The exponential growth of scientific literature has resulted in information overload, challenging researchers to effectively synthesize relevant publications. This paper explores the integration of traditional reference management software with advanced computational techniques, including Large Language Models and Retrieval-Augmented Generation. We introduce PyZoBot, an AI-driven platform developed in Python, incorporating Zoteros reference management with OpenAIs sophisticated LLMs. PyZoBot streamlines knowledge extraction and synthesis from extensive human-curated scientific literature databases. It demonstrates proficiency in handling complex natural language queries, integrating data from multiple sources, and meticulously presenting references to uphold research integrity and facilitate further exploration. By leveraging LLMs, RAG, and human expertise through a curated library, PyZoBot offers an effective solution to manage information overload and keep pace with rapid scientific advancements. The development of such AI-enhanced tools promises significant improvements in research efficiency and effectiveness across various disciplines.

In recent times, a considerable number of research studies have been carried out to address the issue of Missing Value Imputation (MVI). MVI aims to provide a primary solution for datasets that have one or more missing attribute values. The advancements in Artificial Intelligence (AI) drive the development of new and improved machine learning (ML) algorithms and methods. The advancements in ML have opened up significant opportunities for effectively imputing these missing values. The main objective of this article is to conduct a comprehensive and rigorous review, as well as analysis, of the state-of-the-art ML applications in MVI methods. This analysis seeks to enhance researchers' understanding of the subject and facilitate the development of robust and impactful interventions in data preprocessing for Data Analytics. The review is performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) technique. More than 100 articles published between 2014 and 2023 are critically reviewed, considering the methods and findings. Furthermore, the latest literature is examined to scrutinize the trends in MVI methods and their evaluation. The accomplishments and limitations of the existing literature are discussed in detail. The survey concludes by identifying the current gaps in research and providing suggestions for future research directions and emerging trends in related fields of interest.

This study comprehensively analyzes three open star clusters: SAI 16, SAI 81, and SAI 86 using Gaia DR3 data. Based on the ASteCA code, we determined the most probable star candidates (P >= 50%) and estimated the number of star members of each cluster as 125, 158, and 138, respectively. We estimated the internal structural parameters by fitting the King model to the observed RDPs, including the core, limited, and tidal radii. The isochrone fitting to the color-magnitude diagram provided log(age) values of 9.13 +/- 0.04, 8.10 +/- 0.04, and 8.65 +/- 0.04 and distances of 3790 +/- 94 pc, 3900 +/- 200 pc, and 3120 +/- 30 pc for SAI 16, SAI 81, and SAI 86, respectively. We also calculated their projected distances from the Galactic plane (X_sun, Y_sun) as well as their vertical distances (Z_sun), Galactocentric distances (R_gc), and total masses (M_C) in solar units, which are about 142 +/- 12, 302 +/- 17, and 192 +/- 14 for SAI 16, SAI 81, and SAI 86, respectively. Examining the dynamical relaxation state indicates that all three clusters are dynamically relaxed. By undertaking a kinematic analysis of the cluster data, the space velocity was determined. We calculated the coordinates of the apex point (A_o, D_o) using the AD diagram method along with the derivation of the solar motion parameters (S_sun, l_A, b_A). Through our detailed dynamic orbit analysis, we determined that the three SAI clusters belong to the young stellar disc, confirming their membership within this component of the Galactic structure.

Advancements in artificial intelligence (AI), speech recognition systems (ASR), and machine learning have enabled the development of intelligent computer programs called chatbots. Many chatbots have been proposed to provide different services in many areas such as customer service, sales and marketing. However, the use of chatbot as advisers in the field of information security is not yet considered. Furthermore, people, especially normal users who have no technical background, are unaware about many of aspects in information security. Therefore, in this paper we proposed a chatbot that acts as an adviser in information security. The proposed adviser uses a knowledge base with json file. Having such chatbot provides many features including raising the awareness in field of information security by offering accurate advice, based on different opinions from information security expertise, for many users on different. Furthermore, this chatbot is currently deployed through Telegram platform, which is one of widely used social network platforms. The deployment of the proposed chatbot over different platforms is considered as the future work.

Stellar cores may be considered as a nuclear reactor that play important role in injecting new synthesized elements in the interstellar medium Helium burning is an important stage that contribute to the synthesis of key elements such as carbon, through the triple- {\alpha} process, and oxygen. In the present paper, we introduce a computational method for the fractional model of the nuclear helium burning in stellar cores. The system of fractional differential equations is solved simultaneously using series expansion method. The calculations are performed in the sense of modified Riemann-Liouville fractional derivative. Analytic expressions are obtained for the abundance of each element as a function of time. Comparing the abundances calculated at the fractional parameter \alpha=1 , which represents the integer solution, with the numerical solution revealed a good agreement with maximum error \epsilon =0.003. The product abundances are calculated at\alpha=0.25, 0.5, 0.75 to declare the effects of changing the fractional parameters on the calculations.

