bluuebunny/crossref_metadata_2025 huggingface dataset:
822 Mortality in adolescent trauma: a comparison of children’s, mixed and adult major trauma centres
Authors: Evans Jordan , Murch Hannah , Begley Roisin , Roland Damian , Lyttle Mark , Bouamra Omar , Mullen Stephen
Published: June 2022

Aims/Objectives/Background

We aimed to compare adolescent mortality rates between different types of major trauma centre (MTC or level 1; adult, children’s and mixed).

Methods/Design

Data were obtained from TARN (Trauma Audit Research Network) from English sites over a 6-year period (2012–2018), with adolescence defined as 10–24.99 years. Results are presented using descriptive statistics. Patient characteristics were compared using the Kruskal-Wallis test with Dunn’s post-hoc analysis for pairwise comparison and χ2test for categorical variables.

Results/Conclusions

21 033 cases met inclusion criteria. Trauma-related 30-day crude mortality rates by MTC type were 2.5% (children’s), 4.4% (mixed) and 4.9% (adult). Logistic regression accounting for injury severity, mechanism of injury, physiological parameters and ‘hospital ID’, resulted in adjusted odds of mortality of 2.41 (95% CI 1.31 to 4.43; p=0.005) and 1.85 (95% CI 1.03 to 3.35; p=0.041) in adult and mixed MTCs, respectively when compared with children’s MTCs. In three subgroup analyses the same trend was noted. In adolescents aged 14–17.99 years old, those managed in a children’s MTC had the lowest mortality rate at 2.5%, compared with 4.9% in adult MTCs and 4.4% in mixed MTCs (no statistical difference between children’s and mixed). In cases of major trauma (Injury Severity Score >15) the adjusted odds of mortality were also greater in the mixed and adult MTC groups when compared with the children’s MTC. Median length of stay (LoS) and intensive care unit LoS were comparable for all MTC types. Patients managed in children’s MTCs were less likely to have a CT scan (46.2% vs 62.8% mixed vs 64% adult).

Children’s MTC have lower crude and adjusted 30-day mortality rates for adolescent trauma. Further research is required in this field to identify the factors that may have influenced these findings.
853 Mission to mars: using story art to ease anxiety and improve empathy in a children’s emergency department (CED)
Authors: Leanage Navin , Netto Esther , Acheson Jonathan
Published: June 2022

Aims/Objectives/Background

Whilst working in CED one of the most significant challenges has been effective communication with parents and children of all ages. These difficulties can lead to an unclear understanding of a child’s management plan, and specifically its individual steps. This can produce undue anxiety for both parent and child. We decided to focus on creating a tool, to help navigate parents and children through a ketamine reduction of a broken limb. The aims of the digital learning intervention would be to improve patient experience by reducing stress and anxiety and educating them on the plan as it is put into place. This would be in keeping with the Department of Health’s framework for technology enhanced learning as it aims to improve patient outcomes, safety and experience.

Methods/Design

We produced a narrated video story using hand drawn artwork, cartoon effects and video making software plus an optional e-book for parents to read to their children to give a more familiar voice to an already anxious child. The video drew from multiple educational theories and evidence in multimedia education to design an initial protoype. Initially this was specifically for an infant journey through Minor Injuries requiring a ketamine reduction for a bendy limb. Following feedback, the project is being adapted to different journeys ahead of user testing.

Results/Conclusions

Evaluation at multiple levels is presently ongoing and is showing positive feedback however further work is required to fully evaluate this project.

This can overall help both parent and child in an environment they are otherwise unfamiliar with and during a potentially distressing time. The project opens the doors to multiple designs for both inclusivity and diversity. Furthermore, this can be adapted by different departments and different hospitals keeping the patient at the centre of care.
Ear, nose and throat (ENT) manifestations and complications of reflux
Authors: Brar Sabrina , Watters Carolina , Watson Natalie , Birchall Martin , Karagama Yakubu
Published: June 2022

Laryngopharyngeal reflux disease describes the backflow of acidic stomach content towards the larynx and is associated with symptoms such as cough, throat clearing and globus. It is a common presentation in primary care and the sequelae of symptoms that arise from the disease often present in ear, nose and throat clinics. Assessment and examination of patients presenting with reflux symptoms includes questionnaires, as well as direct visualisation of the pharynx and larynx, and takes a multidisciplinary team approach. Treatment options include lifestyle modification, medical therapy and in some specialist centres, surgical management to address the multitude of symptoms associated with the disease.
Quantized noncommutative geometry from multitrace matrix models
Authors: Ydri Badis , Khaled Ramda , Soudani Cherine
Published: June 2022

