bluuebunny/crossref_metadata_2025 huggingface dataset:
Correlations of sieve weights and distributions of zeros
Authors: Walker Aled
Published: May 2022

In this note, we give two small results concerning the correlations of the Selberg sieve weights. We then use these estimates to derive a new (conditional) lower bound on the variance of the primes in short intervals, and also on the so-called “form factor” for the pair correlations of the zeros of the Riemann zeta function. Our bounds ultimately rely on the estimates of Bettin–Chandee for trilinear Kloosterman fractions.
A short note on Manin–Mumford
Authors: Schmidt Harry
Published: May 2022

We give a short proof of Manin–Mumford in the multiplicative group based on the pigeon-hole principle and a simple structure theorem for tori contained in subvarieties of [Formula: see text] due to Bombieri and Gubler. The arguments appear to be new and are applicable in other situations. Our proof is quite elementary and we compare it to previous proofs that rely on machinery such as intersection theory or [Formula: see text]-minimality.
Probabilistic Tsunami Hazard Assessment of Offshore Wave Heights for Southern Region of Pingtung County in Southern Taiwan
Authors: Chen Yen-Lung , Shen Yu-Hsiu , Wu Han-Lun , Hsiao Shih-Chun
Published: May 2022

This study applies the unit tsunami method and probabilistic tsunami hazard analysis to investigate the tsunami hazard for the southern region of Pingtung County in southern Taiwan. The FUNWAVE-TVD model is used to calculate the aleatory and epistemic uncertainties. The aleatory uncertainty is analyzed using a comparison of simulated and observed maximum tsunami wave heights and the epistemic uncertainty is evaluated using a scenario caused by earthquakes in the Manila and Ryukyu subduction zones according to probabilistic seismic hazard analysis in Taiwan using Senior Seismic Hazard Analysis Committee Level 3 methodology. A unit tsunami method database is established for the Manila and Ryukyu subduction zones and used to carry out a probability-based offshore tsunami wave height simulation for the southern region of Pingtung County. A hazard map for tsunamis caused by earthquakes in the Manila and Ryukyu subduction zones is also derived.
Creation of Tsunami Evacuation Time Map for the Mentawai Islands, Indonesia
Authors: Ono Yusuke , Putra Rusnardi Rahmat
Published: May 2022

The Mentawai Islands are located offshore of West Sumatra, Indonesia, and are likely to be hit by a large-scale tsunami in the future. In this study, maps showing tsunami evacuation times (ETs) along the road on the Mentawai Islands were developed to mitigate tsunami impact. We define the tsunami ET as the time required for an evacuee to move to a safe location through the road network from the tsunami inundation area. The safe locations were determined using a digital elevation model and the assumed tsunami inundation heights. The closest safe place for each location on the road network was determined using the shortest path search algorithm for the graph network. First, areas with high tsunami risk on the islands were identified using the obtained ET maps. Second, the appropriateness of the evacuation approach in the Matobe village on Sipora Island was evaluated based on the ET maps. Finally, population exposures to the different ET levels were estimated to quantify the potential risk of loss of life to a tsunami disaster in the Mentawai Islands. This study contributes to the tsunami disaster preparation of residents of the Mentawai Islands.
Photokilling of waterborne-resistant pathogenic bacteria using cobalt-doped zinc oxide doped on reduced graphene oxide nanoparticles
Authors: Khan Ameer Muhammad , Pervez Laiba , Celli Jonathan , Khattak Mutiullah , Ullah Maqdad , Shah ZiaUllah , Khan Muhammad Mustafa , Nadeem Muhammad , Faisal Sulaiman , Nadhman Akhtar
Published: May 2022

This study is aimed at the chemical synthesis of light-activated cobalt-doped zinc oxide and its further doping on reduced graphene oxide (RGO) and assessment of its antibacterial activity on antibiotic-resistant waterborne pathogens including Enterococcus faecalis, Staphylococcus aureus, Klebsiella pneumonia, and Pseudomonas aeruginosa. The synthesized nanoparticles were characterized via UV–vis spectroscopy, scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDS). The minimal inhibitory concentration (MIC) of nanoparticles portrayed a significant killing of both Gram-positive and Gram-negative bacteria. The synthesized nanoparticles were further found as active killers of bacteria in drinking water. Further, these nanoparticles were found photothermally active alongside ROS generators. The photokilling activity makes them ideal replacement candidates for traditional water filters.
A theoretical approach to the computational complexity measure of abstract DEVS simulators
Authors: Diouf Yoro , Maïga Oumar Y. , Traore Mamadou K.
Published: May 2022

DEVS is a sound Modeling and Simulation (M&S) framework that describes a model in a modular and hierarchical way. It comes along with an abstract simulation algorithm which defines its operational semantics. Many variants of such an algorithm have been proposed by DEVS researchers. Yet, the proper interpretation and analysis of the computational complexity of such approaches have not been systematically addressed and defined. As systems become larger and more complex, the efficiency of the DEVS simulation algorithms in terms of time complexity measure becomes a major issue. Therefore, it is necessary to devise a method for computing this complexity. This paper proposes a generic method to address such an issue, taking advantage of the recursion embedded in the triggered-by-message principle of the DEVS simulation protocol. The applicability of the method is shown through the complexity analysis of various DEVS simulation algorithms.
Measurement and simulation of non-premixed flames: A selected review
Authors: Zhou L. X. , Hu L. Y.
Published: May 2022

