Ab initio investigation of layered TMGeTe3 alloys for phase-change applications

Chalcogenide phase-change materials (PCMs) are among the most mature candidates for next-generation memory technology. Recently, CrGeTe3 (CrGT) emerged as a promising PCM due to its enhanced amorphous stability and fast crystallization for embedded memory applications. The amorphous stability of CrGT was attributed to the complex layered structure of the crystalline motifs needed to initiate crystallization. A subsequent computational screening work identified several similar compounds with good thermal stability, such as InGeTe3, CrSiTe3 and BiSiTe3. Here, we explored the substitution of Cr in CrGT with other 3d metals and predicted four additional layered alloys to be dynamically stable, namely ScGeTe3, TiGeTe3, ZnGeTe3 and MnGeTe3. Thorough ab initio simulations performed on both crystalline and amorphous models of these materials indicate the former three alloys to be potential PCMs with sizable resistance contrast. Furthermore, we found that crystalline MnGeTe3 exhibits ferromagnetic behavior, whereas the amorphous state probably forms a spin glass phase. This makes MnGeTe3 a promising candidate for magnetic phase-change applications

Efficiency in Deep Learning: from theoretical foundations to real-world applications

In recent years, terms such as artificial intelligence, machine learning, deep learning, computer vision, and many others have become increasingly prevalent in daily life. These technologies, which can be defined as “learning methodologies” characterized by algorithmic systems capable of extracting information (knowledge) from data, were initially confined to laboratories and research centers. However, thanks to the growing interest in such methodologies, they have gradually permeated corporate environments and our daily lives, becoming essential components in mobile phones, robots, drones, and IoT systems. Generally speaking, we can refer to this category of systems as embedded devices. The spread of such learning methodologies, powered by neural networks (models) combined with specialized hardware such as data centers and clusters of graphic processing units, has led to the development of increasingly powerful and computationally demanding solutions. This concept may be generally expressed as “the bigger the model, the better the performance”. Parallel to this progress aimed at maximizing the model's performance, researchers and scientists all around the world have developed innovative solutions and operations (layers) to improve the learning capability of such architectures. Motivated by the proliferation of embedded devices, characterized by limited computational resources and the need to perform on-board/on-device operations to maintain data privacy, and ensure accurate responses in limited time-frames, this Thesis aims to investigate both mathematically and practically, less-explored research areas related to the efficiency of such models for computer vision tasks. More in detail, we will theoretically analyze and investigate the behavior of fundamental components for neural network learning mechanisms, with a focus on specific layers and elements that characterize the learning procedure, such as self-attention, knowledge distillation, and optimizers. These features, which are essential for both the structure and the learning phase of neural networks, will be crucial in the subsequent stages of this Thesis. More in detail, we will develop computationally efficient solutions in the fields of perception and security, i.e., studying efficient techniques in well-known tasks like monocular depth estimation, 3D mesh reconstruction, and deepfake detection. Additionally, we will look into key elements of neural network efficiency, such as inference time, energy consumption, and their trade-off with estimation performances. Precisely, in contrast to heavy deep learning models, the underlying idea of this Thesis is to develop methodologies that are not only able to “learn” a given task but also “smartly learn” it, i.e., solutions capable of learning the desired task while ensuring good performance with limited inference and training times that can be practically deployed on embedded devices. Moreover, along with these studies, which will be defined as primary in the rest of the manuscript, and to provide an exhaustive perspective of some analyzed tasks, we will also investigate side challenges that emerged in primary researches; such studies will be identified as secondary throughout the manuscript. In conclusion, the purpose of this Thesis is to examine less-explored research areas related to the efficiency of neural network architectures and their applications, with the goal of providing an in-depth view of some open issues and proposing potential solutions, as well as providing the reader valuable hints for further pushing the boundaries of such research fields

