Alternative Measures Research Articles

Skin Cancer: An Insight On Its Association With Aging, Pathogenesis And Treatment Strategies.

Skin cancer is one of the deadly diseases of the skin characterized by pain and uncontrolled growth of cells. The pathogenesis of skin cancer involves the uncontrolled division of abnormal cells in the part of the body affected by an accumulation of genome variation over the course of a lifetime. The incidence of skin cancer has been increasing all over the world and has been reported more in old-aged persons. Furthermore, aging plays a vital role in promoting malignancy. Cancer necessitates lifelong administration of drugs to maintain the quality of life. The major challenge of treatment is the side effects associated with these drugs. Novel and targeted approaches are now formulated to explore as an alternative measure to treat cancer. The current review summarizes the pathogenesis of cancer and its treatment strategies. These approaches are discussed with regard to the drugs, mechanism of action, causative factors, distribution of cancer, mortality rate, and treatment strategies.

Dynamic object detection using sparse LiDAR data for autonomous machine driving and road safety applications

Light Detection And Ranging (LiDAR) sensor offers solutions to extract real-time information of vehicle’s surroundings and can provide a new opportunity to improve the road safety related issues in mixed traffic conditions. This paper explores fundamental principles and combination of algorithms, to accurately detect, classify and track surrounding vehicles on expressways using a vehicle mounted cost-effective LiDAR sensor in dynamic conditions. The proposed approach employs algorithms such as Density-Based Spatial Clustering of Applications with Noise, Random sample consensus, and Kalman filter on 3D LiDAR data for ground and non-ground points segmentation, object clustering, road boundary identification, vehicle classification and tracking. Trajectory data of the tracked vehicles is used to estimate their relative positions and speeds. The surrounding vehicles were classified into three categories: two-wheelers, four-wheelers and heavy-vehicles. The proposed method achieves an accuracy, precision, recall, and F1-score above 94 %, demonstrating its robustness. The resilience and versatility of the proposed algorithm were validated through comprehensive evaluations on a significant dataset spanning over 8000 km (around 3.6 million frames) of driving data collected under varied environmental scenarios on expressway. Further, various applications of proposed methodology in road safety studies are discussed. The approach can extract real-time micro-level trajectory data for surrounding vehicles on expressways in any lighting condition, which is useful for researchers and can be used in connected and autonomous vehicles. It also provides valuable information for assessing surrogate safety measures and identifying road safety issues and suggesting countermeasures, including advanced and cost-effective driver assistance system applications.

Scaling and interpreting treatment effects in clinical trials using restricted mean survival time.

Restricted mean survival time is the expected duration of survival up to a chosen time of restriction . For comparison studies, the difference in restricted mean survival times between two groups provides a summary measure of the treatment effect that is free of assumptions regarding the relative shape of the two survival curves, such as proportional hazards. However, it can be difficult to judge the magnitude of the effect from a comparison of restricted means due to the truncation of observation at time . In this article, we describe additional ways of expressing the treatment effect based on restricted means that can be helpful in this regard. These include the ratio of restricted means, the ratio of life-years (or time) lost, and the average integrated difference between the survival curves, equal to the difference in restricted means divided by These alternative metrics are straightforward to calculate and provide a means for scaling the effect size as an aid to interpretation. Examples from two randomized, multicenter clinical trials in prostate cancer, NRG/RTOG 0521 and NRG/RTOG 0534, with primary endpoints of overall survival and biochemical/radiological progression-free survival, respectively, are presented to illustrate the ideas. The four effect measures (restricted mean survival time difference, restricted mean survival time ratio, time lost ratio, and average survival rate difference) were 0.45 years, 1.05, 0.81, and 0.038 for RTOG 0521 and 1.36 years, 1.17, 0.56, and 0.12 for RTOG 0534 with = 12 and 11 years, respectively. Thus, for example, the 0.45-year difference in the first trial translates into a 19% reduction in time lost and a 3.8% average absolute difference between the survival curves over the 12-year horizon, a modest effect size, whereas the 1.36-year difference in the second trial corresponds to a 44% reduction in time lost and a 12% absolute survival difference, a rather large effect. In addition to the difference in restricted mean survival times, these alternative measures can be helpful in determining whether the magnitude of the treatment effect is clinically meaningful.

