bluuebunny/crossref_metadata_2025 huggingface dataset:
RISK-AVERSION: MATHEMATICAL AND ECONOMIC PERSPECTIVES
Authors: LEVY HAIM
Published: April 2022

A necessary condition for a dominance of prospect [Formula: see text] over prospect [Formula: see text] by all commonly employed investment criteria is [Formula: see text]. Because of this requirement, optimal investment rules fail to distinguish between two alternative prospects where in practice one prospect is preferred over the other by all investors — a paradoxical result. We suggest the general almost second-degree stochastic dominance rule (GASSD) that relaxes this necessary condition, resolves the existing paradoxes and distinguishes between mathematically and economically relevant preferences. In the comparison of stocks and bonds for various horizons, the GASSD rule does not support the idiom “stocks for the long run”.
3D Computational Model for an Electrochemical Gas Separation and Inerting System
Authors: Aryal Utsav Raj , Aziz Majid , Prasad Ajay K.
Published: April 2022

Aircraft fuel tank inerting is employed to reduce the flammability of the fuel vapor in the ullage (air volume above the fuel) by restricting its oxygen concentration to a safe value—12% for commercial aircraft and 9% for military aircraft. Inerting is typically accomplished by displacing oxygen in the ullage with an inert gas like nitrogen. Electrochemical gas separation and inerting system (EGSIS) is an on-board method to generate and supply nitrogen-enriched air (NEA) to the fuel tank. EGSIS combines a polymer electrolyte membrane (PEM) electrolyzer anode which dissociates water to evolve oxygen, and a PEM fuel cell cathode which reduces oxygen from atmospheric air to produce NEA at its outlet. This paper represents the first attempt to model and simulate EGSIS using a three-dimensional, steady state, isothermal model. Various EGSIS performance indicators such as current density, reactant concentration distribution, and polarization curves are studied as a function of operating conditions and design parameters. The results from the computational model are validated against our previous experimental results for various operating conditions. The simulation results reveal the effects of temperature, reactant flowrates, and material property optimization on EGSIS performance. Different operating strategies are explored with the goal of improving system performance.
Construction of Swimmer's Underwater Posture Training Model Based on Multimodal Neural Network Model
Authors: Wen Wei , Yang Tingyu , Fu Yanhao , Liu Siwen
Published: April 2022

Swimming monitoring based on acceleration sensor is an emerging research direction in the field of human motion recognition. As a public sport, swimming has a wide audience. The swimming monitoring system can facilitate people to monitor and record their own swimming data, so as to formulate a reasonable training plan. Aiming at the defects of single modal information representation ability, high contingency, and easy to be influenced by the outside world, this paper adopts the underwater posture training model of swimmers to perform multimodal information fusion. In this paper, a multimodal information fusion method based on evolutionary neural network is proposed, and an intelligent perception information processing model of the intelligent subject system is constructed. Aiming at the defect that the accuracy and speed of the underwater posture monitoring of swimmers cannot be guaranteed in a complex environment, an evolutionary neural network optimized by a multimodal adaptive genetic algorithm is constructed to perform multimodal information fusion to ensure the effectiveness of the system in the face of complex information. Regarding attitude detection, it mainly uses the three dimensions of the angle of movement, the influence of gravity, and the strength and speed of the movement to measure. The MPU6050 module processor has a wide range of applications and is a mold processing tool with high performance and level. It completes the data processing, data calculation, and data storage of the inspection system in this paper. This paper further studies the working principle, structure, and operation process of this module and adjusts the time error in the detection of carrier motion and attitude so that the processing function of this module can play an optimal state. Four kinds of swimming posture measurement experiments were carried out on the swimmers, and the experimental data were analyzed. The whole system is controlled by the host computer man-machine interaction software remotely and in real time through commands. The experimental results show that the system realizes the detection of the basic posture, meets the basic requirements of the system design, and provides a certain foundation for the follow-up research.
Long Noncoding RNA CBR3-AS1 Promotes Stem-like Properties and Oxaliplatin Resistance of Colorectal Cancer by Sponging miR-145-5p
Authors: Xie Liangbao , Cui Guangfei , Li Tao
Published: April 2022

Accumulating articles indicate that long noncoding RNAs (lncRNAs) serve as essential regulators in a plethora of human cancers. In this study, we analyzed the expression profile and functional role of lncRNA CBR3-AS1 in colorectal cancer (CRC). High-expression levels of CBR3-AS1 were found in CRC tissues and cell lines. Upregulated CBR3-AS1 was closely associated with poor prognosis and adverse clinicopathological features of CRC patients. Artificial knockdown of CBR3-AS1 markedly suppressed the proliferation, migration, invasion, and stem-like properties but promoted the apoptosis of CRC cells. Moreover, we observed that CBR3-AS1 could directly bind to miR-145-5p and negatively regulated its expression in CRC. Further experiments also demonstrated that inhibition of miR-145-5p reverted the effects of CBR3-AS1 knockdown on CRC cells. In addition, compared with the parental cells, CBR3-AS1 expression was strikingly increased in oxaliplatin- (OXA-) resistant CRC cells, and the OXA resistance was notably diminished by CBR3-AS1 knockdown. To conclude, our study suggested that CBR3-AS1 serves an oncogenic role in CRC and may be exploited as a novel therapeutic target for CRC patients.
Energy Security and Economic Development Based on Complex Computer System Dynamic Model
Authors: Li Tiantian , Xu Jun
Published: April 2022

