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We classify convex disks with a fixed characteristic foliation and Legendrian boundary, up to contact isotopy relative to the boundary, in every closed overtwisted contact 3-manifold. This classification covers cases where the neighborhood of such a disk is tight or where the boundary violates the Bennequin-Eliashberg inequality. We show that this classification coincides with the formal one, establishing an h-principle for these disks. As a corollary, we deduce that the space of Legendrian unknots that lie in some Darboux ball in a closed overtwisted contact 3-manifold satisfies the h-principle at the level of fundamental groups. Finally, we determine the contact mapping class group of the complement of each Legendrian unknot with non-positive tb invariant in an overtwisted 3-sphere., Comment: 27 pages, 8 figures. Comments are welcome

The research related to digital twins has been increasing in recent years. Besides the mirroring of the physical word into the digital, there is the need of providing services related to the data collected and transferred to the virtual world. One of these services is the forecasting of physical part future behavior, that could lead to applications, like preventing harmful events or designing improvements to get better performance. One strategy used to predict any system operation it is the use of time series models like ARIMA or LSTM, and improvements were implemented using these algorithms. Recently, deep learning techniques based on generative models such as Generative Adversarial Networks (GANs) have been proposed to create time series and the use of LSTM has gained more relevance in time series forecasting, but both have limitations that restrict the forecasting results. Another issue found in the literature is the challenge of handling multivariate environments/applications in time series generation. Therefore, new methods need to be studied in order to fill these gaps and, consequently, provide better resources for creating useful digital twins. In this proposal, it is going to be studied the integration of a BiLSTM layer with a time series obtained by GAN in order to improve the forecasting of all the features provided by the dataset in terms of accuracy and, consequently, improving behaviour prediction.

The interplay between substrate interactions and electron-phonon coupling in two-dimensional (2D) materials presents a significant challenge in understanding and controlling their electronic properties. Here, we present a comparative study of the structural characteristics, phonon dynamics, and electron-phonon interactions in bulk and monolayer NbSe$_2$ on epitaxial bilayer graphene (BLG) using helium atom scattering (HAS). High-resolution helium diffraction reveals a (9x9)0$^{\circ}$ superstructure within the NbSe$_2$ monolayer, commensurate with the BLG lattice, while out-of-plane HAS diffraction spectra indicate a low-corrugated (3$\sqrt{3}$x3$\sqrt{3}$)30$^{\circ}$ substructure. By monitoring the thermal attenuation of the specular peak across a temperature range of 100 K to 300 K, we determined the electron-phonon coupling constant $\lambda_{HAS}$ as 0.76 for bulk 2H-NbSe$_2$. In contrast, the NbSe$_2$ monolayer on graphene exhibits a reduced $\lambda_{HAS}$ of 0.55, corresponding to a superconducting critical temperature (T$_C$) of 1.56 K according to the MacMillan formula, consistent with transport measurement findings. Inelastic HAS data provide, besides a set of dispersion curves of acoustic and lower optical phonons, a soft, dispersionless branch of phonons at 1.7 meV, attributed to the interface localized defects distributed with the superstructure period, and thus termed moir\'e phonons. Our data show that moir\'e phonons contribute significantly to the electron-phonon coupling in monolayer NbSe$_2$. These results highlight the crucial role of the BLG on the electron-phonon coupling in monolayer NbSe$_2$, attributed to enhanced charge transfer effects, providing valuable insights into substrate-dependent electronic interactions in 2D superconductors.

