bluuebunny/crossref_metadata_2025 huggingface dataset:
A two-fluid model for the macroscopic behavior of polar nematic fluids and gels in a nonchiral or a chiral solvent
Authors: Brand Helmut R. , Pleiner Harald
Published: February 2022

We present the macroscopic dynamics of polar nematic liquid crystals in a two-fluid context. We investigate the case of a nonchiral as well as of a chiral solvent. In addition, we analyze how the incorporation of a strain field for polar nematic gels and elastomers in a solvent modifies the macroscopic dynamics. It turns out that the relative velocity between the polar subsystem and the solvent gives rise to a number of cross-coupling terms, reversible as well as irreversible, unknown from the other two-fluid systems considered so far. Possible experiments to study those novel dynamic cross-coupling terms are suggested. As examples we just mention that gradients of the relative velocity lead, in polar nematics to heat currents and in polar cholesterics to temporal changes of the polarization. In polar cholesterics, shear flows give rise to a temporal variation in the velocity difference perpendicular to the shear plane, and in polar nematic gels uniaxial stresses or strains generate temporal variations of the velocity difference.

Graphical abstract
Emerging Optical Microscopy Techniques for Electrochemistry
Authors: Lemineur Jean-François , Wang Hui , Wang Wei , Kanoufi Frédéric
Published: February 2022

An optical microscope is probably the most intuitive, simple, and commonly used instrument to observe objects and discuss behaviors through images. Although the idea of imaging electrochemical processes operando by optical microscopy was initiated 40 years ago, it was not until significant progress was made in the last two decades in advanced optical microscopy or plasmonics that it could become a mainstream electroanalytical strategy. This review illustrates the potential of different optical microscopies to visualize and quantify local electrochemical processes with unprecedented temporal and spatial resolution (below the diffraction limit), up to the single object level with subnanoparticle or single-molecule sensitivity. Developed through optically and electrochemically active model systems, optical microscopy is now shifting to materials and configurations focused on real-world electrochemical applications.
Overcoming Major Barriers to Developing Successful Sensors for Practical Applications Using Functional Nucleic Acids
Authors: Zhang JingJing , Lan Tian , Lu Yi
Published: February 2022

For many years, numerous efforts have been focused on the development of sensitive, selective, and practical sensors for environmental monitoring, food safety, and medical diagnostic applications. However, the transition from innovative research to commercial success is relatively sparse. In this review, we identify four scientific barriers and one technical barrier to developing successful sensors for practical applications, including the lack of general methods to ( a) generate receptors for a wide range of targets, ( b) improve sensor selectivity to overcome interferences, ( c) transduce the selective binding to different optical, electrochemical, and other signals, and ( d) tune dynamic range to match thresholds of detection required for different targets; and the costly development of a new device. We then summarize solutions to overcome these barriers using sensors based on functional nucleic acids that include DNAzymes, aptamers, and aptazymes and how these sensors are coupled to widely available measurement devices to expand their capabilities and lower the barrier for their practical applications in the field and point-of-care settings.
Technologies for Frugal and Sensitive Point-of-Care Immunoassays
Authors: Kinnamon David S. , Heggestad Jacob T. , Liu Jason , Chilkoti Ashutosh
Published: February 2022

Immunoassays are a powerful tool for sensitive and quantitative analysis of a wide range of biomolecular analytes in the clinic and in research laboratories. However, enzyme-linked immunosorbent assay (ELISA)—the gold-standard assay—requires significant user intervention, time, and clinical resources, making its deployment at the point-of-care (POC) impractical. Researchers have made great strides toward democratizing access to clinical quality immunoassays at the POC and at an affordable price. In this review, we first summarize the commercially available options that offer high performance, albeit at high cost. Next, we describe strategies for the development of frugal POC assays that repurpose consumer electronics and smartphones for the quantitative detection of analytes. Finally, we discuss innovative assay formats that enable highly sensitive analysis in the field with simple instrumentation.
Psychosocial Treatments for Bipolar Disorder in Children and Adolescents
Authors: Brickman Haley M. , Fristad Mary A.
Published: February 2022

Evidence suggests that adjunctive psychosocial intervention for the treatment of pediatric bipolar spectrum disorders (BPSDs) is effective, feasible, and highly accepted as both an acute and maintenance treatment for youth with BPSD diagnoses as well as a preventive treatment for high-risk youth who are either asymptomatic or exhibit subsyndromal mood symptoms. Here, we provide a comprehensive review of all known evidence-based interventions, including detailed descriptions of treatment targets and core components, results of clinical trials, and updated research on mediators and moderators of treatment efficacy. Treatments are presented systematically according to level of empirical support (i.e., well established, probably efficacious, possibly efficacious, experimental, or questionable); upcoming and ongoing trials are included when possible. In line with a staging approach, preventive interventions are presented separately. Recommendations for best practices based on age, stage, and additional evidence-based child and family factors shown to affect treatment outcomes are provided.
Major Depression and Its Recurrences: Life Course Matters
Authors: Monroe Scott M. , Harkness Kate L.
Published: February 2022

