Search Results (12)

The digital transformation of the maritime sector, driven by Industry 4.0, is reshaping port operations toward smarter and more sustainable models. This paper analyzed the implementation of Port 4.0 technologies in the Spanish port system through the lens of the Blue Ocean Strategy. By redefining competitive boundaries and applying tools such as the Four Actions Framework and value innovation curves, the study proposes a new strategic vision where ports collaborate rather than compete. Key enabling technologies (such as Big Data, IoT, AI, and Blockchain) were assessed for their capacity to optimize energy use, reduce emissions, and enhance operational efficiency. The findings highlight the potential for a unified, data-driven port ecosystem that creates a new uncontested market space for Spanish ports while promoting environmental and economic sustainability. Full article

In the realm of hydrological engineering, integrating extensive geospatial raster data from remote sensing (Big Data) with sparse field measurements offers a promising approach to improve prediction accuracy in groundwater studies. In this study, we integrated multisource data by applying the LMC to model the spatial relationships of variables and then utilized block support regularization with collocated block cokriging (CBCK) to enhance our predictions. A critical engineering challenge addressed in this study is support homogenization, where we adjusted punctual variances to block variances and ensure consistency in spatial predictions. Our case study focused on mapping groundwater table depth to improve water management and planning in a mixed land use area in Southeast Brazil that is occupied by sugarcane crops, silviculture (Eucalyptus), regenerating fields, and natural vegetation. We utilized the 90 m resolution TanDEM-X digital surface model and STEEP (Seasonal Tropical Ecosystem Energy Partitioning) data with a 500 m resolution to support the spatial interpolation of groundwater table depth measurements collected from 56 locations during the hydrological year 2015–16. Ordinary block kriging (OBK) and CBCK methods were employed. The CBCK method provided more reliable and accurate spatial predictions of groundwater depth levels (RMSE = 0.49 m), outperforming the OBK method (RMSE = 2.89 m). An OBK-based map concentrated deeper measurements near their wells and gave shallow depths for most of the points during estimation. The CBCK-based map shows more deeper predicted points due to its relationship with the covariates. Using covariates improved the groundwater table depth mapping by detecting the interconnection of varied land uses, supporting the water management for agronomic planning connected with ecosystem sustainability. Full article

This study introduces an innovative analytical methodology for examining the interconnections among the atmosphere, ocean, and society. The primary area of interest pertains to the North Atlantic Oscillation (NAO), a notable phenomenon characterised by daily to decadal fluctuations in atmospheric conditions over the Northern Hemisphere. The NAO has a prominent impact on winter weather patterns in North America, Europe, and to some extent, Asia. This impact has significant ramifications for civilization, as well as for marine, freshwater, and terrestrial ecosystems, and food chains. Accurate predictions of the surface NAO hold significant importance for society in terms of energy consumption planning and adaptation to severe winter conditions, such as winter wind and snowstorms, which can result in property damage and disruptions to transportation networks. Moreover, it is crucial to improve climate forecasts in order to bolster the resilience of food systems. This would enable producers to quickly respond to expected changes and make the required modifications, such as adjusting their food output or expanding their product range, in order to reduce potential hazards. The forecast centres prioritise and actively research the predictability and variability of the NAO. Nevertheless, it is increasingly evident that conventional analytical methods and prediction models that rely solely on scientific methodologies are inadequate in comprehensively addressing the transdisciplinary dimension of NAO variability. This includes a comprehensive view of research, forecasting, and social ramifications. This study introduces a new framework that combines sophisticated Big Data analytic techniques and forecasting tools using a generalised additive model to investigate the fluctuations of the NAO and the interplay between the ocean and atmosphere. Additionally, it explores innovative approaches to analyze the socio-economic response associated with these phenomena using text mining tools, specifically modern deep learning techniques. The analysis is conducted on an extensive corpora of free text information sourced from media outlets, public companies, government reports, and newspapers. Overall, the result shows that the NAO index has been reproduced well by the Deep-NAO model with a correlation coefficient of 0.74. Full article

