Relative Performance Research Articles

LG-GNN: Local and Global Information-aware Graph Neural Network for default detection

Default detection, a crucial aspect of individual credit scoring, has attracted considerable attention in research. Previous approaches typically focus on classifying applicants using only explicit attributes, overlooking the importance of latent relations among them. Concurrently, graph-based techniques have emerged as promising tools for credit scoring. However, existing graph-based methods often need to be more accurate in aggregating information from limited neighbors, which can lead to misclassification when the target node has differently labeled neighbors. Motivated by these challenges, we propose a Local and Global Information-aware Graph Neural Network (LG-GNN) approach for default detection. By leveraging the loan applicant relation graph, LG-GNN dynamically learns the representation of the target node from both local and global perspectives. Furthermore, it adaptively fuses the information from these perspectives and employs contrastive learning to enhance feature variations. Extensive experiments demonstrate the superiority of LG-GNN over mainstream methods across several widely used default detection datasets. Specifically, LG-GNN achieves an average relative performance improvement of 47.9% compared to baselines. Moreover, compared to the most competitive default detection methods, LG-GNN exhibits an average performance improvement of 11.9%. Our code is publicly available at https://github.com/BERA-wx/LG-GNN.

Machine learning models and performance dependency on 2D chemical descriptor space for retention time prediction of pharmaceuticals

The predictive modeling of liquid chromatography methods can be an invaluable asset, potentially saving countless hours of labor while also reducing solvent consumption and waste. Tasks such as physicochemical screening and preliminary method screening systems where large amounts of chromatography data are collected from fast and routine operations are particularly well suited for both leveraging large datasets and benefiting from predictive models. Therefore, the generation of predictive models for retention time is an active area of development. However, for these predictive models to gain acceptance, researchers first must have confidence in model performance and the computational cost of building them should be minimal. In this study, a simple and cost-effective workflow for the development of machine learning models to predict retention time using only Molecular Operating Environment 2D descriptors as input for support vector regression is developed. Furthermore, we investigated the relative performance of models based on molecular descriptor space by utilizing uniform manifold approximation and projection and clustering with Gaussian mixture models to identify chemically distinct clusters. Results outlined herein demonstrate that local models trained on clusters in chemical space perform equivalently when compared to models trained on all data. Through 10-fold cross-validation on a comprehensive set containing 67,950 of our company's proprietary analytes, these models achieved coefficients of determination of 0.84 and 3 % error in terms of retention time. This promising statistical significance is found to translate from cross-validation to prospective prediction on an external test set of pharmaceutically relevant analytes. The observed equivalency of global and local modeling of large datasets is retained with METLIN's SMRT dataset, thereby confirming the wider applicability of the developed machine learning workflows for global models.

Positive, negative or graded sustainability labelling? Which is most effective at promoting a shift towards more sustainable product choices?

AbstractSustainability labels convey information about different product attributes, such as its environmental impact, lifespan or ethical performance. The labelling can be either positive (only identifying the most sustainable products available on the market), negative (only identifying the least sustainable products available on the market) or graded (comparing the sustainable performance of a product with that of all other products on the market). We assess the relative performance of these three labelling approaches in terms of influencing product choices. A nationally representative sample of 1243 consumers from Germany, Spain and the Czech Republic participated in an incentive‐compatible online discrete choice experiment with random allocation to different labelling approaches. Compared with positive and negative labels, graded labels were most effective in guiding consumers towards more sustainable product choices. These findings support policy interventions that convey product sustainability with graded labels.

