Application Domains Research Articles

ChatCAD+: Toward a Universal and Reliable Interactive CAD Using LLMs.

The integration of Computer-Aided Diagnosis (CAD) with Large Language Models (LLMs) presents a promising frontier in clinical applications, notably in automating diagnostic processes akin to those performed by radiologists and providing consultations similar to a virtual family doctor. Despite the promising potential of this integration, current works face at least two limitations: (1) From the perspective of a radiologist, existing studies typically have a restricted scope of applicable imaging domains, failing to meet the diagnostic needs of different patients. Also, the insufficient diagnostic capability of LLMs further undermine the quality and reliability of the generated medical reports. (2) Current LLMs lack the requisite depth in medical expertise, rendering them less effective as virtual family doctors due to the potential unreliability of the advice provided during patient consultations. To address these limitations, we introduce ChatCAD+, to be universal and reliable. Specifically, it is featured by two main modules: (1) Reliable Report Generation and (2) Reliable Interaction. The Reliable Report Generation module is capable of interpreting medical images from diverse domains and generate high-quality medical reports via our proposed hierarchical in-context learning. Concurrently, the interaction module leverages up-to-date information from reputable medical websites to provide reliable medical advice. Together, these designed modules synergize to closely align with the expertise of human medical professionals, offering enhanced consistency and reliability for interpretation and advice. The source code is available at GitHub.

Ready-to-use Models Built Using a Diverse Set of 266 Aroma Compounds for the Estimation of Gas Chromatographic Retention Indices for the 50%-Cyanopropylphenyl-50%-Dimethylpolysiloxane Stationary Phase.

Retention index prediction based on the molecule structure is not often used in practice due to low accuracy, the need to use paid software to calculate molecular descriptors (MD), and the narrow applicability domain of many models. In recent years, relatively accurate and versatile deep learning (DL)-based models have emerged. These models are now used in practice as an additional criterion in gas chromatography-mass spectrometry identification. The DB-225ms stationary phase (usually described as 50%-cyanopropylphenyl-50%-dimethylpolysiloxane in available sources) is widely used, but ready-to-use retention index estimation models are not available for it. This study presents such models. The models are linear and use simple constitutional MD and retention indices predicted by DL for the DB-WAX and DB-624 stationary phases as MD (we show that it is their use that allows us to achieve satisfactory accuracy). The accuracy obtained for a completely unseen hold-out test set: root mean square error 73.2; mean absolute error 45.7; median absolute error 22.0. The models were trained using a retention data set of 266 volatile compounds. All calculations can be performed using the convenient open-source software CHERESHNYA. The final equations are implemented as a spreadsheet and a code snippet and are available online: https://doi.org/10.6084/m9.figshare.26800789.

A novel “reinforcing-foaming-recovering” strategy for achieving thermally insulating green foams with ultrafast degradation

Lightweight porous materials have garnered increasing attention in application domains of intelligent construction and flexible thermal insulation, owing to their customizable 3D porous structures and outstanding performances. Nonetheless, the severe environmental pollution resulting from the accumulation of non-degradable plastic waste in the ecosystem has sparked significant interest in developing bio-degradable thermal insulation foams. The preparation of biodegradable foam, however, typically requires operating specialized equipment for long periods under extreme conditions, which diminishes its environmental friendliness as claimed. Herein, we present a pioneering "Reinforcing-Foaming-Recovering" strategy to achieve ultralight and recyclable green foams with excellent thermal insulation properties. This innovative strategy, involving materials reinforcing and low-pressure foaming complemented by nitrogen-assisted gas recovering enables the preparation of low-density (0.08 g/cm3) biodegradable foams with restricted shrinkage ratio (<5%). Thanks to the easy-to-control foaming method, combined with the improved 3D network cellular structures caused by nitrogen-assisted gas exchange, the achieved green foams exhibit favorable thermal insulation (39.8 mW/m·K) and superior biodegradation efficiency (11.5%/2 months). The approach of energy-efficient nitrogen-assisted low-pressure foaming of degradable polymers offers a promising and practical solution for producing eco-friendly and multifunctional porous materials with economic advantages.

