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Data protection requirements heavily increased due to the rising awareness of data security, legal requirements and technological developments. Today, NoSQL databases are increasingly used in security-critical domains. Current survey works on databases and data security only consider authorization and access control in a very general way and do not regard most of today’s sophisticated requirements. Accordingly, the purpose of this paper is to discuss authorization and access control for relational and NoSQL database models in detail with respect to requirements and current state of the art.

This paper follows a systematic literature review approach to study authorization and access control for different database models. Starting with a research on survey works on authorization and access control in databases, the study continues with the identification and definition of advanced authorization and access control requirements, which are generally applicable to any database model. This paper then discusses and compares current database models based on these requirements.

As no survey works consider requirements for authorization and access control in different database models so far, the authors define their requirements. Furthermore, the authors discuss the current state of the art for the relational, key-value, column-oriented, document-based and graph database models in comparison to the defined requirements.

This paper focuses on authorization and access control for various database models, not concrete products. This paper identifies today’s sophisticated – yet general – requirements from the literature and compares them with research results and access control features of current products for the relational and NoSQL database models.

The purpose of this study is to examine the relative importance of climate normals (average long-term temperature and precipitation) in explaining net farm revenue per hectare (NRh) for supplementary irrigated and rainfed cocoa farms in Nigeria.

NRh was estimated for 280 cocoa farmers sampled across seven Nigerian states. It was regressed on climate, household socio-economic characteristics and other control variables by using a Ricardian analytical framework. Marginal calculations were used to isolate the effects of climate change (CC) on cocoa farm revenues under supplementary irrigated and rainfed conditions. Future impacts of CC were simulated using Six CORDEX regional climate model (RCM) ensemble between 2036-2065 and 2071-2100.

Results indicate high sensitivity of NRh to Nigerian climate normals depending on whether farms use supplementary irrigation. Average annual temperature increases and precipitation decreases are associated with NRh losses for rainfed farms and gains for supplementary irrigated cocoa farms. Projections of future CC impacts suggest a wide range of NRh outcomes on supplementary irrigated and rainfed farm revenues, demonstrating the importance of irrigation as an effective adaptation strategy in Nigeria.

This paper uses novel data sets for simulating future CC impacts on land values in Nigeria. CORDEX data constitute the most comprehensive RCMs projections available for Africa.

The study aims to identify the status quo of artificial intelligence in entrepreneurship education with a view to identifying potential research gaps, especially in the adoption of certain intelligent technologies and pedagogical designs applied in this domain.

A scoping review was conducted using six inclusive and exclusive criteria agreed upon by the author team. The collected studies, which focused on the adoption of AI in entrepreneurship education, were analysed by the team with regards to various aspects including the definition of intelligent technology, research question, educational purpose, research method, sample size, research quality and publication. The results of this analysis were presented in tables and figures.

Educators introduced big data and algorithms of machine learning in entrepreneurship education. Big data analytics use multimodal data to improve the effectiveness of entrepreneurship education and spot entrepreneurial opportunities. Entrepreneurial analytics analysis entrepreneurial projects with low costs and high effectiveness. Machine learning releases educators’ burdens and improves the accuracy of the assessment. However, AI in entrepreneurship education needs more sophisticated pedagogical designs in diagnosis, prediction, intervention, prevention and recommendation, combined with specific entrepreneurial learning content and entrepreneurial procedure, obeying entrepreneurial pedagogy.

This study holds significant implications as it can shift the focus of entrepreneurs and educators towards the educational potential of artificial intelligence, prompting them to consider the ways in which it can be used effectively. By providing valuable insights, the study can stimulate further research and exploration, potentially opening up new avenues for the application of artificial intelligence in entrepreneurship education.

Climate analogues have been extensively used in ecological studies to assess the shift of ecoregions due to climate change and the associated impacts on species survival and displacement, but they have hardly been applied to urban areas and their climate shift. This paper aims to use climate analogues to characterize the climate shift of cities and to explore its implications as well as potential applications of this approach.

