Excellent Average Performance Research Articles

Music at Workplace: Is it trully Improving Employees’ Performance?

The purpose of this study was to compare the performance of employees who listened to music while working to those who did not. This study was conducted at the Business Simulation Laboratory, Department of Business Administration of Ujung Pandang State Polytechnic. This study employed an Experimental Research Method with a quantitative methodology. The total participants, 94, were divided into 2 groups, employees with treatment (listening to the music while working) 48 and employees without treatment, with 46 participants. The study's empirical findings indicated that there were productivity differences between employees who got the treatments (listening to music while working) compared to those who did not. The employee who works with music in the workplace performed a more excellent average performance than those who work without music. Listening to favorite songs encourages employees to improve their work efficiency.

Machine Learning Based Non-Enhanced CT Radiomics for the Identification of Orbital Cavernous Venous Malformations: An Innovative Tool.

To evaluate the capability of non-enhanced computed tomography (CT) images for distinguishing between orbital cavernous venous malformations (OCVM) and non-OCVM, and to identify the optimal model from radiomics-based machine learning (ML) algorithms. A total of 215 cases of OCVM and 120 cases of non- OCVM were retrospectively analyzed in this study. A stratified random sample of 268 patients (80%) was used as the training set (172 OCVM and 96 non-OCVM); the remaining data were used as the testing set. Six feature selection techniques and thirteen ML models were evaluated to construct an optimal classification model. There were statistically significant differences between the OCVM and non-OCVM groups in the density and tumor location (P < 0.05), whereas other indicators were comparable (age, gender, sharp, P > 0.05). Linear regression (area under the curve [AUC] = 0.9351; accuracy = 0.8657) and Stochastic Gradient Descent (AUC = 0.9448; accuracy = 0.8806) classifiers, both of which coupled with the f test and L1-based feature selection method, achieved optimal performance. The support vector machine (AUC = 0.9186; accuracy = 0.8806), Random Forest (AUC = 0.9288; accuracy = 0.8507) and eXtreme Gradient Boosting (AUC = 0.9147; accuracy = 0.8507) classifier combined with f test method showed excellent average performance among our study, respectively. The effect of non-enhanced CT images in OCVM not only can help ophthalmologist to find and locate lesion, but also bring great help for the qualitative diagnosis value using radiomic- based ML algorithms.

Histologic subtype classification of non-small cell lung cancer using PET/CT images.

To evaluate the capability of PET/CT images for differentiating the histologic subtypes of non-small cell lung cancer (NSCLC) and to identify the optimal model from radiomics-based machine learning/deep learning algorithms. In this study, 867 patients with adenocarcinoma (ADC) and 552 patients with squamous cell carcinoma (SCC) were retrospectively analysed. A stratified random sample of 283 patients (20%) was used as the testing set (173 ADC and 110 SCC); the remaining data were used as the training set. A total of 688 features were extracted from each outlined tumour region. Ten feature selection techniques, ten machine learning (ML) models and the VGG16 deep learning (DL) algorithm were evaluated to construct an optimal classification model for the differential diagnosis of ADC and SCC. Tenfold cross-validation and grid search technique were employed to evaluate and optimize the model hyperparameters on the training dataset. The area under the receiver operating characteristic curve (AUROC), accuracy, precision, sensitivity and specificity was used to evaluate the performance of the models on the test dataset. Fifty top-ranked subset features were selected by each feature selection technique for classification. The linear discriminant analysis (LDA) (AUROC, 0.863; accuracy, 0.794) and support vector machine (SVM) (AUROC, 0.863; accuracy, 0.792) classifiers, both of which coupled with the ℓ2,1NR feature selection method, achieved optimal performance. The random forest (RF) classifier (AUROC, 0.824; accuracy, 0.775) and ℓ2,1NR feature selection method (AUROC, 0.815; accuracy, 0.764) showed excellent average performance among the classifiers and feature selection methods employed in our study, respectively. Furthermore, the VGG16 DL algorithm (AUROC, 0.903; accuracy, 0.841) outperformed all conventional machine learning methods in combination with radiomics. Employing radiomic machine learning/deep learning algorithms could help radiologists to differentiate the histologic subtypes of NSCLC via PET/CT images.

