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Research on fishing vessels worldwide began in the 1850s and continues to increase to this day. In Turkey, academic studies on fishing vessels commenced with the establishment of the Ata Nutku Ship Model Experiment Laboratory in 1953. Academic studies on fishing vessels in Turkey can be divided into three periods: the early period studies, the İTU fishing vessel series studies, and the Black Sea type fishing vessels studies. The early period studies started in the 1950s and continued until the 1970s, focusing on Taka, Çektirme, and Alametro type fishing vessels. The İTU fishing vessel series studies began in 1979 under the leadership of Prof. Dr. Kemal KAFALI, in collaboration with İTU and TÜBİTAK, based on fishing vessel forms developed through model experiments, and have continued to the present day. The third period, the Black Sea type fishing vessels studies, started in the 1990s and research on these forms is still ongoing. These fishing vessels, generally built in the Black Sea region, are now used in all seas and oceans. This study examined master's and doctoral theses, articles, and conference papers from these three periods. The subjects were analyzed in a cause-and-effect relationship, and the information contained was summarized. • Academic studies on fishing vessels in Turkey. • Doctoral and master's theses, articles and conference papers from 1950 to the present. • Studies on Taka, Çektirme, Alametro. • Studies on ITU fishing boats series. • Studies on the Black Sea type fishing vessel. [ABSTRACT FROM AUTHOR]

This work covers the operational use of the Aerojet General Nucleonics Model 201 Reactor (AGN-201M) at the University of New Mexico (UNM) in Albuquerque, New Mexico. The AGN-201M is a research and test reactor that operates at 5-watts (thermal) and utilizes HALEU fuel encased in polyethylene fuel disks. Operational usage in hours and watt-hours for the 2021 and 2022 fiscal year is provided, as well as licensing data (including training usage), notable research projects, outreach, and educational use in the Nuclear Engineering program at UNM. All research projects described herein have published conference papers, journal articles, or in-progress conference papers, journal articles and benchmarks. [ABSTRACT FROM AUTHOR]

With the prevalence of hardware accelerators as an integral part of the modern systems on chip (SoCs), the ability to model accelerators quickly and accurately within the system in which it operates is critical. This paper presents gem5-SALAMv2 as a novel system architecture for LLVM-based modeling and simulation of custom hardware accelerators integrated into the gem5 framework. It overcomes the inherent limitations of state-of-the-art trace-based pre-register-transfer level (RTL) simulators by offering a truly "execute-in-execute" LLVM-based model. It enables scalable modeling of multiple dynamically interacting accelerators with full-system simulation support. To create long-term sustainable expansion compatible with the gem5 system framework, gem5-SALAM offers a general-purpose and modular communication interface and memory hierarchy integrated into the gem5 ecosystem, streamlining designing and modeling accelerators for new and emerging applications. gem5-SALAMv2 expands upon the framework established in gem5-SALAMv1 with improved LLVM-based elaboration and simulation, improved and more extensible system integration, and new automations to simplify rapid prototyping and design space exploration. 1 1 Conference Paper Extension: This work extends the work presented in gem5-SALAM: A System Architecture for LLVM-based Accelerator Modeling from MICRO 2020 (Rogers et al., 2020). This work expands on the aforementioned work by revamping the gem5-SALAM internals to provide more robust and extensible simulations, introducing new automation tools for expanding and simplifying design space exploration, and demonstrates the new capabilities of gem5-SALAMv2 by exploring multiple configurations of simple neural network architectures. Validation on the MachSuite (Reagen et al., 2014) benchmarks presents a timing estimation error of less than 1% against the Vivado High-Level Synthesis (HLS) tool. Results also show less than a 4% area and power estimation error against Synopsys Design Compiler. Additionally, system validation against implementations on an Ultrascale+ ZCU102 shows an average end-to-end timing error of less than 2%. Lastly, we demonstrate the upgraded capabilities of gem5-SALAMv2 by exploring accelerator platforms for two deep neural networks, LeNet5 and MobileNetv2. In these explorations, we demonstrate how gem5-SALAMv2 can simulate such systems and guide architectural optimizations for these types of accelerator-rich architectures. 2 2 The most up-to-date version of gem5-SALAMv2 is available at https://github.com/TeCSAR-UNCC/gem5-SALAM.. [ABSTRACT FROM AUTHOR]

