bluuebunny/crossref_metadata_2025 huggingface dataset:
A Projection Method for the Estimation of Error Covariance Matrices for Variational Data Assimilation in Ocean Modelling
Authors: Gonzalez-Ondina Jose M. , Sampson Lewis , Shapiro Georgy I.
Published: December 2021

Data assimilation methods are an invaluable tool for operational ocean models. These methods are often based on a variational approach and require the knowledge of the spatial covariances of the background errors (differences between the numerical model and the true values) and the observation errors (differences between true and measured values). Since the true values are never known in practice, the error covariance matrices containing values of the covariance functions at different locations, are estimated approximately. Several methods have been devised to compute these matrices, one of the most widely used is the one developed by Hollingsworth and Lönnberg (H-L). This method requires to bin (combine) the data points separated by similar distances, compute covariances in each bin and then to find a best fit covariance function. While being a helpful tool, the H-L method has its limitations. We have developed a new mathematical method for computing the background and observation error covariance functions and therefore the error covariance matrices. The method uses functional analysis which allows to overcome some shortcomings of the H-L method, for example, the assumption of statistical isotropy. It also eliminates the intermediate steps used in the H-L method such as binning the innovations (differences between observations and the model), and the computation of innovation covariances for each bin, before the best-fit curve can be found. We show that the new method works in situations where the standard H-L method experiences difficulties, especially when observations are scarce. It gives a better estimate than the H-L in a synthetic idealised case where the true covariance function is known. We also demonstrate that in many cases the new method allows to use the separable convolution mathematical algorithm to increase the computational speed significantly, up to an order of magnitude. The Projection Method (PROM) also allows computing 2D and 3D covariance functions in addition to the standard 1D case.
Multilocus Phylogeography of the Tuber mesentericum Complex Unearths Three Highly Divergent Cryptic Species
Authors: Leonardi Marco , Salvi Daniele , Iotti Mirco , Rana Gian Luigi , Paz-Conde Aurelia , Pacioni Giovanni
Published: December 2021

Tuber mesentericum is an edible European black truffle, apparently easy to recognize, but showing a high degree of genetic variability. In this study, we performed an integrative taxonomic assessment of the T. mesentericum complex, combining a multilocus phylogeographic approach with morphological analyses, and including authentic specimens of Vittadini, and Berkeley and Broome. We performed maximum likelihood phylogenetic analyses, based on single and concatenated gene datasets (ITS rDNA, β-tubulin, elongation factor 1-α), and including all available sequences from previous studies. Phylogenetic analyses consistently recovered three reciprocally monophyletic and well-supported clades: clade I, with a wide range across Europe; clade II, specimens collected mainly in the Iberian, Italian, and Balkan peninsulas; and clade III, specimens collected almost exclusively in central Italy. Genetic distance between clades ranged from 10.4% to 13.1% at the ITS region. We also designed new primer pairs specific for each phylogenetic lineage. Morphology of spores, asci, and peridium were investigated on specimens representing the three lineages. Macro- and micromorphological analyses of ascomata revealed only a few, but not diagnostic, differences between the three phylogenetic lineages, thus, confirming that they are morphologically cryptic. By studying authentic specimens of Vittadini, and Berkeley and Broome, it was possible to identify the three clades as T. mesentericum, Tuber bituminatum, and Tuber suave sp. nov., and to designate an epitype for T. mesentericum s.s. and a lectotype for T. bituminatum. Future investigations on volatile organic compound (VOC) composition are needed to define the aroma repertoires in this species complex.
Sugarcane Smut: Current Knowledge and the Way Forward for Management
Authors: Rajput Muhammad Aslam , Rajput Nasir Ahmed , Syed Rehana Naz , Lodhi Abdul Mubeen , Que Youxiong
Published: December 2021

Whip smut of sugarcane is the most serious and widely spread disease of sugarcane and causes a significant reduction in cane quantity and quality. The severity of this disease often depends on the pathogen races, environmental conditions, cultivar genotype and the interaction among these three factors. Under optimum climatic conditions, this disease has the potential to cause total crop failure. Resistance screening is an ongoing process due to the variability among smut pathogen isolates. Multiple races and mutation ability of smut pathogen makes the breeding task more complex. A number of studies on various aspects of the disease epidemiology and management have been published. Due to many overlapping characteristics within the species complex, there is a dearth of information on early detection and strategies to control the smut pathogen. Furthermore, there is a need to coordinate these findings to expedite its research and control. In this paper, we summarize the disease etiology, especially disease impact on the qualitative and quantitative parameters of sugarcane. We also gathered research progress on molecular-based detection and available information on genetic variability in S.scitamineum. The research on the set of management options needed to effectively cope with the disease are reviewed herein. The present review is expected to be helpful for the further investigation on smut resistance in sugarcane.
Fungal Invasive Co-Infection Due to Aspergillus fumigatus and Rhizopus arrhizus: A Rhino-Orbital Presentation
Authors: Ramírez-Hinojosa Juan Pablo , Medrano-Ahumada Salvador , Arenas Roberto , Bravo-Escobar Arturo , Paraguirre-Martínez Sara , Xicohtencatl-Cortes Juan , Martínez-Herrera Erick , Hernández-Castro Rigoberto
Published: December 2021

