Lifelines in case of Natural Disaster Emergencies

In order to handle Natural disasters, emergency areas are often individuated over the territory, close to populated centres. In these areas, rescue services are located which respond with resources and materials for population relief. A method of automatic positioning of these centres in case of a flood or an earthquake is presented. The positioning procedure consists of two distinct parts developed by the research group of Prof Michael G. H. Bell of Imperial College, London, refined and applied to real cases at the University of Bologna under the coordination of Prof Ezio Todini. There are certain requirements that need to be observed such as the maximum number of rescue points as well as the number of people involved. Initially, the candidate points are decided according to the ones proposed by the local civil protection services. We then calculate all possible routes from each candidate rescue point to all other points, generally using the concept of the "hyperpath", namely a set of paths each one of which may be optimal. The attributes of the road network are of fundamental importance, both for the calculation of the ideal distance and eventual delays due to the event measured in travel time units. In a second phase, the distances are used to decide the optimum rescue point positions using heuristics. This second part functions by "elimination". In the beginning, all points are considered rescue centres. During every interaction we wish to delete one point and calculate the impact it creates. In each case, we delete the point that creates less impact until we reach the number of rescue centres we wish to keep.

From Folke Medicine to Medicine Chemistry: Study, Using in Vitro and Cellular Assays, of Receptors Mechanism Involved in the Activities of Natural Compounds

My Doctorate Research has been focused on the evaluation of the pharmacological activity of a natural extract of chestnut wood (ENC) towards the cardiovascular and gastrointestinal system and on the identification of the active compounds. The ENC has been shown to contain more than 10% (w/w) of phenolic compounds, of which tannins as Vescalgin and Castalgin are the more representative. ENC cardiovascular effects have been investigated in guinea pig cardiac preparations; furthermore its activity has been evalueted in guinea pig aorta strips. ENC induced transient negative chronotropic effect in isolated spontaneously beating right atria and simultaneously positive inotropic effect in left atria driven at 1 Hz. Cardiac cholinergic receptors are not involved in the negative chronotropic effect and positive inotropic effects are not related to adrenergic receptors. In vascular smooth muscle, natural extract of chestnut did not significantly change the contraction induced by potassium (80 mM) or that induced by noradrenaline (1μM). In guinea pig ileum, ENC reduced the maximum response to carbachol in a concentrationdependent manner and behaved as a reversible non competitive antagonist. In guinea pig ileum, the antispasmodic activity of ENC showed a significant antispasmodic activity against a variety of different spasmogenic agents including histamine, KCl, BaCl2. In guinea pig proximal colon, stomach and jejunum, ENC reduced the maximum response to carbachol in a concentrationdependent manner and behaved as a reversible non competitive antagonist. ENC contracted gallbladder guinea pig in a reversible and concentration-dependent manner. This effect does not involve cholinergic and cholecystokinin receptors and it is reduced by nifedipine. ENC relaxed Oddi sphincter smooth muscle. The cholecystokinetic and Oddi sphincter relaxing activities occurred also in guinea pigs fed a lithogenic diet. The cholecystokinetic occurred also in human gallbladder. The Fractionation of the extract led to the identification of the active fraction.

Study of novel natural compounds as potential drugs in the treatment of acute leukemias

Introduction: Among all cancer types leukemia represents the leading cause of cancer death in man younger than 40 years. Single-target drug therapy has generally been highly ineffective in treating complex diseases such as cancer. A growing interest has been directed toward multi-target drugs able to hit multiple targets. In this context, plant products, based on their intrinsic complexity, could represent an interesting and promising approach. Aim of the research followed during my PhD was to indentify and study novel natural compounds for the treatment of acute leukemias. Two potential multi-target drugs were identified in Hemidesmus indicus and piperlongumine. Methodology/Principal Findings: A variety of cellular assays and flow cytometry were performed on different cell lines. We demonstrated that Hemidesmus modulates many components of intracellular signaling pathways involved in cell viability and proliferation and alters gene and protein expression, eventually leading to tumor cell death, mediated by a loss of mitochondrial transmembrane potential, raise of [Ca2+]i, inhibition of Mcl-1, increasing Bax/Bcl-2 ratio, and ROS formation. Moreover, we proved that the decoction causes differentiation of HL-60 and regulates angiogenesis of HUVECs in hypoxia and normoxia, by the inhibition of new vessel formation and the processes of migration/invasion. Clinically relevant observations are that its cytotoxic activity was also recorded in primary cells from acute myeloid leukemia (AML) patients. Moreover, both Hemidesmus and piperlongumine showed a selective action toward leukemic stem cell (LSC). Conclusions: Our results indicate the molecular basis of the anti-leukemic effects of Hemidesmus indicus and indentify the mitochondrial pathways, [Ca2+]i, cytodifferentiation and angiogenesis inhibition as crucial actors in its anticancer activity. The ability to selectively hit LSC showed by Hemidesmus and piperlongumine enriched the knowledge of their anti-leukemic activity. On these bases, we conclude that Hemidesmus and piperlongumine can represent a valuable strategy in the anticancer pharmacology.

