Research Issues Research Articles

Using Paradata for Imputation of Missing Values in Sociological Survey Data: Results of Statistical Modeling (Case of Croatia and Slovakia)

Missing values are a common issue in quantitative social researches. One of the ways to handle missing data is by data imputation. This article outlines the challenges of traditional data imputation methods, which often introduce biases, and presents an advanced approach that features integration of paradata—auxiliary information collected during surveys—into the imputation process, using the European Social Survey (ESS) as its dataset. It is proposed that the usage of paradata could enhance predictive models used for imputation. It discusses the practical applications of data imputation, particularly through the lens of sensitive topics such as LGBT issues in socially conservative countries, where missingness could be heavily skewed due to social inacceptability of certain answers. To evaluate the effectiveness of the proposed approach towards imputation, the research employs the approach of using the 'ideal dataset', which is a subset of the original dataset with no missing vales, and then introduces artificial missing values that are not MCAR (Missing Completely at Random) to simulate the real case of missing data. Having artificial missingness allows for evaluation of the imputation procedure by comparing it with the original dataset. The study uses a novel approach towards creation of realistic missing data patterns through clustering based on response patterns. The research uses advanced statistical methods to handle missing data, and incorporates paradata from the survey process to improve the accuracy of predictive models. By comparing statistical metrics such as RMSE, MAE, and R-squared, the article evaluates the effectiveness of these methods in mimicking the original dataset's variability.

Adaptive Fuzzy Tracking Control With Global Prescribed-Time Prescribed Performance for Uncertain Strict-Feedback Nonlinear Systems.

For strict-feedback systems with mismatched uncertainties, adaptive fuzzy control techniques are developed to provide global prescribed performance with prescribed-time convergence. First, a class of prescribed-time prescribed performance functions are designed to quantify the performance constraints of the tracking error. Additionally, a novel error transformation function is provided to eliminate the initial value limitations and resolve the singularity issue in previous research. To ensure the convergence of the tracking error into a prescribed bounded region within a prescribed time and satisfactory transient performance, controllers with or without approximating structures are established. Notably, the settling time and initial condition of the prescribed performance function are completely independent of the initial tracking error and system parameters, thereby improving upon existing results. Furthermore, the disadvantage of the semi-global boundedness of tracking error induced by dynamic surface control can be eliminated through the use of a novel Lyapunov-like energy function. Finally, the effectiveness of the proposed strategies is validated through numerical simulations performed on practical examples.

Socio-economic and Bio-physical Characterization of Wadego Watershed, Oda-Bultum Woreda, West Hararghe zone, Ethiopia

Baseline characterization is important component of watershed development of rural development and natural resource management strategies in many countries. Hence, the study was designed to; assess and document major biophysical and socio-economic constraints and potentials of Wadego watershed. Firstly, Wadego watershed boundary was delineated and its map was developed based on the preliminary outlet identified with the help of GPS reading. The households for interview were selected randomly from the small householders in the watershed based on proportional to population size. Accordingly, data was collected from 120 sample respondents using various instruments such as: key informant interview using semi-structured checklist, focused group discussion, expert interview, unstructured questionnaire and field observation on bio-physical resources and different concerns of watershed management. Collected data was summarized using descriptive statistics. The result indicated, out of the total 120 sample respondents socio-economic status were; 87.5% of them were male while the rest 12.5% were female, 93% were married, 51% were un educated. The major crops grown in the watershed were Maize, Sorghum, Khat, Teff and others while, Barley and Field Pea were the least grown ones. The result of the survey has indicated 37% uses improved variety, while 64% uses local variety. According to respondents, the slop class of the watershed was 56, 29, and 15% flat, medium and steeply slops respectively. Moreover, soil fertility class was 25%, 54% and 21% low, medium and high respectively in the watershed. Generally, the survey result have characterized and documented bio-physical and socio-economic status as: average land owned half hectare, densely populated, different slope class in the watershed, land shortage, feed problems, presence of soil fertility and erosion problems, climatic problems etc. in the watershed. Accordingly, researchable issues on different prioritized problems concerning to; soil fertility management, soil and water conservation, Agroforestry practice, forage development and forestry practices in the watershed have given prioritization and therefore, adapted and generated technologies nearby research center i.e. Mechara Agricultural Research Center and any concerned research institutions and also development work oriented NGO’S has to intervened the watershed by research development work. Accordingly, intervention areas forwarded are: Creating awareness on climate variability and change and SWC techniques, introducing different biophysical soil and water harvesting technologies, demonstration and awareness creation, Participatory demonstration on different Agroforestry and plantation forestry practices, introducing multipurpose plant species like Bamboo variety in the watershed.

