Statistical Techniques Research Articles

Screening levels spatial interpolation of lifetime carcinogenic risk by organochlorine pesticides across catchments of river chenab

The present study has employed an advanced GIS based statistical technique for spatial interpolation of lifetime carcinogenic risk to OCPs in water and sediments from tributaries of River Chenab. The findings revealed that among all detected isomers, DDT exhibited the highest concentrations (mean 14.41 ng/l: range 9.33 – 20.21 ng/l and mean 16.47 ng/l: range 10.55 – 21.24 ng/g) for water and sediment, respectively. Results of OCPs fingerprints revealed the presence of dicofol confirmed fresh input of OCPs isomers along with the DDTs (Dichloro-diphenyl trichloroethane) historical usages in water bodies. The evaluation of ecological risk to benthic organisms’ fish, daphnia and green algae (RQ 2 × 102) indicated that DDTs (DDD, DDE) pose potential hazardous risks (>1) to fish species across all the studied sites. Moreover, Spatial interpolation of the lifetime carcinogenic risk denoted the sites along downstream zone surpassed the permissible limit. The non-carcinogenic risk (∑HQ) ranged from 2 × 10-3 – 1.0 with the highest value for DDT, indicating DDT as a potential hazard through oral exposure (∑HQ≥1). In the case of sediments results of SQGQs (Sediment Quality Guideline Quotient) levels for DDE (Dichloro-diphenyldichloromethane) and DDD (Dichloro-diphenyl dichloroethylene) denoted a severe biological risk to ecological integrities. The findings comprehend the more inclusive monitoring of OCPs usage and distribution in the studied region to reduce risks ecological integrities and to promote good health and wellbeing’s.

Bibliographical Exploration of Public Relations and Diplomacy: Public, Organisational, and Civil Society Perspectives

Abstract: This paper aims to describe, infer and interpret the corpus of references extracted from the book “Diplomacy, Organisations and Citizens. A European Communication Perspective” (Sebastião & Spínola, 2022), a collective volume published as an output of a three-year research and educational project exploring the intersection of public relations with public, organisational and civil society diplo¬macy, from a European perspective. A mixed methods approach of quantita¬tive and statistical techniques in conjunction with social network analysis (SNA) was applied to a corpus formed out of 785 unique entries corresponding to the book references. Borrowing from bibliometric approaches, the techniques of di¬rect citation networks, co-citation networks, and topic networks were used to map perspectives used in the book. Results point to a clear linguistic preference for English, to a saliency of journal articles overlapping with the increase in re¬search accessibility through digitalisation, to a clear dominance of a few journals coming from the fields of public relations and public diplomacy, and to a clear alignment of the perspectives on public, organisational and civil society diplo¬macy at the intersection of public relations and diplomacy, albeit from a debatable European perspective. The research is relevant to any scholar or practitioner in¬terested in how public relations and the democratised forms of diplomacy intersect and influence each other. It can also serve as a roadmap to how the explo¬ration of the bibliographical universe of a research project can be used as a self-reflection and evaluation tool. Keywords: public relations, public diplomacy, organisational diplomacy, civil society diplomacy, bibliography, social network analysis.

Media Representation of Scientists in Jornal Nacional: Reaffirmation of Stereotypes During the First Year of the COVID-19 Pandemic

ABSTRACT In response to the COVID-19 pandemic, scientific authorities were routinely consulted by mainstream media outlets through interviews, statements and/or supporting images. In this framework, our goal in this study was to analyze the media representation of scientists in Brazil during the first year of this global public health crisis. To this end, we applied a research protocol and, using statistical techniques, quantitatively analyzed newscasts on the country’s main TV news program, Jornal Nacional. On this program, scientists were interviewed about “vaccination,” a topic of broad interest given that the vaccine had been described from the onset of the COVID-19 pandemic as a response to cope with the public health crisis. We discuss information about gender, age, and race, the speaking time and screen time of these social actors, the format in which scientists are inserted into news reports, and the presence/absence of science icons. The data reveal that the predominant image of the scientist transmitted to the audience – a mature white man – reinforces stereotypes that persist in the media and in the public understanding of science.