Magnetic fields are ubiquitous across different physical systems of current interest; from the early Universe, compact astrophysical objects and heavy-ion collisions to condensed matter systems. A proper treatment of the effects produced by magnetic fields during the dynamical evolution of these systems, can help to understand observables that otherwise show a puzzling behavior. Furthermore, when these fields are comparable to or stronger than \Lambda_QCD, they serve as excellent probes to help elucidate the physics of strongly interacting matter under extreme conditions of temperature and density. In this work we provide a comprehensive review of recent developments on the description of QED and QCD systems where magnetic field driven effects are important. These include the modification of meson static properties such as masses and form factors, the chiral magnetic effect, the description of anomalous transport coefficients, superconductivity in extreme magnetic fields, the properties of neutron stars, the evolution of heavy-ion collisions, as well as effects on the QCD phase diagram. We describe recent theory and phenomenological developments using effective models as well as LQCD methods. The work represents a state-of-the-art review of the field, motivated by presentations and discussions during the "Workshop on Strongly Interacting Matter in Strong Electromagnetic Fields" that took place in the European Centre for Theoretical Studies in Nuclear Physics and Related Areas (ECT*) in the city of Trento, Italy, September 25-29, 2023., Comment: 325 pages-long review of recent topics of interest in the field of magnetic field effects on QED and QCD matter. To be susbmitted to PNPP

Evidences of vortical effects have been recently found by experiments in heavy ion collisions, instigating new insights into the phase diagram of quantum chromodynamics. Considering the effect of rotations, lattice QCD data shows that the temperatures for deconfinement and chiral symmetry restoration should increase with real angular velocity, and the dominant effects are related to gluonic degrees of freedom. These findings could be essential for quark models in rotating systems that lack gluonic interactions, which predicts the decreasing of the chiral temperature transition with the angular velocity. To address this issue properly, in this work we apply the two-flavor Nambu--Jona-Lasinio model to explore the phase diagram in a rotating rigid cylinder with constant angular velocity in the mean field approximation. To circumvent the absence of gluons, we propose the application of an effective coupling dependent of the angular velocity, fitted to match the pseudocritical temperature of chiral phase transition in the model through lattice QCD data. Our results indicate that the running coupling induces the enhancement of the chiral condensate as a function of angular velocity, strengthening the breaking of chiral symmetry, an effect previously dubbed as chiral vortical catalysis. For the chiral susceptibility we observe stronger fluctuations around the transition temperature when we consider the running coupling. The phase diagram is affected by these findings shifting the critical end point (CEP) to higher temperatures and chemical potentials.

Continuous-variable quantum computing utilizes continuous parameters of a quantum system to encode information, promising efficient solutions to complex problems. Trapped-ion systems provide a robust platform with long coherence times and precise qubit control, enabling the manipulation of quantum information through its motional and electronic degrees of freedom. In this work, quantum operations that can be generated in trapped-ion systems are employed to investigate applications aimed at state preparation in continuous-variable quantum systems., Comment: 17 pages, 5 figures

Differentially private selection mechanisms are fundamental building blocks for privacy-preserving data analysis. While numerous mechanisms exist for single-objective selection, many real-world applications require optimizing multiple competing objectives simultaneously. We present two novel mechanisms for differentially private multi-objective selection: PrivPareto and PrivAgg. PrivPareto uses a novel Pareto score to identify solutions near the Pareto frontier, while PrivAgg enables privacy-preserving weighted aggregation of multiple objectives. Both mechanisms support global and local sensitivity approaches, with comprehensive theoretical analysis showing how to compose sensitivities of multiple utility functions. We demonstrate the practical applicability through two real-world applications: cost-sensitive decision tree construction and multi-objective influential node selection in social networks. The experimental results showed that our local sensitivity-based approaches achieve significantly better utility compared to global sensitivity approaches across both applications and both Pareto and Aggregation approaches. Moreover, the local sensitivity-based approaches are able to perform well with typical privacy budget values $\epsilon \in [0.01, 1]$ in most experiments.

