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In this study, we introduce a novel stretch-based gradient-enhanced damage (GED) model that allows the fracture to localize and also captures the development of a physically diffuse damage zone. This capability contrasts with the paradigm of the phase field method for fracture, where a sharp crack is numerically approximated in a diffuse manner. Capturing fracture localization and diffuse damage in our approach is achieved by considering nonlocal effects that encompass network topology, heterogeneity, and imperfections. These considerations motivate the use of a statistical damage function dependent upon the nonlocal deformation state. From this model, fracture toughness is realized as an output. While GED models have been classically utilized for damage modeling of structural engineering materials, they face challenges when trying to capture the cascade from damage to fracture, often leading to damage zone broadening. This deficiency contributed to the popularity of the phase-field method over the GED model for elastomers and other quasi-brittle materials. Other groups have proceeded with damage-based GED formulations that prove identical to the phase-field method (Lorentz et al., 2012), but these inherit the aforementioned limitations. To address this issue in a thermodynamically consistent framework, we implement two modeling features (a nonlocal driving force bound and a simple relaxation function) specifically designed to capture the evolution of a physically meaningful damage field and the simultaneous localization of fracture, thereby overcoming a longstanding obstacle in the development of these nonlocal strain- or stretch-based approaches. We discuss several numerical examples to understand the features of the approach at the limit of incompressibility, and compare them to the phase-field method as a benchmark for the macroscopic response and fracture energy predictions.

Communication reliability, as defined by 3GPP, refers to the probability of providing a desired quality of service (QoS). This metric is typically quantified for wireless networks by averaging the QoS success indicator over spatial and temporal random variables. Recently, the meta distribution (MD) has emerged as a two-level performance analysis tool for wireless networks, offering a detailed examination of the outer level (i.e., system-level) reliability assessment versus the inner level (i.e., link-level) reliability thresholds. Most existing studies focus on first-order spatiotemporal MD reliability analyses, and the benefits of leveraging MD reliability for applications beyond this structure remain unexplored, a gap addressed in this paper. We present wireless application examples that can benefit the higher-order MD reliability analysis. Specifically, we provide the analysis and numerical results for a second-order spatial-spectral-temporal MD reliability of ultra-wideband THz communication. The results demonstrate the value of the hierarchical representation of MD reliability across three domains and the impact of the inner-layer target reliability on the overall MD reliability measure.

Data scarcity remains a significant challenge in the field of emotion recognition using physiological signals, as acquiring comprehensive and diverse datasets is often prevented by privacy concerns and logistical constraints. This limitation restricts the development and generalization of robust emotion recognition models, making the need for effective synthetic data generation methods more critical. Emotion recognition from physiological signals such as EEG, ECG, and GSR plays a pivotal role in enhancing human-computer interaction and understanding human affective states. Utilizing these signals, this study introduces an innovative approach to synthetic data generation using a Supervised Neural Gas (SNG) network, which has demonstrated noteworthy speed advantages over established models like Conditional VAE, Conditional GAN, diffusion model, and Variational LSTM. The Neural Gas network, known for its adaptability in organizing data based on topological and feature-space proximity, provides a robust framework for generating real-world-like synthetic datasets that preserve the intrinsic patterns of physiological emotion data. Our implementation of the SNG efficiently processes the input data, creating synthetic instances that closely mimic the original data distributions, as demonstrated through comparative accuracy assessments. In experiments, while our approach did not universally outperform all models, it achieved superior performance against most of the evaluated models and offered significant improvements in processing time. These outcomes underscore the potential of using SNG networks for fast, efficient, and effective synthetic data generation in emotion recognition applications., Comment: 14 pages

Revealing statistics of H-defect interactions provides insights into significant ductility loss due to the particular strain partitioning in H-charged structural alloys. Experimental investigation of these interactions is extremely difficult, labor-intensive, and costly. Here, we used MD and GCMC simulations and studied H-diffusion deformation at polycrystalline scale with atomic resolution efficiently. To study H-free surface interactions, large pillars including all possible angles and planes of free surfaces were modeled. To study H-grain boundary interactions, several polycrystalline models containing comparable statistics of low and high angle grain boundaries were examined. We studied the statistics of H-segregation tendencies based on free surface angles and grain boundary types. Dislocation activities were also classified for these various types and total density and strength were compared and analyzed compared to H-free samples. In the free surface model, it was observed that H was evenly distributed along the model's surface. Although the dislocation density was reduced compared to H-free samples, localized bands of dislocations were produced. Additionally in the polycrystalline samples, it was concluded that H tends to segregate along grain boundaries with misorientation angles less than or equal to 25 degrees. However, specific misorientation angle interactions -- namely $>45$ degrees -- led to increased dislocation density in H-charged samples compared to their H-free counterparts., Comment: 27 pages total, 15 pages of text, 137 references, 8 figures