Upcycling Industrial Biomass Wastes Into Aerogels Using Zinc Chloride Salt Hydrates

AbstractEnd‐of‐life paper products, including food packages and rejected fibers from the paper industry, are unrecyclable biomass wastes and thus generally landfilled or incinerated. One of the major obstacles to their recycling is the existence of impurities besides cellulose fiber in the biomass wastes. In this study, a fabrication method is investigated to upcycle biomass wastes directly into high‐performance aerogels without separation of impurities. Surprisingly, this study observes that residual impurities participate in cross‐linking reactions for the aerogel formation. In this study, zinc chloride salt hydrate is applied to convert biomass wastes to aerogel via a dissolution‐regeneration process. The fabricated aerogels exhibited high water absorption capacity (15 times its weight), as well as comparable mechanical strength and thermal insulation performance to the reported cellulose aerogels. In addition, the impurities (i.e., calcium‐based inorganic salt) assisted in the cross‐linking of the cellulose network for the aerogel formation. The scanning electron microscopy (SEM) image of the aerogel generated from the rejected fibers showed a honeycomb inner structure. The rejected fiber aerogels also demonstrated a high compressive modulus of 51 MPa and a low thermal conductivity of 0.029 W m−1 K−1. The results for water absorption and thermal insulation suggest excellent potential across various application domains.

In silico aquatic toxicity prediction of chemicals toward Daphnia magna and fathead minnow using Monte Carlo approaches

The fast-increasing use of chemicals led to large numbers of chemical compounds entering the aquatic environment, raising concerns about their potential effects on ecosystems. Therefore, assessment of the ecotoxicological features of organic compounds on aquatic organisms is very important. Daphnia magna and Fathead minnow are two aquatic species that are commonly tested as standard test organisms for aquatic risk assessment and are typically chosen as the biological model for the ecotoxicology investigations of chemical pollutants. Herein, global quantitative structure–toxicity relationship (QSTR) models have been developed to predict the toxicity (pEC(LC)50) of a large dataset comprising 2106 chemicals toward Daphnia magna and Fathead minnow. The optimal descriptor of correlation weights (DCWs) is calculated using the notation of simplified molecular input line entry system (SMILES) and is used to construct QSTR models. Three target functions, TF1, TF2, and TF3 are utilized to generate 12 QSTR models from four splits, and their statistical characteristics are also compared. The designed QSTR models are validated using both internal and external validation criteria and are found to be reliable, robust, and excellently predictive. Among the models, those generated using the TF3 demonstrate the best statistical quality with R 2 values ranging from 0.9467 to 0.9607, Q 2 values ranging from 0.9462 to 0.9603 and RMSE values ranging from 0.3764 to 0.4413 for the validation set. The applicability domain and the mechanistic interpretations of generated models were also discussed.

Using the super-learner to predict the chemical acute toxicity on rats

With the rapid increase in the number of commercial chemicals, testing methods regarding on median lethal dose (LD50) relying animal experiments face challenges such as high costs and ethical concerns. Classical quantitative structure-activity relationship models relying on single algorithm always lack interpretability and precision, given the complexity of the mechanisms underlying acute toxicity. To address these issues, this study has developed a predictive framework using an ensemble learning model based on Super-learner. Particularly, we first obtained LD50 data for 9843 compounds and constructed 16 meta models using 4 molecular descriptors and machine learning algorithms. The Super-learner model performed well, achieving R² values of 0.61 and 0.64 in five-fold cross-validation and test sets, respectively, with corresponding root mean square errors of 0.55 and 0.64, significantly outperforming the results of individual model. Additionally, we incorporated data filtering and applicability domain methods, which demonstrated that the Super-learner can mitigate the impact of dataset noise to some extent. The model achieved an R² of 0.76 within an applicability domain, ensuring prediction accuracy within the chemical space. Compared to previous studies, the model developed here using Super-learner generally achieved better performance across a larger applicability domain. Finally, we has launched an online tool (http://sltox.hhra.net), allowing users to quickly predict LD50 of compounds, greatly simplifying the chemical safety assessment process. This study not only provides an effective and cost-efficient method for predicting chemical toxicity but also offers technical support and data for risk assessments of chemicals.

