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The Multidimensional Multiple-choice Knapsack Problem (MMKP) is a complex combinatorial optimization problem for which finding high quality feasible solutions is very challenging. Recently, several heuristic approaches and a few exact algorithms have been proposed for its solution. These methods have been able to provide new best-known values for benchmark instances although many of them still remain unclosed to optimality. In this paper, we provide a new variant of the heuristic framework Kernel Search and apply it to the MMKP. The proposed variant keeps the method’s main idea of solving a sequence of restricted mixed-integer subproblems but innovates by partitioning the solution process into two different phases with complementary goals. The first phase strives for feasibility and collects important information to dynamically adapt subproblems’ dimension and solution time in the second phase that is focused on getting high quality solutions. This makes the global approach more scalable and efficient. Computational results on different sets of benchmark instances demonstrate that our method is extremely effective outperforming all state-of-the-art heuristics for the MMKP. The method compares extremely well also with respect to exact approaches running for five hours. The proposed algorithm is able to improve the best-known value of 185 out of 276 open benchmark instances and gets 8 out of 17 optimal solutions for the closed ones. The average deviation from optimality is always negligible.

Edible electronics is an emerging research field targeting electronic devices that can be safely ingested and directly digested or metabolized by the human body. As such, it paves the way...

In the last decade, the development of new flexible electronics is producing pervasive technologies for the Internet of Things (IoT), robotics and wearables transducers, pushing towards the demonstration of highly compliant low cost components on disposable or recyclable substrates. In recent years, there was a marked improvement in these technologies using organic or inorganic transistors. However, the lack of a high-performance passive-wireless devices in flexible electronics is one of the key bottlenecks for compliant IoT and wearables sensing nodes. One of the main building blocks for sorting this issue out is the development of flexible, bendable and stretchable acoustic-based devices, controllable via wireless antenna. In this work, a flexion characterization of a polymeric-based surface acoustic wave (SAW) device is presented. The flexible SAW is fabricated on polyethylene naphthalate (PEN), employing the high performance of the aluminum nitride (AlN) as a piezoelectric layer. The dual-wave modes, Rayleigh and Lamb, have been characterized. The Lamb waves show better results, in terms of strain sensitivity (1.81E- $04~\varepsilon $ ) and responsivity (1.156 kHz/ $\mu \varepsilon $ ), as compared to the Rayleigh wave (4.29E- $04~\varepsilon $ and 0.577 kHz/ $\mu \varepsilon $ , respectively). This polymeric flexible- based SAW device could pave the way for the development of a passive wireless strain sensor.

This is a repository copy of the published article Advanced Materials Technologies 8 (1), 2023,2200731

Edible electronics will facilitate point-of-care testing through safe and cheap devices that are digested or degraded in the body or environment after performing a specific function. Its thrive depends on creating a library of materials that are the basic building blocks for eatable technologies. Edible electrical conductors fabricated with green methods that allow production at a large scale and composed of food derivatives, ingestible in large amounts without risk for human health are needed. Here, conductive pastes made with materials with a high tolerable upper intake limit (major of mg/kg body weight /day) are proposed. Conductive oleogel paste composites, made with biodegradable and food-grade materials like natural waxes, oils, and activated carbon conductive fillers, are presented. The proposed pastes are compatible with manufacturing processes such as direct ink writing and thus are suitable for an industrial scale-up. These conductors are built without the use of solvents, and with tunable electromechanical features and adhesion depending on the composition. They have antibacterial and hydrophobic properties, so that they can be used in contact with food preventing contamination and preserving its organoleptic properties. As a proof-of-principle application, the edible conductive pastes are demonstrated to be effective edible contacts for food impedance analysis, to be integrated for example in smart fruit labels for ripening monitoring.

