bluuebunny/crossref_metadata_2025 huggingface dataset:
An Improved CEEMDAN-FE-TCN Model for Highway Traffic Flow Prediction
Authors: Gao Heyao , Jia Hongfei , Yang Lili
Published: May 2022

With the advent of the data-driven era, deep learning approaches have been gradually introduced to short-term traffic flow prediction, which plays a vital role in the Intelligent Transportation System (ITS). A hybrid predicting model based on deep learning is proposed in this paper, including three steps. Firstly, an improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method is applied to decompose the nonlinear time series of highway traffic flow to obtain the intrinsic mode function (IMF). The fuzzy entropy (FE) is then calculated to recombine subsequences, highlighting traffic flow dynamics in different frequencies and improving prediction efficiency. Finally, the Temporal Convolutional Network (TCN) is adopted to predict the recombined subsequences, and the final prediction result is reconstructed. Two sensors of US101-S on the main road and on-ramp were selected to measure the prediction effect. The results show that the prediction error of the proposed model on two sensors is notably decreased on single-step and multistep prediction, compared with the original TCN model. Furthermore, the proposed improved CEEMDAN-FE-X framework can be combined with prevailing prediction methods to increase the prediction accuracy, among which the improved CEEMDAN-FE-TCN model has the best performance and strong robustness.
Experimental Investigations of the Seismic Performance of a Base-Isolated Uninterruptible Power Supply (UPS) through Shaking Table Tests
Authors: Truong Gia Toai , Lee Seung-Jae , Lee Ji-Eon , Choi Kyoung-Kyu
Published: May 2022

Seismic performance of a UPS through shaking table tests was investigated using the input acceleration time histories with scaling factors of 50%, 100%, and 200%. The first series consists of UPS specimens supported by snip single-corner rectangular pins, and the second series consists of UPS specimens supported by isolator systems. The UPS specimens in the second series significantly reduced the fundamental frequency, peak acceleration, and amplification factors but increased the damping ratio and displacement compared with those of the UPS specimens in the first series. The analytical results obtained from the analytical model agreed well with the experimental results.
Epidemiology of COVID‐19 in Tehran, Iran: A Cohort Study of Clinical Profile, Risk Factors, and Outcomes
Authors: Hatamabadi Hamidreza , Sabaghian Tahereh , Sadeghi Amir , Heidari Kamran , Safavi-Naini Seyed Amir Ahmad , Looha Mehdi Azizmohammad , Taraghikhah Nazanin , Khalili Shayesteh , Karrabi Keivan , Saffarian Afsaneh , Shahsavan Saba , Majlesi Hossein , Allahgholipour Komleh Amirreza , Hatari Saba , Zameni Nadia , Ilkhani Saba , Hajimirzaei Shideh Moftakhari , Ghaffari Aydin , Fallah Mohammad Mahdi , Kalantar Reyhaneh , Naderi Nariman , Bahmaei Parnian , Asadimanesh Naghmeh , Esbati Romina , Yazdani Omid , Shojaeian Fatemeh , Azizan Zahra , Ebrahimi Nastaran , Jafarzade Fateme , Soheili Amirali , Gholampoor Fatemeh , Namazi Negarsadat , Solhpour Ali , Jamialahamdi Tannaz , Pourhoseingholi Mohamad Amin , Sahebkar Amirhossein
Published: May 2022

Background. The outbreak of coronavirus disease 2019 (COVID‐19) dates back to December 2019 in China. Iran has been among the most prone countries to the virus. The aim of this study was to report demographics, clinical data, and their association with death and CFR. Methods. This observational cohort study was performed from 20th March 2020 to 18th March 2021 in three tertiary educational hospitals in Tehran, Iran. All patients were admitted based on the WHO, CDC, and Iran’s National Guidelines. Their information was recorded in their medical files. Multivariable analysis was performed to assess demographics, clinical profile, outcomes of disease, and finding the predictors of death due to COVID‐19. Results. Of all 5318 participants, the median age was 60.0 years, and 57.2% of patients were male. The most significant comorbidities were hypertension and diabetes mellitus. Cough, dyspnea, and fever were the most dominant symptoms. Results showed that ICU admission, elderly age, decreased consciousness, low BMI, HTN, IHD, CVA, dialysis, intubation, Alzheimer disease, blood injection, injection of platelets or FFP, and high number of comorbidities were associated with a higher risk of death related to COVID‐19. The trend of CFR was increasing (WPC: 1.86) during weeks 25 to 51. Conclusions. Accurate detection of predictors of poor outcomes helps healthcare providers in stratifying patients, based on their risk factors and healthcare requirements to improve their survival chance.
Application of Nanoporous Super Thermal Insulation Material in the Prevention and Control of Thermal Hazards in Deep Mining of Metal Mines
Authors: Xiao Yigai , Deng Hongwei , Xie Zhimou , He Wei
Published: May 2022

