Assessment Of Performance Measures Research Articles

Annoyance assessment of the combined effect of railway noise and vibration including physiological measurements

The prediction of annoyance due to railway transportation in residential areas is crucial to correctly dimension noise and vibration reduction solutions. In situ annoyance assessment for multiple exposition scenarios requires to convince many residents to take annoyance tests combined with noise and vibration measurements inside their homes. Such approach is not adapted to large-scale experiments. Therefore, the VibAcouPhysio project aims at studying annoyance due to railway noise and vibration in laboratory conditions. A vibrating platform combined with a high-quality sound reproduction system has been designed for this project to reproduce the noise and vibrations of a train pass-by. Annoyance is estimated by multiple means including questionnaires, cognitive tasks performance assessment and various physiological measurements. Twenty participants were exposed to various train pass-by signals as if they were seating at a table inside their housing. The results show a significant correlation between physiological metrics, the annoyance evaluated in the questionnaires and the physical indicators derived from the noise and vibration signals. The main purpose of the VibAcouPhysio project is to rank various physical indicators describing noise and vibration signals according to their correlation to the perceived or measured annoyance.

Организационна ефективност – концептуална рамка, критерии и модели

The purpose of this study is to present a unified model of organizational effectiveness, bringing together various elements proposed by previous researchers to provide a comprehensive framework that can be used to guide future research, taking into account the organizational context. A unified model is proposed that is holistic, comprehensive and integrated, incorporating systems thinking. It consists of three components – objectives, measures and stakeholder/stakeholder satisfaction – and can be customized by practitioners and researchers to specific contexts in a range of organizations. The framework can be used to help managers select balanced, appropriate organizational performance assessment measures that are context-specific and provide a more holistic approach to guide research.

Routine data in a primary care performance dashboard, Ethiopia.

To explore the feasibility of building a primary care performance dashboard using DHIS2 data from Ethiopia's largest urban (Addis Ababa), agrarian (Oromia) and pastoral (Somali) regions. We extracted 26 data elements reported by 12 062 health facilities to DHIS2 for the period 1July 2022 to 30June 2023. Focusing on indicators of effectiveness, safety and user experience, we built 14 indicators of primary care performance covering reproductive, maternal and child health, human immunodeficiency virus, tuberculosis, noncommunicable disease care and antibiotic prescription. We assessed data completeness by calculating the proportion of facilities reporting each month, and examined the presence of extreme outliers and assessed external validity. At the regional level, average completeness across all data elements was highest in Addis Ababa (82.9%), followed by Oromia (66.2%) and Somali (52.6%). Private clinics across regions had low completeness, ranging from 38.6% in Somali to 58.7% in Addis Ababa. We found only a few outliers (334 of 816 578 observations) and noted that external validity was high for 11 of 14 indicators of primary care performance. However, the 12-month antiretroviral treatment retention rate and proportions of patients with controlled diabetes or hypertension exhibited poor external validity. The Ethiopian DHIS2 contains information for measuring primary care performance, using simple analytical methods, at national and regional levels and by facility type. Despite remaining data quality issues, the health management information system is an important data source for generating health system performance assessment measures on a national scale.

An improved long short-term memory with denoising autoencoder for solving text classification problems

In the past decade, text classification has been a popular research field for automatically categorizing documents into relevant categories. Predicting classes for input samples based on their features is the goal of classification. To limit the dimension of the feature space for precise text categorization, feature selection approaches are widely used for recognizing important and affective features while ignoring unimportant, weak, and chaotic components. This paper proposes a new model referred to as DAE-LSTM for reducing data dimension through the use of a denoising autoencoder. (Long Short-Term Memory (LSTM) is one of the popular text classification approaches, particularly when dealing with sequential datasets. Additionally, the Rectified Linear Unit (Relu) activation function was used instead of the hyperbolic tangent activation function (tanh) to improve the accuracy of the model. Four benchmark datasets were used to evaluate the proposed model against 4 cutting-edge text classification methods, including conventional Bidirectional LSTM, Bidirectional GRU, CNN-LSTM, and CNN-GRU. The suggested model has been found to perform much better than current methods using several kinds of performance assessment measures.