Non-premixed flames are encountered in different combustion facilities. Experimental and numerical studies of non-premixed flames have been made by many investigators, but only a few works reported simultaneous experimental and numerical studies on instantaneous flows and flame structures. In this paper, a review is given for studies on the instantaneous flow and flame structures of non-premixed flames using both particle image velocimetry (PIV) and large-eddy simulation (LES) by us. The LES statistical results are accessed by measurement results. The measured and simulated instantaneous results show the strong interaction between coherent structures and combustion. It is found that larger secondary-air flow leads to early formation of coherent structures and the transition from large vortexes to small vortices. The flame structures are significantly affected by the coherent structures. The spiral vortex structures formed by swirl lead to intensification of combustion
Tweets Topic Classification and Sentiment Analysis Based on Transformer-Based Language Models
Authors: Mandal Ranju , Chen Jinyan , Becken Susanne , Stantic Bela
Published: May 2022

People provide information on their thoughts, perceptions, and activities through a wide range of channels, including social media. The wide acceptance of social media results in vast volume of valuable data, in variety of format as well as veracity. Analysis of such ‘big data’ allows organizations and analysts to make better and faster decisions. However, this data had to be quantified and information has to be extracted, which can be very challenging because of possible data ambiguity and complexity. To address information extraction, many analytic techniques, such as text mining, machine learning, predictive analytics, and diverse natural language processing, have been proposed in the literature. Recent advances in Natural Language Understanding-based techniques more specifically transformer-based architectures can solve sequence-to-sequence modeling tasks while handling long-range dependencies efficiently. In this work, we applied transformer-based sequence modeling on short texts’ topic classification and sentiment analysis from user-posted tweets. Applicability of models is investigated on posts from the Great Barrier Reef tweet dataset and obtained findings are encouraging providing insight that can be valuable for researchers working on classification of large datasets as well as large number of target classes.
Dicationic Ionic Liquid Modified Chitosan as a Sustainable Heterogeneous Catalyst for Knoevenagel Condensation Reactions
Authors: Paliwal Khushboo S. , Mitra Antarip , Biswas Tanmoy , Mandal Saikat , Mahalingam Venkataramanan
Published: May 2022

Ionic liquid (IL) grafted chitosan (CS) is fabricated in a feasible three-step process and used as a catalyst for the Knoevenagel condensation reaction. The IL modified CS (IL-CS) shows higher conversion/yield compared to pristine CS and Pyridine modified CS (Py-CS). The better efficiency of IL-CS can be ascribed to the synergistic effect of H-bonding groups (–OH of CS) and quaternary ammonium groups of ionic liquid. The material is very efficient in converting various functionalized aromatic aldehydes into their benzylidenemalononitrile derivatives. In addition, the catalyst exhibits retention of catalytic activity up to five cycles in the reusability study. The characterization of the recycled material clearly indicates the robust nature of IL-CS. Our work provides a feasible approach towards sustainable catalyst construction for industrially important reactions.
Fatigue crack growth in CNT-reinforced polymer composite
Authors: Dwivedi Kishan , Arora Gaurav , Pathak Himanshu
Published: May 2022

Carbon nanotubes (CNT)-reinforced polymer composite has many engineering applications due to attractive properties, like high strength to weight ratio. These materials are subjected to cyclic fatigue loading during their service life. This work proposed a dual scale modeling approach to predict fatigue crack growth behavior of CNT-reinforced polymer composite under the thermo-mechanical loading environment. The effective orthotropic property of CNT-reinforced polymer composite is estimated using the mean-field homogenization technique in meso-scale modeling. Further, the equivalent composite property is used for macro-scale fatigue crack growth study using higher-order extended finite element method. To present the efficiency of the proposed modeling approach, various combinations of polymer composite (by volume fraction of reinforcement at 7.5%, 10%, 12.5%, 15%, 17.5%, and 20%) are taken for analysis. At macro-scale modeling, XFEM with higher-order enrichment terms is employed to improve the solution accuracy at the crack tip region. The proposed computational approach has been employed with the in-house developed MATLAB code and DIGIMAT software. Several numerical examples are considered with geometrical discontinuities like multiple holes and cracks.
Robust Event-Triggered Control of Nonlinear Systems Under a Global Sector-Bound Condition
Authors: Zhang Pengpeng , Liu Tengfei , Jiang Zhong-Ping
Published: May 2022

This paper studies an event-triggered control problem for nonlinear systems subject to both external disturbances and dynamic uncertainties. It is assumed that the system satisfies a global sector-bound condition. To avoid infinitely fast sampling, a novel event-triggered sampling mechanism is proposed, which uses not only the measured system state but also an estimation of the influence of the disturbances. With the proposed design, the intersampling intervals can be lower bounded by a positive constant, and it is independent of both external disturbances and dynamic uncertainties. Moreover, the closed-loop event-triggered system is proved to be input-to-state stable with respect to the external disturbances. Advanced small-gain techniques are used for the stability analysis of the closed-loop system.
Towards a Soft Science of Soft Robots. A Call for a Place for Aesthetics in Soft Robotics Research
Authors: Jørgensen Jonas
Published: May 2022

