bluuebunny/crossref_metadata_2025 huggingface dataset:
Ghost-dune hollows of the eastern Snake River Plain, Idaho: Their genesis, evolution, and relevance to Martian ghost-dune pits
Authors: Gaylord David R. , Rittenour Tammy M. , Link Paul K. , Turrin Brent D. , Kuntz Mel A.
Published: April 2021

Ghost-dune hollows on the eastern Snake River Plain (ESRP), Idaho, USA, are topographically inverted, crescent-shaped depressions that record the partial encasement of sand dunes by ca. 61 ka basalt lava flows. Deflation of these “ghost” sand dunes produced approximately two dozen, 5–10-m-deep ghost-dune hollows now incompletely filled with pedogenically altered eolian and colluvial sediment. Optically stimulated luminescence (OSL) and 40Ar/39Ar ages constrain a ghost-dune hollow model that illuminates the late Pleistocene to Holocene environmental and climate history of the ESRP. Detrital zircon analyses indicate sand-dune supply routes changed following the burial of Pleistocene Henrys Fork (tributary of the Snake River) alluvium by ca. 70 ka basalt flows. Removal of Henrys Fork alluvium from the eolian supply system made Lake Terreton sediment the primary source for later ESRP sand dunes. Such sediment supply changes highlight the potential impacts of effusive volcanism on sand-dune histories and landscapes. Our results support stratigraphic and sedimentary modeling of comparable ghost-dune 舠pit舡 deposits older than ca. 2 Ga on Mars that may have served as refugia for early life on that planet. Analogous ancient ghost-dune hollow deposits on Earth may also have served as early life refugia.
Water transfer to the deep mantle through hydrous, Al-rich silicates in subduction zones
Authors: Hermann Jörg , Lakey Shayne
Published: April 2021

Constraining deep-water recycling along subduction zones is a first-order problem to understand how Earth has maintained a hydrosphere over billions of years that created conditions for a habitable planet. The pressure-temperature stability of hydrous phases in conjunction with slab geotherms determines how much H2O leaves the slab or is transported to the deep mantle. Chlorite-rich, metasomatic rocks that form at the slab-mantle interface at 50–100 km depth represent an unaccounted, H2O-rich reservoir in subduction processes. Through a series of high-pressure experiments, we investigated the fate of such chlorite-rich rocks at the most critical conditions for subduction water recycling (5–6.2 GPa, 620–800 °C) using two different natural ultramafic compositions. Up to 5.7 GPa, 740 °C, chlorite breaks down to an anhydrous peridotite assemblage, and H2O is released. However, at higher pressures and lower temperatures, a hydrous Al-rich silicate (11.5 Å phase) is an important carrier to enable water transfer to the deep mantle for cold subduction zones. Based on the new phase diagrams, it is suggested that the deep-water cycle might not be in secular equilibrium.
Transient Permian-Triassic euxinia in the southern Panthalassa deep ocean
Authors: Grasby S.E. , Bond D.P.G. , Wignall P.B. , Yin R. , Strachan L.J. , Takahashi S.
Published: April 2021

Both the duration and severity of deep-water anoxic conditions across the Permian-Triassic mass extinction (PTME) are controversial. Panthalassa Ocean circulation models yield varying results, ranging from a well-ventilated deep ocean to rapidly developing northern-latitude, but not southern-latitude, anoxia in response to Siberian Traps–driven global warming. To address this uncertainty, we examined a southern-paleolatitude pelagic record. Trace metal and pyrite framboid data suggest bottom-water euxinic conditions developed in the southern Panthalassa Ocean at the PTME, coincident with enhanced volcanic activity indicated by Hg geochemistry. While a global ocean euxinic event at the PTME placed extraordinary stress on marine life, southern surface waters appear to have recovered more quickly as radiolarian populations returned several million years before they did in northern Panthalassa.
Unexpected clinical outcomes following the implementation of a standardised order set for hepatic encephalopathy
Authors: KC Mandip , Olson Andrew P J , Wang Qi , Lim Nicholas
Published: April 2021

Objective

We evaluated the effect on clinical outcomes of implementing a standardised inpatient order set for patients admitted with hepatic encephalopathy (HE).

Methods

A retrospective review of patients with cirrhosis admitted with HE. Hospital admissions for HE for which the electronic health record (EHR) order set was used were compared with admissions where the order set was not used. Primary outcome was length of hospital stay (LOS). Secondary outcomes were 30-day readmissions, in-hospital complications, in-hospital and 90-day mortality.

Results

There were 341 patients with 980 admissions over the study period: 263 patients with 736 admissions where the order set was implemented, and 78 patients with 244 admissions where the order set was not implemented. Median LOS was 4 days (IQR 3–8) in the order set group compared with 3 days (IQR 2–7) (p<0.001); incidence rate ratio 1.37 (95% CI 1.20 to 1.57), p<0.001. 30-day readmissions rate was 56% in the order set group compared with 40%, p=0.01; OR for readmission was 1.88 (95% CI 1.04 to 3.43), p=0.04. Hypokalaemia occurred in 46% of admissions with order set use compared with 36%, when the order set was not used; p=0.003, OR 1.72 (95% CI 1.22 to 2.43), p=0.002. No significant differences were seen for in-hospital mortality and 90-day mortality.

