Relative Performance Research Articles

Abilities analysis of problem-solving process awareness for elementary school students with different problem-solving performances

Background: Awareness is core ability in problem-solving process, but related performance analysis of problem-solving process awareness for elementary school students is still under study. It is one of the research gaps. This research summarizes problem-solving process awareness dimensions of translation and implementation to study. Former includes recognition and representation, and latter includes strategy and execution, review and check. Objective: According to the results of the literature analysis and research gap, this study aims to explore the problem-solving process awareness ability for elementary school students with different problem-solving performances by utilizing related instruments. Method: This research utilized two instruments of problem-solving process awareness and problem-solving to assess process awareness and problem-solving performances each for elementary school students. There were 389 elementary school students in Chiayi of Taiwan were selected by using stratified and cluster sampling to collect and analyze data. The single-factor multivariate mean test and η 2 effect size value, Roy-Bargman step-down F test and simultaneous confidence interval test were carried out. The analysis above which was conducted by SPSS for Windows. Result and conclusion: It is found that there were significant differences in performances of problem-solving process awareness for students with different problem-solving abilities after adequate data analysis. The performances are considerably different in translation and implementation awareness for students with differing word problem solving ability that translation awareness included recognition and representation awareness, and implementation awareness included strategy and execution, review and check awareness. So, conclusion is listed as follows. It is that students with different problem-solving performances have real differences in translation, implementation and overall awareness of problem-solving process corresponding to research problem.

Beyond apples and oranges: unraveling the complexity in corporate sustainability reporting

PurposeThis investigation highlights the discrepancies in sustainability reporting practices, and their implications for sustainable service.Design/methodology/approachA comparative analysis methodology was employed, examining sustainability reports from similarly situated companies, specifically PepsiCo and Coca-Cola and The Home Depot, Lowe’s and HomePro. This approach was chosen to uncover variances in sustainability reporting and practices within these sectors using the Sustainability Accounting Standards Board (SASB) guidelines which all four firms followed in their sustainability reports.FindingsThe study reveals significant disparities in how companies within the same industry apply SASB guidelines. These inconsistencies highlight a broader issue of non-standardization in sustainability reporting, leading to challenges in effectively evaluating the relative performance of companies in the same sector.Practical implicationsThe findings suggest managers must prioritize standardized and transparent sustainability reporting to build stakeholder acceptance and trust.Originality/valueThis paper contributes to the existing literature by providing a detailed comparison of sustainability practices in two distinct industry sectors. It offers new insights into the challenges and importance of standardizing sustainability reporting and the potential impact on stakeholders.

Double managerial delegation contracts with relative profit performance and environmental performance incentives under emission taxes

AbstractThis paper examines the strategic relations between emission taxes and firms' managerial delegation contracts, which include incentives based on relative profit performance (RPP) and environmental performance (EP). We demonstrate that under quantity (price) competition, the government imposes higher (lower) emission taxes, while the firm owner sets lower (higher) EP incentives and negative (positive) RPP incentives, which leads to lower (higher) welfare. We also explore an endogenous competition mode game and find that quantity (price) competition emerges when both consumers' green willingness‐to‐pay (WTP) and product differentiation are low (high). Our findings suggest that public education on greenness is crucial for enhancing EP incentives in the design of managerial delegation contracts, while intense product market competition may be detrimental to consumers and society, especially when consumers' green WTP is low.

Comparing prospectively assigned trial and real-world lung cancer patients.

Aim: To evaluate the comparability of a probable clinical trial (CT) cohort derived from electronic medical records (EMR) data with a real-world cohort treated with the same therapy and identified using the same inclusion and exclusion criteria to emulate an external control. Methods: We utilized de-identified patient-level structured data sourced from EMRs. We then compared patterns of overall survival (OS) between probable CT patients with those drawn from non-contemporaneous real-world data (RWD) using a two-sided log-rank test, hazard ratios (HRs) using a Cox proportional-hazards model and Kaplan-Meier (KM) survival curves. Each regression estimate was calculated with a corresponding 95% confidence interval. We additionally conducted multiple matching methods to assess their relative performance. Results: Median (standard deviation) OS was 10.2 (0.7) months for the RWD arm and 11.3 (1.3) for the probable CT arm with a Log rank p-value equal to 0.4771. OS in both cohorts is longer than the reported CT median OS of 9.2 (0.6). The HRs generated under all five assessed matching methods (including without adjustment) were not statistically significant at the 95% confidence level. Conclusion: Our results suggest, with caveats noted, that survival patterns between real-world and CT cohorts in this NSCLC setting are not statistically significantly different.

