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In this paper, the heterogeneity of the linkages among financial development, productivity and growth across income groups is emphasized.

An empirical analysis is conducted with an illustrative sample of 130 economies over the period 1991–2019 and classified into four subsamples: Organisation for Economic Co-operation and Development (OECD), developing, least developed and net food importing developing countries. Forecast error variance decompositions and panel vector auto-regressive estimations are computed, with insightful findings.

Higher levels of output stimulate the economic development in the agricultural sector, mainly via the productivity channel and, in the most developed economies, also through access to credit. Differently, in developing and least developed economies, the role of access to credit is marginal. The findings have practical implications for stakeholders involved in the planning of long-run investments. In less developed economies, priorities should be given to investments in technology and innovation, whereas financial markets are more suited to boost the development of the agricultural sector of developed economies.

The authors conclude on the credit–output–productivity nexus and contribute to the literature in (at least) three ways. First, they assess how credit access, agricultural output and agricultural productivity are jointly determined. Second, they use a novel approach, which departs from most of the case studies based on single-country data. Third, they conclude on potential causality links to conclude on policy implications.

Thermo-magnetic convective flow analysis under the impact of thermal radiation for heat and entropy generation phenomena is an active research field for understanding the efficiency of thermodynamic systems in various engineering sectors. This study aims to examine the characteristics of convective heat transport and entropy generation within an inverted T-shaped porous enclosure saturated with a hybrid nanofluid under the influence of thermal radiation and magnetic field.

The mathematical model incorporates the Darcy-Forchheimer-Brinkmann model and considers thermal radiation in the energy balance equation. The complete mathematical model has been numerically simulated through the penalty finite element approach at varying values of flow parameters, such as Rayleigh number (Ra), Hartmann number (Ha), Darcy number (Da), radiation parameter (Rd) and porosity value (e). Furthermore, the graphical results for energy variation have been monitored through the energy-flux vector, whereas the entropy generation along with its individual components, namely, entropy generation due to heat transfer, fluid friction and magnetic field, are also presented. Furthermore, the results of the Bejan number for each component are also discussed in detail. Additionally, the concept of ecological coefficient of performance (ECOP) has also been included to analyse the thermal efficiency of the model.

The graphical analysis of results indicates that higher values of Ra, Da, e and Rd enhance the convective heat transport and entropy generation phenomena more rapidly. However, increasing Ha values have a detrimental effect due to the increasing impact of magnetic forces. Furthermore, the ECOP result suggests that the rising value of Da, e and Rd at smaller Ra show a maximum thermal efficiency of the mathematical model, which further declines as the Ra increases. Conversely, the thermal efficiency of the model improves with increasing Ha value, showing an opposite trend in ECOP.

Such complex porous enclosures have practical applications in engineering and science, including areas like solar power collectors, heat exchangers and electronic equipment. Furthermore, the present study of entropy generation would play a vital role in optimizing system performance, improving energy efficiency and promoting sustainable engineering practices during the natural convection process.

To the best of the authors’ knowledge, this study is the first ever attempted detailed investigation of heat transfer and entropy generation phenomena flow parameter ranges in an inverted T-shaped porous enclosure under a uniform magnetic field and thermal radiation.

The purpose of this study was to investigate how pension funds use charts in popular reports. Popular reports communicate a fund’s financial health to non-technical audiences, and often contain charts, tables, and other graphical elements. Do these graphics meet audiences’ information needs and align with chart best practices?

This study focused on the 60 retirement funds receiving a 2021 popular report award from the Government Finance Officers Association. The author analyzed each graphic’s topic and design.

Most funds presented key topics (such as funding rate and portfolio return), but they generally lacked helpful benchmarks or peer comparisons. A total of 30% of reports had one or more broken charts, where their visual elements did not match the underlying data. A total of 70% of the reports contained at least one badly designed chart. These design flaws included non-zero (truncated) axes, hidden non-zero axes and misleading 3D perspectives.

To the best of the authors’ knowledge, this paper is the first to examine chart quality in pension fund popular reports.

No study has investigated the effects of different parameters on publication bias in meta-analyses using a machine learning approach. Therefore, this study aims to evaluate the impact of various factors on publication bias in meta-analyses.

