bluuebunny/crossref_metadata_2025 huggingface dataset:
Fractal Dimension Feature as a Signature of Severity in Disorders of Consciousness: An EEG Study
Authors: Porcaro Camillo , Marino Marco , Carozzo Simone , Russo Miriam , Ursino Maria , Ruggiero Valentina , Ragno Carmela , Proto Stefania , Tonin Paolo
Published: April 2022

An accurate diagnosis of the disorder of consciousness (DOC) is essential for generating tailored treatment programs. Accurately diagnosing patients with a vegetative state (VS) and patients in a minimally conscious state (MCS), however, might be very complicated, reaching a misdiagnosis of approximately 40% if clinical scales are not carefully administered and continuously repeated. To improve diagnostic accuracy for those patients, tools such as electroencephalography (EEG) might be used in the clinical setting. Many linear indices have been developed to improve the diagnosis in DOC patients, such as spectral power in different EEG frequency bands, spectral power ratios between these bands, and the difference between eyes-closed and eyes-open conditions (i.e. alpha-blocking). On the other hand, much less has been explored using nonlinear approaches. Therefore, in this work, we aim to discriminate between MCS and VS groups using a nonlinear method called Higuchi’s Fractal Dimension (HFD) and show that HFD is more sensitive than linear methods based on spectral power methods. For the sake of completeness, HFD has also been tested against another nonlinear approach widely used in EEG research, the Entropy (E). To our knowledge, this is the first time that HFD has been used in EEG data at rest to discriminate between MCS and VS patients. A comparison of Bayes factors found that differences between MCS and VS were 11 times more likely to be detected using HFD than the best performing linear method tested and almost 32 times with respect to the E. Machine learning has also been tested for HFD, reaching an accuracy of 88.6% in discriminating among VS, MCS and healthy controls. Furthermore, correlation analysis showed that HFD was more robust to outliers than spectral power methods, showing a clear positive correlation between the HFD and Coma Recovery Scale-Revised (CRS-R) values. In conclusion, our work suggests that HFD could be used as a sensitive marker to discriminate between MCS and VS patients and help decrease misdiagnosis in clinical practice when combined with commonly used clinical scales.
Deterministic cellular automata resembling diffusion
Authors: Fukś Henryk , Mudiyanselage Sanchala Abeykoon
Published: April 2022

In this paper, we investigate number-conserving cellular automata with up to five inputs and two states with the goal of comparing their dynamics with diffusion. For this purpose, we introduce the concept of decompression ratio describing expansion of configurations with finite support. We find that a large number of number-conserving rules exhibit abrupt change in the decompression ratio when the density of the initial pattern is increasing, somewhat analogous to the second-order phase transition. The existence of this transition is formally proved for rule 184. Small number of rules exhibit infinite decompression ratio, and such rules may be useful for “engineering” of CA rules which are good models of diffusion, although they will most likely require more than two states.
Structure, dynamics and melting of 19-atom double icosahedral silver cluster
Authors: Ren Shi-Wei
Published: April 2022

In this paper, the structural and dynamical properties of the 19-atom double icosahedral silver cluster are studied by using the classical molecular dynamics. It is found that the melting of the 19-atom cluster starts with the migrations of the surface atoms. The surface atoms can interchange positions with each other atoms through three ways. The first and second ways involve with only part of the surface atoms. The third way can involve the surface atoms at all symmetrical positions. With the increase of temperature, the inner atoms can hop onto the surface of the cluster and the position interchange between the surface atoms and the inner atoms can be realized. With further increase of temperature, the isomerization of the cluster becomes frequent and the motion of the atoms of the cluster becomes more and more disordered. When the temperature reaches the value corresponding to the second peak of the heat capacity, the solid cluster turns into the fully melted state.
A solution algorithm for an inverse source problem for the kinetic equation
Authors: Gölgeleyen İsmet , Hasdemir Muhammed
Published: April 2022

In this work, we deal with an inverse source problem for the kinetic equation. We obtain numerical solution of the problem by using a hybrid method which is composed of finite difference approximation and Lagrange’s polynomial interpolation. We present necessary conditions for the stability of the finite difference scheme by the Von Neumann method. Finally, we test the proposed method by performing several numerical experiments. We conclude that the relative error in reconstruction of the unknown functions is sufficiently small.
Effect of induced magnetic field, chemical reaction and thermal radiation on nonlinear mixed convective flow of Casson fluid over a vertical inclined porous plate embedded in porous medium
Authors: Choudhary Sushila , Yadav Bhagawan Singh
Published: April 2022