In this paper, the geometry of quantum gravity is quantized in the sense of being noncommutative (first quantization) but it is also quantized in the sense of being emergent (second quantization). A new mechanism for quantum geometry is proposed in which noncommutative geometry can emerge from “one-matrix multitrace scalar matrix models” by probing the statistical physics of commutative phases of matter. This is in contrast to the usual mechanism in which noncommutative geometry emerges from “many-matrix singletrace Yang–Mills matrix models” by probing the statistical physics of noncommutative phases of gauge theory. In this novel scenario, quantized geometry emerges in the form of a transition between the two phase diagrams of the real quartic matrix model and the noncommutative scalar phi-four field theory. More precisely, emergence of the geometry is identified here with the emergence of the uniform-ordered phase and the corresponding commutative (Ising) and noncommutative (stripe) coexistence lines. The critical exponents and Wigner’s semicircle law are used to determine the dimension and the metric, respectively. Arguments from the saddle point equation, from Monte Carlo simulation and from the matrix renormalization group equation are provided in support of this scenario.
AI for System - Infusing AI into Cloud Computing Systems
Authors: Hon Hsiao-Wuen
Published: June 2022

In the past fifteen years, the most significant paradigm shift in the computing industry is the migration to cloud computing, which brings unprecedented opportunities of digital transformation to business, society, and human life. The implication of this is profound. It means that cloud computing platforms have become part of the basic infrastructure of the world. Therefore, the non-functional properties of cloud computing platforms, including availability, reliability, performance, efficiency, security, sustainability, etc., become immensely important. The distributed nature, massive scale, and high complexity of cloud computing platforms ranging from storage to networking, computing and beyond present huge challenges to achieve effective and efficient building and operation of such software systems. There is huge wealth of various types of data available throughout the entire development lifecycle of software systems. This is manifested even stronger with the paradigm shift to cloud computing as much more data are available on system runtime and workloads. Leveraging the amount of data, AI for System is to utilize AI/ML technologies to design and build high-quality cloud systems at scale. In this talk, I will first introduce the concept of AI for System and its research landscape. Then using a few projects at Microsoft as examples [1-10], I will talk about the work from Microsoft Research on AI for System and its impact. I will also discuss the research challenges and opportunities in AI for System moving forward.
adPerf: Characterizing the Performance of Third-party Ads
Authors: Pourghassemi Behnam , Bonecutter Jordan , Li Zhou , Chandramowlishwaran Aparna
Published: June 2022

Online advertising (essentially display ads on websites) has proliferated in the last decade to the extent where it is now an integral part of the web. In this paper, we apply an in-depth and first-of-a-kind performance evaluation of web ads. Unlike prior efforts that rely primarily on adblockers, we perform a fine-grained analysis of the web browser's page loading process to demystify the performance cost of web ads. We aim to characterize the cost by every component of an ad, so the publisher, ad syndicate, and advertiser can improve the ad's performance with detailed guidance. For this purpose, we develop a tool, adPerf, for the Chrome browser that classifies page loading workloads into ad-related and main-content at the granularity of browser activities. Our evaluations show that online advertising entails more than 15% of browser page loading workload and approximately 88% of that is spent on JavaScript. On smartphones, this additional cost of ads is 7% lower as we observe mobile pages include fewer and well-optimized ads. We also track the sources and delivery chain of web ads and analyze performance considering the origin of the ad contents. We observe that 2 of the well-known third-party ad domains contribute to 35% of the ads performance cost and surprisingly, top news websites implicitly include unknown third-party ads which in some cases build up to more than 37% of the ads performance cost.
Revisiting Modified Greedy Algorithm for Monotone Submodular Maximization with a Knapsack Constraint
Authors: Tang Jing , Tang Xueyan , Lim Andrew , Han Kai , Li Chongshou , Yuan Junsong
Published: June 2022

Monotone submodular maximization with a knapsack constraint is NP-hard. Various approximation algorithms have been devised to address this optimization problem. In this paper, we revisit the widely known modified greedy algorithm. First, we show that this algorithm can achieve an approximation factor of 0.405, which significantly improves the known factors of 0.357 given by Wolsey and (1-1/e)/2-0.316 given by Khuller et al. More importantly, our analysis closes a gap in Khuller et al.'s proof for the extensively mentioned approximation factor of (1-1/√e)≈0.393 in the literature to clarify a long-standing misconception on this issue. Second, we enhance the modified greedy algorithm to derive a data-dependent upper bound on the optimum, which enables us to obtain a data-dependent ratio typically much higher than 0.405 between the solution value of the modified greedy algorithm and the optimum. It can also be used to significantly improve the efficiency of algorithms such as branch and bound.
Bregman-style Online Convex Optimization with Energy Harvesting Constraints
Authors: Asgari Kamiar , Neely Michael J.
Published: June 2022

This paper considers online convex optimization (OCO) problems where decisions are constrained by available energy resources. A key scenario is optimal power control for an energy harvesting device with a finite capacity battery. The goal is to minimize a time-average loss function while keeping the used energy less than what is available. In this setup, the distribution of the randomly arriving harvestable energy (which is assumed to be i.i.d.) is unknown, the current loss function is unknown, and the controller is only informed by the history of past observations. A prior algorithm is known to achieve O(√T) regret by using a battery with an O(√T) capacity. This paper develops a new algorithm that maintains this asymptotic trade-off with the number of time steps T while improving dependency on the dimension of the decision vector from O(√n) to O(√log(n)). The proposed algorithm introduces a separation of the decision vector into amplitude and direction components. It uses two distinct types of Bregman divergence, together with energy queue information, to make decisions for each component.
Load Balancing Under Strict Compatibility Constraints
Authors: Rutten Daan , Mukherjee Debankur
Published: June 2022