Towards Unstructured Knowledge Integration in Natural Language Processing

In the last decades, Artificial Intelligence has witnessed multiple breakthroughs in deep learning. In particular, purely data-driven approaches have opened to a wide variety of successful applications due to the large availability of data. Nonetheless, the integration of prior knowledge is still required to compensate for specific issues like lack of generalization from limited data, fairness, robustness, and biases. In this thesis, we analyze the methodology of integrating knowledge into deep learning models in the field of Natural Language Processing (NLP). We start by remarking on the importance of knowledge integration. We highlight the possible shortcomings of these approaches and investigate the implications of integrating unstructured textual knowledge. We introduce Unstructured Knowledge Integration (UKI) as the process of integrating unstructured knowledge into machine learning models. We discuss UKI in the field of NLP, where knowledge is represented in a natural language format. We identify UKI as a complex process comprised of multiple sub-processes, different knowledge types, and knowledge integration properties to guarantee. We remark on the challenges of integrating unstructured textual knowledge and bridge connections with well-known research areas in NLP. We provide a unified vision of structured knowledge extraction (KE) and UKI by identifying KE as a sub-process of UKI. We investigate some challenging scenarios where structured knowledge is not a feasible prior assumption and formulate each task from the point of view of UKI. We adopt simple yet effective neural architectures and discuss the challenges of such an approach. Finally, we identify KE as a form of symbolic representation. From this perspective, we remark on the need of defining sophisticated UKI processes to verify the validity of knowledge integration. To this end, we foresee frameworks capable of combining symbolic and sub-symbolic representations for learning as a solution.

Appropriate technologies and natural systems for wastewater treatment in low- and middle-income countries

Wastewater management is an environmental and social burden that primarily affects populations in Low- and Middle-Income Countries and the global environment. Wastewater collection, treatment, and reuse have become urgent, especially considering that 80% of the world's wastewater is untreated or improperly treated and discharged directly into water bodies. In recent years, the role of wastewater treatment plants in a sustainable water cycle has become even more critical, as they are the final destination of the collected wastewater. Indeed, the management of wastewater treatment plants should play an essential role in achieving SDG target 6.3 of the United Nations 2030 Agenda for SD. In this context, water reuse, especially wastewater reuse, plays a key role. This research focuses on investigating the valorization of wastewater resources applying Appropriate Technologies and Natural Systems for wastewater treatment in two different Low- and Middle-Income Countries, the Palestinian Territories and Sub-Saharan Africa. The research objectives are: (1) Determine the characteristics and quality of wastewater in the two case studies analysed. (2) Identify Appropriate Technology to be used in the Palestinian Territories to treat wastewater for reuse in agriculture. (3) Assess the environmental, economic, and social impacts of this project. (4) Assess the feasibility of using natural wetlands for household wastewater treatment in Sub-Saharan region. The first study, conducted in Rafah, Gaza Strip, showed that implementing existing primary treatment plant with a natural secondary treatment plant properly optimized the wastewater quality for reuse in agriculture and was suitable for the study area. The second case study was conducted in Cape Coast, Ghana. It shows that the natural wetland studied is currently overly polluted and threatened by various anthropogenic factors that cannot remove pollutants from the incoming domestic wastewater. Therefore, some recommendations were made in order to improve the efficiency of this natural wetland.

