A novel fine-tuning method is introduced for Non-Intrusive Load Monitoring (NILM), using transfer learning to adapt pre-trained deep learning models for deferrable appliances with distinct short cycles (such as washing machines and dishwashers). This approach enhances model generalization by using limited user-labeled data with readily available, low-frequency aggregate consumption data from smart meters. The method eliminates the need for high-frequency sampling or intrusive sub-metering, and high accuracy in deployable NILM applications. The results show that high accuracy in appliance state recognition is achieved with minimal user interaction, requiring only a small number of labeled appliance activations. The method achieves competitive results compared to state-of-the-art methods, providing a practical and effective NILM solution suitable for widespread adoption by consumers and utility companies.

This study evaluates the effect of three water regimes: full irrigation (I100: 100% of Plant Water Requirement); reduced irrigation (I50: 50% of PWR); no irrigation (I0: rainfed), on durum wheat production in southern Sardinia. The crop performances were tested over two cropping seasons (2022–2023 and 2023–2024) and across two sites (sandy-clay and clay-loam soils). The following traits were considered: grain yield, protein yield, biomass, 1000-grain weight, test weight, protein content, gluten index and harvest index. Deficit irrigation improved grain yield by 53.5 and 65.4% for I50 and I100, respectively when compared to I0. Similar improvements have been registered for most of the traits analysed, with no statistically significant differences between I100 and I50. The agronomic approach was integrated with in situ monitoring, remote sensing and crop modelling with AquaCrop to track soil moisture dynamics, providing suggestions for irrigation decisions. This comprehensive framework led to a Water–Energy–Food analysis to assess costs and benefits of deficit irrigation. The WEF analysis confirmed the sustainability of durum wheat irrigation while Energy Use Efficiency analysis proved that I50 was comparable with I100, the former being even more efficient in 2023. This approach allows to assess the use of deficit irrigation as a tool to enhance durum wheat production in agricultural drought-prone areas in order to preserve staple food security in the Mediterranean region.

Non-intrusive load monitoring (NILM) is the main method used to monitor the energy footprint of a residential building and disaggregate total electrical usage into appliance-related signals. The most common disaggregation algorithms are based on the Hidden Markov Model, while solutions based on deep neural networks have recently caught the attention of researchers. In this work we address the problem through the recognition of the state of activation of the appliances using a fully convolutional deep neural network, borrowing some techniques used in the semantic segmentation of images and multilabel classification. This approach has allowed obtaining high performances not only in the recognition of the activation state of the domestic appliances but also in the estimation of their consumptions, improving the state of the art for a reference dataset.

The application of load disaggregation techniques based on neural networks is often limited to users included in the training dataset. A methodology based on techniques typical of the semantic segmentation of images has been proposed for this task, which allows to obtain a high accuracy and good generalization. We introduce here a novel data augmentation technique for improving forecasts for unmonitored houses that does not require any interaction with the user, nor further measurements of consumption of household appliances.

Important objectives of the four-year enviroGRIDS project encompass the improvement of transnational cooperation, the use of state of the art Information and Communication Technologies for data analysis and sharing and the application of environmental models for monitoring present and predicting future states of the environment for the Black Sea region. In such a transnational context, there is a dire need for the environmental sciences to evolve from a simple, local-scale vision toward a complex, multi-user, multilayered holistic approach. BASHYT (http://swat.crs4.it/) is a Web based, GIS oriented, information and support tool, part of the Black Sea Catchment – Observation System (BSC-OS). It exposes a set of applications for data management, analysis and visualization and a complete server and client side development framework (wiki like) to create Web contents. The core of the portal relies on the hydrological semi distributed SWAT code to model the water cycle and predict the effect of management decisions on water, sediment, nutrient and pesticide yields on large river basins. Furthermore, BASHYT aims at quantifying the interconnectedness between (human and natural) pressures and states of water body receptors at different space and time scales. The aim is to enhance environmental management capacity to assess water resource and to share and process large amounts of key environmental information. Within an experimental and innovative programming environment, modules have been developed to run near real-time applications based on numerical solvers (SWAT is just one example), run pre- and post-processing codes, query and map results through the Web browser. A set of web OGC services and a complete Application Programming Interface (API) are also exposed by the portal. We expect to improve the ways in which land management systems can operate and improve model usability to aid in making management decisions and watershed-scale modeling.

Different scenarios based on environmental changes and water quality models could be used to assess the sustainability and vulnerability of a particular geographical region. The gSWAT and BASHYT platforms provide services and tools supporting the calibration, execution and visualization of the SWAT models. The paper presents the experiments on the execution of different scenarios based on calibrated SWAT model over the Grid infrastructure. Based on the requirements for the SWAT models, lots of input and output data, the high number of simulations that must be performed in the calibration process, the Grid could offer both the storing and execution solutions. This paper presents the interoperability between gSWAT and BASHYT platforms highlighting the architectural components and presenting some test cases to assess the system performances. The research work is supported by the 4-years FP7 Project called Enviro GRIDS - Black Sea Catchment Observation and Assessment System supporting Sustainable Development (http://www.envirogrids.net/) co founded by the European Commission.