Consider a system with N identical single-server queues and M(N) task types, where each server is able to process only a small subset of possible task types. Arriving tasks select d≥2 random compatible servers, and join the shortest queue among them. The compatibility constraints are captured by a fixed bipartite graph GN between the servers and the task types. When GN is complete bipartite, the meanfield approximation is accurate. However, such dense compatibility graphs are infeasible for large-scale implementation. We characterize a class of sparse compatibility graphs for which the meanfield approximation remains valid. For this, we introduce a novel notion, called proportional sparsity, and establish that systems with proportionally sparse compatibility graphs asymptotically match the performance of a fully flexible system. Furthermore, we show that proportionally sparse random compatibility graphs can be constructed, which reduce the server-degree almost by a factor N/ln(N) compared to the complete bipartite compatibility graph.
Magma: A Ground-Truth Fuzzing Benchmark
Authors: Hazimeh Ahmad , Herrera Adrian , Payer Mathias
Published: June 2022

High scalability and low running costs have made fuzz testing the de facto standard for discovering software bugs. Fuzzing techniques are constantly being improved in a race to build the ultimate bug-finding tool. However, while fuzzing excels at finding bugs in the wild, evaluating and comparing fuzzer performance is challenging due to the lack of metrics and benchmarks. For example, crash count---perhaps the most commonly-used performance metric---is inaccurate due to imperfections in deduplication techniques. Additionally, the lack of a unified set of targets results in ad hoc evaluations that hinder fair comparison.

We tackle these problems by developing Magma, a ground-truth fuzzing benchmark that enables uniform fuzzer evaluation and comparison. By introducing real bugs into real software, Magma allows for the realistic evaluation of fuzzers against a broad set of targets. By instrumenting these bugs, Magma also enables the collection of bug-centric performance metrics independent of the fuzzer. Magma is an open benchmark consisting of seven targets that perform a variety of input manipulations and complex computations, presenting a challenge to state-of-the-art fuzzers.

We evaluate seven widely-used mutation-based fuzzers (AFL, AFLFast, AFL++, FairFuzz, MOpt-AFL, honggfuzz, and SymCC-AFL) against Magma over 200,000 CPU-hours. Based on the number of bugs reached, triggered, and detected, we draw conclusions about the fuzzers' exploration and detection capabilities. This provides insight into fuzzer performance evaluation, highlighting the importance of ground truth in performing more accurate and meaningful evaluations.
On Private Peering Agreements between Content and Access Providers: A Contractual Equilibrium Analysis
Authors: Wang Xin , Ma Richard T.B.
Published: June 2022

Driven by the rapid growth of content traffic and the demand for service quality, Internet content providers (CPs) have started to bypass transit providers and connect with access providers directly via private peering agreements. This peering relationship often raises disputes, e.g., Netflix vs. Comcast, and is not well understood. In this paper, we build a peering contract model and propose the concept of contractual equilibrium, based on which we study the formation and evolution of peering contracts. By using market data, we emulate the strategic peering behavior of providers and shed light on the understanding of private peering~agreements.

We reveal that the superiority and market dominance of providers primarily determine their peering strategies. We show that 1) superior providers tend to engage in peering more aggressively, and 2) non-dominant CPs' optimal peering strategies are negatively correlated due to market cannibalism, while the dominant CP often behaves oppositely. Our findings help explain phenomena such as why Netflix and Comcast signed the first peering contract, and reason whether private peering contracts will strengthen in future.
Mean Waiting Time in Large-Scale and Critically Loaded Power of d Load Balancing Systems
Authors: Hellemans Tim , Van Houdt Benny
Published: June 2022

Mean field models are a popular tool used to analyse load balancing policies. In some exceptional cases the waiting time distribution of the mean field limit has an explicit form. In other cases it can be computed as the solution of a differential equation or a recursive relation. In our paper [6] we study the limit of the mean waiting time E[W&955;] as the arrival rate &955; approaches 1 for a number of load balancing policies when job sizes are exponential with mean 1 (i.e. when the system gets close to instability). As E[W_&955;] diverges to infinity, we scale with -log(1-&955;) and present a method to compute the limit lim&955;-> 1- -E[W&955;]/log(1-&955;). We show that this limit has a surprisingly simple form for the load balancing algorithms considered.

