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Introduction: Obstructive Sleep Apnea (OSA) in children is characterized by repeated episodes of partial or complete obstruction of the upper airways that impair normal ventilation and cause hypoxia and sleep disruption. These episodes activate innate and adaptive immunity resulting in the production of proinflammatory cytokines: IL-1β, IL-6, TNF-α, and reactive oxygen species. The hypothalamic–pituitary–adrenal (HPT) axis is also activated with alteration of the circadian rhythm of cortisol synthesis. OSA in children, and even more in adults, induces a systemic inflammatory condition that contributes to the genesis of clinical complications: poor growth, learning disabilities, cardiovascular changes, insulin resistance, and metabolic syndrome. Methods: A total of 42 non-obese children (age 1–15 years) were enrolled among those sent to our sleep center to perform full polysomnography (PSG). After PSG, 6 children did not show OSA (controls), 20 had mild OSA (m OSA), and 16 had medium-severe OSA (MS OSA). In vitro IL-1β, TNF-α, and serum cortisol levels were measured at 2 and 8 am in the analyzed groups. Results: Cortisol levels did not differ between controls and OSA children. At 2 am, there were no differences between controls and OSA in TNF-α production, whereas at 8 am, TNF-α was reduced in MS-OSA. IL-1β production showed no differences between OSA and controls. Conclusions: In our population, only TNF-α production is suppressed in MS-OSA: this might indicate a role of OSA severity in inducing inflammation. In adults, the phenomenon is more pronounced due to the habitual greater severity/duration of OSA, presence of comorbidities (cardiovascular and metabolic), and different immune system function.

Optimal behind-the-meter energy management often requires a day-ahead electric load forecast capable of learning non-linear and non-stationary patterns, due to the spatial disaggregation of loads and concept drift associated with time-varying physics and behavior. There are many promising machine learning techniques in the literature, but black box models lack explainability and therefore confidence in the models’ robustness can’t be achieved without thorough testing on data sets with varying and representative statistical properties. Therefore this work adopts and builds on some of the highest-performing load forecasting tools in the literature, which are Long Short-Term Memory recurrent networks, Empirical Mode Decomposition for feature engineering, and k-means clustering for outlier detection, and tests a combined methodology on seven different load data sets from six different load sectors. Forecast test set results are benchmarked against a seasonal naive model and SARIMA. The resultant skill scores range from −6.3% to 73%, indicating that the methodology adopted is often but not exclusively effective relative to the benchmarks.

In extreme temperature environments, a newly emerging engineering application involves both the active and passive control of structures using cryogenic shape memory alloys, which are smart materials able to recover high deformation below the freezing point. With the objective of carrying out new advances in this area, the present work aims to investigate the Cu-7.5Al-13.5Mn (wt.%) shape memory alloy. Thermal, microstructural, and thermomechanical analyses of as-cast and hot-rolled specimens were performed, taking into account the effects of annealing and solubilization. It was observed that the phase transition occurs at temperatures below 120 K and changes according to the thermo-mechanical path. Specifically, hot-rolling lowers the phase transition temperature range with respect to the as-cast condition–from 34 K to 23 K for Mf, and from 89 K to 80 K for Af. Additionally, when the annealing temperature rises, the phase transformation temperature increases as well, and the alloy loses its cryogenic features when heat treated above 473 K. Finally, loss factors of 0.06 and 0.088, which were respectively found in dynamic and static settings, validate the material’s good damping response.

Shape memory alloys (SMA) are functional materials known for their shape memory and pseudoelastic properties, which originated from a thermoelastic phase transition between two solid phases: austenite and martensite. The ranges of temperature at which austenite and martensite are stable depend primarily on the chemical composition and the thermomechanical history of the alloy. This work presents a broad overview of shape memory alloys presenting the thermoelastic phase transition at cryogenic temperatures—that is, at temperatures below the freezing point of water. Currently, this class of SMA is not very well explored due to the difficulties in conducting both structural and functional experimentations at very low temperatures. However, these materials are of great importance for extreme environments such as space. In this work, the different classes of cryogenic SMA will first be presented as a function of their phase transformation temperatures. Hints of their mechanical performance will also be reported. Cu-based systems have been identified as cryogenic SMA presenting the lowest phase transformation temperatures. The lowest measured Ms (45 K) was found for the Cu-8.8Al-13.1Mn (wt.%) alloy.

