Order Selection Criteria Research Articles

Endocrine oncology : An update on surgical treatments

In the management of solid tumours, routine concepts are increasingly being transformed into individualized patient treatment. Endocrine surgery is traditionally characterized by resection strategies that are adapted to phenotype and genotype of the underlying disease. As complication rates in surgery correlate with the extent of resection, continuous efforts are made to identify selection criteria in order to limit the extent of surgery without compromising the oncological outcome. The aim is to design risk-stratified precision endocrine surgery. Asearch was carried out in PubMed for new and modern strategies and approaches for oncological endocrine surgery. Several developments in surgical technique and technology, molecular pathology, medical therapy, and study data identify the potential to adapt the surgical strategy in all areas of endocrine surgery. According to prevalent data, limited extent of resection in thyroid cancer surgery shows a reduction in complication rates while preserving oncological outcome when adequate selection criteria are implemented. New insights and innovative technologies also influence additional areas in oncological endocrine surgery for parathyroid, adrenal, and neuroendocrine neoplasia. However, the broad practice of these new concepts needs to be evaluated with regard to long-term oncological outcome.

Development of Worth Estimation and Key Target Selection Methods for Effective VE

VE is a powerful technique for analyzing a design with a focus on functionality, reducing costs, and increasing value. However, in order to proceed with the design of VE, the theoretical basis for the VE target selection process, which must be preceded in the preparation stage, is lacking, and as a result, it is difficult to verify the most effective target selection. Thus, a theoretical basis is needed. In this study, we intend to quantify the “Worth” value of the “Cost to Worth” technique and present an objective model of the target selection method using the derived value index (VI). For this purpose, cost worth (Worth C) was calculated based on the performance data of 1008 cases for 15 building types provided by the Domestic Public Procurement Service and the share of 83 detailed processes in seven fields. Then, a survey was conducted targeting the ordering companies, and the final worth (Worth F) was calculated by multiplying the derived customer value (Worth V) weight for each detailed work type. We calculated the value index (VI) by comparing “Cost” and “Worth F” for each detailed construction type of the VE project and developed a model to select construction types that exceed the VI standard value derived using the standard error of the population. Both cost experts and non-cost experts can use the developed model to specify the VE target selection criteria in order to easily select the key target, objectively sort the values that serve as the basis for calculating the value index, and make it possible to more easily select the VE target selection criteria based on theoretical evidence. Also, the model can be used to obtain effective detailed design VE results.

Black widow optimization for reducing the target uncertainties in localization wireless sensor networks

Abstract Localization Wireless Sensor Networks (WSN) represent a research topic with increasing interest due to their numerous applications. However, the viability of these systems is compromised by the attained localization uncertainties once implemented, since the network performance is highly dependent on the sensors location. The Node Location Problem (NLP) aims to obtain the optimal distribution of sensors for a particular environment, a problem already categorized as NP-Hard. Furthermore, localization WSN usually perform a sensor selection for determining which nodes are to be utilized for maximizing the achieved accuracy. This problem, defined as the Sensor Selection Problem (SSP), has also been categorized as NP-Hard. While different metaheuristics have been proposed for attaining a near optimal solution in both problems, no approach has considered the two problems simultaneously, thus resulting in suboptimal solutions since the SSP is biased by the actual node distribution once deployed. In this paper, a combined approach of both problems simultaneously is proposed, thus considering the SSP within the NLP. Furthermore, a novel metaheuristic combining the Black Widow Optimization (BWO) algorithm and the Variable Neighbourhood Descent Chains (VND-Chains) local search, denominated as BWO-VND-Chains, is particularly devised for the first time in the author’s best knowledge for the NLP, resulting in a more efficient and robust optimization technique. Finally, a comparison of different metaheuristic algorithms is proposed over an actual urban scenario, considering different sensor selection criteria in order to attain the best methodology and selection technique. Results show that the newly devised algorithm with the SSP criteria optimization achieves mean localization uncertainties up to 19.66 % lower than traditional methodologies.