In this paper, we present a novel approach to incrementally learn an Abstract Model of an unknown environment, and show how an agent can reuse the learned model for tackling the Object Goal Navigation task. The Abstract Model is a finite state machine in which each state is an abstraction of a state of the environment, as perceived by the agent in a certain position and orientation. The perceptions are high-dimensional sensory data (e.g., RGB-D images), and the abstraction is reached by exploiting image segmentation and the Taskonomy model bank. The learning of the Abstract Model is accomplished by executing actions, observing the reached state, and updating the Abstract Model with the acquired information. The learned models are memorized by the agent, and they are reused whenever it recognizes to be in an environment that corresponds to the stored model. We investigate the effectiveness of the proposed approach for the Object Goal Navigation task, relying on public benchmarks. Our results show that the reuse of learned Abstract Models can boost performance on Object Goal Navigation., Comment: Paper accepted at CVPR2022

This paper shows the optical photoresponse in the IR-Vis-UV range of a AlN-based piezoelectric surface acoustic wave (SAW) delay-line device. The piezoelectric aluminum nitride (AlN) thin film has been sputtered on both silicon rigid substrate and flexible polyethylene naphthalate (PEN) substrate. Both devices have been investigated in their electroacoustic response, by measuring the transfer function S-21 and by laser Doppler vibrometer characterization. The silicon based SAW devices, stimulated by the IR-Vis-UV light, are strongly affected in the out-of-band insertion loss due to the photovoltaic effect. A mathematical model has been implemented to correlate the out-of-band loss with the material's electrical admittance change. In contrast PEN based SAW devices, due to the polymeric nature of the substrate, did not show any variation in the out-of-band loss. Moreover, when exposed to UV light, a frequency downshift of the Rayleigh and Lamb resonances modes have been observed in all the devices, due screening of the photoinduced electrons in the AlN piezoelectric layer which induces an acoustic wave velocity reduction. To the best of our knowledge, this is the first photoresponse study exploiting SAW in the range IR-Vis-UV, suggesting a new detection mode of UV light by a flexible AlN based SAW device. Further development of these devices can lead to a new class of light sensors from UV to IR, based on remote SAW devices.

The automated learning of action models is widely recognised as a key and compelling challenge to address the difficulties of the manual specification of planning domains. Most state-of-the-art methods perform this learning offline from an input set of plan traces generated by the execution of (successful) plans. However, how to generate informative plan traces for learning action models is still an open issue. Moreover, plan traces might not be available for a new environment. In this paper, we propose an algorithm for learning action models online, incrementally during the execution of plans. Such plans are generated to achieve goals that the algorithm decides online in order to obtain informative plan traces and reach states from which useful information can be learned. We show some fundamental theoretical properties of the algorithm, and we experimentally evaluate the online learning of the action models over a large set of IPC domains.

In this work, osteological and paleopathological analyses are combined with liquid-chromatography mass\ud spectrometry to study life and death of 30 non-adult individuals from an Early Medieval Italian funerary context\ud (Valdaro, 7th-8th cent. AD). We estimated individual sex by exploiting sexual differences in enamel-bounded\ud peptides. Enamel proteins were extracted through an acid etching of the whole tooth crowns for 4 samples\ud and through a partial digestion of small enamel chunks for the remaining 26 samples. Both protocols were\ud informative on the sex of the individuals through the identification of amelogenin isoforms (AMELX and\ud AMELY). In addition, low-mineralized tooth germs were analysed and they provided reliable information on the\ud infants’ sex. We observed the presence of 13 males and 17 females among the non-adults of Valdaro, not\ud significantly different from a random sample with an equal frequency of males and females. Cribra cranii and\ud endocranial lesion occurrence showed an association with sex, with higher frequencies in male individuals.

This paper presents an innovative, flexible and biocompatible Ultra High Frequency meander antenna, operating at about 800 MHz, realized by means of sputtering on a Polyethylene Naphthalate substrate, and by means of a multi-material 3D printer. The fabricated antennas were characterized in terms of scattering parameters, showing a good impedance matching and a bandwidth in the order of tens of megahertz, gain and 3D radiation patterns. A numerical model was also introduced to investigate the limits of the proposed technologies in terms of metal thicknesses. The fabricated antenna could be efficiently integrated with Surface Acoustic Wave resonators to realize compact, wireless, wearable and battery-less sensing platforms.