The aim of this paper is to study the combined effects of induced magnetic field and chemical reaction on MHD nonlinear mixed convective flow of Casson fluid over an inclined vertical porous plate embedded in a porous medium. The influence of viscous dissipation, heat source/ sink, and slip phenomena is taken into consideration. The effect of thermal radiation is also considered in the energy equation. The Casson fluid model is used to characterize the non-Newtonian fluid behavior. The main objective here is to analyze the induced magnetic field in a nonlinear mixed convective flow. At first, the appropriate similarity transformation is used to transform the governing nonlinear coupled partial differential equations into nonlinear coupled ordinary differential equations. The nonlinear ordinary differential equations are solved by a shooting technique with the help of bvp4c Matlab package. For the validation of the obtained results through the bvp4c Matlab solver, we have also solved this problem via R-K fourth-order method in Matlab and a good agreement is noted in both the results. The results of different physical parameters involved in the problem on the velocity, temperature, induced magnetic field and concentration are discussed by using graphs. It is noticed that the increasing values of the inclination angle cause rising of the induced magnetic field while induced magnetic field has opposite nature with magnetic parameter and magnetic Prandtl number. With increasing values of the thermal radiation parameter, the temperature profile diminishes. Apart from this, the numerical values of skin friction coefficient, Nusselt number and Sherwood number for the various values of parameters are displayed in tabular form.
DOES FINANCIAL GOVERNANCE STEER FINANCIAL DEVELOPMENT TOWARD SUSTAINABLE DEVELOPMENT?
Authors: DUTTA KUMAR DEBASIS , SAHA MALLIKA
Published: April 2022

Unbridled financial development (FD) leads to financial crisis and undermines sustainable development (SD), which underscores the need for financial governance (FG). Following this backdrop, we seek to explore the role of FG in shaping the FD–SD nexus for a panel of 122 countries covering the period 2000–2017 using advanced econometric methods. Results reveal that the FD–SD relationship is nonlinear, and FG positively influences SD. The impact of FG–FD confluence on SD depends on specific FG instruments. These findings are valuable for policymakers to set FD and FG policies and to achieve SD. The study, to the best of our knowledge, is the first that explains the role of FG in the FD–SD nexus.
Proposal to repeat the Abraham force experiment using giant permittivity materials
Authors: Brevik Iver
Published: April 2022

In the more than 100 years-old Abraham–Minkowski problem in macroscopic electrodynamics, the issue of how to observe the so-called Abraham term [Formula: see text] has been a main point. Such a measurement is rather delicate. Recent years have seen a number of beautiful experiments in radiation optics, but these experiments usually give no information about the Abraham term as this term simply fluctuates out. So, one is left with somewhat indirect verifications of this force, as in the radiation pressure of Jones et al. in the 1950s, testing the radiation pressure on a mirror immersed in a dielectric liquid. Now, there is a different way to test the existence of [Formula: see text], namely to work with low (quasi-stationary) frequencies enabling one to observe the sinusoidal variation of the force directly. These kind of experiments were actually done by Walker et al. in the 1970s, using BaTiO3 as a high-frequency dielectric (permittivity [Formula: see text]). The outcome of these experiments was convincing. Now, in recent years there have appeared dielectric materials with giant permittivities, of order 105 or even higher. It is therefore natural to consider the idea of Walker et al. anew, in order to test if this demanding experiment can be facilitated and give better accuracy. That is the main topic of this paper. The positive outcome of these kinds of experiments clearly supports the Abraham energy–momentum tensor at low frequencies. The Minkowski tensor is unable to predict a torque at all. The low-frequency and the high-frequency regimes are in this way highly contrasted, as it is obvious that in optical experiments the Minkowski tensor is by far the simplest and most convenient one to use. We end this note by commenting upon the use of the Einstein–Laub tensor (1908) in explaining this experiment, and discuss also the influence from air friction.
Hybrid Feature Extraction with Ensemble Classifier for Brain Tumor Classification
Authors: Leena B. , Jayanthi A. N.
Published: April 2022