Participatory Assessment of Agriculture Land and Vegetation Associated Issues and Possible Interventions in Gurage Zone, Ethiopia

Land degradation and depletion of natural forests made agriculture unsustainable and threaten communities and ecosystems as a whole. The objective of this participatory assessment was to identify researchable issues related to agricultural land and vegetation cover in Gurage zone. Accordingly, seven representative districts were selected and from each district two representative kebeles were purposively selected. Focus Group Discussion with farmers, development agents and subject matter specialists and direct observation were important primary sources of information. The survey result depicts in the Gurage zone land degradation, undulating steep slope topography, small and overwhelmed land tenure, inappropriate land use system, cultural landscape mismanagement, deforestation of indigenous trees and expansion of eucalyptus tree, and decreasing enset production are the major identified problems associated with land use and vegetation cover of the study area. In the mixed farming system, enset is produced as a homestead farm and its coverage is decreasing due to disease, lack of processing machines, and shortage of organic matter. Due to the expansion of eucalyptus and land use change, the majority of native trees are disappearing or decreasing in area coverage. Therefore, enset and eucalyptus require suitable policy attention. In the mountainous areas of Gurage; limited land availability, soil degradation, shortage of appropriate farm technologies, water scarcity, lack of knowledge for alpine agriculture and inconvenient topography for mechanized farming machines issues threaten agriculture of area. Despite the fact that there are few attempts to control land degradation; the issue still needs much attention. Appropriate agricultural technologies and knowledge suited for mountain agriculture, dense populations, enset-based farming systems, and small land tenure are important priority research focus areas for future research.

Big Data Analytics and its Approaches: The Digital Transformation of the Future

This study is all about the importance- of methods in big data study, and what they might mean for future- digital growth projects. The rese-arch explores current patte-rns, issues, and smart plans. This gives valuable information for groups figuring out the- tricky mixture of data study and digital growth. The focus of the pape-r is on important methods like batch handling, stream handling, and machine- learning. It looks at their strengths, limits, and use-s. We see the-ir effects on growing and improving. We fore-cast future patterns, tackle issue-s and suggest inventive solutions for succe-ss. This research acts like a guide- for groups that want to line up their digital changes with the- best methods for continuous success.

EDCS: Efficient data collection systems by using bundling technology for effective communications

The energy of Wireless Sensor Network (WSN) in edge network is limited. In order to save energy, data bundling is proposed to reduce the energy consumed in setting up connection links by joining several packets into a single unit. However, although current data bundling schemes can reduce energy consumption, they will increase the delay of data routing, so it makes the multi-hop end to end delay a lot in WSN. Also, there is no appropriate solution to tackle the delayed growing problem caused by data bundling. And the energy consumption of the Acknowledgment (ACK) transmission is not comprehensively considered either. Regarding to those issues of previous researches, in this paper, a Data and ACK Bundling Scheme (DAB) is proposed to aggregate several data or ACK packets into a single unit to reduce the energy consumption as well as the end to end delay of the WSN. In the proposed DAB scheme, not only multiple packets but also ACK bundling into a single unit can reduce energy consumption. More importantly, the parameters of bundling technology are optimized from a global perspective in DAB scheme, so that WSN can not only improve the network lifetime, but also reduce the end to end delay which is impossible in previous studies. The theoretical analysis and experimental results illustrate that, while ensuring the reliability of data collection, our DAB scheme improves the energy efficiency by 29.03 % and reduces the delay by 22.29 % when compared with our traditional data collection scheme.

Progress and Discussion of Perioperative Targeted Therapy in Patients with EGFR-mutated Resectable Non-small Cell Lung Cancer

Lung cancer is still the leading cause of cancer death worldwide. Non-small cell lung cancer (NSCLC) is the main pathological type of lung cancer, accounting for about 80%. Approximately 30% of all patients with NSCLC have resectable early and middle stage disease at the time of diagnosis. Recently, the epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) have made a major breakthrough in the adjuvant targeted therapy of EGFR-mutated resectable NSCLC, and are recommended by the guidelines for clinical use. In this review, we summarize the clinical research progress of perioperative adjuvant targeted therapy for EGFR-mutated resectable NSCLC, and discuss the key issues in the clinical researches. .

