bluuebunny/crossref_metadata_2025 huggingface dataset:
Design and Application of a Citizen Participation Tool to Improve Public Management of Drought Situations
Authors: Casero-Cepeda Juan Francisco , Catalá-Pérez Dani , Cano-Orellana Antonio
Published: October 2022

The growing scarcity of water for human consumption in southern Europe is driving today’s public administrations to search for new ways of optimising its availability. Within this context, the purpose of this paper is to analyse whether citizen participation is an appropriate way of improving the management of available water, as several international organisations suggest. This study is part of a research project carried out by the University of Seville in Spain on behalf of the city of Seville’s metropolitan water supply company, hereinafter EMASESA. A qualitative method is applied in this research using pre-mortem testing techniques, enabling a specific participation tool to be designed, called the EMASESA Water Observatory, which this article describes in detail. The tool produced specific measures aimed at better addressing drought situations. In view of the practical application of this newly designed tool, we conclude that citizen participation is indeed useful in identifying solutions to improve public water policies and drought management. It is also concluded that the tool’s design calling for active participation is a positive factor in its application. Finally, the tool has also demonstrated that it generates knowledge that can be used to address other water-related issues and challenges, beyond those related to water availability.
Landscape Features, Human Disturbance or Prey Availability? What Shapes the Distribution of Large Carnivores in Europe?
Authors: Milanesi Pietro , Puopolo Felice , Zellweger Florian
Published: October 2022

Knowledge concerning the relative importance of biotic and abiotic factors associated with the long-term settlement of large carnivores in human-dominated landscapes is important for effective species conservation. However, identifying the major drivers of large carnivore occurrences at a continental scale is challenging as several factors can act in concert to affect large carnivore distributions. Thus, we investigated the relationship between the permanent and sporadic occurrences of large carnivores (brown bear, Eurasian lynx, grey wolf and wolverine) and three groups of predictor variables (i.e., landscape attributes, human disturbance and prey availability) on the continental scale in Europe. Specifically, we used generalized linear models (GLMs) and variation partitioning to estimate the independent and cumulative effects of the three predictor groups on large carnivore occurrences. The explained variance for permanent vs. sporadic occurrence was highest for the wolverine (42.8% and 29.7%), followed by the brown bear (20.5% and 16.4%), Eurasian lynx (15.2% and 11.6%) and grey wolf (15.8% and 6.1%). Landscape attributes, such as forest cover, shrub-land cover, altitude and slope, were positively related to the occurrence of grey wolf, brown bear and Eurasian lynx, whereas human disturbance (human population density, distance to roads and to human settlements) was negatively related to wolverine occurrence. For all species, shared effects between landscape attributes and human disturbance accounted for a considerable portion of the explained variation in both permanent and sporadic occurrence, and landscape attributes were generally more important for explaining permanent than sporadic occurrence, except for the wolverine. Prey availability was marginally associated with the permanent occurrence of the grey wolf but we found no statistical effect of prey availability on the occurrence of the other large carnivores. In conclusion, the sporadic occurrence of large carnivores in Europe is more stochastic and less predictable than their permanent occurrence. Landscape attributes and their joint effects with human disturbance are the most important factors related to the recolonization dynamics of large carnivores, although wolverine distributions appear strongly limited by human disturbance itself. Domestic prey availability seems to play a subordinate role in driving the recent distribution dynamics of large carnivores at the continental scale. Thus, our results are relevant also in light of the ongoing recolonization of large carnivores which, in many rural areas, caused concerns among the locals, especially in relation to conflicts with human activities. For these reasons, we stress the need to continue and improve large carnivore monitoring across Europe.
Historical Landscape Elements of Abandoned Foothill Villages—A Case Study of the Historical Territory of Moravia and Silesia
Authors: Vavrouchová Hana , Vaishar Antonín , Peřinková Veronika
Published: October 2022

During the second half of the 20th century, a number of settlements disappeared for various reasons, especially in the hilly landscapes of northern Moravia and in the Czech part of Silesia. Currently, in the relevant localities, it is possible to identify preserved original landscape structures (scattered greenery, water elements, original woody plants, terraces, etc.) and other historical landscape elements with heritage potential. The typical elements of the above-mentioned localities of abandoned settlements are agrarian stone walls that document previous agricultural land use. These structures are generally located outside the original building plots on the edges of previously farmed land. Another important historical element is the unused access roads to arable land, which are still visible in lidar pictures. Numerous elements of the extinct settlements also include the remains of building materials and local quarries of building stone. This paper presents and classifies the historical landscape elements and their typology and proposes a methodology for identification and documentation.
Alpha-Phellandrene and Alpha-Phellandrene-Rich Essential Oils: A Systematic Review of Biological Activities, Pharmaceutical and Food Applications
Authors: Radice Matteo , Durofil Andrea , Buzzi Raissa , Baldini Erika , Martínez Amaury Pérez , Scalvenzi Laura , Manfredini Stefano
Published: October 2022