In‐Silico Design of Novel Indole‐Selenide Derivatives as Potential P‐Glycoprotein Inhibitors Against Multi‐Drug Resistance in MCF‐7/ADR Cells: 2D‐QSAR, Molecular Docking, and Dynamics Simulations

AbstractIn this work, a set of thirty‐one novel indole‐selenide derivatives (C1–C31), reported recently as P‐glycoprotein inhibitors against multi‐drug resistance in MCF‐7/ADR cells, have been computationally investigated for the first time by in‐silico approaches, combining 2D‐quantitative structure‐activity relationship based virtual screening (2D‐QSAR‐VS) model, molecular docking, and dynamics simulations. The in‐silico study aims to design new potent molecules with higher anticancer inhibitory activity than observed with in‐vitro assays. The 2D‐QSAR model is built using multiple linear regression (MLR) techniques, and cross‐validated by internal and external parameters, applicability domain (AD) analysis, and Y‐randomization tests, corroborating the Golbreikh and Tropsha criteria. Subsequently, virtual screening was performed on the generated database, considering higher pIC50 values than the most effective in‐vivo C27 inhibitor. Subsequently, molecular docking and dynamics simulations were applied on the selected higher‐scoring ligands showing the best interactions with the PHE643, TYR745, and PRO571 amino acids of the P‐gp receptor (7 A6E), predicting dynamically stable complexes at the time‐scale of 200 ns. The in‐silico outcomes indicate that the selected new ligands have shown promising inhibitory activity for future anti‐cancer therapies, with perspective validating by in‐vitro and in‐vivo studies.

Microblog‐Assisted Synthesis of Schiff Base Network Polymers for SO2/Epoxide Copolymerization and Thermal Degradation Kinetics of the Products

AbstractThis study successfully prepared three environmentally friendly Schiff base network (SNW) polymers, SNW‐1, SNW‐2, and SNW‐3, using melamine and aldehyde compounds as raw materials through a microwave‐assisted synthesis method. The SNW materials were applied to catalyze the copolymerization reaction of SO2 and epoxides. Through detailed analysis of the chemical structure and properties of SNW‐1, SNW‐2, and SNW‐3, their catalytic efficiency was thoroughly investigated. Three thermal degradation kinetic models, Flynn–Wall–Ozawa (FWO), Kissinger, and Coats–Redfern, were employed to calculate the kinetic parameters of the thermal degradation of poly(cyclohexene sulfite) (PCS). Potential thermal degradation mechanisms were speculated. Additionally, the Kissinger model accurately described the thermal degradation kinetics features of PCS. Based on experimental results and kinetic analysis, this paper elucidates the potential mechanism of the copolymerization reaction of SO2 and epoxides. The study indicates that microwave‐assisted synthesis exhibits convenience and high efficiency in preparing efficient catalysts, demonstrating significant potential across various application domains.

Nambu-Goto string as a higher-derivative Liouville theory

I propose a generalization of the Liouville action which corresponds to the Nambu-Goto string like the usual Liouville action corresponds to the Polyakov string. The two differ by higher-derivative terms which are negligible classically but revive quantumly. An equivalence with the four-derivative action suggests that the Nambu-Goto string in four dimensions can be described by the (4,3) minimal model analogously to the critical Ising model on a dynamical lattice. While critical indices are the same as in the usual Liouville theory, the domain of applicability becomes broader. Published by the American Physical Society 2024

Modeling rate of penetration using hybridization of Artificial Neural Network and Artificial Fish Swarm algorithm

This study aims to develop a robust and efficient approach for predicting the Rate of Penetration (ROP) by integrating the Artificial Fish Swarm Algorithm (AFSA) with the Back Propagation (BP) algorithm in an Artificial Neural Network (ANN). The integration of AFSA with Back Propagation is the primary novelty of this work, as it seeks to achieve optimal weight and bias values for the ANN, thereby enhancing prediction accuracy. A dataset of 1944 data points from a vertical well in southern Algeria was utilized. The performance of the developed models was evaluated using correlation coefficient and root mean square error (RMSE). The results demonstrated that the AFSA-ANN model significantly outperforms conventional ANN, Support Vector Regression (SVR), Random Forest regressor (RFR), and Bourgoyne and Young’s model in terms of accuracy. Additionally, the application of leverage method revealed that only 0.98% of the data falls outside the model’s applicability domain, indicating that the data and the model are statistically valid and reliable. The findings of this study have significant implications for the oil and gas field, providing a more accurate and reliable method for predicting ROP, which can lead to more efficient drilling operations and reduced costs.