Pathophysiology, Diagnosis, Prognosis, and Prevention of Poststroke Epilepsy: Clinical and Research Implications.

Poststroke epilepsy (PSE) is associated with higher mortality and poor functional and cognitive outcomes in patients with stroke. With the remarkable development of acute stroke treatment, there is a growing number of survivors with PSE. Although approximately 10% of patients with stroke develop PSE, given the significant burden of stroke worldwide, PSE is a significant problem in stroke survivors. Therefore, the attention of health policymakers and significant funding are required to promote PSE prevention research. The current PSE definition includes unprovoked seizures occurring more than 7 days after stroke onset, given the high recurrence risks of seizures. However, the pathologic cascade of stroke is not uniform, indicating the need for a tissue-based approach rather than a time-based one to distinguish early seizures from late seizures. EEG is a commonly used tool in the diagnostic work-up of PSE. EEG findings during the acute phase of stroke can potentially stratify the risk of subsequent seizures and predict the development of poststroke epileptogenesis. Recent reports suggest that cortical superficial siderosis, which may be involved in epileptogenesis, is a promising marker for PSE. By incorporating such markers, future risk-scoring models could guide treatment strategies, particularly for the primary prophylaxis of PSE. To date, drugs that prevent poststroke epileptogenesis are lacking. The primary challenge involves the substantial cost burden due to the difficulty of reliably enrolling patients who develop PSE. There is, therefore, a critical need to determine reliable biomarkers for PSE. The goal is to be able to use them for trial enrichment and as a surrogate outcome measure for epileptogenesis. Moreover, seizure prophylaxis is essential to prevent functional and cognitive decline in stroke survivors. Further elucidation of factors that contribute to poststroke epileptogenesis is eagerly awaited. Meanwhile, the regimen of antiseizure medications should be based on individual cardiovascular risk, psychosomatic comorbidities, and concomitant medications. This review summarizes the current understanding of poststroke epileptogenesis, its risks, prognostic models, prophylaxis, and strategies for secondary prevention of seizures and suggests strategies to advance research on PSE.

Thermal imaging can reveal variation in stay-green functionality of wheat canopies under temperate conditions.

Canopy temperature (CT) is often interpreted as representing leaf activity traits such as photosynthetic rates, gas exchange rates, or stomatal conductance. This interpretation is based on the observation that leaf activity traits correlate with transpiration which affects leaf temperature. Accordingly, CT measurements may provide a basis for high throughput assessments of the productivity of wheat canopies during early grain filling, which would allow distinguishing functional from dysfunctional stay-green. However, whereas the usefulness of CT as a fast surrogate measure of sustained vigor under soil drying is well established, its potential to quantify leaf activity traits under high-yielding conditions is less clear. To better understand sensitivity limits of CT measurements under high yielding conditions, we generated within-genotype variability in stay-green functionality by means of differential short-term pre-anthesis canopy shading that modified the sink:source balance. We quantified the effects of these modifications on stay-green properties through a combination of gold standard physiological measurements of leaf activity and newly developed methods for organ-level senescence monitoring based on timeseries of high-resolution imagery and deep-learning-based semantic image segmentation. In parallel, we monitored CT by means of a pole-mounted thermal camera that delivered continuous, ultra-high temporal resolution CT data. Our results show that differences in stay-green functionality translate into measurable differences in CT in the absence of major confounding factors. Differences amounted to approximately 0.8°C and 1.5°C for a very high-yielding source-limited genotype, and a medium-yielding sink-limited genotype, respectively. The gradual nature of the effects of shading on CT during the stay-green phase underscore the importance of a high measurement frequency and a time-integrated analysis of CT, whilst modest effect sizes confirm the importance of restricting screenings to a limited range of morphological and phenological diversity.

