Coordination networks incorporating halogen-bond donor sites and azobenzene groups

Two Zn coordination networks, [Zn(1)(Py)2]2(2-propanol)n (3) and [Zn(1)2(Bipy)2](DMF)2n (4), incorporating halogen-bond (XB) donor sites and azobenzene groups have been synthesized and fully characterized. Obtaining 3 and 4 confirms that it is possible to use a ligand wherein its coordination bond acceptor sites and XB donor sites are on the same molecular scaffold (i.e., an aromatic ring) without interfering with each other. We demonstrate that XBs play a fundamental role in the architectures and properties of the obtained coordination networks. In 3, XBs promote the formation of 2D supramolecular layers, which, by overlapping each other, allow the incorporation of 2-propanol as a guest molecule. In 4, XBs support the connection of the layers and are essential to firmly pin DMF solvent molecules through I⋯O contacts, thus increasing the stability of the solvated systems

Continuous haematic pH monitoring in extracorporeal circulation using a disposable florescence sensing element.

During extracorporeal circulation (ECC), blood is periodically sampled and analyzed to maintain the blood-gas status of the patient within acceptable limits. This protocol has well-known drawbacks that may be overcome by continuous monitoring. We present the characterization of a new pH sensor for continuous monitoring in ECC. This monitoring device includes a disposable fluorescence-sensing element directly in contact with the blood, whose fluorescence intensity is strictly related to the pH of the blood. In vitro experiments show no significant difference between the blood gas analyzer values and the sensor readings; after proper calibration, it gives a correlation of R>0.9887, and measuring errors were lower than the 3% of the pH range of interest (RoI) with respect to a commercial blood gas analyzer. This performance has been confirmed also by simulating a moderate ipothermia condition, i.e., blood temperature 32°C, frequently used in cardiac surgery. In ex vivo experiments, performed with animal models, the sensor is continuously operated in an extracorporeal undiluted blood stream for a maximum of 11 h. It gives a correlation of R>0.9431, and a measuring error lower than the 3% of the pH RoI with respect to laboratory techniques

Density functional theory versus quantum Monte Carlo simulations of Fermi gases in the optical-lattice arena

We benchmark the ground state energies and the density profiles of atomic repulsive Fermi gases in optical lattices computed via Density Functional Theory (DFT) against the results of diffusion Monte Carlo (DMC) simulations. The main focus is on a half-filled one-dimensional optical lattices, for which the DMC simulations performed within the fixed-node approach provide unbiased results. This allows us to demonstrate that the local spin-density approximation (LSDA) to the exchange-correlation functional of DFT is very accurate in the weak and intermediate interactions regime, and also to underline its limitations close to the strongly-interacting Tonks-Girardeau limit and in very deep optical lattices. We also consider a three dimensional optical lattice at quarter filling, showing also in this case the high accuracy of the LSDA in the moderate interaction regime. The one-dimensional data provided in this study may represent a useful benchmark to further develop DFT methods beyond the LSDA and they will hopefully motivate experimental studies to accurately measure the equation of state of Fermi gases in higher-dimensional geometries.Comment: 8 pages, 7 figures, plus supplemental material (1 page). Typos correcte

Predictive maintenance: a novel framework for a data-driven, semi-supervised, and partially online prognostic health management application in industries

Prognostic Health Management (PHM) is a predictive maintenance strategy, which is based on Condition Monitoring (CM) data and aims to predict the future states of machinery. The existing literature reports the PHM at two levels: methodological and applicative. From the methodological point of view, there are many publications and standards of a PHM system design. From the applicative point of view, many papers address the improvement of techniques adopted for realizing PHM tasks without covering the whole process. In these cases, most applications rely on a large amount of historical data to train models for diagnostic and prognostic purposes. Industries, very often, are not able to obtain these data. Thus, the most adopted approaches, based on batch and off-line analysis, cannot be adopted. In this paper, we present a novel framework and architecture that support the initial application of PHM from the machinery producers’ perspective. The proposed framework is based on an edge-cloud infrastructure that allows performing streaming analysis at the edge to reduce the quantity of the data to store in permanent memory, to know the health status of the machinery at any point in time, and to discover novel and anomalous behaviors. The collection of the data from multiple machines into a cloud server allows training more accurate diagnostic and prognostic models using a higher amount of data, whose results will serve to predict the health status in real-time at the edge. The so-built PHM system would allow industries to monitor and supervise a machinery network placed in different locations and can thus bring several benefits to both machinery producers and users. After a brief literature review of signal processing, feature extraction, diagnostics, and prognostics, including incremental and semi-supervised approaches for anomaly and novelty detection applied to data streams, a case study is presented. It was conducted on data collected from a test rig and shows the potential of the proposed framework in terms of the ability to detect changes in the operating conditions and abrupt faults and storage memory saving. The outcomes of our work, as well as its major novel aspect, is the design of a framework for a PHM system based on specific requirements that directly originate from the industrial field, together with indications on which techniques can be adopted to achieve such goals

Assembly line balancing and activity scheduling for customised products manufacturing