An electronic questionnaire was created according to some factors extracted from the Cochrane Handbook and AMSTAR-2 tool to identify factors affecting publication bias. Twelve experts were consulted to determine their opinion on the importance of each factor. Each component was evaluated based on its content validity ratio (CVR). In total, 616 meta-analyses comprising 1893 outcomes from PubMed that assessed the presence of publication bias in their reported outcomes were randomly selected to extract their data. The multilayer perceptron (MLP) technique was used in IBM SPSS Modeler 18.0 to construct a prediction model. 70, 15 and 15% of the data were used for the model's training, testing and validation partitions.

There was a publication bias in 968 (51.14%) outcomes. The established model had an accuracy rate of 86.1%, and all pre-selected nine variables were included in the model. The results showed that the number of databases searched was the most important predictive variable (0.26), followed by the number of searches in the grey literature (0.24), search in Medline (0.17) and advanced search with numerous operators (0.13).

The results of this study can help clinical researchers minimize publication bias in their studies, leading to improved evidence-based medicine.

To the best of the author’s knowledge, this is the first study to model publication bias using machine learning.

The modern day has seen an increase in the prevalence of the improvement of high-performance thermal systems for the enhancement of heat transmission. Numerous studies and research projects have been carried out to acquire an understanding of heat transport performance for their functional application to heat conveyance augmentation. The idea of this study is to inspect the entropy production in Darcy-Forchheimer Ree-Eyring nanofluid containing bioconvection flow toward a stretching surface is the topic of discussion in this paper. It is also important to take into account the influence of gravitational forces, double stratification, heat source–sink and thermal radiation. In light of the second rule of thermodynamics, a model of the generation of total entropy is presented.

Incorporating boundary layer assumptions allows one to derive the governing system of partial differential equations. The dimensional flow model is transformed into a non-dimensional representation by applying the appropriate transformations. To deal with dimensionless flow expressions, the built-in shooting method and the BVP4c code in the Matlab software are used. Graphical analysis is performed on the data to investigate the variation in velocity, temperature, concentration, motile microorganisms, Bejan number and entropy production concerning the involved parameters.

The authors have analytically assessed the impact of Darcy Forchheimer's flow of nanofluid due to a spinning disc with slip conditions and microorganisms. The modeled equations are reset into the non-dimensional form of ordinary differential equations. Which are further solved through the BVP4c approach. The results are presented in the form of tables and figures for velocity, mass, energy and motile microbe profiles. The key conclusions are: The rate of skin friction incessantly reduces with the variation of the Weissenberg number, porosity parameter and Forchheimer number. The rising values of the Prandtl number reduce the energy transmission rate while accelerating the mass transfer rate. Similarly, the effect of Nb (Brownian motion) enhances the energy and mass transfer rates. The rate of augments with the flourishing values of bioconvection Lewis and Peclet number. The factor of concentration of microorganisms is reported to have a diminishing effect on the profile. The velocity, energy and entropy generation enhance with the rising values of the Weissenberg number.

According to the findings of the study, a slip flow of Ree-Eyring nanofluid was observed in the presence of entropy production and heat sources/sinks. There are features when the implementations of Darcy–Forchheimer come into play. In addition to that, double stratification with chemical reaction characteristics is presented as a new feature. The flow was caused by the stretching sheet. It has been brought to people's attention that although there are some investigations accessible on the flow of Ree-Eyring nanofluid with double stratification, they are not presented. This research draws attention to a previously unexplored topic and demonstrates a successful attempt to construct a model with distinctive characteristics.

The purpose of this paper is to study haemodynamic flow characteristics and entropy analysis in a bifurcated artery system subjected to stenosis, magnetohydrodynamic (MHD) flow and aneurysm conditions. The findings of this study offer significant insights into the intricate interplay encompassing electro-osmosis, MHD flow, microorganisms, Joule heating and the ternary hybrid nanofluid.

The governing equations are first non-dimensionalised, and subsequently, a coordinate transformation is used to regularise the irregular boundaries. The discretisation of the governing equations is accomplished by using the Crank–Nicolson scheme. Furthermore, the tri-diagonal matrix algorithm is applied to solve the resulting matrix arising from the discretisation.