Quarterly fluctuations in external and internal loads among professional basketball players.

This study aims to explore the variations in external and internal loads on a quarter-by-quarter basis among professional Chinese basketball players. It emphasizes the crucial impact of these variations on optimizing athletic performance and match strategies. An observational longitudinal study design was employed, involving sixteen male players from the National Basketball League during the 2024 season in China. Data collection was facilitated through the use of Catapult S7 devices for measuring external loads and session ratings of perceived exertion (sRPE) for assessing internal loads. Linear mixed-effects models were utilized for the statistical analysis to identify differences in workload intensities across game quarters based on player positions. The Pearson correlation coefficient was used to examine the relationship between external and internal load throughout the game. The analysis uncovered significant positional differences in workload intensities across game quarters. Guards were found to have a higher PlayerLoad™ (PL) per minute in the first quarter, while centers demonstrated an increase in high-intensity accelerations and jumps in the fourth quarter. Furthermore, a significant moderate correlation between sRPE and PL was observed across all game quarters, indicating a link between physical exertion and athletes' perceptions of effort. The study offers new insights into the dynamic physical demands faced by basketball players and the importance of using both objective and subjective measures for a comprehensive assessment of athlete performance and wellbeing. The findings underscore the interconnectedness of physical exertion and athlete perception, providing a foundation for future research and practical applications in the field of basketball science.

Clear Water Scour Depth Prediction using Gradient Boosting Machine and Deep Learning

The scouring process in adjacent to spur dikes has the potential for compromising the stability of riverbanks. Hence, it is necessary for river engineering to conduct precise measurement of maximum scour depth in the vicinity of spur dikes. Nevertheless, the determination of the maximum scour depth has proven to be a challenging task, primarily due to the complex nature of the scour phenomena associated with these structures. In this study, two data-driven models, namely the Gradient Boost Machine (GBM) and Deep Learning (DL), were developed to predict the clear water scour depth near to a spur dike. A total of 154 distinct observations have been collected from previous literatures. A total of 103 observations were utilized for training the model, while 53 observation were allocated for validation purposes. Several performance assessment measures were employed to evaluate the performance of the models, including the correlation coefficient (CC), root-coefficient of determination (R2), scattered plot, variation plot, and box plot. GBM outperformed the DL on the basis of above-mentioned assessment measures. Sensitivity analysis suggests that l/d50 is the most influences input parameter. Thus, the conclusion suggested that both the data-driven model can be used in the prediction of the clear water scour depth around spur dikes but GBM have highest accuracy.

Temporal validation and updating of a prediction model for the diagnosis of gestational diabetes mellitus

ObjectiveThe original Monash gestational diabetes mellitus (GDM) risk prediction in early pregnancy model is internationally externally validated and clinically implemented. We temporally validate and update this model in a contemporary population with a universal screening context and revised diagnostic criteria and ethnicity categories, thereby improving model performance and generalizability. Study Design and SettingThe updating dataset comprised of routinely collected health data for singleton pregnancies delivered in Melbourne, Australia from 2016 to 2018. Model predictors included age, body mass index, ethnicity, diabetes family history, GDM history, and poor obstetric outcome history. Model updating methods were recalibration-in-the-large (Model A), intercept and slope re-estimation (Model B), and coefficient revision using logistic regression (Model C1, original ethnicity categories; Model C2, revised ethnicity categories). Analysis included 10-fold cross-validation, assessment of performance measures (c-statistic, calibration-in-the-large, calibration slope, and expected-observed ratio), and a closed-loop testing procedure to compare models’ log-likelihood and akaike information criterion scores. ResultsIn 26,474 singleton pregnancies (4,756, 18% with GDM), the original model demonstrated reasonable temporal validation (c-statistic = 0.698) but suboptimal calibration (expected-observed ratio = 0.485). Updated model C2 was preferred, with a high c-statistic (0.732) and significantly better performance in closed testing. ConclusionWe demonstrated updating methods to sustain predictive performance in a contemporary population, highlighting the value and versatility of prediction models for guiding risk-stratified GDM care.

