bluuebunny/crossref_metadata_2025 huggingface dataset:
Geodiversity assessment for geomorphosites management: Derborence and Illgraben, Swiss Alps
Authors: Najwer Alicja , Reynard Emmanuel , Zwoliński Zbigniew
Published: September 2022

The paper reports on a geodiversity assessment method at the local scale based on spatial multi-criteria analysis (S-MCA). The study area consists of two geomorphosites: Derborence and Illgraben, located in the Swiss Alps and representative of different types of morphodynamics. Apart from glacial and fluvial landforms, the morphology of these two sites is largely due to extreme phenomena (rockslides, torrential processes). Both areas were incorporated in the list of Swiss geosites for their geomorphological interest. The geodiversity assessment criteria were selected and presented on five factor maps: the relief energy (relative height), the landform fragmentation, the landform preservation, the geological setting and the hydrological factor. The factor maps were aggregated using the weighted linear combination (WLC) technique. The accuracy of the final geodiversity maps of Derborence and Illgraben was verified during a field inspection. The final maps were qualitatively assessed in terms of the geodiversity value of specific areas within the selected geomorphosites. The proposed method allows assessing the intrinsic geodiversity differentiation of large geosites, in particular in protected and conserved areas (PCAs). The results can be used for purposes such as the designation of most valuable parts of the area for the preservation of certain abiotic features, spatial planning or tourism management, at least in the alpine fold mountains.
Geosite determination based on geodiversity assessment utilizing the volcanic history of a near-sea-level explosive eruption-dominated volcanic island: Tūhua/Mayor Island, New Zealand
Authors: Zakharovskyi Vladislav , Kósik Szabolcs , Li Boxin , Németh Károly
Published: September 2022

Tūhua/Mayor Island is located approximately 45 km off the NE coast of the North Island of New Zealand. This island was formed by various explosive and effusive volcanic eruptions commonly influenced by magma–water interaction eruption events occurring since the Pleistocene. The wider area of the SW Pacific contains numerous volcanic islands with a similar type of volcanic evolution. Tūhua/Mayor Island should be studied in more detail to understand the underlying volcanic mechanisms and apply this research to other volcanic islands in the SW Pacific. Mayor Island, also known by its indigenous Māori name
Tūhua
(obsidian in Māori), provides an ideal site for studying current volcanism. The present day island was formed around 150 ka ago and contains several rhyolitic lava-flows from different time periods, pyroclastic-flow deposits generated by small-volume localized eruptions and ignimbrite deposited by large explosive eruptions. Our research utilized a qualitative–quantitative assessment of geodiversity estimates to highlight possible geosites for the collection of precise information about the geological evolution of this area, demonstrating the potential of geoeducational sites. The term geodiversity recognizes geological and geomorphological elements, which have shaped the Earth's surface and underly our abiotic environment. Additionally, volcanic heritage was included in our equation, specifically tailored for Tūhua/Mayor Island and based on expert views (qualitative model). This model allows for a wider diversity for the area of research compared with the original method, which utilized only geological elements. The results show that areas with pyroclastic deposits exposed on the cliffs and in the centre of the collapse caldera should be considered for the further study for geosite planning.
State of the Art of Automation in Sign Language: A Systematic Review
Authors: Kumar Attar Rakesh , Goyal Vishal , Goyal Lalit
Published: September 2022

Sign language is the fundamental communication language of deaf people. Efforts to develop sign language generation systems can make the life of these people smooth and effortless. Despite the importance of sign language generation systems, there is a paucity of a systematic literature review. This is the foremost recognizable scholastic literature review of sign language generation systems. It presents a scholastic database of the literature between 1998 and 2020 and suggests classification criteria to systematize research studies. Four hundred fourteen research studies were recognized and reviewed for their direct pertinence to sign language generation systems. One hundred sixty-two research studies were subsequently chosen, examined, and classified. Each of the 162 chosen research papers was categorized based on 30 sign languages and was further comparatively analyzed based on seven comparison parameters (input form, translation technologies, application domain, use of parsers/grammars, manual/non-manual features, accuracy, and output form). It is evident from our research findings that the majority of research on sign language generation was carried out using data-driven approaches in the absence of proper grammar rules and generated only manual signs. This research study may provide researchers a roadmap toward future research directions and facilitate the compilation of information in the field of sign language generation.
BSML: Bidirectional Sampling Aggregation-based Metric Learning for Low-resource Uyghur Few-shot Speaker Verification
Authors: Zi Yunfei , Xiong Shengwu
Published: September 2022