(1) Background: Anaemia is a common finding in patients with colon cancer and is commonly corrected by blood transfusion prior to surgery. However, the prognostic role of perioperative transfusions is still debated. The aim of the present study was to investigate the role of preoperative anaemia and preoperative blood transfusion in influencing the prognosis in colon cancer. (2) Patients and Methods: Patients undergoing elective surgery for colon cancer at a tertiary referral university hospital between January 2010 and December 2018 were included in a retrospective review of a prospectively collected database. Univariate and regression analyses were performed to identify the prognostic role of preoperative anaemia and preoperative transfusions in this homogeneous cohort of patients. (3) Results: A total of 780 patients were included in the final analysis. The estimated five-year overall survival rate was significantly worse in the anaemic group (83.8% in non-anaemic patients, 60.6% in mild anaemic patients, 61.3% in moderate anaemic patients and 58.4% in severe anaemic patients; log-rank < 0.001 vs. non-anaemic patients). Anaemic status was found to be an independent adverse prognostic factor (hazard ratio (HR): 1.46; 95% confidence interval (CI): 1.02–2.07) during multivariate analysis. Among moderate to severe anaemic patients, no significant association was found between preoperative transfusions and the risk of mortality or recurrence. (4) Conclusions: Preoperative anaemia, regardless of its severity, and not preoperative blood transfusion, was independently associated with a worse prognosis after surgery in patients with colonic cancer.

The increasing penetration of Renewable Energy Sources (RESs) in the energy mix is determining an energy scenario characterized by decentralized power production. Between RESs power generation technologies, solar PhotoVoltaic (PV) systems constitute a very promising option, but their production is not programmable due to the intermittent nature of solar energy. The coupling between a PV facility and a Battery Energy Storage System (BESS) allows to achieve a greater flexibility in power generation. However, the design phase of a PV+BESS hybrid plant is challenging due to the large number of possible configurations. The present paper proposes a preliminary procedure aimed at predicting a family of batteries which is suitable to be coupled with a given PV plant configuration. The proposed procedure is applied to new hypothetical plants built to fulfill the energy requirements of a commercial and an industrial load. The energy produced by the PV system is estimated on the basis of a performance analysis carried out on similar real plants. The battery operations are established through two decision-tree-like structures regulating charge and discharge respectively. Finally, an unsupervised clustering is applied to all the possible PV+BESS configurations in order to identify the family of feasible solutions.

Groin hernia is one of the most common surgical diagnoses worldwide. The indication for surgery in asymptomatic or mildly symptomatic patients is discussed. Some trials have demonstrated the safety of a watchful waiting strategy. During the pandemic, waiting lists for hernia surgery dramatically increased the opportunity to evaluate the natural history of groin hernias. The present study aimed to evaluate the incidence of emergency hernia surgery in a large cohort of patients that were selected and were waiting for elective surgery. This is a retrospective cross-sectional cohort study including all patients evaluated and selected for elective groin hernia surgery at San Gerardo Hospital between 2017 and 2020. Elective and emergency hernia surgeries were recorded for all patients. The incidence of adverse events was also evaluated. Overall, 1423 patients were evaluated, and 964 selected patients (80.3%) underwent elective hernia surgery, while 17 patients (1.4%) required an emergency operation while waiting for an elective operation. A total of 220 (18.3%) patients were still awaiting surgery in March 2022. The overall cumulative risk levels for emergency hernia surgeries were 1%, 2%, 3.2%, and 5% at 12, 24, 36, and 48 months, respectively. There was no association between longer waiting periods and an increased need for emergency surgery. Our study indicates that up to 5% of patients with groin hernia require emergency surgery at 48 months from the evaluation; the increased waiting time for surgery for elective groin hernia repair was not associated with an increased incidence of adverse events.

