bluuebunny/crossref_metadata_2025 huggingface dataset:
[Retracted] Analysis of Environmental Emission Neat Diesel‐Biodiesel–Algae Oil‐Nanometal Additives in Compression Ignition Engines
Authors: Surakasi Raviteja , Khan Mohd Yunus , Sener Arif Senol , Choudhary Tushar , Bhattacharya Sumantha , Singhal Piyush , Singh Bharat , Chowdary Velivela Lakshmikanth
Published: March 2022

The production of biodiesel as an alternative fuel and its use as a mixture and other additives are presented. In the present research work, additive blends with diesel biodiesel from algae oil are physically characterised and an analysis of pollutant emissions is carried out when used in an ignition engine by compression. The measurement of pollutant emissions is carried out through a combined emission analyzer adapted to a system of valves attached to the experimental facility. The properties of each mixture with the polluting emissions are compared with that of the reference diesel. It was found that each of the properties improves compared to that of diesel, reducing most emissions in the use of mixtures with biodiesel.
Certificateless Hybrid Signcryption by a Novel Protocol Applied to Internet of Things
Authors: Zhang Wenzhan , Zhang Yanhui , Guo Chong , An Qi , Guo Yuming , Liu Ximing , Zhang Shijun , Huang Junjia
Published: February 2022

The rapid development of the Internet of Things (IoT) has accelerated the integration of science and technology with life, enabling the public to start enjoying the convenience brought by intelligent living. However, there are multiple resource-constrained sensing devices in IoT, which are always facing various external or internal attacks, making it difficult to ensure the secure transmission of sensitive data in IoT. Therefore, to address the problem of data transmission in resource-constrained devices in IoT, we propose a new certificateless hybrid signcryption scheme for IoT. It is a novel scheme that satisfies confidentiality and unforgeability, showing higher computational efficiency and lower overhead of transmission. To prove that it satisfies the efficent transmission of IoT, we conduct simulation experiments, and the experimental results show that our proposed scheme has higher efficiency than the existing schemes.
A Modified Transfer Matrix Method for Modal Analysis of Stepped Rotor Assembly Applied in the Turbomolecular Pump
Authors: Zhang Yiming , Tang Jiqiang , Wen Tong
Published: February 2022

An accurate modal analysis of the stepped rotor assembly is significant in the design process of rotors. The transfer matrix method (TMM) is widely used in the modal analysis of the rotor. However, the influences of the step and the assembling modes are not considered in the traditional TMM. In this paper, a modified transfer matrix method is presented for the modal calculation of the rotor in the turbomolecular pump. The stiffness reduction of stepped segments and the effect of assembled components are considered in this modified method. First of all, the traditional transfer matrix model is built. Then, the stepped segments of the rotor are changed into conical segments and the lumped mass model of conical segments is calculated. Next, the conicities of conical segments are analyzed and relations between conicities and dimensions of the rotor are discovered. Finally, the stiffness factors which can describe the effects of different assembling modes are introduced and optimized. The optimized stiffness factors are analyzed and explained from the perspective of contact stress. The modal of the stepped rotor assembly could be computed with high accuracy by using this method.
Traffic Sign Detection via Improved Sparse R-CNN for Autonomous Vehicles
Authors: Liang Tianjiao , Bao Hong , Pan Weiguo , Pan Feng
Published: March 2022

Traffic sign detection is an important component of autonomous vehicles. There is still a mismatch problem between the existing detection algorithm and its practical application in real traffic scenes, which is mainly due to the detection accuracy and data acquisition. To tackle this problem, this study proposed an improved sparse R-CNN that integrates coordinate attention block with ResNeSt and builds a feature pyramid to modify the backbone, which enables the extracted features to focus on important information, and improves the detection accuracy. In order to obtain more diverse data, the augmentation method used is specifically designed for complex traffic scenarios, and we also present a traffic sign dataset in this study. For on-road autonomous vehicles, we designed two modules, self-adaption augmentation (SAA) and detection time augmentation (DTA), to improve the robustness of the detection algorithm. The evaluations on traffic sign datasets and on-road testing demonstrate the accuracy and effectiveness of the proposed method.
A Liver Damage Prediction Using Partial Differential Segmentation with Improved Convolutional Neural Network
Authors: Sumathy B. , Dadheech Pankaj , Jain Monika , Saxena Ankur , Hemalatha S. , Liu Wenqi , Nuagah Stephen Jeswinde
Published: February 2022