Combined selection for productivity, adaptability, and stability in corn genotypes for baby corn production

AbstractBaby corn is the name given to the female inflorescence of corn (Zea mays L.) before fertilization, being harvested in 2–3 days after the issuance of the styles‐stigmata. Currently, in Brazil, there is no specific corn cultivar for baby corn production, which leads to the use of popcorn and sweet corn cultivars because they are more accepted in the food industry. Therefore, the objective of this work was to estimate the genetic parameters and determine the efficiency of simultaneous selection based on productivity, adaptability, and stability of corn genotypes for baby corn production in the state of Espírito Santo, Brazil. The evaluations were conducted in 2019 on family properties in six municipalities in the state of Espírito Santo. Eleven corn genotypes were evaluated for baby corn production, being a double hybrid, four native varieties, and six commercial varieties. The design was randomized blocks with four repetitions each. The genetic parameters were estimated, which were obtained using the best linear unbiased prediction (BLUP), and the selection was carried out by the harmonic mean of the relative performance of genotypic values (HMRPGV). It was observed that the environments influenced the performance of the genotypes, configuring specific adaptation. The genotypes AG 1051, Aliança, and BR 106 presented the best performances in all the environments evaluated, considering, simultaneously, the productivity, adaptability, and stability of marketable ears. The HMRPGV method provided optimized selection of corn genotypes to produce baby corn with high productive potential, predictable behavior, and responsive to environmental stimuli.

Temporal Course of Interference Control from Early to Late Young Adulthood: An ERP Study

In the present study, we aimed to investigate the neural dynamics of interference control using event-related potentials (ERPs) to reveal time course of interference control from the beginning to the end of young adulthood. Three groups of participants aged 19–21, 23–27 and 28–44 performed a Stroop task. The results revealed age differences in both accuracy and ERP amplitudes during all aspects of interreference control processing that reflect selective attention (P2), conflict monitoring (N2), conflict evaluation (P3) and interference control (N450). Both younger groups made more errors on incongruent trials compared to participants in their early 30s. The presence of higher P2 and N2 amplitudes, diminished P3 and again higher N450 amplitudes in participants in their early 20s points to a shortage of available resources for top-down control at this age. These results are in accordance with structural and functional studies that show that development of the frontoparietal network, which underlies interference control, continues after adolescence. While brain mechanisms are still developing, the use of accompanying cognitive abilities is still not optimal. The findings that change in neural dynamics and related performance continues into early adulthood challenge current models of cognitive development and call for new directions in developmental theorizing.

Intercomparison of flux-, gradient-, and variance-based optical turbulence (C n2) parameterizations

For free-space optical communication or ground-based optical astronomy, ample data of optical turbulence strength (C n 2) are imperative but typically scarce. Turbulence conditions are strongly site dependent, so their accurate quantification requires in situ measurements or numerical weather simulations. If C n 2 is not measured directly (e.g., with a scintillometer), C n 2 parameterizations must be utilized to estimate it from meteorological observations or model output. Even though various parameterizations exist in the literature, their relative performance is unknown. We fill this knowledge gap by performing a systematic three-way comparison of a flux-, gradient-, and variance-based parameterization. Each parameterization is applied to both observed and simulated meteorological variables, and the resulting C n 2 estimates are compared against observed C n 2 from two scintillometers. The variance-based parameterization yields the overall best performance, and unlike other approaches, its application is not limited to the lowest part of the atmospheric boundary layer (i.e. the surface layer). We also show that C n 2 estimated from the output of the Weather Research and Forecasting model aligns well with observations, highlighting the value of mesoscale models for optical turbulence modeling.

