bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hybrid Zwitterionic Hydrogels with Encoded Differential Swelling and Programmed Deformation for Small‐Scale Robotics
Authors: Bouzari Negin , Nasseri Rasool , Huang Junting , Ganguly Sayan , Tang Xiaowu (Shirley) , Mekonnen Tizazu H. , Aghakhani Amirreza , Shahsavan Hamed 
Published: July 2024

AbstractStimuli‐responsive shape‐morphing hydrogels with self‐healing and tunable physiochemical properties are excellent candidates for functional building blocks of untethered small‐scale soft robots. With mechanical properties similar to soft organs and tissues, such robots enable minimally invasive medical procedures, such as cargo/cell transportation. In this work, responsive hydrogels based on zwitterionic/acrylate chemistry with self‐healing and stimuli‐responsiveness are synthesized. Such hydrogels are then judiciously cut and pasted to form hybrid constructs with predetermined swelling and elastic anisotropy. This method is used to program hydrogel constructs with predetermined 2D‐to‐3D deformation upon exposure to different environmental ionic strengths. Untethered soft robotic functionalities are demonstrated, such as actuation, magnetic locomotion, and targeted transport of soft and light cargo in flooded media. The proposed hydrogel expands the repertoire of functional materials for fabricating small‐scale soft robots.
Risk Management Practices and the Performance of Indonesian and Malaysian Islamic Banks: Does Digitalization Mediate This Nexus?
Authors: Afgani Kurnia Fajar , Marzuki Marziana Madah , Majid Wan Zurina Nik Abdul , Nasser Siti Dalina Tumiran Kamal , Syukur Muhammad , Wiryono Sudarso Kaderi , Rahadi Raden Aswin , Boediman Alfred , Ali Qaisar 
Published: July 2024

ABSTRACTDespite being exceptionally regulated financial institutions, risk management practices (RMPs) of Islamic banking (IBs) have always remained under the limelight due to their impact on financial performance (FP) and the influence of digitalization (DT). This study aims to investigate the impact of RMPs on FP through the mediating effect of DT. To analyze the linkage between these constructs, we created an index for RMPs, five unique proxies (mobile [MB_DEA], big data [BD_DEA], Internet of Things [IoT], cloud computing [CC_DEA], and social media [SM_DEA]) of DT, and Tobin's Q as a proxy of FP and operationalized the theory of contingency. The data between 2009 and 2020 was collected from Indonesian and Malaysian IBs and was analyzed using the two‐step generalized method of moments (GMMs) technique. The empirical findings represent that RMPs and DT have a significant positive effect on FP. Simultaneously, RMPs have a significant positive effect on two proxies of DT (MB_DEA and SM_DEA), and an insignificant positive effect on the remaining three proxies of DT (BD_DEA, IoT_DEA, and CC_DEA). Simultaneously, DT as a mediator plays a significant role between RMPs and FP. This study contributes to systematizing the RMPs of IBs by integrating digitalization which eventually will increase the financial portfolios of IBs.
Opportunities and Challenges of Sustainable Innovation for Climate Change Mitigation in Companies: A Systematic Literature Review
Authors: Kraetzig Elda Rodrigues Steinhorst , Ávila Lucas Veiga , Sálvia Amanda Lange , Bichueti Roberto Schoproni , Brandli Luciana Londero 
Published: July 2024

ABSTRACTThe escalating environmental degradation and tightening regulations worldwide since the 1990s have heightened environmental concerns among organizations, necessitating innovative and sustainable responses to climate change. This article aims to explore opportunities and challenges of sustainable innovations for mitigating climate change within companies. A systematic literature review was conducted, encompassing 42 articles from 2005 to 2022. Most studies originate from European countries, particularly from institutions like Sapienza University of Rome, Jinan University, and Amsterdam Business School. Notable authors include Rosa Maria Dangelico, Ans Kolk, and Jonatan Pinkse. The most cited journals include Business Strategy and the Environment, Journal of Cleaner Production, and Sustainability (MDPI). Identified opportunities for companies embracing climate change mitigation through innovation include enhanced image and competitiveness. Challenges comprise high implementation costs, ambiguous sustainable development guidelines, partnership scarcity, technological risk uncertainties, consumer acceptance concerns, deficient tools for low‐carbon product development, and the sociotechnical system's influence. Moreover, companies face hurdles like reliance on supply chain complementary capabilities and regulatory uncertainties. Future research should prioritize less costly sustainability practices and examine the sociotechnical factor's impact on sustainable innovation adoption. Prohibitive investment costs hinder companies from embracing these practices, highlighting the need for cost‐effective alternatives. Understanding the sociotechnical dynamic's role in driving or obstructing sustainable innovation adoption is crucial for comprehensive analysis and effective strategy development.
Nanozymes for biomedical applications: Multi‐metallic systems may improve activity but at the cost of higher toxicity?
Authors: Phan‐Xuan Thuong , Breitung Ben , Dailey Lea Ann 
Published: July 2024

