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A robust quantum light source operating upon electrical injection at ambient conditions is desirable for practical implementation of quantum technologies, such as quantum key distribution or metrology. Color centers in diamond are promising candidates as they are photostable emitters at room and elevated temperatures. The possibility of their electrical excitation has already been demonstrated within p-i-n diodes. However, this requires the growth of complex diamond structures. In contrast to these conventional approaches, we demonstrate the emission of color centers under electrical pumping in a novel Schottky diode configuration based on hydrogen passivated n-type diamond, which holds promise for integrated single-photon emitting devices based on color centers in diamond., Comment: 14 pages, 4 figures

In this paper, we introduce a novel geometry-aware self-training framework for room layout estimation models on unseen scenes with unlabeled data. Our approach utilizes a ray-casting formulation to aggregate multiple estimates from different viewing positions, enabling the computation of reliable pseudo-labels for self-training. In particular, our ray-casting approach enforces multi-view consistency along all ray directions and prioritizes spatial proximity to the camera view for geometry reasoning. As a result, our geometry-aware pseudo-labels effectively handle complex room geometries and occluded walls without relying on assumptions such as Manhattan World or planar room walls. Evaluation on publicly available datasets, including synthetic and real-world scenarios, demonstrates significant improvements in current state-of-the-art layout models without using any human annotation., Comment: Accepted to ECCV-2024

We performed numerical simulations of the common envelope (CE) interaction between two intermediate-mass asymptotic giant branch (AGB) stars and their low-mass companions. For the first time, formation and growth of dust in the envelope is calculated explicitly. We find that the first dust grains appear as early as $\sim$1-3 yrs after the onset of the CE, and are smaller than grains formed later. As the simulations progress, a high-opacity dusty shell forms, resulting in the CE photosphere being up to an order of magnitude larger than it would be without the inclusion of dust. At the end of the simulations, the total dust yield is $0.0082~M_{\odot}$ ($0.022~M_{\odot}$) for a CE with a $1.7~M_{\odot}$ ($3.7~M_{\odot}$) AGB star. Dust formation does not substantially lead to more mass unbinding or substantially alter the orbital evolution., Comment: 6 pages, 6 figures, contributed talk to the Proceedings IAU Symposium No. 384

Aerodynamic noise is a limitation for further exploitation of wind energy resources. As this type of noise is caused by the interaction of turbulent flow with the airframe, a detailed resolution of the flow is necessary to obtain an accurate prediction of the far-field noise. Computational fluid dynamic (CFD) solvers simulate the flow field but only at a high computational cost, which is much increased when the acoustic field is resolved. Therefore, wind turbine noise predictions using numerical approaches remain a challenge. This paper presents a methodology that couples (relatively fast) wind turbine CFD simulations using actuator lines with a fast turn-around noise prediction method. The flow field is simulated using actuator lines and large eddy simulations. The noise prediction method is based on the Amiet-Schlinker's theory for rotatory noise sources, considering leading- and trailing-edge noise as unique noise sources. A 2D panel code (XFOIL) calculates the sectional forces and boundary layer quantities. The resulting methodology for the noise prediction method is of high fidelity since the wind turbine geometry is accounted for in both flow and acoustics predictions. Results are compared with field measurements of a full-scale wind turbine for two operational conditions, validating the results of this research., Comment: 13 pages, 12 figures

Public data ecosystems (PDEs) represent complex socio-technical systems crucial for optimizing data use in the public sector and outside it. Recognizing their multifaceted nature, previous research pro-posed a six-generation Evolutionary Model of Public Data Ecosystems (EMPDE). Designed as a result of a systematic literature review on the topic spanning three decade, this model, while theoretically robust, necessitates empirical validation to enhance its practical applicability. This study addresses this gap by validating the theoretical model through a real-life examination in five European countries - Latvia, Serbia, Czech Republic, Spain, and Poland. This empirical validation provides insights into PDEs dynamics and variations of implementations across contexts, particularly focusing on the 6th generation of forward-looking PDE generation named "Intelligent Public Data Generation" that represents a paradigm shift driven by emerging technologies such as cloud computing, Artificial Intelligence, Natural Language Processing tools, Generative AI, and Large Language Models (LLM) with potential to contribute to both automation and augmentation of business processes within these ecosystems. By transcending their traditional status as a mere component, evolving into both an actor and a stakeholder simultaneously, these technologies catalyze innovation and progress, enhancing PDE management strategies to align with societal, regulatory, and technical imperatives in the digital era.

