Real-world Cases Research Articles

Electrical-STGCN: An Electrical Spatio-Temporal Graph Convolutional Network for Intelligent Predictive Maintenance

With the rapid improvement of Industrial Internet of Things and artificial intelligence, predictive maintenance (PdM) has attracted great attention from both academia and industrial practitioners. When equipment is running, the electrical attributes have intrinsic relations. Meanwhile, they are changing over time. However, existing PdM models are often limited as they lack considering both attribute interactions and temporal dependence of the dynamic working system. To address the problem, in this article, we propose an electrical spatio-temporal graph convolutional network (Electrical-STGCN) for PdM. First, it takes a sequence of electrical records as input. Next, both attribute interactions and temporal dependence are established to extract features. Then, the extracted features are fed into a prediction component. Finally, the output of the Electrical-STGCN (i.e., remaining useful life) can help the workers decide whether to carry out equipment maintenance. The effectiveness of the proposed method is verified in real-world cases. Our method achieves 85.2% Accuracy and 0.9 F1-Score, which are better than the other approaches.

A Smart Diseases Diagnosis and Classification Strategy of Electronic Healthcare Application Using Novel Hybrid Artificial Intelligence Approaches

In today’s world, the healthcare industry faces difficulties like a scarcity of healthcare professionals, ageing, and rising healthcare costs. Also the classification and decision making process using the data generated via electronic health sensors is of major concern. In the fields of research and medical services, artificial intelligence (AI) is widely employed. However, correct estimate for various illnesses is a significant issue. The implementation of a new hybrid artificial intelligence (AI)-based classifier for helping prediction diagnosis in patients with chronic cancer conditions is examined in this work. Unknown qualities are predicted and given using the Hierarchical Red deer optimization (HRDO) based feature extraction, which is based on realworld cases. The Self-Systemized Generative Fuzzy Algorithm (SSGFA), which finds irregularities in patient data and predicts sickness, is used to create the hybrid classification design. This study’s simulation analysis included datasets for colon, lung, and brain cancer illnesses. The new combination of classifiers’ better performance resulted in total classification with increased accuracy, precision, recall, and F-measure, respectively. In terms of performance indicators, the suggested strategy is also compared to traditional methods. This demonstrates the suggested classification model’s ability to appropriately categorize various illnesses information for categorization.

CaR: A Cutting and Repulsion-Based Evolutionary Framework for Mixed-Integer Programming Problems.

A mixed-integer programming (MIP) problem contains both constraints and integer restrictions. Integer restrictions divide the feasible region defined by constraints into multiple discontinuous feasible parts. In particular, the number of discontinuous feasible parts will drastically increase with the increase of the number of integer decision variables and/or the size of the candidate set of each integer decision variable. Due to the fact that the optimal solution is located in one of the discontinuous feasible parts, it is a challenging task to solve a MIP problem. This article presents a cutting and repulsion-based evolutionary framework (called CaR) to solve MIP problems. CaR includes two main strategies: 1) the cutting strategy and 2) the repulsion strategy. In the cutting strategy, an additional constraint is constructed based on the objective function value of the best individual found so far, the aim of which is to continuously cut unpromising discontinuous feasible parts. As a result, the probability of the population entering a wrong discontinuous feasible part can be decreased. In addition, in the repulsion strategy, once it has been detected that the population has converged to a discontinuous feasible part, the population will be reinitialized. Moreover, a repulsion function is designed to repulse the previously explored discontinuous feasible parts. Overall, the cutting strategy can significantly reduce the number of discontinuous feasible parts and the repulsion strategy can probe the remaining discontinuous feasible parts. Sixteen test problems developed in this article and two real-world cases are used to verify the effectiveness of CaR. The results demonstrate that CaR performs well in solving MIP problems.