Thermal-mechanical evolution behaviors of metal matrix diamond composite by powder bed fusion-laser beam

The thermal damage of diamond and the residual stress have always been the critical issues to be resolved in the development of metal matrix diamond composites. In the presented study, FeCoCrNi high entropy alloys (HEAs)-diamond composites were fabricated by powder bed fusion-laser beam of metals (PBF-LB/M), using un-coated diamond and Ti–Ni coated diamond, respectively. Based on the numerical and experimental analysis, the effect of thermal-mechanical behavior on the microstructures and mechanical properties of the composites was systematically investigated. By establishing the temperature field of the PBF-LB molten pool, the defects, diamond graphitization and in-situ TiC formation were studied. The start-temperature for diamond graphitization in coated samples was 365 °C higher than that of un-coated samples, due to the diffusion barrier effect of TiC. Subsequently, by thermo-mechanical coupling, the property and value of the residual stress exhibited sudden change at the diamond/matrix interface. The TiC intermediate layer significantly relieved the stress concentration at interface, and effectively avoided cleavage fracture of the diamond and the initiation of interfacial cracks. At the moderate molten-pool temperature, the coated diamond composites exhibited optimal performance, as bending strength of 913 MPa, friction coefficient of 0.25 and worn mass loss of 0.67 mg. Finally, the quantitative relationship between PBF-LB molten-pool temperature and the relative density, graphitization degree, residual stress, retention(bending strength) and wear performance was established, to demonstrate the thermal-mechanical evolution of the HEAs-diamond composites by PBF-LB.

The mediatory role of the environmental performance function within the lean-green manufacturing sustainability complex

PurposeThe objective of the study is to explore the mediatory role of the environmental performance of organisations on their economic and social performances. It demonstrates that implementing environmental management techniques should not only be done to comply with environmental regulations, but also as a means of improving social and economic performance.Design/methodology/approachThe data were gathered from the manufacturing industry of Zimbabwe, and 302 useable responses were received. Data analysis was performed through structural equation modelling (SEM) using SMART PLS 3.FindingsImprovement in environmental performance led to improvements in both social and economic performances. Also, environmental performance contributes the greatest total effect; hence, it deserves attention, not only for compliance but also for economic reasons.Originality/valueOur goal is to quantify the extent to which environmental performance might improve the social and, more importantly, the economic performance of organisations. The study also explores the relative importance and performance of lean manufacturing (LM), green manufacturing (GM), social performance and environmental performance for purposes of prioritisation of organisational improvement initiatives.

Prediction of future cognitive decline among cognitively unimpaired individuals using measures of soluble phosphorylated tau or tau tangle pathology.

Plasma p-tau217 and Tau-PET are strong prognostic biomarkers in Alzheimer's disease (AD), but their relative performance in predicting future cognitive decline among cognitively unimpaired (CU) individuals is unclear. In this head-to-head comparison study including 9 cohorts and 1534 individuals, we found that plasma p-tau217 and medial temporal lobe Tau-PET signal showed similar associations with cognitive decline on a global cognitive composite test (R2 PET=0.32 vs R2 PLASMA=0.32, pdifference=0.812) and with progression to mild cognitive impairment (Hazard ratio[HR]PET=1.56[1.43-1.70] vs HRPLASMA=1.63[1.50-1.77], pdifference=0.627). Combined plasma and PET models were superior to the single biomarker models (R2=0.36, p<0.01). Furthermore, sequential selection using plasma p-tau217 and then Tau-PET reduced the number of participants required for a clinical trial by 94%, compared to a 75% reduction when using plasma p-tau217 alone. We conclude that plasma p-tau217 and Tau-PET showed similar performance for predicting future cognitive decline in CU individuals, and their sequential use (i.e., plasma p-tau217 followed by Tau-PET in a subset with high plasma p-tau217) is useful for screening in clinical trials in preclinical AD.