Background. The liver is one of the most significant and most essential organs in the human body. It is divided into two granular lobes, one on the right and one on the left, connected by a bile duct. The liver is essential in the removal of waste products from human food consumption, the creation of bile, the regulation of metabolic activities, the cleaning of the blood by sensitizing digestive management, and the storage of vitamins and minerals. To perform the classification of liver illnesses using computed tomography (CT scans), two critical phases must first be completed: liver segmentation and categorization. The most difficult challenge in categorizing liver disease is distinguishing the liver from the other organs near it. Methodology. Liver biopsy is a kind of invasive diagnostic procedure, widely regarded as the gold standard for accurately estimating the severity of liver disease. Noninvasive approaches for examining liver illnesses, such as blood serum markers and medical imaging (ultrasound, magnetic resonance MR, and CT) have also been developed. This approach uses the Partial Differential Technique (PDT) to separate the liver from the other organs and Level Set Methodology (LSM) for separating the cancer location from the surrounding tissue based on the projected pictures used as input. With the help of an Improved Convolutional Classifier, the categorization of different phases may be accomplished. Results. Several accuracies, sensitivity, and specificity measurements are produced to assess the categorization of LSM using an Improved Convolutional classifier. Approximately, 97.5% of the performance accuracy of the liver categorization is achieved with a 94.5% continuous interval (CI) of [0.6775 1.0000] and an error rate of 2.1%. The suggested method’s performance is compared to that of two existing algorithms, and the sensitivity and specificity provide an overall average of 96% and 93%, respectively, with 95% Continuous Interval of [0.7513 1.0000] and [0.7126 1.0000] for sensitivity and specificity, respectively.
Digital Universal Financial Credit Risk Analysis Using Particle Swarm Optimization Algorithm with Structure Decision Tree Learning-Based Evaluation Model
Authors: Tian Juanjuan , Li Li
Published: February 2022

Predictions of credit risk, model reliability, monitoring, and efficient loan processing are all important factors in decision-making and transparency. Machine learning method is providing these people new hope. However, it is up to banking or nonbanking institutions to determine how they will implement this advanced method in order to decrease human biases in loan decision-making. Objective. This paper proposed the novel machine learning-based credit risk analysis in the digital banking evaluation model. The purpose of this research is to compare various ML algorithms in order to develop an accurate model for credit risk assessment utilising data from a genuine credit registry dataset. Aim is to design the classification-based model using particle swarm optimization (PSO) algorithm with structure decision tree learning (SDTL) in predicting credit risk. This system has the potential to improve quality criteria such as dependability, robustness, extensibility, and scalability. Features have been extracted and classified by the proposed PSO_SL model. Experimental Results. The data have been collected based on real time for credit analysis. Simulation is carried out in Python and optimal results are obtained in comparative analysis with existing techniques. The accuracy obtained by proposed technique is enhanced and the error rate of the design is minimized.
Comprehensive Analysis of Prognostic Value and Immune Infiltration of MMP12 in Esophageal Squamous Cell Carcinoma
Authors: Mao Jing-tao , Lu Qiang , Jing Peng-yu , Li Zi-liang , Yang Xiao-qi , Zhang Ji-peng , Li Zhe
Published: February 2022

Esophageal squamous cell carcinoma (ESCC) is a typical neoplastic disease and a frequent cause of death in China. The prognosis of most ESCC patients is still poor. Previous studies demonstrated that MMP12 is involved in tumor metastasis. However, its clinical significance and association with cancer immunity remained largely unclear. In this study, we first analyzed the expressing pattern of MMPs in ESCC from TCGA datasets and found that several MMPs expression was distinctly increased in ESCC. However, only MMP12 expression was associated with five-year survival of ESCC patients. Then, we focused on MMP12 and found its high expression was positively related to advanced clinical stages of ESCC specimens. KEGG assays revealed MMP12 may influence the activity of several tumor-related pathways, such as the Toll-like receptor signaling pathway, TNF signaling pathway, and IL-17 signaling pathway. Then, we sought to determine whether MMP12 expressions were related to immune cell infiltration in ESCC. We observed that increased MMP12 levels were positively associated with the infiltration levels of mast cells activated and macrophages M0. However, eosinophils, B cells naïve, and mast cells resting exhibited an opposite result. Finally, we showed that knockdown of MMP12 suppressed the proliferation of ESCC cells. Overall, our findings proved that high expression of MMP12 may be a novel and valuable prognostic factor in ESCC.
Thermal Properties of Warm- versus Heated-Needle Acupuncture
Authors: Jo Hyo-Rim , Choi Seong-Kyeong , Sung Won-Suk , Lee Seung-Deok , Lee Byung-Wook , Kim Eun-Jung
Published: March 2022

Background. Warm-needle acupuncture (WA) and fire-needle acupuncture are treatment techniques that use the combination of acupuncture and thermal stimulation. In clinical practice, a new method of fire-needle acupuncture called “heated-needle acupuncture (HA)” has been proposed, wherein the needle is directly heated after insertion. WA and HA share similarities in their methods, and no previous study has sought to assess whether their thermal outcomes are also similar. Methods. We controlled environmental variables and measured the maximum temperatures and temperature changes of a silicon phantom in which K-type thermocouples were embedded at depths of 0, 2, 5, 7, and 10 mm. WA and HA were also performed with acupuncture needles of various thicknesses (0.30 × 40 mm, 0.40 × 40 mm, and 0.50 × 40 mm). Results. Different time-dependent temperature distributions were observed between the two acupuncture methods: HA yielded a higher maximum temperature and temperature change on the surface, whereas WA yielded higher temperatures at the other tested depths. The thermal patterns were similar among the needles of different thicknesses for each method, with the following exception: while the temperature change and maximum temperature did not differ significantly by needle thickness for WA, these parameters increased significantly with needle thickness for HA. Conclusion. The two acupuncture procedures yielded different thermal patterns in a controlled environment. Further studies are necessary to reflect the effect of external environment variables occurring in reality.
Practice of Air Environment Quality Monitoring Data Visualization Technology Based on Adaptive Wireless Sensor Networks
Authors: Yin Fengmei
Published: February 2022