Aspergillosis and mucormycosis are filamentous fungal infections occurring predominantly in immunocompromised patients. Fulminant process with rapid infiltration of the contiguous tissue is distinctive for both type of fungi. The rhinocerebral co-infection by Aspergillus and Mucorales is very rare and is usually associated in immunocompromised patients with a high mortality rate. This rare co-infection leads to difficulties in diagnosis, and therapeutic delays can result in a poor prognosis. Overall, the treatment of choice is surgical debridement and liposomal amphotericin B. This paper describes a combined aspergillosis and mucormycosis case in a diabetes mellitus type 2 patient with chronic ulcerations of the palatal and cheek. To our knowledge, this is the first report of an uncommon co-infection of Aspergillus fumigatus and Rhizopus arrhizus in a rhino-orbital presentation.
Super RaSE: Super Random Subspace Ensemble Classification
Authors: Zhu Jianan , Feng Yang
Published: December 2021

We propose a new ensemble classification algorithm, named super random subspace ensemble (Super RaSE), to tackle the sparse classification problem. The proposed algorithm is motivated by the random subspace ensemble algorithm (RaSE). The RaSE method was shown to be a flexible framework that can be coupled with any existing base classification. However, the success of RaSE largely depends on the proper choice of the base classifier, which is unfortunately unknown to us. In this work, we show that Super RaSE avoids the need to choose a base classifier by randomly sampling a collection of classifiers together with the subspace. As a result, Super RaSE is more flexible and robust than RaSE. In addition to the vanilla Super RaSE, we also develop the iterative Super RaSE, which adaptively changes the base classifier distribution as well as the subspace distribution. We show that the Super RaSE algorithm and its iterative version perform competitively for a wide range of simulated data sets and two real data examples. The new Super RaSE algorithm and its iterative version are implemented in a new version of the R package RaSEn.
FinTechs, BigTechs and Banks—When Cooperation and When Competition?
Authors: Harasim Janina
Published: December 2021

While there is a fast-growing number of studies on FinTech, the relationships between technology companies and banks have received only limited attention in the research literature. Most of the studies on FinTech-bank interactions conducted so far address the questions: why banks collaborate with FinTechs (reasons) and how they do it (forms of cooperation), whereas this paper aims at clarifying when the most likely form of their interaction is cooperation and when competition. To cover this cognitive gap, the conceptual framework to help explain which factors affect the type of interactions between technology companies and banks is presented in this paper. Based on extensive literature review and using the market-based approach, the external factors of the market position of banks and technology companies were examined. It was found that this position and therefore the basic type of interaction depends on the adoption level of FinTechs and BigTechs in individual countries/regions. The adoption of FinTechs and BigTechs turned out to be higher in EMDEs and lower in AEs, which makes it more likely that in the first group of countries tech companies would tend to serve as banks’ competitors, whereas in the second group they would rather collaborate with banks or choose the coopetition strategy. When analyzing internal factors, the resource-based approach and a slightly modified IO theory were applied. In this part, the strategic tool which enables the assessment of the extent to which assets, skills, and features of FinTechs, BigTechs and banks are complementary (which gives the rationale for cooperation) or substitutable (which gives the rationale for competition) was proposed. This study is a critical analysis based on desk research, that contributes to the existing literature by (1) providing a narrow definition of FinTech representing the subjective/institutional approach, (2) considering separately FinTechs and BigTechs, and (3) proposing the strategic tool which helps to assess comparative advantages of banks, FinTechs and BigTechs, and thus makes it easier to choose the most appropriate type of their interaction.
Hidden Supply Chain Risk and Incoterms®: Analysis and Mitigation Strategies
Authors: Davis Jonathan , Vogt John
Published: December 2021

Among the many sources of financial and operational risk in supply chains are the Incoterms®, which are terms of trade used to decide who does what in a cargo movement, when risk passes from seller to buyer and who pays for which part of the movement. Wrong Incoterms® create unexpected costs or risks, at best, and inoperable contracts at worst, with all the challenges implied. This paper analyzes risk in supply chain management (SCM) through the lens of the responsibilities and costs imposed by Incoterms®. The authors also conducted a survey of 100 supply chain decision makers on supply chain contracts creation and Incoterms® knowledge in the population. Failure mode and effect analysis (FMEA) of Incoterms® reveals many scenarios that pose financial, operational, and even legal risk to firms. Results suggest Incoterms® rules are poorly understood by supply chain practitioners in general, are often chosen by personnel who are not aware of the implications of their choices, and are therefore frequently chosen incorrectly or non-strategically, thereby increasing cost and risk. This paper discusses the implications of the analysis and survey results on supply chain performance as well as mitigation strategies for practitioners in strategically using Incoterms® to remove cost, risk, and delay from supply chain transactions.
Difference in Housing Finance Usage and Its Impact on Housing Wealth Inequality in Urban China
Authors: Yu Shan , Cui Can
Published: December 2021

