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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]

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]

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]

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]

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]

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]

• A Mixed Particle Swarm Optimization model is developed for optimal network reconfiguration and DG integration. • Optimal network reconfiguration allow a better penetration of renewable energy in the system. • Optimal integration of DG units dramatically increases the voltage level and the optimal exploitation of the DS. • DG integration along with optimal NR greatly contribute to power losses minimization and voltage level enhancement. This paper introduces a Mixed Particle Swarm Optimization (MPSO) approach for active power loss minimization and voltage profile improvement in the distribution network. The developed technique joins the Binary Particle Swarm Optimization (BPSO) and the conventional PSO algorithms. The first one is devoted to identify the optimal distribution network configuration, while the second one is used to solve Distributed Generation (DG) placement and sizing problems. To evaluate the performance of the developed approach, three different load scenarios were assessed during network reconfiguration (NR) and DG integration. Simulations are conducted on two distribution test systems, namely, the IEEE-33-bus and the IEEE-69-bus. The obtained results clearly demonstrate the performance and the effectiveness of the proposed method to find optimal status of switches, as well as DG locations and sizes. A benchmark comparison is presented to prove the efficiency of the proposed MPSO with regard to other optimization techniques. The results show that MPSO outperforms these techniques in terms of quality of solution, power loss reduction and voltage profile enhancement. This study is an extension of the earlier published conference paper. [ABSTRACT FROM AUTHOR]

Given a set A ⊆ S n of m permutations of { 1 , 2 , ... , n } and a distance function d , the median problem consists of finding a permutation π ∗ that is the "closest" of the m given permutations. Here, we study the problem under the Kendall- τ distance which counts the number of order disagreements between pairs of elements of two permutations. This problem has been proved to be NP-hard when m ≥ 4 , m even. In this article, which is a full version of the conference paper Milosz and Hamel (2016), we investigate new theoretical properties of A that solve the relative order between pairs of elements in median permutations of A , thus drastically reducing the search space of the problem. The resulting preprocessing of the problem is implemented with a Branch-and-Bound solving algorithm. We analyze its performance on randomly generated data and on real data. [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]

A review of publications about educational research in Guinea-Bissau in the last 40 years is the subject of this paper. Published articles have permitted us to map the research terrain and produce new knowledge without collecting new empirical data. We reviewed 148 studies published between 1975 and 2015 in journal articles, theses, monographs, research reports, conference papers, books, edited volumes and grey literature. Moving from title to introduction/abstract and then the entire text we explored thematic focuses, methods, findings, conclusions and funding. This paper argues that education policy receives more attention followed by language issues and it also suggests that the research agenda is mainly due to external pressure with a tendency for applied research. [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]

International conference presentations represent one of the biggest challenges for academics using English as a Lingua Franca (ELF). This paper aims to initiate exploration into the multimodal academic discourse of oral presentations, including the verbal, written, non-verbal material (NVM) and body language modes. It offers a Systemic Functional Linguistic (SFL) and multimodal framework of presentations to enhance mixed-disciplinary ELF academics' awareness of what needs to be taken into account to communicate effectively at conferences. The model is also used to establish evaluation criteria for the presenters' talks and to carry out a multimodal discourse analysis of four well-rated 20-min talks, two from the technical sciences and two from the social sciences in a workshop scenario. The findings from the analysis and interviews indicate that: (a) a greater awareness of the mode affordances and their combinations can lead to improved performances; (b) higher reliance on the visual modes can compensate for verbal deficiencies; and (c) effective speakers tend to use a variety of modes that often overlap but work together to convey specific meanings. However, firm conclusions cannot be drawn on the basis of workshop presentations, and further studies on the multimodal analysis of ‘real conferences’ within specific disciplines are encouraged. [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]

Abstract: This article investigates and discusses 3D property research, as evidenced by conference papers and other publications written in English, to analyze the distribution of interest areas and the occurrence of legal aspects and trends within 3D property research occurring between 2001 and 2011. A total of 156 publications on 3D property were examined. The publications were classified in four different categories, which represent different aspects of 3D property: legal, technical, registration and organizational. More 3D property research has been conducted on technical aspects and registration than legal aspects. In the legal category, most studies addressed national legislation and the practical use of (national) legislation. The authors believe that further fundamental legal research on 3D property is needed. The quantity of research could be increased, for example, by promoting international discussion and increasing the number of comparative legal studies on 3D property rights. Additional and more focused attention should be given to international matters, such as comparative studies on the use of 3D property concepts, the development of (international) 3D property terminology and cooperation between 3D property unit owners. [Copyright &y& Elsevier]

