Archivio Istituzionale della Ricerca - Università degli Studi di Pavia
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Urban Agriculture: Perceived Potentials and Critical Issues in Italy and Singapore
No full textUrban agriculture (UA), the practice of cultivating, processing, and distributing food in and around urban areas, is a process that can grow, mature, and become attuned to urban dynamics only by involving the citizenry, the people who invest energy, time, and skills to strengthen the presence of horticultural products in urban spaces, public and private, within parks and integrated into buildings. The active involvement of people is therefore strategic, and there is a need to inform people about the multiple benefits of urban farming to make the most of the potential of cities. The paper presents the results of a survey aimed at highlighting the role of UA in urban systems. The results of the same questionnaire administered in the Italian territory and in Singapore, two very different contexts in terms of climatic characteristics but also socioeconomic conditions and, therefore, motivation, are presented. This field survey is considered to be a way to analyze the level of familiarity towards UA and a useful tool to identify the policies and strategies that are able to diffuse this practice and engage people
Proton Source Selective Semi-Hydrogenation of Alkynes: A Water-Powered Selective Photocatalyst Based on Nickel Single-Atoms on Poly(Heptazine Imide)
No full textDespite recent advances in photocatalytic hydrogenation reactions using water as a hydrogen source, many existing systems still suffer from limited efficiency, reliance on noble metals, or require complex synthetic procedures. Developing robust, earth-abundant, and synthetically accessible photocatalysts remains a major challenge in the pursuit of sustainable chemical transformations. In this work, we report the preparation of a single-atom nickel photocatalyst embedded in poly(heptazine imide) (Ni-PHI) via a simple and versatile cation exchange strategy. This noble-metal-free photocatalyst enables the selective semi-hydrogenation of alkynes under visible light irradiation using water as the proton source. Photocatalytic tests with phenylacetylene demonstrated a conversion exceeding 98% and a high selectivity for styrene. Moreover, the protocol could be extended to other (internal) alkynes with different substituents. Mechanistic investigations revealed that water molecules adsorb onto Ni(II) sites, promoting a transition from high-spin to low-spin state of the metal center. Under visible-light irradiation, the photogenerated electrons promote the formation of a Ni(I) species, which facilitates proton transfer to the substrate. Spectroscopic studies using near-edge X-ray absorption fine structure (NEXAFS) and multiplet calculations were used to elucidate the reaction mechanism
Photochemistry and Homogeneous Acid Catalysis: a Visible Light Route to ß-Amino Alcohols
No full text“Plasticagglutinated” reef by the genus Sabellaria Lamarck 1818 (Polychaeta, Annelida) in Mediterranean and Atlantic: microplastic pollution and associated risk
No full textMicroplastic pollution in the marine environment is a global threat, particularly in coastal systems, where this anthropogenic pollutant arrives from lands and can temporarily accumulates, before its subsequent transport offshore. Despite microplastics are considered an ubiquitous pollutant, little is known about their effects on benthic organisms that live in this transitional environment, particularly those that utilize the sandy grains to build their external protections (e.g. some marine sedentary polychaetes belonging to the Family Sabellaridae).
This PhD thesis aims to fill this knowledge gap by investigating the possible impact of microplastic pollution on reef-building polychaetes of the genus Sabellaria, specifically S. spinulosa and S. alveolate from both Mediterranean and Atlantic sites. These marine organisms are important ecosystem engineers in temperate coastal areas, where they build large and complex, agglutinated reefs, that in turn, provide essential habitat, foster biodiversity, and also significantly contribute to coastal protection. Given their significant ecological role in the littoral environment, elucidating the effects of microplastics when entered these ecosystems is paramount for effective conservation and management strategies.
In this thesis, the study of microplastics pollution in sabellariid bioconstructions based on a pioneering, multidisciplinary approach that combines specific techniques and methods from both Geological and Biological Sciences. This PhD research successfully integrates traditionally separate aspects by examining both the “geological aspect” of the bioconstruction (the living agglutinated rock) and the “biological aspect” of the reef-building organisms (ecology and organism physiological response). The comparative analysis of microplastics quantified in both bioconstruction and surrounding sediment, offered new insights into the accumulation mechanism, while laboratory manipulation experiments shed light on adverse effects induced by MPs pollution.
