bluuebunny/crossref_metadata_2025 huggingface dataset:
Combination release of chemokines from coated coils to target aneurysm healing
Authors: Laurent Dimitri , Lucke-Wold Brandon , Dodd William S , Martinez Melanie , Chowdhury Muhammad Abdul Baker , Hosaka Koji , Motwani Kartik , Hoh Brian
Published: May 2022

Background

Monocyte chemoattractant protein 1 (MCP-1) and osteopontin (OPN) have been identified separately as key mediators of the aneurysm healing process following coil embolization in the rodent model. The ability of protein coated coils to accelerate this process is currently unknown.

Objective

To create coils coated with both MCP-1 and OPN to target aneurysm healing.

Methods

We used a polymer (poly(glycolide-co-caprolactone)) (Rao pharmaceuticals) (CG910) to test whether coils could be dual coated with active proteins with sequential reliable release. Coils were coated with poly-DL-lactic glycolic acid (PLGA), CG910, and subsequently dipped with protein OPN (inner layer for delayed release) and MCP-1 (outer layer for initial release). Release assays were used to measure protein elution from coils over time. To test in vivo feasibility, coated coils were implanted into carotid aneurysms to determine the effect on aneurysm healing.

Results

The in vitro protein release assay demonstrated a significant amount of OPN and MCP-1 release within 2 days. Using a 200 µg/µL solution of MCP-1 in phosphate-buffered saline, we showed that CG910 coated coils provide effective release of MCP over time. In the carotid aneurysm model, MCP-1 and OPN coated coils significantly increased tissue ingrowth (74% and 80%) compared with PLGA and CG910 coated coils alone (58% and 53%). To determine synergistic impact of dual coating, we measured ingrowth for MCP-1/OPN coils (63%) as well as overlap coefficients for NOX4 and NFκB with CD31.

Conclusions

This study demonstrates that MCP-1 and OPN coated coils are viable and may promote early aneurysm healing. Dual coated coils may have synergistic benefit given different location of protein interaction measured in vivo. Further work is warranted.
Lidocaine infusions for refractory chronic migraine: a retrospective analysis
Authors: Schwenk Eric S , Walter Aaron , Torjman Marc C , Mukhtar Sarah , Patel Harsh T , Nardone Bryan , Sun George , Thota Bhavana , Lauritsen Clinton G , Silberstein Stephen D
Published: May 2022

Introduction

Patients with refractory chronic migraine have poor quality of life. Intravenous infusions are indicated to rapidly ‘break the cycle’ of pain. Lidocaine infusions may be effective but evidence is limited.

Methods

The records of 832 hospital admissions involving continuous multiday lidocaine infusions for migraine were reviewed. All patients met criteria for refractory chronic migraine. During hospitalization, patients received additional migraine medications including ketorolac, magnesium, dihydroergotamine, methylprednisolone, and neuroleptics. The primary outcome was change in headache pain from baseline to hospital discharge. Secondary outcomes measured at the post-discharge office visit (25–65 days after treatment) included headache pain and the number of headache days, and percentage of sustained responders. Percentage of acute responders, plasma lidocaine levels, and adverse drug effects were also determined.

Results

In total, 609 patient admissions met criteria. The mean age was 46±14 years; 81.1% were female. Median pain rating decreased from baseline of 7.0 (5.0–8.0) to 1.0 (0.0–3.0) at end of hospitalization (p<0.001); 87.8% of patients were acute responders. Average pain (N=261) remained below baseline at office visit 1 (5.5 (4.0–7.0); p<0.001). Forty-three percent of patients were sustained responders at 1 month. Headache days (N=266) decreased from 26.8±3.9 at baseline to 22.5±8.3 at the post-discharge office visit (p<0.001). Nausea and vomiting were the most common adverse drug effects and all were mild.

Conclusion

Lidocaine infusions may be associated with short-term and medium-term pain relief in refractory chronic migraine. Prospective studies should confirm these results.
Healthcare utilisation and expenditures attributable to current e-cigarette use among US adults
Authors: Wang Yingning , Sung Hai-Yen , Lightwood James , Yao Tingting , Max Wendy B
Published: May 2022

Aims

This study estimated annual healthcare expenditures attributable to current e-cigarette use among US adults, including current exclusive and dual/poly e-cigarette use.

Methods

Analysing the 2015–2018 National Health Interview Survey data, we estimated the impacts of e-cigarette use on healthcare utilisation among adults aged 18+ years. Healthcare utilisation outcomes were hospital nights, emergency room (ER) visits, doctor visits and home visits. Current e-cigarette use was categorised as exclusive and dual/poly e-cigarette use. The econometric model included two equations: health status as a function of e-cigarette use and other independent variables, and healthcare utilisation as a function of health status, e-cigarette use, and other independent variables. Using an ‘excess utilisation’ approach, we multiplied the e-cigarette-attributable fraction derived from the model by annual health expenditures to calculate healthcare expenditures attributable to current exclusive and dual/poly e-cigarette use, the sum of which were expenditures attributable to all current e-cigarette use.