Conclusion

Implementation of an inpatient EHR order set for use in patients with HE was associated with unexpected clinical outcomes including increased LOS and readmissions. The convenience and advantages of standardisation of patient care should be balanced with a degree of individualisation, particularly in the care of medically complex patients. Furthermore, standardised processes should be evaluated frequently after implementation to assess for unintended consequences.
Masses of positive- and negative-parity hadron ground-states, including those with heavy quarks
Authors: Yin Pei-Lin , Cui Zhu-Fang , Roberts Craig D. , Segovia Jorge
Published: April 2021

A symmetry-preserving treatment of a vector $$\times $$
×
 vector contact interaction is used to compute spectra of ground-state $$J^P = 0^\pm , 1^\pm $$

J
P

=

0
±

,

1
±

$$(f{\bar{g}})$$

(
f

g

¯

)

mesons, their partner diquark correlations, and $$J^P=1/2^\pm , 3/2^\pm $$

J
P

=
1
/

2
±

,
3
/

2
±

(fgh) baryons, where $$f,g,h \in \{u,d,s,c,b\}$$

f
,
g
,
h
∈
{
u
,
d
,
s
,
c
,
b
}

. Results for the leptonic decay constants of all mesons are also obtained, including scalar and pseudovector states involving heavy quarks. The spectrum of baryons produced by this chiefly algebraic approach reproduces the 64 masses known empirically or computed using lattice-regularised quantum chromodynamics with an accuracy of 1.4(1.2)%. It also has the richness of states typical of constituent-quark models and predicts many baryon states that have not yet been observed. The study indicates that dynamical, nonpointlike diquark correlations play an important role in all baryons; and, typically, the lightest allowed diquark is the most important component of a baryon’s Faddeev amplitude.
The Application of the Blockchain Technology in Voting Systems
Authors: Huang Jun , He Debiao , Obaidat Mohammad S. , Vijayakumar Pandi , Luo Min , Choo Kim-Kwang Raymond
Published: April 2021

Voting is a formal expression of opinion or choice, either positive or negative, made by an individual or a group of individuals. However, conventional voting systems tend to be centralized, which are known to suffer from security and efficiency limitations. Hence, there has been a trend of moving to decentralized voting systems, such as those based on blockchain. The latter is a decentralized digital ledger in a peer-to-peer network, where a copy of the append-only ledger of digitally signed and encrypted transactions is maintained by each participant. Therefore, in this article, we perform a comprehensive review of blockchain-based voting systems and classify them based on a number of features (e.g., the types of blockchain used, the consensus approaches used, and the scale of participants). By systematically analyzing and comparing the different blockchain-based voting systems, we also identify a number of limitations and research opportunities. Hopefully, this survey will provide an in-depth insight into the potential utility of blockchain in voting systems and device future research agenda.
Software Testing Effort Estimation and Related Problems
Authors: Bluemke Ilona , Malanowska Agnieszka
Published: April 2021

Although testing effort estimation is a very important task in software project management, it is rarely described in the literature. There are many difficulties in finding any useful methods or tools for this purpose. Solutions to many other problems related to testing effort calculation are published much more often. There is also no research focusing on both testing effort estimation and all related areas of software engineering. To fill this gap, we performed a systematic literature review on both questions. Although our primary objective was to find some tools or implementable metods for test effort estimation, we have quickly discovered many other interesting topics related to the main one. The main contribution of this work is the presentation of the testing effort estimation task in a very wide context, indicating the relations with other research fields. This systematic literature review presents a detailed overview of testing effort estimation task, including challenges and approaches to automating it and the solutions proposed in the literature. It also exhaustively investigates related research topics, classifying publications that can be found in connection to the testing effort according to seven criteria formulated on the basis of our research questions. We present here both synthesis of our finding and the deep analysis of the stated research problems.
A Lightweight Formalism for Reference Lifetimes and Borrowing in Rust
Authors: Pearce David J.
Published: April 2021

Rust is a relatively new programming language that has gained significant traction since its v1.0 release in 2015. Rust aims to be a systems language that competes with C/C++. A claimed advantage of Rust is a strong focus on memory safety without garbage collection. This is primarily achieved through two concepts, namely,
reference lifetimes
and
borrowing
. Both of these are well-known ideas stemming from the literature on
region-based memory management
and
linearity
/
uniqueness
. Rust brings both of these ideas together to form a coherent programming model. Furthermore, Rust has a strong focus on stack-allocated data and, like C/C++ but unlike Java, permits references to local variables.

