Advanced Computational Methods Research Articles

The Physical and Mechanical Properties of Coral Sand

Coral sand interacts with a variety of particles and species in tropical marine habitats due to its special characteristics. particular emphasizes the benefits of using contemporary mathematical technologies to examine the characteristics and behavior of coral sand for engineering applications. Coral sand's composition and creation processes influence its distinctive qualities. Coral sand is mostly made up of the skeletal remains of tiny coral polyps and has a white or off-white appearance due to its high calcium carbonate content. Particularly in the area of geotechnical engineering, the interaction of coral sand particles is of great importance. Particle form, size distribution, and interparticle forces are a few examples of the variables that affect how coral sand behaves as a granular material. Investigations on its geotechnical characteristics, such as its shear strength, permeability, and compressibility, are the main focus of current research. To learn more about the engineering behavior of coral sand, researchers are examining field monitoring approaches and laboratory testing procedures. Additionally, research aims to comprehend how biological activity, cementation, and particle form affect coral sand's mechanical characteristics. In the coral sand study, the benefit of contemporary computation technologies is remarkable. Advanced computer methods, in combination with numerical modeling and simulation approaches, provide precise forecasting of coral sand behavior under various loading and climatic conditions. Engineers may use these technologies to examine foundation design issues, determine the stability of coastal buildings, and create methods for controlling coastal erosion. The study of coral sand and its characteristics has important ramifications for geotechnical engineering. The capacity to assess and construct engineering structures in coral sand settings is improved by the application of contemporary computation technologies.

Distribution of oxygen-containing functional groups on defective graphene: properties engineering and Li adsorption.

In this study, the distribution of oxygen-containing functional groups on graphene with vacancies and topological defects was systematically investigated using advanced computational methods and the structure models for multi-defect graphene oxides (GOs) were proposed. All potential adsorption sites were considered through an automated structure generation program to identify energetically favorable structures. Unlike the pristine graphene surface where oxygen-containing functional groups always aggregate with each other, we observed a tendency for them to preferentially adsorb near defects. Furthermore, they may also be distributed on the same side or both sides of the defective graphene. These multi-defect GOs can exhibit either metallic or semiconducting properties. Notably, upon adsorbing the same oxygen-containing functional groups onto the surface of defective graphene, their electronic characteristics become homogeneous. The coexistence of vacancy/topological defects and oxygen-containing functional groups within the graphene lattice introduces intriguing mechanical anisotropic properties to graphene, including the uncommon negative Poisson's ratio. Additionally, these materials exhibit anisotropic optical behavior, displaying heightened absorption within the infrared and visible regions compared to pristine graphene. Finally, it is found that Li atoms are adsorbed stably on the surfaces of multi-defect GOs via the formation of LinO/LimOH clusters. The research findings presented in this paper, encompassing the development of structural models for multi-defect GOs, could provide crucial insights into the properties and potential applications of graphene oxides.

Equivariant 3D-Conditional Diffusion Model for De Novo Drug Design.

De novo drug design speeds up drug discovery, mitigating its time and cost burdens with advanced computational methods. Previous work either insufficiently utilized the 3D geometric structure of the target proteins, or generated ligands in an order that was inconsistent with real physics. Here we propose an equivariant 3D-conditional diffusion model, named DiffFBDD, for generating new pharmaceutical compounds based on 3D geometric information of specific target protein pockets. DiffFBDD overcomes the underutilization of geometric information by integrating full atomic information of pockets to backbone atoms using an equivariant graph neural network. Moreover, we develop a diffusion approach to generate drugs by generating ligand fragments for specific protein pockets, which requires fewer computational resources and less generation time (65.98% ∼ 96.10% lower). DiffFBDD offers better performance than state-of-the-art models in generating ligands with strong binding affinity to specific protein pockets, while maintaining high validity, uniqueness, and novelty, with clear potential for exploring the drug-like chemical space. The source code of this study is freely available at https://github.com/haichengyi/DiffFBDD.

Influence of ion and hydration atmospheres on RNA structure and dynamics: insights from advanced theoretical and computational methods.

