bluuebunny/crossref_metadata_2025 huggingface dataset:
Benchmarking and Configuring Security Levels in Intermittent Computing
Authors: Krishnan Archanaa S. , Schaumont Patrick
Published: March 2022

Intermittent computing derives its name from the intermittent character of the power source used to drive the computing, typically an energy harvester of ambient energy sources. Intermittent computing is characterized by frequent transitions between the powered and the non-powered state. To enable the processor to quickly recover from unexpected power loss, regular checkpoints store the run-time state of the program, including variables, control information, and machine state. In sensitive applications such as logged measurements, checkpoints must be secured against tamper and replay. We investigate the overhead of creating, securing, and restoring checkpoints with respect to the application. We propose a configurable checkpoint security setting that leverages application properties to reduce overhead of checkpoint security and implement the same using a secure checkpointing protocol. We discuss a prototype implementation for a FRAM-based micro-controller, and we characterize the cost of adding and configuring security to traditional checkpointing using a suite of embedded benchmark applications.
Hardware-friendly User-specific Machine Learning for Edge Devices
Authors: Goyal Vidushi , Das Reetuparna , Bertacco Valeria
Published: March 2022

Machine learning (ML)
on resource-constrained edge devices is expensive and often requires offloading computation to the cloud, which may compromise the privacy of user data. In contrast, the type of data processed at edge devices is user-specific and limited to a few inference classes. In this work, we explore building smaller, user-specific machine learning models, rather than utilizing a generic, compute-intensive machine learning model that caters to a diverse range of users. We first present a hardware-friendly, lightweight pruning technique to create user-specific models directly on mobile platforms, while simultaneously executing inferences. The proposed technique leverages compute sharing between pruning and inference, customizes the backward pass of training, and chooses a pruning granularity for efficient processing on edge. We then propose architectural support to prune user-specific models on a systolic edge ML inference accelerator. We demonstrate that user-specific models provide a speedup of 2.9× and 2.3× on the mobile CPUs for the ResNet-50 and Inception-V3 models.
StyleFusion: Disentangling Spatial Segments in StyleGAN-Generated Images
Authors: Kafri Omer , Patashnik Or , Alaluf Yuval , Cohen-Or Daniel
Published: April 2022

We present
StyleFusion
, a new mapping architecture for StyleGAN, which takes as input a number of latent codes and fuses them into a single style code. Inserting the resulting style code into a pre-trained StyleGAN generator results in a single
harmonized
image in which each semantic region is controlled by one of the input latent codes. Effectively, StyleFusion yields a disentangled representation of the image, providing fine-grained control over each region of the generated image. Moreover, to help facilitate global control over the generated image, a special input latent code is incorporated into the fused representation. StyleFusion operates in a hierarchical manner, where each level is tasked with learning to disentangle a pair of image regions (e.g., the car body and wheels). The resulting learned disentanglement allows one to modify both local, fine-grained semantics (e.g., facial features) as well as more global features (e.g., pose and background), providing improved flexibility in the synthesis process. As a natural extension, StyleFusion allows one to perform semantically-aware cross-image mixing of regions that are not necessarily aligned. Finally, we demonstrate how StyleFusion can be paired with existing editing techniques to more faithfully constrain the edit to the user’s region of interest. Code is available at:
https://github.com/OmerKafri/StyleFusion
.
MaPHeA: A Framework for Lightweight Memory Hierarchy-aware Profile-guided Heap Allocation
Authors: Oh Deok-Jae , Moon Yaebin , Ham Do Kyu , Ham Tae Jun , Park Yongjun , Lee Jae W. , Ahn Jung Ho , Lee Eojin
Published: March 2022

Hardware performance monitoring units (PMUs) are a standard feature in modern microprocessors, providing a rich set of microarchitectural event samplers. Recently, numerous profile-guided optimization (PGO) frameworks have exploited them to feature much lower profiling overhead compared to conventional instrumentation-based frameworks. However, existing PGO frameworks mainly focus on optimizing the layout of binaries; they overlook rich information provided by the PMU about data access behaviors over the memory hierarchy. Thus, we propose MaPHeA, a lightweight

M

emory hierarchy-

a

ware

P

rofile-guided

He

ap

A

llocation framework applicable to both HPC and embedded systems. MaPHeA guides and applies the optimized allocation of dynamically allocated heap objects with very low profiling overhead and without additional user intervention to improve application performance. To demonstrate the effectiveness of MaPHeA, we apply it to optimizing heap object allocation in an emerging DRAM-NVM heterogeneous memory system (HMS), selective huge-page utilization, and controlling the cacheability of the objects with the low temporal locality. In an HMS, by identifying and placing frequently accessed heap objects to the fast DRAM region, MaPHeA improves the performance of memory-intensive graph-processing and Redis workloads by 56.0% on average over the default configuration that uses DRAM as a hardware-managed cache of slow NVM. By identifying large heap objects that cause frequent TLB misses and allocating them to huge pages, MaPHeA increases the performance of the read and update operations of Redis by 10.6% over the transparent huge-page implementation of Linux. Also, by distinguishing the objects that cause cache pollution due to their low temporal locality and applying write-combining to them, MaPHeA improves the performance of STREAM and RADIX workloads by 20.0% on average over the system without cacheability control.
Generalized Euclidean Measure to Estimate Distances on Multilayer Networks
Authors: Coscia Michele
Published: April 2022

