Towards the timely detection of toxicants

We address the problem of enhancing the sensitivity of biosensors to the influence of toxicants, with an entropy method of analysis, denoted as CASSANDRA, recently invented for the specific purpose of studying non-stationary time series. We study the specific case where the toxicant is tetrodotoxin. This is a very poisonous substance that yields an abrupt drop of the rate of spike production at t approximatively 170 minutes when the concentration of toxicant is 4 nanomoles. The CASSANDRA algorithm reveals the influence of toxicants thirty minutes prior to the drop in rate at a concentration of toxicant equal to 2 nanomoles. We argue that the success of this method of analysis rests on the adoption of a new perspective of complexity, interpreted as a condition intermediate between the dynamic and the thermodynamic state.Comment: 6 pages and 3 figures. Accepted for publication in the special issue of Chaos Solitons and Fractal dedicated to the conference "Non-stationary Time Series: A Theoretical, Computational and Practical Challenge", Center for Nonlinear Science at University of North Texas, from October 13 to October 19, 2002, Denton, TX (USA

Compression and diffusion: a joint approach to detect complexity

The adoption of the Kolmogorov-Sinai (KS) entropy is becoming a popular research tool among physicists, especially when applied to a dynamical system fitting the conditions of validity of the Pesin theorem. The study of time series that are a manifestation of system dynamics whose rules are either unknown or too complex for a mathematical treatment, is still a challenge since the KS entropy is not computable, in general, in that case. Here we present a plan of action based on the joint action of two procedures, both related to the KS entropy, but compatible with computer implementation through fast and efficient programs. The former procedure, called Compression Algorithm Sensitive To Regularity (CASToRe), establishes the amount of order by the numerical evaluation of algorithmic compressibility. The latter, called Complex Analysis of Sequences via Scaling AND Randomness Assessment (CASSANDRA), establishes the complexity degree through the numerical evaluation of the strength of an anomalous effect. This is the departure, of the diffusion process generated by the observed fluctuations, from ordinary Brownian motion. The CASSANDRA algorithm shares with CASToRe a connection with the Kolmogorov complexity. This makes both algorithms especially suitable to study the transition from dynamics to thermodynamics, and the case of non-stationary time series as well. The benefit of the joint action of these two methods is proven by the analysis of artificial sequences with the same main properties as the real time series to which the joint use of these two methods will be applied in future research work.Comment: 27 pages, 9 figure

The "15-minutes station": a case study to evaluate the pedestrian accessibility of railway transport in Southern Italy

The recent sustainability challenges that our world is facing have raised, more than ever, the attention to the mobility of passengers and freight in the European and international agendas. The energy transition that has begun globally requires identifying and adopting safe, resilient, and increasingly sustainable mobility solutions. In this perspective, the modal split of passengers plays an essential role. One of the main encouraged policies is to promote an efficient mass rapid transit in urban and suburban areas. More in detail, when considering rail transport, it is necessary to analyze and evaluate the role of stations from at least two points of view: i) the ease of access to the station; ii) the opportunities that can be easily reached in its surroundings, following the concept of the "15-minutes" city. These two issues should be properly addressed to guarantee the role of railway stations as an access point to the transport system and an infrastructural element that can enhance a territory. Starting from these considerations, this research proposes a GIS-based methodology able to analyse railway stations from two points of view: i) walkability, considering the main functional characteristics of the transport network, and ii) impact on the territory, by identifying the services located in an area corresponding to "15-minutes" distances using active modes. For each railway station, the main activities in a 15-minute walking isochrone can be evaluated, both considering the walking distance on the pedestrian network and taking into account the current walkability of each link based on arc characteristics. This allows to study the accessibility of railway stations based on the current pedestrian network and the potential one with ideal characteristics. The method is applied to a case study located in Sicily (Italy), in the case of some urban stations. The final scope is to design a decision-support framework useful for railway station operators and local decision-makers to support strategic decisions regarding the railway system and the planning of appropriate pedestrian transport networks to increase railway station accessibility