Integrating Machine Learning for Enhanced Agricultural Productivity: A Focus on Bananas and Arecanut in the Context of India’s Economic Growth

AbstractAgriculture is one of the sectors that has an important impact, taking into account the problem of sufficient food supply on a global level. The process of predicting the yield of crops is among the most challenging undertakings in the agricultural industry. Agriculture is the main source of income for most developing nations. The purpose of the study is to investigate the significant role that agriculture plays in boosting India's economic growth. Additionally, the research considers the challenges posed by a growing population and a changing environment in terms of agricultural production and food security. The research focuses on analysing the complex characteristics of the agricultural industry, with a particular emphasis on the nutritional importance of tropical fruits, notably bananas and arecanut. These fruits are well-known for their vital nutrients and their role in ensuring world food security. This study acknowledges the importance of sustainable agriculture practices and incorporates sophisticated machine learning algorithms as dynamic tools to forecast crop yields and enhance decision-making processes throughout the crop development cycle. The main aim of this study is to create strong machine learning models and statistical techniques that can accurately predict crop yield by combining a variety of environmental parameters, then assess which models outperform each other. Assist yield projections may provide governments and policymakers with valuable information to make well-informed choices about food security, import–export policies, and resource allocation. It facilitates national- and regional-level food supply planning. The validation method utilises important metrics like R square (R2), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). This present research adds to the continuing discussion on using creative methods to promote sustainable agricultural growth and ensure food security.

Exposome, oxidative stress and inflammation in multiple sclerosis: the EXPOSITION study protocol

Abstract Background The functional exposome approach examines the relationship between biological effects (internal exposome) and the sum of environmental exposures (external exposome). Multiple sclerosis (MS) is characterized by selective demyelination and acute inflammation; miRNAs and microbiota are emerging in the pathogenesis as inflammation and oxidative stress biomarkers. The EXPOSITION study protocol has been designed to study such biomarkers variation in response to the external exposome (air pollution, urbanization, lifestyles) in people with MS (pwMS). Methods EXPOSITION is a cross-sectional observational study collecting a sample of 200 pwMS residing in Lombardy, Northern Italy. Sample size is based on the prediction of differential expression of candidate miRNAs through bioinformatic analyses. All participants will be investigated for social, demographic and clinical data as well as general and specific external exposome factors. The internal exposome factors will be collected for each participant from blood and stool samples, as well as from nasal swabs. Exposome clustering and correlation statistical techniques will be applied and variation of miRNA and microbiota will be tested in association with the variation of the external exposome in urban and extra-urban environments. Results and conclusions The expected results will help pwMS and researchers understanding which biomarkers and which components of the external exposome are associated to oxidative stress and inflammation, to improve precision medicine and contrast disease progression. The relationship between urban exposome and biodiversity is an important dimension in the study of environmental impact on human health. This study may provide preliminary data to assess which nature-based solutions are suitable for enhancing the biodiversity of urban areas. Key messages • The functional exposome approach could be used to link environmental risk factors and disease biomarkers using the right methology. • EXPOSITION will quantify the variation of miRNA and microbiota in association with the variation of external exposome, in urban and extra-urban environments.

