bluuebunny/crossref_metadata_2025_split huggingface dataset:
Noniterative tensor network‐based algorithm for Volterra system identification
Authors: Hu Yangsheng , Tan Li , de Callafon Raymond A. 
Published: March 2022

AbstractVolterra model serves as one of the powerful alternatives to approximate nonlinear dynamics on the basis of input/output observations. The kernel representation of a Volterra model is attractive since it is linear in the kernel coefficients and always stable. Although the kernel representation suffers from the curse of dimensionality, the demand for storage requirements can be relieved via a tensor network (TN) technique. This allows one to approximate complicated coupled nonlinear dynamics with high degree and even multi‐input multi‐output (MIMO) Volterra models. The iterative feature of existing TN‐based algorithms poses challenges for the algorithms to converge to an appropriate solution with both good prediction accuracy and minor overfitting problems. In this article, noniterative TN‐based algorithms with two tuning factors to solve either a linear or ridge regression are proposed for MIMO Volterra system identification. The proposed algorithms do not suffer convergence problems due to its noniterative feature and perform active low‐rank approximations to reduce overfitting problems. Simulations compare different algorithms and investigate the effects for different excitation signals.
Electrochemical Strategy for High‐Resolution Nanostructures in Laser‐Heat‐Mode Resist Toward Next Generation Diffractive Optical Elements
Authors: Wang Zhengwei , Chen Guodong , Wen Ming , Hu Xutao , Liu Xing , Wei Jingsong , Wu Qingsheng , Fu YongQing 
Published: March 2022

AbstractFor achieving high‐resolution nanostructures for next‐generation diffractive optical elements (DOEs) using an environmentally friendly process, an electrochemical development strategy is proposed and developed using AgInSbTe‐based laser heat‐mode resist (AIST‐LHR). Based on the electrical resistivity difference of amorphous and crystalline phases for this resist, an etching selectivity ratio of ≈30:1 (i.e., the etch ratio between the amorphous and crystalline ones) is achieved through the oxidation of Fe3+ ions with the assisted pitting activation etching using Cl− ions in an acid medium. Nanostructures with a minimum feature size down to 41 nm are successfully generated, including grating patterns, meta‐surface optical structures, gears, and English characters. Using a post‐plasma etching process, the nanostructures are successfully transferred from the AIST‐HLR onto silica substrate, and X‐ray grating patterns with a line space of 80 nm are created as a demonstration for its potential applications in DOEs.
Critical roles of the activation of ethylene pathway genes mediated by DNA demethylation in Arabidopsis hyperhydricity
Authors: Gao Hongyang , Xia Xiuying , An Lijia 
Published: March 2022

AbstractHyperhydricity (HH) often occurs in plant tissue culture, seriously influencing the commercial micropropagation and genetic improvement. DNA methylation has been studied for its function in plant development and stress responses. However, its potential role in HH is unknown. In this study, we report the first comparative DNA methylome analysis of normal and hyperhydric Arabidopsis thaliana (L.) Heynh. seedlings using whole‐genome bisulfite sequencing (BS‐seq). We found that the global methylation level decreased in hyperhydric seedlings, and most of the differentially methylated genes were CHH hypomethylated genes. Moreover, the bisulfite sequencing results showed that hyperhydric seedlings displayed CHH demethylation patterns in the promoter of the ACS1 and ETR1 genes, resulting in upregulated expression of both genes and increased ethylene accumulation. Furthermore, hyperhydric seedling displayed reduced stomatal aperture accompanied by decreased water loss and increased phosphorylation of aquaporins accompanied by increased water uptake. While silver nitrate (AgNO3) prevented HH by maintained the degree of methylation in the promoter regions of ACS1 and ETR1 and downregulated the transcription of both genes. AgNO3 also reduced the content of ethylene together with the phosphorylation of aquaporins and water uptake. Taken together, this study suggested that DNA demethylation is a key switch that activates ethylene pathway genes to enable ethylene synthesis and signal transduction, which may subsequently influence aquaporin phosphorylation and stomatal aperture, eventually causing HH; thus, DNA demethylation plays a crucial role in HH. These results provide insights into the epigenetic regulation mechanism of HH and confirm the role of ethylene and AgNO3 in hyperhydricity control.
Bioinspired soft nanovesicles for site‐selective cancer imaging and targeted therapies
Authors: Prasad Rajendra , Conde João 
Published: March 2022