Liquidity Dynamics in Bangladesh: Investigating the Impact of Selected Macroeconomic Indicators Using an ARDL-ECM Method

The study scrutinizes the influence of the selective macroeconomic forces on the liquidity of Bangladesh. The ratio of excess reserve and total deposit liability is used to represent the liquidity of Bangladesh while total domestic credit, lending rate, consumer price index (CPI), excess reserve and exchange rate are selected as the macroeconomic forces. Using yearly data from 1986 to 2021, different time-series techniques have been used. Cointegration test explicates no cointegrating relationship among the variables. It has been found that 58% of the variation of liquidity is explained by the variation of liquidity itself 20% by domestic credit, 19% by consumer price index and another 12% by the other variables like excess reserve, exchange rate and lending rate. From the result of VAR lag order selection criteria, the maximum lag of the series is one. VAR model suggests the stability and no autocorrelation among the variables. From granger causality test, it can be inferred that domestic credit can forecast consumer price index, consumer price index can cause excess reserve of the country, consumer price index can forecast lending rate, domestic credit can cause increase or decrease the liquidity of the country, liquidity can granger cause excess reserve and excess reserve can cause any changes in liquidity. Lastly, excess reserve can predict lending rate. This results will help the policy makers to predict the liquidity trends and the impact of some selected macroeconomic variables contributing to predict the liquidity.

Fusion detection in distributed MIMO radar under hybrid-order Gaussian model

In this paper, we deal with the point-like target fusion detection in a partially homogeneous environment with distributed MIMO radar. Specifically, we consider the imperfect waveform separation problem, which means that the matched filter output contains not only the auto-correlation term of the current matched waveform but also the cross-correlation terms, called waveform residuals, with the remaining transmitted waveforms. To this end, a hybrid-order Gaussian (HOG) model is utilized, where the target amplitude is deterministic but unknown, and the waveform residuals obey the Gaussian distribution. Then two adaptive detectors are developed according to the GLRT and Wald test, named HOG-GLRT and HOG-Wald respectively. At the fusion detection stage, we focus on the problem of signal-to-noise ratio (SNR) diversity, i.e., the detection performance degradation due to the average weighting of spatial diversity channels when the echo SNR differs. Combined with the Model Order Selection criterion and multiple hypothesis test, two modified fusion detectors based on channel selection are proposed, named MHOG-GLRT and MHOG-Wald. Finally, the numerical simulation results show that the HOG-GLRT is sensitive against waveform residuals, while the HOG-Wald exhibits strong robustness. And it also demonstrates the effectiveness of the MHOG-GLRT and MHOG-Wald facing the extreme scene of SNR diversity.

Temporal and Spectral Evolution of Gamma-Ray Burst Broad Pulses: Identification of High-latitude Emission in the Prompt Emission

We perform a detailed analysis of broad pulses in bright gamma-ray bursts (GRBs) to understand the evolution of GRB broad pulses. Using the temporal and spectral properties, we test the high-latitude emission (HLE) scenario in the decaying phase of broad pulses. The HLE originates from the curvature effect of a relativistic spherical jet, where higher-latitude photons are delayed and softer than the observer’s line-of-sight emission. The signature of HLE has not yet been identified undisputedly during the prompt emission of GRBs. The HLE theory predicts a specific relation, ∝ , between the peak energy E p in ν F ν spectra and the spectral flux F ν measured at E p , . We search for evidence of this relation in 2157 GRBs detected by the Gamma-ray Burst Monitor on board the Fermi Gamma-ray Space Telescope from 2008 to 2017. After imposing unbiased selection criteria in order to minimize contamination in a signal by background and overlaps of pulses, we build a sample of 32 broad pulses in 32 GRBs. We perform a time-resolved spectral analysis on each of these 32 broad pulses and find that the evolution of 18 pulses (56%) is clearly consistent with the HLE relation. For the 18 broad pulses, the exponent δ in the relation of ∝ E p δ is distributed as a Gaussian function with a median and width of 1.99 and 0.34, respectively. This result provides a constraint on the emission radius of GRBs with the HLE signature.

