Online adaptive identification of multichannel systems for audio applications.

Impulse responses (IRs) estimation of multi-input acoustic systems is a prerequisite for many audio applications. In this paper, an adaptive identification problem based on the Autostep algorithm is extended to the simultaneous estimation of room IRs for multiple input single output linear time invariant systems without any a priori information. To do so, the proposed algorithm is initially evaluated in a simulated room with several sound sources active at the same time. Finally, an experimental validation is proposed for the cases of a semi-anechoic chamber and an arbitrary room. Special attention is dedicated to the algorithm convergence behavior, considering different meta parameters settings. Results are eventually compared with the other normalized version of the least mean square algorithm.

Stress Work and Hopelessness in Mental Health Workers/Caregivers: an Observational Study in Pandemic and Post COVID-19 Pandemic.

BACKGROUND: WHO has decreed an end to the pandemic crisis from COVID-19. However, the consequences of stress, compassion fatigue, and healthcare workers' expectations are still evident. Also, the hope of ending the problems associated with the pandemic is still present, although the awareness of maintaining a high level of attention is current in the HCWs. METHOD: In our observational study, 102 (65 females, 37 males) mental healthcare workers were evaluated during and post-COVID-19 pandemic. They were divided into different categories of workers: nurses, physicians, psychologists, social assistants, social educators, social health workers, and psychiatric rehabilitation technicians. We used the ProQoL for compassion fatigue, compassion satisfaction, and burnout; BHS for hopelessness. RESULTS: ProQoL data showed a significant increase in compassion satisfaction in post-pandemic (p=0.002) in all professional workers. The same results in burnout and secondary stress subscales (respectively, p=0.018, p=0.000) are evident. The BHS total score indicated that the difference between T0 vs. T1 was not statistically significant (p=0.109). CONCLUSIONS: The collected data during and post-COVID-19 pandemic showed reduced burnout and compassion fatigue in the helping professions. However, in the periods analyzed, no changes in hope are observed.

The Emotional Impact on Mental Health Workers in the Care of Patients with Mental Disorders in the Pandemic and Post COVID-19 Pandemic: a Measure of "Burnout" and "Compassion Fatigue".

BACKGROUND: Emotional pressure, fear, and uncertainties affected healthcare workers (HCWs) who played a significant role during the COVID-19 pandemic. After the pandemic crisis, the consequences on the health of mental HealhCare Workers are still significant. Our work aimed to evaluate burnout and compassion fatigue in HCWs. METHOD: In our observational study, 102 (65 females, 37 males) mental HCWs were evaluated during and post-COVID-19 pandemic. We used the Compassion Fatigue-Short Scale, Balanced Emotional Empathy Scale, and Beck Depression Inventory. RESULTS: Subscale Secondary Trauma Stress of CF-SS demonstrated an increase in the rate in the post-pandemic phase (24.51% in all HCWs). The percentage reached in males was high (37.84%). Instead, the levels of Job Burnout remained constant in the two periods analyzed (during and post-COVID-19). Depressive symptoms remained constant with a prevalence in females of the post-COVID period. CONCLUSION: The results confirm increased stress secondary to the traumatic event, while the levels of job burnout are high. Closely associated with compassion fatigue are levels of empathy that were found to be unchanged.

Modular Structure of the Weyl Algebra.

We study the modular Hamiltonian associated with a Gaussian state on the Weyl algebra. We obtain necessary/sufficient criteria for the local equivalence of Gaussian states, independently of the classical results by Araki and Yamagami, Van Daele, Holevo. We also present a criterion for a Bogoliubov automorphism to be weakly inner in the GNS representation. The main application of our analysis is the description of the vacuum modular Hamiltonian associated with a time-zero interval in the scalar, massive, free QFT in two spacetime dimensions, thus complementing the recent results in higher space dimensions (Longo and Morsella in The massive modular Hamiltonian. arXiv:2012.00565). In particular, we have the formula for the local entropy of a one-dimensional Klein-Gordon wave packet and Araki's vacuum relative entropy of a coherent state on a double cone von Neumann algebra. Besides, we derive the type III 1 factor property. Incidentally, we run across certain positive selfadjoint extensions of the Laplacian, with outer boundary conditions, seemingly not considered so far.

