bluuebunny/crossref_metadata_2025 huggingface dataset:
Vom „Feindstaat“ zum Pfeiler der Vereinten Nationen: 50 Jahre nach seinem Beitritt setzt sich Deutschland weiter für eine starke Weltorganisation ein
Authors: Gareis Sven Bernhard
Published: September 2023

Am 18. September 1973 traten die Bundesrepublik Deutschland und die DDR den Vereinten Nationen (VN) bei. Bereits zuvor konnte sich die Bundesrepublik schon zwanzig Jahre als „aktives Nichtmitglied“ hohes Ansehen als verlässliche Partnerin in der weltweiten multilateralen Zusammenarbeit erarbeiten. Nach der Wiedervereinigung setzte Deutschland diese Politik des umfassenden Engagements fast nahtlos fort. Das 50. Jubiläum seines VN-Beitritts ist daher ein guter Anlass, anhand ausgewählter Politikfelder Deutschlands Beitrag zu den VN zu untersuchen und zu diskutieren, inwieweit das Land seinen selbstgestellten Ansprüchen gerecht wurde – und weiterhin werden will. Dies soll nach einem kurzen Rückblick auf das VN-Engagement beider deutscher Staaten vor und nach ihrem Beitritt unternommen werden.
Die politischen Ursachen des autoritären Populismus. Über Armin Schäfer und Michael Zürn, Die demokratische Regression
Authors: Schmidt Manfred G.
Published: September 2023

Manfred G. Schmidt bespricht den Gehalt des Buches „Die demokratische Regression“ von Schäfer/Zürn zur Weiterentwicklung der Demokratie- und Populismusforschung und diskutiert auf Basis dessen aktuelle Gesellschaftsanalysen. Seine insgesamt ambivalente Diskussion des Buches kommt zu dem Schluss, dass Schäfer/Zürn damit einen vorzüglichen Beitrag zur Populismusanalyse und zur Demokratieforschung vorlegen.
Dynamic contrast-enhanced ultrasound characteristics of renal tumors: VueBox™ quantitative analysis
Authors: Li Ning , Hu Ziyue , Liu Yang , Ding Jianmin , Han Pengcheng , Jing Xiang , Kan Yanmin
Published: September 2023

BACKGROUND: VueBoxtrademark has been used for contrast analysis in DCE-US-based quantitative research. OBJECTIVE: Aim of this study was to use the enhancement-mode and VueBoxtrademark parameters to further evaluate the differential diagnostic value of DCE-US for renal tumors. METHODS: 24 patients with renal tumors, including 7 benign and 17 malignant, were retrospectively analyzed.The DCE-US enhancement-mode and VueBoxtrademark parameters correlated with the histological analyses of tumors were obtained and analyzed. RESULTS: The benign and malignant renal tumors showed significant differences in enhancement degree (P = 0.017) and presence of a pseudocapsule (P = 0.009) and in the VueBoxtrademark parameters FT (P = 0.045) and RT (P = 0.039). Receiver operating characteristic analysis for differential diagnosis of benign and malignant renal tumors showed that AUC for a combination of enhancement degree and presence of a pseudocapsule was greater (AUC = 0.815) than the values for either parameter of enhancement mode alone. Similarly, the AUC for a combination of RT and FT was greater (AUC = 0.798) than the values for RT or FT alone. A comprehensive index obtaining by combining the enhancement-mode and VueBoxtrademark parameters showed the largest AUC (AUC = 0.916) with relatively high accuracy (87.50%), sensitivity (76.50%), and specificity (85.70%). CONCLUSIONS: DCE-US with enhancement mode and quantitative analysis can facilitate preoperative differential diagnosis of benign and malignant renal tumors.
Trading strategy for virtual power plant clusters based on a multi-subject game model
Authors: Wan Ximing , Wen Xuan , Tang Bihong , Sun Qinfei
Published: September 2023

With the rapid development of renewable energy and the continuous growth of new loads, VPP has become an important form of smart grid and energy internet due to its flexible and effective management of distributed energy. During the operation of Virtual power plant, there is a game relationship between the system operator and VPP, and they are in a non-complete information environment. However, most of the current game optimization modeling is under the condition of complete information, and the game model based on complete information cannot solve this problem. This article focuses on the VPP cluster trading problem based on non-complete information game theory, constructs a Bayesian game model for multiple VPPs with multiple subjects under the master-slave game framework by introducing the Bayesian concept to optimize the cluster transactions within VPPs, and verifies the effectiveness of the model through simulation experiments. The experimental results show that the multi-VPP multi-subject Bayesian game model established in the study can guarantee the privacy of each subject and effectively reduce PAR, thus ensuring the security and stability of the VPP network and reducing cost expenditures, which has practicality in actual VPP cluster transactions.
A risk source identification model for network security of power CPS system (CPS) based on fuzzy artificial neural network
Authors: Chen Ze , Lu Ning , Hou Botao , Liu Xin , Zuo Xiaojun
Published: September 2023