Background: Cardiac rhabdomyomas (CRs) are the most common cardiac tumors in newborns. Approximately 80–90% of cases are associated with tuberous sclerosis complex (TSC). In selective cases, Everolimus has resulted in a remarkable tumoral regression effect in children with TS. The optimal dosage for neonates is still unknown. Case presentation: We describe the use of Everolimus in a neonate with multiple biventricular CRs, causing subaortic obstruction, in which a low-dose treatment (0.1 mg/die), in an effort to maintain serum trough levels of 3–7 ng/mL, was successfully used off-label, without adverse effects. Conclusions: We showed that a low-dose Everolimus regimen may be an effective and safe treatment for CR regression in TS neonates, when the minimum therapeutic range was maintained.

Accurate forecast of aggregate end-users electric load profiles is becoming a hot topic in research for those main issues addressed in many fields such as the electricity services market. Hence, load forecast is an extremely important task which should be understood more in depth. In this research paper, the dependency of the day-ahead load forecast accuracy on the basis of the data typology employed in the training of LSTM has been inspected. A real case study of an Italian industrial load with samples recorded every 15 min for the year 2017 and 2018 was studied. The effect in the load forecast accuracy of different dataset cleaning approaches was investigated. In addition, the Generalised Extreme Studentized Deviate hypothesis testing was introduced to identify the outliers present in the dataset. The populations were constructed on the basis of an autocorrelation analysis that allowed for identifying a weekly correlation of the samples. The accuracy of the prediction obtained from different input dataset has been therefore investigated by calculating the most commonly used error metrics, showing the importance of data processing before employing them for load forecast.

Very-short-term photovoltaic power forecast, namely nowcasting, is gaining increasing attention to face grid stability issues and to optimize microgrid energy management systems in the presence of large penetration of renewable energy sources. In order to identify local phenomena as sharp ramps in photovoltaic production, whole sky images can be used effectively. The first step in the implementation of new and effective nowcasting algorithms is the identification of Sun positions. In this paper, three different techniques (solar angle-based, image processing-based, and neural network-based techniques) are proposed, described, and compared. These techniques are tested on real images obtained with a camera installed at SolarTechLab at Politecnico di Milano, Milan, Italy. Finally, the three techniques are compared by introducing some performance parameters aiming to evaluate of their reliability, accuracy, and computational effort. The neural network-based technique obtains the best performance: in fact, this method is able to identify accurately the Sun position and to estimate it when the Sun is covered by clouds.

The increasing penetration of non-programmable renewable energy sources (RES) is enforcing the need for accurate power production forecasts. In the category of hydroelectric plants, Run of the River (RoR) plants belong to the class of non-programmable RES. Data-driven models are nowadays the most widely adopted methodologies in hydropower forecast. Among all, the Artificial Neural Network (ANN) proved to be highly successful in production forecast. Widely adopted and equally important for hydropower generation forecast is the HYdrological Predictions for the Environment (HYPE), a semi-distributed hydrological Rainfall–Runoff model. A novel hybrid method, providing HYPE sub-basins flow computation as input to an ANN, is here introduced and tested both with and without the adoption of a decomposition approach. In the former case, two ANNs are trained to forecast the trend and the residual of the production, respectively, to be then summed up to the previously extracted seasonality component and get the power forecast. These results have been compared to those obtained from the adoption of a ANN with rainfalls in input, again with and without decomposition approach. The methods have been assessed by forecasting the Run-of-the-River hydroelectric power plant energy for the year 2017. Besides, the forecasts of 15 power plants output have been fairly compared in order to identify the most accurate forecasting technique. The here proposed hybrid method (HYPE and ANN) has shown to be the most accurate in all the considered study cases.

Solar radiation is by nature intermittent and influenced by many factors such as latitude, season and atmospheric conditions. As a consequence, the growing penetration of Photovoltaic (PV) systems into the electricity network implies significant problems of stability, reliability and scheduling of power grid operation. Concerning the very short-term PV power production, the power fluctuations are primarily related to the interaction between solar irradiance and cloud cover. In small-scale systems such as microgrids, the adoption of a forecasting tool is a brilliant solution to minimize PV power curtailment and limit the installed energy storage capacity. In the present work, two different nowcasting methods are applied to classify the solar attenuation due to clouds presence on five different forecast horizons, from 1 to 5 min: a Pattern Recognition Neural Network and a Random Forest model. The proposed methods are tested and compared on a real case study: available data consists of historical irradiance measurements and infrared sky images collected in a real PV facility, the SolarTechLAB in Politecnico di Milano. The classification output is a range of values corresponding to the future value assumed by the Clear Sky Index (CSI), an indicator allowing to account for irradiance variations only related to clouds passage, neglecting diurnal and seasonal influences. The developed models present similar performance in all the considered time horizons, reliably detecting the CSI drops caused by incoming overcast and partially cloudy sky conditions.

