Median Operator Research Articles

Enhancing Agricultural Commodity Forecasting: A Median-Based Combination of Time Series Models

This study explores the efficacy of combining forecasts using the median operator to enhance forecasting performance. The traditional approach of assigning equal weights to individual models often struggles with extreme forecasts. A new method Simple Combination of Univariate Models (SCUM) is utilized, which uses the median operator to combine forecasts from four distinct time series models: Exponential Smoothing (ETS), Auto Regressive Integrated Moving Average (ARIMA), Dynamically Optimised Theta Model (DOTM), and Complex Exponential Smoothing (CES). This approach aims to mitigate the influence of extreme forecasts and improve overall accuracy. Our empirical analysis investigates the use of the SCUM approach for the agricultural commodity data. Yearly production of Rice is used, sourced from the Ministry of Agriculture & Farmers Welfare, Government of India. The forecasting performance of the SCUM approach is compared against individual models and a mean-based combined forecast using key performance metrics such as Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and Relative Efficiency. The results show that SCUM outperforms all other models, achieving the lowest RMSE (51.89) and a MAPE of 6.90, indicating superior in-sample forecasting accuracy. ARIMA was the least efficient with a 22.11% relative efficiency, while the Mean combination method (3.33%) was closest to SCUM in performance. The findings suggest that SCUM is not only a viable alternative to traditional methods but also offers significant advantages in improving forecasting accuracy. This research underscores the potential for median-based forecast combinations to achieve superior predictive accuracy and reliability, making it a valuable tool for scholars and practitioners in time series forecasting.

Robust sparse representation based on fitting error decomposition

Sparse representation (SR) of a signal aims at finding the minimum number of atoms for its representation. In several practical scenarios, the signal is vulnerable to outliers and thus robustness is required for SR based algorithms. However, most existing robust schemes are designed on the assumption that the anomalies are independently distributed, which may not perform well encountering complex interferences, especially the correlated ones. To deal with this drawback, a robust SR model is proposed, where both independent and correlated outliers are considered. Specifically, the fitting error is decomposed as the combination of a low-rank component and a sparse part, corresponding to the correlated gross error and independent outlier, respectively. Then, the group sparsity of the representation coefficient is utilized. Moreover, ℓ0-norm and ℓ2,0-norm are adopted as the sparsity regularization for the sparse outlier and representation coefficients, respectively. The solutions to ℓ0/ℓ2,0-norm minimization are generated by the hard-thresholding strategy, where the decision threshold is adaptively determined using median absolute deviation operator. As for the low-rank regularization, due to the NP-hardness of rank minimization, we employ matrix logarithmic norm as the rank surrogate to lessen the approximation gap. Finally, we apply the proposed model to face recognition task, and the excellent performance demonstrates its effectiveness.

The median under orness

Besides the mean, the median is the most widely used single-valued descriptor of data sets. It is well-known that the orness level of the median operator is 1/2. In this paper we provide approximations of the median operator under a given level of orness. We find the exact optimal weighting vector for the 1-norm approximation problem in all conceivable cases and for 2-norm approximation up to nine aggregates. The analytical results show that if the orness level is not 1/2, then not only the middle, but the extreme values also matter.

Fuzzy correlational analysis for dynamic consolidation of virtual machines in cloud computing environment

This theoretical research in fuzzy correlation analysis integrates data uncertainty analysis by measuring the strength of the linear relationship restricted to two fuzzy sets. As the relevant contribution to this research area, this paper presents the axiomatic definition of the n-dimensional generalized fuzzy correlation coefficient (nGCC), assigning to n input fuzzy sets an output value in the interval [−1,1]. Thus, the properties of general overlap functions and fuzzy negations are studied, discussing binary non-normed restricted dissimilarity functions, and the n-dimensional non-normed conjunctive functions. This study provides new methods for the n-dimensional generalized fuzzy correlation coefficient analysis, regarding their applications in solving multi-criteria and decision-making problems founded on fuzzy logic extensions. The n-dimensional non-normed conjunctive aggregation functions are also introduced, as range domain extensions from [0,1] to [−1.1], covering the interpretation of negative to positive variable associations. The proposal correlation analysis promotes a better evaluation and data selection even when more than one algorithm is applied to evaluate the reduction methods in the defuzzification process based on Interval-valued Fuzzy Logic. We also investigate the relevance to determine a reliable result from the fuzzy inference system based on the nGCC methodology. This correlation methodology applied to the Interval Fuzzy Load Balancing for Cloud Computing (Int-FLBCC) model contributes as a flexible approach for virtual machines dynamic consolidation enabling improvements in resource usage and power efficiency, improving the computational system’s energy efficiency. So, the nGCC methodology extends the Int-FLBCC model by adding other degrees of reliability to the results obtained with diverse evaluations through n-dimensional generalized fuzzy correlation coefficient expressions, exploring average aggregations as arithmetic and exponential means and the median operator.