Major depression is one of the most prevalent and debilitating personal and public health conditions worldwide. Less appreciated is that depression's tremendous burdens are not shared equally among all who become depressed. Some will suffer recurrences over the rest of their lives, whereas half or more will never have a recurrence. Based on these two distinctive life course prototypes, we propose a subtype distinction for research on the origins and lifetime course of major depression. A pressing goal is to determine at the time of depression's first onset who will follow which clinical trajectory. The lack of recognition of this distinction has resulted in many obstacles, including conceptual biases, methodological oversights, and definitional dead ends. Current theories are reviewed and compared. The implications for contemporary diagnostic controversies, reevaluating research on treatment and prevention, and enhancing the predictive strength of traditionally weak indicators of recurrences and recurrent depression are discussed.
The Biopsychosocial Puzzle of Painful Sex
Authors: Meana Marta , Binik Yitzchak M.
Published: February 2022

Genital pain associated with sex is a prevalent and distressing problem with a complex research and clinical profile. This article reviews the historical context of the “sexual pain disorders” and the circuitous trajectory that has led from the first mention of painful sex in ancient documents to the latest diagnostic category of genito-pelvic pain penetration disorder in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders as well as in other existing and proposed nomenclatures. Prominent etiologic research and emergent theoretical models are critically assessed, as is the latest treatment outcome research of note. Finally, the review points to a number of extant needs in the research and clinical effort, including an integrated biopsychosocial and multidisciplinary approach, randomized clinical trials, targeting of treatment barriers, and expansion of the entire enterprise to include populations that have not been considered.
Differences/Disorders of Sex Development: Medical Conditions at the Intersection of Sex and Gender
Authors: Sandberg David E. , Gardner Melissa
Published: February 2022

Defined as congenital conditions in which development of chromosomal, gonadal, or anatomic sex is atypical, differences or disorders of sex development (DSDs) comprise many discrete diagnoses ranging from those associated with few phenotypic differences between affected and unaffected individuals to those where questions arise regarding gender of rearing, gonadal tumor risk, genital surgery, and fertility. Controversies exist in numerous areas including how DSDs are conceptualized, how to refer to the set of conditions and those affected by them, and aspects of clinical management that extend from social media to legislative bodies, courts of law, medicine, clinical practice, and scholarly research in psychology and sociology. In addition to these aspects, this review covers biological and social influences on psychosocial development and adjustment, the psychosocial and psychosexual adaptation of people born with DSDs, and roles for clinical psychologists in the clinical management of DSDs.
Reconstructing the Environmental Context of Human Origins in Eastern Africa Through Scientific Drilling
Authors: Cohen Andrew S. , Campisano Christopher J. , Arrowsmith J. Ramón , Asrat Asfawossen , Beck Catherine C. , Behrensmeyer Anna K. , Deino Alan L. , Feibel Craig S. , Foerster Verena , Kingston John D. , Lamb Henry F. , Lowenstein Tim K. , Lupien Rachel L. , Muiruri Veronica , Olago Daniel O. , Owen R. Bernhart , Potts Richard , Russell James M. , Schaebitz Frank , Stone Jeffery R. , Trauth Martin H. , Yost Chad L.
Published: February 2022

Paleoanthropologists have long speculated about the role of environmental change in shaping human evolution in Africa. In recent years, drill cores of late Neogene lacustrine sedimentary rocks have yielded valuable high-resolution records of climatic and ecosystem change. Eastern African Rift sediments (primarily lake beds) provide an extraordinary range of data in close proximity to important fossil hominin and archaeological sites, allowing critical study of hypotheses that connect environmental history and hominin evolution. We review recent drill-core studies spanning the Plio–Pleistocene boundary (an interval of hominin diversification, including the earliest members of our genus Homo and the oldest stone tools), and the Mid–Upper Pleistocene (spanning the origin of Homo sapiens in Africa and our early technological and dispersal history). Proposed drilling of Africa's oldest lakes promises to extend such records back to the late Miocene. ▪ High-resolution paleoenvironmental records are critical for understanding external drivers of human evolution. ▪ African lake basin drill cores play a critical role in enhancing hominin paleoenvironmental records given their continuity and proximity to key paleoanthropological sites. ▪ The oldest African lakes have the potential to reveal a comprehensive paleoenvironmental context for the entire late Neogene history of hominin evolution.
Physics of Melt Extraction from the Mantle: Speed and Style
Authors: Katz Richard F. , Jones David W. Rees , Rudge John F. , Keller Tobias
Published: February 2022