In recent years, text-independent speaker verification has remained a hot research topic, especially for the limited enrollment and/or test data. At the same time, due to the lack of sufficient training data, the study of low-resource few-shot speaker verification makes the models prone to overfitting and low accuracy of recognition. Therefore, a bidirectional sampling aggregation-based meta-metric learning method is proposed to solve the low-accuracy problem of speaker recognition in a low-resource environment with limited data, termed bidirectional sampling multi-scale Fisher feature fusion (BSML). First, the BSML method was used for effective feature enhancement in the feature extraction stage; second, a large number of similar and disjoint tasks were used to train the models to learn how to compare sample similarity; finally, new tasks were used to identify unknown samples by calculating the similarity of the samples. Extensive experiments are conducted on a short-duration text-independent speaker verification dataset generated from the THUYG-20 low-resource Uyghur with limited data, which comprised speech samples of diverse lengths. The experimental result has shown that the metric learning approach is effective in avoiding model overfitting and improving model generalization, with significant results in the identification of short-duration speaker verification in low-resource Uyghur with few-shot. It also demonstrates that BSML outperforms the state-of-the-art deep-embedding speaker recognition architectures and recent metric learning approach by at least 18%–67% in the few-shot test set. The ablation experiments further illustrate that our proposed approaches can achieve substantial improvement over prior methods and achieves better performance and generalization ability.
Emerging Trends of ICT in Airborne Disease Prevention
Authors: Sood Sandeep Kumar , Rawat Keshav Singh , Kumar Dheeraj
Published: September 2022

Information and Communication Technologies (ICT) are becoming indispensable nowadays for the healthcare industry. The utilization of ICT in healthcare services has accelerated even faster after the commencement of the COVID-19 outbreak. This study aims to perform a scientometric analysis of scholarly literature on airborne diseases in the discipline of science and technology. It explores the recent advancement of internet technologies in healthcare to control the prevalence of deadly airborne illnesses by applying analytical approaches. It presents publication trends, citation structure, influential sources, co-citation, and co-occurrence network analysis using the CiteSpace tool. It identifies the important research topics, current research hotspots, most active research areas, and leading technologies in this scientific knowledge domain. It inferred significant results from analyses that will benefit researchers and the academic fraternity across the globe to understand the evolving paths and recent scientific progress of ICT in airborne disease management.
Physical-Assisted Routing for Proactive Avoidance of Nomadic Obstacles in IoT
Authors: Xia Ming , Jin Jiaquan , Liu Biqian , Hu Yu Hen , Wang Xiaoyan , Chi Kaikai
Published: September 2022

With the broadening of the radio spectrum to higher frequency bands, wireless links are more prone to blockages by nomadic obstacles. However, existing routing schemes mostly follow the network-oriented design principle, which makes it difficult to react quickly to sudden obstruction. This article proposes PAR, a novel physical-assisted routing scheme for the Internet of Things. PAR takes a physical-oriented viewpoint attempting to mitigate unexpected link blockages by leveraging sensor observations of obstacles at individual nodes. It analyzes the signatures of sensor measurements to directly estimate the positions and sizes of obstacles and to proactively infer remedial routing decisions even before the transmission failure occurs. During the network deployment time, PAR lets each node learn and calibrate the geographic distribution of neighbors with respect to its sensor measurements by exchanging the sensor observations of a mobile guidance obstacle. During the runtime, an obstacle bypassing algorithm is then developed to find the shortest detour routes by comparing the local sensor measurements with the calibrated positions of neighbors to immediately resume data forwarding. We evaluate the efficacy of PAR using simulation and a real-world testbed. It is observed that PAR significantly improves the success rate while reducing redundant hops and routing control overhead.
On Modality Bias Recognition and Reduction
Authors: Guo Yangyang , Nie Liqiang , Cheng Harry , Cheng Zhiyong , Kankanhalli Mohan , Del Bimbo Alberto
Published: September 2022

Making each modality in multi-modal data contribute is of vital importance to learning a versatile multi-modal model. Existing methods, however, are often dominated by one or few of modalities during model training, resulting in sub-optimal performance. In this article, we refer to this problem as modality bias and attempt to study it in the context of multi-modal classification systematically and comprehensively. After stepping into several empirical analyses, we recognize that one modality affects the model prediction more just because this modality has a spurious correlation with instance labels. To primarily facilitate the evaluation on the modality bias problem, we construct two datasets, respectively, for the colored digit recognition and video action recognition tasks in line with the Out-of-Distribution (OoD) protocol. Collaborating with the benchmarks in the visual question answering task, we empirically justify the performance degradation of the existing methods on these OoD datasets, which serves as evidence to justify the modality bias learning. In addition, to overcome this problem, we propose a plug-and-play loss function method, whereby the feature space for each label is adaptively learned according to the training set statistics. Thereafter, we apply this method on 10 baselines in total to test its effectiveness. From the results on four datasets regarding the above three tasks, our method yields remarkable performance improvements compared with the baselines, demonstrating its superiority on reducing the modality bias problem.
Reducible Theories and Amalgamations of Models
Authors: Aameri Bahar , Grüninger Michael
Published: September 2022