Industrial relations management practice and performance of tertiary institutions in Enugu State Nigeria

The study examined effect of industrial relations management practices on the performance of tertiary institutions in Enugu State. Survey research design was adopted. The population of the study was 11802. Taro Yamani statistical sampling formula was employed to determine the sample size of 387 for the study. The hypotheses were tested using t-test analysis. The findings revealed that expressive participatory management practices had a significant positive effect on the efficiency of tertiary institutions in Enugu State (where t-calculated = 7.39 is higher than the t-table value = 2.776, that employment practices had a significant positive effect on quality of service of tertiary institutions in Enugu State (t-calculated= 6.93 is higher than the t-table = 2.776) and that disengagement practices had a significant positive effect on the efficiency of tertiary institutions in Enugu State ( where the t-value =11.20, whereas the t-table value = 2.77). The study concluded that expressive participatory management, employment practices and disengagement practices influenced the performance of tertiary institutions in Enugu State. The study recommended among others that since industrial democracy effect the performance of tertiary institutions, management should give more room for unions in the organisation to air their views and try as much as possible to align the needs of the employees to that of the institutions so to improve on service delivery

Generation and classification of patch-based land use and land cover dataset in diverse Indian landscapes: a comparative study of machine learning and deep learning models.

In the context of environmental and social applications, the analysis of land use and land cover (LULC) holds immense significance. The growing accessibility of remote sensing (RS) data has led to the development of LULC benchmark datasets, especially pivotal for intricate image classification tasks. This study addresses the scarcity of such benchmark datasets across diverse settings, with a particular focus on the distinctive landscape of India. The study entails the creation of patch-based datasets, consisting of 4000 labelled images spanning four distinct LULC classes derived from Sentinel-2 satellite imagery. For the subsequent classification task, three traditional machine learning (ML) models and three convolutional neural networks (CNNs) were employed. Despite facing several challenges throughout the process of dataset generation and subsequent classification, the CNN models consistently attained an overall accuracy of 90% or more. Notably, one of the ML models stood out with 96% accuracy, surpassing CNNs in this specific context. The study also conducts a comparative analysis of ML models on existing benchmark datasets, revealing higher prediction accuracy when dealing with fewer LULC classes. Thus, the selection of an appropriate model hinges on the given task, available resources, and the necessary trade-offs between performance and efficiency, particularly crucial in resource-constrained settings. The standardized benchmark dataset contributes valuable insights into the relative performance of deep CNN and ML models in LULC classification, providing a comprehensive understanding of their strengths and weaknesses.

Monopoly pricing with unknown demand

AbstractThe optimal pricing of goods, especially when they are new and the innovating firm is a monopolist, must proceed without precise knowledge of the demand curve. This paper provides a pricing method with a relative robustness guarantee by maximizing a performance index which amounts to a worst‐case ratio of the obtained payoff to the best possible payoff. Assuming monotonicity and complementarity of demand in price and the unknown demand parameter, the performance index is fully determined by its behavior at the boundary of the parameter space. This allows for an efficient computation of an optimal robust price. In the linear case, which can also be used for nonlinear demand with bounded slope, the method provides a simple closed‐form solution. A comparison with the standard worst‐case payoff criterion reveals substantial improvements in both absolute and relative performance, at only a small cost relative to the maximized expected profit.

Quantum support vector machines for classification and regression on a trapped-ion quantum computer

Quantum machine learning is a rapidly growing field at the intersection of quantum computing and machine learning. In this work, we examine our quantum machine learning models, which are based on quantum support vector classification (QSVC) and quantum support vector regression (QSVR). We investigate these models using a quantum circuit simulator, both with and without noise, as well as the IonQ Harmony quantum processor. For the QSVC tasks, we use a dataset containing fraudulent credit card transactions and image datasets (the MNIST and the Fashion-MNIST datasets); for the QSVR tasks, we use a financial dataset and a materials dataset. For the classification tasks, the performance of our QSVC models using 4 qubits of the trapped-ion quantum computer was comparable to that obtained from noiseless quantum circuit simulations. The result is consistent with the analysis of our device noise simulations with varying qubit gate error rates. For the regression tasks, applying a low-rank approximation to the noisy quantum kernel, in combination with hyperparameter tuning in ε-SVR, improved the performance of the QSVR models on the near-term quantum device. The alignment, as measured by the Frobenius inner product between the noiseless and noisy quantum kernels, can serve as an indicator of the relative prediction performance on noisy quantum devices in comparison with their ideal counterparts. Our results suggest that the quantum kernel, as described by our shallow quantum circuit, can be effectively used for both QSVC and QSVR tasks, indicating its resistance to noise and its adaptability to various datasets.

