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There are fundamental unresolved questions about the nature of the interplay between digital innovations and water management processes. However, there has been little research on how increasing digital transformation impacts water management and infrastructure in the Global South. This article draws on a socio-technical lens and primary field data to analyse the digital transformation of water management in Ghana's state water utility company. Digital water innovations were found to be recent and delivering relatively limited impacts yet, with value mainly accruing at the utility's operational rather than strategic level, and incremental, not transformative. Digitalisation and datafication also present avenues for power shifts, internal and external struggles, and changes in water management structures and responsibilities. The paper ends with a brief discussion of the implications for water service governance and research and suggestions for using data and information generated from digital water infrastructure to improve services.

This study addresses the challenge of accurately estimating hourly flow and soil moisture by integrating the Soil and Water Assessment Tool (SWAT) with a Land Surface Model (LSM). Our approach enhances SWAT by incorporating spatially distributed soil properties and a physically-based soil moisture process, using the Noah LSM for hourly soil moisture estimation. This integration captures spatial variations in soil moisture and hydraulic properties from remote sensing across the watershed. The parameter sensitivity analysis and the calibration of hourly flow were significantly impacted by the physically-based hourly soil moisture routing and the incorporation of spatially distributed soil properties. Consequently, the modified SWAT model showed improved accuracy in hourly flow simulations for long-term and multiple rainfall events. Validation results showed significant improvements, with Coefficient of Determination (R2) and Nash and Sutcliffe Efficiency (NSE) increasing by 25.95% and 33.3%, respectively, and Percent Bias (PBIAS) decreasing by 85.8%. Notably, the average error for peak flows across eight events decreased by 49%. These findings highlight the importance of initializing soil parameters based on spatial soil moisture distribution and incorporating physical process-based moisture routing to enhance hourly flow simulation accuracy. Future research should focus on validating the physical feasibility of the soil parameter set in the study area with detailed hourly flow and soil moisture data and exploring its applicability in various regions. This study provides valuable insights for the scientific community, water resources, and agricultural decision-makers regarding integrated modeling of soil moisture and hourly flow, which can inform dam operation management, disaster planning, and crop yield improvement.

Fertilization plays a strategic role in the cultivation of wheat, contributing to harvest yield, both in quantitative and qualitative terms. However, similarly to all farm inputs, it has both economic and environmental impacts due to fertilizer dispersion into the environment during its distribution, as well as any excess fertilizer not used by the crop. Precision agriculture, which introduces the possibility of distributing fertilizer following prescription maps, has an immediate effect on dosage compliance according to the request and potential for use by each homogeneous area of the crop. An experimental field (about 15 hectares) at the Council for Agricultural Research and Economics (CREA) of Treviglio (BG) in Northern Italy was fertilized using a centrifugal fertilizer spreader combined with a tractor, equipped with a satellite system to distribute urea led by prescription maps. The purposes of this research were to verify (i) the effect of fertilization, performed with precision agriculture (PA) criteria at a variable rate; (ii) the comparison of the economic impact of the quantities of fertilizer required by precision agriculture compared to the distribution required in previous years (fertilizers administered according to conventional agriculture). The treated areas showed a significant yield improvement (almost 14%) when fertilization was performed according to the prescription map. With a negligible margin of error of less than 0.001, the total amount of fertilizer used was the same in both years.