Energy security has become the most prominent task during certain complex environmental conditions. In any part of the world, energy is the determinant of the country’s economy and hence needs to focus on developing the economy. Energy can be categorized as renewable and nonrenewable resources of the country; irrespective of the type, security has to be provided both at the demand and supply parts. Intelligent system is used for evaluating the economic development and energy security of the country. In this research, decision-making unit (DMU) method is implemented to predict the requirement of energy security and the economic development of the country through energy resources in the dynamic evolving world is considered. The study results proved that the proposed model helps in forecasting the need of economic development and energy security to a greater extent. The proposed model is compared with the existing fuzzy prediction model for the analysis, and it is observed that the proposed model has achieved 98.78% of accuracy in the economic prediction.
Perinatal Catatonia in a Patient with a Twin Pregnancy of Unknown Chorionicity and Gestational Age Presenting in Spontaneous Preterm Labor
Authors: Farias Ryan , Hartnett Janice
Published: April 2022

Catatonia during pregnancy is rare and presents unique challenges due to the potential ramifications to mom and baby of the overall disease state and of potential treatment options. The purpose of this case report is to highlight the complexities in the workup and management of a catatonic patient with concurrent acute obstetric concerns requiring urgent intervention. We report a case of acute catatonia due to underlying major depressive disorder in a patient who presented in spontaneous preterm labor, with a twin pregnancy of unknown chorionicity with no known prenatal care. She underwent an extensive workup with no significant findings on lumbar puncture, brain MRI, metabolic labs, and EEG. After exclusion of several acute underlying conditions, a presumptive diagnosis of catatonia secondary to exacerbation of underlying major depressive disorder was made. She was transferred to an inpatient psychiatric facility postdelivery and treated with a course of lorazepam, aripiprazole, and escitalopram with good effect.
City-Wide NB-IoT Network Monitoring and Diagnosing
Authors: Hu Zhenxian , Xue Guangtao , Chen Yichao , Li Minglu , Wang Ming , Lv Feng
Published: April 2022

NarrowBand-IoT (NB-IoT) is a radio-access technology standardized by 3GPP to support a large set of use cases associated with the rapid deployment of massive machine-type communications. NB-IoT facilitates the connection of devices in inaccessible areas, extends battery life, and reduces device complexity. Unfortunately, the opacity of the underlying schema (i.e., the way that these benefits are achieved) makes it very difficult for most users and developers to manage deployment scenarios. In this study, we built an embedded system comprising a Raspberry Pi with an NB module, referred to as NBPilot, which interacts with NB networks to identify essential signaling messages transmitted by a Qualcomm NB modem. This system gives researchers and developers an unprecedented understanding of network behavior as well as the ability to adjust them to their particular requirements. We employed the-state-of-the-art machine learning techniques for modeling and the analysis of NB performance. The efficacy of the proposed NBPilot system was established by applying it to a metropolitan NB-IoT network with over 2,000 NB sites for the collection and testing of data trace as well as the validation of a cellular station prior to going online. The developed machine learning techniques predict the actual performance (rate, delay, and power consumption) of the user equipment’s connection against the QoS advertised by the operator and pinpoint the affecting factors.
Infinite Product Representation for the Szegö Kernel for an Annulus
Authors: Gafai Nuraddeen S. , Murid Ali H. M. , Wahid Nur H. A. A.
Published: April 2022

The Szegö kernel has many applications to problems in conformal mapping and satisfies the Kerzman-Stein integral equation. The Szegö kernel for an annulus can be expressed as a bilateral series and has a unique zero. In this paper, we show how to represent the Szegö kernel for an annulus as a basic bilateral series (also known as

q

-bilateral series). This leads to an infinite product representation through the application of Ramanujan’s sum. The infinite product clearly exhibits the unique zero of the Szegö kernel for an annulus. Its connection with the basic gamma function and modified Jacobi theta function is also presented. The results are extended to the Szegö kernel for general annulus and weighted Szegö kernel. Numerical comparisons on computing the Szegö kernel for an annulus based on the Kerzman-Stein integral equation, the bilateral series, and the infinite product are also presented.
Dynamic Analysis and Fabrication of Single Screw Conveyor Machine
Authors: Moorthi Sundarraj , Megaraj Meikandan , Nagarajan Lenin , Karthick Alagar , Bharani Murugesan , Patil Pravin P.
Published: April 2022