The Legitimacy of Admission Criteria in Predicting the Achievement of the Students in the Medical Colleges at the University of Hafr Al-Batin

The purpose of this study is to show how the admission criteria can predict first-year college students’ performance. The study uses the data of the students’ high school GPA (HSGPA) and the scores of the Prerequirement standardized tests in the Kingdom of Saudi Arabia(achievement test score (ACT) and aptitude score (APT)), and the data of the first-year college GPA for correlations analysis and inferential statistics. As far as we are aware, no study has been conducted or has been made publicly accessible regarding the validity of admission criteria of the medical colleges at the University of Hafr Batin. The Bayesian Information Criterion and the Akaike Information Criterion are applied as model selection criteria in order to select the best model. Actual data is used to establish the legitimacy of the admission criteria.

Ambulatory appendectomy for acute appendicitis: Can we treat all the patients? A prospective study of 451 consecutive ambulatory appendectomies out of nearly 2,000 procedures

BackgroundAcute appendicitis represents the leading cause of acute gastrointestinal disorders, but only a small series regarding ambulatory appendectomies are available. The aim of this study was to report the results of ambulatory (day-case) appendectomy for acute appendicitis in a large consecutive cohort and to improve selection criteria in order to extend the indications. MethodsAll appendectomy procedures for acute appendicitis (March 2013 to June 2020) were included retrospectively. Criteria to select patients eligible for ambulatory appendectomy were based on our clinico-radiological St-Antoine’s score ≥4. ResultsIn total, 1,730 consecutive patients had an appendectomy for acute appendicitis: 1,279 (74%) in conventional settings and 451 (26%) in ambulatory settings. In the conventional group, 360 (28%) patients had surgery deferred to the next morning, whereas in the ambulatory group, 309 patients (70%) were readmitted the next morning (P < .0001). In the ambulatory group, 376 (83%) patients satisfied the criteria (score ≥4), and 90.9% were discharged on postoperative day 0. Rates of unplanned consultation and readmission were not significantly different (5.1% vs 6.6% P = .243). Multivariate analysis of the entire cohort confirmed absence of radiological perforation as highly predictive of early discharge (odds ratio = 6.073). In our cohort, these patients had an early discharge rate of 86.4% compared to 90.2% in those with a St-Antoine’s score ≥4. Considering only radiological evidence of perforation as a selection criterion for ambulatory appendectomy, 581 more patients would be eligible for ambulatory surgery (+60%). ConclusionAmbulatory surgery for acute appendicitis based on St-Antoine’s score is safe. We propose to extend the indication for ambulatory management to all patients without radiological evidence of perforation.

A geospatial source selector for federated GeoSPARQL querying.

Background: Geospatial linked data brings into the scope of the Semantic Web and its technologies, a wealth of datasets that combine semantically-rich descriptions of resources with their geo-location. There are, however, various Semantic Web technologies where technical work is needed in order to achieve the full integration of geospatial data, and federated query processing is one of these technologies. Methods: In this paper, we explore the idea of annotating data sources with a bounding polygon that summarizes the spatial extent of the resources in each data source, and of using such a summary as an (additional) source selection criterion in order to reduce the set of sources that will be tested as potentially holding relevant data. We present our source selection method, and we discuss its correctness and implementation. Results: We evaluate the proposed source selection using three different types of summaries with different degrees of accuracy, against not using geospatial summaries. We use datasets and queries from a practical use case that combines crop-type data with water availability data for food security. The experimental results suggest that more complex summaries lead to slower source selection times, but also to more precise exclusion of unneeded sources. Moreover, we observe the source selection runtime is (partially or fully) recovered by shorter planning and execution runtimes. As a result, the federated sources are not burdened by pointless querying from the federation engine. Conclusions: The evaluation draws on data and queries from the agroenvironmental domain and shows that our source selection method substantially improves the effectiveness of federated GeoSPARQL query processing.