Software is behind the technological solutions that deliver many services to our society, which means that software security should not be considered a desirable feature anymore but more of a necessity. Protection of software is an endless labor that includes the improvement of security controls but also the understanding of the sources that induce incidents, which in many cases are due to bad implementation or assumptions of controls. As traditional methods may not be efficient in detecting those security assumptions, novel alternatives must be attempted. In this sense, Security Chaos Engineering (SCE) becomes an innovative methodology based on the definition of a steady state, a hypothesis, experiments, and metrics, which allow to identify failing components and ultimately protect assets under cyber risk scenarios. As an extension of a previous work, this paper presents ChaosXploit, an SCE-powered framework that employs a knowledge database, composed of attack trees, to expose vulnerabilities that exist in a software solution that has been previously defined as a target. The use of ChaosXploit may be part of a defensive security strategy to detect and correct software misconfigurations at an early stage. Finally, different experiments are described and executed to validate the feasibility of ChaosXploit in terms of auditing the security of cloud-managed services, i.e., Amazon buckets, which may be prone to misconfigurations and, consequently, targeted by potential cyberattacks.

The world of electrical distribution is rapidly changing and is seeing more and more distributed production and steerable flexibilities. Energy communities are seen as an important innovation for the optimization of electrical consumption at a local level. A central need of the local energy markets inside energy communities is the exchange and circulation of production and consumption data, and therefore the problem of the potential leak of sensitive data must be addressed. In this paper, the context of the Lugaggia Innovation Community, a Self Consumption Community pilot project in southern Switzerland, is introduced together with the blockchain framework that was created for its internal market interaction and the rules designed for its local energy market. A cryptographic protocol from the literature, based on homomorphic encryption, is then proposed for the anonymous aggregation of production and consumption data of the individual households at a resolution of 15 min. The computational overhead associated with the protocol is then experimented and analyzed.

Multisystem inflammatory syndrome in children (MIS-C) is a severe hyperinflammatory disease related to SARS-CoV2 infection, with frequent cardiovascular involvement in the acute setting. The aim of the study was to evaluate the cardiac function at 6 months. Thirty-two patients diagnosed with MIS-C were enrolled and underwent advanced echocardiogram at discharge and at 6 months. According to the left ventricular ejection fraction (LVEF) at admission, the patients were divided into group A (LVEF < 45%) and group B (LVEF ≥ 45%) and the follow-up results were compared. At discharge, all patients had normal LV and RV systolic function (LVEF 61 ± 4.4%, LV global longitudinal strain −22.1%, TAPSE 20.1mm, s’ wave 0.13m/s, RV free wall longitudinal strain −27.8%) with normal LV diastolic function (E/A 1.5, E/e’ 5.7, and left atrial strain 46.5%) and no significant differences at 6 months. Compared to group B, the group A patients showed a reduced, even if normal, LV global longitudinal strain at discharge (−21.1% vs. −22.6%, p-value 0.02), but the difference was no longer significant at the follow-up. Patients with MIS-C can present with depressed cardiac function, but if treated, the cardiac function recovered without late onset of cardiac disease. This favorable result was independent of the severity of acute LV dysfunction.