Type checking in Rust can be viewed as a two-phase process: First, a traditional type checker operates in a flow-insensitive fashion; second, a
borrow checker
enforces an ownership invariant using a flow-sensitive analysis. In this article, we present a lightweight formalism that captures these two phases using a flow-sensitive type system that enforces “
type and borrow safety
.” In particular, programs that are type and borrow safe will not attempt to dereference dangling pointers. Our calculus core captures many aspects of Rust, including copy- and move-semantics, mutable borrowing, reborrowing, partial moves, and lifetimes. In particular, it remains sufficiently lightweight to be easily digested and understood and, we argue, still captures the salient aspects of reference lifetimes and borrowing. Furthermore, extensions to the core can easily add more complex features (e.g., control-flow, tuples, method invocation). We provide a soundness proof to verify our key claims of the calculus. We also provide a reference implementation in Java with which we have model checked our calculus using over 500B input programs. We have also fuzz tested the Rust compiler using our calculus against 2B programs and, to date, found one confirmed compiler bug and several other possible issues.
Natural Language Processing for Requirements Engineering
Authors: Zhao Liping , Alhoshan Waad , Ferrari Alessio , Letsholo Keletso J. , Ajagbe Muideen A. , Chioasca Erol-Valeriu , Batista-Navarro Riza T.
Published: April 2021

Natural Language Processing for Requirements Engineering (NLP4RE) is an area of research and development that seeks to apply natural language processing (NLP) techniques, tools, and resources to the requirements engineering (RE) process, to support human analysts to carry out various linguistic analysis tasks on textual requirements documents, such as detecting language issues, identifying key domain concepts, and establishing requirements traceability links. This article reports on a mapping study that surveys the landscape of NLP4RE research to provide a holistic understanding of the field. Following the guidance of systematic review, the mapping study is directed by five research questions, cutting across five aspects of NLP4RE research, concerning the state of the literature, the state of empirical research, the research focus, the state of tool development, and the usage of NLP technologies. Our main results are as follows: (i) we identify a total of 404 primary studies relevant to NLP4RE, which were published over the past 36 years and from 170 different venues; (ii) most of these studies (67.08%) are solution proposals, assessed by a laboratory experiment or an example application, while only a small percentage (7%) are assessed in industrial settings; (iii) a large proportion of the studies (42.70%) focus on the requirements analysis phase, with quality defect detection as their central task and requirements specification as their commonly processed document type; (iv) 130 NLP4RE tools (i.e., RE specific NLP tools) are extracted from these studies, but only 17 of them (13.08%) are available for download; (v) 231 different NLP technologies are also identified, comprising 140 NLP techniques, 66 NLP tools, and 25 NLP resources, but most of them—particularly those novel NLP techniques and specialized tools—are used infrequently; by contrast, commonly used NLP technologies are traditional analysis techniques (e.g., POS tagging and tokenization), general-purpose tools (e.g., Stanford CoreNLP and GATE) and generic language lexicons (WordNet and British National Corpus). The mapping study not only provides a collection of the literature in NLP4RE but also, more importantly, establishes a structure to frame the existing literature through categorization, synthesis and conceptualization of the main theoretical concepts and relationships that encompass both RE and NLP aspects. Our work thus produces a
conceptual framework
of NLP4RE. The framework is used to identify research gaps and directions, highlight technology transfer needs, and encourage more synergies between the RE community, the NLP one, and the software and systems practitioners. Our results can be used as a starting point to frame future studies according to a well-defined terminology and can be expanded as new technologies and novel solutions emerge.
A Review on Outlier/Anomaly Detection in Time Series Data
Authors: Blázquez-García Ane , Conde Angel , Mori Usue , Lozano Jose A.
Published: April 2021

Recent advances in technology have brought major breakthroughs in data collection, enabling a large amount of data to be gathered over time and thus generating time series. Mining this data has become an important task for researchers and practitioners in the past few years, including the detection of outliers or anomalies that may represent errors or events of interest. This review aims to provide a structured and comprehensive state-of-the-art on unsupervised outlier detection techniques in the context of time series. To this end, a taxonomy is presented based on the main aspects that characterize an outlier detection technique.
A Survey of Binary Code Similarity
Authors: Haq Irfan Ul , Caballero Juan
Published: April 2021

Binary code similarityapproaches compare two or more pieces of binary code to identify their similarities and differences. The ability to compare binary code enables many real-world applications on scenarios where source code may not be available such as patch analysis, bug search, and malware detection and analysis. Over the past 22 years numerous binary code similarity approaches have been proposed, but the research area has not yet been systematically analyzed. This article presents the first survey of binary code similarity. It analyzes 70 binary code similarity approaches, which are systematized on four aspects: (1) the applications they enable, (2) their approach characteristics, (3) how the approaches are implemented, and (4) the benchmarks and methodologies used to evaluate them. In addition, the survey discusses the scope and origins of the area, its evolution over the past two decades, and the challenges that lie ahead.
Mobility Trace Analysis for Intelligent Vehicular Networks
Authors: Celes Clayson , Boukerche Azzedine , Loureiro Antonio A. F.
Published: April 2021