A Data Fusion Method for Non-Destructive Testing by Means of Artificial Neural Networks.

In the aeronautics sector, aircraft parts are inspected during manufacture, assembly and service, to detect defects eventually present. Defects can be of different types, sizes and orientations, appearing in materials presenting a complex structure. Among the different inspection techniques, Non Destructive Testing (NDT) presents several advantages as they are noninvasive and cost effective. Within the NDT methods, Ultrasonic (US) waves are widely used to detect and characterize defects. However, due the so-called blind zone, they cannot be easily employed for defects close to the surface being inspected. On the other hand, another NDT technique such Eddy Current (EC) can be used only for detecting flaws close to the surface, due to the presence of the EC skin effect. The work presented in this article aims to combine the use of these two NDT methods, exploiting their complementary advantages. To reach this goal, a data fusion method is developed, by using Machine Learning techniques such as Artificial Neural Networks (ANNs). A simulated training database involving simulations of US and EC signals propagating in an Aluminum block in the presence of Side Drill Holes (SDHs) has been implemented, to train the ANNs. Measurements have been then performed on an Aluminum block, presenting tree different SDHs at specific depths. The trained ANNs were used to characterize the different real SDHs, providing an experimental validation. Eventually, particular attention has been addressed to the estimation errors corresponding to each flaw. Experimental results will show that depths and radii estimations error were confined on average within a range of 4%, recording a peak of 11% for the second SDHs.

The Emotional Impact of the Operator in the Care of Patients With Mental Disorders during the Pandemic: Measure of Interventions on Compassion Fatigue and Burn-Out.

BACKGROUND: The continuation of the health emergency due to the management of COVID-19 is having a profound effect on all aspects of society, including mental health and physical health. This observational study examined practitioners of psychiatric rehabilitation and therapeutic communities, focusing on the emotional aspects of patient care, in particular the fatigue of compassion, empathy and lack of hope, aspects that could be directly linked to the burnout of health professionals, as found in other similar studies. METHOD: In this study, self-administered scale data was collected in 87 healthcare professionals recruited from 3 different psychiatric rehabilitation communities. In particular, we assessed the fatigue of compassion, vicarious trauma, burnout and hope (hopeless), empathy and depressive symptoms in the two months of May and June 2021. RESULTS: The results obtained after the administration of the following rating scales, ProQOL, BHS, SAVE-9, BDI-II and BEES, showed an overall increase in scores in all professional figures, a significant fatigue of compassion, while the percentage burnout is not present in several groups. The presence of high levels of hope, satisfaction of compassion is indicative of a moderate level of empathy in some professional figures; these high levels can protect workers from the risk of developing work-related stress and depressive symptoms. CONCLUSIONS: The data obtained with this study are not similar to those of previous studies, although they may indicate the importance of factors such as hope, empathy in the care of the patient with psychic disorders in rehabilitation communities, underlining the need for interventions aimed at the emotional management of the care relationship as a tool to improve care and prevent burnout even during times of high stress, such as managing a pandemic.

Li2S growth on graphene: Impact on the electrochemical performance of Li-S batteries.

Lithium-sulfur batteries show remarkable potential for energy storage applications due to their high-specific capacity and the low cost of active materials, especially sulfur. However, whereas there is a consensus about the use of lithium metal as the negative electrode, there is not a clear and widely accepted architectural design for the positive electrode of sulfur batteries. The difficulties arise when trying to find a balance between high-surface-area architectures and practical utilization of the sulfur content. Intensive understanding of the interfacial mechanisms becomes then crucial to design optimized carbon-hosted sulfur architectures with enhanced electrochemical performance. In this work, we use density functional theory (DFT)-based first principles calculations to describe and characterize the growing mechanisms of Li2S active material on graphene, taken as an example of a nonencapsulated carbon host for the positive electrode of Li-S batteries. We first unravel the two growing mechanisms of Li2S supported nanostructures, which explain recent experimental findings on real-time monitoring of interfacial deposition of lithium sulfides during discharge, obtained by means of in situ atomic force microscopy. Then, using a combination of mathematical tools and DFT calculations, we obtain the first cycle voltage plot, explaining the three different regions observed that ultimately lead to the formation of high-order polysulfides upon charge. Finally, we show how the different Li2S supported nanostructures can be characterized in X-ray photoelectron spectroscopy measurements. Altogether, this work provides useful insights for the rational design of new carbon-hosted sulfur architectures with optimized characteristics for the positive electrode of lithium-sulfur batteries.

