Search

While ChatGPT has recently become very popular, AI has a long history and philosophy. This paper intends to explore the promises and pitfalls of the Generative Pre-trained Transformer (GPT) AI and potentially future technologies by adopting a speculative methodology. Speculative future narratives with a specific focus on educational contexts are provided in an attempt to identify emerging themes and discuss their implications for education in the 21st century. Affordances of (using) AI in Education (AIEd) and possible adverse effects are identified and discussed which emerge from the narratives. It is argued that now is the best of times to define human vs AI contribution to education because AI can accomplish more and more educational activities that used to be the prerogative of human educators. Therefore, it is imperative to rethink the respective roles of technology and human educators in education with a future-oriented mindset.

Within scientific debate on post-digital and education, we present a position paper to describe a research project aimed at the design of a predictive model for students' low achievements in mathematics in Italy. The model is based on the INVALSI data set, an Italian large-scale assessment test, and we use decision trees as the classification algorithm. In designing this tool, we aim to overcome the use of economic, social, and cultural context indices as main factors for the prediction of a learning gap occurrence. Indeed, we want to include a suitable representation of students' learning in the model, by exploiting the data collected through the INVALSI tests. We resort to a knowledge-based approach to address this issue and specifically, we try to understand what knowledge is introduced into the model through the representation of learning. In this sense, our proposal allows a students' learning encoding, which is transferable to different students' cohort. Furthermore, the encoding methods may be applied to other large-scale assessments test. Hence, we aim to contribute to a debate on knowledge representation in AI tool for education.

The increasing complexity of the new breed of distributed intelligent systems, such as the Internet of Things, which require a diversity of languages and protocols, can only be tamed with design and programming best practices. Interest is also growing for including the human factor, as advocated by the "ambient intelligence" (AmI) research field, whose focus is on transparently and intelligently supporting people. These new design methodologies are increasingly needed in the toolbox of new electronic and computer engineers, and teaching strategies should be devised that allow students to acquire a systems-level view instead of getting lost in technology-oriented approaches. This paper describes a study carried out over two academic years, in a course in AmI at Politecnico di Torino, Italy. In the course, a project-based learning approach was adopted, in which students design and prototype an AmI system, and their progress is closely monitored throughout the semester. This paper presents the learning goals and teaching strategies, analyzes the learning outcomes from the qualitative and quantitative points of view, and highlights the lessons learned in the process.

The application of Artificial Intelligence (AI) and Machine Learning (ML) models is increasingly leveraged to automate and optimize Data Centre (DC) operations. However, the interpretability and transparency of these complex models pose critical challenges. Hence, this paper explores the Shapley Additive exPlanations (SHAP) values model explainability method for addressing and enhancing the critical interpretability and transparency challenges of predictive maintenance models. This method computes and assigns Shapley values for each feature, then quantifies and assesses their impact on the model's output. By quantifying the contribution of each feature, SHAP values can assist DC operators in understanding the underlying reasoning behind the model's output in order to make proactive decisions. As DC operations are dynamically changing, we additionally investigate how SHAP can capture the temporal behaviors of feature importance in the dynamic DC environment over time. We validate our approach with selected predictive models using an actual dataset from a High-Performance Computing (HPC) DC sourced from the Enea CRESCO6 cluster in Italy. The experimental analyses are formalized using summary, waterfall, force, and dependency explanations. We delve into temporal feature importance analysis to capture the features' impact on model output over time. The results demonstrate that model explainability can improve model transparency and facilitate collaboration between DC operators and AI systems, which can enhance the operational efficiency and reliability of DCs by providing a quantitative assessment of each feature's impact on the model's output. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present a shortened version of the SIS (support intensity scale) obtained by the application of mathematical models and instruments, adopting special algorithms based on the most recent developments in artificial adaptive systems. All the variables of SIS applied to 1,052 subjects with ID (intellectual disabilities) involved in the validation of the Italian version of SIS, were analyzed with the aforementioned artificial adaptive systems. This study has identified 56 items, whose responses are able to explain up to 89% of sample variance. Secondly, these same variables have been analyzed by means of specific semantic networks, in order to demonstrate the plenty of scientific suggestions emerging from such an approach to statistic processing. (Contains 5 figures and 3 tables.)

Geohazard prevention and mitigation are highly complex and remain challenges for researchers and practitioners. Artificial intelligence (AI) has become an effective tool for addressing these challenges. Therefore, for decades, an increasing number of researchers have begun to conduct AI research in the field of geohazards leading to rapid growth in the number of related papers. This has made it difficult for researchers and practitioners to grasp information on cutting-edge developments in the field, thus necessitating a comprehensive review and analysis of the current state of development in the field. In this study, a comprehensive scientometric analysis appraising the state-of-the-art research for geohazard was performed based on 9226 scientometric records from the Web of Science core collection database. Multiple types of scientometric techniques, including coauthor analysis, co-citation analysis, and cluster analysis were employed to identify the most productive researchers, institutions, and hot research topics. The results show that research related to the application of AI in the field of geohazards experienced a period of rapid growth after 2000, with major developments in the field occurring in China, the United States, and Italy. The hot research topics in this field are ground motion, deep learning (DL), and landslides. The commonly used AI algorithms include DL, support vector machine (SVM), and decision tree (DT). The obtained visualization on research networks offers valuable insights and an in-depth understanding of the key researchers, institutions, fundamental articles, and salient topics through animated maps. We believe that this scientometric review offers useful reference points for early-stage researchers and provides valuable in-depth information to experienced researchers and practitioners in the field of geohazard research. This scientometric analysis and visualization are promising for reflecting the global picture of AI-based geohazard research comprehensively and possess potential for the visualization of the emerging trends in other research fields. [ABSTRACT FROM AUTHOR]

The combination of innovation and artificial intelligence (AI), with reference to innovative startups, presents a challenge for firms that start operating in the market. Such a transformation in the entrepreneurial scenario, which is likely to define new business models, has been observed in two European countries: Italy and Romania. In this article, we examine the AI-induced innovations in the regulatory frameworks of both countries. We then provide insights from interviews with CEOs of innovative startups, who discuss the challenges and prospects facing the industry. This paper concludes with some reflections on this evolving framework as a prelude to further studies. [ABSTRACT FROM AUTHOR]