Abstract: Evaluation in academic discourse has received considerable attention from researchers. Much of the work on evaluation has focused, however, on written genres, and less attention has been paid to how evaluation unfolds in spoken academic genres. In our present research, we are interested in disclosing how the interpersonal meaning of evaluation is expressed in the discussion session (hereafter DS) that follows conference paper presentations, since DS has already been defined as an “evaluative forum”, when comparing its phraseological patterns with those of the presentation. Though the study of evaluation in spoken genres has been developed focusing exclusively on linguistic aspects, we assume the non-linguistic message that accompanies the linguistic message has an effect on the interpersonal meaning of the communication. Therefore, the aim of our research is to analyse the evaluative meaning conveyed in DSs that follow paper presentations in an applied linguistics conference. In the study, we draw on a social semiotic theory of language and of kinesics and paralanguage to frame a multimodal exploration of this interpersonal meaning. The comparative analysis between linguistic evaluation and multimodal evaluation reveals the significant contribution of non-linguistics features which are used to intensify linguistic evaluation or to express the speakers’ attitude. [Copyright &y& Elsevier]

The present article reviews and compares the immunomodulatory activities of Crocus sativus (C. sativus) and Nigella sativa (N. sativa) and their main bioactive compounds. Immunomodulatory effects of these plants, especially with respect to Th1 and Th2 cytokines, are discussed based on relevant articles, books, and conference papers published in English until the end of April 2020, that were retrieved from Web of Science, PubMed, Scopus and Google Scholar databases. C. sativus and its constituents increase immunoglobulin (Ig-)G, interleukin 2 (IL)-2, interferon gamma (IFN-γ), and IFN-γ/IL-4 ratio, but decreased IgM, IL-10 and IL-4 secretion. N. sativa extract and thymoquinone reduce the levels of IL-2, -4, -10, and -12, while enhance IFN-γ and serum IgG1 and 2a. The reviewed articles indicate that C. sativus and N. sativa and their constituents could be potentially considered promising treatments for disorders associated with immune-dysregulation such as asthma and cancer. [Display omitted] • Immunomodulatory activities of Crocus sativus , Nigela sativa and their compounds were presented. • C. sativus and its ingredients increased levels of Ig-G, IL-2, and IFN-γ. • C. sativus and its constituents decreased IgM, IL-10 and IL-4 secretion. • N. sativa extract and thymoquinone (TQ) reduced the levels of IL-2, -4, -10, and -12. • N. sativa extract and TQ enhanced the level of IFN-γ, and serum levels of IgG1 and 2a. [ABSTRACT FROM AUTHOR]

There are multiple ways of defining nonmonotonic inference relations based on a conditional knowledge base. While the axiomatic system P is an important standard for such plausible nonmonotonic reasoning, inference relations obtained from system Z or from c-representations have been designed which go beyond system P by selecting preferred models for inference. For any class of models M , we propose the notion of weakly skeptical inference, first introduced in an ECAI conference paper this article revises and extends, that lies between skeptical and credulous inference with respect to M. Weakly skeptical c-inference properly extends skeptical c-inference, but avoids disadvantages of a too liberal credulous c-inference. We extend the concepts of skeptical, weakly skeptical, and credulous c-inference modes by taking models obtained from different minimality criteria into account. We illustrate the usefulness of the obtained inference relations and show that they fulfill various desirable properties put forward for nonmonotonic reasoning. Furthermore, we elaborate in detail the interrelationships among the inference relations when taking the different inference modes and various classes of minimal models into account. [ABSTRACT FROM AUTHOR]

