Application Domains Research Articles

Defects-Rich Heterostructures Trigger Strong Polarization Coupling in Sulfides/Carbon Composites with Robust Electromagnetic Wave Absorption

Defects-rich heterointerfaces integrated with adjustable crystalline phases and atom vacancies, as well as veiled dielectric-responsive character, are instrumental in electromagnetic dissipation. Conventional methods, however, constrain their delicate constructions. Herein, an innovative alternative is proposed: carrageenan-assistant cations-regulated (CACR) strategy, which induces a series of sulfides nanoparticles rooted in situ on the surface of carbon matrix. This unique configuration originates from strategic vacancy formation energy of sulfides and strong sulfides-carbon support interaction, benefiting the delicate construction of defects-rich heterostructures in MxSy/carbon composites (M-CAs). Impressively, these generated sulfur vacancies are firstly found to strengthen electron accumulation/consumption ability at heterointerfaces and, simultaneously, induct local asymmetry of electronic structure to evoke large dipole moment, ultimately leading to polarization coupling, i.e., defect-type interfacial polarization. Such “Janus effect” (Janus effect means versatility, as in the Greek two-headed Janus) of interfacial sulfur vacancies is intuitively confirmed by both theoretical and experimental investigations for the first time. Consequently, the sulfur vacancies-rich heterostructured Co/Ni-CAs displays broad absorption bandwidth of 6.76 GHz at only 1.8 mm, compared to sulfur vacancies-free CAs without any dielectric response. Harnessing defects-rich heterostructures, this one-pot CACR strategy may steer the design and development of advanced nanomaterials, boosting functionality across diverse application domains beyond electromagnetic response.

Customization of 2D Atomic-Molecular Heterojunction with Manipulatable Charge-Transfer and Band Structure.

Manipulating the properties of 2Dmaterials through meticulously engineered artificial heterojunctions holds great promise for novel device applications. However, existing research on the crucial charge-transfer interactions and energy profile regulation is predominantly focused on 2D van der Waals structures formed via weak van der Waals forces, limiting regulatory efficiency at high costs. Herein, a refined atomic-molecular heterojunction strategy featuring strong covalent bonds between organic molecule and 2D violet phosphorus (VP) atomic crystal is developed, which enables enhanced charge-transfer dynamics and customizable band structure regulation at the molecular level. Both experimentally and theoretically, it is demonstrated that grafting efficiency, charge redistribution, and energy gap regulation critically depend on organic electronegativity, providing a low-cost yet high-efficiency regulatory effect on a large scale. As a proof of concept, the novel VP-molecular heterojunctions exhibit optimized performance in diverse application domains, presenting a general platform for future high-performance device applications.

Multi-relational graph contrastive learning with learnable graph augmentation

Multi-relational graph learning aims to embed entities and relations in knowledge graphs into low-dimensional representations, which has been successfully applied to various multi-relationship prediction tasks, such as information retrieval, question answering, and etc. Recently, contrastive learning has shown remarkable performance in multi-relational graph learning by data augmentation mechanisms to deal with highly sparse data. In this paper, we present a Multi-Relational Graph Contrastive Learning architecture (MRGCL) for multi-relational graph learning. More specifically, our MRGCL first proposes a Multi-relational Graph Hierarchical Attention Networks (MGHAN) to identify the importance between entities, which can learn the importance at different levels between entities for extracting the local graph dependency. Then, two graph augmented views with adaptive topology are automatically learned by the variant MGHAN, which can automatically adapt for different multi-relational graph datasets from diverse domains. Moreover, a subgraph contrastive loss is designed, which generates positives per anchor by calculating strongly connected subgraph embeddings of the anchor as the supervised signals. Comprehensive experiments on multi-relational datasets from three application domains indicate the superiority of our MRGCL over various state-of-the-art methods. Our datasets and source code are published at https://github.com/Legendary-L/MRGCL.

Critical insights into data curation and label noise for accurate prediction of aerobic biodegradability of organic chemicals.