Scientific portals are key components of many large-scale Earth Science projects. Through the integration of user-friendly web interfaces within the same environment, researchers and scientists can securely and transparently access to data and computational infrastructures, services, applications, etc. Such web environments provide centric gateways to applications based on workflow and dataflow services and a strong support in accessing to quality information.

Portals provide useful data access functionalities, computational services and procedures for multiple applications and databases. We have developed a GIS oriented Collaborative Working Environment (CWE) on the web, optimized for the environmental sciences, that exposes a development framework and a set of interactive, innovative applications based on hydrological and oceanographic models. The General Estuarine Transport Model (GETM - a 3D numerical hydrodynamic model) and the Soil and Water Assessment Tool (SWAT- a watershed scale model) are coupled within a web based technological framework optimised for data management and report production. Modelling analysis procedures and the web framework have been tested to several study sites around the Sardinian island (Italy). In particular, the oceanographic model is initialised with data from the Ifremer MARS3D-Menor model and the meteorological forcing includes the forecasted air temperature, humidity, pressure, winds and clouds from GFS model at resolution 0.5 degrees. Automatic procedures download this data and perfatm an interpolation in the zone of interest preparing the initial and boundary conditions for the model simulations. The GETM model is run operationally for the forecasted period and results for the sea currents, temperature and salinity are presented using the web-based friendly graphical interface. As an example, we show the application of the tool for the case of the oil spill accident occurred in the Asinara Gulf on 11 of January 2011.

 Abstract—Environmental sciences are moving from a simple, local-scale approach toward complex multilayered, spatially explicit regional ones. The new paradigm is based on integrated and collaborative web tools where the complexity of the technology is transparent to the end user, and interdisciplinary working groups and skills can be enhanced. In this context scientific portals are becoming strategic gateways where end users and stakeholders can securely use innovative applications and researchers and scientists can transparently access to data, computational infrastructures and services. Development frameworks intend to simplify development and integration of such web-based, service oriented environments. BASHYT is a Java platform, based on the model-view-controller (MVC) architectural pattern, to design GIS oriented, Web Information Systems (WIS). The software exposes modules for temporal and spatial (graph, GIS, etc.) analysis to support the dynamic, real time, report production mechanism. At current state, the open source hydrological SWAT and GETM oceanographic models have been interfaced to the BASHYT environment. Our aim is to build an experimental programming platform to run real-time applications based on environmental numerical solvers, run pre- and post-processing codes, query and map results through the web browser. We expect to improve WIS development and maintenance and to improve model usability to address more realistically environmental management. To illustrate the potentiality of the system, we present its use in the EnviroGRIDS and MOMAR projects.

A systematic study of issues related to suspending, migrating and resuming virtual clusters for data-driven HPC applications is presented. The interest is focused on nontrivial virtual clusters, that is where the running computation is expected to be coordinated and strongly coupled. It is shown that this requires that all cluster level operations, such as start and save, should be performed as synchronously as possible on all nodes, introducing the need of barriers at the virtual cluster computing meta-level. Once a synchronization mechanism is provided, and appropriate transport strategies have been setup, it is possible to suspend, migrate and resume whole virtual clusters composed of "heavy" (4 GB RAM, 6 GB disk images) virtual machines in times of the order of few minutes without disrupting parallel computation - albeit of the MapReduce type - running inside them. The approach is intrinsically parallel, and should scale without problems to larger size virtual clusters. (c) 2010 Elsevier B.V. All rights reserved.

Virtualization is an essential enabling technology for building and controlling computing frameworks that can dynamically adapt available physical resources to transient tasks such as the temporary creation of a virtual computing center tailored to the needs of a virtual organization. In this paper we will report on our strategy for the creation of virtual computer clusters based on standard Service-Oriented Architecture (SOA) and hosts virtualization technologies. We will describe our infrastructure designed for dynamical allocation of resources to applications via a general control plane based on workflows of coordinated web services. The control plane is based on logically independent services that are responsible for physical resource management and virtual nodes deployment. The control plane is also responsible for operations on virtual clusters such as their creation, startup and control.