Biodegradable cellulose-based hydrogels are attracting increasing interest in the academic and industrial fields thanks to their high swelling capacity and reproducibility, which allow many novel applications. These properties are enabled by amplification effect of their sensitiveness on a molecular level, translated into macroscopic effects such as a change in swelling degree. The monitoring of the hydrogel state is a crucial step for understanding the response of the hydrogel to external environment. Accordingly, the major aim of this study is to exploit ultrasound to characterize the swelling and degradation of cellulose-based hydrogel with different blend of molecular weight and degree of substitutions. The ultrasonic sensor used herein relies on the determination of a Pulse-echo time of flight. This technique provides dimensional information, thanks to its capability of monitoring the thickness of the swollen/unswollen hydrogel during sorption mechanism. Furthermore, by combining these data with a rheological characterization, the degree of crosslink and its modification during multiple swelling/deswelling cycles (due to ion strength variation) has been monitored. This technique could be an effective, alternative, fast and non-destructive method for real-time hydrogel characterization. Graphical Abstract: [Figure not available: see fulltext.]. © 2018, Springer Science+Business Media B.V., part of Springer Nature.

My research activity focuses on the integration of acting, learning and planning. The main objective is to build a system that is capable to learn how to plan and act in an unknown, dynamic and complex environment. The only knowledge the agent has about the environment is provided by a set of sensor observations which returns continuous measures on the environment. On the learning side, I’m interested in developing algorithms that allow an artificial agent to learn an abstract model of the dynamics of the environment (either an explicit model like a deterministic finite state machine or a model described in a language to express planning domains). The type of abstract model is specified by means of discrete state variables rather than continuous variables representing agent observations. In addition to learning the abstract model, I’m interested in learning probabilistic (generative) models that connects the abstract model with the perceptions of the agents. On the acting and planning side, the artificial agent does not rely on a prior set of execution traces, it rather decides online how to act by means of state-of-art planners. With its own experience, it enriches the planner knowledge, as well as the learned model of the environment. On the learning part, the agent applies techniques for dynamic probabilistic clustering of perceptions in a set of abstract states, neural network for learning transition models, and inductive reasoning for learning action model descriptions. Notice that this is different from Reinforcement Learning where the focus is to learn a policy for achieving a goal, we are interested in learning an abstract model of the environment. We do not have a reward function that encodes the goal to be reached. Indeed in this work an agent does not necessarily need to reach a goal.

The electroacoustic photoresponse of surface acoustic wave (SAW) delay line device, based on piezoelectric aluminum nitride (AlN) thin film sputtered on silicon rigid substrate and flexible polyethylene naphthalate (PEN) substrate, has been investigated. The electroacoustic response of SAW devices has been analyzed by measuring the transfer function S21 under light stimulus of different wavelengths. The S21 out-of-band insertion loss of SAW devices fabricated on silicon is strongly influenced by the photovoltaic effect when the devices are stimulated by light. A mathematical model has been implemented to correlate the out-of-band loss with the material’s electrical admittance. The frequency shift of the resonance frequency modes (Rayleigh and Lamb) has also been characterized for both SAW devices on silicon and PEN substrates, when exposed to UV light. To the best of our knowledge, this is the first detection of UV light by a flexible AlN based SAW device. Further development of these devices exploiting electroacoustic photoresponse phenomena could lead to a new class of remote SAW devices as light sensors in the range between UV to IR.

The development of wearable technology increasingly requires bendable sensing devices operating across multiple domains for opto-electro-mechanical and biochemical transduction. Piezoelectric materials integrated into flexible and transparent device architectures can enable multiple-sensing platforms. It is shown that flexible and compliant surface-acoustic-wave (SAW) piezoelectric devices include all these features and can be applied to the human body. A flexible and transparent aluminum-nitride-(AlN)-based SAW device on a thermoplastic polyethylene naphthalate (PEN) substrate, fabricated by low-temperature sputtering deposition of a multilayered AlN-based stack, is reported for the first time. Two resonant modes, corresponding to Rayleigh and Lamb wave propagation, are shown and compared with a control SAW device on a silicon substrate. A large transmission-signal amplitude, up to 20 dB, is achieved for the Lamb resonance mode around 500 MHz at an acoustic velocity of 10 500 m s−1. The technology is applied to the fabrication of a wearable temperature sensor. Compared to the same piezoelectric stack and SAW technology onto silicon substrates, the AlN/PEN SAW shows better performance and a temperature coefficient frequency as high as ≈810 ppm °C−1. The potential of this flexible SAW device as a wearable temperature sensor based on Rayleigh modes is demonstrated. © 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