Next-generation wireless networks are poised to benefit significantly from the integration of three key technologies (KTs): Rate-Splitting Multiple Access (RSMA), cell-free architectures, and federated learning. Each of these technologies offers distinct advantages in terms of security, robustness, and distributed structure. In this paper, we propose a novel cell-free network architecture that incorporates RSMA and employs machine learning techniques within a federated framework. This combination leverages the strengths of each KT, creating a synergistic effect that maximizes the benefits of security, robustness, and distributed structure. We formally formulate the access point (AP) selection and precoder design for max-min rate optimization in a cell-free MIMO RSMA network. Our proposed solution scheme involves a three-block procedure. The first block trains deep reinforcement learning (DRL) neural networks to obtain RSMA precoders, assuming full connectivity between APs and user equipments (UEs). The second block uses these precoders and principal component analysis (PCA) to assign APs to UEs by removing a subset of AP-UE connections. The final block fine-tunes the RSMA precoders by incorporating the associated APs into a second DRL network. To leverage the distributed nature of the cell-free network, this process is implemented in a Federated Deep Reinforcement Learning (FDRL) structure operating through the cooperation of APs and a central processing unit (CPU). Simulation results demonstrate that the proposed FDRL approach performs comparably to a benchmark centralized DRL scheme. Our FDRL approach, provides a balanced trade-off, maintaining high performance with enhanced security and reduced processing demands.

In this work, we develop a novel neural operator, the Solute Transport Operator Network (STONet), to efficiently model contaminant transport in micro-cracked reservoirs. The model combines different networks to encode heterogeneous properties effectively. By predicting the concentration rate, we are able to accurately model the transport process. Numerical experiments demonstrate that our neural operator approach achieves accuracy comparable to that of the finite element method. The previously introduced Enriched DeepONet architecture has been revised, motivated by the architecture of the popular multi-head attention of transformers, to improve its performance without increasing the compute cost. The computational efficiency of the proposed model enables rapid and accurate predictions of solute transport, facilitating the optimization of reservoir management strategies and the assessment of environmental impacts. The data and code for the paper will be published at https://github.com/ehsanhaghighat/STONet.

Within the so-called scaled quantum theory, the standard bouncing ball problem is analyzed under the presence of a gravitational field and harmonic potential. In this framework, the quantum-classical transition of the density matrix is described by the linear scaled von Neumann equation for mixed states and after it has been particularized to the case of pure states. The main purpose of this work is to show how this theory works for conservative systems and the quantum-classical transition is carried out in a continuous and smooth way, being equivalent to a nonlinear differential wave equation which contains a transition parameter ranging continuously from one to zero and covering all dynamical regimes in-between the two extreme quantum and classical regimes. This parameter can be seen as a degree of quantumness where all intermediate dynamical regimes show quantum features but are fading gradually when approaching to the classical value., Comment: Accepted for publication in Euro. Phys. J. Plus

Recently, the phase field method has been increasingly used for brittle fractures in soft materials like polymers, elastomers, and biological tissues. When considering finite deformations to account for the highly deformable nature of soft materials, the convergence of the phase-field method becomes challenging, especially in scenarios of unstable crack growth. To overcome these numerical difficulties, several approaches have been introduced, with artificial viscosity being among the most widely utilized. This study investigates the energy release rate due to crack propagation in hyperelastic nearly-incompressible materials and compares the phase-field method and a novel gradient-enhanced damage (GED) approach. First, we simulate unstable loading scenarios using the phase-field method, which leads to convergence problems. To address these issues, we introduce artificial viscosity to stabilize the problem and analyze its impact on the energy release rate utilizing a domain J-integral approach giving quantitative measurements during crack propagation. It is observed that the measured energy released rate during crack propagation does not comply with the imposed critical energy release rate, and shows non-monotonic behavior. In the second part of the paper, we introduce a novel stretch-based GED model as an alternative to the phase-field method for modeling crack evolution in elastomers. It is demonstrated that in this method, the energy release rate can be obtained as an output of the simulation rather than as an input which could be useful in the exploration of rate-dependent responses, as one could directly impose chain-level criteria for damage initiation. We show that while this novel approach provides reasonable results for fracture simulations, it still suffers from some numerical issues that strain-based GED formulations are known to be susceptible to.