Optimal estimation of pure states with displaced-null measurements

We revisit the problem of estimating an unknown parameter of a pure quantum state, and investigate ‘null-measurement’ strategies in which the experimenter aims to measure in a basis that contains a vector close to the true system state. Such strategies are known to approach the quantum Fisher information for models where the quantum Cramér-Rao bound (QCRB) is achievable but a detailed adaptive strategy for achieving the bound in the multi-copy setting has been lacking. We first show that the following naive null-measurement implementation fails to attain even the standard estimation scaling: estimate the parameter on a small sub-sample, and apply the null-measurement corresponding to the estimated value on the rest of the systems. This is due to non-identifiability issues specific to null-measurements, which arise when the true and reference parameters are close to each other. To avoid this, we propose the alternative displaced-null measurement strategy in which the reference parameter is altered by a small amount which is sufficient to ensure parameter identifiability. We use this strategy to devise asymptotically optimal measurements for models where the QCRB is achievable. More generally, we extend the method to arbitrary multi-parameter models and prove the asymptotic achievability of the the Holevo bound. An important tool in our analysis is the theory of quantum local asymptotic normality which provides a clear intuition about the design of the proposed estimators, and shows that they have asymptotically normal distributions.

Area measure of skin conductance in the Concealed Information Test

Skin conductance (SC) is one of the indices commonly used in the autonomic Concealed Information Test (CIT), but SC amplitude is sometimes difficult to quantify. This study investigated the applicability of SC area to the CIT as an unambiguous measure of SC. Secondary analyses of an existing dataset indicated that SC area could be used to classify examinees according to their knowledge status, although the equivalence of its performance with the SC amplitude was inconclusive. Classification performance was best when the SC signal was converted to the difference from question onset and summed over 10 s after question onset. SC area produced relatively consistent evaluations of differential responses based on the amplitude for inter-item comparisons. In addition, the classification performance of SC area exceeded the chance level even for participants who showed few measurable amplitudes (low-responsive participants). A possible implication is that a tonic increase in SC occurred in response to the relevant question even in low-responsive participants, who are traditionally excluded from analysis. The use of SC area might contribute to more impartial data evaluation and broader application of the CIT. These results indicate that SC area can be used as an alternative measure of SC in the CIT.

Large Uncertainties When Diagnosing the “Eddy Feedback Parameter” and Its Role in the Signal‐To‐Noise Paradox

AbstractA too‐weak eddy feedback in models has been proposed to explain the signal‐to‐noise paradox in seasonal‐to‐decadal forecasts of the winter Northern Hemisphere. We show that the “eddy feedback parameter” (EFP) used in previous studies is sensitive to sampling and multidecadal variability. When these uncertainties are accounted for, the EFP diagnosed from CMIP6 historical simulations generally falls within the reanalysis uncertainty. We find the EFP is not independent of the sampled North Atlantic Oscillation (NAO). Within the same dataset, a sample containing larger NAO variability will show a larger EFP, suggesting that the link between eddy feedbacks and the signal‐to‐noise paradox could be due to sampling effects with the EFP. An alternative measure of eddy feedback, the barotropic energy generation rate, is less sensitive to sampling errors and delineates CMIP6 models that have weak, strong, or unbiased eddy feedbacks, but shows little relation to NAO variability.

Reliability and concurrent validity of 30-s and 5-time sit-to-stand tests in older adults with knee osteoarthritis.

Sit-to-stand (STS) movement is fundamental in daily life. STS tasks have been used to assess lower limb muscle strength, balance control, and functional abilities in various populations, but limited information is available on their use in older individuals with knee osteoarthritis. We here examined the intra-rater, inter-rater, and test-retest reliability of the 30-s and 5-time STS tests, and calculated the standard error of measurement (SEM) and minimal detectable change (MDC). Moreover, we explored the concurrent validity of these tests by analysing their correlation with knee muscle strength, pain, stiffness, and physical function in older adults with knee osteoarthritis. This cross-sectional study included 60 older participants diagnosed with knee osteoarthritis. Baseline demographic information and data on pain, stiffness, and physical function using the modified Western Ontario and McMaster Universities Osteoarthritis (WOMAC) were obtained. Participants performed the 30-s and 5-time STS tests as well as knee muscle strength assessment using a hand-held dynamometer. After 1week, 30 participants were reassessed to evaluate test-retest reliability. The 30-s and 5-time STS tests had excellent intra-, inter-, and test-retest reliability (intraclass correlation coefficients: 0.990-1.000, 0.996-0.999, and 0.841-0.853, respectively) with a large SEM and MDC. Both STS tests significantly correlated with knee extensor and flexor strength and all modified WOMAC domains (Pearson's correlation coefficients: 0.28-0.61, p < 0.05). The 30-s and 5-time STS tests are reliable assessment tools and correlate with knee muscle strength, pain, stiffness, and physical function in older adults with knee osteoarthritis. Key Points • The 30-s and 5-time STS test can serve as surrogate measures in various aspects of knee health, including muscle strength, pain levels, stiffness, and overall physical function, in older adults with knee osteoarthritis. • Both these STS tests are dependable tools, accessible for both experienced and inexperienced assessors to administer effectively.