• The characteristics that can revolutionize Blockchain technology are thoroughly described. • The WoS literature is being explored with a systematic review on Blockchain. • The current state of Blockchain technology is thoroughly investigated. • Findings of the study highlight some significant implications to the Blockchain community. • A mapping of the major open issues for Blockchain to possible solutions is tabulated. At present times, Blockchain technology is gaining more attraction with every passing day, as it has revolutionized the traditional trade due to its distributed ledger feature, every record in this ledger is secured by rules of cryptography which makes it more secure and tamper-free. This naturally led to the emergence of various types of cryptocurrency, such as Bitcoin, which builds on a technology commonly known as Blockchain. The rapid evolution of research on Blockchain calls for more research studies for investigating and analyzing the current knowledge in this field through a systematic technical study that shows the impact and significance of the related literature since the inception of the technology in 2013. From this point, in this paper, a state-of-the-art review is conducted on the most influential articles, conference papers, and review papers related to Blockchain published from 2013 to 2020 and indexed by the Web of Science Core CollectionTM (WoS) world's literature database. To attain the desired objective, after presenting an inevitable, brief overview of Blockchain technology, the collected papers have closely been analyzed along seven key research questions. Subsequently, vital valuable findings, such as the top 10 influential papers, yearly publications and citation trends, the favorite publication venues, the hottest research areas, and the most supportive funding bodies are reported, which may offer several implications about the status quo and emerging trends and frontiers of Blockchain, to guide towards establishing a baseline for both fresh and experienced researchers and practitioners before initiating a future research project on Blockchain. Furthermore, a rigorous discussion is provided on Blockchain application in diversified domains, along with different versatile use cases. Lastly, a brief insight is presented into open challenges and potential future advancements in the field of Blockchain. Summing up, this paper is meant to assist newbies in exploring and designing new Blockchain solutions, bearing in mind existing demands and challenges. [ABSTRACT FROM AUTHOR]

3D human motion prediction, predicting future poses from a given sequence, is an issue of great significance and challenge in computer vision and machine intelligence, which can help machines in understanding human behaviors. Due to the increasing development and understanding of Deep Neural Networks (DNNs) and the availability of large-scale human motion datasets, the human motion prediction has been remarkably advanced with a surge of interest among academia and industrial community. In this context, a comprehensive survey on 3D human motion prediction is conducted for the purpose of retrospecting and analyzing relevant works from existing released literature. In addition, a pertinent taxonomy is constructed to categorize these existing approaches for 3D human motion prediction. In this survey, relevant methods are categorized into three categories: human pose representation , network structure design , and prediction target. We systematically review all relevant journal and conference papers in the field of human motion prediction since 2015, which are presented in detail based on proposed categorizations in this survey. Furthermore, the outline for the public benchmark datasets, evaluation criteria, and performance comparisons are respectively presented in this paper. The limitations of the state-of-the-art methods are discussed as well, hoping for paving the way for future explorations. [ABSTRACT FROM AUTHOR]

Generative artificial intelligence (GAI) is a rapidly growing field with a wide range of applications. In this paper, a thorough examination of the research landscape in GAI is presented, encompassing a comprehensive overview of the prevailing themes and topics within the field. The study analyzes a corpus of 1319 records from Scopus spanning from 1985 to 2023 and comprises journal articles, books, book chapters, conference papers, and selected working papers. The analysis revealed seven distinct clusters of topics in GAI research: image processing and content analysis, content generation, emerging use cases, engineering, cognitive inference and planning, data privacy and security, and Generative Pre-Trained Transformer (GPT) academic applications. The paper discusses the findings of the analysis and identifies some of the key challenges and opportunities in GAI research. The paper concludes by calling for further research in GAI, particularly in the areas of explainability, robustness, cross-modal and multi-modal generation, and interactive co-creation. The paper also highlights the importance of addressing the challenges of data privacy and security in GAI and responsible use of GAI. [ABSTRACT FROM AUTHOR]