With the increasing importance of financial loans in home purchases in urban China, the role of housing loans in the accumulation of housing wealth needs to be unraveled. Using the data from the 2017 China Household Finance Survey (CHFS), this study investigates the use of housing loans and their impact on housing wealth inequality. It has been found that people with higher socioeconomic status and institutional advantages benefit more from housing provident fund loans and are more likely to fully invoke different financing channels to accumulate housing wealth. On the contrary, disadvantaged groups have to resort to costly market-based mortgages to finance their home purchases. This leads them to fall further behind in housing wealth accumulation. The spatial stratification of housing wealth accompanying the urban hierarchy was also observed and found to be closely linked to the type of housing loans. In this increasingly financialized era, relying on financial instruments in the process of household asset accumulation may further amplify the existing wealth inequality among social groups.
Multi-Scale Features of Regional Poverty and the Impact of Geographic Capital: A Case Study of Yanbian Korean Autonomous Prefecture in Jilin Province, China
Authors: Wang Binyan , Tian Junfeng , Yang Peifeng , He Baojie
Published: December 2021

Poverty is a challenge worldwide. Policy and regulations guiding anti-poverty measures for governments, NGOs, and multilateral institutions have not considered the spatial scale effect of regional poverty, resulting in low-efficiency poverty alleviation actions. This study addressed research gaps by analyzing the multi-scale (county, township, and village) features of regional poverty in Yanbian Korean Autonomous Prefecture in Jilin province, China. It examined the impact of geographic capital and associated spatial heterogeneity from four dimensions: natural environment, transport location, facilities accessibility, and socioeconomic development. The results identified that regional poverty varied at different scales: lower-scale poverty had higher levels of spatial differences, agglomeration, and spatial autocorrelation than higher-scale poverty, and the “island effect” was prominent. The factors potentially impacting regional poverty varied at different scales for geographical capital. At the township scale, only transport location and socioeconomic development dimensions could make significant differences. Factors in all four dimensions could affect village-scale poverty significantly, and the natural environment dimension was more effective than the other three dimensions. The impact of geographic capital and its spatial heterogeneity at the village scale varied, implying that local and diverse anti-poverty measures should increase. This study improves understanding of the multi-scale features of regional poverty and supports the formulation of effective anti-poverty measures.
Low-Level Elevations of Procalcitonin Are Associated with Increased Mortality in Acute Heart Failure Patients, Independent of Concomitant Infection
Authors: Darche Fabrice F. , Biener Moritz , Müller-Hennessen Matthias , Rivinius Rasmus , Stoyanov Kiril M. , Milles Barbara R. , Katus Hugo A. , Frey Norbert , Giannitsis Evangelos
Published: December 2021

We aimed to evaluate the prognostic value of procalcitonin (PCT) in acute heart failure (AHF) patients, especially in those without underlying infection. We enrolled patients presenting with acute dyspnea to the emergency department (ED) of Heidelberg University Hospital and studied the prognostic role of PCT on all-cause death. Of 312 patients, AHF was diagnosed in 139 patients. Of these, 125 patients had AHF without signs of infection, and 14 had AHF complicated by respiratory or other infection. The optimal prognostic PCT cutoff value for mortality prediction was calculated by a receiver operating characteristics curve. In patients with AHF, the prognostic PCT cutoff value was 0.08 ng/mL. The Kaplan–Meier survival analysis showed that AHF patients with PCT values > 0.08 ng/mL had a higher all-cause mortality at 120 days than those with PCT values ≤ 0.08 ng/mL (log-rank p = 0.0123). Similar results could be obtained after subdivision into AHF patients with and without signs of overt infection. In both cases, mortality was higher in patients with PCT levels above the prognostic PCT cutoff than in those with values ranging below this threshold. Moreover, we show that the prognostic PCT cutoff values for mortality prediction ranged below the established PCT cutoff for the guidance of antibiotic therapy. In conclusion, the data of our study revealed that low-level elevations of PCT were associated with an increased mortality in patients with AHF, irrespective of concomitant respiratory or other infection. PCT should thus be further used as a marker in the risk stratification of AHF.
Publisher’s Note: Logics—A New Open Access Journal
Authors: Schelhorn Constanze
Published: December 2021

Logic (from ancient Greek “λογικὴ τέχνη (logiké téchnē)”—“thinking art”, “procedure”) is a multidisciplinary field of research studying the formal principles of reasoning [...]
Maintenance and Inspection of Fiber-Reinforced Polymer (FRP) Bridges: A Review of Methods
Authors: Tang Long
Published: December 2021

