bluuebunny/crossref_metadata_2025 huggingface dataset:
Kolkata-Coventry comparative registry study of acute heart failure: an insight into the impact of public, private and universal health systems on patient outcomes in low–middle income cities (KOLCOV HF Study)
Authors: Banerjee Suvro , Halder Swapan Kumar , Kimani Peter , Tran Patrick , Ali Danish , Roelas Marina , Weight Nicholas , Dungarwalla Moez , Banerjee Prithwish
Published: May 2022

Introduction

Survival gaps in acute heart failure (AHF) continue to expand globally. Multinational heart failure (HF) registries have highlighted variations between countries. Whether discrepancies in HF practice and outcomes occur across different health systems (ie, private, public or universal healthcare) within a city or between countries remain unclear. Insight into organisational care is also scarce. With increasing public scrutiny of health inequalities, a study to address these limitations is timely.

Method

KOLCOV-HF study prospectively compared patients with AHF in public (Nil Ratan Sircar Hospital (NRS)) versus private (Apollo Gleneagles Hospital (AGH)) hospitals of Kolkata, India, and one with universal health coverage in a socioeconomically comparable city of Coventry, England (University Hospitals Coventry & Warwickshire (UHCW)). Data variables were adapted from UK’s National HF Audit programme, collected over 24 months. Predictors of in-hospital mortality and length of hospitalisation were assessed for each centre.

Results

Among 1652 patients, in-hospital mortality was highest in government-funded NRS (11.9%) while 3 miles north, AGH had significantly lower mortality (7.5%, p=0.034), similar to UHCW (8%). This could be attributed to distinct HF phenotypes and differences in clinical and organisational care. As expected, low blood pressure was associated with a significantly greater risk of death in patients served by public hospitals UHCW and NRS.

Conclusion

Marked differences in HF characteristics, management and outcomes exist intra-regionally, and between low–middle versus high-income countries across private, public and universal healthcare systems. Physicians and policymakers should take caution when applying country-level data locally when developing strategies to address local evidence-practice gaps in HF.
VUV photoprocessing of oxygen-containing polycyclic aromatic hydrocarbons: iPEPICO study of the unimolecular dissociation of ionized benzofuran
Authors: Zinck Nicholas , Bodi Andras , Mayer Paul M.
Published: May 2022

Oxygen-containing polycyclic aromatic hydrocarbons (OPAHs) are potential contributors to the 11.3 µm band in interstellar observations. To further explore their role in the interstellar medium, we investigated their fate after photoprocessing by VUV radiation; in particular, we studied the dissociative photoionization of the simplest OPAH, benzofuran, with imaging photoelectron photoion coincidence (iPEPICO) spectroscopy. Ionized benzofuran dissociates by loss of CO, followed by a sequential H-atom loss. The parallel HCO-loss channel, leading to the same bicyclic C7H5+ fragment ion, is not competitive at low excess energies above the ionization threshold. However, the collision-induced dissociation tandem mass spectrometry results suggest that CO and HCO may be formed in parallel at higher energies. A Rice–Ramsperger–Kassel–Marcus fragmentation model reproduced the iPEPICO data well, assuming the initial 1,2-H shift transition state to be rate determining to CO loss. The breakdown diagram and the measured dissociation rates agreed well at the CBS-QB3-calculated activation energy of 2.99 eV, which could be relaxed to 3.25 eV, and only a slight adjustment of the ab initio activation entropy. The model barrier to sequential H loss is larger than the computed H-loss threshold and the breakdown diagram rises less steeply than predicted, which indicates suprastatistical kinetic energy release after the tight H-transfer transition state of the first step. HCO cleavage is possible after a ring-opening transition state, which is looser than and isoenergetic with the CO-loss transition state. However, a subsequent ring formation transition state at 3.85 eV is moderately tight, which suppresses HCO loss at low excess energies.
On the implementation of the dynamically zoned target release reservoir model in the GEM-Hydro streamflow forecasting system
Authors: Gaborit Étienne , Fortin Vincent , Durnford Dorothy
Published: May 2022

This study investigates the benefit of the dynamically zoned target release (DZTR) reservoir model to improve upon the natural lake model for storage and outflow simulations of regulated reservoirs. Simulations were performed with the GEM-Hydro hydrologic model over the Saskatchewan River Basin to study different implementation scenarios for DZTR, applicable for varying degrees of available observed data. Results show that DZTR brings significant improvements upon the natural lake model. Outflow simulations are always better with DZTR than with the natural lake model, while storage performances are sensitive to the implementation methodology considered. Even in the absence of observed lake level data, storage simulations with DZTR are more realistic than the natural lake model. Automatic calibration of the 76 DZTR model parameters did not significantly improve results upon the general implementation methodology. Given the performance variability across reservoirs and the subjective steps involved in the DZTR implementation, it seems risky to implement DZTR without any flow observations downstream of a reservoir.
Quantitative trait locus mapping of rust resistance and agronomic traits in spring wheat
Authors: Ciechanowska Izabela , Semagn Kassa , McCallum Brent , Randhawa Harpinder , Strenzke Klaus , Dhariwal Raman , Virginillo Mark , Iqbal Muhammad , Spaner Dean
Published: May 2022

