Specific Performance Measures Research Articles

#2499 Multicenter observational study on the impact of continuous monitoring of vascular access on the survival and quality of life of patients on dialysis

Abstract Background and Aims AVE is a multicenter observational study aimed at evaluating the improvements in dialysis efficiency, AVF longevity and mortality that can be achieved by implementing both monitoring by a multidisciplinary team. The universal goal of AV monitoring is to identify early events such as stenosis and intervene before thrombosis, thus maximizing the longevity of access and minimizing morbidity. Method This study takes advantage of the opportunity to use data from 19 hemodialysis centers and approximately 1000 patients and to correlate and examine variables that are commonly reported with each treatment, trends in AV use, trends in patient characteristics, and the practice associated with VA. The data were collected by nurses in a specific section of the management database at each treatment for 5 years. Results Trends at the beginning and at the end of the study were evaluated, and Kaplan Maier curves were performed regarding the survival of the VA and the survival of the patients. The average age of 1000 patients enrolled was 73.5 years. Native AVF was used by 79%, AVG by 2% and CVC by 19%. With increasing age, the use of CVC goes from 6% (15-39 years) to 50.5% in patients over 85 years old. The most frequent complications were: Thrombosis 16.5%, Infections 5.5%. VA-related hospitalization days are 28.7% of total days. The average dialysis goals achieved were: QB 290 ml/Min; purified blood 69.7 L; KT/V:1.35. The AVE study, the survival curves evaluated in aggregate were not different between men and women during the observation period (year 2018: p = 0.327; year 2019: p = 0.174; year 2020: p = 0.343). This finding was also confirmed when the survival data were analyzed according to gender and chronological age. No difference was observed between genders while the significant difference (p = 0.000) between chronological age levels was confirmed as shown in the Figure Conclusion By interpreting data collection for specific performance measures using accurate reports, healthcare professionals can highlight AV performance/inefficiencies and provide correct information to clinical staff to support them in their daily clinical practice and decision making.

Revolutionizing performance measures and criteria for the facilities management industry in the UAE

PurposeAmidst the complicated nature of the UAE’s facilities management (FM) industry, the need to recalibrate the existing performance measurement (PM) system measures and criteria has been resonating to ensure their ability to capture the FM industry trends and dynamics, thus enhancing organizational excellence. Therefore, this research aimed to propose a specific PM tool to the country’s FM industry to accurately assess performance and establish strategic enhancements.Design/methodology/approachThe study reviewed literature on the available PM systems to gather the available measures, which were presented to a focus group of seven participants, who were purposively selected based on their expertise in FM and PM implementation in the UAE to adjust them and add ones relevant to the UAE’s FM industry.FindingsThe focus group conducted various changes, from retaining certain measures and criteria, renaming them to simplify or make them more representative of the industry, ranking them based on their importance to limit their numbers, to finally categorizing them as enablers or results. Consequently, the final proposed tool was composed of nine dimensions with 51 measures as performance enablers and three dimensions with 11 measures as performance results. Seven measures were added by the experts, who highlighted their increasing popularity in the UAE’s FM industry.Originality/valueThrough addressing the critical void in literature, this paper develops a specific PM tool aligning with the intricacy of the UAE’s FM industry, thus providing proactive contribution to the industry’s effective and sustainable growth.

Enhanced cardiovascular disease prediction through self-improved Aquila optimized feature selection in quantum neural network & LSTM model.