The investigation reveals that the velocity profile experiences enhancement with an increase in the Debye–Hückel parameter, whereas the magnetic field parameter exhibits the opposite effect, reducing the velocity profile. A comparative study demonstrates the velocity distribution in Au-CuO hybrid nanofluid and Au-CuO-GO ternary hybrid nanofluid. The results indicate a notable enhancement in velocity for the ternary hybrid nanofluid compared to the hybrid nanofluids. Moreover, an increase in the Brinkmann number results in an augmentation in entropy generation.

This study investigates the flow characteristics and entropy analysis in a bifurcated artery system subjected to stenosis, MHD flow and aneurysm conditions. The governing equations are non-dimensionalised, and a coordinate transformation is applied to regularise the irregular boundaries. The Crank–Nicolson scheme is used to model blood flow in the presence of a ternary hybrid nanofluid (Au-CuO-GO/blood) within the arterial domain. The findings shed light on the complex interactions involving stenosis, MHD flow, aneurysms, Joule heating and the ternary hybrid nanofluid. The results indicate a decrease in the wall shear stress (WSS) profile with increasing stenosis size. The MHD effects are observed to influence the velocity distribution, as the velocity profile exhibits a declining nature with an increase in the Hartmann number. In addition, entropy generation increases with an enhancement in the Brinkmann number. This research contributes to understanding fluid dynamics and heat transfer mechanisms in bifurcated arteries, providing valuable insights for diagnosing and treating cardiovascular diseases.

The purpose of this study, thermal radiation and viscous dissipation impacts on double diffusive convection on peristaltic transport of Williamson nanofluid due to induced magnetic field in a tapered channel is examined. The study of propulsion system is on the rise in aerospace research. In spacecraft technology, the propulsion system uses high-temperature heat transmission governed through thermal radiation process. This study will help in assessment of chyme movement in the gastrointestinal tract and also in regulating the intensity of magnetic field of the blood flow during surgery.

The brief mathematical modelling, along with induced magnetic field, of Williamson nanofluid is given. The governing equations are reduced to dimensionless form by using appropriate transformations. Numerical technique is manipulated to solve the highly nonlinear differential equations. The roll of different variables is graphically analyzed in terms of concentration, temperature, volume fraction of nanoparticles, axial-induced magnetic field, magnetic force function, stream functions, pressure rise and pressure gradient.

The key finding from the analysis above can be summed up as follows: the temperature profile decreases and concentration profile increases due to the rising impact of thermal radiation. Brownian motion parameter has a reducing influence on nanoparticle concentration due to massive transfer of nanoparticles from a hot zone to a cool region, which causes a decrease in concentration profile· The pressure rise enhances due to rising values of thermophoresis and thermal Grashof number in retrograde pumping, free pumping and copumping region.

To the best of the authors’ knowledge, a study that integrates double-diffusion convection with thermal radiation, viscous dissipation and induced magnetic field on peristaltic flow of Williamson nanofluid with a channel that is asymmetric has not been carried out so far.

The purpose of this study is to explore existing literature on Building Information Modelling (BIM) in Malaysia and examine the perception of practitioners about the potential of BIM applications in reducing construction waste and enhancing productivity.

First, using 244 bibliographic data extracted from the Scopus database, the paper used scientometric analysis and VOSviewer mapping technique to assess the most impactful publication literature on BIM in Malaysia to identify the existing research gaps. Second, using a structured questionnaire, a total of 100 questionnaires were distributed to practising practitioners who incorporate BIM in the delivery of their projects. Descriptive analysis using cross-tabulation in SPSS software, radar chart, relative importance index and Pearson’s correlation were used to analyze the data.

The research gaps are in the fields of construction projects, buildings, energy efficiency, lifecycle and housing. The findings of the survey indicate that quantity take-off, clash detection, site utilization planning, digital fabrication as well as 4D stimulation were the main BIM applications used among the practitioners in Johor and Selangor.

By assessing the state-of-the-art of BIM and BIM applications in this region, the practical implications of this study provide useful insights to construction stakeholders, funding organizations, policymakers, research institutions, professionals, journal editors, reviewers and researchers to understand the overall trend of BIM in Malaysia and its usage.