Marker-assisted selection requires the identification of molecular markers associated with major genes and quantitative trait loci (QTLs). In this study, we used 167 doubled haploid lines derived from two unregistered spring wheat ( Triticum aestivum L.) parental lines that belong to the Canada Western Special Purpose wheat market class to map QTLs associated with five traits using inclusive composite interval mapping (ICIM). Using ICIM, the least-squares means phenotype data across three to four environments, and a genetic map of 2676 single-nucleotide polymorphisms (SNPs) out of the wheat 90K SNP array, we identified 10 QTLs associated with days to maturity (4A and 5B), plant lodging (4B, 5A, 5D, and 7D), grain yield (2D), leaf rust (4A), and stem rust (1A and 2B). Each QTL individually explained 6.0%–22.3% of the phenotypic variance and together accounted for 8.6%–38.2% of the total variance per trait. Two of the QTLs associated with rusts ( QLr.dms-4A and QSr.dms-1A) had a minor effect (6.0%–9.0%), whereas the second QTL for stem rust ( QSr.dms-2B) had a major effect (22.3%). Although chromosome 2B harbors multiple disease resistance QTLs, the physical location of QSr.dms-2B has not been reported in previous studies. Results from this study provide additional valuable information to wheat researchers; in particular, the area on chromosome 2B should be considered for future analyses.
Cosmic scenario of early universe in context of Euclidean wormhole in dilatonic Einstein–Gauss–Bonnet gravity
Authors: Biswas Gargi , Modak B.
Published: May 2022

In this paper, we present some wormhole configurations using analytic and numerical solutions of the Euclidean field equations considering Einstein–Gauss–Bonnet dilaton coupled interaction in the Robertson–Walker metric. Analytic solutions invoking slow-roll type approximation for some potentials yield identical wormholes in the early era. Eventually, in later era, the universe enters to an expanding era much faster than usual inflation accompanied by oscillations with analytic continuation [Formula: see text]. The numerical solutions of wormholes in terms of the scale factor [Formula: see text] are almost identical for some standard potentials of the dilaton field, except for the evolution of potentials. The Hubble parameter and deceleration parameter obtained by curve fit of [Formula: see text] of the numerical solution of wormhole yield an inflationary era away from the throat of the wormhole using [Formula: see text]. A sharp decay of the potential is also observed.
The photoluminescence responses of two-dimensional atomic layers of MoS2 excited by surface acoustic wave device
Authors: Chung Chan-Yu , Chen Ying-Chung , Chung Chung-Jen , Kao Kuo-Sheng
Published: May 2022

In this study, the surface acoustic wave device combined with an oscillator circuit was used as the excitation platform to explore the photoluminescence responses of the two-dimensional atomic layers of MoS2. The MoS2 layers were prepared using the mechanical exfoliation method, and then transferred onto the SAW delay line area of the platform. Under the varied servo driving voltages of the excitation platform, various power intensities of the oscillator were obtained. It represented that the various energy levels of acoustic waves were propagated underneath the MoS2 layers. From the observation by multiphoton excitation microscopy, the excited fluorescent intensities of the MoS2 species were detected at various levels. Studies have proved that as the driving voltage of the oscillator increases, the interaction between the MoS2 species and the acoustic wave is also enhanced.
Home-Based Self-Employment: Combining Personal, Household and Employment Influences
Authors: Daniel Elizabeth M. , Owen Robyn
Published: May 2022

Despite the significant economic, innovative and social contributions of home-based self-employment, it is an under-researched and under-theorised area. We address this gap by drawing from established entrepreneurial theory to propose and validate a more complete theoretical model that combines personal, household and employment influences. We validate our proposed model by drawing on quantitative data from a large-scale, longitudinal, UK-based, social studies dataset. Our validated model demonstrates how and why antecedent and current household and employment factors, but not personal factors, associated with being home-based interact and provide constitutive affordances that result in a setting for self-employment that is unique in more fundamental ways than simply the home location of the business. Despite being responsible for some of the world’s most innovative and successful businesses, home-based businesses are often denigrated as lacking ambition or growth potential. The results of our analysis vindicate the choices of the home-based self-employed, by demonstrating that basing a business in the home is a rational choice based on an intersection of household and employment characteristics. The data used in this study predates the COVID-19 pandemic. However, it is expected that home-based self-employment will grow significantly following the pandemic in response to increasing acceptance of home-working. It therefore behoves entrepreneurship scholars to have a robust understanding of this previously overlooked type of self-employment if we are to be able to provide guidance to policymakers and self-employment support services.
UNDERSTANDING THE DRIVERS OF RADICAL AND INCREMENTAL INNOVATION PERFORMANCE: THE ROLE OF A FIRM’S KNOWLEDGE-BASED CAPITAL AND ORGANISATIONAL AGILITY
Authors: EISELE SEBASTIAN , GREVEN ANDREA , GRIMM MAREIKE , FISCHER-KREER DENISE , BRETTEL MALTE
Published: May 2022