Within knowledge representation in artificial intelligence, a first-order ontology is a theory in first-order logic that axiomatizes the concepts in some domain. Ontology verification is concerned with the relationship between the intended models of an ontology and the models of the axiomatization of the ontology. In particular, we want to characterize the models of an ontology up to isomorphism and determine whether or not these models are equivalent to the intended models of the ontology. Unfortunately, it can be quite difficult to characterize the models of an ontology up to isomorphism. In the first half of this article, we review the different metalogical relationships between first-order theories and identify which relationship is needed for ontology verification. In particular, we will demonstrate that the notion of logical synonymy is needed to specify a representation theorem for the class of models of one first-order ontology with respect to another. In the second half of the article, we discuss the notion of reducible theories and show we can specify representation theorems by which models are constructed by amalgamating models of the constituent ontologies.
Learning design in science education: perspectives from designing a graduate-level course in evidence-based teaching of science
Authors: Downey Ryan M. , Downey K. Breana , Jacobs Jessica , Korthas Holly , Melchor George S. , Speidell Andrew , Waguespack Hannah , Mulroney Susan E. , Myers Adam K.
Published: September 2022

The need for graduate students and other trainees to learn effective methods for teaching science is greater than ever. In this manuscript, we offer a model course for the training of graduate students in learning theory, curriculum design, and technology use in a biomedical sciences environment.
Energy dissipation during expiration and ventilator-induced lung injury: an experimental animal study
Authors: Busana Mattia , Zinnato Carmelo , Romitti Federica , Palumbo Michela , Gattarello Simone , Sonzogni Aurelio , Gersmann Ann-Kathrin , Richter Annika , Herrmann Peter , Hahn Günter , Brusatori Serena , Maj Roberta , Velati Mara , Moerer Onnen , Meissner Konrad , Barnes Tom , Quintel Michael , Marini John J. , Gattinoni Luciano
Published: September 2022

Energy dissipation within the respiratory system is a factor promoting ventilator-induced lung injury (VILI). In this animal study, we modulated the expiratory flow, reducing the energy dissipated in the system. However, this reduction happened mostly across the endotracheal tube, and only partly in the respiratory system. Therefore, in healthy lungs, the advantage in energy dissipation does not reduce VILI, but the advantages might be more relevant in diseased lungs under injurious ventilation.
Design of Flute Music Remote Teaching System Based on Multi-Pass Scheduling Optimization
Authors: Jing Zhao
Published: September 2022

With the gradual development of the Internet industry, every aspect of people’s life has been affected by the Internet, playing increasingly irreplaceable functions in people’s entertainment, office, and other aspects. Judging from the current development situation, the old Internet digital teaching system has many problems, such as low artificial intelligence, weak information processing ability, and lack of effective learning ability. This paper designs the flute music remote teaching system, which can realize remote music teaching and provide help in providing real-time music teaching. The music learning system includes the user’s access records, the user’s operation and the completion of the test data, the discussion and communication of online participation, the user’s interests, specialties and operation methods, learning progress and scoring, and so on. In addition, it explores and explains all the key steps required by the current distance education model and invents a sample of the distance education model. On this basis, Internet algorithm programs will be used for all key processing functions of the system. The use of Internet algorithm programs is interactive and automated, which greatly enhances the role of the education system. This article first discusses the unique teaching and automated teaching mode of the system, which lays the cornerstone for further reforms in this field in the future.
[Retracted] The Diagnostic Accuracy of Transabdominal and Transvaginal Color Doppler Ultrasound for Pregnant Women with Vasa Previa and Velamentous Cord Insertion
Authors: Liu Qing , Zhang Qiang , Liu Peiwu
Published: September 2022

Objective. The objective of this study is to evaluate feasibility and accuracy of transabdominal color Doppler ultrasound (TA‐CDUS) and transvaginal color Doppler ultrasound (TV‐CDUS) as screening methods for pregnant women with vasa previa (VP) and velamentous cord insertion (VCI). Methods. A retrospective diagnostic accuracy study was performed on 5,434 pregnant women from 2018 to 2021, who underwent both TA‐CDUS and TV‐CDUS. Diagnostic performance of TA‐CDUS and TV‐CDUS was determined using specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and positive and negative likelihood ratios (LR+ and LR-), using the delivery information (gross examination) as the “Gold‐standard”. Patient records were reviewed for demographics and diagnosis. Results. The combination of VP and VCI was diagnosed in 37/5434 (0.68%) women at delivery. The sensitivity, specificity, PPV, NPV, and overall test accuracy of TA‐CDUS were 72.97%, 99.85%, 77.14%, 99.81%, and 99.67%, respectively, for diagnosing VP with VCI. The corresponding values for TV‐CDUS were 89.19%, 99.87%, 82.50%, 99.93%, and 99.80%, respectively. Moreover, the sensitivity of combination of TA‐CDUS and TA‐CDUS in determining VP with VCI was 97.30%, specificity 99.98%, PPV 97.30%, NPV 99.98%, and accuracy 99.96%. No significant difference in the misdiagnosis and missed diagnosis was found between the examination by TA‐CDUS and TV‐CDUS. Conclusions. Both TA‐CDUS and TV‐CDUS can be acceptable diagnostic tools for assessment of pregnant women with VP and VCI, with a better application of TV‐CDUS with higher accuracy. The combination of TA‐CDUS and TV‐CDUS could provide an objective imaging basis for choosing clinical treatment strategies and predicting prognosis.
Effect of Blended NPSB and Nitrogen Application rates on Growth, Yield, and Yield Components of Bread Wheat (Triticum aestivum L.) at Gitilo Dale Research Site of Wallaga University, Western Ethiopia
Authors: Abdisa Jalata Delesa , Gobena Roro Amsalu , Hunduma Dabalo Adugna , Asefa Bebayehu Fayera , Woticha Abdela Tufa
Published: September 2022

