bluuebunny/crossref_metadata_2025 huggingface dataset:
Mediterranean Dietary Pattern and Cardiovascular Risk in Pregnant Women
Authors: Morales Suárez-Varela María , Peraita-Costa Isabel , Marín Alfredo Perales , Marcos Puig Beatriz , Llopis-Morales Agustín , Soriano Jose M.
Published: January 2023

Nutrition during pregnancy is one of the most important factors that determine the health of a mother and the proper development of her fetus. The main objective of this study was to analyze the association between adherence to a Mediterranean dietary (MedDiet) pattern and cardiovascular (CV) risk factors in pregnant women. Accordingly, we carried out an observational, population-based study using data from pregnant women present in a hospital during the entire course of their pregnancy. Adherence to the MedDiet was assessed using the MedDiet score questionnaire. Our study identified that 87.25% (95%CI: 83.48–90.27) of the women had a cardiovascular risk in relation to their dietary intake. Women with diet-related CV risk were more likely to smoke (p = 0.004), weighed more at the beginning of pregnancy, engaged in little physical activity, and had lower adherence to the MedDiet pattern than women without a diet-related CV risk. Dietary analysis showed low consumption of cereals, vegetables, and fish, which failed to satisfy the recommended portions in Spain. Adequate adherence to the MedDiet was found for 54.2% of women who were considered to be without CV risk and 45.8% of women with CV risk. Our data suggest that the MedDiet could be improved in relation to the consumption of cereals, vegetables, and fish during pregnancy in order to reduce CV risk.
High Efficiency Regeneration System from Blueberry Leaves and Stems
Authors: Zhou Yangyan , Li Qing , Wang Zejia , Zhang Yue
Published: January 2023

The main propagation approach is tissue culture in blueberries, and tissue culture is an effective and low-cost method with higher economic efficiency in blueberries. However, there is a lack of stable and efficient production systems of industrialization of tissue culture in blueberries. In this study, the high-efficiency tissue culture and rapid propagation technology system were established based on blueberry leaves and stems. The optimal medium for callus induction was WPM (woody plant medium) containing 2.0 mg/L Forchlorfenuron (CPPU), 0.2 mg/L 2-isopentenyladenine (2-ip) with a 97% callus induction rate and a callus differentiation rate of 71% by using blueberry leaves as explants. The optimal secondary culture of the leaf callus medium was WPM containing 3.0 mg/L CPPU with an increment coefficient of 24%. The optimal bud growth medium was WPM containing 1.0 mg/L CPPU, 0.4 mg/L 2-ip, with which the growth of the bud was better, stronger and faster. The optimal rooting medium was 1/2 Murashige and Skoog (1/2MS) medium containing 2.0 mg/L naphthylacetic acid (NAA), with which the rooting rate was 90% with shorter rooting time and more adventitious root. In addition, we established a regeneration system based on blueberry stems. The optimal preculture medium in blueberry stem explants was MS medium containing 2-(N-morpholino) ethanesulfonic acid (MES) containing 0.2 mg/L indole-3-acetic acid (IAA), 0.1 mg/L CPPU, 100 mg/L NaCl, with which the germination rate of the bud was 93%. The optimal medium for fast plant growth was MS medium containing MES containing 0.4 mg/L zeatin (ZT), 1 mg/L putrescine, 1 mg/L spermidine, 1 mg/L spermidine, which had a good growth state and growth rate. The optimal cultivation for plantlet growth was MS medium containing MES containing 0.5 mg/L isopentene adenine, with which the plantlet was strong. The optimal rooting medium for the stem was 1/2MS medium containing 2.0 mg/L NAA, with which the rooting rate was 93% with a short time and more adventitious root. In conclusion, we found that stem explants had higher regeneration efficiency for a stable and efficient production system of industrialization of tissue culture. This study provides theoretical guidance and technical support in precision breeding and standardization and industrialization in the blueberry industry.
Effect of the Addition of the Fifth Amino Acid to [GADV]-Protein on the Three-Dimensional Structure
Authors: Kato Koichi , Nakayoshi Tomoki , Oyaizu Ryota , Noda Natsuko , Kurimoto Eiji , Oda Akifumi
Published: January 2023

The [GADV]-protein, consisting only of glycine (G), alanine (A), aspartic acid (D), and valine (V), is frequently studied as a candidate for a primitive protein that existed at the beginning of life on Earth. The number of proteogenic amino acids increased during evolution, and glutamic acid may have been added as the fifth amino acid. In this study, we used molecular dynamics simulations to estimate the conformation of random peptides when glutamate is added to G, A, D, and V ([GADVE]), when leucine is added ([GADVL]), and when the frequency of alanine is doubled ([GADVA]). The results showed that the secondary structure contents of the [GADVE]-peptide and [GADVL]-peptide were higher than that of the [GADVA]-peptide. Although the [GADVL]-peptide had a higher secondary structure formation ability than the [GADVE]-peptide, it was less water soluble, suggesting that it may not be a primitive protein. The [GA(D/E)V]-peptide with G:A:D:V:E = 2:2:1:2:1 according to the occurrence ratio in the codon table also increased the secondary structure contents compared to the [GADV]-peptide, indicating that the addition of glutamic acid increased the structure formation ability of the primitive protein candidates.
Current Insights into Diagnosis, Prevention Strategies, Treatment, Therapeutic Targets, and Challenges of Monkeypox (Mpox) Infections in Human Populations
Authors: Patel Mitesh , Adnan Mohd , Aldarhami Abdu , Bazaid Abdulrahman , Saeedi Nizar , Alkayyal Almohanad , Saleh Fayez , Awadh Ibrahim , Saeed Amir , Alshaghdali Khalid
Published: January 2023