Wireless Network-on-Chip (WNoC) has been proposed as a low-latency, versatile, and broadcast-capable complement to current interconnects in the quest for satisfying the ever-increasing communications needs of modern computing systems. However, to realize the promise of WNoC, multiple wireless links operating at several tens of Gb/s need to be created within a computing package. Unfortunately, the highly integrated and enclosed nature of such computing packages incurs significant Co-Channel Interference (CCI) and Inter-Symbol Interference (ISI), not only preventing the deployment of multiple spatial channels, but also severely limiting the symbol rate of each individual channel. In this work, Time Reversal (TR) is proposed as a means to compensate the channel impairments and enable multiple concurrent high-speed links at the chip scale. We offer evidence, via full-wave simulations at 140 GHz, that TR can increase the symbol rate by an order of magnitude and allow the deployment of multiple concurrent links towards achieving aggregate speeds in excess of 100 Gb/s. Finally, the challenges relative to the realization of TR at the chip scale are analyzed from the implementation, protocol support, and architectural perspectives.

The concept of Wireless Network-on-Chip (WNoC) has emerged as a potential solution to address the escalating communication demands of modern computing systems due to their low-latency, versatility, and reconfigurability. However, for WNoC to fulfill its potential, it is essential to establish multiple high-speed wireless links across chips. Unfortunately, the compact and enclosed nature of computing packages introduces significant challenges in the form of Co-Channel Interference (CCI) and Inter-Symbol Interference (ISI), which not only hinder the deployment of multiple spatial channels but also severely restrict the symbol rate of each individual channel. In this paper, we posit that Time Reversal (TR) could be effective in addressing both impairments in this static scenario thanks to its spatiotemporal focusing capabilities even in the near field. Through comprehensive full-wave simulations and bit error rate analysis in multiple scenarios and at multiple frequency bands, we provide evidence that TR can increase the symbol rate by an order of magnitude, enabling the deployment of multiple concurrent links and achieving aggregate speeds exceeding 100 Gb/s. Finally, we evaluate the impact of reducing the sampling rate of the TR filter on the achievable speeds, paving the way to practical TR-based wireless communications at the chip scale.

Matrix-variate data of high dimensions are frequently observed in finance and economics, spanning extended time periods, such as the long-term data on international trade flows among numerous countries. To address potential structural shifts and explore the matrix structure's informational context, we propose a time-varying matrix factor model. This model accommodates changing factor loadings over time, revealing the underlying dynamic structure through nonparametric principal component analysis and facilitating dimension reduction. We establish the consistency and asymptotic normality of our estimators under general conditions that allow for weak correlations across time, rows, or columns of the noise. A novel approach is introduced to overcome rotational ambiguity in the estimators, enhancing the clarity and interpretability of the estimated loading matrices. Our simulation study highlights the merits of the proposed estimators and the effective of the smoothing operation. In an application to international trade flow, we investigate the trading hubs, centrality, patterns, and trends in the trading network.