The Reevaluation of AΩA: Updating the Outdated

Medical schools across the country are reconsidering the place of Alpha Omega Alpha Medical Honor Society (AΩA) as evidence of disparities in membership selection continues to mount. Although AΩA national leadership responded to backlash with a reaffirmation of their motto "Be Worthy to Serve the Suffering," and their commitment to diversity, little meaningful progress has been made to this end. By continuing as an organization based on exclusivity, AΩA is an outdated institution that contributes to the structural racism observed in multiple domains of medical education. As we continue to move away from elements of medical education that perpetuate these inequities (e.g., USMLE Step 1 and USMLE Step 2 Clinical Skills Examinations), the place of AΩA in medical education must be re-examined. Several leaders in diversity and inclusion have led the charge by suspending AΩA participation at their institutions (at least 16 U.S. medical schools) and this trend should continue without drastic changes to AΩA. Rather than pushing responsibility to individual chapters, AΩA national leadership must make significant changes to national selection criteria in order to provide appropriate oversight. We propose that AΩA leadership: (1) convene a national task force to review current selection criteria and make actionable recommendations, (2) incorporate standardized diversity goals into selection criteria to hold chapters accountable and limit internal bias, and 3) report diversity and inclusivity statistics annually to the public to promote organizational accountability. Ultimately, meaningful action is necessary for AΩA to remain an honor and to support the development of inclusive leaders in medicine.

Modeling the Effect of Monetary Policy on Commercial Bank’s Lending Operation in Nigeria using Vector Error Correction Model (1991- 2020)

The study examines the effect of monetary policy on commercial bank’s lending operation in Nigeria between February, 1991 to October, 2020 using the macroeconomic time series variables of exchange rate, interest rate, maximum lending rate and prime lending rate. The vector error correction model was employed to analyze these interactions as well as the effect and pattern of causality among the variables under investigation. Monthly data spanning from February, 1991 to October, 2020 which covered a period of 29 years and 7 months (357 observations) were sourced from the Statistical Bulletin of the Central Bank of Nigeria (CBN). Preliminary statistical approach such as descriptive statistics and time plot were carried out to test if the data set obey the normality assumption and to verify if there is trend on the series. Pre-estimation diagnosis such as unit root test, lag order selection criteria and co-integration test were carried out and the results shows that at level, all variables had unit root, then, at first difference all variables were stationary. The lag order selection criteria chose lag 3 (14.7844*) of Akaike information criteria, but the vector error correction model (VECM) was done at lag 2 indicating losing a lag. The co-integration test shows the presence of long run relationship among the variables. The vector error correction model (VECM) estimated from the results obtained shows that all the variables had positive effect on commercial bank’s lending operation. The post estimation test on vector error correction model such as normality of the residuals, serial correlation and heteroscedasticity shows that the VEC model was multivariate normal, no serial correlation and homoscedastic. The inverse root of AR characteristic polynomial shows that the estimated VECM satisfy the stability condition of the diagnostic test. The variance decomposition test shows that the variables have a very weak influence in predicting one another. Recommendations were made based on the result of findings in the study

Theoretical and practical aspects of the convergence of the SRIVC estimator for over-parameterized models

The Simplified Refined Instrumental Variable method for Continuous-time systems (SRIVC) is one of the most popular direct methods for linear continuous-time system identification. Only recently, some optimal asymptotic properties of this method have been proven. Here we provide a comprehensive analysis of the convergence of the SRIVC method for over-parameterized models. The results we derive are used to discuss practical aspects of the method and to analyze the behavior of the normalized error variance norm, which is a central part in Young’s Information Criterion for model order selection. The theoretical findings are validated through simulation examples.