RNA, a highly charged biopolymer composed of negatively charged phosphate groups, defies electrostatic repulsion to adopt well-defined, compact structures. Hence, the presence of positively charged metal ions is crucial not only for RNA's charge neutralization, but they also coherently decorate the ion atmosphere of RNA to stabilize its compact fold. This feature article elucidates various modes of close RNA-ion interactions, with a special emphasis on Mg2+ as an outer-sphere and inner-sphere ion. Through examples, we highlight how inner-sphere chelated Mg2+ stabilizes RNA pseudoknots, while outer-sphere ions can also exert long-range electrostatic interactions, inducing groove narrowing, coaxial helical stacking, and RNA ring formation. In addition to investigating the RNA's ion environment, we note that the RNA's hydration environment is relatively underexplored. Our study delves into its profound interplay with the structural dynamics of RNA, employing state-of-the-art atomistic simulation techniques. Through examples, we illustrate how specific ions and water molecules are associated with RNA functions, leveraging atomistic simulations to identify preferential ion binding and hydration sites. However, understanding their impact(s) on the RNA structure remains challenging due to the involvement of large length and long time scales associated with RNA's dynamic nature. Nevertheless, our contributions and recent advances in coarse-grained simulation techniques offer insights into large-scale structural changes dynamically linked to the RNA ion atmosphere. In this connection, we also review how different cutting-edge computational simulation methods provide a microscopic lens into the influence of ions and hydration on RNA structure and dynamics, elucidating distinct ion atmospheric components and specific hydration layers and their individual and collective impacts.

Computational toolbox for the analysis of protein-glycan interactions.

Protein-glycan interactions play pivotal roles in numerous biological processes, ranging from cellular recognition to immune response modulation. Understanding the intricate details of these interactions is crucial for deciphering the molecular mechanisms underlying various physiological and pathological conditions. Computational techniques have emerged as powerful tools that can help in drawing, building and visualising complex biomolecules and provide insights into their dynamic behaviour at atomic and molecular levels. This review provides an overview of the main computational tools useful for studying biomolecular systems, particularly glycans, both in free state and in complex with proteins, also with reference to the principles, methodologies, and applications of all-atom molecular dynamics simulations. Herein, we focused on the programs that are generally employed for preparing protein and glycan input files to execute molecular dynamics simulations and analyse the corresponding results. The presented computational toolbox represents a valuable resource for researchers studying protein-glycan interactions and incorporates advanced computational methods for building, visualising and predicting protein/glycan structures, modelling protein-ligand complexes, and analyse MD outcomes. Moreover, selected case studies have been reported to highlight the importance of computational tools in studying protein-glycan systems, revealing the capability of these tools to provide valuable insights into the binding kinetics, energetics, and structural determinants that govern specific molecular interactions.

Paraherquamides - A new hope and great expectations of anthelmintic agents: Computational studies.

Nematode infections impose a significant health and economic burden, particularly as parasites develop resistance to existing treatments and evade host defenses. This study explores the efficacy of 48 paraherquamide analogs, a class of polycyclic spiro-oxindole alkaloids with unique structural features, as potential anthelmintic agents. Employing advanced computational methods, including molecular docking, MM-GBSA, and molecular dynamics simulations, we assessed the interaction of these analogs with the Ls-AchBP receptor, a model for nematode neurotransmission. Among the analogs studied, Paraherquamide K, Mangrovamide A, and Chrysogenamide A showed comparable docking and MM-GBSA scores to the native antagonist. Notably, their binding interactions exhibited slight distinction attributed to structural differences, such as the absence of a di-oxygenated 7-membered ring. Additionally, these analogs demonstrated robust binding stability in the molecular dynamic simulation studies and favorable pharmacokinetic properties in our in-silico ADME assessment. The insights gained from the study highlight the potential of these analogs as a basis for developing new therapeutics for nematode infections. The promising results from this computational analysis set the stage for subsequent in-vivo validations and pre-clinical studies, contributing to the arsenal against parasitic resistance.