Fiber-reinforced polymers (FRPs) are materials that comprise high-strength continuous fibers and resin polymer, and the resins comprise a matrix in which the fibers are embedded. As the technique of FRP production has advanced, FRPs have attained many incomparable advantages over traditional building materials such as concrete and steel, and thus they play a significant role in the strengthening and retrofitting of concrete structures. Bridges that are built out of FRPs have been widely used in overpasses of highways, railways and streets. However, damages in FRP bridges are inevitable due to long-term static and dynamic loads. The health of these bridges is important. Here, we review the maintenance and inspection methods for FRP structures of bridges and analyze the advantages, shortcomings and costs of these methods. The results show that two categories of methods should be used sequentially. First, simple methods such as visual inspection, knock and dragging-chain methods are used to determine the potential damage, and then radiation, modal analysis and load experiments are used to determine the damage mode and degree. The application of FRP is far beyond the refurbishment, consolidation and construction of bridges, and these methods should be effective to maintain and inspect the other FRP structures.
Two-Step Synthesis of Bismuth-Based Hybrid Halide Perovskite Thin-Films
Authors: Trifiletti Vanira , Luong Sally , Tseberlidis Giorgio , Riva Stefania , Galindez Eugenio S. S. , Gillin William P. , Binetti Simona , Fenwick Oliver
Published: December 2021

Lead halide perovskites have been revolutionary in the last decade in many optoelectronic sectors. Their bismuth-based counterparts have been considered a good alternative thanks to their composition of earth-abundant elements, good chemical stability, and low toxicity. Moreover, their electronic structure is in a quasi-zero-dimensional (0D) configuration, and they have recently been explored for use beyond optoelectronics. A significant limitation in applying thin-film technology is represented by the difficulty of synthesizing compact layers with easily scalable methods. Here, the engineering of a two-step synthesis in an air of methylammonium bismuth iodide compact thin films is reported. The critical steps of the process have been highlighted so that the procedure can be adapted to different substrates and application areas.
Optimization Design of Energy-Saving Mixed Flow Pump Based on MIGA-RBF Algorithm
Authors: Lu Rong , Yuan Jianping , Wei Guangjuan , Zhang Yong , Lei Xiaohui , Si Qiaorui
Published: December 2021

Mixed flow pumps driven by hydraulic motors have been widely used in drainage in recent years, especially in emergency pump trucks. Limited by the power of the truck engine, its operating efficiency is one of the key factors affecting the rescue task. In this study, an automated optimization platform was developed to improve the operating efficiency of the mixed flow pump. A three-dimensional hydraulic design, meshing, and computational fluid dynamics (CFD) were executed repeatedly by the main program. The objective function is to maximize hydraulic efficiency under design conditions. Both meridional shape and blade profiles of the impeller and diffuser were optimized at the same time. Based on the CFD results obtained by Optimal Latin Hypercube (OLH) sampling, surrogate models of the head and hydraulic efficiency were built using the Radial Basis Function (RBF) neural network. Finally, the optimal solution was obtained by the Multi- Island Genetic Algorithm (MIGA). The local energy loss was further compared with the baseline scheme using the entropy generation method. Through the regression analysis, it was found that the blade angles have the most significant influence on pump efficiency. The CFD results show that the hydraulic efficiency under design conditions increased by 5.1%. After optimization, the incidence loss and flow separation inside the pump are obviously improved. Additionally, the overall turbulent eddy dissipation and entropy generation were significantly reduced. The experimental results validate that the maximum pump efficiency increased by 4.3%. The optimization platform proposed in this study will facilitate the development of intelligent optimization of pumps.
Motion Planning of Ground Simulator for Space Instable Target Based on Energy Saving
Authors: Bai Xinlin , Li Xiwen , Zhao Zhen , Yang Mingyi , Zhang Zhang , Xu Zhigang , Liu Mingyang , Xia Qi
Published: December 2021

In order to achieve the high-precision motion trajectory in ground experiment of space instable target (SIT) while reducing the energy consumption of the motion simulator, a robot motion planning method based on energy saving is proposed. Observable-based ground robot motion experiment system for SIT is designed and motion planning process is illustrated. Discrete optimization mathematical model of energy consumption of motion simulator is established. The general motion form of the robot joints in ground test is given. The optimal joint path of motion simulator based on energy consumption under discontinuous singularity configuration is solved by constructing the complete energy consumption directed path and Dijkstra algorithm. An improved method by adding the global optimization algorithm is used to decouple the coupled robot joints to obtain the minimum energy consumption path under the continuous singularity configuration of the motion simulator. Simulations are carried out to verify the proposed solution. The simulation data show that total energy saving of motion simulator joints adopting the proposed method under the condition of non-singularity configuration, joints coupled motion with continuous singularity configuration, and coexistence of non-singularity path and continuous singularity path are, respectively, 72.67%, 28.24%, and 62.23%, which proves that the proposed method can meet the requirements of ground motion simulation for SIT and effectively save energy.
Data-Driven Models for Gas Turbine Online Diagnosis
Authors: Castillo Iván González , Loboda Igor , Pérez Ruiz Juan Luis
Published: December 2021