• Laplace or Fourier Transforms are employed to convert from frequency to time domain the transient Ground Potential Rise responses. • Some lightning current functions can not be analytically integrated for the Laplace or Fourier Transform. • Recursive methods are employed to compute transient Ground Potential Rise directly in time domain with lower error in comparison with those computed with classical methods. • Recursive methods do not require Laplace or Fourier Transforms or circuit implementation in EMTP-tool software. • Electromagnetic transient in grounding system with more realistic soils can be computed using different programming languages based on recursive methods. Ground Potential Rise (GPR) is an important factor for a grounding system that must be properly designed to protect people against any dangerous induced voltages and to avoid damages in equipment. In this context, several approaches to assess GPR are available in the literature which can be developed either directly in time domain or frequency-to-time transforms. The purpose of this paper is to investigate the performance of two time-domain recursive methods to compute the transient GPR in grounding systems generated by different lightning currents. First, the grounding impedances are calculated by a full-wave electromagnetic software FEKO with numerical Method of Moments from 100 Hz to 5 MHz. The GPRs are assessed by a recursive convolution method (M 1) and by a recursive trapezoidal integration method (M 2). Both methods employ the Vector Fitting technique on each impedance curve adjusted into a poles-residues form. Then, simulation results from the recursive methods are compared with those obtained with frequency-to-time method using the Numerical Laplace Transform (NLT) and with the equivalent circuit incorporated in the ATP-software. Results show a good agreement between the responses from recursive methods in comparison with those from NLT and ATP-software. As advantages, the recursive methods are an alternative tool when no analytical expressions for lightning currents are known or only measured data is provided. Additionally, the circuit implementation in Electromagnetic Transient (EMT)-type software tools is not needed to compute the transient GPRs in time domain. This work is an extension of a 2019-SIPDA conference paper [1]. [ABSTRACT FROM AUTHOR]

• Flow-induced instabilities constitute important reactor operation and safety issues. • This paper presents an overview of various methods of reactor stability analysis. • Several examples are shown illustrating predictive capabilities of individual methods. Understanding the origins and propagation of flow-induced instabilities is very important for the operation and safety of the current and next generations of boiling water reactors (BWR), as well as for future new reactor types, such as the supercritical-pressure reactors, cooled by either water or CO2. Consequently, substantial (or even extensive) efforts have been made during the past 40+ years to investigate the physical phenomena governing such instabilities, and to develop models and computational tools to simulate the dynamics of marginally-stable/unstable boiling systems. The purpose of this paper is to present an updated perspective on, and discuss new aspects of, selected studies performed by the author and his collaborators, associates and graduate students, aimed at the development of various methods for the analysis of flow-induced instabilities in both boiling and supercritical-pressure systems. The impact of model and method selection on the results of predictions will also be discussed for a large class of systems, including the neutronically-coupled oscillations. Since the current paper is a part of the NED issue commemorating 40 years since the first NURETH–series conference, the list of references includes eleven (11) related papers published before in various NURETH proceedings, as well as other related journal articles and conference papers. [ABSTRACT FROM AUTHOR]

The 12th International Symposium on Applied Plasma Science (ISAPS ′19) was held at University of Yamanashi and Kofu Kinenbi Hotel, Yamanashi, Japan, from September 24 through 28, 2019. The authors of selected research topics presented at ISAPS '19 were invited to write their original papers after the conference. These were peer reviewed and finally 11 papers were compiled in this special issue of Vacuum. [ABSTRACT FROM AUTHOR]

Open innovation (OI) has received considerable attention since its introduction and has positive and negative characteristics. To offer an overview for future research, this article produces a comprehensive review by combining bibliometric analysis and content analysis. A total of 4463 journal articles and 3061 conference papers on OI were extracted from Web of Science (WOS). The synthesized networks were constructed and visualized through CiteSpace software to aid analytical studies. After the descriptive bibliometric analysis of all publications, a content analysis of 213 articles was carried out to develop the conceptual framework of OI. The results reveal that OI still confined itself to limited disciplines and innovation-centric journals but is beginning to put more effort into becoming a more open ecosystem by influencing other disciplines. We also find some gaps in the extant research and provide some research directions in the OI field: empirical studies in various contexts could enrich the external validity of OI, other theoretical perspectives could provide novel views, and OI is on its way to becoming mature but has a long way to go. [ABSTRACT FROM AUTHOR]