Wheat (Triticum aestivum L.) is a very important cereal crop widely produced and consumed in almost all parts of Ethiopia. However, due to abiotic and biotic stresses, its productivity is low as compared to the world average. Among these, soil fertility and unbalanced application of plant nutrients are the major constraints that limit the productivity of the crop in sub-Saharan African countries especially Ethiopia. As a result, an experiment was carried out at the Gitilo Dale Research Site of Wallaga University to determine the combined impact of blended NPSB and urea fertilizers on the growth, yield, and yield-related variables of bread wheat. The experiment used a factorial-arranged in randomized full block design with two factors with four levels each: NPSB (0, 100, 150, and 200 kg·ha−1) and nitrogen (urea) (0, 31.1, 71.65, and 112.2 kg·ha−1) (RCBD). With three replications, there were sixteen treatments. A test crop of the bread wheat variety liben (ETBW 5653) was grown. The phonological and growth parameters of bread wheat were significantly affected by NPSB and nitrogen applications as well as their interactions except for days to 50% emergence. The primary and interaction impacts of NPSB and nitrogen rates affected the yield and all of its constituents, except the number of kernels per spike. The application of 200 kg·ha−1 NPSB plus 112.2 kg·ha−1 nitrogen treatment resulted in the highest above-ground biomass production of bread wheat (12481.00 kg·ha−1), grain yield of 5182.51 kg·ha−1, and a straw yield of 7298.40 kg·ha−1. Hence, to maximize the yield of bread wheat, applying 200 kg·ha−1 of chemically blended NPSB with 112.2 kg·ha−1 nitrogen is recommended rather than applying any straight and complex fertilizers having only macronutrients.
Identification and Validation of an Inflammatory Response-Related Polygenic Risk Score as a Prognostic Marker in Hepatocellular Carcinoma
Authors: Xiaochun Huang , Feixiong Pang , Shengsong Ou , Xiaojiao Wei , Yuju Xu , Yanhua Lai
Published: September 2022

Aims. We hypothesized that the expression patterns of inflammatory response-related genes may be a potential tool for hepatocellular carcinoma (HCC) risk scoring. Background. Inflammatory response plays a pivotal role in the pathogenesis of HCC. Objective. To establish and validate a hallmark inflammatory response gene-based polygenic risk score as a prognostic tool in HCC. Methods. We screened differentially expressed inflammatory response genes and established an inflammatory response-related polygenic risk score (IRPRS) in an HCC-related dataset. Patients with HCC were categorized into high- and low-risk groups according to the median IRPRS, and the overall survival between the two groups was compared. The IRPRS was validated in an independent external dataset. Tumor-infiltrating lymphocytes (TILs) in high- and low-risk groups were compared, and gene set enrichment analysis was performed to characterize high-risk HCC identified using this IRPRS. Results. Four differentially expressed hallmark inflammatory response genes (CD14, AQP9, SERPINE1, and ITGA5) were identified to construct the IRPRS. Patients in the high-risk group had significantly shorter overall survival than those in the low-risk group in both the training set and the test set. Furthermore, the IRPRS remained an independent prognostic factor compared to the routine clinicopathological characteristics. Many cancer-related hallmark gene sets and TILs were significantly enriched in the high-risk group. Conclusions. We established and validated a four-hallmark inflammatory response gene-based polygenic risk score, which could successfully divide patients with HCC into high-risk and low-risk groups. These two risk groups of HCC possess significantly distinct prognostic and biological characteristics.
Bifurcations of a Fractional-Order Four-Neuron Recurrent Neural Network with Multiple Delays
Authors: Fei Yu , Li Rongli , Meng Xiaofang , Li Zhouhong
Published: September 2022

This paper investigates the bifurcation issue of fractional-order four-neuron recurrent neural network with multiple delays. First, the stability and Hopf bifurcation of the system are studied by analyzing the associated characteristic equations. It is shown that the dynamics of delayed fractional-order neural networks not only depend heavily on the communication delay but also significantly affects the applications with different delays. Second, we numerically demonstrate the effect of the order on the Hopf bifurcation. Two numerical examples illustrate the validity of the theoretical results at the end.
The Relation between Morphology of Maxillary Sinus after Augmentation in Three Classification Methods and Residual Bone Height: A Retrospective Study
Authors: Wang Zhi , Jia Qi , Jiang Heng Bo , Han Jianmin , Zou Lidong , Niu Guangliang
Published: September 2022