Alpha-phellandrene is a very common cyclic monoterpene found in several EOs, which shows extensive biological activities. Therefore, the main focus of the present systematic review was to provide a comprehensive and critical analysis of the state of the art regarding its biological activities and pharmaceutical and food applications. In addition, the study identified essential oils rich in alpha-phellandrene and summarized their main biological activities as a preliminary screening to encourage subsequent studies on their single components. With this review, we selected and critically analyzed 99 papers, using the following bibliographic databases: PubMed, SciELO, Wiley and WOS, on 8 July 2022. Data were independently extracted by four authors of this work, selecting those studies which reported the keyword “alpha-phellandrene” in the title and/or the abstract, and avoiding those in which there was not a clear correlation between the molecule and its biological activities and/or a specific concentration from its source. Duplication data were removed in the final article. Many essential oils have significant amounts of alpha-phellandrene, and the species Anethum graveolens and Foeniculum vulgare are frequently cited. Some studies on the above-mentioned species show high alpha-phellandrene amounts up to 82.1%. There were 12 studies on alpha-phellandrene as a pure molecule showed promising biological functions, including antitumoral, antinociceptive, larvicidal and insecticidal activities. There were 87 research works on EOs rich in alpha-phellandrene, which were summarized with a focus on additional data concerning potential biological activities. We believe this data is a useful starting point to start new research on the pure molecule, and, in particular, to distinguish between the synergistic effects of the different components of the OEs and those due to alpha-phellandrene itself. Toxicological data are still lacking, requiring further investigation on the threshold values to distinguish the boundary between beneficial and toxic effects, i.e., mutagenic, carcinogenic and allergenic. All these findings offer inspiration for potential applications of alpha-phellandrene as a new biopesticide, antimicrobial and antitumoral agent. In particular, we believe our work is of interest as a starting point for further studies on the food application of alpha-phellandrene.
The Importance of Livestock Demography and Infrastructure in Driving Foot and Mouth Disease Dynamics
Authors: Gilbertson Kendra , Brommesson Peter , Minter Amanda , Hallman Clayton , Miller Ryan S. , Portacci Katie , Sellman Stefan , Tildesley Michael J. , Webb Colleen T. , Lindström Tom , Beck-Johnson Lindsay M.
Published: October 2022

Transboundary animal diseases, such as foot and mouth disease (FMD) pose a significant and ongoing threat to global food security. Such diseases can produce large, spatially complex outbreaks. Mathematical models are often used to understand the spatio-temporal dynamics and create response plans for possible disease introductions. Model assumptions regarding transmission behavior of premises and movement patterns of livestock directly impact our understanding of the ecological drivers of outbreaks and how to best control them. Here, we investigate the impact that these assumptions have on model predictions of FMD outbreaks in the U.S. using models of livestock shipment networks and disease spread. We explore the impact of changing assumptions about premises transmission behavior, both by including within-herd dynamics, and by accounting for premises type and increasing the accuracy of shipment predictions. We find that the impact these assumptions have on outbreak predictions is less than the impact of the underlying livestock demography, but that they are important for investigating some response objectives, such as the impact on trade. These results suggest that demography is a key ecological driver of outbreaks and is critical for making robust predictions but that understanding management objectives is also important when making choices about model assumptions.
Endothelial Dysfunction in COVID-19: Potential Mechanisms and Possible Therapeutic Options
Authors: Pelle Maria Chiara , Zaffina Isabella , Lucà Stefania , Forte Valentina , Trapanese Vincenzo , Melina Melania , Giofrè Federica , Arturi Franco
Published: October 2022

SARS-CoV-2, a novel coronavirus found in Wuhan (China) at the end of 2019, is the etiological agent of the current pandemic that is a heterogeneous disease, named coronavirus disease 2019 (COVID-19). SARS-CoV-2 affects primarily the lungs, but it can induce multi-organ involvement such as acute myocardial injury, myocarditis, thromboembolic eventsandrenal failure. Hypertension, chronic kidney disease, diabetes mellitus and obesity increase the risk of severe complications of COVID-19. There is no certain explanation for this systemic COVID-19 involvement, but it could be related to endothelial dysfunction, due to direct (endothelial cells are infected by the virus) and indirect damage (systemic inflammation) factors. Angiotensin-converting enzyme 2 (ACE2), expressed in human endothelium, has a fundamental role in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. In fact, ACE2 is used as a receptor by SARS-CoV-2, leading to the downregulation of these receptors on endothelial cells; once inside, this virus reduces the integrity of endothelial tissue, with exposure of prothrombotic molecules, platelet adhesion, activation of coagulation cascades and, consequently, vascular damage. Systemic microangiopathy and thromboembolism can lead to multi-organ failure with an elevated risk of death. Considering the crucial role of the immunological response and endothelial damage in developing the severe form of COVID-19, in this review, we will attempt to clarify the underlying pathophysiological mechanisms.
Diversity of Silica-Scaled Chrysophytes of Two Tropical Islands: Phu Quoc and Con Son (Viet Nam)
Authors: Gusev Evgeniy , Martynenko Nikita , Kapustin Dmitry , Doan-Nhu Hai , Nguyen-Ngoc Lam
Published: October 2022