We present the first installment of the quantum computing (QC) formulation of the electron nuclear dynamics (END) method within the variational quantum simulator (VQS) scheme: END/QC/VQS. END is a time-dependent, variational, on-the-flight, and non-adiabatic method to simulate chemical reactions. END represents nuclei with frozen Gaussian wave packets and electrons with a single-determinantal state in the Thouless non-unitary representation. Within the hybrid quantum/classical VQS, END/QC/VQS evaluates the metric matrix M and gradient vector V of the symplectic END/QC equations on a quantum computer, and calculates basis function integrals and time evolution on a classical computer. To adapt END to QC, we substitute the Thouless non-unitary representation with Fukutome unitary representation. We formulate the first END/QC/VQS version for pure electronic dynamics in chemical models consisting of two-electron units. Therein, Fukutome unitary matrices factorize into products of triads of one-qubit rotational matrices, which leads to a QC encoding of one electron per qubit. We design QC circuits to evaluate M and V in one-electron diatomic molecules. In log2-log2 plots, errors and deviations of those evaluations decrease linearly with the number of shots and with slopes = -1/2. We illustrate an END/QC/VQS simulation with the pure electronic dynamics of H2+. We discuss the present results and future END/QC/QVS extensions.

Warehouse optimization stands as a critical component for enhancing operational efficiency within the industrial sector. By strategically streamlining warehouse operations, organizations can achieve significant reductions in logistical costs such as the necessary footprint or traveled path, and markedly improve overall workflow efficiency including retrieval times or storage time. Despite the availability of numerous algorithms designed to identify optimal solutions for such optimization challenges, certain scenarios demand computational resources that exceed the capacities of conventional computing systems. In this context, we adapt a formulation of a warehouse optimization problem specifically tailored as a binary optimization problem and implement it in a trapped-ion quantum computer., Comment: 8 pages, 3 figures

Federated Learning (FL) is a decentralized machine learning approach that has gained attention for its potential to enable collaborative model training across clients while protecting data privacy, making it an attractive solution for the chemical industry. This work aims to provide the chemical engineering community with an accessible introduction to the discipline. Supported by a hands-on tutorial and a comprehensive collection of examples, it explores the application of FL in tasks such as manufacturing optimization, multimodal data integration, and drug discovery while addressing the unique challenges of protecting proprietary information and managing distributed datasets. The tutorial was built using key frameworks such as $\texttt{Flower}$ and $\texttt{TensorFlow Federated}$ and was designed to provide chemical engineers with the right tools to adopt FL in their specific needs. We compare the performance of FL against centralized learning across three different datasets relevant to chemical engineering applications, demonstrating that FL will often maintain or improve classification performance, particularly for complex and heterogeneous data. We conclude with an outlook on the open challenges in federated learning to be tackled and current approaches designed to remediate and improve this framework., Comment: 46 Pages, 8 figures, Under review in ACS Industrial & Engineering Chemistry Research Journal

In 2023 we have conducted extensive experiments on subjective video quality for the TV 3.0 project at University of Bras\'ilia. A full report on these tests is available at the F\'orum SBTVD website . These tests have evaluated the H.266/VVC codec and a hybrid codec formed by the H.266/VVC and the LCEVC (Low Complexity Enhancement Video Coding) with different resolutions, ranging from 720p to 4K. This report contains the results of additional tests performed for TV 3.0 performed at University of Bras\'ilia. This new experiment consists of two new Video Under Tests (VUTs), one with the H.266/VVC codec at 4K resolution, and the other with the H.266/VVC+LCEVC codec at 4K resolution. In this new test, both codecs have the same GOP size (120 frames) and use the same VVC encoder (MainConcept live encoder). This new experiment follows the same experimental protocol as the previous experiments, in order to be fully comparable to the reported results. This document details the results of the new experiments., Comment: 73 pages

We provide a topological characterization for a family of bypasses with a fixed attaching arc to be contractible. This characterization is formulated in terms of the existence of a bypass that is disjoint from the given family away from the attaching region. As an application, we provide new proofs of several h-principles in overtwisted contact 3-manifolds., Comment: 22 pages, 9 figures