Affective computing has recently gained popularity, especially in the field of human-computer interaction systems, where effectively evoking and detecting emotions is of paramount importance to enhance users experience. However, several issues are hindering progress in the field. In fact, the complexity of emotions makes it difficult to understand their triggers and control their elicitation. Additionally, effective emotion recognition requires analyzing multiple sensor data, such as facial expressions and physiological signals. These factors combined make it hard to collect high-quality datasets that can be used for research purposes (e.g., development of emotion recognition algorithms). Despite these challenges, Virtual Reality (VR) holds promise as a solution. By providing a controlled and immersive environment, VR enables the replication of real-world emotional experiences and facilitates the tracking of signals indicative of emotional states. However, controlling emotion elicitation remains a challenging task also within VR. This research paper introduces the Magic Xroom, a VR platform designed to enhance control over emotion elicitation by leveraging the theory of flow. This theory establishes a mapping between an individuals skill levels, task difficulty, and perceived emotions. In the Magic Xroom, the users skill level is continuously assessed, and task difficulty is adjusted accordingly to evoke specific emotions. Furthermore, user signals are collected using sensors, and virtual panels are utilized to determine the ground truth emotional states, making the Magic Xroom an ideal platform for collecting extensive datasets. The paper provides detailed implementation information, highlights the main properties of the Magic Xroom, and presents examples of virtual scenarios to illustrate its abilities and capabilities., Comment: Proceedings of the 25th International Conference on Mobile Human-Computer Interaction

The bottleneck of alloy design for powder-based additive manufacturing (AM) resides in customized powder production - an expensive and time-consuming process hindering the rapid closed-loop design iterations. This study analyzed an expedited experimental workflow, i.e., multipath laser scanning of induction-melted samples, to mimic rapid solidification of AM to serve as an alternative approach to down-select from the design space. Using Al-Ni-Zr-Er model alloy, comprehensive multi-scale characterizations were performed to compare microstructural features between laser-scanned and laser powder bed fusion (LPBF) samples. Although demonstrating a difference in melt pool geometries, the microstructures in scanning electron microscopy (SEM)- and transmission electron microscopy (TEM)- scale demonstrate a high degree of similarity, in terms of microstructure morphology, grain size, presence of precipitates, and phase distribution. The mechanical performance was evaluated by microhardness tests. The results revealed a 20% reduction in laser-scanned samples compared to LPBF samples, attributed to the thermal history and potential differences in phase fractions. The decreasing trend was also observed in the benchmark alloy showing a 10% absolute error with respect to the model alloy. This study underscores the potential of this workflow to accelerate alloy design in AM by circumventing customized powder production and encourages further exploration across diverse materials and processing parameters.

Additively manufactured (AM) aluminum alloys with high strength and thermal stability have broad applications in turbine engines, vacuum pumps, heat exchangers, and many other industrial systems. Employing precipitates with an L1$_2$ structure to block dislocation motions is a widespread strategy to strengthen aluminum. However, to achieve high strength, a high volume fraction of small precipitates is required, and these characteristics are generally mutually exclusive. Here, we show that for certain compositions of Al alloys, L1$_2$ phases initially precipitate as sub-micron metastable ternary phases under the rapid solidification conditions of powder bed AM, yet the subsequent L1$_2$ phases that precipitate during heat treatment of the sample remain at the nanoscale, imparting high strength. For strength to be retained at elevated temperature, these nanoprecipitates must have low coarsening rates. To inversely design the composition of an alloy to have these target microstructural features, we used hybrid calculation of phase diagram (CALPHAD)-based integrated computational materials engineering (ICME) and Bayesian optimization techniques. We tested our approach by designing an Al-Er-Zr-Y-Yb-Ni model alloy, and the selected composition was manufactured in powder form as AM feedstock. The strength of specimens manufactured via laser powder bed fusion (LPBF) from the designed composition is comparable to that of wrought Al 7075, yet without cracking that occurs upon LPBF of Al 7075. After high-temperature (400$^\circ$C) aging the designed alloy is 50% stronger than the strongest known benchmark printable Al alloy.