Identification and evaluation of cognitive deficits in schizophrenia using "Machine learning".

BACKGROUND: Schizophrenia can be interpreted as a pathology involving the neocortex whose cognitive dysfunctions represent a central and persistent characteristic of the disease, as well as one of the more important symptoms in relation to the impairment of psychosocial functioning and the resulting disabilities. Given the implication of cognitive functions in everyday life, they can better predict the degree of schizophrenia. The study proposes to use Machine Learning techniques to identify the specific cognitive deficits of schizophrenia that mostly characterize the disorder, as well as to develop a predictive system that can diagnose the presence of schizophrenia based on neurocognitive tests. BACKGROUND: The study employs a dataset of neurocognitive assessments carried out on 201 people (86 schizophrenic patients and 115 healthy patients) recruited by the Neuroscience Group of the University of Bari "A. Moro". A data analysis process has been carried out, with the aim of selecting the most relevant features as well as to prepare data for training a number of "off-the-shelf" machine learning methods (Decision Tree, Random Forest, Logistic Regression, k-Nearest Neighbor, Neural Network, Support Vector Machine), which have been evaluated in terms of classification accuracy according to stratified 20-fold cross-validation. RESULTS: Among all variables, 14 were selected as the most influential for the classification problem. The variables with greater influence are related to working memory, executive functions, attention, verbal fluency, memory. The best algorithms turned out to be Support Vector Machine (SVM) and Neural Network, showing an accuracy of 87.8% and 84.8% on a test set. CONCLUSIONS: Machine Learning provides "cheap" and non-invasive methods that potentially enable early intervention with specific rehabilitation interventions. The results suggest the need to integrate a thorough neuropsychological evaluation into the more general diagnostic evaluation of patients with schizophrenia disorder.

Underwater Acoustic Wavefront Visualization by Scanning Laser Doppler Vibrometer for the Characterization of Focused Ultrasonic Transducers.

The analysis of acoustic wave fields is important for a large number of engineering designs, communication and health-related reasons. The visualization of wavefronts gives valuable information about the type of transducers and excitation signals more suitable for the test itself. This article is dedicated to the development of a fast procedure for acoustic fields visualization in underwater conditions, by means of laser Doppler vibrometer measurements. The ultrasonic probe is a focused transducer excited by a chirp signal. The scope of this work is to evaluate experimentally the properties of the sound beam in order to get reliable information about the transducer itself to be used in many kinds of engineering tests and transducer design.

Impact of intrinsic atomic defects on the electronic structure of MoS2 monolayers.

Monolayer MoS2 is a direct band gap semiconductor which has been recently investigated for low-power field effect transistors. The initial studies have shown promising performance, including a high on/off current ratio and carrier mobility with a high-κ gate dielectric. However, the performance of these devices strongly depends on the crystalline quality and defect morphology of the monolayers. In order to obtain a detailed understanding of the MoS2 electronic device properties, we examine possible defect structures and their impact on the MoS2 monolayer electronic properties, using density functional theory in combination with scanning tunneling microscopy to identify the nature of the most likely defects. Quantitative understanding based on a detailed knowledge of the atomic and electronic structures will facilitate the search of suitable defect passivation techniques. Our results show that S adatoms are the most energetically favorable type of defect and that S vacancies are energetically more favorable than Mo vacancies. This approach may be extended to other transition-metal dichalcogenides (TMDs), thus providing useful insights to optimize TMD-based electronic devices.