AbstractCell‐to‐cell communication within the heterogeneous solid tumor environment plays a significant role in the uncontrolled metastasis of cancer. To inhibit the metastasis and growth of cancer cells, various chemically designed and biologically derived nanosized biomaterials have been applied for targeted cancer therapeutics applications. Over the years, bioinspired soft nanovesicles have gained tremendous attention for targeted cancer therapeutics due to their easy binding with tumor microenvironment, natural targeting ability, bio‐responsive nature, better biocompatibility, high cargo capacity for multiple therapeutics agents, and long circulation time. These cell‐derived nanovesicles guard their loaded cargo molecules from immune clearance and make them site‐selective to cancer cells due to their natural binding and delivery abilities. Furthermore, bioinspired soft nanovesicles prevent cell‐to‐cell communication and secretion of cancer cell markers by delivering the therapeutics agents predominantly. Cell‐derived vesicles, namely, exosomes, extracellular vesicles, and so forth have been recognized as versatile carriers for therapeutic biomolecules. However, low product yield, poor reproducibility, and uncontrolled particle size distribution have remained as major challenges of these soft nanovesicles. Furthermore, the surface biomarkers and molecular contents of these vesicles change with respect to the stage of disease and types. Here in this review, we have discussed numerous examples of bioinspired soft vesicles for targeted imaging and cancer therapeutic applications with their advantages and limitations. Importance of bioengineered soft nanovesicles for localized therapies with their clinical relevance has also been addressed in this article. Overall, cell‐derived nanovesicles could be considered as clinically relevant platforms for cancer therapeutics.This article is categorized under:
Biology‐Inspired Nanomaterials > Nucleic Acid‐Based Structures
Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease
Rethinking the Input for Process Mining: Insights from the XES Survey and Workshop
Authors: Wynn Moe Thandar , Lebherz Julian , van der Aalst Wil M. P. , Accorsi Rafael , Di Ciccio Claudio , Jayarathna Lakmali , Verbeek H. M. W. 
Published: March 2022

AbstractAlthough the popularity and adoption of process mining techniques grew rapidly in recent years, a large portion of effort invested in process mining initiatives is still consumed by event data extraction and transformation rather than process analysis. The IEEE Task Force on Process Mining conducted a study focused on the challenges faced during event data preparation (from source data to event log). This paper presents findings from the online survey with 289 participants spanning the roles of practitioners, researchers, software vendors, and end-users. These findings were presented at the XES 2.0 workshop co-located with the 3rd International Conference on Process Mining. The workshop also hosted presentations from various stakeholder groups and a discussion panel on the future of XES and the input needed for process mining. This paper summarises the main findings of both the survey and the workshop. These outcomes help us to accelerate and improve the standardisation process, hopefully leading to a new standard widely adopted by both academia and industry.
Can Deep Neural Networks Learn Process Model Structure? An Assessment Framework and Analysis
Authors: Peeperkorn Jari , vanden Broucke Seppe , De Weerdt Jochen 
Published: March 2022