Scheduling MapReduce Jobs on Identical and Unrelated Processors

We consider non-preemptive scheduling of MapReduce jobs consisting of multiple map-reduce rounds so as to minimize their average weighted completion time on identical or unrelated processor environments. For identical processors, we present LP-based O(1)-approximation algorithms, while for unrelated processors the approximation ratio naturally depends on the maximum number of rounds of any job (which is a small constant in practice). For the single-round case, we substantially improve on previously best known approximation ratios, while also we introduce into our model the crucial cost of the data shuffle phase, i.e., the cost for the transmission of intermediate data from Map to Reduce tasks. Finally, we evaluate our algorithms via simulations in the general case of unrelated processors, comparing them with a lower bound on the optimal cost of the problem as well as with a fast algorithm which combines a simple online assignment of tasks to processors with a standard scheduling policy. As we observe, for random instances that capture data locality issues, our algorithm achieves an excellent average performance.

CD-Based Indices for Link Prediction in Complex Network.

Lots of similarity-based algorithms have been designed to deal with the problem of link prediction in the past decade. In order to improve prediction accuracy, a novel cosine similarity index CD based on distance between nodes and cosine value between vectors is proposed in this paper. Firstly, node coordinate matrix can be obtained by node distances which are different from distance matrix and row vectors of the matrix are regarded as coordinates of nodes. Then, cosine value between node coordinates is used as their similarity index. A local community density index LD is also proposed. Then, a series of CD-based indices include CD-LD-k, CD*LD-k, CD-k and CDI are presented and applied in ten real networks. Experimental results demonstrate the effectiveness of CD-based indices. The effects of network clustering coefficient and assortative coefficient on prediction accuracy of indices are analyzed. CD-LD-k and CD*LD-k can improve prediction accuracy without considering the assortative coefficient of network is negative or positive. According to analysis of relative precision of each method on each network, CD-LD-k and CD*LD-k indices have excellent average performance and robustness. CD and CD-k indices perform better on positive assortative networks than on negative assortative networks. For negative assortative networks, we improve and refine CD index, referred as CDI index, combining the advantages of CD index and evolutionary mechanism of the network model BA. Experimental results reveal that CDI index can increase prediction accuracy of CD on negative assortative networks.

Using the economical order quantity formula for inventory control in one‐warehouse multiretailer systems

AbstractIn this article, we deal with the inventory control of one single product in a distribution network consisting of one warehouse and n retailers where the retailers face continuous demands with constant rates. We study two policies for this problem that are derived from the application of the classical economical ordering quantity formula at each location: the first policy is purely single‐echelon, whereas the second synchronizes the reorder intervals between the different installations and thus constitutes a first step toward a multiechelon perspective. We show that those policies are, respectively, \documentclass{article}\usepackage{mathrsfs}\usepackage{amsmath, amssymb}\pagestyle{empty}\begin{document}\begin{align*}\sqrt{2}\end{align*} \end{document} ‐ and 1.275‐optimal. We then conduct computational experiments and demonstrate that our second policy exhibits excellent average performance and improves substantially over the pure single‐echelon policy: in particular it deviates from Roundy's celebrated algorithm by less than 5%. This result indicates that in this simple setting, a great part of the benefit of shifting to a multiechelon perspective can be obtained by the most natural synchronization scheme between the different installations, and adapting single‐echelon policies can be efficient at managing the inventory in simple distribution networks, confirming observations of practitioners. © 2012 Wiley Periodicals, Inc. Naval Research Logistics, 2012

Scheduling a burn-in oven with non-agreeable release times and due dates

We consider the problem of scheduling semiconductor burn-in operations, where burn-in ovens are modelled as batch processing machines. Most of the studies assume that ready times and due dates of jobs are agreeable (i.e., ri < rj implies di ≤ dj). In many real world applications, the agreeable property assumption does not hold. Therefore, in this paper, scheduling of a single burn-in oven with non-agreeable release times and due dates along with non-identical job sizes as well as non-identical processing of time problem is formulated as a Non-Linear (0-1) Integer Programming optimisation problem. The objective measure of the problem is minimising the maximum completion time (makespan) of all jobs. Due to computational intractability, we have proposed four variants of a two-phase greedy heuristic algorithm. Computational experiments indicate that two out of four proposed algorithms have excellent average performance and also capable of solving any large-scale real life problems with a relatively low computational effort on a Pentium IV computer.