Artificial Intelligence and Machine Learning tools for improving Early Warning systems of volcanic eruptions: the case of Stromboli.

Explosive volcanic blasts can occur suddenly and without any clear precursors. Many volcanoes have erupted in the last years with no evident change in the eruptive parameters and with dramatic consequences for the population living nearby the volcano and the tourists visiting the active areas. In recent years, a big effort has been made to develop Early Warning systems to issue timely alerts to the population. At Stromboli volcano, the development of sensitive instruments to measure the deformation (tilt) of the ground has revealed that the volcano edifice is inflating tens of minutes before the explosion following a recurrent exponential ramp-like pattern. This scale-invariant of ground deformation has allowed the development of a quasi-deterministic Early Warning system which is operative since 2019. In this article we show how Artificial Intelligence and Machine Learning can be successfully applied to improve the efficiency and the sensitivity of Early Warning systems, provided the availability of a comprehensive experimental data set on past explosive events. The approach presented here for the Stromboli case demonstrates promising results also in forecasting the intensity of explosive events, offering valuable insights and new perspectives into the potential risks associated with volcanic activities.

A density-functional theory study of tip electronic structures in scanning tunneling microscopy.

In this work, we report a detailed analysis of the atomic and electronic structures of transition metal scanning tunneling microscopy tips: Rh, Pd, W, Ir, and Pt pyramidal models, and transition metal (TM) atom tips supported on the W surface, by means of ab initio density-functional theory methods. The d electrons of the apex atoms of the TM tips (Rh, Pd, W, Ir, and Pt tetrahedral structures) show different behaviors near the Fermi level and, especially for the W tip, dz(2) states are shown to be predominant near the Fermi level. The electronic structures of larger pyramidal TM tip structures with a single apex atom are also reported. Their obtained density of states are thoroughly discussed in terms of the different d-electron occupations of the TM tips.

A3CarScene: An audio-visual dataset for driving scene understanding.

Accurate perception and awareness of the environment surrounding the automobile is a challenge in automotive research. This article presents A3CarScene, a dataset recorded while driving a research vehicle equipped with audio and video sensors on public roads in the Marche Region, Italy. The sensor suite includes eight microphones installed inside and outside the passenger compartment and two dashcams mounted on the front and rear windows. Approximately 31 h of data for each device were collected during October and November 2022 by driving about 1500 km along diverse roads and landscapes, in variable weather conditions, in daytime and nighttime hours. All key information for the scene understanding process of automated vehicles has been accurately annotated. For each route, annotations with beginning and end timestamps report the type of road traveled (motorway, trunk, primary, secondary, tertiary, residential, and service roads), the degree of urbanization of the area (city, town, suburban area, village, exurban and rural areas), the weather conditions (clear, cloudy, overcast, and rainy), the level of lighting (daytime, evening, night, and tunnel), the type (asphalt or cobblestones) and moisture status (dry or wet) of the road pavement, and the state of the windows (open or closed). This large-scale dataset is valuable for developing new driving assistance technologies based on audio or video data alone or in a multimodal manner and for improving the performance of systems currently in use. The data acquisition process with sensors in multiple locations allows for the assessment of the best installation placement concerning the task. Deep learning engineers can use this dataset to build new baselines, as a comparative benchmark, and to extend existing databases for autonomous driving.

Increasing the Strain Resistance of Si/SiO2 Interfaces for Flexible Electronics.

Understanding the changes that occur in the micro-mechanical properties of semiconductor materials is of utmost importance for the design of new flexible electronic devices, especially to control the properties of newly designed materials. In this work, we present the design, fabrication, and application of a novel tensile-testing device coupled to FTIR measurements that enables in situ atomic investigations of samples under uniaxial tensile load. The device allows for mechanical studies of rectangular samples with dimensions of 30 mm × 10 mm × 0.5 mm. By recording the alternation in dipole moments, the investigation of fracture mechanisms becomes feasible. Our results show that thermally treated SiO2 on silicon wafers has a higher strain resistance and breaking force than the SiO2 native oxide. The FTIR spectra of the samples during the unloading step indicate that for the native oxide sample, the fracture happened following the propagation of cracks from the surface into the silicon wafer. On the contrary, for the thermally treated samples, the crack growth starts from the deepest region of the oxide and propagates along the interface due to the change in the interface properties and redistribution of the applied stress. Finally, density functional theory calculations of model surfaces were conducted in order to unravel the differences in optic and electronic properties of the interfaces with and without applied stress.

