Cross-border critical transportation infrastructure: a multi-level index for resilience assessment

Today, more than ever before, our society depends on interdependent infrastructure systems, such as transportation, energy, water, and telecommunications networks. These systems are often considered critical because they are necessary for the organization, functionality, and stability of a modern industrialized country. However, these infrastructures are vulnerable to accidents, malicious failures, and disruptions that could generate consequences impacting on the economy, health, safety, and welfare of the citizens of a country or of several neighboring countries. The disruption of critical cross-border transportation infrastructure, road or rail, as a result of a major event can affect the area where the event occurs and a wider area. Depending on the type and duration of an event, which can be natural or anthropogenic in origin, it is possible to estimate the impacts on the mobility of people and goods in terms of delays (alternative routes), increased traffic (congestion), and a potential increase in accidents. For instance, in 2019 there was an accident in Rastatt (Germany) that affected rail traffic on the Karlsruhe-Basel line of the Rhine-Alpine corridor in Europe. The rail line was disrupted for more than 50 days, causing disservices and about 2 billion Euro in economic losses in Germany, Switzerland, and Italy. The extended disruption of road and rail sections can have consequences (impacts) not only on the transport system but also on the socio-economic system in a macro-regional context. The research is part of the SICt project - Resilience of Critical Cross-Border Infrastructure developed in the Interreg VA Italy-Switzerland Programme 2014-2020. The work aims to define a RI - Resilience Index for the road and rail transport network falling within the study area. The RI index describes the capability of each network element (i-th link) to cope with a relevant event. The formulation of the index involves the calculation of three independent indicators: i) RIRM - Rescue Management related to the resources that can be activated and used to cope with an event; ii) RIPP - Plans & Management related to the speed with which the necessary resources can be activated and in fact, considers management aspects such as the presence of plans and procedures; iii) RIRN - Network & Traffic related to the robustness of the elements of the transport network. This work aims to present the proposed model and its application to the project area that includes the Lombardy Region (Italy) and the Canton Ticino (Switzerland) within the SICt Project

Cross-border Digital Platform for Transport Critical Infrastructure Resilience: Functionalities and Use-case

The resilience of increasingly interdependent Critical Infrastructure (CI) systems hugely depends on the stakeholder organizations’ ability to exchange information and coordinate, while CI’s cross-border dimension further increases the complexity and challenges. This paper presents the progress in the Lombardy Region (Italy) and Canton Ticino (Switzerland) on the joint capacity to manage disruptive events involving transportation CI between the two countries. We present a cross-border digital platform (Critical Infrastructure Platform – PIC) and its main functionalities for improved cross-border risk and resilience management of CI. A use case, based on a scenario of an intense snowfall along the transboundary motorway impacting both countries, demonstrates how PIC advances the exchange of information, its visualization and analysis in real-time. The use case also shows the practical value of the digital platform and its potential to support the management of cross-border events (and their cascading events) that require the cooperation of Italian and Swiss actors

Chance-constrained Calculation of the Reserve Service Provided by EV Charging Station Clusters in Energy Communities

The concept of energy community is currently under investigation as it is considered central into the energy transition process. One of the main aspects of the successful implementation of community lays in the energy management system that coordinates exchanges among prosumers. This paper deals with the optimal energy management of a local energy community of dc microgrids with electric vehicle charging stations, considering local reserve provided by storage units and vehicle batteries. A two-stage optimal procedure is proposed to assess the optimal scheduling of resources for each community participant. Additionally, the optimal up and down reserve levels able to cover random fluctuations in photovoltaic generation within each EV-based microgrid are determined by a set of specific chance constraints

Comparison between Multistage Stochastic Optimization Programming and Monte Carlo Simulations for the Operation of Local Energy Systems

The paper deals with the day-ahead optimization of the operation of a local energy system consisting of photovoltaic units, energy storage systems and loads aimed to minimize the electricity procurement cost. The local energy system may refer either to a small industrial site or to a residential neighborhood. Two mixed integer linear programming models are adopted, each for a different representation of the battery: A simple energy balance constraint and the Kinetic Battery Model. The paper describes the generation of the scenarios, the construction of the scenario tree and the intraday decision-making procedure based on the solution of the multistage stochastic programming. Moreover, the daily energy procurement costs calculated by using the stochastic programming approach are compared with those calculated by using the Monte Carlo method. The comparison is repeated for two different sizes of the battery and for two load profiles

