Discharge characteristics and discharge simulation model of long air gaps containing a floating conductor

Long air gaps containing a floating conductor are common insulation types in power grids. During the transmission line live-line work, the process of lineman entering the transmission line air gap constitutes a live-line work combined air gap, which is a typical long air gap containing a floating conductor. This thesis investigates the discharge characteristics, the discharge mechanism and a discharge simulation model of long air gaps containing a floating conductor in order to address the engineering issues in live-line work. The innovative achievements of the thesis are as follows: (1) The effect of the gap distance, the floating electrode structure, the switching impulse wavefront time, the altitude, and the deviation of the floating conductor from the axis on the breakdown voltage was determined. (2) The physical process of the discharges in long air gaps containing a floating conductor was determined. The reason why the discharge characteristics of long air gaps containing a floating electrode with complex geometrics and sharp protrusions and long air gaps with a rod-shaped floating electrode are similar has been studied. The formation mechanism of the lowest breakdown voltage area of a long air gap containing a floating conductor is explained. (3) A simulation discharge model of long air gaps containing a floating conductor was established, which can describe the physical process and predict the breakdown voltage. The model can realize the accurate prediction of the breakdown voltage of typical long air gaps containing a floating conductor and live-line work combined air gaps in transmission lines. The findings of the study can provide theoretical reference and technical support for improving the safety of live-line work.

Beyond pollinator reward: steps forward and knowledge gaps on the role of floral nectar in plant-animal interactions.

The work done within the framework of my PhD project has been carried out between November 2019 and January 2023 at the Department of Biological, Geological and Environmental Sciences of the University of Bologna, under the supervision of Prof. Marta Galloni and PhD Gherardo Bogo. A period of three months was spent at the Natural History Museum of Rijeka, under the supervision of Prof. Boštjan Surina. The main aim of the thesis was to investigate further the so-called pollinator manipulation hypothesis, which states that when a floral visitor gets in contact with a specific nectar chemistry, the latter affects its behavior of visit on flowers, with potential repercussions on the plant reproductive fitness. To the purpose, the topic was tackled by means of three main approaches: field studies, laboratory assessments, and bibliographic reviews. This research project contributes to two main aspects. First, when insects encounter nectar-like concentrations of a plethora of secondary metabolites in their food-environment, various aspects of their behavior relevant to flower visitation can be affected. In addition, the results I gained confirm that the combination of field studies and laboratory assessments allows to get more realistic pictures of a given phenomenon than the single approaches. Second, reviewing the existent literature in the field of nectar ecology has highlighted how crucial is to establish the origin of nectar biogenic amines to either confirm or reject the multiple speculations made on the role of nectar microbes in shaping plant-animal interactions.

Mind the gaps: cognitive-inspired AI for high-level visual sensemaking. Towards abstract concept image classification

The abundance of visual data and the push for robust AI are driving the need for automated visual sensemaking. Computer Vision (CV) faces growing demand for models that can discern not only what images "represent," but also what they "evoke." This is a demand for tools mimicking human perception at a high semantic level, categorizing images based on concepts like freedom, danger, or safety. However, automating this process is challenging due to entropy, scarcity, subjectivity, and ethical considerations. These challenges not only impact performance but also underscore the critical need for interoperability. This dissertation focuses on abstract concept-based (AC) image classification, guided by three technical principles: situated grounding, performance enhancement, and interpretability. We introduce ART-stract, a novel dataset of cultural images annotated with ACs, serving as the foundation for a series of experiments across four key domains: assessing the effectiveness of the end-to-end DL paradigm, exploring cognitive-inspired semantic intermediaries, incorporating cultural and commonsense aspects, and neuro-symbolic integration of sensory-perceptual data with cognitive-based knowledge. Our results demonstrate that integrating CV approaches with semantic technologies yields methods that surpass the current state of the art in AC image classification, outperforming the end-to-end deep vision paradigm. The results emphasize the role semantic technologies can play in developing both effective and interpretable systems, through the capturing, situating, and reasoning over knowledge related to visual data. Furthermore, this dissertation explores the complex interplay between technical and socio-technical factors. By merging technical expertise with an understanding of human and societal aspects, we advocate for responsible labeling and training practices in visual media. These insights and techniques not only advance efforts in CV and explainable artificial intelligence but also propel us toward an era of AI development that harmonizes technical prowess with deep awareness of its human and societal implications.