A Research Note on Community Resilience Estimates: New U.S. Census Bureau Data With an Application to Excess Deaths From COVID-19.

In this research note, we describe the results of the first validation study of the U.S. Census Bureau's new Community Resilience Estimates (CRE), which uses Census microdata to develop a tract-level vulnerability index for the United States. By employing administrative microdata to link Social Security Administration mortality records to CRE, we show that CRE quartiles provide more stable predictions of COVID-19 excess deaths than single demographic categorizations such as race or age, as well as other vulnerability measures including the U.S. Centers for Disease Control and Prevention's Social Vulnerability Index (SVI) and the Federal Emergency Management Agency's National Risk Index (NRI). We also use machine learning techniques to show that CRE provides more predictive power of COVID-19 excess deaths than standard socioeconomic predictors of vulnerability such as poverty and unemployment, as well as SVI and NRI. We find that a 10-percentage-point increase in a key CRE risk measure is associated with one additional death per neighborhood during the initial outbreak of COVID-19 in the United States. We conclude that, compared with alternative measures, CRE provides a more accurate predictor of community vulnerability to a disaster such as a pandemic.

Triglyceride-glucose index improves risk prediction beyond traditional risk factors & hypertension mediated organ damage in healthy adults

Background and AimsTriglyceride-glucose (TyG) index, a surrogate measure of insulin resistance, is associated with hypertension mediated organ damage (HMOD) and cardiovascular disease. This study investigated the association between TyG index and major adverse cardiovascular events (MACE) and its interaction with traditional risk factors and HMOD. Methods and ResultsHealthy subjects recruited from the general population were thoroughly examined and followed for MACE using nation-wide registries. Cox proportional hazard models were used to calculate the association between TyG index and MACE occurrence. Models were adjusted for Systematic Coronary Risk Evaluation (SCORE) risk factors, pulse wave velocity, left ventricular mass index, carotid atherosclerotic plaque status, and microalbuminuria. Continuous net reclassification and Harrell’s Concordance index (C-index) were used to assess the added prognostic value of TyG index.During a follow-up period of mean 15.4±4.7 years, MACE were observed in 332(17%) of 1970 included participants. TyG index was associated with MACE; HR=1.44 [95%CI:1.30-1.59] per standard deviation. After adjustment for traditional cardiovascular (CV) risk factors, HR was 1.16 [95%CI:1.03-1.31]. The association between TyG index and MACE remained significant after further adjustment for each HMOD component. However, this finding was evident only in subjects aged 41 or 51 years (HR=1.39; 95%CI:1.15-1.69). Including TyG index in a risk model based on traditional CV risk factors improved C-index with 0.005 (P=0.042). ConclusionIn this population-based study of healthy middle-aged subjects, TyG index was associated with MACE independently of traditional CV risk factors and HMOD. TyG index may have a potential role in future risk prediction systems.

Deployment of carbon removal technologies could reduce the rapid and potentially disruptive pace of decarbonization in South Africa's climate ambitions