The impact of intimate partner violence on adverse birth outcomes in 20 sub-Saharan African countries: propensity score matching analysis

BackgroundIntimate partner violence (IPV) is a significant public health problem, with serious consequences on women's physical, mental, sexual, and reproductive health, as well as birth outcomes. Women who encounter IPV are more likely to experience adverse birth outcomes such as low birth weight, premature delivery, and stillbirth. Although numerous studies are exploring the association between IPV and adverse birth outcomes, they merely used classical models and could not control for potential confounders. The purpose of this study was to ascertain whether there was a causation between IPV and adverse birth outcomes in sub-Saharan Africa (SSA) using a quasi-experimental statistical technique [i.e., propensity score matching (PSM) analysis].MethodThis study used the most recent (2015–22) Demographic and Health Survey (DHS) data from 20 SSA countries. A total weighted sample of 13,727 women was included in this study. IPV (i.e., sexual, physical, emotional, and at least one form of IPV) was the exposure/treatment variable and adverse birth outcomes (preterm delivery, low birth weight, stillbirth, and macrosomia) were the outcome variables of this study. PSM was employed to estimate the impact of IPV on adverse birth outcomes.ResultsThe average treatment effects (ATE) of sexual, physical, emotional, and at least one form of IPV were 0.031, 0.046, 0.084, and 0.025, respectively. Sexual, physical, emotional, and at least one form of IPV increased adverse birth outcomes by 3.1%, 4.6%, 8.4%, and 2.5%, respectively. Findings from the average treatment effect on treated (ATT) showed that women who experienced sexual, physical, emotional, and at least one form of IPV had an increased risk of adverse birth outcomes by 3.6%, 3.7%, 3.3%, and 3.0%, respectively, among treated groups.ConclusionThis study demonstrates a causal relationship between IPV and adverse birth outcomes in SSA countries, indicating a need for programs and effective interventions to mitigate the impact of IPV during pregnancy to reduce related adverse pregnancy outcomes. Furthermore, we suggest further research that investigates the causal effect of IPV on adverse birth outcomes by incorporating additional proximal variables not observed in this study.

Quantifying Residual Soil Moisture through Empirical Orthogonal Functions Analysis to Support Legume-Based Cropping Systems

AbstractThis study investigates spatiotemporal variability of residual soil moisture during the OND (October-November-December) season in Ethiopia and its implications for crop productivity. Employing advanced statistical techniques, we analyze spatial and temporal distribution of soil moisture across Ethiopia from 1981 to 2020, focusing on selected crops including legumes: chickpea, field peas, common bean, soybean and alfalfa, to assess the potential of residual moisture to support post-rainy season cropping. Results indicate pronounced east-west moisture gradients, with eastern regions of Ethiopia exhibiting lower moisture levels (< 60 kg.m-2) compared to western regions (> 150 kg.m-2). The central highlands, which are pivotal for agricultural activities, demonstrate significant variability in moisture (standard deviations > 25 kg.m-2), with implications on agricultural sustainability. The northern and southeastern tips of the country are particularly vulnerable to prolonged drought, where climate change and frequent dry spells exacerbate moisture deficits, consequently impacting crop productivity. Despite these challenges, promising opportunities for future crop production emerge in the southeastern region, which is characterized by increasing moisture trend over time ($$\:\tau\:=0.59$$). Findings further indicate that residual moisture adequately meets and supports crop water requirements in the western, central, and southwestern Ethiopia. In these regions, residual moisture supports more than 90% of cropland water requirements of various crops during the initial and late-season growth stages, whereas water requirement coverage drops to less than 20% during the mid-season growth stage. Therefore, by utilizing residual soil moisture alongside supplemental irrigation, Ethiopian farmers can meet crop water needs for double cropping and enhance resilience to climate variability.