The design and development of machine tools play a vital role in the current economic growth. It facilitates the reduction of manufacturing cost coping with a quickly changing business environment. During the design of the machine tool, the dynamic characteristics need to be calibrated for avoiding uncertainties. This paper investigates the design, dynamic characteristics, and development of a single screw conveyor machine which is used to blend waste plastic material with catalysts. The conveyor machine model is created based on the idea developed from filament extruder machine with Pro-E solid modelling software. Its multibody dynamic analysis was carried out for the selected operational requirements in the ADAMS View. It shows that when the conveyor screw operated above the velocity of 42 m/s, the deformation will occur at a maximum value of 4.23 mm. The rigid body dynamic was carried out in ANSYS to solve the performance forecasting problems at their design stage. The dynamic analysis results were suggested to improve the conveying machine and driving mechanisms design parameters.
Application Value of Real-Time Ultrasonic Elastograph with Serum Human Epididymis Protein 4, Interleukin-33, and Carbohydrate Antigen 153 in Diagnosis of Early Cervical Cancer
Authors: Guo Maoping , Liang Lingling , Wu Litao , Xie Daolin , Li Jian
Published: April 2022

Objective. To explore the application value of real-time ultrasonic elastograph (USE) with serum human epididymis protein 4 HE4, interleukin-33 (IL-33), and carbohydrate antigen 153 (CA153) in the diagnosis of early cervical cancer. Methods. A total of 120 cervical cancer patients treated in our hospital (06, 2019–06, 2021) and meeting the study criteria were screened and divided into the benign group (BG, n = 70) and malignant group (MG, n = 50) according to their final diagnostic results, and healthy females who received physical examination in our hospital in the same period were selected as the control group (CG, n = 60). Patients in the three groups received real-time USE and detection of serum HE4, IL-33, and CA153 so as to analyze the diagnostic value of single examination and combined examination in diagnosing early cervical cancer. Results. The patients’ real-time USE score, Emax, Emean, and elastic fibers were significantly higher in the MG than those in the BG (

P
<
0.05

), and the patients’ real-time USE Emin, stroma ratio and collagen fibers were significantly lower in the MG than those in the BG (

P
<
0.05

); the HE4, IL-33, and CA153 levels were obviously higher in the MG than those in the BG (

P
<
0.05

) and were significantly higher in the BG than those in the CG (

P
<
0.05

); the positive detection rate of combining real-time USE with serum HE4, IL-33, and CA153 was higher than that of single examination, and the diagnostic accuracy rate, sensitivity, specificity, positive predictive value, and negative predictive value of the combined examination were significantly higher than those of single examination (

P
<
0.05

); according to the diagnostic efficacy of single examination and combined examination in diagnosing early cervical cancer by ROC curve, it was combined diagnosis > real-time USE > HE4 > CA153 > IL-33. Conclusion. Combined examination of real-time USE and serum HE4, IL-33, and CA153 has higher diagnostic value in diagnosing early cervical cancer, which can obviously improve the diagnostic accuracy rate of cervical cancer.
Optimal Design of Sliding Bearings Based on Artificial Intelligence Algorithm and CFD Simulation
Authors: Guo Caiping
Published: April 2022

Sliding bearings have a long history, simple manufacturing, and low cost. However, the function of sliding bearings is very large. As a key component of rotating machinery, thrust sliding bearing is directly related to the overall performance of the machine. The machines that can be seen in life, whether simple or complicated, have been used. However, with the development of science and technology and economy, all kinds of new things have been inoculated. One of the current issues is how sliding bearings can continue to keep machinery stable. CFD is the abbreviation of English computational fluid dynamics (computational fluid dynamics). It is developed with the development of computer technology and numerical computing technology. Simply put that CFD is equivalent to “virtually” doing experiments on the computer to simulate the actual fluid flow. The purpose of this article is to study the optimal design of sliding bearings by artificial intelligence algorithms and CFD simulation. This paper uses intelligent design methods to optimize the appearance, shape, and manufacturing process of sliding bearings and to carry the bearing capacity, friction coefficient and temperature rise of sliding bearings. Comprehensive research then establishes the objective function. The finite element algorithm, genetic algorithm, and matrix theory are used to calculate the stability of the sliding bearing under various conditions. The artificial intelligence algorithm is used to sort the calculated data. The CFD simulation is used to obtain the most reasonable result. Algorithm, as well as the appearance, shaft diameter, and material of the plain bearing, maintains stability under various conditions. The experimental data show that the classification using artificial intelligence algorithms and CFD simulation can significantly improve the performance of sliding bearings. It is found that the relationship between the sliding bearings is related to the speed, the diameter of the bearing, and other factors. Experimental data show that artificial intelligence algorithms and CFD simulation can provide reliable data references for the optimal design of sliding bearings, and the optimized sliding bearings can meet the stability under relevant conditions.
Hydrate Dissociation Model with Time Fractional Derivative
Authors: Fang Xinyu , Lian Hairong , Luo Wanjing , Liu Mingzhu , Chen Changfu , Wang Qian
Published: April 2022