The present paper focuses on the silica-scaled chrysophytes from two tropical islands: Con Son (Con Dao Archipelago) and Phu Quoc, located in Viet Nam. Electron microscopy revealed fifty-seven taxa, of which forty-one belong to the genus Mallomonas. The others are species of the genera Synura (5), Paraphysomonas (8), Spiniferomonas (1), Chrysosphaerella (1) and Lepidochromonas (1). This is the first report of the genus Lepidochromonas from Viet Nam. In addition, two species from the genus Paraphysomonas are reported for the first time in the country. Six taxa from the genus Mallomonas and five taxa from the genus Paraphysomonas were not identifiable to the lower rank and may represent new species for science. The overall diversity of the silica-scaled chrysophytes of the islands is very high. A number of rare taxa endemic to Southeast Asia were found.
Suitability of Improved and Ancient Italian Wheat for Bread-Making: A Holistic Approach
Authors: Mefleh Marina , Boukid Fatma , Fadda Costantino
Published: October 2022

Ancient and old wheat grains are gaining interest as a genetic reservoir to develop improved Italian genotypes with peculiar features. In this light, the aim of this study was to assess the baking performance of two improved einkorn (Monlis and Norberto) and two improved emmer (Padre Pio and Giovanni Paolo) genotypes in comparison with two Italian landraces (Garfagnana and Cappelli) and Khorasan. This set was evaluated following a holistic approach considering the flour, dough, and bread properties. The results showed that the flour properties, dough rheology, pasting, and fermentation parameters, as well as the bread properties, significantly differed among the studied genotypes. Cappelli produced the bread with the best quality, i.e., the highest volume and lowest firmness. Despite having the same pedigrees, Giovanni Paolo and Padre Pio resulted in significantly different technological properties. Giovanni Paolo flour showed the highest protein content and provided a dough with a high gas production capacity, resulting in the bread having a similar firmness to Cappelli. Padre Pio flour provided bread having a similar volume to Cappelli but a high firmness similar to Khorasan and Garfagnana. The einkorn genotypes, Monlis and Norberto, showed poor fermentation properties and high gelatinization viscosity that resulted in bread with poor quality. Alternatively, they could be more suitable for making non-fermented flatbreads. Our results showed that the improved wheat showed a high versatility of features, which offers bakers a flexible material to make a genotype of bread types.
Study of the Polysaccharide Production by the Microalga Vischeria punctata in Relation to Cultivation Conditions
Authors: Babich Olga , Budenkova Ekaterina , Kashirskikh Egor , Dolganyuk Vyacheslav , Ivanova Svetlana , Prosekov Alexander , Anokhova Veronika , Andreeva Anna , Sukhikh Stanislav
Published: October 2022

Vischeria punctata is a unicellular microalga that has industrial potential, as it can produce substances with beneficial properties. Among them, endopolysaccharides (accumulated in cells) and exopolysaccharides (released by cells into the culture medium) are of particular interest. This study aimed to investigate the effect of nutrient medium composition on the growth of V. punctata biomass and the synthesis of polysaccharides by microalgae. The effect of modifying a standard nutrient medium and varying cultivation parameters (temperature, time, and extractant type) on the yield of exopolysaccharides produced by the microalgae V. punctate was investigated. The methods of spectrophotometry, ultrasonic extraction, and alcohol precipitation were used in the study. It was found that after 61 days of cultivation, the concentration of polysaccharides in the culture medium was statistically significantly higher (p <0.05) when using a Prat nutrient medium (984.9 mg/g d.w.) than BBM 3N (63.0 mg/g d.w.). It was found that the increase in the V. punctata biomass when cultivated on different nutrient media did not differ significantly. The maximum biomass values on Prat and BBM 3N media were 1.101 mg/g d.w. and 1.120 mg/g d.w., respectively. Neutral sugars and uronic acids were found in the culture media. It follows on from the obtained data that the modified PratM medium was more efficient for extracting polysaccharides from V. punctata. The potential of microalgae as new sources of valuable chemicals (polysaccharides), which can be widely used in technologies for developing novel functional foods, biologically active food supplements, and pharmaceutical substances, was studied.
SARS-CoV-2 and Influenza Vaccines in People with Excessive Body Mass—A Narrative Review
Authors: Drożdżyńska Julia , Jakubowska Wiktoria , Kemuś Marika , Krokowska Martyna , Karpezo Konrad , Wiśniewska Marcelina , Bogdański Paweł , Skrypnik Damian
Published: October 2022