A comprehensive study of the Earth System and its different environments requires understanding of multi-dimensional data acquired with a multitude of different sensors or produced by various models. Here we present a component-wise scalable web-based framework for simultaneous visualisation of multiple data sources. It helps contextualise mixed observation and simulation data in time and space. This work is an extended version of the conference paper (Buck et al., 2021). [Display omitted] • Open-source (EUPL) hybrid application for realtime visualisation of 4D geoscientific data. • Split into native server and web client to utilize the strengths of both platforms. • Desktop builds are released for Windows, Linux, and MacOS. • Viewer used on expedition cruises to plan underwater exploration missions • Presentation and data validation capabilities used by GLODAP [ABSTRACT FROM AUTHOR]

Robot sequential decision-making in the real world is a challenge because it requires the robots to simultaneously reason about the current world state and dynamics, while planning actions to accomplish complex tasks. On the one hand, declarative languages and reasoning algorithms support representing and reasoning with commonsense knowledge. But these algorithms are not good at planning actions toward maximizing cumulative reward over a long, unspecified horizon. On the other hand, probabilistic planning frameworks, such as Markov decision processes (MDPs) and partially observable MDPs (POMDPs), support planning to achieve long-term goals under uncertainty. But they are ill-equipped to represent or reason about knowledge that is not directly related to actions. In this article, we present an algorithm, called iCORPP, to simultaneously estimate the current world state, reason about world dynamics, and construct task-oriented controllers. In this process, robot decision-making problems are decomposed into two interdependent (smaller) subproblems that focus on reasoning to "understand the world" and planning to "achieve the goal" respectively. The developed algorithm has been implemented and evaluated both in simulation and on real robots using everyday service tasks, such as indoor navigation, and dialog management. Results show significant improvements in scalability, efficiency, and adaptiveness, compared to competitive baselines including handcrafted action policies. • This article formally introduces the Integrated Reasoning and Planning (IRP) problem for robots. An IRP problem has a factored state space that is specified by a set of endogenous and exogenous variables. • This article introduces a sequential decision-making algorithm, called Interleaved Commonsense Reasoning and Probabilistic Planning (iCORPP), for addressing IRP problems. • This article presents systematic evaluations of iCORPP using two tasks of mobile robot navigation, and spoken dialog systems, as demonstrated on a real robot. • This article builds on our previous research that appeared in two conference papers (Zhang and Stone, 2015; Zhang et al., 2017). This article unifies their terminology, problem statements, and algorithms. • Compared to our prior work, this article introduces a new problem statement in Section 4.1, which covers the problems addressed in both conference papers. • To address the IRP problem, we have reformulated the iCORPP algorithm (Section 4.2) into a novel form that includes the three key steps of "logical reasoning," "probabilistic reasoning over world states," and "probabilistic reasoning about world dynamics." [ABSTRACT FROM AUTHOR]

Digital platform ecosystem governance refers to a platform owner's decisions and mechanisms that seek to influence complementors and users to build and sustain an ecosystem. The relevance of digital platform ecosystem governance is broadly acknowledged by researchers. However, the extant body of research is fragmented, and varied terminologies are employed, leading to challenges in identifying and recognizing results across different fields. This article provides a multidisciplinary and systematic literature review with the aim of consolidating knowledge on this important topic. Based on an analysis of 103 journal articles and conference papers, this review synthesizes the literature into a conceptual model with five building blocks of platform ecosystem governance. The model aims to create a robust foundation for researchers approaching the topic for the first time and conducting subsequent research. The conceptual model also offers practical guidance for governing ecosystems in a structured manner. Finally, this article provides a research agenda with five areas for future investigation. • Multidisciplinary, systematic literature review on platform ecosystem governance. • Synthesis of the literature into a conceptual model with five building blocks. • Robust foundation for understanding the topic and for future research. • Research agenda with five areas and questions for future investigation. [ABSTRACT FROM AUTHOR]

