bluuebunny/crossref_metadata_2025_split huggingface dataset:
Reciprocity of Cell Mechanics with Extracellular Stimuli: Emerging Opportunities for Translational Medicine
Authors: Li Yiwei , Wong Ian Y. , Guo Ming 
Published: March 2022

AbstractHuman cells encounter dynamic mechanical cues in healthy and diseased tissues, which regulate their molecular and biophysical phenotype, including intracellular mechanics as well as force generation. Recent developments in bio/nanomaterials and microfluidics permit exquisitely sensitive measurements of cell mechanics, as well as spatiotemporal control over external mechanical stimuli to regulate cell behavior. In this review, the mechanobiology of cells interacting bidirectionally with their surrounding microenvironment, and the potential relevance for translational medicine are considered. Key fundamental concepts underlying the mechanics of living cells as well as the extracelluar matrix are first introduced. Then the authors consider case studies based on 1) microfluidic measurements of nonadherent cell deformability, 2) cell migration on micro/nano‐topographies, 3) traction measurements of cells in three‐dimensional (3D) matrix, 4) mechanical programming of organoid morphogenesis, as well as 5) active mechanical stimuli for potential therapeutics. These examples highlight the promise of disease diagnosis using mechanical measurements, a systems‐level understanding linking molecular with biophysical phenotype, as well as therapies based on mechanical perturbations. This review concludes with a critical discussion of these emerging technologies and future directions at the interface of engineering, biology, and medicine.
Mechanically Strong, Hydrostable, and Biodegradable Starch–Cellulose Composite Materials for Tableware
Authors: Niu Shasha , Chang Qing , He Wenqin , Zhao Dandan , Xie Yijun , Deng Xiaoyong 
Published: March 2022

AbstractThe extensive use of nondegradable plastic tableware has brought serious environmental problems. Starch‐based materials are good alternatives to plastics, but poor mechanical properties and intrinsic hydrophilicity limit their utilizations. In order to address this issue, naturally abundant bagasse pulp fiber is used as the reinforcing agent to improve the mechanical properties of starch and polydimethylsiloxane coating (CSB35‐PDMS50) to improve their hydrophobicity and hydrostability. CSB35‐PDMS50 reaches the tensile strength of 21.2 MPa and contact angle of 121°. The enhancement of the mechanical strength can be explained by strong hydrogen bonding between starch and cellulose, as indicated by noncovalent interaction‐reduced density gradient (NCI‐RDG) analysis. CSB35‐PDMS50 retains the low water adsorption rate within 60 h, remains stable in water for 24 h without morphology change, and maintains initial hydrophobicity after continuous abrasion in sandpaper and high‐pressure water spraying due to the formation of Si─O–C bonds. The water absorption capacity of CSB35‐PDMS50 is less than 10% when used as the cap materials for Eppendorf tube for 30 days. Furthermore, CSB35‐PDMS50 demonstrates good biodegradability in the soil with 74% in 100 days. The tableware prepared from ther composites demonstrates good potentials as the competitive substitute for plastics with easy production, high strength, and biodegradability.
High‐entropy alloy stabilized and activated Pt clusters for highly efficient electrocatalysis
Authors: Shi Wenhui , Liu Hanwen , Li Zezhou , Li Chenghang , Zhou Jihan , Yuan Yifei , Jiang Feng , Fu (Kelvin) Kun , Yao Yonggang 
Published: March 2022

AbstractAlthough Pt and other noble metals are the state‐of‐the‐art catalysts for various energy conversion applications, their low reserve, high cost, and instability limit their large‐scale utilization. Herein, we report a hybrid catalysts design featuring noble metal clusters (e.g., Pt) uniformly dispersed and stabilized on high‐entropy alloy nanoparticles (HEA, e.g., FeCoNiCu), denoted as HEA@Pt, which is prepared via ultra‐fast shock synthesis (∼300 ms) for HEA alloying combined with Pt galvanic replacement for surface anchoring. In our design, the HEA core critically ensures high dispersity, stability, and tunability of the surface Pt clusters through high entropy stabilization and core‐shell interactions. As an example in the hydrogen evolution reaction, HEA@Pt achieved a significant mass activity of 235 A/gPt, which is 9.4, 3.6, and 1.9‐times higher compared to that of homogeneous FeCoNiCuPt (HEA‐Pt), Pt, and commercial Pt/C, respectively. We also demonstrated noble Ir stabilized on FeCoNiCrMn nanoparticles (HEA‐5@Ir), achieving excellent anodic oxygen evolution performance and highly efficient overall water splitting when combined with the cathodic HEA@Pt. Therefore, our work developed a general catalysts design strategies by using high entropy nanoparticles for effective dispersion, stabilization, and modulation of surface active sites, achieving a harmonious combination of high activity, stability, and low cost.
“I don’t know how to spell it”: Overcoming Translingual Spelling Hesitancy
Authors: Rowe Lindsey W. 
Published: March 2022