A genetic algorithm to minimize maximum lateness on a batch processing machine

We consider the problem of minimizing maximum lateness on a batch processing machine in the presence of dynamic job arrivals. The batch processing machine can process up to B jobs simultaneously, and the processing time of a batch is given by that of the job with the longest processing time in the batch. We adapt a dynamic programming algorithm from the literature to determine whether a due-date feasible batching exists for a given job sequence. We then combine this algorithm with a random keys encoding scheme to develop a genetic algorithm for this problem. Computational experiments indicate that this algorithm has excellent average performance with reasonable computational burden. Scope and purpose In this paper we address the problem of scheduling a single batch processing machine subject to dynamic job arrivals, which generalizes a number of classical single-machine models from scheduling theory (Pinedo. Scheduling theory, algorithms, and systems. Englewood Cliffs, NJ: Prentice-Hall, 1995.) Batch processing machines, where a number of jobs are processed simultaneously as a batch, are quite common in the metalworking and microelectronics industries. Although an extensive literature on several scheduling models for this type of machine has developed in the last decade, there has been relatively little work on models that consider jobs arriving at the machine dynamically over time. However, such dynamic models are an essential component of decomposition heuristics (Ovacik, Uzsoy. Decomposition methods for complex factory scheduling problems. Massachusetts: Kluwer Academic Publishers, 1997.) used to schedule the factories within which the batch processing machines form an individual workcenter. This paper builds on results developed in prior work (Lee et al. Operations Research 1992;40:764–75.) and (Uzsoy. International Journal of Production Research 1995;33:2605–708.) to develop an effective genetic algorithm (Goldberg. Genetic algorithms in search, optimization and machine learning. Reading, MA: Addison-Wesley, 1989.) for solving this provably intractable scheduling problem. Our procedure combines an optimization algorithm to evaluate job sequences with a random key representation that guarantees feasible solutions after crossover operations. Extensive computational experiments show that it consistently obtains high-quality solutions in modest CPU times.

Improved operability in Hg1−xCdxTe detector arrays

As liquid phase epitaxial (LPE) growth and array fabrication processes have matured to give excellent wafer average performance, the yield limiter for infrared focal plane arrays (IRFPAs), especially large ones, have become outages. In this work, significant progress has been made in identifying the source and eliminating outages from LPE grown Hg1−xCdxTe P-on-n structures. Historically, studies of the sources of outages have employed defect etches to look for dislocations and other crystalline defects, and secondary ion mass spectroscopy (SIMS), imaging SIMS, and sputter initiated resonance ion spectrometry (SIRIS) to look for impurities at critical interfaces. Using these techniques, trends were established, but direct correlation with outages have been observed. In LPE grown materials, where the dislocation densities are always below 5×105 cm−2, and often below 1×105 cm−2 on CdZnTe substrates, dislocations only account for a few outages. In order to understand the source(s) of outages, a failure analysis was performed on several long wavelength IRFPAs. Using a dilute etchant, the metals and then cap layers of some 64×64 pixel IRFPAs which had excellent average performance, but suffered from a high density of pixels with excessive leakage current, were removed. Using a scanning electron microscope with energy dispersive spectroscopy capability, the presence of carbon particles was correlated with excessive leakage current on a 1:1 pixel basis. A series of experiments was then conducted which isolated the source of the particles to the cap layer growth process, which was consequently changed to eliminate them. The process improvements have reduced the particle density to below the measurement limit of the optical measurement technique implemented to monitor the density of particles on witness wafers. These improvements are resulting in IRFPAs with significantly improved operability.

Minimizing makespan on a single batch processing machine with dynamic job arrivals

We consider the problem of minimizing makespan Cmax on a single batch processing machine in the presence of dynamic job arrivals. The batch processing machine can process up to B jobs simultaneously. The processing time of a batch is given by the processing time of the longest job in the batch. We present polynomial and pseudopolynomial-time algorithms for several special cases, develop efficient heuristics for the general problem and evaluate their performance through extensive computational experiments. Our results indicate that several of the heuristics have an excellent average performance with a modest computational burden.