Politecnico di Bari

During the first wave of the COVID-19 pandemic, when Italy, like many other countries across the world, were in trouble to find enough masks and ventilators, Italian public and private organizations showed an extraordinary ability to forge new collaborations. These partnerships have been created in a hurry and without preconceptions, with the sole objective of giving an immediate response to the looming health crisis. Such experiences have been welcomed as a renaissance of the public–private collaborative model. Furthermore, confronting the huge need to quickly spend the Next Gen budget, Italian public authorities seem to have successfully overcome their fears and skepticism toward PPP, which, before the pandemic, hindered the development of partnership models. The chapter offers an overview of the state of the art of PPP in Italy and a vision for strategic PPPs as an instrument to respond to current social, digital, environmental, and economic challenges, not just to increase the pool of public money, but mainly to quickly implement new investments and services.

The transcription factor MYB plays a pivotal role in haematopoietic homoeostasis and its aberrant expression is involved in the genesis and maintenance of acute myeloid leukaemia (AML). We have previously demonstrated that not all AML subtypes display the same dependency on MYB expression and that such variability is dictated by the nature of the driver mutation. However, whether this difference in MYB dependency is a general trend in AML remains to be further elucidated. Here, we investigate the role of MYB in human leukaemia by performing siRNA-mediated knock-down in cell line models of AML with different driver lesions. We show that the characteristic reduction in proliferation and the concomitant induction of myeloid differentiation that is observed in MLL-rearranged and t(8;21) leukaemias upon MYB suppression is not seen in AML cells with a complex karyotype. Transcriptome analyses revealed that MYB ablation produces consensual increase of MAFB expression in MYB-dependent cells and, interestingly, the ectopic expression of MAFB could phenocopy the effect of MYB suppression. Accordingly, in silico stratification analyses of molecular data from AML patients revealed a reciprocal relationship between MYB and MAFB expression, highlighting a novel biological interconnection between these two factors in AML and supporting new rationales of MAFB targeting in MLL-rearranged leukaemias.

HERMES Pathfinder is an in-orbit demonstration consisting of a constellation of six 3U nano-satellites hosting simple but innovative detectors for the monitoring of cosmic high-energy transients. The main objective of HERMES Pathfinder is to prove that accurate position of high-energy cosmic transients can be obtained using miniaturized hardware. The transient position is obtained by studying the delay time of arrival of the signal to different detectors hosted by nano-satellites on low-Earth orbits. In this context, we need to develop novel tools to fully exploit the future scientific data output of HERMES Pathfinder. In this paper, we introduce a new framework to assess the background count rate of a spaceborne, high energy detector; a key step towards the identification of faint astrophysical transients. We employ a neural network to estimate the background lightcurves on different timescales. Subsequently, we employ a fast change-point and anomaly detection technique called Poisson-FOCuS to identify observation segments where statistically significant excesses in the observed count rate relative to the background estimate exist. We test the new software on archival data from the NASA Fermi Gamma-ray Burst Monitor (GBM), which has a collecting area and background level of the same order of magnitude to those of HERMES Pathfinder. The neural network performances are discussed and analyzed over period of both high and low solar activity. We were able to confirm events in the Fermi-GBM catalog, both solar flares and gamma-ray bursts, and found events, not present in Fermi-GBM database, that could be attributed to solar flares, terrestrial gamma-ray flashes, gamma-ray bursts and galactic X-ray flashes. Seven of these are selected and further analyzed, providing an estimate of localisation and a tentative classification.

Gamma-ray spectroscopy and dosimetry are complementary techniques used to locate and identify radioactive sources containing gamma-ray-emitting radioisotopes. Gamma-ray spectroscopy is extensively studied for various applications across multiple fields, including homeland security, environmental radioactivity monitoring, tackling illegal trade of radioiso-topes, and medical sciences. Introducing our newly established startup, Flying DEMon s.r.l., comprised of young researchers, academic professors, and backed by university support. Our venture aims to advance project development, leveraging the grant awarded through the E-TEC2 contest initiated by ENAC. The team will showcase their comprehensive work plan, highlighting the project’s competitiveness and self-sustaining potential. The objective of our startup is to harness cutting-edge technologies in the field of gamma spectroscopy and dosimetry, adaptable for deployment via Unmanned Aircraft Systems (UAS). This innovation holds significant promise for environmental monitoring, facilitating tasks such as pinpointing widespread radioactive sources or identifying concealed and hard-to-reach nuclear waste. Additionally, this advancement holds potential for applications in military, security, and industrial oversight. Our research focus primarily revolves around real-time and rapid gamma-ray analysis in open-field environments. Our group not only supports the core project objectives but also enables its applicability in diverse and non-traditional sectors, such as Agritech.