Intelligent vehicular networks emerge as a promising technology to provide efficient data communication in transportation systems and smart cities. At the same time, the popularization of devices with attached sensors has allowed the obtaining of a large volume of data with spatiotemporal information from different entities. In this sense, we are faced with a large volume of vehicular mobility traces being recorded. Those traces provide unprecedented opportunities to understand the dynamics of vehicular mobility and provide data-driven solutions. In this article, we give an overview of the main publicly available vehicular mobility traces; then, we present the main issues for preprocessing these traces. Also, we present the methods used to characterize and model mobility data. Finally, we review existing proposals that apply the hidden knowledge extracted from the mobility trace for vehicular networks. This article provides a survey on studies that use vehicular mobility traces and provides a guideline for the proposition of data-driven solutions in the domain of vehicular networks. Moreover, we discuss open research problems and give some directions to undertake them.
Syntactic Pattern Recognition in Computer Vision
Authors: Astolfi Gilberto , Rezende Fábio Prestes Cesar , Porto João Vitor De Andrade , Matsubara Edson Takashi , Pistori Hemerson
Published: April 2021

Using techniques derived from the syntactic methods for visual pattern recognition is not new and was much explored in the area called syntactical or structural pattern recognition. Syntactic methods have been useful because they are intuitively simple to understand and have transparent, interpretable, and elegant representations. Their capacity to represent patterns in a semantic, hierarchical, compositional, spatial, and temporal way have made them very popular in the research community. In this article, we try to give an overview of how syntactic methods have been employed for computer vision tasks. We conduct a systematic literature review to survey the most relevant studies that use syntactic methods for pattern recognition tasks in images and videos. Our search returned 597 papers, of which 71 papers were selected for analysis. The results indicated that in most of the studies surveyed, the syntactic methods were used as a high-level structure that makes the hierarchical or semantic relationship among objects or actions to perform the most diverse tasks.
CogTool+
Authors: Yuan Haiyue , Li Shujun , Rusconi Patrice
Published: April 2021

Cognitive modeling tools have been widely used by researchers and practitioners to help design, evaluate, and study computer user interfaces (UIs). Despite their usefulness, large-scale modeling tasks can still be very challenging due to the amount of manual work needed. To address this scalability challenge, we propose CogTool+, a new cognitive modeling software framework developed on top of the well-known software tool CogTool. CogTool+ addresses the scalability problem by supporting the following key features: (1) a higher level of parameterization and automation; (2) algorithmic components; (3) interfaces for using external data; and (4) a clear separation of tasks, which allows programmers and psychologists to define reusable components (e.g., algorithmic modules and behavioral templates) that can be used by UI/UX researchers and designers without the need to understand the low-level implementation details of such components. CogTool+ also supports mixed cognitive models required for many large-scale modeling tasks and provides an offline analyzer of simulation results. In order to show how CogTool+ can reduce the human effort required for large-scale modeling, we illustrate how it works using a pedagogical example, and demonstrate its actual performance by applying it to large-scale modeling tasks of two real-world user-authentication systems.
Polymorphic Iterable Sequential Effect Systems
Authors: Gordon Colin S.
Published: April 2021

Effect systems are lightweight extensions to type systems that can verify a wide range of important properties with modest developer burden. But our general understanding of effect systems is limited primarily to systems where the order of effects is irrelevant. Understanding such systems in terms of a semilattice of effects grounds understanding of the essential issues and provides guidance when designing new effect systems. By contrast, sequential effect systems—where the order of effects is important—lack an established algebraic structure on effects.

We present an abstract polymorphic effect system parameterized by an effect quantale—an algebraic structure with well-defined properties that can model the effects of a range of existing sequential effect systems. We define effect quantales, derive useful properties, and show how they cleanly model a variety of known sequential effect systems.

We show that for most effect quantales, there is an induced notion of iterating a sequential effect; that for systems we consider the derived iteration agrees with the manually designed iteration operators in prior work; and that this induced notion of iteration is as precise as possible when defined. We also position effect quantales with respect to work on categorical semantics for sequential effect systems, clarifying the distinctions between these systems and our own in the course of giving a thorough survey of these frameworks. Our derived iteration construct should generalize to these semantic structures, addressing limitations of that work. Finally, we consider the relationship between sequential effects and Kleene Algebras, where the latter may be used as instances of the former.
In Situ Programming of CAR T Cells
Authors: Parayath Neha N. , Stephan Matthias T.
Published: April 2021

Gene therapy makes it possible to engineer chimeric antigen receptors (CARs) to create T cells that target specific diseases. However, current approaches require elaborate and expensive protocols to manufacture engineered T cells ex vivo, putting this therapy beyond the reach of many patients who might benefit. A solution could be to program T cells in vivo. Here, we evaluate the clinical need for in situ CAR T cell programming, compare competing technologies, review current progress, and provide a perspective on the long-term impact of this emerging and rapidly flourishing biotechnology field.
Vascularized Microfluidics and Their Untapped Potential for Discovery in Diseases of the Microvasculature
Authors: Myers David R. , Lam Wilbur A.
Published: April 2021