The position paper presents an argument that aesthetic theory and practice are pertinent to the rapidly expanding field of soft robotics. Soft robotics as an aesthetic phenomenon is introduced and contextualized drawing on the author's own research and practice. The potential of aesthetic perspectives and approaches in developing and implementing soft robotics are highlighted with a focus on technical design and human-robot interaction.
Fast Automated Abstract Machine Repair Using Simultaneous Modifications and Refactoring
Authors: Cai Cheng-Hao , Sun Jing , Dobbie Gillian , Hóu Zhé , Bride Hadrien , Dong Jin Song , Lee Scott Uk-Jin
Published: May 2022

Automated model repair techniques enable machines to synthesise patches that ensure models meet given requirements. B-repair, which is an existing model repair approach, assists users in repairing erroneous models in the B formal method, but repairing large models is inefficient due to successive applications of repair. In this work, we improve the performance of B-repair using simultaneous modifications, repair refactoring, and better classifiers. The simultaneous modifications can eliminate multiple invariant violations at a time so the average time to repair each fault can be reduced. Further, the modifications can be refactored to reduce the length of repair. The purpose of using better classifiers is to perform more accurate and general repairs and avoid inefficient brute-force searches. We conducted an empirical study to demonstrate that the improved implementation leads to the entire model process achieving higher accuracy, generality, and efficiency.
The Necropolitics of Language Oppression
Authors: Roche Gerald
Published: May 2022

This article explores how language oppression—coerced language loss—contributes to physical death. The context for this investigation is the ongoing crisis of global linguistic diversity, which sees approximately half the world's languages facing language oppression. It is also a crisis of bodies and lives. This article proposes the necropolitics of language oppression as a decolonial anthropological approach for theorizing and confronting this global problem. Drawing on the anthropology of violence, genocide, and the state, within the context of anthropology's colonial turn since the 1970s, this article describes how states within colonial modernity create and exploit population-differentiated death through practices of social death, slow violence, and slow death. This perspective enables a synthesis of literature from linguistics, applied linguistics, sociolinguistics, translation studies, and public health to reveal the links between language oppression and death. The conclusion discusses how the approach developed in this article can help sustain languages and lives.
Theory and Diagnostics of Hot Star Mass Loss
Authors: Vink Jorick S.
Published: May 2022

Massive stars have strong stellar winds that direct their evolution through the upper Hertzsprung–Russell diagram and determine the black hole mass function. Furthermore, wind strength dictates the atmospheric structure that sets the ionizing flux. Finally, the wind directly intervenes with the stellar envelope structure, which is decisive for both single-star and binary evolution, affecting predictions for gravitational wave events. Key findings of current hot star research include: ▪ The traditional line-driven wind theory is being updated with Monte Carlo and comoving frame computations, revealing a rich multivariate behavior of the mass-loss rate [Formula: see text] in terms of M, L, Eddington Γ, Teff, and chemical composition Z. Concerning the latter, [Formula: see text] is shown to depend on the iron (Fe) opacity, making Wolf–Rayet populations, and gravitational wave events dependent on host galaxy Z. ▪ On top of smooth mass-loss behavior, there are several transitions in the Hertzsprung–Russell diagram, involving bistability jumps around Fe recombination temperatures, leading to quasi-stationary episodic, and not necessarily eruptive, luminous blue variable and pre-SN mass loss. ▪ Furthermore, there are kinks. At 100 [Formula: see text] a high Γ mass-loss transition implies that hydrogen-rich, very massive stars have higher mass-loss rates than commonly considered. At the other end of the mass spectrum, low-mass stripped helium stars no longer appear as Wolf–Rayet stars but as optically thin stars. These stripped stars, in addition to very massive stars, are two newly identified sources of ionizing radiation that could play a key role in local star formation as well as at high redshift.
Extracellular Vesicle–Based Multianalyte Liquid Biopsy as a Diagnostic for Cancer
Authors: Lin Andrew A. , Nimgaonkar Vivek , Issadore David , Carpenter Erica L.
Published: May 2022

Liquid biopsy is the analysis of materials shed by tumors into circulation, such as circulating tumor cells, nucleic acids, and extracellular vesicles (EVs), for the diagnosis and management of cancer. These assays have rapidly evolved with recent FDA approvals of single biomarkers in patients with advanced metastatic disease. However, they have lacked sensitivity or specificity as a diagnostic in early-stage cancer, primarily due to low concentrations in circulating plasma. EVs, membrane-enclosed nanoscale vesicles shed by tumor and other cells into circulation, are a promising liquid biopsy analyte owing to their protein and nucleic acid cargoes carried from their mother cells, their surface proteins specific to their cells of origin, and their higher concentrations over other noninvasive biomarkers across disease stages. Recently, the combination of EVs with non-EV biomarkers has driven improvements in sensitivity and accuracy; this has been fueled by the use of machine learning (ML) to algorithmically identify and combine multiple biomarkers into a composite biomarker for clinical prediction. This review presents an analysis of EV isolation methods, surveys approaches for and issues with using ML in multianalyte EV datasets, and describes best practices for bringing multianalyte liquid biopsy to clinical implementation.
Eukaryotic Cell Size Control and Its Relation to Biosynthesis and Senescence
Authors: Xie Shicong , Swaffer Matthew , Skotheim Jan M.
Published: May 2022