The agri-food and rural development sector in Europe is undergoing digitalization, and therefore becoming increasingly data-dependent. However, limited experience in implementing Findable, Accessible, Interoperable, and Reusable (FAIR) data principles has restricted knowledge sharing and reusability. This study aims to investigate the existing FAIR Data Management practices in the agri-food and rural development sector by assessing the FAIRness of project outputs from recent H2020-funded Multi-Actor Projects (MAPs). We conducted a FAIRness assessment of project outputs using both semi-automatic and manual framework, and we also carried out a comprehensive review of the data sharing practices among selected MAPs. Of the investigated MAPs, <10% have achieved FAIR compliance and applied FAIR data management practices. The measured FAIRness of project products, including journal articles, conference papers, and books, is higher than that of other product types such as videos, audios, and presentations. The study highlights the critical need for standardizing the adoption of FAIR data principles and data management practices across both the agri-food and rural development sector but also across bureaucratic data management throughout Europe more widely. Such a change would facilitate broader utilization and re-use of MAPs outputs and results, enhancing decision-making and agri-food and rural development practices. [Display omitted] • The agri-food and rural development sector in Europe is becoming increasingly data-dependent. • The Open Science and the FAIR Data principles enhance decision-making by promoting FAIRness of agri-food research outputs. • The research community in the agri-food and rural development sector has limited experience in applying these principles. • Assessed FAIRness of project outputs in EU-funded agri-food and rural development MAPs. [ABSTRACT FROM AUTHOR]

In applications of AI systems where exact definitions of the important notions of the application domain are hard to come by, the use of traditional logic-based knowledge representation languages such as Description Logics may lead to very large and unintuitive definitions, and high complexity of reasoning. To overcome this problem, we define new concept constructors that allow us to define concepts in an approximate way. To be more precise, we present a family τ EL (m) of extensions of the lightweight Description Logic EL that use threshold constructors for this purpose. To define the semantics of these constructors we employ graded membership functions m , which for each individual in an interpretation and concept yield a number in the interval [ 0 , 1 ] expressing the degree to which the individual belongs to the concept in the interpretation. Threshold concepts C ⋈ t for ⋈ ∈ { < , ≤ , > , ≥ } then collect all the individuals that belong to C with degree ⋈ t. The logic τ EL (m) extends EL with threshold concepts whose semantics is defined relative to a function m. To construct appropriate graded membership functions, we show how concept measures ∼ (which are graded generalizations of subsumption or equivalence between concepts) can be used to define graded membership functions m ∼. Then we introduce a large class of concept measures, called simi-d , for which the logics τ EL (m ∼) have good algorithmic properties. Basically, we show that reasoning in τ EL (m ∼) is NP/coNP-complete without TBox, PSpace-complete w.r.t. acyclic TBoxes, and ExpTime-complete w.r.t. general TBoxes. The exception is the instance problem, which is already PSpace-complete without TBox w.r.t. combined complexity. While the upper bounds hold for all elements of simi-d , we could prove some of the hardness results only for a subclass of simi-d. This article considerably improves on and generalizes results we have shown in three previous conference papers and it provides detailed proofs of all our results. [ABSTRACT FROM AUTHOR]

