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A Smart Factory exploits information and communication technologies (ICT) to improve the production process and the working environment, usually addressing safety concerns. To this concern, factory-grade fire protection systems are governed by several procedures and standards whose application often becomes definitely challenging when the factory premises are dispersed across multiple administrative domains. In such contexts, the Smart Factory approach can prove very effective in the management and coordination of the factory-level fire protection system. However, a catastrophic event may compromise the ICT infrastructure, affecting communication among factory domains and therefore its smart services. A strategy to cope with the latter may be the introduction of mechanisms to handle data analysis on-site for a prompt response while enabling seamless data distribution and processing among neighbouring (federated) ICT infrastructures and emergency operators. In this work, a novel software-defined approach for the adaptive management of a Smart Factory infrastructure is proposed, centered around business logic rewiring and reconfiguration at run-time across different factory domains. Thereby, even in the case of catastrophic (e.g., potentially disruptive) events, working devices of the emergency system can go on with their operations, including transferring data to rescuers and others emergency control systems. To demonstrate the effectiveness of the proposed Software Defined Factory approach, a Federated Fire Protection System operating in an industrial setting is implemented as a case study, able to promptly react and adapt to infrastructure-critical fires and their consequences by leveraging all information and computing facilities pooled over Cloud/Fog/Edge devices spanning the premises.

With the significant impact of Internet services and the evolution in the hardware and software fields, the Internet size and usage scope are continuously covering smaller and resource-constrained devices. Such devices, commonly called IoT devices, with sensing/actuation capabilities, are now capable of managing the complexity of communications over the Internet. However, considering the current IoT ecosystem, this field is still fragmented with respect to the communication technologies, protocols, and data formats used by the different providers. This heterogeneity makes IoT devices/systems unable to communicate seamlessly, leading to limited cooperation and tightly coupled deployments. To overwhelm the interoperability barriers in IoT, we propose, in this paper, a Cloud-based approach with a Dynamic Domain Name System (DDNS) mechanism enabling the IoT devices to communicate using the REST model. An approach that follows the Web of things (WoT) paradigm. In particular, the system enables the IoT devices' hosted resources (e.g., sensors and actuators) to be steered using globally resolvable (over the Internet) Uniform Resource Locators (URLs) even when deployed behind Network Address Translators (i.e., NATs). Besides, the solution considers security aspects related to data transmission by leveraging the use of HTTPS with a Dynamic machnism managing Domain Validation (DV) certificates.

Nummular headache is an unusual facial pain disorder with no evidence-based therapy recommendations. The ketogenic diet is an alternative therapy that demonstrated to be effective in migraineurs, but it was never used in the setting of nummular headache. We describe a 58-years old female patient with nummular headache successfully treated with a 6-months ketogenic diet and botulinum toxin type A injections. Ketogenic diet could be an effective alternative/complementary therapy in nummular headache patients although more studies are needed to confirm our results.

ImportanceAcute symptomatic seizures occurring within 7 days after ischemic stroke may be associated with an increased mortality and risk of epilepsy. It is unknown whether the type of acute symptomatic seizure influences this risk.ObjectiveTo compare mortality and risk of epilepsy following different types of acute symptomatic seizures.Design, Setting, and ParticipantsThis cohort study analyzed data acquired from 2002 to 2019 from 9 tertiary referral centers. The derivation cohort included adults from 7 cohorts and 2 case-control studies with neuroimaging-confirmed ischemic stroke and without a history of seizures. Replication in 3 separate cohorts included adults with acute symptomatic status epilepticus after neuroimaging-confirmed ischemic stroke. The final data analysis was performed in July 2022.ExposuresType of acute symptomatic seizure.Main Outcomes and MeasuresAll-cause mortality and epilepsy (at least 1 unprovoked seizure presenting >7 days after stroke).ResultsA total of 4552 adults were included in the derivation cohort (2547 male participants [56%]; 2005 female [44%]; median age, 73 years [IQR, 62-81]). Acute symptomatic seizures occurred in 226 individuals (5%), of whom 8 (0.2%) presented with status epilepticus. In patients with acute symptomatic status epilepticus, 10-year mortality was 79% compared with 30% in those with short acute symptomatic seizures and 11% in those without seizures. The 10-year risk of epilepsy in stroke survivors with acute symptomatic status epilepticus was 81%, compared with 40% in survivors with short acute symptomatic seizures and 13% in survivors without seizures. In a replication cohort of 39 individuals with acute symptomatic status epilepticus after ischemic stroke (24 female; median age, 78 years), the 10-year risk of mortality and epilepsy was 76% and 88%, respectively. We updated a previously described prognostic model (SeLECT 2.0) with the type of acute symptomatic seizures as a covariate. SeLECT 2.0 successfully captured cases at high risk of poststroke epilepsy.Conclusions and RelevanceIn this study, individuals with stroke and acute symptomatic seizures presenting as status epilepticus had a higher mortality and risk of epilepsy compared with those with short acute symptomatic seizures or no seizures. The SeLECT 2.0 prognostic model adequately reflected the risk of epilepsy in high-risk cases and may inform decisions on the continuation of antiseizure medication treatment and the methods and frequency of follow-up.