The first step of the Phd research was addressed to fill a substantial methodological gap: to effectively extract microplastics from a complex biogenic matrix where microplastics are cemented with the other sedimentary grains. Consequently, a standardized, highly reproducible, and scientifically validated protocol for microplastic extraction, identification and quantification was developed.
Subsequently, comparative field investigations were conducted across two very different environments: the largest known Mediterranean S. spinulosa reef and S. alveolata reefs developed along the Atlantic French coast—among the largest bio-engineered structures in Europe. Findings from both Mediterranean and Atlantic consistently highlighted a passive "trapping mechanism," revealing that these biogenic structures exhibited similar or higher microplastic abundance patterns compared to adjacent shoreface sediments. These observations confirm that Sabellariid reefs act as sedimentary traps for microplastics in the littoral environment.
During the final step of the PhD the potential effect of microplastic on the physiological status of Sabellaria spcimens was assessed. Initially, a comprehensive physiological baseline characterization was established for S. spinulosa, detailing antioxidant defense mechanisms and glycolytic metabolism strategies, including size-related variations in enzyme activity. Subsequently, a controlled laboratory experiment assessed the effects of microplastic exposure on S. alveolata. Employing a multi-proxy approach that monitored feeding behavior alongside stress biomarkers, the experiment yielded robust evidence of stress responses induced by presence of microplastics
Pre-trial detention and EU law: Constitutional constraints, legislative reluctance and non-binding norms
No full textMachine Learning for Predicting Multiple Sclerosis Progression Using Clinical and Environmental Data
No full textMultiple Sclerosis (MS) has become an increasing focus of research in recent years, driven by its growing prevalence and substantial impact on public health. It is a lifelong, chronic neuroinflammatory disorder that progressively impairs neurological functions, often resulting in significant disability. Although therapeutic advances have helped slow disease activity, MS remains a chronic condition, and understanding the drivers of progression is critical for long-term management and for improving patients’ quality of life. Consequently, the study of progression factors has become central to both clinical practice and research.
This work forms part of the BRAINTEASER project, which aims to bring AI-driven care to patients with MS and Amyotrophic Lateral Sclerosis (ALS) across three European countries (Spain, Italy, and Portugal) with clinical and environmental data. Within this framework, this work investigates the role of environmental exposures in MS progression and prediction by integrating them with clinical and demographic data.
The work is organized around three main aims.
Aim 1, addressed in Chapter 3, focuses on the development of a comprehensive longitudinal dataset by integrating clinical and demographic data with multiple types of environmental exposures, including both air pollutants and weather conditions. This constitutes a crucial step toward analyzing the influence of environmental factors on MS progression. To capture both short- and long-term effects, environmental features were aggregated across multiple temporal windows, ranging from one week to several months prior to each patient visit. Since missing data represent a key challenge in medical research, advanced imputation techniques tailored for longitudinal datasets were employed to ensure robust and reliable analyses.
Aim 2, addressed in Chapter 4, investigates the relationship between environmental exposures and disease progression using the Expanded Disability Status Scale (EDSS), the most widely adopted clinical measure of disability in MS. For this purpose, Continuous-Time Markov Models (CTMM) were applied to model transitions between disability states, taking advantage of their ability to handle irregular follow-up intervals and to capture both progression and potential improvement. This approach provides a robust framework for examining how exposures may influence the dynamic course of disability over time.
Aim 3, addressed in Chapter 5, focuses on forecasting Multiple Sclerosis Severity Score (MSSS) classes through sequential data modeling using one of the integrated datasets developed in this research. This study pursues two main objectives. The first, and most central, is to forecast MSSS class, a more recent and refined measure of MS progression compared to EDSS. The second is to assess the contribution of environmental exposures to this predictive task, specifically, to determine their relative importance compared with clinical and demographic variables. This analysis is particularly relevant for forecasting the severity class at a patient’s next clinical visit, an outcome of direct practical value for clinicians.