In the wake of the emergence and worldwide respread of a viral infection called Monkeypox (Mpox), there is a serious threat to the health and safety of the global population. This viral infection was endemic to the western and central parts of Africa, but has recently spread out of this endemic area to various countries, including the United Kingdom (UK), Portugal, Spain, the United States of America (USA), Canada, Sweden, Belgium, Italy, Australia, Germany, France, the Netherlands, Israel, and Mexico. This is a timely review focusing on recent findings and developments in the epidemiology, clinical features, therapeutic targets, diagnosis, prevention mechanisms, research challenges and possible treatment for Mpox. To date (29 November 2022), there have been around 81,225 reported cases of Mpox. In most cases, this illness is mild; however, there is a fatality rate ranging from 1 to 10%, which might be increased due to associated complications and/or secondary infections. There is a real challenge in the diagnosis of Mpox, since its symptoms are very similar to those of other infections, including smallpox and chickenpox. Generally, to prevent/limit the risk and transmission of Mpox, the detection and isolation of infected individuals, as well as hand hygiene and cleanliness, are essential and effective approaches to control/combat this viral infection. Nevertheless, updated information about Mpox from different angles is lacking. Thus, this review provides updated and comprehensive information about the Mpox illness, which should highlight the global burden, pathogenicity, symptoms, diagnosis, prevention measures and possible treatment of this emerging disease.
Method for On-Line Remaining Useful Life and Wear Prediction for Adjustable Journal Bearings Utilizing a Combination of Physics-Based and Data-Driven Models: A Numerical Investigation
Authors: Shutin Denis , Bondarenko Maxim , Polyakov Roman , Stebakov Ivan , Savin Leonid
Published: January 2023

RUL (remaining useful life) estimation is one of the main functions of the predictive analytics systems for rotary machines. Data-driven models based on large amounts of multisensory measurements data are usually utilized for this purpose. The use of adjustable bearings, on the one hand, improves a machine’s performance. On the other hand, it requires considering the additional variability in the bearing parameters in order to obtain adequate RUL estimates. The present study proposes a hybrid approach to such prediction models involving the joint use of physics-based models of adjustable bearings and data-driven models for fast on-line prediction of their parameters. The approach provides a rather simple way of considering the variability of the properties caused by the control systems. It has been tested on highly loaded locomotive traction motor axle bearings for consideration and prediction of their wear and RUL. The proposed adjustable design of the bearings includes temperature control, resulting in an increase in their expected service life. The initial study of the system was implemented with a physics-based model using Archard’s law and Reynolds equation and considering load and thermal factors for wear rate calculation. The dataset generated by this model is used to train an ANN for high-speed on-line bearing RUL and wear prediction. The results show good qualitative and quantitative agreement with the statistics of operation of traction motor axle bearings. A number of recommendations for further improving the quality of predicting the parameters of active bearings are also made as a summary of the work.
Acknowledgment to the Reviewers of Molbank in 2022
Authors:   Molbank Editorial Office
Published: January 2023

High-quality academic publishing is built on rigorous peer review [...]
On the Need for Deconvolution Analysis of Experimental and Simulated Thermoluminescence Glow Curves
Authors: Kitis George , Pagonis Vasilis
Published: January 2023

Simulation studies of thermoluminescence (TL) and other stimulated luminescence phenomena are a rapidly growing area of research. The presence of competition effects between luminescence pathways leads to the complex nature of luminescence signals, and therefore, it is necessary to investigate and validate the various methods of signal analysis by using simulations. The present study shows that in simulations of luminescence signals originating from multilevel phenomenological models, it is not possible to extract mathematically the individual information for each peak in the signal. It is further shown that computerized curve deconvolution analysis is the only reliable tool for extracting the various kinetic parameters. Simulation studies aim to explain experimental results, and therefore, it is necessary to validate simulation results by comparing with experiments. In this paper, testing of simulation results is performed using two methods. In the first method, the influence of competition effects is tested by comparing the input model parameters with the output values from the deconvolution analysis. In the second method, the agreement with experimental results is tested using the properties of well-known glow peaks with very high repeatability among TL laboratories, such as the 110 °C glow peak of quartz.
Film Cooling Performance of a Cylindrical Hole with an Upstream Crescent-Shaped Block in Linear Cascade
Authors: Zhang Chao , Dong Junhuai , Wang Zhan , Zhang Pengfei , Tong Zhiting , Zhang Yue
Published: January 2023

