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Multi-electrode array recording and data analysis methods for molluscan central nervous systems
In this work the use of the central nervous system (CNS) of the aquatic
snail Lymnaea stagnalis on planar multi-electrode arrays (MEAs) was
developed and analysis methods for the data generated were created.
A variety of different combinations of configurations of tissue from the
Lymnaea CNS were explored to determine the signal characteristics
that could be recorded by sixty channel MEAs. In particular, the
suitability of the semi-intact system consisting of the lips, oesophagus,
CNS, and associated nerve connectives was developed for use on
the planar MEA. The recording target area of the dorsal surface of
the buccal ganglia was selected as being the most promising for study
and recordings of its component cells during fictive feeding behaviour
stimulated by sucrose were made. The data produced by this type of
experimentation is very high volume and so its analysis required the
development of a custom set of software tools. The goal of this tool
set is to find the signal from individual neurons in the data streams of
the electrodes of a planar MEA, to estimate their position, and then
to predict their causal connectivity. To produce such an analysis techniques
for noise filtration, neural spike detection, and group detection
of bursts of spikes were created to pre-process electrode data streams.
The Kohonen self-organising map (SOM) algorithm was adapted for
the purpose of separating detected spikes into data streams representing
the spike output of individual cells found in the target system. A
significant addition to SOM algorithm was developed by the concurrent
use of triangulation methods based on current source density
analysis to predict the position of individual cells based on their spike
output on more than one electrode. The likely functional connectivity
of individual neurons identified by the SOM technique were analysed
through the use of a statistical causality method known as Granger
causality/causal connectivity. This technique was used to produce a
map of the likely connectivity between neural sources
On the detection of nearly optimal solutions in the context of single-objective space mission design problems
When making decisions, having multiple options available for a possible realization of the same project can be advantageous. One way to increase the number of interesting choices is to consider, in addition to the optimal solution x*, also nearly optimal or approximate solutions; these alternative solutions differ from x* and can be in different regions – in the design space – but fulfil certain proximity to its function value f(x*). The scope of this article is the efficient computation and discretization of the set E of e–approximate solutions for scalar optimization problems. To accomplish this task, two strategies to archive and update the data of the search procedure will be suggested and investigated. To make emphasis on data storage efficiency, a way to manage significant and insignificant parameters is also presented. Further on, differential evolution will be used together with the new archivers for the computation of E. Finally, the behaviour of the archiver, as well as the efficiency of the resulting search procedure, will be demonstrated on some academic functions as well as on three models related to space mission design
DEVELOPING ENTREPRENEURSHIP IN DIGITAL ECONOMY: THE ECOSYSTEM STRATEGY FOR STARTUPS GROWTH
The transition of the economies towards the digital economy is determining the arising of a type of entrepreneurship based on factors and characteristics widely different from traditional game rules. These changes disclose a series of opportunities for those firms which will be able to adapt at the new parameters and functionalities related to digital technologies diffusion. This contribution underlines some dynamics that should be considered from policy makers who aspire, on the one hand, to promote the emergence of a significant number of startups operating in the digital field and, on the other hand, to nurture the growth process of startups into scale ups. Due to social and economic troubles of many western areas, this latter aspect is even more important. According to a flourishing research stream on entrepreneurship, an interpretative approach to achieving the dual objective is to implement a specific strategy to create an appropriate regional ecosystem. This ecosystem represents a clear change within entrepreneurial policies frame, whose results have so far often been unsatisfactory. Despite its initially selective approach, from an ecosystem many potential benefits can descend. However, creating an ecosystem for digital startup is a complex and burdensome task, which requires a safe and competent guidance, as well as the active involvement of many local actors
A high efficiency input/output coupler for small silicon photonic devices
Coupling light from an optical fibre to small optical waveguides is particularly problematic in semiconductors, since the refractive index of the silica fibre is very different from that of a semiconductor waveguide. There have been several published methods of achieving such coupling, but none are sufficiently efficient whilst being robust enough for commercial applications. In this paper experimental results of our approach called a Dual-Grating Assisted Directional Coupler, are presented. The principle of coupling by this novel method has been successfully demonstrated, and a coupling efficiency of 55% measured
Multi-neuronal refractory period adapts centrally generated behaviour to reward
Oscillating neuronal circuits, known as central pattern generators (CPGs), are responsible for generating rhythmic behaviours such as walking, breathing and chewing. The CPG model alone however does not account for the ability of animals to adapt their future behaviour to changes in the sensory environment that signal reward. Here, using multi-electrode array (MEA) recording in an established experimental model of centrally generated rhythmic behaviour we show that the feeding CPG of Lymnaea stagnalis is itself associated with another, and hitherto unidentified, oscillating neuronal population. This extra-CPG oscillator is characterised by high population-wide activity alternating with population-wide quiescence. During the quiescent periods the CPG is refractory to activation by food-associated stimuli. Furthermore, the duration of the refractory period predicts the timing of the next activation of the CPG, which may be minutes into the future. Rewarding food stimuli and dopamine accelerate the frequency of the extra-CPG oscillator and reduce the duration of its quiescent periods. These findings indicate that dopamine adapts future feeding behaviour to the availability of food by significantly reducing the refractory period of the brain's feeding circuitry
Evolution of Collaborative Networks Supporting Startup Sustainability: Evidences from Digital Firms
The aim of this paper is to investigate whether startup evolution can be conceptualized in a