• CVR determination in (non)-sinusoidal conditions for low-cost digitizers. • Measurement uncertainty analysis using Monte Carlo method for CVR. • Low-cost paradigm related to improvement of metrology in underdeveloped countries. The paradigm of low-cost metrology implies the application of low-cost measuring devices in order to achieve satisfactory precision of the relevant measuring quantities. This is particularly important in underdeveloped countries where it is necessary to improve their metrology capabilities to ensure satisfactory standards for industry requirements. Therefore, the previously published conference paper is extended, and the performance of low-cost sampling devices is investigated for the purpose of high-precision electricity measurement in relevant national metrology institutes or measurement laboratories. The performance of the DMM 3000 device and the DAQ970A digitizers compared with high-resolution (24-bit) ADC-based sampling devices such as the NI PXI 5922 and NI PXI 4461. One of the main contributions of this article is the characterization of the channels of the low-cost digitizers in sinusoidal and non-sinusoidal regimes along with the uncertainty analysis using Monte Carlo method which has been carried out as well. [ABSTRACT FROM AUTHOR]

Machine learning based decision making systems in safety critical areas place high demands on the accuracy and generalization ability of the underlying model. A common strategy to deal with uncertainties and possible mistakes is offered by learning with reject option, i.e. a model can refrain from prediction in ambiguous cases and leave the decision to a human expert. Yet, as for the models themselves, human decision-making is hampered by the fact that reject options are often implemented as black-box rules: Experts cannot readily understand the reasons for rejection. In this work, we propose a model-agnostic framework that enriches classification with reject option by explanation mechanisms. More specifically, we combine conformal prediction as a popular mathematically based technology of certainty estimation with local surrogates derived for the region of interest. This allows us to provide local explanations in terms of example-based explanation methods, including counterfactual, semi-factual, and factual methods. We demonstrate the performance of this technology through a series of benchmarks using 6 different data sets; the associated code is open source. 1 1 This article represents a substantially extended version of the conference paper Artelt et al. (2022). In addition to a more detailed explanation and derivation of the algorithms, we explore semi-factual and factual explanation methods and provide evaluations on realistic benchmark data sets. • New challenge for XAI: explain model uncertainty and reject option • Efficient realization based on counterfactual explanation of local surrogate. • Open source implementation and evaluation on benchmarks. [ABSTRACT FROM AUTHOR]

The 13th International Symposium on Applied Plasma Science (ISAPS '22) was held at IRS (Institute of Space Systems), University of Stuttgart, Stuttgart, Germany, from September 26 through 30, 2022. The selected presentations at ISAPS 2022were invited to write their original papers after the conference. These papers were peer reviewed and finally 6 papers were compiled in this special issue of Vacuum. [ABSTRACT FROM AUTHOR]

The test of security primitives is particularly strategic as any bias coming from the implementation or environment can wreak havoc on the security it is intended to provide. This paper presents how some security properties are tested on hardware security primitives including True Random Number Generation (TRNG), Physically Unclonable Function (PUF), and cryptographic modules. Moreover, we discuss how the sensors embedded to protect cryptographic modules against fault injection attacks should be calibrated over time to fulfill the requirement it was designed for. The testing we discuss in this paper is different from the conventional testing where we consider a fault model and generate test patterns via an ATPG to detect such faults. The test of TRNG and PUF to ensure a high level of security is mainly about the entropy assessment, which requires specific statistical tests. The security against side-channel analysis (SCA) of cryptographic primitives, like the substitution box in symmetric cryptography, is generally ensured by masking. However, the hardware implementation of masking can be damaged by glitches, which create leakages on sensitive variables. Accordingly, a test method is to search for nets of the cryptographic netlist, which are vulnerable to glitches. Finally, the Digital Sensor (DS) is an efficient primitive to detect disturbances and raise alarms in the case of fault injection attack (FIA). The dimensioning of this primitive requires a precise test to take into account the environmental variations including aging. This paper extends on a conference paper presented at DFTS'21 by the same co-authors, where the test methodology for three critical security primitives is presented. In addition, in this paper, we add experimental validation to show how such testing methodology is applied in practice. [ABSTRACT FROM AUTHOR]