In addition, we propose an alternate scaling -log(p&955;) instead of -log(1-&955;), where p&955; is adapted to the policy at hand. This scaling allows us to obtain good approximations of the mean waiting time when &955; is close to one or zero.
The Power of D-hops in Matching Power-Law Graphs
Authors: Yu Liren , Xu Jiaming , Lin Xiaojun
Published: June 2022

This paper studies seeded graph matching for power-law graphs. Assume that two edge-correlated graphs are independently edge-sampled from a common parent graph with a power-law degree distribution. A set of correctly matched vertex-pairs is chosen at random and revealed as initial seeds. Our goal is to use the seeds to recover the remaining latent vertex correspondence between the two graphs. Departing from the existing approaches that focus on the use of high-degree seeds in $1$-hop neighborhoods, we develop an efficient algorithm that exploits the low-degree seeds in suitably-defined D-hop neighborhoods. Specifically, we first match a set of vertex-pairs with appropriate degrees (which we refer to as the first slice) based on the number of low-degree seeds in their D-hop neighborhoods. This approach significantly reduces the number of initial seeds needed to trigger a cascading process to match the rest of graphs. Under the Chung-Lu random graph model with n vertices, max degree Θ(√n), and the power-law exponent 2<β<3, we show that as soon as D>4-β/3-β, by optimally choosing the first slice, with high probability our algorithm can correctly match a constant fraction of the true pairs without any error, provided with only Ω((log n)4-β) initial seeds. Our result achieves an exponential reduction in the seed size requirement, as the best previously known result requires n1/2+ε seeds (for any small constant ε^>0). Performance evaluation with synthetic and real data further corroborates the improved performance of our algorithm.
Nudge: Stochastically Improving upon FCFS
Authors: Grosof Isaac , Yang Kunhe , Scully Ziv , Harchol-Balter Mor
Published: June 2022

The First-Come First-Served (FCFS) scheduling policy is the most popular scheduling algorithm used in practice. Furthermore, its usage is theoretically validated: for light-tailed job size distributions, FCFS has weakly optimal asymptotic tail of response time. But what if we don't just care about the asymptotic tail? What if we also care about the 99th percentile of response time, or the fraction of jobs that complete in under one second? Is FCFS still best? Outside of the asymptotic regime, only loose bounds on the tail of FCFS are known, and optimality is completely open.

In this paper, we introduce a new policy, Nudge, which is the first policy to provably stochastically improve upon FCFS. We prove that Nudge simultaneously improves upon FCFS at every point along the tail, for light-tailed job size distributions. As a result, Nudge outperforms FCFS for every moment and every percentile of response time. Moreover, Nudge provides a multiplicative improvement over FCFS in the asymptotic tail. This resolves a long-standing open problem by showing that, counter to previous conjecture, FCFS is not strongly asymptotically optimal.

This paper represents an abridged version of [2].
Mix and Match: Reorganizing Tasks for Enhancing Data Locality
Authors: Tang Xulong , Kandemir Mahmut Taylan , Karakoy Mustafa
Published: June 2022

Application programs that exhibit strong locality of reference lead to minimized cache misses and better performance in different architectures. In this paper, we target task-based programs, and propose a novel compiler-based approach that consists of four complementary steps. First, we partition the original tasks in the target application into sub-tasks and build a data reuse graph at a sub-task granularity. Second, based on the intensity of temporal and spatial data reuses among sub-tasks, we generate new tasks where each such (new) task includes a set of sub-tasks that exhibit high data reuse among them. Third, we assign the newly-generated tasks to cores in an architecture-aware fashion with the knowledge of data location. Finally, we re-schedule the execution order of sub-tasks within new tasks such that sub-tasks that belong to different tasks but share data among them are executed in close proximity in time. The experiments show that, when targeting a state of the art manycore system, our compiler-based approach improves the performance of 10 multithreaded programs by 23.4% on average.
SADPonzi: Detecting and Characterizing Ponzi Schemes in Ethereum Smart Contracts
Authors: Chen Weimin , Li Xinran , Sui Yuting , He Ningyu , Wang Haoyu , Wu Lei , Luo Xiapu
Published: June 2022

Ponzi schemes are financial scams that lure users under the promise of high profits. With the prosperity of Bitcoin and blockchain technologies, there has been growing anecdotal evidence that this classic fraud has emerged in the blockchain ecosystem. Existing studies have proposed machine-learning based approaches for detecting Ponzi schemes. However, these state-of-the-art approaches face several major limitations, including lacking interpretability, high false positive rates and the weak robustness to evasion techniques, These limitations mean that existing real-world methods for detecting Ponzi schemes are ineffective.

In this paper, we propose SADPonzi, a semantic-aware detection approach for identifying Ponzi schemes in Ethereum smart contracts. Specifically, we propose a heuristic-guided symbolic execution technique to identify investor-related transfer behaviors and the distribution strategies adopted. Experimental result on a well-labelled benchmark suggests that SADPonzi can achieve 100% precision and recall, outperforming all existing machine-learning based techniques. We further apply SADPonzi to all 3.4 million smart contracts deployed by EOAs in Ethereum and identify 835 Ponzi scheme contracts, with over 17 million US Dollars invested by victims. Our observations confirm the urgency of identifying and mitigating Ponzi schemes in the blockchain ecosystem.
A Measurement Study of Wechat Mini-Apps
Authors: Zhang Yue , Turkistani Bayan , Yang Allen Yuqing , Zuo Chaoshun , Lin Zhiqiang
Published: June 2022