Recent advances in the study of epitopes, allergens and immunologic cross-reactivity of edible mango

Mango (Mangifera indica L.) is a tropical fruit that is widely consumed as both fresh fruits and processed products around the world. The high incidence of mango allergy, on the other hand, has sparked widespread concern. Therefore, a summary and analysis of the current status and issues in mango allergen research can guide in-depth study on the mechanism of mango allergy and reveal effective desensitization methods. We described the incidence of fruit allergy, as well as the mechanism and clinical symptoms of mango allergy, in this review. We also looked into the structural properties of mango allergens, the effect of processing methods on mango allergens, prediction methods for mango allergen epitopes, and the current state of research on mango cross-reactive allergens and preventive measures. Finally, the research directions and ideas for the future are proposed and discussed.

Generative Adversarial Network for Wireless Communication: Principle, Application, and Trends

Generative adversarial network (GAN) has attracted wide attention because of its remarkable ability to learn highdimensional and complex data distributions based on game theory and machine learning. In this paper, we focus on the role that GANs play in wireless communications, which has been well researched and developed in recent years. With the gradual increase in communication rates and the complicacy of communication scenarios, some urgent issues have emerged, such as complex channel generation, high-dimensional channel estimation, and insufficient real-world signal acquisition. The powerful nonlinear fitting property of GAN can break through the bottleneck of traditional communication techniques, whose applications can be categorized into data generation, performance optimization, and data classification according to different purposes and network structures. For each category, a universal network structure summarizing the characteristics and targets is investigated, and the performance of GANs in communication is also analyzed with simulation examples. Moreover, the open research issues are discussed at the end to provide some directions for future study.

Some methodological issues in language research: Dealing with transcribed interpreted courtroom data

Transcription is a crucial tool in language research, particularly in discourse analysis, as it provides a distillation of real-time interactions. In the 21st century, researchers are increasingly interested in studying authentic data samples, such as audio-recorded court hearings, to turn evanescent speech into readable and analysable formats. However, transcription involves a complex process of theoretical or methodological issues about language, making it a rich source of examinable data. Researchers need to develop adequate methodologies to make such data available for their research endeavours. This exploratory research presents transcription as a methodology for researchers interested in language and ethnographic methods, addressing critical considerations such as the data to be transcribed, the transcriber responsible, and how to represent it. The paper explores innovations in transcription and presents the benefits and challenges of transcription as a methodology, particularly in language research.

Exploiting Blockchain to Make AI Trustworthy: A Software Development Lifecycle View

Artificial intelligence (AI) is a very powerful technology and can be a potential disrupter and essential enabler. As AI expands into almost every aspect of our lives, people raise serious concerns about AI misbehaving and misuse. To address this concern, international organizations have put forward ethics guidelines for constructing trustworthy AI (TAI), including privacy, transparency, fairness, robustness, accountability, and so on. However, because of the black-box characteristics and complex models of AI systems, it is challenging to translate these guiding principles and aspirations into AI systems. Blockchain, an important decentralized technology, can provide the capabilities of transparency, traceability, immutability, and secure sharing and hence can be used to make AI trustworthy. In this paper, we survey studies on blockchain-based TAI (BTAI) from a software development lifecycle view. We classify the lifecycle of BTAI into four stages: Planning, data collection, model development, and system deployment/use. Particularly, we investigate and summarize the trustworthy issues that blockchain can achieve in the latter three stages, including (1) data transparency, privacy, and accountability; (2) model transparency, privacy, robustness, and fairness; and (3) robustness, privacy, transparency, and fairness of system deployment/use. Finally, we present essential open research issues and future work on developing BTAI systems.

The Loneliness Room: Creative Practice as Critical Pedagogy

ABSTRACT Loneliness has become one of the most pressing research issues of the contemporary age, with a vast literature emerging from health, education, the social sciences, and cultural studies to better understand this “crisis” that has been created by the unequal and alienating forces of neo-liberal capitalism. Very rarely has creative practice been drawn upon to both access the way people feel, experience and understand loneliness, and to translate it into artistic modes/forms that better represent and embody the “pedagogy” of its everydayness. The loneliness room project, a four-year empirical study of loneliness, addressed this gap, inviting participants to submit creative responses and artworks that best captured what loneliness meant to them. In this article, I will explore the creative work through the specific lens of gendered loneliness: reading these submissions through the discourses of patriarchy and heterosexuality, and the “domestic” and “private” spaces that the work captured. Such work not only reveals through a participatory lens the way people envisage loneliness but demonstrate how the ground between ethnography and creative practice can be fruitfully brought together. The work submitted, “teaches us” about the role the creative imagination has in understanding gendered loneliness and the “rooms” that it is housed.