A comprehensive, bottoms-up characterization of two of the most widely used biomedical Ti-containing alloys, NiTi and β-Ti, was carried out applying a novel combination of neutron diffraction, neutron prompt-gamma activation, surface morphology, thermal analysis and mechanical tests, to relate composition, microstructure and physical-chemical-mechanical properties to unknown processing history. The commercial specimens studied are rectangular (0.43 × 0.64 mm~0.017 × 0.025 inch) wires, in both pre-formed U-shape and straight extended form. Practical performance was quantitatively linked to the influence of alloying elements, microstructure and thermo-mechanical processing. Results demonstrated that the microstructure and phase composition of β-Ti strongly depended on the composition, phase-stabilizing elements in particular, in that the 10.2 wt.% Mo content in Azdent resulted in 41.2% α phase, while Ormco with 11.6 wt.% Mo contained only β phase. Although the existence of α phase is probable in the meta-stable alloy, the α phase has never been quantified before. Further, the phase transformation behavior of NiTi directly arose from the microstructure, whilst being highly influenced by thermo-mechanical history. A strong correlation (r = 0.878) was established between phase transformation temperature and the force levels observed in bending test at body temperature, reconfirming that structure determines performance, while also being highly influenced by thermo-mechanical history. The novel methodology described is evidenced as generating a predictive profile of the eventual biomechanical properties and practical performance of the commercial materials. Overall, the work encompasses a reproducible and comprehensive approach expected to aid in future optimization and rational design of devices of metallic origin.

Background: Systemic corticosteroids are associated with reduced mortality in COVID-19-related acute respiratory failure; however, the type and dose has not yet been established. Objectives: To compare the outcomes of dexamethasone vs. methylprednisolone, along with the effects of rescue, short-term, high-dose boluses of corticosteroids. Methods: Before/after and case/control retrospective analysis of consecutive critically ill COVID-19 subjects. Subjects were initially given dexamethasone; however, after review of the local protocol, methylprednisolone was suggested. A three-day course of 1000 mg/day of methylprednisolone was administered in the case of refractory hypoxemia within the first 10 days of treatment. Propensity score-adjusted comparisons were performed. Results: A total of 81 consecutive subjects were included (85% males, 60 ± 10 years, SAPS II 27 ± 7, SOFA 4 [IQR 3, 6] points) and 51 of these subjects (62.9%) received dexamethasone and 29 (35.8%) had methylprednisolone. The groups were well matched for age, comorbidities, and severity at admission. No differences were found in the duration of ICU stay, hospital mortality, or infectious complications between the groups. A total of 22 subjects (27.2%) received a rescue bolus; these subjects had a significantly lower oxygenation, a higher driving pressure, and an increased ventilatory ratio during the first ten days. Short-term/high-dose boluses were associated with higher hospital mortality, longer mechanical ventilation and ICU and hospital stay, and more infectious complications. A subgroup of subjects who received the boluses had significantly improved oxygenation and lower hospital mortality. Conclusions: We were unable to find any difference between dexamethasone or methylprednisolone on the explored outcomes; high-dose boluses of corticosteroids were associated with a worse outcome. However, a subgroup of subjects was identified in whom the high-dose boluses seemed beneficial.

Porous aerogels, formed by subjecting precursor hydrogels using a freeze-drying process, are certainly one of the most studied and synthetized soft materials, thanks to their important features such as elasticity, swelling behavior, softness, and micro and nanosized pores, which guarantee their applicability in various fields. Typically, these systems are synthetized working with natural or synthetic polymers, but in the last years great interest has been given to proper formulated aerogels able to combine polymeric structures with other moieties such as graphene or graphene oxide. This working strategy can be pivotal in many cases to tune important properties of the final system such as toughness, porosity, elasticity, electrical conductivity, or responsive behavior. In this work we propose the synthesis of chitosan graphene oxide aerogels obtained through self-assembly of graphene oxide sheets and chitosan chains. These three-dimensional systems were chemically characterized with IR and XRD technique and their inner structure was investigated through the scanning electron microscopy (SEM). Moreover, we mechanically characterized the material through dynamic mechanical analysis, showing the stability of these systems. Finally, the adsorption ability of these soft materials has been demonstrated using model molecules to simulate water contaminants showing the efficacy of those graphene-based systems even for the removal of anionic dyes. Complete removal of contaminants was obtained at low concentration of dyes in solution (100 mg/L), while with a higher amount of pollutant in the solution (350 mg/L) high sorption capacity (q > 200 mg/g) was observed.