Trial of a Novel Radiation Shielding Device to Protect Staff in the Cardiac Catheter Laboratory

Continuous exposure to low-level scattered radiation to staff performing cardiac angiography and intervention is of concern. A novel shielding solution (NSS) (Rampart IC M1128) has the potential to provide greater shielding for staff present at the table-side. This study aimed to investigate the effectiveness of the NSS compared with a traditional shielding solution (TSS) in a randomized controlled trial that enrolled 100 patients who underwent cardiac angiography and/or intervention which were randomized to the NSS or TSS. Baseline patient characteristics and radiation dose data were collected. Staff who were scrubbed at the table-side wore 5 real-time dosimeters on the head, collar, waist, ankle, and under the apron. The median primary operator radiation dose was significantly lower (p<0.001) for all dosimeter locations with the NSS when compared with the TSS, being reduced by 86%, 80.0%, 100%, and 50.0% for the head, collar, waist, and leg respectively. Median under-apron dose was 0.0µSv for both NSS and TSS. Median second operator dose was reduced by 100%, 100%, and 100% for the head, collar, and waist respectively (p<0.001). Median NSS and TSS dose at the ankle and under apron was 0.0µSv. Median scrub nurse dose was reduced by 50% and 100% for the head and collar respectively (p<0.001). Median NSS and TSS dose at the waist, ankle, and under apron was 0.0µSv. In conclusion, the NSS tested in this study demonstrates a significant decrease in radiation dose to operators and scrub nurses when compared with traditional radiation protection measures.

Intravascular imaging during percutaneous coronary intervention: temporal trends and clinical outcomes in the USA.

Prior trials have demonstrated that intravascular imaging (IVI)-guided percutaneous coronary intervention (PCI) results in less frequent target lesion revascularization and major adverse cardiovascular events (MACEs) compared with standard angiographic guidance. The uptake and associated outcomes of IVI-guided PCI in contemporary clinical practice in the USA remain unclear. Accordingly, temporal trends and comparative outcomes of IVI-guided PCI relative to PCI with angiographic guidance alone were examined in a broad, unselected population of Medicare beneficiaries. Retrospective cohort study of Medicare beneficiary data from 1 January 2013, through 31 December 2019 to evaluate temporal trends and comparative outcomes of IVI-guided PCI as compared with PCI with angiography guidance alone in both the inpatient and outpatient settings. The primary outcomes were 1 year mortality and MACE, defined as the composite of death, myocardial infarction (MI), repeat PCI, or coronary artery bypass graft surgery. Secondary outcomes were MI or repeat PCI at 1 year. Multivariable Cox regression was used to estimate the adjusted association between IVI guidance and outcomes. Falsification endpoints (hospitalized pneumonia and hip fracture) were used to assess for potential unmeasured confounding. The study population included 1 189 470 patients undergoing PCI (38.0% female, 89.8% White, 65.1% with MI). Overall, IVI was used in 10.5% of the PCIs, increasing from 9.5% in 2013% to 15.4% in 2019. Operator IVI use was variable, with the median operator use of IVI 3.92% (interquartile range 0.36%-12.82%). IVI use during PCI was associated with lower adjusted rates of 1 year mortality [adjusted hazard ratio (aHR) 0.96, 95% confidence interval (CI) 0.94-0.98], MI (aHR 0.97, 95% CI 0.95-0.99), repeat PCI (aHR 0.74, 95% CI 0.73-0.75), and MACE (aHR 0.85, 95% CI 0.84-0.86). There was no association with the falsification endpoint of hospitalized pneumonia (aHR 1.02, 95% CI 0.99-1.04) or hip fracture (aHR 1.02, 95% CI 0.94-1.10). Among Medicare beneficiaries undergoing PCI, use of IVI has increased over the previous decade but remains relatively infrequent. IVI-guided PCI was associated with lower risk-adjusted mortality, acute MI, repeat PCI, and MACE.