A novel framework using binary attention mechanism based deep convolution neural network for face emotion recognition

The use of facial emotion recognition (FER) technologies will become more pervasive in our everyday lives. Emotional awareness is advantageous for many types of businesses and areas of life. It is advantageous and important for reasons of security and heath. Deep hierarchical FER systems often focus on the following two main problems; going out of control due to identification factors including lighting, face location, and recognition bias, as well as a lack of training data. We developed each Deep Convolutional Neural Network (DCNN) based on a Binary Attention Mechanism (BAM) for the facial emotion recognition issue in our proposed system. Each image of a face has to be assigned to one of the seven facial emotions. An updated BAM-DCNN model was trained using the original pixel data characteristics. The Histogram of Oriented Gradients (HOG) is used for data preparation. To lessen the overfitting of the models, we used dropout and batch normalization in addition to L2 regularization. The recommended technique enables the detection of human emotion in images by automatically recognizing, extracting, and evaluating diverse face expressions. We extract and examine performance assessment measures from FER datasets, including recognition accuracy, precision, sensitivity, specificity, recall, and F1 score. To demonstrate the effectiveness of our system, we also contrast the recommended technique with the practices now in use.

The relationship of muscle oxygen saturation analyzer with other monitoring and quantification tools in a maximal incremental treadmill test.

Introduction: The study aims to explore whether NIRS derived data can be used to identify the second ventilatory threshold (VT2) during a maximal incremental treadmill test in non-professional runners and to determine if there is a correlation between SmO2 and other valid and reliable exercise performance assessment measures or parameters for maximal incremental test, such as lactate concentration (LT), RPE, HR, and running power (W). Methods: 24 participants were recruited for the study (5 women and 19 men). The devices used consisted of the following: i) a muscle oxygen saturation analyzer placed on the vastus lateralis of the right leg, ii) the Stryd power meter for running, iii) the Polar H7 heart rate band; and iv) the lactate analyzer. In addition, a subjective perceived exertion scale (RPE 1-10) was used. All of the previously mentioned devices were used in a maximal incremental treadmill test, which began at a speed of 8km/h with a 1% slope and a speed increase of 1.2km/h every 3min. This was followed by a 30-s break to collect the lactate data between each 3-min stage. Spearman correlation was carried out and the level of significance was set at p < 0.05. Results: The VT2 was observed at 87,41 ± 6,47% of the maximal aerobic speed (MAS) of each participant. No relationship between lactate data and SmO2 values (p = 0.076; r = -0.156) at the VT2 were found. No significant correlations were found between the SmO2 variables and the other variables (p > 0.05), but a high level of significance and strong correlations were found between all the following variables: power data (W), heart rate (HR), lactate concentration (LT) and RPE (p < 0.05; r > 0.5). Discussion: SmO2 data alone were not enough to determine the VT2, and there were no significant correlations between SmO2 and the other studied variables during the maximal incremental treadmill test. Only 8 subjects had a breakpoint at the VT2 determined by lactate data. Conclusion: The NIRS tool, Humon Hex, does not seem to be useful in determining VT2 and it does not correlate with the other variables in a maximal incremental treadmill test.