Estimating the distance covered by a spreading event on a network can lead to a better understanding of epidemics, economic growth, and human behavior. There are many methods solving this problem—which has been called Node Vector Distance (NVD)—for single layer networks. However, many phenomena are better represented by multilayer networks: networks in which nodes can connect in qualitatively different ways. In this article, we extend the literature by proposing an algorithm solving NVD for multilayer networks. We do so by adapting the Mahalanobis distance, incorporating the graph’s topology via the pseudoinverse of its Laplacian. Since this is a proper generalization of the Euclidean distance in a complex space defined by the topology of the graph, and that it works on multilayer networks, we call our measure the Multi Layer Generalized Euclidean (MLGE). In our experiments, we show that MLGE is intuitive, theoretically simpler than the alternatives, performs well in recovering infection parameters, and it is useful in specific case studies. MLGE requires solving a special case of the effective resistance on the graph, which has a high time complexity. However, this needs to be done only once per network. In the experiments, we show that MLGE can cache its most computationally heavy parts, allowing it to solve hundreds of NVD problems on the same network with little to no additional runtime. MLGE is provided as a free open source tool, along with the data and the code necessary to replicate our results.
Establishing the Medical Actionability of Genomic Variants
Authors: Goddard Katrina A.B. , Lee Kristy , Buchanan Adam H. , Powell Bradford C. , Hunter Jessica Ezzell
Published: April 2022

Actionability is an important concept in medicine that does not have a well-accepted standard definition, nor is there a general consensus on how to establish it. Medical actionability is often conflated with clinical utility, a related but distinct concept. This lack of clarity contributes to practice variation and inconsistent coverage decisions in genomic medicine, leading to the potential for systematic bias in the use of evidence-based interventions. We clarify how medical actionability and clinical utility are distinct and then discuss the spectrum of actionability, including benefits for the person, the family, and society. We also describe applications across the life course, including prediction, diagnosis, and treatment. Current challenges in assessing the medical actionability of identified genomic variants include gaps in the evidence, limited contexts with practice guidelines, and subjective aspects of medical actionability. A standardized and authoritative assessment of medical actionability is critical to implementing genomic medicine in a fashion that improves population health outcomes and reduces health disparities.
Global Change and Emerging Infectious Diseases
Authors: Nova Nicole , Athni Tejas S. , Childs Marissa L. , Mandle Lisa , Mordecai Erin A.
Published: April 2022

Our world is undergoing rapid planetary changes driven by human activities, often mediated by economic incentives and resource management, affecting all life on Earth. Concurrently, many infectious diseases have recently emerged or spread into new populations. Mounting evidence suggests that global change—including climate change, land-use change, urbanization, and global movement of individuals, species, and goods—may be accelerating disease emergence by reshaping ecological systems in concert with socioeconomic factors. Here, we review insights, approaches, and mechanisms by which global change drives disease emergence from a disease ecology perspective. We aim to spur more interdisciplinary collaboration with economists and identification of more effective and sustainable interventions to prevent disease emergence. While almost all infectious diseases change in response to global change, the mechanisms and directions of these effects are system specific, requiring new, integrated approaches to disease control that recognize linkages between environmental and economic sustainability and human and planetary health.
Sarcosine Biosensor Based on Pt/g-C3N4 Nanocomposites with High Electrocatalytic Activity
Authors: Feng Junyuan , Chen Xiaohui , Shi Xinlin , Zheng Weidong , Zhang Xiaofang , Yang Haipeng
Published: March 2022

Sarcosine has been shown to be a new biomarker for prostate cancer that is superior to prostate-specific antigen (PSA). By detecting the concentration of sarcosine in the human body, accurate screening and diagnosis of prostate cancer can be achieved in clinical practice. However, the sarcosine level is extremely low in human serum or urine, and accurate detection of sarcosine concentration is difficult. More work is needed to prepare sarcosine biosensors with good anti-interference performance. In this work, we prepared a sarcosine biosensor using a highly electrocatalytically active nanocomposite comprising platinum/carbon nitride (Pt/g-C3N4) as the electrode modification material. The porous lamellar graphite phase carbon nitride (g-C3N4) prevents the agglomeration of Pt nanoparticles and keeps the Pt nanoparticles in a highly dispersed state. The constructed sarcosine biosensor achieves high sensitivity and can selectively detect low concentrations of sarcosine. Its linear detection range is 2–70 μM, and the limit of detection (LOD) is 0.8 μM (S/N = 3). The prepared sarcosine biosensor shows relatively good performance in terms of resistance to interference caused by electroactive substances and provides the potential to be applied in serum for accurate detection of sarcosine.
Photoresist Removal Using Reactive Oxygen Species Produced by an Atmospheric Pressure Plasma Reactor
Authors: Jang Hyo Jun , Kim Jae Young , Jung Eun Young , Choi Muhan , Tae Heung-Sik
Published: March 2022