There are thousands or even tens of thousands of satellites in Low Earth Orbit (LEO). How to ensure the economic sustainability of LEO satellite constellation construction is an important issue currently. In this article, we envision integrating the popular and promising Internet of Things (IoT) technology with LEO satellite constellations to indirectly provide economic support for LEO satellite construction through paid IoT services. Of course, this can also bring benefits to the development of IoT. LEO Satellites can provide networks for IoT products in areas with difficult conditions, such as deserts, oceans, etc., and Satellite Edge Computing (SEC) can help to reduce the service latency of IoT. Many IoT products rely on Convolutional Neural Networks (CNNs) to provide services, and it is difficult to perform CNN inference on an edge server solely. Therefore, in this article, we use edge-distributed inference to enable the IoT services in the SEC scenario. How to perform edge-distributed inference to shorten inference time is a challenge. To shorten the inference latency of CNN, we propose a framework based on a joint partition, named EDIJP. We use a joint partition method combining data partition and model partition for distributed partition. We model the data partition as a Linear Programming (LP) problem. To address the challenge of trading off computation latency and communication latency, we designed an iterative algorithm to obtain the final partitioning result. By maintaining the original structure and parameters, our framework ensures that the inference accuracy will not be affected. We simulated the SEC environment, based on two popular CNN models, VGG16 and AlexNet, the performance of our method is varified. Compared with local inference, EdgeFlow, and CoEdge, the inference latency by using EDIJP is shorter.

