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This study aims to present a new rapid enhancement digital elevation model (DEM) framework using Google Earth Engine (GEE), machine learning, weighted interpolation and spatial interpolation techniques with ground control points (GCPs), where high-resolution DEMs are crucial spatial data that find extensive use in many analyses and applications.

First, rapid-DEM imports Shuttle Radar Topography Mission (SRTM) data and Sentinel-2 multispectral imagery from a user-defined time and area of interest into GEE. Second, SRTM with the feature attributes from Sentinel-2 multispectral imagery is generated and used as input data in support vector machine classification algorithm. Third, the inverse probability weighted interpolation (IPWI) approach uses 12 fixed GCPs as additional input data to assign the probability to each pixel of the image and generate corrected SRTM elevations. Fourth, gridding the enhanced DEM consists of regular points (E, N and H), and the contour interval is 5 m. Finally, densification of enhanced DEM data with GCPs is obtained using global positioning system technique through spatial interpolations such as Kriging, inverse distance weighted, modified Shepard’s method and triangulation with linear interpolation techniques.

The results were compared to a 1-m vertically accurate reference DEM (RD) obtained by image matching with Worldview-1 stereo satellite images. The results of this study demonstrated that the root mean square error (RMSE) of the original SRTM DEM was 5.95 m. On the other hand, the RMSE of the estimated elevations by the IPWI approach has been improved to 2.01 m, and the generated DEM by Kriging technique was 1.85 m, with a reduction of 68.91%.

A comparison with the RD demonstrates significant SRTM improvements. The suggested method clearly reduces the elevation error of the original SRTM DEM.

The purpose of this study is to examine households’ behavior towards dirty cooking energy utilisation in an environment where relatively higher accessibility to clean energy is noted. Although the low utilisation rate of clean energy can partly be attributed to utility gains anticipated in dirty energy mixes (DEMs) arising out of accessibility constraints, affordances and enablers, it is still unclear on the extend at which each of these contributes towards DEMs manifestation among the seemingly well-to-do households with higher levels of clean energy mixes (CEM) access. This study, therefore, hinges on scrutinising on this lower utilisation patterns despite a seemingly higher accessibility of CEMs, specifically liquified petroleum gases (LPG).

The study is based on a household’s survey that was carried out in 2018, reaching a sample of 393 households using questionnaires in four wards of the Kigamboni district in Tanzania. Subsequent analyses were descriptive as well as inferential based on binary logistic regression analysis where utilisation of DEMs was predicted for both the high and low social economic status (SES) households by incorporating accessibility constraints, affordances and enablers.

The results show, first, if one assumes energy stacking is not an issue, as households become more constrained towards CEMs utilisation, they shift towards DEMs suggesting that the overall effect is a substitution, and second, the complementarity effect ultimately outweighs the substitution effect as households do not shift from DEMs to CEMs rather stack multiple energy. DEMs flourish in this case study area because those with high income are among those in the lowest SES, and some of those with the highest SES are from among the lowest income category, and all of them end up with more DEMs because shifting towards CEMs require income to complement SES.

Policy-wise, removing hurdles in accessing CEMs such as LPG subsidy programme, gas stove provision to the poor, and enhanced LPG awareness will most likely benefits only those who do not stack energy in cooking while strategies targeting those at the lowest SES such as higher education attainment, empower women as a family decision maker, encourage co-occupancy to enlarge the household size and contain urban growth within certain perimeter will have a significant impact only if they raise both incomes and SES.

Despite of the dominance of DEMs for cooking such as charcoal and firewood in Tanzania, CEMs such as LPG, have emerged as complements or alternatives in the household energy basket. The utilisation of such CEMs is, however, still very low despite the accessibility, cost, environmental and health advantages they offer. Accessibility is not the only factor fuelling CEMs; a complementarity must exist between SES and income for the positive transition towards CEMs to be realised.

We examined the effect of management practices on overt and subtle forms of discrimination in remote working contexts. Management practices (i.e. diversity and equality management systems) may influence employees’ perception of the diversity climate and affect the occurrence of discrimination.

To empirically investigate these associations, we administrated an online questionnaire with self-report measures via a 3-wave longitudinal research design.

Data analysis of our sample of N = 153 remotely working employees show that when managers invest in equality and diversity practices, employees perceive their workplace as more inclusive (i.e. diversity climate). In turn, this reduces the occurrence of subtle discriminations. Conversely, this relationship was not significant for overt forms of discrimination.

This result indicates that creating a diversity climate is especially important when combatting subtle forms of discrimination in remote work contexts. We discuss the theoretical and practical implications of our findings in light of managerial studies on discrimination at work and psychological literature on virtual environment and social networking.

