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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Charge-transfer modified embedded atom method dynamic charge potential for Li-Co-O system.

To overcome the limitation of conventional fixed charge potential methods for the study of Li-ion battery cathode materials, a dynamic charge potential method, charge-transfer modified embedded atom method (CT-MEAM), has been developed and applied to the Li-Co-O ternary system. The accuracy of the potential has been tested and validated by reproducing a variety of structural and electrochemical properties of LiCoO2. A detailed analysis on the local charge distribution confirmed the capability of this potential for dynamic charge modeling. The transferability of the potential is also demonstrated by its reliability in describing Li-rich Li2CoO2 and Li-deficient LiCo2O4 compounds, including their phase stability, equilibrium volume, charge states and cathode voltages. These results demonstrate that the CT-MEAM dynamic charge potential could help to overcome the challenge of modeling complex ternary transition metal oxides. This work can promote molecular dynamics studies of Li ion cathode materials and other important transition metal oxides systems that involve complex electrochemical and catalytic reactions.

Understanding the Models of Community Hospital rehabilitation Activity (MoCHA): a mixed-methods study.

INTRODUCTION: To understand the variation in performance between community hospitals, our objectives are: to measure the relative performance (cost efficiency) of rehabilitation services in community hospitals; to identify the characteristics of community hospital rehabilitation that optimise performance; to investigate the current impact of community hospital inpatient rehabilitation for older people on secondary care and the potential impact if community hospital rehabilitation was optimised to best practice nationally; to examine the relationship between the configuration of intermediate care and secondary care bed use; and to develop toolkits for commissioners and community hospital providers to optimise performance. METHODS AND ANALYSIS: 4 linked studies will be performed. Study 1: cost efficiency modelling will apply econometric techniques to data sets from the National Health Service (NHS) Benchmarking Network surveys of community hospital and intermediate care. This will identify community hospitals' performance and estimate the gap between high and low performers. Analyses will determine the potential impact if the performance of all community hospitals nationally was optimised to best performance, and examine the association between community hospital configuration and secondary care bed use. Study 2: a national community hospital survey gathering detailed cost data and efficiency variables will be performed. Study 3: in-depth case studies of 3 community hospitals, 2 high and 1 low performing, will be undertaken. Case studies will gather routine hospital and local health economy data. Ward culture will be surveyed. Content and delivery of treatment will be observed. Patients and staff will be interviewed. Study 4: co-designed web-based quality improvement toolkits for commissioners and providers will be developed, including indicators of performance and the gap between local and best community hospitals performance. ETHICS AND DISSEMINATION: Publications will be in peer-reviewed journals, reports will be distributed through stakeholder organisations. Ethical approval was obtained from the Bradford Research Ethics Committee (reference: 15/YH/0062).

Computational Study of MoS2/HfO2 Defective Interfaces for Nanometer-Scale Electronics.

Atomic structures and electronic properties of MoS2/HfO2 defective interfaces are investigated extensively for future field-effect transistor device applications. To mimic the atomic layer deposition growth under ambient conditions, the impact of interfacial oxygen concentration on the MoS2/HfO2 interface electronic structure is examined. Then, the effect on band offsets (BOs) and the thermodynamic stability of those interfaces is investigated and compared with available relevant experimental data. Our results show that the BOs can be modified up to 2 eV by tuning the oxygen content through, for example, the relative partial pressure. Interfaces with hydrogen impurities as well as various structural disorders were also considered, leading to different behaviors, such as n-type doping, or introducing defect states close to the Fermi level because of the formation of hydroxyl groups. Then, our results indicate that for a well-prepared interface the electronic device performance should be better than that of other interfaces, such as III-V/high-κ, because of the absence of interface defect states. However, any unpassivated defects, if present during oxide growth, strongly affect the subsequent electronic properties of the interface. The unique electronic properties of monolayer-to-few-layered transition-metal dichalcogenides and dielectric interfaces are described in detail for the first time, showing the promising interfacial characteristics for future transistor technology.

Nanopatterning of Group V Elements for Tailoring the Electronic Properties of Semiconductors by Monolayer Doping.

Control of the electronic properties of semiconductors is primarily achieved through doping. While scaling down the device dimensions to the molecular regime presents an increasing number of difficulties, doping control at the nanoscale is still regarded as one of the major challenges of the electronic industry. Within this context, new techniques such as monolayer doping (MLD) represent a substantial improvement toward surface doping with atomic and specific doping dose control at the nanoscale. Our previous work has explained in detail the atomistic mechanism behind MLD by means of density-functional theory calculations (Chem. Mater. 2016, 28, 1975). Here, we address the key questions that will ultimately allow one to optimize the scalability of the MLD process. First, we show that dopant coverage control cannot be achieved by simultaneous reaction of several group V elements, but stepwise reactions make it possible. Second, using ab initio molecular dynamics, we investigate the thermal decomposition of the molecular precursors, together with the stability of the corresponding binary and ternary dopant oxides, prior to the dopant diffusion into the semiconductor surface. Finally, the effect of the coverage and type of dopant on the electronic properties of the semiconductor is also analyzed. Furthermore, the atomistic characterization of the MLD process raises unexpected questions regarding possible crystal damage effects by dopant exchange with the semiconductor ions or the final distribution of the doping impurities within the crystal structure. By combining all our results, optimization recipes to create ultrashallow doped junctions at the nanoscale are finally proposed.

Electronic properties of MoS2/MoOx interfaces: Implications in Tunnel Field Effect Transistors and Hole Contacts.

In an electronic device based on two dimensional (2D) transitional metal dichalcogenides (TMDs), finding a low resistance metal contact is critical in order to achieve the desired performance. However, due to the unusual Fermi level pinning in metal/2D TMD interface, the performance is limited. Here, we investigate the electronic properties of TMDs and transition metal oxide (TMO) interfaces (MoS2/MoO3) using density functional theory (DFT). Our results demonstrate that, due to the large work function of MoO3 and the relative band alignment with MoS2, together with small energy gap, the MoS2/MoO3 interface is a good candidate for a tunnel field effect (TFET)-type device. Moreover, if the interface is not stoichiometric because of the presence of oxygen vacancies in MoO3, the heterostructure is more suitable for p-type (hole) contacts, exhibiting an Ohmic electrical behavior as experimentally demonstrated for different TMO/TMD interfaces. Our results reveal that the defect state induced by an oxygen vacancy in the MoO3 aligns with the valance band of MoS2, showing an insignificant impact on the band gap of the TMD. This result highlights the role of oxygen vacancies in oxides on facilitating appropriate contacts at the MoS2 and MoOx (x < 3) interface, which consistently explains the available experimental observations.