Empirical Data Research Articles

The Microstructure and Corrosion Characteristic of Al-10 wt.%RE (Re= Ce, Nd, La, Y) binary Aluminum Alloys in Natural Seawater

This study presented a thorough investigation into the microstructure, precipitated phases, and corrosion resistance of Al-10wt%Ce, Al-10wt%Nd, Al-10wt%La, and Al-10wt%Y binary alloys when exposed to natural seawater. The microstructural analysis, conducted using optical microscopy, scanning electron microscopy (SEM), X-ray diffraction (XRD), and transmission electron microscopy (TEM), revealed the presence of distinct precipitated phases. The distinct precipitated phases Al3Ce, Al3Nd, Al11La3, and Al3Y, respectively, have different morphologies and are expected to influence the corrosion resistance. The corrosion behavior of these alloys was evaluated through natural seawater immersion tests and electrochemical experiments, which demonstrated varying degrees of corrosion resistance. The average corrosion mass loss rates and average corrosion depths of the alloys in natural seawater were found to follow the order: Al-Y<Al-Ce < Al-La < Al-Nd. Theoretical explanations for the experimental outcomes were provided by analyzing the different types of precipitates, the electronic structures, and the crystallographic structures of the rare earth elements involved. This research contributes valuable theoretical and empirical data, supporting the application of rare earth aluminum alloys in structural materials for marine equipment.

Spatially resolved CFD-DEM model with innovative experimental validation methods to improve understanding of sand retention in oil and gas wells with the consideration of filter-beds on standalone screens

Coupling CFD and DEM is commonly used to study particle-fluid flow in sand retention systems for oil and gas wells, addressing the limitations of laboratory experiments and reliance on empirical data. These numerical studies aid in optimising standalone sand screens, which are favoured over gravel-pack completions for cost-effectiveness. However, such studies overlook the critical role of the bulk filter-bed in retaining permeability for both particulate and fluid phases. This paper presents a robust numerical methodology using resolved CFD-DEM to model a sand retention system that accurately captures filter-bed permeability, which has a greater impact on sand production and retained fluid productivity than the screen itself from a long-term perspective. The numerical model's accuracy is validated through a novel experimental methodology, which involves benchmarking the numerically derived porosity and single-phase permeability against micro-CT imaging of the filter-bed. Results show strong consistency between the numerical model and micro-CT imaging of the laboratory-derived filter-bed. This validated model provides a solid foundation for running more accurate sand retention tests and improving standalone sand screen selection criteria. Future work will explore the effects of varying parameters on the filter-bed formation to determine optimal conditions for maximising sand retention while maintaining hydrocarbon productivity from a long-term perspective.

Unlocking the biochemical and computational parameters of Ceropegia foetida: A scientific approach for functional bioactive compounds from a medicinal food plant

Investigating the therapeutic potentials of medicinal plants remains pivotal in the discovery of novel bioactive compounds for food and pharmaceutical applications. This research delves into the phytochemical composition and biological activities of Ceropegia foetida's methanol extract, employing comprehensive UHPLC-MS for secondary metabolites profiling. The study quantifies the extract's substantial phenolic (76.12 mg GAE/g) and flavonoid (21.58 mg QE/g) contents, revealing a promising correlation with robust antioxidant activities, as evidenced by notable ABTS, FRAP, and CUPRAC assay outcomes. Furthermore, the extract demonstrates significant inhibitory effects on key enzymes implicated in neurodegenerative disorders and diabetes, including acetylcholinesterase (3.56 mg GALAE/g), butyrylcholinesterase (2.91 mg GALAE/g), and tyrosinase (128.31 mg KAE/g). UHPLC-MS analysis confirms the presence of 39 distinct phytochemicals across six primary categories, affirming the extract's complex bioactive profile. In complement to experimental assays, computational analyses via molecular docking simulations provided insights into the interaction mechanisms of identified phytochemicals with the target enzymes. These simulations revealed a substantial binding affinity of the plant's constituents towards enzymes compared to standard inhibitors, highlighting the compounds responsible for C. foetida's bioactivity. Such computational insights, alongside empirical data, suggest that C. foetida merits further exploration as a natural source of therapeutic agents. Overall, the efficacious enzyme inhibition, coupled with the identified phytochemical diversity, underscores the potential of C. foetida as a valuable natural resource for developing nutraceuticals and therapeutic agents. These findings support the further investigation of C. foetida for its applicability in enhancing health and treating chronic conditions.

