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Land value varies at a micro level depending on the location’s economic, geographical and political determinants. The purpose of this study is to present a comprehensive assessment of the determinants affecting land value in the Indian city of Thiruvananthapuram in the state of Kerala.

The global influence of the identified 20 explanatory variables on land value is measured using the traditional hedonic price modeling approach. The localized spatial variations of the influencing parameters are examined using the non-parametric regression method, geographically weighted regression. This study used advertised land value prices collected from Web sources and screened through field surveys.

Global regression results indicate that access to transportation facilities, commercial establishments, crime sources, wetland classification and disaster history has the strongest influence on land value in the study area. Local regression results demonstrate that the factors influencing land value are not stationary in the study area. Most variables have a different influence in Kazhakootam and the residential areas than in the central business district region.

This study confirms findings from previous studies and provides additional evidence in the spatial dynamics of land value creation. It is to be noted that advanced modeling approaches used in the research have not received much attention in Indian property valuation studies. The outcomes of this study have important implications for the property value fixation of urban Kerala. The regional variation of land value within an urban agglomeration shows the need for a localized method for land value calculation.

The aim of this paper is to provide a narrative review of previous research on tourism demand modelling and forecasting and potential future developments.

A narrative approach is taken in this review of the current body of knowledge.

Significant methodological advancements in tourism demand modelling and forecasting over the past two decades are identified.

The distinct characteristics of the various methods applied in the field are summarised and a research agenda for future investigations is proposed.

本文旨在对先前关于旅游需求建模和预测的研究进行叙述性回顾并对未来潜在发展进行展望。

本文采用叙述性回顾方法对当前知识体系进行了评论。

本文确认了过去二十年旅游需求建模和预测方法论方面的重要进展。

本文总结了该领域应用的各种方法的独特特征, 并对未来研究提出了建议。

El objetivo de este documento es ofrecer una revisión narrativa de la investigación previa sobre modelización y previsión de la demanda turística y los posibles desarrollos futuros.

En esta revisión del marco actual de conocimientos sobre modelización y previsión de la demanda turística y los posibles desarrollos futuros,se adopta un enfoque narrativo.

Se identifican avances metodológicos significativos en la modelización y previsión de la demanda turística en las dos últimas décadas.

Se resumen las características propias de los diversos métodos aplicados en este campo y se propone una agenda de investigación para futuros trabajos.

This study aims to propose a refined dynamic network slacks-based measure (DNSBM) to evaluate the efficiency of China's regional green innovation system which consists of basic research, applied research and commercialization stages and explore the influencing factors of the stage efficiency.

A two-step procedure is employed. The first step proposes an improved DNSBM model with flexible settings of stages' input or output efficiency and uses second order cone programming (SOCP) to solve the non-linear problem. In the second step, least absolute shrinkage and selection operator (LASSO) and Tobit models are used to explore the influencing factors of the stage efficiency. Global Dynamic Malmquist Productivity Index (GDMPI) and Dagum Gini coefficient decomposition method are introduced for further discussion of the productivity change and regional differences.

On average, Chinese provincial green innovation efficiency should be improved by 24.11% to become efficient. The commercialization stage outperforms the stages of basic research and applied research. Comparisons between the proposed model and input-oriented, output-oriented and non-oriented DNSBM models show that the proposed model is more advanced because it allows some stages to have output-oriented model characteristics while the other stages have input-oriented model characteristics. The examination of the influencing factors reveals that the three stages of the green innovation system have quite diverse influencing factors. Further discussion reveals that Chinese green innovation productivity has increased by 39.85%, which is driven mainly by technology progress, and the increasing tendency of regional differences between northern and southern China should be paid attention to.

This study proposes an improved dynamic three-stage slacks-based measure (SBM) model that allows calculating output efficiency in some stages and input efficiency in the other stages with the application of SOCP approach. In order to capture productivity change, this study develops a GDMPI based on the DNSBM model. In practice, the efficiency of regional green innovation in China and the factors that influence each stage are examined.