Forecasting of water availability has become of increasing interest in recent decades, especially due to growing human pressure and climate change, affecting groundwater resources towards a perceivable depletion. Numerous research papers developed at various spatial scales successfully investigated daily or seasonal groundwater level prediction starting from measured meteorological data (i.e., precipitation and temperature) and observed groundwater levels, by exploiting data-driven approaches. Barely a few research combine the meteorological variables and groundwater level data with unsaturated zone monitored variables (i.e., soil water content, soil temperature, and bulk electric conductivity), and—in most of these—the vadose zone is monitored only at a single depth. Our approach exploits a high spatial-temporal resolution hydrogeological monitoring system developed in the Conero Mt. Regional Park (central Italy) to predict groundwater level trends of a shallow aquifer exploited for drinking purposes. The field equipment consists of a thermo-pluviometric station, three volumetric water content, electric conductivity, and soil temperature probes in the vadose zone at 0.6 m, 0.9 m, and 1.7 m, respectively, and a piezometer instrumented with a permanent water-level probe. The monitored period started in January 2022, and the variables were recorded every fifteen minutes for more than one hydrologic year, except the groundwater level which was recorded on a daily scale. The developed model consists of three "virtual boxes" (i.e., atmosphere, unsaturated zone, and saturated zone) for which the hydrological variables characterizing each box were integrated into a time series forecasting model based on Prophet developed in the Python environment. Each measured parameter was tested for its influence on groundwater level prediction. The model was fine-tuned to an acceptable prediction (roughly 20% ahead of the monitored period). The quantitative analysis reveals that optimal results are achieved by expoiting the hydrological variables collected in the vadose zone at a depth of 1.7 m below ground level, with a Mean Absolute Error (MAE) of 0.189, a Mean Absolute Percentage Error (MAPE) of 0.062, a Root Mean Square Error (RMSE) of 0.244, and a Correlation coefficient of 0.923. This study stresses the importance of calibrating groundwater level prediction methods by exploring the hydrologic variables of the vadose zone in conjunction with those of the saturated zone and meteorological data, thus emphasizing the role of hydrologic time series forecasting as a challenging but vital aspect of optimizing groundwater management. [ABSTRACT FROM AUTHOR]

On 28 February–2 March 2023, the 2023 States General of Artificial Intelligence (AI) event was held in Italy under the sponsorship of several multinational companies. The purpose of this event was mainly to create a venue for allowing international protagonists of AI to discuss and confront on the recent trends in AI. The aim of this paper is to report on the state of the art of the literature on the most recent control engineering and artificial intelligence methods for managing and controlling energy networks with improved efficiency and effectiveness. More in detail, to the best of the authors' knowledge, the scope of the literature review considered in this paper is specifically limited to recent trends in EV charging, cyber-physical security, and predictive maintenance. These application scenarios were identified in the above-mentioned event as responsible for triggering most of the business needs currently expressed by energy companies. A critical discussion of the most relevant methodological approaches and experimental setups is provided, together with an overview of the future research directions. [ABSTRACT FROM AUTHOR]

This article discusses e-justice platform development and its implications for judicial governance. Procedural decisions and court work processes can now be encoded into the digital court work environment. This may affect fundamental values such as fair procedure and judicial impartiality and independence. The article compares three e-justice platforms (EJP): e-Curia (Court of Justice of the European Union), Civil Trial on Line (TOL) in Italy, and Kwaliteit en Innovatie Rechtspraak (KEI) in the Netherlands. In an innovative approach, it considers EJP's as enterprise information systems. It combines functional and managerial perspectives with those of procedural law and technology. The paper studies the impact of merging procedural law and technology for developing EJP's and for reconciling the requirements of both law and technology in the highly regulated context of courts and judiciaries. The paper finds that, in EJP development and implementation, law and technology's combined effect may affect fair procedure. The article concludes with a discussion of the kind of governance needed to safeguard fair procedure and proper functioning of IT in courts. This need becomes even more pressing with the advent of artificial intelligence (AI). The article finds that resolving these issues is part of the judiciary's responsibility for fair procedures and cannot be left to IT experts. It offers some standards and principles for judicial governance to fulfill judicial responsibility for fair procedures as laid down in the human rights conventions. [ABSTRACT FROM AUTHOR]

All societies should carefully address the child abuse and neglect phenomenon due to its acute and chronic sequelae. Even if artificial intelligence (AI) implementation in this field could be helpful, the state of the art of this implementation is not known. No studies have comprehensively reviewed the types of AI models that have been developed/validated. Furthermore, no indications about the risk of bias in these studies are available. For these reasons, the authors conducted a systematic review of the PubMed database to answer the following questions: "what is the state of the art about the development and/or validation of AI predictive models useful to contrast child abuse and neglect phenomenon?"; "which is the risk of bias of the included articles?". The inclusion criteria were: articles written in English and dated from January 1985 to 31 March 2023; publications that used a medical and/or protective service dataset to develop and/or validate AI prediction models. The reviewers screened 413 articles. Among them, seven papers were included. Their analysis showed that: the types of input data were heterogeneous; artificial neural networks, convolutional neural networks, and natural language processing were used; the datasets had a median size of 2600 cases; the risk of bias was high for all studies. The results of the review pointed out that the implementation of AI in the child abuse and neglect field lagged compared to other medical fields. Furthermore, the evaluation of the risk of bias suggested that future studies should provide an appropriate choice of sample size, validation, and management of overfitting, optimism, and missing data. [ABSTRACT FROM AUTHOR]

The Italian Government planned to invest €15 billion of European funds on National Health Service digitalization and primary care enhancement. The critical burden brought by the pandemic upon hospital care mean these investments could no longer be delayed, considering the extraordinary backlogs of many treatments and the ordinary gaps of fragmented long-term care, in Italy and abroad. National guidelines have been published to standardize interventions across the Italian regions, and telemedicine is frequently mentioned as a key innovation to achieve both goals. The professional resources needed to run the facilities introduced in primary care are defined with great precision, but no details are given on how digitalization and remote care technologies must be implemented in this context. Building on this policy case, this paper focuses on what contribution digitalization and telemedicine can offer to specific primary care innovations, drawing from implemented technology-driven policies which may support the effective stratification, prevention and management of chronic patient needs, including anticipatory healthcare, population health management, adjusted clinical groups, chronic care management, quality and outcomes frameworks, patient-reported outcomes and patient-reported experience. All these policies can benefit significantly from digitalization and remote care technology, provided that some risks and limitations are considered by design. [ABSTRACT FROM AUTHOR]