Recent Advances of Blockchain and Its Applications

Blockchain is an emerging decentralized data collection, sharing, and storage technology, which have provided abundant transparent, secure, tamper-proof, secure, and robust ledger services for various real-world use cases. Recent years have witnessed notable developments of blockchain technology itself as well as blockchain-enabled applications. Most existing surveys limit the scopes on several particular issues of blockchain or applications, which are hard to depict the general picture of current giant blockchain ecosystem. In this paper, we investigate recent advances of both blockchain technology and its most active research topics in real-world applications. We first review the recent developments of consensus and storage mechanisms and communication schema in general blockchain systems. Then extensive literature review is conducted on blockchain-enabled Internet of Things (IoT), edge computing, federated learning, and several emerging applications including healthcare, COVID-19 pandemic, online social network, and supply chain, where detailed specific research topics are discussed in each. Finally, we discuss the future directions, challenges, and opportunities in both academia and industry.

Privacy and Utility of Private Synthetic Data for Medical Data Analyses

The increasing availability and use of sensitive personal data raises a set of issues regarding the privacy of the individuals behind the data. These concerns become even more important when health data are processed, as are considered sensitive (according to most global regulations). Privacy Enhancing Technologies (PETs) attempt to protect the privacy of individuals whilst preserving the utility of data. One of the most popular technologies recently is Differential Privacy (DP), which was used for the 2020 U.S. Census. Another trend is to combine synthetic data generators with DP to create so-called private synthetic data generators. The objective is to preserve statistical properties as accurately as possible, while the generated data should be as different as possible compared to the original data regarding private features. While these technologies seem promising, there is a gap between academic research on DP and synthetic data and the practical application and evaluation of these techniques for real-world use cases. In this paper, we evaluate three different private synthetic data generators (MWEM, DP-CTGAN, and PATE-CTGAN) on their use-case-specific privacy and utility. For the use case, continuous heart rate measurements from different individuals are analyzed. This work shows that private synthetic data generators have tremendous advantages over traditional techniques, but also require in-depth analysis depending on the use case. Furthermore, it can be seen that each technology has different strengths, so there is no clear winner. However, DP-CTGAN often performs slightly better than the other technologies, so it can be recommended for a continuous medical data use case.

An Overview of Emotion in Artificial Intelligence

The field of artificial intelligence (AI) has gained immense traction over the past decade, producing increasingly successful applications as research strives to understand and exploit neural processing specifics. Nonetheless emotion, despite its demonstrated significance to reinforcement, social integration, and general development, remains a largely stigmatized and consequently disregarded topic by most engineers and computer scientists. In this article, we endorse emotion’s value for the advancement of artificial cognitive processing, as well as explore real-world use cases of emotion-augmented AI. A schematization is provided on the psychological-neurophysiologic basics of emotion in order to bridge the interdisciplinary gap preventing emulation and integration in AI methodology, as well as exploitation by current systems. In addition, we overview three major subdomains of AI greatly benefiting from emotion, and produce a systematic survey of meaningful yet recent contributions to each area. To conclude, we address crucial challenges and promising research paths for the future of emotion in AI with the hope that more researchers will develop an interest for the topic and find it easier to develop their own contributions.

Safety of Lithium Battery for New Energy Vehicles: A Literature Review

An overview of the causes of lithium-ion battery fires, what types of extinguishing agents are used when a fire occurs, and how to effectively prevent fires from occurring. It describes in detail the potential factors required for lithium-ion battery fires and related real-world cases, the advantages and disadvantages of various extinguishing agents and whether they can be used in automobiles, and the lithium-ion battery safety systems that have been designed and how they work.

Systematic Construction of Knowledge Graphs for Research-Performing Organizations

Research-Performing Organizations (e.g., research centers, universities) usually accumulate a wealth of data related to their researchers, the generated scientific results and research outputs, and publicly and privately-funded projects that support their activities, etc. Even though the types of data handled may look similar across organizations, it is common to see that each institution has developed its own data model to provide support for many of their administrative activities (project reporting, curriculum management, personnel management, etc.). This creates obstacles to the integration and linking of knowledge across organizations, as well as difficulties when researchers move from one institution to another. In this paper, we take advantage of the ontology network created by the Spanish HERCULES initiative to facilitate the construction of knowledge graphs from existing information systems, such as the one managed by the company Universitas XXI, which provides support to more than 100 Spanish-speaking research-performing organizations worldwide. Our effort is not just focused on following the modeling choices from that ontology, but also on demonstrating how the use of standard declarative mapping rules (i.e., R2RML) guarantees a systematic and sustainable workflow for constructing and maintaining a KG. We also present several real-world use cases in which the proposed workflow is adopted together with a set of lessons learned and general recommendations that may also apply to other domains. The next steps include researching in the automation of the creation of the mapping rules, the enrichment of the KG with external sources, and its exploitation though distributed environments.