GOVERNMANET PUBLIC RELATIONS IN FACING POLITICAL YEAR: TRAINING FOR LOCAL GOVERNMENT OF MAGETAN

The issues faced by Indonesia in the 2019 elections are likely to reemerge in the 2024 elections. In the 2019 elections, digital media became a highly effective tool for disseminating information related to politics and governance. Unfortunately, not all information available on digital media was accurate. Social media, in particular, saw a lot of misinformation and the spread of fake news (hoaxes). This led to a negative perception of the government among the public, which consequently influenced the opinions and political participation of Indonesian society. Therefore, a transformation is needed to support the formation of a positive government image. One of the strategies is maximizing government public relations. On August 8, 2023, the Communication Department at the Faculty of Social and Political Sciences, Universitas Airlangga, held a community service activity on the topic of government public relations training in Magetan Regency to address the political year. The main target of this activity was government officials in Bulukerto Subdistrict, Magetan District, Magetan Regency. The implementation mechanism consisted of three stages. In the initial stage, the community service team conducted surveys and coordinated with local government officials to gain a detailed understanding. During this stage, the Communication Department observed the involvement and performance of government public relations to obtain an initial overview of the strategies or designs used for the training. Subsequently, it was decided that the activity would be conducted through seminars and workshops covering topics such as public speaking, digital government, corporate social responsibility, and government public relations. After the activity, an evaluation was conducted to measure its success. It is hoped that this activity can serve as a foundation for improving the function of government public relations in addressing the political year and can be a supportive factor in ensuring the success of the 2024 elections.

Student selection in higher education – the organisational performance dilemma

ABSTRACT Research on student selection mostly focuses on accepted applicants and the effects of selection procedures. In this sense, most samples seem to be biased, which is well-reflected in the literature. The present study investigates student selection regarding students who had been initially de-selected but finally succeeded in the admission process. Our results show that students from the de-selected group reveal lower performance, i.e. higher dropout, and thus lead to decreases in relative organisational performance. We argue that higher education institutions may be confronted with a performance dilemma if external stakeholders prefer institutional growth, and internal actors prefer maintenance of educational standards. In this situation, reduced graduation rates have the potential to undermine the output legitimacy of higher education institutions. Therefore, we recommend focusing more on the balance between academic standards, stakeholders’ demands, and processes of student selection. From a theoretical point of view, we suggest broadening the perspective and combining selection theory with organisation theory.

Three-way decision based island harmony search algorithm for robust flow-shop scheduling with uncertain processing times depicted by big data

This paper discusses an uncertain two-machine permutation flow-shop scheduling problem (2PFSP) with total weighted tardiness and common due date. Uncertain processing times are described by a large set of discrete scenarios, which is a type of big data. The objective is to minimize the schedule performance under the worst-case scenario. Identifying the worst-case scenario for each evaluated schedule is quite time-consuming in the situation that the scenario set size is large so that the objective evaluation might be computationally expensive. To handle this difficulty, three-way decision is used to preprocess the large-size scenario set to get a reduced scenario set so that the concept of surrogate worst-case scenario is adopted. A hybrid harmony search algorithm of combining three-island framework and the scenario-based local search is developed to solve the discussed problem. Based on the single-scenario knowledge of 2PFSP, a problem-specific scenario-dependent neighborhood structure is constructed under the surrogate worst-case scenario. An extensive experiment was carried out. The computational results show that the application of surrogate worst-case scenario based on three-way decision is effective in reducing the time consuming while keeping schedule performance evaluation. Being compared to the worst-case scenario objective evaluation, for an example in the case of the middle bad-scenario ratio, the surrogate worst-case scenario objective evaluation made the solution algorithm save 12.95 % in average CPU time for all instances while the relative performance difference is only 1.809 % in average. Being compared to possible alternative algorithms derived from the state-of-the-art algorithms, the developed algorithm is advantageous for the addressed problems.

Market volatilities vs oil shocks: Which dominate the relative performance of green bonds?