A-269 IMPACT OF THE COVID-19 PANDEMIC ON SURGERY FOR INDETERMINATE THYROID NODULES: RESULTS FROM A LARGE MULTICENTRIC INTERNATIONAL (THE THYCOVID) STUDY

Abstract Background During the COVID-19 pandemic, planned surgery was ubiquitously suspended or slowed down. We focused our attention on surgery for indeterminate thyroid nodules (ITNs). Methods This was a retrospective multicentric study. Patients with ITNs were included in the study and were divided into three groups based on when they were submitted to thyroidectomy: The main aims of the THYCOVID study were: Results We included 22974 patients with ITNs from 157 centers, mainly located in Europe (76.5%) and Asia (11.8%). The median monthly operations per center were 2 (IQR: 0.9-3.7) in P1, 1.4 (IQR: 0.6-3.4) in P2, and 2.3 (IQR: 1-5) in P3; the differences among the phases were all significant (p&amp;lt;0.001). At pathological examination, the incidence of lymph node metastases was significantly higher in P2 (11.7%) and in P3 (12.2%) than in P1 (9.3%; p&amp;lt;0.0001). Considering the ATA classification for risk of disease recurrence, the incidence of high-risk tumours increased over time from 203 (5.7%) patients in P1, to 198 (6.2%) in P2, and to 155 (7.7%) in P3; the difference between P1 and P3 was significant (p=0.0072). Conclusion Surgical activity for ITNs decreased significantly during the pandemic. In the last phase, an increased incidence of aggressive tumours was found.

Secure State Estimation against Sparse Attacks on a Time-varying Set of Sensors

This paper studies the problem of secure state estimation of a linear time-invariant (LTI) system with bounded noise in the presence of sparse attacks on an unknown, time-varying set of sensors. At each time, the attacker has the freedom to choose an arbitrary set of no more than p sensors and manipulate their measurements without restraint. To this end, we propose a secure state estimation scheme and guarantee a bounded estimation error irrespective of the attack signals subject to 2p-sparse observability and a mild, technical assumption that the system matrix has no degenerate eigenvalues. The proposed scheme comprises a design of decentralized observers for each sensor based on the local observable subspace decomposition. At each time step, the local estimates of sensors are fused by a median operator to obtain a secure estimation, which is then followed by a local detection-and-resetting process of the decentralized observers. The estimation error is shown to be upper-bounded by a constant which is determined only by the system parameters and noise magnitudes. Moreover, we design the detector threshold to ensure that the benign sensors never trigger the detector. The efficacy of the proposed algorithm is demonstrated by its application on a benchmark example of IEEE 14-bus system. We show that our proposed scheme can effectively tolerate sparse attacks on an unknown set of sensors, ensuring a bounded estimation error and effectively detecting and resetting the attacked sensors.

A Survey on Procedural Sedation and Analgesia for Pediatric Facial Laceration Repair in Korea

Background Most children with facial lacerations require sedation for primary sutures. However, sedation guidelines for invasive treatment are lacking. This study evaluated the current status of the sedation methods used for pediatric facial laceration repair in Korea. Methods We surveyed one resident in each included plastic surgery training hospital using face-to-face interviews or e-mail correspondence. The health care center types (secondary or tertiary hospitals), sedation drug types, usage, and dosage, procedure sequence, monitoring methods, drug effects, adverse events, and operator and guardian satisfaction were investigated. Results We included 45/67 hospitals (67%) that used a single drug, ketamine in 31 hospitals and chloral hydrate in 14 hospitals. All health care center used similar sedatives. The most used drug administered was 5 mg/kg intramuscular ketamine (10 hospitals; 32%). The most common chloral hydrate administration approach was oral 50 mg/kg (seven hospitals; 50%). Twenty-two hospitals (71%) using ketamine followed this sequence: administration of sedatives, local anesthesia, primary repair, and imaging work-up. The most common sequence used for chloral hydrate (eight hospitals; 57%) was local anesthesia, administration of sedatives, imaging work-up, and primary repair. All hospitals that used ketamine and seven (50%) of those using chloral hydrate monitored oxygen saturation. Median operator satisfaction differed significantly between ketamine and chloral hydrate (4.0 [interquartile range, 4.0-4.0] vs. 3.0 [interquartile range, 3.0-4.0]; p <0.001). Conclusion The hospitals used various procedural sedation methods for children with facial lacerations. Guidelines that consider the patient's condition and drug characteristics are needed for safe and effective sedation.