This paper begins by outlining the evolution and current state of traditional wireless sensor networks, followed by an examination of the principle and architecture of wireless sensor networks, as well as the challenges that traditional wireless sensor networks face in environmental monitoring. This paper designs and develops a data visualization system for wireless sensor networks based on the visualization system and the advantages of other visualization systems, after analyzing and comparing the current visualization systems for monitoring wireless sensor networks. A real-time monitoring system for urban air quality based on wireless sensor network is designed to solve the problem that the traditional method of combining manual sampling with laboratory analysis for air quality monitoring is expensive and cannot carry out real-time online monitoring for multiple points at the same time. The system’s implementation is based primarily on air quality monitoring data collected in the Shenyang area for visual display and analysis. The air quality monitoring system proposed in this paper has a complete structure and independent modules and is simple to use. The dynamic adaptability of the monitoring system is greatly improved by utilizing the self-adaptability and self-organization of wireless sensor networks, which brings great convenience to air quality monitoring.
Determining the Effect of the Picture Archiving and Communication System (PACS) on Different Dimensions of Users’ Work
Authors: Montazeri Mahdieh , Khajouei Reza
Published: March 2022

The impact of the picture archiving and communication system (PACS) on healthcare costs, information access, image quality, and user workflow has been well studied. However, there is insufficient evidence on the effect of this system on different dimensions of the users’ work. The objective of this study was to evaluate the impact of the PACS on different dimensions of users’ work (external communication, service quality, user intention to use the PACS, daily routine, and complaints on users) and to compare the opinions of different groups of users about the PACS. This study was performed on the PACS users (n = 72) at Kerman University of Medical Sciences, including radiologists, radiology staff, ward heads, and physicians. Data were collected using a questionnaire consisting of two parts: demographic information of the participants and 5-point Likert scale questions concerning the five dimensions of users’ work. Data were analyzed using descriptive statistics, ANOVA, and Pearson’s correlation coefficient statistical tests. The mean of scores given by the PACS users was 4.31 ± 0.86 for external communication, 4.18 ± 0.96 for user intention to use the PACS, 3.91 ± 0.7 for service quality, 3.16 ± 0.56 for daily routine, and 3.08 ± 1.05 for complaints on users. Radiologists and radiology staff had a more positive opinion about the PACS than other clinicians such as physicians (

P
<
0.01

, CI = 95%). Factors such as user age (

P
<
0.01

, CI = 95%), job (

P
<
0.001

, CI = 95%), work experience (

P
<
0.001

, CI = 95%), and PACS training method (

P
=
0.037

, CI = 95%) were related to the impact of the PACS on different dimensions of users’ work. This study showed that the PACS has a positive effect on different dimensions of users’ work, especially on external communication, user intention to use the system, and service quality. It is recommended to implement PACSs in medical centers to support users’ work and to maintain and strengthen the capabilities and functions of radiology departments.
Jaw Manipulation Using Ultrasound-Guided Inferior Alveolar Nerve Block for Trismus in Temporomandibular Disorders
Authors: Kojima Yuki , Sendo Ryozo
Published: February 2022

Temporomandibular disorders are a group of disorders with symptoms that include pain and clicking sounds in the temporomandibular joint and restricted mouth opening. For the treatment of temporomandibular disorders with trismus, herein, we suggest a new approach: “jaw manipulation using the ultrasound-guided inferior alveolar nerve block technique.” A woman in her 60s developed temporomandibular disorders and presented with severe trismus owing to pain in the temporomandibular joint. Ultrasound-guided inferior alveolar nerve block was performed with ropivacaine, which relieved the pain in the patient. Furthermore, we performed jaw manipulation for trismus. Since the analgesic effect lasts for 3 days, self-training can be performed while the pain is relieved. After five sessions of “jaw manipulation using the ultrasound-guided inferior alveolar nerve block technique,” trismus significantly improved in this patient. Ultrasound-guided inferior alveolar nerve block can be effective in relieving temporomandibular disorder-related pain and trismus.
Study on the Relationship between lncRNA Gene Polymorphism and Systemic Lupus Erythematosus
Authors: Guan Xue , Liu Dan , Xing Yuling , Guan Xiuru
Published: February 2022

Background. Systemic lupus erythematosus (SLE) is an autoimmune disease that can cause damage to multiple systems of the body. A number of studies have shown that long-chain noncoding RNA (lncRNA) can participate in the occurrence and development of a variety of autoimmune diseases. This study is aimed at detecting the expression levels of 5 lncRNAs in SLE patients and healthy controls and at exploring the relationship between expression levels and clinical symptoms and laboratory indicators. Methods. The design type of this study is a case-control study. A total of 76 SLE patients and 71 healthy controls were included in the first phase of the study. Real-time fluorescence quantitative polymerase chain reaction was used to detect the expression level of 5 kinds of lncRNAs including lnc7514, lnc0640, lncagf, nc3643, and lnc5150 in PBMCs of two groups of patients; the expression of lncRNAs in the case group and the control group was analyzed. We analyzed the differences in the expression levels of lncRNAs between case and control groups, and explored the association of expression levels with clinical manifestations and laboratory characteristics. SPSS23.0 was used to analyze the expression level and gene polymorphism results; the statistical analysis test level