In the face of a growing number of overweight people and two widely known viral diseases, SARS-CoV-2 and influenza, it is crucial to be aware of the impact of excess body weight on immunisation against these diseases. The aim of this review is to show the effectiveness of SARS-CoV-2 and influenza vaccines in overweight and obese patients. Excessive adipose tissue releases cytokines and maintains local hypoxia, which causes persistent low-grade inflammation. These factors make excess body mass patients’ immune systems weaker. Under such conditions, the humoral response becomes less efficient, leading to a weakened ability to fight against infection and an increased risk of developing lower antibody titres. Vaccines help to reduce morbidity both in normal-weight and excess body mass people, although most studies show that patients with higher BMI tend to lose the antibodies produced more quickly. It is shown that the most effective vaccines (in terms of preventing the infection and potential post-illness complications) are the BNT162b2 vaccine against SARS-CoV-2 and the inactivated influenza vaccine against influenza among both obese and non-obese subjects.
Experimental and Numerical Study on Mixed Lubrication Performance of Journal Bearing Considering Misalignment and Thermal Effect
Authors: Guo Hong , Bao Jianqiao , Zhang Shaolin , Shi Minghui
Published: October 2022

The shaft misalignment under mixed lubrication is an important factor affecting the running performance of the bearing, which can occur under heavy load and unsatisfactory assembly. This paper presents a misaligned journal mixed lubrication model coupling for the asperity contact effect, elastic deformation, viscosity–temperature, and viscosity–pressure effect. The finite difference method was employed to calculate the model, and an experimental apparatus designed in this paper was used to test the friction and temperature characteristics of the specimens. The results show that the pressure field, film thickness, and elastic deformation of the bearing conformed to asymmetric distribution along the axial direction under misalignment conditions and there was a notable end side effect. In addition, the frictional force and side leakage flow were evidently enhanced with the increase in the inclination angle in a certain range. The experimental results showed that there was a visible wear phenomenon on the end sides of the bush and shaft. The research results are beneficial for understanding the mixed lubrication mechanism of misaligned journal bearing.
Exploratory Case Study on Solving Word Problems Involving Triangles by Pre-Service Mathematics Teachers in a Regional University in Australia
Authors: Guo William
Published: October 2022

Studies have shown that solving real-world problems involving triangles is one of the most difficult topics for the pre-service secondary mathematics teachers engaging study and training in metropolitan institutions. We have known little about performances of the pre-service mathematics teachers from regional, rural and remote (RRR) areas engaging study and training in regional universities. This case study intends to explore whether solving word problems involving triangles would also be challenging for the RRR pre-service mathematics teachers, and what unique factors may negatively affect the RRR student teachers’ performances in solving word problems involving triangles. This study compared the works of two groups of the first-year pre-service mathematics teachers who enrolled in an undergraduate education program in a regional university in Australia. The two chosen word questions for comparison were parts of the assignments involving solving triangles to the students. Through statistical analysis, this study indicates that the considerable proportion of no attempts among the enrolled RRR students is the distinctive difference between the metropolitan and RRR pre-service mathematics teachers. Although still challenging, the RRR student teachers who attempted the word problems seemed performed better than the metropolitan students in solving word problems involving triangles.
Resolving Cross-Site Scripting Attacks through Fusion Verification and Machine Learning
Authors: Lu Jiazhong , Wei Zhitan , Qin Zhi , Chang Yan , Zhang Shibin
Published: October 2022

The frequent variations of XSS (cross-site scripting) payloads make static and dynamic analysis difficult to detect effectively. In this paper, we proposed a fusion verification method that combines traffic detection with XSS payload detection, using machine learning to detect XSS attacks. In addition, we also proposed seven new payload features to improve detection efficiency. In order to verify the effectiveness of our method, we simulated and tested 20 public CVE (Common Vulnerabilities and Exposures) XSS attacks. The experimental results show that our proposed method has better accuracy than the single traffic detection model. Among them, the recall rate increased by an average of 48%, the F1 score increased by an average of 27.94%, the accuracy rate increased by 9.29%, and the accuracy rate increased by 3.81%. Moreover, the seven new features proposed in this paper account for 34.12% of the total contribution rate of the classifier.
Determining Reliable Solutions for the Team Orienteering Problem with Probabilistic Delays
Authors: Herrera Erika M. , Panadero Javier , Carracedo Patricia , Juan Angel A. , Perez-Bernabeu Elena
Published: October 2022