An atmospheric pressure (AP) plasma reactor driven by kHz sinusoidal power is developed to investigate effective removal of the photoresist (PR). This reactor can generate oxygen (O) and hydroxyl (OH) radicals simultaneously by introducing small quantities of oxygen (O2) gas and hydrogen peroxide (H2O2) vapor along with the discharge gas. To investigate the effects of O and OH radicals on AP-plasma etching, Helium/O2, helium/O2/H2O2, argon/O2 and argon/O2/H2O2 plasmas are generated and tested. The PR layer with a thickness of 1.6 μm is completely removed within 20 s using AP helium/O2 plasma, and the PR is confirmed to chemically disappear by Fourier transform infrared (FT-IR) measurement. In this case, the etch rate is calculated as several tens of nm/s. When AP argon/O2 plasma is used, the PR etch rate is lowered to several nm/s, and long-duration plasma treatment for PR removal damaged the silicon substrate without complete PR removal.
Effects of postactivation potentiation on mechanical output and muscle architecture during electrically induced contractions in plantar flexors
Authors: Bourgeois Hans , Duchateau Jacques , Baudry Stéphane
Published: March 2022

Postactivation potentiation (PAP), a form of activity-dependent potentiation, is defined as the acute enhancement of muscle contractile properties induced by a conditioning contraction, usually a maximal voluntary contraction. The purpose of our study was to investigate the effects of PAP on force production and muscle architecture in plantar flexor (PF) muscles. The results indicate that the extent of PAP decreased from the twitch to the triplet in PF muscles. Furthermore, this study shows that fascicle shortening and the increase in pennation angle were greater after the maximal voluntary contraction for the twitch, the doublet, and the triplet, with more pronounced effects for the twitch.
Study on Closely Related Citrus CMMs based on Chemometrics and Prediction of Components-Targets-Diseases Network by Ingenuity Pathway Analysis
Authors: Mu Qixuan , Zhang Yaping , Cui Ying , Chai Xin , Liu Junlian , Li Yongzhi , Yu Huijuan , Wang Yuefei
Published: March 2022

As the representatives of closely related Chinese medicinal materials (CMMs) originated from Rutaceae family, Aurantii fructus immaturus (AFI), Aurantii fructus (AF), Citri reticulatae pericarpium viride (CRPV), and Citri reticulatae pericarpium (CRP) have better functions in regulating qi and promoting gastrointestinal motility. However, differences in the quality of closely related Citrus CMMs have not yet been revealed until now. Herein, this study focused on the systematic differentiation and in-depth understanding of closely related Citrus CMMs by a strategy integrating chemometrics and network pharmacology. Determined by ultra performance liquid chromatography, the content of nine flavonoids showed obvious fluctuations in the decoction pieces from different species (Citrus aurantium Linnaeus and Citrus reticulate Blanco) with decreasing levels in the samples of ripe fruits. Decoction pieces from the different species and ripening stages were well distinguished by orthogonal projection to latent structure-discriminate analysis (OPLS-DA) and cluster analysis. As a result, four active components including narirutin, naringenin, hesperidin, and 3,5,6,7,8,3′,4′-heptemthoxyflavone were filtered out by variable importance for the projection (VIP) value (VIP > 1.0), which were regarded as chemotaxonomic markers. Furthermore, a components-targets-diseases network was constructed via ingenuity pathway analysis (IPA), and the correlations were predicted between four chemotaxonomic markers, 223 targets, and three diseases including colitis, breast cancer, and colorectal cancer. The obtained results will be of great significance for identifying closely related Citrus CMMs and conduce to improving the resource utilization of CMMs.
Perioperative Low-Dose Ketamine for Postoperative Pain Management in Spine Surgery: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Authors: Zhou Lijin , Yang Honghao , Hai Yong , Cheng Yunzhong
Published: March 2022

Objective. Although low-dose ketamine has been shown to be generally beneficial in terms of pain control in a variety of major surgery, there is no consensus regarding the effectiveness of supplemental ketamine analgesic use exclusively in spine surgery. The objective of this systematic review and meta-analysis of randomized controlled trials (RCTs) was to assess the efficacy and safety of perioperative low-dose ketamine for pain management and analgesic consumption in patients undergoing spine surgery. Methods. A comprehensive literature search was performed for relevant studies using PubMed, EMBASE, Web of Science, and Cochrane Library. Patients who received perioperative low-dose ketamine were compared to the control group in terms of postoperative pain intensity, opioid consumption, and adverse events. Patients were further categorized by ages and administration times for subgroup analysis. Results. A total of 30 RCTs comprising 1,865 patients undergoing elective spine surgery were included. Significantly lower pain intensity and less opioid consumption at 12 h, 24 h, and 48 h postoperatively and lower incidence of postoperative nausea and vomiting (PONV) were observed in the ketamine group (all

P
<
0.05

). There was no significant difference of central nervous system (CNS) adverse events between groups. However, different efficacy of low-dose ketamine was detected when patients were categorized by ages and administration times. Conclusion. Perioperative low-dose ketamine demonstrated analgesic and morphine-sparing effect with no increased adverse events after spine surgery. However, this effect was not significant in pediatric patients. Only postoperative or intraoperative and postoperative administration could prolong the analgesic time up to 48 h postoperatively. Further studies should focus on the optimal protocol of ketamine administration and its effect on old age participants.
The Order Financing Risk Evaluation under the Financial Supply Chain Model Based on the Logistic Model
Authors: Song Xiaoli , Wu Xian , Liu Chang , Yang Huanhuan
Published: March 2022