α
=
0.05

. Results. The expression level of lnc0640 in PBMCs of SLE patient group was higher than that of healthy control group (

Z
=
−
3.56

,

P
=
0.03

). However, lnc5150 was lower than in healthy controls (

Z
=
−
7.16

,

P
<
0.001

). lnc3643 expression levels were lower in SLE patients of SLE patients with pleurisy was lower than that of patients without pleurisy (

Z
=
−
2.44

,

P
=
0.02

). Low lnc3643 expression levels were observed in PBMCs with SLE patients with rash symptoms (

Z
=
−
2.75

,

P
=
0.013

). SLE expressed lower lnc3643 levels in PBMCs with SLE compared with those without pleurisy (

Z
=
−
2.42

,

P
=
0.02

). The above differences were statistically significant. Association analysis of lncRNA expression levels and clinical manifestations in SLE patients found that SLE was lower than those without rash or pleurisy (both

P
<
0.05

); association analysis of lncRNA expression level and laboratory results found a negative correlation between lnc3643, lnc7514, and SLE disease activity score (SLEDAI-2K), blood sink (ESR), and C-reactive protein (CRP) (all

P
<
0.05

). Conclusions. lnc0640 was overexpressed in PBMCs in SLE patients compared with healthy controls. lnc3643 was negatively correlated with SLEDAI, and expression levels were associated with SLE patients with arthritis, rash, and pleuritis.
Complex Multisnapshot Sparse Bayesian Learning for Offgrid DOA Estimation
Authors: Liu Qinghua , He Yuanxin , Ding Kai , Xie Quanmin
Published: March 2022

Direction of arrival (DOA) estimation has recently been developed based on sparse signal reconstruction (SSR). Sparse Bayesian learning (SBL) is a typical method of SSR. In SBL, the two-layer hierarchical model in Gaussian scale mixtures (GSMs) has been used to model sparsity-inducing priors. However, this model is mainly applied to real-valued signal models. In order to apply SBL to complex-valued signal models, a general class of sparsity-inducing priors is proposed for complex-valued signal models by complex Gaussian scale mixtures (CGSMs), and the special cases correspond to complex versions of several classical priors are provided, which is helpful to analyze the connections with different modeling methods. In addition, the expression of the SBL form of the real- and complex-valued model is unified by parameter values, which makes it possible to generalize and improve the properties of the SBL methods. Finally, the SBL complex-valued form is applied to the offgrid DOA estimation complex-valued model, and the performance between different sparsity-inducing priors is compared. Theoretical analysis and simulation results show that the proposed algorithm can effectively process complex-valued signal models and has lower algorithm complexity.
[Retracted] Research on Digital Image Watermarking Algorithm Based on Scrambling and Singular Value Decomposition
Authors: Pei Lei
Published: February 2022

At present, the digital image watermarking algorithm has not embedded the synchronization signal in the image, resulting in the poor performance of the embedded image in terms of, for example, imperceptibility, anti‐attack ability, and robustness. Therefore, a digital image watermarking algorithm based on scrambling and singular value decomposition is proposed. The digital watermark is preprocessed by dimensionality reduction and encryption; the digital image is processed in sections and embedded in the synchronization signal. The digital watermark image is embedded in the digital image watermark by using the low‐frequency energy ratio technology of sound channel. The watermark image extraction step is designed to extract the watermark image. The transmittance of the degraded image is calculated, the transmittance is refined by relying on the soft matting algorithm and guiding filter, the image contrast in the image frequency domain is enhanced, and the results are mapped to the appropriate visual range to optimize the visual brightness of the image. Finally, according to the uniqueness of singular value matrix, the distributed characteristics of image matrix data are described, the visual effect is enhanced, and the research of digital image watermarking algorithm is completed. The experimental results show that the algorithm can extract the watermark information completely, the image contrast and singular value have been significantly improved, and the algorithm has better anti‐attack performance.
The Changes in Optic Nerve after Orbital Decompression Surgery for Thyroid Eye Disease and Case Reports of Ischemic Optic Neuropathy
Authors: Hsia Yun , Hsiao Chia-Chieh , Wei Yi-Hsuan , Lai I-Wen , Lin Chao-Wen , Liao Shu-Lang
Published: February 2022