As an important energy material, mineral resources have an important role in the rapid growth of the national economy. In recent years, with the enhancement of mining strength and the improvement of mechanized production level, the depth of mining has also increased year by year. Therefore, the number of heat hazards in deep mine mining has increased year by year, and the problem of heat loss in deep mines has become increasingly prominent, which has seriously affected the safe and efficient production of mines. In this paper, the application of nanoporous super insulation materials in the prevention of thermal damage in deep mining of metal mines is studied, and the related theories of nanoporous super insulation materials and the prevention of thermal damage in deep mining of metal mines are understood on the basis of literature data. Nanoporous thermal insulation materials refer to thermal insulation materials with pore diameters in the nanometer range. This material can effectively prevent the collision of gas molecules and prevent the heat conduction of the gas. Then, the application experiment of nanoporous super thermal insulation materials in the prevention and control of heat damage in deep mining of metal mines is carried out. Through experiments, it is concluded that the room temperature thermal conductivity of the experimental material decreases with the increase of the addition of SiO2 aerogel, and the downward trend is linear, and when the heat source temperature is 200°C, the coating surface temperature of the thermal insulation material (excluding aerogel) is 100°C, and the thermal insulation temperature difference is 100°C, while when the coating surface temperature of the aerogel thermal insulation coating is only 60°C, the thermal insulation temperature difference is 140°C. From these data, it can be seen that the thermal insulation performance of nanoporous super thermal insulation materials is better, which verifies the feasibility of its application in the prevention of heat damage in metal mine mining.
Optimized Fault Diagnosis Algorithm under GAN and CNN Hybrid Model
Authors: Zhu Xiaobo , Ye Yunlong
Published: May 2022

The network fault diagnosis algorithm is one of the most important algorithms to solve network transmission problems. In traditional network fault diagnosis, it mainly analyzes and troubleshoots the fault manually by comparing the alarm information of the network performance index with the expert experience database. Diagnosis methods based on manpower analysis will take up much material resources and manpower and increase maintenance costs. Therefore, there is an urgent need for a more efficient and intelligent fault diagnosis technology. In addition, as the current network is becoming more and more complex, and based on this consideration, a semi-supervised fault diagnosis algorithm is studied in this paper. It is a combination of the GAN and CNN models. Meanwhile, the method of combining relief and mutual information is applied to reducing the dimensionality of network feature parameters, and the optimal feature combination is selected. The fluctuations in the convergence of the generated confrontation network model are stabilized. Moreover, the simulation software is used to build the heterogeneous wireless network scenario studied. Meanwhile, an improved fault diagnosis model is constructed to verify that in the case of both GAN and CNN models, the accuracy of fault diagnosis algorithm can reach 98.6%, which is significantly higher than other comparative analysis methods. It has contributed to ensuring the user’s service experience and reducing the cost of network maintenance and operation.
TSVD Regularization-Parameter Selection Method Based on Wilson-θ and Its Application to Vertical Wheel-Rail Force Identification of Rail Vehicles
Authors: Wu Jiaxin , Zhu Tao , Wang Yijun , Lei Cheng , Xiao Shoune
Published: May 2022

A parameter-selection method is proposed to improve the accuracy of the truncated singular value decomposition (TSVD) method, which is based on the Wilson-θ method and the principle of minimum response error, for dynamic load identification. First, using the Green kernel-function matrix, the dynamic load-identification model of the multi-degree-of-freedom system is established. Second, the response corresponding to the dynamic load is identified using the Wilson-θ method, and the minimum error between the response and the input response is obtained. Then, the best regularization parameters are obtained, and the dynamic load is identified using the TSVD regularization method. Finally, the SIMPACK dynamic model of a rail vehicle is established. Taking the German high-interference spectrum as the input, the axle-box displacement and the vertical wheel-rail force of each wheelset at speeds of 100, 160, and 200 km/h are simulated. Taking the simulated axle-box displacement response with 0%, 5%, and 10% noise as the input, the proposed load-identification model and regularization-parameter selection method are used to identify the vertical wheel-rail force of a rail vehicle. The effects of different track spectra on the identification results are considered. The results indicate that this method has a high identification accuracy for the wheel-rail vertical dynamic load. With an increase in the vehicle speed, the correlation coefficient for identifying the dynamic load decreases, but the correlation remains strong. At the speed of 200 km/h, when the input response noise level is 0%, the dynamic load identification correlation coefficient is 0.9556, which corresponds to extremely strong correlation. When the input response contains 5% noise, this method has stronger robustness than L-curve method, and the dynamic load identification correlation coefficient is 0.6354, which corresponds to strong correlation. The proposed load-identification model and regularization-parameter selection method have important theoretical and engineering application value for wheel-rail force monitoring and safety assessment of running trains.
Fetal Zone Steroids Show Discrete Effects on Hyperoxia-Induced Attenuation of Migration in Cultured Oligodendrocyte Progenitor Cells
Authors: Sunny Donna E. , Krüger Elisabeth L. , Hammer Elke , Völker Uwe , Heckmann Matthias
Published: May 2022