The most fundamental feature of cellular form is size, which sets the scale of all cell biological processes. Growth, form, and function are all necessarily linked in cell biology, but we often do not understand the underlying molecular mechanisms nor their specific functions. Here, we review progress toward determining the molecular mechanisms that regulate cell size in yeast, animals, and plants, as well as progress toward understanding the function of cell size regulation. It has become increasingly clear that the mechanism of cell size regulation is deeply intertwined with basic mechanisms of biosynthesis, and how biosynthesis can be scaled (or not) in proportion to cell size. Finally, we highlight recent findings causally linking aberrant cell size regulation to cellular senescence and their implications for cancer therapies.
Mechanochemical Principles of Spatial and Temporal Patterns in Cells and Tissues
Authors: Bailles Anaïs , Gehrels Emily W. , Lecuit Thomas
Published: May 2022

Patterns are ubiquitous in living systems and underlie the dynamic organization of cells, tissues, and embryos. Mathematical frameworks have been devised to account for the self-organization of biological patterns, most famously the Turing framework. Patterns can be defined in space, for example, to form stripes; in time, such as during oscillations; or both, to form traveling waves. The formation of these patterns can have different origins: purely chemical, purely mechanical, or a combination of the two. Beyond the variety of molecular implementations of such patterns, we emphasize the unitary principles associated with them, across scales in space and time, within a general mechanochemical framework. We illustrate where such mechanisms of pattern formation arise in biological systems from cellular to tissue scales, with an emphasis on morphogenesis. Our goal is to convey a picture of pattern formation that draws attention to the principles rather than solely to specific molecular mechanisms.
Adhesion-Based Self-Organization in Tissue Patterning
Authors: Tsai Tony Y.-C. , Garner Rikki M. , Megason Sean G.
Published: May 2022

Since the proposal of the differential adhesion hypothesis, scientists have been fascinated by how cell adhesion mediates cellular self-organization to form spatial patterns during development. The search for molecular tool kits with homophilic binding specificity resulted in a diverse repertoire of adhesion molecules. Recent understanding of the dominant role of cortical tension over adhesion binding redirects the focus of differential adhesion studies to the signaling function of adhesion proteins to regulate actomyosin contractility. The broader framework of differential interfacial tension encompasses both adhesion and nonadhesion molecules, sharing the common function of modulating interfacial tension during cell sorting to generate diverse tissue patterns. Robust adhesion-based patterning requires close coordination between morphogen signaling, cell fate decisions, and changes in adhesion. Current advances in bridging theoretical and experimental approaches present exciting opportunities to understand molecular, cellular, and tissue dynamics during adhesion-based tissue patterning across multiple time and length scales.
The Aftermath of Debt Surges
Authors: Kose M. Ayhan , Ohnsorge Franziska L. , Reinhart Carmen M. , Rogoff Kenneth S.
Published: May 2022

Debt in emerging markets and developing economies is at its highest level in half a century. In about 9 in 10 emerging markets and developing economies, debt is higher now than it was in 2010, and in half of the emerging markets and developing economies, debt is more than 30 percentage points of GDP higher than in 2010. This article reviews a menu of options that in the past have helped lower debt burdens. It examines orthodox options (enhancing growth, fiscal consolidation, privatization, and wealth taxation) and heterodox options (inflation, financial repression, and debt default and restructuring). The mix of feasible options depends on country characteristics and the type of debt. However, none of these options comes without political, economic, and social costs. The challenges associated with debt reduction raise questions of global governance, including to what extent advanced economies can better support emerging market and developing economies in cushioning prospective shocks.
The Great Gatsby Curve
Authors: Durlauf Steven N. , Kourtellos Andros , Tan Chih Ming
Published: May 2022

This paper provides a synthesis of theoretical and empirical work on the Great Gatsby Curve, the positive empirical relationship between cross-sectional income inequality, and persistence of income across generations. We present statistical models of income dynamics that mechanically give rise to the relationship between inequality and mobility. Five distinct classes of theories are developed, including models on family investments, skills, social influences, political economy, and aspirations, each providing a behavioral mechanism to explain the relationship. Finally, we review empirical studies that provide evidence of the curve for a range of contexts and socioeconomic outcomes as well as explore evidence on mechanisms.
A Journey Through Genetics to Biology
Authors: van Heyningen Veronica
Published: May 2022