Purpose. To demonstrate the changes in the retinal nerve fiber layer (RNFL) after orbital decompression for thyroid eye disease (TED). Methods. We retrospectively enrolled 52 surgical TED patients, 30 nonsurgical TED patients, and 30 control subjects. Five surgical TED eyes with disc edema were excluded. The surgical TED patients were classified into the “dysthyroid optic neuropathy (DON)” group (16 eyes) and the “non‐DON” group (83 eyes). Optical coherence tomography (OCT) and visual field (VF) examinations were performed preoperatively and 6 months later. The control subjects and nonsurgical TED patients received two OCT examinations at 6‐month intervals. The postoperative changes in the RNFL thickness were compared between groups. Three cases with severe postoperative vision loss were presented additionally. Results. The changes in the RNFL thickness of the controls (0.5 ± 3.4 μm) and the nonsurgical TED patients (0.3 ± 2.8 μm) were significantly smaller than the surgical TED patients (P < 0.001). The DON group (−9.2 ± 9.2 μm) had greater RNFL thickness reduction than the non‐DON group (−3.9 ± 5.4 μm) (P = 0.002). Bone removal decompression was associated with decreased RNFL in the non‐DON (P = 0.025; β = −2.49) and DON (P = 0.042; β = −9.43) groups. Three cases who were hard to operate due to extensive fibrosis experienced severe vision loss postoperatively due to anterior ischemic optic neuropathy, posterior ischemic optic neuropathy, and posterior ciliary artery occlusion, respectively. Conclusions. TED patients experienced subclinical optic nerve injury and significant RNFL loss after the orbital decompression surgery. Aggressive manipulation during decompression surgery may lead to dreadful vision loss. Tailored surgical plans and delicate manipulation are warranted.
Analysis of Clinical Related Factors of Severe Mycoplasma pneumoniae Pneumonia in Children Based on Imaging Diagnosis
Authors: Wang Xiaoshuai , Lin Xiaofei
Published: February 2022

Severe pneumonia is a common acute respiratory disease in children, and it has a rapid onset and violent onset, which often affects the whole body. Moreover, typical clinical manifestations and signs often cannot be taken seriously or covered up in clinical work. Due to the short time for treatment, it is easy to cause improper diagnosis and treatment, aggravate the disease and further deteriorate, and even threaten the life of the child. In order to achieve early intervention and treatment of severe Mycoplasma pneumoniae pneumonia in children, reduce or shorten the course of the disease, and improve the cure rate, this paper combines the imaging diagnosis to study the analysis of clinical related factors of severe Mycoplasma pneumoniae pneumonia in children. In addition, this paper analyzes the experimental data with hospital case samples, conducts statistical research on the analysis of clinical related factors of severe Mycoplasma pneumoniae pneumonia in children, and proposes effective coping strategies.
Basketball Training Posture Monitoring Based on Intelligent Wearable Device
Authors: Hou Xiangfeng , Qi Bo
Published: February 2022

Aiming at the problems of low monitoring accuracy, long time, and poor effect in the current basketball training posture monitoring method, a basketball training posture monitoring method based on intelligent wearable devices is proposed. By analyzing the concept and classification of intelligent wearable devices, the attitude monitoring technology based on intelligent wearable devices is studied. A two-stage Kalman filter is used to correct the error caused by the drift of the gyroscope signal in the intelligent wearable device by constructing an adaptive acceleration error covariance matrix. The time sequence of collecting acceleration and angular velocity signals is segmented, and the characteristics of basketball training posture are extracted from the sensor signals of the intelligent wearable device. The SVM classification algorithm is used to monitor the basketball training posture and realize the basketball training posture monitoring. The experimental results show that the basketball training posture monitoring effect of the proposed method is better, which can effectively improve the monitoring accuracy and shorten the monitoring time.
Performance Degradation Estimation of High-Speed Train Bogie Based on 1D-ConvLSTM Time-Distributed Convolutional Neural Network
Authors: Ren Junxiao , Jin Weidong , Li Liang , Wu Yunpu , Sun Zhang
Published: February 2022

High-speed train bogies are essential for the safety and comfort of train operation. The performance of the bogie usually degrades before it fails, so it is necessary to detect the performance degradation of a high-speed train bogie in advance. In this paper, with two key dampers on the bogie taken as experimental objects (lateral damper and yaw damper), a novel 1D-ConvLSTM time-distributed convolutional neural network (CLTD-CNN) is proposed to estimate the performance degradation of a high-speed train bogie. The proposed CLTD-CNN is an encoder-decoder structure. Specifically, the encoder part of the proposed structure consists of a time-distributed 1D-CNN module and a 1D-ConvLSTM. The decoder part consists of a 1D-ConvLSTM and a simple time-CNN with residual connections. In addition, an auxiliary training part is introduced into the structure to support CLTD-CNN in learning the performance degradation trend characteristic, and a special input format is designed for this structure. The whole structure is end-to-end and does not require expert knowledge or engineering experience. The effectiveness of the proposed CLTD-CNN is tested by the high-speed train CRH380A under different performance states. The experimental results demonstrate the superiority of CLTD-CNN. Compared to other methods, the estimation error of CLTD-CNN is the smallest.
Crystals-Dilithium on ARMv8
Authors: Kim Youngbeom , Song Jingyo , Youn Taek-Young , Seo Seog Chung
Published: February 2022

