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With the shift from the industrial era to the digital era and the increasing importance of digital transformation (DT) in organizations, a new role in the top management team of organizations emerged, that of the chief digital officer (CDO). This emerging phenomenon relates to a Chief (C) - level executive position, focused on the development and implementation of the digital strategy in organizations. To provide a better understanding of the nature of this phenomenon, as well as to develop the basis from which scholars to be based on for further developing this research stream, we conduct a systematic review of 29 articles published in highly esteemed business journals. By synthesizing the review findings, we identified emerging themes and core areas of inquiry, and clarified the nature, typologies and role of CDOs in organizations. Moreover, based on the synthesis and gaps identified in this review, as well as via the transfer of theory from the CEO-TMT literature, several research propositions are developed for scholars to test in the future and a new concept is emerged, that of the Multifunctional CDO Leadership. In conclusion this study provides various implications to both research and management practice.

New digital technologies, combined with the challenges faced by the pandemic, have prompted several organizations, including small and medium sized enterprises (SMEs), to innovate their business models. We extent this article by studying the impact of strategic leaders’ entrepreneurial persistence on digital transformation and business model innovation (BMI). We also examine the role of market-sensing dynamic capability in these relationships. To test our hypotheses, we use survey data from 229 owners/CEOs and senior managers of SMEs based in United Arab Emirates. The results show that entrepreneurial persistence positively influences the adoption of digital technologies and BMI. Moreover, the adoption of digital technologies has a positive effect on BMI and mediates the direct effects of entrepreneurial persistence on BMI. The mediating influence is positively moderated by market sensing dynamic capability.

Wood-boring insect pests pose a significant threat to orchards, potentially leading to tree mortality. In the initial stages of infestation, no visible symptoms are apparent, but as infestations progress, rapid and widespread symptoms emerge, resulting in accelerated tree decline. Therefore, the timely detection of early wood-boring insect symptoms is critical for effective pest control, necessitating advanced methods such as remote sensing. In this study, remote sensing is utilized to identify the early symptoms of peach flatheaded root borer (PFRB) infestation in trees. A multispectral sensor attached to a UAV captures aerial imagery data from stone fruit and pome fruit orchards. These data undergo processing in photogrammetric and GIS programs, where NDVI, NDRE, and the tree crown area are computed. On-site observations confirm PFRB infestations. Various machine-learning models, including logistic regression (LR), artificial neural network (NN), random forest (RF), and extreme gradient boosting (XGBoost), are compared using mean NDVI values, mean NDRE values, crown area, mean temperature, and mean relative humidity. Mean NDVI values emerge as the most crucial factor for predicting PFRB infestation across all machine-learning models. The XGBoost model proves the most effective, achieving an accuracy of 0.85, with marginal variations from the other tested models.

Causal discovery is a highly promising tool with a broad perspective in the field of biology. In this study, a causal structure robustness assessment algorithm is proposed and employed on the causal structures obtained, based on transcriptomic, proteomic, and the combined datasets, emerging from a quantitative proteogenomic atlas of 15 sweet cherry (Prunus avium L.) cv. ‘Tragana Edessis’ tissues. The algorithm assesses the impact of intervening in the datasets of the causal structures, using various criteria. The results showed that specific tissues exhibited an intense impact on the causal structures that were considered. In addition, the proteogenomic case demonstrated that biologically related tissues that referred to the same organ induced a similar impact on the causal structures considered, as was biologically expected. However, this result was subtler in both the transcriptomic and the proteomic cases. Furthermore, the causal structures based on a single omic analysis were found to be impacted to a larger extent, compared to the proteogenomic case, probably due to the distinctive biological features related to the proteome or the transcriptome. This study showcases the significance and perspective of assessing the causal structure robustness based on omic databases, in conjunction with causal discovery, and reveals advantages when employing a multiomics (proteogenomic) analysis compared to a single-omic (transcriptomic, proteomic) analysis.

The concept of sustainable intensification in agriculture necessitates the implementation of management practices that prioritize sustainability without compromising productivity. However, the effects of such practices are known to depend on environmental conditions, and are therefore expected to change as a result of a changing climate. We study the impact of crop diversification on productivity in the context of climate change. We leverage heterogeneous Earth Observation data and contribute a data-driven approach based on causal machine learning for understanding how crop diversification impacts may change in the future. We apply this method to the country of Cyprus throughout a 4-year period. We find that, on average, crop diversification significantly benefited the net primary productivity of crops, increasing it by 2.8%. The effect generally synergized well with higher maximum temperatures and lower soil moistures. In a warmer and more drought-prone climate, we conclude that crop diversification exhibits promising adaptation potential and is thus a sensible policy choice with regards to agricultural productivity for present and future. Comment: Accepted for oral presentation at ISPRS Geospatial Week 2023

Planet Earth is facing numerous imminent challenges, from climate change to ecological dysfunction, which are largely attributed to anthropogenic activities [...]

Cyprus experiences the highest level of water stress among European Union countries due to several interplaying factors such as rainfall variability and increasing water demand. These instigate the nation to build dams on almost all rivers of the island to satisfy the requirements for drinking water and irrigation. Many studies have been primarily conducted on assessing water availability for various uses, particularly for drinking water supply and irrigation. However, there is still a gap/less explored area in terms of a better understanding of changes in surface water over time. Thus, this study aims to evaluate the water surface area variation in Cyprus over the past four decades based on remote sensing products, timeseries analysis and trend detection. The result reveals a statistically significant increasing trend (p < 0.05) in water surface area between 1984–2021. However, following the completion of the final reservoir in 2010, a statistically significant decreasing trend (p < 0.05) was observed in the permanent water surface area. This decline is related to both climatic variability and increased water demands. We observed cycles of 6, 8, and 11 years in permanent water. These cycles indicate a recurring pattern of water scarcity, with severe implication already observed on both economic activity and agriculture. The recent decade has witnessed a decline in rainfall, and this is evident through the decrease in vegetation greenness in rainfed agricultural regions, highlighting its impact. Therefore, the findings of this study underscore not only the necessity for the development of infrastructure aimed at conserving water, but also reinforces the need to discuss water use priorities in Cyprus.

This review paper aims to provide a meta-analysis of the scientific literature for heritage documentation and monitoring using geo-information sensors. The study initially introduces the main types of geomatic sensors that are currently widely used for heritage studies. Although the list provided here is indicative rather than exhaustive, it provides a general overview of the variety of sensors used for different observation scales. The study next focuses on the existing literature, based on published documents. Targeted queries were implemented to the Scopus database to extract the relevant information. Filtering was then applied to the results so as to limit the analysis on the specific thematic sub-domains that is applied for heritage documentation and monitoring. These domains include, among other close-range and underwater photogrammetry, Terrestrial Laser Scanner, Unmanned Aerial Vehicles platforms, and satellite observations. In total, more than 12,000 documents were further elaborated. The overall findings are summarized and presented here, providing further insights into the current status of the domain.