A geospatial source selector for federated GeoSPARQL querying

Background: Geospatial linked data brings into the scope of the Semantic Web and its technologies, a wealth of datasets that combine semantically-rich descriptions of resources with their geo-location. There are, however, various Semantic Web technologies where technical work is needed in order to achieve the full integration of geospatial data, and federated query processing is one of these technologies. Methods: In this paper, we explore the idea of annotating data sources with a bounding polygon that summarizes the spatial extent of the resources in each data source, and of using such a summary as an (additional) source selection criterion in order to reduce the set of sources that will be tested as potentially holding relevant data. We present our source selection method, and we discuss its correctness and implementation. Results: We evaluate the proposed source selection using three different types of summaries with different degrees of accuracy, against not using geospatial summaries. We use datasets and queries from a practical use case that combines crop-type data with water availability data for food security. The experimental results suggest that more complex summaries lead to slower source selection times, but also to more precise exclusion of unneeded sources. Moreover, we observe the source selection runtime is (partially or fully) recovered by shorter planning and execution runtimes. As a result, the federated sources are not burdened by pointless querying from the federation engine. Conclusions: The evaluation draws on data and queries from the agroenvironmental domain and shows that our source selection method substantially improves the effectiveness of federated GeoSPARQL query processing.

Scale-Invariant and consistent Bayesian information criterion for order selection in linear regression models

The Bayesian information criterion (BIC) is one of the most well-known criterion used for model order estimation in linear regression models. However, in its popular form, BIC is inconsistent as the noise variance tends to zero given that the sample size is small and fixed. Several modifications of the original BIC have been proposed that takes into account the high-SNR consistency, but it has been recently observed that the performance of the high-SNR forms of BIC highly depends on the scaling of the data. This scaling problem is a byproduct of the data dependent penalty design, which generates irregular penalties when the data is scaled and often leads to greater underfitting or overfitting losses in some scenarios when the noise variance is too small or large. In this paper, we present a new form of the BIC for order selection in linear regression models where the parameter vector dimension is small compared to the sample size. The proposed criterion eliminates the scaling problem and at the same time is consistent for both large sample sizes and high-SNR scenarios.

It is about who you know (and how you help them): Insights from staff and donors about how to recruit and retain a panel of committed anti-D donors.

Maintaining a panel of committed anti-D donors is crucial for the production of anti-D immunoglobulin to prevent haemolytic disease of the foetus and newborn. Given low numbers of donors in the Australian panel, there is a need to better understand motivators and barriers specific to anti-D donors. A qualitative approach was used to gather perspectives of staff and current anti-D donors in Australia. Focus groups were held with staff involved with the anti-D programme. An asynchronous online discussion forum and interviews were conducted with donors. All data were coded using deductive and inductive thematic analysis. Staff stressed the importance of recruiting donors who met their own informal criteria as well as the formal selection criteria in order to maximize the chances of donors committing to making regular plasma donations. In contrast, donors were motivated by having a personal connection to anti-D, the recipient group and being eligible to join the programme. Support from staff and understanding the value of their donations also helped donors overcome concerns about the risks of joining the programme and reduced barriers to remaining in the programme. Anti-D donors in Australia are motivated by multiple factors, including knowing who the recipient is, and dedicated staff are integral to building donors' commitment through education and support. Findings suggest the current approach to recruitment could be broadened to include all donors who meet formal selection criteria, with retention enhanced by reinforcing and rewarding the motives identified by donors for donating.

Robust Spectral Analysis of Multi-Channel Sinusoidal Signals in Impulsive Noise Environments

Robust spectral analysis of the sinusoidal signals corrupted by impulsive noise poses a big challenge in the signal processing community. In this paper, we address the issue of robust spectral analysis for multi-channel sinusoidal signals, including order detection and parameter estimation. The successive robust low-rank decomposition is firstly designed to extract the common signal subspace from the multi-channel data matrix. Subsequently, the number of sinusoidal poles is determined with a model order selection criterion, based on the so-obtained subspaces. With the signal order information, the sinusoidal parameters and outliers are jointly estimated according to the maximum a posteriori criterion. To find a robust initial guess of the sinusoidal parameters, an estimator based on robust weighted linear prediction is developed. Additionally, the performance analysis is provided, which includes computational complexity, convergence verification of the sinusoidal parameter estimation, and asymptotic consistency of the signal order detection. Simulation results demonstrate the advantages of the proposed robust spectral analysis framework compared to state-of-the-art schemes.