Brain tumor is one of the most severe nervous system disorders affecting the health of humans and critically it will lead to death. The most elevated disease that causes a major death rate is Glioma, i.e. a primary intracranial tumor. One of the widely used techniques in medical imaging is Magnetic Resonance Imaging (MRI) which turned out as the principle diagnosis model for the analysis of glioma and its treatment. However, the brain tumor segmentation and classification process are more complicated problems to execute. This paper intends to introduce a novel brain tumor classification model that includes four major phases: (i) Pre-processing (ii) Segmentation (iii) Brain Feature extraction (iv) Brain tumor Classification. Initially, the input image is subjected to the pre-processing phase, in which the image is pre-processed under a certain process. The pre-processed images are then subjected to the segmentation phase, which is carried out by the k-means clustering. Subsequently, the segmented images are subjected to the brain feature extraction phase, in which the features are extracted using the hybrid Principal Component Analysis (PCA)-GIST feature extraction method. Then, these features are given as the input to the classification process, where the ensemble classifier is exploited for the same. Moreover, the proposed ensemble technique includes k-Nearest Neighbor (k-NN), Optimized Neural Network (NN), Random Forest (RF), and Support Vector Machine (SVM). To precisely detect the tumor classification, the NN training is performed using Elephant Herding Optimization with mutation operations (EHOMO) Algorithm via selecting the optimal weights.
Utility-Prioritized Differential Privacy for Quantitative Biomedical Data
Authors: He Muqing , Zou Deqing , Qiang Weizhong , Wu Wenbo , Xu Shouhuai , Deng Xianjun , Jin Hai
Published: April 2022

In the context of heavy investments in life sciences, one consequence of rapid biotechnical developments is the generation of very large amounts of biological data that not only support bioinformatics research used in medical and health services by data analysis but also cause genetic privacy breaches for the data originators, limiting biomedical data sharing. However, the original intention of medical informatization is to improve medical efficiency and break the information barriers between medical institutions, which is also limited by genetic privacy risks from growing bioinformatics data. To share biomedical data correlated with genes usefully under the premise of ensuring genetic privacy, with the concept of medical reference interval, we design a query strategy based on a flexible differential privacy algorithm that setting weights for noise added according to the purpose of the inquirer. We generate a series of biomedical data to test the effectiveness of our query system, the test results show that our algorithm ensures data’s utility with acceptable privacy breaches that addresses the problem of biomedical data sharing to a certain extent.
Improved State-Space All-Digital Filters via Series Approximations
Authors: Rahmani Ibrahim , Mitiche Lahcene , Adamou-Mitiche Amel Baha Houda
Published: April 2022

Recently, variable digital filters (VDFs) have become an essential research topic. This paper presents a simple method for the design of infinite impulse response (IIR) VDFs with high tuning accuracy. First, VDFs were created using a state-space model based on a frequency transformation, which requires the inverse matrix and the square root that leads to high computational cost. Then a new algorithm was implemented to generate the same VDFs absent the complex calculations that utilized negative binomial and Taylor series approximations in a simple state-space formulation. This algorithm provides high-accuracy approximations of the inverse matrix and square root compared to other algorithms that have been developed. Thus, the proposed VDFs have high tuning accuracy with respect to finite wordlength effects.
CMOS Compatible First-Order Current Mode Universal Filter Structure and its Possible Tunable Variant
Authors: Mohan Jitendra , Chaturvedi Bhartendu , Jitender
Published: April 2022

A novel first-order universal filter structure supporting current mode operation is proposed in this paper. The filter has a single active building block based realization and utilizes an extra-X second-generation current conveyor as an active building block. Additionally, the proposed circuit employs a grounded resistor and a grounded capacitor as passive components. The circuit has the ability to deliver the all-pass, high-pass, and low-pass functionalities simultaneously without meeting any passive components matching constraint. High output impedance and load insensitive outputs are other remarkable signal processing features offered by the proposed filter structure. Theoretical behavior of the proposed filter is described by presenting ideal, non-ideal, parasitic, and stability analyses. Additionally, the resistorless variant of the proposed filter is also shown to impart the tunability feature. Personal simulation program with integrated circuit emphasis (PSPICE) simulation results are presented to verify the theoretically described performance of the proposed universal filter. Complementary metal oxide semiconductor (CMOS) realization of extra-X second-generation current conveyor is utilized for the purpose of simulations, therefore, the filter is CMOS compatible.
Runtime Assurance of Learning-Based Lane Changing Control for Autonomous Driving Vehicles
Authors: Wang Qiang , Kou Guang , Chen Longquan , He Ying , Cao Weipeng , Pu Geguang
Published: April 2022