As Artificial Intelligence (AI) becomes more and more pervasive in our everyday life, new questions arise about its ethical and social impacts. Such issues concern all stakeholders involved in or committed to the design, implementation, deployment, and use of the technology. The present document addresses these preoccupations by introducing and discussing a set of practical obligations and recommendations for the development of applications and systems based on AI techniques. With this work we hope to contribute to spreading awareness on the many social challenges posed by AI and encouraging the establishment of good practices throughout the relevant social areas. As points of novelty, the paper elaborates on an integrated view that combines both human rights and ethical concepts to reap the benefits of the two approaches. Moreover, it proposes innovative recommendations, such as those on redress and governance, which add further insight to the debate. Finally, it incorporates a specific focus on the Italian Constitution, thus offering an example of how core legislations of Member States might contribute to further specify and enrich the EU normative framework on AI. [ABSTRACT FROM AUTHOR]

This paper proposes an energy management system based on an Artificial Neural Network (ANN) to be integrated with the standard ISO 50001 and aims to describe the definition and the enhancement of the energy baselines by means of artificial intelligence techniques applied and tested on the real electrical absorption data of the auxiliary units of different thermal power plants in Italy. Power plant optimized operations are important both for cost and energy performance reasons with related effects on the environment in the next future energy transition scenario. The improvement of the energy baselines consists in determining more accurate consumption monitoring models that are able to track inefficiencies and absorption drifts through data analytics and Artificial Intelligence. Starting from the analysis of the energy vectors at the production site level, we performed a multi-scale analysis to define the consumption at macro areas level and finally find the most relevant consumption units within the plants. A comparison of different ANNs applied to several real power plant data was performed to model complex plant architecture and optimize energy savings with respect to pre-set thresholds according to the ISO 50001 standard procedure. The energy baselines are determined through the analysis of the data available in the power plants' Distributed Control System (DCS), and we can identify the consumption derived from the unit's proper operation. Based on the reported numerical simulations, improved baselines have been reached up to a 5% threshold for different plant sub-units, thus representing a relevant overall saving in terms of alert threshold definition and related control efficiency: a potential saving of about 140 MWh throughout the considered three-year dataset was obtained taking into account a cooling tower sub-unit, representing a considerable economic benefit. The results obtained highlight the neural technique efficiency in defining more accurate energy baselines and represents a valuable tool for large energy plant asset management to face relevant energy drifts observed in the last years of plant operation. [ABSTRACT FROM AUTHOR]

Incorrect pesticide use in plant protection often involve a risk to the health of operators and consumers and can have negative impacts on the environment and the crops. The application of artificial intelligence techniques can help the reduction of the volume sprayed, decreasing these impacts. In Italy, the production of ready-to-eat salad in greenhouses requires usually from 8 to 12 treatments per year. Moreover, inappropriate sprayers are frequently used, being originally designed for open-field operations. To solve this problem, a small vehicle suitable for moving over rough ground (named "rover"), was designed, able to carry out treatments based on a single row pass in the greenhouse, devoted to reduce significantly the sprayed product amount. To ascertain its potential, the prototype has been tested at two growth stages of some salad cultivars, adopting different nozzles and boom settings. Parameters such as boom height, nozzle spacing and inclination, pump pressure and rover traveling speed were studied. To assess the effectiveness of the spraying coverage, for each run several water-sensitive papers were placed throughout the vegetation. Compared to the commonly distributed mixture volume (1000 L/ha), the prototype is able to reduce up to 55% of product sprayed, but still assure an excellent crop coverage. [ABSTRACT FROM AUTHOR]

This paper investigates the role of local knowledge specializations from universities in the artificial intelligence (AI) startup creation process. The empirical analysis is grounded in the Knowledge Spillovers Theory of Entrepreneurship. It provides evidence of the interplay between the presence of university knowledge specializations and the creation of AI startups located in Italian NUTS3 regions. More precisely, we analyze the mix of different types of university knowledge specializations able to foster the creation of AI startups, demonstrating that competencies in technological domains, such as computer science, need to be complemented with domains of applications for the emergence of AI technology. The findings indicate that the creation of AI startups is closely tied to the local knowledge present in the region. The article also proposes an empirical approach to identify and classify innovative startups operating in AI domains. Our work offers important implications for regional innovation policies and the management of AI in entrepreneurship. [ABSTRACT FROM AUTHOR]

Industrial robots are a strategic future technology and an important part of the development of artificial intelligence, and they are a necessary means for the intelligent transformation of manufacturing industry. Based on global industrial robot trade data from 1998 to 2017, this paper applies the dynamic complex network analysis method to reveal the spatial and temporal variation characteristics and trade status evolution of the global industrial robot trade network. The results show that the global industrial robot network density has steadily increased, and the industrial robot trade has been characterized by ‘diversification’. The number of major industrial robot exporters in the world is increasing, and the import market is increasingly diversified. The export market structure is relatively tight, the centrality of the global industrial robot trade network shows a downward trend, and the dissimilarity of the ‘core-edge’ clusters decreases year by year. The trade status of ‘catch-up’ countries represented by China has rapidly increased. However, Japan, Germany, and Italy are still in the central position of the industrial robot trade. Moreover, trade of the ‘catch-up’ countries’ is dominated by imports, and exports of industrial robot products are insufficient. Finally, policy suggestions are provided according to the results. [ABSTRACT FROM AUTHOR]

Nowadays, an infectious disease outbreak is considered one of the most destructive effects in the sustainable development process. The outbreak of new coronavirus (COVID-19) as an infectious disease showed that it has undesirable social, environmental, and economic impacts, and leads to serious challenges and threats. Additionally, investigating the prioritization parameters is of vital importance to reducing the negative impacts of this global crisis. Hence, the main aim of this study is to prioritize and analyze the role of certain environmental parameters. For this purpose, four cities in Italy were selected as a case study and some notable climate parameters-such as daily average temperature, relative humidity, wind speed-and an urban parameter, population density, were considered as input data set, with confirmed cases of COVID-19 being the output dataset. In this paper, two artificial intelligence techniques, including an artificial neural network (ANN) based on particle swarm optimization (PSO) algorithm and differential evolution (DE) algorithm, were used for prioritizing climate and urban parameters. The analysis is based on the feature selection process and then the obtained results from the proposed models compared to select the best one. Finally, the difference in cost function was about 0.0001 between the performances of the two models, hence, the two methods were not different in cost function, however, ANN-PSO was found to be better, because it reached to the desired precision level in lesser iterations than ANN-DE. In addition, the priority of two variables, urban parameter, and relative humidity, were the highest to predict the confirmed cases of COVID-19.