Multivariate probabilistic forecasting of electricity prices with trading applications

This study extends recently introduced neural networks approach, based on a regularized distributional multilayer perceptron (DMLP) technique for a multivariate case electricity price forecasting. The performance of a fully connected architecture and a LSTM architecture of neural networks are tested. Different from previous studies we incorporate dependence between multiple exchanges (EPEX and Nord Pool). The empirical data application analyzes two auctions in the day-ahead electricity market for the United Kingdom market. Along with statistical evaluation of probabilistic forecasts we develop a flexible bidding strategy based on risk-adjusted investor utility function. The trading application leverages the differences of the two exchanges by having long/short positions in both. Our findings demonstrate while DMLP shows similar performance compared to the benchmarks, the algorithm is considerably less computationally costly. LASSO Quantile Regression is better in terms if statistical evaluation of distributional fit, while DMLP outperforms in terms of Sharpe ration (by 18%) in the trading application.

Parental control and bullying: Variable-centered and person-centered approaches.

Previous research has established a connection between parental control and bullying. However, the nuanced relationships involving different combinations of psychological control (PPC) and behavioral control (PBC), along with traditional and cyberbullying, remain ambiguous. Thus, this study investigated the associations of specific parental control dimensions and profiles with traditional and cyberbullying, focusing on the potential mediating mechanism of moral disengagement. A sample of 831 students (48.4% were female; Mage = 18.98 years; SDage = 1.08) was analyzed. Through latent profile analysis, three control profiles emerged: low PPC and PBC (40.31%), low PPC and high PBC (29.00%), and high PPC and PBC (30.69%). The findings indicated a positive correlation between PPC and traditional/cyberbullying. Moral disengagement mediated the relationship between PPC/PBC and traditional/cyberbullying. Particularly, high PPC and PBC were linked to cyberbullying. Moral disengagement mediated the relationship between high PPC and PBC and traditional/cyberbullying. These insights provide valuable empirical data for developing bullying interventions that consider parental control and moral disengagement.

High-temperature heat pumps in industrial heating networks: A study on energy use, emissions, and economics

Industrial heat significantly contributes to global primary energy use and primarily relies on fossil-fuel combustion. Recovering residual heat in the industry offers a means to reduce overall energy use. However, the temperature and amount of residual heat available varies widely across industrial sites. Some may have a large amount of residual heat available at relatively high temperatures, while others may not have any residual heat available. Furthermore, for most industries, the residual heat available is at temperatures below 100 °C, while the heat demands are at higher temperatures. Hence, clustering these industries by industrial heating networks, using high-temperature heat pump integration, could offer a promising solution. Research on this concept regarding energy use, emissions and economics is however lacking in the literature. This study however distinguishes and subsequently compares three different heat network configurations in terms of their energy use, carbon emissions and financial appraisal. These configurations include a ‘consumer based heat upgrading network’, a ‘supplier based heat upgrading network’ and a ‘supplier based heat upgrading network with an additional hot water network’. For this purpose a generic methodology is developed, using first and second law principles completed with empirical data for performance and costs. The methodology is applied to data collected from ten companies clustered within the North Sea Port, Ghent (Belgium). The results indicate that the first configuration exhibits the most efficient use of energy and consequently also has the lowest carbon emissions. In addition it also has the lowest levelized cost of heat. This configuration shows, depending on maximum supply temperature of the heat pump, a potential reduction in carbon emissions ranging from 70 % to 80 % in comparison to natural gas boilers. Considering low gas prices, a positive financial appraisal is difficult without carbon taxation. On the other hand, an evaluation during the energy crisis of 2021–2022 indicates that the even without carbon taxation, the levelized cost of heat decreases by 19 % compared to a gas boiler at a maximum heat pump temperature of 160 °C. It was also found that in scenarios of dynamic energy prices a hybrid configuration of a consumer based heat upgrading network and a natural gas boiler could lower the LCOH compared to the individual solutions, by up to 7.6 % compared to the best individual solution. This is done by activating the technology with the lowest operational cost in each time frame.