Assessing the performance and productivity of labour in building construction projects through the application of work-based training practices

PurposeSources highlight that lack of systematic labour training components results in low performance and productivity of labour, which leads the construction industry of many countries to face various challenges. This study aims to quantify the variations in the performance and productivity levels of labour in building construction projects through the applications of effective work-based training components.Design/methodology/approachA comprehensive literature review and a series of experts’ discussions with action-oriented communication approaches were conducted to develop a set of practices related to labour training, performance assessment and productivity measurements within a framework. The developed practices were applied to around 100 labourers working on nine building construction projects through a construction supervisory training programme.FindingsThe study presents the detailed patterns of the significant changes in labour performance and productivity levels. The majority of trained labourers have grown to perform the work process with some relevant theoretical and operational knowledge and skills. The overall results spotlight the significant behavioural changes that can be observed in workforce operations by improving labour performance, which resulted in implementing effective labour-rewarding practices within a framework.Research limitations/implicationsAlthough the study findings were limited to the Sri Lankan context, the proposed practices can be applied to the industry practices of the construction sector of other developing countries and the other developing industries in similar ways/scenarios.Practical implicationsThe study outcomes contribute to uplifting the work qualities of labourers with life-long learning opportunities and unlocking the potential barriers for expanding the local labour supply while controlling the excessive inclination of the local firms towards foreign labour. This paper describes further implications and future scopes of the study elaborately.Originality/valueThe study provides generalised mechanisms and practices that transform the labour characteristics and add new attributes for strengthening the values of construction supervision practices to obtain well-improved work outputs. The study outcomes reinforce the chain relationships among the training elements, labour performance and productivity levels, leading to upgrading current planning and operational management practices, especially adding constructive mechanisms in resource levelling and productivity benchmarking practices.

Classification of Music Genres using Feature Selection and Hyperparameter Tuning

The ability of music to spread joy and excitement across lives, makes it widely acknowledged as the human race's universal language. The phrase "music genre" is frequently used to group several musical styles together as following a shared custom or set of guidelines. According to their unique preferences, people now make playlists based on particular musical genres. Due to the determination and extraction of appropriate audio elements, music genre identification is regarded as a challenging task. Music information retrieval, which extracts meaningful information from music, is one of several real - world applications of machine learning. The objective of this paper is to efficiently categorise songs into various genres based on their attributes using various machine learning approaches. To enhance the outcomes, appropriate feature engineering and data pre-processing techniques have been performed. Finally, using suitable performance assessment measures, the output from each model has been compared. Compared to other machine learning algorithms, Random Forest along with efficient feature selection and hyperparameter tuning has produced better results in classifying music genres.

Development of a three tiered cognitive hybrid machine learning algorithm for effective diagnosis of Alzheimer’s disease

Alzheimer's disease (AD) is one of the most frequent neurodegenerative disorders in the elderly subjects. Since early detection can prevent or delay cognitive decline in the older subjects, it is desirable to develop effectual protocols for the diagnosis of the disease. Most of the existing diagnostic tools fail to improvise timely disease prognosis in susceptible patients. Keeping this fact into consideration, we developed a cognitive-based 3-tiered machine learning (ML) algorithm employing baseline characteristics to predict AD or mild cognitive impairment (MCI) to construct psychometric test results. Earlier machine learning based AD diagnosis methods used a binary or multinomial classification technique. We relied on the development of a sophisticated hybrid cognitive ML algorithm that provides an accurate and precise prediction of the disease. We built an ML model using cognitive and demographic data. The prediction method consisted of a three-step process. Alzheimer’s Disease Neuroimaging Initiative (ADNI) database was used to develop a novel prediction algorithm. Considering the fact that nineteen ML and deep learning classifiers could not adequately classify ADNI data, we created a 2-layer model stacking procedure. Model stacking outperformed six ML classifier combinations, including Logistic Regression, Naïve Bayes, Support Vector Machine, Decision Trees, Random Forest, and eXtreme Gradient Boosting. The performance of the as-proposed model was evaluated employing seven performance assessment measures and four classification error indicators. Each model was evaluated in three separate strategical assessment modules. In the first experiment, XGB, Random Forest, and SVM achieved 89.63% accuracy, while Random Forest achieved 93.90% accuracy in the second experiment. Experiment 2 improved the classification and performance of overall prediction. In the third experiment, hybrid modeling, the accuracy increased significantly, with experiment 1 giving 90.24% accuracy and experiment 2 yielding 95.12% accuracy. The as-proposed model successfully predicted early AD and MCI in an effective manner. We were able to reduce nineteen classifiers into four classifiers (from experiment-1) and six classifiers (from experiment-2) and subsequently into one meta-learner (19 → 4 → 1 and 19 → 6 → 1), with high predictive power. Finally, we performed a thorough comparative analysis of different ADNI datasets to validate our findings.