A new mobile computing paradigm, dubbed mini-app, has been growing rapidly over the past few years since being introduced by WeChat in 2017. In this paradigm, a host app allows its end-users to install and run mini-apps inside itself, enabling the host app to build an ecosystem around (much like Google Play and Apple AppStore), enrich the host's functionalities, and offer mobile users elevated convenience without leaving the host app. It has been reported that there are over millions of mini-apps in WeChat. However, little information is known about these mini-apps at an aggregated level. In this paper, we present MiniCrawler, the first scalable and open-source WeChat mini-app crawler that has indexed over 1,333,308 mini-apps. It leverages a number of reverse engineering techniques to uncover the interfaces and APIs in WeChat for crawling the mini-apps. With the crawled mini-apps, we then measure the resource consumption, API usage, library usage, obfuscation rate, app categorization, and app ratings at an aggregated level in this work.
PredictRoute: A Network Path Prediction Toolkit
Authors: Singh Rachee , Tench David , Gill Phillipa , McGregor Andrew
Published: June 2022

Accurate prediction of network paths between arbitrary hosts on the Internet is of vital importance for network operators, cloud providers, and academic researchers. We present PredictRoute, a system that predicts network paths between hosts on the Internet using historical knowledge of the data and control plane. In addition to feeding on freely available traceroutes and BGP routing tables, PredictRoute optimally explores network paths towards chosen BGP prefixes. PredictRoute's strategy for exploring network paths discovers 4X more autonomous system (AS) hops than other well-known strategies used in practice today. Using a corpus of traceroutes, PredictRoute trains probabilistic models of routing towards prefixes on the Internet to predict network paths and their likelihood. PredictRoute's AS-path predictions differ from the measured path by at most 1 hop, 75% of the time. We expose PredictRoute's path prediction capability via a REST API to facilitate its inclusion in other applications and studies. We additionally demonstrate the utility of PredictRoute in improving real-world applications for circumventing Internet censorship and preserving anonymity online.
The Role of Viral RNA Degrading Factors in Shutoff of Host Gene Expression
Authors: Gaucherand Léa , Gaglia Marta Maria
Published: June 2022

Many viruses induce shutoff of host gene expression (host shutoff) as a strategy to take over cellular machinery and evade host immunity. Without host shutoff activity, these viruses generally replicate poorly in vivo, attesting to the importance of this antiviral strategy. In this review, we discuss one particularly advantageous way for viruses to induce host shutoff: triggering widespread host messenger RNA (mRNA) decay. Viruses can trigger increased mRNA destruction either directly, by encoding RNA cleaving or decapping enzymes, or indirectly, by activating cellular RNA degradation pathways. We review what is known about the mechanism of action of several viral RNA degradation factors. We then discuss the consequences of widespread RNA degradation on host gene expression and on the mechanisms of immune evasion, highlighting open questions. Answering these questions is critical to understanding how viral RNA degradation factors regulate host gene expression and how this process helps viruses evade host responses and replicate.
The Case for Studying New Viruses of New Hosts
Authors: Stenglein Mark D.
Published: June 2022

Virology has largely focused on viruses that are pathogenic to humans or to the other species that we care most about. There is no doubt that this has been a worthwhile investment. But many transformative advances have been made through the in-depth study of relatively obscure viruses that do not appear on lists of prioritized pathogens. In this review, I highlight the benefits that can accrue from the study of viruses and hosts off the beaten track. I take stock of viral sequence diversity across host taxa as an estimate of the bias that exists in our understanding of host-virus interactions. I describe the gains that have been made through the metagenomic discovery of thousands of new viruses in previously unsampled hosts as well as the limitations of metagenomic surveys. I conclude by suggesting that the study of viruses that naturally infect existing and emerging model organisms represents an opportunity to push virology forward in useful and hard to predict ways.
Viral G Protein–Coupled Receptors Encoded by β- and γ-Herpesviruses
Authors: Rosenkilde Mette M. , Tsutsumi Naotaka , Knerr Julius M. , Kildedal Dagmar F. , Garcia K. Christopher
Published: June 2022

Herpesviruses are ancient large DNA viruses that have exploited gene capture as part of their strategy to escape immune surveillance, promote virus spreading, or reprogram host cells to benefit their survival. Most acquired genes are transmembrane proteins and cytokines, such as viral G protein–coupled receptors (vGPCRs), chemokines, and chemokine-binding proteins. This review focuses on the vGPCRs encoded by the human β- and γ-herpesviruses. These include receptors from human cytomegalovirus, which encodes four vGPCRs: US27, US28, UL33, and UL78; human herpesvirus 6 and 7 with two receptors: U12 and U51; Epstein-Barr virus with one: BILF1; and Kaposi's sarcoma-associated herpesvirus with one: open reading frame 74, ORF74. We discuss ligand binding, signaling, and structures of the vGPCRs in light of robust differences from endogenous receptors. Finally, we briefly discuss the therapeutic targeting of vGPCRs as future treatment of acute and chronic herpesvirus infections.
Critical Role of Pits in Suppressing Li Dendrites Revealed by Continuum Mechanics Simulation and In Situ Experiment
Authors: Gao Li Ting , Huang Pingyuan , Guo Zhan-Sheng
Published: June 2022