In the team orienteering problem, a fixed fleet of vehicles departs from an origin depot towards a destination, and each vehicle has to visit nodes along its route in order to collect rewards. Typically, the maximum distance that each vehicle can cover is limited. Alternatively, there is a threshold for the maximum time a vehicle can employ before reaching its destination. Due to this driving range constraint, not all potential nodes offering rewards can be visited. Hence, the typical goal is to maximize the total reward collected without exceeding the vehicle’s capacity. The TOP can be used to model operations related to fleets of unmanned aerial vehicles, road electric vehicles with limited driving range, or ride-sharing operations in which the vehicle has to reach its destination on or before a certain deadline. However, in some realistic scenarios, travel times are better modeled as random variables, which introduce additional challenges into the problem. This paper analyzes a stochastic version of the team orienteering problem in which random delays are considered. Being a stochastic environment, we are interested in generating solutions with a high expected reward that, at the same time, are highly reliable (i.e., offer a high probability of not suffering any route delay larger than a user-defined threshold). In order to tackle this stochastic optimization problem, which contains a probabilistic constraint on the random delays, we propose an extended simheuristic algorithm that also employs concepts from reliability analysis.
Optimized np Attribute Control Chart Using Triple Sampling
Authors: Muñoz Jose Jorge , Campuzano Manuel J. , Mosquera Jaime
Published: October 2022

This paper studies an attribute control chart for monitoring the number of nonconforming items using a triple sampling (TS-np) which has not yet been applied to attribute control charts. The chart design and procedure for the decision about the state of the process are given. Mathematical expressions for the average run length (ARL) for in-control and out-of-control processes and the average sample number (ASN) are given. A bi-objective genetic algorithm that seeks to minimize the ASN and the probability of type 2 error is implemented in order to optimize the design of the TS-np control chart. A comparison between TS-np, single sampling np (SS-np), double sampling np (DS-np), and multiple dependent state repetitive sampling (MDSRS) control charts is carried out in terms of the out-of-control average run length (ARL1). Tables of ARL1 values for TS-np are presented in comparison with MDSRS and DS-np for various scenarios. The operation of the proposed control chart is shown through simulated data. Finally, it is concluded that the proposed TS-np chart has a better performance in terms of ARL1 detecting small and moderate shifts in the process nonconforming rate in-control (p0) compared with MDSRS and DS-np.
Survey on the Application of Artificial Intelligence in ENSO Forecasting
Authors: Fang Wei , Sha Yu , Sheng Victor S.
Published: October 2022

Climate disasters such as floods and droughts often bring heavy losses to human life, national economy, and public safety. El Niño/Southern Oscillation (ENSO) is one of the most important inter-annual climate signals in the tropics and has a global impact on atmospheric circulation and precipitation. To address the impact of climate change, accurate ENSO forecasts can help prevent related climate disasters. Traditional prediction methods mainly include statistical methods and dynamic methods. However, due to the variability and diversity of the temporal and spatial evolution of ENSO, traditional methods still have great uncertainty in predicting ENSO. In recent years, with the rapid development of artificial intelligence technology, it has gradually penetrated into all aspects of people’s lives, and the climate field has also benefited. For example, deep learning methods in artificial intelligence can automatically learn and train from a large amount of sample data, obtain excellent feature representation, and effectively improve the performance of various learning tasks. It is widely used in computer vision, natural language processing, and other fields. In 2019, Ham et al. used a convolutional neural network (CNN) model in ENSO forecasting 18 months in advance, and the winter ENSO forecasting skill could reach 0.64, far exceeding the dynamic model with a forecasting skill of 0.5. The research results were regarded as the pioneering work of deep learning in the field of weather forecasting. This paper introduces the traditional ENSO forecasting methods and focuses on summarizing the various latest artificial intelligence methods and their forecasting effects for ENSO forecasting, so as to provide useful reference for future research by researchers.
Deep Spatial-Temporal Neural Network for Dense Non-Rigid Structure from Motion
Authors: Wang Yaming , Wang Minjie , Huang Wenqing , Ye Xiaoping , Jiang Mingfeng
Published: October 2022

Dense non-rigid structure from motion (NRSfM) has long been a challenge in computer vision because of the vast number of feature points. As neural networks develop rapidly, a novel solution is emerging. However, existing methods ignore the significance of spatial–temporal data and the strong capacity of neural networks for learning. This study proposes a deep spatial–temporal NRSfM framework (DST-NRSfM) and introduces a weighted spatial constraint to further optimize the 3D reconstruction results. Layer normalization layers are applied in dense NRSfM tasks to stop gradient disappearance and hasten neural network convergence. Our DST-NRSfM framework outperforms both classical approaches and recent advancements. It achieves state-of-the-art performance across commonly used synthetic and real benchmark datasets.
The Enhanced Fixed Point Method: An Extremely Simple Procedure to Accelerate the Convergence of the Fixed Point Method to Solve Nonlinear Algebraic Equations
Authors: Filobello-Nino Uriel , Vazquez-Leal Hector , Huerta-Chua Jesús , Martínez-Castillo Jaime , Herrera-May Agustín L. , Sandoval-Hernandez Mario Alberto , Jimenez-Fernandez Victor Manuel
Published: October 2022