Digital game-based learning in mathematics education at primary school level: A systematic literature review

In recent decades, digital game-based learning (DGBL) has become a trend among scholars and practitioners in many parts of the world. Therefore, there were some systematic literature reviews in the past few years conducted to identify the trends of DGBL research with diverse subjects and educational levels, however, there is a lack of review that focuses only on mathematics education at primary school levels. This study seeks to provide a comprehensive overview of DGBL research within the context of elementary mathematics learning. Using PRISMA (2020) flow diagram, this study identified 45 articles related to the topic during the period of 2006-2023. Papers were coded and analyzed by years, regions, elementary mathematics topics, research issues, outcomes, research approaches, research design, data collection tools, game genres, and gameplay mode. The results from this systematic review identified the trends in DGBL research related to elementary mathematics learning, highlighted gaps in existing literature, provided insights, and oriented future studies on the topic. The findings of the research reveal a pronounced interest among scholars in the content topics of elementary mathematics, highlighting the research issues that attract attention, the methodologies employed in studies, and the types of games and gaming modes frequently utilized for elementary school children. The article discusses the trends of DGBL within elementary mathematics education, offering in-depth analyses and identifying research gaps that could guide future directions.

Ataxic person prediction using feature optimized based on machine learning model

Ataxic gait monitoring and assessment of neurological disorders belong to important areas that are supported by digital signal processing methods and artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) techniques. This paper uses spatio-temporal data from Kinect sensor to optimize machine learning model to distinguish between ataxic and normal gait. Existing ML-based methodologies fails to establish feature correlation between different gait parameters; thus, exhibit very poor performance. Further, when data is imbalanced in nature the existing ML-based methodologies induces higher false positive. In addressing the research issues this paper introduces an extreme gradient boost (XGBoost)-based classifier and enhanced feature optimization (EFO) by modifying the standard cross validation (SCV) mechanism. Experiment outcome shows the proposed ataxic person identification model achieves very good result in comparison with existing ML-based and DL-based ataxic person identification methodologies.

Economics of Education and Digital Learning for Human Capital Development in Pakistan: A Critical Review

The objective of the research is to better understand how digital learning, specifically how it affects employability, skills, and economic growth, may enhance the development of human capital within the framework of Pakistan's educational system. This research critically examines the economics of education in Pakistan by addressing significant research issues. It examines the financial barriers to education, focusing on issues including financial inequality and procedural hurdles. The research examines how initiatives for curriculum reform and teacher training help educational institutions support human capital development. It also examines the effects of online education, demonstrating its ability to close knowledge gaps and improve digital literacy. Despite obstacles like the digital divide, this research emphasizes the significance of strategic measures to guarantee equal access to high-quality education and build a trained, flexible workforce essential for Pakistan's socio-economic success. However, implement evidence-based policies to enhance the management of education. Research in the future should concentrate on inventive financing approaches and reliable data.

Survey on data replication in cloud systems

In a distributed environment, replication is the most investigated phenomenon. Replication is a way of storing numerous copies of the same data at different locations. Whenever data is needed, it will be fetched from the nearest accessible copy, avoiding delays and improving system performance. To manage the replica placement strategy in the Cloud, three key challenges must be addressed. The challenges in determining the best time to make replicas were generated, the kind of files to replicate, as well as the best location to store the replicas. This survey conducts a review of 65 articles published on data replication in the cloud. The literature review examines a series of research publications and offers a detailed analysis. The analysis begins by presenting several replication strategies in the reviewing articles. Analysis of each contributor’s performance measures is conducted. Moreover, this survey offers a comprehensive examination of data auditing systems. This work also determines the analytical evaluation of replication handling in the cloud. Furthermore, the evaluation tools used in the papers are examined. Furthermore, the survey describes a lot of research issues & limitations that might help researchers support better future work on pattern mining for data replication in the cloud.

Developing biobased and degradable polyazomethines from hemin

The development of biobased polymers is emerging as a critical issue for researchers in this era, especially for conjugated or semiconducting polymers, due to the lack of natural conjugated building blocks. In addition, it is of urgent importance to develop degradable polymers to diminish the increasing amount of plastic waste. Therefore, we aim to utilize hemin-based materials as a building block to develop semi-conjugated, degradable, and biobased polymers in this study. Accordingly, hemin derived from bovine is modified to remove the ferric ion and functionalize with amine or aldehyde groups. The aldehyde-functionalized protoporphyrin is copolymerized with p-phenylenediamine, 2,7-fluorenediamine, and amine-functionalized protoporphyrin to form a series of polyazomethines of PDA, FDA, and PPIX. The semi-conjugated polyazomethines exhibit varied electrostatic potential distributions and frontier energy levels based on their compositions; in addition, all the polyazomethines present high biobased contents spanning the range of 60–80%. Notably, PPIX can be rapidly depolymerized by acid hydrolysis with 0.01 mol L−1 of HCl within several hours, guaranteed by the evolutions of optical absorption and size-exclusion chromatography, which show that the polyazomethines degrade to monomers to short-chain oligomers. In the aspect of the optoelectronic device application, the semi-conjugated characteristics of the hemin-based polyazomethines give rise to their high performance as a photoresponsive polymer electret in photonic transistor memory. By fine-tuning the molecular structure and energy levels, the developed polyazomethine of FDA achieves decent data discernibility and long-termed cyclic operation as a component of memory electronics, thereby rendering green electronics exceptionally economical and environmentally feasible.