AbstractTeachers in English‐medium classrooms can support students’ translingual composing by inviting students to write using all their language resources. However, students in these contexts often express hesitancy about their ability to spell in languages beyond English. This article describes support strategies teachers can use to scaffold students’ translingual composing and help them overcome hesitancy about spelling in languages beyond English. These support strategies center on helping students draw on available resources, including their existing knowledge, peers and family members, and digital tools to write/spell in languages beyond English.
Diffusion Monte Carlo approaches for studying nuclear quantum effects in fluxional molecules
Authors: DiRisio Ryan J. , Finney Jacob M. , McCoy Anne B. 
Published: March 2022

AbstractDiffusion quantum Monte Carlo (DMC) provides a powerful approach for obtaining the ground state energy and wave function of molecules, ions, and molecular clusters. The approach is uniquely well suited for studies of fluxional molecules, which undergo large amplitude vibrational motions even in their ground state. In contrast to the electronic structure problem, where the wave function must be antisymmetric with respect to exchange of any pair of electrons, the wave function for the ground vibrational state is nodeless. This greatly simplifies the application of DMC for vibrational problems. Because there is not a single potential function that can be used to describe the intramolecular and intermolecular interactions in all molecular systems, most methods that are used to describe nuclear quantum effects rely on a carefully chosen zero‐order description of the molecular vibrations. In contrast, DMC calculations can be performed in Cartesian coordinates, making the DMC algorithm easily transferable between different chemical systems. In this contribution, the theory that underlies DMC will be discussed along with important considerations for performing DMC calculations. Extensions for evaluating vibrationally excited states and molecular properties are also discussed. Insights that can be obtained from DMC calculations are illustrated in the context of the protonated water clusters.This article is categorized under:
Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods
Theoretical and Physical Chemistry > Spectroscopy
Diversity in Nitridosilicate Chemistry: The Nitridoalumosilicate Ca4(AlSiN5) and the Nitridosilicate Silicide Ca12Si4[SiN4]
Authors: Link Lukas , Niewa Rainer 
Published: March 2022

AbstractSingle crystals of the calcium nitridoalumosilicate Ca4(AlSiN5) and the nitridosilicate silicide Ca12Si4[SiN4] were grown from sodium flux. Ca4(AlSiN5) crystallizes in the noncentrosymmetric space group Pna21 and presents a framework of AlN4 and SiN4 tetrahedra sharing vertices. Ca12Si4[SiN4] crystallizes in P
. Its crystal structure features mutually isolated nitridosilicate tetrahedra [SiIVN4]8− next to Si4− Zintl anions, with silicon both in its highest and lowest possible oxidation state. Crystals of the isotypic compound Sr12Ge4[GeN4] were obtained, but the crystal structure could not be satisfactorily refined due to unsolved twinning issues.
A Pyridyl Zn (II) Chelate for the Sensitive Detection of Aβ Fibrils
Authors: Yao Yusi , Li Wenhui , Han Qiuqin , Lv Guanglei , Li Chunxia , Sun Anyang 
Published: March 2022

AbstractAmyloid‐β (Aβ) fibrils play a pivotal role in driving neuroinflammation and the progression of tau pathology in Alzheimer's disease (AD). In addition, Aβ oligomers are presumed to be closely associated with pathogenesis of AD. Currently, although a few Aβ antibodies are available, fluorescent probes still play an important role in detecting Aβ pathology. Herein, we designed and synthesized a pyridyl zinc chelate KZ and tested its potential specificity to Aβ species. The probe KZ showed a good binding to fibrillar Aβ aggregates and an excellent anti‐interference feature in vitro. Importantly, KZ could display a subset of Aβ deposits recognized by OC antibody in brain sections of AD mouse models. Thus, this probe KZ could be used as a sensitive tracer for detecting Aβ fibrils or/and fibrillar oligomers.
Prescriptive Process Monitoring Under Resource Constraints: A Causal Inference Approach
Authors: Shoush Mahmoud , Dumas Marlon 
Published: March 2022

AbstractPrescriptive process monitoring is a family of techniques to optimize the performance of a business process by triggering interventions at runtime. Existing prescriptive process monitoring techniques assume that the number of interventions that may be triggered is unbounded. In practice, though, interventions consume resources with finite capacity. For example, in a loan origination process, an intervention may consist of preparing an alternative loan offer to increase the applicant’s chances of taking a loan. This intervention requires time from a credit officer. Thus, it is not possible to trigger this intervention in all cases. This paper proposes a prescriptive monitoring technique that triggers interventions to optimize a cost function under fixed resource constraints. The technique relies on predictive modeling to identify cases that are likely to lead to a negative outcome, in combination with causal inference to estimate the effect of an intervention on a case’s outcome. These estimates are used to allocate resources to interventions to maximize a cost function. A preliminary evaluation suggests that the approach produces a higher net gain than a purely predictive (non-causal) baseline.
On the Performance Analysis of the Adversarial System Variant Approximation Method to Quantify Process Model Generalization
Authors: Theis Julian , Mokhtarian Ilia , Darabi Houshang 
Published: March 2022