Exploring the use of meal delivery services in Belgium

Abstract Background Ready-to-eat meal delivery services have become commonplace in recent years and their growth accelerated during the COVID-19 pandemic. However, the potential ramifications on dietary habits and subsequent health outcomes remain largely unexplored. Given the well-established links between unhealthy dietary patterns and chronic diseases such as obesity, type II diabetes, and cardiovascular diseases, understanding the determinants and public health implications of ready-to-eat meal delivery services use is imperative. This study aims to investigate the determinants associated with the use of ready-to-eat meal delivery services and their implications for health behaviours and outcomes. Methods This cross-sectional study is conducted amongst adults residing in Flanders or Brussels, Belgium. A quantitative online survey commenced in April 2024 and is scheduled to conclude by June 2024. Structural equation models will be used to assess determinants and implications of ready-to-eat meal delivery services use. Latent class analysis will be used to identify user profiles and subsequent structural equation modelling will assess determinants of these profiles and their associated health (behaviour) outcomes. Results Preliminary results suggest meal delivery usage was higher in younger and middle-income users. Main reasons of use amongst users (n = 139) were more time for leisure activities (66%) and supermarket avoidance (64%). Main reasons for non-use amongst non-users (n = 69) were not wanting to spend money with meal delivery services (70%) and preference to cook own meals (81%). Data collection is currently ongoing. Further analysis is planned, and full results will be available by November 2024. Conclusions This study will provide a better understanding of drivers of meal delivery services use in Belgium and their potential impact on public health. It will also help inform public health strategies targeted at improving dietary habits. Key messages • Recognising that meal delivery services will become ever present in the future, insights into why people order meals for delivery and the potential health implications of such services are essential. • The use of advanced statistical techniques highlights the potential of the study to offer novel insights into determinants and ramifications of meal delivery services usage.

Communication Strategies & Radio Talk Shows

The majority of the consumers of radio stations’ programming are the youth, most of whom are students in high schools, colleges, and universities. The findings revealed that majority of the respondents agreed that radio talk show presenters have effectively analysed their respective audiences so as to communicate effectively. In view of this, the research article seeks to determine the communication strategies that are employed in presenting radio talk shows, as observed, through listening, by university students in the city of Eldoret. Uses and Gratifications theory guided this study. The study utilized a mixed method research approach that made use of both quantitative and qualitative methods of data collection and analysis. The target population of the study were students from university campuses within Eldoret, and the sample size was 300. The researcher adopted a stratified sampling and simple random sampling technique to arrive at the sample that would best fit or present the intended purpose of the research. Data was collected using a questionnaire and Focus Group Discussion. The collected data was analysed using descriptive statistical techniques that included frequencies, percentages. The study established that effective communication strategies by presenters are crucial in engaging university students. Radio talk show presenters should analyse their audience effectively and employ communication strategies that foster engagement, such as active listening and utilizing multiple modes of communication.

Some notes on the consequences of pretreatment of multivariate data

With the advent of data technologies, we have various types of data, such as structured, unstructured and semi-structured. Performing certain statistical or machine learning techniques may require careful preprocessing or pretreatment of the data to make them suitable for analysis. For example, given a data matrix X, which represents n multivariate observations or cases on p variables or features, the columns/rows of X may be pretreated before applying statistical or machine learning techniques to the data. While centering and/or scaling the variables do not alter the correlation structure nor the graphical representation of the data, centering/scaling the observations do. We investigate various row pretreatment methods more closely and show with theoretical proofs and numerical examples that centering/scaling the rows of X changes both the graphical structure of the observations in the multi-dimensional space and the correlation structure among the variables. There may be good reasons for performing row centering/scaling on the data and we are not against it, but analysts who use such row operations should be aware of the geometrical and correlation structures one has performed on the data and should also demonstrate that the process results in a new, more appropriate structure for their questions.

A scoping review of the epidemiology of systemic sclerosis and its organ manifestations: 2018-2024.

Updates from large, observational cohorts and new statistical techniques have resulted in new data on the epidemiology of systemic sclerosis (SSc). This scoping review uses data from 2018 to 2024 to describe the current understanding of the epidemiology of SSc and several of its organ- manifestations. Our review identified new estimates for the global incidence and prevalence of SSc (1.4-8.6 per 100 000 person-years and 17.6-18.9 per 100 000 individuals, respectively). Mortality rates remain high, though mortality at younger ages has decreased. interstitial lung disease and pulmonary arterial hypertension remain the most common causes of death for patients with SSc. Literature on gastrointestinal (GI) manifestations of SSc was scarce, and we identified significant heterogeneity in results. Furthermore, data on the epidemiology of racial, ethnic and sex-based disparities was lacking. New techniques for the evaluation of the epidemiology of SSc highlight the high morbidity and mortality of SSc, and a growing prevalence rate compared with prior eras. Further research is needed to address notable heterogeneity in the reporting of epidemiological data and understudied disease manifestations, including GI disease and health disparities in disease outcomes.