The market performance of green bonds determines whether they will make a significant contribution to future financial markets and sustainable investment. It is therefore important to conduct a thorough study of the dominant factors affecting the market performance of green bonds, particularly their performance relative to conventional bonds. Thus, the main objective of this paper is to investigate and compare the impact of market volatilities (mainly VIX, OVX, and MOVE) and oil shocks (including supply shock, demand shock, and speculative demand shock) on the relative performance of green bonds. The main findings indicate that when volatilities coincide with oil shocks, the former consistently exert a more pronounced influence on the relative performance of green bonds. This phenomenon is particularly evident in the post-COVID-19 period. Concerning crude oil shocks, oil supply and demand shocks have opposite effects on the relative performance of green bonds, with the supply shock having a negative impact and the demand shock having a positive impact. Furthermore, our analysis shows that volatilities and oil shocks tend to have significant effects on the relative performance of green bonds during periods of heightened market risk aversion, while their impact remains negligible in a stable market environment. The empirical results may provide novel insights and significant implications for the further development of the green bond market.

The dynamics of competition and decision-making

AbstractWe examine the underlying cognitive mechanisms that govern how competitions play out over time. We used cognitive modeling to examine the dynamic effects of time remaining and relative performance (whether the person is winning or losing) on effort and strategy. In this experiment, participants completed a competitive decision-making task with varying time limits and starting scores, in a repeated-measures design. Participants were tasked with scoring more points than their computerized opponent during a certain time frame, gaining and losing points for correct and incorrect decisions, respectively. The results showed that as the competition deadline approached and as participants drew ahead of their opponent within a competition, they increased effort and became more cautious. Furthermore, the effect of relative score on effort and caution changed over the course of a competition as the deadline approached. These results highlight the importance of considering dynamics when working to understand how competitions unfold as well as the underlying cognitive mechanisms that give rise to the dynamic behavior.

Hierarchical thermal modeling and surrogate-model-based design optimization framework for cold plates used in battery thermal management systems

The continued advancement of battery-powered electric vehicles (EVs) towards higher energy and power densities poses significant thermal challenges for batteries. This work proposes a generalized design optimization framework for battery thermal management systems (BTMS), responding to the need for EVs with enhanced battery thermal performance, safety, and lifetime. This framework combines hierarchical thermal modeling and surrogate-model-based design optimization, explicitly tailored for liquid-cooled cold plates commonly used in EV BTMS. The hierarchical thermal modeling component decouples the battery cell, battery module, and cold plate models to reduce computational modeling costs while preserving the relative thermal performance of different cold plate designs. The design optimization component leverages our pioneer deep encoder–decoder hierarchical (DeepEDH) convolutional neural network surrogate modeling methodology to predict the cold plate’s full pressure, velocity, and temperature fields. Computationally efficient DeepEDH neural networks replace costly transient thermal simulations of cold plates and compute the objectives for evolutionary optimization using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). This work’s novel modeling and optimization framework produces optimal cold plate designs that consider battery-module-specific design features, including distributed battery cell heat generation, packaging materials, and heat-spreading characteristics. The proposed framework is evaluated through a cold plate design for modular BTMS, thoroughly assessing the impact of optimization objectives, flow path constraints, modeling assumptions, and design variable choices. The results demonstrate the effectiveness of the combined hierarchical thermal modeling and design optimization framework, with the optimized cold plates achieving reductions of 6.26K, 6.29K (22.7%), and 16.16Pa (18.7%) for maximum temperature, maximum temperature difference, and pressure loss, respectively. Moreover, compared to other methodologies that apply direct optimization without hierarchical and surrogate models, our approach significantly reduces the computational cost — from approximately 4800h to 13.5h. Our generalized multi-objective optimization framework is an effective and efficient design tool that can be leveraged to advance thermal management innovations for next-generation battery systems.

Critical assessment of missense variant effect predictors on disease-relevant variant data.