Melt extraction from the partially molten mantle is among the fundamental processes shaping the solid Earth today and over geological time. A diversity of properties and mechanisms contribute to the physics of melt extraction. We review progress of the past ∼25 years of research in this area, with a focus on understanding the speed and style of buoyancy-driven melt extraction. Observations of U-series disequilibria in young lavas and the surge of deglacial volcanism in Iceland suggest this speed is rapid compared to that predicted by the null hypothesis of diffuse porous flow. The discrepancy indicates that the style of extraction is channelized. We discuss how channelization is sensitive to mechanical and thermochemical properties and feedbacks, and to asthenospheric heterogeneity. We review the grain-scale physics that underpins these properties and hence determines the physical behavior at much larger scales. We then discuss how the speed of melt extraction is crucial to predicting the magmatic response to glacial and sea-level variations. Finally, we assess the frontier of current research and identify areas where significant advances are expected over the next 25 years. In particular, we highlight the coupling of melt extraction with more realistic models of mantle thermochemistry and rheological properties. This coupling will be crucial in understanding complex settings such as subduction zones. ▪ Mantle melt extraction shapes Earth today and over geological time. ▪ Observations, lab experiments, and theory indicate that melt ascends through the mantle at speeds ∼30 m/year by reactively channelized porous flow. ▪ Variations in sea level and glacial ice loading can cause significant changes in melt supply to submarine and subaerial volcanoes. ▪ Fluid-driven fracture is important in the lithosphere and, perhaps, in the mantle wedge of subduction zones, but remains a challenge to model.
Political Risk and International Investment Law
Authors: Moehlecke Carolina , Wellhausen Rachel L.
Published: February 2022

International investment law provides a means for states to mitigate political risks that foreign investors face inside their borders. Its status quo includes thousands of international investment agreements (IIAs) and Investor–State Dispute Settlement (ISDS), a dispute resolution mechanism in which foreign, private investors sue host states in ad hoc international tribunals in pursuit of monetary compensation for property rights violations. In this review, we survey the vast contemporary literature on this regime to evidence the ways in which scholars have challenged the purported original goals of international investment law and its distributional consequences. In light of this literature's accomplishments, we highlight opportunities for a refocusing of international relations scholars’ research agenda on dynamics of continuity and change in the regime. The status quo in international investment law is fragile, and, in our view, the regime is on the brink of a major shift toward prioritizing state sovereignty well above political risk mitigation.
Wind Excited Action around Tall Building Having Different Corner Configurations
Authors: Meena Rahul Kumar , Raj Ritu , Anbukumar S.
Published: February 2022

Aeroelastic instabilities are common in square or rectangular plan shape structures due to the development of powerful vortices by the rolling motion of the separated shear layers. Windward corner modifications such as corner cut, recession, rounded, and slotted help to reduce instabilities. Codal recommendations about common shapes like square, rectangle, triangle, and circular shapes are available in different international standards, but they did not provide the detailed analysis results for the different regular shapes with corner configurations. Although analysis was performed on various plan shapes of tall buildings using the computational fluid dynamics technique through k‐ε turbulence model, a very small number of studies were performed on particular shapes with different corners having same plan area and height. The mean pressure coefficients of Model‐A and Model‐B were compared with various international standards and wind tunnel data, respectively, for validation, showing a nearly equivalent consistency; however, international standards consist of coefficients at 0° and 90° wind angles only. Wind effects on building shapes having different corners change the characteristics of the separated shear layer and reduce the downstream wake which helps to reduce drag and lift forces simultaneously. Recent study shows that the windward pressure distribution pattern is almost independent of building size and height. Therefore incorporation of corners in building helps to reduce the forces caused by extreme wind. A very large amount of numerical simulation data about wind pressure is generated, which can be used by the designer while designing such a building for wind load. Comparison have been made between buildings having different corners under same wind speed, and Model‐D (round corner) performed very well against the wind.
Seismic Random Noise Denoising Using Mini-Batch Multivariate Variational Mode Decomposition
Authors: Wu Guoning , Liu Guochang , Wang Junxian , Fan Pingping
Published: February 2022

Seismic noise attenuation plays an important role in seismic interpretation. The empirical mode decomposition, synchrosqueezing wavelet transform, variational mode decomposition, etc., are often applied trace by trace. Multivariate empirical mode decomposition, multivariate synchrosqueezing wavelet transform, and multivariate variational mode decomposition were proposed for lateral continuity consideration. Due to large input data, mini-batch multivariate variational mode decomposition is proposed in this paper. The proposed method takes advantages both of variational mode decomposition and multivariate variational mode decomposition. This proposed method firstly segments the input data into a series of smaller ones with no overlapping and then applies multivariate variational mode decomposition to these smaller ones. High frequency-domain noise is filtered through sifting. Finally, the denoised smaller ones are concatenated to form components (or intrinsic mode functions) of the input signal. Synthetic and field data experiments validate the proposed method with different batch sizes and achieve higher signal-to-noise ratio than the variational mode decomposition method.
Optimal Medical Image Size Reduction Model Creation Using Recurrent Neural Network and GenPSOWVQ
Authors: Sridhar Chethana , Pareek Piyush Kumar , Kalidoss R. , Jamal Sajjad Shaukat , Shukla Prashant Kumar , Nuagah Stephen Jeswinde
Published: February 2022