• Estimated battery SOH using CNN model based on the raw data. • A high tolerance to the partial charging condition. • Introduced transfer learning method to reduce the offline training data. • Satisfied generality to different types of batteries. State of health (SOH) estimation is essential to the health diagnostic of lithium-ion battery. The data-driven approach with charging feature extraction is promising for online SOH estimation and has been widely explored over years. However, their deployment can be barriered by the lack of complete charging data in real-world applications. Motivated by this, this paper proposes an artificial intelligence-based SOH estimator using the transient phase between constant current (CC) and constant voltage (CV) charging, which is easily obtained in real-world charging scenarios. Specifically, a convolutional neural network (CNN) model is proposed to explain the relationship between the charging data and the SOH. Following this endeavor, the transfer learning is exploited for model mitigation and SOH estimation on different battery types, relying on much reduced amount of data for efficient CNN model re-training. The validation experiments are conducted based on the aging data obtained on LiNiCoAlO 2 (NCA) and LiCoO 2 (LCO) cells. Results suggest that the proposed method realizes accurate SOH estimation requiring only a short segment from the CC-CV transient phase, so that can meet a broad range of real-world charging scenarios. Moreover, the efficient model transfer promises expected performance with different battery types. The short version of the paper was presented at ICAE2021, Nov 29 - Dec 5, 2021. This paper is a substantial extension of the short version of the conference paper. [ABSTRACT FROM AUTHOR]

Knowledge discovery for data streaming requires online feature selection to reduce the complexity of real-world datasets and significantly improve the learning process. This is achieved by selecting highly relevant subsets and minimising irrelevant and redundant features. However, researchers have difficulties in addressing various forms of data. The goal of this article is to present a state-of-the-art review of feature subset selection based on the data form for the high-dimensional data used in online streaming. Through a systematic literature review assessing journal and conference papers from the past five years, detailed discussions on traditional feature selection and online feature selection were presented. Subsequently, a taxonomy of the challenges related to OFS provides a comprehensive review of state-of-the-art OFS and the benchmark methods. Several data forms were identified based on the extensive review: group stream, multi-label, capricious, imbalance, and feature drift. Using critical analysis, the evaluation metrics of online feature selection methods were compared from the perspectives of threshold initialisation, accuracy, high dimensionality, running time, relevancy, and redundancy for the optimal feature subset. An online feature selection framework was derived to illustrate the relationship between the application area, data form, online feature selection methods, evaluation metrics, and tools. Finally, the findings and potential directions for future research were thoroughly discussed. It is suggested that future researchers explore the derived framework and aim to advance each method. • Online feature selection methods for high dimensional online streaming data according to data forms and correlation measures. • A taxonomy of OFS challenges and the OFS framework was derived to illustrate the relationship of its ecosystems. • Significant findings in the adjustment of threshold dimensionality of data, relevancy and redundancy state. [ABSTRACT FROM AUTHOR]