Microengineering advances have enabled the development of perfusable, endothelialized models of the microvasculature that recapitulate the unique biological and biophysical conditions of the microcirculation in vivo. Indeed, at that size scale (<100 μm)—where blood no longer behaves as a simple continuum fluid; blood cells approximate the size of the vessels themselves; and complex interactions among blood cells, plasma molecules, and the endothelium constantly ensue—vascularized microfluidics are ideal tools to investigate these microvascular phenomena. Moreover, perfusable, endothelialized microfluidics offer unique opportunities for investigating microvascular diseases by enabling systematic dissection of both the blood and vascular components of the pathophysiology at hand. We review ( a) the state of the art in microvascular devices and ( b) the myriad of microvascular diseases and pressing challenges. The engineering community has unique opportunities to innovate with new microvascular devices and to partner with biomedical researchers to usher in a new era of understanding and discovery of microvascular diseases.
The Geometry of Information Coding in Correlated Neural Populations
Authors: Azeredo da Silveira Rava , Rieke Fred
Published: April 2021

Neurons in the brain represent information in their collective activity. The fidelity of this neural population code depends on whether and how variability in the response of one neuron is shared with other neurons. Two decades of studies have investigated the influence of these noise correlations on the properties of neural coding. We provide an overview of the theoretical developments on the topic. Using simple, qualitative, and general arguments, we discuss, categorize, and relate the various published results. We emphasize the relevance of the fine structure of noise correlation, and we present a new approach to the issue. Throughout this review, we emphasize a geometrical picture of how noise correlations impact the neural code.
A Secure Asymmetric Optical Image Encryption Based on Phase Truncation and Singular Value Decomposition in Linear Canonical Transform Domain
Authors: Sangwan Anshula , Singh Hukum
Published: April 2021

A new asymmetric optical double image encryption algorithm is proposed, which combines phase truncation and singular value decomposition. The plain text is encrypted with two-stage phase keys to obtain a uniformly distributed cipher text and two new decryption keys. These keys are generated during the encryption process and are different from encryption keys. It realizes asymmetric encryption and improves the security of the system. The unscrambling keys in the encryption operation are mainly related to plain text. At the same time, the system is more resistant to selective plain text attacks; it also improves the sensitivity of decryption keys. With the application of phase truncation, the key space expanded and the security of the cryptographic system is enhanced. The efficacy of the system is calculated by evaluating the estimated error between the input and retrieved images. The proposed technique provides innumerable security keys and is robust against various potential attacks. Numerical simulations verify the effectiveness and security of the proposed technique.
Bibliometric Study of the National Scientific Production of All Peruvian Schools of Dentistry in Scopus
Authors: Mayta-Tovalino Frank , Pacheco-Mendoza Josmel , Diaz-Soriano Ana , Perez-Vargas Fernando , Munive-Degregori Arnaldo , Luza Silvia
Published: April 2021

Objective. To perform a bibliometric analysis of the national scholarly output of all dental schools in Peru in Scopus through a retrospective study after the promulgation of the Peruvian University Law 30220 in 2014. Methods. This was a descriptive, comparative, retrospective, and cross-sectional study. A search of the Scopus database was performed to identify scholarly output in dentistry between 2014 and 2019. A total of 287 scientific articles with affiliation with the dental faculties of Peruvian public and private universities were evaluated. The data was extracted from Scopus using a complex formula developed from the words of the thesaurus MeSh (Medline) and Emtree (base) with words related to dentistry combined with the AF-IDs of the Peruvian universities. Results. The Top 10 in the scholarly output of all the Peruvian public and private dental schools were as follows: firstly, the Universidad Peruana Cayetano Heredia (UPCH), with 79 scholarly outputs publications and 5.2 citations per article, followed by the Universidad Nacional Mayor de San Marcos (UNMSM) with 53 scholarly outputs publications and 2.6 citations per article, and in third place, the Universidad Cientifica del Sur (UCSUR) with 49 scholarly outputs publications. In general, it was found that, of the 30 Faculties of Dentistry in Peru, only 10 in the public and private universities have a scholarly output greater than 5 to belong to the top 10. In addition, it was found that, according to the share of publications per journal quartile by the CiteScore Percentile of all the public and private Peruvian Faculties of Dentistry, in 2019, it presented the highest number of scientific publications in all quartiles with 20,33,14 and 43 articles in the quartiles Q1, Q2, Q3, and Q4, respectively. The highest number of scientific publications was produced in 2019 with 20, 33, 14, and 43 articles in quartiles Q1, Q2, Q3, and Q4, respectively. Conclusion. The UPCH, UNMSM, and UCSUR dental schools were the most productive. Both public and private universities presented an evident increase in their scientific publications in Scopus after the promulgation of the University Law 30220 in 2014.
How Does Capital Account Liberalization Affect Systemic Financial Risks? Evidence from China
Authors: Zheng Zhiyong , He Jian , Bian Yang , Feng Chen , Zhang Mengting
Published: April 2021