Le sfide della transizione energetica. Competitività e resilienza dei territori

Il libro analizza la complessità della transizione energetica e il suo impatto sugli equilibri globali, con un focus particolare sulle sfide tecnologiche, economiche e ambientali del contesto economico e sociale di Civitavecchia. Il primo capitolo esplora l’evoluzione storica delle crisi energetiche, dalla rivoluzione industriale fino agli shock del XX e XXI secolo, evidenziando il ruolo del cambiamento climatico e delle recenti crisi geopolitiche, come quella ucraina. L’analisi include l’emergere di paradigmi innovativi, come l’Industria 4.0, e modelli di sviluppo sostenibile basati sulla Creazione di Valore Condiviso e la Teoria degli Stakeholders. Il secondo capitolo si concentra sulla città di Civitavecchia come caso studio, proponendo una strategia di transizione energetica e riqualificazione territoriale. Viene delineato un piano strategico che punta a trasformare le sfide ambientali ed economiche in opportunità di sviluppo sostenibile, attraverso l’innovazione tecnologica e il potenziamento delle PMI e delle start-up locali. L’analisi territoriale affronta temi come l’istruzione, il pendolarismo, l'economia del mare e la gestione delle risorse ambientali, offrendo una visione integrata per una crescita resiliente e inclusiva. Questo lavoro si propone di fornire una prospettiva multidisciplinare sulle dinamiche della transizione energetica, combinando storia, economia, tecnologia, società e politiche territoriali. Attraverso l’esame di modelli teorici e applicazioni pratiche, il libro rappresenta un contributo significativo per comprendere e affrontare le sfide del futuro energetico globale e nello specifico del contesto italiano

An integrated approach to prevent and control healthcare-associated infections in the intensive care units of Umberto I teaching hospital of Rome: active patient surveillance, epidemiological and genotypic analysis of bacterial isolates and analysis of compliance to hand hygiene precaution among healthcare workers

Healthcare-associated infections (HAIs) pose a significant risk in intensive care units (ICUs), making surveillance and control essential for preventing hospital-acquired infections. In this study, conducted in the ICUs of Umberto I hospital, a multidisciplinary approach was adopted to prevent and manage HAIs during the SARS-CoV-2 pandemic. This approach integrated the analysis of risk factors, molecular characterization of pathogens to identify epidemic clusters, and evaluation of healthcare workers’ compliance with hand hygiene (HH). Findings indicate that the pandemic initially had a negative impact on HAI incidence in ICUs, followed by improvements in clinical outcomes. These improvements were attributed to more effective management of critically ill COVID-19 patients, growing population immunity, and a decrease in SARS-CoV-2 virulence. Despite progress, a rise in HAI incidence was also observed in neonatal ICUs, underscoring the need for continuous optimization in the care of vulnerable patients. Molecular analysis of Acinetobacter baumannii isolates further revealed an alarming spread of multidrug-resistant strains, necessitating enhanced infection control measures and the development of long-term strategies. HH compliance rates remain suboptimal, with lower adherence before patient contact and significant variability across wards, highlighting the challenges in achieving uniform compliance. In conclusion, an integrated approach combining active surveillance, molecular typing, and monitoring of healthcare worker behaviour proves valuable in controlling and preventing HAIs. To maximize its impact, it is essential to address logistical challenges, encourage staff participation, and promote continuous training to improve adherence to preventive practices, thereby enhancing patient outcomes and overall quality of care

Multicomponent signals interference detection exploiting HP-splines frequency parameter

Multicomponent signals play a key role in many application fields, such as biology, audio processing, seismology, air traffic control and security. They are well represented in the time-frequency plane where they are mainly characterized by special curves, called ridges, which carry information about the instantaneous frequency (IF) of each signal component. However, ridges identification usually is a difficult task for signals having interfering components and requires the automatic localization of time-frequency interference regions (IRs). This paper presents a study on the use of the frequency parameter of a hyperbolic-polynomial penalized spline (HP-spline) to predict the presence of interference regions. Since HP-splines are suitably designed for signal regression, it is proved that their frequency parameter can capture the change caused by the interaction between signal components in the time-frequency representation. In addition, the same parameter allows us to define a data-driven approach for IR localization, namely HP-spline Signal Interference Detection (HP-SID) method. Numerical experiments show that the proposed HP-SID can identify specific interference regions for different types of multicomponent signals by means of an efficient algorithm that does not require explicit data regression

Hands as tools: how manual behavior shapes actions and spontaneous and task-evoked brain activity