In this paper, we shall investigate fractional partial differential equations with fractional moving boundary condition to study the dissociation of natural gas hydrate under heat injection. The moving boundary separates the hydrate reservoir into the dissociated zone and the hydrate one. By using the self-similar transformation and Wright function, we obtain the explicit solutions for two zones. We present simulations with steam and hot water injection and show the dissociation temperature in graphical mode from injection temperature to reservoir temperature with respect to the time, distance, and fractional order. Our analysis of fractional model turns out to be a successful generalization of the classical one; i.e., it can well describe the dissociation of natural gas hydrate and is theoretically consistent with the existing integer hydrate dissociation model. When the factional order tends to 1, the “limit solution” becomes the classical one.
Removal of Methylene Blue from Aqueous Solution Using Black Tea Wastes: Used as Efficient Adsorbent
Authors: Ullah Azmat , Zahoor Muhammad , Din Waqar Ud , Muhammad Mian , Khan Farhat Ali , Sohail Aamir , Ullah Riaz , Ali Essam A. , Murthy H. C. Ananda
Published: April 2022

The biosorbent black tea wastes (BTW) after preliminary treatments was used in this study for the removal of methylene blue (M.B) from aqueous solution. The removal of M.B from aqueous solution was studied as a function of time, initial concentration of M.B temperature, pH, and BTW dosage. The optimum time for equilibration was achieved in 3 min. The optimum dosage of adsorbent was found to be 0.4 g. Various kinetic models were applied to the sorption kinetic data in which the obtained data was best explained by the pseudo-second-order model ([Formula: see text]) with a rate constant K2 of 0.0714–0.0763 g.mg-1 min-1. Additionally, the calculated amount of adsorption was approximately equal to the experimentally determined value. The isotherm data was best fitted to the Langmuir model rather than the Freundlich model. The intraparticle diffusion model exhibited the process to be diffusion dependent. The various organic functional groups on the surface of BTW played a significant role in the sorption of the selected dye. Consequently, BTW has the prospective to act as a potential sorbent for the removal of other contaminants from aquatic media as well.
Theory and Computations for the Nonlinear Burgers’ Equation via the Use of Sinc-Galerkin Method
Authors: Al-Momani Anwar , Al-Khaled Kamel
Published: April 2022

In this research, Burgers’ equation, which is well known to be nonlinear partial differential equation, has many applications for studying some physical phenomena in the disciplines we mention, water waves, plasma waves, and ion acoustic plasma waves. The paper demonstrates a comprehensive performance assessment for the use of sinc methodology to solve Burgers’ equation; the method used in this research depends mainly on the use of the sinc function as a basis, where the equation under consideration was converted into an integral equation of Volterra type. Then, thex−derivatives were approximated and replaced by their corresponding sinc matrices and the resulting integral equation was also treated by approximating the integral via the use of definite integral formula for sinc functions. The method used is the sinc method, taking into account the domain on thex−axis and the time axistt, where transformation functions intervened to make the problem applicable in their domains. The solution has been converted into an algebraic equation that is easy to deal with through any iterative method. As for the proof, the convergence of the resulting solution has been proven using the fixed-point method. It turns out that the approximate solution to the problem under consideration converges to the exact solution with an exponential order. Some examples were presented, and the effectiveness and accuracy of the method were shown by displaying the results in tables and graphs, which showed the efficacy and ease of the sinc method.
Thyroid Dysfunction after Gonadotropin-Releasing Hormone Agonist Administration in Women with Thyroid Autoimmunity
Authors: Marin Loris , Ambrosini Guido , Noventa Marco , Filippi Flavia , Ragazzi Eugenio , Dessole Francesco , Capobianco Giampiero , Andrisani Alessandra
Published: April 2022

GnRH agonists (GnRHa) are a useful tool for pretreatment before artificial endometrial preparation for frozen-thawed embryo-transfer (FET). Their prolonged administration has been associated with thyroid dysfunction, both hyper and hypothyroidism. The aim of this study is to investigate the impact of GnRHa administration on thyroid function in women undergoing artificial endometrial preparation. Seventy-eight euthyroid women undergoing endometrial preparation with hormone replacement for FET were retrospectively reviewed. They were divided into two groups according to pretreatment with GnRHa (group A, 42 women) or with an oral contraceptive (group B, 36 women). Group A was subsequently divided into two subgroups according to thyroid autoimmunity presence. Thyroid function has been evaluated and compared among groups and subgroups. Our results did not show any statistically significant differences in age, body mass index, and basal thyroid stimulating hormone (TSH). Total estradiol dosage, duration of treatment, and endometrial thickness were comparable among groups. When TSH was measured 14 days after embryo transfer, no significant differences between the two groups were reported. Among women of group A, TSH was significantly higher only in women with thyroid autoimmunity. GnRHa seems to be associated with thyroid dysfunction in women with thyroid autoimmunity undergoing hormone replacement therapy for FET.
Alpha Power Moment Exponential Model with Applications to Biomedical Science
Authors: Shrahili M. , Hassan Amal S. , Almetwally Ehab M. , Ghorbal Anis Ben , Elbatal Ibrahim
Published: April 2022