In order to improve the effect and accuracy of risk source identification, this paper studied the network security risk source identification model of power CPS system based on fuzzy artificial neural network. The network security risk source index system of power CPS system was constructed, and the dimension of index data was reduced by principal component analysis. Fuzzy theory is used to process the index data after dimension reduction, and the comprehensive membership vector of each index is obtained. The dynamic clustering algorithm is used to determine the number of hidden layer units of radial basis function neural network, and the network security risk source identification model is established. Finally, the quantitative value of risk source identification is output. The experimental results show that the model can effectively reduce the dimension of the network security risk source index data of the power CPS system. The optimal distance threshold of the hidden layer is 4.2, and the optimal number of units is 6. In the final identification results, four severe risk sources and five moderate risk sources were obtained, and the quantitative values of risk source identification of each index were 63, 70, 71, 77, 65, 89 and 96, respectively, indicating that the model can effectively identify network security risk sources of power CPS systems. With the increase of the proportion of communication nodes removed, when there are various types of security vulnerability information, the mean square error value of the model is always lower than the set threshold, indicating that the model has high recognition accuracy.
Study on the coordinated development of national traditional sports and tourism brands based on big data platforms from the perspective of “The Belt and Road”
Authors: Cao Peng , Xiao Jing
Published: September 2023

The Belt and Road (B&R) plan is put out within the framework of global economics and strategic growth. This study examines the written material of popular tourist sites along B&R and the tourism assets from the viewpoint of B&R, based on the wireless network and AI technology, and using a big data platform and the Internet of Things (IoT) User Generated Content (UGC) network structure. To manage tourist pictures from customers’ views, online travel notes are first utilized as examples. Next, tourism texts’ keywords are extracted using Python big data and AI technology to understand consumers’ perceptions of scenic spot preferences, tourism facilities and services, and social and cultural customs. The findings demonstrate that, when compared to the conventional tourism brand development strategy, the integrated development strategy based on the AI big data platform can not only increase the effectiveness of managing tourists’ perceptions of scenic locations but can also encourage the common development of national sports event components and intelligent tourism image management. Several sports tourist boutique picturesque locations have also been built along B&R following years of development of intelligent tourism and sports projects, which will strengthen the effect of multicultural exchanges.
An interpretable decision tree ensemble model for imbalanced credit scoring datasets
Authors: My Bui T.T. , Ta Bao Q.
Published: September 2023

Credit scoring is a typical example of imbalanced classification, which poses a challenge to conventional machine learning algorithms and statistical classifiers when attempting to accurately predict outcomes for defaulting customers. In this paper, we propose a credit scoring classifier called Decision Tree Ensemble model (DTE). This model effectively addresses the challenge of imbalanced data and identifies significant features that influence the likelihood of credit status. An experiment demonstrates that DTE exhibits superior performance metrics in comparison to well-known based-tree ensemble classifiers such as Bagging, Random Forest, and AdaBoost, particularly when integrated with resampling techniques for handling imbalanced data.
Analyzing the impact of educators’ ability to develop student’s skills in the digital era using fuzzy models
Authors: Yuan Jinhai , Li Sisi , Fan Xin
Published: September 2023

Educators across different fields disseminate their knowledge and utilize digital technologies to improve student skills for their careers and sustainability. Students’ skills that are improved are verified based on assessment and knowledge application over different circumstances. The article investigates the impact of the effective educator’s knowledge assessment and their role in student skill development. The motivation for the research arises from the realization that teachers’ knowledge and their capacity for transferring skills and information to students successfully play a significant role in the quality of education. The goal of the study is to develop a GA2M that has been verified and can be used by educators to improve their performance, enhance student’s results, and eventually progress educational practices to use Fuzzy methods for reasoning and to include new rules for improving knowledge to bridge the knowledge gap between educators and students’ skill growth. It requires a great effect by the educator to enhance their ability over successive performance improvement. This article analyzes the ability for better improvement using the proposed Guided Ability Assessment Model (GA2M). The proposed model discards the negative impact of the ability on students’ skill deterioration. Besides, the ratio of skill improvement across various new abilities and exposures is analyzed using Fuzzy inference. This analysis frames the interference using knowledge rules required for different circumstances. These rules are framed using existing skill implications and problem-solving ability. This proposed model proposes new rules for development of various abilities of educators. Based on their current ability, further training process for the educator’s skill development is prescribed. Therefore, the inference for fuzzification is performed for the positive impact on students’ skill development. If the inference succeeds, the assessment is leveraged between skill guidance and ability.
Detecting anomalies in fetal electrocardiogram records using deep learning models
Authors: Sowmya S. , Jose Deepa
Published: September 2023

