Phonocardiogram (PCG) signal analysis is a critical, widely-studied technology to noninvasively analyze the heart's mechanical activity. Through evaluating heart sounds, this technology has been chiefly leveraged as a preliminary solution to automatically diagnose Cardiovascular diseases among adults; however, prenatal tasks such as fetal gender identification have been relatively less studied using fetal Phonocardiography (FPCG). In this work, we apply common PCG signal processing techniques on the gender-tagged Shiraz University Fetal Heart Sounds Database and study the applicability of previously proposed features in classifying fetal gender using both Machine Learning and Deep Learning models. Even though PCG data acquisition's cost-effectiveness and feasibility make it a convenient method of Fetal Heart Rate (FHR) monitoring, the contaminated nature of PCG signals with the noise of various types makes it a challenging modality. To address this problem, we experimented with both static and adaptive noise reduction techniques such as Low-pass filtering, Denoising Autoencoders, and Source Separators. We apply a wide range of previously proposed classifiers to our dataset and propose a novel ensemble method of Fetal Gender Identification (FGI). Our method substantially outperformed the baseline and reached up to 91% accuracy in classifying fetal gender of unseen subjects.

Developed by OpenAI, ChatGPT (Conditional Generative Pre-trained Transformer) is an artificial intelligence technology that is fine-tuned using supervised machine learning and reinforcement learning techniques, allowing a computer to generate natural language conversation fully autonomously. ChatGPT is built on the transformer architecture and trained on millions of conversations from various sources. The system combines the power of pre-trained deep learning models with a programmability layer to provide a strong base for generating natural language conversations. In this study, after reviewing the existing literature, we examine the applications, opportunities, and threats of ChatGPT in 10 main domains, providing detailed examples for the business and industry as well as education. We also conducted an experimental study, checking the effectiveness and comparing the performances of GPT-3.5 and GPT-4, and found that the latter performs significantly better. Despite its exceptional ability to generate natural-sounding responses, the authors believe that ChatGPT does not possess the same level of understanding, empathy, and creativity as a human and cannot fully replace them in most situations.

The present study, investigates the modulation of plasma wakefields in dense magnetized plasma driven by relativistic electron beams under transverse RF excitation. A self consistent theoretical framework, comprising the RF vector potential, Maxwells equations, and relativistic electron motion, is extended through full 3D electromagnetic particle in cell simulations. The results reveal systematic amplification and reshaping of wakefields under the combined action of external magnetic fields and RF drivers. Variations in the cyclotron to plasma frequency ratio dictate the radial positioning and gyromotion of plasma electrons, sharpening transverse confinement and stabilizing blowout structures. The RF amplitude introduces progressive modulation of radial excursions and transverse forces, enhancing wakefield symmetry and depth. Current density distributions confirm the nonlinear scaling with RF strength, evolving from weak perturbations into sharply structured ion channels. Scalar potentials and longitudinal fields exhibit pronounced sensitivity to pulse shape, polarization angle, frequency ratio, and driver density, each parameter producing distinct oscillatory features and confinement regimes. Plasma density sets the field strength and radial localization, while the modulation parameter governs the emergence of fine scale oscillatory bands, producing smooth to multiband transitions in longitudinal electric fields. Across all conditions, simulations confirm the reinforcement of ponderomotive force, resulting in controlled narrowing of electron sheaths, sharper scalar potential gradients, and extended acceleration zones.

Over the past few years, emergence of cloud computing has notably made an evolution in the IT industry by putting forward an "everything as a service" idea .Cloud Computing is of growing interest to companies throughout the world, but there are many barriers associated with its adoption which should be eliminated. This paper aims to investigate Cloud Computing and discusses the drivers and inhibitors of its adoption. Moreover, an attempt has been made to identify the key stakeholders of Cloud Computing and outline the current security challenges. A SWOT analysis which consists of strengths, weaknesses, opportunities and threats has also carried out in which Cloud Computing adoption for SMEs (Small and Medium-sized Enterprises) is evaluated. Finally, the paper concludes with some further research areas in the field of Cloud Computing.