The lack of gas turbine field data, especially faulty engine data, and the complexity of fault embedding into gas turbines on test benches cause difficulties in representing healthy and faulty engines in diagnostic algorithms. Instead, different gas turbine models are often used. The available models fall into two main categories: physics-based and data-driven. Given the models’ importance and necessity, a variety of simulation tools were developed with different levels of complexity, fidelity, accuracy, and computer performance requirements. Physics-based models constitute a diagnostic approach known as Gas Path Analysis (GPA). To compute fault parameters within GPA, this paper proposes to employ a nonlinear data-driven model and the theory of inverse problems. This will drastically simplify gas turbine diagnosis. To choose the best approximation technique of such a novel model, the paper employs polynomials and neural networks. The necessary data were generated in the GasTurb software for turboshaft and turbofan engines. These input data for creating a nonlinear data-driven model of fault parameters cover a total range of operating conditions and of possible performance losses of engine components. Multiple configurations of a multilayer perceptron network and polynomials are evaluated to find the best data-driven model configurations. The best perceptron-based and polynomial models are then compared. The accuracy achieved by the most adequate model variation confirms the viability of simple and accurate models for estimating gas turbine health conditions.
Homoclinic Orbits in Several Classes of Three-Dimensional Piecewise Affine Systems with Two Switching Planes
Authors: Chen Yanli , Wang Lei , Yang Xiaosong
Published: December 2021

The existence of homoclinic orbits or heteroclinic cycle plays a crucial role in chaos research. This paper investigates the existence of the homoclinic orbits to a saddle-focus equilibrium point in several classes of three-dimensional piecewise affine systems with two switching planes regardless of the symmetry. An analytic proof is provided using the concrete expression forms of the analytic solution, stable manifold, and unstable manifold. Meanwhile, a sufficient condition for the existence of two homoclinic orbits is also obtained. Furthermore, two concrete piecewise affine asymmetric systems with two homoclinic orbits have been constructed successfully, demonstrating the method’s effectiveness.
Quasi-Mapping and Satisfying IoT Availability with a Penalty-Based Algorithm
Authors: Rahmani Amir Masoud , Ali Naqvi Rizwan , Ali Saqib , Hosseini Mirmahaleh Seyedeh Yasaman , Hosseinzadeh Mehdi
Published: December 2021

The Internet of things and medical things (IoT) and (IoMT) technologies have been deployed to simplify humanity’s life, which the complexity of communications between their layers was increased by rising joining the applications to IoT and IoMT-based infrastructures. The issue is challenging for decision-making and the quality of service where some researchers addressed the reward-based methods to tackle the problems by employing reinforcement learning (RL) algorithms and deep neural networks (DNNs). Nevertheless, satisfying its availability remains a challenge for the quality of service due to the lack of imposing a penalty to the defective devices after detecting faults. This paper proposes a quasi-mapping method to transfer the roles of sensors and services onto a neural network’s nodes to satisfy IoT-based applications’ availability using a penalty-backwarding approach into the NN’s weights and prunes weak neurons and synaptic weights (SWs). We reward the sensors and fog services, and the connection weights between them when are covered the defective nodes’ output. Additionally, this work provides a decision-making approach to dedicate the suitable service to the requester using employing a threshold value in the NN’s output layer according to the application. By providing an intelligent algorithm, the study decides to provide a service based on its availability and updating initial information, including faulty devices and new joined components. The observations and results prove decision-making accuracy for different IoT-based applications by approximately 95.8–97% without imposing the cost. The study reduces energy consumption and delay by approximately 64.71% and 47.4% compared without using neural networks besides creating service availability. This idea affects deploying IoT infrastructures to decision-making about providing appropriate services in critical situations because of removing defective devices and joining new components by imposing penalties and rewards by the designer, respectively.
Hierarchical Approach to Identifying Fluid Flow Models in a Heterogeneous Porous Medium
Authors: Musakaev Emil , Rodionov Sergey , Musakaev Nail
Published: December 2021

A three-dimensional numerical hydrodynamic model fairly accurately describes the processes of developing oil and gas fields, and has good predictive properties only if there are high-quality input data and comprehensive information about the reservoir. However, under conditions of high uncertainty of the input data, measurement errors, significant time and resource costs for processing and analyzing large amounts of data, the use of such models may be unreasonable and can lead to ill-posed problems: either the uniqueness of the solution or its stability is violated. A well-known method for dealing with these problems is regularization or the method of adding some additional a priori information. In contrast to full-scale modeling, currently there is active development of reduced-physics models, which are used, first of all, in conditions when it is required to make an operational decision, and computational resources are limited. One of the most popular simplified models is the material balance model, which makes it possible to directly capture the relationship between reservoir pressure, flow rates and the integral reservoir characteristics. In this paper, it is proposed to consider a hierarchical approach when solving the problem of oil field waterflooding control using material balance models in successive approximations: first for the field as a whole, then for hydrodynamically connected blocks of the field, then for wells. When moving from one level of model detailing to the next, the modeling results from the previous levels of the hierarchy are used in the form of additional regularizing information, which ultimately makes it possible to correctly solve the history matching problem (identification of the filtration model) in conditions of incomplete input information.
An Analytical Method of Electromagnetic Wave Scattering by a Highly Conductive Sphere in a Lossless Medium with Low-Frequency Dipolar Excitation
Authors: Stefanidou Eleni , Vafeas Panayiotis , Kariotou Foteini
Published: December 2021