Since its inception in 1969, Chemico-Biological Interactions (CBI) has persistently published high quality research articles. As part of the journal's golden anniversary (50 years), we performed an electronic search on Scopus to get all publications details. Based on citescore, ranking & percentile, CBI holds 21st position in the top 113 relevant journals (in 2018). CBI also completed publications of 8005 manuscripts in March 2020. The highest documents were articles (6972/87.09%) followed by conference papers (588/7.34%) and reviews (252/3.14%). The maximum number of publications (385) was recorded in 2019, followed by 366 (in 2010) and 336 in 2016. Furthermore, details of the top 50 countries, top 50 authors and top 20 institutes with total publications, h-index, total citations and without selfcitations (WSC) are provided. USA (2371), China (786) and United Kingdom (658) are the top three countries, O'Brien, P.J (48), Maser, E. (45) and Lockridge, O. (35) are the top three authors and Karolinska Institutet (144), Stockholm University (102) & Ministry of Education China (94) are the top three institutes involved in research publications. More than eighty-four thousand (84,000) key words were amassed from scopus and after critical analysis we proposed a common sequence and connectivity. The top 200 articles, 200 reviews and 200 conference papers were analyzed by Vosviewer for various parameters. While, the top three (3) research articles and reviews are briefly described. The bibliometric analyses confirm a noteworthy growth of CBI in research publications and scientometric performance. • The 1st bibliometric study of chemico-biological interactions. • The data about top authors, universities and countries are provided. • The top 200 articles, reviews and conference papers were analyzed by Vosviewer. [ABSTRACT FROM AUTHOR]

This work follows up a previous paper at conference Cyberworlds 2018 for automatic border approximation of cutaneous melanoma and other skin lesions from macroscopic medical images. Given a set of feature points on the boundary of the skin lesion obtained by a dermatologist, we introduce a new method for automatic least-squares B-spline curve fitting of such feature points. The method is based on the original cuckoo search algorithm used in the conference paper but with three major modifications: (1) we use an enhanced version of the algorithm in which the parameters change dynamically with the generations; (2) this improved method is coupled with the Luus-Jaakola local search heuristics for better performance; (3) the original Bézier curves are now replaced by the more powerful and more general B-spline curves, providing extra flexibility and lower polynomial degree. The new method (called memetic improved cuckoo search algorithm) has been applied to a benchmark comprised of ten medical images of skin lesions. The computer results show that it performs very well and yields a border curve enclosing the lesion and fitting the feature points with good accuracy. Furthermore, a comparison with ten alternative methods in the literature (six standard mathematical methods for B-spline fitting, two state-of-the art methods in medical imaging, the method in our conference paper and the non-memetic version of our new method) shows that it outperforms all these methods in terms of numerical accuracy for the instances in our reference benchmark. [ABSTRACT FROM AUTHOR]

The current Virtual Special Issue of the Energy Journal contains cutting-edge developments in energy research that were presented at the 21st Conference on Process Integration for Energy Saving and Pollution Reduction, held in Prague, Czech Republic, on 25–29 August 2018. Conference PRES hosted more than 234 oral and 221 poster presentations. Out of these papers, the 34 contributions in this VSI represent a selection of the most significant developments in the area of energy research, and each article includes extensions and additional results beyond the conference paper. Conference PRES is an annual symposium that aims to attract thought-leaders and academics to facilitate an ongoing discussion around energy, the impact on the environment, and the way forward. This paper provides an overview of each contribution, the context of their development, and groups them into four research themes: (1) Heat Exchangers and Heat Exchanger Networks, (2) System Optimisation, (3) System Design and Analysis, and (4) Process and System Synthesis. [ABSTRACT FROM AUTHOR]

The rapid development of information and communication technologies (ICT) has enabled the prevailing digital transformation (i.e. digitalization), where physical products can be readily digitized in the virtual space and seamlessly interconnected. Meanwhile, industries are ever increasingly adopting service business models (i.e. servitization), so as to offer not only physical products but also services as a solution bundle to meet individual customer needs. Such convergence of both digitalization and servitization (i.e. digital servitization) has triggered an emerging IT-driven business paradigm, smart product-service systems (Smart PSS). As a novel paradigm coined in 2014, to the authors' knowledge, only 2 conference papers have provided some literature review to date, and many issues remain uncovered or not comprehensively investigated. Aiming to fill this gap, this paper has conducted a systematic review of Smart PSS or related papers published ever since its first brought up to date (30/06/2019), and selected 97 representative items together with other 37 supplementary works to summarize the tendency towards Smart PSS, its business and technical aspects, current challenges, and future perspectives. From the survey, it is found that several hybrid concerns are the key challenges faced, and self-adaptiveness with sustainability, advanced IT infrastructure, human-centric perspectives, and circular lifecycle management are the core future perspectives to explore. It is hoped that this work can attract more open discussions and provide useful insights to both academics and industries in their exploration and implementation of Smart PSS. [ABSTRACT FROM AUTHOR]