Impact of the COVID-19 pandemic on surgery for severe endometriosis in the UK: a national database study.

The COVID-19 pandemic has had a significant effect on healthcare services, particularly affecting patients who suffer from chronic conditions. However, the pandemic's effect on endometriosis surgery is not yet known. To determine the impact of the COVID-19 pandemic on surgery for severe endometriosis in the UK at a national, regional and centre-level. The British Society for Gynaecological Endoscopy (BSGE) collects data nationally on all operations for severe endometriosis which involve dissection of the pararectal space. Annual audits of this database were obtained from the BSGE. Publicly available data on COVID-19 cases and population were obtained from the UK Office for National Statistics. Numbers of annual BSGE-registered endometriosis operations. A total of 8204 operations were performed. The number of operations decreased by 49.4% between 2019 and 2020 and then increased in 2021, but remained 10.5% below average pre-pandemic levels, indicating at least 980 missed operations between 2019-2020. Median operations per centre decreased by 51.0% in 2020 (IQR 29.4% - 75.0%) and increased in 2021 but remained 33% below pre-pandemic levels. There was no change in the type of surgery performed. All 11 administrative regions of Great Britain had reduced numbers of operations in 2020 compared with the average for 2017-2019, with a median 44.2% decrease (range 13.3% - 67.5%). Regional reduction in operations was correlated with COVID-19 infection rates (r=0.54, 95% CI of r 0.022 - 1.00, p=0.043). The number of operations performed annually in the UK for severe endometriosis fell dramatically during the COVID-19 pandemic and is yet to normalise. This study shows the dramatic effect that the COVID-19 pandemic has had on UK services for endometriosis surgery, which may continue to affect patients and clinicians for a considerable time to come.

Byzantine-robust variance-reduced federated learning over distributed non-i.i.d. data

We consider the federated learning problem where data on workers are not independent and identically distributed (i.i.d.). During the learning process, an unknown number of Byzantine workers may send malicious messages to the central node, leading to remarkable learning error. Most of the Byzantine-robust methods address this issue by using robust aggregation rules to aggregate the received messages, but rely on the assumption that all the regular workers have i.i.d. data, which is not the case in many federated learning applications. In light of the significance of reducing stochastic gradient noise for mitigating the effect of Byzantine attacks, we use a resampling strategy to reduce the impact of both inner variation (that describes the sample heterogeneity on every regular worker) and outer variation (that describes the sample heterogeneity among the regular workers), along with a stochastic average gradient algorithm to gradually eliminate the inner variation. The variance-reduced messages are then aggregated with a robust geometric median operator. We prove that the proposed method reaches a neighborhood of the optimal solution at a linear convergence rate and the learning error is determined by the number of Byzantine workers. Numerical experiments corroborate the theoretical results and show that the proposed method outperforms the state-of-the-arts in the non-i.i.d. setting.

Temporal Trends, Practice Variation, and Associated Outcomes With IVUS Use During Peripheral Arterial Intervention

BackgroundIntravascular ultrasound (IVUS) has been shown in limited prospective studies to improve procedural outcomes for patients undergoing lower extremity peripheral arterial intervention (PVI). ObjectivesThe authors aimed to study temporal trends, practice variation, and associated outcomes with the use of IVUS during PVI among Medicare beneficiaries. MethodsAll PVIs performed from 2016 to 2019 among Medicare beneficiaries aged >65 years were included. Temporal trends in IVUS use were stratified by procedural location (inpatient, outpatient, or ambulatory surgery center [ASC]/office-based laboratory [OBL]) and physician specialty. The primary outcome was major adverse limb events (MALE). Inverse probability weighting was used to account for differences in baseline characteristics. Cox regression with competing risks was used to estimate weighted hazard ratios. ResultsDuring the study period, 543,488 PVIs were included, of which 63,372 (11.7%) used IVUS. A substantial growth in IVUS use was observed, which was driven by procedures performed in ASCs/OBLs (23.6% increase from quarter 1 of 2016 through quarter 4 of 2019). Among operators who used IVUS, there was also notable variation in use (median operator use 5.4% of cases; IQR: 2.2%-15.0%; range, <1%-100%). In weighted analysis, IVUS use during PVI was associated with a lower risk of MALE through a median of 514 days (adjusted hazard ratio: 0.73; 95% CI: 0.70-0.75; P < 0.0001). ConclusionsIn contemporary nationwide data, IVUS use during PVI has increased since 2016, driven by growth in the ASC/OBL setting. However, there remains substantial variation in operator practice. When used during PVI, IVUS was associated with a lower risk of short- and long-term MALE.