As a developing country, South Africa faces the risk of having to undertake disruptive actions to meet its internationally pledged emission reduction targets. It has become necessary to find alternative measures for South Africa to meet its climate targets without such a disruptive and aggressive transformation of its energy sector. To truly reach net-zero CO2 emissions, South Africa must ensure that residual emissions from its recalcitrant sectors are equally compensated by carbon dioxide removal (CDR). Given the potential for CDR technologies to delay the need for rapid emissions cuts, we leverage this characteristic to explore their role in helping South Africa achieve its climate targets on time, but through a less disruptive and overly aggressive pace of energy system decarbonization. Using an integrated assessment model, we show that while reaching its emission reduction targets, the presence of novel CDR (nCDR) approaches could significantly reduce South Africa's mitigation costs to $155-240/tCO2 instead of $190–590/tCO2 by 2050 in the absence of nCDR. Furthermore, the availability of nCDR may allow for a more gradual transition towards renewable and nuclear power generation compared to scenarios without nCDR. Importantly, we reveal that nCDR could help avoid asset stranding of up to 4–6 GW and $15–25 billion in associated costs between 2016 and 2050. However, this comes at the cost of higher residual greenhouse gas emissions due to the continued reliance on fossil fuels under nCDR availability. Overall, South Africa's priority must remain on decarbonization, especially in sectors where fuel switching and energy efficiency are feasible. Nonetheless, the complementary role of nCDR must not be underestimated, and South Africa should begin investing in these emerging technologies to support its ambitious climate goals.

Cosine similarity and distance measures for [formula omitted] quasirung orthopair fuzzy sets: Applications in investment decision-making

Similarity measures and distance measures are used in a variety of domains, such as data clustering, image processing, retrieval of information, and recognizing patterns, in order to measure the degree of similarity or divergence between elements or datasets. p,q− quasirung orthopair fuzzy (p,q− QOF) sets are a novel improvement in fuzzy set theory that aims to properly manage data uncertainties. Unfortunately, there is a lack of research on similarity and distance measure between p,q− QOF sets. In this paper, we investigate different cosine similarity and distance measures between to p,q− quasirung orthopair fuzzy sets (p,q− ROFSs). Firstly, the cosine similarity measure and the Euclidean distance measure for p,q− QOFSs are defined, followed by an exploration of their respective properties. Given that the cosine measure does not satisfy the similarity measure axiom, a method is presented for constructing alternative similarity measures for p,q− QOFSs. The structure is based on the suggested cosine similarity and Euclidean distance measures, which ensure adherence to the similarity measure axiom. Furthermore, we develop a cosine distance measure for p,q− QOFSs that connects similarity and distance measurements. We then apply this technique to decision-making, taking into account both geometric and algebraic perspectives. Finally, we present a practical example that demonstrates the proposed justification and efficacy of the proposed method, and we conclude with a comparison to existing approaches.

Evaluating targeted COVID-19 vaccination strategies with agent-based modeling.

We evaluate approaches to vaccine distribution using an agent-based model of human activity and COVID-19 transmission calibrated to detailed trends in cases, hospitalizations, deaths, seroprevalence, and vaccine breakthrough infections in Florida, USA. We compare the incremental effectiveness for four different distribution strategies at four different levels of vaccine supply, starting in late 2020 through early 2022. Our analysis indicates that the best strategy to reduce severe outcomes would be to actively target high disease-risk individuals. This was true in every scenario, although the advantage was greatest for the intermediate vaccine availability assumptions and relatively modest compared to a simple mass vaccination approach under high vaccine availability. Ring vaccination, while generally the most effective strategy for reducing infections, ultimately proved least effective at preventing deaths. We also consider using age group as a practical surrogate measure for actual disease-risk targeting; this approach also outperforms both simple mass distribution and ring vaccination. We find that quantitative effectiveness of a strategy depends on whether effectiveness is assessed after the alpha, delta, or omicron wave. However, these differences in absolute benefit for the strategies do not change the ranking of their performance at preventing severe outcomes across vaccine availability assumptions.

How Do Quantitative Tissue Imaging Outcomes in Acute Ischemic Stroke Relate to Clinical Outcomes?