Regular, systematic, and independent assessment of computational tools used to predict the pathogenicity of missense variants is necessary to evaluate their clinical and research utility and suggest directions for future improvement. Here, as part of the sixth edition of the Critical Assessment of Genome Interpretation (CAGI) challenge, we assess missense variant effect predictors (or variant impact predictors) on an evaluation dataset of rare missense variants from disease-relevant databases. Our assessment evaluates predictors submitted to the CAGI6 Annotate-All-Missense challenge, predictors commonly used by the clinical genetics community, and recently developed deep learning methods for variant effect prediction. To explore a variety of settings that are relevant for different clinical and research applications, we assess performance within different subsets of the evaluation data and within high-specificity and high-sensitivity regimes. We find strong performance of many predictors across multiple settings. Meta-predictors tend to outperform their constituent individual predictors; however, several individual predictors have performance similar to that of commonly used meta-predictors. The relative performance of predictors differs in high-specificity and high-sensitivity regimes, suggesting that different methods may be best suited to different use cases. We also characterize two potential sources of bias. Predictors that incorporate allele frequency as a predictive feature tend to have reduced performance when distinguishing pathogenic variants from very rare benign variants, and predictors supervised on pathogenicity labels from curated variant databases often learn label imbalances within genes. Overall, we find notable advances over the oldest and most cited missense variant effect predictors and continued improvements among the most recently developed tools, and the CAGI Annotate-All-Missense challenge (also termed the Missense Marathon) will continue to assess state-of-the-art methods as the field progresses. Together, our results help illuminate the current clinical and research utility of missense variant effect predictors and identify potential areas for future development.

Spillovers from incentive schemes on distributional preferences and expectations

In the workplace, incentive schemes may spill over on distributional preferences and beliefs, which underlie an employee’s willingness to cooperate. In an online experiment, we analyze how different incentive schemes and the corresponding feedback affect distributional preferences and beliefs. In six different treatments, we vary the incentive scheme (competitive vs team incentives) for a real-effort task and the feedback participants receive at the end of the real-effort task. Subsequently, we measure participants’ social value orientation (SVO), a proxy for distributional preferences, and the corresponding beliefs about other’s SVO. If no feedback is provided, participants show stronger SVOs if they are incentivized by team incentives in comparison to piece-rate remuneration. Surprisingly, this positive effect prevails under competitive incentives without feedback. With feedback about relative performance, the spillover effects differ between the incentive schemes. Under competitive incentives, participants show lower SVOs (negative spillovers), but only for low performers. Under team incentives, we find negative spillovers on preferences for high performers and positive spillovers for low performers. We find no evidence for spillovers on beliefs.

A multicriteria assessment of food safety measures for a large dairy farm in hot weather conditions

Dairy farms are vulnerable to the impacts of climate change through the alteration of raw milk quality and pressure on animal health. Measures in dairy farms are necessary to reduce microbiological risks that may impact animal health and may be passed on to humans through the consumption of contaminated dairy products. However, these additional controls should incur lower environmental impact, have a low cost of implementation, minimal impact on milk property, and sufficient effectiveness to control risks. This study selected a dairy farm located under hot weather conditions to demonstrate how these challenges may be considered. Our objective was to present how a multi-criteria decision analysis (MCDA) could be used to select the appropriate mitigation strategy for this farm among fifteen potential food safety measures.The MCDA framework brought together ten criteria classified into four supra criteria: stakeholder acceptance, food safety effectiveness, environmental impact, and impact on milk properties. The relative performances of various food safety measures scored against the ten criteria were expressed either in qualitative or quantitative values. Ultimately, the outranking MCDA technique, PROMETHEE II, was used to rank the measures.This study ranked the selected four food safety measures, namely, sand bedding, chitosan supplementation, cooling mister operation, and phage spray, after a series of preselection filters. It was found that none of these dominated the others on the ten criteria. However, MCDA has allowed the determination of the best compromise among the selected measures. It was found that an increase in the frequency of changing the sand bedding ranked first, and an increase in the operation of cooling misters was ranked last.The study demonstrated the benefit of MCDA in combining criteria of different nature (stakeholder acceptance, food safety effectiveness, environmental impact, milk properties), values, and scales to prioritize food safety measures. The approach can be applied to other dairy farms eager to limit the impacts of climate change while guaranteeing food safety.