AbstractNanozymes are nanomaterials with intrinsic enzyme‐like activity with selected advantages over native enzymes such as simple synthesis, controllable activity, high stability, and low cost. These materials have been explored as surrogates to natural enzymes in biosensing, therapeutics, environmental protection, and many other fields. Among different nanozymes classes, metal‐ and metal oxide‐based nanozymes are the most widely studied. In recent years, bi‐ and tri‐metallic nanomaterials have emerged often showing improved nanozyme activity, some of which even possess multifunctional enzyme‐like activity. Taking this concept even further, high‐entropy nanomaterials, that is, complex multicomponent alloys and ceramics like oxides, may potentially enhance activity even further. However, the addition of various elements to increase catalytic activity may come at the cost of increased toxicity. Since many nanozyme compositions are currently being explored for in vivo biomedical applications, such as cancer therapeutics, toxicity considerations in relation to nanozyme application in biomedicine are of vital importance for translation.This article is categorized under:
Therapeutic Approaches and Drug Discovery > Emerging Technologies
Toxicology and Regulatory Issues in Nanomedicine > Toxicology of Nanomaterials
Diagnostic Tools > Diagnostic Nanodevices
White‐Light‐Emitting Ln‐MOF as Luminescent Probe for Selective Detection of Nitroaromatic Compounds
Authors: Wang Yihui , Xiao Xitong , Liu Jiayi , Dong Zhenghong , Wang Fengqin , Wang Xiaoqing 
Published: July 2024

AbstractMetal‐organic frameworks (MOFs) as an efficient and convenient sensing material for environmental pollutants have attracted widespread attention in recent years. Here in, we synthesized a series of lanthanide co‐doping MOFs, EuxTb1‐x‐MOFs (x=0, 0.005, 0.00667, 0.01, 0.02 and 1) under solvothermal conditions. The luminescence colors can be adjusted by changing metal molar mass ratios and altering excitation wavelength. Fortunately, a near white‐light‐emitting material Eu0.01Tb0.99‐MOF was obtained under the excitation at 350 nm, whose CIE coordinates of (0.3444, 0.3409) are very close to standard white light coordinates (0.3333, 0.3333). We further studied the fluorescence sensing performances of Eu‐MOF, Tb‐MOF and Eu0.01Tb0.99‐MOF towards NACs. The results indicate that all MOFs can selectively detect 4‐nitroaniline, 4‐nitrophenol and 2‐nitrophenol with good sensitivity and recyclability.
Marabou 2.0: A Versatile Formal Analyzer of Neural Networks
Authors: Wu Haoze , Isac Omri , Zeljić Aleksandar , Tagomori Teruhiro , Daggitt Matthew , Kokke Wen , Refaeli Idan , Amir Guy , Julian Kyle , Bassan Shahaf , Huang Pei , Lahav Ori , Wu Min , Zhang Min , Komendantskaya Ekaterina , Katz Guy , Barrett Clark 
Published: July 2024

AbstractThis paper serves as a comprehensive system description of version 2.0 of the Marabou framework for formal analysis of neural networks. We discuss the tool’s architectural design and highlight the major features and components introduced since its initial release.
Enchanting Program Specification Synthesis by Large Language Models Using Static Analysis and Program Verification
Authors: Wen Cheng , Cao Jialun , Su Jie , Xu Zhiwu , Qin Shengchao , He Mengda , Li Haokun , Cheung Shing-Chi , Tian Cong 
Published: July 2024