Non-contrast computer tomography detects the presence of hyperdense middle cerebral artery sign (HMCAS). Studies on the prognostic value of HMCAS among patients undergoing mechanical thrombectomy (MT) are conflicting. A retrospective analysis of consecutive patients with acute ischemic stroke due to middle cerebral artery occlusion, presenting with or without HMCAS, who underwent MT, was performed. We enrolled 191 patients (HMCAS +, n = 140; HMCAS -, n = 51). Prevalence of successful recanalization was significantly higher in patients with HMCAS than in those without HMCAS (92.1% versus 74.5%, p = 0.001). Patients with HMCAS had a better clinical outcome than those HMCAS - (54.3% versus 37.3%, p = 0.037, for three-month favorable outcome; 62.9% versus 39.3%, p = 0.004, for major neurological improvement at discharge; 8.6% versus 19.6%, p = 0.035, for in-hospital mortality; 14.3% versus 27.5%, p = 0.035, for intracranial hemorrhage; 2.9% versus 17.6%, p = 0.001, for symptomatic intracranial hemorrhage). Multivariate analyses confirmed that HMCAS represents an independent predictor of three-month favorable outcome (OR 2.48, 95% CI 1.10-5.58, p = 0.028), major neurological improvement at discharge (OR 2.40, 95% CI 1.09-5.20, p = 0.030), in-hospital mortality (OR 0.29, 95% CI 0.010-0.81, p = 0.018), presence of ICH (OR 0.49, 95% CI 0.25-0.97, p = 0.042) and presence of SICH (OR 0.16, 95% CI 0.04-0.63, p = 0.009). HMCAS presence predicts favorable outcome in patients undergoing MT. This result may indicate that hyperdense clots are more likely to respond to MT than isodense ones. This effect is mediated by reduction in hemorrhagic transformation.

Accidente cerebrovascular isquémico; Tratamiento de reperfusión; Factores de riesgo Ischemic Stroke; Reperfusion treatment; Risk factors Accident cerebrovascular isquèmic; Tractament de reperfusió; Factor de risc Objective The purpose of this study was to identify risk factors for acute symptomatic seizures and post-stroke epilepsy after acute ischemic stroke and evaluate the effects of reperfusion treatment. Methods We assessed the risk factors for post-stroke seizures using logistic or Cox regression in a multicenter study, including adults from 8 European referral centers with neuroimaging-confirmed ischemic stroke. We compared the risk of post-stroke seizures between participants with or without reperfusion treatment following propensity score matching to reduce confounding due to treatment selection. Results In the overall cohort of 4,229 participants (mean age 71 years, 57% men), a higher risk of acute symptomatic seizures was observed in those with more severe strokes, infarcts located in the posterior cerebral artery territory, and strokes caused by large-artery atherosclerosis. Strokes caused by small-vessel occlusion carried a small risk of acute symptomatic seizures. 6% developed post-stroke epilepsy. Risk factors for post-stroke epilepsy were acute symptomatic seizures, more severe strokes, infarcts involving the cerebral cortex, and strokes caused by large-artery atherosclerosis. Electroencephalography findings within 7 days of stroke onset were not independently associated with the risk of post-stroke epilepsy. There was no association between reperfusion treatments in general or only intravenous thrombolysis or mechanical thrombectomy with the time to post-stroke epilepsy or the risk of acute symptomatic seizures. Interpretation Post-stroke seizures are related to stroke severity, etiology, and location, whereas an early electroencephalogram was not predictive of epilepsy. We did not find an association of reperfusion treatment with risks of acute symptomatic seizures or post-stroke epilepsy.

In Cloud computing deployments, specifically in the Infrastructure-as-a-Service (IaaS) model, networking is one of the core enabling facilities provided for the users. The IaaS approach ensures significant flexibility and manageability, since the networking resources and topologies are entirely under users’ control. In this context, considerable efforts have been devoted to promoting the Cloud paradigm as a suitable solution for managing IoT environments. Deep and genuine integration between the two ecosystems, Cloud and IoT, may only be attainable at the IaaS level. In light of extending the IoT domain capabilities’ with Cloud-based mechanisms akin to the IaaS Cloud model, network virtualization is a fundamental enabler of infrastructure-oriented IoT deployments. Indeed, an IoT deployment without networking resilience and adaptability makes it unsuitable to meet user-level demands and services’ requirements. Such a limitation makes the IoT-based services adopted in very specific and statically defined scenarios, thus leading to limited plurality and diversity of use cases. This article presents a Cloud-based approach for network virtualization in an IoT context using the de-facto standard IaaS middleware, OpenStack, and its networking subsystem, Neutron. OpenStack is being extended to enable the instantiation of virtual/overlay networks between Cloud-based instances (e.g., virtual machines, containers, and bare metal servers) and/or geographically distributed IoT nodes deployed at the network edge.

To date, very few studies focused their attention on efficacy and safety of recanalisation therapy in acute ischemic stroke (AIS) patients with cancer, reporting conflicting results. We retrospectively analysed data from our database of consecutive patients admitted to the Udine University Hospital with AIS that were treated with recanalisation therapy, i.e. intravenous thrombolysis (IVT), mechanical thrombectomy (MT), and bridging therapy, from January 2015 to December 2019. We compared 3-month dependency, 3-month mortality, and symptomatic intracranial haemorrhage (SICH) occurrence of patients with active cancer (AC) and remote cancer (RC) with that of patients without cancer (WC) undergoing recanalisation therapy for AIS. Patients were followed up for 3 months. Among the 613 AIS patients included in the study, 79 patients (12.9%) had either AC (n = 46; 7.5%) or RC (n = 33; 5.4%). Although AC patients, when treated with IVT, had a significantly increased risk of 3-month mortality [odds ratio (OR) 6.97, 95% confidence interval (CI) 2.42–20.07, p = 0.001] than WC patients, stroke-related deaths did not differ between AC and WC patients (30% vs. 28.8%, p = 0.939). There were no significant differences between AC and WC patients, when treated with MT ± IVT, regarding 3-month dependency, 3-month mortality and SICH. Functional independence, mortality, and SICH were similar between RC and WC patients. In conclusion, recanalisation therapy might be used in AIS patients with nonmetastatic AC and with RC. Further studies are needed to explore the outcome of AIS patients with metastatic cancer undergoing recanalisation therapy.