Stars everywhere: Revealing the prevalence of star performers using empirical data published in entrepreneurship research

Scholars have long called for moving beyond a narrow focus on average performance toward a more direct investigation of the variance in performance. While a few studies have evaluated star entrepreneurs, most empirical research continues to focus on average performers. This lacuna has constrained not only the development of theories but also the accumulation of data on the distribution of performance. In response, this study uses simulations and heuristics to extract distributional information from descriptive statistics commonly reported in published research (i.e., mean, standard deviation, and sample size). Applying this approach to studies recently published in high-impact entrepreneurship journals shows that (a) the suggested methodology can provide rough estimates of the skew and shape of performance distributions, and (b) right-skewed, heavy-tailed distributions featuring star performers are ubiquitous in entrepreneurship, thus reinforcing calls for more direct studies of performance distributions in entrepreneurship.

"I Learned as I Went": an online distance education case study.

Interactions between deaf and hard of hearing (DHH) students and instructors in online distance education (ODE) increased exponentially during the COVID pandemic. To understand this phenomena, we conducted a comprehensive literature review about evolving ODE formats customized for deaf student's needs. This literature shows increasing multimodal, multilingual, and interactive features. Next, we examined empirical data from a collective case study implemented to better understand ODE phenomena from the perspectives of DHH students and faculty in higher education. We used 4 data collection strategies: (1) in-depth, semi-structured faculty interviews, (2) observations of teaching-learning interactions, (3) focus-groups featuring undergraduate and graduate DHH students, and (4) curriculum document analyses. We coded the dataset using MAXQDA software and uncovered 10 triangulated themes; 4 focus on instructors, 4 center students, and 2 describe student-faculty interactions. Overall, this qualitative analysis is a particularizing account of our participant's lifeworlds; however, we close with general recommendations for improving ODE practices through research.

What can we infer about mutation calling by using time-series mutation accumulation data and a Bayesian Mutation Finder?

Accurate estimates of mutation rates derived from genome-wide mutation accumulation (MA) data are fundamental to understanding basic evolutionary processes. The rapidly improving high-throughput sequencing technologies provide unprecedented opportunities to identify single nucleotide mutations across genomes. However, such MA derived data are often difficult to analyze and the performance of the available methods of analysis is not well understood. In this study, we used the existing Bayesian Genotype Caller adapted for MA data that we refer to as Bayesian Mutation Finder (BMF) for identifying single nucleotide mutations while considering the characteristics of the data. We compared the performance of BMF with the widely used Genome Analysis Toolkit (GATK) by applying these two methods to time-series MA data as well as simulated data. The time-series data were obtained by propagating Daphnia pulex over an average of 188 generations and performing whole-genome sequencing of 14 MA lines across three time points. The results indicate that BMF enables more accurate identification of single nucleotide mutations than GATK especially when applied to the empirical data. Furthermore, BMF involves the use of fewer parameters and is more computationally efficient than GATK. Both BMF and GATK found surprisingly many candidate mutations that were not confirmed at later time points. We systematically infer causes of the unconfirmed candidate mutations, introduce a framework for estimating mutation rates based on genome-wide candidate mutations confirmed by subsequent sequencing, and provide an improved mutation rate estimate for D. pulex.

Political Breakthrough in a Rural Community during the 2011 Sarawak State Election: An Ethnographic Perspective

This article draws on empirical data and ethnographic evidence as a springboard to explore issues affecting narratives and observations made during the 2011 Sarawak State Election. It throws light on ways in which communities and cultures at the periphery of the Malaysian nation state grapple with the potentials and consequences of elections in the process of economic, political and social transformation. Eschewing aspects of candidates’ selections, electoral campaign process and actual voting behaviour of individual voters, the main analysis is of the formulation of local understandings and meanings of the state elections in two rural settlements of Ba’ Kelalan and Bario located close to the border between Sarawak and Kalimantan. This article suggests that an account of their experiences is a necessity in order to understand what functions and whose interest elections serve, and most importantly what election actually does and means within particular circumstances of local history. As a political phenomenon, elections can look different from the perspective of those close to the centre of power in contrast to those with little power within a political hierarchy.