Governance structure and its links with justice in the context of cooperative organizations

Objective: Cooperative organizations have unique characteristics in their processes and control structures. Given the nature of the relationships that permeate them, justice has stood out as the basis of these relationships, given that the organizations involved seek justice when comparing the rewards and costs involved in interactions. The objective of this essay is to present a theoretical model that makes it possible to explain the links between the governance package (PGov) and the cooperative members' perception of justice and how both reflect on the relational performance of cooperative organizations.Method: This study adopts the theoretical essay method, and assumes the epistemological dimension of structuralism, which generally seeks to explore the structure of the phenomenon and, through its essence, determine its determinant links and interrelationships (Triviños, 1997).Originality/Relevance: Despite advances in understanding the role of control and governance in interorganizational relationships (Ding, Dekker, &amp; Groot, 2013), studies that consider the perspective of justice are still scarce (Dhaundiyal &amp; Coughlan, 2020; Su, Zhang and Guo (2020), and who strive to understand the impacts of these links on performance, which is a differential of the model presented in this essay.Results: This study proposes a model for analyzing the links between the PGov, Organizational Justice and its effects on relational performance. To do so, it was based on the governance and management control structures defended by van der Meer-Kooistra and Scapens (2008), in organizational justice, outlined in its three aspects: distributive justice, procedural justice and interactional justice, addressed by Luo (2007), as well as the notions of relational performance adopted by Su et al. (2020).Theoretical/methodological/practical contributions: The application of the model can bring practical contributions, specifically for the review of the cooperative's PGov and the governance structures to be reformulated (or not), in order to reinforce the justice perceived by the cooperative members. In this way, it is believed that the use of the model will enable the ideal channels of justice to be identified, enhancing the reach of the governance structure and the achievement of the objectives of cooperative organizations.

Machine learning assisted prediction of land surface temperature (LST) based on major air pollutants over the Annamayya District of India

Remote sensing (RS), Geographic information systems (GIS), and Machine learning can be integrated to predict land surface temperatures (LST) based on the data related to carbon monoxide (CO), Formaldehyde (HCHO), Nitrogen dioxide (NO2), Sulphur dioxide (SO2), absorbing aerosol index (AAI), and Aerosol optical depth (AOD). In this study, LST was predicted using machine learning classifiers, i.e., Extra trees classifier (ET), Logistic regressors (LR), and Random Forests (RF). The accuracy of the LR classifier (0.89 or 89%) is higher than ET (82%) and RF (82%) classifiers. Evaluation metrics for each classifier are presented in the form of accuracy, Area under the curve (AUC), Recall, Precision, F1 score, Kappa, and MCC (Matthew’s correlation coefficient). Based on the relative performance of the ML classifiers, it was concluded that the LR classifier performed better. Geographic information systems and RS tools were used to extract the data across spatial and temporal scales (2019 to 2022). In order to evaluate the model graphically, ROC (Receiver operating characteristic) curve, Confusion matrix, Validation curve, Classification report, Feature importance plot, and t- SNE (t-distributed stochastic neighbour embedding) plot were used. On validation of each ML classifier, it was observed that the RF classifier returned model complexity due to limited data availability and other factors yet to be studied post data availability. Sentinel-5-P and MODIS data are used in this study.

Comparison of two propensity score-based methods for balancing covariates: the overlap weighting and fine stratification methods in real-world claims data