The Internet of Things (IoT) is transforming various domains, including smart energy management, by enabling the integration of complex digital and physical components in distributed cyber-physical systems (DCPSs). The design of DCPSs has so far been focused on performance-related, non-functional requirements. However, with the growing power consumption and computation expenses, sustainability is becoming an important aspect to consider. This has led to the concept of energy-aware DCPSs, which integrate conventional non-functional requirements with additional attributes for sustainability, such as energy consumption. This research activity aimed to investigate and develop energy-aware architectural models and edge/cloud computing technologies to design next-generation, AI-enabled (and, specifically, deep-learning-enhanced), self-conscious IoT-extended DCPSs. Our key contributions include energy-aware edge-to-cloud architectural models and technologies, the orchestration of a (possibly federated) edge-to-cloud infrastructure, abstractions and unified models for distributed heterogeneous virtualized resources, innovative machine learning algorithms for the dynamic reallocation and reconfiguration of energy resources, and the management of energy communities. The proposed solution was validated through case studies on optimizing renewable energy communities (RECs), or energy-aware DCPSs, which are particularly challenging due to their unique requirements and constraints; in more detail, in this work, we aim to define the optimal implementation of an energy-aware DCPS. Moreover, smart grids play a crucial role in developing energy-aware DCPSs, providing a flexible and efficient power system integrating renewable energy sources, microgrids, and other distributed energy resources. The proposed energy-aware DCPSs contribute to the development of smart grids by providing a sustainable, self-consistent, and efficient way to manage energy distribution and consumption. The performance demonstrates our approach’s effectiveness for consumption and production (based on RMSE and MAE metrics). Our research supports the transition towards a more sustainable future, where communities adopting REC principles become key players in the energy landscape.

Storage and analysis of video surveillance data is a significant challenge, requiring video interpretation and event detection in the relevant context. To perform this task, the low-level features including shape, texture, and color information are extracted and represented in symbolic forms. In this work, a methodology is proposed, which extracts the salient features and properties using machine learning techniques and represent this information as Linked Data using a domain ontology that is explicitly tailored for detection of certain activities. An ontology is also developed to include concepts and properties which may be applicable in the domain of surveillance and its applications. The proposed approach is validated with actual implementation and is thus evaluated by recognizing suspicious activity in an open parking space. The suspicious activity detection is formalized through inference rules and SPARQL queries. Eventually, Semantic Web Technology has proven to be a remarkable toolchain to interpret videos, thus opening novel possibilities for video scene representation, and detection of complex events, without any human involvement. The proposed novel approach can thus have representation of frame-level information of a video in structured representation and perform event detection while reducing storage and enhancing semantically-aided retrieval of video data.

In a Smart City scenario, the authors envisioned an IoT-Cloud framework for the management of boards and resources scattered over a geographic area. It can also become a tool to let device owners contribute freely to the infrastructure. In this paper, we present an authorization and delegation model for the IoT-Cloud based on blockchain technology. We focus on smart city’s design and simulation of smart contracts to address the specific characteristics of smart environments and analyze access control and delegation mechanism in IoT. In particular, the scheme is implemented for the Ethereum platform in the form of smart contracts. We present the theoretical analysis of the proposed solution. We perform experiments on local testnet Ganache and public testnet Rinkeby to evaluate the performance of the presented models. Finally, we discuss the results and limitations of the system and possible solutions to issues.

Nowadays Cloud computing permeates almost every domain in Information and Communications Technology (ICT) and, increasingly, most of the action is shifting from large, dominant players toward independent, heterogeneous, private/hybrid deployments, in line with an ever wider range of business models and stakeholders. The rapid growth in the numbers and diversity of small and medium Cloud providers is bringing new challenges in the as-a-Services space. Indeed, significant hurdles for smaller Cloud service providers in being competitive with the incumbent market leaders induce some innovative players to “federate” deployments in order to pool a larger, virtually limitless, set of resources across the federation, and stand to gain in terms of economies of scale and resource usage efficiency. Several are the challenges that need to be addressed in building and managing a federated environment, that may go under the “Security”, “Interoperability”, “Versatility”, “Automatic Selection” and “Scalability” labels. The aim of this paper is to present a survey about the approaches and challenges belonging to the “Automatic Selection” category. This work provides a literature review of different approaches adopted in the “Automatic and Optimal Cloud Service Provider Selection”, also covering “Federated and Multi-Cloud” environments.

BackgroundPosterior reversible encephalopathy syndrome (PRES), reversible cerebral vasoconstriction syndrome (RCVS), or the coexistence of these two entities shares similar risk factors and clinical features. For these conditions, a common origin has been supposed. Even if the majority of patients show a favorable course and a good prognosis, a small percentage of cases develop neurological complications. Up to date, only about 30 cases of PRES associated with Guillain-Barré syndrome (GBS) have been reported in the literature.CasesHere, we present two cases of a particularly aggressive PRES/RCVS overlap syndrome, associated with acute motor axonal neuropathy (AMAN) and acute inflammatory demyelinating polyneuropathy (AIDP) variants of GBS, respectively, presenting with similar initial clinical aspects and developing both an atypical and unfavorable outcome. On MRI examination, the first patient showed typical aspects of PRES, while, in the second case, radiological features were atypical and characterized by diffusion restriction on the apparent diffusion coefficient (ADC) map. The first patient demonstrated rapid worsening of clinical conditions until death; the second one manifested and maintained neurological deficits with a permanent disability.ConclusionsWe suggest that PRES may conceal RCVS aspects, especially in most severe cases or when associated with a dysimmune syndrome in which autoimmune system and endothelial dysfunction probably play a prominent role in the pathogenesis. Although the role of IVIg treatment in the pathogenesis of PRES has been proposed, we suggest that GBS itself should be considered an independent risk factor in developing PRES.

(1) Background: Stroke is one of the most frequent causes of status epilepticus (SE) in adults. Patients with stroke and SE have poorer prognosis than those with stroke alone. We described characteristics and prognosis of early- and late-onset post-stroke SE (PSSE). (2) Methods: We retrospectively analyzed consecutive stroke patients who experienced a first SE between August 2012 and April 2021, comparing clinical characteristics, stroke, and SE features between early- versus late-onset SE in relation to patients’ outcome. (3) Results: Forty stroke patients experienced PSSE. Fourteen developed an early-onset SE (35%) and twenty-six a late-onset SE (65%). Early-onset SE patients had a slightly higher NIHSS score at admission (6.9 vs. 6.0; p = 0.05). Early-onset SE was more severe than late-onset, according to STESS (Status Epilepticus Severity Score) (3.5 vs. 2.8; p = 0.05) and EMSE (Epidemiology-based Mortality score in Status Epilepticus) score (97.0 vs. 69.5; p = 0.04); furthermore, it had a significant impact on disability at 3-month and 1-year follow-up (p = 0.03 and p = 0.02). SE recurrence and seizures relapse were observed mainly in cases of late-onset SE. (4) Conclusions: Early-onset SE seems to be associated with higher disability in short- and long-term follow-up as possible expression of severe acute brain damage.