This paper develops a neural network surrogate model based on a discrete lattice approach to investigate the influence of complex microstructures on the emergent behavior of biological networks.

The adaptability of network-forming organisms, such as, slime molds, relies on fluid-to-solid state transitions and dynamic behaviors at the level of the discrete microstructure, which continuum modeling methods struggle to capture effectively. To address this challenge, we present an optimized approach that combines lattice spring modeling with machine learning to capture dynamic behavior and develop nonlinear constitutive relationships.

This integrated approach allows us to predict the dynamic response of biological materials with heterogeneous microstructures, overcoming the limitations of conventional trial-and-error lattice design. The study investigates the microstructural behavior of biological materials using a neural network-based surrogate model. The results indicate that our surrogate model is effective in capturing the behavior of discrete lattice microstructures in biological materials.

The combination of numerical simulations and machine learning endows simulations of the slime mold Physarum polycephalum with a more accurate description of its emergent behavior and offers a pathway for the development of more effective lattice structures across a wide range of applications.

The novelty of this research lies in integrating lattice spring modeling and machine learning to explore the dynamic behavior of biological materials. This combined approach surpasses conventional methods, providing a more holistic and accurate representation of emergent behaviors in organisms.

This paper aims to examine various indicators related to corruption and determine their impact on financial globalization in emerging countries. It will consider other factors that may impact financial globalization and focus on how corruption within political, executive and public sector institutions can affect this process.

This paper uses a generalized method of moments (GMM) for a data sample of emerging countries covering 2000–2020. Corruption measurements are derived from the varieties of democracy data sets and Transparency International. It also includes data on foreign direct investment, portfolio flows, foreign exchange and international debt as separate indicators of financial globalization. These measures provide more detailed information on the types of financial transactions occurring across countries.

The results reveal that foreign investors may be less likely to enter certain sectors of the economy due to concerns about unethical practices and difficulties navigating the regulatory landscape in countries with high levels of corruption. This can lead to underdevelopment in sectors that are attractive to foreign investment and a reliance on a narrow range of sectors.

This paper offers valuable insights by integrating corruption and financial globalization indicators, using the GMM for robust analysis. It highlights how corruption influences foreign investment decisions, potentially leading to sectoral underdevelopment and overreliance in emerging countries.

The present study aims to evaluate the effect of climate change on the flood hazard potential in the Kelantan River Basin using current and future scenarios.

The intensity-duration-frequency (IDF) was used to estimate the current 50- and 100-year return period 24-h design rainfall, and the climate change factor (CCF) was used to compute the future design rainfall. The CCF was calculated from the rainfall projections of two global climate models, CGCM1 and CCSM3, with different pre-processing steps applied to each. The IDF data were used in the rainfall-runoff-inundation model to simulate current and future flood inundation scenarios.

The estimated CCF values demonstrate a contrast, whereby each station had a CCF value greater than one for CGCM1, while some stations had a CCF value of less than one for CCSM3. Therefore, CGCM1 projected an aggravation and CCSM3 a reduction of flood hazard for future scenarios. The study reveals that topography plays an essential role in calculating the CCF.

To the best of the author’s knowledge, this is the first study to examine flood projections in the Kelantan River Basin. It is, therefore, hoped that these results could benefit local managers and authorities by enabling them to make informed decisions regarding flood risk mitigation in a climate change scenario.

Cross-cultural cognitive paradoxes have frequently broken the existing boundaries of knowledge and stimulated demands for knowledge creation (KC), and such paradoxes have triggered and will continue to trigger novel risks in the context of international business (IB). Given the nascency of relevant issues, this study aims to develop a more comprehensive understanding of KC across cultures by proposing a Yin-Yang dialectical systems theory of KC as micro-foundation to more systematically frame the risk/paradox-resolving mechanism elicited by cultural collisions.

This paper is conceptual in nature. The authors first critically review the literature to lay a broad theoretical foundation. Integrating the philosophy- and praxis-based views, the authors reposition knowledge as a Yin-Yang dialectical system of knowing, with yin representing the tacit while yang represents the explicit. Next, the authors justify the underling logic of realising KC through a contradiction-resolving process. On this basis, the authors draw upon the Yijing’s Later Heaven Sequence (LHS) as the source domain of a heuristic metaphor to reconceptualise KC as a dynamic capability in the IB context.

Using the LHS paradigm to metaphorically map the intricate patterns of interaction and interconnectivity among the involved individuals, organisations and all related stakeholders, this research identifies and theorises the overall dynamic capability of KC in the IB context, which comprises five sets of processes: contradiction, conflict, communication, compromise and conversion.