SQL Injection Attack Detection Using Machine Learning Algorithm

The structured query language injection attack (SQLIA) is a well-known cyberattack targeting vulnerabilities in web-based applications; it is used to carry out illegal information control language, bypass confirmation measures, and get access to restricted data. There was some consideration given to existing systematic reviews in the literature. Contemporary systematic reviews frequently incorporate both older and more contemporary works in the topic. Therefore, we restricted ourselves to recently published works. For the current study, I used information from 2012 through 2020. Encryption, XML, design coordination, parsing, and machine learning are just some of the methods and systems that can be used to spot and prevent SQL injection attacks. The Machine Learning (ML) process, which has been proved to be important for SQLIA relief, is applied with the help of guarded coding. Machine learning approaches require a large amount of data for model preparation and only handle a few number of attack types. The use of ML methods may alleviate a particularly challenging vision impairment SQL injection attack. In the Waikato Climate for Data Exploration study, we looked at the following methods: Logistic Regression (LRN), Stochastic Gradient Descent (SDG), Sequential Minimal Optimization (SMO), Bayes Network (BNK), Instance-Based Learner (IBK), Multilayer Perceptron (MLP), Naive Bayes (NB), and J48. Wait (70%) and 10-fold Cross Validation assessment procedures were used to survey the presentation of the regulated learning grouping calculations to choose the optimal calculation. Accuracy values for SMO, IBK, and J48 were found to be 98.7785%, 98.4285%, and 98.2985% using the Cross Validation method, and 98.7956%, 98.1526%, and 100 using the Hold-Out method. Using the Cross Validation method SMO took IBK and J48 10.15 seconds, 0.06 seconds, and 14.12 seconds, whereas using the Hold-Out method SMO took 9.71 seconds, 0.16 seconds, and 14.28 seconds to construct their models. Based on the findings, IBK was chosen as the classifier for SQLIA detection and prevention since it was the fastest to train a model using the Cross Validation strategy and had the best overall performance. Not only is accuracy essential when choosing an algorithm for predictive analytics, but also a variety of performance assessment indicators.

Adaptive Scheduling Algorithm Based Task Loading in Cloud Data Centers

Cloud computing is a global storage framework whereby users use the tools, including the computation, storage, network, etc., that are provided automatically. The computational has led the cloud data centers to be stored in digital services that many consumers distribute. The biggest problem with cloud data centers is handling the millions of users’ continuous proposals. Therefore, in this paper, Adaptive Scheduling Algorithm Based Task Loading (ASA-TL) has been proposed to manage cloud data centers’ task to be stored in digital devices. ASA is implemented in which the task is shared between all the current virtual servers,and the cloud data are protected from overloading. The data assignment is made by taking account of the importance and status of the digital device that helps to assign task fairly and use them efficiently. TL manages these requests efficiently, and the task input must be equally and reliably allocated between various processors. The experimental result suggests that ASA-TL achieves the highest performance assessment measurements, including response time, processing time in the data center and overall expense.