The focus of this work is to enhance state-of-the-art Machine Learning (ML) models that can predict the aerobic biodegradability of organic chemicals through a data-centric approach. To do that, an already existing dataset that was previously used to train ML models was analyzed for mismatching chemical identifiers and data leakage between test and training set and the detected errors were corrected. Chemicals with high variance between study results were removed and an XGBoost was trained on the dataset. Despite extensive data curation, only marginal improvement was achieved in the classification model's performance. This was attributed to three potential reasons: (1) a significant number of data labels were noisy, (2) the features could not sufficiently represent the chemicals, and/or (3) the model struggled to learn and generalize effectively. All three potential reasons were examined and point (1) seemed to be the most decisive one that prevented the model from generating more accurate results. Removing data points with possibly noisy labels by performing label noise filtering using two other predictive models increased the classification model's balanced accuracy from 80.9% to 94.2%. The new classifier is therefore better than any previously developed classification model for ready biodegradation. The examination of the key characteristics (molecular weight of the substances, proportion of halogens present and distribution of degradation labels) and the applicability domain indicate that no/not a large share of difficult-to-learn substances has been removed in the label noise filtering, meaning that the final model is still very robust.

A Methodology to Measure Glucose Metabolism by Quantitative Analysis of PET Images.

Positron emission tomography (PET) with F-18 fluorodeoxyglucose (FDG) tracer is the standard clinical technique to measure myocardial and vessel metabolism and viability and to investigate the metabolic syndrome associated with cardiovascular diseases. The quantitative analysis of PET images allows one to study the cardiovascular physiological processes, by extracting quantitative parameters from the analysis of the tracer kinetic. Here, we propose a new methodology to quantify and evaluate the evolution of glucose metabolism inside the myocardium and the large vascular structures over time. We merge and analyze PET and CT cardiac images, extracting different volumes of interest (VOI) and performing quantitative measurements. To validate it, we apply the methodology to merge images of the aorta vessel for patients affected by metabolic syndrome. The application of the proposed approach to the use case reveals a correlation between administered drugs and metabolic syndrome, measuring the glucose metabolic rate (MRGlu) in both the myocardium and aorta. The proposed methodology can be used to evaluate some cardiovascular risk indexes of diabetic patients, too. The proposed methodology can also be deployed to analyze other application domains.

Design and implementation of a scalable IoT-based real-time environmental monitoring and alarm system: an experimental study

Digital transformation is crucial for organizations to survive, be competitive, and grow in the modern age. IoT is the key to enabling this transformation by connecting devices and optimizing processes. This paper presents the design and implementation of a generic IoT-based real-time environmental monitoring and alarm system. The platform is validated by applying it to a manufacturing plant scenario, where various sensors simulate industrial conditions. Scalable message distribution systems such as MQTT and Apache Kafka facilitate reliable data transmission. A microservice architecture is constructed for the backend services to ensure uninterrupted and high throughput services in the application domain. Instead of deploying a real WSN, traffic generation services were chosen to minimize costs, provide greater control and flexibility, and facilitate faster, scalable testing in a controlled environment. The platform also features an integrated alarm system with an event definition module, which allows users to define custom action rules. This flexible, scalable, and resilient architecture can be used across a wide range of application domains that require digital transformation. The experimental study demonstrates the platform's capabilities and great potential for broader IoT applications.

Architecture decisions in quantum software systems: An empirical study on Stack Exchange and GitHub

Context:Quantum computing provides a new dimension in computation, utilizing the principles of quantum mechanics to potentially solve complex problems that are currently intractable for classical computers. However, little research has been conducted about the architecture decisions made in quantum software development, which have a significant influence on the functionality, performance, scalability, and reliability of these systems. Objective:The study aims to empirically investigate and analyze architecture decisions made during the development of quantum software systems, identifying prevalent challenges and limitations by using the posts and issues from Stack Exchange and GitHub. Methods:We used a qualitative approach to analyze the obtained data from Stack Exchange Sites and GitHub projects — two prominent platforms in the software development community. Specifically, we collected data from 385 issues (from 87 GitHub projects) and 70 posts (from 3 Stack Exchange sites) related to architecture decisions in quantum software development. Results:The results show that in quantum software development (1) architecture decisions are articulated in six linguistic patterns, the most common of which are Solution Proposal and Information Giving, (2) the two major categories of architectural decisions are Implementation Decision and Technology Decision, (3) Software Development Tools are the most common application domain among the twenty application domains identified, (4) Maintainability is the most frequently considered quality attribute, and (5) Design Issues and High Error Rates are the major limitations and challenges that practitioners face when making architecture decisions in quantum software development. Conclusions:Our results show that the limitations and challenges encountered in architecture decision-making during the development of quantum software systems are strongly linked to the particular features (e.g., quantum entanglement, superposition, and decoherence) of those systems. These issues mostly pertain to technical aspects and need appropriate measures to address them effectively.

