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• A novel combined interval prediction algorithm is proposed in this paper. • Hybrid feature selection strategy is designed in the proposed system. • Multi-objective optimization mechanism reflects strong search capabilities. • Four interval prediction models cover the inherent modes of sequence. Traditional point prediction approaches can not reflect the uncertainty, which brings greater risks to decision-makers. To fill this gap, this paper extends a feature selection strategy that relies solely on correlation and redundant feature judgment, proposes a novel combined interval prediction algorithm, 3-Mcip (Combined Interval Prediction Based on Maximize Relevancy, Minimize Redundancy and Maximize Synergy) system, and solves the tradeoff between prediction accuracy and interval width. This system first designs a hybrid feature selection strategy to optimally select candidate variables and reduce model input redundancy. Secondly, the structure of the four ANN models is improved to accommodate the results of feature selection, and an optimization mechanism is introduced to search for the Pareto optimal solution set. In order to measure the comprehensive performance of the 3-Mcip system, hourly power load data and related candidate variables from Pittsburgh and Washington, D.C are considered. The numerical results show that the 3-Mcip system has coverage rates of 53.3333, 90.1667, and 99.4479 for Site 1 at different levels of interval width coefficients, which not only achieves perfect prediction of power load but also analyzes uncertainty. It is also helpful for power system managers to better capture the fluctuation range of future load and improve the flexibility of smart grid dispatching. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) has been deemed a promising technology to achieve high spectral efficiency. This paper investigates transmit power optimization for a multi-user multiple-input single-output (MISO) NOMA downlink system endowed with energy harvesting and physical layer security. More specifically, the system is designed to satisfy the data-link security requirements of its legitimate users while charging energy harvesting devices. To achieve that goal, we investigate a joint design on beamforming vectors and artificial noise (AN) covariance matrix. Then, an iterative difference-of-two-convex-function (D.C.) algorithm is proposed by applying the successive convex approximation (SCA) approach. To further reduce the computational complexity, a sub-optimal alternating D.C. (ADC) algorithm is devised by decoupling beamforming and mapping the AN to the null space of legitimate users. In the ADC algorithm, optimal null space and beamforming vectors are alternately solved by convex programming. Furthermore, the convergence of the proposed D.C. and ADC algorithms is derived. Extensive simulation results show that the two proposed AN-aided algorithms significantly reduce the transmit power cost over OMA and NOMA scheme without AN, especially when there are more eavesdroppers. [ABSTRACT FROM AUTHOR]

Objectives: Osteochondritis dissecans (OCD) is a focal idiopathic alteration of subchondral bone and/or its precursor with risk for instability and disruption of adjacent articular cartilage that may result in premature osteoarthritis. There are limited evidence-based guidelines to predict which lesions will heal with non-operative treatment. Therefore, this study primarily aims to design and train a classification algorithm to determine whether a patient with OCD of the knee will heal with non-operative treatment from intake visit characteristics. Methods: The Research in OsteoChondritis of the Knee (ROCK) cohort is the largest repository of OCD subjects, which has collected data on over 1400 subjects with OCD knee lesions. It is a longitudinal study at 23 participating institutions. It is registered with ClinicalTrials.gov (NCT02771496). The cohort size and fine level of detail permit optimal algorithm development and allow the present study to consider whether anatomic, image-based, or demographic factors play a role in a lesion's nonoperative healing capacity. The ROCK cohort included all patients seeking care for OCD lesions at any of the participating institutions, except those specified by the following exclusion criteria: (1) diagnosis of a focal chondral defect, (2) patients 26 years or older at the time of enrollment, (3) patient records missing data regarding OCD lesion location, (4) patient records with an incomplete or unverified screening form, and (5) patient records with an incomplete or unverified initial visit form. Subjects were further excluded from the study if data were missing for any feature required for model development. Additional inclusion criteria were that each patient meets the necessary definitions for failure or success of nonoperative management. Failure of nonoperative management was defined as the crossover from nonoperative management to surgery at any point at or beyond three-month follow-up. Successful healing was defined as complete healing on imaging with full return to sports participation. To replicate previous studies examining the healing potential of OCD lesions with logistic regression (Wall et al., JBJS, 2008; Krause et al., AJSM, 2013) a multivariate logistic regression model was developed to ascertain the effects of age, normalized lesion dimensions, and isolated pain or mechanical symptoms on the likelihood of the success of nonoperative management. A second model added sex, race, lesion plane on sagittal and coronal MRI, and lesion anatomical location (lateral femoral condyle, medial femoral condyle, or trochlea) as additional features. Next, a suite of machine learning algorithms, including a random forest classifier, a neural network, linear and radial-kernel support vector machines, a k-nearest-neighbor classifier, and a generalized boosted classifier, was developed using all available features. Each model type was hyperparameter tuned with five-fold cross-validation repeated ten times to select the best model. The raw classification accuracy and area under the receiving operating curve (AUC) were recorded. Results: The current study population includes 81 subjects. The average age in the study cohort was 12.10 years and 38.3% of subjects were female. 72.8% of lesions occurred in the medial femoral condyle, 18.5% in the lateral femoral condyle, and 6.2% in the trochlea. A full description of relevant cohort characteristics is included in Table 1. The logistic regression model developed by previous studies had a cross-validated accuracy of 65.3% and an AUC of 0.645. Only normalized lesion width was associated with an increased likelihood that a lesion would heal nonoperatively (OR = 1.16, CI = 1.04-1.31, p = 0.013). The extended regression model had a cross-validated accuracy of 71.2% and an AUC of 0.750. In this model, coronal lesion location in the lateral or medial-most zone (OR = 0.02, CI = 0.00-0.31, p = 0.012) and sagittal lesion location in the posterior zone (OR = 0.03, CI = 0.00-0.018, p < 0.001) on MRI were associated with an increased likelihood of successful nonoperative treatment. By contrast, increased normalized lesion width was associated with an increased likelihood of nonoperative failure (OR = 1.67, CI = 1.30-2.32, p<0.001). Table 2 compares the effect sizes, odds ratios, and significance for predictors included in the two logistic regression models. A hyperparameter-tuned generalized boosted classifier had the highest cross-validated accuracy and AUC of any model at 74.8% and 0.762, respectively. Regarding the importance of predictor variables: normalized lesion width was the most important variable in the generalized boosting model, followed by lesion location in the posterior sagittal zone, age, the presence of mechanical symptoms, and normalized lesion length. Other variables were not used to determine whether lesions would heal nonoperatively. Table 3 compares the performance characteristics of the three models. Conclusions: Machine learning models can predict which OCD lesions will heal with nonoperative management. Canonical models such as logistic regression can determine risk factors for nonoperative failure and serve as a clinician decision-aid with acceptable sensitivity and specificity profiles following the first encounter with a patient. Advanced machine learning algorithms, such as generalized boosted classifiers, can produce better predictive accuracy for clinical use. Normalized lesion width, lesion location in the posterior sagittal zone, patient age, the presence of mechanical symptoms, and normalized lesion length were the most important variables for successful lesion classification in the best-performing model. In the future, model performance should be tested on new, prospective patient data and compared to clinician decision-making to validate its performance. [ABSTRACT FROM AUTHOR]