AbstractProcess mining algorithms discover a process model from an event log. The resulting process model is supposed to describe all possible event sequences of the underlying system. Generalization is a process model quality dimension of interest. A generalization metric should quantify the extent to which a process model represents the observed event sequences contained in the event log and the unobserved event sequences of the system. Most of the available metrics in the literature cannot properly quantify the generalization of a process model. A recently published method called Adversarial System Variant Approximation leverages Generative Adversarial Networks to approximate the underlying event sequence distribution of a system from an event log. While this method demonstrated performance gains over existing methods in measuring the generalization of process models, its experimental evaluations have been performed under ideal conditions. This paper experimentally investigates the performance of Adversarial System Variant Approximation under non-ideal conditions such as biased and limited event logs. Moreover, experiments are performed to investigate the originally proposed sampling parameter value of the method on its performance to measure the generalization. The results confirm the need to raise awareness about the working conditions of the Adversarial System Variant Approximation method and serve to initiate future research directions.
Process Mining in Trusted Execution Environments: Towards Hardware Guarantees for Trust-Aware Inter-organizational Process Analysis
Authors: Müller Marcel , Simonet-Boulogne Anthony , Sengupta Souvik , Beige Oliver 
Published: March 2022

AbstractProcess mining techniques enable business process analysis on event logs extracted from information systems. Currently, industry applications and research in process mining predominantly analyze intra-organizational processes. Intra-organizational processes deal with the workflows within a single organization. However, analyzing inter-organizational processes across separate companies has the potential to generate further insights. Process analysts can use these insights for optimizations such as workflow improvements and process cost reductions. It is characteristic for inter-organization process analysis that it is not possible to uncover the insights by analyzing the event logs of a single organization in isolation. On the other hand, privacy and trust issues are a considerable obstacle to adopting inter-organizational process mining applications. The independent companies fear competitive disadvantages by letting third parties access their valuable process logs. This paper proposes a concept for inter-organizational process mining using trusted execution environments in a decentralized cloud. The hardware-based approach aims to technically prevent data leakage to unauthorized parties without the need for a trusted intermediary. The contributions of this paper are theoretical and identify future research challenges for implementing the concept.
Quantifying the Re-identification Risk in Published Process Models
Authors: Maatouk Karim , Mannhardt Felix 
Published: March 2022

AbstractEvent logs are the basis of process mining operations such as process discovery, conformance checking, and process optimization. Sensitive information may be obtained by adversaries when re-identifying individuals that relate to the traces of an event log. This re-identification risk is dependent on the assumed background information of an attacker. Multiple techniques have been proposed to quantify the re-identification risks for published event logs. However, in many scenarios there is no need to release the full event log, a discovered process model annotated with frequencies suffices. This raises the question on how to quantify the re-identification risk in published process models. We propose a method based on generating sample traces to quantify this risk for process trees annotated with frequencies. The method was applied on several real-life event logs and process trees discovered by Inductive Miner. Our results show that there can be still a significant re-identification risk when publishing a process tree; however, this risk is often lower than that for releasing the original event log.
Visualizing Trace Variants from Partially Ordered Event Data
Authors: Schuster Daniel , Schade Lukas , van Zelst Sebastiaan J. , van der Aalst Wil M. P. 
Published: March 2022

AbstractExecuting operational processes generates event data, which contain information on the executed process activities. Process mining techniques allow to systematically analyze event data to gain insights that are then used to optimize processes. Visual analytics for event data are essential for the application of process mining. Visualizing unique process executions—also called trace variants, i.e., unique sequences of executed process activities—is a common technique implemented in many scientific and industrial process mining applications. Most existing visualizations assume a total order on the executed process activities, i.e., these techniques assume that process activities are atomic and were executed at a specific point in time. In reality, however, the executions of activities are not atomic. Multiple timestamps are recorded for an executed process activity, e.g., a start-timestamp and a complete-timestamp. Therefore, the execution of process activities may overlap and, thus, cannot be represented as a total order if more than one timestamp is to be considered. In this paper, we present a visualization approach for trace variants that incorporates start- and complete-timestamps of activities.
Linac: A Smart Environment Simulator of Human Activities
Authors: Di Federico Gemma , Nikolajsen Erik Ravn , Azam Mamuna , Burattin Andrea 
Published: March 2022