Order financing, as an innovative business in the financial supply chain model of banks and other financial institutions, has served more and more small and medium‐sized enterprises in recent years, and has also brought new profit points to the financial and logistics industries. Businesses urgently need to carry out relevant risk evaluation research on the risks brought by banks and other financial institutions. Based on this background, this article has carried out relevant research on the construction and evaluation method of the risk evaluation index system of order financing business. Through project research and literature review, the research on the risk evaluation theory and method of order financing carried out by banks and other financial institutions is not perfect. The role‐played in order financing and the key risk variables faced by financial institutions, and innovatively proposed an evaluation method based on the logistics model. Based on the analysis of order financing risk factors, and through literature review and expert interviews, an evaluation index system initially constructed from five aspects: financing companies, downstream core companies, logistics companies, supply chain operations and external environment. Through questionnaire surveys and statistical analysis of the survey results, a complete, qualitied the risk evaluation index system for order financing business was finally determined. Taking the order‐financing project jointly carried out by commercial banks and logistics companies as an example.
Applying Optimized ANN Models to Estimate Dew Point Pressure of Gas Condensates
Authors: Han Luo , Sarvazizi Saeed
Published: April 2022

It is economically and technically essential to promptly and accurately estimate the dew point pressure (DPP) of gas condensate to, for example, characterize fluids, evaluate the performance of reservoirs, plan and develop reservoirs for gas condensates, and design/optimize a production system. Indeed, it is difficult to experimentally explore the DPP. Furthermore, experimental tests are time-consuming and complicated. Therefore, it is required to develop an accurate, reliable DPP estimation framework. This paper introduces artificial neural network (ANN) models coupled with optimization algorithms, including a genetic algorithm (GA) and particle swarm optimization (PSO), for DPP estimation. A total of 721 data points were employed to train and test the algorithm. In addition, the outlier data were identified and excluded. The root-mean-squared error (RMSE) and the coefficient of determination (R2) were calculated to be 230.42 and 0.982 for the PSO-ANN model and 0.0022 and 0.997 for the GA-ANN model, respectively. The model estimates were found to be in good agreement with the experimental dataset. Therefore, it can be said that the proposed method is efficient and effective.
Rock Dynamic Fracture Characteristics by Mini‐Linear Shaped Charge Jet Penetration: A Case Study
Authors: Xu Zhenyang , Liu Xin , Mo Hongyi , Guo Lianjun , Yang Jun
Published: March 2022

An insignificant number of studies have focused on employing penetration using mini‐linear shaped charge jets for directionally controlled splitting of massive rocks. In this study, we adopt a numerical calculation method to simulate the penetration and formation of the main splitting surface of a concrete specimen, considering a wedge angle of 45° in both processes. The surface on the principal axial plane is found to split first due to linear jet penetration. In the case of single primary‐plane splitting, cracks appear at both ends of the long axis of the penetration crack and the splitting surface extends diagonally from the center of the penetration. A transverse crack separates the splitting surface and the radial‐fracture surface, and the degree of fracture decreases along the direction of the height of the specimen. Finally, a realistic physical model demonstrating penetration using a mini‐linearshaped charge jet is established. It is a rapid and safe blasting technology to handle hazardous massive rocks during emergency rescuing or mining.
A Data Symmetry Algorithm-Based Security Awareness Model for Emergency Wireless Communication under Multisensor Fusion
Authors: Liang Ye , Gao Nan
Published: April 2022

Due to the characteristics of sensor networks, wireless sensor networks (WSN) have a series of security threats in their applications. For example, wireless sensor networks are vulnerable to attacks such as eavesdropping, data tampering, and jamming. The processing capacity of sensor nodes is limited, and its integrity and confidentiality are greatly threatened. Nodes are easily captured or counterfeited; control of the energy of nodes; encryption and decryption of data depends on the distribution and management of keys; how to ensure the legal networking and access of nodes, etc. With the deepening and gradual promotion of WSN research, these security threats have become an urgent problem to be solved. In the context of multisensor fusion, this research realized the construction of a security perception model of emergency wireless communication based on the Bicycle data symmetric encryption algorithm and the use of secret sharing technology. The 16 bit Bicycle encryption and decryption time is about 1/40 of the encryption time, and the 32 bit Bicycle decryption time is about 1/30 of the encryption time, 16 bit encryption is slower than 32 bit encryption and faster decryption.
Enhancing the Mechanical Behaviour and Antibacterial Activity of Bioepoxy Using Hybrid Nanoparticles for Dental Applications
Authors: Mohammed Mohammed Razzaq , Hadi Ahmed Namah
Published: March 2022