Organisational agility, a specific dynamic capability, constitutes a key success factor in today’s dynamic business environments. However, it is unclear whether and how firms’ knowledge-based capital enables organisational agility. Moreover, mechanisms through which organisational agility translates knowledge-based capital into innovation performance are poorly understood. This study fills these gaps by investigating direct effects of knowledge-based capital (i.e., human, social, and organisational capital) on organisational agility and, in turn, indirect effects on radical and incremental innovation performance. Leveraging survey data from 385 German firms, this study contributes to the organisational agility and the strategic innovation management literature. First, our analysis validates human and social capital as antecedents of organisational agility, yielding evidence for the premises of the knowledge-based view in a novel context. Second, it substantiates the dynamic capability view as a pivotal theoretical lens for explaining the mechanisms underlying the relationships between firms’ knowledge-based capital, organisational agility, and incremental innovation performance.
FREE CASH FLOW, MANAGERIAL OWNERSHIP, AND AGENCY COST: A NONLINEAR EVIDENCE FROM NIGERIAN QUOTED CONSUMER AND INDUSTRIAL GOODS FIRMS
Authors: YERO JIBRIL IBRAHIM , ABUBAKAR NUHU , HAMMAN AISHA MUHAMUD , SAIDU SANI
Published: May 2022

This study focuses on a nonlinear approach in assessing the effect of free cash flow and managerial ownership on the agency cost of firms listed under the Consumer and Industrial Goods sector in the Nigerian Stock Exchange (NSE). Based on the extant theories, firms with high free cash flow are more exposed to agency costs. Existing literature however shows that managerial ownership could moderate this tendency. In this study, we argue that the extent to which managerial ownership moderates the effect of free cash flow on firms’ agency costs is dependent upon the level of the ownership, and thus, the moderation effect is not entirely linear. The study therefore empirically investigates whether free cash flow has a significant positive effect on agency costs; whether managerial ownership has a nonlinear effect on free cash flow; and whether the moderating effect of managerial ownership on the association of free cash flow and agency costs is nonlinear (not the same at different levels [Formula: see text]25% and above 25%). We estimate the panel regression using data from 2009 to 2015 to test three main hypotheses. We measured free cash flow using the cash flow approach and adopted the inverse of asset utilization to proxy for agency costs. The findings reveal that while free cash flow linearly and negatively affects agency costs, managerial ownership has a nonlinear effect on free cash flow and on the effect of free cash flow on agency costs. In line with the findings, this study concludes that managerial ownership is not a straight jacket remedy for tacking problems associated with agency costs that results from having excess free cash flow. Further studies should consider other proxies of agency costs.
Exploring Sonification Mapping Strategies for Spatial Auditory Guidance in Immersive Virtual Environments
Authors: Gao Zihan , Wang Huiqiang , Feng Guangsheng , Lv Hongwu
Published: May 2022

Spatial auditory cues are important for many tasks in immersive virtual environments, especially guidance tasks. However, due to the limited fidelity of spatial sounds rendered by generic Head-Related Transfer Functions (HRTFs), sound localization usually has a limited accuracy, especially in elevation, which can potentially impact the effectiveness of auditory guidance. To address this issue, we explored whether integrating sonification with spatial audio can enhance the perceptions of auditory guidance cues so user performance in auditory guidance tasks can be improved. Specifically, we investigated the effects of sonification mapping strategy using a controlled experiment that compared four elevation sonification mapping strategies: absolute elevation mapping, unsigned relative elevation mapping, signed relative elevation mapping, and binary relative elevation mapping. In addition, we examined whether azimuth sonification mapping can further benefit the perception of spatial sounds. The results demonstrate that spatial auditory cues can be effectively enhanced by integrating elevation and azimuth sonification, where the accuracy and speed of guidance tasks can be significantly improved. In particular, the overall results suggest that binary relative elevation mapping is generally the most effective strategy among four elevation sonification mapping strategies, which indicates that auditory cues with clear directional information are key to efficient auditory guidance.
A Survey on IoT Profiling, Fingerprinting, and Identification
Authors: Safi Miraqa , Dadkhah Sajjad , Shoeleh Farzaneh , Mahdikhani Hassan , Molyneaux Heather , Ghorbani Ali A.
Published: May 2022

The proliferation of heterogeneous Internet of things (IoT) devices connected to the Internet produces several operational and security challenges, such as monitoring, detecting, and recognizing millions of interconnected IoT devices. Network and system administrators must correctly identify which devices are functional, need security updates, or are vulnerable to specific attacks. IoT profiling is an emerging technique to identify and validate the connected devices’ specific behaviour and isolate the suspected and vulnerable devices within the network for further monitoring. This article provides a comprehensive review of various IoT device profiling methods and provides a clear taxonomy for IoT profiling techniques based on different security perspectives. We first investigate several current IoT device profiling techniques and their applications. Next, we analyzed various IoT device vulnerabilities, outlined multiple features, and provided detailed information to implement profiling algorithms’ risk assessment/mitigation stage. By reviewing approaches for profiling IoT devices, we identify various state-of-the-art methods that organizations of different domains can implement to satisfy profiling needs. Furthermore, this article also discusses several machine learning and deep learning algorithms utilized for IoT device profiling. Finally, we discuss challenges and future research possibilities in this domain.
Change of Optical Properties of Carbon-Doped Silicon Nanostructures under the Influence of a Pulsed Electron Beam
Authors: Darmenkulova M. B. , Aitzhanov M. B. , Zhumatova Sh. A. , Ibraimov M. K. , Sagidolda Ye.
Published: May 2022

