Diverse Domains Research Articles

A hybrid approach of ConvLSTMBNN-DT and GPT-4 for real-time anomaly detection decision support in edge–cloud environments

Anomaly detection is a critical requirement across diverse domains to promptly identify abnormal behavior. Conventional approaches often face limitations with uninterpretable anomaly detection results, impeding efficient decision-making processes. This paper introduces a novel hybrid approach, the convolutional LSTM Bayesian neural network with nonparametric dynamic thresholding (ConvLSTMBNN-DT) for prediction-based anomaly detection. In addition, the model integrates fine-tuned generative pre-training version 4 (GPT-4) to provide human-interpretable explanations in edge–cloud environments. The proposed method demonstrates exceptional performance, achieving an average F1−score of 0.91 and an area under the receiver operating characteristic curve (AUC) of 0.86. Additionally, it effectively offers comprehensible decision-support explanations.

RLclean: An unsupervised integrated data cleaning framework based on deep reinforcement learning

Data cleaning, a prerequisite to subsequent data analysis, has always been the focus of data science research. Datasets with errors can severely detract from the quality of downstream analytical results. Unfortunately, despite the proliferation of various data cleaning methods, it remains a time-consuming problem and frequently entails considerable labor expenses. In reality, errors are often heterogeneous and require different solutions. As a result, stand-alone methods often inadequate for addressing dirty data with multiple types of errors, while studies have demonstrated that combining such methods always require human intervention and the result remains unsatisfactory.In this paper, we propose an unsupervised integrated data cleaning framework, namely RLclean. Based on deep reinforcement learning, RLclean takes advantages of multiple data cleaning techniques, enabling it to effectively clean multiple types of errors and achieve satisfactory results. Additionally, it eliminates the need for costly human involvement, as the cleaning strategy is learned by data-driven, which further allows the framework to self-adapt to diverse domains. RLclean mainly consists of two parts: (i) an integrated error detection model that unites multiple techniques to detect different types of errors from multiple views; and (ii) an integrated data repair model that learns the optimal repair operations and repairs dirty data in an unsupervised manner. Extensive experiments on benchmark datasets have demonstrated the superiority of RLclean over state-of-the-art methods.

Sentiment analysis with machine learning and deep learning: A survey of techniques and applications

Sentiment analysis is the task of automatically identifying the sentiment expressed in text. It has become increasingly important in many applications such as social media monitoring, product reviews analysis, and customer feedback evaluation. With the advent of deep learning techniques, sentiment analysis has seen significant improvements in performance and accuracy. This paper presents a comprehensive survey of machine learning and deep learning methods for sentiment analysis at the document, sentence, and aspect levels. We first provide an overview of traditional machine learning approaches to sentiment analysis and their limitations. We then look into various machine learning and deep learning architectures that have been successfully applied to this task. Additionally, we discuss the challenges of dealing with different data modalities, such as visual and multimodal data, and how both techniques have been adapted to address these challenges. Furthermore, we explore the applications of sentiment analysis in diverse domains, including social media, product reviews, and healthcare. Finally, we highlight the current limitations of deep learning approaches for sentiment analysis and outline potential future research directions. This survey aims to provide researchers and practitioners with a comprehensive understanding of the state-of-the-art deep learning techniques for sentiment analysis and their practical applications.

Flexible Electronics: Advancements and Applications of Flexible Piezoelectric Composites in Modern Sensing Technologies.

The piezoelectric effect refers to a physical phenomenon where piezoelectric materials generate an electric field when subjected to mechanical stress or undergo mechanical deformation when subjected to an external electric field. This principle underlies the operation of piezoelectric sensors. Piezoelectric sensors have garnered significant attention due to their excellent self-powering capability, rapid response speed, and high sensitivity. With the rapid development of sensor techniques achieving high precision, increased mechanical flexibility, and miniaturization, a range of flexible electronic products have emerged. As the core constituents of piezoelectric sensors, flexible piezoelectric composite materials are commonly used due to their unique advantages, including high conformability, sensitivity, and compatibility. They have found applications in diverse domains such as underwater detection, electronic skin sensing, wearable sensors, targeted therapy, and ultrasound diagnostics for deep tissue. The advent of flexible piezoelectric composite materials has revolutionized the design concepts and application scenarios of traditional piezoelectric materials, playing a crucial role in the development of next-generation flexible electronic products. This paper reviews the research progress on flexible piezoelectric composite materials, covering their types and typical fabrication techniques, as well as their applications across various fields. Finally, a summary and outlook on the existing issues and future development of these composite materials are provided.