IMPLEMENTASI METODE SINGULAR SPECTRUM ANALYSIS (SSA) PADA PERAMALAN INDEKS LQ45

Stock market index forecasting is one of the research studies to obtain stock market index prediction using statistical and computational techniques, one of the techniques that can be used is Singular Spectrum Analysis (SSA). SSA aims to decompose a time series into a set of independent components which have nonstatic tendencies such as trends, oscillations, and noise. SSA can be applied without any prior assumptions such as stationarity, linearity, and normality which seem impossible to fulfill by the fluctuation of stock market index data. One of the stock indices listed in Indonesia Stock Exchange (IDX) is LQ45. The LQ45 index contains 45 selected stocks that have great performance in liquidity and fundamental aspects. LQ45 Index data has a nonstatic structure so it is suitable to be predicted using the SSA method. The forecasting uses the daily data of the LQ45 Index for the period of July 25, 2022 – November 14, 2022 with a window length (L) of 30. From the forecasting process, 4 groups were acquired and Mean Absolute Percentage Error (MAPE) value was 1.69% to forecast the next 20 days. These results indicate that the forecasting process using SSA for LQ45 Index of the period is accurate.

Solar powered integrated multi sensors to monitor inland lake water quality using statistical data fusion technique with Kalman filter

This study proposes a data-driven statistical model using multi sensor fusion and Kalman filtering for real-time water quality assessment in lakes. A recursive estimation technique, the Kalman Filter, is employed to handle uncertainties and enhance computational efficiency. The fusion process integrates data from sensors monitoring parameters like chlorophyll concentration, surface water elevation, temperature, and precipitation, producing Markov features to capture temporal transitions and environmental dynamics. Data synchronization and fusion are achieved through recursive KF methods, enabling real-time adaptive management in response to environmental fluctuations such as seasonal changes, precipitation (6–18%), and evaporation rates (1.2–11.9 mm/day). Over a 30-day evaluation period, the model accurately predicted chlorophyll concentrations, reaching 128 mg/m3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {mg}/\\hbox {m}^{3}$$\\end{document} in mid-level inflow regions (3.6 m water elevation) compared to 86 mg/m3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {mg}/\\hbox {m}^{3}$$\\end{document} in extreme inflow areas (5.5 m). The integration of Markov feature extraction and eigenvalue estimation enhanced prediction stability and sensitivity, with the KF maintaining computational efficiency at 7.8 ms per computation cycle. The model’s accuracy was validated by achieving a residual error of less than 0.05 with minimal noise interference. Overall, the system provides a resilient and precise framework for real-time lake water quality assessment, capable of handling multi-parameter uncertainties and dynamic environmental changes, thereby supporting informed decision-making for aquatic ecosystem management.

Artificial intelligence reveals past climate extremes by reconstructing historical records

The understanding of recent climate extremes and the characterization of climate risk require examining these extremes within a historical context. However, the existing datasets of observed extremes generally exhibit spatial gaps and inaccuracies due to inadequate spatial extrapolation. This problem arises from traditional statistical methods used to account for the lack of measurements, particularly prevalent before the mid-20th century. In this work, we use artificial intelligence to reconstruct observations of European climate extremes (warm and cold days and nights) by leveraging Earth system model data from CMIP6 through transfer learning. Our method surpasses conventional statistical techniques and diffusion models, showcasing its ability to reconstruct past extreme events and reveal spatial trends across an extensive time span (1901-2018) that is not covered by most reanalysis datasets. Providing our dataset to the climate community will improve the characterization of climate extremes, resulting in better risk management and policies.