Infarct volume and other imaging markers are increasingly used as surrogate measures for clinical outcome in acute ischemic stroke research, but how improvements in these imaging surrogates translate into better clinical outcomes is currently unclear. We investigated how changes in infarct volume at 24 hours alter the probability of achieving good clinical outcome (modified Rankin Scale [mRS] 0-2). Data are from endovascular thrombectomy patients from the randomized controlled ESCAPE-NA1 (Efficacy and Safety of Nerinetide for the Treatment of Acute Ischaemic Stroke) trial. Infarct volume at 24 hours was manually segmented on non-contrast computed tomography or diffusion-weighted magnetic resonance imaging. Probabilities of achieving good outcome based on infarct volume were obtained from a multivariable logistic regression model. The probability of good outcome was plotted against infarct volume using linear spline regression. A total of 1,099 patients were included in the analysis (median final infarct volume 24.9 mL [interquartile range: 6.6-92.2]). The relationship between total infarct volume and good outcome probability was nearly linear for infarct volumes between 0 mL and 250 mL. In this range, a 10% increase in the probability of achieving mRS 0-2 required a decrease in infarct volume of approximately 34.0 mL (95% confidence interval: -32.5 to -35.6). At infarct volumes above 250 mL, the probability of achieving mRS 0-2 probability was near zero. The relationships of tissue-specific infarct volumes and parenchymal hemorrhage volume generally showed similar patterns, although variability was high. There seems to be a near-linear association between total infarct volume and probability of achieving good outcome for infarcts up to 250 mL, whereas patients with infarct volumes greater than 250 mL are highly unlikely to have a favorable outcome.

Clustering algorithm based on DINNSM and its application in gene expression data analysis.

Selecting an appropriate similarity measurement method is crucial for obtaining biologically meaningful clustering modules. Commonly used measurement methods are insufficient in capturing the complexity of biological systems and fail to accurately represent their intricate interactions. This study aimed to obtain biologically meaningful gene modules by using the clustering algorithm based on a similarity measurement method. A new algorithm called the Dual-Index Nearest Neighbor Similarity Measure (DINNSM) was proposed. This algorithm calculated the similarity matrix between genes using Pearson's or Spearman's correlation. It was then used to construct a nearest-neighbor table based on the similarity matrix. The final similarity matrix was reconstructed using the positions of shared genes in the nearest neighbor table and the number of shared genes. Experiments were conducted on five different gene expression datasets and compared with five widely used similarity measurement techniques for gene expression data. The findings demonstrate that when utilizing DINNSM as the similarity measure, the clustering results performed better than using alternative measurement techniques. DINNSM provided more accurate insights into the intricate biological connections among genes, facilitating the identification of more accurate and biological gene co-expression modules.

Triglyceride-glucose index is associated with myocardial ischemia and poor prognosis in patients with ischemia and no obstructive coronary artery disease

BackgroundIschemia and no obstructive coronary artery disease (INOCA) is increasingly recognized and associated with poor outcomes. The triglyceride-glucose (TyG) index is a reliable alternative measure of insulin resistance significantly linked to cardiovascular disease and adverse prognosis. We investigated the association between the TyG index and myocardial ischemia and the prognosis in INOCA patients.MethodsINOCA patients who underwent both coronary angiography and myocardial perfusion imaging (MPI) were included consecutively. All participants were divided into three groups according to TyG tertiles (T1, T2, and T3). Abnormal MPI for myocardial ischemia in individual coronary territories was defined as summed stress score (SSS) ≥ 4 and summed difference score (SDS) ≥ 2. SSS refers to the sum of all defects in the stress images, and SDS is the difference of the sum of all defects between the rest images and stress images. All patients were followed up for major adverse cardiac events (MACE).ResultsAmong 332 INOCA patients, 113 (34.0%) had abnormal MPI. Patients with higher TyG index had a higher rate of abnormal MPI (25.5% vs. 32.4% vs. 44.1%; p = 0.012). Multivariate logistic analysis showed that a high TyG index was significantly correlated with abnormal MPI in INOCA patients (OR, 1.901; 95% CI, 1.045–3.458; P = 0.035). During the median 35 months of follow-up, 83 (25%) MACE were recorded, and a higher incidence of MACE was observed in the T3 group (T3 vs. T2 vs. T1: 36.9% vs. 21.6% vs. 16.4%, respectively; p = 0.001). In multivariate Cox regression analysis, the T3 group was significantly associated with the risk of MACE compared to the T1 group (HR, 2.338; 95% CI 1.253–4.364, P = 0.008).ConclusionThis study indicates for the first time that the TyG index is significantly associated with myocardial ischemia and poor prognosis among INOCA patients.