The term Industry 4.0 has been coined in Germany in 2011 and since then, it has officially started the so-called fourth industrial revolution. In the herein presented review, the industrial digitalization process of Italy is explored and cross-compared to the German one, in order to unearth and analyze the intrinsic systemic key-elements, which are responsible for the differentiated digital industrial development among the examined countries. As evidenced by the analytical study, which also entails the results of a survey conducted over 238 companies in Italy, the main responsible factor for the industrial digital gap among the countries seems to be represented by a digital holistic-based approach across the entire value chain of the product. This finding, besides being reflected into the very different nature of the two Industry 4.0 policy frameworks, is also supported by survey data, since the majority of the investigated performed interventions can mainly be applied to the production process, rather than to other crucial steps of the value chain. In this regard, the survey analysis suggests also that a lack of perception of the beneficial potential effect of the change of "digital paradigm" from the production process to the product itself is yet lacking within the Italian industrial mindset. Finally, the survey results support the widely discussed synergistic beneficial effect among ecology and digitalization. Indeed, the majority of the interviewed companies have reported that many of the carried-out digitalization measures have positively affected the energetic and sustainability-related dimensions. [ABSTRACT FROM AUTHOR]

The agricultural sector is amidst an industrial revolution driven by the integration of sensing, communication, and artificial intelligence (AI). Within this context, the internet of things (IoT) takes center stage, particularly in facilitating remote livestock monitoring. Challenges persist, particularly in effective field communication, adequate coverage, and long-range data transmission. This study focuses on employing LoRa communication for livestock monitoring in mountainous pastures in the north-western Alps in Italy. The empirical assessment tackles the complexity of predicting LoRa path loss attributed to diverse land-cover types, highlighting the subtle difficulty of gateway deployment to ensure reliable coverage in real-world scenarios. Moreover, the high expense of densely deploying end devices makes it difficult to fully analyze LoRa link behavior, hindering a complete understanding of networking coverage in mountainous environments. This study aims to elucidate the stability of LoRa link performance in spatial dimensions and ascertain the extent of reliable communication coverage achievable by gateways in mountainous environments. Additionally, an innovative deep learning approach was proposed to accurately estimate path loss across challenging terrains. Remote sensing contributes to land-cover recognition, while Bidirectional Long Short-Term Memory (Bi-LSTM) enhances the path loss model's precision. Through rigorous implementation and comprehensive evaluation using collected experimental data, this deep learning approach significantly curtails estimation errors, outperforming established models. Our results demonstrate that our prediction model outperforms established models with a reduction in estimation error to less than 5 dB, marking a 2X improvement over state-of-the-art models. Overall, this study signifies a substantial advancement in IoT-driven livestock monitoring, presenting robust communication and precise path loss prediction in rugged landscapes. [ABSTRACT FROM AUTHOR]

Respiratory malignancies, encompassing cancers affecting the lungs, the trachea, and the bronchi, pose a significant and dynamic public health challenge. Given that air pollution stands as a significant contributor to the onset of these ailments, discerning the most detrimental agents becomes imperative for crafting policies aimed at mitigating exposure. This study advocates for the utilization of explainable artificial intelligence (XAI) methodologies, leveraging remote sensing data, to ascertain the primary influencers on the prediction of standard mortality rates (SMRs) attributable to respiratory cancer across Italian provinces, utilizing both environmental and socioeconomic data. By scrutinizing thirteen distinct machine learning algorithms, we endeavor to pinpoint the most accurate model for categorizing Italian provinces as either above or below the national average SMR value for respiratory cancer. Furthermore, employing XAI techniques, we delineate the salient factors crucial in predicting the two classes of SMR. Through our machine learning scrutiny, we illuminate the environmental and socioeconomic factors pertinent to mortality in this disease category, thereby offering a roadmap for prioritizing interventions aimed at mitigating risk factors. [ABSTRACT FROM AUTHOR]

This paper gives a detailed technical overview of some of the activities carried out in the context of the "Wireless Sensor networks for city-Wide Ambient Intelligence (WISEWAI)" project, funded by the Cassa di Risparmio di Padova e Rovigo Foundation, Italy. The main aim of the project is to demonstrate the feasibility of large-scale wireless sensor network deployments, whereby tiny objects integrating one or more environmental sensors (humidity, temperature, light intensity), a microcontroller and a wireless transceiver are deployed over a large area, which in this case involves the buildings of the Department of Information Engineering at the University of Padova. We will describe how the network is organized to provide full-scale automated functions, and which services and applications it is configured to provide. These applications include long-term environmental monitoring, alarm event detection and propagation, single-sensor interrogation, localization and tracking of objects, assisted navigation, as well as fast data dissemination services to be used, e.g., to rapidly re-program all sensors over-the-air. The organization of such a large testbed requires notable efforts in terms of communication protocols and strategies, whose design must pursue scalability, energy efficiency (while sensors are connected through USB cables for logging and debugging purposes, most of them will be battery-operated), as well as the capability to support applications with diverse requirements. These efforts, the description of a subset of the results obtained so far, and of the final objectives to be met are the scope of the present paper. [ABSTRACT FROM AUTHOR]