Polymeric based surface acoustic wave (SAW) devices represent one of the most interesting platform for the development of wireless passive sensors for IoT and smart packaging applications. An important feature to be addressed in order to develop a compact and conformable wireless SAW sensor is the increase of the working frequency which allows an easy antenna integration and miniaturization. In this work, we present a disposable polymeric SAW temperature sensor device working in the GHz range, based on aluminum nitride (AlN) and built on a 125 μm thick film of polyethylene naphthalate. The flexible device has been compared to the same SAW device fabricated onto silicon substrates. The polymeric based SAW device shows three operating wave modes corresponding to the Rayleigh, Love, and Lamb, highlighting that the Lamb mode exhibits a resonance frequency as high as 1.325 GHz, corresponding to a phase wave velocity of 10,600 m/s, an electromechanical coupling of 2.91 % and Q factor of 109. Temperature coefficient of frequency (TCF) values of 149, 109, 53 ppm/°C have been calculated for the Rayleigh, Love and Lamb waves, respectively. The different behavior of the three SAW modes let us envision the development of a multiple sensing platforms based on different modes in the same device (e.g. temperature, microbiological contamination, light exposure).

Over the last decades, great effort has gone into developing new biosensor technologies for applications in different fields such as disease diagnosis and detection of pollutants in water and food. Global developments in robotic, IoT technologies and in healthcare sensors require new flexible sensor technologies that are low cost and built from sustainable and reusable or recyclable materials. One of the most promising technologies is based on the development of surface acoustic wave (SAW) flexible biosensors, which are highly reproducible, reliable and wirelessly controllable. This work presents for the first time a novel aluminum nitride (AlN)-based conformable SAW immunosensor fabricated on recyclable polyethylene naphthalate. We apply it to the detection of E.Coli using a faster and innovative functionalization method that exploit Protein-A/antibody affinity. A higher sensitivity (Limit of detection-LoD, 6.54*105 CFU/ml) of the Lamb wave traveling on the polymeric device has been obtained in comparison with SAWs traveling on AlN on silicon substrate (LoD, 1.04*106 CFU/ml). Implementation of a finite element method allowed for the estimation of the single E.Coli mass of approximately 9*10−13 g. This work demonstrates the high biosensing potential of flexible polymeric SAW devices for bacteria contamination control in food chain, water and smart packaging.

Archaeological fieldwork at the Etruscan settlement of Forcello (Bagnolo San Vito, Mantova) has been carried out since 1983; excavations led to the discovery of structures and floor levels assigned to nine frequentation phases, dated from 540 to 380-375 BC. This paper offers an overview of the most recent findings, as the Attic ceramic fragments from the aristocratic dwelling called “Casa dei Velna”, whose decoration can be attributed to the Orchard Painter.

Alginate micro beads containing Lactobacillus kefiri (the principal bacteria present in the kefir probiotic drink) were produced by a novel technique based on dual aerosols spaying of alginate based solution and CaCl2 as cross linking agent. Carboxymethylcellulose (CMC) has been also added to the alginate in order to change the physic-chemical properties (viscosity and permeability) of the microbeads. Calcium alginate and CMC are biopolymers that can be used for developing oral drug-delivery systems. These biopolymers have been reported to show a pH-dependent swelling behaviour. Calcium alginate and CMC have also been known to possess an excellent mucoadhesive property. The loaded microbeads have been characterized in terms of morphology, chemical composition and stability in different conditions mimicking the gastric environment. In this study, we demonstrate the feasibility of a continuous fabrication of alginate microbeads in a range of 50–70 μm size, encapsulating L. kefiri as active ingredient. The technique involves the use of a double aerosols of alginate based solution and CaCl2 as crosslinking agent. Moreover, the encapsulation process was proved to be effective and not detrimental to bacteria viability. At the same time, it was verified the protective efficacy of the microcapsules against the gastric environment using both SGF pH 1.2 (fasted state) and pH 2.2 (feed state).