The impact of digital finance on financial inclusion in Zimbabwe

This study looks at how digital finance affects financial inclusion in Zimbabwe. There has been a noticeable change in Zimbabwe's financial services sector with the introduction of digital technology, especially mobile money and digital banking services. The present study investigates the ways in which digital finance initiatives have facilitated the expansion of financial services accessibility, the advancement of financial literacy, and the promotion of economic empowerment among marginalised communities in Zimbabwe. This study offers insights into the potential obstacles related to digital finance in achieving financial inclusion objectives in Zimbabwe through an extensive analysis of current research and empirical data.

IMPACT OF BEHAVIOURAL FINANCE ON RISK PERCEPTION, PSYCHOLOGICAL BEHAVIOUR, AND FINANCIAL DECISIONS OF MOROCCAN INVESTORS

The paper deals with behavioural finance as a stream of finance challenging the principles of classical finance by adding psychology to the reasons that could explain observed anomalies within financial markets and which, at the same time, are caused by real human behaviour. The aim of this paper is to explore investors' behavioural, biases and their disturbances of rationality, focusing mostly on the factors of loss aversion, cultural influences, and overconfidence, and how these elements influence investment decisions.It is based on empirical data and behavioural experiments, which help to show how such biases deviate from efficient market theories; this work also tests some methods that can mitigate these negative effects, therefore providing insights on how to integrate the teaching of behavioural finance more effectively into the decision-making process of investors and experts within finance. Precisely, this work is aimed at attempting to provide a model of psychological behaviour for Moroccan investors, based on an investigation carried out in the field among 93 regular investors in the Casablanca Stock Exchange.The main findings of the study underline how behavioural biases deviate from predictions by efficient market theories. This paper has only focused on the specific context of Moroccan investors, yet giving insight into how psychological factors impact investment decisions through new insights into life where these financial behaviours take place.There is, hence, uniqueness to the research through the theoretical and practical approaches integrated therein, with the use of empirical data and behavioural experiments that sustain the discussion on behavioural finance. Presenting the case of Moroccan investors adds a significant contribution to proposing a context-specific model, which may bring important implications for financial decision-making within the region.

Images as data – modelling data interactions in social science and humanities research

PurposeThe expanded reuse of images as research data in the social sciences and humanities necessitates the understanding of scholars’ real-life interactions with the type of data. The aim of this study is to analyse activities constituting image data interactions in social science and humanities research and to provide a model describing the data interaction process.Design/methodology/approachThe study is based on interviews with 21 scholars from various academic backgrounds utilising digital and print images collected from external sources as empirical research data. Qualitative content analyses were executed to analyse image data interactions throughout the research process in three task types: contemporary, historical and computational research.FindingsThe findings further develop the task-based information interaction model (Järvelin et al., 2015) originally created to explain the information interaction process. The enhanced model presents five main image data interaction activities: Data gathering, Forming dataset, Working with data, Synthesizing and reporting and Concluding, with various sub-activities. The findings show the variety of image data interactions in different task types.Originality/valueThe developed model contributes to understanding critical points in image data interactions and provides a model for future research analysing research data interactions. The model may also be used, for example, in designing better research services and infrastructures by identifying support needs throughout the research process.

Using outcome measures in sub-national level performance management: When and under what circumstances?