Nowadays, Artificial intelligence (AI), combined with the digitalization of healthcare, can lead to substantial improvements in Patient Care, Disease Management, Hospital Administration, and supply chain effectiveness. Among predictive analytics tools, time series forecasting represents a central task to support healthcare management in terms of bookings and medical services predictions. In this context, the development of flexible frameworks to provide robust and reliable predictions became a central point in this healthcare innovation process. This paper presents and discusses a multi-source time series fusion and forecasting framework relying on Deep Learning. By combining weather, air-quality and medical bookings time series through a feature compression stage which preserves temporal patterns, the prediction is provided through a flexible ensemble technique based on machine learning models and a hybrid neural network. The proposed system is able to predict the number of bookings related to a specific medical examination for a 7-days horizon period. To assess the proposed approach's effectiveness, we rely on time series extracted from a real dataset of administrative e-health records provided by the Campania Region health department, in Italy. • We propose a multi-source time-series fusion methodology relying on Deep Learning. • We design a robust framework for daily 7-days-horizon predictions. • We assess the proposed approach on a real medical bookings dataset. [ABSTRACT FROM AUTHOR]

Monitoring soil organic carbon (SOC) typically assumes conducting a labor-intensive soil sampling campaign, followed by laboratory testing, which is both expensive and impractical for generating useful, spatially continuous data products. The present study leverages the power of machine learning (ML) and, in particular, deep neural networks (DNNs) for segmentation, as well as satellite imagery, to estimate the SOC remotely. We propose a new two-stage pipeline for remote SOC estimation, which relies on using a DNN trained to classify land cover to perform feature extraction, while the SOC estimation is performed by a different ML model. The first stage is an image segmentation DNN with the U-Net architecture, which is trained to estimate the land cover for an observed geographical region, based on multi-spectral images taken by the Sentinel-2 satellite constellation. This estimator is subsequently used to extract the latent feature vector for each of the output pixels, by rolling back from the output (dense) layer of the U-Net and accessing the last available convolutional layer of the same dimension as our desired output. The second stage is trained on a set of feature vectors extracted at the coordinates for which manual SOC measurements exist. We tested a variety of ML models and report on their performance. Using the best extremely randomized trees model, we generated a spatially continuous map of SOC estimations for the region of Tuscany, in Italy, with a resolution of 10 m, to share with the researchers as a means of validating the results and to demonstrate the efficiency of the proposed approach, which can can easily be scaled to create a global continuous SOC map. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is fast becoming a general purpose technology with outstanding impacts in industries worldwide, thus supporting the Industry 4.0 revolution. In particular, the energy sector is one of those that has taken more advantages from the implementation of AI approaches, especially Machine Learning models, for several applications, including energy performance benchmarking of buildings. However, the black-box approach could lead to a lack of result interpretability thus preventing the effective application of AI in some real-world scenarios. For this reason, eXplainable Artificial Intelligence (XAI) tools can be effectively embedded within an AI-based Energy Analytics methodology in order to enhance the explainability of the model results. In this paper, we propose an explainable AI-based benchmarking framework for estimating the membership to specific energy performance classes of a large set of Energy Performance Certificates (EPCs) of flats. The classification is obtained by leveraging different black-box classifiers characterized by high accuracy, yet their inference mechanism is not human-readable. Therefore, a generalizable XAI methodology, based on the combination of a local explainer together with a clustering algorithm, is employed to explain the model results and causal effects between the predictors and target variable to better understand the model behaviour, and the motivations behind correct and wrong performed classifications. The paper provides a general methodological approach capable to exploit a limited number of instances to extract, explain and interpret inference mechanisms learnt by the model that are useful for the end-user. The framework was tested on about 100,000 EPCs of flats located in Italy. • A data-driven benchmarking process of building energy performance is proposed. • A set of about 100,000 energy performance certificates of buildings is analysed. • Five classification algorithms are considered for developing the benchmarking tool. • Clustering analysis and XAI are coupled for extracting human-readable patterns. • Classification results are explained for understanding the model behaviour. [ABSTRACT FROM AUTHOR]

The aim of this study was to develop and test an artificial intelligence (AI)-based algorithm for detecting common technical errors in canine thoracic radiography. The algorithm was trained using a database of thoracic radiographs from three veterinary clinics in Italy, which were evaluated for image quality by three experienced veterinary diagnostic imagers. The algorithm was designed to classify the images as correct or having one or more of the following errors: rotation, underexposure, overexposure, incorrect limb positioning, incorrect neck positioning, blurriness, cut-off, or the presence of foreign objects, or medical devices. The algorithm was able to correctly identify errors in thoracic radiographs with an overall accuracy of 81.5% in latero-lateral and 75.7% in sagittal images. The most accurately identified errors were limb mispositioning and underexposure both in latero-lateral and sagittal images. The accuracy of the developed model in the classification of technically correct radiographs was fair in latero-lateral and good in sagittal images. The authors conclude that their AI-based algorithm is a promising tool for improving the accuracy of radiographic interpretation by identifying technical errors in canine thoracic radiographs. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic affected the perinatal emotional landscape in Italy, a country that had high mortality and implemented a strict lockdown during the pandemic. This study explores the emotions and challenges of pregnant and postpartum women during the pandemic, using AI-based mixed methods. The study analyzed 1774 women from the national survey COVID-ASSESS: 1136 pregnant and 638 postpartum women. The survey had qualitative questions on emotions and feelings related to birth, communication with healthcare professionals, media, and peers and family. We used natural language processing and machine learning to classify emotions, identify themes, and extract citations from the data. Fear and anxiety replaced joy as dominant emotions during the pandemic: trust and joy decreased by 49.3% and 36.4%, respectively, while sadness and fear increased by 52.3% and 49.3%, respectively. The pandemic also induced loneliness, isolation, frustration, and anger. Women faced challenges related to birth, communication with HCPs, media, and peers and family. They also used coping strategies such as self-care, news limitation, and trying to cultivate gratitude and hope. This study provides a comprehensive exploration of the perinatal emotional landscape of Italian women during the pandemic. The findings underscore the significant psychological impact of the pandemic and also highlight women's resilience and coping strategies. [ABSTRACT FROM AUTHOR]