Big data has revolutionized science and technology leading to the transformation of our societies. High-performance computing (HPC) provides the necessary computational power for big data analysis using artificial intelligence and methods. Traditionally, HPC and big data had focused on different problem domains and had grown into two different ecosystems. Efforts have been underway for the last few years on bringing the best of both paradigms into HPC and big converged architectures. Designing HPC and big data converged systems is a hard task requiring careful placement of data, analytics, and other computational tasks such that the desired performance is achieved with the least amount of resources. Energy efficiency has become the biggest hurdle in the realization of HPC, big data, and converged systems capable of delivering exascale and beyond performance. Data locality is a key parameter of HPDA system design as moving even a byte costs heavily both in time and energy with an increase in the size of the system. Performance in terms of time and energy are the most important factors for users, particularly energy, due to it being the major hurdle in high-performance system design and the increasing focus on green energy systems due to environmental sustainability. Data locality is a broad term that encapsulates different aspects including bringing computations to data, minimizing data movement by efficient exploitation of cache hierarchies, reducing intra- and inter-node communications, locality-aware process and thread mapping, and in situ and transit data analysis. This paper provides an extensive review of cutting-edge research on data locality in HPC, big data, and converged systems. We review the literature on data locality in HPC, big data, and converged environments and discuss challenges, opportunities, and future directions. Subsequently, using the knowledge gained from this extensive review, we propose a system architecture for future HPC and big data converged systems. To the best of our knowledge, there is no such review on data locality in converged HPC and big data systems. Full article

Vegetation productivity, as the basis of the material cycle and energy flow in an ecosystem, directly reflects the information of vegetation change. At the ecosystem level, the gross primary productivity (GPP) refers to the amount of organic carbon fixed by plant bodies. How to accurately estimate the spatiotemporal variation of vegetation productivity of the forest ecosystem in the Altay Mountains in northwest China has become a critical issue to be addressed. The Altay Mountains, with rich forest resources, are located in a semi-arid climate zone and are sensitive to global climate changes, which will inevitably have serious impacts on the function and structure of forest ecosystems in northwest China. In this paper, to reveal the variation trends of vegetation gross primary productivity (GPP) and its response to surface meteorological factors in the Altay Mountains in northwest China, daily temperature and precipitation data from the period of 2000–2017 were collected from seven meteorological stations in Altay prefecture and its surrounding areas; the data were analyzed by using the MODIS GPP model, moving average trend analysis, linear regression analysis and the climate tendency rate method. The results show that: (1) The spatial distribution pattern of GPP in the whole year was almost the same as that in the growing season of vegetation in the Altay Mountains. In the whole mountain range, the proportion of the area which had a GPP value of 400–600 g c/m² had the highest value; the proportion of the annual and growing season of this area was 41.10% and 40.88%, respectively, which was mainly distributed in the middle and west alpine areas of the Altay Mountains. (2) There was a big gap in the GPP value in the different stages of the vegetation growing season (April to September), which reached the highest value in July, the area with a GPP of 100–150 g c/m² was the highest, with 36.15%. (3) The GPP of the Altay Mountains showed an overall increasing trend, but the annual fluctuation was relatively large. In 2003, 2008, 2009 and 2014, the GPP showed lower values, which were 385.18 g c/m², 384.90 g c/m², 384.49 g c/m² and 393.10 g c/m², respectively. In 2007, 2011 and 2016, the GPP showed higher values, which were 428.49 g c/m², 428.18 g c/m² and 446.61 g c/m². (4) In 64.85% of the area of the Altay Mountains, the GPP was positively correlated with annual average temperature, and in 36.56% of the area, the correlation coefficient between temperature and GPP ranged from −0.2 to 0. In 71.61% of the area of the Altay Mountains, the GPP was positively correlated with annual accumulated precipitation, and in 28.39% of the area, the GPP was negatively correlated with annual accumulated precipitation. Under the scenario of global climate change, our study has quantitatively analyzed the long-term dynamics of vegetation GPP and its responses to meteorological factors in the Altay Mountains, which would be helpful for evaluating and estimating the variation trends of forest ecosystems in China, and has important guiding significance for policy formulation to protect forest resources and improve the local ecological environment. Full article