Stress hyperglycemia may impair outcomes in patients with acute ischemic stroke (AIS) undergoing mechanical thrombectomy (MT). The glucose-to-glycated hemoglobin ratio (GAR) was used to measure stress hyperglycemia. Data from our database of consecutive patients admitted to the Udine University Hospital with AIS who were treated with MT between January 2015 and December 2020 were retrospectively analyzed. We included 204 patients in the study and stratified them into four groups according to the quartiles of GAR (Q1–Q4). The higher the GAR index, the more severe the stress hyperglycemia was considered. Patients with more severe stress hyperglycemia showed a higher prevalence of 3-month poor outcome (Q1, 53.1%; Q2, 40.4%; Q3, 63.5%; Q4, 82.4%;p= 0.001), 3-month mortality (Q1, 14.3%; Q2, 11.5%; Q3, 15.4%; Q4, 31.4%;p= 0.001), and symptomatic intracranial hemorrhage (Q1, 2%; Q2, 7.7%; Q3, 7.7%; Q4, 25.4%;p= 0.001). After controlling for several confounders, severe stress hyperglycemia remained a significant predictor of 3-month poor outcome (OR 4.52, 95% CI 1.4–14.62,p= 0.012), 3-month mortality (OR 3.55, 95% CI 1.02–12.29,p= 0.046), and symptomatic intracranial hemorrhage (OR 6.89, 95% CI 1.87–25.36,p= 0.004). In summary, stress hyperglycemia, as measured by the GAR index, is associated with a detrimental effect in patients with AIS undergoing MT.

A visit from qualified and authorized personnel to troubleshoot customer-premises equipment (CPE) on-site is an example of a situation where both local wireless connectivity and access to the Cloud may be needed, the former to discover proximal equipment to probe and diagnose upon, the latter to promptly send useful data back to the company providing services to the customer, e.g., without waiting for the repair session to be over and the personnel have left the premises. In such a scenario, the company support service may take advantage of a dynamic coupling and interaction among the Cloud, mobiles (as personnel-borne equipment), and IoT devices (as bolt-on addition to the, possibly legacy, CPE). For this purpose, a software architecture for the collection and transmission of metrics of interest through a combined system, featuring a Cloud instance and a mobile-based agent, has been implemented. The reference architecture is composed of IoT-Cloud and edge/mobile devices, specifically smartphones and embedded boards, the latter equipped with a Cloud-controlled agent as a software component, with the goal to collect useful metrics from hardware devices of an energy substation, as a way to support remote and on-site diagnostics through technologies such as Bluetooth Low Energy. We present the reference architecture, its implementation, and some preliminary results on the effectiveness of the proposed solution.

The Internet of Medical Things (IoMT), combined with interconnected wearable devices and medical-grade sensors, can play an essential role in healthcare evolution. By exploiting the data generated by the plethora of interconnected devices (vital parameters, location-based info, patients activity and more), advanced ICT systems can be put in place with predicting capabilities. This way potentially critical situations, that may evolve in serious complications to patients’ well-being, can be promptly recognized and successfully addressed, first of all, to save lives and secondarily to limit economical damages. To support continuous patient monitoring in public and private healthcare, this paper proposes "DILoCC", an architecture to manage wearable devices, sensors and applications, that uses a Distributed Incremental Learning (DIL) approach to exploit cooperation among the sensing devices and increase the overall system efficiency through the mitigation of "Catastrophic Forgetting" consequences.

Smart Building/Environment management is an interesting topic that, although widely investigated in the literature, has not seen wide adoption in real case studies. Current Smart Building solutions provide facilities for: i) automatically managing HVAC (Heating, Ventilation and Air Conditioning) systems; ii) energy management, iii) building automation. Current solutions are tightly coupled with the underlying IT infrastructure of the building and cannot be rearranged according to the events, catastrophic or otherwise, that may modify the building structure (e.g., a disruption to sections of a building). In this work, we present a novel approach for Smart Buildings (we refer to it as "Software Defined Building 2.0") to customize and reprogram IT infrastructure powering buildings, this way enabling cooperation among Cyber-Physical Systems (CPSs) operating within. We analyze an architecture enabling this approach, evaluating overhead management and its impact on systems’ performance.

To enable and support smart environments, a recent ICT trend promotes pushing computation from the remote Cloud as close to data sources as possible, resulting in the emergence of the Fog and Edge computing paradigms. Together with Cloud computing, they represent a stacked architecture, in which raw datasets are first pre-processed locally at the Edge and then vertically offloaded to the Fog and/or the Cloud. However, as hardware is becoming increasingly powerful, Edge devices are seen as candidates for offering data processing capabilities, able to pool and share computing resources to achieve better performance at a lower network latency—a pattern that can be also applied to Fog nodes. In these circumstances, it is important to enable efficient, intelligent, and balanced allocation of resources, as well as their further orchestration, in an elastic and transparent manner. To address such a requirement, this article proposes an OpenStack-based middleware platform through which resource containers at the Edge, Fog, and Cloud levels can be discovered, combined, and provisioned to end users and applications, thereby facilitating and orchestrating offloading processes. As demonstrated through a proof of concept on an intelligent surveillance system, by converging the Edge, Fog, and Cloud, the proposed architecture has the potential to enable faster data processing, as compared to processing at the Edge, Fog, or Cloud levels separately. This also allows architects to combine different offloading patterns in a flexible and fine-grained manner, thus providing new workload engineering patterns. Measurements demonstrated the effectiveness of such patterns, even outperforming edge clusters.