Cerebral oxygenation disturbances contribute to the pathogenesis of brain lesions in preterm infants with white matter damage. These children are at risk of developing long-term neurodevelopmental disabilities. Preterm birth is associated with sudden hormonal changes along with an untimely increase in oxygen tissue tension. There is a persistent high postnatal production of fetal zone steroids (FZS), which serve in the fetoplacental unit as precursors for placental estrogen synthesis during pregnancy. The role of FZS in events associated with oxygenation differences and their impact on the developing white matter is not well understood. Therefore, we investigated the effect of hyperoxia (80% O2) and subsequent administration of FZS on the protein composition and migration capabilities of immature oligodendrocytes using the OLN93 (rat-derived OPC) cell line as an experimental model. We tested the effect of the FZS, dehydroepiandrosterone (DHEA), 16α-OH-DHEA, and adiol (5-androstene-3β, 17β-diol). After 24-hour exposure to hyperoxia, we monitored the changes in the proteome profile following treatment and observed significant alterations in pathways regulating cytoskeletal remodelling, cell migration, and cell survival. Additionally, hyperoxia leads to impaired migration of the OLN93 cells in culture. Administration of the FZS showed positive effects on the migration process under normoxic conditions in general. However, under hyperoxic conditions, the trend was less prominent. The observed effects could be related to changes in levels of cofilin/LIMK pathway-associated proteins. Adiol had a negative effect when administered together with estradiol, and the proteomic data reveal the activation of ephrin receptor signalling that might be responsible for the attenuation of migration. The results suggest that FZS can differentially regulate pathways involved in the migration of OLN93 cells. A deeper insight into the precise role of endogenous FZS would be an essential prerequisite for developing new treatment strategies including supplementation of estradiol and other steroids in preterm infants.
Thermal and Waterproof Properties of Foamed Concrete with Nano SiO2 Aerogel and Organosilicon Waterproofing Agent
Authors: Cheng Baojun , Gu Xiaowei , Gao Yuxin , Ma Pengfei , Kang Shengrong
Published: May 2022

This paper aimed to improve the thermal and waterproof properties of foamed concrete through the synergic work of nano SiO2 aerogel powder (NSAP) and organosilicon waterproofing agent (OWA). Hence, several series of foamed concrete with a 0–3.0% of OWA and with 1–4% of NSAP addition were developed. The results show that OWA can decrease the dry bulk density, compressive strength, water absorption rate, and thermal conductivity. With the addition of NSAP, the water absorption rate reached a minimum value of 9.2% at 3.0% NSAP, while the thermal conductivity continued decreasing. Through the microscopic pore size distribution, XRD, and SEM, it was found that the addition of NSAP had no effect on the type of hydration product, but it can increase the average pore size and the amount of macropore and weaken the interfacial adhesion between hydration products. Comparing the heat transmission model, the presence of NSAP increased the heat transmission pathway and resistance, resulting in lower thermal conductivity.
Quantitative Evaluation of the Adaptability of the Shield Machine Based on the Analytic Hierarchy Process (AHP) and Fuzzy Analytic Hierarchy Process (FAHP)
Authors: Xie Jiangsheng , Liu Bo , He Lu , Zhong Weiling , Zhao Haiqing , Yang Xingzhi , Mai Tianrui
Published: May 2022

Tunnel engineering based on the shield method is an extremely complex project. During the construction process, many uncertain factors are often faced, which makes it difficult to effectively and quantitatively evaluate the effectiveness of the shield machine. To solve this problem, this paper proposes an adaptability evaluation method for shield machines based on the fuzzy analytic hierarchy process (FAHP) and analytic hierarchy process (AHP). First, the selection and equipment matching of Earth pressure balance shield machines in the shield interval between Tashuiqiao Station and Erxianqiao Station of Chengdu Metro Line 17 are introduced. Then, using APH for screening, the final evaluation indices of the adaptability of the shield machines in this project are determined. Next, referring to the existing research results and fuzzy mathematics method, the membership function of the evaluation indices and adaptability evaluation model of the shield machine are constructed. Finally, the rationality and accuracy of the fuzzy comprehensive evaluation model of shield adaptability are verified through onsite shield construction, and the evaluation results are consistent with the actual situation.
Eccentricity-Based Topological Descriptors of First Type of Hex-Derived Network
Authors: Babar Usman , Naseem Asim , Shaker Hani , Zobair Mian Muhammad , Ali Haidar
Published: May 2022

Graph theory has made significant progress in mathematical chemistry and has gained a lot of traction among scientists due to its numerous applications in mathematical chemistry. The numerical invariants of a molecular structure are known as molecular topological descriptors, and they are highly effective for predicting their bioactivity. A number of such indices are examined and applied in pharmaceutical research, chemistry, medication development, and other fields. The eccentricity-based Zagreb indices, total eccentricity, geometric arithmetic

G

A

4

, atom-bond connectivity

A
B

C

5

, and average eccentricity indices of a hex-derived network of first type are computed in this article. We also provide analytically closed formulas for these descriptors, which may be used to investigate the underlying topologies.
A Mathematical Analysis on the New Fractal-Fractional Model of Second-Hand Smokers via the Power Law Type Kernel: Numerical Solutions, Equilibrium Points, and Sensitivity Analysis
Authors: Rezapour S. , Etemad S. , Sinan M. , Alzabut J. , Vinodkumar A.
Published: May 2022

The second-hand smoke is a phenomenon that needs to be investigated, and its effects on the health of the people are to be examined. To analyze such an issue, the mathematical models are the best tools that help us to study the dynamical behaviors of this phenomenon. For this purpose, in the present paper, we consider a three-compartmental fractal-fractional mathematical model of a specific population of smokers or people that are exposed to second-hand smoke. By assuming some conditions on