Recent works have already demonstrated that placing a crescent-shaped block upstream of a cylindrical hole could enhance the cooling performance of flat-plate films. The flow and cooling performance of the crescent-shaped block applied over the pressure and suction sides of the blade is investigated in this article. The Reynolds-averaged Navier-Stokes equations are solved with the Shear Stress Transport model for turbulence closure. Two optimized blocks are obtained from the flat-plate film cooling in our previous work, and two positions on the pressure and suction sides are tested. The blowing ratio varies from 0.5 to 2.0. The results show that when the block is applied on the blade surface, it yields a different cooling performance compared with the flat plate due to different geometry curvature and pressure gradient. The cooling performance on the suction side is slightly higher than on that on the pressure side, while the aerodynamic loss on the suction side is much higher. For the different blocks, the qualitative change of cooling performance vs. blowing ratios held on turbine blades is quite close to that of flat plates. The optimized smaller block in the flat plate provides better cooling performance at lower blowing ratios, while the larger block is superior when the blowing ratios are higher.
Modification and Validation of 1D Loss Models for the Off-Design Performance Prediction of Centrifugal Compressors with Splitter Blades
Authors: Yang Xiuxin , Liu Yan , Zhao Guang
Published: January 2023

One-dimensional (1D) aerodynamic performance predictions are very often conducted by researchers and designers during the preliminary design of centrifugal compressors. This paper focuses on a 1D prediction method for centrifugal compressors with splitter blades, which is rarely seen in the open literature. One-dimensional prediction of aerodynamic overall performance is made for centrifugal compressors with different technical design specifications. However, the aerodynamic overall prediction accuracy relies on the accuracy of the 1D-loss-models used. Therefore, an optimum combination of loss models is proposed by summarizing a variety of loss models presented in the public literature. In addition, an optimization method is utilized to optimize some coefficients involved in loss models in order to improve the generality of the combined model. The modified models obtained in this study are proved to have good predictive accuracy.
A 4D-Printed Self-Folding Spatial Mechanism with Pre-Stressed Response Properties
Authors: Zhang Wencai , Li Duanling
Published: January 2023

Exploring the transformation of spatial mechanisms from their unfolded to controlled folding states to meet the requirements of various application scenarios has long been a hot topic in mechanical structure research. Although conventional spatial mechanisms can be designed to meet almost any application scenario, the design’s complex and excessive combinations of structural components, kinematic pairs, and drive units are unavoidable. It introduces many problems, such as poor reliability, drive complexity, and control difficulties. Based on 4D printing technology, the design of self-folding spatial mechanisms that use pre-stressed response properties under predetermined thermal excitation to achieve different shrinkage ratios integrates the control and drive system and the structural components and kinematic pairs. It brings novel features of self-folding while effectively avoiding many problems associated with conventional mechanical design. Further, the pre-stressed response model introduces the self-folding spatial mechanisms’ excitation, morphing, and driving investigation. Self-folding spatial mechanisms with different shrinkage ratios were prepared via fused deposition modeling, which verified the theoretical analysis and pre-stress response model and the design’s correctness and feasibility by experiments. The existing 4D printing technology lacks a paradigmatic design method in the application field. Contrarily, this work organically combined the conventional mechanical structure design with materials and fabrication via fused deposition modeling. A systematic study of self-folding spatial mechanisms from structural design to morphing control was carried out. This design is expected to introduce a novel paradigm of 4D printing technology in conventional mechanical design and has considerable application prospects in spherical radar calibration mechanisms.
Hydrogeological Study in Tongchuan City Using the Audio-Frequency Magnetotelluric Method
Authors: Xu Zhimin , Xin Huicui , Weng Yuren , Li Guang
Published: January 2023

Tongchuan City, located in Shaanxi Province, northwest China, has limited groundwater resources. Rational planning and exploitation of groundwater are crucial to the sustainable development of the city, for which investigating the distribution of groundwater is the premise. Traditional resistivity sounding methods are often used to detect groundwater; however, these methods are not applicable in the study area where thick Quaternary loess is extensively distributed. In this study, we arranged five audio-frequency magnetotelluric (AMT) profiles to detect the deep clastic rock groundwater and carbonate karst fissure groundwater in Tongchuan. Firstly, we analyzed the electromagnetic interference (EMI) noises in Tongchuan City, revealing that the main EMI is power frequency interference (PFI). We used the dictionary learning processing technology to suppress the PFI. Secondly, the two-dimensional (2D) nonlinear conjugate gradient method was employed to invert a 2D electrical structure model for the area shallower than 1 km. We analyzed the characteristics of the electrical structure and its geological significance. Lastly, the three-dimensional (3D) electrical structure model of the study area was inverted using the 3D nonlinear conjugate gradient method, and the spatial distribution characteristics of the water-bearing strata were further analyzed. The results show that the PFI in urban environment can be suppressed by the dictionary learning processing technology. In Tongchuan city, the distribution of clastic rock fissure water is controlled by folds and faults, as well as the thickness of sandstone layers, and that of the carbonate karst fissure water is mainly controlled by faults. On this basis, we infer that the water-bearing areas are in the middle east and south of the study area.
On Deceiving Malware Classification with Section Injection
Authors: da Silva Adeilson Antonio , Pamplona Segundo Mauricio
Published: January 2023