Blood cell classification and counting are vital for the diagnosis of various blood-related diseases, such as anemia, leukemia, and thrombocytopenia. The manual process of blood cell classification and counting is time-consuming, prone to errors, and labor-intensive. Therefore, we have proposed a DL based automated system for blood cell classification and counting from microscopic blood smear images. We classify total of nine types of blood cells, including Erythrocyte, Erythroblast, Neutrophil, Basophil, Eosinophil, Lymphocyte, Monocyte, Immature Granulocytes, and Platelet. Several preprocessing steps like image resizing, rescaling, contrast enhancement and augmentation are utilized. To segment the blood cells from the entire microscopic images, we employed the U-Net model. This segmentation technique aids in extracting the region of interest (ROI) by removing complex and noisy background elements. Both pixel-level metrics such as accuracy, precision, and sensitivity, and object-level evaluation metrics like Intersection over Union (IOU) and Dice coefficient are considered to comprehensively evaluate the performance of the U-Net model. The segmentation model achieved impressive performance metrics, including 98.23% accuracy, 98.40% precision, 98.25% sensitivity, 95.97% Intersection over Union (IOU), and 97.92% Dice coefficient. Subsequently, a watershed algorithm is applied to the segmented images to separate overlapped blood cells and extract individual cells. We have proposed a BloodCell-Net approach incorporated with custom light weight convolutional neural network (LWCNN) for classifying individual blood cells into nine types. Comprehensive evaluation of the classifier's performance is conducted using metrics including accuracy, precision, recall, and F1 score. The classifier achieved an average accuracy of 97.10%, precision of 97.19%, recall of 97.01%, and F1 score of 97.10%.

In this study, we considered the optical wavelength of Gaia DR3 to analyze poorly studied three newly open star clusters namely OCSN 203, OCSN 213, and OCSN 244 clusters with ASTECA code. Here, we identified candidates of 227, 200, and 551 with highly probable ($P \geq 50\%$) members. Fitting King's profile within RDPs allows us to estimate inner stellar structures like core (0.190 $\le r_{\rm c}$ (pc) $\le$ 1.284) and the limiting (0.327 $\le r_{\rm cl}$ (pc) $\le 1.302$) radii. Constructing CMDs fitted with suitable log age (yr) between (log t; 6.52 - 7.05) and metallicities (Z; 0.01308-0.01413) isochrones. Therefore, the estimated photometric parameters with CMDs, reflect the heliocentric distances are 332 $\pm$ 18, 529 $\pm$ 23, and 506 $\pm$ 23 (pc) for OCSN 203, OCSN 213, and OCSN 244, respectively. Furthermore, the collective mass ($M_{\rm C}$) in solar mass units calculated with MLR as 67 $\pm$ 8.19, 91 $\pm$ 9.54, and 353 $\pm$ 18.79. Additionally, LF determined that the mean absolute magnitudes are 9.54 $\pm$ 3.09, 8.52 $\pm$ 2.92, and 7.60 $\pm$ 2.76 for these clusters, respectively. The overall mass function reflects the slopes ($\alpha$) for Salpeter within the uncertainty are ($\alpha_{OCSN203}$ = 2.41 $\pm$ 0.06), ($\alpha_{OCSN213}$ = 2.13 $\pm$ 0.07), and ($\alpha_{OCSN244}$ = 2.28 $\pm$ 0.07). The results of this study which employed a dynamical analysis over varying timescales indicate that OCSN 203 and OCSN 244 are clusters that have undergone significant relaxation, with a dynamical evolution parameter ($\tau$) that is much greater than one. In contrast, OCSN 213 exhibits characteristics of a non-relaxed cluster. A kinematic analysis of these open clusters was carried out, encompassing aspects of their apex position ($A_o,D_o$) using the AD diagrams. At the end, we found that the three OCSN clusters are young stellar disc members using dynamic orbit parameters.

The study presents both photometric and kinematic analyses of the non-relaxed open cluster Stock 3 with Gaia DR3 which found to be positioned at 2.945 $\pm$ 0.700 kpc and having an age of 16.00 $\pm$ 4.00 Myr. We analyse the data to infer the membership and thus determine the total mass, IMF and the dynamical and kinematical status.

The kinematic parameters identified from high-velocity stars situated within 100 kpc are examined and analyzed. We included three high velocity programs comprising 591, 87, and 519 stars as a function of distances ranging from 0.10 to nearly 109 kpc. In this analysis, we will determine the spatial velocities (U, V, W) in galactic coordinates along with their velocity dispersion (sigma_1, sigma_2, sigma_3), the convergent point (Ao, Do), and therefore, the solar motion (S_sun).