Maxillary sinus augmentation is critical to oral implantology, particularly in some cases. The morphology of the sinus floor reflects the lifting effect to a certain extent; however, there has been limited research on the morphology after sinus augmentation. The present study aims to investigate the relationship between residual bone height (RBH) and the morphology of the sinus floor and determine whether a correlation exists between the different evaluation classifications. Maxillary sinus floor augmentation procedures were performed in 56 patients and 68 teeth using the sinus crest approach (SCA). Imaging results obtained after one year of sinus augmentation were analyzed and simultaneously classified along the coronal plane, the sagittal plane, and the biplane (coronal-sagittal). The higher the RBH, the closer the result tends to be to A, A’, or type 1 (more satisfactory). There was a significant correlation between the three different evaluation classifications (

p
<
0.05

). The morphology of perforation cases was involved in types C, D, C’, and D’. A more satisfactory post-lifting morphology (tent type and flat type) is probably related to an optimal preoperative bone height, and an unsatisfactory post-lifting morphology is related to a low preoperative sinus floor height. The sagittal plane evaluation correlates with the coronal plane and biplane evaluation and is thus more recommended.
Correlation between Endometrial Vascular Endothelial Growth Factor Expression and Pregnancy Outcome of Frozen–Thawed Embryo Transfer in Patients with Repeated Implantation Failure
Authors: Jin Rui , Ma Wenye , Tang Dawei , Liu Fang , Bai Gang , Reng Mengmeng
Published: September 2022

Objective. Vascular endothelial growth factor (VEGF) is a well-known angiogenic factor that is essential to numerous physiological and pathological processes. VEGF also contributes to embryo implantation by promoting embryo development, enhancing endometrial receptivity (ER), and promoting interactions between the endometrium and developing embryo. Changes in VEGF expression are linked to repeated implantation failure (RIF). Control endometrial tissues demonstrated an increase in VEGF expression during the implant window period, which promoted early villous vascularization and embryo implantation. The purpose of this study is to investigate the relationship between RIF and the expression of ER markers, such as VEGF during the implantation window stage. Methods. The Yinchuan Maternal and Child Health Hospital collected 192 cases of FET endometrial tissues in the implantation window stage between January 2019 and December 2021. Immunohistochemistry was utilized to measure the levels of VEGF expression in patients with RIF (RIF group,

n
=
82

) and patients with a successful pregnancy (control group,

n
=
110

). The relationship between VEGF and the RIF group was analyzed using Spearman’s correlation coefficient. Results. VEGF levels were significantly lower during the implantation window stage (

P
<
0.05

). Conclusion. VEGF was expressed in planting window stage. The decrease of VEGF during the implantation window was correlated with RIF.
Analysis of the Visual Design and Expression Effect of Virtual Reality and Three-Dimensional Space Technology in Art Space
Authors: Du Wei , Li Qian
Published: September 2022

The application of information technology has realized the transformation of people’s production and lifestyle and also promoted the improvement of people’s aesthetic level. Currently, the application of information technology in the art industry is getting stronger and stronger, and using its advanced methods and technologies of information display, this study aims to realize the exploration of art space under virtual reality technology to promote the study of the expression effect of visual design. This study proposes the use of virtual reality technology to guide the study of the effect of visual design expression in art spaces, which helps to analyze and solve a series of problems that objectively exist today in terms of single visual design and people’s aesthetic fatigue, and draws on the principles and algorithms of virtual reality technology to conduct research on the effect of visual design and expression in art spaces. In the experiment for the algorithm of the three-dimensional model, it can be seen from table that the score of one-dimensional space is around 4, the score of two-dimensional space is around 5.6, while the average score of three-dimensional space use is around 8.2; the score is very high, thus we get the following conclusion: three-dimensional space, no matter once or now, in the visual art of art space, as well as the expression effect is a great role, so the study of virtual reality. It is therefore of great vitality and social significance to study how virtual reality can be used to maximum effect in the visual arts and expressions of all art spaces.
Application Design of Linguistics in Computer Technology under Artificial Intelligence Background
Authors: Zhang Lixia
Published: September 2022

Computer technology is called the fourth industrial revolution, indicating the importance of computer technology. At the same time, as the most important way for our daily exchanges, it is also connected to computer technology. This article is intended to study the application design of linguistics in computer technology in controversy. This paper proposes a documentary conversion algorithm based on a BP neural network and has made corresponding analysis for linguistics. It designed a documentary conversion system for the application of language technology. The experimental results show that the language has more in-depth applications in computer technology, and the documentary conversion system designed herein can reach more than 97% of the accuracy of 20 people in the system.
Experimental Study on Strengthening Root‐Soil Composite with Different Root Contents by Using MICP
Authors: Zheng Xuegui , Huang Wei , Tan Zhijie , Wang Huihui , Zhu Jianfeng , Luo Yi , Zhou Yuni
Published: September 2022