AbstractPredictive process monitoring concerns itself with the prediction of ongoing cases in (business) processes. Prediction tasks typically focus on remaining time, outcome, next event or full case suffix prediction. Various methods using machine and deep learning have been proposed for these tasks in recent years. Especially recurrent neural networks (RNNs) such as long short-term memory nets (LSTMs) have gained in popularity. However, no research focuses on whether such neural network-based models can truly learn the structure of underlying process models. For instance, can such neural networks effectively learn parallel behaviour or loops? Therefore, in this work, we propose an evaluation scheme complemented with new fitness, precision, and generalisation metrics, specifically tailored towards measuring the capacity of deep learning models to learn process model structure. We apply this framework to several process models with simple control-flow behaviour, on the task of next-event prediction. Our results show that, even for such simplistic models, careful tuning of overfitting countermeasures is required to allow these models to learn process model structure.
Remaining Time Prediction for Processes with Inter-case Dynamics
Authors: Pourbafrani Mahsa , Kar Shreya , Kaiser Sebastian , van der Aalst Wil M. P. 
Published: March 2022

AbstractProcess mining techniques use event data to describe business processes, where the provided insights are used for predicting processes’ future states (Predictive Process Monitoring). Remaining Time Prediction of process instances is an important task in the field of Predictive Process Monitoring (PPM). Existing approaches have two key limitations in developing Remaining Time Prediction Models (RTM): (1) The features used for predictions lack process context, and the created models are black-boxes. (2) The process instances are considered to be in isolation, despite the fact that process states, e.g., the number of running instances, influence the remaining time of a single process instance. Recent approaches improve the quality of RTMs by utilizing process context related to batching-at-end inter-case dynamics in the process, e.g., using the time to batching as a feature. We propose an approach that decreases the previous approaches’ reliance on user knowledge for discovering fine-grained process behavior. Furthermore, we enrich our RTMs with the extracted features for multiple performance patterns (caused by inter-case dynamics), which increases the interpretability of models. We assess our proposed remaining time prediction method using two real-world event logs. Incorporating the created inter-case features into RTMs results in more accurate and interpretable predictions.
Active Anomaly Detection for Key Item Selection in Process Auditing
Authors: Post Ruben , Beerepoot Iris , Lu Xixi , Kas Stijn , Wiewel Sebastiaan , Koopman Angelique , Reijers Hajo 
Published: March 2022

AbstractProcess mining allows auditors to retrieve crucial information about transactions by analysing the process data of a client. We propose an approach that supports the identification of unusual or unexpected transactions, also referred to as exceptions. These exceptions can be selected by auditors as “key items”, meaning the auditors wants to look further into the underlying documentation of the transaction. The approach encodes the traces, assigns an anomaly score to each trace, and uses the domain knowledge of auditors to update the assigned anomaly scores through active anomaly detection. The approach is evaluated with three groups of auditors over three cycles. The results of the evaluation indicate that the approach has the potential to support the decision-making process of auditors. Although auditors still need to make a manual selection of key items, they are able to better substantiate this selection. As such, our research can be seen as a step forward with respect to the usage of anomaly detection and data analysis in process auditing.
An Event Data Extraction Approach from SAP ERP for Process Mining
Authors: Berti Alessandro , Park Gyunam , Rafiei Majid , van der Aalst Wil M. P. 
Published: March 2022

AbstractThe extraction, transformation, and loading of event logs from information systems is the first and the most expensive step in process mining. In particular, extracting event logs from popular ERP systems such as SAP poses major challenges, given the size and the structure of the data. Open-source support for ETL is scarce, while commercial process mining vendors maintain connectors to ERP systems supporting ETL of a limited number of business processes in an ad-hoc manner. In this paper, we propose an approach to facilitate event data extraction from SAP ERP systems. In the proposed approach, we store event data in the format of object-centric event logs that efficiently describe executions of business processes supported by ERP systems. To evaluate the feasibility of the proposed approach, we have developed a tool implementing it and conducted case studies with a real-life SAP ERP system.
Verifying Guideline Compliance in Clinical Treatment Using Multi-perspective Conformance Checking: A Case Study
Authors: Grüger Joscha , Geyer Tobias , Kuhn Martin , Braun StephanA. , Bergmann Ralph 
Published: March 2022