The appropriate capability of handling several forces exerted inside the mouth, and preventing the adhesion and proliferation of oral microorganisms are among the most vital factors for achieving effective alternative dental materials to the damaged native. Nevertheless, lack of mechanical and antimicrobial properties of dental resins hinders their use in most clinical applications in dentistry. In the present study, the main aim was to provide bioepoxy composite biomaterials that could meet the required mechanical and antibacterial properties for dental related fields. Herein, highly biocompatible epoxy and hybrid reinforcing materials were utilised to produce a composite material, which could have features resembling those of original dental parts. Various weight fractions of nanosilver/nano-alumina particles at 1, 2, and 3 wt% were incorporated into the bioepoxy for improving the mechanical and antibacterial characteristics of the biocompatible epoxy resin. Three-point bending and Izod impact tests were performed to evaluate the flexure and impact strengths of the obtained nanocomposites. The morphology of pristine bioepoxy and nanoparticle reinforced bioepoxy composites was characterized by scanning electron microscopy. The influence of these fillers on the bioepoxy resin antibacterial sensitivity was assessed using the agar diffusion technique. Nanofiller contents have been revealed to have a remarkable role to play in tuning the mechanical properties of the nanocomposites; the flexure strength and modulus were higher when the total ratio of hybrid reinforcement was 2 wt%. In contrast, the addition of higher percentage of hybrid nanoparticles could cause deterioration in the flexure characteristics of nanocomposites, yet they were better than those of pristine epoxy. Regarding the impact strength, the enhancement in this property was only observed for the composite containing 1 wt% of AgNps-Al2O3; the impact strength was dropped gradually beyond this ratio. The antibacterial effectiveness of the nanocomposites was demonstrated to positively depend on the increase in AgNps mass fraction. Among all evaluated unmodified and modified bioepoxy, the nanocomposite containing 2.5 wt% of AgNps had the higher antibacterial activity against Escherichia coli and Staphylococcus aureus. Based on the attainable outcomes, the prepared composites, particularly at moderate levels of Al2O3-AgNps, could provide biocomposites having the potential to be utilised in several biomedical fields, particularly in dental technology.
New Robust Tensor PCA via Affine Transformations and

L

2,1

Norms for Exact Tubal Low-Rank Recovery from Highly Corrupted and Correlated Images in Signal Processing
Authors: Liang Peidong , Zhang Chentao , Likassa Habte Tadesse , Guo Jielong
Published: March 2022

In this latest work, the Newly Modified Robust Tensor Principal Component Analysis (New RTPCA) using affine transformation and

L

2,1

norms is proposed to remove the outliers and heavy sparse noises in signal processing. This process is done by decomposing the original data matrix as the low-rank heavy sparse noises. The determination of the potential variables is casted as constrained convex optimization problem, and the Alternating Direction Method of Multipliers (ADMM) method is considered to reduce the computational loads in an iterative manner. The simulation results validate the effectiveness of the new method as compared with that of the state-of-the-art methods.
Considerations of Autonomy in Guiding Decisions around the Feedback of Individual Genetic Research Results from Genomics Research: Expectations of and Preferences from Researchers in Botswana
Authors: Kasule Mary , Matshaba Mogomotsi , Mwaka Erisa , Wonkam Ambroise , de Vries Jantina
Published: March 2022

Background. The Human Health and Heredity (H3Africa) Consortium continues to generate large amounts of genomic data leading to new insights into health and disease among African populations. This has however generated debate among stakeholders involved in developing, implementing, and applying ethical standards and policies for the return of individual genetic research results. The key questions are about when results must, should, may, or must not be returned and by whom. This study aimed to explore the views on the feedback of individual pertinent and incidental genetic research results of researchers, ethics committee members, and policymakers in Botswana. Methods. In-depth interviews were conducted with 16 key stakeholders from academic, research institutions, and regulatory bodies in Botswana. An analysis of the coded data was done through an iterative process of analytic induction to document and interpret themes and patterns. Results. Overall, the study indicated that researchers have at least a partial obligation to return individual genetic research results to research participants. Respondents placed emphasis on the ethical principle of autonomy. They felt that it was inappropriate for researchers to make decisions about the return of results on participants’ behalf except in situations of avoiding participant self-harm or harm to society. Conclusion. Findings helped to highlight the importance of considering participants’ autonomy in the development of sustainable and credible guidelines for feedback of findings from genomics research in Botswana, which can be explained during community engagement and consent processes. Such guidelines would ultimately be used to develop policies, guide African genomics research, and promote participant autonomy, transparency, and possibly participant trust in research.
On the Janowski Starlikeness of the Coulomb Wave Functions
Authors: Noreen Saddaf , Raza Mohsan , Deniz Erhan , Malik Sarfraz Nawaz
Published: March 2022

In this article, we are interested in finding sufficient conditions on

A
,
B
,
L

, and

η

which ensure the normalized Coulomb wave function to be Janowski starlike. Sufficient conditions are also obtained for

g

L
,
η

/
z
∈
P

A
,
B

, which readily yield conditions for

g

L
,
η

to be close-to-convex.
COVID-19 Risk Prediction for Diabetic Patients Using Fuzzy Inference System and Machine Learning Approaches
Authors: Aggarwal Alok , Chakradar Madam , Bhatia Manpreet Singh , Kumar Manoj , Stephan Thompson , Gupta Sachin Kumar , Alsamhi S. H. , AL-Dois Hatem
Published: April 2022