Among NiTi-based alloys, one of the most promising and exploited alloys is NiTiCu, since the addition of Cu in substitution of Ni in the binary equiatomic NiTi has a significant influence on the martensitic transformation and the thermomechanical properties of the system. A high content of Cu improves the damping properties at the expense of phase homogeneity and workability. The present study focuses on an alloy with a high copper content, i.e., 20 at.%. For this specific composition, the correlation between the thermal treatments, microstructure, formation of secondary phases, and damping properties are investigated by several analyses. The microscopic observation, together with the compositional analysis, allowed the determination of four different phases in the alloy. Both the calorimetry and dynamic thermo mechanical measurements, which confirmed the high damping ability of the alloy, provided a characterization of the martensitic transition. Finally, the electron backscatter diffraction (EBSD) analysis detected the different crystallographic structures (i.e., cubic austenite, orthorhombic martensite, and cubic (face-centered) NiTi2) and their orientation in the different phases. Therefore, the present work aims to improve the knowledge of the role of secondary phases in the optimization of the NiTiCu20 alloy as a valuable alternative to typical alloys used for damping purposes.

Dyskinesias are characterized by abnormal repetitive involuntary movements due to dysfunctional neuronal activity. Although levodopa-induced dyskinesia, characterized by tic-like abnormal involuntary movements, has no clinical treatment for Parkinson’s disease patients, animal studies indicate that Riluzole, which interferes with glutamatergic neurotransmission, can improve the phenotype. The rat model of Levodopa-Induced Dyskinesia is a unilateral lesion with 6-hydroxydopamine in the medial forebrain bundle, followed by the repeated administration of levodopa. The molecular pathomechanism of Levodopa-Induced Dyskinesia is still not deciphered; however, the implication of epigenetic mechanisms was suggested. In this study, we investigated the striatum for DNA methylation alterations under chronic levodopa treatment with or without co-treatment with Riluzole. Our data show that the lesioned and contralateral striata have nearly identical DNA methylation profiles. Chronic levodopa and levodopa + Riluzole treatments led to DNA methylation loss, particularly outside of promoters, in gene bodies and CpG poor regions. We observed that several genes involved in the Levodopa-Induced Dyskinesia underwent methylation changes. Furthermore, the Riluzole co-treatment, which improved the phenotype, pinpointed specific methylation targets, with a more than 20% methylation difference relative to levodopa treatment alone. These findings indicate potential new druggable targets for Levodopa-Induced Dyskinesia.

(1) Introduction: To date, the sensitivity of the Comprehensive Complication Index (CCI) in a homogeneous cohort of colonic resections for oncologic purposes has not been reported. The present study aims to compare the CCI with the conventional Clavien–Dindo classification (CDC) in colon cancer patients. (2) Methods: The clinical data of patients submitted to an elective colectomy for adenocarcinoma were retrieved from a prospectively maintained database. Postoperative complications and length of stay were reviewed, and CDC and CCI scores were calculated for each patient. The association of the CCI and the CDC with the length of stay, prolongation of stay and readmission rate were assessed and compared. (3) Results: The overall postoperative morbidity was 26.9%. In particular, 157 (20.4%) patients had more than one complication. A strong correlation between the two scoring systems was observed (r = 99.4%; 95%CI: 99.3–99.5%). In multivariate analysis, CCI had a higher predictive ability for all endpoints. Regarding subgroup analysis, the difference between the CCI and CDC was progressively increased when evaluating outcome measures in complicated and multi-complicated patients. (4) Conclusion: Both scoring systems adequately report the overall burden of postoperative complications. The CCI showed a greater ability than the CDC to predict hospital stay, particularly in patients with multiple postoperative complications.

The present work focused on the microstructural, thermal, electrical, and damping characterization of NiMnGa samples produced through a powder pressing and a sintering process; the effect of sintering times and of the starting powder size were evaluated. Moreover, an observation of the evolution of martensitic transformation typical of NiMnGa ferromagnetic shape memory alloy was conducted in comparison with the cast material behavior and in correlation with the material densification. The optimum powder size and sintering time for the process, i.e., 50 µm or lower and 72 h, were identified considering the investigated physical properties of the sintered samples in comparison to the cast material. The corresponding sample showed the best compromise between density, thermal and electrical properties, and damping and functional behaviour. In general, the outcomes of this study could be the basis of a useful tool for production processes that include a sintering step as well as being a starting point for the evaluation of an alternative low cost fabrication method of this alloy.