Dendrite growth and surface pitting are critical factors hindering the development of ultra-high energy density rechargeable lithium battery. However, the mechanism of dendrite growth promoted by pits on the surface of lithium metal remains unclear. In this study, we propose a combination of continuum mechanics simulations and develop an in situ experimental observation device to investigate the effects of pit size, curvature of pit edge, overpotential, and lithium-ion concentration gradient on dendrite growth. Results show that a larger size and curvature of pits can reduce the deposition rate of lithium and dendrite morphology significantly. Larger overpotential can aggravate dendritic nucleation and thereby promotes dendrite growth. Uneven lithium-ion concentration gradient distribution significantly influences the direction of the dendrite growth and leads to the formation of branches. The lithium deposition behavior near the ideal pit predicted by continuum mechanics is consistent with that observed in the in situ experiments. These results lay the basis for future studies to determine the effect of the surface morphology of lithium metal electrodes on the electrodeposition stability and performance of lithium metal batteries.
Voltage Dependence of Nanopattern Morphology in Electropolished Aluminum: A Theoretical Study
Authors: Yuan Yuan , Zhang Dan , Zhang Guoxu , Zhang Peng , Zhang Feihu , Lin Qianru , Yang Chunhui , Gan Yang
Published: June 2022

The crystallographic orientation and voltage dependence of nanopattern morphology and size of electropolished Al has been revealed and established. However, the reported models failed to explain these experimental results. Here we developed an improved model that explicitly includes the calculated anisotropic interfacial energy of clean Al/ethanol interface for the perchloric acid-ethanol based solution with high volume percentage of ethanol. The model predicts existing regions of six types of nanopattern morphologies based on the Turing pattern bifurcation criterion, and reasonably explains experimental results. These findings help gain deeper insights into the nanopattern formation mechanism of electropolished Al and other metals.
High-Performance Non-Enzymatic Glucose Sensor Based on Boron-Doped Copper Oxide Nanbundles
Authors: Zhang Chongchao , Yin Hang , Bai Xiao , Yang Ziyin
Published: June 2022

In this paper, for the first time, boron-doped copper oxide (B-CuO) was explored as an excellent electrocatalyst for glucose oxidation, which was synthesized by a simple method. The nanomaterials were characterized by transmission electron microscopy (TEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), X-ray photoelectron spectroscopy (XPS), and Raman spectroscopy (Raman). The results show that B-CuO presents a spindle structure with rich pores, which favors exposure of accessible active sites. Moreover, the doping of B significantly accelerates the electron transfer rate. Owing to these unique features, the enzymeless sensor based on B-CuO exhibited excellent performance for glucose analysis with a high sensitivity (1546.13 μA·mM−1·cm−2), a wide detection range (0.2 μM−1.1 mM), and a low detection limit (0.16 μM). This study demonstrated B-CuO as a new electrocatalyst for electrochemical sensing of glucose.
Quantifying Cross‐Correlations between Economic Policy Uncertainty and Bitcoin Market: Evidence from Multifractal Analysis
Authors: Ma Junjun , Wang Tingting , Zhao Ruwei
Published: June 2022

We investigate the dynamic correlation between the Bitcoin price (BTC) and the U.S. economic policy uncertainty index (USEPU) from the perspective of multifractality. Utilizing the multifractal detrended cross‐correlation analysis (MF‐DCCA), we confirm a long‐range cross‐correlation between BTC and USEPU. Moreover, the empirical results of MF‐DCCA show that the power‐law properties and multifractal characteristics between BTC and USEPU are significant. We further examine the long‐range dependency of cross‐correlation between BTC and USEPU series via the Hurst exponent test and confirm the durable cross‐correlation. Finally, we introduce another multifractal indicator and examine the extent of multifractality among time series. The empirical results indicate that the BTC series, USEPU series, and the cross‐correlation of BTC‐USEPU present apparent multifractality, where BTC shows the strongest degree of multifractality.
Modal Choice for the Driverless City: Scenario Simulation Based on a Stated Preference Survey
Authors: Cordera Rubén , González-González Esther , Nogués Soledad , Arellana Julián , Moura José L.
Published: June 2022