Survey on haptic technologies for virtual reality applications during COVID-19

&lt;p&gt;This paper presents a comprehensive survey on the advancements and applications of haptic technologies, which are methods that facilitate the sense of touch and movement, in virtual reality (VR) during the COVID-19 pandemic. It aims to identify and classify the various domains in which haptic technologies have been utilized or can be adapted to combat the unique challenges posed by the pandemic or public health emergencies in general. Existing reviews and surveys that concentrate on the applications of haptic technologies during the Covid-19 pandemic are often limited to specific domains; this survey strives to identify and consolidate all application domains discussed in the literature, including healthcare, medical training, education, social communication, and fashion and retail. Original research and review articles were collected from the Web of Science Core Collection as the main source, using a combination of keywords (like ‘haptic’, ‘haptics’, ‘touch interface’, ‘tactile’, ‘virtual reality’, ‘augmented reality’, 'Covid-19', and ‘pandemic’) and Boolean operators to refine the search and yield relevant results. The paper reviews various haptic devices and systems and discusses the technological advancements that have been made to offer more realistic and immersive VR experiences. It also addresses challenges in haptic technology in VR, including fidelity, ethical, and privacy considerations, and cost and accessibility issues.&lt;/p&gt;

Atomic Layer Processing (ALP): Ubi es et Quo Vadis?

AbstractAtomic Layer Processing (ALP) techniques have transformed materials engineering by enabling atomic/molecular‐level control over composition, fidelity in structure replication, and properties. Tracing its origins to pioneering molecular layering and atomic layer deposition work in the mid‐20th century, this multifaceted field has remarkably diversified to include molecular layer deposition (MLD), atomic layer etching (ALE), area‐selective deposition (ASD), and vapor‐phase infiltration (VPI) processes. ALP is making great impacts across diverse disciplines – facilitating semiconductor miniaturization through ultrathin dielectric films, improving battery materials and engineering catalysts for energy applications, creating bioactive surfaces for advanced biomaterials, and promoting sustainable membranes for environmental remediation. As ALP techniques continue evolving through integration with additive manufacturing, machine learning, and in situ diagnostics, new frontiers in materials design are emerging, driven by the growing focus on environmental considerations like renewable precursors, energy‐efficient processes, and waste minimization. This perspective article examines ALP's historical development, highlights current state‐of‐the‐art applications across selected fields, and provides insights into the anticipated future trajectory, emerging application domains, and the pivotal role of academic‐industry‐research laboratory collaborations in catalyzing ALP innovations and facilitating its widespread adoption as a transformative manufacturing platform.

Histone Methylation-Mediated Reproductive Toxicity to Consumer Product Chemicals in Caenorhabditis elegans: An Epigenetic Adverse Outcome Pathway (AOP).

The significance of histone methylation in epigenetic inheritance underscores its relevance to disease and the chronic effects of environmental chemicals. However, limited evidence of the causal relationships between chemically induced epigenetic changes and organismal-level effects hinders the application of epigenetic markers in ecotoxicological assessments. This study explored the contribution of repressive histone marks to reproductive toxicity induced by chemicals in consumer products in Caenorhabditis elegans, applying the adverse outcome pathway (AOP) framework. Triclosan (TCS) and tetrabromobisphenol A (TBBPA) exposures caused reproductive toxicity and altered histone methyltransferase (HMT) and histone demethylase (HDM) activities, increasing the level of trimethylation of H3K9 and H3K27. Notably, treatment with an H3K27-specific HMT inhibitor alleviated reproductive defects and the transcriptional response of genes related to vitellogenin, xenobiotic metabolism, and oxidative stress. Comparison of points of departure (PODs) based on calculated benchmark concentrations (BMCs) revealed the sensitivity of histone-modifying enzyme activities to these chemicals. Our findings suggest that the 'disturbance of HMT and HDM' can serve as the molecular initiating event (MIE) leading to reproductive toxicity in the epigenetic AOP for TCS and TBBPA. The study extended the biological applicability of these enzymes by identifying model species with analogous protein sequences and functions. This combined approach enhances the essentiality, empirical support, and taxonomic domain of applicability (tDOA), which are crucial considerations for ecotoxicological AOPs. Given the widespread use and environmental distribution of chemicals in consumer products, this study proposes histone-modifying enzyme activity as an effective screening tool for reproductive toxicants and emphasizes the integration of epigenetic mechanisms into a prospective ERA.