Medical diagnosis is always a time and a sensitive approach to proper medical treatment. Automation systems have been developed to improve these issues. In the process of automation, images are processed and sent to the remote brain for processing and decision making. It is noted that the image is written for compaction to reduce processing and computational costs. Images require large storage and transmission resources to perform their operations. A good strategy for pictures compression can help minimize these requirements. The question of compressing data on accuracy is always a challenge. Therefore, to optimize imaging, it is necessary to reduce inconsistencies in medical imaging. So this document introduces a new image compression scheme called the GenPSOWVQ method that uses a recurrent neural network with wavelet VQ. The codebook is built using a combination of fragments and genetic algorithms. The newly developed image compression model attains precise compression while maintaining image accuracy with lower computational costs when encoding clinical images. The proposed method was tested using real-time medical imaging using PSNR, MSE, SSIM, NMSE, SNR, and CR indicators. Experimental results show that the proposed GenPSOWVQ method yields higher PSNR SSIMM values for a given compression ratio than the existing methods. In addition, the proposed GenPSOWVQ method yields lower values of MSE, RMSE, and SNR for a given compression ratio than the existing methods.
The Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) Pandemic: Are Africa’s Prevalence and Mortality Rates Relatively Low?
Authors: Mariam Solomon H.
Published: February 2022

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the cause of coronavirus disease 19 (COVID-19), has been rapidly spreading since December 2019, and within a few months, it turned out to be a global pandemic. The disease affects primarily the lungs, but its pathogenesis spreads to other organs as well. However, its mortality rates vary, and in the majority of infected people, there are no serious consequences. Many factors including advanced age, preexisting health conditions, and genetic predispositions are believed to exacerbate outcomes of COVID-19. The virus contains several structural proteins including the spike (S) protein with subunits for binding, fusion, and internalization into host cells following interaction with host cell receptors and proteases (ACE2 and TMPRSS2, respectively) to cause the subsequent pathology. Although the pandemic has spread into all countries, most of Africa is thought of as having relatively less prevalence and mortality. Several hypotheses have been forwarded as reasons for this and include warmer weather conditions, vaccination with BCG (i.e., trained immunity), and previous malaria infection. From genetics or metabolic points of view, it has been proposed that most African populations could be protected to some degree because they lack some genetic susceptibility risk factors or have low-level expression of allelic variants, such as ACE2 and TMPRSS2 that are thought to be involved in increased infection risk or disease severity. The frequency of occurrence of α-1 antitrypsin (an inhibitor of a tissue-degrading protease, thereby protecting target host tissues including the lung) deficiency is also reported to be low in most African populations. More recently, infections in Africa appear to be on the rise. In general, there are few studies on the epidemiology and pathogenesis of the disease in African contexts, and the overall costs and human life losses due to the pandemic in Africa will be determined by all factors and conditions interacting in complex ways.
Bank Financial Risk Prediction Model Based on Big Data
Authors: Peng Hua , Lin Yicheng , Wu Mingzheng
Published: February 2022

Financial risk prediction is an important technique to systematically predict the unforeseeable risks in banking systems. The issues involving ill-timing and low accuracy in the current risks prediction methods necessitate an effective risk prediction method. Akin to the use of big data in various domains, the technology has a significant role in financial services and can be used to accurately and timely predict the possibilities of risks. In this paper, an effective hybrid method is proposed to aptly and effectively predict financial risks in the banking systems. The method utilizes the Lasso and linear regression algorithms via the big data features and framework technologies. By proper formalization of the bank financial risk problems, the risk data is obtained and processed. To filter the initial text features and preprocess the annual report text data, the information gain method is used. With the Bag-of-Words (BoW) and the word frequency reverse document frequency weighting method, the text features of financial risk prediction are extracted. The bank financial risk prediction model is constructed based on weighted fusion adaptive random subspace algorithm. The prediction results obtained are integrated so as to realize the bank financial risks in a seamless way. The experimental results show that the proposed method can effectively improve the prediction accuracy and consumes comparatively lesser time in risk prediction.
Posterior Capsule Opacification after Cataract Surgery via Implantation with Hydrophobic Acrylic Lens Compared with Silicone Intraocular Lens: A Systematic Review and Meta-Analysis
Authors: Kwon Ye Rin , Hwang Yoo Na , Kim Sung Min
Published: February 2022