The possible future introduction of Autonomous Vehicles (AVs) and Shared Autonomous Vehicles (SAVs) raises questions about how they might affect the demand for transport and especially modal choice. In this research, a stated preference (SP) survey and a modelling process using Mixed Logit are proposed to simulate the future market share of AVs/SAVs and how their introduction into the system could change the modal choice, especially in relation to active and public transport modes. An efficient SP survey design has been developed based on the state-of-the-art information and carried out in 2020 among citizens of two medium-sized Southern European cities within a car-intensive region. The design considered different trip purposes (compulsory, leisure), different trip distances, and attributes not taken into account before, such as comfort and the physical characteristics of the terrain for the active modes. The model results suggest that AVs and SAVs were the preferred transport modes for most respondents, accounting for more than 58% of the market share in the scenarios presented. Also, we detected some socioeconomic differences in the propensity to use this mode of transport showing that men living in high-income households and car users were more prone to use autonomous alternatives. The models allowed us to simulate different scenarios, such as experiencing higher costs for using the AV alternative. Policies imposing a higher cost for the AV alternative but lower costs and waiting times for the SAV and public transport alternatives could decrease the AV’s market share favouring more sustainable modes. The above scenario showed that achieving a more sustainable future mobility system considering AVs requires an in-depth transport demand knowledge and adequate transport policies.
Quantitative Data Integration Analysis Method for Cross-Studies: Obstructive Sleep Apnea as an Example
Authors: Zhou Rong , Zhou Shengrong , Xia Qiguang , Zhang Tiejun , Zhang Guoqing
Published: June 2022

Objective. In recent years, the prevalence of obstructive sleep apnea (OSA) has gradually increased. The diagnosis of this multiphenotypic disorder requires a combination of several indicators. The objective of this study was to find significant apnea monitor indicators of OSA by developing a strategy for cross-study screening and integration of quantitative data. Methods. Articles related to sleep disorders were obtained from the PubMed database. A sleep disorder dataset and an OSA dataset were manually curated from these articles. Two evaluation indexes, the indicator coverage ratio (ICR) and the study integrity ratio (SIR), were used to filter out OSA indicators from the OSA dataset and create profiles including different numbers of indicators and studies for analysis. Data were analyzed by the meta 4.18-0 package of R, and the

p

value and standard mean difference (SMD) values were calculated to evaluate the change of each indicator. Results. The sleep disorder dataset was constructed based on 178 studies from 119 publications, the OSA dataset was extracted from 89 studies, 284 sleep-related indicators were filtered out, and 22 profiles were constructed. Apnea hypopnea index was significantly decreased in all 22 profiles. Total sleep time (TST) (min) showed no significant differences in 21 profiles. There were significant increases in rapid eye movement (REM) (%TST) in 18 profiles, minimum arterial oxygen saturation (SaO2) in 9 profiles, REM duration in 3 profiles, and slow wave sleep duration (%TST) and pulse oximetry lowest point in 2 profiles. There were significant decreases in apnea index (AI) in 14 profiles; arousal index and

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<
90

(%TST) in 8 profiles; N1 stage (%TST) in 7 profiles; and hypopnea index, N1 stage (% sleep period time (%SPT)), N2 stage (%SPT), respiratory arousal index, and respiratory disorder index in 2 profiles. Conclusion. The proposed data integration strategy successfully identified multiple significant OSA indicators.
Molecular Mechanisms of Hawthorn Extracts in Multiple Organs Disorders in Underlying of Diabetes: A Review
Authors: Gheitasi Izadpanah , Savari Feryal , Akbari Ghaidafeh , Mohammadi Jamshid , Fallahzadeh Ali Reza , Sadeghi Hossein
Published: June 2022

Diabetes mellitus (DM) is one of the most important metabolic disorders associated with chronic hyperglycemia and occurs when the body cannot manage insulin secretion, insulin action, or both. Autoimmune destruction of pancreatic beta cells and insulin resistance are the major pathophysiological factors of types 1 and 2 of DM, respectively. Prolonged hyperglycemia leads to multiple organs dysfunctions, including nephropathy, neuropathy, cardiomyopathy, gastropathy, and micro- and macrovascular disorders. The basis of the metabolic abnormalities in carbohydrate, fat, and protein in diabetes is insufficient action of insulin on various target tissues. Medicinal plants are rich sources of bioactive chemical compounds with therapeutic effects. The beneficial effects of leaves, fruits, and flowers extracts of Crataegus oxyacantha, commonly called hawthorn, belonging to the Rosaceae family, are widely used as hawthorn-derived medicines. Data in this review have been collected from the scientific articles published in databases such as Science Direct, Scopus, PubMed, Web of Science, and Scientific Information Database from 2000 to 2021. Based on this review, hawthorn extracts appear both therapeutic and protective effects against diabetic-related complications in various organs through molecular mechanisms, such as decreasing triglyceride, cholesterol, very low density lipoprotein and increasing the antioxidant activity of superoxide dismutase, catalase, glutathione peroxidase, total antioxidant capacity, decreasing malondialdehyde level, and attenuating tumor necrosis factor alpha, interleukin 6 and sirtuin 1/AMP-activated protein kinase (AMPK)/nuclear factor kappa B (NF-κB) pathway and increasing the phosphorylation of glucose transporter 4, insulin receptor substrate 1, AKT and phosphoinositide 3-kinases, and attenuating blood sugar and regulation of insulin secretion, insulin resistance, and improvement of histopathological changes in pancreatic beta cells. Collectively, hawthorn can be considered as one new target for the research and development of innovative drugs for the prevention or treatment of DM and related problems.
Energy Efficient and Secure Information Dissemination in Heterogeneous Wireless Sensor Networks Using Machine Learning Techniques
Authors: Dudeja Deepak , Hera Sabeena Yasmin , Doohan Nitika Vats , Dubey Nilesh , Mahaveerakannan R. , Ahanger Tariq Ahamed , Hinga Simon Karanja
Published: June 2022