The moment exponential distribution has recently been generalized by a number of authors. The two-parameter alpha power-transformed moment exponential (APTME) distribution is introduced. In terms of fit, the APTME distribution outperforms the moment exponential distribution. Exact expressions for ordinary moments, incomplete and conditional moments, the moment generating function, the cumulant generating function, and information measures are obtained for some APTME distribution features. To estimate the model parameters, six well-known frequentist techniques were applied. The behavior of the various estimators was investigated using a simulated exercise. To examine the practical significance of the APTME distribution, real-world datasets were used. In terms of performance, we show that the APTME distribution beats other models.
Assessment of Factors Affecting Time to Recovery from COVID-19: A Retrospective Study in Ethiopia
Authors: Dessie Anteneh Mengist , Feleke Sefineh Fenta , Anley Denekew Tenaw , Anteneh Rahel Mulatie , Demissie Zelalem Animut
Published: April 2022

Background. The average duration of recovery from COVID-19 and influencing factors, which would help inform optimal control strategies, remain unclear. Moreover, studies regarding this issue are limited in Ethiopia, and no region-wise studies were conducted. Hence, this study aimed to investigate the median recovery time from COVID-19, and its predictors among patients admitted to Amhara regional state COVID-19 treatment centers, Ethiopia. Methods. A facility-based retrospective follow-up study was conducted at Amhara regional state COVID-19 treatment centers from 13 March 2020 through 30 March 2021. Data were entered using EpiData version 3.1, and STATA version 14 was used for analysis. A Kaplan–Meier curve was used to estimate survival time, and the Cox regression model was fitted to identify independent predictors.

P

value with 95% CI for the hazard ratio was used for testing the significance at alpha 0.05. Results. Six hundred twenty-two cases followed, and 540 observations developed an event at the end of the follow-up. The median time to recovery was 11 days with an interquartile range of 9–14 days. Most of the patients were recovered from COVID-19 between days seven and fourteen. In the first six days of admission, only 4.2% of cases had recovered, but by day 14, 73.8% had recovered. Patients without comorbid illness/s were faster to recover than their counterparts (AHR = 1.44 : 95% CI: 1.10, 1.91) and those who have signs and symptoms on admission (AHR = 0.42 : 95% CI: 0.30, 0.60) and old-aged (AHR = 0.988; 95% CI: 0.982, 0.994) took longer to recover. Conclusion. In conclusion, a relatively short median recovery time was found in this study. Significant predictors for delayed recovery from COVID-19 were older age, presence of symptoms at admission, and having at least one comorbid condition. These factors should be placed under consideration while developing a strategy for quarantining and treating COVID-19 patients.
Neonatal Tactile Stimulation Downregulates Dendritic Spines in Layer V Pyramidal Neurons of the WAG/Rij Rat Somatosensory Cortex
Authors: Ilbay Gul , Balıkcı Aymen , Köktürk Sibel , Yılmaz Melda Yardımoglu , Ates Nurbay , Baydemır Canan , Balcı Sibel
Published: April 2022

Objective. The aim of our study is to examine the effects of neonatal tactile stimulations on the brain structures that previously defined as the focus of epilepsy in the Wistar-Albino-Glaxo from Rijswijk (WAG/Rij) rat brain with genetic absence epilepsy. Methods. In the present research, morphology and density of dendritic spines were analyzed in layer V pyramidal neurons of the somatosensory cortex (SoCx) of WAG/Rij rats (nonstimulated control, tactile-stimulated, and maternal separated rats) and healthy Wistar (nonepileptic) rats. To achieve this, a Golgi-Cox method was used. Results. Dendritic spine number in layer V of the SoCx has been detected significantly higher in adult WAG/Rij rats at postnatal day 150 in comparison to nonepileptic adult control Wistar rats (

p
<
0.001

). Moreover, quantitative analyses of dendrite structure in adult WAG/Rij rats showed a decrease in dendrite spine density of pyramidal neurons of SoCx which occurred in early neonatal exposure to maternal separation (MS) and tactile stimulation (TS) (

p
<
0.001

). Conclusions. Our findings provide the first evidence that tactile stimulations during the early postnatal period have a long-term impact on dendrite structure in WAG/Rij rat’s brain and demonstrate that neonatal tactile stimulation can regulate dendritic spines in layer V in pyramidal neurons of SoCx in epileptic brains.
A Novel Approach for Monitoring the Ecoenvironment of Alpine Wetlands using Big Geospatial Data and Cloud Computing
Authors: Wang Haijun , Kong Xiangdong , Luo Ji , Li Pengju , Xie Tianhui , Yi Xiaobo , Wang Fang , Xiao Jie
Published: April 2022

