Empirical Assessment Research Articles

Symmetry estimating R × C vote transfer matrices from aggregate data

Abstract Ecological inference methods are devised to estimate unknown inner-cells of 2-way contingency tables by inferring conditional distribution probabilities. This outlines one of the more long-standing social science problems, chiefly frequent in political science and sociology. To solve the problem, ecological inference algorithms consider an asymmetric relationship, with a main characteristic (e.g. race or social class) mapped to rows impacting on a dependent variable, usually the vote, mapped to columns. The problem arises because different solutions are reached depending on how variables are assigned to rows and columns. The models are asymmetric. In this paper, we propose 2 new sets of ecological inference algorithms and explore if accuracy could be improved by handling the problem in a symmetric way. We assess the accuracy of the proposed methods using real data from more than 550 concurrent elections where the true district-level cross-classifications of votes (straight- and split-tickets) are known. Our empirical assessment clearly identifies the symmetric solutions as more accurate. They outperform asymmetric methods 90% of the time and reduce error, on average, by 11%. Our results are based on data from simultaneous elections, so further research is required to see whether our conclusions can be maintained in other ecological inference contexts. Interested readers can easily use the proposed methods as they are implemented in the R package lphom.

Empirical assessment of ChatGPT’s answering capabilities in natural science and engineering

ChatGPT is a powerful language model from OpenAI that is arguably able to comprehend and generate text. ChatGPT is expected to greatly impact society, research, and education. An essential step to understand ChatGPT’s expected impact is to study its domain-specific answering capabilities. Here, we perform a systematic empirical assessment of its abilities to answer questions across the natural science and engineering domains. We collected 594 questions on natural science and engineering topics from 198 faculty members across five faculties at Delft University of Technology. After collecting the answers from ChatGPT, the participants assessed the quality of the answers using a systematic scheme. Our results show that the answers from ChatGPT are, on average, perceived as “mostly correct”. Two major trends are that the rating of the ChatGPT answers significantly decreases (i) as the educational level of the question increases and (ii) as we evaluate skills beyond scientific knowledge, e.g., critical attitude.

Stimulus Avoidance Assessment: A Systematic Literature Review.

Board certified behavior analysts are ethically required to first address destructive behavior using reinforcement-based and other less intrusive procedures before considering the use of restrictive or punishment-based procedures (ethics standard 2.15; Behavior Analyst Certification Board, 2020). However, the inclusion of punishment in reinforcement-based treatments may be warranted in some cases of severe forms of destructive behavior that poses risk of harm to the client or others. In these cases, behavior analysts are required to base the selection of treatment components on empirical assessment results (ethics standard 2.14; Behavior Analyst Certification Board, 2020). One such preintervention assessment is the stimulus avoidance assessment (SAA), which allows clinicians to identify a procedure that is likely to function as a punisher. Since the inception of this assessment approach, no studies have conducted a systematic literature review of published SAA cases. These data may be pertinent to examine the efficacy, generality, and best practices for the SAA. The current review sought to address this gap by synthesizing findings from peer-reviewed published literature including (1) the phenomenology and epidemiology of the population partaking in the SAA; (2) procedural variations of the SAA across studies (e.g., number of series, session length); (3) important quality indicators of the SAA (i.e., procedural integrity, social validity); and (4) how the SAA informed final treatment efficacy. We discuss findings in the context of the clinical use of the SAA and suggest several avenues for future research.

Traceability and reuse mechanisms, the most important properties of model transformation languages