Diffusion entropy and waiting time statistics of hard x-ray solar flares

We analyze the waiting time distribution of time distances $\tau$ between two nearest-neighbor flares. This analysis is based on the joint use of two distinct techniques. The first is the direct evaluation of the distribution function $\psi(\tau)$, or of the probability, $\Psi(tau)$, that no time distance smaller than a given $\tau$ is found. We adopt the paradigm of the inverse power law behavior, and we focus on the determination of the inverse power index $\mu$, without ruling out different asymptotic properties that might be revealed, at larger scales, with the help of richer statistics. The second technique, called Diffusion Entropy (DE) method, rests on the evaluation of the entropy of the diffusion process generated by the time series. The details of the diffusion process depend on three different walking rules, which determine the form and the time duration of the transition to the scaling regime, as well as the scaling parameter $\delta$. With the first two rules the information contained in the time series is transmitted, to a great extent, to the transition, as well as to the scaling regime. The same information is essentially conveyed, by using the third rules, into the scaling regime, which, in fact, emerges very quickly after a fast transition process. We show that the significant information hidden within the time series concerns memory induced by the solar cycle, as well as the power index $\mu$. The scaling parameter $\delta$ becomes a simple function of $\mu$, when memory is annihilated. Thus, the three walking rules yield a unique and precise value of $\mu$ if the memory is wisely taken under control, or cancelled by shuffling the data. All this makes compelling the conclusion that $\mu = 2.138 \pm 0.01$.Comment: 23 pages, 13 figure

Insulin pump failures in Italian children with Type 1 diabetes: retrospective 1-year cohort study

AimsInsulin pump failure and/or malfunction requiring replacement have not been thoroughly investigated. This study evaluated pump replacement in children and adolescents with Type 1 diabetes using insulin pump therapy.MethodsData were collected for all participants younger than 19 years, starting insulin pump therapy before 31 December 2013. For each child, age, disease duration, date of insulin pump therapy initiation, insulin pump model, failure/malfunction/replacement yes/no and reason were considered for the year 2013.ResultsData were returned by 40 of 43 paediatric centres belonging to the Diabetes Study Group of the Italian Society of Paediatric Endocrinology and Diabetology. In total, 1574 of 11 311 (13.9%) children and adolescents with Type 1 diabetes were using an insulin pump: 29.2% Animas VIBE, 9.4% Medtronic MiniMed 715/515, 34.3% Medtronic MiniMed VEO, 24.3% Accu-Check Spirit Combo and 2.8% other models. In 2013, 0.165 insulin pump replacements per patient-year (11.8% due to pump failure/malfunction and 4.7% due to accidental damage) were recorded. Animas VIBE (22.1%) and Medtronic MiniMed VEO (17.7%) were the most replaced.ConclusionsIn a large cohort of Italian children and adolescents with Type 1 diabetes, insulin pump failure/malfunction and consequent replacement are aligned with rates previously reported and higher in more sophisticated pump models

A Multicenter Retrospective Survey regarding Diabetic Ketoacidosis Management in Italian Children with Type 1 Diabetes

We conducted a retrospective survey in pediatric centers belonging to the Italian Society for Pediatric Diabetology and Endocrinology. The following data were collected for all new-onset diabetes patients aged 0-18 years: DKA (pH < 7.30), severe DKA (pH < 7.1), DKA in preschool children, DKA treatment according to ISPAD protocol, type of rehydrating solution used, bicarbonates use, and amount of insulin infused. Records (n = 2453) of children with newly diagnosed diabetes were collected from 68/77 centers (87%), 39 of which are tertiary referral centers, the majority of whom (n = 1536, 89.4%) were diagnosed in the tertiary referral centers. DKA was observed in 38.5% and severe DKA in 10.3%. Considering preschool children, DKA was observed in 72%, and severe DKA in 16.7%. Cerebral edema following DKA treatment was observed in 5 (0.5%). DKA treatment according to ISPAD guidelines was adopted in 68% of the centers. In the first 2 hours, rehydration was started with normal saline in all centers, but with different amount. Bicarbonate was quite never been used. Insulin was infused starting from third hour at the rate of 0.05-0.1 U/kg/h in 72% of centers. Despite prevention campaign, DKA is still observed in Italian children at onset, with significant variability in DKA treatment, underlying the need to share guidelines among centers

Vortex dynamics in evolutive flows: a weakly chaotic phenomenon

We make use of a wavelet method to extract, from experimental velocity signals obtained in an evolutive flow, the dominating velocity components generated by vortex dynamics. We characterize the resulting time series complexity by means of a joint use of data compression and of an entropy diffusion method. We assess that the time series emerging from the wavelet analysis of the vortex dynamics is a weakly chaotic process with a vanishing Kolmogorov-Sinai entropy and a power-law growth of the information content. To reproduce the Fourier spectrum of the experimental signal, we adopt a harmonic dependence on time with a fluctuating frequency, ruled by an inverse power-law distribution of random events. The complexity of these fluctuations is determined by studying the corresponding artificial sequences. We reproduce satisfactorily both spectral and complex properties of the experimental signal by locating the complexity of the fluctuating process at the border between the stationary and the nonstationary states