We investigate how to modify executable files to deceive malware classification systems. This work’s main contribution is a methodology to inject bytes across a malware file randomly and use it both as an attack to decrease classification accuracy but also as a defensive method, augmenting the data available for training. It respects the operating system file format to make sure the malware will still execute after our injection and will not change its behavior. We reproduced five state-of-the-art malware classification approaches to evaluate our injection scheme: one based on Global Image Descriptor (GIST) + K-Nearest-Neighbors (KNN), three Convolutional Neural Network (CNN) variations and one Gated CNN. We performed our experiments on a public dataset with 9339 malware samples from 25 different families. Our results show that a mere increase of 7% in the malware size causes an accuracy drop between 25% and 40% for malware family classification. They show that an automatic malware classification system may not be as trustworthy as initially reported in the literature. We also evaluate using modified malware alongside the original ones to increase networks robustness against the mentioned attacks. The results show that a combination of reordering malware sections and injecting random data can improve the overall performance of the classification. All the code is publicly available.
An Offline Weighted-Bagging Data-Driven Evolutionary Algorithm with Data Generation Based on Clustering
Authors: Guo Zongliang , Lin Sikai , Suo Runze , Zhang Xinming
Published: January 2023

In recent years, a variety of data-driven evolutionary algorithms (DDEAs) have been proposed to solve time-consuming and computationally intensive optimization problems. DDEAs are usually divided into offline DDEAs and online DDEAs, with offline DDEAs being the most widely studied and proven to display excellent performance. However, most offline DDEAs suffer from three disadvantages. First, they require many surrogates to build a relatively accurate model, which is a process that is redundant and time-consuming. Second, when the available fitness evaluations are insufficient, their performance tends to be not entirely satisfactory. Finally, to cope with the second problem, many algorithms use data generation methods, which significantly increases the algorithm runtime. To overcome these problems, we propose a brand-new DDEA with radial basis function networks as its surrogates. First, we invented a fast data generation algorithm based on clustering to enlarge the dataset and reduce fitting errors. Then, we trained radial basis function networks and carried out adaptive design for their parameters. We then aggregated radial basis function networks using a unique model management framework and demonstrated its accuracy and stability. Finally, fitness evaluations were obtained and used for optimization. Through numerical experiments and comparisons with other algorithms, this algorithm has been proven to be an excellent DDEA that suits data optimization problems.
Alternative Method to Resolve the Principal–Principal Conflict—A New Perspective Based on Contract Theory and Negotiation
Authors: Zhou Yuxun , Rahman Mohammad Mafizur , Khanam Rasheda , Taylor Brad R.
Published: January 2023

Liquidated damages mechanisms have been analyzed from a legal perspective and applied to real-world contracts. Due to the lack of application of LDs to common agency theory, this research explores whether the liquidated damages mechanism can resolve principal–principal conflicts under common agency. We combine the moral hazard model of common agency with non-cooperative dynamic game theory to analyze the influence of the liquidated damages mechanism on the agent and principals under the condition of complete information and incomplete information. We find that liquidated damages are the key factors affecting the optimal contract between the principal and agent. Since the agent does not terminate the current contract, a principal–principal conflict arises when another principal wishes to enter into a new contract with the common agent. We find that the agent terminates the existing contract and signs a new one with another principal. The injured party requires liquidated damages from the breaching party. Therefore, they will negotiate the number of liquidated damages. Liquidated damages cause the bargaining game to generate a unique subgame perfect Nash equilibrium and sequential equilibrium. We prove that only when liquidated damages belong to a specific interval does a mechanism generating the Pareto optimal solutions to solve the principal–principal conflicts under common agency exist. A common way to resolve this conflict is ensuring that the minority is subordinate to the majority. For the first time, we study how the liquidated damages mechanism solves multi-principal conflict. This is another perspective that can be used to solve the conflict. Therefore, our paper expands the method of resolving the conflict and extends the theory of common agency; we first show the delegation process. Our research can be applied to various situations and provide a rational decision-making basis for participants.
Inverse Problem to Determine Two Time-Dependent Source Factors of Fractional Diffusion-Wave Equations from Final Data and Simultaneous Reconstruction of Location and Time History of a Point Source
Authors: Janno Jaan
Published: January 2023