Results

Current exclusive and dual/poly e-cigarette use, with 0.2% and 3.5% prevalence in 2015–2018, were associated with higher odds of reporting poor health status than never tobacco users. Poor health status was associated with higher odds of using the four healthcare services and a greater number of ER and doctor visits. Annual healthcare expenditures attributable to all current e-cigarette use was $15.1 billion ($2024 per user) in 2018, including $1.3 billion attributable to exclusive e-cigarette use ($1796 per user) and $13.8 billion attributable to dual/poly e-cigarette use ($2050 per user).

Conclusion

Adult current e-cigarette use was associated with substantial excess healthcare utilisation and expenditures.
Scaling behaviour of braided active channels: a Taylor’s power law approach
Authors: De Bartolo Samuele , Rizzello Stefano , Ferrari Ennio , Frega Ferdinando , Napoli Gaetano , Vitolo Raffaele , Scaraggi Michele , Fallico Carmine , Severino Gerardo
Published: May 2022

At a channel (reach) scale, braided channels are fluvial, geomorphological, complex systems that are characterized by a shift of bars during flood events. In such events water flows are channeled in multiple and mobile channels across a gravel floodplain that remain in unmodified conditions. From a geometrical point of view, braided patterns of the active hydraulic channels are characterized by multicursal nature with structures that are spatially developed by either simple- and multi-scaling behavior. Since current studies do not take into account a general procedure concerning scale measurements, the latter behavior is still not well understood. The aim of our investigation is to analyze directly, through a general procedure, the scaling behavior of hydraulically active channels per transect and per reach analyzed. Our generalized stochastic approach is based on Taylor’s law, and the theory of exponential dispersion distributions. In particular, we make use of a power law, based on the variance and mean of the active channel fluctuations. In this way we demonstrate that the number of such fluctuations with respect to the unicursal behavior of the braided rivers, follows a jump-process of Poisson and compound Poisson–Gamma distributions. Furthermore, a correlation is also provided between the scaling fractal exponents obtained by Taylor’s law and the Hurst exponents.
Virtual element method for the Helmholtz transmission eigenvalue problem of anisotropic media
Authors: Meng Jian , Mei Liquan
Published: May 2022

In this paper, we propose a conforming virtual element method for the Helmholtz transmission eigenvalue problem of anisotropic media. By using [Formula: see text]-coercivity theory, the spectral approximation theory of compact operator and the projection and interpolation error estimates, we prove the spectral convergence of the discrete scheme and the optimal a priori error estimates for the discrete eigenvalues and eigenfunctions. The virtual element method has great flexibility in handling polygonal meshes, which motivates us to construct a fully computable a posteriori error estimator for the virtual element method. Then the upper bound of the approximation error is derived from the residual equation and the inf-sup condition. In turn, the related lower bound is established by using the bubble function strategy. Finally, we provide numerical examples to verify the theoretical results, including the optimal convergence of the virtual element scheme on uniformly refined meshes and the efficiency of the estimator on adaptively refined meshes.
Interpolation and stability estimates for edge and face virtual elements of general order
Authors: Beirão da Veiga L. , Mascotto L. , Meng J.
Published: May 2022

We develop interpolation error estimates for general order standard and serendipity edge and face virtual elements in two and three dimensions. Contextually, we investigate the stability properties of the associated [Formula: see text] discrete bilinear forms. These results are fundamental tools in the analysis of general order virtual elements, e.g. for electromagnetic problems.
Black holes decohere quantum superpositions
Authors: Danielson Daine L. , Satishchandran Gautam , Wald Robert M.
Published: May 2022

We show that if a massive body is put in a quantum superposition of spatially separated states, the mere presence of a black hole in the vicinity of the body will eventually destroy the coherence of the superposition. This occurs because, in effect, the gravitational field of the body radiates soft gravitons into the black hole, allowing the black hole to acquire “which path” information about the superposition. A similar effect occurs for quantum superpositions of electrically charged bodies. We provide estimates of the decoherence time for such quantum superpositions. We believe that the fact that a black hole will eventually decohere any quantum superposition may be of fundamental significance for our understanding of the nature of black holes in a quantum theory of gravity.
Scientific and Technological Innovation Rapid Emergency Resource Constraint-Improved Particle Swarm Optimization Project Scheduling Method
Authors: Chen Manyi
Published: May 2022

In the environment of scientific and technological innovation, the human and material resources that can reach the project threshold are often limited. For enterprise projects that require high R&D and production level to produce rapid emergency resources, they often face the problem of difficult effective allocation of emergency resources, resulting in project delay. Therefore, in the process of high-tech projects, it is necessary to deal with the lack of emergency scientific and technological resources. Therefore, based on the particle swarm optimization algorithm which is easy to be realized by scientific and technological innovation enterprises, this study improves its two shortcomings of easy local optimization and low performance, constructs the CD-PSO project scheduling model, verifies the performance of the improvement points, and tests the performance of the model in the actual project case of enterprise rapid emergency resource scheduling. The results show that the optimal solution of CD-PSO model in the three sample sets is higher than that of D-PSO model in the same group, and the average scheduling duration is the shortest, which is 350 days, and the proportion of reaching the optimal duration is 100%. The operation effect is obviously better than other scheduling models. The CD-PSO project scheduling model designed in this study is easier to implement and can calculate the optimal solution of the first project more efficiently. It not only has excellent performance, but also has a strong popularization foundation.
Experimental Investigations and Multi-Response Optimization of Dynamic Magnetic Field-Assisted Electrochemical Spark Drilling Using Grey Relational Analysis
Authors: Singh Roopa , Singh D. K. , Singh Jeeoot
Published: May 2022