Hydrophobic acrylic intraocular lens (IOL) is the most popular material in cataract surgery. Posterior capsule opacification (PCO) is a long-term complication of cataract surgery. It can impair vision and adversely affect the prognosis of IOL delamination. The objective of this study was to perform a systematic review and meta-analysis to provide an updated evaluation of long-term complications and visual function after implantation with hydrophobic acrylic and silicone intraocular lenses. PubMed, Embase, and Cochrane Library were searched from January 2000 until March 2021. Randomized controlled trials (RCTs) and retrospective studies were finally included. The main outcomes were PCO value and neodymium-doped yttrium aluminum garnet (Nd : YAG) capsulotomy rate. Subgroup analysis was performed to compare hydrophobic acrylic and silicone IOLs during the follow-up period. Sensitivity analysis was also performed. The meta-analysis included a total of 17 studies. When the follow-up period was considered, the results of the analysis revealed higher PCO value (Group 3: standardized mean difference (SMD), −0.59; 95% confidence interval (CI), −0.90 to −0.28) and Nd : YAG capsulotomy rate (Group 3: risk ratio (RR), 0.60; 95% CI, 0.40–0.89) for hydrophobic acrylic IOLs than silicone IOLs during a long-term (≥6 years) follow-up. In conclusion, both the PCO value and the Nd : YAG capsulotomy rates were higher in hydrophobic acrylic IOLs group than the silicone IOLs group at long-term use (more than 6 years) after implantation.
The Construction and Empirical Analysis of the Company’s Financial Early Warning Model Based on Data Mining Algorithms
Authors: Wang Aiqun , Yu Hongxiang
Published: February 2022

With the rapid advancement of the informatization process, enterprise informatization management has received more and more attention. Facing the increasingly complex and changeable social and economic environment, the difficulty of enterprise risk management has gradually increased. How to establish an efficient risk management mechanism for early warning of corporate risks is the goal that companies seek. Traditional statistical analysis can no longer satisfy the processing of massive financial data. Therefore, how to find useful information for the financial risk early warning management of the enterprise from the large amount of financial data information generated by the business activities of the enterprise is a problem that enterprises urgently need to solve at present. The continuous improvement and innovation of data mining technology and the good performance of research and analysis of massive data have made the two closely linked. First, this study introduces the theories of financial risk early warning and data mining technology; second, it introduces the research process of financial risk early warning model and elaborates the three data mining techniques used in this study; then combined with the actual situation of listed companies in my country, it constructs financial risk early warning index system; and finally, 77 listed manufacturing companies and their matching companies that were first processed by ST in 2005‐2007 were used as research samples, based on the financial data of the 2.4 years before being processed by ST and CXISP. It is found that the financial risk early warning model established by data mining technology has strong early warning capabilities. From the perspective of the prediction capabilities of the three models, the closer the time to ST, the higher the accuracy of the prediction; from the perspective of short‐term early warning, the three models have better prediction effects, but from the perspective of long‐term early warning, the prediction effects of neural networks and decision trees are better than logistic regression of statistical analysis; data mining techniques based on knowledge discovery are not only suitable for short‐term early warning but also for longer‐term early warning. Therefore, data mining can be applied to financial risk early warning analysis to achieve the purpose of using data mining technology for decision support.
Effect of Operating Room Nursing Management on Nosocomial Infection in Orthopedic Surgery: A Meta-Analysis
Authors: He Yuying , Chen Jun , Chen Yufeng , Qian Huangjing
Published: February 2022

Objective. This study aimed to provide scientific management methods to prevent nosocomial infection based on the systematical evaluation of the effect of operating room nursing management on nosocomial infection in orthopedic surgery. Methods. PubMed, Web of Science, Embase, China National Knowledge Internet, and Wanfang Databases were systematically searched for relevant studies published from 2013 to 2020. In this meta-analysis, comprehensive estimates of effect size estimates and 95% confidence intervals (CIs) for nursing satisfaction and incidence of infection were obtained. Results. Twenty studies with 2962 orthopedic patients were included in the meta-analysis. The experimental group received operating room nursing management while the routine nursing management was given for the control group. Meta-results showed that, in comparison with the control group, the nursing satisfaction in the experimental group was increased (OR = 6.22, 95% CI: 4.63–8.35,

P
<
0.001

), while the incidence of infection was reduced (OR = 0.20, 95% CI: 0.15–0.28,

P
<
0.001

), and the differences had statistical significance. Conclusions. Operating room nursing management could reduce the incidence of infection while prevent nosocomial infection in orthopedic surgery, which could be utilized to guide the hospital management.
Application of Improved U-Net Convolutional Neural Network for Automatic Quantification of the Foveal Avascular Zone in Diabetic Macular Ischemia
Authors: Meng Yongan , Lan Hailei , Hu Yuqian , Chen Zailiang , Ouyang Pingbo , Luo Jing
Published: February 2022