Microbial induced carbonate precipitation (MICP) in coordination with vegetation protection is a novel technology in the field of slope reinforcement. Urea in MICP can promote vegetation growth and change the root content in the soil. However, existing studies often ignore the influence of MICP on root growth, while root content has a significant impact on the ability of vegetation to cement soil and protect slopes. In this paper, through the laboratory direct shear test, the strength variation characteristics of root‐soil composites with different root contents before and after reinforcement were analyzed, and the influence of root content changes on the strength of root‐soil composites in the MICP collaborative slope protection project was studied. The results showed that: (1) the strength of the root‐soil composite was improved significantly after MICP treatment. When the root content was 1.5%, the maximum strength peak value was increased by 32.6%, and the cohesive force was increased by 49.2%. (2) MICP reaction has no effect on the root content of the peak intensity, indicating that MICP has no negative effect on vegetation growth and can be combined with vegetation protection. The results show that MICP synergistic slope protection has strong engineering application value.
Prediction and Analysis of Housing Price Based on the Generalized Linear Regression Model
Authors: Li Xinshu
Published: September 2022

In order to improve the reliability of housing price prediction and analysis, this article combines the generalized linear regression model to build a real estate price prediction model and analyzes the basic knowledge of data mining. On the basis of this prior knowledge, this article investigates the cluster analysis algorithm and selects the generalized linear regression model as the research focus based on its definition and the characteristics of stock data. Moreover, this article analyzes the estimation methods of the generalized linear regression model and the nonparametric regression model, and then gives the estimation method of a partial linear model. In addition, this article verifies the validity of the model proposed in this article by means of simulation research. Through the simulation and comparison experiments, it can be seen that the housing price prediction system based on the generalized regression model proposed in this article has a high housing price prediction accuracy.
Evaluation of Urban Economic Benefit Value Calculation Method Based on Smart City
Authors: Zhu Weihang , Wang Manting
Published: September 2022

Economic growth has not only made life more comfortable for people but has also increased the demands and expectations of the living environment. Urban planning, layout, construction, transportation, environmental protection, etc. are increasingly attracting the attention of the government. Due to the increase in residents, there are many problems in cities, such as traffic congestion, smog, etc. The relevant departments hope to use intelligent means to effectively alleviate these problems, and many cities have achieved good results. As a part of urban economics research, analyzing the economic benefits of a city helps to study how to develop the city. Smart sensor and communication technology is an advanced technology for smart manufacturing and the Internet of Things. As an advanced sensor tool, it is of great significance for exploring the economic benefits of smart cities. This paper discusses the calculation method of urban economic benefit value based on smart cities and evaluates the calculation method. The purpose of this paper is to find out the calculation method applicable to the urban economic benefit and to propose an effective evaluation method for the urban economic benefit, so as to have a more complete and effective calculation and evaluation of the urban economic benefit. This paper introduces the smart city, combining intelligent sensing and communication technology, and designs the economic benefit calculation method and evaluation model. Finally, this paper designs an experiment of economic benefit calculation and evaluation. First, 18 cities in China are used as examples to explore the relationship between indicators and GDP. It is concluded that there is a long-term and stable relationship between GDP and household savings balance at the end of the year and that this does not exist with the average number of on-the-job workers. Then we took Wuhan Metro Line 5 as an example to evaluate its economic benefits. The experiment shows that the largest weight value is the investment return period, with a weight of 0.54, and the evaluation level of Wuhan Metro Line 5 is 4.
Day Ahead Electricity Price Forecasting Based on the Deep Belief Network
Authors: Cao Man , Wang Yajun , Liu Jinning , Yin Zhiyong , Guo Xin , Ren Xiaokun
Published: September 2022

With the reform of electric power system, major progress has been made in the construction of the electricity market. Electricity prices are a key influencing factor in the electricity market, and each participant trades electricity based on the price of electricity. Therefore, improving the accuracy of electricity price forecasts is important for every player in the electricity market. Prediction using single-layer neural networks has limited accuracy. Due to the high accuracy of machine learning in forecasting, the method of deep belief network is used to predict the price of electricity in the future. Real data from the U.S. PJM electricity market are used for simulation and compared with the prediction models of other neural networks. The results show that the prediction accuracy of the deep belief network model is higher, and the use of the deep belief network can provide an effective method for China’s electricity sales companies to predict electricity prices.
A New Dynamic Balance Framework Based on Blind Source Separation under Multiple Fault Conditions
Authors: Lang Bo , Zhang Xinyi , Xiao Jun , Lu Shouhang , Li Bing
Published: September 2022