Magnetic field-assisted electrochemical spark drilling (MA-ECSD) is a cost-effective triplex hybrid machining technique that has been developed to enhance the machining depth and surface roughness of insulated and hard-to-scribe materials. The study presented adopts a reformed approach for creation of dynamic magnetic field during drilling of Sodalime glass where a 34 AWG copper wire coiled electromagnet has been installed in the in-house designed and fabricated setup of MA-ECSD. The experimental plan is based on Box–Behnken design (BBD) of Response Surface Methodology (RSM) and significance of parameters is determined using ANOVA. Multi-objective optimization (MOO) is performed by applying Grey Relational Analysis (GRA). A noncontact optical profilometer measures the machining depths and surface roughness of drilled holes. The installed electromagnet generated dynamic magnetic field intensity (MFI) ranging between 0.00 and 0.18 Tesla. Preliminary experiments were conducted to select and set the range of input parameters. Significant effect of voltage, NaOH concentration and MFI on machining depth and surface roughness is found and optimal parameter settings obtained are 24[Formula: see text]V, 30[Formula: see text]wt% and 0.09 Tesla. Machining depth increased by about 13.03% with rise in voltage-NaOH concentration and surface roughness improved by 25.3% with elevation in voltage-MFI. Dynamic MFI generated from electromagnet helped in smooth motion of electrolyte in the fine space amidst cathode and glass slide due to magnetohydrodynamic effect (MHD) which resulted in enhanced machining depth and surface roughness. The experimental and predicted results obtained after confirmatory test are appreciable which is evident from SEM images and images obtained from Optical profilometer.
Environmental Unconcern and Recycled Paper Products Purchase: The Moderating Role of Environmental Knowledge
Authors: Delistavrou Antonia , Tilikidou Irene
Published: May 2022

Recent and forthcoming technological innovations highlight recycled paper products as a consumer market with promising potential. This study presents the examination of the recycled paper products purchasing behavior (RPPPB), as well as some of the factors that were assumed to affect these specific pro-environmental consumers’ choices. Instead of the impact of the customary positive environmental attitudes, this study focused on those negative attitudes, namely Environmental Unconcern, that might be found able to inhibit RPPB. In addition, an effort was made to examine the role of an objective environmental knowledge scale, namely the Eco-literacy scale. A survey among 224 consumers residing in the Municipality of Thessaloniki, Greece was conducted. Multiple regression indicated that just Environmental Knowledge and not the Eco-literacy scale is a predictor of RPPPB. Further, SEM analysis though verified the theoretical assumption that knowledge moderates the relationship between attitudes and behavior. It was concluded that the negative direct relationship between environmental unconcern and the recycled paper products’ purchasing behavior strengthens in the group of knowledgeable consumers. Implications are provided for all stakeholders that engage in the recycled paper products market.
On the Significance of Category Prediction for Code-Comment Synchronization
Authors: Yang Zhen , Keung Jacky Wai , Yu Xiao , Xiao Yan , Jin Zhi , Zhang Jingyu
Published: May 2022

Software comments sometimes are not promptly updated in sync when the associated code is changed. The inconsistency between code and comments may mislead the developers and result in future bugs. Thus, studies concerning code-comment synchronization have become highly important, which aims to automatically synchronize comments with code changes. Existing code-comment synchronization approaches mainly contain two types, i.e., (1) deep learning-based (e.g., CUP), and (2) heuristic-based (e.g., HebCUP). The former constructs a neural machine translation-structured semantic model, which has a more generalized capability on synchronizing comments with software evolution and growth. However, the latter designs a series of rules for performing token-level replacements on old comments, which can generate the completely correct comments for the samples fully covered by their fine-designed heuristic rules. In this article, we propose a composite approach named
CBS
(i.e.,
Classifying Before Synchronizing
) to further improve the code-comment synchronization performance, which combines the advantages of CUP and HebCUP with the assistance of inferred categories of
Code-Comment Inconsistent (CCI)
samples. Specifically, we firstly define two categories (i.e., heuristic-prone and non-heuristic-prone) for CCI samples and propose five features to assist category prediction. The samples whose comments can be correctly synchronized by HebCUP are heuristic-prone, while others are non-heuristic-prone. Then, CBS employs our proposed
Multi-Subsets Ensemble Learning (MSEL)
classification algorithm to alleviate the class imbalance problem and construct the category prediction model. Next, CBS uses the trained MSEL to predict the category of the new sample. If the predicted category is heuristic-prone, CBS employs HebCUP to conduct the code-comment synchronization for the sample, otherwise, CBS allocates CUP to handle it. Our extensive experiments demonstrate that CBS statistically significantly outperforms CUP and HebCUP, and obtains an average improvement of 23.47%, 22.84%, 3.04%, 3.04%, 1.64%, and 19.39% in terms of Accuracy, Recall@5,
Average Edit Distance (AED)
,
Relative Edit Distance (RED)
, BLEU-4, and
Effective Synchronized Sample (ESS)
ratio, respectively, which highlights that category prediction for CCI samples can boost the code-comment synchronization performance.
The Global Care Ecosystems of 3D Printed Assistive Devices
Authors: Savage Saiph , Flores-Saviaga Claudia , Rodney Rachel , Savage Liliana , Schull Jon , Mankoff Jennifer
Published: May 2022