This work proposes the Enhanced Fixed Point Method (EFPM) as a straightforward modification to the problem of finding an exact or approximate solution for a linear or nonlinear algebraic equation. The proposal consists of providing a versatile method that is easy to employ and systematic. Therefore, it is expected that this work contributes to breaking the paradigm that an effective modification for a known method has to be necessarily long and complicated. As a matter of fact, the method expresses an algebraic equation in terms of the same equation but multiplied for an adequate factor, which most of the times is just a simple numeric factor. The main idea is modifying the original equation, slightly changing it for others in such a way that both have the same solution. Next, the modified equation is expressed as a fixed point problem and the proposed parameters are employed to accelerate the convergence of the fixed point problem for the original equation. Since the Newton method results from a possible fixed point problem of an algebraic equation, we will see that it is relatively easy to get modified versions of the Newton method with orders of convergence major than two. We will see in this work the convenience of this procedure.
CFD Model Studies of Dust Dispersion in Driven Dog Headings
Authors: Tutak Magdalena , Brodny Jarosław , John Antoni , Száva Janos , Vlase Sorin , Scutaru Maria Luminita
Published: October 2022

Dust is one of the most burdensome hazards found in the environment. It is composed of crushed solids that pose a threat to the health and life of people, machines and machine components. At high concentration levels, it can reduce visibility. All of these negative phenomena occur during the process of underground mining, where dust hazards are common. The negative impact of dust on the efficacy of the mining process prompts research in this area. The following study presents a method developed for model studies of dust dispersion in driven dog headings. This issue is immensely important due to the fact that these dog headings belong to a group of unidirectional excavations (including tunnelling). This paper presents the results of model studies on dust dispersion in driven dog headings. The main focus is on the analysis of the distribution of dust concentration along a dog heading during the mining process. In order to achieve this goal, a model test method based on the finite volume method, which is included in the group of CFD methods, was developed. Analyses were carried out for two different values of dust emission from the face of the excavation for the transient state. The results made it possible to determine areas with the highest potential for dust concentration. The size and location of these areas are mainly dependent on the amount of dust emissions during the mining process. The results can support the process of managing dust prevention and protection of workers during the mining excavation process.
Electric Vehicle Fire Risk Assessment Based on WBS-RBS and Fuzzy BN Coupling
Authors: Chen Jianhong , Li Kai , Yang Shan
Published: October 2022

(1) Background: In recent years, electric vehicle fire accidents have occurred frequently. Studying the risk factors leading to electric vehicle fire can take corresponding safety measures to reduce the occurrence of electric vehicle fire accidents. (2) Methods: The Work Breakdown Structure (WBS) was constructed to decompose the electric vehicle system, the Risk Breakdown Structure (RBS) was constructed to decompose the risk of electric vehicle fire accidents, a WBS-RBS coupling matrix was built to identify the risk factors that lead to electric vehicle fire accidents in the electric vehicle system, and the fuzzy Bayesian network was used to evaluate the risk of electric vehicle fire accidents. (3) Results: In this study, the electric vehicle was divided into four systems, and 15 risk factors leading to electric vehicle fire were found. The first risk factor was external collision ignition, followed by battery failure, artificial modification, battery-pack flooding, and charging equipment failure, and safety measures were proposed; (4) Conclusions: The results show that the WBS-RBS and fuzzy BN coupling research method can identify the risk factors leading to an electric vehicle fire, and the risk factors were ranked, providing a reference for the safety protection of electric vehicles.
On Coevaluation Behavior and Equivalence
Authors: Zúñiga Angel , Bel-Enguix Gemma
Published: October 2022

Coevaluation, the coinductive interpretation of standard big-step evaluation rules, is a concise form of semantics, with the same number of rules as in evaluation, which intends to simultaneously describe finite and infinite computations. However, it is known that it is only able to express an infinite computations subset, and, to date, it remains unknown exactly what this subset is. More precisely, coevaluation behavior has several unusual features: there are terms for which evaluation is infinite but that do not coevaluate, it is not deterministic in the sense that there are terms which coevaluate to any value v, and there are terms for which evaluation is infinite but that coevaluate to only one value. In this work, we describe the infinite computations subset which is able to be expressed by coevaluation. More importantly, we introduce a coevaluation extension that is well-behaved in the sense that the finite computations coevaluate exactly as in evaluation and the infinite computations coevaluate exactly as in divergence. Consequently, no unusual features are presented; in particular, this extension captures all infinite computations (not only a subset of them). In addition, as a consequence of thiswell-behavior, we present the expected equivalence between (extended) coevaluation and evaluation union divergence.
Distributed Optimization for Second-Order Multi-Agent Systems over Directed Networks
Authors: Yang Feiyang , Yu Zhiyong , Huang Da , Jiang Haijun
Published: October 2022

This paper studies a generalized distributed optimization problem for second-order multi-agent systems (MASs) over directed networks. Firstly, an improved distributed continuous-time algorithm is proposed. By using the linear transformation method and Lyapunov stability theory, some conditions are obtained to guarantee all agents’ states asymptotically reach the optimal solution. Secondly, to reduce unnecessary communication transmission and control cost, an event-triggered algorithm is designed. Moreover, the convergence of the algorithm is proved, and the Zeno behavior can be avoided based on strict theoretic analysis. Finally, one example is given to verify the good performance of the proposed algorithms.
Optimal Neural Network Model for Short-Term Prediction of Confirmed Cases in the COVID-19 Pandemic
Authors: Milić Miljana , Milojković Jelena , Jeremić Miljan
Published: October 2022