This paper is the first to use science mapping using scientometrics to reveal the current BIM research in “Malaysia only”. Relying on the identified gaps, the study further examined the usage of BIM applications in Malaysian construction projects.

This study aims to present the current state of published literature concerning halal tourism and hospitality studies.

This research discovered 314 Scopus documents from 2003 until 2023 regarding existing studies related to this field. The present study then used a bibliometric analysis of this data set. The present research analysed the documents' bibliographical data based on year, source title, country of origin, institution, authors, keywords and citations. Microsoft Excel, bibliometrix, Harzing's Publish or Perish and VOSviewer software applications were used to conduct a frequency analysis, evaluate the impact and map bibliometric networks regarding halal hospitality. This study has presented the evolution of publications in the research field by year, the top players in source titles, countries, institutions and authors. This paper also evaluated the intellectual structure of existing research concerning halal tourism and hospitality and presented the most influential documents in this field. There were 112.21 citations per year, 7.21 per paper and 3.14 authors per paper.

The findings revealed that the study trend concerning halal tourism and hospitality had attracted the attention of academics and developed into a significant field in 2016. Malaysia and Indonesia have been the major contributors to halal hospitality research, with the highest proportion of authors.

In general, this study also has limitations similar to other studies. The research data were derived solely from the Scopus database, which has the advantage of being the most comprehensive database indexing all scientific works, even though it does not easily cover all available sources.

The present research has differed from previous research in that it examined literature published in Scopus concerning research on halal hospitality from 2003 to 2022. It analysed bibliographic data by year, source title, country, institution, author, keywords and citations.

This study aims to predict overnight stays in Italy at tourist accommodation facilities through a nonlinear, single factor, stochastic model called CIR#. The contribution of this study is twofold: in terms of forecast accuracy and in terms of parsimony (both from the perspective of the data and the complexity of the modeling), especially when a regular pattern in the time series is disrupted. This study shows that the CIR# not only performs better than the considered baseline models but also has a much lower error than other additional models or approaches reported in the literature.

Typically, tourism demand tends to follow regular trends, such as low and high seasons on a quarterly/monthly level and weekends and holidays on a daily level. The data set consists of nights spent in Italy at tourist accommodation establishments as collected on a monthly basis by Eurostat before and during the COVID-19 pandemic breaking regular patterns.

Traditional tourism demand forecasting models may face challenges when massive amounts of search intensity indices are adopted as tourism demand indicators. In addition, given the importance of accurate forecasts, many studies have proposed novel hybrid models or used various combinations of methods. Thus, although there are clear benefits in adopting more complex approaches, the risk is that of dealing with unwieldy models. To demonstrate how this approach can be fruitfully extended to tourism, the accuracy of the CIR# is tested by using standard metrics such as root mean squared errors, mean absolute errors, mean absolute percentage error or average relative mean squared error.

The CIR# model is notably simpler than other models found in literature and does not rely on black box techniques such as those used in neural network (NN) or data science-based models. The carried analysis suggests that the CIR# model outperforms other reference predictions in terms of statistical significance of the error.

The proposed model stands out for being a viable option to the Holt–Winters (HW) model, particularly when dealing with irregular data.

The proposed model has demonstrated superiority even when compared to other models in the literature, and it can be especially useful for tourism stakeholders when making decisions in the presence of disruptions in data patterns.

The novelty lies in the fact that the proposed model is a valid alternative to the HW, especially when the data are not regular. In addition, compared to many existing models in the literature, the CIR# model is notably simpler and more transparent, avoiding the “black box” nature of NN and data science-based models.