Activation of surface hydroxyl groups by modification of H-terminated Si(111) surfaces.

Chemical functionalization of semiconductor surfaces, particularly silicon oxide, has enabled many technologically important applications (e.g., sensing, photovoltaics, and catalysis). For such processes, hydroxyl groups terminating the oxide surface constitute the primary reaction sites. However, their reactivity is often poor, hindering technologically important processes, such as surface phosphonation requiring a lengthy postprocessing annealing step at 140 °C with poor control of the bonding geometry. Using a novel oxide-free surface featuring a well-defined nanopatterned OH coverage, we demonstrate that hydroxyl groups on oxide-free silicon are more reactive than on silicon oxide. On this model surface, we show that a perfectly ordered layer of monodentate phosphonic acid molecules is chemically grafted at room temperature, and explain why it remains completely stable in aqueous environments, in contrast to phosphonates grafted on silicon oxides. This fundamental understanding of chemical activity and surface stability suggests new directions to functionalize silicon for sensors, photovoltaic devices, and nanoelectronics.

Mechanisms for Graphene Growth on SiO2 Using Plasma-Enhanced Chemical Vapor Deposition: A Density Functional Theory Study.

Plasma-enhanced chemical vapor deposition (PE-CVD) of graphene layers on dielectric substrates is one of the most important processes for the incorporation of graphene in semiconductor devices. Graphene is moving rapidly from the laboratory to practical implementation; therefore, devices may take advantage of the unique properties of such nanomaterial. Conventional approaches rely on pattern transfers after growing graphene on transition metals, which can cause nonuniformities, poor adherence, or other defects. Direct growth of graphene layers on the substrates of interest, mostly dielectrics, is the most logical approach, although it is not free from challenges and obstacles such as obtaining a specific yield of graphene layers with desired properties or accurate control of the growing number of layers. In this work, we use density-functional theory (DFT) coupled with ab initio molecular dynamics (AIMD) to investigate the initial stages of graphene growth on silicon oxide. We select C2H2 as the PE-CVD precursor due to its large carbon contribution. On the basis of our simulation results for various surface models and precursor doses, we accurately describe the early stages of graphene growth, from the formation of carbon dimer rows to the critical length required to undergo dynamical folding that results in the formation of low-order polygonal shapes. The differences in bonding with the functionalization of the silicon oxide also mark the nature of the growing carbon layers as well as shed light of potential flaws in the adherence to the substrate. Finally, our dynamical matrix calculations and the obtained infrared (IR) spectra and vibrational characteristics provide accurate recipes to trace experimentally the growth mechanisms described and the corresponding identification of possible stacking faults or defects in the emerging graphene layers.

Understanding the preferential adsorption of CO2 over N2 in a flexible metal-organic framework.

The unusual uptake behavior and preferential adsorption of CO(2) over N(2) are investigated in a flexible metal-organic framework system, Zn(2)(bdc)(2)(bpee), where bpdc = 4,4'-biphenyl dicarboxylate and bpee = 1,2-bis(4-pyridyl)ethylene, using Raman and IR spectroscopy. The results indicate that the interaction of CO(2) with the framework induces a twisting of one of its ligands, which is possible because of the type of connectivity of the carboxylate end group of the ligand to the metal center and the specific interaction of CO(2) with the framework. The flexibility of the bpee pillars allows the structure to respond to the twisting, fostering the adsorption of more CO(2). DFT calculations support the qualitative picture derived from the experimental analysis. The adsorption sites at higher loading have been identified using a modified van der Waals-Density Functional Theory method, showing that the more energetically favorable positions for the CO(2) molecules are closer to the C═C bond of the bpee and the C-C bond of the bpdc ligands instead of the benzene and pyridine rings of these ligands. These findings are consistent with changes observed using Raman spectroscopy, which is useful for detecting both specific guest-host interactions and structural changes in the framework.