The current research involves an analytical method of electromagnetic wave scattering by an impenetrable spherical object, which is immerged in an otherwise lossless environment. The highly conducting body is excited by an arbitrarily orientated time-harmonic magnetic dipole that is located at a reasonable remote distance from the sphere and operates at low frequencies for the physical situation under consideration, wherein the wavelength is much bigger than the size of the object. Upon this assumption, the scattering problem is formulated according to expansions of the implicated magnetic and electric fields in terms of positive integer powers of the wave number of the medium, which is linearly associated to the implied frequency. The static Rayleigh zeroth-order case and the initial three dynamic terms provide an excellent approximation for the obtained solution, while terms of higher orders are of minor significance and are neglected, since we work at the low-frequency regime. To this end, Maxwell’s equations reduce to a finite set of interrelated elliptic partial differential equations, each one accompanied by the perfectly electrically conducting boundary conditions on the metal sphere and the necessary limiting behavior as we move towards theoretical infinity, which is in practice very far from the observation domain. The presented analytical technique is based on the introduction of a suitable spherical coordinated system and yields compact fashioned three-dimensional solutions for the scattered components in view of infinite series expansions of spherical harmonic modes. In order to secure the validity and demonstrate the efficiency of this analytical approach, we invoke an example of reducing already known results from the literature to our complete isotropic case.
An Efficient Satellite Resource Cooperative Scheduling Method on Spatial Information Networks
Authors: Fan Huilong , Yang Zhan , Wu Shimin , Zhang Xi , Long Jun , Liu Limin
Published: December 2021

To overcome the low timeliness of resource scheduling problems in spatial information networks, we propose a method based on a dynamic reconstruction of resource request queues and the autonomous coordinated scheduling of resources. First, we construct a small satellite network and combine the graph maximum flow theory to solve the link resource planning problem during inter-satellite data transmission. In addition, we design a multi-satellite resource scheduling algorithm with minimal time consumption based on graph theory. The algorithm is based on graph theory to reallocate the resource request queue to satellites with idle processing resources. Finally, we simulate the efficient resource scheduling capability in the spatial information network and empirically compare our approaches against two representative swarm intelligence baseline approaches and show that our approach has significant advantages in terms of performance and time consumption during resource scheduling.
The Geometrical Characterizations of the Bertrand Curves of the Null Curves in Semi-Euclidean 4-Space
Authors: Sun Jianguo , Zhao Yanping
Published: December 2021

According to the Frenet equations of the null curves in semi-Euclidean 4-space, the existence conditions and the geometrical characterizations of the Bertrand curves of the null curves are given in this paper. The examples and the graphs of the Bertrand pairs with two different conditions are also given in order to supplement the conclusion of this paper more intuitively.
An Improved Alternating CQ Algorithm for Solving Split Equality Problems
Authors: He Yan-Juan , Zhu Li-Jun , Tan Nan-Nan
Published: December 2021

The CQ algorithm is widely used in the scientific field and has a significant impact on phase retrieval, medical image reconstruction, signal processing, etc. Moudafi proposed an alternating CQ algorithm to solve the split equality problem, but he only obtained the result of weak convergence. The work of this paper is to improve his algorithm so that the generated iterative sequence can converge strongly.
MP-CE Method for Space-Filling Design in Constrained Space with Multiple Types of Factors
Authors: You Yang , Jin Guang , Pan Zhengqiang , Guo Rui
Published: December 2021

Space-filling design selects points uniformly in the experimental space, bringing considerable flexibility to the complex-model-based and model-free data analysis. At present, space-filling designs mostly focus on regular spaces and continuous factors, with a lack of studies into the discrete factors and the constraints among factors. Most of the existing experimental design methods for qualitative factors are not applicable for discrete factors, since they ignore the potential order or spatial distance between discrete factors. This paper proposes a space-filling method, called maximum projection coordinate-exchange (MP-CE), taking into account both the diversity of factor types and the complexity of factor constraints. Specifically, the maximum projection criterion and distance criterion are introduced to capture the “bad” coordinates, and the coordinate-exchange and the optimization of experimental design are realized by solving one-dimensional constrained optimization problem. Meanwhile, by adding iterative perturbations to the traditional coordinate exchange process, the adjacent areas of the local optimal solution are explored and the optimum performances of the current optimal solution are retained, while the shortcomings of random restart are effectively avoided. Experiments in the regular space and constraint space, as well as experimental design for the terminal interception effectiveness of a missile defense system, show that the MP-CE method significantly outperforms existing popular space-filling design methods in terms of space-projection properties, while yielding comparable or superior space-filling properties.
Deep Learning and Device-Assisted Enteroscopy: Automatic Detection of Gastrointestinal Angioectasia
Authors: Mascarenhas Saraiva Miguel , Ribeiro Tiago , Afonso João , Andrade Patrícia , Cardoso Pedro , Ferreira João , Cardoso Hélder , Macedo Guilherme
Published: December 2021