A new methodology for accidents analysis: The case of the state road 36 in Italy

6noEvery year more than 1.35 million people die for road accidents and several million suffer serious injuries, which force them to live with compromised health conditions. Over the last decades, road safety research has focused on improving modelling techniques. However, due to the lack of expertise and statistical skills, such approaches might not be used by local authorities and road managers for road safety evaluation purposes. This paper proposes an operational methodology to analyze road accidents with the aim of increasing road safety. More specifically, the methodology enables to identify the most critical road segments to prioritize economic resources allocation accordingly. By using the data collected by the Road Police Department of Lombardy Region (in Italy) from 2014 to 2018, this methodology has been successfully applied to State Road 36, which is recognized as one of the busiest roads in Italy with a very high number of accidents occurring every year. The proposed methodology may support public administrations and road managers - involved in the definition and implementation of safety measures - to reduce the number of road accidents identifying and implementing prioritized interventions. Moreover, the methodology is general enough to be applied to each segment of a generic road infrastructure.openopenBorghetti F.; Marchionni G.; De Bianchi M.; Barabino B.; Bonera M.; Caballini C.Borghetti, F.; Marchionni, G.; De Bianchi, M.; Barabino, B.; Bonera, M.; Caballini, C

Deep Long Short-term Memory Structures Model Temporal Dependencies Improving Cognitive Workload Estimation

Using deeply recurrent neural networks to account for temporal dependence in electroencephalograph (EEG)-based workload estimation is shown to considerably improve day-to-day feature stationarity resulting in significantly higher accuracy (p \u3c .0001) than classifiers which do not consider the temporal dependence encoded within the EEG time-series signal. This improvement is demonstrated by training several deep Recurrent Neural Network (RNN) models including Long Short-Term Memory (LSTM) architectures, a feedforward Artificial Neural Network (ANN), and Support Vector Machine (SVM) models on data from six participants who each perform several Multi-Attribute Task Battery (MATB) sessions on five separate days spread out over a month-long period. Each participant-specific classifier is trained on the first four days of data and tested using the fifth’s. Average classification accuracy of 93.0% is achieved using a deep LSTM architecture. These results represent a 59% decrease in error compared to the best previously published results for this dataset. This study additionally evaluates the significance of new features: all combinations of mean, variance, skewness, and kurtosis of EEG frequency-domain power distributions. Mean and variance are statistically significant features, while skewness and kurtosis are not. The overall performance of this approach is high enough to warrant evaluation for inclusion in operational systems

Cross-Participant EEG-Based Assessment of Cognitive Workload Using Multi-Path Convolutional Recurrent Neural Networks

Applying deep learning methods to electroencephalograph (EEG) data for cognitive state assessment has yielded improvements over previous modeling methods. However, research focused on cross-participant cognitive workload modeling using these techniques is underrepresented. We study the problem of cross-participant state estimation in a non-stimulus-locked task environment, where a trained model is used to make workload estimates on a new participant who is not represented in the training set. Using experimental data from the Multi-Attribute Task Battery (MATB) environment, a variety of deep neural network models are evaluated in the trade-space of computational efficiency, model accuracy, variance and temporal specificity yielding three important contributions: (1) The performance of ensembles of individually-trained models is statistically indistinguishable from group-trained methods at most sequence lengths. These ensembles can be trained for a fraction of the computational cost compared to group-trained methods and enable simpler model updates. (2) While increasing temporal sequence length improves mean accuracy, it is not sufficient to overcome distributional dissimilarities between individuals’ EEG data, as it results in statistically significant increases in cross-participant variance. (3) Compared to all other networks evaluated, a novel convolutional-recurrent model using multi-path subnetworks and bi-directional, residual recurrent layers resulted in statistically significant increases in predictive accuracy and decreases in cross-participant variance