Several diagnostic criteria have been developed to effectively diagnose systemic lupus erythematosus (SLE). Three criteria are most common, namely the American College of Rheumatology (ACR)-1997, the Systemic Lupus International Collaborating Clinics (SLICC)-2012, and the European League Against Rheumatism (EULAR/ACR)-2019. Whether they also apply to juvenile SLE is unclear. To examine the diagnostic accuracy of ACR-1997, SLICC-2012, and EULAR/ACR-2019 for juvenile SLE. A comprehensive search of PubMed, Cochrane, and Embase was conducted up to 26 March 2022. We included all study designs in which patients had any index tests for ACR-1997, SLICC-2012, or EULAR/ACR-2019; both full-text papers and conference abstracts published in English were used. Exclusion criteria were as follows: (1) case reports; (2) adult subjects; or (3) did not report sufficient information to acquire true positive, false positive, true negative, and false negative values of diagnostic criteria. Two authors independently screened studies, extracted relevant data, and assessed the risk of bias. First, a meta-analysis of the diagnostic accuracy of EULAR/ACR-2019 and a hierarchical summary receiver operating characteristic (HSROC) model was performed to estimate sensitivity and specificity with 95% confidence intervals (CIs). We then carried out a network meta-analysis to compare the performances of these three diagnostic criteria. In total, 17 relevant studies that included 2339 juvenile SLE patients were eligible to analyze pooled accuracy. In the meta-analysis, 10 studies (1613 cases) reported the diagnostic performance of EULAR/ACR-2019, showing a pooled sensitivity of 0.92 (95% CI, 0.89–0.95), pooled specificity of 0.89 (0.77–0.95), and area under HSROC of 0.96 (0.94–0.97). In the network meta-analysis, the SLICC-2012 (0.94, 0.92–0.96) had the highest sensitivity, followed by EULAR/ACR-2019 (0.93, 0.90–0.95), and ACR-1997 (0.78, 0.72–0.82); the ACR-1997 (0.96, 0.92–0.98) demonstrated the highest specificity. EULAR/ACR-2019 (0.92, 0.87–0.96) and SLICC-2012 (0.92, 0.86–0.96) had the similar specificity. We found that the applicability of the new EULAR/ACR-2019 criteria in juvenile SLE is not yet the best diagnostic tool. PROSPERO CRD42022321514. • This study represents the first network meta-analysis of diagnostic test accuracy to compare the performance of ACR-1997, SLICC-2012, and EULAR/ACR-2019 criteria for juvenile systemic lupus erythematosus. • For EULAR/ACR-2019, we determined the performance of the new EULAR/ACR-2019 criteria to have an area under the curve value of 0.96. • Among all of the included studies, we found EULAR/ACR-2019 not to be the best tool for diagnosing juvenile systemic lupus erythematosus; SLICC-2012 has better sensitivity, and ACR-1997 has better specificity. [ABSTRACT FROM AUTHOR]

• Electricity, the most direct and effective use of RES, is in center of energy transition. • An interdisciplinary procedure suitable for the planning of RES is presented. • Key points and procedures for the assessment of large-scale RES installation is presented. • Simulation on Eygiption case shows the technical and economical viability. Due to the environmental impacts brought by current energy schemes, the energy transition, a new paradigm shift from fossil fuels to renewable energy, has been widely accepted and is being realized through collective international, regional, and local efforts. Electricity, as the most direct and effective use of renewable energy sources (RES), plays a key role in the energy transition. In this paper, we first discuss a viable pathway to energy transition through the electricity triangle, highlighting the role of RES in electricity generation. Further, we propose methodologies for the planning of wind and solar PV, as well as how to address their uncertainty in generation expansion problems. Finally, by using a web-based tool, "RES-PLAT" 1 1 https://res-plat.est.polito.it. , we demonstrate the scheme in a case study in Egypt, which evaluates the impacts and benefits of a large-scale RES expansion. 2 2 The short version of the paper was presented at Applied Energy Symposium: MIT A+ B, August 13–14, Boston. This paper is a substantial extension of the short version of the conference paper. [ABSTRACT FROM AUTHOR]

• We propose a two-stage framework for entity alignment from the perspective of combining the advantages of both symbol-based and embedding-based methods. • A series of symbol-based methods are adopted to align the relation pairs in stage I. • Symbol-based methods and a hybrid embedding model are combined to match the entity pairs in stage II. • Experimental results from real-word datasets demonstrate that our proposed method is effective. • Ablation studies illustrate our proposed strategies are versatile and can also be applied to other embedding models. The objective of entity alignment is to judge whether entities refer to the same object in the real world. Methods for entity alignment can be grossly divided into two groups: conventional symbol-based entity alignment methods and embedding-based entity alignment methods. Both groups of methods have advantages and disadvantages (which are detailed in Section 1). Therefore, combining the advantages of both methods might be a promising strategy. However, to the best of our knowledge, only the RTEA algorithm that was proposed in our previous conference paper (Proceeding of Pacific Rim International Conference on Artificial Intelligence, pp. 162–175, 2019) utilizes this strategy for entity alignment. This manuscript is an extended version of that conference paper, in which an improved algorithm, namely, ESEA (combining e mbedding-based and s ymbol-based methods for e ntity a lignment), is proposed based on the following steps. First, a novel method for combining embedding models with symbol-based models is proposed. Entities with high vector similarities are obtained through a hybrid embedding model, and the final aligned entity pairs are calculated via symbol-based methods. Second, a series of symbol-based methods, instead of only the edit distance method in the original version, are combined with embedding-based methods for relation alignment. Third, we combine symbol-based and embedding-based methods in a more complicated framework with the objective of better exploiting the advantages of both methods. The experimental results on real-world datasets demonstrate that the proposed method outperformed several state-of-the-art embedding-based entity alignment approaches and outperformed our previous RTEA method. An example of fusing multiple financial knowledge graphs (KGs) from different sources. Through aligning and integrating multi-source heterogeneous data, we could observe that Jack Ma (also called Mr. Ma) is not only the principal founder of the Alibaba Group, but also a director of Softbank. Therefore, a more complete knowledge graph with rich information can be obtained, which is essential for applications such as financial search and financial question answering [Display omitted]. [ABSTRACT FROM AUTHOR]