Circular economy has gained much interest over the last decade as an industrial approach aimed at overcoming the traditional “take-make-dispose” economic model. Several studies argue that the implementation of circular economy principles by companies may require them to design a circular business model. Designing a circular business model implies the adoption of managerial practices that address the business model dimensions of value creation, value transfer, and value capture. Existing research highlights that such practices can be adopted by exploiting digital technologies such as 3-D printing. Moreover, earlier scholarly research shows that the ability of a digital technology such as 3-D printing to enable a specific managerial practice depends upon its features. However, a full understanding of the role that 3-D printing can play in enabling the adoption of these managerial practices—by leveraging its peculiar features—is still lacking. Therefore, in this article, we aim to investigate the relationship between 3-D printing features and managerial practices for circular business model design. To this aim, an interactive and interpretive research approach inspired by the design research methodology has been carried out. Leveraging such an approach, this article proposes a novel framework linking the 3-D printing features to the managerial practices that can be adopted in each business model dimension. The framework has been developed and validated through an application case conducted with a company operating in the manufacturing industry.

In the past decades, brain–computer interfaces (BCIs) have been among the fastest-growing technologies and a very prolific research field. Typically, sending input to a computer requires the user to use their hands (e.g., for controlling the mouse and keyboard), their eyes (e.g., in gaze tracking), or realize some type of physical action. On the contrary, the objective of BCIs is to enable direct communication between the user’s mind and different types of computers and devices, that is, without involving any muscular pathways or visible actions. To this end, BCIs rely on systems that acquire the brain activity and process it so that the acquired signals can be analyzed or utilized to control third-party software or devices. Indeed, this involves a sophisticated process consisting of multiple tasks, each of which can be realized in numerous ways. This chapter aims to detail the process, describe the state-of-the-art technologies, present the most recent ones, and highlight future directions for the field. Specifically, after a high-level introduction, the authors assemble information on the most common recording methods for brain signals that are being used in BCI paradigms. Then, the authors focus on EEG-based BCIs and provide an overview on brain control signals, including event-related potentials, slow cortical potentials, and sensorimotor rhythms, which can be used to derive users’ intents and related computational methods employed to extract useful patterns. These methods vary from traditional signal analysis techniques to machine and deep learning approaches. Advantages and limitations of the latter are thoroughly discussed to provide an insight into the state-of-the-art BCI systems. BCIs are expected to compensate for neurological impairment (e.g., brain stroke) of disabilities and allow affected people to live an independent life. Though being unarguably developed for non-clinical purposes, especially with the recent technological progress, medical applications of BCIs are still the main focus of this chapter. Assistance is one of the primary objectives of a medical BCI to which various applications are oriented. For instance, a speller may be exploited as a communication system for patients with amyotrophic lateral sclerosis or spinal injuries, allowing them to write down characters that can be used to browse the Internet. Such interfaces have also the potential to perform domestic tasks, such as regulating lighting system and watching the TV. Another assistive role of BCIs is that of movement support, since they can perform movements in place of people with either upper- or lower-limb disorders. Rehabilitation is another aim that is pursued with BCI in order to cope with those pathologies that reduce cognitive and motor functions, thus determining the address of the intervention, which is a portion of the neurological or musculoskeletal system, respectively. Brain signals might be utilized as health markers in the context of disease prevention as well, notably in the case of degenerative pathologies, like Parkinson’s disease, tumors, as well as those ones with less deadly effects (e.g., schizophrenia and motor disabilities). BCI offers a feedback that may be useful in the treatment of mental diseases, as in the case of epilepsy, or spelling improvement in dyslexic patients.