COVID-19 is one of the largest issues that humanity still has to cope with and has an impact on the daily lives of billions of people. Researchers from all around the world have made various attempts to establish accurate mathematical models of COVID-19 spread. In many branches of science, it is difficult to make accurate predictions about short time series with extremely irregular behavior. Artificial neural networks (ANNs) have lately been extensively used for such applications. Although ANNs may mimic the nonlinear behavior of short time series, they frequently struggle to handle all turbulences. Alternative methods must be used as a result. In order to reduce errors and boost forecasting confidence, a novel methodology that combines Time Delay Neural Networks is suggested in this work. Six separate datasets are used for its validation showing the number of confirmed daily COVID-19 infections in 2021 for six world countries. It is demonstrated that the method may greatly improve the individual networks’ forecasting accuracy independent of their topologies, which broadens the applicability of the approach. A series of additional predictive experiments involving state-of-the-art Extreme Learning Machine modeling were performed to quantitatively compare the accuracy of the proposed methodology with that of similar methodologies. It is shown that the forecasting accuracy of the system outperforms ELM modeling and is in the range of other state-of-the art solutions.
An Improved Gray Wolf Optimization Algorithm with a Novel Initialization Method for Community Detection
Authors: Kang Yan , Xu Zhongming , Wang Haining , Yuan Yanchong , Yang Xuekun , Pu Kang
Published: October 2022

Community discovery (CD) under complex networks is a hot discussion issue in network science research. Recently, many evolutionary methods have been introduced to detect communities of networks. However, evolutionary optimization-based community discovery still suffers from two problems. First, the initialization population quality of the current evolutionary algorithm is not good, resulting in slow convergence speed, and the final performance needs to be further improved. Another important issue is that current methods of CD have inconsistent network detection performance at different scales, showing a dramatic drop as the network scale increases. To address such issues, this paper proposes an algorithm based on the novel initial method and improved gray wolf optimization (NIGWO) to tackle the above two problems at the same time. In this paper, a novel initialization strategy is proposed to generate a high-quality initial population and greatly accelerate the convergence speed of population evolution. The strategy effectively fused the elite substructure of the community and different features based on the dependency and other features among nodes. Moreover, an improved GWO is presented with two new search strategies. An improved hunting prey stage is proposed to retain the excellent substructures of populations and quickly improve the community structure. Furthermore, new mutation strategies from node level to community level are designed in an improved encircling prey stage. Specifically, boundary nodes are mutated according to a proposed function to improve the search efficiency and save the computation assumption. Numerous experiments have proven our method obtains more excellent performance in most networks compared with 11 state-of-the-art algorithms.
Plug-and-Play-Based Algorithm for Mixed Noise Removal with the Logarithm Norm Approximation Model
Authors: Liu Jinhua , Wu Jiayun , Xu Mulian , Huang Yuanyuan
Published: October 2022

During imaging and transmission, images are easily affected by several factors, including sensors, camera motion, and transmission channels. In practice, images are commonly corrupted by a mixture of Gaussian and impulse noises, further complicating the denoising problem. Therefore, in this work, we propose a novel mixed noise removal model that combines a deterministic low-rankness prior and an implicit regularization scheme. In the optimization model, we apply the matrix logarithm norm approximation model to characterize the global low-rankness of the original image. We further adopt the plug-and-play (PnP) scheme to formulate an implicit regularizer by plugging an image denoiser, which is used to preserve image details. The above two building blocks are complementary to each other. The mixed noise removal algorithm is thus established. Within the framework of the PnP scheme, we address the proposed optimization model via the alternating directional method of multipliers (ADMM). Finally, we perform extensive experiments to demonstrate the effectiveness of the proposed algorithm. Correspondingly, the simulation results show that our algorithm can recover the global structure and detailed information of images well and achieves superior performance over competing methods in terms of quantitative evaluation and visual inspection.
Quantitative Analysis of the Balance Property in Factorial Experimental Designs 24 to 28
Authors: Ramírez-Tapia Ricardo , Ríos-Lira Armando Javier , Pantoja-Pacheco Yaquelin Verenice , Vázquez-López José Antonio , Ruelas-Santoyo Edgar Augusto
Published: October 2022