The purpose of this paper is to propose a new grey prediction model, GOFHGM (1,1), which combines generalised fractal derivative and particle swarm optimisation algorithms. The aim is to address the limitations of traditional grey prediction models in order selection and improve prediction accuracy.

The paper introduces the concept of generalised fractal derivative and applies it to the order optimisation of grey prediction models. The particle swarm optimisation algorithm is also adopted to find the optimal combination of orders. Three cases are empirically studied to compare the performance of GOFHGM(1,1) with traditional grey prediction models.

The study finds that the GOFHGM(1,1) model outperforms traditional grey prediction models in terms of prediction accuracy. Evaluation indexes such as mean squared error (MSE) and mean absolute error (MAE) are used to evaluate the model.

The research study may have limitations in terms of the scope and generalisability of the findings. Further research is needed to explore the applicability of GOFHGM(1,1) in different fields and to improve the model’s performance.

The study contributes to the field by introducing a new grey prediction model that combines generalised fractal derivative and particle swarm optimisation algorithms. This integration enhances the accuracy and reliability of grey predictions and strengthens their applicability in various predictive applications.

The current study uses an advanced machine learning method and aims to investigate whether auditors perceive financial statements that are principles-based as less risky. More specifically, this study aims to explore the association between principles-based accounting standards and audit pricing and between principles-based accounting standards and the likelihood of receiving a going concern opinion.

The study uses an advanced machine-learning method to understand the role of principles-based accounting standards in predicting audit fees and going concern opinion. The study also uses multiple regression models defining audit fees and the probability of receiving going concern opinion. The analyses are complemented by additional tests such as economic significance, firm fixed effects, propensity score matching, entropy balancing, change analysis, yearly regression results and controlling for managerial risk-taking incentives and governance variables.

The paper provides empirical evidence that auditors charge less audit fees to clients whose financial statements are more principles-based. The finding suggests that auditors perceive financial statements that are principles-based less risky. The study also provides evidence that the probability of receiving a going-concern opinion reduces as firms rely more on principles-based standards. The finding further suggests that auditors discount the financial numbers supplied by the managers using rules-based standards. The study also reveals that the degree of reliance by a US firm on principles-based accounting standards has a negative impact on accounting conservatism, the risk of financial statement misstatement, accruals and the difficulty in predicting future earnings. This suggests potential mechanisms through which principles-based accounting standards influence auditors’ risk assessments.

The authors recognize the limitation of this study regarding the sample period. Prior studies compare rules vs principles-based standards by focusing on the differences between US generally accepted accounting principles (GAAP) and international financial reporting standards (IFRS) or pre- and post-IFRS adoption, which raises questions about differences in cross-country settings and institutional environment and other confounding factors such as transition costs. This study addresses these issues by comparing rules vs principles-based standards within the US GAAP setting. However, this limits the sample period to the year 2006 because the measure of the relative extent to which a US firm is reliant upon principles-based standards is available until 2006.

The study has major public policy suggestions as it responds to the call by Jay Clayton and Mary Jo White, the former Chairs of the US Securities and Exchange Commission (SEC), to pursue high-quality, globally accepted accounting standards to ensure that investors continue to receive clear and reliable financial information globally. The study also recognizes the notable public policy implications, particularly in light of the current Chair of the International Accounting Standards Board (IASB) Andreas Barckow’s recent public statement, which emphasizes the importance of principles-based standards and their ability to address sustainability concerns, including emerging risks such as climate change.

The study has major public policy suggestions because it demonstrates the value of principles-based standards. The study responds to the call by Jay Clayton and Mary Jo White, the former Chairs of the US SEC, to pursue high-quality, globally accepted accounting standards to ensure that investors continue to receive clear and reliable financial information as business transactions and investor needs continue to evolve globally. The study also recognizes the notable public policy implications, particularly in light of the current Chair of the IASB Andreas Barckow’s recent public statement, which emphasizes the importance of principles-based standards and their ability to address sustainability concerns, including emerging risks like climate change. The study fills the gap in the literature that auditors perceive principles-based financial statements as less risky and further expands the literature by providing empirical evidence that the likelihood of receiving a going concern opinion is increasing in the degree of rules-based standards.