AbstractFormal verification provides a rigorous and systematic approach to ensure the correctness and reliability of software systems. Yet, constructing specifications for the full proof relies on domain expertise and non-trivial manpower. In view of such needs, an automated approach for specification synthesis is desired. While existing automated approaches are limited in their versatility,i.e., they either focus only on synthesizing loop invariants for numerical programs, or are tailored for specific types of programs or invariants. Programs involving multiple complicated data types (e.g., arrays, pointers) and code structures (e.g., nested loops, function calls) are often beyond their capabilities. To help bridge this gap, we presentAutoSpec, an automated approach to synthesize specifications for automated program verification. It overcomes the shortcomings of existing work in specification versatility, synthesizing satisfiable and adequate specifications for full proof. It is driven by static analysis and program verification, and is empowered by large language models (LLMs).AutoSpecaddresses the practical challenges in three ways: (1) drivingAutoSpecby static analysis and program verification, LLMs serve as generators to generate candidate specifications, (2) programs are decomposed to direct the attention of LLMs, and (3) candidate specifications are validated in each round to avoid error accumulation during the interaction with LLMs. In this way,AutoSpeccan incrementally and iteratively generate satisfiable and adequate specifications. The evaluation shows its effectiveness and usefulness, as it outperforms existing works by successfully verifying 79% of programs through automatic specification synthesis, a significant improvement of 1.592x. It can also be successfully applied to verify the programs in a real-world X509-parser project.
Verifying Global Two-Safety Properties in Neural Networks with Confidence
Authors: Athavale Anagha , Bartocci Ezio , Christakis Maria , Maffei Matteo , Nickovic Dejan , Weissenbacher Georg 
Published: July 2024

AbstractWe present the first automated verification technique for confidence-based 2-safety properties, such as global robustness and global fairness, in deep neural networks (DNNs). Our approach combines self-composition to leverage existing reachability analysis techniques and a novel abstraction of the softmax function, which is amenable to automated verification. We characterize and prove the soundness of our static analysis technique. Furthermore, we implement it on top of Marabou, a safety analysis tool for neural networks, conducting a performance evaluation on several publicly available benchmarks for DNN verification.
General Anticipatory Runtime Verification
Authors: Hipler Raik , Kallwies Hannes , Leucker Martin , Sánchez César 
Published: July 2024

AbstractRuntime verification is a technique for monitoring a system’s behavior against a formal specification. Monitors must produce verdicts that are sound with respect to the specification. Anticipation is the ability to immediately produce verdicts when the monitor can confidently predict the inevitability of the verdict.Stream runtime verification is a specialized form of runtime verification tailored to the monitoring and verification of data streams. In this paper we study anticipatory monitoring for stream runtime verification. More specifically, we present an algorithm with anticipation for monitoring of Lola specifications, which we then extend to exploit assumptions and tolerate uncertainties. As perfect anticipation is in general not computable, we use techniques from abstract interpretation, especially widening, to approximate anticipatory monitoring verdicts. Finally, we report on three empirical cases studies using a prototype implementation of a symbolic instantiation of our approach.
Proactive Real-Time First-Order Enforcement
Authors: Hublet François , Lima Leonardo , Basin David , Krstić Srđan , Traytel Dmitriy 
Published: July 2024

AbstractModern software systems must comply with increasingly complex regulations in domains ranging from industrial automation to data protection. Runtime enforcement addresses this challenge by empowering systems to not only observe, but also actively control, the behavior of target systems by modifying their actions to ensure policy compliance. We propose a novel approach to the proactive real-time enforcement of policies expressed in metric first-order temporal logic (MFOTL). We introduce a new system model, define an expressive MFOTL fragment that is enforceable in that model, and develop a sound enforcement algorithm for this fragment. We implement this algorithm in a tool calledWhyEnfand carry out a case study on enforcing GDPR-related policies. Our tool can enforce all policies from the study in real-time with modest overhead. Our work thus provides the first tool-supported approach that can proactively enforce expressive first-order policies in real time.
Predictive Monitoring with Strong Trace Prefixes
Authors: Ang Zhendong , Mathur Umang 
Published: July 2024