In quantum information, quantum correlations--particularly entanglement, EPR steering, and quantum discord--stand as interesting resources, powering breakthroughs from secure communication and quantum teleportation to efficient quantum computation. Here, we unveil a theoretical framework for generating and controlling these vital quantum phenomena within a double-cavity molecular optomechanical (McOM) system. This innovative approach leverages strong interactions between confined optical fields and collective molecular vibrations, creating a versatile environment for exploring robust quantum correlations. Our findings reveal that judiciously optimizing the coupling strength between the cavity field and the molecular collective mode leads to a remarkable enhancement of entanglement, quantum steering, and quantum discord. We demonstrate that cavity-cavity quantum correlations can be effectively mediated by the molecular collective mode, enabling a unique pathway for inter-cavity quantum connectivity. Moreover, the quantum entanglement generated in our McOM system exhibits robustness against thermal noise, persisting at temperatures approaching 1000 K. This strong resilience, qualifies molecular optomechanics as a compelling architecture for scalable, room-temperature quantum information processing and the practical realization of quantum networks.

High dimensionality in datasets produced by microarray technology presents a challenge for Machine Learning (ML) algorithms, particularly in terms of dimensionality reduction and handling imbalanced sample sizes. To mitigate the explained problems, we have proposedhybrid ensemble feature selection techniques with majority voting classifier for micro array classi f ication. Here we have considered both filter and wrapper-based feature selection techniques including Mutual Information (MI), Chi-Square, Variance Threshold (VT), Least Absolute Shrinkage and Selection Operator (LASSO), Analysis of Variance (ANOVA), and Recursive Feature Elimination (RFE), followed by Particle Swarm Optimization (PSO) for selecting the optimal features. This Artificial Intelligence (AI) approach leverages a Majority Voting Classifier that combines multiple machine learning models, such as Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), to enhance overall performance and accuracy. By leveraging the strengths of each model, the ensemble approach aims to provide more reliable and effective diagnostic predictions. The efficacy of the proposed model has been tested in both local and cloud environments. In the cloud environment, three virtual machines virtual Central Processing Unit (vCPU) with size 8,16 and 64 bits, have been used to demonstrate the model performance. From the experiment it has been observed that, virtual Central Processing Unit (vCPU)-64 bits provides better classification accuracies of 95.89%, 97.50%, 99.13%, 99.58%, 99.11%, and 94.60% with six microarray datasets, Mixed Lineage Leukemia (MLL), Leukemia, Small Round Blue Cell Tumors (SRBCT), Lymphoma, Ovarian, andLung,respectively, validating the effectiveness of the proposed modelin bothlocalandcloud environments.

With the emergence of Information and Communication Technologies (ICT) and wireless embedded sensing devices into modern vehicles, Intelligent Transport System (ITS) becomes a reality and an indispensable component of smart cities. The purpose of ITS is to improve road safety and traffic efficiency as well as offering infotainments services. In fact, warning drivers in the right time about dangerous situations on the road and providing them with prior information about traffic will undoubtedly leads to enhance driver's safety and reduce traffic congestion. Technically speaking, ITS is based on self-organizing wireless networks, known as vehicular ad-hoc networks (VANETs). Mobile vehicles in VANET might play the role of stationary sensors in infrastructure-based networks. They can detect, gather and disseminate real-time data about traffic, driving conditions and potential hazards on roads. In this respect, we review in this study, recent developments on the design of VANET protocols and applications. We first introduce the architecture of VANETs then we review their unique characteristics and applications. Thereafter, we discuss the main research challenges and open issues to be considered for designing efficient and a cost-effective VANET protocols and applications.

The aim of this paper is to establish and study the linear canonical Dunkl wavelet transform. We begin by introducing the generalized translation operator and generalized convolution product for the linear canonical Dunkl transform and we establish their basic properties. Next, we introduce the new proposed wavelet transform and we investigate its fundamentals properties. In the end, we derive some uncertainty inequalities for the desired wavelet transform as applications.

This study uses the density functional theory (DFT) approach with GGA-PBE to assess the effect of substituting alkali metals in Rb$_{2}$CaH and Cs-doped Rb$_{2}$CaH$_{4}$ on their hydrogen storage potential. To address the challenges associated with predicting accurate electronic properties in materials containing heavier elements such as cesium, spin-orbit coupling (SOC) effects have been incorporated into our calculations. The mechanical robustness of both Rb$_{2}$CaH$_{4}$ and Cs-doped Rb$_{2}$CaH$_{4}$, as demonstrated by their mechanical properties, highlights these materials as promising candidates due to their stability in hydrogen storage applications. Anisotropic factors show that all materials exhibit anisotropy, suggesting a directional dependency in their properties. The Pugh ratio indicates that Rb$_{2}$CaH$_{4}$ and Cs-doped Rb$_{2}$CaH$_{4}$ are brittle materials. Based on the calculated band gap, the electronic band structure analysis, conducted using both HSE06 and GGA-PBE, shows that Rb$_{2}$CaH$_{4}$ and Cs-doped Rb$_{2}$CaH$_{4}$ are wide-bandgap materials. Rb$_{2}$CaH$_{4}$ and Cs-doped Rb$_{2}$CaH$_{4}$ exhibit the highest optical conductivity, absorption coefficient, and energy loss function among optoelectronic materials, emphasizing their superior absorption and electron transfer capabilities. The hydrogen storage capacity has been evaluated for practical applications; Rb$_{2}$CaH$_{4}$ and Cs-doped Rb$_{2}$CaH$_{4}$ show the highest gravimetric and volumetric capacities.