Low Resource Summarization using Pre-trained Language Models

With the advent of Deep Learning-based Artificial Neural Network models, Natural Language Processing (NLP) has witnessed significant improvements in textual data processing in terms of its efficiency and accuracy. However, the research is mostly restricted to high-resource languages such as English, and low-resource languages still suffer from a lack of available resources in terms of training datasets as well as models with even baseline evaluation results. Considering the limited availability of resources for low-resource languages, we propose a methodology for adapting self-attentive transformer-based architecture models (mBERT, mT5) for low-resource summarization, supplemented by the construction of a new baseline dataset (76.5k article, summary pairs) in a low-resource language, Urdu. Choosing news (a publicly available source) as the application domain has the potential to make the proposed methodology useful for reproducing in other languages with limited resources. Our adapted summarization model urT5 with up to 44.78% reduction in size as compared to mT5 can capture contextual information of the low-resource language effectively with an evaluation score (up to 46.35 ROUGE-1, 77 BERTScore) on par with state-of-the-art models in the high-resource language of English (PEGASUS: 47.21, BART: 45.14 on XSUM Dataset) . The proposed method provided a baseline approach toward extractive as well as abstractive summarization with competitive evaluation results in a limited resource setup.

QSPR modeling to predict surface tension of psychoanaleptic drugs using the hybrid DA-SVR algorithm

A robust Quantitative Structure-Property Relationship (QSPR) model was presented to predict the surface tension property of psychoanaleptic (psychostimulant and antidepressant) drugs. A dataset of 112 molecules was utilized, and three feature selection methods were applied: genetic algorithm combined with Ordinary Least Squares (GA-OLS), Partial Least Squares (GA-PLS), and Support Vector Machines (GA-SVM), each identifying ten pertinent AlvaDesc descriptors. The models were constructed using the Dragonfly Algorithm combined with the Support Vector Regressor (DA-SVR), with the GA-SVM-based model emerging as the top performer. Rigorous statistical metrics validate its superior predictive accuracy (R2 = 0.98142, Q2LOO = 0.98142, RMSE = 1.12836, AARD = 0.78746). Furthermore, an external test set of ten compounds was employed for model validation and extrapolation, along with assessing the applicability domain, further underscoring the model’s reliability. The selected descriptors (X0Av, VE1sign_B(e), ATSC1e, MATS6v, P_VSA_ppp_A, TDB01u, E1s, R2m+, N-067, SssO) collectively elucidate the key structural factors influencing surface tension in the studied drugs. The model provides excellent predictions and can be used to determine the surface tension of new psychoanaleptic drugs. Its outcomes will guide the design of novel medications with targeted surface tension properties.

Empirical estimation of metal powder bed fusion technological improvement rate

AbstractThis study empirically estimates the technological improvement rate (TIR) of metal powder bed fusion (PBF) technology, widely used in aerospace, automotive, and medical industries. PBF's continuous long-term adoption growth is driven by its ability to enhance manufacturing efficiency in terms of time and raw material use, as well as its capability to produce high-quality, high-strength, complex-shaped parts. Measuring the technological development of PBF is crucial as itis enlarging its application domain and is increasingly considered a viable alternative to traditional manufacturing technologies across a broader range of applications. We resorted to the literature to collect information and assess which technical parameters are most relevant to measure the capabilities of PBF. With those, we established an ideal functional performance metric (FPM) capable of comprehensively assessing PBF's technological performance improvement. Considering all available data sources and PBF machines ever made commercially available, a data set of technical parameters was constructed. This was followed by a data curation process focusing on data availability and reliability. The resultant practical FPM was used to estimate the TIR of PBF technology. By employing regression analysis, we estimate a yearly improvement of 26.8%. This empirical rate comes as a more accurate and reliable substitute to the previously indirectly estimated patent-derived rate of 33.3%. Our findings underscore PBF's capability of keeping pace with its growing significance and wider industrial applications. The results of this study provide a key metric for those in the industry and research, confirming the rapid performance growth and establishing a standard for future industrial uses.