The authors propose a methodology to match the current climate of cities with the future climate of other locations and to characterize cities’ climate shift velocity. Employing a sample of 90 European cities, the authors demonstrate the applicability of this method and characterize their climate shift from 1951 to 2100.

Results show that cities’ climate shift follows rather strictly north-to-south transects over the European continent and that the average southward velocity is expected to double throughout the twenty-first century. These rapid shifts will have direct implications for urban infrastructure, risk management and public health services.

These findings appear to be potentially useful for raising awareness of stakeholders and urban dwellers about the pace, magnitude and dynamics of climate change, supporting identification of the future climate impacts and vulnerabilities and implementation of readily available adaptation options, and strengthening cities’ cooperation within climate-related networks.

Effective rail surface defects detection method is the basic guarantee to manufacture high-quality rail. However, the existed visual inspection methods have disadvantages such as poor ability to locate the rail surface region and high sensitivity to uneven reflection. This study aims to propose a bionic rail surface defect detection method to obtain the high detection accuracy of rail surface defects under uneven reflection environments.

Through this bionic rail surface defect detection algorithm, the positioning and correction of the rail surface region can be computed from maximum run-length smearing (MRLS) and background difference. A saliency image can be generated to simulate the human visual system through some features including local grayscale, local contrast and edge corner effect. Finally, the meanshift algorithm and adaptive threshold are developed to cluster and segment the saliency image.

On the constructed rail defect data set, the bionic rail surface defect detection algorithm shows good recognition ability on the surface defects of the rail. Pixel- and defect-level index in the experimental results demonstrate that the detection algorithm is better than three advanced rail defect detection algorithms and five saliency models.

The bionic rail surface defect detection algorithm in the production process is proposed. Particularly, a method based on MRLS is introduced to extract the rail surface region and a multifeature saliency fusion model is presented to identify rail surface defects.

The paper aims to investigate the possible changes in mean temperature in the Mongolian Plateau associated with the 1.5 and 2°C global warming targets and how snow changes in the Mongolian Plateau when the mean global warming is well below 2°C or limited to 1.5°C.

In total, 30 model simulations of consecutive temperature and precipitation days from Coupled Model Inter-comparison Project Phase 5 (CMIP5) are assessed in comparison with the 111 meteorological monitoring stations from 1961–2005. Multi-model ensemble and model relative error were used to evaluate the performance of CMIP5 models. Slope and the Mann–Kendall test were used to analyze the magnitude of the trends and evaluate the significance of trends of snow depth (SD) from 1981 to 2014 in the Mongolian Plateau.

Some models perform well, even better than the majority (80%) of the models over the Mongolian Plateau, particularly HadGEM2-CC, CMCC-CM, BNU-ESM and GFDL-ESM2M, which simulate best in consecutive dry days (CDD), consecutive wet days (CWD), cold spell duration indicator (CSDI) and warm spell duration indicator (WSDI), respectively. Emphasis zones of WSDI on SD were deeply analysed in the 1.5 and 2 °C global warming period above pre-industrial conditions, because it alone has a significant negative relation with SD among the four indices. It is warmer than before in the Mongolian Plateau, particularly in the southern part of the Mongolian Plateau, indicating less SD.

Providing climate extremes and SD data sets with different spatial-temporal scales over the Mongolian Plateau. Zoning SD potential risk areas and proposing adaptations to promote regional sustainable development.

This paper aims to examine the effects of socio-emotional wealth importance (SEWi) in family firms and family firm-specific HR practices, namely professionalization and bifurcation bias, on their entrepreneurial orientation (EO).

The paper surveyed 133 small and medium-sized family firms in the USA. The respondents were recruited through Prolific Academic.

When SEWi is low, a family firm becomes more similar to a non-family firm, thereby enjoying the benefits associated with EO. When SEWi is high, a family firm leverages the unique resources and capabilities specific to family firms. Moderate SEWi levels are associated with lower EO levels. Additionally, the results support the argument that professionalization (involving non-family managers, formalization and decentralization) fosters EO, while bifurcation bias hinders its development.