ϕ

-

ψ

-contractions and compact operators, we prove some theorems in relation to the existence of solutions. The Banach principle for the usual contractions is used for proving the uniqueness of solutions. Next, by some notions of functional analysis, two types of Ulam-Hyers stability for the fractal-fractional second-hand smoker model are established. Moreover, we have a steady-state analysis and obtain equilibrium points and basic reproduction number

R

0

. Then, we investigate the sensitivity of the fractal-fractional system with respect to each parameter. For numerical simulation, the Adams-Bashforth (AB) method is used to derive numerical schemes for plotting and simulating the approximate solutions. Finally, the obtained solutions are tested with real data and different values of fractal dimensions and fractional orders.
Epigenetic Regulation of IFI44L Expression in Monocytes Affects the Functions of Monocyte-Derived Dendritic Cells in Systemic Lupus Erythematosus
Authors: Luo Shuaihantian , Wu Ruifang , Li Qianwen , Zhang Guiying
Published: May 2022

Background. Interferon-inducible 44 like (IFI44L) is a newly discovered interferon-induced gene and has been reported to overexpress in systemic lupus erythematosus (SLE). However, little is known about the mechanism and function of IFI44L overexpression in SLE. In this study, we aimed to investigate the epigenetic mechanism of IFI44L overexpression in SLE monocyte and its potential functions contributing to the pathogenesis of SLE. Methods. We collected peripheral blood from 20 SLE patients and 20 healthy controls. Expression of IFI44L in monocytes and effects of different signal transducers and activators of transcription (STAT) pathway inhibitors on IFI44L expression were detected. Recruitment of ten-eleven translocation protein (TET) by STAT and methylation of IFI44L promoter were evaluated. Effects of IFI44L overexpression on the expression of surface markers on monocyte-derived dendritic cells (Mo-DCs) were analyzed. T cell differentiation mediated by Mo-DCs and related cytokines production were also analyzed. Results. Expression level of IFI44L was significantly increased in SLE monocyte. IFI44L expression was decreased most significantly in STAT3 inhibitor compared with other inhibitors. STAT3 regulated IFI44L expression and interacted with TET2 which induced DNA demethylation of IFI44L promoter. Overexpression of IFI44L in monocyte enhanced the maturation and functions of Mo-DC by upregulating costimulatory receptors and inducing Th1/Th17-related cytokines when cocultured with naïve CD4+ T cells. Conclusion. TET2 recruited by STAT3 induces DNA demethylation of IFI44L promoter which promotes IFI44L overexpression in monocyte contributing to the pathogenesis of SLE by enhancing the maturation and functions of Mo-DC. IFI44L is expected to become a new target for treatment of SLE.
Observation on the Efficacy of Moxibustion Combined with Ear Acupoint Pressing Beans in Treating Patients with Phlegm Stasis Syndrome Vertigo
Authors: Liu Caidan , Luo Huanwen , Wang Zhongying , Luo Hong , Yu Yanlan
Published: May 2022

Objective. The aim of this study is to investigate the efficacy of moxibustion combined with an ear acupoint pressing bean in the treatment of patients with phlegm stasis syndrome vertigo. Methods. 60 patients with vertigo identified as phlegm stasis syndrome who were hospitalized in our department from May 2020 to May 2021 were selected and divided into a control group and a treatment group of 30 cases each according to the random number method. The control group was treated with conventional treatment and care, and the treatment group was treated with moxibustion combined with ear acupressing beans on top of the conventional group. The treatment effects, the dizziness disorder inventory (DHI), Pittsburgh sleep quality index (PSQI), Hamilton anxiety score (HAMA), TCM symptom score, and blood flow parameters (left vertebral artery flow velocity (LVA), right vertebral artery flow velocity (RVA), and basilar artery flow velocity (BA)) of the two groups were compared with each other during and after the treatment. Results. After implementation, the treatment efficiency of the treatment group was higher than that of the control group, and the treatment group had lower PSQI, HAMA, and DHI scores as well as TCM symptom scores such as vertigo, head heavy as a wrap, chest tightness, and nausea and vomiting than the control group (

P
<
0.05

). In addition, LVA, RVA, and BA were all higher in the treatment group than in the control group after treatment (

P
<
0.05

). Conclusion. Moxibustion combined with ear acupoint pressing bean treatment can clearly improve patients’ sleep quality, psychological state, relieve patients’ various symptoms caused by vertigo, improve blood flow parameters, and have better efficacy in the treatment of phlegm stasis syndrome vertigo.
Mechanical and Water Absorption Properties of Jute/Palm Leaf Fiber-Reinforced Recycled Polypropylene Hybrid Composites
Authors: Gideon Rotich , Atalie Desalegn
Published: May 2022

