument has to be assessed. Agreement and reliability are both important parameters in determining the quality of an instrument. This article will discuss some issues related to methods comparison study in medicine for the benefit of medical professional and researcher.
This is a narrative review and this article review the most common statistical methods used to assess agreement and reliability of medical instruments that measure the same continuous outcome. The two methods discussed in detail were the Bland-Altman Limits of Agreement, and Intra-class Correlation Coefficient (ICC). This article also discussed some issues related to method comparison studies including the application of inappropriate statistical methods, multiple statistical methods, and the strengths and weaknesses of each method. The importance of appropriate statistical method in the analysis of agreement and reliability in medicine is also highlighted in this article.
There is no single perfect method to assess agreement and reliability; however researchers should be aware of the inappropriate methods that they should avoid when analysing data in method comparison studies. Inappropriate analysis will lead to invalid conclusions and thus validated instrument might not be accurate or reliable. Consequently this will affect the quality of care given to a patient.
It is the first time to do investigation the reliability and validity of thirty kinetic and isotherm models for describing the behaviors of adsorption of silver nanoparticles (AgNPs) onto different adsorbents. The purpose of this study is therefore to assess the most reliable models for the adsorption of AgNPs onto feasibility of an adsorbent. The fifteen kinetic models and fifteen isotherm models were used to test secondary data of AgNPs adsorption collected from the various data sources. The rankings of arithmetic mean were estimated based on the six statistical analysis methods of using a dedicated software of the MATLAB Optimization Toolbox with a least square curve fitting function. The use of fractal-like mixed 1, 2-order model for describing the adsorption kinetics and that of Fritz-Schlunder and Baudu models for describing the adsorption isotherms can be recommended as the most reliable models for AgNPs adsorption onto the natural and synthetic adsorbent materials. The application of thirty models have been identified for the adsorption of AgNPs to clarify the usefulness of both groups of the kinetic and isotherm equations in the rank order of the levels of accuracy, and this significantly contributes to understandability and usability of the proper models and makes to knowledge beyond the existing literatures.
Preventive tests and diagnosis of in-service power transformer are important for early fault prediction and increased reliability of electricity supply. However, some existing diagnostic techniques require transformer outage before the measurement can be performed and need expert knowledge and experiences to interpret the measurement results. Other measurement techniques such as chemical analyses of insulating oil may cause significant variance to measurement results due to different practices in oil sampling, storage, handling and transportation of oil. A cost-effective measuring technique, which is simple, providing fast and an accurate measurement results, is therefore highly required. The extended application of Polarisation and Depolarisation (PDC) measurement for characterisation of different faults conditions in-service power transformer has been presented in this paper. Earlier studies on polarisation and depolarisation current of oil samples from in-service power transformer shows that depolarisation has provided significant information about the change of material properties due to faults in power transformer. In this paper, a new approach based on Depolarisation Current Ratio Index (DRI) was developed for identifying and classifying different transformer fault conditions. The DRI at time interval of 4s to 100s was analysed and the results show that DRI of depolarisation current between 5/100s and 10/100s provides higher correlation on the incipient faults in power transformer.
This work demonstrates the high performance graphene oxide (GO)/PEDOT:PSS doubled decked hole transport layer (HTL) in the PCDTBT:PC71BM based bulk heterojunction organic photovoltaic device. The devices were tested on merits of their power conversion efficiency (PCE), reproducibility, stability and further compared with the devices with individual GO or PEDOT:PSS HTLs. Solar cells employing GO/PEDOT:PSS HTL yielded a PCE of 4.28% as compared to either of individual GO or PEDOT:PSS HTLs where they demonstrated PCEs of 2.77 and 3.57%, respectively. In case of single GO HTL, an inhomogeneous coating of ITO caused the poor performance whereas PEDOT:PSS is known to be hygroscopic and acidic which upon direct contact with ITO reduced the device performance. The improvement in the photovoltaic performance is mainly ascribed to the increased charge carriers mobility, short circuit current, open circuit voltage, fill factor, and decreased series resistance. The well matched work function of GO and PEDOT:PSS is likely to facilitate the charge transportation and an overall reduction in the series resistance. Moreover, GO could effectively block the electrons due to its large band-gap of ~3.6 eV, leading to an increased shunt resistance. In addition, we also observed the improvement in the reproducibility and stability.
Semiconductor metal oxide (SMO) as a sensing layer for gas detection has been widely used. Many researches have been performed to enhance the sensing performance including its sensitivity, reliability and selectivity. Electrical sensors that use resistivity as an indicator of its sensing are popular and well established. However, the optical based sensor is still much to explore in detecting gas. By integrating it with SMO, the sensor offers good alternative to overcome some drawbacks from electrical sensors.
In the recent past, Internet of Things (IoT) plays a significant role in different applications such as health care, industrial sector, defense and research etc.… It provides effective framework in maintaining the security, privacy and reliability of the information in internet environment. Among various applications as mentioned health care place a major role, because security, privacy and reliability of the medical information is maintained in an effective way. Even though, IoT provides the effective protocols for maintaining the information, several intermediate attacks and intruders trying to access the health information which in turn reduce the privacy, security and reliability of the entire health care system in internet environment. As a result and to solve the issues, in this research Learning based Deep-Q-Networks has been introduced for reducing the malware attacks while managing the health information. This method examines the medical information in different layers according to the Q-learning concept which helps to minimize the intermediate attacks with less complexity. The efficiency of the system has been evaluated with the help of experimental results and discussions.