Alpine wetlands in western Sichuan plateau (WSCP) are located on the eastern edge of the Qinghai-Tibet Plateau (QTP), where the ecological environment is very sensitive to global climate change. Being naturally driven coupled with unreasonable human development activities, alpine wetlands have experienced serious ecological and environmental issues such as drought, inversion, and desertification. However, due to the limitations of data sources and calculation models, it is impossible for us to deeply understand the change mechanism and spatial difference of the ecological environment of the alpine wetland (EEAW) in previous studies. In view of this, an innovative approach for monitoring the EEAW change has been proposed in this paper. We employ the approach to perform the EEAW change trend analysis, and some meaningful characteristics were founded. Specifically, it includes the fol1owing aspects. The air temperature increase is relatively significant, while the precipitation change has obvious spatial differentiation, and even some region’s precipitation experienced a decrease especially in plot1. In Haizishan, Lugu Lake, and Bari Lake, we explored an interesting phenomenon that the precipitation increases first and then decrease, and the turning point occurred around 1999. Increases in air temperature and evaporation have aggravated the drought in high-latitude areas. The drought situation has been alleviated in high-altitude areas due to the acceleration of snow melt water. Wetland vegetation and biomass presented an overall increasing trend, but the degradation also occurs in some area, including Zoige and Lugu Lake area. The human activity disturbances of wetland degradation mainly include the settlements expansion, agricultural development, and the ecotourism prosperity. Among them, targeted poverty alleviation projects have accelerated the urbanization in WSCP, and the development of agriculture and tourism has increased the interference of wetlands. Additionally, we have used Landsat images and national wetland survey data (1999, 2013, and 2020 year) from the past two decades to verify the EEAW trend and confirm the reliability of the analysis results using this approach.
Research on Brand Illustration Innovative Design Modeling Based on Industry 4.0
Authors: Liu Yueyan , Ping Zou
Published: April 2022

With China’s attention to the requirements of brand illustration innovative design, under the background of Industry 4.0, the advantages of applying 3D modeling method in the field of traditional illustration design are becoming more and more prominent. Based on this, this paper studies the modeling method of brand illustration innovative design based on Industry 4.0 and constructs a 3D analysis model of illustration design based on cattle cooperative hybrid algorithm. This paper innovates the innovative design method of brand illustration under the background of Industry 4.0 from the three aspects of illustration structure, illustration style, and illustration creation method, uses the cattle cooperative hybrid algorithm to quantify its innovation, and realizes the intelligent evaluation of its innovation and the quantitative representation of modeling method based on the illustration database. The experimental results show that the illustration innovative design analysis model based on cattle collaborative hybrid algorithm can effectively combine the industrial 4.0 background, realize the modeling innovation at the three-dimensional level, and significantly improve its innovative design efficiency and modeling success rate.
Early Stroke Prediction Methods for Prevention of Strokes
Authors: Kaur Mandeep , Sakhare Sachin R. , Wanjale Kirti , Akter Farzana
Published: April 2022

The emergence of the latest technologies gives rise to the usage of noninvasive techniques for assisting health-care systems. Amongst the four major cardiovascular diseases, stroke is one of the most dangerous and life-threatening disease, but the life of a patient can be saved if the stroke is detected during early stage. The literature reveals that the patients always experience ministrokes which are also known as transient ischemic attacks (TIA) before experiencing the actual attack of the stroke. Most of the literature work is based on the MRI and CT scan images for classifying the cardiovascular diseases including a stroke which is an expensive approach for diagnosis of early strokes. In India where cases of strokes are rising, there is a need to explore noninvasive cheap methods for the diagnosis of early strokes. Hence, this problem has motivated us to conduct the study presented in this paper. A noninvasive approach for the early diagnosis of the strokes is proposed. The cascaded prediction algorithms are time-consuming in producing the results and cannot work on the raw data and without making use of the properties of EEG. Therefore, the objective of this paper is to devise mechanisms to forecast strokes on the basis of processed EEG data. This paper is proposing time series-based approaches such as LSTM, biLSTM, GRU, and FFNN that can handle time series-based predictions to make useful decisions. The experimental research outcome reveals that all the algorithms taken up for the research study perform well on the prediction problem of early stroke detection, but GRU performs the best with 95.6% accuracy, whereas biLSTM gives 91% accuracy and LSTM gives 87% accuracy and FFNN gives 83% accuracy. The experimental outcome is able to measure the brain waves to predict the signs of strokes. The findings can certainly assist the physicians to detect the stroke at early stages to save the lives of the patients.
Dynamic Posture Prediction Model in Informationized Combat Environment
Authors: Zhu Rongxin
Published: April 2022

The several local wars that broke out at the turn of the century have shown to the world that the rapid development of high and new technology with information technology as the core is effectively promoting the pace of world military reform, and its most essential feature is information dominance. In local wars under modern high-tech conditions, rapid response and multiarms joint and coordinated operations have become the key factors to win, which requires multiarms, multiweapon platforms, and each combat unit software to work autonomously and dynamically. This research mainly discusses the dynamic posture prediction model in the informationized combat environment. It designs the organization of command elements, related data (products and tools), competency, personnel matching checklists, and activities in joint operational planning and relies on activities to model the binding of groups, capabilities, and related data. Finally, it forms process segments through the combined design of activities and then sets the final state and background knowledge constraints on the process segments to construct the process knowledge, turning each combat unit into an agent unit, so that the combat software unit has the performance characteristics of the agent’s autonomy, reactivity, and sociality. At the same time, it has good communication performance and autonomous negotiation ability, so it can realize the autonomous connection, communication, and cooperative work among multiagent systems. During the process execution or business processing, the seat staff can input the goal and background of the activity according to the current operating environment. It relies on the background constraints of the process knowledge to quickly match the corresponding process knowledge, insert the process fragments in the process knowledge into the original process, realize the rapid and efficient decomposition of the process redefinition, and effectively ensure the orderly development of the combat command. Techniques and methods for automating integration testing of combat systems are provided. These technologies are further applied to the process of combat system integration tests to improve the efficiency of combat system integration. In addition, through the research on the integration testing technology of combat systems, the problems of low generality of integration testing software and many structural repetitive codes can also be solved and the existing integration testing technology can be further improved. In the research, the time-consuming of the script interpretation module accounts for a relatively low proportion of about 5% of the simulation. The article embodies the platform to explore the realization of multiagent-based dynamic cooperative operations, to achieve the purpose of simulating the battlefield situation, rehearsing the combat plan, and evaluating the strike effect. This study will improve the efficiency of overall combat system integration.
Optimization of Urban Waste Transportation Route Based on Genetic Algorithm
Authors: Zhang Yanling , Luo Xu , Han Xiaoxuan , Lu Yongxing , Wei Jiacheng , Yu Chunyu
Published: April 2022