AbstractThe identification and construction of datasets of human activities is an extremely time-consuming and resource intensive task, yet researchers cannot refrain from such datasets. The publicly available datasets may not reflect all the researchers’ requirements and are not scrupulously documented. In addition, these datasets can cope with just a limited and predefined set of behaviors. To address these challenges, we developed an instrument that allows to simulate the behavior of agents interacting with an environment. The environment is a customized configuration, equipped with sensors. The simulation generates as output a stream of events stemming from activated sensors. In addition, the agents behavior is not fully deterministic, so as to reflect the dynamic nature of human beings and to be as realistic as possible.
China Mainland’s Energy Transition: How to Overcome Financial, Societal, and Institutional Challenges in the Long Term
Authors: Lin Jiaqiao , Zhao Ang 
Published: March 2022

AbstractThe 2060 carbon neutrality goal announced by President Xi in 2020 sets the tone for the long-term Chinese climate policy. China has made considerable achievements in greening its energy mix in the past decade; solar power and wind power capacity both ranked top as of 2019. The current number of electric vehicles are, moreover, close to half of the total number of EVs in the world. However, there are conflicting signals from the coal power development plan and the increasing dependence on natural gas. There are many questions like these to be answered in a more reasonable and independent way. Discussion of China’s energy transition will start with the energy mix and energy governance structure, supplemented by the interaction of energy transition with climate change and air pollution issues. In particular, the institutional setting for national energy and climate policy will be discussed in this chapter, followed by inclusion of key elements that impact the interaction between energy democracy and energy transition process. Civil society organizations and industrial associations have been playing an important role in achieving a decentralized renewable energy system and accordingly promoting energy democracy. Special focus will be placed on the renewable energy corporates’ involvement in the transition.
Energy Democracy and Energy Transition in Taiwan
Authors: Lin Tze-Luen , Cheng Fang-Ting 
Published: March 2022

AbstractThe issue of post-carbon energy transition has received growing attention worldwide for several decades. Being one of the top 20 CO2 emitters in the world, Taiwan embarked on energy transitions in 2016 as a key strategy to address the climate change issue as well as to enhance its energy security. Moreover, it also plans to phase out nuclear power by 2025. The overall policy goal focuses not only on the energy technology shift, but also industrial structure transformation and environmental benefit improvement. This chapter outlines the energy transition from a multilevel governance perspective to explore prime movers, and the changing power relationships between central and local governments in implementing the transition in Taiwan. How has the current energy system been protected by the status quo? In what policy agendas has the energy transition been addressed through the current energy structures? What are the major obstacles for governments to achieve more effective energy governance? What institutional transitions might be required? This Chapter discusses why the political dimension is critical when it comes to post-carbon energy actions and how energy governance adapts to these challenges.
Universality (beyond leading log) of soft radiative corrections to $$ \hat{q} $$ in p⊥ broadening and energy loss
Authors: Arnold Peter 
Published: March 2022

Abstract
It has been known for many years that soft radiation can give potentially large double-logarithm corrections to p⊥ broadening of a high-energy particle traveling through QCD matter, but that this soft radiation correction can be absorbed into an effective value $$ \hat{q} $$

q
̂

eff for the medium p⊥-broadening parameter $$ \hat{q} $$

q
̂

. Here “soft” means high energy compared to medium scales but soft compared to the original high-energy particle traveling through the medium. A similar situation arises in the case of soft corrections to hard splitting of a high-energy particle, such as hard g→gg, where double logarithms can also be absorbed using the same effective $$ \hat{q} $$

q
̂

eff. In this paper, I study whether the same holds true for potentially large, subleading, single-logarthim corrections. The correspondence is more indirect for single logarithms, but I show (in the large-Nc limit) that single logarithms from soft radiation in the case of p⊥ broadening also determine the single logarithms from soft radiative corrections to hard g→gg splitting. Along the way, there is an interesting variation of the original BDMPS-Z calculation of splitting rates in the $$ \hat{q} $$

q
̂

approximation. I also discuss how, for soft-radiative corrections to hard splitting processes, there are two different types of “$$ \hat{q} $$

q
̂

eff” that come into play, which differ by “iπ” terms that multiply single logarithms.
Mirror thermodynamic Bethe ansatz for AdS3/CFT2
Authors: Frolov Sergey , Sfondrini Alessandro 
Published: March 2022

Abstract
We consider superstrings on the pure-Ramond-Ramond AdS3 × S3 × T4 background. Using the recently-proposed dressing factors for the worldsheet S matrix, we formulate the string hypothesis for the mirror Bethe-Yang equations, and use it to derive the canonical mirror thermodynamic Bethe ansatz (TBA) equations of the model. For the first time, these equations account for all massive and massless modes of the model, and do not resort to any limit or special kinematics. We also discuss the simplified mirror TBA equations and the Y-system of the model.