This paper presents a novel approach to extract scientifically valuable information about Earth's physical phenomena from unconventional sources, such as multi-modal social media posts. Employing a state-of-the-art large language model (LLM), Gemini 1.5 Pro (Reid et al. 2024), we estimate earthquake ground shaking intensity from these unstructured posts. The model's output, in the form of Modified Mercalli Intensity (MMI) values, aligns well with independent observational data. Furthermore, our results suggest that LLMs, trained on vast internet data, may have developed a unique understanding of physical phenomena. Specifically, Google's Gemini models demonstrate a simplified understanding of the general relationship between earthquake magnitude, distance, and MMI intensity, accurately describing observational data even though it's not identical to established models. These findings raise intriguing questions about the extent to which Gemini's training has led to a broader understanding of the physical world and its phenomena. The ability of Generative AI models like Gemini to generate results consistent with established scientific knowledge highlights their potential to augment our understanding of complex physical phenomena like earthquakes. The flexible and effective approach proposed in this study holds immense potential for enriching our understanding of the impact of physical phenomena and improving resilience during natural disasters. This research is a significant step toward harnessing the power of social media and AI for natural disaster mitigation, opening new avenues for understanding the emerging capabilities of Generative AI and LLMs for scientific applications.

Taking a system of two coupled qubits described by a X-shaped state and interacting through an anisotropic Heisenberg XY interaction, we examine the evolution of quantum entanglement and a few quantum correlations beyond entanglement, local quantum uncertainty and measurement-induced nonlocality, under the environmental decoherence both for zero and finite temperatures. The relation between concurrence and log negativity as two well-known quantifiers of entanglement is argued. It will be proven that measurement-induced nonlocality equals correlated coherence. The interaction of qubits with the environment causes quantum entanglement to suddenly die for independent qubits, but other correlations do not experience this phenomenon. The time of entanglement sudden death is calculated analytically for zero temperature, while numerically for finite temperatures. Contrary to its usual destructive role, the environment plays a constructive role in some situations, inducing quantum correlations even when the initial quantum correlations are zero. The steady state quantum correlations, being independent of the initial state, are found to remain all non-zero for low, finite temperatures. It is found that quantum correlations beyond entanglement are more robust against temperature than entanglement. The zero-temperature steady state exhibits less local quantum uncertainty than the other correlations., Comment: Accepted for publications in EPJP

In this work, we consider different theoretical aspects and simple applications of the Milburn equation which is governed by a parameter controlling what is known as intrinsic decoherence. The main goal is to show some similarities also observed when external decoherence is considered. Linear entropy, Ehrenfest relations, probability density current, the Wigner representation as well as the relation to a Lindbladian master equation are analyzed in terms of this intrinsic decoherence, leading to new insights on the Milburn dynamics. Interference of two wave packets, tunneling and the bouncing ball problem are also studied under this perspective., Comment: Accepted for publication in EPJ Plus

This article presents results from an experiment in which 44 human subjects interact with a dynamic system to perform 40 trials of a command-following task. The reference command is unpredictable and different on each trial, but all subjects have the same sequence of reference commands for the 40 trials. The subjects are divided into 4 groups of 11 subjects. One group performs the command-following task without preview of the reference command, and the other 3 groups are given preview of the reference command for different time lengths into the future (0.5 s, 1 s, 1.5 s). A subsystem identification algorithm is used to obtain best-fit models of each subject's control behavior on each trial. The time- and frequency-domain performance, as well as the identified models of the control behavior for the 4 groups are examined to investigate the effects of reference-command preview. The results suggest that preview tends to improve performance by allowing the subjects to compensate for sensory time delay and approximate the inverse dynamics in feedforward. However, too much preview may decrease performance by degrading the ability to use the correct phase lead in feedforward., Comment: Preprint submitted to IEEE Transactions on Cybernetics

In this work, we proposed a smooth transition wave equation from a quantum to classical regime in the framework of von Neumann formalism for ensembles and then obtained an equivalent scaled equation. This led us to develop a scaled statistical theory following the well-known Wigner-Moyal approach of quantum mechanics. This scaled nonequilibrium statistical mechanics has in it all the ingredients of the classical and quantum theory described in terms of a continuous parameter displaying all the dynamical regimes in between the two extreme cases. Finally, a simple application of our scaled formalism consisting of reflection from a mirror by computing various quantities, including probability density plots, scaled trajectories, and arrival times, was analyzed.