Learning techniques such as deep reinforcement learning have been increasingly used in the controller design for autonomous vehicles, e.g., lane changing controllers. Although the use of learning techniques can remarkably increase the level of autonomy, their presence poses a great challenge for safety assurance due to the black-box and data-driven nature of learning techniques. In this work, we study the problem of how to safeguard the learning-based lane changing controller for collision avoidance. Our solution leverages on the runtime assurance framework, in particular the Simplex architecture to bound the behavior of the learning-based controller and to provide safety guarantees. The basic idea is to encompass the learning-based controller with a safety-by-construction controller and a decision module, which monitors the output of the learning-based controller at runtime and implements a switching logic between these two controllers according to the changing environment. We present the detailed design of the decision module and formally prove its correctness for collision avoidance. We also carry out a comprehensive experimental evaluation in a set of realistic highway scenarios using the SUMO simulator. The simulation results show that our proposed solution can not only provide safety guarantee for the learning-based lane changing controller, but also maintain a considerable level of efficiency in different volumes of traffic flow.
An Intelligent Decision Framework for Loan Allocation Schemes
Authors: Hu Min , Li Qiao , Pan Ling
Published: April 2022

With the constant development of economy, how to reasonably allocate limited loans to enterprises has been an interesting issue. As a result, adaptive decision for optimal loan allocation schemes is well worth investigating. Although some researchers had utilized machine learning-based techniques to deal with such issue, they cannot handle well the scenarios where total capital amount is limited. To bridge such gap, this paper proposes an intelligent decision framework for loan allocation schemes in complex social systems. First of all, the expressions about profit and risk of the bank side are deduced, separately. On this basis, the dynamic planning approach is adopted to formulate a set of optimization model. Specifically, such model is established from two aspects: profit maximization and risk minimization. Hence, two groups of decision results can be reached from two different perspectives by searching optimal solution of the planning model. This work also gives a case study on a real-world dataset to present process of the planning model. Thus, two referential results are provided with use of optimization solution tool.
Identities of tropical matrix semigroups and the plactic monoid of rank 4
Authors: Aird Thomas
Published: April 2022

We study semigroup varieties generated by full and upper triangular tropical matrix semigroups and the plactic monoid of rank 4. We prove that the upper triangular tropical matrix semigroup [Formula: see text] generates a different semigroup variety for each dimension [Formula: see text]. We show a weaker version of this fact for the full matrix semigroup: full tropical matrix semigroups of different prime dimensions generate different semigroup varieties. For the plactic monoid of rank 4, [Formula: see text], we find a new set of identities satisfied by [Formula: see text] shorter than those previously known, and show that the semigroup variety generated by [Formula: see text] is strictly contained in the variety generated by [Formula: see text].
IMPORTANCE MEASUREMENT OF THE INFLUENCING FACTORS OF LONG-TERM NURSING STATUS IN LONG-TERM NURSING INSURANCE BASED ON MULTIPLE LINEAR REGRESSION, RANDOM FOREST AND XGBOOST MODELS
Authors: JI YANHAN , LIU XIANGDONG
Published: April 2022

Long-term care for the elderly has become one of the prominent social problems globally when the ratios of persons whose ages over 65 steadily increase in almost all countries. One of the solution approaches that could be adapted is called long-term care insurance provided by insurance companies. However, companies need to classify care status types based on price or to provide supports utilizing its organizational structures such as departmental communication, business selection, and market segmentation since long-term care consists of many factors. The motivation of this research aims at filling the gap since there exists no comprehensive research concerning these factors that have impacts on the long-term care status for the elderly. To determine those factors, machine learning (ML) algorithms such as multiple linear regression, random forest, and the XGBoost are selected to be employed. Then, those factors and their important variables are utilized to predict insurance pricing. The 2018 Chinese (CHARLS) data set is used to determine factors that have key impacts on long-term care status in the elderly. Finally, all models are combined as a comprehensive model to generate better prediction accuracies innovatively. The results show that the three ML models can provide relatively consistent important measures of risk factors in determining the nursing status of the elderly. On the other hand, the prediction accuracy of the random forest and the XGBoost was improved by 0.6% and 1%, respectively, when compared to multiple linear regression. Besides, the results show that when the ratios of 2.6, 3.7, 3.7 are assigned to the results of the three models, the prediction accuracy of the comprehensive model is higher in the test set than that of the multiple linear regression, which contributes 1.92% more. The main innovation of this research is to construct a comprehensive model, a weighted combination of three models, with better prediction accuracy. Eventually, the long-term care insurance business can utilize the comprehensive model to classify the long-term care status of the elderly.
Portrait growth in contracting, regular branch groups
Authors: Šunić Zoran , Uria-Albizuri Jone
Published: April 2022