In this paper, two inverse problems for the fractional diffusion-wave equation that use final data are considered. The first problem consists in the determination of two time-dependent source terms. Uniqueness for this inverse problem is established under an assumption that given space-dependent factors of these terms are “sufficiently different”. The proof uses asymptotical properties of Mittag–Leffler functions. In the second problem, the aim is to reconstruct a location and time history of a point source. The uniqueness for this problem is deduced from the uniqueness theorem for the previous problem in the one-dimensional case.
Dual-Domain Image Encryption in Unsecure Medium—A Secure Communication Perspective
Authors: Mahalingam Hemalatha , Veeramalai Thanikaiselvan , Menon Anirudh Rajiv , S. Subashanthini , Amirtharajan Rengarajan
Published: January 2023

With the growing demand for digitalization, multimedia data transmission through wireless networks has become more prominent. These multimedia data include text, images, audio, and video. Therefore, a secure method is needed to modify them so that such images, even if intercepted, will not be interpreted accurately. Such encryption is proposed with a two-layer image encryption scheme involving bit-level encryption in the time-frequency domain. The top layer consists of a bit of plane slicing the image, and each plane is then scrambled using a chaotic map and encrypted with a key generated from the same chaotic map. Next, image segmentation, followed by a Lifting Wavelet Transform, is used to scramble and encrypt each segment’s low-frequency components. Then, a chaotic hybrid map is used to scramble and encrypt the final layer. Multiple analyses were performed on the algorithm, and this proposed work achieved a maximum entropy of 7.99 and near zero correlation, evidencing the resistance towards statistical attacks. Further, the keyspace of the cryptosystem is greater than 2128, which can effectively resist a brute force attack. In addition, this algorithm requires only 2.1743 s to perform the encryption of a 256 × 256 sized 8-bit image on a host system with a Windows 10 operating system of 64-bit Intel(R) Core(TM) i5-7200U CPU at 2.5 GHz with 8 GB RAM.
The Optical Path Method for the Problem of Oblique Incidence of a Plane Electromagnetic Wave on a Plane-Parallel Scatterer
Authors: Belov Aleksandr , Dombrovskaya Zhanna
Published: January 2023

A number of actual problems of integrated photonics are reduced to an oblique incidence of radiation on a plane-parallel scatterer. For such problems, an approximate method of integrating the Maxwell equations along the beam propagation direction is proposed. As a result, the original two-dimensional problem is reduced to a one-dimensional one, and recently proposed one-dimensional bicompact schemes are used to solve it. This approach provides a significant reduction of computational costs compared to traditional two-dimensional methods such as finite differences and finite elements. To verify the proposed method, calculations of test and applied problems with known exact reflection spectra are carried out.
Semi-Markovian Discrete-Time Telegraph Process with Generalized Sibuya Waiting Times
Authors: Michelitsch Thomas M. , Polito Federico , Riascos Alejandro P.
Published: January 2023

In a recent work we introduced a semi-Markovian discrete-time generalization of the telegraph process. We referred to this random walk as the ‘squirrel random walk’ (SRW). The SRW is a discrete-time random walk on the one-dimensional infinite lattice where the step direction is reversed at arrival times of a discrete-time renewal process and remains unchanged at uneventful time instants. We first recall general notions of the SRW. The main subject of the paper is the study of the SRW where the step direction switches at the arrival times of a generalization of the Sibuya discrete-time renewal process (GSP) which only recently appeared in the literature. The waiting time density of the GSP, the ‘generalized Sibuya distribution’ (GSD), is such that the moments are finite up to a certain order r≤m−1 (m≥1) and diverging for orders r≥m capturing all behaviors from broad to narrow and containing the standard Sibuya distribution as a special case (m=1). We also derive some new representations for the generating functions related to the GSD. We show that the generalized Sibuya SRW exhibits several regimes of anomalous diffusion depending on the lowest order m of diverging GSD moment. The generalized Sibuya SRW opens various new directions in anomalous physics.
A Novel Zeroing Neural Network for Solving Time-Varying Quadratic Matrix Equations against Linear Noises
Authors: Li Jianfeng , Qu Linxi , Li Zhan , Liao Bolin , Li Shuai , Rong Yang , Liu Zheyu , Liu Zhijie , Lin Kunhuang
Published: January 2023

The solving of quadratic matrix equations is a fundamental issue which essentially exists in the optimal control domain. However, noises exerted on the coefficients of quadratic matrix equations may affect the accuracy of the solutions. In order to solve the time-varying quadratic matrix equation problem under linear noise, a new error-processing design formula is proposed, and a resultant novel zeroing neural network model is developed. The new design formula incorporates a second-order error-processing manner, and the double-integration-enhanced zeroing neural network (DIEZNN) model is further proposed for solving time-varying quadratic matrix equations subject to linear noises. Compared with the original zeroing neural network (OZNN) model, finite-time zeroing neural network (FTZNN) model and integration-enhanced zeroing neural network (IEZNN) model, the DIEZNN model shows the superiority of its solution under linear noise; that is, when solving the problem of a time-varying quadratic matrix equation in the environment of linear noise, the residual error of the existing model will maintain a large level due to the influence of linear noise, which will eventually lead to the solution’s failure. The newly proposed DIEZNN model can guarantee a normal solution to the time-varying quadratic matrix equation task no matter how much linear noise there is. In addition, the theoretical analysis proves that the neural state of the DIEZNN model can converge to the theoretical solution even under linear noise. The computer simulation results further substantiate the superiority of the DIEZNN model in solving time-varying quadratic matrix equations under linear noise.
On the Control over the Distribution of Ticks Based on the Extensions of the KISS Model
Authors: Kolokoltsov Vassili N.
Published: January 2023