In order to assess the fetus health and make timely decisions throughout pregnancy, Fetal Electrocardiography (FECG) monitoring is essential. Huge datasets for electrocardiograms are freely accessible from Physionet ATM Dataset1- Abdominal and Direct Fetal ECG Database (adfecgdb), Dataset2- Fetal ECG Synthetic Database (fecgsyndb), Dataset3- Non-Invasive Fetal ECG Database(nifecgdb). In this study, categorization is done based on normal and abnormal (Atrial fibrillation) FECG from three online dataset which contains FECG recordings as major details. Deep learning models like Transfer Learning (TL) and Convolutional Neural Networks (CNN) are being investigated. The composite abdominal signal and the FECG are separated using a wavelet transform approach. The best model for categorizing the parameters of the FECG is determined through a comparative analysis and performance is improved using Continuous Wavelet Transform (CWT). The accuracy of the CNN-based technique is found to be 98.59%, whereas the accuracy of the transfer learning model is 99.01% for FECG classification. The computation of metric parameters for all the datasets is done. The classification of normal and abnormal (Atrial fibrillation) is best performed in TL model compared to CNN. Real-time data analysis is done for PQRST plotting and comparative study is done using Net Reclassification Improvement (NRI) and obtained NRI = 13%, z static 0f 3.7641, p-Value of 0.00016721. Acute Myocardial Infraction (AMI) identification is done based on ST segment of Maternal ECG (MECG) images to analyze the heart attack risk. The proposed work can be utilized to track FECG waveforms in real-time for wearable technology because of its end-to-end properties and expandable intrinsic for diagnosing multi-lead heart disorders.
Offline writer identification using deep feature concatenation
Authors: Afzali Parvaneh , Rezapour Abdoreza , Rezaee Jordehi Ahmad
Published: September 2023

Handwriting is an individual trait that serves as evidence to authenticate a particular writer. Identifying the writer of a handwritten text has shown encouraging results in examining historical and forensic documents. In this paper, we propose a novel offline writer identification system based on the challenging analysis of small amount of data to extract distinct patterns. In our deep network, the feature extraction process relies on a specially designed dual-path architecture, and the resulting embeddings are concatenated to produce the final learned features. To deal with a variety of uncertainties such as high intra-class variations and noises, we leverage the fuzzy logic in the design of a custom Convolutional Neural Network (CNN) with a type-2 fuzzy activation function for the first path. Additionally, the second path utilizes the transfer learning-based CNN to enhance the discriminability of the learned features. Our method allows for text-independent writer identification, eliminating the need for identical handwriting samples to train and test the model. Considering that various factors can influence the handwriting style, a dataset containing right-to-left handwriting samples is assembled. The proposed method is evaluated on our developed dataset and four widely-known public datasets, namely KHATT, CVL, Firemaker, and IAM. High accuracy values are achieved, with results of 99.85%, 99.83%, 99.79%, 99.64%, and 98.17% for each dataset, respectively. One noteworthy aspect of this study is that the evaluation results on diverse datasets demonstrate the applicability of the proposed model to various languages. Moreover, the model performs effectively in real-world scenarios with limited handwritten data.
Fuzzy comprehensive evaluation model in the evaluation of English teaching quality
Authors: Wang Liyang , Yang Xiao
Published: September 2023

Evaluating English teaching quality is vital for improving knowledge-based developments through communication for different aged students. Teaching quality assessment relies on the teachers’ and students’ features for constructive progression. With the development of computational intelligence, optimization and machine learning techniques are widely adapted for teaching quality assessment. In this article, a Quality-centric Assessment Model aided by Fuzzy Optimization (QAM-FO) is designed. This optimization approach validates the student-teacher features for a balanced model assessment. The distinguishable features for improving students’ oral and verbal communication from different teaching levels (basic, intermediate, and proficient) are extracted. The extracted features are the crisp input for the fuzzy optimization such that the recurring fuzzification detains the least fit feature. Such features are replaced by the level-based teaching and performance feature that differs from the previous fuzzy input. This replacement is pursued until a maximum recommendable feature (performance/ learning) is identified. The identified feature is applicable for different teaching levels for improving the quality assessment. Therefore, the proposed optimization approach provides different feasible recommendations for teaching improvements.
Secure IoT data management and sharing architecture for information security using cryptographic technique
Authors: Mishra Rajiv Kumar , Yadav Rajesh Kumar , Nath Prem
Published: September 2023