Aanalisis Kinerja Keuangan Pemerintah Desa Di Kabupaten Jepara (Studi Kasus Pemerintah Desa Jambu)

Financial performance, namely providing direction on financial performance, providing an assessment of performance measures to government management, providing input or evaluation of performance achievement and improving the system on government performance. The purpose of this study is to find out the financial performance of the Jambu Village Government the object under study is the Budget Realization Report data. Jambu Village Income and Expenditure Budget 2016-2018. The method used in this study is a qualitative method using primary and secondary data. The data used in this study is the Jambu Village Budget Realization Report 2016-2018 after data is processed using financial ratios in the form of Independence Ratios, Efficiency Ratios, Dependency Ratios, Activity Ratios, and Growth Ratios. Results from the 2016-2018 Jambu Village Financial Performance Analysis study showed that the Independence Ratio was very poor, the Efficiency Ratio was good or Efficient, the Dependency Ratio was very high to the Provincial or District Governments, the Activity Ratio was still unbalanced between the Ratio of Direct Shopping and Indirect Expenditure The Activity Ratio is Dominant Direct Spending, PADes rowth Ratio for 2016-2017 is very Low while 2017-2018 is very high, Growth Ratio of total PADes in 2016-2017 is very low and in 2017-2018 is relatively moderate, Direct Shopping Activity Ratio 2016 Ratio -2017 is very low, while 2017-2018 is very high while the Indirect Expenditure Activity Ratio for 2016-2017 is relatively moderate. Whereas 2017-2018 is very low. DOI: 10.26905/ap.v6i1.4103

Short-term availability and performability analysis for a large-scale multi-state system based on robotic sensors

This paper presents dynamic (short-term) availability and performability analysis for highly responsive large-scale distributed robotic systems. Such systems are intended for the exploration and measurement of different parameters in aggressive environments by using robotic sensors. The typical system, which is considered in this paper, consists of N platforms and m robotic sensors, which initially are installed on each platform. Platforms are distributed on a safe distance from the aggressive (dangerous) environment to be explored. At some time, sensors should be shot from the platform to the environment. Then each platform begins to process all data gathered by its sensors. Each sensor, when it is installed on a platform, and each platform are individually represented as a three-state component – completely available state, minor repairable state, and replacement state. Thus, the entire system and its components are interpreted as a multi-state system. In general, numbers N and m may be enough big. In such cases, the system is large-scale. The problem is to find the short-term availability and the performance for an entire large-scale system which strongly depends on the initial conditions of the system.It has been shown, that there is a great difference between steady-state and short-term availability and performance indices which significantly depend on the initial states of all robotic sensors and platforms. For a highly responsive system, this difference is very important. The main obstacle in finding a solution to the problem is its large multi-dimensionality. To overcome this difficulty, the Lz-transform method was used. This paper suggests a multi-state model for the system and measures for system availability and performance assessment. The paper has also developed an algorithm for an Lz-transform application to evaluate these measures and presents recommendations for the prevention of availability and performance decrease in the initial short-time period.A numerical example is presented to illustrate the proposed method.

Using Open Public Meetings and Elections to Promote Inward Transparency and Accountability: Lessons From Zambia.

Background: Community-led governance can ensure that leaders are accountable to the populations they serve and strengthen health systems for maternal care. A key aspect of democratic accountability is electing respective governance bodies, in this case community boards, and holding public meetings to inform community members about actions taken on their behalf. After helping build and open 10 maternity waiting homes (MWHs) in rural Zambia as part of a randomized controlled trial, we assisted community governance committees to plan and execute annual meetings to present performance results and, where needed, to elect new board members. Methods: We applied a principally qualitative design using observation and analysis of written documentation of public meetings to answer our research question: how do governance committees enact inward transparency and demonstrate accountability to their communities. The analysis measured participation and stakeholder representation at public meetings, the types and purposes of accountability sought by community members as evidenced by questions asked of the governance committee, and responsiveness of the governance committee to issues raised at public meetings. Results: Public meetings were attended by 6 out of 7 possible stakeholder groups, and reports were generally transparent. Stakeholders asked probing questions focused mainly on financial performance. Governance committee members were responsive to questions raised by participants, with 59% of answers rated as fully or mostly responsive (showing understanding of and answering the question). Six of the 10 sites held elections to re-elect or replace governance committee members. Only 2 sites reached the target set by local stakeholder committees of 50% female membership, down from 3 at formation. To further improve transparency and accountability, community governance committees need to engage in advance preparation of reports, and should consult with stakeholders on broader measures for performance assessment. Despite receiving training, community-level governance committees lacked understanding of the strategic purpose of open public meetings and elections, and how these relate to democratic accountability. They were therefore not motivated to engage in tactics to manage stakeholders effectively. Conclusion: While open meetings and elections have potential to enhance good governance at the community level, continuous training and mentoring are needed to build capacity and enhance sustainability.