The interrelationship between obesity and race in breast cancer prognosis: a prospective cohort study

BackgroundObesity is associated with an increased breast cancer risk in postmenopausal women and may contribute to worse outcomes. Black women experience higher obesity and breast cancer mortality rates than non-Black women. We examined associations between race, obesity, and clinical tumor stage with breast cancer prognosis.MethodsWe conducted a prospective cohort study in 1,110 breast cancer patients, using univariable and multivariable Cox regression analyses to evaluate the effects of obesity, race/ethnicity, and clinical tumor stage on progression-free and overall survival (PFS and OS).Results22% of participants were Black, 64% were Hispanic White, and 14% were non-Hispanic White or another race. 39% of participants were obese (body mass index [BMI] ≥ 30 kg/m2). In univariable analyses, tumor stage III-IV was associated with worse PFS and OS compared to tumor stage 0-II (hazard ratio [HR] = 4.68, 95% confidence interval [CI] = 3.52–6.22 for PFS and HR = 5.92, 95% CI = 4.00-8.77 for OS). Multivariable analysis revealed an association between Black race and worse PFS in obese (HR = 2.19, 95% CI = 1.06–4.51) and non-obese (HR = 2.11, 95% CI = 1.05–4.21) women with tumors staged 0-II. Obesity alone was not associated with worse PFS or OS.ConclusionsResults suggest a complex interrelationship between obesity and race in breast cancer prognosis. The association between the Black race and worse PFS in tumor stages 0-II underscores the importance of early intervention in this group. Future studies are warranted to evaluate whether alternative measures of body composition and biomarkers are better prognostic indicators than BMI among Black breast cancer survivors.

Good Governance Measurement using Rotberg's Output-Based Approach for Kyrgyzstan

This study evaluates the concept of good governance by comparing traditional inputbased measures with a novel output-based methodology. Indices like the Worldwide Governance Indicators (WGI) are widely used for cross-country comparisons but face criticisms for their reliance on input and capacity-based approaches, which may not accurately reflect governance outcomes experienced by citizens. Rotberg (2016) proposed an output-centred method that assesses governance quality through tangible political goods provisions, utilizing a bottom-up approach. This research applies Rotberg’s methodology to measure good governance in Kyrgyzstan using data on 36 indexes over a period from 1991 to 2019. Missing values were imputed using Principal Component Analysis. Comparative analysis of Rotberg’s index with the WGI reveals divergent trends, explained by differences in methodology and underlying data. The research findings highlight the need for the design of a rigorous methodology for the computation of the output-based good governance index for developing countries. Addressing the identified issues will facilitate the widespread adoption and refinement of this alternative measure, fostering better governance practices worldwide.

An integrated cost based approach for warehouse performance evaluation: A new multiphase model

Warehouses represent key links in domestic and international commodity flows. The increasing shortage of workers and increasing costs on the one hand, and the increasing number and stricter demands of users on the other hand lead warehouse managers to realize their operations as efficiently as possible. A proposed model has an objective of enabling companies to monitor warehouse performance in an authoritative, reliable, and simple way and define appropriate corrective measures accordingly. The proposed empirical research consists of three stages, where in the first stage a combination of Principal Component Analysis-Data Envelopment Analysis methods was applied in order to determine efficient warehouses based on 90 decision making units. In the second phase, a completely new method called Interval Fuzzy Rough Pivot Pair-wise Relative Criteria Importance Assessment method used for determining criteria weights was developed and applied, which is one of the most important novelties of this study. In the last phase, the Interval Fuzzy Rough Measurement of Alternatives and Ranking according to the Compromise Solution method was applied to rank the alternatives. Twelve criteria were observed to evaluate 21 alternatives. Based on the results, it was concluded that salary stood out as the most important criterion, while amortization stood out as the least significant criterion. On the other hand, alternatives A9 and A10 stood out as the best-ranked alternatives while A1, A2, and A3 stood out as the least efficient ones. The paper provides clear scientific contributions that are reflected in the reduction of the gap that was observed after reviewing the literature where there is a lack of papers dealing with this task. Also, the combination of methods applied in the paper has not been used so far, so it can be said that this paper represents an excellent basis for further research. The model has practical contributions as it allows decision-makers to make quality decisions regarding the operation of their warehouses in different time periods or observation periods, as well as it represents a decision support tool that can be used for better warehouse management.