Modification of the hopfield neural network model for solving the task of optimal task allocation in a group of mobile robots

In the context of group interaction among mobile robots, there arises the challenge of task distribution within the group, considering the robots' characteristics and the working environment. This study aims to modify the Hopfield neural network and develop methodologies for its application in solving the task allocation problem for an arbitrary number of tasks within a group of mobile robots. To achieve this, the Hopfield neural network is represented as a graph. An algorithm is presented, demonstrating the transition from the initial problem to the Traveling Salesman Problem (TSP). The application of the Hopfield model to the task distribution problem in a group of robots is described, along with the development of an optimization function calculation algorithm. An assessment is conducted to evaluate the impact of neural network parameters on the quality and speed of solving the optimization problem. By comparing it with other heuristic methods (genetic and ant colony algorithms), the domains of application for the modified algorithm are determined.

Recent progress in the synthesis and electrocatalytic application of MXene‐based metal phosphide composites

AbstractIn the domain of novel catalyst design and application, metal phosphides have attracted widespread interest due to their unique electronic structure and potential catalytic activity. Various types of supports that can effectively anchor metal phosphides have been reported, among which MXene have received significant attention due to their two‐dimensional (2D) structure, adjustable composition, and composite variability. This work mainly aims to elucidate the preparation of novel MXene carriers and their unique roles in loading metal phosphides and participating in catalytic reactions. We will clarify the preparation strategy of MXene, the interaction between metal phosphides and MXene carriers, to explain the stabilization of metal phosphide active sites and the rational adjustment of electronic structure. In addition, we will comprehensively summarize recent research progress of MXene‐based metal phosphide composites, with particular emphasis on advancements in the synergistic effect of heterostructures. Regarding applications, we review the utilization of MXene‐based metal phosphide composites in electrocatalysis, including hydrogen evolution reaction (HER), oxygen evolution reaction (OER), and oxygen reduction reaction (ORR). Finally, some fundamental challenges and prospects for the efficient electrocatalysis of MXene‐based metal phosphide composites are introduced.

Computer-aided pattern scoring (C@PS): a novel cheminformatic workflow to predict ligands with rare modes-of-action

The identification, establishment, and exploration of potential pharmacological drug targets are major steps of the drug development pipeline. Target validation requires diverse chemical tools that come with a spectrum of functionality, e.g., inhibitors, activators, and other modulators. Particularly tools with rare modes-of-action allow for a proper kinetic and functional characterization of the targets-of-interest (e.g., channels, enzymes, receptors, or transporters). Despite, functional innovation is a prime criterion for patentability and commercial exploitation, which may lead to therapeutic benefit. Unfortunately, data on new, and thus, undruggable or barely druggable targets are scarce and mostly available for mainstream modes-of-action only (e.g., inhibition). Here we present a novel cheminformatic workflow—computer-aided pattern scoring (C@PS)—which was specifically designed to project its prediction capabilities into an uncharted domain of applicability.