Densenet-201 for Skin Melanoma Classification: A Comprehensive Performance Evaluation and Analysis

Skin cancer stands out as the predominant malignancy, necessitating prompt detection and intervention due to its potentially fatal nature. Distinguishing between cancerous and benign skin lesions poses a formidable challenge to visual assessment, underscoring the intricacy of accurate cancer detection. The inherent similarity in the appearances of various lesions further compounds the precision required for effective skin cancer identification. The rapidly evolving technological landscape, notably in the fields of machine learning and deep learning, has seen greater interest in resolving the categorization issues inherent in skin lesions. In our proposed research work, we deploy a deep learning model incorporating a pre-trained DenseNet architecture. This strategic utilization of advanced computational methods aims to enhance the discriminatory capabilities in skin cancer identification. For our research work, we used Melanoma cancer dataset, which contain 10540 dermoscopic labeled images. The study involves extensive preprocessing, including dataset inspection, label encoding, and distribution analysis. The research focuses on training DenseNet-201 architecture, evaluating its performance, and interpreting the results through various metrics.

Reproducible Research Practices in Magnetic Resonance Neuroimaging: A Review Informed by Advanced Language Models.

MRI has progressed significantly with the introduction of advanced computational methods and novel imaging techniques, but their wider adoption hinges on their reproducibility. This concise review synthesizes reproducible research insights from recent MRI articles to examine the current state of reproducibility in neuroimaging, highlighting key trends and challenges. It also provides a custom generative pretrained transformer (GPT) model, designed specifically for aiding in an automated analysis and synthesis of information pertaining to the reproducibility insights associated with the articles at the core of this review.

Agnostic-Specific Modality Learning for Cancer Survival Prediction from Multiple Data.

Cancer is a pressing public health problem and one of the main causes of mortality worldwide. The development of advanced computational methods for predicting cancer survival is pivotal in aiding clinicians to formulate effective treatment strategies and improve patient quality of life. Recent advances in survival prediction methods show that integrating diverse information from various cancer-related data, such as pathological images and genomics, is crucial for improving prediction accuracy. Despite promising results of existing approaches, there are great challenges of modality gap and semantic redundancy presented in multiple cancer data, which could hinder the comprehensive integration and pose substantial obstacles to further enhancing cancer survival prediction. In this study, we propose a novel agnostic-specific modality learning (ASML) framework for accurate cancer survival prediction. To bridge the modality gap and provide a comprehensive view of distinct data modalities, we employ an agnostic-specific learning strategy to learn the commonality across modalities and the uniqueness of each modality. Moreover, a cross-modal fusion network is exerted to integrate multimodal information by modeling modality correlations and diminish semantic redundancy in a divide-and-conquer manner. Extensive experiment results on three TCGA datasets demonstrate that ASML reaches better performance than other existing cancer survival prediction methods for multiple data.

Deep Learning for the Accurate Prediction of Triggered Drug Delivery.

The need to mitigate the adverse effects of chemotherapy has driven the exploration of innovative drug delivery approaches. One emerging trend in cancer treatment is the utilization of Drug Delivery Systems (DDSs), facilitated by nanotechnology. Nanoparticles, ranging from 1 nm to 1000 nm, act as carriers for chemotherapeutic agents, enabling precise drug delivery. The triggered release of these agents is vital for advancing this novel drug delivery system. Our research investigated this multifaceted delivery capability using liposomes and metal organic frameworks as nanocarriers and utilizing all three targeting techniques: passive, active, and triggered. Liposomes are modified using targeting ligands to render them more targeted toward certain cancers. Moieties are conjugated to the surfaces of these nanocarriers to allow for their binding to receptors overexpressed on cancer cells, thus increasing the accumulation of the agent at the tumor site. A novel class of nanocarriers, namely metal organic frameworks, has emerged, showing promise in cancer treatment. Triggering techniques (both intrinsic and extrinsic) can be used to release therapeutic agents from nanoparticles, thus enhancing the efficacy of drug delivery. In this study, we develop a predictive model combining experimental measurements with deep learning techniques. The model accurately predicts drug release from liposomes and MOFs under various conditions, including low- and high-frequency ultrasound (extrinsic triggering), microwave exposure (extrinsic triggering), ultraviolet light exposure (extrinsic triggering), and different pH levels (intrinsic triggering). The deep learning-based predictions significantly outperform linear predictions, proving the utility of advanced computational methods in drug delivery. Our findings demonstrate the potential of these nanocarriers and highlight the efficacy of deep learning models in predicting drug release behavior, paving the way for enhanced cancer treatment strategies.