Individuals with pre-existing diabetes seem to be vulnerable to the COVID-19 due to changes in blood sugar levels and diabetes complications. As observed globally, around 20–50% of individuals affected by coronavirus had diabetes. However, there is no recent finding that diabetic patients are more prone to contract COVID-19 than nondiabetic patients. However, a few recent findings have observed that it could be at least twice as likely to die from complications of diabetes. Considering the multifold mortality rate of COVID-19 in diabetic patients, this study proposes a COVID-19 risk prediction model for diabetic patients using a fuzzy inference system and machine learning approaches. This study aimed to estimate the risk level of COVID-19 in diabetic patients without a medical practitioner’s advice for timely action and overcoming the multifold mortality rate of COVID-19 in diabetic patients. The proposed model takes eight input parameters, which were found as the most influential symptoms in diabetic patients. With the help of the various state-of-the-art machine learning techniques, fifteen models were built over the rule base. CatBoost classifier gives the best accuracy, recall, precision, F1 score, and kappa score. After hyper-parameter optimization, CatBoost classifier showed 76% accuracy and improvements in the recall, precision, F1 score, and kappa score, followed by logistic regression and XGBoost with 75.1% and 74.7% accuracy. Stratified k-fold cross-validation is used for validation purposes.
Numerical Simulation Study on the Strengthening Mechanism of Rock Materials under Impact Loads
Authors: Liu Xin , Wang Kai , Tang Chunan , Qian Xikun
Published: March 2022

Based on an analysis of real stress in rock specimens, the mechanisms of inertia and cracks on the dynamic properties of concrete material are discussed in this paper. A dynamic enhancement factor is introduced on the basis of static mesodamage constitutive theory, and a rate-dependent three-dimensional rock dynamic constitutive model is established. The orthogonal design and range method are used to analyze the sensitivity of the dynamic damage constitutive parameters, and a differential evolution algorithm is used to invert the relevant parameters of the constitutive model. Based on the drop hammer impact model established by the rock dynamic damage constitutive model, the AE distribution, crack propagation mode, and load-displacement curve of the rock sample are obtained. The numerical simulation results show that rock specimens under dynamic loading have different failure forms, mechanical response mechanisms, and energy evolution than those under static loading. Under dynamic loading, the rock specimen forms more cracks and releases more elastic energy. Furthermore, it shows greater strength characteristics. Inhomogeneities and inertial effects of rock materials are the fundamental reasons for the increase in the dynamic strength of rock materials. A reduction in inertial effects is the main reason for strength reduction in rock samples with hole defects.
Effectiveness of Experimental Whitening Toothpastes Containing Colorants on the Optical Properties of Enamel
Authors: Vilhena Fabiano Vieira , Frederico de Oliveira Graeff Carlos , Svizero Nádia da Rocha , D’Alpino Paulo Henrique Perlatti
Published: March 2022

Objective. This in vitro study investigated the whitening potential of experimental toothpastes containing optical colorants in their formulations in comparison with commercial products. The chemical and physical characteristics of the toothpastes, the morphology, and elemental analysis of the enamel surface after treatment were also analyzed. Materials and Methods. One hundred twenty-five bovine incisor teeth were randomly divided into five groups according to the treatment: (i) experimental PHTALOX dental gel (PHT); (ii) experimental blue silica dental gel (SDG); (iii) Sensodyne Whitening Repair & Protect (WRP); (iv) Sensodyne True White (STW); (v) Snow White Toothpaste (SWS). The whiteness index differences (ΔWID) and color alteration (CIELab-ΔE, CIEDE2000-ΔE00) were calculated after color change analysis using a spectrophotometer before and after the enamel treatment (n = 25). The surface and cross-sectional micromorphology were assessed using scanning electron microscopy. The elemental analyses were determined with energy-dispersive X-ray spectroscopy (EDS). The pH, particle size, zeta potential, and polydispersity index of toothpaste were evaluated. Data was statistically analyzed (ANOVA/Tukey, 5%). Results. Whitening toothpastes containing optical colorants were effective for whitening the enamel, as whiter teeth were observed following treatment (higher means of ΔWID). In addition, when the parameters ΔEab and ΔE00 were evaluated, these toothpastes were graded as very good effectiveness (grade 4). Other toothpastes were graded as 3 (good effectiveness). PHT had a neutral pH and a larger mean particle size (412.8 nm). Elemental analysis demonstrated enamel with a silicon-enriched mineral layer on the enamel surface treated with SDG. The Ca/P ratio after enamel treatment varied from 1.74 (SWS) to 2.04 (SDG and WRP). Conclusions. Experimental whitening toothpastes containing optical colorants are effective at bleaching the enamel. The synergism among the different parameters analyzed seems to positively affect the color change after brushing with whitening toothpastes containing optical colorants.
Cervical Cancer and Its Screening: Assessing the Knowledge, Awareness, and Perception among Health and Allied Students
Authors: Aga Syed Sameer , Yasmeen Nusrath , Khan Muhammad Anwar
Published: April 2022