Effects of a dietary crude fibre concentrate on growth in weaned piglets

Many fibre sources can help the adaptation of piglets at weaning, improving the growth. In this study, the effects of a dietary crude fibre concentrate (CFC) on piglet’s growth was investigated. From 31 to 51 days of age, 108 weaned piglets (D×(Lw×L)), had access to two isofibrous, isoenergetic and isonitrogenous diets, supplemented with 1% of CFC (CFC group) or not (control (CON) group). From days 52 to 64 all piglets received the same starter diet. During the dietary treatment period the CFC group showed higher average daily gain, average daily feed intake and feed efficiency (P<0.001) than CON group. At 64 days of age, BW was higher in CFC group compared with CON group (P<0.001). Blood samples were collected at days 31, 38, 45 and 52 of age. From days 31 to 52 significant differences in the somatotropic axis between groups were observed. In particular, growth hormone levels were higher only at the end of the 1st week of dietary treatment (P<0.05) in CFC group animals compared with CON group animals. The IGF-I trend was similar between groups even if the IGF-I levels were higher in the CFC group than CON group 1 week after starting treatment (P<0.01). The IGF-binding protein 3 (IGFBP-3) levels were higher in the first 2 weeks of dietary treatment and lower in the 3rd week in CON group compared with CFC group (P<0.01). Specifically, the IGFBP-3 profile was consistent with that of IGF-I in CFC group but not in CON group. At the same time, an increase of leptin in CFC compared with CON group was observed (P<0.05). Piglets fed the CFC diet showed a lower diarrhoea incidence (P<0.05) and a lower number of antibiotic interventions (P<0.05) than CON diet from 31 to 51 days of age. Pig-major acute-phase protein plasma level (P<0.01) and interleukin-6 gene expression (P<0.05) were higher in CON group than CFC group at the end of 1st week of dietary treatment. In conclusion, this study showed that CFC diet influences the hormones related to energy balance enhancing the welfare and growth of piglets. Furthermore, the increase in feed intake during 3 weeks of dietary treatment improved the feed efficiency over the entire post-weaning period

Evaluation of safety and efficacy of DNA vaccines against bovine herpesvirus-1 (BoHV-1) in calves.

Four DNA vaccines against BoHV-1 were evaluated for their efficacy in calves. Twelve animals were divided into four groups which were injected with four different DNA vaccines: pVAX-tgD (Vaccine A); pVAX-tgD co-immunised with pVAX-48CpG (Vaccine B); pVAX-UbiLacl-tgD-L (Vaccine C); pVAX-UbiLacl-tgD-L co-immunised with pVAX-48CpG (Vaccine D). Three additional calves were given the plasmid vector and served as controls. Ninety days after the first vaccination all calves were challenge infected with BoHV-1. All animals developed a severe form of infections bovine rhinotracheitis. Only the calves given the pVAX-tgD co-immunised with pVAX-48CpG (Vaccine B) developed humoral antibodies against BoHV-1 between 56 and 90 days after the first vaccination, whereas in calves of other groups and in the controls, antibodies appeared only after the infection. In the calves vaccinated with either pVAX-tgD (Vaccine A) or pVAX-tgD combined with pVAX-48CpG (Vaccine B), BoHV-1-specific IFN-gamma secreting cells were detected in PBMCs 90 days after the first vaccination and their number increased after challenge exposure. In the other groups the IFN-gamma secreting cells were detected after virus infection and at low values

Environmental Effects on Oxygen Isotope Enrichment of Leaf Water in Cotton Leaves

The oxygen isotope enrichment of bulk leaf water (Db) was measured in cotton (Gossypium hirsutum) leaves to test the Craig- Gordon and Farquhar-Gan models under different environmental conditions. Db increased with increasing leaf-to-air vapor pressure difference (VPd) as an overall result of the responses to the ratio of ambient to intercellular vapor pressures (ea/ei) and to stomatal conductance (gs). The oxygen isotope enrichment of lamina water relative to source water ðD1Þ; which increased with increasing VPd, was estimated by mass balance between less enriched water in primary veins and enriched water in the leaf. The Craig-Gordon model overestimated Db (and D1Þ; as expected. Such discrepancies increased with increase in transpiration rate (E), supporting the Farquhar-Gan model, which gave reasonable predictions of Db and D1 with an L of 7.9 mm, much less than the total radial effective length Lr of 43 mm. The fitted values of L for D1 of individual leaves showed little dependence on VPd and temperature, supporting the assumption that the Farquhar-Gan formulation is relevant and useful in describing leaf water isotopic enrichment