The popularity of 3D printed assistive technology has led to the emergence of new ecosystems of care, where multiple stakeholders (makers, clinicians, and recipients with disabilities) work toward creating new upper limb prosthetic devices. However, despite the increasing growth, we currently know little about the differences between these care ecosystems. Medical regulations and the prevailing culture have greatly impacted how ecosystems are structured and stakeholders work together, including whether clinicians and makers collaborate. To better understand these care ecosystems, we interviewed a range of stakeholders from multiple countries, including Brazil, Chile, Costa Rica, France, India, Mexico, and the U.S. Our broad analysis allowed us to uncover different working examples of how multiple stakeholders collaborate within these care ecosystems and the main challenges they face. Through our study, we were able to uncover that ecosystems with multi-stakeholder collaborations exist (something prior work had not seen), and these ecosystems showed increased success and impact. We also identified some of the key follow-up practices to reduce device abandonment. Of particular importance are to have ecosystems put in place follow-up practices that integrate formal agreements and compensations for participation (which do not need to be just monetary). We identified that these features helped to ensure multi-stakeholder involvement and ecosystem sustainability. We finished the article with socio-technical recommendations to create vibrant care ecosystems that include multiple stakeholders in the production of 3D printed assistive devices.
Deep Dive into ZGC: A Modern Garbage Collector in OpenJDK
Authors: Yang Albert Mingkun , Wrigstad Tobias
Published: May 2022

ZGC is a modern, non-generational, region-based, mostly concurrent, parallel, mark-evacuate collector recently added to OpenJDK. It aims at having GC pauses that do not grow as the heap size increases, offering low latency even with large heap sizes. The ZGC C++ source code is readily accessible in the OpenJDK repository, but reading it (25 KLOC) can be very intimidating, and one might easily get lost in low-level implementation details, obscuring the key concepts. To make the ZGC algorithm more approachable, this work provides a thorough description on a high-level, focusing on the overall design with moderate implementation details. To explain the concurrency aspects, we provide a SPIN model that allows studying races between mutators and GC threads, and how they are resolved in ZGC. Such a model is not only useful for learning the current design (offering a deterministic and interactive experience) but also beneficial for prototyping new ideas and extensions. Our hope is that our detailed description and the SPIN model will enable the use of ZGC as a building block for future GC research, and research ideas implemented on top of it could even be adopted in the industry more readily, bridging the gap between academia and industry in the context of GC research.
Survey and Taxonomy of Adversarial Reconnaissance Techniques
Authors: Roy Shanto , Sharmin Nazia , Acosta Jaime C. , Kiekintveld Christopher , Laszka Aron
Published: May 2022

Adversaries are often able to penetrate networks and compromise systems by exploiting vulnerabilities in people and systems. The key to the success of these attacks is information that adversaries collect throughout the phases of the cyber kill chain. We summarize and analyze the methods, tactics, and tools that adversaries use to conduct reconnaissance activities throughout the attack process. First, we discuss what types of information adversaries seek and how and when they can obtain this information. Then, we provide a taxonomy and detailed overview of adversarial reconnaissance techniques. The taxonomy introduces a categorization of reconnaissance techniques based on the source as third-party and human-, and system-based information gathering. This article provides a comprehensive view of adversarial reconnaissance that can help in understanding and modeling this complex but vital aspect of cyber attacks as well as insights that can improve defensive strategies, such as cyber deception.
Review—Human-Body Powered Biosensing Textiles: Body-Power Generating Wearables Based on Textiles for Human Biomonitoring
Authors: Harimurti Suksmandhira , Hesar Milad Eyvazi , Soekoco Asril Senoaji , Jessika Jessika , Rizalputri Lavita Nuraviana , Althof Raih Rona , Refantero Gema , Utari Listya , Idrissi Akram , Gries Thomas , Yuliarto Brian , Ingebrandt Sven , Anshori Isa
Published: May 2022