Scalable Query Answering Under Uncertainty to Neuroscientific Ontological Knowledge: The NeuroLang Approach.

Researchers in neuroscience have a growing number of datasets available to study the brain, which is made possible by recent technological advances. Given the extent to which the brain has been studied, there is also available ontological knowledge encoding the current state of the art regarding its different areas, activation patterns, keywords associated with studies, etc. Furthermore, there is inherent uncertainty associated with brain scans arising from the mapping between voxels-3D pixels-and actual points in different individual brains. Unfortunately, there is currently no unifying framework for accessing such collections of rich heterogeneous data under uncertainty, making it necessary for researchers to rely on ad hoc tools. In particular, one major weakness of current tools that attempt to address this task is that only very limited propositional query languages have been developed. In this paper we present NeuroLang, a probabilistic language based on first-order logic with existential rules, probabilistic uncertainty, ontologies integration under the open world assumption, and built-in mechanisms to guarantee tractable query answering over very large datasets. NeuroLang's primary objective is to provide a unified framework to seamlessly integrate heterogeneous data, such as ontologies, and map fine-grained cognitive domains to brain regions through a set of formal criteria, promoting shareable and highly reproducible research. After presenting the language and its general query answering architecture, we discuss real-world use cases showing how NeuroLang can be applied to practical scenarios.

Validation of Wearable Device Consisting of a Smart Shirt with Built-In Bioelectrodes and a Wireless Transmitter for Heart Rate Monitoring in Light to Moderate Physical Work

Real-time monitoring of heart rate is useful for monitoring workers. Wearable heart rate monitors worn on the upper body are less susceptible to artefacts caused by arm and wrist movements than popular wristband-type sensors using the photoplethysmography method. Therefore, they are considered suitable for stable and accurate measurement for various movements. In this study, we conducted an experiment to verify the accuracy of our developed and commercially available wearable heart rate monitor consisting of a smart shirt with bioelectrodes and a transmitter, assuming a real-world work environment with physical loads. An exercise protocol was designed to light to moderate intensity according to international standards because no standard exercise protocol for the validation simulating these works has been reported. This protocol includes worker-specific movements such as applying external vibration and lifting and lowering loads. In the experiment, we simultaneously measured the instantaneous heart rate with the above wearable device and a Holter monitor as a reference to evaluate mean absolute percentage error (MAPE). The MAPE was 0.92% or less for all exercise protocols conducted. This value indicates that the accuracy of the wearable device is high enough for use in real-world cases of physical load in light to moderate intensity tasks such as those in our experimental protocol. In addition, the experimental protocol and measurement data devised in this study can be used as a benchmark for other wearable heart rate monitors for use for similar purposes.

Physics-constrained Gaussian process regression for soil moisture dynamics

Soil moisture (SM) is a crucial variable in the hydrological cycle and several recent studies have been conducted to model it using a hybrid strategy integrating physical-based and data-driven approaches. However, such methods pay little attention to the flaws of the introduced physics constraints, and the inherent nonlinearity of the soil flow process poses a challenge to their real-world application. In this study, a new physics-constrained scheme is proposed to exploit or avoid the adverse effects of the model error. Based on the reasonable assessment of model uncertainty, traditional physical model simulations are dynamically weighted to fuse into a data-driven framework based on Gaussian process regression (GPR) to utilize the respective strengths of both paradigms. For comparison, we also implement a scheme that introduces the gradient from the data as an implicit constraint. The effectiveness of the proposed paradigm is tested with a series of real-world cases. The results show that the physical model based on Richards' equation runs the risk of failing to guarantee even a basic water balance in real soil moisture simulations, which raises a red flag about the introduction of physics constraints, such as in the deep layers of the Goodwell site, the coefficient of determination (R2) predicted by the physical model is even less than 0.2 due to the neglect of the preferential flow process. In contrast, the proposed hybrid scheme significantly improves the accuracy and robustness of the soil moisture retrieval by weighing the reliability of the prediction results, with the R2 of the surface layer at Las Cruses site improving to 0.82 compared to 0.45 for the purely data-driven model. The comparison of the different forms of constraints further demonstrates the generalizability of our proposed constraint scheme.