Hybrid composites have proven endless benefits to the research communities in terms of environmental friendliness, mechanical properties, and development of new material. The present work explores the tensile, flexural, impact strengths, and water absorption properties of jute and palm leaf fiber-reinforced hybrid composites. Five types of hybrid composites were fabricated by varying jute and palm leaf fiber percentage of J100:P0, J25:P75, J50:P50, J25:P75, and J0:P100% with a constant weight ratio of polypropylene matrix. The findings showed that jute-palm leaf fiber-reinforced composite had positive effect on tensile, flexural, and impact strength. Experimental results showed that the J25:P75% (jute and palm leaf fiber) exhibited the highest tensile strength of 62.2 MPa and flexural strength of 82.26 MPa due to the optimal interfacial properties between the fibers and matrix. The impact strength of composites increased as the proportion of palm leaf fiber increased. Water absorption increased with jute fiber content, reaching a maximum of 1.26% at composite J100:P0, with lower water absorption at J0:P100% composite due to the higher moisture content of jute fiber.
Luteolin Inhibits the Biofilm Formation and Cytotoxicity of Methicillin-Resistant Staphylococcus aureus via Decreasing Bacterial Toxin Synthesis
Authors: Sun Yixuan , Sun Fengjun , Feng Wei , Wang Qian , Liu Fang , Xia Peiyuan , Qiu Xuewen
Published: May 2022

Owing to the fact that luteolin has antibacterial activity against Staphylococcus aureus (S. aureus) and methicillin-resistant S. aureus (MRSA), its specific mechanism in MRSA is worthy of investigation, which is the focus of this study. Initially, the collected S. aureus strains were treated with luteolin. Then, the minimum inhibitory concentration (MIC) of luteolin against the S. aureus strains was measured by the broth microdilution. The growth curves, biofilm formation, and cytotoxicity of treated S. aureus were detected using a microplate reader. The live and dead bacteria were evaluated using confocal laser scanning microscopy, the bacterial morphology was observed using scanning electron microscopy, and the S. aureus colony-forming unit (CFU) numbers were assessed. The levels of alpha hemolysin (α-hemolysin), delta hemolysin (δ-hemolysin), and hlaA were detected via western blot and RT-PCR. The mortality of mouse model with S. aureus systemic infection was analyzed, and the levels of IL-6, IL-8, IL-10, and TNF-α were quantitated using ELISA. Concretely, the MIC of luteolin against MRSA N315 was 64 μg/mL. Luteolin at 16 μg/mL did not affect the growth of MRSA N315, but inhibited the biofilm formation and CFU, and promoted the morphological changes and death of MRSA N315. Luteolin decreased the cytotoxicity and the levels of α-hemolysin, δ-hemolysin, and hlaA in MRSA N315, elevated MRSA-reduced mice survival rate, and differentially modulated the inflammatory cytokine levels in MRSA-infected mice. Collectively, luteolin inhibits biofilm formation and cytotoxicity of MRSA via blocking the bacterial toxin synthesis.
Deep Active Learning Framework for Lymph Node Metastasis Prediction in Medical Support System
Authors: Zhuang Qinghe , Dai Zhehao , Wu Jia
Published: May 2022

Assessing the extent of cancer spread by histopathological analysis of sentinel axillary lymph nodes is an important part of breast cancer staging. With the maturity and prevalence of deep learning technology, building auxiliary medical systems can help to relieve the burden of pathologists and increase the diagnostic precision and accuracy during this process. However, such histopathological images have complex patterns that are difficult for ordinary people to understand and require professional medical practitioners to annotate. This increases the cost of constructing such medical systems. To reduce the cost of annotating and improve the performance of the model as much as possible, in other words, using as few labeled samples as possible to obtain a greater performance improvement, we propose a deep learning framework with a three-stage query strategy and novel model update strategy. The framework first trains an auto-encoder with all the samples to obtain a global representation in a low-dimensional space. In the query stage, the unlabeled samples are first selected according to uncertainty, and then, coreset-based methods are employed to reduce sample redundancy. Finally, distribution differences between labeled samples and unlabeled samples are evaluated and samples that can quickly eliminate the distribution differences are selected. This method achieves faster iterative efficiency than the uncertainty strategies, representative strategies, or hybrid strategies on the lymph node slice dataset and other commonly used datasets. It reaches the performance of training with all data, but only uses 50% of the labeled. During the model update process, we randomly freeze some weights and only train the task model on new labeled samples with a smaller learning rate. Compared with fine-tuning task model on new samples, large-scale performance degradation is avoided. Compared with the retraining strategy or the replay strategy, it reduces the training cost of updating the task model by 79.87% and 90.07%, respectively.
Pretension Design and Analysis of Deployable Mesh Antenna considering the Effect of Gravity
Authors: Su Guanlong , Ma Xiaofei , Li Yang , Fan Yesen , Wang Hui
Published: May 2022

The difference between the space and the earth environment has significantly influenced the shape accuracy of the antenna reflector surface. With the increasing demand for the aperture of the antenna reflector, gravity has become one of the main factors that restrict the accuracy. In this paper, a new method for pretension design considering the effect of gravity is proposed. The design surface can be well restored to the ideal surface in orbit. Meanwhile, this method can avoid flipping antenna reflectors or extensive experiments for modification during ground adjustment. Then, the feasibility and effectiveness of the design method are validated by several numerical simulations. Moreover, the results are compared with the previous method and the differences have been discussed in detail. Finally, the effects of cable radius, cable length, and elastic modulus of the mesh reflector have been researched, respectively.
Two-Year Follow-Up of Clinical Efficacy of Femtosecond Laser, Modified Capsular Tension Ring, and Iris Hook-Assisted Surgical Treatment of Lens Subluxation in Patients with Elevated Intraocular Pressure
Authors: Wang Chao , Rui Yuhua , Zhou Yi , Hu Tu , Xia Xiaobo , Jiang Jian
Published: May 2022