Accurate detection of brain tumors could save lots of lives and increasing the accuracy of this binary classification even as much as a few percent has high importance. Neural Gas Networks (NGN) is a fast, unsupervised algorithm that could be used in data clustering, image pattern recognition, and image segmentation. In this research, we used the metaheuristic Firefly Algorithm (FA) for image contrast enhancement as pre-processing and NGN weights for feature extraction and segmentation of Magnetic Resonance Imaging (MRI) data on two brain tumor datasets from the Kaggle platform. Also, tumor classification is conducted by Support Vector Machine (SVM) classification algorithms and compared with a deep learning technique plus other features in train and test phases. Additionally, NGN tumor segmentation is evaluated by famous performance metrics such as Accuracy, F-measure, Jaccard, and more versus ground truth data and compared with traditional segmentation techniques. The proposed method is fast and precise in both tasks of tumor classification and segmentation compared with other methods. A classification accuracy of 95.14 % and segmentation accuracy of 0.977 is achieved by the proposed method., Comment: 7 pages

Progressive aneurysmal dilatation is a well-recognized complication in patients with chronic type B aortic dissection (cTBAD), which may lead to a delayed rupture and create a life-threatening condition. However, our understanding of such aortic expansion in cTBAD remains weak. In the present paper, we propose to use numerical simulations to study the role of growth and remodeling (G\&R) in aneurysmal dilatation after cTBAD. We set up a 3D finite-element model of G\&R for aortic dissection within an open-source code. Constitutive equations, momentum balance equations, and equations related to the mechanobiology of the artery were formulated based on the homogenized constrained mixture theory. The model was first applied to idealized aortic geometries with cylindrical and toric shapes to demonstrate its feasibility and efficiency. The model was then applied to a patient-specific aortic segment to show its potential in more relevant and complex patient-specific clinical applications. It was found that the G\&R tends to naturally trigger the aneurysmal dilatation after dissection, in order to restore its tensional equilibrium. Our results indicated that the value of the gain parameter, related to collagen G\&R, plays an important role in the stability of aortic expansion after cTBAD. A small gain parameter will induce an excessive aneurysmal degeneration whilst a large gain parameter helps to recover a stabilized state of the artery after dissection. Finally, it was found that other mechanobiology-related parameters, such as the circumferential length of the dissection, as well as the pressure in the false lumen, may also be determinant for the stability of aneurysmal dilatation after cTBAD. Both a wide tear and an elevated false lumen pressure favor an unstable development of aortic expansion after cTBAD. As future work, the present model will be validated through predictions of aneurysmal dilatation in patient-specific clinical cases, in comparison with datasets followed over a significant period of time.

A transition period from regular vehicles (RVs) to autonomous vehicles (AVs) is imperative. This article explores both types of vehicles using a route choice model, formulated as a stochastic multi-class traffic assignment (SMTA) problem. In RVs, cross-nested logit (CNL) models are used since drivers do not have complete information and the unique characteristics of CNL. AVs, however, are considered to behave in a user equilibrium (UE) due to complete information about the network. The main innovation of this article includes the introduction of three solution methods for SMTA. Depending on the size of the network, each method can be used. These methods include solving the nonlinear complementary problem (NCP) with GAMS software, the decomposition-assignment algorithm, and the modified Wang's algorithm. Through the modification of Wang's algorithm, we have increased the convergence speed of Wang's algorithm and shown its numerical results for the Nguyen and Sioux Falls networks. As it is not possible to consider all paths in the traffic assignment, we proposed a creative path generation-assignment (PGA) algorithm. This algorithm generates several attractive paths for each origin-destination (OD), and the modified Wang's algorithm assigns traffic flow. Keywords: Autonomous Vehicles, Stochastic Multi-class Traffic Assignment, Cross-Nested Logit Model