In the work, porous silicon with observed photoluminescence was made from a p-type silicon substrate doped with boron and crystallographic orientation (100) using the method of electrochemical etching in a solution containing H2(SiF6) (silicon hydrofluoric acid) and ethyl alcohol. Thin carbon films were sprayed by high-frequency magnetron sputtering at room temperature onto the surface of porous silicon. The resulting carbon-doped thin films of porous silicon were irradiated on a pulsed electron booster and comparisons were made with nonirradiated films of porous silicon. To understand the effect of carbon on the properties of porous silicon films samples were analyzed by Raman spectroscopy, spectrophotometry, and scanning probe microscopy (SPM). The results of the SPM showed that the roughness of the samples increases after carbon doping on the surface of porous silicon. Thus, for the first time, experimental results were obtained on the effect of irradiation on carbon-doped porous silicon obtained in a solution containing hydrogen hexafluorosilicate H2(SiF6) and a significant change in its optical properties was shown. The results of the study showed that irradiated samples of carbon-doped porous silicon have better photoluminescence compared to nonirradiated samples.
Analysis and Optimization of Flute Playing and Teaching System Based on Convolutional Neural Network
Authors: Xu Cong
Published: May 2022

Influenced by cultural background, economic development, social system, education system, and other factors, there is still a big gap between Chinese institutions and developed countries in flute teaching, even with our neighbors, South Korea and Japan. Under the influence of cultural background, economic development, social system, and educational system, there is still a very big gap between Chinese colleges and universities and developed countries in flute teaching, even with our neighbors, South Korea and Japan. Because of its local perception and weight-sharing structure, the convolutional neural network is closer to the biological neural network in the real world. The weight-sharing structure reduces the complexity of the neural network, which can avoid the complexity of feature extraction and classification process in data reconstruction. This paper studies the analysis and optimization of flute playing and teaching system based on a convolutional neural network. By applying local perception field and parameter sharing in a convolutional neural network at the same time and adding multiple filters, it can not only effectively reduce the number of parameters but also extract features layer by layer. In the process of convolution, the parameters of the characteristic map obtained by each layer decrease layer by layer, but the number increases gradually. Based on the analysis of the problems faced by the flute performance teaching, this paper puts forward the corresponding solutions in order to promote the flute performance teaching in China to achieve better results.
Measurement of Trade Export Level Based on Computer Simulation Technology
Authors: Yang Shuai
Published: May 2022

This study uses computer simulation technology to process trade export data and real demand to create a trade export level measurement model based on computer simulation technology in order to enhance the measurement impact of trade export level. Moreover, this paper analyzes the classic complex network model, combines the trade export level measurement model to improve the algorithm, and builds the trade export level system with the support of the improved algorithm. In addition, this paper combines a complex network model to construct an intelligent model that can be used for trade level measurement. The model can perform an intelligent analysis of multiple factors and can evaluate the level of trade from the actual situation. Finally, this paper verifies the algorithm model of this paper through experimental research. From the research results, it can be seen that the method proposed in this paper has certain effects.
Study on Susceptibility and Mechanism of Reheat Cracking in Welded Joint of Type 347 Austenitic Stainless Steel
Authors: Wang Jianjun , Yu Shurong
Published: May 2022

The reheat cracking susceptibility of welded joints has been evaluated by 900°C high-temperature constant load test and 750°C–900°C high-temperature slow strain rate tensile (SSRT) test on welded joints of TP347 steel, and the influencing factors and mechanism of reheat cracking in welded joints have been further investigated by microcosmic characterization techniques of SEM and EDS, etc. The result shows the critical fracture stress of TP347 steel welded joints at 900°C is 24 MPa, which is much lower than 80% of the high-temperature yield strength of base material, indicating that the reheat crack of the welded joint is highly sensitive at this temperature; the average reduction of area (RoA) of welded joints obtained by 750°C–900°C SSRT test at each temperature is below 20%, which means all welded joints have a reheat cracking susceptibility in this temperature range, and the reheat cracking susceptibility increases with the rising of temperature. Based on study and analysis using microcosmic methods of optical microscope, scanning electron microscope, and energy spectrum analysis, it is found that welded joints of TP347 steel have a high reheat cracking susceptibility because the high temperature accelerates the diffusion of Cr, Nb, and impurity elements to the grain boundary, and a large quantity of carbides and low-melting-point eutectics are collected and precipitated at the grain boundary to reduce the plasticity of the grain boundary. Since there is a high welding residual stress in welded joints, cavities are formed under the action of the stress at a high temperature, and the merging of cavities causes the formation of cracks which extend along the grain boundary.
Factor Analysis and Regression Prediction Model of Swimmers’ Performance Structure Based on Mixed Genetic Neural Network
Authors: Yuan Rui , Han Yuexing
Published: May 2022