Cardiovascular disease (CVD) stands as a pervasive catalyst for illness and mortality on a global scale, underscoring the imperative for sophisticated prediction methodologies within the ambit of healthcare data analysis. The vast volume of medical data available necessitates effective data mining techniques to extract valuable insights for decision-making and prediction. While machine learning algorithms are commonly employed for CVD diagnosis and prediction, the high dimensionality of datasets poses a performance challenge. This research paper presents a novel hybrid model for predicting CVD, focusing on an optimal feature set. The proposed model encompasses four main stages namely: preprocessing, feature extraction, feature selection (FS), and classification. Initially, data preprocessing eliminates missing and duplicate values. Subsequently, feature extraction is performed to address dimensionality issues, utilizing measures such as central tendency, qualitative variation, degree of dispersion, and symmetrical uncertainty. FS is optimized using the self-improved Aquila optimization approach. Finally, a hybridized model combining long short-term memory and a quantum neural network is trained using the selected features. An algorithm is devised to optimize the LSTM model's weights. Performance evaluation of the proposed approach is conducted against existing models using specific performance measures. Far dataset-1, accuracy-96.69%, sensitivity-96.62%, specifity-96.77%, precision-96.03%, recall-97.86%, F1-score-96.84%, MCC-96.37%, NPV-96.25%, FPR-3.2%, FNR-3.37% and for dataset-2, accuracy-95.54%, sensitivity-95.86%, specifity-94.51%, precision-96.03%, F1-score-96.94%, MCC-93.03%, NPV-94.66%, FPR-5.4%, FNR-4.1%. The findings of this study contribute to improved CVD prediction by utilizing an efficient hybrid model with an optimized feature set. We have proven that our method accurately predicts cardiovascular disease (CVD) with unmatched precision by conducting extensive experiments and validating our methodology on a large dataset of patient demographics and clinical factors. QNN and LSTM frameworks with Aquila feature tuning increase forecast accuracy and reveal cardiovascular risk-related physiological pathways. Our research shows how advanced computational tools may alter sickness prediction and management, contributing to the emerging field of machine learning in healthcare. Our research used a revolutionary methodology and produced significant advances in cardiovascular disease prediction.

Effect of altered gene expression in lipid metabolism on cognitive improvement in patients with Alzheimer's dementia following fecal microbiota transplantation: a preliminary study.

The brain-gut axis has emerged as a potential target in neurodegenerative diseases, including dementia, as individuals with dementia exhibit distinct gut microbiota compositions. Fecal microbiota transplantation (FMT), the transfer of fecal solution from a healthy donor to a patient, has shown promise in restoring homeostasis and cognitive enhancement. This study aimed to explore the effects of FMT on specific cognitive performance measures in Alzheimer's dementia (AD) patients and investigate the relationship between cognition and the gut microbiota by evaluating changes in gene expression following FMT. Five AD patients underwent FMT, and their cognitive function [Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Clinical Dementia Rating Scale Sum of Boxes (CDR-SOB)] was assessed before and after FMT. The patients' fecal samples were analyzed with 16S rRNA to compare the composition of their gut microbiota. We also assessed modifications in the serum mRNA expression of patients' genes related to lipid metabolism using serum RNA sequencing and quantitative real-time polymerase chain reaction. Significant improvements in cognitive function, as measured by the MMSE (pre- and post-FMT was 13.00 and 18.00) and MoCA were seen. The MoCA scores at 3 months post-FMT (21.0) were the highest (12.0). The CDR-SOB scores at pre- and post-FMT were 10.00 and 5.50, respectively. Analysis of the gut microbiome composition revealed changes via 16S rRNA sequencing with an increase in Bacteroidaceae and a decrease in Enterococcaceae. Gene expression analysis identified alterations in lipid metabolism-related genes after FMT. These findings suggest a link between alterations in the gut microbiome, gene expression related to lipid metabolism, and cognitive function. The study highlights the importance of gut microbiota in cognitive function and provides insights into potential biomarkers for cognitive decline progression. FMT could complement existing therapies and show potential as a therapeutic intervention to mitigate cognitive decline in AD.

'Led by Intelligence': A Scoping Review on the Experimental Evaluation of Intelligence-Led Policing.