一般来说, 旅游需求往往遵循规律的趋势, 例如季度/月的淡季和旺季, 以及日常的周末和假期。该数据集包括欧盟统计局在打破常规模式的2019冠状病毒病大流行之前和期间每月收集的在意大利旅游住宿设施度过的夜晚。

本研究旨在通过一个名为cir#的非线性单因素随机模型来预测意大利游客住宿设施的过夜住宿情况。这项研究的贡献是双重的:在预测准确性方面和在简洁方面(从数据和建模复杂性的角度来看), 特别是当时间序列中的规则模式被打乱时。我们表明, cir#不仅比考虑的基线模型表现更好, 而且比文献中报告的其他模型或方法具有更低的误差。

当大量搜索强度指标被作为旅游需求指标时, 传统的旅游需求预测模型将面临挑战。此外, 鉴于准确预测的重要性, 许多研究提出了新的混合模型或使用各种方法的组合。因此, 尽管采用更复杂的方法有明显的好处, 但风险在于处理难使用的模型。为了证明这种方法能有效地扩展到旅游业, 使用RMSE、MAE、MAPE或AvgReIMSE等标准指标来测试cir#的准确性。

cir#模型明显比文献中发现的其他模型简单, 并且不依赖于黑盒技术, 例如在神经网络或基于数据科学的模型中使用的技术。所进行的分析表明, cir#模型在误差的统计显著性方面优于其他参考预测。

这个模型作为Holt-Winters模型的一个拟议模型, 特别是在处理不规则数据时。

即使与文献中的其他模型相比, 所提出的模型也显示出优越性, 并且在数据模式中断时对旅游利益相关者做出决策特别有用。

创新之处在于所提出的模型是Holt-Winters模型的有效替代方案, 特别是当数据不规律时。此外, 与文献中的许多现有模型相比, cir#模型明显更简单、更透明, 避免了神经网络和基于数据科学的模型的“黑箱”性质。

Normalmente, la demanda turística tiende a seguir tendencias regulares, como temporadas altas y bajas a nivel trimestral/mensual y fines de semana y festivos a nivel diario. El conjunto de datos consiste en las pernoctaciones en Italia en establecimientos de alojamiento turístico recogidas mensualmente por Eurostat antes y durante la pandemia de COVID-19, rompiendo los patrones regulares.

El presente estudio pretende predecir las pernoctaciones en Italia en establecimientos de alojamiento turístico mediante un modelo estocástico no lineal de un solo factor denominado CIR#. La contribución de este estudio es doble: en términos de precisión de la predicción y en términos de parsimonia (tanto desde la perspectiva de los datos como de la complejidad de la modelización), especialmente cuando un patrón regular en la serie temporal se ve interrumpido. Demostramos que el CIR# no sólo aplica mejor que los modelos de referencia considerados, sino que también tiene un error mucho menor que otros modelos o enfoques adicionales de los que se informa en la literatura.

Los modelos tradicionales de previsión de la demanda turística pueden enfrentarse a desafíos cuando se adoptan cantidades masivas de índices de intensidad de búsqueda como indicadores de la demanda turística. Además, dada la importancia de unas previsiones precisas, muchos estudios han propuesto modelos híbridos novedosos o han utilizado diversas combinaciones de métodos. Así pues, aunque la adopción de enfoques más complejos presenta ventajas evidentes, el riesgo es el de enfrentarse a modelos poco manejables. Para demostrar cómo este enfoque puede extenderse de forma fructífera al turismo, se comprueba la precisión del CIR# utilizando métricas estándar como RMSE, MAE, MAPE o AvgReIMSE.

El modelo CIR# es notablemente más sencillo que otros modelos encontrados en la literatura y no se basa en técnicas de caja negra como las utilizadas en los modelos basados en redes neuronales o en la ciencia de datos. El análisis realizado sugiere que el modelo CIR# supera a otras predicciones de referencia en términos de significación estadística del error.

El modelo propuesto destaca por ser una opción viable al modelo Holt-Winters, sobre todo cuando se trata de datos irregulares.

El modelo propuesto ha demostrado su superioridad incluso cuando se compara con otros modelos de la bibliografía, y puede ser especialmente útil para los agentes del sector turístico a la hora de tomar decisiones cuando se producen alteraciones en los patrones de datos.

La novedad radica en que el modelo propuesto es una alternativa válida al Holt-Winters especialmente cuando los datos no son regulares. Además, en comparación con muchos modelos existentes en la literatura, el modelo CIR# es notablemente más sencillo y transparente, evitando la naturaleza de “caja negra” de los modelos basados en redes neuronales y en ciencia de datos.