SmartME has been one of the first initiatives in Italy to realize a Smart City through the use of open technologies. Thanks to the use of low cost sensor-powered devices scattered over the city area, different “smart” services have been deployed having the Stack4Things framework as the common underlying middleware. In this paper, we present the results obtained after 2 years of project highlighting the vertical solutions that have been proposed in different areas ranging from environmental monitoring to parking management.

It is not known whether the current territorial organization for acute revascularization treatments in ischemic stroke patients guarantees similar time to treatment and functional outcomes among different levels of institutional stroke care. We aimed to assess the impact of time to treatment on functional outcomes in ischemic stroke patients who received intravenous thrombolysis (IVT) alone, bridging (IVT plus thrombectomy), or primary thrombectomy in level 1 and level 2 Stroke Units (SUs) in Triveneto, a geographical macroarea in Northeast of Italy. We conducted an analysis of data prospectively collected from 512 consecutive ischemic stroke patients who received IVT and/or mechanical thrombectomy in 25 SUs from September 17th to December 9th 2018. The favorable outcome measures were mRS score 0–1 and 0–2 at 3months. The unfavorable outcome measures were mRS score 3–5 and death at 3months. We estimated separately the possible association of each variable for time to treatment (onset-to-door, door-to-needle, onset-to-needle, door-to-groin puncture, needle-to-groin puncture, and onset-to-groin puncture) with 3-month outcome measures by calculating the odds ratios (ORs) with two-sided 95% confidence intervals (CI) after adjustment for pre-defined variables and variables with a probability value ≤ 0.10 in the univariate analysis for each outcome measure. Distribution of acute revascularization treatments was different between level 1 and level 2 SUs (p < 0.001). Among 182 patients admitted to level 1 SUs (n = 16), treatments were IVT alone in 164 (90.1%), bridging in 12 (6.6%), and primary thrombectomy in 6 (3.3%) patients. Among 330 patients admitted to level 2 SUs (n = 9), treatments were IVT alone in 219 (66.4%), bridging in 74 (22.4%), and primary thrombectomy in 37 (11.2%) patients. Rates of excellent outcome (51.4% vs 45.9%), favorable outcome (60.1% vs 58.7%), unfavorable outcome (33.3% vs 33.8%), and death (9.8% vs 11.3%) at 3months were similar between level 1 and 2 SUs. No significant association was found between time to IVT alone (onset-to-door, door-to-needle, and onset-to-needle) and functional outcomes. After adjustment, door-to-needle time ≤ 60min (OR 4.005, 95% CI 1.232–13.016), shorter door-to-groin time (OR 0.991, 95% CI 0.983–0.999), shorter needle-to-groin time (OR 0.986, 95% CI 0.975–0.997), and shorter onset-to-groin time (OR 0.994, 95% CI 0.988–1.000) were associated with mRS 0–1. Shorter door-to-groin time (OR 0.991, 95% CI 0.984–0.998), door-to-groin time ≤ 90min (OR 12.146, 95% CI 2.193–67.280), shorter needle-to-groin time (OR 0.983, 95% CI 0.972–0.995), and shorter onset-to-groin time (OR 0.993, 95% CI 0.987–0.999) were associated with mRS 0–2. Longer door-to-groin time (OR 1.007, 95% CI 1.001–1.014) and longer needle-to-groin time (OR 1.019, 95% CI 1.005–1.034) were associated with mRS 3–5, while door-to-groin time ≤ 90min (OR 0.229, 95% CI 0.065–0.808) was inversely associated with mRS 3–5. Longer onset-to-needle time (OR 1.025, 95% CI 1.002–1.048) was associated with death. Times to treatment influenced the 3-month outcomes in patients treated with thrombectomy (bridging or primary). A revision of the current territorial organization for acute stroke treatments in Triveneto is needed to reduce transfer time and to increase the proportion of patients transferred from a level 1 SU to a level 2 SU to perform thrombectomy.

Thanks to the impact of the advancement in the hardware field, the network size and usage scope of the Internet are continuously growing. Indeed, new smart devices, e.g., sensors, actuators, home appliances are becoming strong enough to communicate and exchange data over the Internet. Accordingly, this distributed ecosystem with sensing/actuation capabilities is introducing new market opportunities with innovative services including, e.g., environmental monitoring, traffic monitoring, homes/buildings control. To conceive new IoT services, enabling the smart devices to join the Internet and expose their capabilities/data through the Web is fundamental. For this purpose, Web Application Programming Interfaces (APIs) or what we refer to also as RESTful Web Services is a paradigm that can enhance the IoT application scope by making smart things part of the Web. In this paper, based on our Stack4Things (S4T) Cloud middleware, we introduce a new approach for exposing services running on IoT devices to the Web so that they become reachable using globally resolvable Uniform Resource Locators (URLs). We emphasized security issues as well by implementing, on the devices, an automated mechanism capable of managing X.509 certificates issuance to enable secure communications using Hypertext Transfer Protocol Secure (HTTPS).

With the great impact of the advancement in the hardware field, major efforts have been made to create large-scale networks of IoT devices (e.g., embedded systems, sensors, and actuators) that can interact with each other as well as their surrounding environments. However, most of the deployments are based on proprietary and tightly-coupled systems. To overwhelm the application layer interoperability issues, the Web of Things (WoT) paradigm aims at making smart things an integral part of the Web and thus, enabling them to communicate through Web standards/protocols. Therefore, applications/services making use of smart things become easier to conceive by means of Web standards/protocols (e.g., HTTP, Websockets). This paper proposes an approach enabling the smart devices to join the Web using features provided by the Stack4Things (S4T) OpenStack-based middleware. Thereby, user agents, typically, Web browsers and/or smart devices can make use of the resources of (other) IoT devices using globally resolvable Uniform Resource Locators (URLs) regardless of their underlying used protocol stacks.