We address a question of Grigorchuk by providing both a system of recursive formulas and an asymptotic result for the portrait growth of the first Grigorchuk group. The results are obtained through analysis of some features of the branching subgroup structure of the group. More generally, we provide recursive formulas for the portrait growth of any finitely generated, contracting, regular branch group, based on the coset decomposition of the groups that are higher in the branching subgroup structure in terms of the lower subgroups. Using the same general approach we fully describe the portrait growth for all non-symmetric Grigorchuk–Gupta–Sidki groups (GGS-groups) and for the Apollonian group.
On the uniform bound of reducibility index of parameter ideals of idealizations
Authors: Nam Pham Hong
Published: April 2022

Let [Formula: see text] be a Noetherian local ring and [Formula: see text] a finitely generated [Formula: see text]-module. In this paper, we study the polynomial type and the sequential polynomial type of the idealization [Formula: see text] of [Formula: see text] and [Formula: see text] introduced by Cuong [On the least degree of polynomials bounding above the differences between lengths and multiplicities of certain systems of parameters in local rings, Nagoya Math. J. 125 (1992) 105–114] and Nhan-Dung-Chau [A measure for non-sequential Cohen Macaulayness of finitely generated modules, J. Algebra 468 (2016) 275–295], respectively. In addition, we give a uniform bound for the reducibility index of parameter ideals of idealizations whose polynomial type and sequential polynomial type are at most one.
Numerical semigroups with unique Apéry expansions
Authors: Pandit Sudip , Saha Joydip , Sengupta Indranath
Published: April 2022

In this paper, we carry out a fairly comprehensive study of two special classes of numerical semigroups, one generated by the sequence of partial sums of an arithmetic progression and the other one generated by a shifted geometric progression, in embedding dimension [Formula: see text]. Both these classes have the common feature that they have unique expansions of the Apéry set elements.
Prediction of nucleosome dynamic interval based on long–short-term memory network (LSTM)
Authors: Liu Jianli , Zhou Deliang , Jin Wen
Published: April 2022

Nucleosome localization is a dynamic process and consists of nucleosome dynamic intervals (NDIs). We preprocessed nucleosome sequence data as time series data (TSD) and developed a long short-term memory network (LSTM) model for training time series data (TSD; LSTM-TSD model) using iterative training and feature learning that predicts NDIs with high accuracy. Sn, Sp, Acc, and MCC of the obtained LSTM model is 91.88%, 92.72%, 92.30%, and 84.61%, respectively. LSTM model could precisely predict the NDIs of yeast 16 chromosome. The NDIs contain 90.29% of nucleosome core DNA and 91.20% of nucleosome central sites, indicating that NDIs have high confidence. We found that the binding sites of transcriptional proteins and other proteins are outside NDIs, not in NDIs. These results are important for analysis of nucleosome localization and gene transcriptional regulation.
Channel-based coherence of quantum states
Authors: Zhang Cheng-Yang , Wang Pu , Bai Li-Hua , Guo Zhi-Hua , Cao Huai-Xin
Published: April 2022

Quantum coherence is one of the most fundamental and striking features in quantum physics. Considered the standard coherence (SC), the partial coherence (PC) and the block coherence (BC) as variance of quantum states under some quantum channels (QCs) [Formula: see text], we propose the concept of channel-based coherence of quantum states, called [Formula: see text]-coherence for short, which contains the SC, PC and BC, but does not contain the positive operator-valued measure (POVM)-based coherence. By our definition, a state [Formula: see text] is said to be [Formula: see text]-incoherent if it is a fixed point of a QC [Formula: see text], otherwise, it is said to be [Formula: see text]-coherent. First, we find the set [Formula: see text] of all [Formula: see text]-incoherent states for some given channels [Formula: see text] and prove that the set [Formula: see text] forms a nonempty compact convex set for any channel [Formula: see text]. Second, we define [Formula: see text]-incoherent operations ([Formula: see text]-IOs) and prove that the set of all [Formula: see text]-IOs is a nonempty convex set. We also establish some characterizations of a [Formula: see text]-IO in terms of its Kraus operators. Lastly, we discuss the problem of quantifying [Formula: see text]-coherence and prove some related properties.
Computer Simulations of Blood Flows in Right Coronary Arteries with Multiple Stenoses
Authors: Liu Biyue
Published: April 2022