Objectives.The foveal avascular zone (FAZ) is a biomarker for quantifying diabetic macular ischemia (DMI), to automate the identification and quantification of the FAZ in DMI, using an improved U-Net convolutional neural network (CNN) and to establish a CNN model based on optical coherence tomography angiography (OCTA) images for the same purpose. Methods. The FAZ boundaries on the full-thickness retina of

6
×
6
 
mm

en face OCTA images of DMI and normal eyes were manually marked. Seventy percent of OCTA images were used as the training set, and ten percent of these images were used as the validation set to train the improved U-Net CNN with two attention modules. Finally, twenty percent of the OCTA images were used as the test set to evaluate the accuracy of this model relative to that of the baseline U-Net model. This model was then applied to the public data set sFAZ to compare its effectiveness with existing models at identifying and quantifying the FAZ area. Results. This study included 110 OCTA images. The Dice score of the FAZ area predicted by the proposed method was 0.949, the Jaccard index was 0.912, and the area correlation coefficient was 0.996. The corresponding values for the baseline U-Net were 0.940, 0.898, and 0.995, respectively, and those based on the description data set sFAZ were 0.983, 0.968, and 0.950, respectively, which were better than those previously reported based on this data set. Conclusions. The improved U-Net CNN was more accurate at automatically measuring the FAZ area on the OCTA images than the traditional CNN. The present model may measure the DMI index more accurately, thereby assisting in the diagnosis and prognosis of retinal vascular diseases such as diabetic retinopathy.
Chest CT Scan Features to Predict COVID-19 Patients’ Outcome and Survival
Authors: Mehrabi Nejad Mohammad-Mehdi , Abkhoo Aminreza , Salahshour Faeze , Salehi Mohammadreza , Gity Masoumeh , Komaki Hamidreza , Kolahi Shahriar
Published: February 2022

Background. Providing efficient care for infectious coronavirus disease 2019 (COVID-19) patients requires an accurate and accessible tool to medically optimize medical resource allocation to high-risk patients. Purpose. To assess the predictive value of on-admission chest CT characteristics to estimate COVID-19 patients’ outcome and survival time. Materials and Methods. Using a case-control design, we included all laboratory-confirmed COVID-19 patients who were deceased, from June to September 2020, in a tertiary-referral-collegiate hospital and had on-admission chest CT as the case group. The patients who did not die and were equivalent in terms of demographics and other clinical features to cases were considered as the control (survivors) group. The equivalency evaluation was performed by a fellowship-trained radiologist and an expert radiologist. Pulmonary involvement (PI) was scored (0–25) using a semiquantitative scoring tool. The PI density index was calculated by dividing the total PI score by the number of involved lung lobes. All imaging parameters were compared between case and control group members. Survival time was recorded for the case group. All demographic, clinical, and imaging variables were included in the survival analyses. Results. After evaluating 384 cases, a total of 186 patients (93 in each group) were admitted to the studied setting, consisting of 126 (67.7%) male patients with a mean age of 60.4 ± 13.6 years. The PI score and PI density index in the case vs. the control group were on average 8.9 ± 4.5 vs. 10.7 ± 4.4 (

p

value: 0.001) and 2.0 ± 0.7 vs. 2.6 ± 0.8 (

p

value: 0.01), respectively. Axial distribution (p value: 0.01), cardiomegaly (

p

value: 0.005), pleural effusion (p value: 0.001), and pericardial effusion (

p

value: 0.04) were mostly observed in deceased patients. Our survival analyses demonstrated that PI score ≥ 10 (

p

value: 0.02) and PI density index ≥ 2.2 (

p

value: 0.03) were significantly associated with a lower survival rate. Conclusion. On-admission chest CT features, particularly PI score and PI density index, are potential great tools to predict the patient’s clinical outcome.
Processing of the Conceptual Semantics of Verbs and Clauses in English Learners under the Background of Wireless Communication and Artificial Intelligence
Authors: Mao Jiadai
Published: February 2022