Ticks and tick-borne diseases present a well-known threat to the health of people in many parts of the globe. The scientific literature devoted both to field observations and to modeling the propagation of ticks continues to grow. To date, the majority of the mathematical studies have been devoted to models based on ordinary differential equations, where spatial variability was taken into account by a discrete parameter. Only a few papers use spatially nontrivial diffusion models, and they are devoted mostly to spatially homogeneous equilibria. Here we develop diffusion models for the propagation of ticks stressing spatial heterogeneity. This allows us to assess the sizes of control zones that can be created (using various available techniques) to produce a patchy territory, on which ticks will be eventually eradicated. Using averaged parameters taken from various field observations we apply our theoretical results to the concrete cases of the lone star ticks of North America and of the taiga ticks of Russia. From the mathematical point of view, we give criteria for global stability of the vanishing solution to certain spatially heterogeneous birth and death processes with diffusion.
Using Machine Learning in Predicting the Impact of Meteorological Parameters on Traffic Incidents
Authors: Aleksić Aleksandar , Ranđelović Milan , Ranđelović Dragan
Published: January 2023

The opportunity for large amounts of open-for-public and available data is one of the main drivers of the development of an information society at the beginning of the 21st century. In this sense, acquiring knowledge from these data using different methods of machine learning is a prerequisite for solving complex problems in many spheres of human activity, starting from medicine to education and the economy, including traffic as today’s important economic branch. Having this in mind, this paper deals with the prediction of the risk of traffic incidents using both historical and real-time data for different atmospheric factors. The main goal is to construct an ensemble model based on the use of several machine learning algorithms which has better characteristics of prediction than any of those installed when individually applied. In global, a case-proposed model could be a multi-agent system, but in a considered case study, a two-agent system is used so that one agent solves the prediction task by learning from the historical data, and the other agent uses the real time data. The authors evaluated the obtained model based on a case study and data for the city of Niš from the Republic of Serbia and also described its implementation as a practical web citizen application.
Prediction of Acute Respiratory Distress Syndrome in Traumatic Brain Injury Patients Based on Machine Learning Algorithms
Authors: Wang Ruoran , Cai Linrui , Zhang Jing , He Min , Xu Jianguo
Published: January 2023

Background: Acute respiratory distress syndrome (ARDS) commonly develops in traumatic brain injury (TBI) patients and is a risk factor for poor prognosis. We designed this study to evaluate the performance of several machine learning algorithms for predicting ARDS in TBI patients. Methods: TBI patients from the Medical Information Mart for Intensive Care-III (MIMIC-III) database were eligible for this study. ARDS was identified according to the Berlin definition. Included TBI patients were divided into the training cohort and the validation cohort with a ratio of 7:3. Several machine learning algorithms were utilized to develop predictive models with five-fold cross validation for ARDS including extreme gradient boosting, light gradient boosting machine, Random Forest, adaptive boosting, complement naïve Bayes, and support vector machine. The performance of machine learning algorithms were evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy and F score. Results: 649 TBI patients from the MIMIC-III database were included with an ARDS incidence of 49.5%. The random forest performed the best in predicting ARDS in the training cohort with an AUC of 1.000. The XGBoost and AdaBoost ranked the second and the third with an AUC of 0.989 and 0.815 in the training cohort. The random forest still performed the best in predicting ARDS in the validation cohort with an AUC of 0.652. AdaBoost and XGBoost ranked the second and the third with an AUC of 0.631 and 0.620 in the validation cohort. Several mutual top features in the random forest and AdaBoost were discovered including age, initial systolic blood pressure and heart rate, Abbreviated Injury Score chest, white blood cells, platelets, and international normalized ratio. Conclusions: The random forest and AdaBoost based models have stable and good performance for predicting ARDS in TBI patients. These models could help clinicians to evaluate the risk of ARDS in early stages after TBI and consequently adjust treatment decisions.
A Real-World Cost-Effectiveness Analysis of Rivaroxaban versus Vitamin K Antagonists for the Treatment of Symptomatic Venous Thromboembolism: Lessons from the REMOTEV Registry
Authors: Kepka Sabrina , Cordeanu Elena-Mihaela , Zarca Kevin , Frantz Anne-Sophie , Ohlmann Patrick , Andres Emmanuel , Bilbault Pascal , Durand-Zaleski Isabelle , Stephan Dominique
Published: January 2023