The issue of dental implant placement relative to the alveolar crest, whether in supracrestal, equicrestal, or subcrestal positions, remains highly controversial, leading to conflicting data in various studies. Three-dimensional (3D) Finite Element Analysis (FEA) can offer insights into the biomechanical aspects of dental implants and the surrounding bone. A 3D model of the jaw was generated using computed tomography (CT) scans, considering a cortical thickness of 1.5 mm. Subsequently, Morse cone implant–abutment connection implants were virtually positioned at the model’s center, at equicrestal (0 mm) and subcrestal levels (−1 mm and −2 mm). The findings indicated the highest stress within the cortical bone around the equicrestally placed implant, the lowest stress in the −2 mm subcrestally placed implant, and intermediate stresses in the −1 mm subcrestally placed implant. In terms of clinical relevance, this study suggested that subcrestal placement of a Morse cone implant–abutment connection (ranging between −1 and −2 mm) could be recommended to reduce peri-implant bone resorption and achieve longer-term implant success.

Photonic Integrated Circuits (PIC) provide a solution to overcome the main limitations of electronics, such as the operating frequency and heat generation, pushing the so-called “More than Moore” concept to increase the capacity and the speed of data transmission. In large data centers and optical transmission systems, PICs are interconnected by using fiber-to-chip couplers, which are crucial to improve system performance. An ultra-low loss interconnection (< 1 dB) over a wide bandwidth (≈ 50 nm) is the gold standard. In this context, the silicon nitride (Si <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> ) platform is a promising candidate, with propagation losses of the order of dB/m at 1550 nm. Here, we propose the design and the experimental results for a silicon nitride-based fiber-to-chip interconnect, acting as a high aspect ratio waveguide Spot-Size Converter (SSC). A coupling loss less than 0.20 dB over the entire C-band and within a footprint of 1,800 μm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> has been experimentally demonstrated, suggesting the proposed interconnect as a promising solution for next-generation high-density PICs.

Safety and security issues for Critical Infrastructures are growing as attackers adopt drones as an attack vector flying in sensitive airspaces, such as airports, military bases, city centers, and crowded places. Despite the use of UAVs for logistics, shipping recreation activities, and commercial applications, their usage poses severe concerns to operators due to the violations and the invasions of the restricted airspaces. A cost-effective and real-time framework is needed to detect the presence of drones in such cases. In this contribution, we propose an efficient radio frequency-based detection framework called URANUS. We leverage real-time data provided by the Radio Frequency/Direction Finding system, and radars in order to detect, classify and identify drones (multi-copter and fixed-wings) invading no-drone zones. We adopt a Multilayer Perceptron neural network to identify and classify UAVs in real-time, with 90% accuracy. For the tracking task, we use a Random Forest model to predict the position of a drone with an MSE ≈ 0.29, MAE ≈ 0.04, and R2 ≈ 0.93. Furthermore, coordinate regression is performed using Universal Transverse Mercator coordinates to ensure high accuracy. Our analysis shows that URANUS is an ideal framework for identifying, classifying, and tracking UAVs that most Critical Infrastructure operators can adopt.

The spatial planning process is considered an extremely complex system, as it is made up of different variables that interrelate and interact with each other. Effectively addressing this spatial complexity necessitates a multidisciplinary approach, as unified methodologies may prove insufficient. Specifically, in urban planning, it is increasingly crucial to prioritize bike lanes, bike stations, and pedestrian zones, for functional transportation infrastructures. This approach can enhance cities by improving air quality, reducing emissions, and boosting public health and safety through physical activity and accident prevention. However, implementing these changes requires careful planning, community engagement, and collaboration with stakeholders. This paper proposes a hybrid model for identifying optimal locations for bike lanes, bike stations, and pedestrian zones adopting Real-Time Spatial Delphi and Generative Adversarial Networks (GANs). The Real-Time Spatial Delphi is a modified version of the traditional Delphi method that incorporates real-time feedback and visualization of group response in real-time, aiming to achieve a convergence of opinions among experts on the territory. Nevertheless, these judgments are a spatial representation not visible in reality, and with the spread of AI models, different implementations can support the planning process, such as the use of Generative Adversarial Networks (GANs). In this case, the GANs models can be exploited by adopting pre-existing location images resulting from experts’ judgments to illustrate the proposed intervention’s visual impact. This approach can help stakeholders, policymakers and citizens visualize the proposed changes and assess their potential impact more accurately. To demonstrate the effectiveness of our hybrid model, we apply it to the city of Dublin.