The rise of wearable technology has gradually shifted modern health monitoring from clinical to personal use. Smart wearables can collect physiological signals and show them directly on a smartphone. In contemporary healthcare scenarios, this big data could aid medical doctors in online health analysis. Most currently available wearables are designed to monitor specific health parameters, while the combination of many devices is practically not convenient and not cost-effective. Therefore, a strong trend is towards the development of multifunctional devices. This demands, however, alternative sources of power other than conventional batteries. The concept of human-body-powered biosensing textiles (HBBTs) addresses this challenge. By harvesting energy produced from the human body such as motion, pressure, vibration, heat, and metabolites and converting them into electricity, HBBTs could potentially work without a battery. Additionally, the textiles themselves provide a suitable substrate for interconnects and biosensors, such that a system based on HBBTs could provide multifunctional health monitoring. This review explains the fundamental theories, the classification, the energy-conversion efficiency assessment, and the possible biomonitoring applications of HBBTs. Furthermore, we discuss the challenges for technology maturity and the perspectives of HBBTs in shaping the future of health monitoring.
Dynamic Elucidation of Lithium Insertion Reaction into MgMn2O4 Spinel
Authors: Ariyoshi Kingo , Masuda Shumpei
Published: May 2022

Since the expansion of Li-ion battery applications from portable electronic devices to electric vehicles and renewable energy storage, high-power capability is becoming increasingly important as a battery performance metric. Elucidation of the reaction mechanism of Li insertion materials is a major task in the battery research field, because it offers crucial insights into both the kinetics of the Li insertion reaction and the development of high-power Li-ion batteries. In this study, the mechanism for Li insertion into MgMn2O4 spinel, across the entire reaction range, was elucidated by fitting the current response during constant-potential discharge reaction using solid-state kinetic reaction (nucleation-growth, diffusion, and contraction) models. The fitting results revealed that the Li insertion reaction in the dynamic (non-equilibrium) process proceeds via nucleation-growth followed by solid-state Li-ion diffusion (single-phase), while Li insertion into MgMn2O4 proceeds through a two-phase coexistence reaction in the equilibrium state, as observed by ex situ XRD analysis. The finding that the reaction mechanisms in the dynamic and equilibrium processes are different indicates that the kinetics of the Li insertion reaction should be considered through a dynamic rather than an equilibrium process viewpoint.
Chemokines, soluble PD-L1, and immune cell hyporesponsiveness are distinct features of SARS-CoV-2 critical illness
Authors: Morrell Eric D. , Bhatraju Pavan K. , Sathe Neha A. , Lawson Jonathan , Mabrey Linzee , Holton Sarah E. , Presnell Scott R. , Wiedeman Alice , Acosta-Vega Carolina , Mitchem Mallorie A. , Liu Ted , Chai Xin-Ya , Sahi Sharon , Brager Carolyn , Orlov Marika , Sakr Sana S. , Sader Anthony , Lum Dawn M. , Koetje Neall , Garay Ashley , Barnes Elizabeth , Cromer Gail , Bray Mary K. , Pipavath Sudhakar , Fink Susan L. , Evans Laura , Long S. Alice , West T. Eoin , Wurfel Mark M. , Mikacenic Carmen
Published: May 2022

Critically ill patients manifest many of the same immune features seen in coronavirus disease 2019 (COVID-19), including both “cytokine storm” and “immune suppression.” However, direct comparisons of molecular and cellular profiles between contemporaneously enrolled critically ill patients with and without severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) are limited. We sought to identify immune signatures specifically enriched in critically ill patients with COVID-19 compared with patients without COVID-19. We enrolled a multisite prospective cohort of patients admitted under suspicion for COVID-19, who were then determined to be SARS-CoV-2-positive ( n = 204) or -negative ( n = 122). SARS-CoV-2-positive patients had higher plasma levels of CXCL10, sPD-L1, IFN-γ, CCL26, C-reactive protein (CRP), and TNF-α relative to SARS-CoV-2-negative patients adjusting for demographics and severity of illness (Bonferroni P value < 0.05). In contrast, the levels of IL-6, IL-8, IL-10, and IL-17A were not significantly different between the two groups. In SARS-CoV-2-positive patients, higher plasma levels of sPD-L1 and TNF-α were associated with fewer ventilator-free days (VFDs) and higher mortality rates (Bonferroni P value < 0.05). Lymphocyte chemoattractants such as CCL17 were associated with more severe respiratory failure in SARS-CoV-2-positive patients, but less severe respiratory failure in SARS-CoV-2-negative patients ( P value for interaction < 0.01). Circulating T cells and monocytes from SARS-CoV-2-positive subjects were hyporesponsive to in vitro stimulation compared with SARS-CoV-2-negative subjects. Critically ill SARS-CoV-2-positive patients exhibit an immune signature of high interferon-induced lymphocyte chemoattractants (e.g., CXCL10 and CCL17) and immune cell hyporesponsiveness when directly compared with SARS-CoV-2-negative patients. This suggests a specific role for T-cell migration coupled with an immune-checkpoint regulatory response in COVID-19-related critical illness.
APP processing: a biochemical competition influenced by exercise-induced signaling mediators?
Authors: Marko Daniel M. , MacPherson Rebecca E. K.
Published: May 2022