Systematic evaluations and integration of Assam indigenous Joha rice starch in intelligent packaging films for monitoring food freshness using beetroot extract

It is becoming increasingly important to have starch sources with different physicochemical properties to meet the needs of new applications in food, packaging, bioplastic, and pharmaceutical industries. The first part of this study dealt with the isolation of starch from culturally, geographically, nutritionally esteemed, and high-yielding Assam Joha rice. Fine and uniform particle size (6.3 ± 0.09 μm), high amylose content (28 ± 1.03 %), swelling behavior, viscoelastic rheological behavior, moderate gelatinization temperature (66 ± 1.7 °C), thermostable nature, type A crystallographic pattern with high (45 ± 3.3 %) crystallinity, and suitable microbial quality make the Joha rice derived starch physico-chemically and functionally suitable for potential applications in diverse domains. The latter part of the study focuses on one of the applications of derived starch as a suitable matrix for intelligent packaging films with the incorporation of betanin-enriched beetroot extract (BRE) as a bio-based pH sensor. The addition of 1.0 % w/v BRE to the starch film (starch-BRE III) significantly increased its functionality by reducing UV–visible light transmittance and water vapor permeability, along with enhancing flexibility and hydrophobicity due to intermolecular bonding between BRE and the starch film matrix. Moreover, starch-BRE films with different BRE concentrations were successfully used to monitor the real-time freshness of white meat (chicken and fish) and Indian cottage cheese samples. Overall, the results indicated that starch-BRE III has great potential as an intelligent packaging material for monitoring food freshness.

Regulatory Frameworks for AI-Enabled Medical Device Software in China: Comparative Analysis and Review of Implications for Global Manufacturer.

The China State Council released the new generation artificial intelligence (AI) development plan, outlining China's ambitious aspiration to assume global leadership in AI by the year 2030. This initiative underscores the extensive applicability of AI across diverse domains, including manufacturing, law, and medicine. With China establishing itself as a major producer and consumer of medical devices, there has been a notable increase in software registrations. This study aims to study the proliferation of health care-related software development within China. This work presents an overview of the Chinese regulatory framework for medical device software. The analysis covers both software as a medical device and software in a medical device. A comparative approach is employed to examine the regulations governing medical devices with AI and machine learning in China, the United States, and Europe. The study highlights the significant proliferation of health care-related software development within China, which has led to an increased demand for comprehensive regulatory guidance, particularly for international manufacturers. The comparative analysis reveals distinct regulatory frameworks and requirements across the three regions. This paper provides a useful outline of the current state of regulations for medical software in China and identifies the regulatory challenges posed by the rapid advancements in AI and machine learning technologies. Understanding these challenges is crucial for international manufacturers and stakeholders aiming to navigate the complex regulatory landscape.

Indonesian Stock Price Prediction Using Neural Basis Expansion Analysis for Interpretable Time Series Method

The stock market is an attractive investment venue for many individuals and companies. However, unexpected share price fluctuations can cause significant financial losses. In stock investment, predicting stock price movements is the most frequently discussed topic because it allows investors to make the right investment decisions to make big profits. Therefore, a model is needed to predict future stock prices, one strategy for maximising investment profits. New state-of-the-art deep learning architectures for time series forecasting are being developed yearly, making them more accurate than ever. The most commonly used network for such a solution is Long Short-Term Memory (LSTM) architecture, but it has limitations such as long training time and interpretability. This study aims to evaluate another state-of-theart solution, Neural Basis Expansion Analysis for Interpretable Time Series (N-BEATS), in comparison with LSTM by utilising historical data of PT Bank Central Asia Tbk (one of the banking companies in Indonesia) from 25 March 2013 to 21 March 2023. N-BEATS is a relatively new variable method that can produce accurate predictions using neural networks. This architecture has advantages such as interpretability, seamless applicability across diverse target domains without requiring modifications, and fast training. Based on tests carried out with prediction errors measured using the Mean Average Percentage Error (MAPE), it was found that the N-BEATS model outperformed the LSTM model with a MAPE value of 1.05 percent. In conclusion, this research shows the use of a new method of deep learning algorithms to predict stock prices, which contributes to facilitating stock buying and selling decisions by investors.