The time series theory is set in this work under the domain of general elliptically contoured distributions. The advent of a time series approach that is in accordance with the expected reality of dependence between errors, transfers the increasingly complex and difficult to handle correlation analysis into a discipline that models volatility from a new view of a likelihood based on dependent probabilistic samples. The equally important problem of model selection is strengthened, but at the same time criticized with the introduction of degrees of evidence of significant difference in the modified BIC criterion . The demanding scale of differentiation puts a well-known database in trouble by observing insignificant relevance between the hierarchical models most used in the theory of time series under independence, such as Arch, Garch, Tgarch and Egarch. For extreme cases where the probabilistic independence of the samples is exceptionally demonstrated by an expert, the article also proposes the theory of time series under elliptical models, but with the same demanding comparison of the degrees of evidence of differences of the modified BIC. The example studied under this approach also does not denote any advantage of the hierarchical models studied. Such a new perspective for a likelihood based on dependent probabilistic samples has arisen naturally in similar context in finance under the setting of multivector variate distributions.

We introduce a novel approach to reduce the number of times required for reprogramming memristors on bit-sliced compute-in-memory crossbars for deep neural networks (DNNs). Our idea addresses the limited non-volatile memory endurance, which restrict the number of times they can be reprogrammed. To reduce reprogramming demands, we employ two techniques: (1) we organize weights into sorted sections to schedule reprogramming of similar crossbars, maximizing memristor state reuse, and (2) we reprogram only a fraction of randomly selected memristors in low-order columns, leveraging their bit-level distribution and recognizing their relatively small impact on model accuracy. We evaluate our approach for state-of-the-art models on the ImageNet-1K dataset. We demonstrate a substantial reduction in crossbar reprogramming by 3.7x for ResNet-50 and 21x for ViT-Base, while maintaining model accuracy within a 1% margin., Comment: 5 pages, 10 figures

The Travelling Salesman Problem - TSP is one of the most explored problems in the scientific literature to solve real problems regarding the economy, transportation, and logistics, to cite a few cases. Adapting TSP to solve different problems has originated several variants of the optimization problem with more complex objectives and different restrictions. Metaheuristics have been used to solve the problem in polynomial time. Several studies have tried hybridising metaheuristics with specialised heuristics to improve the quality of the solutions. However, we have found no study to evaluate whether the searching mechanism of a particular metaheuristic is more adequate for exploring hybridization. This paper focuses on the solution of the classical TSP using high-level hybridisations, experimenting with eight metaheuristics and heuristics derived from k-OPT, SISR, and segment intersection search, resulting in twenty-four combinations. Some combinations allow more than one set of searching parameters. Problems with 50 to 280 cities are solved. Parameter tuning of the metaheuristics is not carried out, exploiting the different searching patterns of the eight metaheuristics instead. The solutions' quality is compared to those presented in the literature.

Plasticity Loss is an increasingly important phenomenon that refers to the empirical observation that as a neural network is continually trained on a sequence of changing tasks, its ability to adapt to a new task diminishes over time. We introduce Self-Normalized Resets (SNR), a simple adaptive algorithm that mitigates plasticity loss by resetting a neuron's weights when evidence suggests its firing rate has effectively dropped to zero. Across a battery of continual learning problems and network architectures, we demonstrate that SNR consistently attains superior performance compared to its competitor algorithms. We also demonstrate that SNR is robust to its sole hyperparameter, its rejection percentile threshold, while competitor algorithms show significant sensitivity. SNR's threshold-based reset mechanism is motivated by a simple hypothesis test that we derive. Seen through the lens of this hypothesis test, competing reset proposals yield suboptimal error rates in correctly detecting inactive neurons, potentially explaining our experimental observations. We also conduct a theoretical investigation of the optimization landscape for the problem of learning a single ReLU. We show that even when initialized adversarially, an idealized version of SNR learns the target ReLU, while regularization-based approaches can fail to learn.