Purpose. To evaluate the outcomes of femtosecond laser, modified capsular tension ring, and iris hook-assisted surgical treatment of lens subluxation in patients with elevated intraocular pressure (IOP). Methods. Fifteen patients with lens subluxation and elevated IOP were enrolled in this study. All patients underwent femtosecond-laser-assisted cataract surgery/phacoemulsification/intraocular lens implantation/modified capsular tension ring (MCTR) implantation with iris hook assistance. Uncorrected visual acuity (UCVA), best corrected visual acuity (BCVA), IOP, number of glaucoma medication complications, endothelial cell density (ECD), and tilt of the lens were recorded before and after surgery. All patients were observed for 24 months postoperatively. Results. UCVA and BCVA increased significantly at 1 month, 6 months, 12 months, and 24 months, compared with preoperative UCVA and BCVA (

P
<
0.001

). IOP significantly decreased at 1 month, 3 months, 6 months, 12 months, and 24 months, compared with preoperative IOP (

P
<
0.001

). 3 patients received glaucoma medications to control IOP after surgery. All medications were discontinued at 3 months postoperatively. Conjunctival redness or hemorrhage was observed in 11 patients (73.3%); transient corneal edema was observed in 3 patients (20.0%); and posterior capsule opacification occurred in 1 patient (6.67%). The ECD and tilt of the lens are within an acceptable range. Conclusion. The combined use of a femtosecond laser, MCTR, and iris hooks is an effective and safe method for treating patients with lens subluxation and elevated IOP.
Alarm Signal S100-Related Signature Is Correlated with Tumor Microenvironment and Predicts Prognosis in Glioma
Authors: Wang Lin-jian , Lv Peipei , Lou Yongli
Published: May 2022

Glioma are the most common malignant central nervous system tumor and are characterized by uncontrolled proliferation and resistance to therapy. Dysregulation of S100 proteins may augment tumor initiation, proliferation, and metastasis by modulating immune response. However, the comprehensive function and prognostic value of S100 proteins in glioma remain unclear. Here, we explored the expression profiles of 17 S100 family genes and constructed a high-efficient prediction model for glioma based on CGGA and TCGA datasets. Immune landscape analysis displayed that the distribution of immune scores, ESTIMATE scores, and stromal scores, as well as infiltrating immune cells (macrophages M0/M1/M2, T cell CD4+ naïve, Tregs, monocyte, neutrophil, and NK activated), were significant different between risk-score subgroups. Overall, we demonstrated the value of S100 protein-related signature in the prediction of glioma patients’ prognosis and determined its relationship with the tumor microenvironment (TME) in glioma.
Adaptive Bandwidth Prediction and Smoothing Glitches in Low-Latency Live Streaming
Authors: Wu Dapeng , Cui Linfeng , Tang Tong , Wang Ruyan
Published: May 2022

HTTP adaptive streaming (HAS) technologies such as dynamic adaptive streaming over HTTP (DASH) and common media application format (CMAF) are now used extensively to deliver live streaming services to large numbers of viewers. However, in dynamic networks, inaccurate bandwidth prediction may result in the wrong request of bitrate, and short-term network fluctuations may produce glitches, causing unnecessary bitrate switching, thereby degrading clients' Quality of Experience (QoE). To tackle this, we propose adaptive bandwidth prediction and smoothing glitches in low-latency live streaming (called APSG) in this article. Concretely, firstly, the size of random bandwidth fluctuations is exploited as the weight of exponentially weighted moving average (EWMA) for adaptive bandwidth prediction; in addition to bandwidth prediction and buffer occupancy, glitches phenomena under a stable network environment are taken into account to enhance the viewing experience of clients. Finally, experimental results show that compared to traditional ABR algorithms under a stable network environment, APSG could reduce the number of bitrate switches and latency by up to 72.6% and 27.3%, respectively; under a dynamic network environment, APSG could reduce the number of bitrate switches and latency by up to 53.8% and 23.6%, respectively.
Optimizing Convolutional Neural Networks with Transfer Learning for Making Classification Report in COVID-19 Chest X-Rays Scans
Authors: Amin Samina , Alouffi Bader , Uddin M. Irfan , Alosaimi Wael
Published: May 2022