Blockchain Technology: Benefits, Challenges, Applications, and Integration of Blockchain Technology with Cloud Computing

The real-world use cases of blockchain technology, such as faster cross-border payments, identity management, smart contracts, cryptocurrencies, and supply chain–blockchain technology are here to stay and have become the next innovation, just like the Internet. There have been attempts to formulate digital money, but they have not been successful due to security and trust issues. However, blockchain needs no central authority, and its operations are controlled by the people who use it. Furthermore, it cannot be altered or forged, resulting in massive market hype and demand. Blockchain has moved past cryptocurrency and discovered implementations in other real-life applications; this is where we can expect blockchain technology to be simplified and not remain a complex concept. Blockchain technology’s desirable characteristics are decentralization, integrity, immutability, verification, fault tolerance, anonymity, audibility, and transparency. We first conduct a thorough analysis of blockchain technology in this paper, paying particular attention to its evolution, applications and benefits, the specifics of cryptography in terms of public key cryptography, and the challenges of blockchain in distributed transaction ledgers, as well as the extensive list of blockchain applications in the financial transaction system. This paper presents a detailed review of blockchain technology, the critical challenges faced, and its applications in different fields. Blockchain in the transaction system is explained in detail with a summary of different cryptocurrencies. Some of the suggested solutions are given in the overall study of the paper.

MAC Plus Study: A Retrospective Study Correlating DNA Variants, Clinical Course and Outcomes from 187 Patients Who Underwent Testing Using an Atypical Hemolytic Uremic Syndrome and Thrombotic Microangiopathy Genetic Sequencing Panel

Atypical hemolytic uremic syndrome (aHUS) is a rare, potentially fatal disease that is often difficult to diagnose. The clinical presentation and pathogenesis of aHUS can overlap with several other rare, serious diseases (e.g., inherited thrombotic thrombocytopenia (TTP), shiga-toxin hemolytic uremic syndrome (STEC-HUS), and C3 glomerulopathy (C3G)). In many cases, a positive genetic finding can help clarify the diagnosis, guide treatment decisions, and inform prognosis. We have sequenced >2000 patients with a genetic panel targeting a collection of rare thrombotic microangiopathy (TMA) diseases and here we report the clinical course and outcomes for a subset (n=187). We cataloged all of the rare variants detected from sequencing ADAMTS13, C2, C3, C3AR1, CD46 (MCP), CFB, CFD, CFH, CFHR1, CFHR2, CFHR3, CFHR4, CFHR5, CFI, DGKE, MASP2, MMACHC, THBD, PLG, WT1 and the variant in C5 causing eculizumab resistance (p.Arg885). We collected data including clinical lab values, potential triggers, diagnoses, treatments, and outcomes. Our cohort here is distinct in that it is not a collection of patients clinically diagnosed with a specific disease (e.g., aHUS); instead, this cohort comprises real-world cases where the physicians sought out genetic testing for their patients as a part of the diagnostic picture. Physicians may be especially likely to seek out genetics when the diagnosis is especially murky. Patients treated with eculizumab responded well (52 responded, 7 did not respond, 8 unknown). Among patients who initiated eculizumab therapy, 34/67 discontinued its use and zero reported disease relapse. Of the 34 discontinuing eculizumab, 8 had a non-aHUS, non-TMA diagnosis while the 33 patients continuing therapy had only 1 with a non-aHUS, non-TMA diagnosis. In this cohort, 38% (71/187) ended up with a diagnosis of aHUS, 14% (27/187) were diagnosed with C3G/C3G-TMA, and 21% (40/187) were diagnosed with some other form of thrombotic microangiopathy (e.g., hypertension-TMA). Only 19 patients had a clear-cut positive genetic finding with 13 of those being the large, homozygous deletion of CFHR1 that is associated with CFH autoantibodies. 52 cases were negative for genetics while 86 had one or more rare variants of unknown significance. The difficulty in interpreting rare variants in the context of very rare, partially penetrant diseases like aHUS underscores the need for more genetic and clinical data to be made publicly available. The collection of data here will be a valuable resource for clinicians and scientists actively conducting research on these rare diseases and perhaps even more valuable for the clinical scientists and genetic counselors seeking context and comparisons for their own patients' variants, where the biggest problem currently is that most rare variants for these diseases are unfortunately variants of unknown significance.