Access to legal information and, in particular, to legal literature is examined for the creation of a search and retrieval system for Italian legal literature. The design and implementation of services such as integrated access to a wide range of resources are described, with a particular focus on the importance of exploiting metadata assigned to disparate legal material. The integration of structured repositories and Web documents is the main purpose of the system: it is constructed on the basis of a federation system with service provider functions, aiming at creating a centralized index of legal resources. The index is based on a uniform metadata view created for structured data by means of the OAI approach and for Web documents by a machine learning approach, which, in this paper, has been assessed as regards document classification. Semantic searching is a major requirement for legal literature users and a solution based on the exploitation of Dublin Core metadata, as well as the use of legal ontologies and related terms prepared for accessing indexed articles have been implemented. [ABSTRACT FROM AUTHOR]

The small hyperspectral imager Compact High- Resolution Imaging Spectrometer (CHRIS) is the most important instrument for Earth observation included in the payload of the European Space Agency Third-Part Mission Project for On-Board Autonomy (PROBA)-1 satellite. This instrument has provided dozens of images in several target areas in the world, and a good number of acquisitions are available for the test site of Frascati and Tor Vergata, Italy. This paper reports several results concerning the generation of thematic maps obtained from CHRIS mode-3 imagery. The potential of the use of different configurations for the input vector exploiting multispectral, multiangular, and multitemporal measurements has been investigated, and the results have been evaluated and compared in terms of accuracy in the classification. The core of the decision task has been developed using the neural network methodology. Indeed, this approach is characterized by a particular ease in performing nonlinear mapping of a multidimensional set of inputs into the output one. [ABSTRACT FROM AUTHOR]

Background: Rapid advancements in artificial intelligence (AI) have led to the adoption of AI-driven symptom checkers in primary care. This study aimed to evaluate both patients' and physicians' attitudes towards these tools in Italian general practice settings, focusing on their perceived utility, user satisfaction, and potential challenges. Methods: This feasibility study involved ten general practitioners (GPs) and patients visiting GP offices. The patients used a chatbot-based symptom checker before their medical visit and conducted anamnestic screening for COVID-19 and a medical history algorithm concerning the current medical problem. The entered data were forwarded to the GP as medical history aid. After the medical visit, both physicians and patients evaluated their respective symptoms. Additionally, physicians performed a final overall evaluation of the symptom checker after the conclusion of the practice phase. Results: Most patients did not use symptom checkers. Overall, 49% of patients and 27% of physicians reported being rather or very satisfied with the symptom checker. The most frequent patient-reported reasons for satisfaction were ease of use, precise and comprehensive questions, perceived time-saving potential, and encouragement of self-reflection. Every other patient would consider at-home use of the symptom checker for the first appraisal of health problems to save time, reduce unnecessary visits, and/or as an aid for the physician. Patients' attitudes towards the symptom checker were not significantly associated with age, sex, or level of education. Most patients (75%) and physicians (84%) indicated that the symptom checker had no effect on the duration of the medical visit. Only a few participants found the use of the symptom checker to be disruptive to the medical visit or its quality. Conclusions: The findings suggest a positive reception of the symptom checker, albeit with differing focus between patients and physicians. With the potential to be integrated further into primary care, these tools require meticulous clinical guidance to maximize their benefits. Trial registration: The study was not registered, as it did not include direct medical intervention on human participants. [ABSTRACT FROM AUTHOR]

Copyright of JLIS.it: Italian Journal of Library, Archives & Information Science is the property of Firenze University Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To evaluate the adoption of an integrated eHealth platform for televisit/monitoring/consultation during the COVID-19 pandemic. Methods: During the lockdown imposed by the Italian government during the COVID19 pandemic spread, a dedicated multi-professional working group was set up in the Radiation Oncology Department with the primary aim of reducing patients' exposure to COVID-19 by adopting de-centralized/remote consultation methodologies. Each patient's clinical history was screened before the visit to assess if a traditional clinical visit would be recommended or if a remote evaluation was to be preferred. Real world data (RWD) in the form of patient-reported outcomes (PROMs) and patient reported experiences (PREMs) were collected from patients who underwent televisit/teleconsultation through the eHealth platform. Results: During the lockdown period (from 8 March to 4 May 2020) a total of 1956 visits were managed. A total of 983 (50.26%) of these visits were performed via email (to apply for and to upload of documents) and phone call management; 31 visits (1.58%) were performed using the eHealth system. Substantially, all patients found the eHealth platform useful and user-friendly, consistently indicating that this type of service would also be useful after the pandemic. Conclusions: The rapid implementation of an eHealth system was feasible and well-accepted by the patients during the pandemic. However, we believe that further evidence is to be generated to further support large-scale adoption. [ABSTRACT FROM AUTHOR]

In the coming years, missions to the Moon and Mars shall be the new goals of space flight. The complexity of these missions due to the great distance from Earth and the unforeseen obstacles to settle on another planet have given rise to great concerns for crew health and survival. The need for advanced crew autonomy and a different approach to surgical emergency require new protocols and devices to help future crew medical officers and other crew members in a task of unprecedented difficulty. Hence, the increasing variety of schedules, devices, and protocols being developed. A serious health problem, such as an emerging surgical disease or severe trauma, can jeopardize the mission and survival of the entire crew. Many other difficulties are present in deep-space missions or settlements on other planets, such as communication and supply, also medical, delays, and shortage, and the presence of radiation. Progress in advanced technologies as well as the evolution of robotic surgery and the use of artificial intelligence are other topics of this review. In this particular area of research, even if we are still very far from an "intelligent robot", this evolution must be evaluated in the light of legislative and ethical considerations. This topic was presented at the annual meeting of the American College of Surgeons—Italy Chapter in 2021. [ABSTRACT FROM AUTHOR]