The coronavirus disease (COVID-19) outbreak, which began in December 2019, has claimed numerous lives and impacted all aspects of human life. COVID-19 was deemed an outbreak by the World Health Organization (WHO) as time passed, putting a tremendous strain on substantially all countries, particularly those with poor health services and delayed reaction times. This recently identified virus is highly contagious. Controlling the rapid spread of this infection requires early detection of infected people through comprehensive screening. For COVID-19 viral diagnosis and follow-up, chest radiography imaging is an excellent tool. Deep learning (DL) has been used for a variety of healthcare purposes, including diabetic retinopathy detection, image classification, and thyroid diagnosis. DL is a useful strategy for combating the COVID-19 outbreak because there are so many streams of medical images (e.g., X-rays, CT, and MRI). In this study, we used the benchmark chest X-ray scan (CXRS) dataset for both COVID-19-infected and noninfected patients. We evaluate the results of DL-based convolutional neural network (CNN) models after preprocessing the scans and using data augmentation. Transfer learning (TL) is used to improve the algorithm’s classification performance for chest radiography imaging. Finally, features of the attention and feature interweave modules are combined to create a more accurate feature map. The architecture is trained for COVID-19 CXRS using CNN, and the newly generated feature layer is applied to TL architecture. The experimental results found that training enhances the CNN + TL algorithm’s ability to classify CXRS with an overall detection accuracy of 99.3%, precision (0.97), recall (0.98), f-measure (0.98), and receiver operating characteristic (ROC) curve (area = 0.97). The results show that further training improves the classification architecture’s performance by 99.3%.
Acoustic Signal NLOS Identification Method Based on Swarm Intelligence Optimization SVM for Indoor Acoustic Localization
Authors: Kan Ruixiang , Wang Mei , Zhou Zou , Zhang Peng , Qiu Hongbing
Published: May 2022

The demand for an indoor localization system is increasing, and related research is also becoming more universal. Previous works on indoor localization systems mainly focus on the acoustic signals in Line of Sight (LOS) scenario to obtain accurate localization information, but their effectiveness in Nonline of Sight (NLOS) scenario remains comparatively untouched. These works are usually less efficient as the acoustic signals often bring diffraction, refraction, scattering, energy decays, and so on in NLOS environments. So the system needs adjusting accordingly in a complex NLOS scenario based on NLOS identification results. Therefore, the identification of NLOS acoustic signal turns out to be significant in the indoor localization system. If the system only uses original support vector machine (SVM) to complete NLOS identification, the result turns out to be poor by our test. To address this challenge, we propose a novel indoor localization system, named ZKLocPro, which utilizes an advanced swarm intelligence method to optimize the traditional SVM classification model to deal with NLOS acoustic signal identification. Its results can help the system adjust the localization process if necessary in a complex NLOS scenario. Obviously, it is also significant to build our own NLOS data set, which is suitable for an indoor localization system’s situation. Specifically, four methods are added: (1) new LOS and NLOS acoustic localization signal sample production, rearrangement, and reselecting process; (2) advanced parameter optimization process; (3) elitist strategy; and (4) inertia weight nonlinear decrement. The experimental result shows that our system is efficient and performs better than state-of-the-art congeneric works even in a complex NLOS scenario.
Prediction of Response to Radiotherapy by Characterizing the Transcriptomic Features in Clinical Tumor Samples across 15 Cancer Types
Authors: Xu Yu , Tang Chao , Wu Yan , Luo Ling , Wang Ying , Wu Yongzhong , Shi Xiaolong
Published: May 2022

Purpose. Radiotherapy (RT) is one of the major cancer treatments. However, the responses to RT vary among individual patients, partly due to the differences of the status of gene expression and mutation in tumors of patients. Identification of patients who will benefit from RT will improve the efficacy of RT. However, only a few clinical biomarkers were currently used to predict RT response. Our aim is to obtain gene signatures that can be used to predict RT response by analyzing the transcriptome differences between RT responder and nonresponder groups. Materials and Methods. We obtained transcriptome data of 1664 patients treated with RT from the TCGA database across 15 cancer types. First, the genes with a significant difference between RT responder (R group) and nonresponder groups (PD group) were identified, and the top 100 genes were used to build the gene signatures. Then, we developed the predictive model based on binary logistic regression to predict patient response to RT. Results. We identified a series of differentially expressed genes between the two groups, which are involved in cell proliferation, migration, invasion, EMT, and DNA damage repair pathway. Among them, MDC1, UCP2, and RBM45 have been demonstrated to be involved in DNA damage repair and radiosensitivity. Our analysis revealed that the predictive model was highly specific for distinguishing the R and PD patients in different cancer types with an area under the curve (AUC) ranging from 0.772 to 0.972. It also provided a more accurate prediction than that from a single-gene signature for the overall survival (OS) of patients. Conclusion. The predictive model has a potential clinical application as a biomarker to help physicians create optimal treatment plans. Furthermore, some of the genes identified here may be directly involved in radioresistance, providing clues for further studies on the mechanism of radioresistance.
Serum 25(OH)D Levels Modify the Association between Triglyceride and IR: A Cross-Sectional Study
Authors: Gong Rongpeng , Tang Xin , Jiang Ziying , Luo Gang , Dong Chaofan , Han Xiuxia
Published: May 2022

Background. Triglycerides and 25(OH)D had been reported as correlates of IR, but the results suggest substantial heterogeneity across races. In addition, little research reported on whether different 25(OH)D levels affect triglycerides and IR. Therefore, a similar study on the US population would be a great addition to the current one. This study investigated the association between triglycerides and IR at different 25(OH)D levels. Methods. A total of 19,926 participants were included, each containing specific indicators for the study project. IR was estimated as a HOMA-IR index ≥2.73. Four multivariate logistic regression models were developed to analyze the association between TG and IR and whether different 25(OH)D levels influenced this association. Smoothed fitting curves were plotted. Results. Triglyceride was significantly associated with IR (OR: 1.3, 95 CI %), while this association received different 25(OH)D levels (Pfor interaction <0.001). The effect value OR was 1.33 with the high levels, and its effect value OR was 1.28 with the low levels. Conclusion. This study demonstrates that triglyceride levels are significantly associated with insulin in the US adult population and can be used as a predictor of IR. This correlation was compromised at different 25 (OH)D levels, so future studies need to be explored in more ethnically diverse contexts.
Multidimensional Nonlinear Landscape Planning Based on Parameter Feature Extraction and Multimedia Technology
Authors: Liu Tiesheng
Published: May 2022