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

In recent times, stakeholders have called on corporate organizations especially those charged with governance to embrace full disclosure on non-financial issues, especially sustainability reporting. Based on this premise, this study aims to examine the influence of corporate board and assurance on sustainability reporting practices (SRP) of selected 80 firms from 8 countries in sub-Saharan Africa.

To measure the corporate board, the authors use both board variables and audit committee variables. Also, the authors adapted the sustainability score model as used by previous authors in the field of sustainability disclosure to measure SRPs. The analysis was done using both ordered logistic regression and probit regression models.

The results show that the combination of board corporate and assurance has a positive and significant impact on the sustainability reporting practice of selected firms in sub-Saharan Africa.

The study places emphasis on the need for strong collaboration between the corporate board and external assurance in evaluating and enhancing the quality of sustainability disclosure.

The study bridged the gap in the literature in the area of corporate board, assurance and SRP of corporate firms which has received little attention within sub-Saharan Africa.

This paper presents an experimental investigation in establishing the relationship between FDM process parameters and tensile strength of polycarbonate (PC) samples using the I-Optimal design.

I-optimal design methodology is used to plan the experiments by means of Minitab-17.1 software. Samples are manufactured using Stratsys FDM 400mc and tested as per ISO standards. Additionally, an artificial neural network model was developed and compared to the regression model in order to select an appropriate model for optimisation. Finally, the genetic algorithm (GA) solver is executed for improvement of tensile strength of FDM built PC components.

This study demonstrates that the selected process parameters (raster angle, raster to raster air gap, build orientation about Y axis and the number of contours) had significant effect on tensile strength with raster angle being the most influential factor. Increasing the build orientation about Y axis produced specimens with compact structures that resulted in improved fracture resistance.

The fitted regression model has a p-value less than 0.05 which suggests that the model terms significantly represent the tensile strength of PC samples. Further, from the normal probability plot it was found that the residuals follow a straight line, thus the developed model provides adequate predictions. Furthermore, from the validation runs, a close agreement between the predicted and actual values was seen along the reference line which further supports satisfactory model predictions.

This study successfully investigated the effects of the selected process parameters - raster angle, raster to raster air gap, build orientation about Y axis and the number of contours - on tensile strength of PC samples utilising the I-optimal design and ANOVA. In addition, for prediction of the part strength, regression and ANN models were developed. The selected ANN model was optimised using the GA-solver for determination of optimal parameter settings.

The proposed ANN-GA approach is more appropriate to establish the non-linear relationship between the selected process parameters and tensile strength. Further, the proposed ANN-GA methodology can assist in manufacture of various industrial products with Nylon, polyethylene terephthalate glycol (PETG) and PET as new 3DP materials.

The research objective starts from the argument that small-scale multinational corporations’ (SMNCs’) managerial behavior toward auditing decisions is influenced by their personal value, especially when the auditing process is not mandatory. This study aims to examine how national culture-religiosity affects that decision. The authors further examine how foreign-owned MNCs might behave differently from local MNCs, although the host country’s cultural-religiosity value might influence that decision.

This study obtains the data from three sources: Hofstede Framework, Pew Research Center and World Bank Enterprise Survey in cross-sectional mode. The final sample consists of 8,590 SMNCs from 45 countries as the observations. This study uses robust regression analysis to test the effects of culture, religiosity and controlling shareholders on the audited financial statements decision.

The regression results support the hypothesis, whereas cultural-religiosity values are associated with the audited financial report. The findings confirm stakeholder theory and institutional theory.

This study fills a gap in the literature by providing empirical evidence on the cultural and religiosity effects on the accounting decision of SMNCs. The results can be used as the foundation for future research related to MNCs’ managerial behavior toward accounting policies, especially with the psychosocial factors.