Fair and optimal prediction via post‐processing

AbstractWith the development of machine learning algorithms and the increasing computational resources available, artificial intelligence has achieved great success in many application domains. However, the success of machine learning has also raised concerns about the fairness of the learned models. For instance, the learned models can perpetuate and even exacerbate the potential bias and discrimination in the training data. This issue has become a major obstacle to the deployment of machine learning systems in high‐stakes domains, for example, criminal judgment, medical testing, online advertising, hiring process, and so forth. To mitigate the potential bias exhibited by machine learning models, fairness criteria can be integrated into the training process to ensure fair treatment across all demographics, but it often comes at the expense of model performance. Understanding such tradeoffs, therefore, is crucial to the design of optimal and fair algorithms. My research focuses on characterizing the inherent tradeoff between fairness and accuracy in machine learning, and developing algorithms that can achieve both fairness and optimality. In this article, I will discuss our recent work on designing post‐processing algorithms for fair classification, which can be applied to a wide range of fairness criteria, including statistical parity, equal opportunity, and equalized odds, under both attribute‐aware and attribute‐blind settings, and is particularly suited to large‐scale foundation models where retraining is expensive or even infeasible. I will also discuss the connections between our work and other related research on trustworthy machine learning, including the connections between algorithmic fairness and differential privacy as well as adversarial robustness.

A proof-of-concept multi-tiered Bayesian approach for the integration of physiochemical properties and toxicokinetic time-course data for Daphnia magna

The use of in silico and in vitro methods, commonly referred to as New Approach Methodologies (NAMs), has been proposed to support environmental (and human) chemical safety decisions, ensuring enhanced environmental protection. Toxicokinetic models developed for environmentally relevant species are fundamental to the deployment of a NAMs-based safety strategy, enabling the conversion between external and internal chemical concentrations, although they require historical toxicokinetic data and robust physical models to be considered a viable solution. Daphnia magna is a key model organism in ecotoxicology albeit with limited and scattered quantitative toxicokinetic data, as for most invertebrates, resulting in a lack of robust toxicokinetic models. Moreover, current D. magna models are chemical specific, which restricts their applicability domain. One aim of this study was to address the current data availability limitations by collecting toxicokinetic time-course data for D. magna covering a broad chemical space and assessing the dataset's uniqueness. The collated toxicokinetic dataset included 48 time-courses for 30 chemicals from 17 studies, which was developed into an R package named AquaTK, with 11 studies unique to our work when compared to existing databases. Subsequently, a proof-of-concept Bayesian analysis was developed to estimate the steady-state concentration ratio (internal concentration / external concentration) from the data at three different levels of precision given three different data availability scenarios for environmental risk assessment. Specifically, an atrazine case study illustrates the multi-level modelling approach providing improvements (uncertainty reductions) in predictions of ratios for increasing amounts of data availability. Our work provides a consistent and self-contained Bayesian framework that irrespective of the hierarchy or resolution of individual experiments, can utilise the available information to generate optimal predictions of steady-state concentration ratios in D. magna. This approach is paramount to supporting the implementation of a NAMs based environmental safety paradigm shift in environmental risk assessment.

Automated model selection for multivariate anomaly detection in manufacturing systems

AbstractAs machine learning is widely applied to improve the efficiency and effectiveness of manufacturing systems, the automated selection of appropriate algorithms and hyperparameters becomes increasingly important. This paper presents a model selection approach to multivariate anomaly detection for applications in manufacturing systems using a multi-output regression-based meta-learning method. The proposed method exploits the capabilities of meta-learning to explore and learn the intricate relationships within multivariate data sets in order to select the best anomaly detection model. It also facilitates the construction of an ensemble of algorithms with dynamically assigned weights based on their respective performance levels. In addition to the framework, new meta-features for the application domain are presented and evaluated. Experiments show the proposed method can be successfully applied to achieve significantly better results than benchmark approaches. This enables an automated selection of algorithms that can be used for enhanced anomaly detection under changing operating conditions.