Intelligence-led policing (ILP) was introduced in the 1990s as a proactive approach to policing, but to date, there is a lack of studies that have synthesized and summarized the central characteristics and insights of (quasi-)experimental studies related to ILP. This study aims to address this gap by synthesizing and characterizing the central characteristics of 38 quasi-experimental and experimental studies related to ILP. In this study, a scoping review is conducted on different quasi-experimental and experimental studies that relate to the framework of ILP. It was found that most studies within the domain of ILP focus on testing the crime reduction effects of using spatio-temporal crime intelligence to deploy police resources more efficiently and effectively. However, some studies have combined different types of crime intelligence or used solely offender-related intelligence. Several statistical-methodological challenges were also identified that should be considered when designing experimental research within the domain of ILP. Additionally, most studies focused solely on measuring crime reduction, with few focusing on secondary effects of interventions. The review concludes that future evaluation studies should consider evaluating the use of different types of crime intelligence and establish specific, objective, and realistic criteria for measuring specific performance measures such as crime disruption. Future experimental research within the domain of ILP should consider applying the 3-i model, evaluating each leg of ILP thoroughly. The limitations of the study are also discussed. This review provides valuable insights for future research and development of ILP-related approaches.

Research on Evaluation Model of Entrepreneurship Education Based on BP Neural Network

The evaluation system of entrepreneurial practice and curriculum teaching mostly depends on the evaluation of people. Qualitative evaluation lacking specific performance and quantitative measurement is carried out through small groups. This evaluation method lacks specific quantitative and weight basis. Therefore, the evaluation results often depend on the emotional bias and cognitive bias of individuals, and the subjectivity in the evaluation process is too strong. To solve this problem, BP neural network (BP-net) is introduced into the evaluation system of entrepreneurial practice and course teaching. The model has higher adaptability and nonlinear processing ability, so the evaluation results with higher objectivity and lower deviation rate can be obtained in the evaluation. According to the evaluation characteristics of entrepreneurial practice and course teaching, a combined update design is carried out, and a new momentum term is introduced to enhance the overall operation effect of the model. Finally, the model is tested by means of performance comparison and empirical analysis. The results show that the performance score of the model reaches 4.93 points of the highest value, and the R value of the regression fitting process is stable at more than 0.90. It can conduct a comprehensive and objective analysis for different index dimensions, and split out the advantages and disadvantages of entrepreneurial practice and course teaching. This model can evaluate the quality of entrepreneurial practice and course teaching, promote the self-improvement of colleges, and create a better innovation and entrepreneurship environment for students.

Re-Conceptualizing Kaepernick’s Kneeling Protests and His Banishment From the NFL as an Infringement on His “Right to Work”

A nascent literature is emerging that analyzes the case of Kaepernick who was “locked out” of the National Football League (NFL) beginning in 2017 because he chose to protest police brutality, systemic racism, and white supremacy. Using status expectations states theory and prototypicality theory, our research re-conceptualizes Kaepernick’s lock-out as an infringement on his right to work. First, we utilize a modified case-study approach comparing his experiences to those of six other Black male athletes who were “locked out.” Second, we utilize data and “matched cases” to demonstrate empirically that Kaepernick was locked out of the league while quarterbacks who were less qualified (based on specific performance measures) were allowed to continue working. Our analysis demonstrates that Kaepernick was denied his “right to work” because he, like other Black male athletes before him, challenged structural racism and white supremacy.

Testosterone levels mediate the dynamics of motor oscillatory coding and behavior in developing youth

Recent investigations have studied the development of motor-related oscillatory responses to delineate maturational changes from childhood to young adulthood. While these studies included youth during the pubertal transition period, none have probed the impact of testosterone levels on motor cortical dynamics and performance. We collected salivary testosterone samples and recorded magnetoencephalography during a complex motor sequencing task in 58 youth aged 9–15 years old. The relationships between testosterone, age, task behavior, and beta (15–23 Hz) oscillatory dynamics were examined using multiple mediation modeling. We found that testosterone mediated the effect of age on movement-related beta activity. We also found that the effect of age on movement duration was mediated by testosterone and reaction time. Interestingly, the relationships between testosterone and motor performance were not mediated by beta activity in the left primary motor cortex, which may indicate the importance of higher-order motor regions. Overall, our results suggest that testosterone has unique associations with neural and behavioral indices of complex motor performance, beyond those already characterized in the literature. These findings are the first to link developmental changes in testosterone levels to maturation of beta oscillatory dynamics serving complex motor planning and execution, and specific measures of motor performance.