Dyslexia is the most widespread specific learning disorder and significantly impair different cognitive domains. This, in turn, negatively affects dyslexic students during their learning path. Therefore, specific support must be given to these students. In addition, such a support must be highly personalized, since the problems generated by the disorder can be very different from one to another. In this work, we explored the possibility of using AI to suggest the most suitable supporting tools for dyslexic students, so as to provide a targeted help that can be of real utility. To do this, we relied on recommendation algorithms, which are a branch of machine learning, that aim to detect personal preferences and provide the most suitable suggestions. We hence implemented and trained three collaborative-filtering recommendation models, namely an item-based, a user-based and a weighted-hybrid model, and studied their performance on a large database of 1237 students' information, collected with a self-evaluating questionnaire regarding all the most used supporting strategies and digital tools. Each recommendation model was tested with three different similarity metrics, namely Pearson correlation, Euclidean distance and Cosine similarity. The obtained results showed that a recommendation system is highly effective in suggesting the optimal help tools/strategies for everyone. This demonstrates that the proposed approach is successful and can be used as a new and effective methodology to support students with dyslexia., Comment: 36 pages, 4 figures and 6 tables. Preprint submitted to Expert Systems with Applications

This paper is devoted to the study of inverse source problems for coupled systems of heat equations with constant or spatial--dependent coupling terms and whose internal measurements involve a reduced number of observed states. The analysis is developed for two kind of systems: the first one consists of parabolic equations with zero order coupling terms (or the so-called non-self-adjoint matrix potential) and whose possibly space-dependent coefficients. The second one consists of parabolic equations with coupling in the diffusion matrix. In all configurations the source is decomposed in separate variables, where the temporal part is known and scalar, whereas the spatial dependence is an unknown vector field. This work builds on previous methodologies for the recovery of source in scalar equations and Stokes fluids, thus expanding the field to include coupled systems of second order parabolic equations. Numerical algorithms through the finite element method in 1D and 2D are performed. Several examples showing that the algorithms make possible to recover space-dependent sources.

This paper analyzes the relation between bank profit performance and business models. Using a machine learning-based approach, we propose a methodological strategy in which balance sheet components' contributions to profitability are the identification instruments of business models. We apply this strategy to the European Union banking system from 1997 to 2021. Our main findings indicate that the standard retail-oriented business model is the profile that performs best in terms of profitability, whereas adopting a non-specialized business profile is a strategic decision that leads to poor profitability. Additionally, our findings suggest that the effect of high capital ratios on profitability depends on the business profile. The contributions of business models to profitability decreased during the Great Recession. Although the situation showed signs of improvement afterward, the European Union banking system's ability to yield returns is still problematic in the post-crisis period, even for the best-performing group., Comment: 46 pages, 10 tables, 3 figures, submitted version of a paper published in Research in International Business and Finance

To examine the relation between profitability and business models (BMs) across bank sizes, the paper proposes a research strategy based on machine learning techniques. This strategy allows for analyzing whether size and profit performance underlie BM heterogeneity, with BM identification being based on how the components of the bank portfolio contribute to profitability. The empirical exercise focuses on the European Union banking system. Our results suggest that banks with analogous levels of performance and different sizes share strategic features. Additionally, high capital ratios seem compatible with high profitability if banks, relative to their size peers, adopt a standard retail BM., Comment: 14 pages, 1 figure, 3 tables, accepted version of an article published in Finance Research Letters

Learning disorders are neurological conditions that affect the brain's ability to interconnect communication areas. Dyslexic students experience problems with reading, memorizing, and exposing concepts; however the magnitude of these can be mitigated through both therapies and the creation of compensatory mechanisms. Several efforts have been made to mitigate these issues, leading to the creation of digital resources for students with specific learning disorders attending primary and secondary education levels. Conversely, a standard approach is still missed in higher education. The VRAIlexia project has been created to tackle this issue by proposing two different tools: a mobile application integrating virtual reality (VR) to collect data quickly and easily, and an artificial intelligencebased software (AI) to analyze the collected data for customizing the supporting methodology for each student. The first one has been created and is being distributed among dyslexic students in Higher Education Institutions, for the conduction of specific psychological and psychometric tests. The second tool applies specific artificial intelligence algorithms to the data gathered via the application and other surveys. These AI techniques have allowed us to identify the most relevant difficulties faced by the students' cohort. Our different models have obtained around 90\% mean accuracy for predicting the support tools and learning strategies., Comment: 7 pages, 5 figures, 3 tables, MetroXRAINE 2022 Conference, VRAILEXIA european project