In this innovative practice full paper we present the implementation of the distant laboratory for the Internet of Things teaching and training. The recent outbreak of the SARS-COV-2 virus and related COVID-19 pandemic throughout the world has caused governments across the world to shut down schools and universities, to slow down the spread of the coronavirus that is causing the disease. As a result, some universities and schools have switched from physical classrooms to virtual or online classrooms. This approach is working well for theoretical subjects and courses, but it is not straight forward in the case of laboratory subjects and courses that require access to hardware resources. The IOT-OPEN.EU remote laboratory infrastructure presented in this paper is a timely solution. In this paper, we present current advances in distant learning, distant laboratory models, and the IOT-OPEN.EU remote laboratory implemented as part of the IOT-OPEN.EU ERASMUS+ project, along with short analysis on current advances in distant learning, where students are interacting with physical hardware remote way.

In this work, we propose a low-cost Smart and Healthy Intelligent Room System (SHIRS), able to monitor Indoor Air Quality (IAQ) by enhancing edge-based computation. SHIRS exploits the ability to run Machine Learning (ML) algorithms to infer humans presence (headcount) from environmental data analysis. Experimental results show the validity of the proposed approach, demonstrate the potential of edge-based computing and push towards the adoption of smart integrated Cloud-IoT frameworks for environmental monitoring and control.

With the widespread emergence of the Internet of Things (IoT), our environment and locations are turning progressively into smart environments ranging from individual houses/offices to schools, factories, and hospitals. Even more interesting, with the rise of Fog/Edge paradigms, the IoT application scope has been extended to provide critical services. By pushing resources such as compute and storage to the network edge, IoT-based services are taking benefits from their proximity to provide better performances. However, albeit an exciting development in and by itself, Edge/Fog computing platforms currently do not provide a convenient level of flexibility and efficiency to support the dynamic composition of services with a data-oriented approach. In this context, the Function-as-a-Service computing paradigm rises as a convenient/suitable paradigm to be adopted in the IoT landscape. For the sake of providing flexible IoT Edge/Fog deployments, this paper introduces a system providing FaaS services based on a distributed IoT infrastructure. Besides, we provide a dashboard based on Node-RED that exploits, in the backend, the FaaS system to make the users able to conceive customized applications using the resources (i.e., sensors and actuators) that the IoT devices can host.

Recently, Industrial Wireless Sensor Networks (IWSN) have gained a lot of interest from both research and industry communities as an evolution of WSN specifically tailored for Industry 4.0 applications and their requirements. Thanks to the set of benefits IWSN technology introduces, it is considered to be a sustainable solution for industrial system monitoring. Yet, this approach has several drawbacks stemming from the limited resources (i.e., compute and storage) available on-board network devices. Although Cloud-based WSN data management solutions are widely adopted, issues related to this approach persist (e.g., high latency, bandwidth consumption, storage costs). This paper introduces an innovative platform enhancing IWSN architectures by adding a processing layer at the network edge: an approach that follows the Fog Computing paradigm. Remote management and programmability of the Fog layer are only some of the most challenging requirements. We show how our approach is suitable for industrial scenarios by applying it to a representative use case, i.e., the monitoring of asynchronous electric motors.

No study investigated the possible detrimental effect of stress hyperglycemia on patients affected acute ischemic stroke (AIS) undergoing intravenous thrombolysis (IVT). A new index, the glucose-to-glycated hemoglobin ratio (GAR), has been developed for assessing stress hyperglycemia. We retrospectively analyzed data from a prospectively collected database of consecutive patients admitted to the Udine University Hospital with AIS that were treated with IVT from January 2015 to December 2019. Four hundred and fourteen consecutive patients with AIS undergoing IVT entered the study. The patients were then stratified into four groups by quartiles of GAR (Q1–Q4). The higher GAR index was, the more severe stress hyperglycemia was considered. Prevalence of 3 months poor outcome (37.7% for Q1, 34% for Q2, 46.9% for Q3, and 66.7% for Q4, p for trend = 0.001), 3 months mortality (10.5% for Q1, 7.5% for Q2, 11.2% for Q3, and 27.1% for Q4, p for trend = 0.001), and symptomatic intracranial hemorrhage (0.9% for Q1, 0.9% for Q2, 5.1% for Q3, and 17.7% for Q4, p for trend = 0.001) was significant different among the four groups. AIS patients with severe stress hyperglycemia had a significantly increased risk of 3 months poor outcome (OR 2.43, 95% CI 1.14–5.22, p = 0.02), 3 months mortality (OR 2.38, 95% CI 1.01–5.60, p = 0.04), and symptomatic intracranial hemorrhage (OR 16.76, 95% CI 2.09–134.58, p = 0.008) after IVT. In conclusion, we demonstrated that stress hyperglycemia, as measured by the GAR index, is associated to worse outcome in AIS patients undergoing IVT.

Background: Admission hyperglycemia impairs outcome in acute ischemic stroke (AIS) patients undergoing mechanical thrombectomy (MT). Since hyperglycemia in AIS represents a dynamic condition, we tested whether the dynamic patterns of hyperglycemia, defined as blood glucose levels &gt, 140 mg/dl, affect outcomes in these patients. Methods: We retrospectively analyzed data of 200 consecutive patients with prospective follow-up. Based on blood glucose level, patients were distinguished into 4 groups: (1) persistent normoglycemia, (2) hyperglycemia at baseline only, (3) hyperglycemia at 24-h only, and (4) persistent (at baseline plus at 24-h following MT) hyperglycemia. Results: AIS patients with persistent hyperglycemia have a significantly increased risk of poor functional outcome (OR 6.89, 95% CI 1.98&ndash, 23.94, p = 0.002, for three-month poor outcome, OR 11.15, 95% CI 2.99&ndash, 41.52, p = 0.001, for no major neurological improvement), mortality (OR 5.37, 95% CI 1.61&ndash, 17.96, p = 0.006, for in-hospital mortality, OR 4.43, 95% CI 1.40&ndash, 13.97, p = 0.01, for three-month mortality), and hemorrhagic transformation (OR 6.89, 95% CI 2.35&ndash, 20.21, p = 0.001, for intracranial hemorrhage, OR 5.42, 95% CI 1.54&ndash, 19.15, p = 0.009, for symptomatic intracranial hemorrhage) after endovascular treatment. These detrimental effects were partially confirmed after also excluding diabetic patients. The AUC-ROC showed a very good performance for predicting three-month poor outcome (0.76) in-hospital mortality (0.79) and three-month mortality (0.79). Conclusions: Our study suggests that it is useful to perform the prolonged monitoring of glucose levels lasting 24-h after MT.