Skills Acquisition and Engagement with the Use of a Structured Journal Club Curriculum.

Journal clubs are a common element of many residency programs. However, there is little data showing that they improve residents' ability to interpret and use the scientific literature. The authors developed a structured journal club curriculum intended to improve scientific literacy skills among residents and increase acceptability and participation in meaningful discussion of high-quality articles among both residents and facilitators. This study examined the impact of the curriculum on objective skills, subjective skills, and engagement. Psychiatry residents from three academic medical centers completed 10-12 virtual journal clubs during academic year 2022-2023. At the start and end of that year, residents completed a knowledge assessment and a self-rating of scientific literacy skills; at the end of the year, residents self-rated participation in, preparation for, and attitudes towards journal club sessions and assessment of scientific literacy skills. Twenty-seven residents completed self-reports at both time points. Performance on the objective skills assessment did not improve over the course of the year. Subjectively, residents reported increased understanding of common statistical techniques, ability to determine if techniques used in an article are appropriate, and the extent to which information learned from articles informs clinical practice. In the sample of 62 resident engagement surveys at follow-up, the journal club curriculum was well-received. Use of a structured journal club curriculum was associated with improvements in self-rated scientific literacy skills among residents and had high acceptability. More work is needed to understand the impact of teaching methodology on journal club success.

Linguistic variation in mediated diplomatic communication: a full multi-dimensional analysis of interpreted language in Chinese Regular Press Conferences

The integration of corpus linguistics within translation studies has revolutionised our understanding of mediated language. This study endeavours to advance this burgeoning field by employing a full multi-dimensional analysis to investigate linguistic variation in interpreted language within the specialised context of diplomatic discourse. Specifically, the research examines the co-occurring patterns of linguistic features in interpreted diplomatic language vis-à-vis its non-interpreted counterpart. Employing a multivariate statistical technique, this investigation conducted a factor analysis of 113 linguistic variables, yielding five distinct linguistic dimensions: (1) Involved vs. Informational Production, (2) Objective vs. Addressee-focused Narration, (3) Literate-Oral Continuum, (4) Information Elaboration, and (5) Narrative vs. Non-narrative Concerns. The resulting patterns demonstrate that interpreted diplomatic language tends to be more informative, objective, less elaborated, non-narrative, and aligns more closely with formal registers compared to its non-interpreted counterpart, although both navigate the literate-oral continuum. This study delineates the prevailing co-occurrence patterns in interpreted and non-interpreted diplomatic languages and seeks to elucidate the potential factors shaping these linguistic variations by situating these patterns within the context of diplomatic communication. In doing so, it contributes to a nuanced understanding of how specialised contexts influence mediated language use. The findings have significant implications for corpus-based interpreting studies, shedding light on the multi-dimensional nature of interpreted language and informing the development of targeted pedagogical approaches for diplomatic interpreter training.

Spiritual Intelligence and Subjective Vitality Among Young Adults and Old Ages

The presented study examined the difference between spiritual intelligence and subjective vitality among young adults and old ages. The sample for the present study consisted of 60 young adults and 60 old ages (including males and females) using descriptive sampling method. The tools used for the study are spiritual intelligence scale, subjective vitality scale and socio-demographic questionnaire. The collected data were analysed using appropriate statistical techniques and frequencies. The findings showed that there is a significant relationship between spiritual intelligence and subjective vitality and a significant difference between both variables among young adults and old ages.