Investigating the Use of AR Glasses for Content Annotation on Mobile Devices

Mobile devices such as smartphones and tablets have limited display size and input capabilities that make a variety of tasks challenging. Coupling the mobile device with Augmented Reality eyewear such as smartglasses can help address some of these challenges. In the specific context of digital content annotation tasks, this combination has the potential to enhance the user experience on two fronts. First, annotations can be offloaded into the air around the mobile device, freeing precious screen real-estate. Second, as smartglasses often come equipped with a variety of sensors including a camera, users can annotate documents with pictures or videos of their environment, captured on the spot, hands-free, and from the wearer's perspective. We present AnnotAR, a prototype that we use as a research probe to assess the viability of this approach to digital content annotation. We use AnnotAR to gather users' preliminary feedback in a laboratory setting, and to showcase how it could support real-world use cases.

A general framework of surrogate-assisted evolutionary algorithms for solving computationally expensive constrained optimization problems

The objective and constraints of expensive constrained optimization problems (ECOPs) are often evaluated using simulations with different computational costs. However, the existing algorithms always assume that both the objective and constraints can be simultaneously obtained after one expensive simulation, i.e., the most time-consuming one among the evaluations, based on parallel computing. This will affect the optimization efficiency since it is not necessary to call all the simulations of objective and constraints for each solution. Therefore, a general framework of surrogate-assisted evolutionary algorithms (GF-SAEAs) is proposed to adaptively arrange search strategies based on actual simulation cost differences. Specifically, all constraints are classified into several constraint levels by effectively quantifying the computational cost differences of objective and constraints, and a level-by-level feasible region-driven local search strategy is designed to locate potential sub-feasible regions for each constraint level. Then three different search mechanisms are employed to explore and exploit these located regions. Additionally, an adaptive population regeneration strategy is utilized to restart the algorithm and prevent premature convergence. In summary, GF-SAEAs can maintain a good balance between feasibility and convergence. Experimental studies on benchmark problems and two real-world cases show that GF-SAEAs performs better than other state-of-the-art algorithms.

Deep learning-accelerated optimization algorithm for controller parameters optimization of doubly-fed induction generators

In this work, a cooperative Gray wolf Optimizer with adaptive differential Evolution (GOE) is proposed for the multimodal controller parameters optimization of doubly-fed induction generators (DFIGs) based on maximum power point tracking (MPPT) strategies. Moreover, the optimization process of the GOE is accelerated by a deep fully connected model (DFCM). The GOE contains a cooperative gray wolf optimizer (GWO) and adaptive differential evolution (ADE). The cooperative GWO contains alpha, beta, delta, and omega wolves to explore and exploit optimization problems and achieves optimization tasks wider and deeper than GWO. The ADE cooperates with the cooperative GWO to solve global optimization over continuous spaces. The simulation results on seven uni-model benchmark functions show that the GOE accelerated by DFCM obtains acceptable fitness values with 39.99% lesser computation time than the symmetry adapted stochastic search (SASS) algorithm and 80.72% lesser computation time than the Lévy flights-success-history based adaptive differential evolution with constraint handling technique (COLSHADE) algorithm, which are the winners of the CEC2020 Competition on Real-World Single Objective Constrained Optimization. Furthermore, the simulation results on DFIG with MPPT strategies in three real-world cases verify that the GOE accelerated by DFCM can effectively obtain global optimization solutions for non-smooth problems with 99.51% lesser average computation time than the SASS algorithm, 99.63% less than the COLSHADE algorithm, and 89.52% less than other methods. In addition, the accelerated GOE algorithm by DFCM has the feature of faster convergence.

No! Re-imagining Data Practices Through the Lens of Critical Refusal

Critical refusal is an active process; an informed practice of investigating power differences in order to generate more just and equitable alternatives to the status quo. In this paper, we examine what it means to utilize critical refusal as a tool for investigating unequal power dynamics that are produced and reified by data practices. We illustrate the generative capacity of critical refusal by drawing on declarations from The Feminist Data Manifest-No to examine data practices across three real-world cases. By pairing a conceptual exploration of critical refusal with real-world examples, we make a theoretical contribution that is grounded in concrete approaches for generating alternative data practices in ways that account for interlocking struggles across contexts and communities.