Background and Objectives: Device-assisted enteroscopy (DAE) allows deep exploration of the small bowel and combines diagnostic and therapeutic capacities. Suspected mid-gastrointestinal bleeding is the most frequent indication for DAE, and vascular lesions, particularly angioectasia, are the most common etiology. Nevertheless, the diagnostic yield of DAE for the detection of these lesions is suboptimal. Deep learning algorithms have shown great potential for automatic detection of lesions in endoscopy. We aimed to develop an artificial intelligence (AI) model for the automatic detection of angioectasia DAE images. Materials and Methods: A convolutional neural network (CNN) was developed using DAE images. Each frame was labeled as normal/mucosa or angioectasia. The image dataset was split for the constitution of training and validation datasets. The latter was used for assessing the performance of the CNN. Results: A total of 72 DAE exams were included, and 6740 images were extracted (5345 of normal mucosa and 1395 of angioectasia). The model had a sensitivity of 88.5%, a specificity of 97.1% and an AUC of 0.988. The image processing speed was 6.4 ms/frame. Conclusions: The application of AI to DAE may have a significant impact on the management of patients with suspected mid-gastrointestinal bleeding.
Volumetric Modulated Arc Therapy Capabilities for Treating Lower-Extremity Skin Affected by Several Merkel Cell Carcinoma Nodules: When Technological Advances Effectively Achieve the Palliative Therapeutic Goal while Minimising the Risk of Potential Toxicities
Authors: Ferini Gianluca , Valenti Vito , Puliafito Ivana , Illari Salvatore Ivan , Marchese Valentina Anna , Borzì Giuseppina Rita
Published: December 2021

The peculiar and rare clinical condition below clearly requires a customized care approach in the context of personalized medicine. An 80-year-old female patient who was subjected in 2018 to surgical removal of a cutaneous Merkel cell carcinoma (MCC) nodule located on the posterior surface of the left thigh and to three subsequent palliative radiotherapy treatments developed a fourth relapse in October 2020, with fifteen nodular metastases located in the left thigh and leg. Since the overall macroscopic disease was still exclusively regionally located and microscopic spread was likely extended also to clinically negative skin of the thigh and leg, we performed an irradiation of the whole left lower extremity. For this purpose the total target (65.5 cm) was divided into three sub-volumes. Dose prescription was 30 Gy in 15 daily fractions. A sequential boost of 10 Gy in 5 daily fractions was planned for macroscopic nodules. Plans were calculated by means of volumetric modulated arc therapy (VMAT) with the field overlap technique. Thanks to this, we obtained a homogeneous dose distribution in the field junction region; avoidance structures were delineated in the central part of the thigh and leg with the aim of achieving an optimal superficial dose painting and to reduce bone exposure to radiation. This case study demonstrates that VMAT allows for a good dose coverage for circumferential cutaneous targets while sparing deeper organs at risk. A reproducible image-guided set-up is fundamental for an accurate and safe dose delivery. However, local treatments such as radiotherapy for very advanced MCC of the lower extremities might have limited impact due to the high probability of systemic progression, as illustrated in this case. Radiation is confirmed as being effective in preventing MCC nodule progression toward skin wounding.
Accuracy of Opportunistic Bone Mineral Density Assessment on Staging Computed Tomography for Gynaecological Cancers
Authors: O’Gorman Catherine Anne , Milne Sarah , Lambe Gerard , Sobota Aleksandra , Beddy Peter , Gleeson Noreen
Published: December 2021

Background and Objectives: Women with gynecological cancers constitute a high-risk cohort for loss of bone density. International guidance stipulates women undergoing cancer treatments associated with bone loss should have a quantitative assessment of bone density. Access to Dual-energy X-ray Absorptiometry (DXA) is limited. This study aimed to assess the accuracy of opportunistic bone density measurement on staging computed tomography (CT) scans for gynaecological malignancies, in comparison to the gold standard DXA. Materials and Methods: Women with a staging CT scan of the abdomen and pelvis for a new diagnosis of gynecological cancer were recruited. DXA was performed within 6 weeks of treatment for gynaecological cancer. Lumbar bone density was measured by CT attenuation values, in Hounsfield units (HU), of the anterior trabecular region. Correlations between CT and DXA parameters were analysed. Receiver Operating Characteristic(ROC) curves for diagnosis of low bone density and osteoporosis were analysed. Results: Final cohort included 48 of 50 women recruited. There was good diagnostic accuracy for abnormal bone density and osteoporosis, with areas under the ROC curve at L1 of 0.77 (p = 0.002) and 0.80 (p = 0.020) respectively. CT-HU of 170–190 yielded sensitivities of 87–90%, positive predictive values of 75–84% and negative predictive values of 71–75% for the diagnosis of low bone mineral density. CT-HU of 90–110 yielded specificities of 85–93% for the diagnosis of osteoporosis. Moderate correlations were found between CT-HU and both DXA T-scores and diagnostic categories. Conclusions: This is the first study to assess the opportunistic application of CT in the assessment of bone health in women with gynaecological cancer, a cohort at high-risk of osteoporosis. The correlation between bone density assessment in CT-HU and DXA, and strong AUC values for the diagnosis of low bone density (0.77) and osteoporosis (0.80) support this pragmatic solution in resolving the care-gap in cancer treatment-induced bone loss, often associated with poor access to DXA.
The Development of a Low-Cost Method for Monitoring Methane Leakage from the Subsurface of Natural Gas Fields
Authors: Farhan Muhammad Alfiza , Sugai Yuichi , Widodo Nuhindro Priagung , Syafrizal Syafrizal
Published: December 2021