Gamification for Mental Health and Health Psychology: Insights at the First Quarter Mark of the 21st Century.

The concept of gamification, originally rooted in digital media and game design, has increasingly emerged as a pivotal element in psychology and engagement strategies. Its application spans diverse domains within healthcare, offering innovative solutions to transform patient care through behavior modification and heightened engagement. This contemporary perspective explores theoretical frameworks and provides a critical interdisciplinary examination of current advancements in gamification for mental health and health psychology, identifying existing knowledge gaps and projecting future trends and research directions within these contexts. Based on this, concise criteria for designing, implementing, and evaluating gamification in healthcare, aligned with acknowledged best practices to promote ethical, fair, and trustworthy systems, are outlined, establishing a robust theoretical framework to design gamified interventions and scientifically evaluate their impact.

Noise Aware Fully Integrated Low Power and Low Inrush Current Fast Transient Response LDO

The complexity of Systems-on-Chip (SoC) designs necessitates robust linear regulator architectures to ensure stable operations and efficient power management in modern devices. In response to this demand, low-dropout (LDO) voltage regulators have emerged as a focal point for their scalability and superior performance across diverse application domains. This paper proposes an LDO linear regulator characterized by high power supply rejection ratio (PSR) and rapid transient response. The design of this LDO emphasizes low power consumption and minimal inrush current while incorporating advanced techniques to enhance PSR and stability across varying frequencies. Despite its compact footprint and low-power profile, this LDO achieves remarkable PSR across a broad spectrum of frequencies. To achieve swift transient response, the proposed design integrates variable biasing and transient boost capacitance. The variable bias structure enhances the slew rate and PSR of the LDO, contributing to its overall performance optimization. Additionally, the strategic placement and utilization of transient-boost capacitance leverage its voltage characteristics to bolster transient response without introducing additional quiescent current, thereby further enhancing circuit stability.

AG-MSTLN-EL: A Multi-source Transfer Learning Approach to Brain Tumor Detection.

The analysis of medical images (MI) is an important part of advanced medicine as it helps detect and diagnose various diseases early. Classifying brain tumors through magnetic resonance imaging (MRI) poses a challenge demanding accurate models for effective diagnosis and treatment planning. This paper introduces AG-MSTLN-EL, an attention-aided multi-source transfer learning ensemble learning model leveraging multi-source transfer learning (Visual Geometry Group ResNet and GoogLeNet), attention mechanisms, and ensemble learning to achieve robust and accurate brain tumor classification. Multi-source transfer learning allows knowledge extraction from diverse domains, enhancing generalization. The attention mechanism focuses on specific MRI regions, increasing interpretability and classification performance. Ensemble learning combines k-nearest neighbor, Softmax, and support vector machine classifiers, improving both accuracy and reliability. Evaluating the model's performance on a dataset with 3064 brain tumor MRI images, AG-MSTLN-EL outperforms state-of-the-art models in terms of all classification measures. The model's innovative combination of transfer learning, attention mechanism, and ensemble learning provides a reliable solution for brain tumor classification. Its superior performance and high interpretability make AG-MSTLN-EL a valuable tool for clinicians and researchers in medical image analysis.