Under the normal conditions of the new economy, people’s living standards have improved; the amount of urban domestic waste has increased suddenly; and the differentiation of waste has become more and more complicated. However, the garbage transportation method at this stage has been difficult to meet the needs of reality. Therefore, it is particularly urgent to reform the original and simple treatment methods and adopt the latest and more reasonable garbage transportation. The urban waste transportation route optimization described in this article is quite complicated in the specific problems faced in the optimization of the urban domestic waste transportation route. This paper proposes to combine computer science to study the system optimization model and combine information management and other disciplines to develop and apply research on the dynamic management software of urban waste logistics systems. In the modern service industry, logistics occupies an important position. The optimization of urban waste transportation routes based on genetic algorithms also promotes the development of high-end modern service industries and is an important task to promote urban development and maintained hygiene and improve the healthcare of the citizens. The optimization of urban waste transportation routes is conducive to creating a clean and tidy urban environment.
Innovative Research on the Teaching Mode of Preschool Education Courses under the Background of Wireless Communication and Big Data
Authors: Zhao Jie
Published: April 2022

With the development of science and technology, big data and wireless communication technology are gradually applied in the field of education. As people nowadays have higher and higher requirements for the orientation and characteristics of kindergarten curriculum, kindergarten curriculum presents a development trend of “a hundred flowers bloom.” However, the curriculum setting of kindergartens in our country is affected by many factors, and it is not uncommon to see unscientific phenomena. To solve these problems, there is an urgent need for the teaching of relevant curriculum theory. Based on wireless communications and big data background before school education teaching mode, on the basis of the literature on preschool education course teaching mode of related theory to understand, then by using the method of questionnaire survey on the present stage preschool education curriculum innovation present situation investigation, through the results of the survey, at the present stage, the main problem existing in the innovation of preschool education curriculum teaching mode is the shortage of educational resources, accounting for 44%, followed by the faculty.
Phase Demodulation of Rotor Torsional Vibration Measurement under Time-Varying Speed
Authors: Chen Moli , Zheng Nan , Zheng Jun , Jin Zhu , Luo Guihuo
Published: April 2022

When torsional vibration is measured with the zebra-tape method, the modulation signals can be contaminated by electrical noise, environmental noise, step noise, sensor nonlinearities, and so on. Although the amplitudes of these types of noise are very weak relative to the amplitude of the ideal pulse test signal, they may introduce large-scale random broad-spectrum noise to the signal phases. Such noise is difficult to remove using frequency domain, time-frequency-domain, or threshold sampling methods and seriously affects the accuracy of torsional vibration measurements. This paper presents a phase demodulation algorithm based on downsampling and local resampling (DSLR) to improve the accuracy of torsional vibration measurements. To verify the proposed DSLR algorithm, torsional vibration simulations and experiments are conducted under a time-varying rotation speed. The results show that the angular displacement signal obtained from the torsional vibration test using zebra tapes is the superposition of the dynamic and static angular displacements, with the latter generated because of the varying rotation speed with respect to time. The DSLR algorithm effectively reduces the phase noise and demodulates the pulse signal phases. The work in this paper provides a method for obtaining reference torsional vibration measurements of rotors based on the zebra-tape method under time-varying speeds.
Fuzzy Logic System Implementation on the Performance Parameters of Health Data Management Frameworks
Authors: Vyas Sonali , Gupta Shaurya , Bhargava Deepshikha , Boddu Rajasekhar
Published: April 2022

The development of wireless sensors and wearable devices has led health care services to the new paramount. The extensive use of sensors, nodes, and devices in health care services generate an enormous amount of health data which is generally unstructured and heterogeneous. Many generous methods and frameworks have been developed for efficient data exchange frameworks, security protocols for data security and privacy. However, very less emphasis has been devoted to structuring and interpreting health data by fuzzy logic systems. The wireless sensors and device performances are affected by the remaining battery/energy, which induces uncertainties, noise, and errors. The classification, noise removal, and accurate interoperation of health data are critical for taking accurate diagnosis and decision making. Fuzzy logic system and algorithms were found to be effective and energy efficient in handling the challenges of raw medical data uncertainties and data management. The integration of fuzzy logic is based on artificial intelligence, neural network, and optimization techniques. The present work entails the review of various works which integrate fuzzy logic systems and algorithms for enhancing the performance of healthcare-related apps and framework in terms of accuracy, precision, training, and testing data capabilities. Future research should concentrate on expanding the adaptability of the reasoning component by incorporating other features into the present cloud architecture and experimenting with various machine learning methodologies.
Linc00261 Inhibited High-Grade Serous Ovarian Cancer Progression through miR-552-ATG10-EMT Axis
Authors: Wang Lin , Wang Hongcai , Chen Jiuwei
Published: April 2022