Background. Cervical cancer (CC) is the fourth most common cancers affecting women. It being preventable, a robust and effective national education cum screening program is the key to curb its increasing incidence. Objectives. This cross-sectional study was designed to investigate the knowledge, awareness, and perceptions (KAP) towards CC etiology, its signs and symptoms and risk factors among Health Professions Students’ (HPSs) in King Saud Bin Abdulaziz University for Health Sciences (KSAU-HS), Jeddah Campus. Methods. Data was collected using predesigned and validated study questionnaire. The respondents’ KAP were compared across gender, colleges, and levels of education. Data analysis was done using SPSS v 20.0 (IBM Corporation) at 95% CI. Results. A total of 580 HPS responded to the survey, 128 (22.1%) were males, while 452 (77.9%) were females with the

mean
 
age
±
SD

20.36
±
1.74

years. Although 70% of the respondents had heard about CC, but only 27.9% and 18.6% knew that CC is common among women and it is transmissible, respectively. Additionally, the knowledge of relationship between viral infection and vaccination was also dismal (34.1%) and only 18.6% of the students had knowledge that CC was a transmissible disease. Interestingly, students from college of medicine had more knowledge and awareness about CC (

p
<
0.05

). Conclusions. Since HPSs had considerable low knowledge about CC, we identify the dire need to implement an effective education programs, curricular activities, and awareness campaigns for HPSs as well as general population to augment the learning process effectively.
Research on Stock Price Time Series Prediction Based on Deep Learning and Autoregressive Integrated Moving Average
Authors: Xiao Daiyou , Su Jinxia
Published: March 2022

Different from traditional algorithms and model, machine learning is a systematic and comprehensive application of computer algorithms and statistical models, and it has been widely used in many fields. In the field of finance, machine learning is mainly used to study the future trend of capital market price. In this paper, to predict the time-series data of stock, we applied the traditional models and machine learning models for forecasting the linear and non-linear problem, respectively. First, stock samples that occurred from year 2010 to 2019 at the New York Stock Exchange are collected. Next, the ARIMA (autoregressive integrated moving average model) model and LSTM (long short-term memory) neural network model are applied to train and predict stock price and stock price subcorrelation. Finally, we evaluate the proposed model by several indicators, and the experiment results show that: (1) Stock price and stock price correlation are accurately predicted by the ARIMA model and LSTM model; (2) compared with ARIMA, the LSTM model performance better in prediction; and (3) the ensemble model of ARIMA-LSTM significantly outperforms other benchmark methods. Therefore, our proposed method provides theoretical support and method reference for investors about stock trading in China stock market.
Investigating the Effect of Calcium Lignosulfonate on the Durability and Performance of Asphalt Mixtures
Authors: Fatemi Saeed , Bazaz Jafar Bolouri , Ziaee Seyed Ali
Published: April 2022

Using bitumen modifiers to prolong the asphalt mixture’s life has been considered a practical approach in constructing asphalt mixtures. Among various bitumen modifiers, biomass ones have been more noteworthy because of their abundance, cost-effectiveness, renewability, and sustainability. Calcium lignosulfonate (CLS), as a plant-based bitumen modifier, has been studied to enhance the rheological properties of bitumen. However, the feasibility of using CLS as a rutting retarder of asphalt mixtures is still unclear. This study focused on the durability of asphalt mixtures containing CLS powder as a bitumen modifier. Specifically, extensive laboratory tests were conducted to determine the performance of asphalt mixtures against traffic loadings. In this study, to begin, CLS-modified bitumen was manufactured using a high-shear mixer at various CLS dosages (5, 10, 15, and 20 wt.%). Afterward, the optimum bitumen content of asphalt mixtures was found using the Marshall mix design technique. Finally, a series of dynamic tests, including resilient modulus, dynamic creep, and wheel-tracking tests, were combined with Zhou’s three-stage model and Tukey method to determine the influence of CLS on asphalt mixture’s rutting potential. The Tukey method found that asphalt mixtures containing 15% CLS had the highest rutting performance among the samples tested under repetitive loadings. Meanwhile, the surface morphology and elemental analysis of CLS powder were investigated using a field emission scanning electron microscope (FESEM) and by energy-dispersive X-ray spectroscopy (EDX), respectively. The FESEM and EDX results showed that the CLS powder had a bumpy structure and its chemical structure contained metal oxide, carbon, magnesium, calcium, and silicon. The results indicated that the asphalt mixtures became stiff owing to the combination of CLS and bitumen, which significantly affected the rutting improvement of asphalt mixtures. Moreover, Zhou’s three-stage model revealed that the incorporation of CLS into bitumen enhanced the rutting resistance capacity of asphalt mixtures against traffic loadings.
Marine Propeller Optimization Based on a Novel Parametric Model
Authors: Wang Hao , Zheng Long , Chen Shunhuai
Published: March 2022

This paper presents a novel parametric model of marine propellers based on Non-Uniform Rational B-Splines. It involves eight parameters and five categories of spanwise parameter distributions, which are utilized for determining hydrofoil and blade shapes. 20 different hydrofoils and 5 types of well-known marine propellers are employed to detect the accuracy of the proposed parametric model. Furthermore, a propeller optimization problem was addressed with the aid of the parametric model. In the propeller optimization problem, a common AU-series propeller is treated as the baseline propeller. The proposed parametric model is used for the representation and deformation of the propeller geometric model. A hydrodynamic performance evaluation model is developed based on gene expression programming. Also, non-dominated sorting genetic algorithm II is used in the applications of the propeller optimization problem. The results demonstrate that the accuracy of the proposed parametric model satisfies the engineering requirements well, and a propeller with higher efficiency than the baseline propeller can be derived by settling the propeller optimization problem.
Determination of Glyphosate, Glufosinate, and Their Major Metabolites in Tea Infusions by Dual-Channel Capillary Electrophoresis following Solid-Phase Extraction
Authors: Nguyen Manh Huy , Nguyen Thanh Dam , Vu Minh Tuan , Duong Hong Anh , Pham Hung Viet
Published: April 2022