Decoherence is a well established process for the emergence of classical mechanics in open quantum systems. However, it can have two different origins or mechanisms depending on the dynamics one is considering, speaking then about intrinsic decoherence for isolated systems and environmental decoherence due to dissipation/fluctuations for open systems. This second mechanism can not be considered for backflow since no thermal fluctuation terms can be added in the formalism in order to keep an important requirement for the occurrence of this effect: only contributions of positive momenta along time should be maintained. The purpose of this work is to analyze the backflow effect in the light of the underlying intrinsic decoherence and the dissipative dynamics. For this goal, we first deal with the Milburn approach where a mean frequency of the unitary evolution steps undergone for the system is assumed. A comparative analysis is carried out in terms of the Lindblad master equation. Second, the so-called quantum-to-classical transition wave equation is analyzed from a linear scaled Schr\"odinger equation which is derived and expressed in terms of a continuous parameter covering from the quantum to the classical regime as well as all in-between dynamical non-classical regimes. This theoretical analysis is inspired by the Wentzel-Kramers-Brillouin approximation. And third, in order to complete our analysis, the transition wave equation formalism is also applied to dissipative backflow within the Caldirola-Kanai approach where the dissipative dynamics comes from an effective Hamiltonian. In all the cases treated here, backflow is gradually suppressed as the intrinsic decoherence process is developing, paying a special attention to the classical limit. The route to classicality is not unique., Comment: Accepted for publication in JPA

Some aspects of quantum damped harmonic oscillator (DHO) obeying a Markovian master equation are considered in the absence of thermal noise. The continuity equation is derived and Bohmian trajectories are constructed. As a solution of the master equation, we take a single coherent state and compute analytically the relative entropy of coherence, $C_r$, in the energy, position and momentum bases. Although $C_r$ is constant in both the position and the momentum bases, it is a decreasing function of time in the energy basis becoming zero at long times, revealing its role as the preferred basis. Then, quantum coherence is computed for a superposition of two coherent states, a cat state, and also a superposition of two cat states in the energy basis as a function of separation, in the complex plane, between the two superposed states. It is seen that the quantum coherence increases with this separation. Furthermore, quantum coherence of superposition is compared to that of decomposed states in the superposition. Finally, considering a system of two non-interacting DHOs, the effect of quantum statistics is studied on the coherence of reduced single-particle states, the joint detection probability and the mean square separation of particles. Our computations show that the single-particle coherence for antisymmetric states is always less than that of symmetric ones. Furthermore, boson anti-bunching and fermion bunching is seen in this open system. This behavior of bosons is the matter-wave analogue of photon anti-bunching seen in a modified Hanbury Brown-Twiss (HBT) interferometer., Comment: Accepted for publication in Int. J. Quantum Information (IJQI)

Earthquake monitoring workflows are designed to detect earthquake signals and to determine source characteristics from continuous waveform data. Recent developments in deep learning seismology have been used to improve tasks within earthquake monitoring workflows that allow the fast and accurate detection of up to orders of magnitude more small events than are present in conventional catalogs. To facilitate the application of machine-learning algorithms to large-volume seismic records, we developed a cloud-based earthquake monitoring workflow, QuakeFlow, that applies multiple processing steps to generate earthquake catalogs from raw seismic data. QuakeFlow uses a deep learning model, PhaseNet, for picking P/S phases and a machine learning model, GaMMA, for phase association with approximate earthquake location and magnitude. Each component in QuakeFlow is containerized, allowing straightforward updates to the pipeline with new deep learning/machine learning models, as well as the ability to add new components, such as earthquake relocation algorithms. We built QuakeFlow in Kubernetes to make it auto-scale for large datasets and to make it easy to deploy on cloud platforms, which enables large-scale parallel processing. We used QuakeFlow to process three years of continuous archived data from Puerto Rico, and found more than a factor of ten more events that occurred on much the same structures as previously known seismicity. We applied Quakeflow to monitoring frequent earthquakes in Hawaii and found over an order of magnitude more events than are in the standard catalog, including many events that illuminate the deep structure of the magmatic system. We also added Kafka and Spark streaming to deliver real-time earthquake monitoring results. QuakeFlow is an effective and efficient approach both for improving realtime earthquake monitoring and for mining archived seismic data sets.