Start Generating: Harnessing Generative Artificial Intelligence for Sociological Research

How can generative artificial intelligence (GAI) be used for sociological research? The author explores applications to the study of text and images across multiple domains, including computational, qualitative, and experimental research. Drawing upon recent research and stylized experiments with DALL·E and GPT-4, the author illustrates the potential applications of text-to-text, image-to-text, and text-to-image models for sociological research. Across these areas, GAI can make advanced computational methods more efficient, flexible, and accessible. The author also emphasizes several challenges raised by these technologies, including interpretability, transparency, reliability, reproducibility, ethics, and privacy, as well as the implications of bias and bias mitigation efforts and the trade-offs between proprietary models and open-source alternatives. When used with care, these technologies can help advance many different areas of sociological methodology, complementing and enhancing our existing toolkits.

Quantum Biochemistry: Unveiling the Quantum Coherence in Enzyme Catalysis

Objective: This study investigates the influence of quantum coherence in enzyme catalysis, aiming to elucidate its role in biochemical processes. The primary objective is to unravel quantum effects within various enzymes (A-F) involved in crucial biochemical pathways. Methodology: Employing a multidisciplinary approach, advanced experimental techniques and computational methods were utilized. Quantum tunneling rates were measured through Reaction Progress Kinetic Analysis (RPKA) with High-Performance Liquid Chromatography (HPLC). Femtosecond-resolved spectroscopy captured quantum coherence times, while Two-Dimensional Infrared Spectroscopy (2D IR) probed vibrational coupling. Ultrafast Laser Spectroscopy provided insights into enzyme dynamics. Density Functional Theory (DFT) calculations and Ab Initio Simulations complemented experimental findings. Results: The results reveal distinct quantum signatures across all enzymes. Notably, Enzyme A demonstrates a quantum tunneling rate of 3.2 x 10^-2 s^-1. Quantum coherence times in Enzyme B showcase unprecedented femtosecond scales, while other enzymes exhibit diverse behaviors. DFT calculations for Enzyme E predict a 30% reduction in energy barriers. Ab Initio Simulations of Enzyme F unveil persistent entanglement states. Conclusion: The observed quantum phenomena suggest a profound interplay between quantum coherence and enzyme catalysis, emphasizing the enzyme-specific nature of quantum effects. The implications of energy barrier reduction and entanglement states provide insights into potential quantum-assisted catalytic mechanisms.

The Evaluation and Development of University English Teaching Quality Based on Wireless Network Artificial Intelligence

This study introduces a novel approach to address deficiencies in prior teaching quality assessment systems by establishing a mathematical model for evaluation. Utilizing a neural network trained via a particle swarm optimization algorithm (PSO), the method develops a BP (Backpropagation) model fine-tuned by PSO to capture the intricate relationships among diverse indicators influencing teachers’ teaching quality assessment and resulting evaluations. Empirical findings highlight the effectiveness of artificial neural networks in constructing a comprehensive evaluation framework accommodating a wide spectrum of systematic assessments. This approach not only optimizes teaching methodologies but also augments overall teaching efficacy and the quality of educational delivery in a holistic manner. Moreover, it fosters the cultivation of multifaceted individuals proficient in English application skills, contributing to the development of high-quality talent in practical and complex domains. The convergence of advanced mathematical modeling techniques and computational methods, alongside the utilization of numerous indicators, aligns with combinatorial principles, exploring the permutations and relationships of diverse factors impacting teaching quality assessment.