The extensive use of sensor technology in every sphere of life, along with the continuous digitization of society, makes it realistic to anticipate that the planet will soon be patched with small-sized devices all over the place in the not-too-distant future. These devices give monitoring and surveillance capabilities, as well as access to a vast digital universe of information, among other things. Finding data and information, as well as processing enquiries, is made much easier thanks to the seamless transmission of information over wireless media, which is based on the “anywhere, anytime, everywhere” paradigm that allows information to be sent anywhere, at any time. Sensing networks came into existence as a consequence of the downsizing of wireless devices that are capable of receiving information from a source, transferring it, and processing it. Sensor networks, although they share many of the features, uses, and limits of ad hoc networks, have their own set of capabilities that are unique to them. While performing their responsibilities, sensor networks must contend with a variety of security issues, including unsecured wireless channels, physical compromise, and reprogramming. Because of the small size and ubiquitous availability of wireless sensor networks, compromise attacks are the most severe kind of attack in this environment (WSNs). With the proliferation of wireless sensor networks (WSNs), it is becoming more difficult to rely only on machine learning techniques. We sought to tackle the security challenge by developing a key management system as well as a secure routing mechanism. We are building scalable key management approaches that are resistant to node compromise and node replication attacks, which we will demonstrate in our proposed study, by using deployment-driven localization of security information and leveraging distributed key management. Using a security-aware selection of the next hop on the route to the destination, we were able to design safe routing algorithms that were effective.
Effects of Oxidized Fish Oil on the Growth, Immune and Antioxidant Capacity, Inflammation-Related Gene Expression, and Intestinal Microbiota Composition of Juvenile Sea Urchin (Strongylocentrotus intermedius)
Authors: Li Min , Tang Lu , Heqiu Yuqing , Lv Deliang , Ding Jun , Chang Yaqing , Zuo Rantao
Published: June 2022

A 60 d feeding experiment was conducted to investigate the effects of oxidized fish oil (OF) on survival, growth, immune and antioxidant capacity, inflammation-related gene expression, and intestinal microbiota of juvenile sea urchin (Strongylocentrotus intermedius). Six feeds were formulated by including equal amounts of fish oil with different oxidation levels (0, 50, 100, 150, 200, and 300 meq/kg). The feed OF0 and fresh kelp (Laminaria japonica) were used as the control groups. Results showed that the weight gain rate (WGR) of sea urchins significantly decreased as the oxidation level increased. When the oxidation level of fish oil was equal to or above 150 meq/kg, the WGR of sea urchins was significantly lower than those fed the control diets. The activities of digestive enzymes (pepsin and amylase), immune enzymes (alkaline phosphatase and acid phosphatase), and antioxidant enzymes (superoxide dismutase, catalase, and glutathione S-transferase) first decreased significantly as the oxidation level increased to 150 or 200 meq/kg and then bounced to values comparable to or even higher than those in the control feed group, while the transcription of immune, antioxidation, and inflammation-related genes was upregulated by the increasing oxidation level of fish oil. Oxidized fish oil decreased the intestinal microbial diversity and the relative abundance of Firmicutes. Vibrio was the dominant genus of microbiota in the intestine of sea urchins fed almost all formulated feeds (except for OF150), with its abundance above 20%. Comparably, the abundance of Vibrio in the intestine of sea urchins fed fresh kelp was only 2.32%. These results showed that oxidized fish oil had negative effects on the growth, immune response and antioxidant capacity, and bacterial diversity of juvenile S. intermedius. The abnormal increase of antioxidant enzyme activities and malondialdehyde contents at relatively higher oxidation level (>150 meq/kg) could be due to the oxidative stress occurrence inside the body of sea urchins. Sea urchins fed formulated feeds host more Vibrio than those fed fresh kelp, and this could be a potential incidence of disease occurrence.
Classification of All Single Traveling Wave Solutions of (
3
+
1
)-Dimensional Jimbo-Miwa Equation with Space-Time Fractional Derivative
Authors: Han Tianyong , Li Zhao , Wen Jiajin , Yuan Jun
Published: June 2022

In this paper, the complete discrimination system method is used to construct the single traveling wave solutions for the (

3
+
1

)-dimensional Jimbo-Miwa equations with space-time fractional derivative. As a result, we get the exact traveling wave solutions of the (

3
+
1

)-dimensional Jimbo-Miwa equation with space-time fractional derivative, which include rational function solutions, Jacobian elliptic function solutions, hyperbolic function solutions, and trigonometric function solutions. Some graphical representations of the solutions are also provided. Finally, the obtained solution is compared with the existing literature.