Estimation of Heart Rate From Tracheal Sounds Recorded for the Sleep Apnea Syndrome Diagnosis.

Obstructive sleep apnea is a common sleep disorder with a high prevalence and often accompanied by significant snoring activity. To diagnose this condition, polysomnography is the standard method, where a neck microphone could be added to record tracheal sounds. These can then be used to study the characteristics of breathing, snoring or apnea. In addition cardiac sounds, also present in the acquired data, could be exploited to extract heart rate. The paper presents new algorithms for estimating heart rate from tracheal sounds, especially in very loud snoring environment. The advantage is that it is possible to reduce the number of diagnostic devices, especially for compact home applications. Three algorithms are proposed, based on optimal filtering and cross-correlation. They are tested firstly on one patient presenting significant pathology of apnea syndrome, with a recording of 509 min. Secondly, an extension to a database of 16 patients is proposed (16 hours of recording). When compared to a reference ECG signal, the final results obtained from tracheal sounds reach an accuracy of 81% to 98% and an RMS error from 1.3 to 4.2 bpm, according to the level of snoring and to the considered algorithm.

Core-Shell Nanocomposites for Improving the Structural Stability of Li-Rich Layered Oxide Cathode Materials for Li-Ion Batteries.

The structural stability of Li-rich layered oxide cathode materials is the ultimate frontier to allow the full development of these family of electrode materials. Here, first-principles calculations coupled with cluster expansion are presented to investigate the electrochemical activity of phase-separation, core-shell-structured xLi2MnO3·(1 - x)LiNiCoMnO2 nanocomposites. The detrimental surface effects of the core region can be countered by the Li2MnO3 shell, which stabilizes the nanocomposites. The operational voltage windows are accurately determined to avoid the electrochemical activation of the shell and the subsequent structural evolution. In particular, the dependence of the activation voltage with the shell thickness shows that relatively high voltages can still be obtained to meet the energy density needs of Li-ion battery applications. Finally, activation energies of Li migration at the core-shell interface must also be analyzed carefully to avoid the outbreak of a phase transformation, thus making the nanocomposites suitable from a structural viewpoint.

Ab Initio Study on Surface Segregation and Anisotropy of Ni-Rich LiNi1-2yCoyMnyO2 (NCM) (y ≤ 0.1) Cathodes.

Advances in ex situ and in situ (operando) characteristic techniques have unraveled unprecedented atomic details in the electrochemical reaction of Li-ion batteries. To bridge the gap between emerging evidences and practical material development, an elaborate understanding on the electrochemical properties of cathode materials on the atomic scale is urgently needed. In this work, we perform comprehensive first-principle calculations within the density functional theory + U framework on the surface stability, morphology, and elastic anisotropy of Ni-rich LiNi1-2yCoyMnyO2 (NCM) (y ≤ 0.1) cathode materials, which are strongly related to the emerging evidence in the degradation of Li-ion batteries. On the basis of the surface stability results, the equilibrium particle morphology is obtained, which is mainly determined by the oxygen chemical potential. Ni-rich NCM particles are terminated mostly by the (012) and (001) surfaces for oxygen-poor conditions, whereas the termination corresponds to the (104) and (001) surfaces for oxygen-rich conditions. Besides, Ni surface segregation predominantly occurs on the (100), (110), and (104) nonpolar surfaces, showing a tendency to form a rocksalt NiO domain on the surface because of severe Li-Ni exchange. The observed elastic anisotropy reveals that an uneven deformation is more likely to be formed in the particles synthesized under poor-oxygen conditions, leading to crack generation and propagation. Our findings provide a deep understanding of the surface properties and degradation of Ni-rich NCM particles, thereby proposing possible solution mechanisms to the factors affecting degradation, such as synthesis conditions, coating, or novel nanostructures.