The massive amounts of data produced and gathered by smart devices through the internet support a wide range of applications, considerably improving our daily lives. Data sharing among smart devices must be safeguarded due to the sensitivity of the data involved in the transmission. The Internet of Things (IoT) environment must be protected from unauthorised access due to a variety of variables, including its attractiveness to cybercriminals, previous successful cyber-attacks, and consumers’ perceptions of security and reliability. Blockchain technology appears to be one promising technology that appears to address these security challenges extremely effectively. However, given the volume and rate at which smart devices generate data, Blockchain appears to be inefficient for storing it. The pace of data collection in the IoT context and the speed of transaction confirmation in the Blockchain network are the two key elements behind this. We connect the Blockchain and the Inter-Planetary File System (IPFS) in this study to permit data recording on a distributed storage and a mechanism to restrict access to recorded data to authorised organisations only. Over the Blockchain network, the access policy definition for safe data sharing and cryptographic hash content is stored. The real IoT-generated data, on the other hand, is collected via a distributed storage network, which improves availability and security. The proposed scheme’s analysis and performance evaluation show that it is secure and feasible. Furthermore, simulations are undertaken to assess the operating costs of smart contracts and to test the efficacy and viability of the suggested architecture.
Hamy mean operator under complex picture fuzzy environment and its application to disaster management program
Authors: Khan Muhammad Ishfaq , Almazrooei Abdullah Eqal , Yanhong Li , Ibrar Muhammad , Nazif Fatima , Latif Abdul
Published: September 2023

Disaster Management Program plays a critical role in coordinating and implementing strategies to address emergencies and disasters, ranging from natural events like hurricanes, earthquakes, and wildfire to human-made incidents such as industrial accidents or terrorist attacks. Simultaneously, it is widely studied as a typical multi-attribute decision-making (MADM) problem. This paper investigates the concept of complex picture fuzzy sets (CPFS), an extension of picture fuzzy sets (PFS), achieved by the inclusion of a phase term. The existence of phase terms expand the scope of CPFS from real line to a complex plane of unit disc and highlight its originality by demonstrating its capacity to handle both vagueness and periodicity simultaneously. In this paper, the complex picture fuzzy Hamy mean operator (CPFHM) and complex picture fuzzy dual Hamy mean operator (CPFDHM) is studied. The reason of selecting complex picture fuzzy Hamy mean operator (CPFHMO) is that it can find interrelationship among multi-input variables. Then the various properties of CPFHM and CPFDHM operator are described in depth. A multi-attributes group decision-making (MAGDM) technique for solving group decision-making problems is proposed based on these operators. The validity of the present technique is demonstrated by analyzing a disaster management problem. Furthermore we check the sensitivity of parameter k and apply the validity test on our proposed technique. Finally, a comprehensive comparison is provided between the proposed model and specific existing approaches, illustrating that the suggested decision model is superior and more advantageous than the existing employed methodologies.
RETRACTED: Hybrid interior ideals and hybrid bi-ideals in ternary semigroups
Authors: Catherine Grace John J. , Deepika M. , Elavarasan B.
Published: September 2023

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.
Double-local conditional probability based fast calculation method for approximation regions of local rough sets
Authors: Xu Yi , Zhou Meng
Published: September 2023

As an important extension of classical rough sets, local rough set model can effectively process data with noise. How to effectively calculate three approximation regions, namely positive region, negative region and boundary region, is a crucial issue of local rough sets. Existing calculation methods for approximation regions are based on conditional probability, the time complexity is O (|X||U||C|). In order to improve the computational efficiency of three approximation regions of local rough sets, we propose a double-local conditional probability based fast calculation method. First, to improve the computational efficiency of equivalence class, we define the double-local equivalence class. Second, based on the double-local equivalence class, we define the double-local conditional probability. Finally, given the probability thresholds and a local equivalence class, the monotonicity of double-local conditional probability is proved, on this basis, a double-local conditional probability based fast calculation method for approximation regions of local rough sets is proposed, and the time complexity is O (MAX (|X|2|C|, |X||XC||C|)). Experimental results based on 9 datasets from UCI demonstrate the effectiveness of the proposed method.
Passenger flow prediction and management method of urban public transport based on SDAE model and improved Bi-LSTM neural network
Authors: Xian Luo , Tian Lan
Published: September 2023