life cycle model intended as an unpredictable sequence of stages, where startups need to find actors
with whom to collaborate to acquire knowledge and resources supporting the effectiveness and the
sustainability of their mission. The creation and implementation of collaborative networks is observed
through the lens of the holistic approach to the entrepreneurial ecosystem, whose purpose is to
build “bridges” between different actors through the creation of communities of best practices
or entrepreneurial networks. The creation of a specific ecosystem is suggested to ease the
new digital entrepreneurship generation toward acquiring an appropriate level of knowledge,
skills, financial facilitations, and entrepreneurial culture. Following a multiple case study analysis
based on nine successful Italian digital firms, the empirical evidence seems to confirm that firms
collaborate with different actors in different stages, as knowledge and resource networks play a
critical role in sustaining the evolution and success of new firms
Dataset for multimodal fake news detection and verification tasks
The proliferation of online disinformation and fake news, particularly in the context of breaking news events, demands the development of effective detection mechanisms. While textual content remains the predominant medium for disseminating misleading information, the contribution of other modalities is increasingly emerging within online outlets and social media platforms. However, multimodal datasets, which incorporate diverse modalities such as texts and images, are not very common yet, especially in low-resource languages. This study addresses this gap by releasing a dataset tailored for multimodal fake news detection in the Italian language. This dataset was originally employed in a shared task on the Italian language. The dataset is divided into two data subsets, each corresponding to a distinct sub-task. In sub-task 1, the goal is to assess the effectiveness of multimodal fake news detection systems. Sub-task 2 aims to delve into the interplay between text and images, specifically analyzing how these modalities mutually influence the interpretation of content when distinguishing between fake and real news. Both sub-tasks were managed as classification problems. The dataset consists of social media posts and news articles. After collecting it, it was labeled via crowdsourcing. Annotators were provided with external knowledge about the topic of the news to be labeled, enhancing their ability to discriminate between fake and real news. The data subsets for sub-task 1 and sub-task 2 consist of 913 and 1350 items, respectively, encompassing newspaper articles and tweets
Evaluating Pre-Trained Transformers on Italian Administrative Texts
In recent years, Transformer-based models have been widely used in NLP for various downstream tasks and in different domains. However, a language model explicitly built for the Italian administrative language is still lacking. Therefore, in this paper, we decided to compare the performance of five different Transformer models, pre-trained on general purpose texts, on two main tasks in the Italian administrative domain: Name Entity Recognition and multi-label document classification on Public Administration (PA) documents. We evaluate the performance of each model on both tasks to identify the best model in this particular domain. We also discuss the effect of model size and pre-training data on the performances on domain data. Our evaluation identifies UmBERTo as the best-performing model, with an accuracy of 0.71, an F1 score of 0.89 for multi-label document classification, and an F1 score of 0.87 for NER-PA
Potential energy savings from circular economy scenarios based on construction and agri-food waste in Italy
In this study, our aim was to explore the potential energy savings obtainable from the recycling of 1 tonne of Construction and Demolition Waste (C&DW) generated in the Metropolitan City of Naples. The main fraction composing the functional unit are mixed C&DW, soil and stones, concrete, iron, steel and aluminium. The results evidence that the recycling option for the C&DW is better than landfilling as well as that the production of recycled aggregates is environmentally sustainable since the induced energy and environmental impacts are lower than the avoided energy and environmental impacts in the life cycle of recycled aggregates. This LCA study shows that the transition to the Circular Economy offers many opportunities for improving the energy and environmental performances of the construction sector in the life cycle of construction materials by means of internal recycling strategies (recycling C&DW into recycled aggregates, recycled steel, iron and aluminum) as well as external recycling by using input of other sectors (agri-food by-products) for the manufacturing of construction materials. In this way, the C&D sector also contributes to realizing the energy and bioeconomy transition by disentangling itself from fossil fuel dependence
THE REASONS OF THE VERNACULAR ARCHITECTURE FOR THE REGULATION OF CONTEMPORARY INTERVENTIONS. TWO EXAMPLES OF RURAL ARCHITECTURE ON DANUBE DELTA AND THE VESUVIUS
Abstract. The development and growth of the territory has for centuries been conditioned by the availability of resources on site. The minor architecture which is presented as a vast and varied repertoire of unique architectural forms, perfected over time to meet the needs of living places, is the repository of the formal and cultural testimonies that represent the integration between man and environment, which took place in a constant process of adaptation and enhancement of limits and resources in terms of climate, materials, soil morphology and geology. The "not only formal" result of this growth process is a consolidated iconography that summarizes the profound reasons for building through techniques developed according to the characteristics of the available materials and the needs of life and daily work, an absolute synthesis between form and function that gives rise to the repertoire of the lexicon of the architecture of a place and of the landscape. Starting from these reflections, the proposed study seeks to investigate the reasons for the constructive lexicon of some examples of vernacular architecture related to different contexts, identifying the reasons for the constructive choices in terms of relationships between the function of technical elements and construction characteristics; the purpose of this approach is to regulate constructive interventions in consolidated settlements of vernacular architecture by proposing a study methodology that highlights the rules and reasons for those constructive choices so that purely formal distortions and misunderstandings do not occur in current practices. The selected case studies are the rural settlements of Terzigno, a municipality in the province of Naples (Italy) on the slopes of Vesuvius and some of the rural settlements in the Danube Delta, in Romania
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