Computer-aided simulation and testing of spatial linkages with joint mechanical errors

Tolerance allocation influences production costs in a big way. For this reason it is very important to have an accurate study about the effects of manufacturing errors on the functioning and performances of linkages. In this paper, the authors present a computer-aided methodology based on a 3D geometrical approach using the dual-algebra fundamentals. The purpose is to give ail useful tool which can be integrated into CAD software in order to evaluate the performances of spatial mechanisms with mechanical errors. The proposed methodology has been validated by means of experimental tests on a Cardan joint mechanism with clearances, misalignments and dimensional errors. Copyright (c) 2005 John Wiley & Sons, Ltd

Fog-Driven Context-Aware Architecture for Node Discovery and Energy Saving Strategy for Internet of Things Environments

The consolidation of the Fog Computing paradigm and the ever-increasing diffusion of Internet of Things (IoT) and smart objects are paving the way toward new integrated solutions to efficiently provide services via short-mid range wireless connectivity. Being the most of the nodes mobile, the node discovery process assumes a crucial role for service seekers and providers, especially in IoT-fog environments where most of the devices run on battery. This paper proposes an original model and a fog-driven architecture for efficient node discovery in IoT environments. Our novel architecture exploits the location awareness provided by the fog paradigm to significantly reduce the power drain of the default baseline IoT discovery process. To this purpose, we propose a deterministic and competitive adaptive strategy to dynamically adjust our energy-saving techniques by deciding when to switch BLE interfaces ON/OFF based on the expected frequency of node approaching. Finally, the paper presents a thorough performance assessment that confirms the applicability of the proposed solution in several different applications scenarios. This evaluation aims also to highlight the impact of the nodes' dynamic arrival on discovery process performance

Learning Opinion Dynamics From Social Traces

Opinion dynamics - the research field dealing with how people's opinions form and evolve in a social context - traditionally uses agent-based models to validate the implications of sociological theories. These models encode the causal mechanism that drives the opinion formation process, and have the advantage of being easy to interpret. However, as they do not exploit the availability of data, their predictive power is limited. Moreover, parameter calibration and model selection are manual and difficult tasks. In this work we propose an inference mechanism for fitting a generative, agent-like model of opinion dynamics to real-world social traces. Given a set of observables (e.g., actions and interactions between agents), our model can recover the most-likely latent opinion trajectories that are compatible with the assumptions about the process dynamics. This type of model retains the benefits of agent-based ones (i.e., causal interpretation), while adding the ability to perform model selection and hypothesis testing on real data. We showcase our proposal by translating a classical agent-based model of opinion dynamics into its generative counterpart. We then design an inference algorithm based on online expectation maximization to learn the latent parameters of the model. Such algorithm can recover the latent opinion trajectories from traces generated by the classical agent-based model. In addition, it can identify the most likely set of macro parameters used to generate a data trace, thus allowing testing of sociological hypotheses. Finally, we apply our model to real-world data from Reddit to explore the long-standing question about the impact of backfire effect. Our results suggest a low prominence of the effect in Reddit's political conversation.Comment: Published at KDD202

Tracking the evolution of riverbed morphology on the basis of uav photogrammetry

Unmanned aerial vehicle (UAV) photogrammetry has recently become a widespread technique to investigate and monitor the evolution of different types of natural processes. Fluvial geomorphology is one of such fields of application where UAV potentially assumes a key role, since it allows for overcoming the intrinsic limits of satellite and airborne-based optical imagery on one side, and in situ traditional investigations on the other. The main purpose of this paper was to obtain extensive products (digital terrain models (DTMs), orthophotos, and 3D models) in a short time, with low costs and at a high resolution, in order to verify the capability of this technique to analyze the active geomorphic processes on a 12 km long stretch of the French–Italian Roia River at both large and small scales. Two surveys, one year apart from each other, were carried out over the study area and a change detection analysis was performed on the basis of the comparison of the obtained DTMs to point out and characterize both the possible morphologic variations related to fluvial dynamics and modifications in vegetation coverage. The results highlight how the understanding of different fluvial processes may be improved by appropriately exploiting UAV-based products, which can thus represent a low-cost and non-invasive tool to crucially support decisionmakers involved in land management practices

Sensor-embedded face masks for detection of volatiles in breath: a proof of concept study

The correlation between breath volatilome and health is prompting a growing interest in the development of sensors optimized for breath analysis. On the other hand, the outbreak of COVID-19 evidenced that breath is a vehicle of infection; thus, the introduction of low-cost and disposable devices is becoming urgent for a clinical implementation of breath analysis. In this paper, a proof of concept about the functionalization of face masks is provided. Porphyrin-based sensors are among the most performant devices for breath analysis, but since porphyrins are scarcely conductive, they make use of costly and bulky mass or optical transducers. To overcome this drawback, we introduce here a hybrid material made of conducting polymer and porphyrins. The resulting material can be easily deposited on the internal surface of standard FFP face masks producing resistive sensors that retain the chemical sensitivity of porphyrins implementing their combinatorial selectivity for the identification of volatile compounds and the classification of complex samples. The sensitivity of sensors has been tested with respect to a set of seven volatile compounds representative of diverse chemical families. Sensors react to all compounds but with a different sensitivity pattern. Functionalized face masks have been tested in a proof-of-concept test aimed at identifying changes of breath due to the ingestion of beverages (coffee and wine) and solid food (banana- and mint-flavored candies). Results indicate that sensors can detect volatile compounds against the background of normal breath VOCs, suggesting the possibility to embed sensors in face masks for extensive breath analysis