In the era of big data, the exponentially increasing data volume and emerging technical tools have put forward new requirements for enterprise information management. Therefore, it is of great significance to enhance the core competitiveness of enterprises to explore how big data can empower the innovation of enterprise information management. Intelligent transportation system combines a variety of technologies and applies them to a large-scale transportation management system, so as to make a reasonable dispatch of traffic conditions. Aiming at the problem of the relatively low accuracy of bus passenger flow forecasting with the existing models, a short-term passenger flow prediction model combining Stacked Denoising Auto Encoder (SDAE) and improved bidirectional Long-short Term Memory network (Bi-LSTM) is proposed. First, the SDAE model is used to fill in the missing bus passenger flow data, the characteristics of the bus passenger flow data are effectively utilized, and the data with rich information is used to predict the missing values with high accuracy. Second, Bi-LSTM model combined with attention mechanism is used for short-term bus passenger flow prediction. Considering that the data sequence of bus passenger flow is relatively long and there is a two-way information flow, the BiLSTM neural network is used for prediction tasks, and the influence of key factors is highlighted through attention weights to mine the internal laws of passenger flow data. The experimental results show that the proposed method achieves the lowest prediction error among all the comparison methods in the task of short-term bus passenger flow prediction on the public transportation dataset, with MAE, MRE, and RMSE values of 6.014, 0.052, and 9.874, respectively. These findings confirmed the effectiveness of the new model in the passenger flow prediction field.
Soybean futures price prediction with dual-stage attention-based long short-term memory: a decomposition and extension approach
Authors: Fan Kun , Hu Yanrong , Liu Hongjiu , Liu Qingyang
Published: September 2023

Accurately predicting soybean futures fluctuations can benefit various market participants such as farmers, policymakers, and speculators. This paper presents a novel approach for predicting soybean futures price that involves adding sequence decomposition and feature expansion to an Long Short-Term Memory (LSTM) model with dual-stage attention. Sequence decomposition is based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method, a technique for extracting sequence patterns and eliminating noise. The technical indicators generated enrich the input features of the model. Dual-stage attention are finally employed to learn the spatio-temporal relationships between the input features and the target sequence. The research is founded on data related to soybean contract trading from the Dalian Commodity Exchange. The suggested method surpasses the comparison models and establishes a fresh benchmark for future price forecasting research in China’s agricultural futures market.
Real-time intention prediction for upper limb configuration coordinates in reaching motions
Authors: Nie Kuang , Langari Reza
Published: September 2023

Surface electromyography (sEMG) signals have great potential for predicting upper limb motion. Although prior investigations have explored diverse applications of sEMG signal analysis, but few studies have focused on real-time motion prediction within the context of upper limb configuration space. Additionally, previous research has not adequately considered individual variability in sEMG features. This study aims to accomplish two main objectives. Firstly, it seeks to examine the dissimilarities in signal distribution across different subjects when employing various features. Additionally, the study aims to establish a correlation between signal distribution patterns and the model’s predictive accuracy. Secondly, the study introduced a personalized standardization (PSD) technique, which will serve to normalize the shape of the signal distribution across different subjects, thereby addressing the inter-individual differences in sEMG features. A bi-directional long short-term memory (Bi-LSTM) network is employed to estimate the real-time moving intention of the upper limb after applying the PSD technique. The analysis of signal distribution involved nine combinations of features, encompassing six features, namely mean absolute value (MAV), wave length (WL), variance (VAR), root mean square (RMS), mean frequency (MNF) and median frequency (MDF). To assess predictive capabilities, several models were evaluated. Remarkably, the distribution analysis clearly demonstrated that the shape of the signal distribution notably influences the model’s performance. Accroding to results, the incorporation of the PSD technique resulted in a notable improvement in the accuracy of the Bi-LSTM model, which leds to an enhancement of up to 2.8 percentage points in predictive accuracy. Additionally, the Bi-LSTM model emerged as the highest-performing model among all the compared models during the analysis. These findings underscore the importance of considering individual variability in sEMG features when developing predictive models for upper limb motion and highlight the potential benefits of employing the PSD technique to enhance model performance.
RETRACTED: Fractal based feature extraction technique for classifying EEG signal for color visualization tasks
Authors: Saranya K. , Paulraj M. , Hema C.R. , Nithya S.
Published: September 2023

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.
Pharmacogenetic markers of development of angioneurotic edema as a secondary side effect to enalapril in patients with essential arterial hypertension
Authors: Sychev Ivan V. , Denisenko Natalia P. , Kachanova Anastasiya A. , Lapshtaeva Anna V. , Abdullaev Sherzod P. , Goncharova Ludmila N. , Mirzaev Karin B. , Sychev Dmitry A.
Published: September 2023