Constrained multiobjective optimization problems (CMOPs) are commonly found in real-world applications. CMOP is a complex problem that needs to satisfy a set of equality or inequality constraints. This paper proposes a variant of the bidirectional coevolution algorithm (BiCo) with differential evolution (DE). The novelties in the model include the DE differential mutation and crossover operators as the main search engine and a non-dominated sorting selection scheme. Experimental results on two benchmark test suites and eight real-world CMOPs suggested that the proposed model reached better overall performance than the original model., Comment: 6 pages

This paper introduces the inverse modeling constrained multi-objective evolutionary algorithm based on decomposition (IM-C-MOEA/D) for addressing constrained real-world optimization problems. Our research builds upon the advancements made in evolutionary computing-based inverse modeling, and it strategically bridges the gaps in applying inverse models based on decomposition to problem domains with constraints. The proposed approach is experimentally evaluated on diverse real-world problems (RWMOP1-35), showing superior performance to state-of-the-art constrained multi-objective evolutionary algorithms (CMOEAs). The experimental results highlight the robustness of the algorithm and its applicability in real-world constrained optimization scenarios., Comment: 6 pages, 1 figure, 1 algorithm, and 2 tables

Characterizing the host galaxies of astrophysical transients is important to many areas of astrophysics, including constraining the progenitor systems of core-collapse supernovae, correcting Type Ia supernova distances, and probabilistically classifying transients without photometric or spectroscopic data. Given the increasing transient discovery rate in the coming years, there is substantial utility in providing public, transparent, reproducible, and automatic characterization for large samples of transient host galaxies. Here we present Blast, a web application that ingests live streams of transient alerts, matches transients to their host galaxies, and performs photometry on coincident archival imaging data of the host galaxy. The photometry is then used to infer both global host-galaxy properties and galaxy properties within 2 kpc of the transient location by using the Prospector Bayesian inference framework, with an acceleration in evaluation speed achieved via simulation-based inference. Blast provides host-galaxy properties to users via a web browser or an application program interface. The software can be extended to support alternative photometric or SED-fitting algorithms, and can be scaled via an asynchronous worker queue across multiple compute nodes to handle the processing of large volumes of transient alerts for upcoming transient surveys. Blast has been ingesting newly discovered transients from the Transient Name Server since mid-2024, and has currently measured SED parameters for more than 6000 transients. The service is publicly available at https://blast.scimma.org/., Comment: submitted to PASP

Autism Spectrum Disorder (ASD) is a neurodivergent condition with a wide range of characteristics and support levels. Individuals with ASD can exhibit various combinations of traits such as difficulties in social interaction, communication, and language, alongside restricted interests and repetitive activities. Many adults with ASD live independently due to increased awareness and late diagnoses, which help them manage long-standing challenges. Predictability, clarity, and minimized sensory stimuli are crucial for the daily comfort of autistic individuals. In mobile applications, autistic users face significant cognitive overload compared to neurotypicals, resulting in higher effort and time to complete tasks. Urban mobility apps, essential for daily routines, often overlook the needs of autistic users, leading to cognitive overload issues. This study investigates the accessibility of urban mobility apps for autistic individuals using the Interfaces Accessibility Guide for Autism (GAIA). By evaluating various apps, we have identified a common gap regarding accessibility for people with Autism Spectrum Disorder (ASD). This limitation relates to the absence of a functionality that allows users on the autism spectrum to customize the characteristics of the textual and visual elements of the software, such as changing the text font, altering the font type, and adjusting text colors, as well as native audio guidance within the applications themselves. Currently, the only function in this context is for visually impaired people, which completely changes the user experience in terms of navigation., Comment: Author pre-print of article published in the XXIII Brazilian Symposium on Human Factors in Computing Systems (IHC'24)