In this study, various models with different curvatures of bend and different inter-stenotic distances are created. The computations are carried out and the numerical results are compared with the computer simulation in a right coronary artery model reconstructed using the basic information from a coronary artery segment of a patient. Our results show that the curvature of bend significantly affects the wall shear stress (WSS) and the pressure drop (PD) in curved artery with two moderate stenoses. The location of the distal stenosis strongly influences the flow pattern downstream, while the effect of the location of the proximal stenosis is insignificant.
Managing R&D and Innovation Projects: An Integrated Conceptual Model for Technology Transfer
Authors: Rocha António , Lima Rui M. , Amorim Marlene , Romero Fernando , Cruz-Cunha Manuela
Published: April 2022

Projects start with an idea, which is deployed into a set of actions to deliver expected or foreseen outcomes, and they should be carried out in a cohesive and unified context. In this paper, contributions are devised to integrate project management and technology transfer practices to drive the transformation of knowledge into innovation within the scope of research and development projects at universities and research laboratories or centers. These contributions are presented in the form of a conceptual model that includes three interrelated groups. The first group is focused on project ideas’ definition and selection, the second group on project management, and the third group on knowledge and technology transfer, these groups are supported by improvement, innovation and project management guidelines and standards. To obtain insights to define the model, quantitative and qualitative methods have been used and the data was processed using content analysis and descriptive and inferential techniques meant to ensure the complementarity of the data to obtain a holistic understanding of the practices under analysis. The approach was exploratory and descriptive, but also analytical in the sense that it states the issues at stake to support project management and technology transfer.
How Social Networks Influence Organizational Innovation Adoption
Authors: Jacob Axel , Teuteberg Frank
Published: April 2022

The purpose of this paper is to provide a framework that explains the interdependency of organizations and different social networks in the process of organizational innovation adoption (OIA). We first develop a taxonomy of social networks relevant to OIA and then a conceptual framework based on a review of existing frameworks. Furthermore, a case study was conducted with two cases and six embedded units of analysis. The review of existing frameworks for OIA shows that the importance of social networks has not yet been fully addressed. The analysis of the cases reveals four types of influence (comparison, argumentation, reciprocal learning, and alignment) through which social networks influence the OIA on micro, meso and macro levels. Our results help to better understand how the adoption of innovations proceeds in organizations against the background of the increasing importance of social networks and concepts such as open innovation.
Monotonicity properties of functionals under Ricci flow on manifolds without and with boundary
Authors: Bracken Paul
Published: April 2022

In this paper, the idea of the Ricci flow is introduced and its significance and importance to related problems in mathematics had been discussed. Several functionals are defined and their behavior is studied under Ricci flow. A unique minimizer is shown to exist for one of the functionals. This functional evaluated at the minimizer is strictly increasing. The results for the first functional considered are extended to manifold with boundary. Finally, two physically motivated examples are presented.
A New Expression of Internal Stiffness for Load Path Analysis in Structures
Authors: Zhao Shengjie , Wu Nan , Karnaoukh Sviatoslaw
Published: April 2022

The load transfer inside a given structure can be expressed by load paths. Based on the concept of internal stiffness that is independent of stress analysis, the [Formula: see text]* index is a powerful tool to visualize the load paths by measuring strain energy caused by displacement at the loading point. In this short communication, a scalar index [Formula: see text]* is introduced as a new expression of internal stiffness to visualize the load paths with a greater precision. The load paths are defined as the streamlines following the decay direction of the internal stiffness. Based on the numerical simulations, [Formula: see text]* load paths have higher load transfer efficiency compared to [Formula: see text]* counterparts. The experimental study of an [Formula: see text]-bracket with stiffeners placed on the main load paths further verifies the effectiveness of the [Formula: see text]* index for load path analysis. The proposed concept for load path analysis showcases great potential in design evaluation and optimization for load-bearing components from a macroscopic view.
On Blo-algebras
Authors: Endam Joemar C. , Mamhot Alice A.
Published: April 2022