Capital account liberalization typically results in higher volumes of capital inflows and outflows for a country, yet abnormal cross-border capital flows may lead to overall financial risk accumulation, in turn causing tremendous damages to the economy. Using a time-varying parameter structural vector autoregression model with stochastic volatility (SV-TVP-SVAR), we identify time-varying effects of capital account liberalization on four types of systemic financial risks in China. Empirical results demonstrate that capital account liberalization, in the short run, can effectively curb the accumulation of macroeconomic and sudden stop risks. On the other hand, capital account liberalization may heighten credit crunch and asset bubble risks to varying degrees. We also find that some important capital account liberalization measures are double-edged: reform policies are likely to increase macroeconomic risk when optimizing the financing structure and reducing credit crunch risk.
The Prevalence of Hypertension and Associated Risk Factors among Secondary School Teachers in Bahir Dar City Administration, Northwest Ethiopia
Authors: Damtie Destaw , Bereket Ayehu , Bitew Denekew , Kerisew Bizuayehu
Published: April 2021

Background. Hypertension is one of the noncommunicable cardiovascular diseases (CVDs), and its prevalence is rising in middle- and low-income countries. It is not given enough attention in the developing countries like Ethiopia. Not enough data and studies about hypertension are available in Ethiopia. This study aimed to determine the prevalence of hypertension and its associated risk factors among secondary school teachers in Bahir Dar city administration. Methods. An institutional-based cross-sectional survey was conducted among secondary school teachers in Bahir Dar. Two hundred twenty-two randomly selected teachers were interviewed, and data related to the demographic, behavioral, health, and dietary characteristics of the individuals were recorded. Blood pressure data were taken. Logistic regression analysis had been used to assess independent risk factors for hypertension.p-values of less than 0.05 were considered statistically significant. Results. The overall prevalence of hypertension in the study was 29.28%. Age 41 to 50 (AOR: 2.506; 95% CI: 1.103–5.694; andp=0.028), having self-reported diabetes mellitus (AOR: 8.595; 95% CI: 2.795–26.424; andp<0.0001), having a family history of hypertension (AOR: 3.387; 95% CI: 1.579–7.285; andp=0.002), khat chewing (AOR: 5.426; 95% CI: 1.811–16.256; andp=0.003), physical inactivity (AOR: 5.212; 95% CI: 1.974–13.763; andp=0.001), and presence of self-reported repeated stress (AOR: 3.027; 95% CI: 1.404–6.527; andp=0.005) were the risk factors associated with hypertension. Conclusions. Different intervention measures with a particular emphasis on prevention by introducing lifestyle modifications are highly recommended to mitigate and control hypertension.
Reexamining Spatiotemporal Disparities of Financial Development in China Based on Functional Data Analysis
Authors: Wang Deqing , Ye Tianzhi , Tian Sihua , Wang Xu
Published: April 2021

Essentially, financial development is a continuous and time-varying process. This paper explicitly accounts for this by introducing functional data analysis to convert discrete observations into a smooth curve in order to represent the continuous process of financial development at the regional level. Regional disparities in financial development in China are reexamined from three dimensions: financial scale enlargement, financial structure adjustment, and financial intermediary efficiency. Disparities are then decomposed using an extended functional Theil index. It is necessary to introduce functional data into the research of financial development level. Through the functional research of China’s financial development level index of three different dimensions, the financial development level can be studied at the level of speed and acceleration, which fills up the research gap. The results imply that (1) the disparity in the level of financial scale enlargement and disparity in velocity and acceleration of financial intermediary efficiency are both statistically significant across regions, while the regional disparity in financial structure adjustment is insignificant, and (2) the downward trends of the total disparity in three dimensions are all dominated by the declining intraregional disparities. Since all 31 provinces have broken the traditional regional division, China does not need to implement region-specific protective policies in strengthening its deepening financial reforms.
Empirical Analysis of the Matching Degree between Energy Equipment Manufacturing and Market Demand: A Global Perspective
Authors: Deng Yirui , Chen Yimin , Gao Guowei
Published: April 2021

The study of matching degree between energy equipment manufacturing and market demand is crucial for energy enterprises to adjust business strategies, expand market share, and develop sustainably. Considering that the current electricity market evaluation indicators are rarely selected from a global perspective and a single evaluation method may lead to one‐sided results, this article takes the technology and equipment related to electric energy as the research object and selects six indicators, including technical standards, qualification certification, export methods, after‐sales service, market concentration, and product concentration. By analyzing the supply and demand characteristics of major global regional markets and the situation of Chinese power enterprises in these markets, we propose matching model cluster including osculating value method, rank‐sum ratio method, ideal point method, entropy value method, and efficacy coefficient method to conduct the matching degree study. The results show that the overall market matching degree of Chinese power companies is good, especially in Southeast Asia, Central Asia, and Africa. For markets with a low degree of matching, we analyze the reasons based on the matching indicators values to provide companies with corresponding strategies and recommendations.
Rethinking Separable Convolutional Encoders for End-to-End Semantic Image Segmentation
Authors: Wang Lin , Wang Xingfu , Hawbani Ammar , Xiong Yan , Zhang Xu
Published: April 2021