BACKGROUND: Angioneurotic edema is the most dangerous complication in angiotensin-converting enzyme inhibitors (ACEIs) therapy. Based on the current data, the clinical and genetic predictors of angioedema development are still understudied, which demonstrates the relevance of this study. OBJECTIVE: To reveal the pharmacogenetic predictors of the angioedema as a secondary side effect to enalapril in patients with essential arterial hypertension. METHODS: The study enrolled 111 subjects randomized into two groups: study group, patients with the angioedema as a secondary side effect to enalapril; and control group, patients without adverse drug reaction. All patients underwent pharmacogenetic testing. RESULTS: An association between the development of the angioneurotic edema and the genotypes AA rs2306283 of gene SLCO1B1, TT rs4459610 of gene ACE, and CC rs1799722 of gene BDKRB2 in patients was revealed. CONCLUSION: The findings justify further investigations of the revealed genetic predictors of angioedema with larger-size patient populations.
Semiparametric transformation model:A hierarchical Bayesian approach
Authors: Achcar Jorge Alberto , Barili Emerson , Martinez Edson Zangiacomi
Published: September 2023

The use of semiparametric or transformation models has been considered by many authors in the analysis of lifetime data in the presence of censoring and covariates as an alternative and generalization of the usual proportional hazards, the proportional odds models, and the accelerated failure time models, extensively used in lifetime data analysis. The inferences for the proportional hazards model introduced by Cox (1972) are usually obtained by maximum likelihood estimation methods assuming the partial likelihood function introduced by Cox (Cox, 1975). In this study, we consider a hierarchical Bayesian analysis of the proportional hazards model assuming the complete likelihood function obtained from a transformation model considering the unknown hazard function as a latent unknown variable under a Bayesian approach. Some applications with real time medical data illustrate the proposed methodology.
A quasi Rama distribution
Authors: Shanker Rama , Das Hrittika , Shukla Kamlesh Kumar
Published: September 2023

A two-parameter quasi Rama distribution which contains the Rama distribution as particular case has been proposed. Its statistical properties based on moments have been discussed. The hazard rate function, mean residual life function, mean deviations and stochastic ordering of the distribution have been derived and studied. The estimation of parameters using method of moments and maximum likelihood methods has been discussed. A simulation study has been presented to know the performance of maximum likelihood estimates. The goodness of fit of the proposed distribution on two datasets relating to failure times has been presented.
The gamma-Maxwell regression for COVID-19 mortality rates of the 50 U.S. largest cities
Authors: da Costa N.S.S. , Cordeiro G.M.
Published: September 2023

A new parametric regression model is developed based on the gamma-Maxwell distribution. Monte Carlo simulations show the accuracy of the maximum likelihood estimators. The proposed model explains COVID-19 mortality rates of the 50 U.S. largest cities.
Study of the stress-deformation state of the soil base and foundations for equipment under significant horizontal loads
Authors: Zhupanenko Iryna
Published: September 2023

The study of the possibility of calculating foundations for stability against displacement along the sole and overturning under significant horizontal loads was carried out by the LIRA-SAPR computer software complex.

The study was carried out by analyzing the amount of horizontal (sliding) and vertical (overturning) displacement of the foundation under the shunting winch under two variants of the engineering and geological conditions of the construction site.

Through "manual" calculation, it was found that the stability of the foundation is ensured in the first variant of geological conditions, but not in the second one. Therefore, such geological conditions are illustrative for the study of calculation possibilities using the software complex.

The foundation with width b=1.8 m, length l=4.2 m and height h=2.35 m is modeled by volume finite elements. The bottom layer, which is in contact with the soil base, is modeled by quadrilateral finite elements of the slab (slab on an elastic base).

Friction under the footing is modeled by finite stiffness links (single-node finite elements) that are added at each node of the slab that models the bottom layer of the foundation.

The stiffness of such finite elements in the direction of the global axis X and axis Y is determined automatically in the "GRUNT" system according to the methodology of SNiP 2.02.05-87.

Summarizing the conducted research, we can say that the advanced capabilities of the LIRA-SAPR PC, in particular, the implementation of the algorithm for determining the stiffness coefficients of the base according to the SNiP 2.02.05-87 method, allow calculating foundations for resistance against displacement along the sole and overturning by numerical methods. This allows, firstly, to completely automate the calculation of the foundations, and secondly, during the automated calculation, the negative influence of the neighboring foundations is additionally taken into account, which is relevant, since the foundations for machines and mechanisms are mainly subjected to significant horizontal loads.