A B-algebra is an algebra [Formula: see text] of type (2, 0) satisfying: (I) [Formula: see text], (II) [Formula: see text], (III) [Formula: see text]. A B[Formula: see text]-algebra is an algebra [Formula: see text] such that [Formula: see text] is a B-algebra and [Formula: see text] is a poset with [Formula: see text] compatible on [Formula: see text]. In this paper, we introduce and characterize lattice ordered B-algebras (or B[Formula: see text]-algebras). A B[Formula: see text]-algebra is a B[Formula: see text]-algebra which is a lattice under its partial order. We show that a B[Formula: see text]-algebra is both distributive and torsion-free.
General Randić index of trees with given number of branching vertices
Authors: Vetrík Tomáš
Published: April 2022

Let [Formula: see text] be the set of all trees with [Formula: see text] vertices and [Formula: see text] branching vertices. We present lower bounds on the general Randić index for trees in [Formula: see text] for [Formula: see text]. Those bounds generalize lower bounds on the second Zagreb index for trees in [Formula: see text] presented by Borovićanin in 2015, since the second Zagreb index is a special case of the general Randić index. We also correct the lower bound of Borovićanin for [Formula: see text].
Connectivity of superpower graphs of some non-abelian finite groups
Authors: Kumar Ajay , Selvaganesh Lavanya , Tamizh Chelvam T.
Published: April 2022

For a finite group [Formula: see text] the superpower graph [Formula: see text] of [Formula: see text] is an undirected simple graph with vertex set [Formula: see text] and two vertices in [Formula: see text] are adjacent in [Formula: see text] if and only if the order of one divides the order of the other in the group [Formula: see text]. In this paper, we give sharp bounds for the vertex connectivity of superpower graphs [Formula: see text] and [Formula: see text] of dihedral group [Formula: see text] and dicyclic group [Formula: see text]
A POST-COVID-19 ECONOMIC ASSESSMENT OF THE CHILEAN NDC REVISION
Authors: BABONNEAU FRÉDÉRIC , VIELLE MARC
Published: April 2022

Last year, Chile updated its Nationally Determined Contributions, moving from intensity-based emissions reductions to an effective emissions target. This paper aims to assess the economic and environmental impacts of this change in the current context of high uncertainty Chile faces with social protests and the COVID-19 pandemic. Using the computable general equilibrium model GEMINI-E3, we performed a sensitivity analysis assuming different levels of economic growth through 2030. Though at first glance the revised commitments appear more ambitious, we found that they could lead to higher emissions in low-growth scenarios. The results show that intensity-based emissions targets indeed become less stringent when assuming high levels of economic growth and thus may result in highly uncertain effective emissions in 2030. On the other hand, given the uncertainty surrounding Chilean economic growth, the updated commitments would be politically more amenable as it would lead to lower welfare losses. In addition, we analyze different redistribution schemes of a CO2 tax and we show that a per capita redistribution rule makes the CO2 tax more progressive and thus fiscally more acceptable.
Proposing a Method to Solve Inverse Kinematics and Dynamics of a Human Upper Limb Rehabilitation Robot
Authors: Nguyen Trung , Truong Son , Hoang Hai
Published: April 2022

A rehabilitation robot is a device that has been proving its positive effectiveness in the process of helping patients recover quickly after a stroke. Researching, designing, and manufacturing robot models in general and upper limb rehabilitation robots in particular are very practical. In this study, we proposed to combine the use of an algorithm and a physical modeling method to shorten the calculation process and design an upper limb rehabilitation robot. First, an exoskeleton upper limb rehabilitation robot model (UExosVN) was briefly described. Next, in turn, all the important problems including inverse kinematics, inverse dynamics for this robot model were proposed and solved by using optimization algorithms and physical modeling methods. The model was evaluated in the critical movement of daily operations. The results after the testing process have proven the accuracy and effectiveness of the proposed methods.
An Experimental Study of Convolutional Neural Networks for Functional and Subject Classification of Web Pages
Authors: Artene Codruţ-Georgian , Vecliuc Dumitru-Daniel , Tibeică Marius Nicolae , Leon Florin
Published: April 2022