Neurodegenerative diseases, such as Alzheimer’s disease (AD), are becoming more common in aging our society. One specific neuropathological hallmark of this disease is excessive accumulation of amyloid-β (Aβ) peptides, which can aggregate to form the plaques commonly associated with this disease. These plaques are often observed well before clinical diagnosis of AD. At the cellular level, both the production and aggregation of Aβ peptides in the brain are detrimental to neuronal cell production, survival, and function, as well as often resulting in neuronal dysfunction and death. Exercise and physical activity have been shown to improve overall health, including brain health, and in the last several years there has been evidence to support that exercise may be able to regulate Aβ peptide production in the brain. Exercise promotes the release of a wide array of signaling mediators from various metabolically active tissues and organs in the body. These exercise-induced signaling mediators could be the driving force behind some of the beneficial effects observed in brain with exercise. This review will aim to discuss potential exercise-induced signaling mediators with the capacity to influence various proteins involved in the formation of Aβ peptide production in the brain.
Effect of Fibre Aspect-Ratio on the Fresh and Strength Properties of Steel Fibre Reinforced Self-Compacting Concrete
Authors: Sivanantham Panneerselvam Arul , Prabhu Ganapathy Ganesh , Vimal Arokiaraj George Gabriel , Sunil Killi
Published: May 2022

This paper presents the findings of investigation, which was instigated to assess the effects of fibre aspect-ratio on the fresh and mechanical properties of SCC. The investigational parameters were aspect-ratio of the fibre and fibre addition rate. Two different aspect-ratios (64 and 73) of steel fibre were used and the fibre addition rate was between 0.2% and 1.0% with the increment of 0.2% and the fibres were added to the SCC in volume fraction. A series of tests were performed to determine the impact of fibre aspect-ratio and fibre inclusion rate on the engineering/mechanical properties of SCC. The presence of steel fibre in the SCC lessened the workability; furthermore, the upsurge in the inclusion rate of steel fibre abridged the workability further. The inclusion of fibre boosted the compressive strength of the SCC moderately, and conversely, an overwhelming increase in flexural and tensile strength was observed. The rise in the fibre aspect-ratio caused a 2.53% augmentation in the compressive strength of the SCC. Nevertheless, more than 11% improvement in tensile strength was observed, because of the bridging/anchorage established by the fibre. From the obtained results, it can be inferred that the impact of fibre aspect-ratio on the compressive strength of the SCC is not substantial; however, the tensile strength properties of the SCC can be enhanced moderately by increasing the fibre aspect-ratio.
The Application of Web‐Based Scientific Computing System in Innovation and Entrepreneurship
Authors: Cheng Tingli
Published: May 2022

In the past ten years, the number of newly added labor forces in China has continued to grow. At the same time, with the increase in the number of university candidates, the number of university employments is also increasing. Due to employment pressure from society and alumni, the government promotes innovation and entrepreneurship and promotes innovative slogans through entrepreneurship. Universities have become the people who are concerned about this slogan. As an important tool for solving large‐scale computer problems, scientific computing is increasingly used in various fields of science and engineering. With the development of high‐performance computing technology, various parallel computers have appeared and are widely used. This article uses a scientific computing system to conduct research on innovation and entrepreneurship. The entire scientific computing system is a typical three‐tier architecture, and the small service program is responsible for analyzing the scientific computing request in the request and generating related calculation expressions. Get the comprehensive evaluation level of innovation and entrepreneurship risk: excellent score is 3.45, good score is 2.56, general score is 1.68, and difference is 0.68.
A Modified Fiber Bridging Model for High Ductility Cementitious Composites Based on Debonding-Slipping Rupture Analysis
Authors: Cong Ding , Liping Guo , Jinming Ren , Yongming Wang , Xinyu Li , Yuan Gao , Wanpeng Liu , Ruize Li
Published: May 2022

Modified micromechanical bridging model is established with consideration of the fiber rupture effect at debonding and slipping stages. The bridging model includes the debonding and slipping rupture of fibers and establishes the fiber/matrix interfacial parameters (friction

τ

0

, chemical bonding force

G

d

, slip-hardening coefficient

β

). A different interfacial bonding can cause fiber rupture. The influence of the interfacial conditions on the fiber rupture risk was investigated. In the modified bridging model, the effective bridging stress, the debonding rupture stress, and the slipping rupture stress were clearly identified. Finally, single-fiber pullout tests with different embedded lengths were carried out to validate the bridging model. The relationship between the fiber bridging stress and the crack opening predicted by the bridging model was consistent with the experimental results. This modified micromechanical bridging model can be used to quantitatively calculate the actual fiber bridging capacity and to predict the ductility of the high ductility cementitious composites reinforced by different types of fibers.
Sports Economic Mining Algorithm Based on Association Analysis and Big Data Model
Authors: Zhou Fujian
Published: May 2022