Tropical sandy beaches provide essential ecosystem services and support many local economies. In recent times, however, there has been a massive infrastructure expansion in popular tourist destinations worldwide. To investigate the shoreline variability at a popular tourist destination in Mexico, we used the novel semi-automatic CoastSat program (1980 to 2020) and the climate dataset ERA5 (wave energy and direction). We also measured the beach cross-shore distance and the foredune height with topographic surveys. The results indicate that the section of real estate seafront infrastructure in the study site presents a considerable shoreline erosion due to the fragmentation between the foredune ridge and the beach berm, based on the in situ transects. Moreover, foredune corridors with cross-shore distances of up to 70 to 90 m and dune heights of 8 m, can be seen in the short unobstructed passages between buildings. In the south section we found the coastline in a much more stable condition because this area has not had coastal infrastructures, as of yet. For the most part, the remote sensing analysis indicates constant erosion since 1990 in the real estate section (mainly seafront hotels) and an overall accretion pattern at the unobstructed beach-dune locations. This study demonstrates the catastrophic consequences of beach fragmentation due to unplanned real estate developments, by combining in situ surveys and a freely available big-data approach (CoastSat). Full article

The recent upsurge of smart cities’ applications and their building blocks in terms of the Internet of Things (IoT), Artificial Intelligence (AI), federated and distributed learning, big data analytics, blockchain, and edge-cloud computing has urged the design of the upcoming 6G network generation, due to their stringent requirements in terms of the quality of services (QoS), availability, and dependability to satisfy a Service-Level-Agreement (SLA) for the end users. Industries and academia have started to design 6G networks and propose the use of AI in its protocols and operations. Published papers on the topic discuss either the requirements of applications via a top-down approach or the network requirements in terms of agility, performance, and energy saving using a down-top perspective. In contrast, this paper adopts a holistic outlook, considering the applications, the middleware, the underlying technologies, and the 6G network systems towards an intelligent and integrated computing, communication, coordination, and decision-making ecosystem. In particular, we discuss the temporal evolution of the wireless network generations’ development to capture the applications, middleware, and technological requirements that led to the development of the network generation systems from 1G to AI-enabled 6G and its employed self-learning models. We provide a taxonomy of the technology-enabled smart city applications’ systems and present insights into those systems for the realization of a trustworthy and efficient smart city ecosystem. We propose future research directions in 6G networks for smart city applications. Full article

This study discusses how to create strategic value through energy big data and how to promote stakeholder interaction mechanisms in the evolution of the energy big data ecosystem. We use differential game methods to study the interaction between one power grid enterprise (PG) and one technology supplier (TS) under three different cost-sharing contracts: without cost-sharing contract, cost-sharing contract, and two-way subsidy contract. The effectiveness of the dynamic equilibrium strategies under different contracts is verified via numerical simulations. The results show that under the centralized decision scenario, the technological innovation investment, the degree of technological advancement of PG and TS, and the total profit of the supply chain system are superior to the decentralized decision scenario. The extent of TS technology innovation investment depends on the share rate of PG. Technology innovation investment and the profits of energy big data service supply chain stakeholders will increase with the sensitivity coefficient of technological advancement. Compared with contracts without cost-sharing and with cost-sharing, the two-way subsidy contract can provide the Pareto optimal solution for the investment trajectory of technological innovation and long-term profits. Theoretically, this study reveals a new perspective in the research on the relationship between power grid enterprises and technology suppliers under dynamic technology innovation. In practice, this study facilitates power grid enterprises and technology suppliers to form a closer cooperative relationship in the energy big data ecosystem. More importantly, it is helpful for power grid enterprises to make optimal transaction decisions at different stages of energy big data ecosystem evolution. Full article

With the development of the Energy Internet and the Internet of Things, diversified social production activities are making the interactions between energy, business, and information flow among physical, social, and information systems increasingly complex. As the carrier of information and the hub between physical and social systems, the effective management of energy big data has attracted the attention of scholars. This work indicates that China’s energy companies have carried out a series of activities that are centered on energy big data collection, as well as development and exchange, and that the energy big data ecosystem has begun to take shape. However, the research on and the application of energy big data are mainly limited to micro-level fields, and the development of energy big data in China remains disordered because the corresponding macro-level instructive governance frameworks are lacking. In this work, to facilitate the sustainable development of the energy big data ecosystem and to solve existing problems, such as the difficult-to-determine governance boundaries and the difficult-to-coordinate interests, and to analyze the structure and mechanism of the energy big data ecosystem, data curation is introduced into energy big data governance, and a paradigm is constructed for sustainable energy big data curation that encompasses its full life cycle, including the planning, integration, application, and maintenance stages. Key paradigmatic issues are analyzed in-depth, including data rights, fusion, security, and transactions. Full article