The PRFashion24 dataset is a comprehensive Persian dataset collected from various online fashion stores, spanning from April 2020 to March 2024. With 767,272 reviews, it is the first dataset in its kind that encompasses diverse categories within the fashion industry in the Persian language. The goal of this study is to harness deep learning techniques, specifically Long Short-Term Memory (LSTM) networks and a combination of Bidirectional LSTM and Convolutional Neural Network (BiLSTM-CNN), to analyze and reveal sentiments towards online fashion shopping. The LSTM model yielded an accuracy of 81.23%, while the BiLSTM-CNN model reached 82.89%. This research aims not only to introduce a diverse dataset in the field of fashion but also to enhance the public's understanding of opinions on online fashion shopping, which predominantly reflect a positive sentiment. Upon publication, both the optimized models and the PRFashion24 dataset will be available on GitHub.

Melanoma is caused by the abnormal growth of melanocytes in human skin. Like other cancers, this life-threatening skin cancer can be treated with early diagnosis. To support a diagnosis by automatic skin lesion segmentation, several Fully Convolutional Network (FCN) approaches, specifically the U-Net architecture, have been proposed. The U-Net model with a symmetrical architecture has exhibited superior performance in the segmentation task. However, the locality restriction of the convolutional operation incorporated in the U-Net architecture limits its performance in capturing long-range dependency, which is crucial for the segmentation task in medical images. To address this limitation, recently a Transformer based U-Net architecture that replaces the CNN blocks with the Swin Transformer module has been proposed to capture both local and global representation. In this paper, we propose Att-SwinU-Net, an attention-based Swin U-Net extension, for medical image segmentation. In our design, we seek to enhance the feature re-usability of the network by carefully designing the skip connection path. We argue that the classical concatenation operation utilized in the skip connection path can be further improved by incorporating an attention mechanism. By performing a comprehensive ablation study on several skin lesion segmentation datasets, we demonstrate the effectiveness of our proposed attention mechanism.

For the first time in an extragalactic source, we detect linearly polarized water maser emission associated with the molecular accretion disk of NGC 1068. The position angles of the electric polarization vectors are perpendicular to the axes of filamentary structures in the molecular accretion disk. The inferred magnetic field threading the molecular disk must lie within approximately 35 degrees of the sky plane. The orientation of the magnetic fields relative to the disk plane implies that the maser region is unstable to hydromagnetically powered outflow; we speculate that the maser region may be the source of the larger scale molecular outflow found in ALMA studies. The new VLBI observations also reveal a compact radio continuum source, NGC 1068*, aligned with the near-systemic maser spots. The molecular accretion disk must be viewed nearly edge-on, and the revised central mass is (16.6 +/- 0.1) million solar masses.

In this paper, a novel objective evaluation metric for image fusion is presented. Remarkable and attractive points of the proposed metric are that it has no parameter, the result is probability in the range of [0, 1] and it is free from illumination dependence. This metric is easy to implement and the result is computed in four steps: (1) Smoothing the images using Gaussian filter. (2) Transforming images to a vector field using Del operator. (3) Computing the normal distribution function ({\mu},{\sigma}) for each corresponding pixel, and converting to the standard normal distribution function. (4) Computing the probability of being well-behaved fusion method as the result. To judge the quality of the proposed metric, it is compared to thirteen well-known non-reference objective evaluation metrics, where eight fusion methods are employed on seven experiments of multimodal medical images. The experimental results and statistical comparisons show that in contrast to the previously objective evaluation metrics the proposed one performs better in terms of both agreeing with human visual perception and evaluating fusion methods that are not performed at the same level.

In this chapter we will introduce an effective equation of state (EoS) model based on polytropes that serves to study the so called "mass twins" scenario, where two compact stars have approximately the same mass but (significant for observation) quite different radii. Stellar mass twin configurations are obtained if a strong first-order phase transition occurs in the interior of a compact star. In the mass-radius diagram of compact stars, this will lead to a third branch of gravitationally stable stars with features that are very distinctive from those of white dwarfs and neutron stars. We discuss rotating hybrid star sequences in the slow rotation approximation and in full general relativity and draw conclusions for an upper limit on the maximum mass of nonrotating compact stars that has recently be deduced from the observation of the merger event GW170817.