[ES] Internet of Things (IoT) se ha convertido en una realidad omnipresente que está transformando la forma en la que interactuamos con el entorno y cómo los dispositivos y objetos se conectan, comunican e intercambian información, abriendo un abanico de posibilidades y oportunidades sin precedentes. Sus aplicaciones son casi infinitas y gracias a su gran potencial, hoy en día está presente en todo tipo de ámbitos. Artificial Internet of Things (AIoT) se considera la siguiente etapa de Internet. Se trata de un nuevo paradigma de red que combina IoT con la inteligencia artificial (IA) dando lugar a sistemas altamente inteligentes y autónomos, capaces de comprender, razonar y aprender de manera similar a los seres humanos. Sin embargo, a pesar de su potencial, uno de los desafíos clave que enfrenta AIoT desde sus inicios es la falta de una arquitectura de referencia estandarizada que proporcione un conjunto de funcionalidades básicas, estructuras de información y mecanismos que sirvan de modelo para desarrollar e implementar estos sistemas. Esta falta de estandarización está dando lugar a una serie de problemas y desafíos en su implementación y adopción generalizada. En primer lugar, la falta de estándares comunes dificulta la interoperabilidad entre diferentes dispositivos y sistemas AIoT. Además, la ausencia de una arquitectura de referencia complica el desarrollo coherente y eficiente. Por último, se observan dificultades para escalar los sistemas. Sin una estructura común y estándares interoperables, resulta más complicado integrar y administrar grandes cantidades de dispositivos y datos. En esta tesis se presenta una arquitectura de referencia AIoT multidominio. El diseño de la arquitectura considera las recomendaciones de la ITU propuestas en UIT-T Y.2066. Además, la arquitectura propuesta se alinea a su vez con otras dos arquitecturas de referencia: IIRA y RAMI 4.0. Como consecuencia, la arquitectura presenta una naturaleza versátil y adaptable, lo que le permite ajustarse a las necesidades y requerimientos de diferentes contextos y dominios. La arquitectura presentada ha sido implementada y validada en cuatro casos de uso desarrollados en el contexto de cuatro proyectos de investigación. En los proyectos ACTIVAGE - H2020 LSP y DIATOMIC Open Call nº2 - H2020 la arquitectura facilita la implementación de servicios cognitivos de asistencia para personas mayores. En el proyecto COSIBAS - Programa Internacional ITEA3 incorpora a un sistema IoT heredado capacidades cognitivas para mejorar la gestión del tráfico marino. Por último, en OPTIMAI - H2020 se incorporan capacidades cognitivas a una línea de producción de antenas para detectar anomalías. [CA] Internet of Things (IoT) s'ha convertit en una realitat omnipresent que està transformant la manera com interactuem amb l'entorn i com els dispositius i objectes es connecten, comuniquen i intercanvien informació, obrint un ventall de possibilitats i oportunitats sense precedents. Les seves aplicacions són quasi infinites i gràcies al seu gran potencial, avui dia és present en tot tipus d'àmbits. Artificial Internet of Things (AIoT) es considera la següent etapa de la Internet. Es tracta d'un nou paradigma de xarxa que combina IoT amb la intel·ligència artificial (IA) donant lloc a sistemes altament intel·ligents i autònoms, capaços de comprendre, raonar i aprendre de manera similar a els éssers humans. Tot i això, malgrat el seu potencial, un dels desafiaments clau que enfronta AIoT des dels seus inicis és la manca d'una arquitectura de referència estandarditzada que proporcioni un conjunt de funcionalitats bàsiques, estructures d'informació i mecanismes que serveixin de model per desenvolupar implementar aquests sistemes. Aquesta manca d'estandardització dona lloc a una sèrie de problemes i desafiaments en la seva implementació i adopció generalitzada. En primer lloc, la manca d'estàndards comuns dificulta la interoperabilitat entre diferents dispositius i sistemes AIoT. A més a més, l'absència d'una arquitectura de referència complica el desenvolupament coherent i eficient. Per acabar, s'observen dificultats per escalar els sistemes. Sense una estructura comuna i estàndards interoperables, és més complicat integrar i administrar grans quantitats de dispositius i dades. En aquesta tesi es presenta una arquitectura de referència AIoT multidomini. El disseny de l'arquitectura considera les recomanacions de la ITU proposades a UIT-T Y.2066. A més, l'arquitectura proposada s'alinea alhora amb dues arquitectures de referència més: IIRA i RAMI 4.0. Com a conseqüència, l'arquitectura presenta una naturalesa versàtil i adaptable, cosa que permet ajustar-se a les necessitats i requeriments de diferents contextos i dominis de la IoT. L'arquitectura presentada ha estat implementada i validada en quatre casos d'ús desenvolupats en el context de quatre projectes de recerca. Als projectes ACTIVAGE - H2020 LSP i DIATOMIC Open Call nº2 - H2020 l'arquitectura facilita la implementació de serveis cognitius d'assistència per a gent gran. Al projecte COSIBAS - Programa Internacional ITEA3 incorpora a un sistema IoT heretat capacitats cognitives per millorar la gestió del trànsit marí. Per acabar, a OPTIMAI - H2020 s'incorporen capacitats cognitives a una línia de producció d'antenes per detectar anomalies. [EN] Internet of Things (IoT) has become an omnipresent reality that is transforming the way we interact with the environment and how devices and objects connect, communicate, and exchange information, opening a range of unprecedented possibilities and opportunities. Its applications are almost infinite, and thanks to its tremendous potential, it is now present in all kinds of fields. Artificial Internet of Things (AIoT) is considered the next stage of the Internet. It is a new network paradigm that combines IoT with Artificial Intelligence (AI), resulting in highly intelligent and autonomous systems capable of understanding, reasoning, and learning similar to humans. However, despite its potential, one of the key challenges that AIoT has faced since its inception is the lack of a standardized reference architecture that provides a set of basic functionalities, information structures, and mechanisms to serve as a model for developing and implementing these systems. This lack of standardization has led to a series of problems and challenges in its implementation and widespread adoption. Firstly, the lack of common standards hinders interoperability between different AIoT devices and systems. Additionally, the absence of a reference architecture complicates coherent and efficient development. Lastly, scaling the systems poses difficulties. Without a common structure and interoperable standards, integrating and managing large amounts of devices and data becomes more complex. This thesis presents a multidomain AIoT reference architecture that incorporates recommendations from the ITU, as proposed in UIT-T Y.2066. Furthermore, the proposed architecture aligns with two other reference architectures: IIRA and RAMI 4.0. As a result, the architecture exhibits versatility and adaptability, allowing it to meet the needs and requirements of different IoT contexts and domains. The presented architecture has been implemented and validated in four use cases developed in the context of four research projects. In the ACTIVAGE - H2020 LSP and DIATOMIC Open Call nº2 - H2020 projects, the architecture facilitates the implementation of cognitive assistance services for the elderly. In the COSIBAS - International ITEA3 Program, it incorporates cognitive capabilities into a legacy IoT system to improve maritime traffic management. Finally, in OPTIMAI - H2020, cognitive capabilities are integrated into an antenna production line to detect anomalies. La arquitectura presentada en esta tesis ha sido implementada y validada en cuatro casos de uso desarrollados en el contexto de cuatro proyectos de investigación. En los proyectos ACTIVAGE - H2020 LSP y DIATOMIC Open Call nº2 - H2020 la arquitectura facilita la implementación de servicios cognitivos de asistencia para personas mayores. En el proyecto COSIBAS - Programa Internacional ITEA3 incorpora a un sistema IoT heredado capacidades cognitivas para mejorar la gestión del tráfico marino. Por último, en OPTIMAI - H2020 se incorporan capacidades cognitivas a una línea de producción de antenas para detectar anomalías.