AbstractRuntime predictive analyses enhance coverage of traditional dynamic analyses based bug detection techniques by identifying a space of feasible reorderings of the observed execution and determining if any reordering in this space witnesses the violation of some desired safety property. The most popular approach for modelling the space of feasible reorderings is through Mazurkiewicz’s trace equivalence. The simplicity of the framework also gives rise to efficient predictive analyses, and has been the de facto means for obtaining space and time efficient algorithms for monitoring concurrent programs.In this work, we investigate how to enhance the predictive power of trace-based reasoning, while still retaining the algorithmic benefits it offers. Towards this, we extend trace theory by naturally embedding a class of prefixes, which we call strong trace prefixes. We formally characterize strong trace prefixes using an enhanced dependence relation, study its predictive power and establish a tight connection to the previously proposed notion of synchronization-preserving correct reorderings developed in the context of data race and deadlock prediction. We then show that despite the enhanced predictive power, strong trace prefixes continue to enjoy the algorithmic benefits of Mazurkiewicz traces in the context of prediction against co-safety properties, and derive new algorithms for synchronization-preserving data races and deadlocks with better asymptotic space and time usage. We also show that strong trace prefixes can capture more violations of pattern languages. We implement our proposed algorithms and our evaluation confirms the practical utility of reasoning based on strong prefix traces.
Wilson loops and random matrices
Authors: Bergner Georg , Gautam Vaibhav , Hanada Masanori , Holden Jack 
Published: July 2024

Abstract
Linear confinement with Casimir scaling of the string tension in confining gauge theories is a consequence of a certain property of the Polyakov loop related to random matrices. This mechanism does not depend on the details of the theories (neither the gauge group nor dimensions) and explains approximate Casimir scaling below string-breaking length. In this paper, we study 3d SU(2) pure Yang-Mills theory numerically and find the same random-matrix behavior for rectangular Wilson loops. We conjecture that this is a universal feature of strongly coupled confining gauge theories.
Weak Hopf symmetry and tube algebra of the generalized multifusion string-net model
Authors: Jia Zhian , Tan Sheng , Kaszlikowski Dagomir 
Published: July 2024

Abstract
We investigate the multifusion generalization of string-net ground states and lattice Hamiltonians, delving into their associated weak Hopf symmetries. For the multifusion string-net, the gauge symmetry manifests as a general weak Hopf algebra, leading to a reducible vacuum string label; the charge symmetry, serving as a quantum double of gauge symmetry, constitutes a connected weak Hopf algebra. This implies that the associated topological phase retains its characterization by a unitary modular tensor category (UMTC). The bulk charge symmetry can also be captured by a weak Hopf tube algebra. We offer an explicit construction of the weak Hopf tube algebra structure and thoroughly discuss its properties. The gapped boundary and domain wall models are extensively discussed, with these 1d phases characterized by unitary multifusion categories (UMFCs). We delve into the gauge and charge symmetries of these 1d phases, as well as the construction of the boundary and domain wall tube algebras. Additionally, we illustrate that the domain wall tube algebra can be regarded as a cross product of two boundary tube algebras. As an application of our model, we elucidate how to interpret the defective string-net as a restricted multifusion string-net.
Lattice QCD calculation of the pion distribution amplitude with domain wall fermions at physical pion mass
Authors: Baker Ethan , Bollweg Dennis , Boyle Peter , Cloët Ian , Gao Xiang , Mukherjee Swagato , Petreczky Peter , Zhang Rui , Zhao Yong 
Published: July 2024

Abstract
We present a direct lattice QCD calculation of the x-dependence of the pion distribution amplitude (DA), which is performed using the quasi-DA in large momentum effective theory on a domain-wall fermion ensemble at physical quark masses and spacing a ≈ 0.084 fm. The bare quais-DA matrix elements are renormalized in the hybrid scheme and matched to $$ \overline{\textrm{MS}} $$

MS
¯

with a subtraction of the leading renormalon in the Wilson-line mass. For the first time, we include threshold resummation in the perturbative matching onto the light-cone DA, which resums the large logarithms in the soft gluon limit at next-to-next-to-leading log. The resummed results show controlled scale-variation uncertainty within the range of momentum fraction x ∈ [0.25, 0.75] at the largest pion momentum Pz ≈ 1.85 GeV. In addition, we apply the same analysis to quasi-DAs from a highly-improved-staggered-quark ensemble at physical pion mass and a = 0.076 fm. By comparison we find with 2σ confidence level that the DA obtained from chiral fermions is flatter and lower near x = 0.5.
Dynamic modelling and efficiency prediction for forging operations under a screw press
Authors: Song Heyu , Durand Camille , Baudouin Cyrille , Bigot Régis 
Published: July 2024