ContextDedicated model transformation languages are claimed to provide many benefits over the use of general purpose languages for developing model transformations. However, the actual advantages and disadvantages associated with the use of model transformation languages are poorly understood empirically. There is little knowledge and even less empirical assessment about what advantages and disadvantages hold in which cases and where they originate from. In a prior interview study, we elicited expert opinions on what advantages result from what factors surrounding model transformation languages as well as a number of moderating factors that moderate the influence.ObjectiveWe aim to quantitatively asses the interview results to confirm or reject the influences and moderation effects posed by different factors. We further intend to gain insights into how valuable different factors are to the discussion so that future studies can draw on these data for designing targeted and relevant studies.MethodWe gather data on the factors and quality attributes using an online survey. To analyse the data and examine the hypothesised influences and moderations, we use universal structure modelling based on a structural equation model. Universal structure modelling produces significance values and path coefficients for each hypothesised and modelled interdependence between factors and quality attributes that can be used to confirm or reject correlation and to weigh the strength of influence present.ResultsWe analyzed 113 responses. The results show that the MTL capabilities Tracing and Reuse Mechanisms are most important overall. Though the observed effects were generally 10 times lower than anticipated. Furthermore, we found that moderation effects need to be individually assessed for each influence on a quality attribute. The moderation effects of a single moderating variable vary significantly for each influence, with the strongest effects being 1000 times higher than the weakest.ConclusionThe empirical assessment of MTLs is a complex topic that cannot be solved by looking at a single stand-alone factor. Our results provide clear indication that evaluation should consider transformations of different sizes and use-cases that go beyond mapping one elements attributes to another. Language development on the other hand should focus on providing practical, transformation specific reuse mechanisms that allow MTLs to excel in areas such as maintainability and productivity compared to GPLs.

Plant Adaptation and Phosphorus Limitation in Tropical Forests: A Theoretical and Empirical Assessment

Ecosystem dynamics are shaped by plant adaptation to environmental stress, yet the conditions under which this occurs remain poorly understood. We developed a theoretical framework to predict how strategies used by tropical trees to cope with low-phosphorus (P) availability (that is, traits related to P uptake, and use) influence growth under P limitation. We then tested this framework against data on tree species in Borneo and a meta-analysis of results from pantropical nutrient addition experiments. Our theoretical framework predicts that plant traits associated with low-P environments, including enhanced P allocation to leaves, efficient P resorption, and root phosphatase activity, alleviate the negative effects of P scarcity more strongly for “inefficiently” growing plants, represented by large trees and old-growth forests, compared with saplings or secondary forests. In agreement with this prediction, changes in traits related to low-P environments increased the potential relative growth rate of large trees more than small trees in Borneo. Finally, theoretical expectation was supported by a meta-analysis which revealed stronger P limitation in saplings and secondary forests than in old-growth forests. Together, these findings provide a novel framework to interpret the relationship between resource constraints and plant performance and reinforce the importance of accounting for plant adaption to predict ecosystem responses to P limitation in tropical forests.

Outcomes of control and monitoring of a widespread riparian invader (Tamarix spp.): a comparison of synthesis approaches

Effective ecological restoration requires empirical assessment to determine outcomes of projects, but conclusions regarding the effects of restoration treatments on the whole ecosystem remain rare. Control of invasive shrubs and trees in the genus Tamarix and associated riparian restoration in the American Southwest has been of interest to scientists and resource managers for decades; dozens of studies have reported highly variable outcomes of Tamarix control efforts, as measured by a range of response variables, temporal and spatial scales and monitoring strategies. We conducted a literature search and review, meta-analysis and vote count (comparison of numerical outcomes lacking reported variances and/or sample sizes) on published papers that quantitatively measured a variety of responses to control of Tamarix. From 96 publications obtained through a global search on terms related to Tamarix control, we found 52 publications suitable for a meta-analysis (n = 777 comparisons) and 63 publications suitable for two vote counts (n = 1,460 comparisons total; 622 comparisons reported as statistically significant) of response to Tamarix control. We estimated responses to control by treatment type (e.g. cut-stump treatment, burning, biocontrol) and ecosystem component (e.g. vegetation, fauna, fluvial processes). Finally, we compared results of the various synthesis methods to determine whether the increasingly stringent requirements for inclusion led to biased outcomes. Vegetation metrics, especially measures of Tamarix response, were the most commonly assessed. Ecosystem components other than vegetation, such as fauna, soils and hydrogeomorphic dynamics, were under-represented. The meta-analysis showed significantly positive responses by vegetation overall to biocontrol, herbicide and cut-stump treatments. This was primarily due to reduction of Tamarix cover; impacts on replacement vegetation were highly variable. We found concordance amongst our varied synthesis approaches, indicating that increased granularity from stricter quantitative techniques does not come at the cost of a biased sample. Overall, our results indicate that common control methods are generally effective for reducing Tamarix, but the indirect effects on other aspects of the ecosystem are variable and remain understudied. Given that this is a relatively well-studied invasive plant species, our results also illustrate the limitations of not only individual studies, but also of reviews for measuring the impact of invasive species control. We call on researchers to investigate the less commonly studied responses to Tamarix control and riparian restoration including the effects on fauna, soil and hydrogeomorphic characteristics.