With the implementation of national strategies such as sports power and national fitness, the sports economy has become an important element of high-quality national development, and the demand for sports economy and management talents is greatly increased. Particularly in the new area with big data as the typical feature, the teaching content, teaching method, and teaching mode of sports economics and management majors have put forward new requirements. The continuous progress of storage and network technology has prompted the generation of massive multisource spatiotemporal data in various fields. The advantage of association analysis algorithms is that they are easy to code and implement. The relationships found by association analysis can take two forms: frequent itemsets or association rules. We use correlation analysis methods to perform correlation learning between sports economy and related big data and thus improve the development of sports economy. Mining and analyzing the relevant big data can precisely reveal the problems of sports economic development and can realize the fine management of sports, thus contributing to the healthy development of sports. Mastering the skills of acquiring, analyzing, and applying big data is the core content of sports economic analysis. The sports economy has refined and intelligent management means, and its adoption of virtual reality reflects the current situation and development trend of the sports business, which further highlights the status and role of multisource big data in the sports economy. Based on these, this paper proposed a sports economy mining algorithm in view of the correlation analysis and big data model. Then, we verified the effectiveness of the model through experiments, which laid the foundation for the development of the sports economy.
Prevalence of algD, pslD, pelF, Ppgl, and PAPI-1 Genes Involved in Biofilm Formation in Clinical Pseudomonas aeruginosa Strains
Authors: Rajabi Hakime , Salimizand Himen , Khodabandehloo Mazaher , Fayyazi Amirhossein , Ramazanzadeh Rashid
Published: May 2022

Introduction. Biofilm formation is one of the main virulence factors in Pseudomonas aeruginosa infections. This study is aimed at investigating the presence of genes involved in biofilm formation in clinical P. aeruginosa isolates. Material and Methods. A cross‐sectional study was conducted on 112 P. aeruginosa isolates. The biofilm formation assay was performed on all isolates. Antimicrobial resistance was determined by the disk diffusion method, and the presence of genes was detected by polymerase chain reaction. Isolates were typed with Rep‐PCR. Results. The results of biofilm formation demonstrated that 85 strains (75.9%) were biofilm producers, and 27 strains (24.1%) were nonproducer isolates. Antibiotic susceptibility pattern in biofilm‐positive and biofilm‐negative isolates obtained from hospitalized patients showed a high rate of antibiotic resistance to amoxicillin with 95.7% and 92.3%, respectively. Based on PCR amplification results, the frequency of genes involved in biofilm formation among all isolates was as follows: algD (78.6%), pelF (70.5%), pslD (36.6%), Ppgl (0%), and PAPI-1 (77.6%). Rep‐PCR typing demonstrated that 112 P. aeruginosa isolates were classified into 57 types according to 70% cut‐off. The predominant type was A which contained 15 isolates. Moreover, 7 isolates were clustered in genotype B, followed by C type (6), D (4), E (4), F (4), G (4), H (3), I (3), J (3 isolates), and 12 genotypes, each containing two isolates. Also, 35 isolates were distributed in scattered patterns and showed single types. Conclusion. Study results showed significant association between biofilm formation and resistance to antibiotics such as ceftazidime and meropenem. Analysis of Rep‐PCR patterns indicated that the evaluated isolates were heterogeneous, relatively.
Chinese Clinical Named Entity Recognition with ALBERT and MHA Mechanism
Authors: Li Dongmei , Long Jiao , Qu Jintao , Zhang Xiaoping
Published: May 2022

Traditional clinical named entity recognition methods fail to balance the effectiveness of feature extraction of unstructured text and the complexity of neural network models. We propose a model based on ALBERT and a multihead attention (MHA) mechanism to solve this problem. Structurally, the model first obtains character-level word embeddings through the ALBERT pretraining language model, then inputs the word embeddings into the iterated dilated convolutional neural network model to quickly extract global semantic information, and decodes the predicted labels through conditional random fields to obtain the optimal label sequence. Also, we apply the MHA mechanism to capture intercharacter dependencies from multiple aspects. Furthermore, we use the RAdam optimizer to boost the convergence speed and improve the generalization ability of our model. Experimental results show that our model achieves an F1 score of 85.63% on the CCKS-2019 dataset—an increase of 4.36% compared to the baseline model.
A Study on Insulation Monitoring Technology of High-Voltage Cables in Underground Coal Mines Based on Decision Tree
Authors: Fu Wenjun , Xu Ying , Gao Yan
Published: May 2022

The insulation state of high-voltage cables in coal mines directly influences the reliability of power supply in coal mines and the level of safe production. In this paper, the degradation mechanism of cable insulation is analyzed, and an online monitoring technology of cable insulation in coal mines based on decision tree is proposed, the technical principle of the judgment method of cable degradation based on decision tree is studied, the feasibility of this technology is verified through simulation, the existing online monitoring solutions for cable insulation are analyzed, and a wide-area synchronous measurement and monitoring system for cable insulation is designed. This technology has been applied in Chinese coal mine enterprises in China and achieved a good effect.
An AI-Enabled Internet of Things Based Autism Care System for Improving Cognitive Ability of Children with Autism Spectrum Disorders
Authors: Abdel Hameed Mohamed , Hassaballah M. , Hosney Mosa E. , Alqahtani Abdullah
Published: May 2022