There is growing interest in using outcome-based measures in sub-national level health system performance management, particularly in high-income countries. Increasingly, population health indicators used for making international comparisons are being applied at a sub-national level. This study aims to understand whether and under what circumstances population health outcome-based measures can be used for performance measurement and management at the sub-national level health systems. We have integrated empirical population-based data with key health system expert perspectives to evaluate the appropriateness of two population health indicators – amenable mortality and ambulatory-sensitive hospitalization of young children. Our assessment focused on two key aspects: (i) the technical validity of these indicators, ensuring they accurately measure these outcomes, and (ii) the functionality and legitimacy of performance information, determining whether it meets stakeholders' program or policy needs and supports strategic decision-making. Overall, we found that the 'intermediate' outcome measure, childhood ambulatory sensitive hospitalization, was more useful for identifying district-level health system performance variation than the 'end' outcome measure, amenable mortality. Performance information based on childhood ambulatory-sensitive hospitalization is more appropriate for improving decision-making, and it is more likely to be accepted by a wide range of stakeholders involved in health system performance improvement.

Spatial distribution of corrosion products from a bridge pier

AbstractThis paper studies the spatial distribution of corrosion by-products by a bridge pier within a conductive medium. An electrochemical impedance spectroscopy (EIS) technique was used to investigate an uncoated metallic bridge pier submerged in static distilled water. An equivalent circuit model, derived from EIS results, served as the foundation for the study. Further, the role of diffusion was analysed, considering its significance in characterising the transfer of particles from the pier into the surrounding water. This exploration revealed the complex interaction between the diffusion processes of various corrosion by-products as a function of distance. In addition, by evaluating the spatial distribution of iron (II) corrosion by-products and modelling nanoparticle diffusion, the research examined the impact of diffusion and concentration on corrosion particle transmission. The findings, analysed via Nyquist and Bode plots, demonstrate significant differences between theoretical and empirical diffusion coefficients. Results indicated that under natural corrosion conditions, the primary product of the corrosion reaction, iron (II), disperses into the medium when oxidation occurs. The elevated resistivity due to the presence of iron (II) underscores the diffusion effect, leading to corrosion product precipitation and reaching saturation level. Additionally, the results demonstrated ideal values for the diffusion coefficient, which are crucial for advanced corrosion modelling. The results emphasised the need for empirical data to improve corrosion prediction models and informed maintenance strategies for submerged structures.

The relationship between the level of preschoolers’ cognitive development and the success of their adjustment to primary school

Introduction. The article studies the problem of children’s adaptation to systematic learning at primary school, which is significant in a rapidly changing world, determining both the conditions of the educational environment and the requirements for the level of preschoolers’ preparation. The purpose of this study is to evaluate the influence of the level of preschoolers’ cognitive development on their adaptation to systematic learning at primary school. Materials and Methods. The methodology of the study is based on a systematic approach that allows the authors to reveal psychophysiological characteristics of the subject, the complex of which provides the most complete picture of the child’s development. The following methods were used: theoretical analysis, synthesis; diagnostic methods, allowing to assess the level of cognitive development and academic performance of the examined children; methods of mathematical statistics for processing empirical data. Results. Through theoretical analysis and generalization of research data, it is shown that the majority of children have developed basic cognitive functions by the older preschool age. However, carrying out a comprehensive diagnosis of the preschoolers’ functional development in preparatory groups, the authors identified low levels of voluntary regulation, visual-spatial perception and visual-motor coordination in a third of the examined preschoolers. Based on the data analysis of these children’ academic performance at primary school, the authors identified influence of the level of the above mentioned cognitive functions in preschool age on academic performance at primary school. Longitudinal analysis showed a gradual increase in academic performance in this group of students by the third year at primary school. Conclusions. Based on the results obtained, the authors showed that the level of cognitive functions development, as well as social and communication skills, significantly influences the successful adaptation of first-year primary schoolchildren and subsequent academic performance at primary school. In conclusion, the authors point out that it is necessary to conduct a comprehensive assessment of cognitive development in older preschool age in order to identify individual characteristics and predict possible risks of maladjustment at school.