In this study, we utilize photometric and kinematic data from \textit{Gaia} DR3 and the {\sc ASteCA} package to analyze the sparsely studied open clusters, King 2 and King 5. For King 2, we identify 340 probable members with membership probabilities exceeding 50\%. Its mean proper motion components are determined as $(\mu_\alpha\cos\delta,~\mu_\delta) = (-1.407 \pm 0.008, -0.863 \pm 0.012)$ mas yr$^{-1}$, and its limiting radius is derived as $6.94_{-1.06}^{+0.22}$ arcminutes based on radial density profiles. The cluster has an estimated age of $4.80 \pm 0.30$ Gyr, a distance of $6586 \pm 164$ pc, and a metallicity of $\text{[Fe/H]} = -0.25$ dex ($z = 0.0088$). We detect 17 blue straggler stars (BSSs) concentrated in its core, and its total mass is estimated to be $356 \pm 19~M_{\odot}$. The computed apex motion is $(A_o,~D_o) = (-142^\circ.61 \pm 0^\circ.08, -63^\circ.58 \pm 0^\circ.13)$. Similarly, King 5 consists of 403 probable members with mean proper motion components $(\mu_\alpha\cos\delta,~\mu_\delta) = (-0.291 \pm 0.005, -1.256 \pm 0.005)$ mas yr$^{-1}$ and a limiting radius of $11.33_{-2.16}^{+5.45}$ arcminutes. The cluster's age is determined as $1.45 \pm 0.10$ Gyr, with a distance of $2220 \pm 40$ pc and a metallicity of $\text{[Fe/H]} = -0.15$ dex ($z = 0.0109$). We identify 4 centrally concentrated BSSs, and the total mass is estimated as $484 \pm 22~M_{\odot}$. The apex motion is calculated as $(A_o,~D_o) = (-115^\circ.10 \pm 0^\circ.09, -73^\circ.16 \pm 0^\circ.12)$. The orbital analysis of King 2 and King 5 indicates nearly circular orbits, characterized by low eccentricities and minimal variation in their apogalactic and perigalactic distances. King 2 and King 5 reach maximum heights of $499 \pm 25$ pc and $177 \pm 2$ pc from the Galactic plane, respectively, confirming their classification as young stellar disc population.

Water covers 71% of the Earth's surface, where the steady increase in oceanic activities has promoted the need for reliable maritime communication technologies. The existing maritime communication systems involve terrestrial, aerial, and satellite networks. This paper presents a holistic overview of the different forms of maritime communications and provides the latest advances in various marine technologies. The paper first introduces the different techniques used for maritime communications over the RF and optical bands. Then, we present the channel models for RF and optical bands, modulation and coding schemes, coverage and capacity, and radio resource management in maritime communications. After that, the paper presents some emerging use cases of maritime networks, such as the Internet of Ships (IoS) and the ship-to-underwater Internet of things (IoT). Finally, we highlight a few exciting open challenges and identify a set of future research directions for maritime communication, including bringing broadband connectivity to the deep sea, using THz and visible light signals for on-board applications, and data-driven modeling for radio and optical marine propagation.

Advancements in information technology have led to the sharing of users' data across borders, raising privacy concerns, particularly when destination countries lack adequate protection measures. Regulations like the European General Data Protection Regulation (GDPR) govern international data transfers, imposing significant fines on companies failing to comply. To achieve compliance, we propose a privacy framework based on Milner's Bigraphical Reactive Systems (BRSs), a formalism modelling spatial and non-spatial relationships between entities. BRSs evolve over time via user-specified rewriting rules, defined algebraically and diagrammatically. In this paper, we rely on diagrammatic notations, enabling adoption by end-users and privacy experts without formal modelling backgrounds. The framework comprises predefined privacy reaction rules modelling GDPR requirements for international data transfers, properties expressed in Computation Tree Logic (CTL) to automatically verify these requirements with a model checker and sorting schemes to statically ensure models are well-formed. We demonstrate the framework's applicability by modelling WhatsApp's privacy policies.