The development of information network and digital technology has improved people’s quality of life. Parametric design can reflect the thinking logic of users and the level of computer-aided design. Taking parameter features as the research object, this study makes multidimensional nonlinear landscape planning with multimedia technology. The application of multimedia technology will pave the way for the reference of computer design program, lead to multidimensional nonlinear landscape image analysis, sample and analyze it, and change the parameter characteristics through simulation to realize the optimization of landscape design. The research data show that the landscape correlation degree is 0.344–0.724. The similarity coefficient is between 0.433–0.645. The contribution level is between 0.323–0.774. The ambiguity factor is between 0.555–0.433. In this study, by simulating the spatial vision of the scene and changing the parameters, we can find the best scheme and get good results. Verify the real validity of the experimental data.
Artificial Intelligence Technology Based on Deep Learning in Building Construction Management System Modeling
Authors: Wang Hongbo , Hu Yan
Published: May 2022

In order to explore the application of AI technology in construction management system modeling, the author proposed the application of a deep learning-based AI technology in construction management system modeling. The 3 D reconstruction deep learning model is first introduced, and then the model idea of the construction progress reliability control system is designed based on BIM (building information model). Second, the construction process of the 4dbim model is described, and the construction method is introduced. The construction of the model provides data information for the construction schedule reliability control system. Finally, the three functional modules of progress monitoring, progress reliability early warning, and progress prediction are realized by combining the S-curve comparison method, and the work of the system is described through case simulation. The early warning result is from June 7 to June 11, the progress deviation is between (−2%, 2%), and the progress is basically controlled. On June 13, the planned workload was 81.099%, and the actual cumulative workload was 7.099%, which was 4% less than the planned workload. The project progress was out of date, so it needs to be closely tracked. On June 15, the planned workload was 85.511%, and the actual cumulative workload was 80.899%, 4.5% less than the planned workload. The forecast result is the line forecast of the actual cumulative completion percentage on June 17. After calculation, the forecast result on June 17 is 84.311%. The progress deviation on June 17 was 5.21%. If no timely delay is taken on June 15, the delay will get worse. In addition, the system can predict the completion period of the project. When the actual percentage of cumulative completion is greater than or equal to 100%, it indicates that the project has been completed. Therefore, we can calculate the completion period of the three-storey project, and the construction period is about June 29 or 30. When the simulation can be carried out, the simulation number is set as 1000 times, the completion probability of the project is only 40%, and the completion probability is not too high. Artificial intelligence technology can realize progress monitoring, progress reliability early warning, and progress prediction. This system model prepares for software development and is conducive to improving the progress reliability control level of construction enterprises. Starting from the schedule planning subsystem and the schedule control subsystem, this paper studies the application of the artificial intelligence technology based on deep learning in the modeling of a building construction management system. The results show that this technology can effectively improve the efficiency of the building construction schedule management. Compared with the existing management methods, it shows great advantages in terms of operating costs and ease of use. It also promotes the application of artificial intelligence technology in the construction phase.
Risk Management of Investment Projects Based on Artificial Neural Network
Authors: Deng Limei , Chang Ying
Published: May 2022

The benefit evaluation of investment projects is the key to the whole investment activities. This paper mainly describes the risk management of investment projects using an artificial neural network. It generally adopts the index system of project risk through modern scientific measurement methods, to evaluate whether the investment project of artificial neural network is feasible or not. It establishes a benefit evaluation model based on an artificial neural network, from the analysis and consideration of 4 groups of experiments, comparing four sets of data: BP network convergence rate, artificial neural network identification efficiency, enterprise risk, artificial neural network output, and error; it is concluded that the relative risk is reduced by about 20% after using the artificial neural network. This also verifies the feasibility of artificial neural networks in the application of raw materials.
Research on Prediction and Analysis of Real Estate Market Based on the Multiple Linear Regression Model
Authors: Liu Guangjie
Published: May 2022

Since the housing reform in the 1990s, China’s real estate market has expanded and developed rapidly. It has quickly become the pillar of China’s national economy and made a great contribution to China’s economic growth. However, China’s real estate market started late and its development is not perfect, and house prices show obvious volatility. Accurate prediction of house prices is conducive to the government issuing appropriate regulatory policies, helping investors formulate correct investment strategies, and guiding the healthy and long-term development of the real estate market. Based on the statistical data of the real estate market, this study analyzes the influencing factors of the real estate price, establishes a multiple linear regression model, uses the least square method to solve the unknown parameters of the model, and finally constructs a house price prediction model to predict and analyze the real estate market. The results show that the maximum error of the real estate price prediction model is no more than 8%. The above results show that the house price prediction model has high accuracy and can predict house prices accurately and efficiently.