Although my engagement with human genetics emerged gradually, and sometimes serendipitously, it has held me spellbound for decades. Without my teachers, students, postdocs, colleagues, and collaborators, I would not be writing this review of my scientific adventures. Early gene and disease mapping was a satisfying puzzle-solving exercise, but building biological insight was my main goal. The project trajectory was hugely influenced by the evolutionarily conserved nature of the implicated genes and by the pace of progress in genetic technologies. The rich detail of clinical observations, particularly in eye disease, makes humans an excellent model, especially when complemented by the use of multiple other animal species for experimental validation. The contributions of collaborators and rivals also influenced our approach. We are very fortunate to work in this era of unprecedented progress in genetics and genomics.
The Genetics of Brugada Syndrome
Authors: Cerrone Marina , Costa Sarah , Delmar Mario
Published: May 2022

Brugada syndrome is a heritable channelopathy characterized by a peculiar electrocardiogram (ECG) pattern and increased risk of cardiac arrhythmias and sudden death. The arrhythmias originate because of an imbalance between the repolarizing and depolarizing currents that modulate the cardiac action potential. Even if an overt structural cardiomyopathy is not typical of Brugada syndrome, fibrosis and structural changes in the right ventricle contribute to a conduction slowing, which ultimately facilitates ventricular arrhythmias. Currently, Mendelian autosomal dominant transmission is detected in less than 25% of all clinical confirmed cases. Although 23 genes have been associated with the condition, only SCN5A, encoding the cardiac sodium channel, is considered clinically actionable and disease causing. The limited monogenic inheritance has pointed toward new perspectives on the possible complex genetic architecture of the disease, involving polygenic inheritance and a polygenic risk score that can influence penetrance and risk stratification.
Is Agricultural Insurance Fulfilling Its Promise for the Developing World? A Review of Recent Evidence
Authors: Kramer Berber , Hazell Peter , Alderman Harold , Ceballos Francisco , Kumar Neha , Timu Anne G.
Published: May 2022

Innovations in agricultural index insurance have raised expectations that the private sector can overcome shortcomings associated with more traditional indemnity-based products like multiperil crop insurance and strengthen agricultural risk management at scale across developing countries. This article updates previous reviews on agricultural insurance but differs in that it goes beyond the prognosis that recent innovations can help make insurance more commercially viable. As such, it addresses two important challenges that have received limited attention. First, it distinguishes different types of farm households and recognizes that many are excluded from the insurance market, describing additional innovations that can help make insurance more accessible to these excluded groups. Second, it acknowledges that insurance for catastrophic risks is unaffordable for most farmers and summarizes new developments in disaster assistance and safety net programs that can provide broader protection against these risks. The review concludes that cost-benefit analyses of subsidized insurance programs will be crucial for guiding public spending decisions.
Natural Selection, Intracellular Bottlenecks of Virus Populations, and Viral Superinfection Exclusion
Authors: Perdoncini Carvalho Camila , Ren Ruifan , Han Junping , Qu Feng
Published: May 2022

Natural selection acts on cellular organisms by ensuring the genes responsible for an advantageous phenotype consistently reap the phenotypic advantage. This is possible because reproductive cells of these organisms are almost always haploid, separating the beneficial gene from its rival allele at every generation. How natural selection acts on plus-strand RNA viruses is unclear because these viruses frequently load host cells with numerous genome copies and replicate thousands of progeny genomes in each cell. Recent studies suggest that these viruses encode the Bottleneck, Isolate, Amplify, Select (BIAS) mechanism that blocks all but a few viral genome copies from replication, thus creating the environment in which the bottleneck-escaping viral genome copies are isolated from each other, allowing natural selection to reward beneficial mutations and purge lethal errors. This BIAS mechanism also blocks the genomes of highly homologous superinfecting viruses, thus explaining cellular-level superinfection exclusion.
6,7-Dimethoxycoumarin Influences the Erythroid Differentiation of Human Chronic Myelogenous Leukemia K562 Cells through Regulating FOXO3/p27 Signal Pathway
Authors: Zheng Li , Wu Yu , Wang Fu , Shi Hui , Xu Bo , Yang Aihua
Published: May 2022

Objective. To study the pharmacological activity and the mechanism of action of natural compounds derived from 6,7-dimethoxycoumarin on the differentiation of human chronic myeloid leukemia K562 cells. Methods. We use MTT assay (Sigma-Aldrich, USA) to detect cell viability; use flow cytometry to analyze DNA content for cell cycle analysis; use benzidine staining to synthesize hemoglobin to determine K562 cell differentiation; use western blot analysis and qPCR to detect the expression levels of FOX03, P27, CDK4, and their phosphorylation; and use the AOBS laser scanning confocal system (Leica, Wetzlar, Germany) to analyze and quantify the number of positive green spots. The statistical methods used are one-way analysis of variance (ANOVA) and Dunnett’s test to analyze within and between groups. Results. In order to explore the effect of 6,7-dimethoxycoumarin on the differentiation of K562 cell erythrocytes, it was concluded that 6,7-dimethoxycoumarin promotes the differentiation of K562 cell erythrocytes; the proliferation of K562 cells was detected by MTT method, and the results showed that 6,7-dimethoxycoumarin can inhibit the proliferation of K562 cells; to evaluate the effect of 6,7-dimethoxycoumarin on the proliferation of K562 cells, the results showed that 6,7-dimethoxycoumarin increased the expression of FOXO3, P27, CDK4, and CDK65, and decreased the phosphorylation of CDK4 and CDK6 proteins. To further explore the effect of knocking out FOXO3 on cell differentiation, the results show that 6,7-dimethoxycoumarin can reduce the differentiation and proliferation of K562 cells by increasing the expression of FOXO3. Conclusion. This study extended the understanding of the pharmacological activity of 6,7-dimethoxycoumarin and may provide a potential new target for the treatment of chronic myelogenous leukemia. However, we still need to further study the specific molecular capabilities of 6.7 dimethylcoumarin to understand their possible capture mechanism.
SegChaNet: A Novel Model for Lung Cancer Segmentation in CT Scans
Authors: Cifci Mehmet Akif
Published: May 2022