Crystals-Dilithium is one of the digital-signature algorithms in NIST’s ongoing post-quantum cryptography (PQC) standardization final round. Security and computational efficiency concerning software and hardware implementations are the primary criteria for PQC standardization. Many studies were conducted to efficiently apply Dilithium in various environments; however, they are focused on traditionally used PC and 32-bit Advanced RISC Machine (ARM) processors (Cortex-M4). ARMv8-based processors are more advanced embedded microcontrollers (MCUs) and have been widely used for various IoT devices, edge computing devices, and On-Board Units in autonomous driving cars. In this study, we present an efficient Crystals-Dilithium implementation on ARMv8-based MCU. To enhance Dilithium’s performance, we optimize number theoretic transform (NTT)-based polynomial multiplication, the core operation of Dilithium, by leveraging ARMv8’s architectural properties such as large register sets and NEON engine. We apply task parallelism to NTT-based polynomial multiplication using the NEON engine. In addition, we reduced the number of memory accesses during NTT-based polynomial multiplication with the proposed merging and register-holding techniques. Finally, we present an interleaved NTT-based multiplication simultaneously executed with ARM processor and NEON engine. This implementation can further optimize performance by eliminating the ARM processor latency with NEON overheads. Through the proposed optimization methods, for Dilithium 3, we achieved a performance improvement of about 43.83% in key pair generation, 113.25% in signing, and 41.92% in verification compared to the reference implementation submitted to the final round of the NIST PQC competition.
A New Resultant Vibration Acceleration Model of a Planetary Gear Train and Fault Response Analysis
Authors: Meng Rui , Cai Xin , Guo Xingwen
Published: February 2022

A new resultant vibration acceleration model is proposed to reveal its signal characteristics more accurately in the healthy and faulty state. First, an analytical lateral-torsional coupled dynamic model is developed with consideration of time-varying mesh stiffness and damping, static transmission error, and gear backlash. Then, the effect of gear backlash and damping on the system is analyzed, and a numerical velocity signal in the healthy and faulty state is carried out. Considering the effects of transmission paths, the resultant acceleration signal along the vertical direction is constructed as the weighted vibration summation. This signal includes a vertical component of the vibration along the meshing lines of both sun-planet and ring-planet pairs. Moreover, it also contains the vertical component of the planet gears, sun gear, and planet carrier acceleration relative to their own supporting bearings. Finally, the simulation results from the resultant signal model are experimentally validated and analyzed in both time and frequency domains.
Influence of Masonry Infill on Seismic Design Factors of Reinforced-Concrete Buildings
Authors: Shendkar Mangeshkumar R. , Kontoni Denise-Penelope N. , Işık Ercan , Mandal Sasankasekhar , Maiti Pabitra Ranjan , Harirchian Ehsan
Published: February 2022

Infill walls are the most common separator panels in typical reinforced-concrete (RC) frame structures. It is crucial to investigate the influence of the infill walls on the earthquake behavior of RC frames. The load resistance of infill materials was often not taken into account in the designing phase, whereas the infill walls have significant contributions to the structural behavior under lateral and vertical loadings. A three-dimensional 4-story RC building is designed, and in order to make a realistic model, different infill walls configurations were taken into account with the openings in the infill. Four different models were created for structural analysis for infill wall effects, namely, full RC infilled frame (Model I), corner infill at ground story RC infilled frame (Model II), open ground story RC infilled frame (Model III), and bare RC frame (Model IV). Static adaptive pushover analysis has been performed for all structural models by using the SeismoStruct software. The double strut nonlinear cyclic model was used for modeling the infill walls. In this study, three different compressive strengths of infill walls are taken into consideration, and the effects on seismic design factors (namely, the response reduction factor, the ductility, the overstrength factor, and the deflection factor) are calculated. The obtained values of the response reduction factor (R) are compared with the given values in the BIS code. The results show that the R factors of all RC infilled frames are decreased when the compressive strength of the masonry infill reduces. However, the R values of bare frames are less than the corresponding values recommended in the BIS code. It is worth noting that the National Earthquake Hazards Reduction Program (NEHRP) provisions underestimate the deflection factors of the reinforced-concrete (RC) frames according to the evaluated deflection factors of the herein studied RC frames.
Trends of Inequalities in Early Initiation of Breastfeeding in Ethiopia: Evidence from Ethiopian Demographic and Health Surveys, 2000‐2016
Authors: Amare Tsegaw , Dellie Endalkachew , Amare Getasew
Published: February 2022