Numerical Analysis of Aluminium Façade Components

This paper commences with a scientific literature review of current research that underlines the environmental benefits to be gained from using smaller quantities of raw materials in the construction industry, with particular emphasis on a sustainable approach to façade design. Life cycle assessment modelling is advocated to validate the sustainability of building structures to achieve optimal solutions. A real-life application of the design of an aluminium façade bracket is presented, demonstrating that a weight reduction of up to 35-45% is attainable by exploiting the post-elastic properties of a material. The work described ranges from a discussion of the current conventional numerical techniques adopted by the industry to the most recent and advanced computational methods permitted by the introduction of Eurocode 9. This code facilitates a substantial enhancement in structural performance by incorporating an evaluation of the material's elastic-hardening behaviour and allows for a noteworthy reduction in component size and increased geometric design flexibility.

A novel method leveraging time series data to improve subphenotyping and application in critically ill patients with COVID-19

Computational subphenotyping, a data-driven approach to understanding disease subtypes, is a prominent topic in medical research. Numerous ongoing studies are dedicated to developing advanced computational subphenotyping methods for cross-sectional data. However, the potential of time-series data has been underexplored until now. Here, we propose a Multivariate Levenshtein Distance (MLD) that can account for address correlation in multiple discrete features over time-series data. Our algorithm has two distinct components: it integrates an optimal threshold score to enhance the sensitivity in discriminating between pairs of instances, and the MLD itself. We have applied the proposed distance metrics on the k-means clustering algorithm to derive temporal subphenotypes from time-series data of biomarkers and treatment administrations from 1039 critically ill patients with COVID-19 and compare its effectiveness to standard methods. In conclusion, the Multivariate Levenshtein Distance metric is a novel method to quantify the distance from multiple discrete features over time-series data and demonstrates superior clustering performance among competing time-series distance metrics.

Analysis of Shallow Water Equation Based on Tsunami Simulations: Evidence from the Pacific Ocean

Tsunamis, particularly prevalent in the Pacific Ocean's "Ring of Fire" region, present both a scientific intrigue and a societal concern due to their potential for devastation. Central to understanding and predicting these phenomena is the Shallow Water Equations (SWEs), which describe the horizontal motion of water waves. This study delves into the specific behaviors of tsunamis in the Pacific coast, utilizing comprehensive oceanographic and seismological data from the Pacific Oceanographic Institute (POI) spanning two decades. Through the lens of the SWEs, the impacts of bathymetric features and coastal topography on tsunamis are analyzed from the perspective of propagation, wave period, and frequency. Findings highlighted the significant role of solitons or solitary waves and the destructive force they exert, especially in shallow waters. While the SWE-based models have greatly assisted in developing real-time warning systems, reducing fatalities and damages, they also present certain limitations, e.g., assuming a flat seafloor and neglecting factors such as Earth's rotation, vertical fluid motion, and real-world marine conditions like turbulence. The study underscores the need for continuous refinement of these models, emphasizing the integration of observational data with advanced computational methods to enhance tsunami prediction and preparedness.

Epidermis lesion detection via optimized distributed capsule neural network

Skin cancer, encompassing various forms such as melanoma, basal cell carcinoma, and others, remains a significant global health concern, often proving fatal if not diagnosed and treated in its early stages. The challenge of accurately diagnosing skin cancer, particularly melanoma, persists even for experienced dermatologists due to the intricate and unpredictable nature of its symptoms. To address the need for more accurate and efficient skin cancer detection, a novel Golden Hawk Optimization-based Distributed Capsule Neural Network (GHO-DCaNN) is proposed. This novel technique leverages advanced computational methods to improve the reliability and precision of skin cancer diagnosis. An optimized clustering-based segmentation approach is introduced, integrating the innovative Sewer Shad Fly Optimization (SSFO), which combines elements of both mayfly and moth flame optimization. This integration enhances the accuracy of lesion boundary delineation and feature extraction. The core of the innovation lies in the optimized distributed capsule neural network, which is trained using the Hybrid GHO. This optimizer, inspired by the behaviors of the golden eagle and fire hawk, ensures the effectiveness of epidermis lesion detection, pushing the boundaries of skin cancer diagnosis methods. The achievements based on the metrics, like specificity, sensitivity, and accuracy show 97.53%, 99.05%, and 98.83% for 90% of training and 97.83%, 99.50%, and 99.06% for k-fold of 10, respectively.

