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.
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.
METHOD:
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.
CONCLUSION:
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.
The conversion of plastic waste into fuel by pyrolysis has been recognized as a potential strategy for commercialization. The amount of plastic waste is basically different for each country which normally refers to non-recycled plastics data; consequently, the production target will also be different. This study attempted to build a model to predict fuel production from different non-recycled plastics data. The predictive model was developed via Levenberg-Marquardt approach in feed-forward neural networks model. The optimal number of hidden neurons was selected based on the lowest total of the mean square error. The proposed model was evaluated using the statistical analysis and graphical presentation for its accuracy and reliability. The results showed that the model was capable to predict product yields from pyrolysis of non-recycled plastics with high accuracy and the output values were strongly correlated with the values in literature.
The Forms and Functions of Aggression Questionnaire is widely used to measure the aggressive behavior in the peer context by many researchers. This scale was developed by Little, Jones, Henrich, and Hawley in 2003. It consists of four principle dimensions of aggressive behavior: overt and relational aggression, instrumental and reactive aggression. The objectives of this study were (i) to evaluate the revised and translated version of this scale by CFA, (ii) to know the reliability of the model, and (iii) to prove the validity of the model. The Chinese version appears to be a valid and reliable instrument for the assessment of relational aggression against sibling. The resulting model will help future researchers, especially related to aggression in the sibling context.
This paper outlines an alternative algorithm for solving general second order ordinary differential equations (ODEs). Normally, the numerical method was designed for solving higher order ODEs by converting it into an n-dimensional first order equations with implementation of constant step length. Nevertheless, this involved a lot of computational complexity which led to consumption a lot of time. Consequently, a direct block multistep method with utilization of variable step size strategy is proposed. This method was developed for computing the solution at four points simultaneously and the derivation based on numerical integration as well as using interpolation approach. The convergence of the proposed method is justified under suitable conditions of stability and consistency. Five numerical examples are considered and some comparisons are made with the existing methods for demonstrating the validity and reliability of the proposed algorithm.
Microgrids help to achieve power balance and energy allocation optimality for the defined load networks. One of the major challenges associated with microgrids is the design and implementation of a suitable communication-control architecture that can coordinate actions with system operating conditions. In this paper, the focus is to enhance the intelligence of microgrid networks using a multi-agent system while validation is carried out using network performance metrics i.e., delay, throughput, jitter, and queuing. Network performance is analyzed for the small, medium and large scale microgrid using Institute of Electrical and Electronics Engineers (IEEE) test systems. In this paper, multi-agent-based Bellman routing (MABR) is proposed where the Bellman-Ford algorithm serves the system operating conditions to command the actions of multiple agents installed over the overlay microgrid network. The proposed agent-based routing focuses on calculating the shortest path to a given destination to improve network quality and communication reliability. The algorithm is defined for the distributed nature of the microgrid for an ideal communication network and for two cases of fault injected to the network. From this model, up to 35%-43.3% improvement was achieved in the network delay performance based on the Constant Bit Rate (CBR) traffic model for microgrids.
Executive functions (EFs) are a set of high-level cognitive and behavioral monitoring skills that are important to employees' work performance. The 25-item Executive Skills Questionnaire-Revised (ESQ-R) measures executive dysfunction in five dimensions (e.g., emotional regulation). Nevertheless, the usability of this newly developed scale for employees remains unclear. The present study evaluated the psychometric properties of the adopted ESQ-R for working adults in Malaysia. A total of 325 employees responded to an online survey consisted of the ESQ-R, Executive Function Index (EFI), self-rated creativity scale (SRCS), and 9-item Utretch Work Engagement Scale (UWES-9) and Employee Well-being Scale. Several CFAs were conducted to compare three competing models. While all models showed a good fit, the 5-factor second-order model that is in line with the theoretical structure is preferable. The ESQ-R showed excellent internal consistency. Moreover, the ESQ-R score was negatively correlated with EFI, creativity, and UWES-9 scores, supporting the convergent, discriminant, and concurrent validity. The ESQ-R score also explained incremental variance in well-being above and beyond scores of the UWES-9 and SRCS. Taken together, the ESQ-R is a useful tool for assessing employees' executive dysfunction and suggesting intervention programs helping employees with deficits in EFs.
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.
Nanowires have been utilized widely in the generation of high-performance nanosensors. Laser ablation, chemical vapor, thermal evaporation and alternating current electrodeposition are in use in developing nanowires. Nanowires are in a great attention because of their submicron feature and their potentials in the front of nanoelectronics, accelerated field effect transistors, chemical- and bio-sensors, and low power consuming light-emitting devices. With the control of nanowire size and concentration of dopant, the electrical sensitivity and other properties of nanowires can be tuned for the reproducibility. Nanowires comprise of arrays of electrodes that form a nanometer electrical circuit. One of advantages of nanowires is that they can be fabricated in nanometer-size for various applications in different approaches. Several studies have been conducted on nanowires and researchers discovered that nanowires have the potential in the applications with material properties at the nanometer scale. The unique electrical properties of nanowires have made them to be promising for numerous applications. Nowadays, for example, MOS field-effect transistors are largely used as fundamental building elements in electronic circuits. Also, the dimension of MOS transistors is gradually decreasing to the nanoscale based on the prediction made by Moor's law. However, their fabrication is challenging. This review summarized different techniques in the fabrication of nanowires, global nanowire prospect, testing of nanowires to understand the real electrical behavior using higher resolution microscopes, and brief applications in the detection of biomolecules, disease such as corona viral pandemic, heavy metal in water, and applications of nanowires in agriculture.