Dyslexia is a neurodevelopmental disorder that is estimated to affect about 5-10% of the population. In particular, phonological dyslexia causes problems in connecting the sounds of words with their written forms. This results in difficulties such as slow reading speed, inaccurate reading, and difficulty decoding unfamiliar words. Moreover, dyslexia can also be a challenging and frustrating experience for students as they may feel misunderstood or stigmatized by their peers or educators. For these reasons, the use of compensatory tools and strategies is of crucial importance for dyslexic students to have the same opportunities as non-dyslexic ones. However, generally, people underestimate the problem and are not aware of the importance of support methodologies. In the light of this, the main purpose of this paper is to propose a virtual reality (VR) serious game through which teachers, students and, in general, non-dyslexic people could understand which are some of the issues of student with dyslexia and the fundamental utility of offering support to them. In the game, players must create a potion by following a recipe written in an alphabet that is specifically designed to replicate the reading difficulties experienced by individuals with dyslexia. The task must be solved first without any help and then by receiving supporting tools and strategies with the idea that the player can put himself in the place of the dyslexic person and understand the real need for support methodologies., Comment: 5 pages, 5 figures, MetroXRAINE 2023

Jewelry recognition is a complex task due to the different styles and designs of accessories. Precise descriptions of the various accessories is something that today can only be achieved by experts in the field of jewelry. In this work, we propose an approach for jewelry recognition using computer vision techniques and image captioning, trying to simulate this expert human behavior of analyzing accessories. The proposed methodology consist on using different image captioning models to detect the jewels from an image and generate a natural language description of the accessory. Then, this description is also utilized to classify the accessories at different levels of detail. The generated caption includes details such as the type of jewel, color, material, and design. To demonstrate the effectiveness of the proposed method in accurately recognizing different types of jewels, a dataset consisting of images of accessories belonging to jewelry stores in C\'ordoba (Spain) has been created. After testing the different image captioning architectures designed, the final model achieves a captioning accuracy of 95\%. The proposed methodology has the potential to be used in various applications such as jewelry e-commerce, inventory management or automatic jewels recognition to analyze people's tastes and social status., Comment: 6 pages, 5 figures, MetroXRAINE 2023 Conference

Structured hand gestures that incorporate visual motions and signs are used in sign language. Sign language is a valuable means of daily communication for individuals who are deaf or have speech impairments, but it is still rare among hearing people, and fewer are capable of understand it. Within the academic context, parents and teachers play a crucial role in supporting deaf students from childhood by facilitating their learning of sign language. In the last years, among all the teaching tools useful for learning sign language, the use of Virtual Reality (VR) has increased, as it has been demonstrated to improve retention, memory and attention during the learning process. The ISENSE project has been created to assist students with deafness during their academic life by proposing different technological tools for teaching sign language to the hearing community in the academic context. As part of the ISENSE project, this work aims to develop an application for Spanish and Italian sign language recognition that exploits the VR environment to quickly and easily create a comprehensive database of signs and an Artificial Intelligence (AI)-based software to accurately classify and recognize static and dynamic signs: from letters to sentences., Comment: 6 pages, 4 figures, MetroXRAINE 2023 Conference, ISENSE european project