AbstractClinical guidelines support physicians in the evidence-based treatment of patients. The technical verification of guideline compliance is not trivial, since guideline knowledge is usually represented textually and none of the approaches to computer-interpretable guideline representation has yet been able to establish itself. Due to the procedural nature of treatment sequences, this case study examines the applicability of a guideline process model to real hospital data for verification of guideline compliance. For this purpose, the limitations and challenges in the transformation of clinical data into an event log and in the application of conformance checking to align the data with the guideline reference model are investigated. As a data set, we use treatment data of skin tumor patients from a cancer registry enriched by hospital information system data. The results show the difficulty of applying process mining to medically complex and heterogeneous data and the need for complex preprocessing. The variability of clinical processes makes the application of global conformance checking algorithms challenging. In addition, the work shows the semantic weakness of the alignments and the need for new semantically sensitive approaches.
Root Cause Analysis in Process Mining with Probabilistic Temporal Logic
Authors: Van Houdt Greg , Depaire Benoît , Martin Niels 
Published: March 2022

AbstractProcess mining is a research domain that enables businesses to analyse and improve their processes by extracting insights from event logs. While determining the root causes of, for example, a negative case outcome can provide valuable insights for business users, only limited research has been conducted to uncover true causal relations within the process mining field. Therefore, this paper proposes AITIA-PM, a novel technique to measure cause-effect relations in event logs based on causality theory. The AITIA-PM algorithm employs probabilistic temporal logic to formally yet flexibly define hypotheses and then automatically tests them for causal relations from data. We demonstrate this by applying AITIA-PM on a real-life dataset. The case study shows that, after a well-thought-out hypotheses definition and information extraction, the AITIA-PM algorithm can be applied on rich event logs, expanding the possibilities of meaningful root cause analysis in a process mining context.
Articulations of Ethics: Energy Worlds and Moral Selves
Authors: High Mette M. 
Published: March 2022

AbstractThis chapter introduces the notion of ‘regimes of ethics’ to explore the diverse ways that ethics is articulated in corporate capitalism, particularly in industries that are involved in the extraction of natural resources such as oil and gas. I show how companies draw on corporate social responsibility frameworks as public-facing demonstrations of ethics for stakeholder engagement, while also generating greater company loyalty and pride among employees. While corporate social responsibility (CSR) provides an explicit corporate strategy, the professional codes of ethics that apply to engineering practices in the USA specifically anchor ethics in individual decision-making. Animated by a generally conservative, if not stultifying, ethos, engineering ethics sees practices such as whistleblowing and breaking ranks as epitomising individual ethical action. Co-existing with and sometimes challenging these two formal regimes of ethics are also industry actors’ own moral convictions. This third regime of ethics draws on my ethnographic research in the oil and gas industry in Colorado, USA. By discussing these various articulations of ethics, my aim is to take seriously formal rules and codes as well as industry actors’ own ethical sensibilities. I suggest that rather than existing as entrenched hardened moral worlds, these multiple and competing regimes of ethics indicate the underdetermined nature of ethical life.
Challenging the Transition of Civilization: Theory and Practice of “Energy Democracy”
Authors: Sasaki Hiroshi 
Published: March 2022

AbstractToday, wind powers and solar powers have become the main stream of energies. Everyone, from major electric power utilities to mega capital investors, has entered the renewable energy sector, increasingly and rapidly expanding the mass capital investment projects in the world. It is not rare to find cases of such mega projects developing confrontations with local communities. Since 1990s, the privatization of energy sector has been significant, with Nordic countries and Germany seeing the advancement of privatizations among community-operated energy utilities. Recently, however, there has been the movement to re-review such trend “to revive public utilities”. The recent reversal of privatization trend means that the conventional way of decision-making participated by local governments and handful corporations is no longer appropriate, and there is a rise of common understanding that the decision-making and governance method of local communities must be open and distributed horizontally. The rapid progress of ICT in recent years has raised awareness of risks in the governance system dominated by private companies, while raising technical capabilities to realize new and open decision-making and governance in local communities.
Transboundary Cooperative Governance Toward Energy Transition in East Asia
Authors: Otsuka Kenji 
Published: March 2022