Study on Common Mental Disorders (CMD) in medical residents victims of aggressions in the Medical Residency Program in the state of Paraná

Medical residency entails supervised on-the-job training, where the dual role of worker and learner subjects’ residents to constant internal tension, potentially triggering common mental disorders (CMD). This cross-sectional, quantitative study involved 165 medical residents, administering three simultaneous virtual questionnaires on sociodemographic data, aggressions, abuse, mistreatment, and CMD. A significance level of 5% was utilized. Among residents frequently subjected to aggression, abuse, and mistreatment, 89.23% were classified as possible CMD cases (p < 0.001). Preceptors and supervisors were the most reported aggressors (66.15%). "Depreciation or humiliation" (p = 0.018), "assignment of punitive rather than educational tasks" (p = 0.022), and "threat of harm" (p = 0.008) were the most associated frequent aggression modalities with the classification of possible CMD cases. Imbalance among emotional, mental, and social spheres is linked to poor technical performance and inefficiency in patient relations.

Some convergently three-term trust region conjugate gradient algorithms under gradient function non-Lipschitz continuity

This paper introduces two three-term trust region conjugate gradient algorithms, TT-TR-WP and TT-TR-CG, which are capable of converging under non-Lipschitz continuous gradient functions without any additional conditions. These algorithms possess sufficient descent and trust region properties, and demonstrate global convergence. In order to assess their numerical performance, we compare them with two classical algorithms in terms of restoring noisy gray-scale and color images as well as solving large-scale unconstrained problems. In restoring noisy gray-scale images, we set the performance of TT-TR-WP as the standard, then TT-TR-CG takes around 2.33 times longer. The other algorithms around 2.46 and 2.41 times longer, respectively. In solving the same color images, the proposed algorithms exhibit relative good performance over other algorithms. Additionally, TT-TR-WP and TT-TR-CG are competitive in unconstrained problems, and the former has wide applicability while the latter has strong robustness. Moreover, the proposed algorithms are both more outstanding than the baseline algorithms in terms of applicability and robustness.

Impact of coating and microstructure on wear resistance of tool steels for wood cutting: a novel approach to quantification and analysis of wear-related damages

Squaring, a wood transformation process, is an operation which consists of introducing the logs into a squaring machine which then uses sharp tools to cut the wood into pieces with high surface quality. Tool steels used in this process experience significant wear, damaging the wood surface and hence leading to substantial scrape rate. This study investigates the wear resistance of three tool steels specifically designed for wood cutting applications: modified AISI A8, modified steels with 0% and 1% tungsten, and powder metallurgy prepared W360 steel. Furthermore, the influence of a PVD coating on the wear resistance of the three alloys was investigated. ASTM G65 abrasive wear tests were conducted using the dry sand/rubber wheel abrasion test. A methodology using a non-contact 3D measurement system and specialized software was developed, allowing for a thorough quantitative assessment of the wear of these steels. The results revealed that the coated A8mod + 1%W steel exhibits the best resistance among the coated steels. Despite the excellent intrinsic resistance of W360 steel, the coating did not provide a significant improvement for this steel, showing only a 10% reduction in wear. Microstructural analysis revealed that the predominant wear mechanisms were abrasion and impact. The relative performance of each steel was quantified and is reported. Field trials conducted under actual cutting conditions, indicate the superiority of W360 steel in terms of resilience to wear and impacts compared to other tested alloys, while confirming the effectiveness of surface treatments in mitigating material wear. However, A8 steel modified with 1% tungsten exhibits increased wear under coating.