Background and motivation: Lung computed tomography (CT) techniques are high-resolution and are well adopted in the intensive care unit (ICU) for COVID-19 disease control classification. Most artificial intelligence (AI) systems do not undergo generalization and are typically overfitted. Such trained AI systems are not practical for clinical settings and therefore do not give accurate results when executed on unseen data sets. We hypothesize that ensemble deep learning (EDL) is superior to deep transfer learning (TL) in both non-augmented and augmented frameworks. Methodology: The system consists of a cascade of quality control, ResNet–UNet-based hybrid deep learning for lung segmentation, and seven models using TL-based classification followed by five types of EDL's. To prove our hypothesis, five different kinds of data combinations (DC) were designed using a combination of two multicenter cohorts—Croatia (80 COVID) and Italy (72 COVID and 30 controls)—leading to 12,000 CT slices. As part of generalization, the system was tested on unseen data and statistically tested for reliability/stability. Results: Using the K5 (80:20) cross-validation protocol on the balanced and augmented dataset, the five DC datasets improved TL mean accuracy by 3.32%, 6.56%, 12.96%, 47.1%, and 2.78%, respectively. The five EDL systems showed improvements in accuracy of 2.12%, 5.78%, 6.72%, 32.05%, and 2.40%, thus validating our hypothesis. All statistical tests proved positive for reliability and stability. Conclusion: EDL showed superior performance to TL systems for both (a) unbalanced and unaugmented and (b) balanced and augmented datasets for both (i) seen and (ii) unseen paradigms, validating both our hypotheses. [ABSTRACT FROM AUTHOR]

Understanding mechanical processes occurring on faults requires detailed information on the microseismicity that can be enhanced today by advanced techniques for earthquake detection. This problem is challenging when the seismicity rate is low and most of the earthquakes occur at depth. In this study, we compare three detection techniques, the autocorrelation FAST, the machine learning EQTransformer, and the template matching EQCorrScan, to assess their ability to improve catalogues associated with seismic sequences in the normal fault system of Southern Apennines (Italy) using data from the Irpinia Near Fault Observatory (INFO). We found that the integration of the machine learning and template matching detectors, the former providing templates for the cross-correlation, largely outperforms techniques based on autocorrelation and machine learning alone, featuring an enrichment of the automatic and manual catalogues of factors 21 and 7, respectively. Since output catalogues can be polluted by many false positives, we applied refined event selection based on the cumulative distribution of their similarity level. We can thus clean up the detection lists and analyse final subsets dominated by real events. The magnitude of completeness decreases by more than one unit compared to the reference value for the network. We report b -values associated with sequences smaller than the average, likely corresponding to larger differential stresses than for the background seismicity of the area. For all the analysed sequences, we found that main events are anticipated by foreshocks, indicating a possible preparation process for main shocks at subkilometric scales. [ABSTRACT FROM AUTHOR]

Epidemiological and public health resonance of sarcopenia in late life requires further research to identify better clinical markers useful for seeking proper care strategies in preventive medicine settings. Using a machine-learning approach, a search for clinical and fluid markers most associated with sarcopenia was carried out across older populations from northern and southern Italy. A dataset of adults >65 years of age (n = 1971) made up of clinical records and fluid markers from either a clinical-based subset from northern Italy (Pavia) and a population-based subset from southern Italy (Apulia) was employed (n = 1312 and n = 659, respectively). Body composition data obtained by dual-energy X-ray absorptiometry (DXA) were used for the diagnosis of sarcopenia, given by the presence of either low muscle mass (i.e., an SMI < 7.0 kg/m2 for males or <5.5 kg/m2 for females) and of low muscle strength (i.e., an HGS < 27 kg for males or <16 kg for females) or low physical performance (i.e., an SPPB ≤ 8), according to the EWGSOP2 panel guidelines. A machine-learning feature-selection approach, the random forest (RF), was used to identify the most predictive features of sarcopenia in the whole dataset, considering every possible interaction among variables and taking into account nonlinear relationships that classical models could not evaluate. Then, a logistic regression was performed for comparative purposes. Leading variables of association to sarcopenia overlapped in the two population subsets and included SMI, HGS, FFM of legs and arms, and sex. Using parametric and nonparametric whole-sample analysis to investigate the clinical variables and biological markers most associated with sarcopenia, we found that albumin, CRP, folate, and age ranked high according to RF selection, while sex, folate, and vitamin D were the most relevant according to logistics. Albumin, CRP, vitamin D, and serum folate should not be neglected in screening for sarcopenia in the aging population. Better preventive medicine settings in geriatrics are urgently needed to lessen the impact of sarcopenia on the general health, quality of life, and medical care delivery of the aging population. [ABSTRACT FROM AUTHOR]

Structural health monitoring of civil infrastructure, such as bridges and buildings, has become a trending topic in the last few years. The key factor is the technological push given by new technologies that permit the acquisition, storage, processing and visualisation of data in real time, thus assessing a structure's health condition. However, data related to anomaly conditions are difficult to retrieve, and, by the time those conditions are met, in general, it is too late. For this reason, the problem becomes unsupervised, since no labelled data are available, and anomaly detection algorithms are usually adopted in this context. This research proposes a novel algorithm that transforms the intrinsically unsupervised problem into a supervised one for condition monitoring purposes. Considering a bridge equipped with N sensors, which measure static structural quantities (rotations of the piers) and environmental parameters, exploiting the relationships between different physical variables and determining how these relationships change over time can indicate the bridge's health status. In particular, this algorithm involves the training of N models, each of them able to estimate the quantity measured via a sensor by using the others' N − 1 measurements. Hence, the system can be represented by the ensemble of the N models. In this way, for each sensor, it is possible to compare the real measurement with the predicted one and evaluate the residual between the two; this difference can be addressed as a symptom of changes in the structure with respect to the condition regarded as nominal. This approach is applied to a real test case, i.e., Candia Bridge in Italy, and it is compared with a state-of-the-art anomaly detector (namely an autoencoder) in order to validate its robustness. [ABSTRACT FROM AUTHOR]

One method to reduce CO2 emissions from vehicle exhaust is the use of liquified petroleum gas (LPG) fuel. The global use of this fuel is high in European countries such as Poland, Romania, and Italy. There are a small number of computational models for the purpose of estimating the emissions of LPG vehicles. This work is one of the first to present a methodology for developing microscale CO2 emission models for LPG vehicles. The developed model is based on data from road tests using the portable emission measurement system (PEMS) and on-board diagnostic (OBDII) interface. This model was created from a previous exploratory data analysis while using gradient-boosting machine learning methods. Vehicle velocity and engine RPM were chosen as the explanatory variables for CO2 prediction. The validation of the model indicates its good precision, while its use is possible for the analysis of continuous CO2 emissions and the creation of emission maps for environmental analyses in urban areas. The validation coefficients for the selected gradient-boosting method of modelling CO2 emissions for an LPG vehicle are the R2 test of 0.61 and the MSE test of 0.77. [ABSTRACT FROM AUTHOR]