The Internet of Things (IoT) paradigm has revolutionized several industries (e.g., manufacturing, health, transport, education, among others) by allowing objects to connect to the Internet and, thus, enabling a variety of novel applications. In this sense, IoT devices have become an essential component of smart cities, allowing many novel and useful services, but, at the same time, bringing numerous cybersecurity threats. The paper at hand proposes BlockSIEM, a blockchain-based and distributed Security Information and Event Management (SIEM) solution framework for the protection of the aforementioned smart city services. The proposed SIEM relies on blockchain technology to securely store and access security events. Such security events are generated by IoT sentinels that are in charge of shielding groups of IoT devices. The IoT sentinels may be deployed in smart city scenarios, such as smart hospitals, smart transport systems, smart airports, among others, ensuring a satisfactory level of protection. The blockchain guarantees the non-repudiation and traceability of the registry of security events due to its features. To demonstrate the feasibility of the proposed approach, our proposal is implemented using Ethereum and validated through different use cases and experiments.

To evaluate if glutamine (GLN) supplementation may affect primary outcomes in patients undergoing major elective abdominal operations, we performed a systematic literature review of randomized clinical trials (RCTs) published from 1983 to 2013 and comparing intravenous glutamine dipeptide supplementation to no supplementation in elective surgical abdominal procedures. A meta-analysis for each outcome (overall and infectious morbidity and length of stay) of interest was carried out. The effect size was estimated by the risk ratio (RR) or by the weighted mean difference (WMD). Nineteen RCTs were identified with a total of 1243 patients (640 receiving GLN and 603 controls). In general, the studies were underpowered and of medium or low quality. GLN supplementation did not affect overall morbidity (RR = 0.84, 95% CI 0.51 to 1.36; p = 0.473) and infectious morbidity (RR = 0.64; 95% CI = 0.38 to 1.07; p = 0.087). Patients treated with glutamine had a significant reduction in length of hospital stay (WMD = −2.67; 95% CI = −3.83 to −1.50; p < 0.0001). In conclusion, GLN supplementation appears to reduce hospital stay without affecting the rate of complications. The positive effect of GLN on time of hospitalization is difficult to interpret due to the lack of significant effects on surgery-related morbidity.

Neutron scattering in combination with scanning electron and atomic force microscopy were employed to quantitatively resolve elemental composition, nano- through meso- to metallurgical structures and surface characteristics of two commercial stainless steel orthodontic archwires—G&H and Azdent. The obtained bulk composition confirmed that both samples are made of metastable austenitic stainless steel type AISI 304. The neutron technique’s higher detection sensitivity to alloying elements facilitated the quantitative determination of the composition factor (CF), and the pitting resistance equivalent number (PREN) for predicting austenite stability and pitting-corrosion resistance, respectively. Simultaneous neutron diffraction analyses revealed that both samples contained additional martensite phase due to strain-induced martensite transformation. The unexpectedly high martensite content (46.20 vol%) in G&H was caused by combination of lower austenite stability (CF = 17.37, p = .03), excessive cold working and inadequate thermal treatment during material processing. Together, those results assist in revealing alloying recipes and processing history, and relating these with corrosion resistance and mechanical properties. The present methodology has allowed access to unprecedented length-scale (μm to sub-nm) resolution, accessing nano- through meso-scopic properties. It is envisaged that such an approach can be extended to the study and design of other metallic (bio)materials used in medical sciences, dentistry and beyond.

Continuous authentication was introduced to propose novel mechanisms to validate users’ identity and address the problems and limitations exposed by traditional techniques. However, this methodology poses several challenges that remain unsolved. In this paper, we present a novel framework, PALOT, that leverages IoT to provide context-aware, continuous and non-intrusive authentication and authorization services. To this end, we propose a formal information system model based on ontologies, representing the main source of knowledge of our framework. Furthermore, to recognize users’ behavioral patterns within the IoT ecosystem, we introduced a new module called “confidence manager”. The module is then integrated into an extended version of our early framework architecture, IoTCAF, which is consequently adapted to include the above-mentioned component. Exhaustive experiments demonstrated the efficacy, feasibility and scalability of the proposed solution.