Who cares about academic welfare in empirical data collection? Exploring the deficit in personal and psychological care for researchers in research ethics processes

ABSTRACT Ethics applications in academic research rarely account for the personal care and wellbeing of the researchers who undertake research. This paper reflects on the practice of carrying out research where the experiences of the participant and reflections of the researcher themselves are closely aligned. Such instances encourage the revisiting of difficult, troubling, and sometimes traumatic periods of the researchers’ personal life history. This article reflects on research conducted during a doctoral thesis which uses qualitative methods to understand the experiences of thirteen working-class professional services and administrative staff. I suggest that this type of personal reflection is involuntary, it is not actively pursued yet becomes inseparable from the conduct of research. Echoing the work of Brown and Wild ([2022]. Ethical Dilemmas in Education: Considering Challenges and Risks in Practice. Open University Press) this paper calls for additional support mechanisms to be implemented for researchers who conduct this sort of involuntary reflexivity in qualitative research, making suggestions for practical solutions within UK institutions.

Impacting Elements of Metaverse Platforms’ Intentional Use in Cultural Education: Empirical Data Drawn from UTAUT, TTF, and Flow Theory

This study aims to address the need for design guidelines in developing a cultural-heritage-based metaverse educational system. Using the UTAUT, the TTF model, and Flow Theory, a theoretical framework is constructed. Through qualitative research based on the GT, three user perception factors—presence, interactivity, and narrativity—are introduced as external variables to explore the relationship between these factors and users’ willingness to adopt the cultural heritage metaverse system. The study examines this relationship from the dual perspectives of user perception and technology acceptance. A scale was designed to test the theoretical model empirically, and 298 valid responses were collected through a structured process involving GT coding, pre-testing, and formal surveys. The findings indicate that interactivity, narrativity, and presence significantly enhance the flow experience, while factors such as performance expectancy, effort expectancy, social influence, facilitating conditions, technology–task fit, and flow positively influence users’ intention to adopt the system. Among these, technology–task fit emerged as the most influential factor. This integrated approach reduces subjectivity and bias in criteria determination, enhancing the objectivity and precision of cultural heritage metaverse system assessments and making the system more responsive to user needs.

The Impact of Social Networks on Language Learning: A Comprehensive Analysis

The ever-increasing pervasiveness of social networks has radically changed how language learning is conceptualized and practically experienced. This paper explores the role that popular social networking platforms such as Facebook and Instagram, and some language-specific applications such as HelloTalk and Tandem, play in language acquisition. A comprehensive analysis has been done of the existing literature and is supplemented with empirical data gathered through surveys and interviews conducted with language learners themselves. Thus, the investigation aims at pointing out the opportunities created by social networks in respect of vocabulary and grammar acquisition and of intercultural understanding, as well as the critical challenges that come along with the observation of non-standard language forms and the tendency toward digital distractions. Conclusions are drawn that even though social networks offer deeply authentic input and social interaction contributing to the development of linguistic and cultural competence, they need to be carefully embedded within a structured language education in order to optimize the learning effect. Furthermore, the paper discusses implications for educators and recommendations for good practice in using social networks as supplementary language learning tools.

Rmacrostrat : An R package for accessing and retrieving data from the Macrostrat geological database

The geological record is a vast archive of information that provides the only empirical data about the evolution of the Earth. In recent years, concentrated efforts have been made to compile macrostratigraphic data into the online centralized database Macrostrat. Macrostrat is a global stratigraphic database containing information regarding surface and subsurface rock units and their respective ages, lithologies, geographic extents, and various other associated metadata. However, these raw data are currently directly accessible only through the Macrostrat application programming interface, which is a barrier to potential users that are less familiar with such services. This data accessibility hurdle currently prevents full capitalization of the value offered by Macrostrat, particularly its potential to improve understanding of the geological and biological evolution of the Earth. Here, we introduce rmacrostrat, an R package that interfaces with the Macrostrat database to access and retrieve a variety of geological, paleontological, and economic data directly into the R programming environment. In this article, we provide details about how the package can be installed, its implementation, and potential use cases. For the latter, we showcase how rmacrostrat can be used to visualize regional stratigraphic columns, produce regional geologic outcrop maps, and investigate temporal trends in macrostratigraphic units. We hope that this package will make geological data more readily accessible and in turn will facilitate new research utilizing Earth system data.