Virtualization is an essential enabling technology for the construction and control of computing facilities that can dynamically adapt available physical resources to transient tasks such as the temporary creation of a virtual computing center tailored to the needs of a virtual organization. In this paper we will describe our strategy for the creation of virtual computer clusters based on standard SOA and hosts virtualization technologies and we will report on our ongoing work on the application of the latter to the deployment and management of a research cluster with 140 dual core cpu. Our deployment mechanism, as well as the system management, is delegated to a control plane based on workflows of coordinated web services. The control plane is based on two logically independent modules, the first is responsible of the physical resources and the deployment on the hardware of virtual Xen hypervisor images, while the second manages operations on virtual clusters such as their creation, startup and control. Low level operations e.g., the control of a running image on a given computational host - are directly provided by atomic web services, in this specific case a WSRF service running in the dom0 of each participating physical Xen host, while all logic above that level is implemented as BPEL scripts.

Environmental sciences are evolving from a simple, local-scale approach toward complex multilayered, spatially explicit regional endeavours. Advances in computer simulation and high performance computing in recent years greatly extends the possibilities in this field, and changes the ways in which land management systems operate. The Datacrossing DSS is a decision support system that relies on a basin-scale groundwater model and a geographically distributed GIS to support decision makers, through a user-friendly web interface, in the field of sustainable water resources management. The portal, for the general user, exposes hydrological applications based on geochemical field data, geophysical surveys, and results of the finite element hydrological CODESA 3-D model quantifying the impact of point/nonpoint pollution. Within an experimental collaborative environment, modules have been developed to run real-time applications based on numerical solvers, run pre- and post-processing codes, and query and map results through the web browser. Our aim is to build a collaborative platform that, by introducing the computational and data-sharing advantages of a GRID infrastructure, promotes joint initiatives and encourages cooperation among interdisciplinary teams operating in the environmental sciences. To illustrate the potential of our decision support system, we present its application to a complex industrial area in Sardinia, Italy (the Portoscuso site) and a less-impacted aquifer in Morocco, province of Tetuan (the Oued aquifer).

Current bioinformatics applications require both management of huge amounts of data and heavy computation: fulfilling these requirements calls for simple ways to implement parallel computing. MapReduce is a general-purpose parallelization technology that appears to be particularly well adapted to this task. Here we report on its application, using its open source implementation Hadoop, to two relevant algorithms: BLAST and GSEA. The first is characterized by streaming computation on large data sets, while the second requires a multi-pass computational strategy on relatively small data sets. The analysis of these algorithms is relevant to a wide class of complex applications, e.g., structural genomics and genome-wide association studies, since they typically contain a mixture of these two computational flavors. Our results are very promising and indicate that the framework could have a wide range of bioinformatics applications while maintaining good computational efficiency, scalability and ease of maintenance.

BACKGROUND:Cardiac calcifications are a frequent occurrence in uraemic subjects and are probably connected to the increased cardiovascular mortality of haemodialysis patients. There is substantial support to the hypothesis that low levels of serum PTH in haemodialysis patients are associated with increased vascular and cardiac calcium deposits, due to decreased buffering capacity of bone in low turnover osteodystrophy. The present study has been carried out on a cohort of patients on haemodialysis, with exclusion of previously parathyroidectomized patients, with the aim to evaluate the association between PTH serum levels and coronary calcifications.METHODS:The study has been carried out in a cohort of 197 haemodialysis patients. There were 133 males and 64 females. Twenty-two patients had diabetes mellitus. Average age was 58.6 +/- 12.9 years. Patients were divided into groups of intact PTH levels, 0-150 (A), 150-300 (B), 300-600 (C) and >600 (D) pg/ml.RESULTS:The values of coronary scores in the PTH groups were as follows: (A) 624.7 +/- 939, (B) 866.4 +/- 1080, (C) 1202.8 +/- 1742.3 and (D) 1872.7 +/- 2961.9. The difference between coronary calcium scores was significant (P < 0.01). A general linear model identified serum calcium and dialysis age as independent factors of calcium deposits in the high PTH group.CONCLUSIONS:No prominent association between low PTH serum levels and the severity of coronary calcium deposits in haemodialysis patients was found while increased levels of PTH, with special regard to very elevated levels, associated with more frequent hypercalcaemia and hyperphosphataemia, should be considered a major risk factor of coronary calcifications and cardiac events.

The Datacrossing DSS (http://datacrossing.crs4.it) is a basin-scale groundwater model that relies on a geographically distributed GIS to support decision makers, through a user-friendly Web interface, in the field of sustainable water resources management. The portal, for the general user, exposes hydrological applications based on complex models that make use of large volumes of distributed data available in a GRID infrastructure. Free software and in-house technologies are combined to transparently and automatically deploy the applications. Our objective is to build a development platform that by introducing the computational and data-sharing advantages of the GRID infrastructure promotes joint initiatives and encourages cooperation among multidisciplinary teams operating in environmental sciences. To illustrate the potential of our data-grid DSS, we present its application to a Sardinian case history where a coastal aquifer is threatened by the leakage of highly toxic inorganic residuals from the Portoscuso industrial settlements.