With the development of economy, people put forward higher demands on material life and spiritual life, and sports have become an indispensable part of daily life. Ten km freestyle, although it has been started late, is loved by people all over the world, and it is getting more and more attention in the world of competitive sports. Through the theoretical research of 10 km freestyle, it can be seen that most of the research on this project is focused on the training, selection of materials, and how to improve the technique of crossing obstacles or pools, while the research on the performance of 10 km freestyle is limited to the analysis of the development trend of the performance and no research on the prediction of the actual performance, which to some extent restricts the scientific development of 10 km freestyle. The efficiency of a suitable learning is a factor that influences whether the training and learning of a BP neural network is stable or not. It can also directly determine the choice of weights. When the learning rate is chosen to be large, it increases the speed of learning and makes the stability of the network change. This has limited the scientific development of 10 km freestyle to a certain extent, and it is also a good entry point for the research of 10 km freestyle. BP neural network is one of the most widely used neural network models, which can be used for classification, clustering, prediction, and other related problems. In this study, we propose a comprehensive evaluation method of 10 km freestyle performance based on BP neural network and try to establish a neural network evaluation model by combining the athletes’ physiological and biochemical indexes and sports performance. In this study, the normalized index values are used as the network input, and the athletes’ performance in 10 km freestyle is used as the network output to predict the athletes’ performance in 10 km freestyle. This study provides a theoretical basis for adjusting the condition of 10 km freestyle athletes and improving their performance. Optimize the reasonable allocation of resources between coaches and athletes, and increase the opportunities for coaches and athletes to communicate and learn from outside.
Effect of Preventive Analgesia with Nalbuphine and Dexmedetomidine in Endoscopic Sinus Surgery
Authors: Yu Pan , Zhang Jie , Zou Yan , Wang Jun
Published: May 2022

Background. The study was to assess the efficacy and safety of nalbuphine combined with dexmedetomidine for preventive analgesia in endoscopic sinus surgery. Methods. 110 patients with deviation of the nasal septum were randomized into the nalbuphine group (group N), dexmedetomidine combined with nalbuphine group (group DN), and saline group (group C). Fifteen minutes before the induction of anesthesia, patients in group N were injected nalbuphine 0.2 mg/kg intravenously; patients in group DN received intravenous infusion of dexmedetomidine 0.5 μg/kg and injection of nalbuphine 0.2 mg/kg; patients in group C received 0.9% saline. Mean arterial pressure (MAP), heart rate (HR), numerical rating scale (NRS) scores, quality of recovery-40 (QoR-40) scores, the need for remedial analgesia, the consumption of remifentanil and propofol, and the incidence of adverse reactions were recorded. Results. MAP, HR, and NRS scores of the DN group were significantly lower and the QoR-40 scores were higher than those of groups N and C (

P
<
0.001

). The need for remedial analgesia, the consumption of remifentanil and propofol, and the incidence of nausea in the DN group were the lowest among the three groups (

P
<
0.001

). Conclusion. Preventive analgesia with nalbuphine and dexmedetomidine in endoscopic sinus surgery can not only maintain hemodynamic stability but also reduce intraoperative anesthetic dosage, postoperative pain, and improve the quality of postoperative recovery without affecting the revival and extubation time.
Comparison of Two Waves of COVID-19 in Critically Ill Patients: A Retrospective Observational Study
Authors: Jana Kundan R. , Yap Ernie , Janga Kalyana C. , Greenberg Sheldon
Published: May 2022

Background. The SARS-CoV-2 virus caused the global COVID-19 pandemic, with waxing and waning course. This study was conducted to compare outcomes in the first two waves, in mechanically ventilated patients. Methods. This retrospective observational study included all mechanically ventilated COVID-19 patients above 18 years of age, between March 2020 and January 2021. Patients were grouped into first wave from March 2020 to July 2020, and second wave from August 2020 to January 2021. Outcome measures were mortality, the development of acute kidney injury (AKI), and need for renal replacement therapy (RRT). Univariate and multivariate cox regression analysis were used to delineate risk factors for the outcome measures. Results. A total of 426 patients, 285 in the first wave and 185 in the second wave, were included. The incidence of AKI was significantly lower in the second wave (72% vs. 63%;

p
=
0.04

). There was no significant difference in mortality (70% vs. 63%;

p
=
0.16

) and need for RRT (36% vs. 30%;

p
=
0.1

). Risk factors for mortality were increasing age and AKI in both waves, and chronic kidney disease (CKD) (adj. HR 1.7; 95% CI 1.02–2.68;

p
=
0.04

) in the second wave. Risk factors for AKI were CKD in both the waves, while it was diabetes (adj. HR 1.4; 95% CI 1.02–1.95;

p
=
0.04

) and increasing age in the first wave. Remdesivir (adj. HR 0.5; 95% CI 0.3–0.7;

p
<
0.01

) decreased the risk of AKI, and convalescent plasma (adj. HR 0.5; 95% CI 0.3–0.9;

p
=
0.02

) decreased the risk of mortality in the first wave, however, such benefit was not observed in the second wave. Conclusions. Our study shows a decrease in the incidence of AKI in critically ill patients, however, the reason for this decrease is still unknown. Studies comparing the waves of the pandemic would not only help in understanding disease evolution but also to develop tailored management strategies.
The eIF4A Inhibitor Silvestrol Blocks the Growth of Human Glioblastoma Cells by Inhibiting AKT/mTOR and ERK1/2 Signaling Pathway
Authors: Zhang Wei , Gong Pian , Tian Qi , Han Shoumeng , Wang Jianfeng , He Peibang , Guo Yujia , Wang Guijun , Chen Qianxue , Huang Jie , Li Mingchang
Published: May 2022