Comparing linear discriminant analysis and supervised learning algorithms for binary classification-A method comparison study.

In psychology, linear discriminant analysis (LDA) is the method of choice for two-group classification tasks based on questionnaire data. In this study, we present a comparison of LDA with several supervised learning algorithms. In particular, we examine to what extent the predictive performance of LDA relies on the multivariate normality assumption. As nonparametric alternatives, the linear support vector machine (SVM), classification and regression tree (CART), random forest (RF), probabilistic neural network (PNN), and the ensemble k conditional nearest neighbor (EkCNN) algorithms are applied. Predictive performance is determined using measures of overall performance, discrimination, and calibration, and is compared in two reference data sets as well as in a simulation study. The reference data are Likert-type data, and comprise 5 and 10 predictor variables, respectively. Simulations are based on the reference data and are done for a balanced and an unbalanced scenario in each case. In order to compare the algorithms' performance, data are simulated from multivariate distributions with differing degrees of nonnormality. Results differ depending on the specific performance measure. The main finding is that LDA is always outperformed by RF in the bimodal data with respect to overall performance. Discriminative ability of the RF algorithm is often higher compared to LDA, but its model calibration is usually worse. Still LDA mostly ranges second in cases it is outperformed by another algorithm, or the differences are only marginal. In consequence, we still recommend LDA for this type ofapplication.

Separation of arteries and veins in retinal fundus images with a new CNN architecture

ABSTRACT Retinal blood vessels are directly or indirectly associated with many diseases. The retinal blood vessels consist of artery and vein vessels. With the automatic correct identification of these vessels, many diseases can be prevented. In this paper, a method is proposed to separate between arteries and veins on retinal blood vessel images. In the proposed method, firstly, the image preprocessing step is applied. Then, image patches are obtained from pre-processed retinal fundus images. These patches are prepared as input to the deep learning network architecture. The proposed deep learning network architecture is presented as a new CNN architecture. This architecture decides whether the blood vessel pixels in the fundus image are arteries or veins. The proposed method was evaluated on publicly available given DRIVE, INSPIRE datasets, and the recently created LES-AV dataset. The performance of the proposed method was evaluated by using the most commonly used sensitivity, specificity, and accuracy performance measures. The accuracy measure for all vessel pixels is 0.9110 for DRIVE, 0.9654 for INSPIRE, and 0.9531 for LES-AV dataset. The proposed method is compared with other state-of-the-art artery/vein separation methods. The experimental results of the proposed method are promising. This method is suitable for automatic artery/vein separation.

Omega 3 fatty acid supplementation improves physical performance: a systematic review and meta-analysis of RCTs

Abstract Background Omega 3 polyunsaturated fatty acids (n-3, PUFA) show promise for improving muscle mass, strength and measures of physical performance that may ameliorate sarcopenia. Purpose To perform an updated meta-analysis of n-3 interventions with detailed subgroup analysis on physical performance/strength and muscle mass, with sub-group analysis on duration, age, BMI, sex, and location, amongst others. Methods We followed The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) –checklist for systematic reviews and meta-analysis. Literature was searched (ISI, Scopus and PubMed databases up to October 2021) with terms related to n-3 (alpha linolenic acid, docosahexaenoic acid, eicosapentaenoic acid, fish oil etc.) muscle mass, strength and physical performance (using the 30 s Chair Stand Test [30CST] for muscle strength and endurance and the Timed Up an Go Test [TUG] for physical performance; both are useful and simple tools that can be used to assess sarcopenia). Human n-3 randomised controlled trials (RCTs) were included and assessed for methodological quality (Cochrane quality assessment tool). Weighted mean differences (WMDs) with 95% confidence intervals (CIs) and random-effect model analysis, and I-squared (I2) statistic were used to assess outcomes and heterogeneity respectively. Subgroup and sensitivity analysis were performed, and Begg's and Egger's tests evaluated publication bias. Statistical analysis used STATA 11 software and a p-value of <0.05 was considered statistically significant. Results The primary meta-analyses for functional performance (TUG, 7 RCTs, 8 interventions, n=259) and strength (30CST, 5 RCTs, 6 interventions, n=180), favoured n-3 PUFA (p<0.001). TUG (weighted mean change (WMD): −0.35 s; 95% CI: −0.53, −0.18; I2=0.0%) (Figure 1A) and 30CST (WMD: 2.23 repetitions; 95% CI: 1.34, 3.32; I2=67.6%) (Figure 1B). Subgroup analysis for TUG was significant for female sex (WMD: 2.92 reps; 95% CI: 2.37, 3.46; I2=33.1%), BMI ≥25 kg/m2 (WMD: −0.36 s; 95% CI: −0.58, −0.17; I2=16.9%), and for 30CST by duration ≥16 weeks (WMD: 2.60 reps; 95% CI: 1.37, 3.83; I2=0.0%) (all p<0.001). There were no significant effects on muscle mass and handgrip strength, and subgroup analysis showed negligible influence. Publication bias was minimal and sensitivity analysis did not influence findings. Conclusions We showed novel favourable effects of n-3 PUFA on specific measures of strength and physical performance in some populations but not on measures of muscle mass and other strength tests. The beneficial effects of n-3 on these measures may be due to various mechanisms that regulate muscle quality (rather than quantity). Further studies are needed to elucidate the dose, duration and other influential variables. Funding Acknowledgement Type of funding sources: None.