Mapping the Evolution of Intrusion Detection in Big Data: A Bibliometric Analysis

This study provides a comprehensive analysis of the dynamic amalgamation of intrusion detection and big data, revealing trends and patterns within cybersecurity research. The investigation reveals a notable surge in scholarly output from 2018 onwards, reflecting heightened interest and exploration within the field. Dominant themes such as "intrusion detection," "big data," and "machine learning" underscore the integration of security concerns with advanced technologies. Geographical influences showcase diverse contributions, with varying citation impacts from countries like India, China, and Saudi Arabia. Author contributions reveal a balance between prolific authors and impactful contributions from authors with fewer publications. Recommendations include fostering interdisciplinary collaborations, integrating advanced computational methods, and conducting longitudinal studies to gauge sustained impacts. This research underscores collaboration dynamics, thematic evolution, and global influences as pivotal facets within the realm of intrusion detection and big data, guiding future research to fortify digital security in an ever-evolving technological landscape.

Autonomous and intelligent optical tweezers for improving the reliability and throughput of single particle analysis

Optical tweezers are an interesting tool to enable single cell analysis, especially when coupled with optical sensing and advanced computational methods. Nevertheless, such approaches are still hindered by system operation variability, and reduced amount of data, resulting in performance degradation when addressing new data sets. In this manuscript, we describe the deployment of an automatic and intelligent optical tweezers setup, capable of trapping, manipulating, and analyzing the physical properties of individual microscopic particles in an automatic and autonomous manner, at a rate of 4 particle per min, without user intervention. Reproducibility of particle identification with the help of machine learning algorithms is tested both for manual and automatic operation. The forward scattered signal of the trapped PMMA and PS particles was acquired over two days and used to train and test models based on the random forest classifier. With manual operation the system could initially distinguish between PMMA and PS with 90% accuracy. However, when using test datasets acquired on a different day it suffered a loss of accuracy around 24%. On the other hand, the automatic system could classify four types of particles with 79% accuracy maintaining performance (around 1% variation) even when tested with different datasets. Overall, the automated system shows an increased reproducibility and stability of the acquired signals allowing for the confirmation of the proportionality relationship expected between the particle size and its friction coefficient. These results demonstrate that this approach may support the development of future systems with increased throughput and reliability, for biosciences applications.

A Comprehensive Study on the Role of Machine Learning in 5G Security: Challenges, Technologies, and Solutions

Fifth-generation (5G) mobile networks have already marked their presence globally, revolutionizing entertainment, business, healthcare, and other domains. While this leap forward brings numerous advantages in speed and connectivity, it also poses new challenges for security protocols. Machine learning (ML) and deep learning (DL) have been employed to augment traditional security measures, promising to mitigate risks and vulnerabilities. This paper conducts an exhaustive study to assess ML and DL algorithms’ role and effectiveness within the 5G security landscape. Also, it offers a profound dissection of the 5G network’s security paradigm, particularly emphasizing the transformative role of ML and DL as enabling security tools. This study starts by examining the unique architecture of 5G and its inherent vulnerabilities, contrasting them with emerging threat vectors. Next, we conduct a detailed analysis of the network’s underlying segments, such as network slicing, Massive Machine-Type Communications (mMTC), and edge computing, revealing their associated security challenges. By scrutinizing current security protocols and international regulatory impositions, this paper delineates the existing 5G security landscape. Finally, we outline the capabilities of ML and DL in redefining 5G security. We detail their application in enhancing anomaly detection, fortifying predictive security measures, and strengthening intrusion prevention strategies. This research sheds light on the present-day 5G security challenges and offers a visionary perspective, highlighting the intersection of advanced computational methods and future 5G security.