One of the most significant issues as attended a lot in recent years is that of recognizing the sentiments and emotions in social media texts. The analysis of sentiments and emotions is intended to recognize the conceptual information such as the opinions, feelings, attitudes and emotions of people towards the products, services, organizations, people, topics, events and features in the written text. These indicate the greatness of the problem space. In the real world, businesses and organizations are always looking for tools to gather ideas, emotions, and directions of people about their products, services, or events related to their own. This article uses the Twitter social network, one of the most popular social networks with about 420 million active users, to extract data. Using this social network, users can share their information and opinions about personal issues, policies, products, events, etc. It can be used with appropriate classification of emotional states due to the availability of its data. In this study, supervised learning and deep neural network algorithms are used to classify the emotional states of Twitter users. The use of deep learning methods to increase the learning capacity of the model is an advantage due to the large amount of available data. Tweets collected on various topics are classified into four classes using a combination of two Bidirectional Long Short Term Memory network and a Convolutional network. The results obtained from this study with an average accuracy of 93%, show good results extracted from the proposed framework and improved accuracy compared to previous work.

Medical consumer question answering (CQA) is crucial for empowering patients by providing personalized and reliable health information. Despite recent advances in large language models (LLMs) for medical QA, consumer-oriented and multilingual resources, particularly in low-resource languages like Persian, remain sparse. To bridge this gap, we present PerMedCQA, the first Persian-language benchmark for evaluating LLMs on real-world, consumer-generated medical questions. Curated from a large medical QA forum, PerMedCQA contains 68,138 question-answer pairs, refined through careful data cleaning from an initial set of 87,780 raw entries. We evaluate several state-of-the-art multilingual and instruction-tuned LLMs, utilizing MedJudge, a novel rubric-based evaluation framework driven by an LLM grader, validated against expert human annotators. Our results highlight key challenges in multilingual medical QA and provide valuable insights for developing more accurate and context-aware medical assistance systems. The data is publicly available on this https URL

Contracting the $h$-deformation of $\SL(2,\Real)$, we construct a new deformation of two dimensional Poincaré algebra, the algebra of functions on its group and its differential structure. It is also shown that the Hopf algebra is triangular, and its universal R matrix is also constructed explicitly. Then, we find a deformation map for the universal enveloping algebra, and at the end, give the deformed mass shells and Lorentz transformation.

Recently, the number of non-standard percolation models has proliferated. In all these models, there exists a phase transition at which long range connectivity is established, if local connectedness increases through a threshold $p_c$. In ordinary (site or bond) percolation on regular lattices, this is a well understood second-order phase transition with rather precisely known critical exponents, but there are non-standard models where the transitions are in different universality classes (i.e. with different exponents and scaling functions), or even are discontinuous or hybrid. It was recently claimed that certain scaling functions are in all such models given by extreme-value theory and thus independent of the precise universality class. This would lead to super-universality (even encompassing first-order transitions!) and would be a major break-through in the theory of phase transitions. We show that this claim is wrong.

Recently it is shown that there are three families of stochastic one-dimensional non-equilibrium lattice models for which the single-shock measures form an invariant subspace of the states of these models. Here, both the stationary states and dynamics of single-shocks on a one-dimensional lattice are studied. This is done for both an infinite lattice and a finite lattice with boundaries. It is seen that these models possess both static and dynamical phase transitions. The static phase transition is the well known low-high density phase transition For the ASEP. The BCRW, and AKGP models also show the same phase transition. Double-shocks on a one-dimensional lattice are also investigated. It is shown that at the stationary state the contribution of double-shocks with higher width becomes small, and the main contribution comes from thin double-shocks.

Understanding the structure and dynamics of biological networks is one of the important challenges in system biology. In addition, increasing amount of experimental data in biological networks necessitate the use of efficient methods to analyze these huge amounts of data. Such methods require to recognize common patterns to analyze data. As biological networks can be modeled by graphs, the problem of common patterns recognition is equivalent with frequent sub graph mining in a set of graphs. In this paper, at first the challenges of frequent subgrpahs mining in biological networks are introduced and the existing approaches are classified for each challenge. then the algorithms are analyzed on the basis of the type of the approach they apply for each of the challenges.