Identification and Prioritization of Key Performance Indicators for the Construction Small and Medium Enterprises

The central purpose of this study is to propose a set of key performance indicators (KPIs) to measure the performance of construction small and medium enterprises (SMEs) that have been ignored in the performance management literature so far. Secondly, this study aims to determine the most crucial KPIs by using the fuzzy VIKOR method to improve cost-effectiveness in the performance measurement of construction SMEs. At the first stage of this study, KPIs proposed by the existing studies were identified via a literature survey. Then, the KPIs extracted from the literature survey were verified, and eight new KPIs were proposed as a result of focus group discussions with 12 participants who are owners/managers of construction SMEs. Additionally, the Balanced Scorecard (BSC) was modified in line with the needs of construction SMEs, and each KPI was grouped into a BSC perspective. A questionnaire survey followed this grouping to gather data associated with the KPIs. Based on these data, KPIs were prioritized by using the fuzzy VIKOR. It is found out that external indicators such as “effectiveness of monitoring market conditions” are determined as the most important KPIs, in contrast to the findings in the studies about large-scale companies. Furthermore, “Attracting new customers”; “Reliability of financial performance” and, “Competency of managers” are identified as important indicators. Four KPIs proposed by experts during the focus group discussion are placed among the most important KPIs, which highlights the need for a specific performance measurement system (PMS) for construction SMEs.

The joint effects of performance measurement system design and TMT cognitive conflict on innovation ambidexterity

Prior research examining the consequences of specific performance measurement system (PMS) design attributes is largely focused on processes and decisions at an individual level. We extend this research by examining how PMS design attributes interact with group socio-cognitive processes to influence firm level outcomes. Specifically, we examine how the interaction between two PMS design attributes related to comprehensiveness – scope and integration – and top management team (TMT) cognitive conflict affect the achievement of innovation ambidexterity. Based on cross-sectional data collected from a survey of TMT members of 90 firms operating in innovative industries, we find that PMSs with broad scope positively interact with cognitive conflict, leading to higher innovation ambidexterity, while highly integrated PMSs negatively interact with cognitive conflict, leading to lower innovation ambidexterity. Overall, our study demonstrates the importance of disentangling the interplay of different PMS design attributes with group socio-cognitive processes for understanding how firms achieve innovation ambidexterity.