Comparison of time-domain and frequency-domain models for vibration prediction of a rail transit viaduct paved with floating slab track

Noise from rail transit viaducts often has a negative effect on surrounding buildings and nearby residents. To reduce the bridge vibration and radiated noise, floating slab tracks have been widely adopted in rail transit viaducts. To calculate the vibration of coupled track-bridge systems, both time-domain and frequency-domain methods can be applied although their relative performance is not very clear. This study aims to compare the two approaches and then provide some guidelines in choosing the appropriate method for predicting the vibration. The principle and methodology of the two calculation methods are firstly introduced, and the models for the train, floating slab track and bridge are developed using similar parameters. To further improve the efficiency and accuracy of the frequency-domain method, a half period method and a weighted average method are proposed to calculate the average responses of a certain point on the rail, floating slab and bridge to approximate the response during the pass-by. An indirect method is used to estimate the wheel–rail combined roughness from the measured rail acceleration when the train is running on the bridge, and compared with the direct measurement rail roughness. For the vibration of the bridge and floating slab, comparisons are made between results obtained from the time-domain and frequency-domain methods with different train speeds, and between results from experiment and simulation with different roughness. It is found that the wheel–rail contact forces and other structural responses from the two approaches show good consistency in the frequency region above 20 Hz. It is shown that a good approximation of the response during the pass-by can be obtained using the frequency-domain method by adopting the half period average or a simplified weighted average of two wheel position cases. When using the measured roughness and estimated roughness, the simulated vibration of the floating slab and bridge match well with the experimental results, and the indirect method can be used to modify direct measurement roughness in lower frequency regions. In the vibration prediction, the rail vibration or roughness, and other parameters in the vehicle–track–bridge system, should be measured accurately to ensure high reliability.

The Effects of Relative Performance Information and Transparency on Knowledge Sharing and Productive Effort

Using a real-effort experiment, we investigate individuals’ simultaneous decisions regarding how much effort to exert and how to allocate this effort when working on a productive task (productive effort) and sharing knowledge (knowledge-sharing effort). Specifically, we examine the effects of relative performance information (RPI) and the transparency of individual knowledge-sharing decisions (transparency). We find that RPI increases productive effort but reduces knowledge-sharing effort over time. In contrast, transparency does not influence productive effort but increases knowledge-sharing effort. Furthermore, the results indicate that transparency can counteract the adverse effect of RPI on knowledge-sharing effort over time without impeding productive effort. We link our results to the psychological concepts of social comparison, impression management, and reciprocity. Implications for research and practice are discussed.

The evolution of sensor-based technologies in dairy production

Sensor-based management is growing rapidly in dairy farming. Activity, behavior and rumination monitors and data from au­tomated milking systems and calf feeders are established man­agement tools and hold promise for earlier or more efficient de­tection of health problems. However, gaps remain in validation and especially in turning streams of data into actionable infor­mation. On average, dairy herds can achieve comparable re­productive performance with management emphasizing estrus detection by activity monitors or timed insemination programs, but herd-specific variables will affect relative performance. Sensor-based screening of fresh cows may be useful to save la­bor or reduce disruptions to cows’ routines, but more validation is needed before this can augment or offset skilled, rational de­tection of health problems.

Mean Performance and Genetic Variability Exploration for Fruit Yield and its Attributing Traits in Tomato (Solanum lycopersicum L.)