Low-Power Energy-Based Spike Detector ASIC for Implantable Multichannel BMIs

Advances in microtechnology have enabled an exponential increase in the number of neurons that can be simultaneously recorded. To meet high-channel count and implantability demands, emerging applications require new methods for local real-time processing to reduce the data to transmit. Nonlinear energy operators are widely used to distinguish neural spikes from background noise featuring a good tradeoff between hardware resources and accuracy. However, they require an additional smoothing filter, which affects both area occupation and power dissipation. In this paper, we investigate a spike detector, based on a series of two nonlinear energy operators, and a simple and adaptive threshold, based on a three-point median operator. We show that our proposal provides good accuracy compared to other energy-based detectors on a synthetic dataset at different noise levels. Based on the proposed technique, a 1024-channel neural signal processor was designed in a 28 nm TSMC CMOS process by using latch-based static random-access memory (SRAM), demonstrating a total power consumption of 1.4 μW/ch and a silicon area occupation of 230 μm2/ch. These features, together with a comparison with the state of the art, demonstrate that our proposal constitutes an alternative for the development of next-generation multichannel neural interfaces.

Variation in Case-Mix Across Hospitals: Analysis of The Society of Thoracic Surgeons Congenital Heart Surgery Database

BackgroundThe Society of Thoracic Surgeons (STS) Congenital Heart Surgery Database was queried to document variation of patient characteristics, procedure types, and programmatic case-mix. MethodsAll index cardiac operations in patients less than 18 years of age in the STS Congenital Heart Surgery Database (July 2016 to June 2020) were eligible for inclusion except patients weighing ≤2.5 kg undergoing isolated patent ductus arteriosus closure. At the hospital level, we describe variations in patient and procedural characteristics known from previous analyses to be associated with outcomes. We also report variations across hospitals of programmatic case-mix. ResultsData were analyzed from 117 sites (90 322 total operations, 87 296 total index cardiac operations eligible for STAT [STS-European Association for Cardio-Thoracic Surgery] 2020 Mortality Score). The median annual total index cardiac operations eligible for STAT 2020 Mortality Score per hospital was 157 (interquartile range [IQR], 94-276). Wide variability was documented in total annual index cardiac operations eligible for STAT 2020 Mortality Score per hospital (ratio 90th/10th percentile = 9.01), operations in neonates weighing <2.5 kg (ratio 90th/10th percentile = 4.09), operations in patients with noncardiac anatomic abnormalities (ratio 90th/10th percentile = 3.46), and operations in patients with preoperative mechanical ventilation (ratio 90th/10th percentile = 3.97). At the hospital level, the median percentage of all index cardiac operations in STAT 2020 Mortality Category 5 was 3.7% (IQR, 1.7%-4.9%), the median percentage of all index cardiac operations in STAT 2020 Mortality Category 4 or 5 was 24.4% (IQR, 19.0%-28.4%), the median hospital-specific mean STAT Mortality Category was 2.39 (IQR, 2.20-2.47), and the median hospital-specific mean STAT Mortality Score was 0.86 (IQR, 0.73-0.91). ConclusionsSubstantial variation of patient characteristics, procedure types, and case-mix exists across pediatric and congenital cardiac surgical programs. Knowledge about programmatic case-mix augments data about indirectly standardized programmatic observed-to-expected (O/E) mortality. Indirectly standardized O/E ratios do not provide a complete description of a given pediatric and congenital cardiac surgical program. The indirectly standardized programmatic O/E ratios associated with a given program apply only to its specific case-mix of patients and may represent a quite different case-mix than that of another program.