This research highlights that KC is simultaneously activated and constrained by human actions as well as by the socially constructed context in which it emerges, which helps individuals, organisations and policy makers more clearly frame the novel risks induced by cross-cultural cognitive conflicts in the IB context.

The authors synthesise Yin-Yang dialectics with the approach of collective phronesis, proposing a novel, praxis-oriented Yin-Yang dialectical systems theory of KC. It provides a deeper understanding of the epistemological paradox inherent in all knowledge, thus enabling KC to be rationalised by a sounder logical reasoning. By fusing the macro and micro perspectives on KC, the authors also enrich existing theory and future theory building in the domain of knowledge management.

This paper aims to clarify the relationship between foreign direct investment (FDI), democracy and carbon intensity. This study examines the influence of types of democracy on the relationship between inward FDI and carbon intensity. For this purpose, it uses five varieties of democracy, including a composite democracy indicator as moderating variables.

This study applies the fixed-effects panel quantile regression approach that considers unobserved heterogeneity and distributional heterogeneity using panel data from 160 countries during 1990–2020. By taking into account sudden changes in the volume of inward FDI, an event study is conducted across various sub-samples of democracy to check the robustness of the results.

The results show that FDI has a significantly negative impact on carbon intensity of the host country in the upper quantiles. In general, different types of democracy have a significant positive impact on carbon intensity across different quantiles. After considering the other factors, including industry intensity, trade openness, green technology, fossil fuel dependency and International Environmental Agreements, there is evidence that all types of democracy moderate the relationship between FDI and carbon intensity, thereby supporting the halo effect hypothesis. In addition, the interaction effects have a significant negative impact on carbon intensity of low- and high-carbon-intensive countries.

This paper offers several contributions to the literature on the effect of FDI and democracy on carbon intensity. This study overcomes the limitations related to the conceptualization and measurement of democracy found in the literature. While prior research has predominately concentrated on how democracy promotes the selection of FDI host-country locations, this study seeks to answer the question of whether democracy type has any effect on inward FDI, thus contributing to improving carbon intensity. Furthermore, this paper analyses the interaction effect on carbon intensity in different countries with different carbon intensity levels separately.

Present study aimed to integrate Geographic Information Systems (GIS) and Building Information Modeling (BIM) in conjunction with multicriteria decision-making (MCDM) to enhance infrastructure investment planning.

This analysis combines GIS databases with BIM simulations for a novel highway project. Around 150 potential alternatives were simulated, narrowed to 25 more effective routes and 3 options underwent in-depth analysis using PROMETHEE method for decision-making, based on environmental, cost and safety criteria, allowing for comprehensive cross-perspective comparisons.

A comprehensive framework proposed was validated through a case study. Demonstrating its adaptability with customizable parameters. It aids decision-making, cost estimation, environmental impact analysis and outcome prediction. Considering these critical factors, this study holds the potential to advance new techniques for assessment and planning railways, power lines, gas and water.

The study acknowledges limitations in GIS data quality, particularly in underdeveloped areas or regions with limited technology access. It also overlooks other pertinent variables, like social, economic, political and cultural issues. Thus, conclusions from these simulations may not entirely represent reality or diverse potential scenarios.

The proposed method automates decision-making, reducing subjectivity, aids in selecting effective alternatives and considers environmental criteria to mitigate negative impacts. Additionally, it minimizes costs and risks while demonstrating adaptability for assessing diverse infrastructures.

By integrating GIS and BIM data to support a MCDM workflow, this study proposes to fill the existing research gap in decision-making prioritization and mitigate subjective biases.

This paper applies an original construct of “subjective risk intelligence (SRI)” to the small business context. By leveraging on its multidimensionality, it aims to shed light on the existing ambiguities in the analysis of the relationship between the entrepreneurial attitude towards risk evaluation and firms’ financial stability.

The empirical investigation refers to the Italian context, where an ad hoc survey has been administered to a sample of small businesses. Based on both a linear and a semiparametric regression, results show a significant relationship between SRI and firm’s financial structure, and that such relationship is basically nonlinear.

Evidence shows that entrepreneurs with a high level of risk intelligence run highly leveraged firms. Moreover, in the light of the non-linearity of such relationship, higher levels of risk intelligence are associated with a greater capacity of the entrepreneur to govern the financial balance of the enterprise only up to a certain threshold. Over this threshold, risk intelligence generates overconfidence leading the entrepreneur to a reckless behaviour in taking financial risks.

From a theoretical point of view, the paper contributes to the literature by shedding lights on the complexity of the relationship between risk intelligence and small businesses. From a policy point of view, findings suggest that, to train new entrepreneurs, the educational system aims should focus on the development of two specific “soft skills”: the ability to manage emotions and the ability to glimpse opportunities even in uncertain situations.