With the development of science and technology, the middle volume and neural network in the semantic image segmentation of the codec show good development prospects. Its advantage is that it can extract richer semantic features, but this will cause high costs. In order to solve this problem, this article mainly introduces the codec based on a separable convolutional neural network for semantic image segmentation. This article proposes a codec based on a separable convolutional neural network for semantic image segmentation research methods, including the traditional convolutional neural network hierarchy into a separable convolutional neural network, which can reduce the cost of image data segmentation and improve processing efficiency. Moreover, this article builds a separable convolutional neural network codec structure and designs a semantic segmentation process, so that the codec based on a separable convolutional neural network is used for semantic image segmentation research experiments. The experimental results show that the average improvement of the dataset by the improved codec is 0.01, which proves the effectiveness of the improved SegProNet. The smaller the number of training set samples, the more obvious the performance improvement.
Computing Exact Values for Gutman Indices of Sum Graphs under Cartesian Product
Authors: Alanazi Abdulaziz Mohammed , Farid Faiz , Javaid Muhammad , Munagi Augustine
Published: April 2021

Gutman index of a connected graph is a degree-distance-based topological index. In extremal theory of graphs, there is great interest in computing such indices because of their importance in correlating the properties of several chemical compounds. In this paper, we compute the exact formulae of the Gutman indices for the four sum graphs (S-sum, R-sum, Q-sum, and T-sum) in the terms of various indices of their factor graphs, where sum graphs are obtained under the subdivision operations and Cartesian products of graphs. We also provide specific examples of our results and draw a comparison with previously known bounds for the four sum graphs.
Game Analysis of Dynamic Planning Evolution for the Optimization of Community Governance Structure in Smart Cities
Authors: Liang Gaofeng
Published: April 2021

In this paper, we study the structural optimization of community governance in smart cities and optimize the structure based on the game analysis of dynamic planning evolution. Theoretically, the connotation and extension of the concept of “self‐organization” are cleared, and the environment and conditions of community self‐organization formation, the formation process of community self‐organization, and the types of community self‐organization are discussed. The so‐called “self‐organization” is a process of spontaneously moving from disorder to order, and the environment and conditions of community self‐organization are mainly the openness, nonequilibrium state, and nonlinearity of the system. The key parameters of the contract with the change of the values of each factor make up for the inability to collect data for the empirical study in this paper; then, it presents four linear stages along with the cause such as self‐creation, self‐expansion, self‐maintenance, and self‐degeneration, respectively. The types of community self‐organization mainly include community self‐organization with random aggregation, socialized community self‐organization, and stable community self‐organization with autonomy orientation. By systematically sorting out the relationship between community self‐organization and government, while confirming the positive significance of community self‐organization to community cultural governance, various complex conflicts and game relationships will deeply affect the actual effectiveness of cultural governance and provide useful knowledge reference for future community cultural governance.
Financial Crisis Early Warning Based on Panel Data and Dynamic Dual Choice Model
Authors: Du Qingyu
Published: April 2021

Based on the research of currency crisis pressure index, bank crisis pressure index, and asset bubble crisis pressure index, this paper introduces an external shock pressure index reflecting the impact of global economic changes on economy and synthesizes systemic financial crisis pressure based on the above four pressure indexes; then, all the alternative early warning indicators and the systemic risk pressure index constructed in this paper were tested for Granger causality. We build financial systemic risk pressure indexes, including currency crisis pressure (CCP) banking crisis pressure (BCP) index, bubble crisis pressure (PBP) index, and external shock pressure (ESP) index to predict financial crises. Finally, four indicators that have a significant impact on the systemic financial crisis pressure index were selected, namely, the stock price index change rate, industrial added value growth rate, domestic and foreign real deposit interest rate differential, and foreign direct investment as a percentage of GDP. A dynamic Logit model with lagging binary variables is constructed, and compared with the traditional static Logit line, the actual dynamic fitting effect is better than the static Logit model. The dynamic Logit model is used to predict the early warning status of systemic financial crisis in 2020, and the forecast of various early warning indicators is realized by the ARIMA model. The final prediction results show that the probability of a systemic financial crisis in China in 2020 is extremely low, almost zero. This is in line with the overall improvement in the international economic situation in 2020 and the steady growth of the domestic economy.
Evaluation of Fracture Toughness, Color Stability, and Sorption Solubility of a Fabricated Novel Glass Ionomer Nano Zirconia-Silica-Hydroxyapatite Hybrid Composite Material
Authors: Aldhuwayhi Sami D. , Sajjad Arbaz , Bakar Wan Zaripah W. , Mohamad Dasmawati , Kannan Thirumulu P. , Moheet Imran A.
Published: April 2021

The aim of this study was to investigate the effects of adding a nano zirconia-silica-hydroxyapatite (nanoZrO2-SiO2-HA) composite synthesized using a one-pot sol-gel technique to a conventional glass ionomer cement (GIC), which was then characterized using X-ray diffraction (XRD). Following the characterization studies, further investigations were carried out after the addition of nanoZrO2-SiO2-HA to cGIC (GIC nanoZrO2-SiO2-HA) at various percentages (~5% to 9%) to compare their fracture toughness, color stability, and sorption- solubility in relation to cGIC (Fuji IX). The XRD diffractogram indicated the presence of peaks for ZrO2, SiO2, and HA. The fracture toughness of GIC 5%nanoZrO2-SiO2-HA was statistically higher than that of other percentages of GIC nanoZrO2-SiO2-HA and cGIC. The highest values recorded were fracture toughness (