In addition, the conducted research revealed the absence of a kinematic criterion for assessing the stability of the foundation. In manual calculation, the criterion for ensuring the stability of the foundation against displacement is the condition of static equilibrium. When calculating by the method of finite elements, implemented in PC "LIRA" and similar, displacements are decisive, so it is considered promising to study the issue of the criterion of stability of the foundation by displacements.
Research on effective pit fencing solutions for the construction of a two-level parking lot of a multi-story building
Authors: Bondareva Liudmyla , Mekheda Kostiantyn
Published: September 2023

A study on the effective placement of the piles in the retaining wall of the pit was carried out. A comparison of internal forces and horizontal displacements of the retaining wall piles was drawn among three variants of their placements in the wall. The influence on the calculation results of the detailing of the soil excavation process during modeling using the Plaxis 2D was examined. Based on the research on the stress-strain state (STS) of the retaining wall piles using the spatial setting of the problem, a comparison to the problem with the flat setting was drawn. Additionally, an analysis of the bending moments and horizontal displacements in the retaining wall piles with a spatial setting of the problem was made.

Based on the obtained results, information on the effectiveness of different variants of the pile placement in the retaining wall of the pit, as well as the convergence of the calculation results with different detailing of the excavation process modeling is presented. Also, a comparison of the values of internal forces, horizontal displacements, and the elevation of the pit bottom between flat and spatial problem settings was made. The distribution of forces between piles of the retaining wall using a spatial problem setting is presented.

The finite element model consists of the soil base and retaining wall structure. Modeling of the base was executed using the Hardening soil model, in which the following soil characteristics are used during the calculation: γ – the specific weight of the soil in its natural state; γsat – the specific weight of the soil in the water-saturated state; c – cohesion; φ – the effective stress friction angle; ψ – dilatancy angle; E50 – stiffness for primary loading; Eoed – oedometer loading stiffness; Eur – stiffness for un-/reloading. The calculation was made using the Plaxis program complex.

Today, the effective and the most rational way of retaining wall design during the construction of buildings and structures with underground parts is significant. A successful choice of the construction of the excavation fencing not only ensures the reliability of the construction but also allows using building materials in the most efficient way. In addition, the choice of the method of the problem definition and its detailing are important factors when approaching the modeling process of similar problems, as they affect the accuracy of the obtained results, the degree of labor-intensiveness, and the time spent on the calculation.
Analysis of existing methods for calculating retaining walls from bored piles
Authors: Mykhaylovsky Denys , Komar Oleh , Khoronzhevskyi Maksym
Published: September 2023

An overview analysis of the methods of calculating retaining walls from driven piles was performed. Analytical calculation of piles in accordance with SNiP 2.02.03–85, calculation taking into account the requirements of change No. 1 DBN V.2.1-10-2009 on the calculation of horizontal stiffness of piles and static calculation of the section using the Lira-CAD software complex were considered, and calculation of a plane cross-section using the finite element method according to the Coulomb-Mohr model in the Plaxis 2D software complex.With population growth and the expansion of cities, the need for new residential, commercial and industrial buildings increases. Modern urbanization leads to an increase in the population density and densification of buildings. This requires the construction of multi-story buildings and infrastructure to ensure efficient use of the limited land area, construction on areas with significant elevation differences, or the construction of underground parking lots. Under such conditions, engineers increasingly have to solve construction problems in the conditions of compacted buildings, or in conditions of significant differences in the height of the building site.One of the most common engineering solutions to solve these problems is the implementation of massive retaining walls in the soil in the form of bored piles connected by a binding grid. The calculation of such structures requires special attention to the determination of the initial parameters of the considered problem and the choice of an approach to the calculation and analysis of the stress-strain state of the elements of the pit enclosure structures and the base.On the example, the calculation of the pile was performed in the engineering and geological conditions of the city of Kyiv with the same calculation parameters and different calculation methods. For the reliability of the comparison of loading results for all three cases, it was accepted according to DSTU NB V.2.1-31:2014. The obtained movements of the piles at the level of the bottom of the pit and the top of the pile were analyzed in three ways, and based on them, preliminary conclusions were formulated with prospects for further research.
Genesis of Syncretic Nature of Ukrainian Musical
Authors: Derkach Svitlana , Melnyk Myroslava , Chystiakov Oleg
Published: September 2023