Restoring Deteriorated Images using Deep Learning Techniques: Region Filling, Median Filter

Image restoration is a technique for recovering images from corrupted images that have blur and noise, lowering the image's quality. Motion blur, low resolution, moisture in the atmosphere, and other factors can all contribute to image noise. For noise removal, there are a variety of restoration techniques and a spatial domain filter. To eliminate blur and scratches in deteriorated photographs, an image restoration method has been developed. Deep learning has gained popularity as a method for image restoration during the last few years. Denoising and other image restoration operations are necessary steps in many image processing applications. Image fusion using the stacked median operator, low resolution detail improvement using guided super sampling, and repeated visual consistency assessment and refining are the three processes in the restoration process. Two VAEs (Variational Autoencoders) are trained in this model to translate old and clean pictures into two latent spaces, respectively. This is due to the fact that they are all using supervised learning, which is a difficulty created by the domain gap between the original image and the ones synthesized for training. The suggested project offers a cost-effective solution that can deal with noise, picture rotations, and occlusions.

Particle Size, Mass Concentration, and Microbiota in Dental Aerosols

Many dental procedures are considered aerosol-generating procedures that may put the dental operator and patients at risk for cross-infection due to contamination from nasal secretions and saliva. This aerosol, depending on the size of the particles, may stay suspended in the air for hours. The primary objective of the study was to characterize the size and concentrations of particles emitted from 7 different dental procedures, as well as estimate the contribution of the nasal and salivary fluids of the patient to the microbiota in the emitted bioaerosol. This cross-sectional study was conducted in an open-concept dental clinic with multiple operators at the same time. Particle size characterization and mass and particle concentrations were done by using 2 direct reading instruments: Dust-Trak DRX (Model 8534) and optical particle sizer (Model 3330). Active bioaerosol sampling was done before and during procedures. Bayesian modeling (SourceTracker2) of long-reads of the 16S ribosomal DNA was used to estimate the contribution of the patients’ nasal and salivary fluids to the bioaerosol. Aerosols in most dental procedures were sub-PM1 dominant. Orthodontic debonding and denture adjustment consistently demonstrated more particles in the PM1, PM2.5, PM4, and PM10 ranges. The microbiota in bioaerosol samples were significantly different from saliva and nasal samples in both membership and abundance (P < 0.05) but not different from preoperative ambient air samples. A median of 80.15% of operator exposure was attributable to sources other than the patients’ salivary or nasal fluids. Median operator’s exposure from patients’ fluids ranged from 1.45% to 2.75%. Corridor microbiota showed more patients’ nasal bioaerosols than oral bioaerosols. High-volume saliva ejector and saliva ejector were effective in reducing bioaerosol escape. Patient nasal and salivary fluids are minor contributors to the operator’s bioaerosol exposure, which has important implications for COVID-19. Control of bioaerosolization of nasal fluids warrants further investigation.

Impact of Operator Volumes and Experience on Outcomes After Percutaneous Coronary Intervention: Insights From the Veterans Affairs Clinical Assessment, Reporting and Tracking (CART) Program

BackgroundRecent analyses of the volume-outcome relationship for percutaneous coronary intervention (PCI) have suggested a less robust association than previously reported. It is unknown if novel factors such as lifetime operator experience influence this relationship. ObjectivesTo assess the relationship between annual volumes and outcomes for PCI and determine whether lifetime operator experience modulates the association. MethodsAnnual PCI volumes for facilities and operators within the Veterans Affairs Healthcare System and their relationship with 30-day mortality following PCI were described. The influence of operator lifetime experience on the volume-outcome relationship was assessed. Hierarchical logistic regression was used to adjust for patient and procedural factors. Results57,608 PCIs performed from 2013 to 2018 by 382 operators and 63 institutions were analyzed. Operator annualized PCI volume averaged 47.6 (standard deviation [SD] 49.1) and site annualized volume averaged 189.2 (SD 105.2). Median operator experience was 9.0 years (interquartile range [IQR] 4.0–15.0). There was no independent relationship between operator annual volume, institutional volume, or operator lifetime experience with 30-day mortality (p > 0.10). However, the interaction between operator volume and lifetime experience was associated with a marginal decrease in mortality (odds ratio [OR] 0.9998, 95% CI 0.9996–0.9999). ConclusionsThere were no significant associations between facility or operator-level procedural volume and 30-day mortality following PCI in a nationally integrated healthcare system. There was a marginal association between the interaction of operator lifetime experience, operator annual volume, and 30-day mortality that is unlikely to be clinically relevant, though does suggest an opportunity to explore novel factors that may influence the volume-outcome relationship.