In this review we deal with open (dissipative and stochastic) quantum systems within the Bohmian mechanics framework which has the advantage to provide a clear picture of quantum phenomena in terms of trajectories, originally in configuration space. The gradual decoherence process is studied from linear and nonlinear Schr\"odinger equations through Bohmian trajectories as well as by using the so-called quantum-classical transition differential equation through scaled trajectories. This transition is governed by a continuous parameter, the transition parameter, covering these two extreme open dynamical regimes. Thus, two sources of decoherence of different nature are going to be considered. Several examples will be presented and discussed in order to illustrate the corresponding theory behind each case, namely: the so-called Brownian-Bohmian motion leading to quantum diffusion coefficients, dissipative diffraction in time, dissipative tunnelling for a parabolic barrier under the presence of an electric field and stochastic early arrivals for the same type of barrier. In order to simplify the notations and physical discussion, the theoretical developments will be carried out in one dimension throughout all this wok. One of the main goals is to analyze the gradual decoherence process existing in these open dynamical regimes in terms of trajectories, leading to a more intuitive way of understanding the underlying physics in order to gain new insights., Comment: Review Article; accepted for publication in Found. Phys

Some unexplored decoherence aspects within the Caldeira-Leggett master equation are analyzed and discussed. The decoherence process is controlled by the two environment parameters, the relaxation rate or friction and the temperature, leading to a gradual transition from the quantum to classical regime. Arrival time distributions, nonminimum-uncertainty-product or stretching Gaussian wave packets, identical particles and diffraction in time display interesting features during the decoherence process undergone by the time dependent interference patterns. We show that the presence of a constant force field does not affect the decoherence, {\it positive} values of the stretching parameter reduces the rate of decoherence, the symmetry of the wave function for identical particles plays no role when open dynamics are considered; and diffraction in time and space is gradually washed out by increasing the temperature and/or relaxation rate in the zero dissipation limit within the so-called quantum shutter problem., Comment: Accepted for publication in EPJP

Background: Bladder cancer is the 10th most common cancer worldwide, and its prevalence is increasing, especially in developing countries. Objective: In the present study, we employed gene expression profiles from the GSE163209 data set in the GEO database to identify potential molecular and genetic markers in BC patients. Methods: The data set comprised 217 samples, with 113 stage Ta tumor tissue samples and 104 stage T1 tumor tissue samples. The top 766 genes were chosen. P.value<0.0001 and |logFC|=1 was used to change the cutoff criteria for defining DEGs. Moreover, the MCODE plugin and cytoHubba plugin were employed to produce a module and detect 20 hub genes in these DEGs. We used GO and KEGG pathway enrichment analyses to get a better understanding of these DEGs. Results: The KEGG pathway enrichment results indicated that the top genes were mainly involved: Systemic lupus erythematosus, Alcoholism, and Viral carcinogenesis. SLE activation in the renal glomeruli could explain the connection between this disease's route and bladder cancer, and according to our results and previous researches, heavy alcohol intake can increase the risk of BC in males and particular populations. Conclusion: According to our hub genes, we can consider CDK1 and NDC80 as bladder cancer biomarkers. Not much research has been done on the effect of this gene on bladder cancer., Comment: Talk in 4th International biotechnology congress of Islamic Republic of Iran (2021)

Breast cancer is the most common cancer in women worldwide, and discovering the biomarkers of this disease became so vital nowadays and Cell in Cell structure could be one of them, and it may be used as an available proxy for tumor malignancy. (CICs) are unusual in that keep morphologically healthy cells within another cell. They are found in various human cancers and result from active cell-cell interaction, and it has different kinds. In this study, we analyzed the microarray data from GEO (GSE103865) to genetically evaluate CICs' incidence in samples obtained from breast cancer patients to understand the relationship between the rate of CIC and the prognosis of breast cancer. The preprocessing was performed using R software. The DAVID website was used to analyze gene ontology (GO) and Gene and Genome (KEGG) pathways. The protein-protein interactions (PPIs) of the obtained DEGs were assessed using the STRING website, and hub modules in Cytoscape and cytoHubba were screened. According to the results from analyzing the 20 hub genes, we understood that overexpression of our Top genes is effective in focal adhesion, ECM-receptor interaction, platelet activation and PI3K-Akt signaling pathway, which shows that changes in these pathways could be the reason the overexpression of CICs in breast cancer. These data and research by many others have uncovered various genes involved in CIC formation and have started to give us an idea of why they are formed and how they could contribute to breast cancer, Comment: Presented as a poster in 4th International biotechnology congress of Islamic Republic of Iran (2021)