Humans establish interactions with external objects through stereotypical upper-limb movements. These regularities may generate probabilistic representations about the body and internal models (i.e., prior) for adaptive sensorimotor control. Recent studies show that, in the absence of any task, spontaneous brain activity patterns resemble those evoked by the execution of ecological hand movements. These observations suggest that, even at rest, the brain preserves a hand representation, likely for efficient motor control. Through hands, humans manipulate tools that can be incorporated into the body schema (i.e., brain representation of the body). Using neuroimaging studies, we tested whether spontaneous activity patterns more strongly resemble patterns evoked by the observation of visual stimuli depicting hands vs non-hands and regular vs perturbed object-related arm and hand movements. Then, in two behavioral studies, we explored whether humans could embody a bionic tool (i.e., experience it as part of the body) and thus if this would affect behavior and the body schema. Results showed that spontaneous activity patterns code for the visual representation of human hands in somatomotor brain regions and for regular upper-limb movements in the dorsal attention network. Furthermore, we found that the virtual grafting of a bionic tool elicits a sense of embodiment like or even stronger than its natural counterpart (i.e., a virtual hand) and that tool use can alter the body representation through changes in muscular intensity and kinematics parameters. We suggest that hand shape and regular movements are more represented in spontaneous activity than control stimuli, likely due to replay mechanisms for processing and interpreting information to which we are regularly exposed. Our studies also indicate that the natural use of bionic tools can change human behavior, opening new research and application possibilities, especially for amputees struggling to embody prosthetic limbs

Surface modifications and functionalization of highly aligned multi-walled carbon nanotubes

Highly aligned multi-wall carbon nanotubes (MWCNTs) prepared as vertical arrays onto a silicon substrate, were functionalized and ion-bombarded in clean controlled conditions, to modify their electronic properties. We studied their morphology and electronic states, before and after induced modifications, by X-ray photoelectron spectroscopy (XPS), Raman spectroscopy and Scanning Electron Microscopy (SEM). Ion-beam induced modifications were obtained by medium energy (3 keV) noble gas ions of different mass (He, Ne, Ar), so to generate controlled defects without causing chemical interaction with the carbon matrix. Ion irradiation leads to change in morphology, deformation of the carbon (C) honeycomb lattice and different structural defects in MWCNTs. One of the major effects is the production of bond distortions, as determined by micro-Raman and micro-X-ray photoelectron spectroscopy. We observe an increase of sp3 distorted bonds at higher binding energy with respect to the expected sp2 associated signal of the C 1s core level, and increase of dangling bonds. Furthermore, the surface damage as determined by the XPS C 1s core level is equivalent upon bombarding with ions of different mass, while the impact and density of defects in the lattice of the MWCNTs, as determined by micro-Raman and SEM, are dependent on the bombarding ion mass, huge damage for lighter helium ions, smaller damage for heavier argon ions. CNT functionalization has been achieved by exposing the nanotubes to atomic deuterium in clean and controlled ultra-high-vacuum conditions. Bonding of deuterium (D) atoms on the C mesh of the nanotubes has been established, so to modify the electronic response, without changing the average morphology of the CNT arrays. X-ray photoelectron spectroscopy of the C 1s core level provides clear evidence of deuterium and carbon chemical interaction, by evidencing the establishment of sp3 bonds with suppression of the π plasmon excitation. We demonstrate ∼70 at. % D:C percentage of deuterated carbon atoms in the MWCNTs. The electronic structure modification induced by D chemisorption also affects the energy loss spectrum extrinsically excited by the outgoing photoelectrons, showing quenching of the π-plasmon. Ultraviolet photoelectron spectroscopy showed the opening of an energy gap in the valence band of the D-MWCNTs, with the valence band maximum at about 3.2 eV below the Fermi level. The bond distortion is also evidenced by the modification of the Raman response at the deuterated nanotubes. These results on controlled increase of sp3 distorted bonds via ion bombardment and increasing D:C bonds via atomic deuterium irradiation on the MWCNTs, open new functionalization perspective. This work represents that the molecular cracking of D2 in an ultra-high vacuum is an efficient way to obtain stable, homogeneous and high uptake of deuterium atoms with minimal induction of defects. Ion bombardment opens up the ways to improve and increase atomic deuterium uptake on ion-bombarded MWCNTs, towards potential applications in hydrogen storage into solid state

La vicenda artistica e culturale. L’età moderna e contemporanea

Il contributo affronta il tema delle vicende artistiche e dell'evoluzione della scena autoriale romana dal 1911 sino al 2025. Artisti, spazi allestitivi e musei raccontano al possibile fruitore della "Guida Rossa" di una città poliedrica in continuo cambiamento: dalla Quadriennale di Roma all'Estate Romana sino all'apertura di spazi contemporanei come il museo MAXXI e alle prospettive artistiche future per la città

From memory to identity: the impact of mild cognitive impairment on autobiographical memory and its implications for clinical practice and research