We performed numerical simulations of the common envelope (CE) interaction between thermally-pulsing asymptotic giant branch (AGB) stars of 1.7~\Msun and 3.7~\Msun, respectively, and a 0.6~\Msun compact companion. We use tabulated equations of state to take into account recombination energy. For the first time, formation and growth of dust is calculated explicitly, using a carbon dust nucleation network with a C/O abundance ratio of 2.5 (by number). The first dust grains appear within $\sim$1--3~yrs after the onset of the CE, forming an optically thick shell at $\sim$10--20~au, growing in thickness and radius to values of $\sim$400--500~au over $\sim$40~yrs, with temperatures around 400~K. Most dust is formed in unbound material, having little effect on mass ejection or orbital evolution. By the end of the simulations, the total dust yield is $\sim8.4\times10^{-3}$~\Msun and $\sim2.2\times10^{-2}$~\Msun for the CE with a 1.7~\Msun and a 3.7~\Msun AGB star, respectively, corresponding to a nucleation efficiency close to 100\%, if no dust destruction mechanism is considered. Despite comparable dust yields to single AGB stars, \textit{in CE ejections the dust forms a thousand times faster, over tens of years as opposed to tens of thousands of years}. This rapid dust formation may account for the shift in the infrared of the spectral energy distribution of some optical transients known as luminous red novae. Simulated dusty CEs support the idea that extreme carbon stars and "water fountains" may be objects observed after a CE event., Comment: 18 pages, 17 figures, 2 tables

We present iFusion, a novel 3D object reconstruction framework that requires only two views with unknown camera poses. While single-view reconstruction yields visually appealing results, it can deviate significantly from the actual object, especially on unseen sides. Additional views improve reconstruction fidelity but necessitate known camera poses. However, assuming the availability of pose may be unrealistic, and existing pose estimators fail in sparse view scenarios. To address this, we harness a pre-trained novel view synthesis diffusion model, which embeds implicit knowledge about the geometry and appearance of diverse objects. Our strategy unfolds in three steps: (1) We invert the diffusion model for camera pose estimation instead of synthesizing novel views. (2) The diffusion model is fine-tuned using provided views and estimated poses, turned into a novel view synthesizer tailored for the target object. (3) Leveraging registered views and the fine-tuned diffusion model, we reconstruct the 3D object. Experiments demonstrate strong performance in both pose estimation and novel view synthesis. Moreover, iFusion seamlessly integrates with various reconstruction methods and enhances them., Comment: Code: https://github.com/chinhsuanwu/ifusion, Project page: https://chinhsuanwu.github.io/ifusion

Open government and open (government) data are seen as tools to create new opportunities, eliminate or at least reduce information inequalities and improve public services. More than a decade of these efforts has provided much experience, practices, and perspectives to learn how to better deal with them. This paper focuses on benchmarking of open data initiatives over the years and attempts to identify patterns observed among European countries that could lead to disparities in the development, growth, and sustainability of open data ecosystems. To do this, we studied benchmarks and indices published over the last years (57 editions of 8 artifacts) and conducted a comparative case study of eight European countries, identifying patterns among them considering different potentially relevant contexts such as e-government, open government data, open data indices and rankings, and others relevant for the country under consideration. Using a Delphi method, we reached a consensus within a panel of experts and validated a final list of 94 patterns, including their frequency of occurrence among studied countries and their effects on the respective countries. Finally, we took a closer look at the developments in identified contexts over the years and defined 21 recommendations for more resilient and sustainable open government data initiatives and ecosystems and future steps in this area., Comment: 46 pages

Designing terahertz sensors with high sensitivity to detect nanoscale thin films and single biomolecule presents a significant challenge, and addressing these obstacles is crucial for unlocking their full potential in scientific research and advanced applications. This work presents a strategy for the design optimization of metamaterial sensors employed in the detection of small amounts of dielectric materials. The sensors usually utilize the shift of the resonance frequency as an indicator of the presence of the analyte. The amount of shifting depends on intrinsic properties (electric field distribution, quality factor, and mode volume) of the bare cavity, as well as the overlap volume of its high-electric-field zone(s) and the analyte. Guided by the simplified dielectric perturbation theory, interdigitated electric split-ring resonators (ID-eSRR) are devised to significantly enhance the detection sensitivity for thin-film analytes compared to eSRRs without interdigitated fingers in the SRR gap region. The fingers of the ID-eSRR metamaterial sensor redistribute the electric field, creating strongly localized field enhancements that substantially boost the interaction with the analyte. Additionally, the periodic change of the orientation of the inherent anti-phase electric field in the interdigitated structure reduces radiation loss, leading to a higher Q-factor. Experiments with e-beam-fabricated ID-eSRR sensors operating at around 300 GHz demonstrate a remarkable frequency shift of 33.5 GHz upon deposition of a SiO2 layer with a thickness of 150 nm as an analyte simulant. The figure of merit (FOM) improves by over 50 times compared to structures without interdigitated fingers. This rational design option opens a promising avenue for highly sensitive detection of thin films and trace biomolecules., Comment: 16pages, 6 figures