The present study was conducted to access the relative performance, genetic variability, genetic advance and heritability in thirty genotypes of tomato crop. The crop was sown under Randomized Block Design (RBD) in three replications at Regional Research Station Karnal, CCS Haryana Agricultural University, during the Autumn Winter season of 2021-22. The observations were recorded for eight parameters pertaining to the fruit yield and its attributing traits in tomato. Statistics from analysis of variance showed substantial differences among the genotypes that unveils the plausible presence of significant genetic variability which could be positively exploited in crop improvement programs. The results obtained in the experiment showed that Pusa Ruby (38 days) was earliest to flower in 50% plants followed by EC-631351 (40 days). Genotype Selection 7 took the least (66) number of days to first picking and genotype Kashi Hemant took the maximum (155 days) number of days to last picking. Genotype EC-615056 recorded the maximum (57.71 g) average fruit weight and genotypes namely EC-631457, EC-631357 and Pusa Early Dwarf observed highest fruit yield per plant. Narrow differences were recorded for the phenotypic and genotypic coefficients of variation for all the characters, which denotes that environment had very little influence in expression of the characters and phenotype truly represents the genotype. High to moderate magnitude of genotypic coefficient of variation and phenotypic coefficient of variation was recorded for almost all the characters in our investigation. Plant characters days to first picking, days to 50% flowering, days to last picking, plant height at harvest recorded high (>70%) magnitude of heritability in broad sense implying least influence of environment over these characters. High heritability coupled with high genetic advance was observed for characters like days to 50% flowering and plant height at harvesting.

Comparison of four D-dimer assays in the context of venous thromboembolism in the emergency department.

This observational study conducted across seven emergency care units compares the efficacy of four D-dimer detection methods, namely HemosIL D-dimer HS (HS), HemosIL D-dimer HS-500 (HS-500), VIDAS D-dimer (VIDAS), and HemosIL AcuStar D-dimer (ACUSTAR). The primary focus is on patients with a clinical suspicion of deep venous thrombosis (DVT) or pulmonary embolism (PE). A total of 149 samples were collected from patients with suspected DVT or PE. The confirmation of DVT/PE was based on calf ultrasound or computed tomography-Angiography. Direct comparisons were made between the different detection methods, considering both their analytical performance and clinical utility. Additionally, the impact of an age-adjusted cut-off on the diagnostic accuracy of each method was assessed. The results revealed comparable negative predictive value, sensitivity, and specificity across the methods, with a notable exception of increased specificity for HS compared with HS-500 (50.8% vs. 41.5%, p = 0.03). Further analysis incorporating an age-adjusted cut-off demonstrated a significant improvement in specificity for HS. When using the age-adjusted cut-off, HS exhibited a substantial increase in specificity compared with HS-500 (63.1% vs. 49.2%, p = 0.004) and demonstrated significantly higher specificity compared with VIDAS (63.1% vs. 53.8%, p = 0.04). The study emphasizes the nonuniversal effect of an age-adjusted cut-off and discusses the potential necessity for different cut-off values, particularly in the case of HS-500. These findings contribute to the understanding of D-dimer detection methods in the context of DVT and PE, providing insights into their relative performances and the potential optimization through age-adjusted cut-offs.

An Artificial Intelligence Driven Approach for Classification of Ophthalmic Images using Convolutional Neural Network: An Experimental Study.

Early disease detection is emphasized within ophthalmology now more than ever, and as a result, clinicians and innovators turn to deep learning to expedite accurate diagnosis and mitigate treatment delay. Efforts concentrate on the creation of deep learning systems that analyze clinical image data to detect disease-specific features with maximum sensitivity. Moreover, these systems hold promise of early accurate diagnosis and treatment of patients with common progressive diseases. DenseNet, ResNet, and VGG-16 are among a few of the deep learning Convolutional Neural Network (CNN) algorithms that have been introduced and are being investigated for potential application within ophthalmology. In this study, the authors sought to create and evaluate a novel ensembled deep learning CNN model that analyzes a dataset of shuffled retinal color fundus images (RCFIs) from eyes with various ocular disease features (cataract, glaucoma, diabetic retinopathy). Our aim was to determine (1) the relative performance of our finalized model in classifying RCFIs according to disease and (2) the diagnostic potential of the finalized model to serve as a screening test for specific diseases (cataract, glaucoma, diabetic retinopathy) upon presentation of RCFIs with diverse disease manifestations. We found adding convolutional layers to an existing VGG-16 model, which was named as a proposed model in this article that, resulted in significantly increased performance with 98% accuracy (p<0.05), including good diagnostic potential for binary disease detection in cataract, glaucoma, diabetic retinopathy. The proposed model was found to be suitable and accurate for a decision support system in Ophthalmology Clinical Framework.