R&D investment, innovation, and export performance: An analysis of SME and large firms

ABSTRACT There is a paucity of literature investigating the simultaneous relationships among R&D, innovation, and exports and assessing the separate roles of R&D and innovation from an empirical perspective. We address this research gap and contribute to the literature by examining the “learning to export” and “learning by exporting” hypotheses in SMEs and large firms. Using instrumental variable (IV) regressions and path analyses, the empirical assessment provides evidence that R&D and innovation should be regarded as two separate constructs. We also find significant mediating effects between R&D and Export via Innovation and between Export and R&D via Innovation only in the large company cohort. SMEs do not show evidence of simultaneity among R&D, innovation, and exporting nor do the results provide support for the “learning by doing” hypothesis in SMEs. Our findings assist in understanding how the variables interact across different firm sizes, which is important for targeted resource support and policies.

Tax Compliance in Slovenia: An Empirical Assessment of Tax Knowledge and Fairness Perception

Complex tax systems can result in tax evasion, which further impacts the revenues necessary to achieve sustainable development goals. Enhancing taxpayer education, tax knowledge, and tax fairness perception is essential for boosting revenues to support societal sustainability. The aim of this study was to assess the levels of tax knowledge and tax fairness perception within the Slovene taxpayer population, with a specific focus on the differences related to gender and settlement size. Further, the connections between tax knowledge and various aspects of tax fairness were explored. The Kruskal–Wallis test was used to assess the statistical significance of gender and settlement size differences and the Kendall’s coefficient of rank to determine the association between the tax knowledge and fairness perception dimensions. The results provide evidence that highlights disparities in tax knowledge between male and female taxpayers (p-value = 0.0116). Additionally, this study demonstrates that settlement size does not significantly impact tax knowledge perception among Slovene taxpayers (p-value = 0.2067). However, tax fairness encompasses various dimensions, and our research reveals no disparities based on gender (p-value = 0.7263) or settlement size (p-value = 0.2786). When assessing the correlation between tax knowledge and tax fairness perception, the results indicate statistically significant but weak correlations in both directions, depending on the specific fairness dimension.

Worlds apart? An empirical assessment of education as a social boundary among young people in Flanders

ABSTRACT Educational tracking refers to the grouping of students based on their academic abilities. Although tracking has been intensively debated in terms of its impact on academic outcomes, this study assesses whether educational tracks effectively constitute a social boundary between youth. Data from 11 surveys (Total N = 38,372) gathered among pupils enrolled in secondary education in Flanders were used to assess to what extent young people’s educational track position relates to a wide range of opinions and behavior in general (471 outcomes) and the potential moderating role of gender and migration status. Subsequently, we focused on indicators that refer to intergroup processes (i.e. cross-track friendships and perceived contempt). Educational differences are especially large for tastes, indicators of lifestyle, and societal attitudes. Moreover, we find that differences in educational track position are connected with perceived status differences and constitute an important structuring factor regarding their friendships.

Shades of support: An empirical assessment of D&I policy support in organizations

AbstractIn this research, we aim to develop a better understanding of the different ways in which employees can advance or resist the diversity and inclusion (D&I) policies implemented by their organization. To this end, we complement prior work by distinguishing between employees' attitudinal and behavioral opposition versus support for D&I policies. We combine these to distinguish different combinations of attitudinal and behavioral responses that characterize specific groups of employees, which we label opponents, bystanders, reluctants, and champions. In a large‐scale survey study conducted among employees from seven organizations located in the Netherlands (n = 2913), we find empirical support for the validity of this taxonomy and its value in understanding the likelihood that employees advance or resist D&I policies. Furthermore, we find more convergence between attitudinal and behavioral support when employees perceive a more positive climate for inclusion. Together, these results advance existing scholarly work by providing both a theoretical account of and empirical evidence for the different ways in which D&I policies may find support or resistance from employees. In addition, our work offer practitioners a practical tool to examine the likelihood that D&I policies meet support or opposition from their employees and therefore enables them to design and implement more effective D&I interventions.