Among the challenges in systematic inquiry into elder abuse and neglect (EAN) is the lack of standardized tool of measurement. Existing literature demonstrates diverse tools being used, with the Conflict Tactics Scale (CTS) and its versions being the most common. The Malaysian Elder Mistreatment Project (MAESTRO) utilized the Modified CTS developed and used by the National Study of Elder Abuse and Neglect in Ireland (NSEA-I). This article aimed to validate this Malay version of the modified CTS for use in the Malaysian context and by Malay-speaking populations across Southeast Asia while highlighting the various practical and methodological challenges encountered along the process. Data were collected from 1927 older respondents who lived in Kuala Pilah district. Preliminary data screening led to the dropping of 10 items due to 0 variance. Further four items were deleted during CFA due to low loading. The indicators of neglect factor were made into a composite factor due to high collinearity. The final scale had acceptable reliability and validity. This tool is likely to assist in assessing and detecting EAN more quickly and conveniently. It will also assist future researches of EAN in taking into account the issues that arise in the measurement of EAN.
Background: The confinement measures during COVID-19 had a massive effect on physical and psychological health in public. This study assessed the impact of containment and coping behaviour among the Malaysia public during the COVID-19 pandemic. Questions assessing the impact of containment and coping behaviours were developed and psychometrically tested. Methods: Exploratory factor analysis (EFA) was conducted with the items using principal component analysis extraction and Varimax rotation. Partial least squares structural equation modelling was used to determine the relationship between coping and impact. Results: The 13-item of impact and 10-item coping instruments were developed with three dimensions identified through EFA. Both scales demonstrated excellent composite reliability and good convergent validity. The survey findings revealed that the impact on individual psychological aspects was prominent, followed by well-being and lifestyle. Mindfulness and physical coping strategies were most commonly reported. Coping through seeking help from health professionals and hotlines had a positive direct effect on well-being and lifestyle (b = 0.231, p < 0.001), psychological (B = 0.132, p < 0.001), and employment-related (0.194, p < 0.001) impacts. Coping through mindfulness practise had a negative effect on well-being and lifestyle-related impact (B = -0.180, p < 0.001) and employment-related impact (B = -0.096, p = 0.008). Conclusions: Despite some limitation, the scales for measuring impact and coping behaviours have the potential to be used as a measurement tool in future studies. Findings highlight the enormous impact of the pandemic on psychological well-being and lifestyles. Health authorities should support individual coping as it was found to be an important resilience-related factor to mitigate the impacts of containment during the pandemic.
Evidence on the role of the oral microbiome in health and disease is changing the way we understand, diagnose, and treat ailments. Numerous studies on diseases affecting the oral cavity have revealed a large amount of data that is invaluable for the advancements in diagnosing and treating these diseases. However, the clinical translation of most of these exploratory data is stalled by variable methodology between studies and non-uniform reporting of the data.Understanding the key areas that are gateways to bias in microbiome studies is imperative to overcome this challenge faced by oral microbiome research. Bias can be multifactorial and may be introduced in a microbiome research study during the formulation of the study design, sample collection and storage, or the sample processing protocols before sequencing. This chapter summarizes the recommendations from literature to eliminate bias in the microbiome research studies and to ensure the reproducibility of the microbiome research data.
Aerospace equipages encounter potential radiation footprints through which soft errors occur in the memories onboard. Hence, robustness against radiation with reliability in memory cells is a crucial factor in aerospace electronic systems. This work proposes a novel Carbon nanotube field-effect transistor (CNTFET) in designing a robust memory cell to overcome these soft errors. Further, a petite driver circuit to test the SRAM cells which serve the purpose of precharge and sense amplifier, and has a reduction in threefold of transistor count is recommended. Additionally, analysis of robustness against radiation in varying memory cells is carried out using standard GPDK 90 nm, GPDK 45 nm, and 14 nm CNTFET. The reliability of memory cells depends on the critical charge of a device, and it is tested by striking an equivalent current charge of the cosmic ray's linear energy transfer (LET) level. Also, the robustness of the memory cell is tested against the variation in process, voltage and temperature. Though CNTFET surges with high power consumption, it exhibits better noise margin and depleted access time. GPDK 45 nm has an average of 40% increase in SNM and 93% reduction of power compared to the 14 nm CNTFET with 96% of surge in write access time. Thus, the conventional MOSFET's 45 nm node outperforms all the configurations in terms of static noise margin, power, and read delay which swaps with increased write access time.