Background. Early initiation of breastfeeding (EIBF) is a costless practice with numerous neonates’ survival benefits. Thus, any disparity results in an unacceptably high neonatal death rate but socioeconomic disparities on EIBF have not been well explored in Ethiopia. Therefore, this study is aimed at assessing the socioeconomic inequalities of EIBF in Ethiopia from 2000 to 2016. Methods. The Ethiopian demographic and health survey data and the World Health Organization’s Health Equity Assessment Toolkit were used to investigate the inequalities in EIBF across the wealth quintile, education, residence, and subnational region. Difference, ratio, slope index inequality (SII), relative index inequality (RII), and population attributable risk (PAR) were used as equity summary measures. Results. In Ethiopia, EIBF practice was 47.4% in 2000, 66.2% in 2005, 51.5% in 2011, and 73.3% in 2016. Wealth‐related inequality was observed in the 2000, 2005, and 2011 survey years with SII of ‐7.1%, ‐8.8%, and 8.7%, respectively, whereas educational‐related inequality was observed in 2005 and 2011 with SII of ‐11.7% and 6.5%, respectively. However, significant change in wealth‐, education‐, and residence‐related inequalities was detected in 2011. Regional inequality on EIBF was observed in all survey years with a difference of 35.7%, 38.0%, 29.1%, and 48.5% in the 2000, 2005, 2011, and 2016 survey years, respectively. But a significant change in regional inequality was noted in 2016 with a PAR of 17.2%. Conclusions. In Ethiopia, the wealth‐, residence‐, and educational‐related inequalities of EIBF increased significantly between the years 2000 and 2011. However, regional inequality persistently increased from 2000 to 2016. Overall, one‐sixth of the national level EIBF was decreased due to regional disparity in 2016. The northern regions of Ethiopia (Tigray, Afar, and Amhara) poorly performed compared to the peer regions. Therefore, interventions targeting them would significantly improve the national level of EIBF.
The Role of Symptom Duration and Serologic Factors in the Relapse of IgG4-Related Ophthalmic Disease following Surgery: A Retrospective Cohort Study
Authors: Liu Siyu , Yue Zifan , Zeng Chengcheng , Huang Xiao , Li Jian , Diao Jiale , Chen Xinxin , Wei Ruili , Yang Weihua
Published: February 2022

IgG4-related disease (IgG4-RD) affects multiple organs and is characterized by immune-mediated inflammation and fibrosis; IgG-RD affecting orbital tissue is known as IgG4-related ophthalmic disease (IgG4-ROD). This research is aimed at exploring whether symptom duration and common serologic factors, such as IgG, IgE, and eosinophils, are potential risk factors for IgG4-ROD patient relapse after surgery and identifying possible causes of the positive correlation between symptom duration and relapse. This retrospective cohort study included 40 IgG4-ROD patients after surgery. Auxiliary inspection results were obtained before surgery and during follow-up, and relapse risk factors were identified based on previous studies. We used the Spearman rank correlation test to reveal the relationship between symptom duration and relapse time and identified the optimal cutoff value for symptom duration by X-tile. Then, we divided the patients into the long-duration and short-duration groups. Kaplan–Meier survival analyses and log-rank tests were performed to identify the relationship between symptom duration and relapse using X-tile software. Finally, we studied the relationship between previously studied relapse risk factors and symptom duration. The survival curves of the long-duration and short-duration groups were obviously different, and the baseline serum IgG, IgE, and eosinophil levels and asthma concomitant rate were significantly different between the long-duration and short-duration groups. Furthermore, the baseline serum IgG (

r
=
0.485

,

P
=
0.002

), IgE (

r
=
0.350

,

P
=
0.037

), and eosinophil (

r
=
0.6535

,

P
<
0.0001

) levels were positively correlated with symptom duration. Our study shows that IgG4-ROD symptom duration is significantly positively correlated with relapse rate and negatively correlated with relapse time. Symptom duration was positively correlated with serum baseline IgG4, IgE, and eosinophil levels and asthma history, which were potential risk factors for disease relapse. We recommended that IgG4-ROD patients with symptom durations greater than 96 months continue to receive maintenance steroid therapy longer than 1 year postsurgery to reduce the relapse rate.
Image Defect Recognition Method Based on Deep Learning Network
Authors: Di Xiaolian , Li Li
Published: March 2022

Deep learning techniques are used to identify weld image defects in the process of image defect recognition. In this paper, a transfer learning method based on convolutional neural networks is proposed for the recognition problem of deep neural network models on weld flaw detection image data sets. Designing interdomain heterogeneous transfer learning with the pretrained model on the large data set, the interdomain heterogeneous transfer learning is used to transfer the pretrained model in the source data domain to the weld inspection image data set according to the difference of the content in the source and target data domains, and the effectiveness of the transfer learning in weld inspection image defect recognition is verified by fine-tuning the whole network by training the parameters of different layers using the frozen layer method. The effect of freezing different layers on the recognition performance of the model is also investigated.
Curcumin Inhibits Papillary Thyroid Cancer Cell Proliferation by Regulating lncRNA LINC00691
Authors: Li Zhihua , Gao Youbing , Li Lijun , Xie Shanshan
Published: February 2022