Fiber orientation and orientation factors in steel fiber‐reinforced concrete beams with hybrid fibers: A critical review

AbstractFiber orientation is of paramount importance for the design of fiber‐reinforced concrete (FRC) structural elements, because it markedly influences the postcracking properties of such material. For this reason, structural codes introduce orientation factors which aim to correlate the real mechanical properties of the structural element with the ones determined from standard beams. Although the need of considering fiber orientation in design codes is commonly accepted, the orientation factors are still based on a limited number of research studies, raising the need to better determine fiber orientation to improve the current standards and support the design process of FRC elements. In this research, a steel fiber‐reinforced concrete (SFRC) with a hybrid system of macro and microfibers is steered into a broad range of fiber orientations and cast into standard beams. Besides measuring the mechanical performance of these SFRC beams, three different methods for assessing fiber orientation are employed, namely electromagnetic induction, image analysis, and micro‐computed tomography. The comparison between the outcomes of the different methods provides detailed information about the accuracy and suitability of each method, considering the corresponding domain of applicability at structural level. Finally, a critical review of the most common 2D and 3D orientation parameters found in literature is performed, and the equations are adapted to account for the hybrid system of fibers.

A comprehensive survey of UPPAAL‐assisted formal modeling and verification

AbstractUPPAAL is a formal modeling and verification tool based on timed automata, capable of effectively analyzing real‐time software and hardware systems. In this article, we investigate research on UPPAAL‐assisted formal modeling and verification. First, we propose four research questions considering tool characteristics, modeling methods, verification means and application domains. Then, the state‐of‐the‐art methods for model specification and verification in UPPAAL are discussed, involving model transformation, model repair, property specification, as well as verification and testing methods. Next, typical application cases of formal modeling and verification assisted by UPPAAL are analyzed, spanning across domains such as network protocol, multi‐agent system, cyber‐physical system, rail traffic and aerospace systems, cloud and edge computing systems, as well as biological and medical systems. Finally, we address the four proposed questions based on our survey and outline future research directions. By responding to these questions, we aim to provide summaries and insights into potential avenues for further exploration in this field.

Blind non-linear spectral unmixing with spatial coherence for hyper and multispectral images

Multi and hyperspectral images have become invaluable sources of information, revolutionizing various fields such as remote sensing, environmental monitoring, agriculture and medicine. In this expansive domain, the multi-linear mixing model (MMM) is a versatile tool to analyze spatial and spectral domains by effectively bridging the gap between linear and non-linear interactions of light and matter. This paper introduces an upgraded methodology that integrates the versatility of MMM in non-linear spectral unmixing, while leveraging spatial coherence (SC) enhancement through total variation theory to mitigate noise effects in the abundance maps. Referred to as non-linear extended blind end-member and abundance extraction with SC (NEBEAE-SC), the proposed methodology relies on constrained quadratic optimization, cyclic coordinate descent algorithm, and the split Bregman formulation. The validation of NEBEAE-SC involved rigorous testing on various hyperspectral datasets, including a synthetic image, remote sensing scenarios, and two biomedical applications. Specifically, our biomedical applications are focused on classification tasks, the first addressing hyperspectral images of in-vivo brain tissue, and the second involving multispectral images of ex-vivo human placenta. Our results demonstrate an improvement in the abundance estimation by NEBEAE-SC compared to similar algorithms in the state-of-the-art by offering a robust tool for non-linear spectral unmixing in diverse application domains.

A Review and Analysis of Evaluation Practices in VIS Domain Applications.

This paper presents a review and analysis of evaluation practices within the visualization and visual analytics (VIS) domain, with a focus on domain application work accepted at the IEEE VIS conference from 2018 to 2022. Through the analysis of 140 pertinent papers, we establish a detailed classification principle for evaluation practices, using the Who, When, What, and How indicators. This principle covers facets such as analysis methods, targets, scenarios, participant expertise, and stages of occurrence. By systematically categorizing the application domains presented in these works, we apply our established classification principle to discern and categorize the evaluation practices within them, identifying the prevailing characteristics and trends. The paper explores the variety of evaluation methods employed across different application domains and observes the distinctions in their usage. In conclusion, we provide insights and highlight concerns for conducting evaluations in upcoming domain application research. Our findings are intended to inform and guide subsequent studies in a similar context. All information about the reviewed papers, the coding results, and the detailed data analysis process can be accessed at https://github.com/narrating-complexity/vis_evaluation_analysis.

Deep Learning-Based Image Classification and Segmentation on Digital Histopathology for Oral Squamous Cell Carcinoma: A Systematic Review and Meta-Analysis.