The most frequently identified central nervous system tumor in adults is glioblastoma multiforme (GBM). GBM prognosis remains poor despite multimodal treatment, i.e., surgery and radiation therapy with concurrent temozolomide-based chemotherapy. Silvestrol, an eIF4A inhibitor, has been demonstrated to be able to kill tumor cells in previous studies. In this study, it was found that silvestrol considerably attenuated the proliferative potential of U251 and U87 glioma cells and reduced expression of cyclin D1. In addition, silvestrol reduced the level of ERK1/2 and decreased the levels of AKT phosphorylation. Unfortunately, the effect of silvestrol in inhibiting GBM cells was greatly reduced with hypoxia, and the downregulation in AKT/mTOR and ERK1/2 were also rescued with an upregulation of HIF1α, which warranted further research. Taken together, silvestrol exerted antitumor effects in GBM cells by inhibiting the AKT/mTOR and ERK1/2 signaling cascades.
Impact of the Validity Analysis Model and Multirelational Data Clustering Based on the Trust Probability
Authors: Zhang Zhongzhen , Talha Muhammad
Published: May 2022

In view of the present poor impact of multi relational data clustering, a multirelational data clustering effectiveness analysis model based on trusted probability is created. The construction of the multirelational data clustering trusted probability evaluation model, standardization of the multirelational data clustering trusted probability evaluation index, optimization of the multirelational data clustering trusted probability analysis process, and data clustering processing quality and finally, investigations show that the validity analysis model of multirelational data clustering based on credible probability is more effective in practice and satisfies the research goals entirely.
A Deep Learning Filter that Blocks Phishing Campaigns Using Intelligent English Text Recognition Methods
Authors: Tang Yonghui , Wu Fei
Published: May 2022

Most of the sophisticated attacks in the modern age of cybercrime are based, among other things, on specialized phishing campaigns. A challenge in identifying phishing campaigns is defining a classification of patterns that can be generalized and used in different areas and campaigns of a different nature. Although efforts have been made to establish a general labeling scheme in their classification, there is still limited data labeled in such a format. The usual approaches are based on feature engineering to correctly identify phishing campaigns, exporting lexical, syntactic, and semantic features, e.g., previous phrases. In this context, the most recent approaches have taken advantage of modern neural network architectures to record hidden information at the phrase and text levels, e.g., Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs). However, these models lose semantic information related to the specific problem, resulting in a variation in their performance, depending on the different data sets and the corresponding standards used for labeling. In this paper, we propose to extend word embeddings with word vectors that indicate the semantic similarity of each word with each phishing campaigns template tag. These embedded keywords are calculated based on semantic subfields corresponding to each phishing campaign tag, constructed based on the automatic extraction of keywords representing these tags. Combining general word integrations with vectors is calculated based on word similarity using a set of sequential Kalman filters, which can then power any neural architecture such as LSTM or CNN to predict each phishing campaign. Our experiments use a data indicator to evaluate our approach and achieve remarkable results that reinforce the state-of-the-art.
Prediction and Analysis of the Physical Test Scores Based on BP Neural Network and Principal Component Analysis Algorithm
Authors: Qu Jiale
Published: May 2022

College students’ physical fitness test can comprehensively evaluate students’ physical health level from many aspects such as students’ body shape, quality, function, and sports ability. Through the analysis and prediction of college students’ physical fitness test results, it can be used to understand the actual physical quality of college students, formulate the evaluation standard of students’ physical health level, and assist physical education teachers to formulate reasonable teaching plans. For students with different physical quality, different training plans can be formulated to improve their actual physical quality. Aiming at this background, this paper puts forward a method based on BP neural network and principal component analysis algorithm of college students test score prediction algorithm, the BP neural network, and principal component analysis of machine learning algorithm successfully applied to physical test comprehensive performance prediction, implements the college students test transcript accurate prediction, and can effectively assist PE teachers to develop reasonable teaching plan. For students with different physical quality, different training plans can be formulated to improve their actual physical quality.
Serum Scavenging Capacity and Folliculogenesis Impact following Flaxseed Consumption in the First-Generation Mice Pups
Authors: Pourjafari Fahimeh , Haghpanah Tahereh , Grazia Palmerini Maria , Ezzatabadipour Massood
Published: May 2022