Accurate lung tumor identification is crucial for radiation treatment planning. Due to the low contrast of the lung tumor in computed tomography (CT) images, segmentation of the tumor in CT images is challenging. This paper effectively integrates the U-Net with the channel attention module (CAM) to segment the malignant lung area from the surrounding chest region. The SegChaNet method encodes CT slices of the input lung into feature maps utilizing the trail of encoders. Finally, we explicitly developed a multiscale, dense-feature extraction module to extract multiscale features from the collection of encoded feature maps. We have identified the segmentation map of the lungs by employing the decoders and compared SegChaNet with the state-of-the-art. The model has learned the dense-feature extraction in lung abnormalities, while iterative downsampling followed by iterative upsampling causes the network to remain invariant to the size of the dense abnormality. Experimental results show that the proposed method is accurate and efficient and directly provides explicit lung regions in complex circumstances without postprocessing.
Numerical Simulation and Experimental Research of Cavitation Jets in Dual-Chamber Self-Excited Oscillating Pulsed Nozzles
Authors: Shi Dongyan , Xing Yulin , Wang Lifu , Chen Zhuo
Published: May 2022

Self-excited oscillating pulsed cavitation jet has many advantages, such as easy operation, simply maintenance, and low investment costs. Based on the structural parameters of the traditional Helmholtz and organ pipe single-chamber self-excited oscillating pulsed nozzles, Helmholtz + Helmholtz, organ pipe + organ pipe, and Helmholtz + organ pipe dual-chamber self-excited oscillating pulsed nozzles with different structures are established in series, and the internal flow field of the dual chamber is studied to obtain the best combination nozzle by comparing numerical simulation and erosion experiments. The results indicate that the cleaning effect is the best when adopting the combined structure of Helmholtz + organ pipe, which brings out the largest volume fraction of the nozzle cavity with higher gas content and higher kinetic energy with larger jet velocity. By comparing the erosion experiments, it is found that the flushing area and erosion depth of the nozzle with the ratio of cavity and wall (D/a) of 2.50 is significantly higher than the nozzle with the ratio of cavity and wall (D/a) of 5.00. The pulse pressure of the flushing target increases by 0.0044 MPa, the pressure amplitude of the former changes significantly in the same cavitation cycle, and the cleaning effect is best.
A Multi-Secret Reputation Adjustment Method in the Secret Sharing for Internet of Vehicles
Authors: Wu Jingbang , Chen Xiyu , Zhang Luyuan , Zhou Shufen , Wang Daoshun
Published: May 2022

Secure data sharing for the Internet of Vehicles (IoV) has drawn much attention in developing smart cities and smart transportation. One way to achieve that is based on the reputation of the participant vehicles and the interaction between them. Most of the reputation adjustment schemes are aimed at single-secret situations, and there are cases where shared information leaks after reconstruction; and the shared information of all participants need to be updated again. This paper uses binary asymmetric polynomials to realize the asynchronous reconstruction of secrets, that is, multiple secrets are distributed at once, and each secret is independent of each other when it is restored. Each participant holds the shared divided information part of a one-variable polynomial. When the secret is reconstructed, the shared information part is not directly shown to ensure that the shared information is not leaked. Furthermore, we provide another way to keep the secret unchanged and modify the secret threshold.
Information Extraction from the Text Data on Traditional Chinese Medicine: A Review on Tasks, Challenges, and Methods from 2010 to 2021
Authors: Zhang Tingting , Huang Zonghai , Wang Yaqiang , Wen Chuanbiao , Peng Yangzhi , Ye Ying
Published: May 2022

Background. The practice of traditional Chinese medicine (TCM) began several thousand years ago, and the knowledge of practitioners is recorded in paper and electronic versions of case notes, manuscripts, and books in multiple languages. Developing a method of information extraction (IE) from these sources to generate a cohesive data set would be a great contribution to the medical field. The goal of this study was to perform a systematic review of the status of IE from TCM sources over the last 10 years. Methods. We conducted a search of four literature databases for articles published from 2010 to 2021 that focused on the use of natural language processing (NLP) methods to extract information from unstructured TCM text data. Two reviewers and one adjudicator contributed to article search, article selection, data extraction, and synthesis processes. Results. We retrieved 1234 records, 49 of which met our inclusion criteria. We used the articles to (i) assess the key tasks of IE in the TCM domain, (ii) summarize the challenges to extracting information from TCM text data, and (iii) identify effective frameworks, models, and key findings of TCM IE through classification. Conclusions. Our analysis showed that IE from TCM text data has improved over the past decade. However, the extraction of TCM text still faces some challenges involving the lack of gold standard corpora, nonstandardized expressions, and multiple types of relations. In the future, IE work should be promoted by extracting more existing entities and relations, constructing gold standard data sets, and exploring IE methods based on a small amount of labeled data. Furthermore, fine-grained and interpretable IE technologies are necessary for further exploration.
Using the Improved SSD Algorithm to Motion Target Detection and Tracking
Authors: Yan Yongjiang
Published: May 2022