Causality-inspired Domain Expansion network for single domain generalization

Most existing single domain generalization (SDG) methods focus on extending the distribution of the training data to achieve good generalizability in the target domain by synthesizing out-of-domain data. However, in practice, the generated data are insufficient to cover all possible shifts. When the target distributions are not covered, the spurious associations between irrelevant features and labels captured by a model will deteriorate the generalization performance. To alleviate this problem, in this paper, we first analyze that augmenting the source domain with samples of new styles (e.g., different backgrounds) can mitigate spurious associations between irrelevant features and labels from a causal perspective, which can reduce the interference of non-causal features on the classification labels then propose a novel Causality-inspired Domain Expansion Network (CDEN) for SDG. Specifically, CDEN contains a domain augmentation subnetwork and a representation learning subnetwork. The former uses a special style generator module AdaIN∗ to generate out-of-domain data with the same semantic information but different styles from the source domain data to help the model capture true associations between discriminative features and labels. The latter introduces a novel class-level contrastive learning, instance-level contrastive learning, and invariant risk minimization to help the model further eliminate spurious associations. CDEN progressively generates diverse style data and learns domain invariant representations containing few spurious associations by optimizing the two subnetworks in an adversarial learning manner. We conduct extensive experiments on three benchmark datasets to evaluate the superiority of CDEN, comparing it to fifteen state-of-the-art methods.

Giant Piezoelectric Effects of Topological Structures in Stretched Ferroelectric Membranes.

Freestanding ferroelectric oxide membranes emerge as a promising platform for exploring the interplay between topological polar ordering and dipolar interactions that are continuously tunable by strain. Our investigations combining density functional theory (DFT) and deep-learning-assisted molecular dynamics simulations demonstrate that DFT-predicted strain-driven morphotropic phase boundary involving monoclinic phases manifest as diverse domain structures at room temperatures, featuring continuous distributions of dipole orientations and mobile domain walls. Detailed analysis of dynamic structures reveals that the enhanced piezoelectric response observed in stretched PbTiO_{3} membranes results from small-angle rotations of dipoles at domain walls, distinct from conventional polarization rotation mechanism and adaptive phase theory inferred from static structures. We identify a ferroelectric topological structure, termed "dipole spiral," which exhibits a giant intrinsic piezoelectric response (>320 pC/N). This helical structure, possessing a rotational zero-energy mode, unlocks new possibilities for exploring chiral phonon dynamics and dipolar Dzyaloshinskii-Moriya-like interactions.

Terpyridine-Decorated Polymer Nanosphere Latex: Template Nanocarriers for the Synthesis of Cu-CeO2 Hollow Spheres.

Water-soluble polymers with the ability to complex metal ions through complexing ligands have attracted significant interest in diverse domains, such as optical or catalyst applications. In this paper, we successfully synthesized, through a one-pot process combining polymerization-induced self-assembly and reversible addition-fragmentation chain transfer polymerization, aqueous dispersions of terpyridine-decorated poly[poly(ethylene glycol)methyl ether methacrylate]-b-poly(methyl methacrylate) (tpy-PPEGMA-b-PMMA) amphiphilic block copolymers. The in-situ formation of well-defined amphiphilic block copolymers and their self-assembly led to nanosphere latex with the hydrodynamic diameters increasing from 17 to 52 nm and the length of the copolymers increasing from 21,000 to 51,000 g·mol-1. These aqueous dispersed tpy-PPEGMA-b-PMMA nanospheres effectively complex metal ions, such as Cu2+, in a stoichiometric ratio of 2:1. Subsequently, these metal-complexed nanospheres were employed as soft template nanocarriers to control, on the nanometer scale, the dispersion of metal on a nanostructured support. This is exemplified by the synthesis of copper supported on cerium oxide hollow spheres (Cu-CeO2) using Cu2+-tpy-PPEGMA-b-PMMA as template nanocarriers and CeO2 nanoparticles. This novel assembly engineering strategy for the preparation of atomically dispersed metal on a nanostructured support was highlighted through the utilization of Cu-CeO2 hollow spheres as an electrocatalyst for the nitrate reduction reaction (NO3RR) to NH3. These encouraging outcomes emphasize the potential of metal-metal oxide-nanostructured materials to treat contaminated water sources with nitrate while allowing the green production of ammonia.

Gold-Catalyzed Lactone Synthesis: Advancements and Insights.

Lactones represent a class of fundamental structural motifs ubiquitous in nature, holding significance across diverse scientific domains such as pharmaceuticals, natural products, drug discovery, and industry. Despite their simplicity, the synthesis of lactones has garnered considerable interest due to their pivotal roles. Gold, traditionally regarded as a noble metal, has emerged as an efficient catalyst, challenging conventional perceptions. The utilization of gold in lactone synthesis has captivated researchers, leading to the development of numerous effective methodologies. Motivated by this, we present a comprehensive compilation of reports on the gold-catalyzed synthesis of lactones, encompassing literature till date.

Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology.

Advancements in imaging technologies have revolutionized our ability to deeply profile pathological tissue architectures, generating large volumes of imaging data with unparalleled spatial resolution. This type of data collection, namely, spatial proteomics, offers invaluable insights into various human diseases. Simultaneously, computational algorithms have evolved to manage the increasing dimensionality of spatial proteomics inherent in this progress. Numerous imaging-based computational frameworks, such as computational pathology, have been proposed for research and clinical applications. However, the development of these fields demands diverse domain expertise, creating barriers to their integration and further application. This review seeks to bridge this divide by presenting a comprehensive guideline. We consolidate prevailing computational methods and outline a roadmap from image processing to data-driven, statistics-informed biomarker discovery. Additionally, we explore future perspectives as the field moves toward interfacing with other quantitative domains, holding significant promise for precision care in immuno-oncology.

Robust image tamper detection and recovery with self-embedding watermarking using SPIHT and LDPC

In today’s digital landscape, the pervasive use of digital images across diverse domains has led to growing concerns regarding their authenticity and reliability. The potential for malicious manipulation of these images underscores the critical need to develop robust methods for detecting tampering and ensuring their integrity. Fragile watermarking has been found to have extensive applications for tamper detection and recovery. An image watermarking technique for tamper detection, correction, and recovery is presented in this study. The proposed method employs a self-embedding method to generate the reference watermark from the original image, which has the advantage of superior tamper detection, localization, recovery capabilities, and robustness against attacks. Set Partitioning in the Hierarchical Tree (SPIHT) algorithm is applied to generate a reference watermark of the original image. Low-Density Parity Check (LDPC) is employed for error correction, providing higher-quality reconstruction to recover the original image. Schur decomposition processed the watermarked image blocks to generate authentication bits for each block to enhance tampering detection. The proposed watermarking method was evaluated using PSNR, SSIM, BER, and NC metrics for grayscale and colored images. The technique demonstrated high robustness in tamper detection and recovery against various malicious attacks. Comparative analysis with existing methods shows the efficacy of the proposed method.

Mosaic and cocktail capsid-virus-like particle vaccines for induction of antibodies against the EPCR-binding CIDRα1 domain of PfEMP1.

The sequestration of Plasmodium falciparum-infected erythrocytes to the host endothelium is central to the pathogenesis of malaria. The sequestration is mediated by the parasite´s diverse Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1) variants, which bind select human receptors on the endothelium. Severe malaria is associated with PfEMP1 binding human endothelial protein C receptor (EPCR) via their CIDRα1 domains. Antibodies binding and inhibiting across the sequence diverse CIDRα1 domains are likely important in acquired immunity against severe malaria. In this study, we explored if immunization with AP205 bacteriophage capsid-virus-like particles (cVLPs) presenting a mosaic of diverse CIDRα1 protein variants would stimulate broadly reactive and inhibitory antibody responses in mice. Three different mosaic cVLP vaccines each composed of five CIDRα1 protein variants with varying degrees of sequence conservation of residues at and near the EPCR binding site, were tested. All mosaic cVLP vaccines induced functional antibodies comparable to those induced by matched cocktails of cVLPs decorated with the single CIDRα1 variant. No broadly reactive responses were observed. However, the vaccines did induce some cross-reactivity and inhibition within the CIDRα1 subclasses included in the vaccines, demonstrating potential use of the cVLP vaccine platform for the design of multivalent vaccines.

Impact of protein domains on the MEL2 granule, a cytoplasmic ribonucleoprotein complex maintaining faithful meiosis progression in rice.