Flaxseed is a source of antioxidants utilized for female infertility treatment in traditional medicine. This study investigated the effects of flax hydroalcoholic extract and flaxseeds during prenatal and postnatal (PND) periods on folliculogenesis and serum total antioxidant capacity (TAC). Pregnant NMRI mice received 500 and 1000 mg/kg of flax extract (LE) and the same doses of flaxseed (LS). Female pups received the same regimen for 56 days. The body, ovarian morphometry, follicle development, and TAC levels were evaluated. The ovarian weight significantly increased in the LE1000 group compared to the LS500 group. The LE500 group had a considerably lower number of primary and antral follicles compared to the CTL and LS1000 groups. The number of antral follicles significantly increased in the LE1000 group compared to the LS500 and LE500 groups. The number of preovulatory follicles was higher in the LE1000 group. A significant increase in the TAC levels was detected in the LS500, LS1000, and LE1000 groups. LE showed a dose-dependent protective effect on the folliculogenesis in F1, which is more evident with the dosage of 1000 mg/kg. This could be related to the strongest antioxidant property of LE1000, as shown by the highest levels of TAC.
Fresh, Mechanical, and Microstructural Properties Investigation on the Combined Effect of Biomedical Waste Incinerator Ash and Bagasse Ash for High-Strength Concrete
Authors: Girma Menker , Asteray Belachew
Published: May 2022

The combination effect of supplementary cementitious materials in the production of high-strength concrete production is an effective way to reduce the amount of cement required while contributing to environmental sustainability and cost. This study aims to assess the microstructural investigation on the combined effect of biomedical waste incinerator ash (BWIA) and bagasse ash (BA) as a partial replacement of cement in high-strength concrete production. The cement was partially replaced with BA (0%, 2.5%, 5%, and 7.5%) and BWIA (0%, 2.5%, 5%, and 7.5%). The mix design was done as per the ACI 211-4R-93 mix design standard. Slump, slump flowability, density, and compaction factor tests were conducted for freshly mixed concrete. Mechanical properties of the hardened concrete from four different mixes were also determined for the 7- and 28-day cured specimens. The microstructural properties of the hardened concrete for all mixes were also investigated using scanning electron microscopy (SEM) and X-ray diffraction (XRD) tests. Based on the experimental results, the compressive strength of high-strength concrete at every 2.5% BA and 2.5% BWIA replacement of cement in the concrete mix at 7 days of curing was slightly decreased, while at 28 days of curing, the compressive strength of the control mix (51.8 MPa) decreased as compared to the mix codes' compressive strength of the BWIA and BA5 and BWIA and BA10 mix codes, at 54.8 MPa and 52.5 MPa, respectively. The SEM micrographs showed that the partial replacement of cement by the BWIA and BA leads to a decrease in the pore proportion in the enlarged interfacial transition zone (ITZ), reduced CH crystals, and a denser C–S–H gel as compared to the control specimen. The XRD pattern showed the existence of portlandite, ettringite, okenite, quartz, and calcite in the cement and aggregate phases. As a result, the usage of BWIA and BA has a significant impact on the properties of high-strength concrete in fresh, hardened, and microstructure high-strength concrete.
A Direct Reinforcement Learning Approach for Nonautonomous Thermoacoustic Generator
Authors: Do Trong Tan , Tran Duc Chuyen , Le Duy Tung , Dao Phuong Nam
Published: May 2022

For nonautonomous nonlinear systems, the optimal control design is affected by the terms of partial derivative. If a reinforcement learning (RL) strategy is developed to approximate the optimal control scheme in nonautonomous nonlinear systems, then the closed control system might be unstabilizing. Therefore, in this article, the approach of direct RL law for a nonautonomous thermoacoustic generator (TAG) is investigated. We establish the mathematical model of TAG by partial differential equations (PDEs) and then transforming them into time varying nonlinear systems. The direct RL technique with Newton–Leibniz formula is implemented to consider the partial derivative term from classical policy iteration (PI) method by modifying the computation using data collection between the two sampling times. Finally, several simulation studies with some comparisons are conducted to validate the theoretical analyses.
Performance Analysis of Product Channel for Relaying-Assisted Edge Computing in IoT Networks
Authors: Zhu Fusheng , Chen Liming , Zhou Wen , Deng Dan , Rao Yanyi , Tang Yajuan , Liu Jun , Zhang Yuwei , Wang Jing , Cui Tao
Published: May 2022

In this paper, we analyze the typical product channel which is often encountered in wireless relaying channels, for relaying-assisted edge computing in Internet of Things (IoT) networks. Such analysis is of vital importance, as it is often encountered in wireless transmission. Specifically, we firstly derive a closed-form expression of the transmission outage probability in product channels, through solving involved complicated multivariate integral. We then simplify the expression through some approximation to the involved Bessel function, which can help obtain some meaningful findings to the system design. We finally provide some numerical results to verify that the presented analysis on the production channels is effective.
Analyzing the Role of Emotional Intelligence on the Performance of Small and Medium Enterprises (SMEs) Using AI-Based Convolutional Neural Networks (CNNs)
Authors: Serbaya Suhail H.
Published: May 2022