In this work, momentum-space decoherence using minimum and nonminimum-uncertainty-product (stretched) Gaussian wave packets in the framework of Caldeira-Leggett formalism and under the presence of a linear potential is studied. As a dimensionless measure of decoherence, purity, a quantity appearing in the definition of the {\it linear entropy}, is studied taking into account the role of the stretching parameter. Special emphasis is on the open dynamics of the well-known cat states and bosons and fermions compared to distinguishable particles. For the cat state, while the stretching parameter speeds up the decoherence, the external linear potential strength does not affect the decoherence time; only the interference pattern is shifted. Furthermore, the interference pattern is not observed for minimum-uncertainty-product-Gaussian wave packets in the momentum space. Concerning bosons and fermions, the question we have addressed is how the symmetry of the wave functions of indistinguishable particles is manifested in the decoherence process, which is understood here as the loss of being indistinguishable due to the gradual emergence of classical statistics with time. We have observed that the initial bunching and anti-bunching character of bosons and fermions, respectively, in the momentum space are not preserved as a function of the environmental parameters, temperature and damping constant. However, fermionic distributions are slightly broader than the distinguishable ones and these similar to the bosonic distributions. This general behavior could be interpreted as a residual reminder of the symmetry of the wave functions in the momentum space for this open dynamics., Comment: Accepted for publication in Entropy

Earthquake monitoring by seismic networks typically involves a workflow consisting of phase detection/picking, association, and location tasks. In recent years, the accuracy of these individual stages has been improved through the use of machine learning techniques. In this study, we introduce a new, end-to-end approach that improves overall earthquake detection accuracy by jointly optimizing each stage of the detection pipeline. We propose a neural network architecture for the task of multi-station processing of seismic waveforms recorded over a seismic network. This end-to-end architecture consists of three sub-networks: a backbone network that extracts features from raw waveforms, a phase picking sub-network that picks P- and S-wave arrivals based on these features, and an event detection sub-network that aggregates the features from multiple stations and detects earthquakes. We use these sub-networks in conjunction with a shift-and-stack module based on back-projection that introduces kinematic constraints on arrival times, allowing the model to generalize to different velocity models and to variable station geometry in seismic networks. We evaluate our proposed method on the STanford EArthquake Dataset (STEAD) and on the 2019 Ridgecrest, CA earthquake sequence. The results demonstrate that our end-to-end approach can effectively pick P- and S-wave arrivals and achieve earthquake detection accuracy rivaling that of other state-of-the-art approaches.

Earthquake phase association algorithms aggregate picked seismic phases from a network of seismometers into individual earthquakes and play an important role in earthquake monitoring. Dense seismic networks and improved phase picking methods produce massive earthquake phase data sets, particularly for earthquake swarms and aftershocks occurring closely in time and space, making phase association a challenging problem. We present a new association method, the Gaussian Mixture Model Association (GaMMA), that combines the Gaussian mixture model for phase measurements (both time and amplitude), with earthquake location, origin time, and magnitude estimation. We treat earthquake phase association as an unsupervised clustering problem in a probabilistic framework, where each earthquake corresponds to a cluster of P and S phases with hyperbolic moveout of arrival times and a decay of amplitude with distance. We use a multivariate Gaussian distribution to model the collection of phase picks for an event, the mean of which is given by the predicted arrival time and amplitude from the causative event. We carry out the pick assignment for each earthquake and determine earthquake parameters (i.e., earthquake location, origin time, and magnitude) under the maximum likelihood criterion using the Expectation-Maximization (EM) algorithm. The GaMMA method does not require the typical association steps of other algorithms, such as grid-search or supervised training. The results on both synthetic test and the 2019 Ridgecrest earthquake sequence show that GaMMA effectively associates phases from a temporally and spatially dense earthquake sequence while producing useful estimates of earthquake location and magnitude.