Nowadays, end customers require personalized products to match their specific needs. Thus, production systems must be extremely flexible. Companies typically exploit assembly lines to manufacture produces in great volumes. The development of assembly lines distinguished by mixed or multi models increases their flexibility concerning the number of product variants able to be manufactured. However, few scientific contributions deal with customizable products, i.e., produces which can be designed and ordered requiring or not a large set of available accessories. This manuscript proposes an original two-step procedure to deal with the multi-manned assembly lines for customized product manufacturing. The first step of the procedure groups the accessories together in clusters according to a specific similarity index. The accessories belonging to a cluster are typically requested together by customers and necessitate a significant mounting time. Thus, this procedure aims to split accessories belonging to the same cluster to different assembly operators avoiding their overloads. The second procedure step consists of an innovative optimization model which defines tasks and accessory assignment to operators. Furthermore, the developed model defines the activity time schedule in compliance with the task precedencies maximizing the operator workload balance. An industrial case study is adopted to test and validate the proposed procedure. The obtained results suggest superior balancing of such assembly lines, with an average worker utilization rate greater than 90%. Furthermore, in the worst case scenario in terms of customer accessories requirement, just 4 line operators out of 16 are distinguished by a maximum workload greater than the cycle time

Machine Learning Scoring Functions for Drug Discoveries from Experimental and Computer-Generated Protein-Ligand Structures: Towards Per-Target Scoring Functions

In recent years, machine learning has been proposed as a promising strategy to build accurate scoring functions for computational docking finalized to numerically empowered drug discovery. However, the latest studies have suggested that over-optimistic results had been reported due to the correlations present in the experimental databases used for training and testing. Here, we investigate the performance of an artificial neural network in binding affinity predictions, comparing results obtained using both experimental protein-ligand structures as well as larger sets of computer-generated structures created using commercial software. Interestingly, similar performances are obtained on both databases. We find a noticeable performance suppression when moving from random horizontal tests to vertical tests performed on target proteins not included in the training data. The possibility to train the network on relatively easily created computer-generated databases leads us to explore per-target scoring functions, trained and tested ad-hoc on complexes including only one target protein. Encouraging results are obtained, depending on the type of protein being addressed.Comment: 22 pages, 8 figure

The Equation of State of a Low-Temperature Fermi Gas with Tunable Interactions

Interacting fermions are ubiquitous in nature and understanding their thermodynamics is an important problem. We measure the equation of state of a two-component ultracold Fermi gas for a wide range of interaction strengths at low temperature. A detailed comparison with theories including Monte-Carlo calculations and the Lee-Huang-Yang corrections for low-density bosonic and fermionic superfluids is presented. The low-temperature phase diagram of the spin imbalanced gas reveals Fermi liquid behavior of the partially polarized normal phase for all but the weakest interactions. Our results provide a benchmark for many-body theories and are relevant to other fermionic systems such as the crust of neutron stars.Comment: 28 pages, 7 figure

Resíduo do processo de fabricação de papel reciclado - potencial de uso como insumo agrícola e/ou florestal.

Resumo

The Impact of Specific Viruses on Clinical Outcome in Children Presenting with Acute Heart Failure

Abstract: The presence and type of viral genomes have been suggested as the main etiology for inflammatory dilated cardiomyopathy. Information on the clinical implication of this finding in a large population of children is lacking. We evaluated the prevalence, type, and clinical impact of specific viral genomes in endomyocardial biopsies (EMB) collected between 2001 and 2013 among 63 children admitted to our hospital for acute heart failure (median age 2.8 years). Viral genome was searched by polymerase chain reaction (PCR). Patients underwent a complete two-dimensional echocardiographic examination at hospital admission and at discharge and were followed-up for 10 years. Twenty-seven adverse events (7 deaths and 20 cardiac transplantations) occurred during the follow-up. Viral genome was amplified in 19/63 biopsies (35%); PVB19 was the most commonly isolated virus. Presence of specific viral genome was associated with a significant recovery in ejection fraction, compared to patients without viral evidence (p < 0.05). In Cox-regression analysis, higher survival rate was related to virus-positive biopsies (p < 0.05). When comparing long-term prognosis among different viral groups, a trend towards better prognosis was observed in the presence of isolated Parvovirus B19 (PVB19) (p = 0.07). In our series, presence of a virus-positive EMB (mainly PVB19) was associated with improvement over time in cardiac function and better long-term prognosis

The quest for a molecular capsule assembled via halogen bonds

A halogen-bonded capsule is obtained via directed assembly of a rigid tetra(3-pyridyl) cavitand and a flexible tetra(4-iodotetrafluorophenyl) calix[4]arene. The pyridyl nitrogen atoms from one cavitand molecule interact with the iodine atoms of a single calixarene molecule through short and directional I…N halogen bonds. The flexibility of the ethylenedioxy moieties on the calixarene platform results in positional flexibility of the iodotetrafluorobenzene sites which, coupled with a supramolecular chelating effect, allow for an effective partner-induced geometric fitting between four nitrogen atoms on the cavitand and four iodine atoms on the calixarene