We compare the 24-hour ahead forecasting performance of two methods commonly used for the prediction of the power output of photovoltaic systems. Both methods are based on Artificial Neural Networks (ANN), which have been trained on the same dataset, thus enabling a much-needed homogeneous comparison currently lacking in the available literature. The dataset consists of an hourly series of simultaneous climatic and PV system parameters covering an entire year, and has been clustered to distinguish sunny from cloudy days and separately train the ANN. One forecasting method feeds only on the available dataset, while the other is a hybrid method as it relies upon the daily weather forecast. For sunny days, the first method shows a very good and stable prediction performance, with an almost constant Normalized Mean Absolute Error, NMAE%, in all cases (1% < NMAE% < 2%); the hybrid method shows an even better performance (NMAE% < 1%) for two of the days considered in this analysis, but overall a less stable performance (NMAE% > 2% and up to 5.3% for all the other cases). For cloudy days, the forecasting performance of both methods typically drops; the performance is rather stable for the method that does not use weather forecasts, while for the hybrid method it varies significantly for the days considered in the analysis.

The Internet of Things (IoT) became established during the last decade as an emerging technology with considerable potentialities and applicability. Its paradigm of everything connected together penetrated the real world, with smart devices located in several daily appliances. Such intelligent objects are able to communicate autonomously through already existing network infrastructures, thus generating a more concrete integration between real world and computer-based systems. On the downside, the great benefit carried by the IoT paradigm in our life brings simultaneously severe security issues, since the information exchanged among the objects frequently remains unprotected from malicious attackers. The paper at hand proposes COSMOS (Collaborative, Seamless and Adaptive Sentinel for the Internet of Things), a novel sentinel to protect smart environments from cyber threats. Our sentinel shields the IoT devices using multiple defensive rings, resulting in a more accurate and robust protection. Additionally, we discuss the current deployment of the sentinel on a commodity device (i.e., Raspberry Pi). Exhaustive experiments are conducted on the sentinel, demonstrating that it performs meticulously even in heavily stressing conditions. Each defensive layer is tested, reaching a remarkable performance, thus proving the applicability of COSMOS in a distributed and dynamic scenario such as IoT. With the aim of easing the enjoyment of the proposed sentinel, we further developed a friendly and ease-to-use COSMOS App, so that end-users can manage sentinel(s) directly using their own devices (e.g., smartphone).

Undoubtedly, the adoption of the Internet of Things (IoT) paradigm has impacted on our every-day life, surrounding us with smart objects. Thus, the potentialities of this new market attracted the industry, so that many enterprises developed their own IoT platforms aiming at helping IoT services’ developers. In the multitude of possible platforms, selecting the most suitable to implement a specific service is not straightforward, especially from a security perspective. This paper analyzes some of the most prominent proposals in the IoT platforms market-place, performing an in-depth security comparison using five common criteria. These criteria are detailed in sub-criteria, so that they can be used as a baseline for the development of a secure IoT service. Leveraging the knowledge gathered from our in-depth study, both researchers and developers may select the IoT platform which best fits their needs. Additionally, an IoT service for monitoring commercial flights is implemented in two previously analyzed IoT platforms, giving an adequate detail level to represent a solid guideline for future IoT developers.

In surgical patients, malnutrition is an important risk factor for post-operative complications. In undernourished patients undergoing major gastrointestinal procedures, preoperative enteral nutrition (EN) should be preferred whenever feasible. It may be given either orally or by feeding tubes, depending on patient compliance. Early oral intake after surgery should be encouraged, but if an insufficient postoperative oral intake is anticipated, tube feeding should be initiated as soon as possible. The use of immunomodulating formulas offers significant advantages when compared to standard feeds and the positive results on postoperative complications seem independent from the baseline nutritional status. In malnourished patients, the optimal timing and dose of immunonutrition is unclear, but consistent data suggest that they should be treated peri-operatively for at least two weeks.