Time Series Forecasting with Trend and Seasonal Patterns using NARX Network Ensembles

In this research, we propose a Nonlinear Auto-Regressive network with exogenous inputs (NARX) model with a different approach, namely the determination of the main input variables using a stepwise regression and exogenous input using a deterministic seasonal dummy. There are two approaches in making a deterministic seasonal dummy, namely the binary and the sine-cosine dummy variables. Approximately half the number of input variables plus one is contained in the neurons of the hidden layer. Furthermore, the resilient backpropagation learning algorithm and the tangent hyperbolic activation function were used to train each network. Three ensemble operators are used, namely mean, median, and mode, to solve the overfitting problem and the single NARX model's weakness. Furthermore, we provide an empirical study using actual data, where forecasting accuracy is determined by Mean Absolute Percent Error (MAPE). The empirical study results show that the NARX model with binary dummy exogenous is the most accurate for trend and seasonal with multiplicative properties data patterns. For trend and seasonal with additive properties data patterns, the NARX model with sine-cosine dummy exogenous is more accurate, except the fact that the NARX model uses the mean ensemble operator. Besides, for trend and non-seasonal data patterns, the most accurate NARX model is obtained using the mean ensemble operator. This research also shows that the median and mode ensemble operators, which are rarely used, are more accurate than the mean ensemble operator for data that have trend and seasonal patterns. The median ensemble operator requires the least average computation time, followed by the mode ensemble operator. On the other hand, all of our proposed NARX models' accuracy consistently outperforms the exponential smoothing method and the ARIMA method.

Restoration and Enhancement of Historical Stereo Photos.

Restoration of digital visual media acquired from repositories of historical photographic and cinematographic material is of key importance for the preservation, study and transmission of the legacy of past cultures to the coming generations. In this paper, a fully automatic approach to the digital restoration of historical stereo photographs is proposed, referred to as Stacked Median Restoration plus (SMR+). The approach exploits the content redundancy in stereo pairs for detecting and fixing scratches, dust, dirt spots and many other defects in the original images, as well as improving contrast and illumination. This is done by estimating the optical flow between the images, and using it to register one view onto the other both geometrically and photometrically. Restoration is then accomplished in three steps: (1) image fusion according to the stacked median operator, (2) low-resolution detail enhancement by guided supersampling, and (3) iterative visual consistency checking and refinement. Each step implements an original algorithm specifically designed for this work. The restored image is fully consistent with the original content, thus improving over the methods based on image hallucination. Comparative results on three different datasets of historical stereograms show the effectiveness of the proposed approach, and its superiority over single-image denoising and super-resolution methods. Results also show that the performance of the state-of-the-art single-image deep restoration network Bringing Old Photo Back to Life (BOPBtL) can be strongly improved when the input image is pre-processed by SMR+.

A New Approach for Forecasting Crude Oil Prices Using Median Ensemble Empirical Mode Decomposition and Group Method of Data Handling

The accuracy of time series forecasting is more important and can assist organizations to take up-to-date decisions for better planning and management. Several classical econometrics and computational approaches show promising results for the ordinary time series forecasting tasks, but they are not satisfactory in crude oil price forecasting. Ensemble empirical mode decomposition (EEMD) not only resolves the problem of nonlinearity and nonstationarity of time series prediction but also creates some problems (i.e., mood mixing and splitting). In this study, we proposed a new hybrid method that combines the median ensemble empirical mode decomposition and group method of data handling (MEEMD-GMDH) to reduce mood splitting problems and forecast crude oil price. MEEMD is achieved by replacing the mean operator with the median operator during the EEMD process. For testing and validation purposes of the different models, the two-seat stamp benchmarked crude oil price data are used (i.e., Brent and West Texas Intermediate (WTI)). To check the proposed model performance, different evaluation measures are used including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Diebold-Mariano (DM) test. All the forecasting accuracy measures confirmed that our proposed model performs well in crude oil prices forecasting as compared to other hybrid models.