Olive quick decline syndrome (OQDS) is a disease that has been seriously affecting olive trees in southern Italy since around 2009. During the disease, caused by Xylella fastidiosa subsp. pauca sequence type ST53 (Xf), the flow of water and nutrients within the trees is significantly compromised. Initially, infected trees may not show any symptoms, making early detection challenging. In this study, young artificially infected plants of the susceptible cultivar Cellina di Nardò were grown in a controlled environment and co-inoculated with additional xylem-inhabiting fungi. Asymptomatic leaves of olive plants at an early stage of infection were collected and analyzed using nuclear magnetic resonance (NMR), hyperspectral reflectance (HSR), and chemometrics. The application of a spectranomic approach contributed to shedding light on the relationship between the presence of specific hydrosoluble metabolites and the optical properties of both asymptomatic Xf-infected and non-infected olive leaves. Significant correlations between wavebands located in the range of 530–560 nm and 1380–1470 nm, and the following metabolites were found to be indicative of Xf infection: malic acid, fructose, sucrose, oleuropein derivatives, and formic acid. This information is the key to the development of HSR-based sensors capable of early detection of Xf infections in olive trees.

This paper presents a critical review on the measuring methods and parameters affecting nano-tribology in the context of nano-scale wear. Nano-scale wear phenomena play a crucial role in various industries, including micro/nano-systems, electronics, and biotechnology. The review begins by discussing the significance of nano-scale wear and its impact on device performance, lifespan, durability, energy efficiency, cost savings, and environmental sustainability. It then delves into the measuring methods employed to assess nano-scale wear, including scanning probe microscopy (SPM) techniques such as atomic force microscopy (AFM) and friction force microscopy (FFM). The capabilities of AFM and FFM in studying the roughness of surface, adhesion, friction, scratch, abrasion, and nano-scale material transfer are highlighted. Additionally, the review explores the parameters affecting nano-wear, such as lubrication strategies, stress levels, sliding velocity, and atomic-scale reactions. The article concludes by emphasizing the importance of advanced microscopy techniques in understanding tribological mechanisms at different scales, bridging the gap between macro and nano-tribology studies.

In this work, we report on the implementation of a multi-quantum cascade laser (QCL) module as an innovative light source for quartz-enhanced photoacoustic spectroscopy (QEPAS) sensing. The source is composed of three different QCLs coupled with a dichroitic beam combiner module that provides an overlapping collimated beam output for all three QCLs. The 3λ-QCL QEPAS sensor was tested for detection of NO2, SO2, and NH3 in sequence in a laboratory environment. Sensitivities of 19.99 mV/ppm, 19.39 mV/ppm, and 73.99 mV/ppm were reached for NO2, SO2, and NH3 gas detection, respectively, with ultimate detection limits of 9 ppb, 9.3 ppb, and 2.4 ppb for these three gases, respectively, at an integration time of 100 ms. The detection limits were well below the values of typical natural abundance of NO2, SO2, and NH3 in air.

In this paper we address an extension of the sequential pattern mining problem which aims at detecting the significant differences between frequent sequences with respect to given classes. The resulting problem is known as contrast sequential pattern mining, since it merges the two notions of sequential pattern and contrast pattern. For this problem we present a declarative approach based on Answer Set Programming (ASP). The efficiency and the scalability of the ASP encoding are evaluated on two publicly available datasets, iPRG and UNIX User, by varying parameters, also in comparison with a hybrid ASP-based approach.

Network function virtualization (NFV) supports the rapid development of service function chain (SFC), which efficiently connects a sequence of network virtual function instances (VNFIs) placed into physical infrastructures. Current SFC migration mechanisms usually keep static SFC deployment after finishing certain objectives, and deployment methods mostly provide static resource allocation for VNFIs. Therefore, the adversary has enough time to plan for devastating attacks for in-service SFCs. Fortunately, moving target defense (MTD) was proposed as a game-changing solution to dynamically adjust network configurations. However, existing MTD methods mostly depend on attack-defense models, and lack adaptive mutation period. In this paper, we propose an Intelligence-Driven Service Function Chain Migration (ID-SFCM) scheme. Firstly, we model a Markov decision process (MDP) to formulate the dynamic arrival or departure of SFCs. To remove infeasible actions from the action space of MDP, we formalize the SFC deployment as a constrained satisfaction problem. Then, we design a deep reinforcement learning (DRL) algorithm named model-based adaptive proximal policy optimization (MA-PPO) to enable attack-resistant migration decisions and adaptive migration period. Finally, we evaluate the defense performance by multiple attack strategies and two realistic datasets called CICIDS-2017 and LYCOS-IDS2017 respectively. Simulation results highlight the effectiveness of ID-SFCM compared with representative solutions.