Cytoplasmic ribonucleoprotein (RNP) granules are membraneless structures composed of various RNAs and proteins that play important roles in post-transcriptional regulation. While RNP granules are known to regulate the meiotic entry in some organisms, little is known about their roles in plants. In this study, we observed the cytoplasmic granular structures of rice RNA-binding protein MEIOSIS ARRESTED AT LEPTOTENE2 (MEL2), which contributes to the control of meiotic entry timing, in leaf protoplasts and spore mother cells. We performed colocalization analysis with known cytoplasmic RNP factors, and domain deletion analysis to assess their impact on granule formation and meiosis progression. Conservation of MEL2 domains across plant species was also explored. Our results indicated that MEL2 granules colocalized with processing body and stress granule factors. The maintenance of granule properties modulated by LOTUS domain and the intrinsically disordered region (IDR) is essential for proper MEL2 function in meiosis progression. MEL2-like proteins widely found in plant kingdom conserved LOTUS domain followed by the IDR despite their diverse domain structures, suggesting the functional conservation of these domains among plant species. This study highlights the role of MEL2 granule dynamics and its impact on meiotic transition and progression.

Jaccard-constrained dense subgraph discovery

Finding dense subgraphs is a core problem in graph mining with many applications in diverse domains. At the same time many real-world networks vary over time, that is, the dataset can be represented as a sequence of graph snapshots. Hence, it is natural to consider the question of finding dense subgraphs in a temporal network that are allowed to vary over time to a certain degree. In this paper, we search for dense subgraphs that have large pairwise Jaccard similarity coefficients. More formally, given a set of graph snapshots and input parameter α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha$$\\end{document}, we find a collection of dense subgraphs, with pairwise Jaccard index at least α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha$$\\end{document}, such that the sum of densities of the induced subgraphs is maximized. We prove that this problem is NP-hard and we present a greedy, iterative algorithm which runs in Onk2+m\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathcal {O}} \\mathopen {} \\left( nk^2 + m\\right)$$\\end{document} time per single iteration, where k is the length of the graph sequence and n and m denote number of vertices and total number of edges respectively. We also consider an alternative problem where subgraphs with large pairwise Jaccard indices are rewarded. We do this by incorporating the indices directly into the objective function. More formally, given a set of graph snapshots and a weight λ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\lambda$$\\end{document}, we find a collection of dense subgraphs such that the sum of densities of the induced subgraphs plus the sum of Jaccard indices, weighted by λ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\lambda$$\\end{document}, is maximized. We prove that this problem is NP-hard. To discover dense subgraphs with good objective value, we present an iterative algorithm which runs in On2k2+mlogn+k3n\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathcal {O}} \\mathopen {}\\left( n^2k^2 + m \\log n + k^3 n\\right)$$\\end{document} time per single iteration, and a greedy algorithm which runs in On2k2+mlogn+k3n\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathcal {O}} \\mathopen {}\\left( n^2k^2 + m \\log n + k^3 n\\right)$$\\end{document} time. We show experimentally that our algorithms are efficient, they can find ground truth in synthetic datasets and provide good results from real-world datasets. Finally, we present two case studies that show the usefulness of our problem.

From diabetes to diverse domains: the multifaceted roles of GLP-1 receptor agonists.

Glucagon-like Peptide-1 (GLP-1) receptor agonists (GLP-1RAs) emerged as a primary treatment for type-2 diabetes mellitus (T2DM), however, their multifaceted effects on various target organs beyond glycemic control opened a new era of treatment. We conducted a comprehensive literature search using databases including Scopus, Google Scholar, PubMed, and the Cochrane Library to identify clinical, in-vivo, and in-vitro studies focusing on the diverse effects of GLP-1 receptor agonists. Eligible studies were selected based on their relevance to the varied roles of GLP-1RAs in T2DM management and their impact on other physiological functions. Numerous studies have reported the efficacy of GLP-1RAs in improving outcomes in T2DM, with demonstrated benefits including glucose-dependent insulinotropic actions, modulation of insulin signaling pathways, and reductions in glycemic excursions. Additionally, GLP-1 receptors are expressed in various tissues and organs, suggesting their widespread physiological functions beyond glycemic control potentially include neuroprotective, anti-inflammatory, cardioprotective, and metabolic benefits. However, further scientific studies are still underway to maximize the benefits of GLP-1RAs and to discover additional roles in improving health benefits. This article sought to review not only the actions of GLP1RAs in the treatment of T2DM but also explore its effects on potential targets in other disorders.