AbstractAccurate predictions concerning a forging process can be obtained by numerical simulation, but only with a thorough knowledge of the main process variables. The material flow behavior and the interface effects are already well studied in the literature, but not the machine behavior, although it is required to estimate blow efficiency and deduce the energy actually transmitted to the workpiece. In this paper, an experimental methodology was applied to determine a spring-mass-damping model and its associated parameters for a screw press. The model and its parameters were identified with press strikes performed without billet. For validation, simulations were performed to predict blows on copper billets. The model’s predictions were in good agreement with the experimental measurements for ten consecutive blows on a copper billet. The decrease of process efficiency and the evolution from inelastic blows to elastic blows were correctly depicted by the model.
Socio-demographic determinants of myelofibrosis outcomes in an underserved center and the SEER national database
Authors: Yan John , Hammami M. Bakri , Wei John X. , Shah Nishi , Goldfinger Mendel , Mantzaris Ioannis , Kornblum Noah , Gritsman Kira , Sica Alejandro , Cooper Dennis , Feldman Eric , Konopleva Marina , Pradhan Kith , Thakur Rahul , Vegivinti Charan , Qasim Asma , Verma Amit , Goel Swati 
Published: July 2024

AbstractThe influence of demographic characteristics and social determinants on cancer outcomes is widely recognized in various malignancies but remains understudied in myelofibrosis (MF). This study aims to investigate social and demographic variables associated with MF survival. We retrospectively reviewed data of biopsy-proven MF patients from the Surveillance, Epidemiology and End Results (SEER) database (2000–2021) and Montefiore Medical Center (2000–2023), an underserved inner-city hospital. The SEER cohort included 5,403 MF patients and was predominantly Non-Hispanic (NH) White (82%) with a median age of 69 years. The age-adjusted incidence rate of MF was 0.32 cases per 100,000 person-years, increasing annually by 1.3% from 2000 to 2021. Two- and five- year overall survival rates were 69% and 42%, respectively. Worse cause-specific survival was associated with older age, male sex, and diagnosis before 2011 (year of Ruxolitinib approval). NH-Black ethnicity, unmarried status and lower median income were independent predictors of worse overall survival. The single-center analysis included 84 cases, with a median age of 66 years. NH-White patients comprised 37% of the sample, followed by NH-Black (28.5%). Two- and five- year overall survival rates were 90% and 61%, respectively, with NH-Black patients exhibiting the lowest median survival, although the difference was not statistically significant. Age was a significant predictor of worse survival in this cohort. NH-Black and Hispanic patients lived in areas with higher socioeconomic and demographic stress compared to NH-White patients. Overall, this study highlights the association of social and demographic factors with MF survival and emphasizes the need for equitable healthcare and further exploration of social-demographic factors affecting MF survival.
Physical Zero-Knowledge Proof Protocols for Topswops and Botdrops
Authors: Komano Yuichi , Mizuki Takaaki 
Published: July 2024

AbstractSuppose that a sequence of $${\varvec{n}}$$

n

cards, numbered 1 to $${\varvec{n}}$$

n

, is placed face up in random order. Let $${\varvec{k}}$$

k

be the number on the first card in the sequence. Then take the first $${\varvec{k}}$$

k

cards from the sequence, rearrange that subsequence of $${\varvec{k}}$$

k

cards in reverse order, and return them to the original sequence. Repeat this prefix reversal until the number on the first card in the sequence becomes 1. This is a one-player card game called Topswops. The computational complexity of Topswops has not been thoroughly investigated. For example, letting $${\varvec{f}}({\varvec{n}})$$

f

(

n

)

denote the maximum number of prefix reversals for Topswops with $${\varvec{n}}$$

n

cards, values of $${\varvec{f}}({\varvec{n}})$$

f

(

n

)

for $${\varvec{n}}\ge 20$$

n

≥
20

remain unknown. In general, there is no known efficient algorithm for finding an initial sequence of $${\varvec{n}}$$

n

cards that requires exactly $$\ell $$
ℓ
prefix reversals for any integers $${\varvec{n}}$$

n

and $${\varvec{\ell }}$$

ℓ

. In this paper, using a deck of cards, we propose a physical zero-knowledge proof protocol that allows a prover to convince a verifier that the prover knows an initial sequence of $${\varvec{n}}$$

n

cards that requires $${\varvec{\ell }}$$

ℓ

prefix reversals without leaking knowledge of that sequence. We also deal with Botdrops, a variant of Topswops.