It measures like me: An IoTs algorithm in WSNs based on heuristics behavior and clustering methods

Our work stems from the consideration that nodes of a wireless sensor network, deployed on a general topology, should follow a bio-inspired approach to respect the trustability, information load, risk and energy-saving requirements, under bounded conditions of time, knowledge and computational power. It allows to introduce a multi-agent model related to Internet of Things and heuristics models, in order to obtain a smart organized network with nodes that have both social and human cognition. Our model is based on a hierarchical clustering method and an aggregation/rejection mechanism, that follows sociological and heuristics theories. The model follows the principle of sense of community and the logic of tie for similarity. The main target is to integrate the inherent cooperation of a multi-agent system with node intelligence of Internet of Things and also with the “Satisficing” of heuristic decisions in order to get a social smart behavior of the whole network.     

Blind source extraction for robust speech recognition in multisource noisy environments

This paper proposes and describes a complete system for Blind Source Extraction (BSE). The goal is to extract a target signal source in order to recognize spoken commands uttered in reverberant and noisy environments, and acquired by a microphone array. The architecture of the BSE system is based on multiple stages: (a) TDOA estimation, (b) mixing system identification for the target source, (c) on-line semi-blind source separation and (d) source extraction. All the stages are effectively combined, allowing the estimation of the target signal with limited distortion.While a generalization of the BSE framework is described, here the proposed system is evaluated on the data provided for the CHiME Pascal 2011 competition, i.e. binaural recordings made in a real-world domestic environment. The CHiME mixtures are processed with the BSE and the recovered target signal is fed to a recognizer, which uses noise robust features based on Gammatone Frequency Cepstral Coefficients. Moreover, acoustic model adaptation is applied to further reduce the mismatch between training and testing data and improve the overall performance. A detailed comparison between different models and algorithmic settings is reported, showing that the approach is promising and the resulting system gives a significant reduction of the error rate.     

Reflective engagement in cultural history: a Lacanian perspective on Pasifika teachers in Aotearoa New Zealand

How do we understand our own cultural histories and how do these understandings impact on our senses of self? This paper addresses the case of Pacific islander migration into New Zealand. It is based on a study fuelled by a group of Pacific island teachers exploring their own experiences of becoming teachers in New Zealand schools. The paper raises some theoretical issues relating to cultural identity as understood in relation to educational practices at the interfaces of cultures. By examining how notions of Pacific cultural identity for individuals are held in place by particular self-images the paper asks how such identities might be seen as reaching out to possible futures. The paper concludes with some tentative thoughts on how an individual might work towards strengthening a historical sense of self and a more productive reflective/reflexive conception of engagement in a situation of cultural minority.     

Evidence for Biological Age Acceleration and Telomere Shortening in COVID-19 Survivors

The SARS-CoV-2 infection determines the COVID-19 syndrome characterized, in the worst cases, by severe respiratory distress, pulmonary and cardiac fibrosis, inflammatory cytokine release, and immunosuppression. This condition has led to the death of about 2.15% of the total infected world population so far. Among survivors, the presence of the so-called persistent post-COVID-19 syndrome (PPCS) is a common finding. In COVID-19 survivors, PPCS presents one or more symptoms: fatigue, dyspnea, memory loss, sleep disorders, and difficulty concentrating. In this study, a cohort of 117 COVID-19 survivors (post-COVID-19) and 144 non-infected volunteers (COVID-19-free) was analyzed using pyrosequencing of defined CpG islands previously identified as suitable for biological age determination. The results show a consistent biological age increase in the post-COVID-19 population, determining a DeltaAge acceleration of 10.45 ± 7.29 years (+5.25 years above the range of normality) compared with 3.68 ± 8.17 years for the COVID-19-free population (p < 0.0001). A significant telomere shortening parallels this finding in the post-COVID-19 cohort compared with COVID-19-free subjects (p < 0.0001). Additionally, ACE2 expression was decreased in post-COVID-19 patients, compared with the COVID-19-free population, while DPP-4 did not change. In light of these observations, we hypothesize that some epigenetic alterations are associated with the post-COVID-19 condition, particularly in younger patients (< 60 years).     

A framework for the analysis of acoustical cardiac signals

Skilled cardiologists perform cardiac auscultation, acquiring and interpreting heart sounds, by implicitly carrying out a sequence of steps. These include discarding clinically irrelevant beats, selectively tuning in to particular frequencies and aggregating information across time to make a diagnosis. In this paper, we formalize a series of analytical stages for processing heart sounds, propose algorithms to enable computers to approximate these steps, and investigate the effectiveness of each step in extracting relevant information from actual patient data. Through such reasoning, we provide insight into the relative difficulty of the various tasks involved in the accurate interpretation of heart sounds. We also evaluate the contribution of each analytical stage in the overall assessment of patients. We expect our framework and associated software to be useful to educators wanting to teach cardiac auscultation, and to primary care physicians, who can benefit from presentation tools for computer-assisted diagnosis of cardiac disorders. Researchers may also employ the comprehensive processing provided by our framework to develop more powerful, fully automated auscultation applications.     

Tracking of multidimensional TDOA for multiple sources with distributed microphone pairs

This paper presents a general framework for tracking the time differences of arrivals of multiple acoustic sources recorded by distributed microphone pairs. Tracking is based on a three-stage analysis. Complex-valued propagation models are extracted at different time instants and frequencies using either the independent component analysis or the phase of the cross-power spectrum evaluated at each microphone pair. In both cases, approximated densities of the propagation time delays are derived through the generalized state coherence transform. A sequential Bayesian tracking scheme with an integrated activity detection is finally implemented through disjoint particle filters based on a track-before-detect strategy. Experiments on both synthetic and real data recorded by two distributed microphone pairs show that the proposed framework can detect and track up to five sources simultaneously active in a reverberant environment.     

Technology Strategy and Knowledge Dynamics: The Case of Biotech¹

This study starts with the lack of clarity revealed in the literature so far as to whether technology is a source of differentiation between firms. The use of patent data allows the evolution of the technological profiles of 99 firms active in biotechnology-related applications to be traced. It is tested whether firms converge or diverge in terms both of their technology portfolios and of the way in which they combine their sets of technologies. It is found that the sources of technological heterogeneity between firms change over time, even when controlling for inter-sectoral differences (as in such sectors as pharmaceuticals, food processing, or chemicals). In brief, firms rely on an increasingly similar scientific corpus, the exploitation of which is gradually becoming firm specific.