Artificial intelligence (AI)-based tools have shown promise in histopathology image analysis in improving the accuracy of oral squamous cell carcinoma (OSCC) detection with intent to reduce human error. This systematic review and meta-analysis evaluated deep learning (DL) models for OSCC detection on histopathology images by assessing common diagnostic performance evaluation metrics for AI-based medical image analysis studies. Diagnostic accuracy studies that used DL models for the analysis of histopathological images of OSCC compared to the reference standard were analyzed. Six databases (PubMed, Google Scholar, Scopus, Embase, ArXiv, and IEEE) were screened for publications without any time limitation. The QUADAS-2 tool was utilized to assess quality. The meta-analyses included only studies that reported true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) in their test sets. Of 1267 screened studies, 17 studies met the final inclusion criteria. DL methods such as image classification (n = 11) and segmentation (n = 3) were used, and some studies used combined methods (n = 3). On QUADAS-2 assessment, only three studies had a low risk of bias across all applicability domains. For segmentation studies, 0.97 was reported for accuracy, 0.97 for sensitivity, 0.98 for specificity, and 0.92 for Dice. For classification studies, accuracy was reported as 0.99, sensitivity 0.99, specificity 1.0, Dice 0.95, F1 score 0.98, and AUC 0.99. Meta-analysis showed pooled estimates of 0.98 sensitivity and 0.93 specificity. Application of AI-based classification and segmentation methods on image analysis represents a fundamental shift in digital pathology. DL approaches demonstrated significantly high accuracy for OSCC detection on histopathology, comparable to that of human experts in some studies. Although AI-based models cannot replace a well-trained pathologist, they can assist through improving the objectivity and repeatability of the diagnosis while reducing variability and human error as a consequence of pathologist burnout.

An approach simulating interacting solid, liquid, and gas domains for dynamic seal applications

AbstractIn seal applications a solid elastic body (the seal) ensures the separation of a liquid (e.g., hydraulic oil in one chamber) from a gas (e.g., air in another chamber). In the case of a dynamic seal, the rod is moving and transports a thin liquid film from the liquid chamber into the air chamber. The seal gap consists of a converging gap, a minimum distance between seal and rod and a diverging gap, where transported liquid detaches from the seal. Between the three states of matter (gas, liquid, gas) appear the following contact conditions: liquid‐to‐solid, gas‐to‐liquid, and gas‐to‐solid. A classical fluid‐structure‐interaction is technically possible with limited effort, but is not sufficient to distinguish between the liquid and the gas. A solution can make use of the fact, that the liquid has significant dimensions only in the liquid chamber and is pressurized only there. There is a thin liquid film on the rod in the air chamber, but it has dimensions clearly lower than the air chamber and has the same pressure as the air. Therefore a simulation is possible with the “variable viscosity approach”: Liquid and gas are modeled in one single fluid domain. Differences between liquid and gas are included by pressure‐dependent fluid parameters, especially viscosity and density. The resulting violation of the mass balance is very small as the low mass flow through the seal lip is low. The presented approach allows to apply a fluid‐structure simulation with special fluid properties to model a system with three states of matter. Advantages are a less complex model with less complex contact conditions and a reduced simulation time.

Harnessing Dynamic Heterogeneous Redundancy to Empower Deep Learning Safety and Security

The rapid development of deep learning (DL) models has been accompanied by various safety and security challenges, such as adversarial attacks and backdoor attacks. By analyzing the current literature on attacks and defenses in DL, we find that the ongoing adaptation between attack and defense makes it impossible to completely resolve these issues. In this paper, we propose that this situation is caused by the inherent flaws of DL models, namely non-interpretability, non-recognizability, and non-identifiability. We refer to these issues as the Endogenous Safety and Security (ESS) problems. To mitigate the ESS problems in DL, we propose using the Dynamic Heterogeneous Redundant (DHR) architecture. We believe that introducing diversity is crucial for resolving the ESS problems. To validate the effectiveness of this approach, we conduct various case studies across multiple application domains of DL. Our experimental results confirm that constructing DL systems based on the DHR architecture is more effective than existing DL defense strategies.