A diamond-based sensor utilizing nitrogen-vacancy (NV) center ensembles permits the analysis of micron-sized samples through NMR techniques at room temperature. Current efforts are directed towards extending the operating range of NV centers into high magnetic fields, driven by the potential for larger nuclear spin polarization of the target sample and the presence of enhanced chemical shifts. Especially interesting is the access to J-couplings as they carry information of chemical connectivity inside molecules. In this work, we present a protocol to access J-couplings in both homonuclear and heteronuclear cases with NV centers at high magnetic fields. Our protocol leads to a clear spectrum exclusively containing J-coupling features with high resolution. This resolution is limited primarily by the decoherence of the target sample, which is mitigated by the noise filtering capacities of our method., Comment: 4 pages, 2 figures + Supplemental Material

Wireless communications in the terahertz band have been recently proposed as complement to conventional wired interconnects within computing packages. Such environments are typically highly reverberant, hence showing long channel impulse responses and severely limiting the achievable rates. Fortunately, this communications scenario is static and can be pre-characterized, which opens the door to techniques such as time reversal. Time reversal acts a spatial matched filter and has a spatiotemporal focusing effect, which allows not only to increase the achievable symbol rates, but also to create multiple spatial channels. In this paper, the multi-user capability of time reversal is explored in the context of wireless communications in the terahertz band within a computing package. Full-wave simulations are carried out to validate the approach, whereas modulation streams are simulated to evaluate the error rate as a function of the transmitted power, symbol rate, and number of simultaneous transmissions.

Time domain astronomy is advancing towards the analysis of multiple massive datasets in real time, prompting the development of multi-stream machine learning models. In this work, we study Domain Adaptation (DA) for real/bogus classification of astronomical alerts using four different datasets: HiTS, DES, ATLAS, and ZTF. We study the domain shift between these datasets, and improve a naive deep learning classification model by using a fine tuning approach and semi-supervised deep DA via Minimax Entropy (MME). We compare the balanced accuracy of these models for different source-target scenarios. We find that both the fine tuning and MME models improve significantly the base model with as few as one labeled item per class coming from the target dataset, but that the MME does not compromise its performance on the source dataset.

Observations from past missions such as RXTE and Beppo-SAX suggested the presence of inverse Compton (IC) scattering at hard X-ray energies within the intracluster medium of some massive galaxy clusters. In subsequent years, observations by, e.g., Suzaku, and now NuSTAR, have not been able to confirm these detections. We report on NuSTAR hard X-ray searches for IC emission in two massive galaxy clusters, Abell 665 and Abell 2146. To constrain the global IC flux in these two clusters, we fit global NuSTAR spectra with three models: single (1T) and two-temperature (2T) models, and a 1T plus power law component (T$+$IC). The temperature components are meant to characterize the thermal ICM emission, while the power law represents the IC emission. We find that the 3-30 keV Abell 665 and 3-20 keV Abell 2146 spectra are best described by thermal emission alone, with average global temperatures of $kT = (9.15\pm 0.1)$ keV for Abell 665 and $kT = (8.29\pm 0.1)$ keV for Abell 2146. We constrain the IC flux to $F_{\rm NT} < 0.60 \times 10^{-12}$ erg s$^{-1}$ cm$^{-2}$ and $F_{\rm NT} < 0.85 \times 10^{-12}$ erg s$^{-1}$ cm$^{-2}$ (20-80 keV) for Abell 665 and Abell 2146, respectively both at the 90% confidence level. When we couple the IC flux limits with 1.4 GHz diffuse radio data from the VLA, we set lower limits on the average magnetic field strengths of $>$0.14 $\mu$G and $>$0.011 $\mu$G for Abell 665 and Abell 2146, respectively., Comment: 19 pages, 15 figures