Explaining long-term bond yields synchronization dynamics in Europe

We present a novel empirical assessment of the determinants of sovereign yield synchronization dynamics in the European Monetary Union. This topic has been seldom addressed in the existing macroeconometric literature. We use a time-varying measure of government bond yields synchronization and Bayesian Model Averaging methods to show that the persistence of synchronization measures differs significantly between GIIPS countries (Greece, Ireland, Italy, Portugal and Spain) and the rest of the monetary union, as well as across periods characterized by whether the zero lower bound of interest rates was binding or not. The degree of synchronization in inflation rates with the rest of the currency area is a robust predictor of the synchronization of sovereign yields, as opposed to economic fundamentals describing the fiscal positions of individual countries. An out-of-sample forecasting exercise reveals that accounting for the most relevant economic fundamentals can lead to improvements in the directional accuracy of the forecasts of yield synchronization rates for GIIPS countries.

BLOCKCHAIN IN U.S. ACCOUNTING: A REVIEW: ASSESSING ITS TRANSFORMATIVE POTENTIAL FOR ENHANCING TRANSPARENCY AND INTEGRITY

This study critically examines the transformative potential of blockchain technology in enhancing transparency ansd integrity within U.S. accounting practices. Employing a systematic literature review and content analysis methodology, the research draws on a range of peer-reviewed articles, industry reports, and authoritative databases. The study's objectives include understanding the evolution of blockchain in accounting, assessing its impact on transparency and integrity, identifying challenges in adoption, and exploring future trends and stakeholder implications. Key findings reveal that blockchain acts as a catalyst for transparency and integrity in accounting, offering a decentralized and immutable record-keeping system that significantly reduces the likelihood of fraud and errors. The technology's integration into accounting practices is evolving, indicating a future of automated, real-time auditing processes and enhanced financial reporting. However, challenges such as regulatory ambiguity, technical complexities, and scalability issues persist, necessitating strategic solutions. The study recommends that industry leaders and policymakers proactively embrace blockchain technology through education, regulatory framework development, and collaboration between technology developers and the accounting industry. Standard-setting bodies are also encouraged to update accounting standards in light of blockchain's implications. Future research directions include exploring the long-term impact of blockchain on various accounting aspects, empirical assessments of its real-world impact, and strategies to overcome adoption barriers. In conclusion, the study highlights blockchain's significant role in revolutionizing accounting practices, emphasizing the need for continued exploration and strategic integration of this technology in the accounting sector.
 Keywords: Blockchain Technology, Accounting Transparency, Regulatory Frameworks, Financial Integrity.

Drought response of the boreal forest carbon sink is driven by understorey–tree composition

The boreal forest is an important global carbon sink, but its response to drought remains uncertain. Here, we compiled biometric- and chamber-based flux data from 50 boreal forest stands to assess the impact of the 2018 European summer drought on net ecosystem production (NEP) across a 68 km2 managed landscape in northern Sweden. Our results reveal a non-uniform reduction in NEP (on average by 80 ± 16 g C m−2 yr−1 or 57 ± 13%) across the landscape, which was greatest in young stands of 20–50 years (95 ± 39 g C m−2 yr−1), but gradually decreased towards older stands (54 ± 57 g C m−2 yr−1). This pattern was attributed to the higher sensitivity of forest-floor understorey to drought and its decreasing contribution to production relative to trees during stand development. This suggests that an age-dependent shift in understorey–tree composition with increasing stand age drives the drought response of the boreal forest NEP. Thus, our study advocates the need for partitioning ecosystem responses to improve empirical and modelling assessments of carbon cycle–climate feedbacks in boreal forests. It further implies that the forest age structure may strongly determine the carbon sink response to the projected increase in drought events across the managed boreal landscape.