Accordingly, the mass unbalance of the rotors is usually the major cause of excessive vibration. The information extracted at the fundamental frequency is often employed to fix the unbalance issue. However, other rotor faults like rotor bending and bearing-failure effect also generate additional components to the characteristics. Thus, it is necessary to isolate the corresponding features and obtain the intrinsic causes of the multiple failures. In this paper, a productive hybrid method is successfully developed to deal with the root mass unbalance problem with additional force interference by integrating the superiority of different methods, including Ensemble Empirical Mode Decomposition (EEMD) and Nonnegative Matrix Factorization (NMF), where EEMD is used to obtain sensitive IMFs and NMF is employed to acquire the inherent source signal, respectively. Meanwhile, a root dynamic balancing and implemental framework is also developed to accomplish the task of vibration reduction. For verification, a serial of simulations and experimental investigations have been analysed to demonstrate the preferable potentialities of the proposed method. In particular, a standard Bently Nevada rotor rig with a specifically designed device was employed to simulate appended faults by adjusting the additional forces during the experimental steps. The analysis results show that the proposed method can isolate and extract the unbalance faults from the raw vibration signals and achieve accurate correction balancers, where a nearly identical correction angle has been achieved, which indicates that the optimal installation position has been successfully figured out.
An Effective Biomass for the Adsorption of Methylene Blue Dye and Treatment of River Water
Authors: Siddiqui Shaziya H. , Uddin Mohammad Kashif , Isaac Runit , Aldosari Obaid F.
Published: September 2022

Neolamarckia cadamba leaves were used as an adsorbent for methylene blue removal as well as for the reduction in the water quality parameters of the sewage belts of Yamuna River in Allahabad, Uttar Pradesh, India. A variety of water quality parameters were assessed, including chemical oxygen demand, biological oxygen demand, total dissolved solids, conductivity, hardness, pH, and temperature. A great deal of degradation in the water quality parameters collected from three locations within the Yamuna River in Allahabad, Uttar Pradesh, India, has been observed by treating with Neolamarckia cadamba leaves. The adsorption studies were carried out by the batch method, and the effect of various parameters such as contact time, pH, initial concentration, and temperature was assessed. The maximum removal of methylene blue was obtained at pH 5 after 120 minutes of equilibrium time. In addition to fitting the Langmuir isotherm most accurately, the adsorbent also followed pseudokinetics of the second order. The maximum monolayer adsorption capacity of used biomass was 101 mg/g using 50 mg/L methylene blue solution. It is evident from the thermodynamic data that the adsorption is exothermic. Also, the spontaneity of the interaction between adsorbent and adsorbate decreases with temperature. Energy-dispersive X-ray spectroscopy, scanning electron microscopy, and Fourier transform infrared spectroscopy techniques were used to characterize the adsorbent. SEM images revealed that the biomass consisted of irregular spherical lumps with a porous structure, which provided effective adsorption sites. A chemical composition analysis of the biomass by EDAX shows that the chemical composition of oxygen and carbon decreases, whereas nitrogen’s chemical composition increases.
Masses of Single, Double, and Triple Heavy Baryons in the Hyper-Central Quark Model by Using GF-AEIM
Authors: Abu-shady M. , Fath-Allah H. M.
Published: September 2022

By using the generalized fractional analytical iteration method (GF-AEIM), the single, the double, and the triple heavy baryons masses are calculated in the hyper-central model in the two cases. In the first case, the potential is a combination of Coulombic potential, the linear confining potential, and the harmonic oscillator potential. In the second case, we add the hyperfine interaction. The energy eigenvalues and the baryonic wave function are obtained in the fractional forms. The present results are a good agreement with experimental data and are improved with other recent works.
Distance‐Based Topological Descriptors on Ternary Hypertree Networks
Authors: Yu Yun , Xavier D. Antony , Varghese Eddith Sarah , Mathew Deepa , Siddiqui Muhammad Kamran , Fufa Samuel Asefa
Published: September 2022

Topological indices are numeric parameters which portray the topology of a subatomic structure. In QSAR/QSPR analysis, topological descriptors play a vital role to examine the topology of a network. An interconnection network is a structure whose components are connected physically according to some pattern. In this paper, an interconnection network, ternary hypertree, which is a structural combination of complete ternary tree and hypertree, is introduced. We have evaluated the topological descriptors grounded on the distances for the ternary hypertree. The analytical expressions for Wiener, different types of Szeged, and Mostar indices are determined.
Energy Consumption and QoS Optimization Coverage Mechanism in Wireless Sensor Networks Based on Swarm Sensing Algorithm
Authors: Wang Gang , Feng Jiayin , Li Guolin , Song Jinling , Jia Dongyan
Published: September 2022

Objective. To study energy consumption and QoS optimization coverage mechanism in wireless sensor networks based on swarm sensing algorithm. Methods. The swarm sensor algorithm is reported to optimize the configuration of wireless sensor nodes, improve network service performance, and provide feedback on effective sensor placement. Results. Two WSN coverage optimization schemes were proposed in this essay. The first scheme applied drosophila intelligent algorithm to WSN coverage optimization, and the second scheme integrated particle swarm optimization algorithm and improved firefly algorithm to complement each other. Both schemes can achieve good results in practical application. Scheme one can increase the coverage rate by 6.36% compared with previous schemes, while scheme two can increase it by 6.48%. Both schemes are basically the same, so they can be applied in the optimization of WSN node deployment. Conclusion. As an optimization method, group perception algorithm is inspired by biology. Through the continuous enrichment and development of this algorithm, it has been effectively used in the coverage optimization of the wireless sensor networks.
Research on Risk Assessment and Prediction of RMB Internationalization Based on the PCA-SA-BPNN Model
Authors: Hu Shili , Du Jie
Published: September 2022