A knowledge-enhanced graph-based temporal-spatial network for natural gas consumption prediction

The accurate prediction of natural gas consumption plays a central role in long-distance pipeline system production and transportation planning, and it becomes even more important during present political situation. The existing prediction methods for natural gas consumption barely consider spatial correlations and domain knowledge. As a result, the study proposes a novel deep learning prediction method (knowledge-enhanced graph-based temporal-spatial network, abbreviated to KE-GB-TSN) for predicting natural gas consumption by integrating domain knowledge into association graph construction and capturing temporal-spatial features via a hybrid deep learning network. This study first applies the domain knowledge that analyses the operation technique of the natural gas pipeline network and combines the historical data to establish an association graph. Subsequently, the historical data and association graphs are input to a hybrid deep learning network to predict natural gas consumption. The comparative experiments are conducted by taking real-world cases of natural gas consumption as examples. At last, a sensitivity analysis of different components combination is carried out to exhibit the significance of each component in the proposed model. The results prove that the proposed model is capable of achieving more accurate and efficient predicted results compared to the advanced models, such as decision trees and gated recurrent units. The Mean Absolute Relative Errors and Root Mean Squared Relative Errors gotten by the proposed model are less than 0.11 and 0.14 in all cases, indicating an improvement compared to previous works. Additionally, it is also suggested that domain knowledge and temporal-spatial correlations are crucial for the excellent performance of the prediction model.

Electronic Health Records at People's Hands Across Europe: The InteropEHRate Protocols.

Sharing of personal health data could facilitate and enhance the quality of care and the conduction of further research studies. However, these data are still underutilized due to legal, technical, and interoperability challenges, whereas the data subjects are not able to manage, interact, and decide on what to share, with whom, and for what purposes. This barrier obstacles continuity of care across in the European Union (EU), and neither healthcare providers nor data researchers nor the citizens are benefiting through efficient healthcare treatment and research. Despite several national-level EU studies and research activities, cross-border health data exchange and sharing is still a challenging task, which is addressed only under specific cases and scenarios. This manuscript presents the InteropEHRate research project along with its key innovations, aiming to offer Electronic Health Records (EHRs) at peoples' hands across the EU, via the exploitation of three (3) different protocol families, namely the Device-to-Device (D2D), Remote-to-Device (R2D), and Research Data Sharing (RDS) protocols. These protocols facilitate efficient, secure, privacy preserving, and General Data Protection Regulation (GDPR) compliant health data sharing across the EU, covering different real-world use cases.

Multisurrogate-Assisted Ant Colony Optimization for Expensive Optimization Problems With Continuous and Categorical Variables.

As an effective optimization tool for expensive optimization problems (EOPs), surrogate-assisted evolutionary algorithms (SAEAs) have been widely studied in recent years. However, most current SAEAs are designed for continuous/ combinatorial EOPs, which are not suitable for mixed-variable EOPs. This article focuses on one kind of mixed-variable EOP: EOPs with continuous and categorical variables (EOPCCVs). A multisurrogate-assisted ant colony optimization algorithm (MiSACO) is proposed to solve EOPCCVs. MiSACO contains two main strategies: 1) multisurrogate-assisted selection and 2) surrogate-assisted local search. In the former, the radial basis function (RBF) and least-squares boosting tree (LSBT) are employed as the surrogate models. Afterward, three selection operators (i.e., RBF-based selection, LSBT-based selection, and random selection) are devised to select three solutions from the offspring solutions generated by ACO, with the aim of coping with different types of EOPCCVs robustly and preventing the algorithm from being misled by inaccurate surrogate models. In the latter, sequence quadratic optimization, coupled with RBF, is utilized to refine the continuous variables of the best solution found so far. By combining these two strategies, MiSACO can solve EOPCCVs with limited function evaluations. Three sets of test problems and two real-world cases are used to verify the effectiveness of MiSACO. The results demonstrate that MiSACO performs well in solving EOPCCVs.