Tragedy of the Commons in Crowd Work-Based Research

Academic scholars have leveraged crowd work platforms such as MTurk to conduct research and collect data, but the data quality crisis in crowd work has been an alarming phenomenon recently. Though prior studies have discussed data quality and validity issues in crowd work via surveys and experiments, they kind of neglected to explore the scholars’ and particularly the IRB's ethical concerns and the related policies in various ethical guidelines for crowd work-based research in these respects. In this study, we interviewed 17 scholars from six disciplines and 15 IRB directors and analysts in the U.S. and analyzed 28 research guidance documents to fill these gaps. We identified common themes among our interviewees and documents but also discovered distinctive and even opposing views regarding the approval rate, rejection, and internal/external research validity. Based on the findings, we discussed a potential Tragedy of the Commons regarding data quality deterioration and the disciplinary differences regarding validity in crowd work-based research. We further explored the origin of the data quality and validity issues in crowd work-based research. We advocated the IRB's ethical concerns in crowd work-based research be heard and respected further and be reflected in the ethical guidance for crowd work-based research. Finally, we proposed our research implications, limits, and future work.

Ethical issues in research with second victims: A scoping review.

Second victim is the name given to the healthcare personnel-most often a nursing professional-involved with the error that led to the adverse event to a patient and who, as a result, have experienced negative psychological effects. Research with second victims has increased over the years, however concerns exist with regards to the ethical risks imposed upon these individuals. To explore the extent to which research with second victims of adverse events in healthcare settings adhere to ethical requirements. A scoping review was conducted following Arksey and O'Malley's methodological framework and using the following databases: PUBMED, Web of Science, and SCOPUS. Original research of any study design focused on second victims and published in English, Spanish, or Portuguese in 2014-2023 were included. A critical narrative approach was used to discuss the findings. The review followed ethical guidelines emphasizing accurate authorship attribution and truthful data reporting. Fifteen studies using qualitative (n = 2), quantitative (n = 10), and mixed-method (n = 3) designs were included. Over half were not assessed by a research ethics committee, with questionable reasons given by the authors. One-third did not refer to having used an informed consent. In two studies, participants were recruited by their workplace superiors, which could potentially right to autonomy and voluntary participation. Over half of the included studies with second victims did not comply with fundamental ethical aspects, with risk to inflict respect for individual autonomy, confidentiality, and of not causing any harm to participants. Healthcare personnel involved in adverse events are most often nursing professionals; therefore, any breach of ethics in research with this population is likely to directly affect their rights as research participants. We provide recommendations to promote better research practices with second victims towards safeguarding their rights as research participants.

CREDO: a friendly Customizable, REproducible, DOcker file generator for bioinformatics applications

BackgroundThe analysis of large and complex biological datasets in bioinformatics poses a significant challenge to achieving reproducible research outcomes due to inconsistencies and the lack of standardization in the analysis process. These issues can lead to discrepancies in results, undermining the credibility and impact of bioinformatics research and creating mistrust in the scientific process. To address these challenges, open science practices such as sharing data, code, and methods have been encouraged.ResultsCREDO, a Customizable, REproducible, DOcker file generator for bioinformatics applications, has been developed as a tool to moderate reproducibility issues by building and distributing docker containers with embedded bioinformatics tools. CREDO simplifies the process of generating Docker images, facilitating reproducibility and efficient research in bioinformatics. The crucial step in generating a Docker image is creating the Dockerfile, which requires incorporating heterogeneous packages and environments such as Bioconductor and Conda. CREDO stores all required package information and dependencies in a Github-compatible format to enhance Docker image reproducibility, allowing easy image creation from scratch. The user-friendly GUI and CREDO's ability to generate modular Docker images make it an ideal tool for life scientists to efficiently create Docker images. Overall, CREDO is a valuable tool for addressing reproducibility issues in bioinformatics research and promoting open science practices.