We introduce $\textit{sorted weight sectioning}$ (SWS): a weight allocation algorithm that places sorted deep neural network (DNN) weight sections on bit-sliced compute-in-memory (CIM) crossbars to reduce analog-to-digital converter (ADC) energy consumption. Data conversions are the most energy-intensive process in crossbar operation. SWS effectively reduces this cost leveraging (1) small weights and (2) zero weights (weight sparsity). DNN weights follow bell-shaped distributions, with most weights near zero. Using SWS, we only need low-order crossbar columns for sections with low-magnitude weights. This reduces the quantity and resolution of ADCs used, exponentially decreasing ADC energy costs without significantly degrading DNN accuracy. Unstructured sparsification further sharpens the weight distribution with small accuracy loss. However, it presents challenges in hardware tracking of zeros: we cannot switch zero rows to other layer weights in unsorted crossbars without index matching. SWS efficiently addresses unstructured sparse models using offline remapping of zeros into earlier sections, which reveals full sparsity potential and maximizes energy efficiency. Our method reduces ADC energy use by 89.5% on unstructured sparse BERT models. Overall, this paper introduces a novel algorithm to promote energy-efficient CIM crossbars for unstructured sparse DNN workloads., Comment: 5 pages, 4 figures

Federated Kolmogorov-Arnold Networks (F-KANs) have already been proposed, but their assessment is at an initial stage. We present a comparison between KANs (using B-splines and Radial Basis Functions as activation functions) and Multi- Layer Perceptrons (MLPs) with a similar number of parameters for 100 rounds of federated learning in the MNIST classification task using non-IID partitions with 100 clients. After 15 trials for each model, we show that the best accuracies achieved by MLPs can be achieved by Spline-KANs in half of the time (in rounds), with just a moderate increase in computing time., Comment: 10 pages, 5 figures, for associated code see https://github.com/artsasse/fedkan

In this paper we consider a massive self-interacting scalar quantum field in a Lorentz-violation scenario based on a Horava-Lifshitz model. Specifically, we investigate the vacuum energy density and its loop correction, up to first order in the self-interaction coupling constant, and also the topological mass generation. These quantities are also analyzed in the case where the field is massless. The scalar vacuum state is perturbed by the presence of two large parallel plates, placed at a distance L from each other, due to the imposition of Dirichlet boundary condition on the two plates. To perform this study, the effective potential approach in quantum field theory is applied., Comment: 14 pages, 3 Figures. arXiv admin note: text overlap with arXiv:2405.14656

We introduce a Retrieval-Augmented Generation (RAG) system for translating user questions into accurate federated SPARQL queries over bioinformatics knowledge graphs (KGs) leveraging Large Language Models (LLMs). To enhance accuracy and reduce hallucinations in query generation, our system utilises metadata from the KGs, including query examples and schema information, and incorporates a validation step to correct generated queries. The system is available online at chat.expasy.org.

Background. In the last decades, several life science resources have structured data using the same framework and made these accessible using the same query language to facilitate interoperability. Knowledge graphs have seen increased adoption in bioinformatics due to their advantages for representing data in a generic graph format. For example, yummydata.org catalogs more than 60 knowledge graphs accessible through SPARQL, a technical query language. Although SPARQL allows powerful, expressive queries, even across physically distributed knowledge graphs, formulating such queries is a challenge for most users. Therefore, to guide users in retrieving the relevant data, many of these resources provide representative examples. These examples can also be an important source of information for machine learning, if a sufficiently large number of examples are provided and published in a common, machine-readable and standardized format across different resources. Findings. We introduce a large collection of human-written natural language questions and their corresponding SPARQL queries over federated bioinformatics knowledge graphs (KGs) collected for several years across different research groups at the SIB Swiss Institute of Bioinformatics. The collection comprises more than 1000 example questions and queries, including 65 federated queries. We propose a methodology to uniformly represent the examples with minimal metadata, based on existing standards. Furthermore, we introduce an extensive set of open-source applications, including query graph visualizations and smart query editors, easily reusable by KG maintainers who adopt the proposed methodology. Conclusions. We encourage the community to adopt and extend the proposed methodology, towards richer KG metadata and improved Semantic Web services.