Wildfires in forest ecosystems exert substantial ecological, economic, and social impacts. The effectiveness of fire management hinges on precise pre-fire risk assessments to inform mitigation efforts. This study aimed to investigate the relationship between predictions from pre-fire risk assessments and outcomes observed through post-fire burn severity analyses. In this study, forest fire risk was assessed through the Fuzzy Analytical Hierarchy Process (FAHP), in which fire-oriented factors were used as input. The degree of burn was determined by the Random Forest method using 11,519 training points and 400 test points on Sentinel-2 satellite images under three different classes. According to the results obtained from 266 selected test points located within the forest, all primary factors put forth increased high burn severity. Climate, in particular, emerged as the most significant factor, accounting for 52% of the overall impact. However, in cases of high fire severity, climate proved to be the most effective risk factor, accounting for 67%. This was followed by topography with 50% accuracy at a high fire intensity. In the risk assessment based on the FAHP method, climate was assigned the highest weight value among the other factors (32.2%), followed by topography (27%). To evaluate the results more comprehensively, both visually and statistically, two regions with different stand canopy characteristics were selected within the study area. While high burn severity had the highest accuracy in the Case 1 area, moderate burn severity had the highest in the Case 2 area. During the days of the fire, the direction of spreading was obtained from the MODIS images. In this way, the fire severity was also interpreted depending on the direction of fire progression. Through an analysis of various case studies and literature, this research underlines both the inherent strengths and limitations of predicting forest fire behavior-based pre-fire risk assessments. Furthermore, it emphasizes the necessity of continuous improvement to increase the success of forest fire management.

Intangible cultural heritage (ICH) strengthens community identity and represents a resource for sustainable community development. The current extensive access to the Internet has facilitated the digitization of communication, including ICH. The aim of this article is to highlight the importance of both digitizing ICH and digital communication about ICH for local development. The results of a longitudinal research study on local ICH resources available on the official websites of Făgăraș Land administrative territorial units (ATUs) before and after the COVID-19 pandemic, together with the results of a content analysis on ICH on the social media accounts of the same ATUs, indicate the digitization of ICH as a sustainable local development resource whose potential can be better exploited. In Făgăraș Land there are also successful initiatives in the fields of both ICH digitization and digital communication about ICH local resources. The Museum of Canvases and Stories in Mândra ATU and the use of social media for communication about ICH in the case of Drăguș ATU are related to such initiatives and are presented as case studies in this article, highlighting the potential of ICH as a resource for sustainable community development in Făgăraș Land.

The integration of cultural heritage in education facilitates critical thinking, experiential learning, cross-cultural collaborative learning and ultimately, quality learning experiences. This process is further enhanced by the increasing adoption of digital technology, which makes education more accessible. However, some countries in the European Union have low digital literacy and a high student dropout rate. Also, the use of cultural heritage in education is declining as young learners are becoming increasingly unaware of their cultural identity. Within this framework, a study of mixed methods (questionnaires and interviews) was conducted in three European countries to examine digital and cultural heritage competencies among young learners. The results of the paper reveal how digital cultural heritage increases learners’ resilience by promoting competences for digital transformation, which in turn enhances learning and engagement with cultural heritage. Drawing on our findings, the paper proposes a new innovative hybrid model within the framework of sustainable education (SE).