In this study, two analytical procedures were developed and validated using dual-channel capillary electrophoresis-coupled contactless conductivity detection (CE-C4D) followed by solid-phase extraction (SPE) for simultaneous determination of glyphosate (GLYP), glufosinate (GLUF), and their two major metabolites, aminomethylphosphonic acid (AMPA) and 3-(methylphosphinico) propionic acid (MPPA), respectively, in a popular beverage such as tea infusions. GLYP, GLUF, and AMPA were analyzed in the first channel using background electrolyte (BGE) of 1 mM histidine (His) adjusted to pH 2.75 by acetic acid (Ace). In contrast, MPPA was quantified in the second channel with a BGE of 30 mM His adjusted to pH 6.7 by 3-(N-morpholino) propanesulfonic acid (MOPS) and 10 µM of cetyltrimethylammonium bromide (CTAB). In addition, the samples of tea infusions were treated using SPE with 10 mL of 0.5 mM HCl in methanol as eluent. At the optimized conditions, the method detection limit (MDL) of GLYP, GLUF, AMPA, and MPPA is 0.80, 1.56, 0.56, and 0.54 μg/l, respectively. The methods were then applied to analyze four target compounds in 16 samples of tea infusions. GLYP was found in two infusion samples of oolong tea with concentrations ranging from 5.34 to 10.74 µg/L, and GLUF was recognized in three samples of green tea infusion in the range of 45.1–53.9 µg/L.
Machine Learning with Quantum Seagull Optimization Model for COVID-19 Chest X-Ray Image Classification
Authors: Ragab Mahmoud , Alshehri Samah , Alhakamy Nabil A. , Alsaggaf Wafaa , Alhadrami Hani A. , Alyami Jaber
Published: March 2022

Early and accurate detection of COVID-19 is an essential process to curb the spread of this deadly disease and its mortality rate. Chest radiology scan is a significant tool for early management and diagnosis of COVID-19 since the virus targets the respiratory system. Chest X-ray (CXR) images are highly useful in the effective detection of COVID-19, thanks to its availability, cost-effective means, and rapid outcomes. In addition, Artificial Intelligence (AI) techniques such as deep learning (DL) models play a significant role in designing automated diagnostic processes using CXR images. With this motivation, the current study presents a new Quantum Seagull Optimization Algorithm with DL-based COVID-19 diagnosis model, named QSGOA-DL technique. The proposed QSGOA-DL technique intends to detect and classify COVID-19 with the help of CXR images. In this regard, the QSGOA-DL technique involves the design of EfficientNet-B4 as a feature extractor, whereas hyperparameter optimization is carried out with the help of QSGOA technique. Moreover, the classification process is performed by a multilayer extreme learning machine (MELM) model. The novelty of the study lies in the designing of QSGOA for hyperparameter optimization of the EfficientNet-B4 model. An extensive series of simulations was carried out on the benchmark test CXR dataset, and the results were assessed under different aspects. The simulation results demonstrate the promising performance of the proposed QSGOA-DL technique compared to recent approaches.
Quality of Evidence Supporting the Role of Curcuma Longa Extract/Curcumin for the Treatment of Osteoarthritis: An Overview of Systematic Reviews
Authors: Chen Wenqiang , Shi Hongshuo , Deng Pin , Yang Zhenguo , Liu Wenbin , Qi Lu , Dong Chengda , Si Guomin , Guo Dong , Wang Lei
Published: March 2022

Background. Well known for its good anti-inflammatory effect, curcuma longa extract (CLE)/curcumin (C) has a potential effect on osteoarthritis (OA), and a large number of researchers have completed several systematic reviews/meta-analyses (SRs/MAs) in this research area. However, the methodological and evidentiary quality of these SRs/MAs need to be further evaluated, and whether these findings provide reliable evidence for clinicians remains controversial. Methods. Two researchers collected data from seven databases for SRs/MAs that are about randomized controlled trials (RCTs) on CLE/C for OA. Assessment was made for the SRs/MAs included in this article by means of the Assessment System for Evaluating Methodological Quality 2 (AMSTAR-2), the Risk of Bias in Systematic (ROBIS) scale, the list of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), and the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system. Results. Nine published SRs/MAs were included in our study. According to the results of the AMSTAR-2 assessment, only one SR/MA was assessed as high quality. According to the ROBIS evaluation results, only 2 SRs/MAs have a low risk of bias. According to the results of the PRISMA checklist assessment, only 2 SRs/MAs studies fully reported the checklist, while other studies had reporting flaws. According to GRADE, a total of 59 effect sizes extracted from the included SRs/MAs were evaluated, among which no effect size was rated as high. Conclusions. CLE/C may be an effective and safe complementary treatment for OA. However, further standard SRs/MAs and RCTs are needed to provide an evidence-based medical rationale for this.