This study discusses how to facilitate the barrier-free circulation of energy big data among multiple entities and how to balance the energy big data ecosystem under government supervision using dynamic game theory. First, we define the related concepts and summarize the recent studies and developments of energy big data. Second, evolutionary game theory is applied to examine the interaction mechanism of complex behaviors between power grid enterprises and third-party enterprises in the energy big data ecosystem, with and without the supervision of government. Finally, a sensitivity analysis is conducted on the main factors affecting co-opetition, such as the initial participation willingness, distribution of benefits, free-riding behavior, government funding, and punitive liquidated damages. The results show that both government supervision measures and the participants’ own will have an impact on the stable evolution of the energy big data ecosystem in the dynamic evolution process, and the effect of parameter changes on the evolution is more significant under the state of no government supervision. In addition, the effectiveness of the developed model in this work is verified by simulated analysis. The present model can provide an important reference for overall planning and efficient operation of the energy big data ecosystem. Full article

In Texas and elsewhere, the looming realities of rapid population growth and intensifying effects of climate change mean that the things we rely on to live—water, energy, dependable infrastructure, social cohesion, and an ecosystem to support them—are exposed to unprecedented risk. Limited resources will be in ever greater demand and the environmental stress from prolonged droughts, record-breaking heat waves, and destructive floods will increase. Existing long-term trends and behaviors will not be sustainable. That is our current trajectory, but we can still change course. Significant advances in information communication technologies and big data, combined with new frameworks for thinking about urban places as social–ecological–technical systems, and an increasing movement towards transdisciplinary scholarship and practice sets the foundation and framework for a metropolitan observatory. Yet, more is required than an infrastructure for data. Making cities inclusive, safe, resilient, and sustainable will require that data become actionable knowledge that change policy and practice. Research and development of urban sustainability and resilience knowledge is burgeoning, yet the uptake to policy has been slow. An integrative and holistic approach is necessary to develop effective sustainability science that synthesizes different sources of knowledge, relevant disciplines, multi-sectoral alliances, and connections to policy-makers and the public. To address these challenges and opportunities, we developed a conceptual framework for a “metropolitan observatory” to generate standardized long-term, large-scale datasets about social, ecological, and technical dimensions of metropolitan systems. We apply this conceptual model in Texas, known as the Texas Metro Observatory, to advance strategic research and decision-making at the intersection of urbanization and climate change. The Texas Metro Observatory project is part of Planet Texas 2050, a University of Texas Austin grand challenge initiative. Full article

Considering that the largest part of end-use energy consumption worldwide is associated with the buildings sector, there is an inherent need for the conceptualization, specification, implementation, and instantiation of novel solutions in smart buildings, able to achieve significant reductions in energy consumption through the adoption of energy efficient techniques and the active engagement of the occupants. Towards the design of such solutions, the identification of the main energy consuming factors, trends, and patterns, along with the appropriate modeling and understanding of the occupants’ behavior and the potential for the adoption of environmentally-friendly lifestyle changes have to be realized. In the current article, an innovative energy-aware information technology (IT) ecosystem is presented, aiming to support the design and development of novel personalized energy management and awareness services that can lead to occupants’ behavioral change towards actions that can have a positive impact on energy efficiency. Novel information and communication technologies (ICT) are exploited towards this direction, related mainly to the evolution of the Internet of Things (IoT), data modeling, management and fusion, big data analytics, and personalized recommendation mechanisms. The combination of such technologies has resulted in an open and extensible architectural approach able to exploit in a homogeneous, efficient and scalable way the vast amount of energy, environmental, and behavioral data collected in energy efficiency campaigns and lead to the design of energy management and awareness services targeted to the occupants’ lifestyles. The overall layered architectural approach is detailed, including design and instantiation aspects based on the selection of set of available technologies and tools. Initial results from the usage of the proposed energy aware IT ecosystem in a pilot site at the University of Murcia are presented along with a set of identified open issues for future research. Full article