Wireless Network-on-Chip (WNoC) is a promising paradigm to overcome the versatility and scalability issues of conventional on-chip networks for current processor chips. However, the chip environment suffers from delay spread which leads to intense Inter-Symbol Interference (ISI). This degrades the signal when transmitting and makes it difficult to achieve the desired Bit Error Rate (BER) in this constraint-driven scenario. Time reversal (TR) is a technique that uses the multipath richness of the channel to overcome the undesired effects of the delay spread. As the flip-chip channel is static and can be characterized beforehand, in this paper we propose to apply TR to the wireless in-package channel. We evaluate the effects of this technique in time and space from an electromagnetic point of view. Furthermore, we study the effectiveness of TR in modulated data communications in terms of BER as a function of transmission rate and power. Our results show not only the spatiotemporal focusing effect of TR in a chip that could lead to multiple spatial channels, but also that transmissions using TR outperform, BER-wise, non-TR transmissions it by an order of magnitude

We carried out 3D smoothed particle hydrodynamics simulations of the common envelope binary interaction using the approximation of Bowen to calculate the dust opacity in order to investigate the resulting dust-driven accelerations. We have simulated two types of binary star: a 1.7 and a 3.7 $M_{\odot}$ thermally-pulsating, asymptotic giant branch stars with a 0.6 $M_{\odot}$ companion. We carried out simulations using both an ideal gas and a tabulated equations of state, with the latter considering the recombination energy of the envelope. We found that the dust-driven wind leads to a relatively small increase in the unbound gas, with the effect being smaller for the tabulated equation of state simulations and for the more massive primary. Dust acceleration does contribute to envelope expansion with only a slightly elongated morphology, if we believe the results from the tabulated equation of state as more reliable. The Bowen opacities in the outer envelopes of the two models, at late times, are large enough that the photosphere of the post-inspiral object is about ten times larger compared to the same without accounting for the dust opacities. As such, the prediction of the appearance of the transient would change substantially if dust is included., Comment: 14 Pages, 13 Figures. Accepted for publication in MNRAS

The detection of individual molecules and their dynamics has been a long-standing challenge in the field of nanotechnology. In this work, we present a method that utilizes a nitrogen vacancy (NV) center and a dangling-bond on the diamond surface to measure the coupling between two electronic targets tagged on a macromolecule. To achieve this, we design a multi-tone dynamical decoupling sequence that leverages the strong interaction between the nitrogen vacancy center and the dangling bond. In addition, this sequence minimizes the impact of decoherence finally resulting in an increased signal-to-noise ratio. This proposal has the potential to open up new avenues for fundamental research and technological innovation in distinct areas such as biophysics and biochemistry., Comment: 9 pages, 6 figures

In this work we study the influence of isotropic and anisotropic fluids on the spherically symmetric warp metric. We evaluate the energy conditions and the influence of including a cosmological constant type term. We find that, considering this term, there is a trade-off between the weak and strong energy conditions. The obtained solutions are numerical and we solve the system for both the stationary and the full regime. The influence of imposing the zero expansion condition has been explored. We find a wide diversity of behaviours for the solutions. In general there are regions of spacetime where the energy conditions can be at least partially satisfied. Finally, we calculate the value of the total mass using the density found in the numerical simulations, finding examples where it remains positive during the entire evolution of the system., Comment: 34 pages, 25 figures