The leakage of methane from the subsurface on the coalfield or natural gas field invariably becomes an important issue nowadays. In notable addition, materials such as activated carbon, zeolites, and Porapak have been successfully identified as adsorbents. Those adsorbents could adsorb methane at atmospheric pressure and room temperature. Therefore, in this scholarly study, a new method using adsorbents to detect points of methane leakage that can cover a wide-scale area was developed. In the beginning, the most capable adsorbent should be determined by quantifying adsorbed methane amount. Furthermore, checking the possibility of adsorption in the column diffusion and desorption method of adsorbents is equally necessary. The most capable adsorbent was activated carbon (AC), which can adsorb 1.187 × 10−3 mg-CH4/g-AC. Hereinafter, activated carbon successfully can adsorb methane through column diffusion, which simulates the situation of on-site measurement. The specific amount of adsorbed methane when the initial concentrations of CH4 in a bag were 200 ppm, 100 ppm, and 50 ppm was found to be 0.818 × 10−3 mg-CH4/g-AC, 0.397 × 10−3 mg-CH4/g-AC, 0.161 × 10−3 mg-CH4/g-AC, respectively. Desorption of activated carbon analysis shows that methane concentration increases during an hour in the temperature bath under 80 °C. In conclusion, soil methane leakage points can be detected using activated carbon by identifying the observed methane concentration increase.
Multiphoton Absorption Simulation of Sapphire Substrate under the Action of Femtosecond Laser for Larger Density of Pattern-Related Process Windows
Authors: Cai Xintian , Ji Chaoyue , Li Changkai , Tian Zhiqiang , Wang Xuan , Lei Cheng , Liu Sheng
Published: December 2021

It is essential to develop pattern-related process windows on substrate surface for reducing the dislocation density of wide bandgap semiconductor film growth. For extremely high instantaneous intensity and excellent photon absorption rate, femtosecond lasers are currently being increasingly adopted. However, the mechanism of the femtosecond laser developing pattern-related process windows on the substrate remains to be further revealed. In this paper, a model is established based on the Fokker–Planck equation and the two-temperature model (TTM) equation to simulate the ablation of a sapphire substrate under the action of a femtosecond laser. The transient nonlinear evolutions such as free electron density, absorption coefficient, and electron–lattice temperature are obtained. This paper focuses on simulating the multiphoton absorption of sapphire under femtosecond lasers of different wavelengths. The results show that within the range of 400 to 1030 nm, when the wavelength is large, the number of multiphoton required for ionization is larger, and wider and shallower ablation pits can be obtained. When the wavelength is smaller, the number of multiphoton is smaller, narrower and deeper ablation pits can be obtained. Under the simulation conditions presented in this paper, the minimum ablation pit depth can reach 0.11 μm and the minimum radius can reach 0.6 μm. In the range of 400 to 1030 nm, selecting a laser with a shorter wavelength can achieve pattern-related process windows with a smaller diameter, which is beneficial to increase the density of pattern-related process windows on the substrate surface. The simulation is consistent with existing theories and experimental results, and further reveals the transient nonlinear mechanism of the femtosecond laser developing the pattern-related process windows on the sapphire substrate.
Ionic-Liquid Gating in Two-Dimensional TMDs: The Operation Principles and Spectroscopic Capabilities
Authors: Vaquero Daniel , Clericò Vito , Salvador-Sánchez Juan , Quereda Jorge , Diez Enrique , Pérez-Muñoz Ana M.
Published: December 2021

Ionic-liquid gating (ILG) is able to enhance carrier densities well above the achievable values in traditional field-effect transistors (FETs), revealing it to be a promising technique for exploring the electronic phases of materials in extreme doping regimes. Due to their chemical stability, transition metal dichalcogenides (TMDs) are ideal candidates to produce ionic-liquid-gated FETs. Furthermore, as recently discovered, ILG can be used to obtain the band gap of two-dimensional semiconductors directly from the simple transfer characteristics. In this work, we present an overview of the operation principles of ionic liquid gating in TMD-based transistors, establishing the importance of the reference voltage to obtain hysteresis-free transfer characteristics, and hence, precisely determine the band gap. We produced ILG-based bilayer WSe2 FETs and demonstrated their ambipolar behavior. We estimated the band gap directly from the transfer characteristics, demonstrating the potential of ILG as a spectroscopy technique.