Revisiting the Classical Theory of Investment: An Empirical Assessment from the European Union

In alignment with classical investment theory, this study explores the enduring relationships and causal linkages among total private investment, profit rate, unit labour costs, and demand growth within the European Union throughout the period spanning from 1961 to 2019. The empirical approach adopted involves the use of advanced econometric techniques designed to address cross-sectional dependence and slope heterogeneity. As a first stage, we examine stationarity and cointegration by employing second-generation panel unit root and cointegration tests. Subsequently, we estimate long-run equations through estimators intended to control for cross-sectional dependence and slope heterogeneity. As a further step, we use the Dumitrescu-Hurlin procedure to examine potential bidirectional causality between the variables and detect whether there exists endogeneity in the data. Finally, we apply the dynamic common correlated effects estimator mean group with instrumental variables to control for the potential presence of endogeneity. The outcomes of the analysis underscore a positive association between private investment and the profit rate, unit labour costs, and demand growth, thus providing robust empirical support for the classical theory of investment.

Proposed Model for Real-Time Anomaly Detection in Big IoT Sensor Data for Smart City

A smart city represents an advanced urban environment that utilizes digital technologies to improve the well-being of residents, efficiently manage urban operations, and prioritize long-term sustainability. These technologically advanced cities collect significant data through various Internet of Things (IoT) sensors, highlighting the crucial importance of detecting anomalies to ensure both efficient operation and security. However, real-time identification of anomalies presents challenges due to the sheer volume, rapidity, and diversity of the data streams. This manuscript introduces an innovative framework designed for the immediate detection of anomalies within extensive IoT sensor data in the context of a smart city. Our proposed approach integrates a combination of unsupervised machine learning techniques, statistical analysis, and expert feature engineering to achieve real-time anomaly detection. Through an empirical assessment of a practical dataset obtained from a smart city environment, we demonstrate that our model outperforms established techniques for anomaly detection.

An empirical assessment of whether urban green ecological networks have the capacity to store higher levels of carbon

Carbon–neutral growth is a crucial long-term climatic aim in the context of global warming. This paper introduces complex network theory and explores its potential application to achieve this goal. Specifically, we investigate the spatial and temporal distribution of nodes and sources in the ecological network, and examine whether a relationship exists between the topological index of network nodes and the landscape pattern index of ecological source areas. We also determine the contribution of nodes to the carbon stock of the entire network by exploring the correlation between the carbon stock of nodes and sources to develop an optimization strategy based on the synergistic effect of node-source carbon enhancement. Finally, we test the effect of network optimization through robustness. Our results show that: (1) The correlation topological feature index analysis reveals that the degree distribution of the node network's topological characteristics becomes dispersed and modular, exhibiting the characteristics of small-world networks according to a large clustering coefficient. The heterogeneity and extent of ecological source landscapes have increased by modularity index but remain distributed and locally fragmented; (2) According to correlation analysis, by enhancing the eccentricity of the node topology, the patch cohesion index (COHESION) of the ecological source site can maximize the contribution of the node to the enhancement of the carbon stock benefits of the source site; (3) According to the tests on the robustness of nodes and edges and the robustness of network links, network stability is improved and carbon sink capacity is enhanced. Simultaneously, the restoration and rejuvenation of ecological space through national ecological construction projects can effectively improve the carbon sink within the organized region, contributing to the carbon neutrality aim. This research gives scientific and quantifiable references for potential ecological construction projects for sustainable cities and the optimization of urban ecological space structure.

Novel Approaches for the Empirical Assessment of Evapotranspiration over the Mediterranean Region

The hydrological cycle should be scrutinized and investigated under recent climate change scenarios to ensure global water management and to increase its utilization. Although the FAO proposed the use of the Penman–Monteith (PM) equation worldwide to predict evapotranspiration (ET), which is one of the most crucial components of the hydrological cycle, its complexity and time-consuming nature, have led researchers to examine alternative methods. In this study, the performances of numerous temperature-driven ET methods were examined relative to the PM using daily climatic parameters from central stations in 11 districts of the Kahramanmaras province. Owing to its geographical location and other influencing factors, the city has a degraded Mediterranean climate with varying elevation gradients, while its meteorological patterns (i.e., temperature and precipitation) deviate from those of the main Mediterranean climate. A separate evaluation was performed via ten different statistical metrics, and spatiotemporal ET variability was reported for the districts. This study revealed that factors such as altitude, terrain features, slope, aspect geography, solar radiation, and climatic conditions significantly impact capturing reference values, in addition to temperature. Moreover, an assessment was conducted in the region to evaluate the effect of modified ET formulae on simulations. It can be drawn as a general conclusion that the Hargreaves–Samani and modified Blaney–Criddle techniques can be utilized as alternatives to PM in estimating ET, while the Schendel method exhibited the lowest performance throughout Kahramanmaras.