AbstractThe last decade has witnessed several events that had a serious impact on people’s attitudes toward environmental sustainability regionally and globally. This chapter depicts how transboundary cooperative initiatives by states and nonstate actors tackle transboundary air pollution and climate change in East Asia. It also examines the opportunities and challenges we face in the very recent landscape shift toward carbon neutrality and the deepening concerns for climate emergencies. Multilateral cooperative institutions in East Asia have focused on the monitoring of air pollutants and information sharing of related policies and measures among member countries. It should also be noted that there are some transboundary coalitions of independent scholars and research-type NGOs in Northeast Asia who conduct joint research on the decarbonization of energy systems and disseminate up-to-date knowledge and information on decentralized nature-based renewable energy. For further development of transboundary cooperation in East Asia, opportunities exist as an increasing potential for multilateral policy dialogue beyond the borders and broadening partnerships for local and transboundary coalitions with global alliances on the one hand; and challenges in just and safe transition, decarbonization of overseas financing, and seeking energy resilience on the other.
Conway subgroup symmetric compactifications redux
Authors: Baykara Zihni Kaan , Harvey Jeffrey A. 
Published: March 2022

Abstract
We extend the investigation in [1] of special toroidal compactifications of heterotic string theory for which the half-BPS states provide representations of subgroups of the Conway group. We also explore dual descriptions of these theories and find that they are all linked to either F-theory or type IIA string theory on K3 surfaces with symplectic automorphism groups that are the same Conway subgroups as those of the heterotic dual. The matching with type IIA K3 dual theories includes both the matching of symmetry groups and a comparison between the Narain lattice on the heterotic side and the cohomology lattice on the type IIA side. We present twelve examples where we can identify a type IIA dual K3 orbifold theory as the dual description of the heterotic theory. In addition, we include a supplementary Mathematica package that performs most of the computations required for these comparisons.
Host plant resistance for fall armyworm management in maize: relevance, status and prospects in Africa and Asia
Authors: Prasanna Boddupalli M. , Bruce Anani , Beyene Yoseph , Makumbi Dan , Gowda Manje , Asim Muhammad , Martinelli Samuel , Head Graham P. , Parimi Srinivas 
Published: March 2022

Abstract
Key message
Sustainable control of fall armyworm (FAW) requires implementation of effective integrated pest management (IPM) strategies, with host plant resistance as a key component. Significant opportunities exist for developing and deploying elite maize cultivars with native genetic resistance and/or transgenic resistance for FAW control in both Africa and Asia.

Abstract
The fall armyworm [Spodoptera frugiperda (J.E. Smith); FAW] has emerged as a serious pest since 2016 in Africa, and since 2018 in Asia, affecting the food security and livelihoods of millions of smallholder farmers, especially those growing maize. Sustainable control of FAW requires implementation of integrated pest management strategies, in which host plant resistance is one of the key components. Significant strides have been made in breeding elite maize lines and hybrids with native genetic resistance to FAW in Africa, based on the strong foundation of insect-resistant tropical germplasm developed at the International Maize and Wheat Improvement Center, Mexico. These efforts are further intensified to develop and deploy elite maize cultivars with native FAW tolerance/resistance and farmer-preferred traits suitable for diverse agro-ecologies in Africa and Asia. Independently, genetically modified Bt maize with resistance to FAW is already commercialized in South Africa, and in a few countries in Asia (Philippines and Vietnam), while efforts are being made to commercialize Bt maize events in additional countries in both Africa and Asia. In countries where Bt maize is commercialized, it is important to implement a robust insect resistance management strategy. Combinations of native genetic resistance and Bt maize also need to be explored as a path to more effective and sustainable host plant resistance options. We also highlight the critical gaps and priorities for host plant resistance research and development in maize, particularly in the context of sustainable FAW management in Africa and Asia.