Background and objectives: Venous thromboembolism (VTE) represents a health and economic burden with consequent healthcare resource utilization. Direct oral anticoagulants (DOACs) have emerged as the mainstay option for VTE treatment but few data exist on their cost-effectiveness as compared to the standard therapy (vitamin K antagonists (VKAs)). This study aimed to assess the cost-effectiveness of rivaroxaban compared to VKAs in VTE treatment by calculating the incremental cost effectiveness ratio (ICER). Materials and methods: We conducted a prospective observational study based on the REMOTEV registry, including patients hospitalized for VTE from 23 October 2013 to 31 July 2015, to evaluate the impact of the anticoagulant treatment (DOACs versus VKAs) on 6-month complications: major or clinically relevant non-major bleeding, VTE recurrence and all-cause death. Rivaroxaban was the only DOAC prescribed in this study. The ICER was calculated as the difference in costs divided by the difference in effectiveness. Results: Among the 373 patients included, 279 were treated with rivaroxaban (63.1 ± 17.9 years old; 49% men) and 94 with VKAs (71.3 ± 16.6 years old; 46% men). The mean cost was EUR 5662 [95% CI 6606; 9060] for rivaroxaban and EUR 7721 [95% CI 5130; 6304] for VKAs, while effectiveness was 0.0586 95% CI [0.0114; 0.126] for DOACs and 0.0638 [95% CI 0.0208; 0.109] for VKAs. The rivaroxaban treatment strategy was dominant with costs per patient EUR 2059 lower [95% CI −3582; −817] and a higher effectiveness of 0.00527 [95% CI −0.0606; 0.0761] compared to VKAs. Conclusions: This study provides real-world evidence that rivaroxaban is not only an efficient and safe alternative to VKAs for eligible VTE patients, but also cost-saving.
Platinum Nanoparticles Immobilized on Electrospun Membranes for Catalytic Oxidation of Volatile Organic Compounds
Authors: Soukup Karel , Topka Pavel , Kupčík Jaroslav , Solcova Olga
Published: January 2023

Structured catalytic membranes with high porosity and a low pressure drop are particularly suitable for industrial processes carried out at high space velocities. One of these processes is the catalytic total oxidation of volatile organic compounds, which is an economically feasible and environmentally friendly way of emission abatement. Noble metal catalysts are typically preferred due to high activity and stability. In this paper, the preparation of a thermally stable polybenzimidazole electrospun membrane, which can be used as a support for a platinum catalyst applicable in the total oxidation of volatile organic compounds, is reported for the first time. In contrast to commercial pelletized catalysts, high porosity of the membrane allowed for easy accessibility of the platinum active sites to the reactants and the catalytic bed exhibited a low pressure drop. We have shown that the preparation conditions can be tuned in order to obtain catalysts with a desired platinum particle size. In the gas-phase oxidation of ethanol, acetone, and toluene, the catalysts with Pt particle sizes 2.1 nm and 26 nm exhibited a lower catalytic activity than that with a Pt particle size of 12 nm. Catalysts with a Pt particle size of 2.1 nm and 12 nm were prepared by equilibrium adsorption, and the higher catalytic activity of the latter catalyst was ascribed to more reactive adsorbed oxygen species on larger Pt nanoparticles. On the other hand, the catalyst with a Pt particle size of 26 nm was prepared by a solvent evaporation method and contained less active polycrystalline platinum. Last but not least, the catalyst containing only 0.08 wt.% of platinum achieved high conversion (90%) of all the model volatile organic compounds at moderate temperatures (lower than 335 °C), which is important for reducing the costs of the abatement technology.
Esterification of Acetic Acid by Flow-Type Membrane Reactor with AEI Zeolite Membrane
Authors: Sekine Yuma , Sakai Motomu , Matsukata Masahiko
Published: January 2023

AEI-type zeolite membrane for dehydration was prepared, and a flow-type membrane reactor for the esterification of acetic acid and ethanol by AEI membrane was developed. A synthesized AEI membrane had suitable molecular sieving property for gas separation (H2/i-butane and CO2/CH4) and pervaporation (H2O/acetic acid). AEI membrane showed H2O permeance of 6.2 × 10−7 mol m−2 s−1 Pa−1 with a separation factor of 67 at 363 K for the equimolar mixture of H2O/acetic acid. AEI membrane maintained stable performance under acidic conditions. The yield of ethyl acetate at 363 K in a flow-type membrane reactor with AEI membrane successfully exceeded the equilibrium of 69.1%, reaching 89.0%. The flow rate of feed solution strongly affected the conversion of acetic acid and the space–time yield (STY) of ethyl acetate. Due to the more significant proportion of water selectively removed from the reaction system at a lower feed flow rate, the thermodynamic equilibrium shifted significantly, resulting in higher conversions. In contrast, STY increased with increasing feed flow rate. Our flow-type membrane reactor exhibited a relatively large STY of 430 kg m−3 h−1 compared with the batch-type membrane reactor previously reported.
Effects of Natural Rhamnolipid Mixture on Dioleoylphosphatidylcholine Model Membrane Depending on Method of Preparation and Sterol Content
Authors: Potapov Konstantin , Gordeev Alexander , Biktasheva Liliya , Rudakova Maya , Alexandrov Artem
Published: January 2023