This paper combines principal component analysis, a BP neural network, and a simulated annealing algorithm, to construct a PCA-SA-BPNN risk forecast model to evaluate and predict the RMB internationalization risk status of China. First, we analyze the risk of RMB internationalization and its transmission mechanism from the perspective of the economic characteristics of neighboring countries and trading partner countries. Second, we use the FASP index system construction method for reference to construct a forecast index system for macro- and microrisks brought about by RMB internationalization. Then, the weight of each index is determined through index common degree analysis and principal component analysis, and the risk of RMB internationalization is divided. On this basis, the risks of RMB internationalization in China from 2000 to 2019 are divided into four categories. Based on the BP neural network algorithm optimized by the simulated degradation algorithm, the PCA-SA-BPNN model of RMB internationalization risk forecast is constructed. Finally, the validity of the model is verified by experimental verification, and the risk status of RMB internationalization in 2020 is simulated and predicted. The research results show that the risk status of RMB internationalization in 2020 is basically safe, and the risks of RMB internationalization mainly come from macroeconomic growth risks and systemic risks of the financial system.
Methylammonium Chloride Additive in Lead Iodide Optimizing the Crystallization Process for Efficient Perovskite Solar Cells
Authors: Wang Hongqiao , Zhang Jingquan
Published: September 2022

Perovskite solar cells (PSCs) were fabricated using a two-step spin-coating method with MACl added to the inorganic layer. The properties of the perovskite films were characterized by SEM, XRD, PL, UV-vis spectra, etc. The morphology of the PbI2 film was significantly changed, and the formation of MACl-related intermediate phase was observed at the grain boundaries. The grain size and crystallinity of the perovskite film increased, and the morphology at grain boundaries was optimized, while the composition of perovskite remained unchanged. The introduction of MACl improved the open circuit voltage (VOC) and fill factor (FF) of PSCs, and the optimal device efficiency reached 21.59%. This paper presents a new approach using additives in inorganic layers to optimize the crystallization process for efficient PSCs.
Effect of Tuber Size and Intra-Row Spacing on the Yield and Quality of Potato (Solanum tuberosum L.) Varieties
Authors: Regasa Miressa , Garedew Weyessa , Olika Ambecha
Published: September 2022

A field experiment was conducted at Nono Benja District from December 2013 to May 2014 under irrigation to assess the response of different sizes of seed tubers and intra-row spacing on the yield and quality of potato varieties. The treatments consisted of a factorial combination of two varieties (Jalenie and Gudenie): five levels of tuber size (20–35, 36–50, 51–65, 66–80, and 81–95 g) and five levels of intra-row spacing (20, 25, 30, 35, and 40 cm). The treatments were arranged in a split-split plot design (variety on main plot, intra-row spacing on subplot, and tuber size on sub-sub plot) with three replications. There were significant interaction effects of variety, intra-row spacing, and tuber size (

P
<
0.05

) on plant height, number of main stems per plant, average tuber weight, tuber number per plant, total tuber yield, marketable tuber yield, unmarketable tuber yield, large tuber mean yield, medium tuber mean yield, and small tuber mean yield. Variety of tuber size and intra-row spacing was also influenced by dry matter content and tuber specific gravity. The interaction effect of intra-row spacing and seed tuber size was significantly (

P
<
0.05

) influenced by the number of days to emergence. The day to flowering was also significantly (

P
<
0.05

) influenced by the interaction effect of variety and intra-row spacing. The maximum plant height (80.38 cm) was recorded with variety Gudenie with a tuber size of 66–80 g and planted at an intra-row spacing of 35 cm. The maximum (9.79) number of main stems was recorded in variety Jalenie with a tuber size of 81–95 g and planted at intra-row spacing of 40 cm. The maximum of total tuber yields (24.84 to/ha) was recorded from Jalenie variety with tuber size 66–80 g and planted at intra-row spacing of 25 cm. The maximum (10.18 t/ha) larger tuber was recorded with variety Gudenie with a tuber size of 66–80 g and planted at 25 cm. Variety Jalenie with tuber size of 66–80 g and planted at 25 cm produced the maximum (8.04 t/ha) medium-sized tubers. The maximum small tuber (8.59 t/ha) was recorded from both varieties with intra-row spacing of 20 cm and tuber size 81–95 g. Variety Gudenie planted at intra-row spacing of 40 cm gave higher (19.98%) dry matter content, and variety Gudenie with a tuber size of 66–80 g gave higher (20.61%) dry matter content. The higher (1.077) tuber specific gravity was obtained with variety Gudenie planted at intra-row spacing of 35 and 40 cm, and the higher (1.074) tuber specific gravity was registered with variety Gudenie with a tuber size of 66–80 g and/or 81–95 g and planted at intra-row spacing of 40 cm. The study needs in-depth investigation by repeating in multilocations with more seasons, varieties, processing quality, and other agronomic practices of the crop to reach a conclusive recommendation. The result of this research was presented to Jimma University since 2015.