Human emotion detection is necessary for social interaction and plays an important role in our daily lives. Artificial intelligence research is rising, focusing on automated emotion detection. The capability to identify the emotion, which is considered one of the traits of emotional intelligence, is a component of human intelligence. Although the study is limited dependent on facial expressions or voice is flourishing, it is identifying emotions via body movements, a less researched issue. To attain emotional intelligence, this study suggests a deep learning approach. Here initially the video can be converted into image frames after the converted image frames can be preprocessed using the Glitter bandpass butter worth filter and contrast stretch histogram equalization. Then from the enhanced image, the features can be clustered using the hybrid Gaussian BIRCH algorithm. Then the specialized features are retrieved from the body of human gestures using the AdaDelta bacteria foraging optimization algorithm, and the selected features are fed to a supervised Kernel Boosting LENET deep-learning algorithm. The experiment is conducted using Geneva multimodal emotion portrayals (GEMEPs) corpus data set. This data set includes, human body gestures portraying the archetypes of five emotions, such as anger, fear, joy, pride, and sad. In these emotion detection techniques, the suggested Kernel Boosting LENET classifier achieves 98.5% accuracy, 94% precision, 95% sensitivity, and F-Score 93% outperformed better than the other existing classifiers. As a result, emotional acknowledgment may help small and medium enterprises (SMEs) to improve their performance and entrepreneurial orientation. The correlation coefficient of 188 and the significance coefficient of 0.00 show that emotional intelligence and SMEs performance have a significant and positive association.
Optimal Deep Transfer Learning-Based Human-Centric Biomedical Diagnosis for Acute Lymphoblastic Leukemia Detection
Authors: Hamza Manar Ahmed , Albraikan Amani Abdulrahman , Alzahrani Jaber S. , Dhahbi Sami , Al-Turaiki Isra , Al Duhayyim Mesfer , Yaseen Ishfaq , Eldesouki Mohamed I.
Published: May 2022

Human-centric biomedical diagnosis (HCBD) becomes a hot research topic in the healthcare sector, which assists physicians in the disease diagnosis and decision-making process. Leukemia is a pathology that affects younger people and adults, instigating early death and a number of other symptoms. Computer-aided detection models are found to be useful for reducing the probability of recommending unsuitable treatments and helping physicians in the disease detection process. Besides, the rapid development of deep learning (DL) models assists in the detection and classification of medical-imaging-related problems. Since the training of DL models necessitates massive datasets, transfer learning models can be employed for image feature extraction. In this view, this study develops an optimal deep transfer learning-based human-centric biomedical diagnosis model for acute lymphoblastic detection (ODLHBD-ALLD). The presented ODLHBD-ALLD model mainly intends to detect and classify acute lymphoblastic leukemia using blood smear images. To accomplish this, the ODLHBD-ALLD model involves the Gabor filtering (GF) technique as a noise removal step. In addition, it makes use of a modified fuzzy c-means (MFCM) based segmentation approach for segmenting the images. Besides, the competitive swarm optimization (CSO) algorithm with the EfficientNetB0 model is utilized as a feature extractor. Lastly, the attention-based long-short term memory (ABiLSTM) model is employed for the proper identification of class labels. For investigating the enhanced performance of the ODLHBD-ALLD approach, a wide range of simulations were executed on open access dataset. The comparative analysis reported the betterment of the ODLHBD-ALLD model over the other existing approaches.
Mechanical Behavior of Plastic Strips‐Reinforced Expansive Soils Stabilized with Waste Marble Dust
Authors: Amena Shelema , Kabeta Worku Firomsa
Published: May 2022

Expansive soil needs to undergo treatment to be used as safe foundation soil for roads and buildings. From an environmental conservation and economical point of view, the usage of agricultural and industrial wastes is the best option. In this study, the effects of utilizing plastic waste and marble waste dust on the engineering properties of expansive soils were examined. Various laboratory tests were carried out on sampled expansive soil by adding 10, 15, and 20% of marble and 0.25, 0.5, and 0.75% of 5 × 8 mm2 plastic strips. The laboratory test results showed that there are good enhancements on strength parameters due to the addition of marble dust and plastic strips. With an increase in percentages of marble dust and plastic strips, California Bearing Ratio (CBR) values rise. With the addition of marble dust, unconfined compressive strength (UCS) values increase linearly, while they increase only up to 0.5% with the addition of plastic strips. As the proportions of marble dust and plastic strips increase, the soil’s free swell and CBR swell are decreased significantly. This shows that environmental pollution waste marble dust and plastic strips can be utilized to strengthen the weak subgrade soil and minimize its swelling properties. Therefore, this study found out that the expansive soil treated with polyethylene terephthalate (PET) plastic and marble dust can be used as a subgrade material since it fulfills the minimum requirement needed by standards.
False Positive Hemianopia
Authors: Ben-Avi Ravid , Nahum Addy , Kruger Joshua M.
Published: May 2022

A 70-year-old woman was urgently referred for neuro-ophthalmic evaluation when a routine visual field test demonstrated a pattern of bilateral nasal hemianopia. Detailed inspection of the visual field study revealed the hemianopias to be artifactual for the following reasons: (1) it was performed with an excessive number of false positive responses; (2) the grey scale plot had white patches, consistent with abnormally high sensitivity; (3) the total deviation probability maps were normal, indicating that no tested points had poor scores. Confrontational visual field testing was normal in all zones for both eyes. Repeated visual field testing showed no evidence of a true hemianopia. Even automated visual fields with highly specific abnormalities can merely be testing artifact. Scrutinize all components of the report before determining the clinical implications.