Rhamnolipids as biosurfactants have a potentially wide range of applications, for example, as “green” surfactants or components of drug delivery systems, which is associated with the features of their interaction with cell membranes. However, as noted in the literature, those kind of features have not been sufficiently studied now. This paper presents a study of the interaction of a natural mixture of rhamnolipids produced by bacteria of the rhizosphere zone of plants Pseudomonas aeruginosa with model membranes—liposomes based on dioleoylphosphatidylcholine (DOPC), depending on the method of their preparation and the content of sterols—ergosterol, cholesterol, lanosterol. Liposomes with rhamnolipids were prepared by two protocols: with film method from a mixture of DOPC and rhamnolipids; with film method from DOPC and injection of water solution of rhamnolipids. Joint analysis of the data of 31P NMR spectroscopy and ATR-FTIR spectroscopy showed that in the presence of rhamnolipids, the mobility of the head group of the DOPC phospholipid increases, the conformational disorder of the hydrophobic tail increases, and the degree of hydration of the C=O and P=O groups of the phospholipid decreases. It can be assumed that, when prepared from a mixture, rhamnolipids are incorporated into the membrane in the form of clusters and are located closer to the middle of the bilayer; while when prepared by injection, rhamnolipid molecules migrate into the membrane in the form of individual molecules and are located closer to the head part of phospholipids. The sterol composition of the model membrane also affects the interaction of rhamnolipids with the membrane. Here it is worth noting the possible presence of type of interaction between rhamnolipids and ergosterol differ from other investigated sterols, due to which rhamnolipid molecules are embedded in the area where ergosterol is located.
Recovery of Metals from Wastewater—State-of-the-Art Solutions with the Support of Membrane Technology
Authors: Staszak Katarzyna , Wieszczycka Karolina
Published: January 2023

This paper discusses the most important research trends in the recovery of metals from industrial wastewater using membrane techniques in recent years. Particular attention is paid to the preparation of new membranes with the required filtration and separation properties. At the same time, possible future applications are highlighted. The aspects discussed are divided into metals in order to clearly and comprehensibly list the most optimal solutions depending on the composition of the wastewater and the possibility of recovering valuable components (metalloids, heavy metals, and platinum group metals). It is shown that it is possible to effectively remove metals from industrial wastewater by appropriate membrane preparation (up to ~100%), including the incorporation of functional groups, nanoparticles on the membrane surface. However, it is also worth noting the development of hybrid techniques, in which membrane techniques are one of the elements of an effective purification procedure.
Rapid Electrodeposition and Corrosion Behavior of Zn Coating from a Designed Deep Eutectic Solvent
Authors: Chen Jiayi , Zhu Mengjun , Gan Mingtao , Wang Xiuli , Gu Changdong , Tu Jiangping
Published: January 2023

This work aimed to develop a new type of deep eutectic solvent containing high concentrations of zinc ions as an electrolyte to improve the electrodeposition rate for zinc plating. Two typical deep eutectic solvent systems, choline chloride (ChCl)–urea and ChCl–ethylene glycol (EG), were combined to prepare a stable electrolyte at room temperature with a zinc ion concentration up to 2 M. Cyclic voltammetry experiments of the electrolyte at different temperatures were conducted. The effects of key electrodeposition parameters (bath temperature and current density) on the morphology, structure, and corrosion resistance of zinc coatings deposited on mild steel were investigated. It was found that the crystal orientation of the as-deposited zinc particle is related to the electrodeposition temperature and current density. The experimental results show that the zinc coating deposited at 60 °C and the current density of 4 mA·cm−2 exhibited the most compact and crack-free morphology, thus had the optimum corrosion resistance property.
Analysis of Design Method and Mechanical Properties of Plug-In Composite Shear Wall
Authors: Sun Xiang , Liu Peiyu , Jiang Zhelong , Yang Yuqing , Wang Zhe , Mu Zaigen
Published: January 2023

Assembly buildings are an important direction for the future development of the construction field. They can be prefabricated in the factories and then assembled on-site, which significantly improves construction efficiency. The shear walls are the most important lateral force-resisting elements in building structures, and at this stage, there are more and more studies on the prefabricated shear wall. In this paper, a new composite shear wall structure is proposed. The composite shear wall is a part of a prefabricated building, which is prefabricated into a single shear wall unit in the factory. During the construction, the upper and lower prefabricated shear wall units are connected by the plug-in. The design methods of splicing connection are given for the design of this composite shear wall structure. Eleven composite wall models under different parameters are established by using the finite element method, especially the fine modeling of the upper and lower connection parts. Compared with the conventional composite shear wall model of the same dimensions, the mechanical behaviors of the two models are similar. In the simulation of cyclic loading, the new composite shear wall shows good ductility and energy dissipation capacity, and also meets the established requirements of building seismic performance. Therefore, it can be concluded that the new prefabricated composite shear walls have good development prospects and application values.