The development of wireless communication and artificial intelligence decision-making has allowed our scientific research to be carried out on a more convenient and precise basis. This kind of all-round progress is also very obvious in the field of linguistic research. The research presented to explain the observation that people may use one of the alternate clauses to describe the picture they see. This linguistic observation leads us to the assumption that there may exist a functional cortex connecting visual information of an event with a group of corresponding clauses. Between the visual information and a group of clauses can be understood as a shared conceptual content of both. It is well supported by the PET and the ERP experiments. In other words, if a group of alternate clauses are connected with the same visual information, they are said to have the same conceptual content. In our linguistic model, this shared conceptual content is represented as a conceptual frame. In most linguistic studies, however, alternate syntactic structures are said to be derived from different meanings of the same verb via different argument structures. The difference between the conceptual frame and its corresponding argument structures needs more substantial evidence from brain imaging for the plausibility of conceptual frame. To answer this question, we attempt to investigate the relationship between a group of alternate clauses and two different pictures via priming effect manifested by an ERP component. Also, we make full use of wireless communication and artificial intelligence decision-making technology to construct. This technical background allows this research to fully simulate the practical environment while also making more assumptions about uncertainty. In the clause-picture priming experiment, clauses are treated as prime stimuli, designated as S1. The pictures are treated as target stimuli, designated as S2. One picture is conceptually congruent with all the clauses, and the other is not. The ERP component N270 is expected to be sensitive to the degree to which the S2 (the target stimulus) is in agreement with S1 (stimulus). After many simulation experiments, the experimental framework of wireless communication and artificial intelligence decision-making technology architecture provides a lot of reference data for research. The results of the experiment show that the semantics of each individual clause of an event was consistent with the semantics of its following congruent picture but inconsistent with that of the incongruent picture. Therefore, each clause of an event, though has different grammatical structures, shares the same semantics. This shared semantics is understood as the conceptual frame.
Network Clustering Algorithm Based on Fast Detection of Central Node
Authors: Jia Ziruo , Qi Fuqiang
Published: February 2022

Based on scale-free and density-based complex networks and numerical clustering algorithm, a graph clustering algorithm based on fast detection of central nodes is proposed. Through the calculation of local density and comprehensive clustering of nodes in the network, the clustering center in the network can be found quickly and noncentral nodes can be divided into the clustering center according to the nearest neighbor principle, thus avoiding parameter limitations such as the number of clustering to be set in advance using conventional classic social network detection algorithm. The experimental comparison and analysis in the real network indicate that the graph clustering algorithm based on fast detection of the central node is highly effective and efficient.
Research on Low-Resistance Grounding Fault Line Selection Based on VMD with PE and K-Means Clustering Algorithm
Authors: Cao Wensi , Li Chen , Li Zhaohui , Wang Shuo , Pang Heyuan
Published: February 2022

Aiming at the fault line selection problem in the single-phase grounding system of the distribution network, a new fault line selection method based on VMD and permutation entropy feature extraction combined with K-means clustering algorithm is proposed. This method is a hybrid algorithm that can effectively identify fault line selection. Firstly, a simulation model is built and its zero sequence current is collected. The variational modal decomposition method is used to decompose the collected zero-sequence current into multiple intrinsic modal functions, which can not only effectively reduce the influence of harmonic components and noise in the characteristic signal but also facilitate the calculation. The extracted intrinsic mode function is calculated by permutation entropy (PE), and the calculated entropy value is constructed into a matrix to highlight the fault characteristics of the line; then, the matrix is subjected to K-means cluster analysis through the preprocessing algorithm and the faulty line is correctly distinguished. Then, regression verification is performed. Finally, it is verified by the recorded wave data of the real test site and then analyzed and compared with other algorithms. The proposed method shows that when a single-phase ground fault occurs, the ground fault line selection can be effectively identified under different transition resistances, grounding resistances, and fault distances. Therefore, this method can accurately identify the fault line selection, and the accuracy rate is 100%, which has a certain use value.
Severe Chest Pain in a Young Patient with Behçet’s Disease: A Rare Manifestation
Authors: Reed Steen , McGee Michael , Sritharan Shanathan
Published: February 2022

A 32-year-old man with a background of Behçet’s disease developed severe chest pain. The onset coincided with an episode of sacroiliitis. The patient was diagnosed with pericarditis and was successfully treated with a combination of anti-inflammatory agents. Pericarditis is a rare manifestation of Behçet’s disease.
Influence of Natural Gas Hydrate’s Property Weakening from Dissociation on Subsidence of Seabed Sediments: A Simulation Study
Authors: Ma Shuzhi , Li Hongbo , Jia Hongbiao
Published: February 2022