Computer vision-based motion target detection and tracking, which is widely used in video surveillance, human-computer interaction, range interpretation, and other fields, is one of the current research hotspots in the field of computer vision. In engineering scenarios, the two are inseparable and need to work together to accomplish specific tasks. The related research is progressing rapidly, but there is still room for improving its timeliness, accuracy, and automation. In this paper, we summarize and classify some classical target detection methods, analyze the basic principles of convolutional neural networks, and analyze the classical detection algorithms based on region suggestion and deep regression networks. After that, we improve the SSD algorithm for the shortage of low-level feature convolution layers, which has insufficient feature extraction and leads to poor detection of small targets. For the motion target tracking problem, this paper studies the motion target tracking method based on support vector machine and proposes the tracking method of support vector regression and the corresponding online support vector regression solution method based on the analysis of support vector tracking method and structural support vector tracking method. In this paper, we propose a tracking method that fuses structural support vector machines and correlation filtering. The structure is based on the idea of Inception, which adds and replaces some feature convolution layers of the original network while maintaining the original lightweight backbone. The final experiments on the VOC data set demonstrate that the improved algorithm improves the average detection accuracy by 2.6% compared to the original algorithm and basically maintains the real-time speed as well. Experimental simulations on a subset of VOC data (human set) show a significant improvement in AP values and more effective and reliable detection tracking of moving targets. The stability and accuracy of motion target detection and tracking are improved by setting parameters, such as confidence level; the effectiveness and continuity of detection and tracking are judged by setting the interframe centroid distance.
Several Alkaloids in Chinese Herbal Medicine Exert Protection in Acute Kidney Injury: Focus on Mechanism and Target Analysis
Authors: Rui Yixin , Li Sheng , Luan Fei , Li Dan , Liu Rong , Zeng Nan
Published: May 2022

Objectives. Acute kidney injury (AKI) is a loose set of kidney diseases accompanied by a variety of syndromes, which is a serious threat to human life and health. Some alkaloids are derived from various Chinese herbs have been widely concerned in the improvement of AKI. This review provides the research progress of alkaloids in AKI experimental models and discusses the related molecular mechanisms. Key Findings. Alkaloids can protect AKI through various mechanisms including antioxidant stress, improvement of mitochondrial damage, reduction of cell death, induction of autophagy, and inhibition of inflammation. These mechanisms are mainly related to the activation of Nrf2/HO-1 signaling pathway, inhibition of ferroptosis and apoptosis, regulation of PINK1/Parkin pathway, inhibition of TLR4/NF-κB pathway and NLRP3 inflammatory bodies, upregulation of Klotho protein level and so on. In addition, there are a few alkaloids that have certain toxicity on the kidney. Conclusion. Alkaloids have been shown to significantly improve AKI, but only in pharmacological studies. This paper summarizes the main experimental models currently used in AKI research and describes some representative alkaloids based on recent research. Their potential roles in the prevention and treatment of AKI through different mechanisms are highlighted.
Seroprevalence and Risk Factors of African Horse Sickness in Three Agroecological Zones of Cameroon
Authors: Ndebé Mohamed M. F. , Mouiche Mohamed M. M. , Moffo Frédéric , Poueme Rodrigue N. S. , Awah-Ndukum Julius
Published: May 2022

African horse sickness (AHS), a highly fatal arbovirosis of equines is endemic in sub-Saharan Africa. However, its epidemiology is poorly known in Cameroon. This study aimed to investigate the prevalence profile and risk factors of African horse sickness in Cameroon. Horse sera were subjected to the ELISA blocking test for the determination of antibodies against African horse sickness virus, and positive samples were submitted to capture ELISA to determine the presence of antigens. Potential risk factors associated with AHS were assessed based on the information collected in the field. The chi-square test and the odd ratio (OR) were used to test the association between serology and the different variables. Of the 336 sera obtained, 198 were positive for antibodies with a prevalence of 58.93% (CI: 53.67–64.19). From the 198 positive sera to antibodies, only one revealed positivity to antigens with a prevalence of 0.51% (CI: 0–1.5). Agroecological zone I (94.31%, CI: 91.83–96.79, OR: 34.92) was significantly (

p
<
0.05

) associated with the higher risk of disease dissemination than agroecological zone II (66.67%, CI: 61.63–71.71, OR: 4.21) and agroecological zone III (32.18%, CI: 27.18–37.18; OR: 1). Males (63.59%, CI: 58.44–68.74, OR: 1) were significantly (

p
<
0.05

) affected than females (50.42%, CI: 45.07–55.77; OR: 0.58). Horses of more than 8 years (76.00%, CI: 71.43–80.57) were significantly (

p
<
0.05

) at risk than young animals of less than 3 years old (32.14%, CI: 27.15–37.13, OR: 0.15). This study highlights a high seroprevalence of antibodies of African horse sickness in Cameroon. Agroecological zone, age, and the importation of horses were highly associated with the distribution of disease at the national level.
Integrated Network Pharmacology and Clinical Study to Reveal the Effects and Mechanisms of Bushen Huoxue Huatan Decoction on Polycystic Ovary Syndrome
Authors: Ding Jie , Shanshan Mei , Mengcheng Cai , Danying Zhang , Jin Yu
Published: May 2022