Dataset characteristics influence the performance of different interpolation methods for soil salinity spatial mapping.

This study compared the performance of different interpolation methods for mapping soil salinity of three different agricultural fields having the same land use but different dataset characteristics. Four common spatial interpolation methods including global polynomial interpolation (GPI), inverse distance weighted (IDW), ordinary kriging (OK), and radial basis functions (RBF) were employed for mapping soil EC. The performance of interpolation methods in predicting soil EC was evaluated based on mean bias error, root mean square error, mean absolute percentage error, and coefficient of determinations criteria. Results showed that dataset characteristics, including central tendency and distribution, were significantly different among the studied fields. Experimental semivariogram and fitted model parameters indicated that three studied fields were also different in their spatial dependence strength. Considering all of the performance assessment measures used, the best interpolation method for fields A and C was OK and IDW for field B. The performance of interpolation methods was found to be affected by data characteristics of the studied fields, which were mostly ascribed to management practices. This study suggests in order to obtain accurate mapping of soil salinity in agricultural fields, it is essential to first find the best spatial interpolation method compatible with the characteristics of the collected data from the selected agricultural land.

How prior knowledge affects problem-solving performance in a medical simulation game: Using game-logs and eye-tracking

Computer-based simulation games provide an environment to train complex problem-solving skills. Yet, it is largely unknown how the in-game performance of learners varies with different levels of prior knowledge. Based on theories of complex-skill acquisition (e.g., 4C/ID), we derive four performance aspects that prior knowledge may affect: (1) systematicity in approach, (2) accuracy in visual attention and motor reactions, (3) speed in performance, and (4) cognitive load. This study aims to empirically test whether prior knowledge affects these four aspects of performance in a medical simulation game for resuscitation skills training. Participants were 24 medical professionals (experts, with high prior knowledge) and 22 medical students (novices, with low prior knowledge). After pre-training, they all played one scenario, during which game-logs and eye-movements were collected. A cognitive-load questionnaire ensued. During game play, experts demonstrated a more systematic approach, higher accuracy in visual selection and motor reaction, and a higher performance speed than novices. Their reported levels of cognitive load were lower. These results indicate that prior knowledge has a substantial impact on performance in simulation games, opening up the possibility of using our measures for performance assessment.

Efficiency assessment of Indian electronics retail stores using DEA

Retail plays an important role in creating a link between upstream stages of a supply chain and the end customers. Supply chains can create higher customer value and competitive advantage if retailers are capable of extending their capabilities and performance. It is imperative for retail businesses to monitor their performance based on appropriate performance measures and devise strategies for continuous improvements. This study presents the methodology for selection of performance assessment measures integrating fuzzy Delphi and data envelopment analysis (DEA) selective measure methods; and then compute the relative efficiency of multiple stores of an Indian Electronics retail chain following the DEA method. The overall performance of a retail chain rest on the individual store's performance and hence it is all the more important for the inefficient stores to benchmark the performance of the efficient stores. Subsequent to efficiency measurement, super efficiency scores are computed to rank the efficient stores and input/output slack are determined for inefficient stores to help practitioners in planning strategies for benchmarking and performance enhancement.