Pancreatic Cancer Prediction Using Hierarchical Convolutional Neural Network and Visual Geometry Group16 CNN Approach on Accuracy and Specificity Performance Measures

The main aim of the work is to measure the accuracy, sensitivity, and specificity in the detection of pancreatic cancer. Materials and methods: The dataset of 7390 images obtained from Kaggle for training (80%) and testing (20%) of the predictive model developed in Matlab and statistical analysis is done using SPSS software. The hierarchical convolutional neural network (HCNN) is used for developing the model and compared with the visual geometry group (VGG16) based model. Result: The predictive model developed using the HCNN algorithm shows an improvement in accuracy 94.8221.4705, specificity 88.49801.78406, and sensitivity 90.04601.79226 than VGG16 model with accuracy 94.3760, specificity 88.39200.1663, and sensitivity of 88.92600.5808 with the significance of 0.437, 0.497, and 0.165. Conclusion: The outcome of the study confirms that the HCNN based model appears to be of higher accuracy than the VGG16 based model.

Metabolic Rate Diversity Shapes Group Performance in Honeybees.

AbstractThe metabolic theory of ecology posits that the functional properties at any level of biological organization are a function of the metabolic rate (MR) of its constituent units, although we know little about how heterogeneity among them shapes group-level performance. Using honeybees as a model system, we leveraged the differences in MR associated with "slow" and "fast" malate dehydrogenase alleles to breed genetic lines with low MR and high MR, respectively, and created four experimental groups with different phenotypic compositions. We then measured MR, energetic intake, thermoregulation, and survival of these groups in low- and high-resource conditions. Contrary to expectations, neither the monomorphic fast groups nor the polymorphic (1∶1 slow-to-fast) groups showed a consistent advantage over other groups, but the performance of all groups was consistently better under high-resource conditions. MR had a strong influence on the other performance traits, more so under low-resource conditions. We quantified the effect of heterogeneity in the polymorphic groups as a diversity effect and found different impacts of metabolic heterogeneity, depending on the specific performance measure in question. We discuss these results in the context of how MR plays an important role in shaping division of labor and social evolution.

Market Misreaction? Leverage and Mergers and Acquisitions

Using a large database of U.S. mergers and acquisitions (M&As) announced from 2010 through 2017, we examine the effects of capital ratio (leverage) on the announcement period stock price reaction as well as on longer-term stock returns and performance, for banks, making comparisons with non-banks. We compare announcement period reactions (computed in different ways) for lower (lower than sample median) capitalized banks and non-banks with that for higher capitalized banks and non-banks. We confirm our results using multivariate analyses—after controlling for year and industry fixed effects—and we check the associations of capital ratio with announcement period abnormal returns, longer-term performance, as well as certain bank-specific and non-bank specific performance measures. For banks, we find that a lower capital ratio of acquirers at the time of the announcement of the M&A is significantly associated with negative announcement period abnormal returns. However, for these banks, the longer-run abnormal returns and performance are positive. The opposite is true for non-bank M&A announcements: higher equity ratios (lower leverage) of acquirers as at the time of the announcement is significantly associated with negative announcement period abnormal returns. Yet, for such non-banks, the longer-run abnormal returns and performance are positive. This shows that the market may misreact, on average, to both bank and non-bank M&A announcements based on the acquirer’s leverage at the time of the announcement.

VNN model cross validation towards Accuracy, Sensitivity, Specificity and kappa performance measures of β-caryophyllene using a restricted-unrestricted applicability domain on Artificial Intelligence & Machine Learning approach based in-silico prediction