Infiltration Models in EnergyPlus: Empirical Assessment for a Case Study in a Seven-Story Building

The current decarbonization transition to be achieved by 2050 according to the European Council has given great prominence to the use of Digital Twins as tools for energy management. For their correct operation, it is essential to control the uncertainties of the energy models, which lead to differences between the measured and predicted data. One of the key parameters that is most difficult to assess numerically is air leakage. The existent infiltration models available in EnergyPlus were developed to be applied in low-rise residential buildings with fewer than three stories. Therefore, it is common to rely on air leakage equations employing predefined coefficients. This research presents an empirical assessment of the performance of two EnergyPlus air leakage models, the “Effective Leakage Area” and the “Flow Coefficient”, in predicting dynamic infiltration within the attic of a seven-story building. Blower door tests, along with the application of CO2 tracer gas, were conducted to establish coefficients for the models. Then, they were evaluated in three independent periods according to the criteria established in the American Society for Testing Material D5157 Standard. Those models that only used in situ coefficients consistently met the standard across all three periods, demonstrating for both equations their accurate performance and reliability. For the best model derived from tracer gas data, the R2 and NMSE values are 0.94 and 0.019, respectively. In contrast, the model developed using blower door test data and EnergyPlus default values presented a 64% reduction in accuracy compared to the best one. This discrepancy could potentially lead to misleading energy estimates. Although other software options exist for estimating infiltration, this study specifically targets EnergyPlus users. Therefore, these findings offer valuable insights to make more informed decisions when implementing the infiltration models into energy simulations for high-rise buildings using EnergyPlus.

Leveraging Django and Machine Learning to Craft a Health Forecasting System, Tailoring Personalized Wellness Strategies

Abstract: The goal of this thesis is to develop a health forecasting system that combines Django with cutting-edge machine learning in order to customize wellness plans according to personal health information. The study addresses model selection, data preprocessing, and user interface creation as it investigates the potential synergies between machine learning and web development. Predictive health analytics potential and gaps are identified through a review of the literature. The goal of the project is to create a real-time, scalable health forecasting system while protecting user privacy. Neural networks and decision trees are two examples of machine learning methods that are utilized. The efficacy of the system in delivering precise health projections and augmenting user involvement in proactive wellness management is evaluated through empirical assessment and case studies. In order to further personalized healthcare solutions, practitioners, researchers, and developers can benefit greatly from the research's insightful contributions.

Exploring the Impact of Artificial Intelligence Tools in Engineering Pedagogy: A Qualitative Survey of Academic Experiences

Abstract: Artificial Intelligence (AI) is revolutionizing the landscape of Engineering Education, albeit with limited empirical qualitative assessment of its potential and perceived benefits or challenges. This paper investigates the integration and experiences of academic professionals with AI tools in their pedagogical practices. The study employs a comprehensive survey asking specific questions relating to the familiarity, motivation, usage, training, benefits, challenges, ethical considerations, and future implications of AI tools like ChatGPT, Google Bard, and Microsoft Bing. The results illustrate a broad understanding and adoption of AI tools, and their instrumental role in enhancing educational methods and pedagogical approaches. However, the findings also highlight the need for adequate training and ethical guidelines for responsible AI use. Additionally, perceived challenges are revealed, underscoring the importance of addressing them to harness AI's full potential in education. Overall, this research contributes to the understanding of AI's value in education from an academic standpoint, outlining its role, advantages, challenges, and future expectations. This study is essential in the ongoing discourse of AI in education, and it can inform the development of training, policies, and strategies for effective and responsible AI integration in the future of engineering education.