Data regarding the possible relationship of insomnia and EDS with mortality are inconclusive. The aim of this study was to investigate the association between these sleep complaints and the risk of long-term (20 years) all-cause mortality in older adults. Between April 2000 and March 2001, 750 subjects aged 65 years and older, who resided in the seventh district of Udine, were recruited. Data on sociodemographic characteristics, past medical history, and pharmacological treatment were collected. Dementia was diagnosed using a comprehensive neurological and neuroradiological assessment. Older adults were interviewed by neuropsychologists trained in sleep disturbances in order to assess the presence of sleep complaints. Vital status was followed over 20 years until March 2020. Older male adults affected by insomnia and EDS were significantly more likely to die over the follow-up period. Indeed, males reporting poor sleep and daytime somnolence had a 60% and 48% higher chance of dying than subjects who were not affected by these sleep complaints, respectively. The HR was attenuated after adjusting for confounding variables among insomniacs, whereas that of somnolent men strengthened. Differently from men, insomnia and EDS did not have any impact on mortality in older women. In conclusion, older male adults affected by insomnia and EDS had a significant increased risk of mortality, which is independent of cancer, depression, dementia, cardiovascular diseases, and sleeping pill use.

Intravenous thrombolysis (IVT) in patients with a low National Institutes of Health Stroke Scale (NIHSS) score of 0&ndash, 5 remains controversial. IVT should be used in patients with mild but nevertheless disabling symptoms. We hypothesize that response to IVT of patients with &ldquo, mild stroke&rdquo, may depend on their level of functional dependence (FD) at hospital admission. The aims of our study were to investigate the effect of IVT and to explore the role of FD in influencing the response to IVT. This study was a retrospective analysis of a prospectively collected database, including 389 patients stratified into patients receiving IVT (IVT+) and not receiving IVT (IVT &minus, ) just because of mild symptoms. Barthel index (BI) at admission was used to assess FD, dividing subjects with BI score &lt, 80 (FD+) and with BI score &gt, 80 (FD&minus, ). The efficacy endpoints were the rate of positive disability outcome (DO+) (3-month mRS score of 0 or 1), and the rate of positive functional outcome (FO+) (mRS score of zero or one, plus BI score of 95 or 100 at 3 months). At the multivariate analysis, IVT treatment was an independent predictor of DO+ (OR 3.12, 95% CI 1.34&minus, 7.27, p = 0.008) and FO+ (OR: 4.70, 95% CI 2.38&minus, 9.26, p = 0.001). However, FD+ IVT+ patients had a significantly higher prevalence of DO+ and FO+ than those FD+ IVT&ndash, Differently, IVT treatment did not influence DO+ and FO+ in FD&ndash, patients. In FD+ patients, IVT treatment represented the strongest independent predictor of DO+ (OR 6.01, 95% CI 2.59&ndash, 13.92, p = 0.001) and FO+ (OR 4.73, 95% CI 2.29&ndash, 9.76, p = 0.001). In conclusion, alteplase seems to improve functional outcome in patients with &ldquo, However, in our experience, this beneficial effect is strongly influenced by FD at admission.

In recent years, statistics have confirmed that the number of elderly people is increasing. Aging always has a strong impact on the health of a human being; from a biological of point view, this process usually leads to several types of diseases mainly due to the impairment of the organism. In such a context, healthcare plays an important role in the healing process, trying to address these problems. One of the consequences of aging is the formation of pressure ulcers (PUs), which have a negative impact on the life quality of patients in the hospital, not only from a healthiness perspective but also psychologically. In this sense, e-health proposes several approaches to deal with this problem, however, these are not always very accurate and capable to prevent issues of this kind efficiently. Moreover, the proposed solutions are usually expensive and invasive. In this paper we were able to collect data coming from inertial sensors with the aim, in line with the Human-centric Computing (HC) paradigm, to design and implement a non-invasive system of wearable sensors for the prevention of PUs through deep learning techniques. In particular, using inertial sensors we are able to estimate the positions of the patients, and send an alert signal when he/she remains in the same position for too long a period of time. To train our system we built a dataset by monitoring the positions of a set of patients during their period of hospitalization, and we show here the results, demonstrating the feasibility of this technique and the level of accuracy we were able to reach, comparing our model with other popular machine learning approaches.

Supplemental Digital Content is available in the text., Background and Purpose: We aimed to investigate the rate of hospital admissions for cerebrovascular events and of revascularization treatments for acute ischemic stroke in Italy during the coronavirus disease 2019 (COVID-19) outbreak. Methods: The Italian Stroke Organization performed a multicenter study involving 93 Italian Stroke Units. We collected information on hospital admissions for cerebrovascular events from March 1 to March 31, 2020 (study period), and from March 1 to March 31, 2019 (control period). Results: Ischemic strokes decreased from 2399 in 2019 to 1810 in 2020, with a corresponding hospitalization rate ratio (RR) of 0.75 ([95% CI, 0.71–0.80] P

Nowadays, the study of nucleic acids (DNA/RNA) has become a digital science thanks to the advent of modern massive parallel sequencing technologies, better known with the acronym NGS standing for next-generation sequencing, and to the availability of a vast amount of genetic data easily accessible from publicly available databases. Due to the quantity and complexity of such data, its processing requires strong computer science knowledge and skills. This background includes topics such as programming and scripting languages, command-line interfaces, low-level data management tools, which are not always part of the toolbox of molecular biologists and geneticists. The need to adapt to entirely new IT tools and workflows slow down even the more experienced researchers, thus dedicated and customizable GUIs would be much more preferable and conducive. In this paper, we tackle this issue by proposing a preliminary architecture for a framework providing the following benefits: i) it supports the post-NGS analysis process definition phase (commonly called pipeline definition) via a graphical dashboard designed with NodeRED; ii) it automatically deploys the workflows on top of a cluster of computational resources, according to the Function-as-a-Service paradigm, i.e., treating each step of the pipeline as a function to be executed within Linux-based containers, pre-configured with all the necessary dependencies; iii) it runs such containers taking care automatically of resource load balancing. Finally, the framework is thought to include human feedback in the loop, thanks to the availability of a smart notification system, allowing the researcher to monitor the workflows and make any decision needed for its continuation.