Access Control Verification in Smart Contracts Using Colored Petri Nets

This paper presents an approach for the verification of access control in smart contracts written in the Digital Asset Modeling Language (DAML). The approach utilizes Colored Petri Nets (CPNs) and their analysis tool CPN Tools. It is a model-driven-based approach that employs a new meta-model for capturing access control requirements in DAML contracts. The approach is supported by a suite of tools that fully automates all of the steps: parsing DAML code, generating DAML model instances, transforming the DAML models into CPN models, and model checking the generated CPN models. The approach is tested using several DAML scripts involving access control extracted from different domains of blockchain applications.

Multi-Camera Lighting Estimation for Mobile Augmented Reality

Lighting estimation is an important and long-standing task in computer vision and graphics communities[1]. Over the past few years, we have observed a rising demand for creating immersive user experiences in mobile augmented reality (AR) applications. In particular, environment lighting estimation is essential to render visually coherent virtual objects that need to be overlaid on physical scenes in mobile AR. In this work, we explore the key research questions in lighting understanding for an emerging application domain, mobile AR on multi-camera handheld devices, particularly try-on applications where end users leverage handheld mobile devices, such as smartphones, to overlay products of interest on their faces. Mobile AR try-on apps promote online shopping and often require photorealism to provide user experiences on par with physical try-on.

Recommender Systems Applications: Data Sources, Features, and Challenges

In recent years, there has been growing interest in recommendation systems, which is matched by their widespread adoption across various sectors. This can be attributed to their effectiveness in reducing an avalanche of data into individualized information that is meaningful, relevant, and can easily be absorbed by a single person. Several studies have recently navigated the landscape of recommendation systems, attending to their approaches, challenges, and applications, as well as the evaluation metrics necessary for effective implementation. This systematic review investigates the understudied aspects of recommendation systems, including the data input into the systems and their features or outputs. The data in (input) and data out (features) are both diverse and vary significantly from not just one application domain to another, but also from one application use case to another, which is a distinction that has not been thoroughly addressed in the past. In addition, this study explores several application domains, providing a comprehensive breakdown of the categorical data consumed by these systems and the features, or outputs, of these systems. Without focusing on any particular journals or their rankings, this study collects and reviews articles on recommendation systems published from 2018 to April 2024, in four top-tier research repositories, including IEEE Xplore Digital Library, Springer Link, ACM Digital Library, and Google Scholar.

SensorHUB – a Novel, Open-source Software Stack for Enhanced Accessibility and Secure Interoperability in IoT Project Management

Abstract. The Internet of Things (IoT) seeks to achieve seamless device connectivity and data sharing, thereby enhancing automation, efficiency, and insights in various fields of application. By delivering real-time data, IoT is essential for systems that aim to model and map the physical world to digital formats, such as digital twins or metaverses. In the recent years, there has been a surge in high-quality commercial IoT products available to various sectors. However, these solutions often come with high costs and proprietary interfaces, which hinder seamless interoperability. For users seeking cost-effective and flexible solutions, open-source software and international standards present viable alternatives. Nonetheless, despite technological progress, the adoption of open and standardized IoT software solutions is still predominantly seen among technology-affine groups or within research and development initiatives. We propose the sensorHUB, a novel IoT stack, which builds upon the Open Geospatial Consortium’s (OGC) data standard SensorThings API and enhances existing solutions towards more accessible, production-oriented solutions for diverse application domains and user groups. Key features include the provision of a new, state-of-the-art graphical user interface for managing data flows and the access to related software modules, and a secure and flexible user and access management.