Experimental designs are built by using orthogonal balanced matrices. Balance is a desirable property that allows for the correct estimation of factorial effects and prevents the identity column from aliasing with factorial effects. Although the balance property is well known by most researchers, the adverse effects caused by the lack or balance have not been extensively studied or quantified. This research proposes to quantify the effect of the lack of balance on model term estimation errors: type I error, type II error, and type I and II error as well as R2, R2adj, and R2pred statistics under four balance conditions and four noise conditions. The designs considered in this research include 24–28 factorial experiments. An algorithm was developed to unbalance these matrices while maintaining orthogonality for main effects, and the general balance metric was used to determine four balance levels. True models were generated, and a MATLAB program was developed; then a Monte Carlo simulation process was carried out. For each true model, 50,000 replications were performed, and percentages for model estimation errors and average values for statistics of interest were computed.
Ocular Biometrics with Low-Resolution Images Based on Ocular Super-Resolution CycleGAN
Authors: Lee Young Won , Kim Jung Soo , Park Kang Ryoung
Published: October 2022

Iris recognition, which is known to have outstanding performance among conventional biometrics techniques, requires a high-resolution camera and a sufficient amount of lighting to capture images containing various iris patterns. To address these issues, research is actively conducted on ocular recognition to include a periocular region in addition to the iris region, which also requires a high-resolution camera to capture images, indicating limited applications due to costs and size limitation. Accordingly, this study proposes an ocular super-resolution cycle-consistent generative adversarial network (OSRCycleGAN) for ocular super-resolution reconstruction, and additionally proposes a method to improve recognition performance in case that ocular images are acquired at a low-resolution. The results of the experiment conducted using open databases, namely, CASIA-iris-Distance and Lamp v4, and IIT Delhi iris database, showed that the equal error rate of recognition of the proposed method was 3.02%, 4.06% and 2.13% for each database, respectively, which outperformed state-of-the-art methods.
Energy Transfer through a Magnetized Williamson Hybrid Nanofluid Flowing around a Spherical Surface: Numerical Simulation
Authors: Alzu’bi Oruba Ahmad Saleh , Alwawi Firas A. , Swalmeh Mohammed Z. , Sulaiman Ibrahim Mohammed , Hamarsheh Abdulkareem Saleh , Ibrahim Mohd Asrul Hery
Published: October 2022

A computational simulation of Williamson fluid flowing around a spherical shape in the case of natural convection is carried out. The Lorentz force and constant wall temperature are taken into consideration. In addition, upgrader heat transfer catalysts consisting of multi-walled carbon tubes, molybdenum disulfide, graphene oxide, and molybdenum disulfide are employed. The Keller box approach is used to solve the mathematical model governing the flow of hybrid Williamson fluid. To validate our findings, the key parameters in the constructed model are set to zero. Next, the extent of the agreement between our results and published results is observed. Numerical and graphical results that simulate the impressions of key parameters on physical quantities related to energy transmission are obtained, discussed, and analyzed. According to the results of this study, increasing the value of the Weissenberg number causes an increase in both the fluid temperature and drag force, while it also leads to a decrease in both the velocity of the fluid and the rate of energy transmission. Increasing the magnetic field intensity leads to a reduction in the rate of heat transfer, drag force, and fluid velocity while it has an appositive effect on temperature profiles.
Computational Analysis of Fractional Diffusion Equations Occurring in Oil Pollution
Authors: Singh Jagdev , Alshehri Ahmed M. , Momani Shaher , Hadid Samir , Kumar Devendra
Published: October 2022

The fractional model of diffusion equations is very important in the study of oil pollution in the water. The key objective of this article is to analyze a fractional modification of diffusion equations occurring in oil pollution associated with the Katugampola derivative in the Caputo sense. An effective and reliable computational method q-homotopy analysis generalized transform method is suggested to obtain the solutions of fractional order diffusion equations. The results of this research are demonstrated in graphical and tabular descriptions. This study shows that the applied computational technique is very effective, accurate, and beneficial for managing such kind of fractional order nonlinear models occurring in oil pollution.
Determinants of Investment Awareness: A Moderating Structural Equation Modeling-Based Model in the Saudi Arabian Context
Authors: Ali Mohamed Ali Shabeeb , Ammer Mohammed Abdullah , Elshaer Ibrahim A.
Published: October 2022

In line with today’s economy, investment and financial awareness are necessary for success and an individual’s well-being, specifically for the younger generations. Therefore, this study aims to examine the relationships between financial literacy, saving behavior, a lack of self-control, family financial socialization, and investment awareness. Further, it investigates the moderating role of both family financial socialization and the lack of self-control in these relationships. Employing a quantitative study technique and partial least squares structural equation modeling (PLS-SEM), we analyzed a sample of 409 students representing young adults at King Faisal University, specifically in the School of Business. Our results indicate that financial literacy, saving behavior, and family financial socialization are significantly and positively related to investment awareness. Interestingly and as expected, a lack of self-control negatively and significantly affects investment awareness. For the moderating impact, it was found that the connection between financial literacy, saving behavior, and investment awareness is positively and strongly moderated by family financial socialization. Likewise, a lack of self-control significantly and negatively moderated the association between financial literacy, saving behavior, and investment awareness. The results of this study provide substantial implications for regulators, educational organizations, individuals, and their families.