Performances of Various Order Selection Criteria for Autoregressive Process

Performances of Various Order Selection Criteria for Autoregressive Process

Prognostic Factors to Determine Survivorship of Meniscal Allograft Transplant: A Systematic Review.

Background:There is much room for improvement and optimization of meniscal allograft survivorship.Purpose:To understand prognostic factors for survivorship using evidence-based selection criteria in order to identify patients who would best benefit from meniscal allograft transplant (MAT).Study Design:Systematic review; Level of evidence, 4.Methods:We conducted this systematic review to analyze prognostic factors for survivorship of MAT. The Cochrane Central Register, PubMed publisher, Embase.com, and Web of Science databases were searched through August 8, 2019. Included studies entailed patients of any age who received MAT with a reported association between prognostic factors and survivorship of the allograft. Two reviewers independently screened all titles and abstracts for eligibility, extracted the data, assessed the risk of bias using the Newcastle-Ottawa Scale, and performed a best-evidence synthesis.Results:The review included 18 studies with a total of 1920 patients. The mean follow-up time was 6.0 years (range, 2.1-11.2 years). A total of 20 prognostic factors were identified and shown to be associated with survivorship of MAT. Strong evidence was found that severe cartilage damage was associated with poor survivorship. Strong evidence was also found showing that sex, knee compartment, surgical side, concomitant anterior cruciate ligament (ACL) reconstruction, and concomitant osteotomy for malalignment had no effect on survivorship. Moderate evidence was found that body mass index (<36), tobacco use, and arthroscopic versus open procedure had no influence on survivorship. Conflicting evidence was found that older age and kissing cartilage lesions (lesions on both the femur and tibia vs on a single side) decreased survivorship.Conclusion:Severe cartilage damage decreases the survivorship of MAT. Concomitant ACL reconstruction and osteotomy showed no relationship to survivorship. Many determinants showed conflicting and limited evidence. Older age may be of interest and should be further studied.

Novel model selection criteria for LMARS: MARS designed for biological networks

ABSTRACT In higher dimensions, the loop-based multivariate adaptive regression splines (LMARS) model is used to build sparse and complex gene structure nonparametrically by correctly defining its interactions in the network. Also, it prefers to apply the generalized cross-validation (GCV) value as its original model selection criterion in order to select the best model, in turn, represent the true network structure. In this study, we suggest to modify the model selection procedure of LMARS by changing GCV with our Kullback–Leibler information-based criteria, namely, consistent Akaike information criterion (CAIC), CAIC with Fisher information matrix and information complexity. Thereby, we aim to compare the performance of our proposal model selection criteria together with the state-of-art model selection criteria, namely, AIC and Bayesian information criterion by comparing their accuracy with GCV while modelling different dimensional and topological biological networks under both simulated and real datasets.

A fuzzy AHP approach to evaluating differences between ocean container carriers and their customers

The study has investigated ocean container carrier selection criteria in order to determine the differences and similarities between carriers and their customers in the Turkish market. In application, four separate groups have used: carriers, freight forwarders, shippers, and consignees. For the first time, differences in perspective between carriers and their customers are studied in Turkey, eliminating the gap in knowledge about this market. It is also the first study to utilise fuzzy analytical hierarchy process (AHP) method to examine the differences in perceptions between ocean container carriers, freight forwarders, shippers, and consignees. This study contributes to the literature by employing both a new region and an unused method. The study's findings are that the most important sub-criterion for carriers and freight forwarders was low freight; meanwhile, the most important criterion for shippers was determined to be equipment availability. The most important sub-criterion of consignees was on-time notification of the customer.