Canine circovirus and Canine adenovirus type 1 and 2 in dogs with parvoviral enteritis

Canine parvovirus type 2 (CPV-2) is one of the most relevant pathogens associated with enteritis in dogs and is frequently reported in association with the detection of other pathogens in faeces. In this study the concomitant presence of Canine circovirus (CanineCV) and Canine adenovirus (CAdV) DNA in faecal or intestine samples of 95 dogs with parvovirus enteritis sampled in Italy (1995–2017) was investigated and the viruses identified were genetically characterised. Potential correlations with the antigenic variant of CPV-2 and with signalment data and outcome were evaluated. Twenty-eight of 95 (29.5%) CPV-2 infected dogs tested positive to other viruses: 7/28 were also positive to CanineCV, 1/28 to CAdV-1, 18/28 to CAdV-2, 1/28 to CanineCV and CAdV-2, and 1/28 to CAdV-1 and CAdV-2. The frequency of CAdV DNA detection and coinfections was significantly higher in purebred dogs compared to mixed breed ones (P = 0.002 and 0.009, respectively). The presence of coinfection was not associated with any other relevant data available, including CPV-2 variant and final outcome. The detection of CanineCV in a dog sampled in 2009 allowed to backdating its circulation in dogs. The eight CanineCV completely sequenced were phylogenetically related to the CanineCV identified in dogs, wolves and a badger from Europe, USA, Argentina and China. Nine CAdV were partially sequenced and phylogenetic analysis showed a separate branch for the oldest CAdV-2 identified (1995). From the results obtained in this study population, CanineCV and CAdV coinfections in dogs with parvoviral enteritis did not result in more severe disease

Protection against pertussis in humans correlates to elevated serum antibodies and memory B cells

Pertussis is a respiratory infection caused by Bordetella pertussis that may be particularly severe and even lethal in the first months of life when infants are still too young to be vaccinated. Adults and adolescents experience mild symptoms and are the source of infection for neonates. Adoptive maternal immunity does not prevent pertussis in the neonate. We compared the specific immune response of mothers of neonates diagnosed with pertussis and mothers of control children. We show that women have pre-existing pertussis-specific antibodies and memory B cells and react against the infection with a recall response increasing the levels specific serum IgG, milk IgA, and the frequency of memory B cells of all isotypes. Thus, the maternal immune system is activated in response to pertussis and effectively prevents the disease indicating that the low levels of pre-formed serum antibodies are insufficient for protection. For this reason, memory B cells play a major role in the adult defense. The results of this study suggest that new strategies for vaccine design should aim at increasing long-lived plasma cells and their antibodies

Proline enantiomers discrimination by (L)-prolinated porphyrin derivative Langmuir-Schaefer films: proof of concept for chiral sensing applications

A porphyrin derivative functionalized with the L-enantiomer of proline amino acid was characterized at the air-pure water interface of the Langmuir trough. The porphyrin derivative was dissolved in dichloromethane solution, spread at the air-subphase interface and investigated by acquiring the surface pressure vs. area per molecule Langmuir curves. It is worth observing that the behavior of the molecules of the porphyrin derivative floating film was substantially influenced by the presence of L-proline amino acid dissolved in the subphase (10(-5) M); on the contrary, the physical chemical features of the floating molecules were only slightly influenced by the D-proline dissolved in the subphase. Such an interesting chirality-driven selection was preserved when the floating film was transferred onto solid supports by means of the Langmuir-Schaefer method, but it did not emerge when a spin-coating technique was used for the layering of the tetrapyrrolic derivatives. The obtained results represent proof of concept for the realization of active molecular layers for chiral discrimination: porphyrin derivatives, due to their intriguing spectroscopic and supramolecular properties, can be functionalized with the chiral molecule that should be detected. Moreover, the results emphasize the crucial role of the deposition technique on the features of the sensing layers

Chiral recognition with broad selective sensor arrays

The detection and discrimination of chiral analytes has always been a topical theme in food and pharmaceutical industries and environmental monitoring, especially when dealing with chiral drugs and pesticides, whose enantiomeric nature assessment is of crucial importance. The typical approach matches novel chiral receptors designed ad hoc for the discrimination of a target enantiomer with emerging nanotechnologies. The massive synthetic efforts requested and the difficulty of analyzing complex matrices warrant the ever-growing exploitation of sensor array as an alternative route, using a limited number of chiral or both chiral and achiral sensors for the stereoselective identification and dosing of chiral compounds. This review aims to illustrate a little-explored winning strategy in chiral sensing based on sensor arrays. This strategy mimics the functioning of natural olfactory systems that perceive some couples of enantiomeric compounds as distinctive odors (i.e., using an array of a considerable number of broad selective receptors). Thus, fundamental concepts related to the working principle of sensor arrays and the role of data analysis techniques and models have been briefly presented. After the discussion of existing examples in the literature using arrays for discriminating enantiomers and, in some cases, determining the enantiomeric excess, the remaining challenges and future directions are outlined for researchers interested in chiral sensing applications