Wide-reaching interest in use of plant derived secondary metabolites (PDSMs) has been growing world over. This implies that the beneficial effects of plant based natural products (PBNPs) being rediscovered and harnessed for the development of novel drugs; drug-leads and structural background for drug design. Survey of literature on the indigenous use of traditional medicinal knowledge bestows ethnopharmacological potentials of PBNPs that has inspired drug design and development through research in drug discovery; PBNPs provide baseline for the development of drug leads against various pharmacological targets. Studies indicate that Cymbopogon martini Essential Oil (CMEO) exhibits a wide range of biological activities (anticancer, antidiabetic, antibacterial, antifungal, antioxidant, hepatoprotective and insecticide) attributed to the presence of bio-active secondary metabolites (BASM). However, wide-spread application of CMEO is limited because of typical odor, color and taste. In the present study, In-silico Absorption, Distribution, Metabolism, Elimination, Toxicity (ADMET) studies of β-caryophyllene (BCP) from Cymbopogon martinii is envisaged. β-caryophyllene enhances wound healing through multiple routes therefore, Non AMES toxic, Non-carcinogens, and biodegradable nature of β-caryophyllene prospects this compound as considered as an apt lead for the development of drugs for the treatment of cuts and wounds besides other diseases viz., diabetics, cancer, metabolic inflammation and other long term complications associated with these diseases. In particular, BCP inhibits cytochrome P450 isoforms (CYP3A4) that metabolize xenobiotics and results in adverse effects. Pharmacokinetic properties and predicted score values of β-caryophyllene have highlighted that both pharmacological and toxicological aspects need to be further investigated before put to clinical trials.
 Keywords: ADMET; β-caryophyllene; Cymbopogon martinii; Cardiotoxicity; Cytotoxicity; Drugability; Pharmacokinetics; Bioactivity; Essential Oil;

Disaggregated versus Holistic Performance Evaluations in a Promotion Setting

ABSTRACT We examine how a holistic versus disaggregated performance evaluation system affects supervisors' subjective evaluations of their subordinates in a promotion setting. A survey of actual supervisors and subordinates indicates that, compared to a holistic system, respondents prefer a disaggregated system and believe it provides more useful feedback and gives a fairer evaluation. We next conduct an experiment with M.B.A. students to examine whether the two types of evaluation systems can lead supervisors to rate their subordinates' job performance differently in a promotion setting. Results indicate that when supervisors are concurrently considering which subordinate to promote in the future, they inflate the overall rating of the subordinate who is best suited for promotion under a holistic system, but not under a disaggregated system. Results from a supplemental experiment suggest that supervisors under a holistic system mentally inflate the rating of the specific performance measure most relevant to the upcoming promotion decision. Data Availability: Data available upon request.

Rethinking business performance in global value chains

This paper develops a theoretical foundation for rethinking business performance in global value chains amid the Covid-19 fallout. Specifically, we synthesize business performance into three potentially reinforcing but also conflicting performance systems: 1) operational efficiency, 2) market effectiveness and 3) financial resilience to examine their effect on a) profitability, b) growth and c) solvency. While some specific measures of business performance will suffice in times of stability and growth, they could make firms operating in global value chains vulnerable in times of adversity. Our comprehensive theoretical framework contributes to our understanding of the dynamic interplay of conflicting performance systems. We discuss implications for assessing business performance and provide directions for further research.

Cerebral Blood Flow Is Not a Direct Surrogate of Behavior: Performance Models Suggest a Role for Functional Meta-Networks.

BackgroundFunctional brain imaging has become the dominant approach to the study of brain-behavior relationships. Unfortunately, the behavior half of the equation has been relegated to second-class status when it is not ignored completely. Different approaches to connectivity, based on temporally correlated physiological events across the brain, have ascended in place of behavior. A performance-based analysis has been developed as a simple, basic approach to incorporating specific performance measures obtained during imaging into the analysis of the imaging data identifying clinically relevant regions.MethodsThis paper contrasts performance-based lateralized regional cerebral blood flow (CBF) predictors of speech rate during Positron Emission Tomography with the values of these regions and their opposite hemisphere homologs in which a performance-based model was not applied. Five studies were examined: two that utilized normal speakers, one that utilized ataxic speakers, and two that examined Parkinsonian speakers.ResultsIn each study, the predictors were lateralized but the blood flow values that contributed to the performance-based analysis were bilateral. The speech-rate predictor regions were consistent with clinical studies on the effects of focal brain damage.ConclusionsThis approach has identified a basic, reproducible blood flow network that has predicted speech rate in multiple normal and neurologic groups. While the predictors are lateralized consistent with lesion data, the blood flow values of these regions are neither lateralized nor distinguished from their opposite hemisphere homologs in their magnitudes. The consistent differences between regional blood flow values and their corresponding regression coefficients in predicting performance suggests the presence of functional meta-networks that orchestrate the contributions of specific brain regions in support of mental and behavioral functions.