To identify the key drivers of consumption, we analyzed consumer preferences and estimated the willingness to pay for craft beer compared with industrial products in Italy. For this purpose, we conducted an ad hoc survey of 469 craft beer drinkers and set up an econometric strategy using a machine learning estimation technique. The main results show that young consumers, the ability to perceive and evaluate quality, and the frequency of consumption are the main profile elements that, more than others, orient preferences. In the meantime, sustaining local beer producers, sharing good time with friends, and the perception of beer as healthier compared with other drinks are also important. The most preferable product attributes are can packaging and the search for unique taste. [ABSTRACT FROM AUTHOR]

In this work, a new hydroelectric basin modelling approach is described and applied to the Pontecosi basin, Italy. Several types of data sources were used to learn the model: a number of weather stations, satellite observations, the reanalysis dataset, and basin data. With the goal of predicting the water level of the basin, the model was composed by three cascade modules. Firstly, different spatial interpolation methods, such as Kriging, Radial Basis Function, and Natural Neighbours, were compared and applied to interpolate the weather stations data nearby the basin area to infer the main environmental variables (air temperature, air humidity, precipitation, and wind speed) in the basin area. Then, using these variables as inputs, a neural network was trained to predict the mean soil moisture concentration over the area, also to improve the low availability due to satellite orbits. Finally, a non-linear auto regressive exogenous input (NARX) model was trained to simulate the basin level with different prediction horizons, using the data from the previous modules and past basin data (water level, discharge flow rate, and turbine flow rate). Accurate predictions of the basin water level were achieved within 1 to 6 h ahead, with mean absolute errors (MAE) between 2 cm and 10 cm, respectively. [ABSTRACT FROM AUTHOR]

The diagnosis, evaluation, and treatment planning of pancreatic pathologies usually require the combined use of different imaging modalities, mainly, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). Artificial intelligence (AI) has the potential to transform the clinical practice of medical imaging and has been applied to various radiological techniques for different purposes, such as segmentation, lesion detection, characterization, risk stratification, or prediction of response to treatments. The aim of the present narrative review is to assess the available literature on the role of AI applied to pancreatic imaging. Up to now, the use of computer-aided diagnosis (CAD) and radiomics in pancreatic imaging has proven to be useful for both non-oncological and oncological purposes and represents a promising tool for personalized approaches to patients. Although great developments have occurred in recent years, it is important to address the obstacles that still need to be overcome before these technologies can be implemented into our clinical routine, mainly considering the heterogeneity among studies. [ABSTRACT FROM AUTHOR]

Context—Anomaly detection in a data center is a challenging task, having to consider different services on various resources. Current literature shows the application of artificial intelligence and machine learning techniques to either log files or monitoring data: the former created by services at run time, while the latter produced by specific sensors directly on the physical or virtual machine. Objectives—We propose a model that exploits information both in log files and monitoring data to identify patterns and detect anomalies over time both at the service level and at the machine level. Methods—The key idea is to construct a specific dictionary for each log file which helps to extract anomalous n-grams in the feature matrix. Several techniques of Natural Language Processing, such as wordclouds and Topic modeling, have been used to enrich such dictionary. A clustering algorithm was then applied to the feature matrix to identify and group the various types of anomalies. On the other side, time series anomaly detection technique has been applied to sensors data in order to combine problems found in the log files with problems stored in the monitoring data. Several services (i.e., log files) running on the same machine have been grouped together with the monitoring metrics. Results—We have tested our approach on a real data center equipped with log files and monitoring data that can characterize the behaviour of physical and virtual resources in production. The data have been provided by the National Institute for Nuclear Physics in Italy. We have observed a correspondence between anomalies in log files and monitoring data, e.g., a decrease in memory usage or an increase in machine load. The results are extremely promising. Conclusions—Important outcomes have emerged thanks to the integration between these two types of data. Our model requires to integrate site administrators' expertise in order to consider all critical scenarios in the data center and understand results properly. [ABSTRACT FROM AUTHOR]

Background: The previous COVID-19 lung diagnosis system lacks both scientific validation and the role of explainable artificial intelligence (AI) for understanding lesion localization. This study presents a cloud-based explainable AI, the "COVLIAS 2.0-cXAI" system using four kinds of class activation maps (CAM) models. Methodology: Our cohort consisted of ~6000 CT slices from two sources (Croatia, 80 COVID-19 patients and Italy, 15 control patients). COVLIAS 2.0-cXAI design consisted of three stages: (i) automated lung segmentation using hybrid deep learning ResNet-UNet model by automatic adjustment of Hounsfield units, hyperparameter optimization, and parallel and distributed training, (ii) classification using three kinds of DenseNet (DN) models (DN-121, DN-169, DN-201), and (iii) validation using four kinds of CAM visualization techniques: gradient-weighted class activation mapping (Grad-CAM), Grad-CAM++, score-weighted CAM (Score-CAM), and FasterScore-CAM. The COVLIAS 2.0-cXAI was validated by three trained senior radiologists for its stability and reliability. The Friedman test was also performed on the scores of the three radiologists. Results: The ResNet-UNet segmentation model resulted in dice similarity of 0.96, Jaccard index of 0.93, a correlation coefficient of 0.99, with a figure-of-merit of 95.99%, while the classifier accuracies for the three DN nets (DN-121, DN-169, and DN-201) were 98%, 98%, and 99% with a loss of ~0.003, ~0.0025, and ~0.002 using 50 epochs, respectively. The mean AUC for all three DN models was 0.99 (p < 0.0001). The COVLIAS 2.0-cXAI showed 80% scans for mean alignment index (MAI) between heatmaps and gold standard, a score of four out of five, establishing the system for clinical settings. Conclusions: The COVLIAS 2.0-cXAI successfully showed a cloud-based explainable AI system for lesion localization in lung CT scans. [ABSTRACT FROM AUTHOR]