The purpose of the study is to analyse the formation and development of the musical and to determine its syncretic nature. The research methodology. The method of systematic analysis of the genesis of the musical and its syncretic nature was applied; the axiological method made it possible to generalise the place and role of the musical in the entertainment culture of Ukrainians; the empirical method is based on the study of development trends and the application of the latest practices in domestic musicals. The scientific novelty lies in the general outline of the genesis of the musical, the current state of the Ukrainian musical genre as an integral cultural model, and the generalisation of the syncretic nature of the musical, which goes beyond the established stage canons. Conclusions. The genesis of the musical as a genre is connected with the process of constant syncretism of different genres of stage art, as well as pop music, cinematography, modern technologies, and is in constant adaptation to any changes in society and its system of values. Contemporary directors and producers try to syncretise as many different types and genres of art as possible in a musical, combine circus elements, modern technologies, and social networks to create a unique stage product, to maximise the boundaries of the genre, which actually reveals the syncretic nature of the contemporary musical. Thus, it can be stated that the musical, due to its syncretic nature, reflects a system of values that correlates with the values of the majority of society.
Key words: musical, syncretism, musical theatre, musical director, musical artist, new technologies, commercialisation.
Conditional Effect of Environmental Degradation and Institutional Environment on Human Development in Developing Countries: Evidence from Method of the Moment-Quantile Regression with Fixed Effect
Authors: Kamalu Kabiru , Binti Wan Ibrahim Wan Hakimah
Published: September 2023

The study examines the conditional effect of environmental degradation and institutional environment on the conditional distribution of human development, using the Method of the Moment Quantile Regression (MM-QR) technique with fixed effect, Fully Modified OLS (FMOLS) and Dynamic OLS (DOLS) estimators. The study uses strongly balanced data from 20 developing countries with the full data from 1996 to 2021. The findings confirmed heterogenous effects of environmental degradation and institutional environment on human development. The results reveals that lower environmental degradation promote human development across all levels of human development, but the effect higher in countries with low human development. The study shows that institutional environment has positive effect across all levels of human development, with higher effect in countries with low human development. The findings for the control variables show that financial development, population growth and FDI promote human development across all levels of human development. However, the strength of the coefficients also increases with higher quantiles. The results also confirmed unidirectional causality running from environmental degradation and institutional environment to human development, while bidirectional causality between financial development and human development. To promote human development, policymakers at developing countries should focus on climate action policies and building strong institutions.
ORAL INTERPRETATION OF QS. AL-AHZAB:33 BY KH. SYA'RONI AHMAD: MAQASHIDI ANALYSIS
Authors: Rohmah Miftahur
Published: September 2023

This paper discusses the oral interpretation of Kyai Sya'roni Ahmadi Kudus, who textually interpreted QS. Al-Ahzāb [33]:33. His interpretation is contrary to the current phenomenon, so the purpose of this paper is to find out the maqashid of Kyai Sya'roni's interpretation regarding the better women at home, and when they leave the house, there must be a companion or surveillance. This paper used descriptive analysis through the maqashidi Abdul Mustaqim interpretation approach. The results were obtained after applying the maqashidi interpretation method, including maslahah analysis, Qur'anic text and theme analysis, the historical context of revelation, the social context of Kyai Sa'roni in interpreting the al-Qur'an, and also social science analysis. By applying maqashid analysis, this paper argued that better women at home are a direction to preserve women's dignity. There are four alternative surveillance methods for women, e.g., close friends, security officers, constitution, and digital security system. The maqashid aspect of this verse is hifẓ al-nasl.
Biodegradable porous silica nanoparticles – Potential solutions in cancer treatment
Authors:   Center for Innovative Materials and Architectures (INOMAR), Vietnam National University Ho Chi Minh City, Vietnam, Đạt Ngọc Xuân Mai ,   Vietnam National University Ho Chi Minh City, Vietnam, Nguyễn Vy Trần Hạnh ,   Center for Innovative Materials and Architectures (INOMAR), Vietnam National University Ho Chi Minh City, Vietnam; Vietnam National University Ho Chi Minh City, Vietnam, Doan Tan Le-Hoang ,   Center for Innovative Materials and Architectures (INOMAR), Vietnam National University Ho Chi Minh City, Vietnam; Vietnam National University Ho Chi Minh City, Vietnam
Published: September 2023

Biodegradable porous silica nanoparticles (B-PMO) have been developed as a great promising in targeted drug delivery. In this review, we introduce our research for design, synthesis and development of B-PMO nanomaterials with biodegradable organic framework for anticancer drugs delivery applications. The biodegradable organic linker, incorporated into the structure, responds to the specific conditions in the tumor and then nanoparticles can be degraded and further eliminated from the body. The accumulation of NPs into tumor spheroid and inhibit of tumor growth were demonstrated. In particular, anticancer drug-loaded B-PMO inhibited the tumor growth in a chicken egg model and reduced significantly side effects toward the organs of the chicken embryo. The results of research and development of biodegradable-based silica nanomaterials show their potential in cancer treatment.