1.35
±
0.15
 
MPa
.

m

1
/
2

), leading to an increase of ∼57%, as compared to cGIC. Overall, the color change (

Δ
E

) values for GIC 5% nano Zr-Si-HA group were lower than those of cGIC over a one-month period and were between slight and perceptible. In addition, GIC 5%nanoZrO2-SiO2-HA recorded lower sorption values (

23.64
±
2.3
 
μ
gm

m

−
3

) as compared to cGIC (

36.28
±
2.6
 
μ
gm

m

−
3

) and higher solubility (

66.46
±
2.4
 
μ
gm

m

−
3

) as compared to cGIC (

56.76
±
1.6
 
μ
gm

m

−
3

). The addition of nanoZrO2-SiO2-HA to cGIC significantly enhanced its physicomechanical properties. Based on the results of our study, GIC nanoZrO2-SiO2-HA has the potential to be suggested as a restorative dental material with diverse applications ranging from cavity restoration, core build-up, and as a luting material.
Chemerin-9 Attenuates Experimental Abdominal Aortic Aneurysm Formation in ApoE−/− Mice
Authors: Chen Shuxiao , Han Chenglin , Bian Shuai , Chen Jianfeng , Feng Xuedong , Li Gang , Wu Xuejun
Published: April 2021

Chronic inflammation plays an essential role in the pathogenesis of abdominal aortic aneurysm (AAA), a progressive segmental abdominal aortic dilation. Chemerin, a multifunctional adipocytokine, is mainly generated in the liver and adipose tissue. The combination of chemerin and chemokine-like receptor 1 (CMKLR1) has been demonstrated to promote the progression of atherosclerosis, arthritis diseases, and Crohn’s disease. However, chemerin-9 acts as an analog of chemerin to exert an anti-inflammatory effect by binding to CMKLR1. Here, we first demonstrated that AAA exhibited higher levels of chemerin and CMKLR1 expression compared with the normal aortic tissues. Hence, we hypothesized that the chemerin/CMKLR1 axis might be involved in AAA progression. Moreover, we found that chemerin-9 treatment markedly suppressed inflammatory cell infiltration, neovascularization, and matrix metalloproteinase (MMP) expression, while increasing the elastic fibers and smooth muscle cells (SMCs) in Ang II-induced AAA in ApoE−/− mice. This demonstrated that chemerin-9 could inhibit AAA formation. Collectively, our findings indicate a potential mechanism underlying AAA progression and suggest that chemerin-9 can be used therapeutically.
Use of Network Pharmacology to Explore the Mechanism of Gegen (Puerariae lobatae Radix) in the Treatment of Type 2 Diabetes Mellitus Associated with Hyperlipidemia
Authors: Yuan Guozhen , Shi Shuai , Jia Qiulei , Shi Jingjing , Shi Shuqing , Zhang Xuesong , Shou Xintian , Zhu Xueping , Hu Yuanhui
Published: April 2021

Rapid increases in metabolic disorders, such as type 2 diabetes mellitus (T2DM) and hyperlipidemia, are becoming a substantial challenge to worldwide public health. Traditional Chinese medicine has a long history and abundant experience in the treatment of diabetes and hyperlipidemia, and Puerariae lobatae Radix (known as Gegen in Chinese) is one of the most prevalent Chinese herbs applied to treat these diseases. The underlying mechanism by which Gegen simultaneously treats diabetes and hyperlipidemia, however, has not been clearly elucidated to date. Therefore, we systematically explored the potential mechanism of Gegen in the treatment of T2DM complicated with hyperlipidemia based on network pharmacology. We screened the potential targets of Gegen, T2DM, and hyperlipidemia in several online databases. Then, the hub targets were analyzed by performing protein-protein interaction, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment assays, and finally, the complicated connections among compounds, targets, and pathways were visualized in Cytoscape. We found that isoflavones, including daidzein, genistein, and puerarin, as well as β-sitosterol, are the key active ingredients of Gegen responsible for its antidiabetic and antihyperlipidemia effects, which mainly target AKR1B1, EGFR, ESR, TNF, NOS3, MAPK3, PPAR, CYP19A1, INS, IL6, and SORD and multiple pathways, such as the PI3K-Akt signaling pathway; the AGE-RAGE signaling pathway in diabetic complications, fluid shear stress, and atherosclerosis; the PPAR signaling pathway; insulin resistance; the HIF-1 signaling pathway; the TNF signaling pathway; and others. These active ingredients also target multiple biological processes, including the regulation of glucose and lipid metabolism, the maintenance of metabolic homeostasis, and anti-inflammatory and antioxidant pathways. In conclusion, Gegen is a promising therapeutic phytomedicine for T2DM with hyperlipidemia that targets multiple proteins, biological processes, and pathways.