An intelligent methodology and its prototype system are developed for automatically discovering legal precedents using semantic analysis. The concept of the trademark legal precedent recommendation was originated from our TE2020 conference paper. The approach is to identify matching cases related to given seed case with respect to their legal case brief attributes using advanced text mining techniques. In the paper, dynamic topic modeling is further developed to analyze the dataset over three time-sequential cohorts to identify trademark law topics varied over time. Further, the prototype system was demonstrated and verified using real trademark case analysis with satisfactory results. [ABSTRACT FROM AUTHOR]

Ever since California experienced its energy crisis two decades ago, dynamic pricing of electricity has been the topic of discussion in numerous conferences and papers. Scores of pilots involving some 400 treatments of time-varying rates have been done to assess customer response to dynamic pricing around the globe, beginning with California's Statewide Pricing Pilot that ran from 2003 to 04. In the past few years, we are witnessing large scale deployment of static time-varying rates in states such as California and Michigan. Colorado is about to embark on that journey. However, other than OG&E's deployment of dynamic pricing, we have not seen much deployment of dynamic pricing in North America. California's power outages in August 2002 have rekindled interest in the topic. But almost all of the discussion about dynamic pricing has focused on summer peaking utilities. In this paper, we discuss the experience of Hydro Quebec, a winter peaking utility in Canada. Hydro Quebec has tested both critical-peak pricing and peak-time rebates. The results are very encouraging and quite consistent with results from summer peaking utilities. The article is based on a question and answer format with Frederic Pelletier, who advises Hydro Quebec on tariff strategy. He had posted some results from their dynamic pricing deployment on LinkedIn in response to some results I had posted from three-pilots with TOU rates that had been carried out in Maryland. I put a few questions to Frederic and this article evolved out of that conversation. In the article that follows, the questions are mine and the answers are exclusively his. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms rely on the population initialization strategy to determine a set of candidate solutions, which provide the preliminary knowledge of the problem landscape for the subsequent evolutionary process. However, the distribution of the initial population is rarely concerned in the multimodal evolutionary community. Moreover, a large initial population has not been attractive enough for evolutionary multimodal optimization, because it is difficult and challenging to achieve a balance between diversity and convergence within limited computing resources. As an extended version of our previous conference paper, this paper focuses on the construction of a large initial population that is beneficial for evolutionary multimodal optimization. First, we propose an improved density-based population initialization strategy to generate a uniform initial population with a certain degree of randomness. Then, after acquiring the raw fitness landscape of the problem, a fitness-weighted density-based population initialization strategy is proposed to explore potential global peaks. Finally, we design a multi-species cooperative coevolution algorithm for multimodal optimization, which balances the exploration and exploitation of such a population in the evolutionary process. Experimental results demonstrate that the proposed algorithm is better than the mainstream algorithms on the CEC2013 multimodal optimization benchmark. [ABSTRACT FROM AUTHOR]