In recent years, long non-coding RNAs (lncRNAs) play an important role in a multitude of pathways across species; however, their functions are still unknown. In this study, we demonstrate that Linc00261 is downregulation in high-grade serous ovarian cancer (HGSOC) and can inhibit cell proliferation and migration of high-grade serous ovarian cancer cells. We further validate the targeting interactions among Linc00261, miR-552, and ATG10. Interestingly, they all play important roles for regulating epithelial-mesenchymal transition (EMT) progression. Collectively, these findings suggest that Linc00261, a mediator of EMT progression, can target oncogenic miR-552, elevating ATG10 expression, to prevent high-grade serous ovarian cancer tumorigenesis and may serve as a potential novel therapeutic target.
Determinants of Rural Household Food Security Status in North Shewa Zone, Amhara Region, Ethiopia
Authors: Fikire Abebaw Hailu , Zegeye Mesele Belay
Published: April 2022

Food insecurity is one of the most serious problems in developing countries, especially in Ethiopia. Therefore, it is important to understand the barriers to improving the state of food security in the country. Thus, this study aims to investigate the determinants of food security of rural households in the North Shewa zone in the Amhara region, Ethiopia. A sample of 796 farm households was considered. This paper used the calorie intake method per day to measure household food security status and a logit model to investigate the determinants of food security. The results show that family size, age of the household head, educational level of the head, off-farm activities, monthly income of the household, and distance from the market are the major determinants of rural household food security in the North Shewa Zone. The findings suggest that expanding the access to education in farm households, expanding the access to off-farm activities to increase household income, and expanding market access to farm households are important to improve rural food security status in the study area.
The Ball-Relaxed Gradient-Projection Algorithm for Split Feasibility Problem
Authors: Li Xiaochun , Liu Xiaoxiao , Gao Fugen
Published: April 2022

In this paper, we concern with the split feasibility problem (SFP) whenever the convex sets involved are composed of level sets. By applying Gradient-projection algorithm which is used to solve constrained convex minimization problem of a real valued convex function, we construct two new algorithms for the split feasibility problem and prove that both of them are convergent weakly to a solution of the feasibility problem. In the end, as an application, we obtain a new algorithm for solving the split equality problem.
The Efficacy of Tumor Characterization for Colorectal Lesions with Blue Light Imaging of a Compact Light-Emitting Diode Endoscopic System Compared to a Laser Endoscopic System: A Pilot Study
Authors: Inoue Ken , Yoshida Naohisa , Kobayashi Reo , Tomita Yuri , Hashimoto Hikaru , Sugino Satoshi , Hirose Ryohei , Dohi Osamu , Yasuda Hiroaki , Yasuda Ritsu , Murakami Takaaki , Inada Yutaka , Itoh Yoshito
Published: April 2022

Background: A compact and cost-effective light source-processor combined 3-color light-emitting diode (LED) endoscopic system (ELUXEO-Lite: EP-6000, Fujifilm Co., Tokyo) with a magnified colonoscope (EC-6600ZP, Fujifilm Co.) has been released. Aims: In this study, we analyzed the efficacy of this system for colorectal tumor characterization with magnified blue light imaging (BLI-LED) and image’s subjective and objective evaluations, compared to a magnified blue laser imaging (BLI-LASER) using a standard LASER endoscopic system. Methods: We retrospectively reviewed 37 lesions observed with both BLI-LED and BLI-LASER systems from 2019 using the Japanese narrow band imaging classification. Two representative magnified images, one BLI-LED and one BLI-LASER, of the same area of a lesion were evaluated for diagnostic accuracy and visualization quality by three experts and three non-experts. Their color difference values (CDVs) and brightness values (BVs) were also calculated as objective indicators. Results: Among 37 lesions, mean tumor size was

18.9
±
13.1

 mm, and 21 lesions were nonpolypoid. Histopathology revealed 14 sessile serrated lesions, 7 adenomas, 12 high-grade dysplasias and T1a cancers, and 4 T1b cancers. The diagnostic accuracy rates of BLI-LED/BLI-LASER of experts and non-experts were 90.1% and 87.4% (

p
=
0.52

) and 89.2% and 89.2% (

p
=
0.99

). The percentages of instances where BLI-LED images were better, the two imaging types were equivalent, or BLI-LASER images were better were 16%/83%/1% for experts and 19%/58%/23% for non-experts (

p
<
0.001

). CDVs and BVs between BLI-LED and BLI-LASER were not significantly different (CDVs:

p
=
0.653

, BVs:

p
=
0.518

). Conclusions: BLI-LED using the compact system was noninferior to BLI-LASER for colorectal tumor characterization and image quality.