In this paper a toy model of quantum topology is reviewed to study effects of matter and gauge fields on the topology fluctuations. In the model a collection of N one dimensional manifolds are considered where a set of boundary conditions on states of Hilbert space specifies a set of all topologies perceived by quantum particle and probability of having a specific topology is determined by a partition function over all the topologies in the context of noncommutative spectral geometry. In general the topologies will be fuzzy with the exception of a particular case which is localized by imposing a specific boundary condition. Here fermions and bosons are added to the model. It is shown that in the presence of matter, the fuzziness of topology will be dependent on N, however for large N the dependence is removed similar to the case without matter. Also turning on a particular background gauge field, can overcome the fuzziness of topology to reach a localized topology with classical interpretation. It can be seen that for large N more opportunities can be provided for choosing the background gauge field to localize the fuzzy topology.

Let ${\rm dim}(G)$ and $D(G)$ respectively denote the metric dimension and the distinguishing number of a graph $G$. It is proved that $D(G) \le {\rm dim}(G)+1$ holds for every connected graph $G$. Among trees, exactly paths and stars attain the bound, and among connected unicyclic graphs such graphs are $t$-cycles for $t\in \{3,4,5\}$. It is shown that for any 1\leq n< m, there exists a graph $G$ with $D(G)=n$ and ${\rm dim}(G)=m$. Using the bound $D(G) \le {\rm dim}(G)+1$, graphs with $D(G) = n(G)-2$ are classified.

The propagation of laser pulses in tapered magnetized plasma channels is analyzed using the fluid theory of cold plasmas. This study focuses on laser propagation's key instabilities: forward Raman scattering and self-modulation instability. The influence of plasma density, laser intensity, dc magnetic field strength, and laser polarization on the growth rates of these instabilities is thoroughly examined. Analytical and numerical computations of the number of e-foldings for forward Raman scattering are performed to quantify its impact. The results reveal that increased plasma density and laser intensity significantly enhance the growth rates of these instabilities, leading to amplified Raman-scattered wave intensity and modulation of the laser envelope. Moreover, the strength of the dc magnetic field plays a pivotal role: it boosts instability growth rates for right-handed circularly polarized laser pulses while suppressing them for left-handed circularly polarized pulses. These findings highlight that reducing the growth rates of instabilities can facilitate the stable propagation of laser pulses in tapered plasma channels.

Crocus sativus is a triploid sterile plant with red stigmas belonging to the family of Iridaceae, and sub-family Crocoideae. Crocin, picrocrocin, and safranal are three major carotenoid derivatives that are responsible for the color, taste, and specific aroma of Crocus. Saffron flowers are harvested manually and used as spice, dye, or medicinal applications. The natural propagation rate of most geophytes including saffron is relatively low. An in vitro multiplication technique like micropropagation has been used for the propagation of saffron. To understand the efficiency of this alternative and study the molecular basis of apocarotenoid biosynthesis/accumulation, the RT-PCR method was performed on perianth explants that were cultured on MS medium to observe the level of expression of zeaxanthin cleavage dioxygenase (CsZCD) gene during stigma development, and also the impact of soil EC on its expression. The present study was conducted at Plant molecular and physiology Lab, Alzahra University, Tehran, Iran during 2011-2013. Stigma-like structures (SLSs) on calli were collected from immature perianth explants from floral buds of corms that were collected from Ghaen city, and compared to (Torbat-e Haidariye, Mardabad, and Shahroud cities) for investigating the impact of different soil EC on CsZCD expression. The results indicated that the CsZCD gene was highly expressed in fully developed red SLSs in perianth of cultured samples of Shahroud with the highest salinity. In this research, a close relationship between soil EC and second metabolites regulation is studied. Overall, these results will pave the way for understanding the molecular basis of apocarotenoid biosynthesis and other aspects of stigma development in C. sativus.