We study the properties of a system composed of strongly interacting matter with an isospin imbalance, using as an effective description of QCD the two-flavor Linear Sigma Model with quarks. From the one-loop effective potential, including the two light quarks, pions and sigma contributions, and enforcing the restrictions imposed by chiral symmetry, we show that the development of an isospin condensate comes together with the emergence of a Goldstone mode that provides a constraint for the chiral and isospin condensates as a result of a non-trivial mixing between the charged pions and the sigma. We compute the thermodynamical quantities of interest and in particular the sound velocity squared, showing that it presents a maximum for an isospin chemical potential similar to the one reported by lattice QCD results and also with a similar height. Therefore, we attribute the origin of the peak of the sound velocity to the proper treatment of the Goldstone mode and to the non-trivial mixing of the charged pions and sigma in the isospin condensed phase., Comment: 7 pages, 2 figures

Quantum computing with qudits, an extension of qubits to multiple levels, is a research field less mature than qubit-based quantum computing. However, qudits can offer some advantages over qubits, by representing information with fewer separated components. In this article, we present QuForge, a Python-based library designed to simulate quantum circuits with qudits. This library provides the necessary quantum gates for implementing quantum algorithms, tailored to any chosen qudit dimension. Built on top of differentiable frameworks, QuForge supports execution on accelerating devices such as GPUs and TPUs, significantly speeding up simulations. It also supports sparse operations, leading to a reduction in memory consumption compared to other libraries. Additionally, by constructing quantum circuits as differentiable graphs, QuForge facilitates the implementation of quantum machine learning algorithms, enhancing the capabilities and flexibility of quantum computing research., Comment: 18 pages, 7 figures

Most stars are born in the crowded environments of gradually forming star clusters. Dynamical interactions between close-passing stars and the evolving UV radiation fields from proximate massive stars are expected to sculpt the protoplanetary discs in these clusters, potentially contributing to the diversity of planetary systems that we observe. Here, we investigate the impact of cluster environment on disc demographics by implementing simple protoplanetary disc evolution models within $N$-body simulations of gradual star cluster formation. We consider a range of star formation efficiency per free-fall time, $\epsilon_{\rm ff}$, and mass surface density of the natal cloud environment, $\Sigma_{\rm cl}$, both of which affect the overall duration of cluster formation. We track the interaction history of all stars to estimate the dynamical truncation of the discs around stars involved in close encounters. We also track external photoevaporation of the discs due to the ionizing radiation field of the nearby high- and intermediate-mass ($> 5 M_\odot$) stars. We find that $\epsilon_{\rm ff}$, $\Sigma_{\rm cl}$, and the degree of primordial binarity have major influences on the masses and radii of the disc population. In particular, external photo-evaporation has a greater impact than dynamical interactions in determining the fate of discs in our clusters., Comment: Submitted to MNRAS. 16 pages, 9 figures. Comments welcome

We study the symmetry breaking and restoration behavior of a self-interacting charged scalar field theory under the influence of a constant electric field and finite temperature. Our study is performed in the context of the optimized perturbation theory. The dependence of the effective potential with constant electric fields is established by means of the bosonic propagators in the Schwinger proper-time method. Explicit analytical expressions for the electric and thermal contributions are found. Our results show a very weak decreasing behavior of the vacuum expectation value as a function of the electric field, which is strengthened by the temperature effect. A first-order phase transition that occurs at zero/weak electric fields changes to a second-order phase transition under strong electric fields. The critical temperature for the phase transition exhibited a very weak dependence on the electric field. Additionally, we computed the vacuum persistence probability rate for the interacting theory, finding a peak at the critical point. The maximum value of this rate at the critical point is found to be independent of the coupling constant but depended solely on the magnitude of the electric field., Comment: Comments and clarifications have been added, two figures included, references reorganized and typos corrected. Matches version published in Phys. Rev. D 110, 116024 (2024)