The research activity presented in this thesis work falls within a period of historical significance for the study of age-related conditions, since the United Nations General Assembly has designated the period between 2021 and 2030 as the UN Decade of Healthy Ageing. In this context, healthcare systems need to provide early support and resources for the management of age-related conditions. Past autobiographical memories are extremely significant to older people, as well as to their caregivers, since they are associated with one’s identity and emotional state, as well as with mood, social functioning, and abilities such as problem-solving. Many researchers have focused on studying the changes in the functioning of autobiographical memory in patients with dementia. While the effects of Alzheimer’s disease on autobiographical memory are well-researched, its impact on milder forms of decline, such as MCI, remains debated. Consequently, this project aims to investigate how autobiographical memory functions in the context of both normal and pathological aging. In light of these considerations, this work will be divided into five chapters: the first two chapters will focus on a bibliographic introduction to the main theories and models related to the concepts of mild cognitive impairment (MCI) and autobiographical memory; the third chapter describes a systematic review of the literature aiming to analyze the functioning of autobiographical memory in patients with MCI; the fourth chapter illustrates the attempt to revise a new instrument for assessing autobiographical memory in the Italian population, the Autobiographical Memory of the Self (Memoria Autobiografica del Sé; MA-SElf); finally, the final chapter depicts a study aiming to investigate whether the MA-SElf discriminates between individuals with a normal cognitive profile and patients with MCI

Pervasiveness in conflictual relationship patterns and defensive functioning: unpacking their role in borderline personality disorder

This dissertation unfolds through three studies, with the first two stemming from a randomized controlled trial (RCT) (NCT03717818) conducted at Lausanne University Hospital and the University of Lausanne (project SNSF100014_179457/1) funded by the Swiss National Science Foundation and granted to Prof. Dr. Ueli Kramer as principal investigator from October 2018 to January 2024. The trial was concerned with the mechanisms of change in 10-session General Psychiatric Management (GPM), a brief psychodynamically-oriented psychiatric treatment for Borderline Personality Disorder (BPD). The first article explores how the pervesiveness of core conflictual relationship themes (CCRT) and overall defensive functioning (ODF) interact to predict changes in symptom severity among two groups: BPD-GPM and BPD-TAU (Treatment-as-Usual). 60 patients with BPD (75% female, mean age 29.6) were interviewed with Relationship Anecdote Paradigm at pretreatment to assess ODF and three defensive categories (mature, neurotic, immature) using the Defense Mechanisms Rating Scales-Q-sort (DMRS-Q), and the CCRTs to calculate the overall CCRT pervasiveness (OCP). Symptom severity changes were measured using the Zanarini Rating Scale for BPD, subtracting intake scores from post-treatment scores. Results showed that lower CCRT pervasiveness predicted a significant decrease in affective disturbance, and more frequent use of mature defenses significantly predicted improvements in disturbed relationships, with these effects being more pronounced in GPM compared to TAU. Instead, TAU led to a worsening of affective disturbance for individuals with lower OCP. No significant results were found regarding the prediction and interaction of ODF with OCP in altering borderline symptom.The findings of the first study highlighted that while some patients benefit significantly from this brief intervention, others experienced mixed outcomes, with some even worsening in certain symptom domains. This prompted me to examine and illustrate the treatment process of a 22-year-old patient with BPD from the same RCT, based on the rationale of the “Cases within Trials” (CWT) Model (Fishman et al., 2017). An in-depth investigation of her treatment process revealed a reduction in the CCRT pervasiveness, both overall and in each component; however, a decline in defensive functioning warranted further examination due to its potential impact on increasing affective and relational symptom severity. My exploration of defense mechanisms sparked a profound interest in its implications for psychopathology and the psychotherapy process, ultimately guiding me to develop the third study of my dissertation. This final study investigated the psychometric properties of the Turkish version of the Defense Mechanisms Rating Scales-Self-Report-30 (DMRS-SR-30) within the general population. It represents a crucial first step toward conducting further research on defensive functioning and its impact on therapeutic outcomes within a different linguistic and cultural context. Overall, this dissertation1 aims to contribute to the development of an empirically grounded comprehensive model of BPD while addressing current shortcomings in the literature regarding predictors and outcome changes following brief treatments. It underscores the importance of moving beyond the divide between science and practice, advocating for the integration of both quantitative and qualitative approaches. By doing so, the dissertation seeks to empower clinicians to utilize evidence-based decision trees when selecting the most appropriate psychological interventions for their patients