In recent years, a new trend of advanced applications with huge demands in terms of Quality of Service (QoS) is gaining ground. Even though Cloud computing provides mature management facilities with ubiquitous capabilities, novel requirements and workloads, foisted by new services, start to expose its weaknesses. In this context, a new Information and Communication Technologies (ICT) trend aims at pushing computation from the Cloud to be much close as possible to data sources, raising in the evolution of new paradigms namely Fog and Mist computing. Specifically, the Fog computing paradigm exploits powerful nodes such as servers, routers, and cloudlets that are coupled with the end devices or their access networks accordingly; they are ”relatively” close by the data sources. Whereas Mist computing, which is a lightweight form of Fog computing, pushes the resources even closer. Precisely, Mist computing uses particular nodes that could reside within the same network (e.g., Local Area Network (LAN)) as the end-devices. Considering the advancement that the hardware is knowing nowadays, Fog and Mist nodes are seen suitable to provide resources such as processing, storage, and networking in the proximity of data sources; thereby, the requirements of the new services could be met. Together with the Cloud, the Fog and Mist paradigms introduce a stacked architecture for data processing where a data pre-processing could be performed at the Mist level, then offloaded vertically to the upper layers (i.e., Fog nodes or the Cloud). In these circumstances, it is fundamental to build a management system able to provision efficiently the Fog/Mist-based applications. For this purpose, the Operating System (OS)-level virtualization using containerization technologies, considering its light footprint, fits as a suitable solution to provide Fog/Mist services. The industrial-grade Cloud middlewares, such as OpenStack, which is a reference architecture for Infrastructure-as-a-Service solutions, are still far away from incorporating this new trend. This article proposes an OpenStack-based middleware platform through which containers can be deployed/managed at the Fog/Mist levels.

The 2018 West Nile Virus (WNV) season in Europe was characterized by an extremely high infection rate and an exceptionally higher burden when compared to previous seasons. Overall, there was a 10.9-fold increase in incidence in Italy, with 577 human cases, 230 WNV neuroinvasive diseases (WNNV) and 42 WNV-attributed deaths. Methods: in this paper we retrospectively reported the neurological presentation of 7 patients admitted to University Hospital of Udine with a diagnosis of WNNV, especially focusing on two patients who presented with atypical severe brain stem involvement. Conclusions: the atypical features of some of these forms highlight the necessity to stay vigilant and suspect the diagnosis when confronted with neurological symptoms. We strongly encourage clinicians to consider WNNV in patients presenting with unexplained neurological symptoms in mild climate-areas at risk.

In clinical practice, direct oral anticoagulants (DOACs) are often started earlier (≤ 7 days) than in randomized clinical trials after stroke. We aimed to develop a nomogram model incorporating time of DOAC introduction ≤ 7 days of stroke onset in combination with different degrees of stroke radiological/neurological severity at the time of treatment to predict the probability of unfavorable outcome. We conducted a multicenter prospective study including 344 patients who started DOAC 1–7 days after atrial fibrillation-related stroke onset. Computed tomography scan 24–36 h after stroke onset was performed in all patients before starting DOAC. Unfavorable outcome was defined as modified Rankin Scale (mRS) score > 2 at 3 months. Based on multivariate logistic model, the nomogram was generated. We assessed the discriminative performance by using the area under the receiver operating characteristic curve (AUC–ROC) and calibration of risk prediction model by using the Hosmer–Lemeshow test. Onset-to-treatment time for DOAC (OR: 1.21, p = 0.030), NIH Stroke Scale (NIHSS) score at the time of treatment (OR: 1.00 for NIHSS = 0–5; OR: 2.67, p = 0.016 for NIHSS = 6–9; OR: 26.70, p

Recent technological advances led to the rapid and uncontrolled proliferation of intelligent surveillance systems (ISSs), serving to supervise urban areas. Driven by pressing public safety and security requirements, modern cities are being transformed into tangled cyber-physical environments, consisting of numerous heterogeneous ISSs under different administrative domains with low or no capabilities for reuse and interaction. This isolated pattern renders itself unsustainable in city-wide scenarios that typically require to aggregate, manage, and process multiple video streams continuously generated by distributed ISS sources. A coordinated approach is therefore required to enable an interoperable ISS for metropolitan areas, facilitating technological sustainability to prevent network bandwidth saturation. To meet these requirements, this paper combines several approaches and technologies, namely the Internet of Things, cloud computing, edge computing and big data, into a common framework to enable a unified approach to implementing an ISS at an urban scale, thus paving the way for the metropolitan intelligent surveillance system (MISS). The proposed solution aims to push data management and processing tasks as close to data sources as possible, thus increasing performance and security levels that are usually critical to surveillance systems. To demonstrate the feasibility and the effectiveness of this approach, the paper presents a case study based on a distributed ISS scenario in a crowded urban area, implemented on clustered edge devices that are able to off-load tasks in a “horizontal” manner in the context of the developed MISS framework. As demonstrated by the initial experiments, the MISS prototype is able to obtain face recognition results 8 times faster compared with the traditional off-loading pattern, where processing tasks are pushed “vertically” to the cloud.

Infrastructure as a service is a successful cloud-based utility paradigm. Its effectiveness stems from employing virtualization and enabling cloud providers to leverage elastic datacenter capabilities. To expand the reach and benefits of IaaS, the cloud must embrace the Internet of Things infrastructure as virtualized I/O resources and first-class objects at the edge.