Unlike previous studies, this paper posits a non-linear, U-shaped relationship between SEWi and EO. It contributes to the field by empirically investigating the effects of professionalization and bifurcation bias on EO in family firms.

Effective customer journey design (ECJD) is considered a key variable in customer experience management and an essential source of brand meaning and pro-brand behavior. Although previous research has confirmed its importance for driving brand attitudes and loyalty, the role of consumer-brand identification as a social identity-based influence in this relationship has not yet been discussed. Drawing on construal level and social identity theories, this paper aims to investigate whether effective journeys and the resulting overall journey experience are equally powerful in driving brand loyalty among customers with different levels of consumer-brand identification.

The present article develops and tests a research model using data from the European and US service sectors (N = 1,454) to investigate how and when ECJD affects service brand loyalty.

Across two cultural contexts, four service industries and 33 service brands, the results reveal that ECJD is a crucial driver of service brand loyalty for customers with low consumer-brand identification. Moreover, the findings show that different aspects of journey effectiveness positively impact the valence of customers’ experience related to those journeys – a process that is ultimately decisive for their brand loyalty.

This study is unique because it generates theoretical and practical knowledge by combining the literature streams of customer journey design, customer experience and branding. Furthermore, this work demonstrates that consumer-brand identification is a critical boundary condition to be considered in the relationship between ECJD and brand loyalty in services.

The authors analyzed the relationship between learning orientation (LO) and performance in micro, small and medium-sized enterprises (MSMEs) by investigating the moderating role of environmental dynamism to answer the need for systematic research of models between LO and firm performance (FP).

The authors investigated the (in)direct relationship between LO and FP. The authors collected data from 182 MSMEs operating in technology parks (TPs) in Poland. The authors used two methods in the quantitative empirical research. The authors used linear regression models to test the hypotheses, which allowed for a global assessment of relationships among all analyzed variables. Dynamic capabilities (DCs) framework guided the study.

The study results show that FP benefits from LO-related behaviors. LO is an important stimulant of FP. Meanwhile, the authors did not classify market dynamism (MD) as a moderator of the LO-FP relationship.

By design, the authors surveyed only MSMEs open to participate in the survey, which potentially limits generalizability. Furthermore, future researchers may consider other types of strategic orientations (SOs) to further explain the impact of multiple SOs on FP in specific industries.

This article presents arguments that allow for recognizing LO as a strategic organizational factor shaping FP.

This study’s fundamental objective is to assess climate change impact on reference evapotranspiration (ET_o) patterns in Egypt under the latest shared socioeconomic pathways (SSPs) of climate change scenarios. Additionally, the study considered the change in the future solar radiation and actual vapor pressure and predicted them from historical data, as these factors significantly impact changes in the ET_o.

The study utilizes data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) models to analyze reference ET_o. Six models are used, and an ArcGIS tool is created to calculate the monthly average ET_o for historical and future periods. The tool considers changes in actual vapor pressure and solar radiation, which are the primary factors influencing ET_o.

The research reveals that monthly reference ET_o in Egypt follows a distinct pattern, with the highest values concentrated in the southern region during summer and the lowest values in the northern part during winter. This disparity is primarily driven by mean air temperature, which is significantly higher in the southern areas. Looking ahead to the near future (2020–2040), the data shows that Aswan, in the south, continues to have the highest annual ET_o, while Kafr ash Shaykh, in the north, maintains the lowest. This pattern remains consistent in the subsequent period (2040–2060). Additionally, the study identifies variations in ET_o , with the most significant variability occurring in Shamal Sina under the SSP585 scenario and the least variability in Aswan under the SSP370 scenario for the 2020–2040 time frame.

This study’s originality lies in its focused analysis of climate change effects on ET_o, incorporating crucial factors like actual vapor pressure and solar radiation. Its significance becomes evident as it projects ET_o patterns into the near and distant future, providing indispensable insights for long-term planning and tailored adaptation strategies. As a result, this research serves as a valuable resource for policymakers and researchers in need of in-depth, region-specific climate change impact assessments.