BackgroundTwo propensity score (PS) based balancing covariate methods, the overlap weighting method (OW) and the fine stratification method (FS), produce superb covariate balance. OW has been compared with various weighting methods while FS has been compared with the traditional stratification method and various matching methods. However, no study has yet compared OW and FS. In addition, OW has not yet been evaluated in large claims data with low prevalence exposure and with low frequency outcomes, a context in which optimal use of balancing methods is critical. In the study, we aimed to compare OW and FS using real-world data and simulations with low prevalence exposure and with low frequency outcomes.MethodsWe used the Texas State Medicaid claims data on adult beneficiaries with diabetes in 2012 as an empirical example (N = 42,628). Based on its real-world research question, we estimated an average treatment effect of health center vs. non-health center attendance in the total population. We also performed simulations to evaluate their relative performance. To preserve associations between covariates, we used the plasmode approach to simulate outcomes and/or exposures with N = 4,000. We simulated both homogeneous and heterogeneous treatment effects with various outcome risks (1-30% or observed: 27.75%) and/or exposure prevalence (2.5-30% or observed:10.55%). We used a weighted generalized linear model to estimate the exposure effect and the cluster-robust standard error (SE) method to estimate its SE.ResultsIn the empirical example, we found that OW had smaller standardized mean differences in all covariates (range: OW: 0.0–0.02 vs. FS: 0.22–3.26) and Mahalanobis balance distance (MB) (< 0.001 vs. > 0.049) than FS. In simulations, OW also achieved smaller MB (homogeneity: <0.04 vs. > 0.04; heterogeneity: 0.0-0.11 vs. 0.07–0.29), relative bias (homogeneity: 4.04–56.20 vs. 20–61.63; heterogeneity: 7.85–57.6 vs. 15.0-60.4), square root of mean squared error (homogeneity: 0.332–1.308 vs. 0.385–1.365; heterogeneity: 0.263-0.526 vs 0.313-0.620), and coverage probability (homogeneity: 0.0–80.4% vs. 0.0-69.8%; heterogeneity: 0.0-97.6% vs. 0.0-92.8%), than FS, in most cases.ConclusionsThese findings suggest that OW can yield nearly perfect covariate balance and therefore enhance the accuracy of average treatment effect estimation in the total population.

Use of Dampers to Improve the Overspeed Control System with Movable Arms for Butterfly Wind Turbines

To reduce the cost of small wind turbines, a prototype of a butterfly wind turbine (6.92 m in diameter), a small vertical-axis type, was developed with many parts made of extruded aluminum suitable for mass production. An overspeed control system with movable arms that operated using centrifugal and aerodynamic forces was installed for further cost reduction. Introducing this mechanism eliminates the need for large active brakes and expands the operating wind speed range of the wind turbine. However, although the mechanism involving the use of only bearings is simple, the violent movement of the movable arms can be a challenge. To address this in the present study, dampers were introduced on the movable arm rotation axes to improve the movement of the movable arms. To predict the behavior of a movable arm and the performance of the wind turbine with the mechanism, a simulation method was developed based on the blade element momentum theory and the equation of motion of the movable arm system. A comparison of experiments and predictions with and without dampers demonstrated qualitative agreement. In the case with dampers, measurements confirmed the predicted increase in the rotor rotational speed when the shorter ailerons installed perpendicularly to the movable arms were used to achieve the inclination. Field experiments of the generated power at a wind speed of 6 m/s (10 min average) showed relative performance improvements of 11.4% by installing dampers, 91.3% by shortening the aileron length, and 57.6% by changing the control target data. The movable arm system with dampers is expected to be a useful device for vertical-axis wind turbines that are difficult to control.

Keeping Up with the Joneses during an Economic Crisis: The Effect of Different Types of Pay Cuts on Employee Performance

ABSTRACT Many firms implement pay cuts to reduce labor costs during organizational crises and there are different ways to distribute pay cuts among employees. We experimentally investigate how employees respond to equal-share pay cuts compared to performance-based pay cuts. We predict and find that the effect of different ways of allocating pay cuts is moderated by employees’ relative performance before the pay cuts. Compared to equal-share pay cuts, performance-based pay cuts lead to higher performance but only for employees who underperform their peers before the pay cuts. Performance-based pay cuts also result in higher team performance. Interestingly, we also find that when pay cuts are caused by an organizational crisis, low performers do not perceive it as unfair to receive a larger share of pay reduction. Our findings contribute to both literature and practice on pay reduction during organizational crises. Data availability: Data are available from the authors upon request. JEL Classifications: D91; J33; M40.