The dissociation of natural gas hydrate in the seabed during hydrate production can lead to substantial weakening in soil mechanic properties, which in turn results in the deformation and even destruction of the seafloor sediments, thereby posing a potential threat to the safety of the undersea engineering infrastructures. A better understanding of the effect of soil mechanical weakening on seabed deformation will benefit the prediction and/or determination of geological conditions for safer hydrate production. In this work, a three‐dimensional model of the submarine geological body was established and engineering properties of hydrate reservoir under different degrees of hydrate dissociation were determined. Such model and engineering parameters were applied for simulation calculation on examining the effect of hydrate dissociation degree on soil subsidence using FLAC3D software. The simulation results show that the subsidence of sediments is centrally distributed within the dissociation area and the subsidence of hydrate‐bearing sediment’s top surface is much more severe than that of the overlying layer. Subsidence on the horizontal section above the hydrate reservoir presents an essentially symmetric dish‐like subsidence trough, with the greatest subsidence in the centre and approximately constant subsidence beyond the trough range. The radius of the subsidence trough increases with the increase of the degree of dissociation, and the increasing extent in the early dissociation stage (lower dissociation degree) is greater than in the middle and the later stage (higher dissociation degree). As the degree of hydrate dissociation increases, subsidence of both the overlying layer and the HBS increases. Especially when the decomposition exceeds a certain threshold, the seafloor subsidence increases rapidly. The simulation results in this work are of guiding significance for deformation monitoring and infrastructure deformation prevention in practical hydrate production projects.
A Brain-Computer Interface for Teleoperation of a Semiautonomous Mobile Robotic Assistive System Using SLAM
Authors: Nandikolla Vidya , Ghoslin Bryan , Matsuno Kevin , Medina Portilla Daniel A.
Published: February 2022

The proposed assistive hybrid brain-computer interface (BCI) semiautonomous mobile robotic arm demonstrates a design that is (1) adaptable by observing environmental changes with sensors and deploying alternate solutions and (2) versatile by receiving commands from the user’s brainwave signals through a noninvasive electroencephalogram cap. Composed of three integrated subsystems, a hybrid BCI controller, an omnidirectional mobile base, and a robotic arm, the proposed robot has commands mapped to the user’s brainwaves related to a set of specific physical or mental tasks. The implementation of sensors and the camera systems enable both the mobile base and the arm to be semiautonomous. The mobile base’s SLAM algorithm has obstacle avoidance capability and path planning to assist the robot maneuver safely. The robot arm calculates and deploys the necessary joint movement to pick up or drop off a desired object selected by the user via a brainwave controlled cursor on a camera feed. Validation, testing, and implementation of the subsystems were conducted using Gazebo. Communication between the BCI controller and the subsystems is tested independently. A loop of prerecorded brainwave data related to each specific task is used to ensure that the mobile base command is executed; the same prerecorded file is used to move the robot arm cursor and initiate a pick-up or drop-off action. A final system test is conducted where the BCI controller input moves the cursor and selects a goal point. Successful virtual demonstrations of the assistive robotic arm show the feasibility of restoring movement capability and autonomy for a disabled user.
Deformation Control Monitoring of Basement Excavation at Field Construction Site: A Case of Hydraulic Servo Steel Enhancement Geotechnology
Authors: Nangulama Horris K. , Jian Zhou
Published: February 2022

This study presents two field cases of deep excavation using the hydraulic servo geotechnology as an enhancement to the lateral support. The first excavation process was preceded with numerical simulation to investigate the deformation of excavation pit with and without servo geotechnology. Optimised axial loading capacities from the numerical simulation were adopted at the actual excavation site. On the second excavation site, servo geotechnology was applied directly on the excavation process during actual construction without prior numerical simulation. The results showed that the servo steel support geotechnology restrained above 70% of lateral displacements of the excavated walls, and ground surface influence zone settlements were kept within the acceptable warning limits. Prior application of the numerical simulation before field excavation optimised axial loads and kept the deformation caused by excavation within project limits. The numerical simulation of servo geotechnology deformation control capacity for deep excavation projects is validated, and this study provides a practical and significant reference on similar projects.
The Toxic Influence of Excess Free Iron on Red Blood Cells in the Biophysical Experiment: An In Vitro Study
Authors: Kozlova E. , Sherstyukova E. , Sergunova V. , Kozlov A. , Gudkova O. , Inozemtsev V. , Chernysh A.
Published: February 2022

Iron is needed for life-essential processes, but free iron overload causes dangerous clinical consequences. The study of the role of red blood cells (RBCs) in the influence of excess free iron in the blood on the pathological consequences in an organism is relevant. Here, in a direct biophysical experiment in vitro, we studied the action of free iron overload on the packed red blood cell (pRBC) characteristics. In experiments, we incubated pRBCs with the ferrous sulfate solution (Fe2+). Wе used free iron in a wide range of concentrations. High

Fe

2
+

concentrations made us possible to establish the pattern of the toxic effect of excess iron on pRBCs during a reduced incubation time in a biophysical experiment in vitro. It was found that excess free iron causes changes in pRBC morphology, the appearance of bridges between cells, and the formation of clots, increasing the membrane stiffness and methemoglobin concentration. We created a kinetic model of changes in the hemoglobin derivatives. The complex of simultaneous distortions of pRBCs established in our experiments can be taken into account when studying the mechanism of the toxic influence of excess free iron in the blood on pathological changes in an organism.