Digitalisation Empowerment of Business to Digital Innovation in the Achievement of SDGs: A Survey on Creative Msme Actors in Indonesia

Objective: This study aims to test and analyse the relationship patterns of digital business-based training, empowerment of MSME actors, digital self-efficacy and digital innovation in creative MSMEs for the achievement of SDGs in MSMEs in West Java and Banten Provinces, Indonesia. Theoretical Framework: Digital literacy is needed so that a person has the ability to access information related to digital-based media. MSMEs are required to have renewable digital-based business methods in order to provide optimal and satisfying services. When MSMEs have high digital literacy through digital-based training, they could increase high self-efficacy. Empowering MSMEs based on digital business is very necessary in the digital era. Human resources in the organisation is expected to have digital competencies (skills, knowledge, attitude). Also, employees are expected to increase digital self-efficacy in order to increase digital innovation and MSME performance in the long term. Method: This study uses quantitative method by distributing questionnaires to 250 natural colour batik MSMEs in West Java and Banten Provinces. 207 questionnaires are filled in completely and then processed with Partial Least Square statistical techniques. Results and Discussion: This study finds that digital-based business training has a significant positive impact on the empowerment of MSME actors, and digital self-efficacy of MSME actors. In addition, empowerment of MSME players has significant positive impact on digital innovation and digital self-efficacy. On the other hand, digital self-efficacy of MSME actors has significant positive impact on digital innovation and is able to mediate the effect of business digital-based training on digital innovation. However, empowerment of MSME actors cannot mediate the effect of business digital-based training on digital innovation. Research Implications: This study provide guidance for the government and MSME empowerment organisations to design more targeted digital-based training. In addition, this study highlights the importance of supporting technological infrastructure and providing wider access to digital technology, especially for creative MSMEs in Indonesia. This study also enriches the literature on the role of digital training on digital empowerment and digital self-efficacy. Likewise, it provides implications for the role of digital empowerment and digital self-efficacy in encouraging digital innovation. Originality/Value: This study offers a business digitalisation empowerment model for creative MSMEs in order to increase digital innovation for creative MSMEs in achieving SDGs in Indonesia. The research model considers digital-based business training, empowerment of MSME players, and digital self-efficacy.

Interval-Valued Random Matrices

This paper introduces a novel approach that combines symbolic data analysis with matrix theory through the concept of interval-valued random matrices. This framework is designed to address the complexities of real-world data, offering enhanced statistical modeling techniques particularly suited for large and complex datasets where traditional methods may be inadequate. We develop both frequentist and Bayesian methods for the statistical inference of interval-valued random matrices, providing a comprehensive analytical framework. We conduct extensive simulations to compare the performance of these methods, demonstrating that Bayesian estimators outperform maximum likelihood estimators under the Frobenius norm loss function. The practical utility of our approach is further illustrated through an application to climatology and temperature data, highlighting the advantages of interval-valued random matrices in real-world scenarios.

Urban Road Transport Infrastructure Planning and Development Implications on Socio-Economic Growth in the City of Douala, Cameroon

Cities are the major contributors to socio- economic growth with easy access to amenities which stem through road transport infrastructure development. This study was conducted to assess urban road transport infrastructure planning and examine development implications on socio-economic growth in the City of Douala. A descriptive research design was used with primary and secondary data collected and analyzed using descriptive and inferential statistical techniques. Results revealed that, transport infrastructure in Douala have been structured through urban development planning policy which influences surface spatial expansion of socio- economic activities. The planning policies have contributed to the rapid expansion and growth through the initiations of master plan, land use plan and sector plan. Infrastructural development on road transport depicts a physical expansion and construction of road networks which influences the socio-economic growth of the study area. Stakeholders’ actions on construction, reconstruction and rehabilitation, investments and maintenance of road infrastructure have helped to curb down some transportation challenges and creates easy access to facilities within different sectors of the study area. However, road transport infrastructure which is well-developed boasts socio-economic activities and growth. The state of road transport network, its diversity and characteristic features is defined by its prevalence within different areas in Douala. Contrarily, other transport infrastructures which are not functioning appropriately due to lapses in physical planning possess enormous implications on transportation in the study area. The conclusion drawn was that the manner in which urban planning policies are implemented determines the level of development in road transport infrastructure and socio-economic growth of Douala. The study recommends an effective application of planning policies to be integrated by implicating all the actors of urban managers and transportation planners for improved growth in the study area.