Smart monitoring and assisted living systems for cognitive health assessment play a central role in assessment of individuals’ health conditions. Autistic children suffer from some difficulties including social skills, repetitive behaviors, speech and nonverbal communication, and accommodating to the environment around them. Thus, dealing with autistic children is a serious public health problem as it is hard to determine what they feel with a lack of emotional cognitive ability. Currently, no medical treatments have been shown to cure autistic children, with most of the social assistive research to date focusing on Autism Spectrum Disorder (ASD) without suggesting a real treatment. In this paper, we focus on improving cognitive ability and daily living skills and maximizing the ability of the autistic child to function and participate positively in the community. Through utilizing intelligent systems based Artificial Intelligence (AI) and Internet of Things (IoT) technologies, we facilitate the process of adaptation to the world around the autistic children. To this end, we propose an AI-enabled IoT system embodied in a sensor for measuring the heart rate to predict the state of the child and then sending the state to the guardian with feeling and expected behavior of the child via a mobile application. Further, the system can provide a new virtual environment to help the child to be capable of improving eye contact with other people. This way is represented in pictures of these persons in 3D models that break this child’s fear barrier. The system follows strategies that have focused on social communication skill development particularly at young ages to be more interactive with others.
Brand Marketing Leveraging the Advantage of Emoji Pack Relying on Association Rule Algorithm in Data Mining Technology
Authors: Tian Huan
Published: May 2022

The random and disordered configuration of expression package image, text, and context is in line with the emotional consumption trend of young groups, so it has become a kind of image symbol favored by young groups. How to integrate the text and images related to emoticons and carry out emotion analysis in a specific consumer environment is the focus of attention. In view of these needs and defects, this study uses the association rule algorithm in data mining technology to explore brand marketing targeting different consumers. Taking the nonverbal symbols on social media as the research object, this study collects and mines the download ranking and appreciation of the expression package of “WeChat expression open platform.” The persistence of users’ use of expression package has the characteristics of power distribution. When choosing, there are sprouting attributes and dynamic and serialized selection preferences. The advantages of emoticon package are used, brand communication and marketing are strengthened, and effective brand marketing analysis is implemented. The simulation results show that by taking advantage of the strong communication and appeal of the expression package, the brand will help to expand the contact surface of the marketing brand concept. Secondly, as a gathering community, expression packs are divided into different circle groups between and within groups, which helps brand marketing realize precision marketing based on “strong connection” with the help of expression packs and promote the occurrence of consumer behavior.
The Effect of Comprehensive Rehabilitation Nursing on the Rehabilitation of Sports-Induced Ankle Joint Injuries
Authors: Qiao Yu , Zhang Bin , Zhang Lei
Published: May 2022

The purpose of participating in sports is to enhance physical fitness and promote physical and mental health. The occurrence of sports injuries will cause physical and mental injuries to athletes, especially the ankle joint injuries that often occur. Therefore, it is particularly important to understand the causes, symptoms, treatment, and prevention of athletes’ ankle injuries. This article aims at studying the significance of comprehensive rehabilitation nursing for the rehabilitation of sports-induced ankle joint injuries and introduces the concepts of comprehensive rehabilitation nursing and ankle joint injuries. This article proposes how to use comprehensive rehabilitation nursing to recover ankle injuries caused by sports based on smart medical care. In the experiment of this article, it can be seen from Table 5 that the recovery speed of 5 athletes with ankle joint injuries before rehabilitation training was 8% (lowest) and 14% (highest). The recovery rate after rehabilitation care is up to 25%. It can be seen that comprehensive rehabilitation care is of great help to athletes with ankle injuries. As can be seen from Table 4, the number of ankle injuries in acute secondary injuries is 25, accounting for 33%, which is the highest; the number of contusions is 15, accounting for 21%, which is the lowest ratio, so it can be seen that the ankle joint injury in sports is the most serious. Therefore, the application of comprehensive rehabilitation care needs to be popularized, which is of great significance to ankle joint injuries. The ankle joint is one of the most easily sprained joints in the human body, not only because of the flexibility of the ankle joint, but also because of the anatomical characteristics of the ankle joint. The main cause of ankle injuries is inappropriate behavior in sports.
Mechanics Design of Conical Spiral Structure for Flexible Coilable Antenna Array
Authors: Wang Hairui , Zhang Yao , Chen Siyu , Ma Yinji , Wang Heling , Chen Ying , Feng Xue
Published: May 2022

Limited by the effective launch capacity of a rocket, the deployable antenna is very important in the design of spaceborne antenna array. Compared to traditional deployable antenna, flexible coilable antenna array has higher surface precision and better vibration control and therefore is more suitable for high frequency communication. In order to minimize the weight of satellite and reduce cost of its launch, a design guideline to the geometry parameters of flexible coilable antenna array is crucial. Existing models cannot be directly applied to interaction and large deformation between coilable membrane and conical spiral antenna in the flexible coilable antenna array. Hence, the geometry parameters of the conical spiral structure and the thickness of the coilable membrane in the flexible coilable antenna array have not been optimized yet. In this paper, the interaction between the coilable membrane and the concial spiral antenna is analyzed in the antenna array. A concise formula is derived to predict the critical force that flattens the conical spiral antenna by a coiling scroll. Combined with a theoretical model to predict the deformation of the membrane, the model provides an important theoretical support for the lightweight design and mechanical design of flexible coilable antenna array, such as the thickness of the coilable membrane. The proposed design is validated by experiments. The above findings have potential applications in the effective reduction of antenna array weight and satellite launch costs.