Objective. Bushen Huoxue Huatan Decoction (BHHD) is a classic prescription for treating polycystic ovary syndrome (PCOS). This study aims to explore the effects and possible mechanisms of BHHD on PCOS by integrating network pharmacology and clinical study. Methods. The components and potential drug targets of BHHD were analysed using the TCMSP platform, and the potential pathogenesis targets for PCOS were analysed using the GeneCards and OMIM databases. Subsequently, a disease-compound-target network diagram was established to identify the targets of BHHD treatment on PCOS. In addition, protein-protein interaction analysis, KEGG pathway analysis, and Gene Ontology biological analysis were carried out to reveal the mechanisms of BHHD. To further validate the analysis, a clinical trial involving 62 PCOS patients was conducted. All patients were treated with BHHD for 3 months and the ovulation rate, anthropometric indicators, clinical symptoms, and serological indicators were measured and compared before and after treatment. Results. The network pharmacology analysis showed that quercetin, luteolin, and kaempferol are the most significant active components in BHHD; STAT3, Jun, AKT1, MAPK3, MAPK1, and TP53 are the most critical drug targets; regulating hormones, reversing insulin (INS) resistance, exerting anti-inflammatory effects, and improving fertility might be the most important mechanisms of BHHD in the treatment of PCOS. After BHHD administration, the cyclic ovulation rate and the clinical symptoms including acanthosis nigricans and acne of patients were obviously improved. The serum endocrine levels of luteinising hormone (LH), LH/follicle-stimulating hormone, testosterone, dehydroepiandrosterone sulphate, insulin (INS), and area under the INS curve were evidently reversed, and the serum inflammatory factors levels including human interleukin (IL)-18, IL-16, IL-1β, IL-8, macrophage migration inhibitory factor, and human leukocyte differentiation antigen CD40 ligand were greatly reduced. Conclusion. BHHD has a good therapeutic effect on PCOS, and its mechanisms may be related to regulating hormone levels, improving insulin resistance, alleviating inflammation, and promoting pregnancy.
[Retracted] Revealing the Longevity Code of Humans with up to Extreme Longevity in Guangxi Based on Physical Examination Indicators and Personalized Biomarkers of Aging
Authors: Li He , Ren Minhong , He Qianzu , Gao Jie , Li Quanyang
Published: May 2022

Background. The pursuit of health and longevity is the eternal theme of humanity. Guangxi has a remarkable phenomenon of longevity in long‐lived regions and ranks with the highest number of longevity villages in China, thus providing a natural advantage for health longevity research. Methods. In this study, we selected 117 natives of a longevity area in Guangxi, covering a large age range (38‐118 years old) as subjects to measure peripheral leukocyte telomere length (LTL). Nineteen physical examination indicators and two inflammatory factor levels were measured. Results. Pearson’s analysis revealed a significant negative correlation between age and LTL (r = −0.3694, p = 0.003), as well as alanine aminotransferase, albumin, total bilirubin, direct bilirubin, γ‐glutamyltransferase, triglycerides, Interleukin‐10, and tumor necrosis factor type‐α. Systolic blood pressure and blood urea nitrogen were positively correlated with age. In addition, LTL decreased in people aged 38–89 years, and an upward trend was observed in people aged older than 90 years. Conclusions. Longevity individuals have characteristics, such as longer LTL, good hepatic function, and lower triglycerides and inflammation levels.
Research on Finite Element Structure of Vehicle Suspension Control Arm Based on Neural Network Sensing Control
Authors: Zhu Yancong , Jiang Weisheng , Qi Ruiya
Published: May 2022

To improve the adaptive control of the neural network under the influence of vehicle suspension control, the neural network control method is proposed. The specific content of the method analyzes the nonlinear properties of vehicle suspensions, proposes neural network-based adaptive control strategies, and develops neural network-based nonlinear algorithms and neural identifiers. Genetic algorithms perform predictive control of rear suspension through a compensation network. The experimental results show that the model structure is order

n
=
m
=
2

, the AN1 network node is 4-6-1, the AN2 network node is 5-4-1, the AN3 network node is 6-4-1, and the learning correction rate is

α
=
0.90

. In the actual simulation calculation, the number of nodes in the hidden layer of the network is increased, and the minimum number of nodes is chosen to determine the structure of the network, since the control effect obtained is not fundamentally changed. The suspension, which is controlled by the neural network’s adaptive control, has a vibration-reducing effect and is more effective by increasing the control of the rear suspension. The neural network has been shown to be able to effectively control the vehicle’s control arm.