Multi-objective clustering algorithms have superiority over its single objective counterparts as they include additional knowledge on properties of data in the form of objectives to discover the underlying clusters present in various datasets. This paper proposes a distributed clustering algorithm using multi-objective whale optimization (DMOWOA) for peer to peer network. The algorithm minimizes two objective functions to perform clustering, namely : Total Euclidean Deviation and Total Symmetrical Deviation. Both objective values are shared using diffusion method of cooperation to obtain correct partitioning at each peer. A single solution from the non-dominated solutions is selected as final solution based on its minimum distance to origin in the normalized objective space. The proposed algorithm's performance is evaluated on four synthetic and five real-life wireless sensor network datasets (Canada weather station dataset, Delhi air pollution content dataset, Intel laboratory dataset, Washington cook agronomy farm dataset and Thames river water quality dataset). The comparison is carried out with multi-objective distributed particle swarm optimization (DMOPSO), distributed K-Means (DK-Means) and other seven recently developed nature inspired multi-objective clustering techniques. The proposed algorithm in most of the cases outperforms the existing techniques in terms of statistical measures Minkowski Score, Dunn index and Silhouette index. The average rank of the proposed algorithm is also better in Kruskal–Wallis statistical test. • Distributed multi-objective clustering method is proposed in peer to peer network. • The algorithm is based on multi-objective whale optimization. • An optimal solution is chosen from the obtained Pareto optimal front. • The proposed algorithm is validated on nine distributed datasets. • Proposed approach shows superiority over existing clustering approaches. [ABSTRACT FROM AUTHOR]

This article is based on a talk given at the 1988 TIMS/ORSA meeting in Washington, D.C., about developments since the 1974 Lanchester Prize was awarded to P. J. Kolesar and W. E. Walker in 1974, for a paper that described an algorithm for dynamically repositioning available fire companies in response to changing patterns of availabilities. The life history of that algorithm motivates a vision for the future of the applied side of the OR profession. [ABSTRACT FROM AUTHOR]

Travel speed ties directly to travel time, so it is an important measure for quantifying arterial performance. However, accurately estimating link travel speed for urban arterials is difficult because of traffic fluctuations and stop-and-go conditions caused by signal control. This research proposes a two-step empirical approach to effectively estimate the link-journey speeds using only advance loop detector outputs. The first step is to estimate the spot speed on the basis of advance loop measurements using Athol's algorithm. The robust regression technique can be used to calibrate the speed estimation parameter (or -factor) in Athol's algorithm. The second step is to use the proposed simplified speed estimation model to estimate the link speed using only the calculated loop-spot speed without any knowledge of signal timing plans. Traffic operations in the central business district of the City of Bellevue, Washington, are simulated in the VISSIM traffic simulation model. The test results show that only 50 cycles of data are needed to calibrate the -factor in loop-speed estimation and the same datasets can be used to calibrate the proposed link-speed model. Using this model, the average mean absolute error over the study links is reduced from 4.24 to 1.51 mph. With proper calibration, this average error can be further reduced to 0.91 mph. The results are encouraging and satisfactory. The results also show that the accuracy of speed estimation may be further increased when more data are applied for calibration. [ABSTRACT FROM AUTHOR]