Towards cheaper, safer and low environmental impact materials for energy storage devices: natural resources-derived carbon based electrodes and aqueous electrolytes for supercapacitors

The growing market of electrical cars, portable electronics, photovoltaic systems..etc. requires the development of efficient, low-cost, and low environmental impact energy storage devices (ESDs) including batteries and supercapacitors.. Due to their extended charge-discharge cycle, high specific capacitance, and power capabilities supercapacitors are considered among the most attractive ESDs. Over the last decade, research and development in supercapacitor technology have accelerated: thousands of articles have been published in the literature describing the electrochemical properties of the electrode materials and electrolyte in addition to separators and current collectors. Carbon-based supercapacitor electrodes materials have gained increasing attention due to their high specific surface area, good electrical conductivity, and excellent stability in harsh environments, as well as other characteristics. Recently, there has been a surge of interest in activated carbon derived from low-cost abundant sources such as biomass for supercapacitor electrode materials. Also, particular attention was given to a major challenging issue concerning the substitution of organic solutions currently used as electrolytes due to their highest electrochemical stability window even though their high cost, toxicity, and flammability. In this regard, the main objective of this thesis is to investigate the performances of supercapacitors using low cost abundant safe, and low environmental impact materials for electrodes and electrolytes. Several prototypes were constructed and tested using natural resources through optimization of the preparation of appropriate carbon electrodes using agriculture by-products waste or coal (i.e. Argan shell or Anthracite from Jerrada). Such electrodes were tested using several electrolyte formulations (aqueous and water in salt electrolytes) beneficing their non-flammability, lower cost, and environmental impact; the characteristics that provide a promising opportunity to design safer, inexpensive, and environmentally friendly devices compared to organic electrolytes.

Analysis of technological accidents triggered by natural events (Natech) in the perspective of climate change

The severe accidents deriving from the impact of natural events on industrial installations have become a matter of growing concern in the last decades. In the literature, these events are typically referred to as Natech accidents. Several peculiarities distinguish them from conventional industrial accidents caused by internal factors, such as the possible occurrence of multiple simultaneous failures, and the enhanced probability of cascading events. The research project provides a comprehensive overview of Natech accidents that occurred in the Chemical and Process Industry, allowing for the identification of relevant aspects of Natech events. Quantified event trees and probability of ignition are derived from the collected dataset, providing a step forward in the quantitative risk assessment of Natech accidents. The investigation of past Natech accidents also demonstrated that wildfires may cause technological accidents. Climate change and global warming are promoting the conditions for wildfire development and rapid spread. Hence, ensuring the safety of industrial facilities exposed to wildfires is paramount. This was achieved defining safety distances between wildland vegetation and industrial equipment items. In addition, an innovative methodology for the vulnerability assessment of Natech and Domino scenarios triggered by wildfires was developed. The approach accounted for the dynamic behaviour of wildfire events and related technological scenarios. Besides, the performance of the emergency response and the related intervention time in the case of cascading events caused by natural events were evaluated. Overall, the tools presented in this thesis represent a step forward in the Quantitative Risk Assessment of Natech accidents. The methodologies developed also provide a solid basis for the definition of effective strategies for risk mitigation and reduction. These aspects are crucial to improve the resilience of industrial plants to natural hazards, especially considering the effects that climate change may have on the severity of such events.

Advancing digital healthcare research and machine learning-aided knowledge extraction amidst the COVID-19 pandemic

The rapid progression of biomedical research coupled with the explosion of scientific literature has generated an exigent need for efficient and reliable systems of knowledge extraction. This dissertation contends with this challenge through a concentrated investigation of digital health, Artificial Intelligence, and specifically Machine Learning and Natural Language Processing's (NLP) potential to expedite systematic literature reviews and refine the knowledge extraction process. The surge of COVID-19 complicated the efforts of scientists, policymakers, and medical professionals in identifying pertinent articles and assessing their scientific validity. This thesis presents a substantial solution in the form of the COKE Project, an initiative that interlaces machine reading with the rigorous protocols of Evidence-Based Medicine to streamline knowledge extraction. In the framework of the COKE (“COVID-19 Knowledge Extraction framework for next-generation discovery science”) Project, this thesis aims to underscore the capacity of machine reading to create knowledge graphs from scientific texts. The project is remarkable for its innovative use of NLP techniques such as a BERT + bi-LSTM language model. This combination is employed to detect and categorize elements within medical abstracts, thereby enhancing the systematic literature review process. The COKE project's outcomes show that NLP, when used in a judiciously structured manner, can significantly reduce the time and effort required to produce medical guidelines. These findings are particularly salient during times of medical emergency, like the COVID-19 pandemic, when quick and accurate research results are critical.