The most notable finding is that several environmental exposures exert a stronger-than-expected influence on transitions between disability states when progression is considered (Aim 2). In addition, Aim 3 highlights the key potential of environmental exposures, in some cases surpassing well-established demographic and clinical features, traditionally regarded as top predictors in MS research, for forecasting outcomes at the next visit.Multiple Sclerosis (MS) has become an increasing focus of research in recent years, driven by its growing prevalence and substantial impact on public health. It is a lifelong, chronic neuroinflammatory disorder that progressively impairs neurological functions, often resulting in significant disability. Although therapeutic advances have helped slow disease activity, MS remains a chronic condition, and understanding the drivers of progression is critical for long-term management and for improving patients’ quality of life. Consequently, the study of progression factors has become central to both clinical practice and research.
This work forms part of the BRAINTEASER project, which aims to bring AI-driven care to patients with MS and Amyotrophic Lateral Sclerosis (ALS) across three European countries (Spain, Italy, and Portugal) with clinical and environmental data. Within this framework, this work investigates the role of environmental exposures in MS progression and prediction by integrating them with clinical and demographic data.
The work is organized around three main aims.
Aim 1, addressed in Chapter 3, focuses on the development of a comprehensive longitudinal dataset by integrating clinical and demographic data with multiple types of environmental exposures, including both air pollutants and weather conditions. This constitutes a crucial step toward analyzing the influence of environmental factors on MS progression. To capture both short- and long-term effects, environmental features were aggregated across multiple temporal windows, ranging from one week to several months prior to each patient visit. Since missing data represent a key challenge in medical research, advanced imputation techniques tailored for longitudinal datasets were employed to ensure robust and reliable analyses.
Aim 2, addressed in Chapter 4, investigates the relationship between environmental exposures and disease progression using the Expanded Disability Status Scale (EDSS), the most widely adopted clinical measure of disability in MS. For this purpose, Continuous-Time Markov Models (CTMM) were applied to model transitions between disability states, taking advantage of their ability to handle irregular follow-up intervals and to capture both progression and potential improvement. This approach provides a robust framework for examining how exposures may influence the dynamic course of disability over time.
Aim 3, addressed in Chapter 5, focuses on forecasting Multiple Sclerosis Severity Score (MSSS) classes through sequential data modeling using one of the integrated datasets developed in this research. This study pursues two main objectives. The first, and most central, is to forecast MSSS class, a more recent and refined measure of MS progression compared to EDSS. The second is to assess the contribution of environmental exposures to this predictive task, specifically, to determine their relative importance compared with clinical and demographic variables. This analysis is particularly relevant for forecasting the severity class at a patient’s next clinical visit, an outcome of direct practical value for clinicians.
The most notable finding is that several environmental exposures exert a stronger-than-expected influence on transitions between disability states when progression is considered (Aim 2). In addition, Aim 3 highlights the key potential of environmental exposures, in some cases surpassing well-established demographic and clinical features, traditionally regarded as top predictors in MS research, for forecasting outcomes at the next visit
Digital orientation and the hybrid tipping point: Balancing in-person and remote work
No full textAt what point does adding more office time start eroding, rather than enhancing, organizational performance?
Although remote, hybrid, and on-site work have each been studied in isolation, managers still lack evidence-based guidance on the proper mix of these arrangements for organizational results. Existing research rarely connects work-configuration choices to firm-level performance, nor does it consider how digital capabilities might affect the balance. Guided by socio-technical systems (STS) theory, we analyze 27,451 small and medium-sized enterprises (SMEs) across 30 countries to explore whether different intensities of in-person work and their interplay with digital orientation translate into meaningful shifts in revenue growth. By focusing on organizational outcomes rather than individual productivity, our study seeks to uncover strategic inflection points that can inform organizational leaders seeking to navigate the complexities of the post-pandemic business landscape