Papillary thyroid cancer (PTC) is a type of epithelial-derived differentiated TC that reportedly accounts for a majority of TCs. Curcumin, a polyphenolic compound and a member of the Zingiberaceae (ginger) family derived from turmeric plants, can exhibit anticancer effects. Herein, we aimed to investigate the effect of curcumin on PTC and elucidate underlying mechanisms. Accordingly, PTC B-CPAP cells were treated with curcumin, in combination with/without long noncoding RNA LINC00691 inhibition, to determine the effect of curcumin and its relationship with LINC00691 in PTC cells. We observed that curcumin treatment decreased B-CPAP cell proliferation and promoted apoptosis. Curcumin inhibited LINC00691 expression in B-CPAP cells. Curcumin administration or si-LINC00691 transfection alone promoted ATP levels, inhibited glucose uptake and lactic acid levels, and inhibited lactate dehydrogenase A and hexokinase 2 protein expression in B-CPAP cells, which were further enhanced by combination treatment. Moreover, curcumin administration or si-LINC00691 transfection alone inhibited p-Akt activity, further suppressed by combination treatment. Akt inhibition promoted apoptosis and suppressed the Warburg effect in B-CPAP cells. In conclusion, our findings indicate that curcumin promotes apoptosis and suppresses proliferation and the Warburg effect by inhibiting LINC00691 in B-CPAP cells. The precise molecular mechanism might be mediated through the Akt signaling pathway, providing a theoretical basis for the treatment of PTC with curcumin.
Rumor Dynamic Model considering Intentional Spreaders in Social Network
Authors: Wang Yan , Qing Feng , Wang Lei
Published: February 2022

The development of network technology has created various platforms and methods for information dissemination. When rumors spread in social networks, they will rapidly spread and may cause social harm. Also, there are groups in social networks that create and spread rumors for the purpose of profit, thus expanding the scope of rumors. Therefore, based on the theory of complex network propagation dynamics, the study of the propagation law of rumors and the design of effective prevention and control strategies is of practical importance and theoretical significance for understanding the propagation laws of rumors and controlling the outbreak of rumors. The spreading process of rumors on social network platforms is focused here. The intentional spreader based on the classic rumor‐spreading model is introduced. First, 2SIR rumor‐spreading models on homogeneous and heterogeneous networks are established, respectively. Second, the steady‐state analysis was separately carried out, and the corresponding propagation critical value was obtained: in the homogeneous network, the condition for the large‐scale spread of rumors is ; in the heterogeneous network, the condition for the large‐scale spread of rumors is . Finally, the simulation calculation and model feasibility verification were carried out on the model. The results show that the theoretical propagation threshold corresponds with the simulation results. According to the simulation results, the final influence of rumors has significantly decreased with decreasing values of β (intentional spreading rate) instead of α (unintentional spreading rate). It can be concluded that in the real‐life rumor control process, more resources need to be invested in reducing the rate of intentional transmission instead of being indiscriminately put on controlling all spreaders of rumors.
Analysis of Heat Transfer Rate for Different Annulus Shape Properties-Enhanced Beeswax-Based Phase Change Material for Thermal Energy Storage
Authors: Mishra Durgesh Kumar , Bhowmik Sumit , Pandey Krishna Murari
Published: February 2022

Thermal exploration of melting phenomena of four different annulus shapes’ (circular-circular, circular-rectangular, rectangular-circular, and rectangular-rectangular) beeswax-based phase change material for latent heat energy storage system is discussed here. To overcome the low efficiency in heat transfer because of low thermal conductivity of beeswax (the energy storage material in the current study), the composite-beeswax approach has been investigated numerically. Three metal oxides (Al2O3, MgO, and SiO2) with different volume concentrations (1%, 3%, and 5%) containing high thermal conductivity are dispersed into the pure beeswax. The 2-d continuity and momentum as well as energy transport mathematical model inside the composite-beeswax system have been adopted for the study, and we validated the model by available results in the literature. The obtained results revealed that both the melting rate and heat transfer are improved slightly in the case of rectangular-rectangular annulus shape. It was also shown from the result that the melting rate and heat transfer are improved significantly with the presence of metal oxide particles. Moreover, it was obtained that the types of particles have not affected noticeably the melting rate and heat transfer. For the three investigated nanoparticles, temperature of the system attained 346.4 K at 15000 s of process; at the same time, it is 341 K as noted for the beeswax, which shows an improvement of 1.4% in the case of circular-circular annulus and Al2O3. In the same conditions, melt fraction increases from 0.58 for beeswax to 0.91 for composite unit, thus showing 56.8% of improvement in the melting rate.
Inversion Attacks against CNN Models Based on Timing Attack
Authors: Chen Zhaohe , Tang Ming , Li Jinghai
Published: February 2022

Model confidentiality attacks on convolutional neural networks (CNN) are becoming more and more common. At present, model reverse attack is an important means of model confidentiality attacks, but all of these attacks require strong attack ability, meanwhile, the success rates of these attacks are low. We study the time leakage of CNN running on the SoC (system on-chip) system and propose a reverse method based on side-channel attack. It uses the SDK tool-profiler to collect the time leakage of different networks of various CNNs. According to the linear relationship between time leakage, calculation, and memory usage parameters, we take the profiling attack to establish a mapping library of time and the different networks. After that, the smallest difference between the measured time of unknown models and the theoretical time in the mapping library is considered to be the real parameters of the unknown models. Finally, we can reverse other layers even the entire model. Based on the experiments, the reverse success rate of common convolutional layers is above 78.5%, and the reverse success rates of different CNNs (such as AlexNet, ConvNet, LeNet, etc.) are all above 67.67%. Moreover, the results show that the success rate of our method is 10% higher than the traditional methods on average. In the adversarial sample attack, the success rate reached 97%.