Information filtering and information retrieving applications are based on web page classification methods. Usually, web pages serve different functionalities or develop different topics or subjects. The diversity of web page content increases the need for automatic web page classification, making it a challenging task at the same time. Considering that the main component of the content of a web page is most often represented by the text and the classification of the text is a problem intensively studied in the last years, with researchers reporting state-of-the-art results for various methods, the idea of applying these methods on the text extracted from web pages could lead to important results. In this work, we revisit our experimental study on convolutional neural networks for multi-label multi-language web page classification with a new approach that consists of dividing the classification problem into functional classification and subject classification of web pages. From the experimental evaluation, one may conclude that the separation of the functional and subject classification of web pages leads to an improvement of the overall results.
The Influence of Internal Monitoring on Compliance with Effluent Limits
Authors: Brandes Julia , Earnhart Dietrich
Published: April 2022

Using unique data on chemical manufacturing facilities regulated under the Clean Water Act (CWA), this paper analyzes the effect of one particular environmental management practice — internal monitoring — on regulated facilities’ compliance with wastewater discharge limits. To deepen our understanding of the role played by internal monitoring, we extend our analysis by exploring the interaction between internal monitoring and self-audits, which should share a similar purpose to internal monitoring by providing systematic information to regulated facilities on their pollution abatement efforts. Baseline empirical results robustly demonstrate that increases in the extent of internal monitoring significantly improve facilities’ environmental compliance. The extended empirical results offer moderate evidence that audits complement internal monitoring (and vice versa): as facilities audit more frequently, the productive effect of increased internal monitoring grows.
A Content Analysis of the Strictest Water Resources Management Policy in China
Authors: Cheng Zhe , Wang Nina , Ouyang Rulin , Wang Huanming , Song Zhe
Published: April 2022

Policy intervention is a critical measure to address water resources challenges and improve water governance capacity. The strictest water resources management (SWRM) policy is an important water policy system that aims to deal with water shortage, water pollution, and institutional dysfunction in China. This study conducts a quantitative analysis for policy text characteristics of the SWRM through the methods of text mining and content analysis. First, we construct an analytical framework with the combination of policy instruments and policy targets, and then code and classify policy instruments in policy text and conduct statistical analysis. Finally, the research conclusions and policy suggestions are put forward. The results show that major policy instruments are structurally imbalanced in China’s SWRM policy. China’s government prefers to issue a mandatory, standardized, and restrictive policies in water resources management. Most of the policy instruments are focused on institutional management, followed by resources allocation, technological progress, and the ecological environment. This study contributes to the knowledge body of water policy evaluation and water governance, provides decision-making references for optimizing and promoting China’s water resources management policy, and offers a peer reference for water governance in other developing countries.
Real-Time Camera Localization during Robot-Assisted Telecystoscopy for Bladder Cancer Surveillance
Authors: Gong Chen , Zhou Yaxuan , Lewis Andrew , Chen Pengcheng , Speich Jason R. , Porter Michael P. , Hannaford Blake , Seibel Eric J.
Published: April 2022

Telecystoscopy can lower the barrier to access critical urologic diagnostics for patients around the world. A major challenge for robotic control of flexible cystoscopes and intuitive teleoperation is the pose estimation of the scope tip. We propose a novel real-time camera localization method using video recordings from a prior cystoscopy and 3D bladder reconstruction to estimate cystoscope pose within the bladder during follow-up telecystoscopy. We map prior video frames into a low-dimensional space as a dictionary so that a new image can be likewise mapped to efficiently retrieve its nearest neighbor among the dictionary images. The cystoscope pose is then estimated by the correspondence among the new image, its nearest dictionary image, and the prior model from 3D reconstruction. We demonstrate performance of our methods using bladder phantoms with varying fidelity and a servo-controlled cystoscope to simulate the use case of bladder surveillance through telecystoscopy. The servo-controlled cystoscope with 3 degrees of freedom (angulation, roll, and insertion axes) was developed for collecting cystoscope videos from bladder phantoms. Cystoscope videos were acquired in a 2.5D bladder phantom (bladder-shape cross-section plus height) with a panorama of a urothelium attached to the inner surface. Scans of the 2.5D phantom were performed in separate arc trajectories each of which is generated by actuation on the angulation with a fixed roll and insertion length. We further included variance in moving speed, imaging distance and existence of bladder tumors. Cystoscope videos were also acquired in a water-filled 3D silicone bladder phantom with hand-painted vasculature. Scans of the 3D phantom were performed in separate circle trajectories each of which is generated by actuation on the roll axis under a fixed angulation and insertion length. These videos were used to create 3D reconstructions, dictionary sets, and test data sets for evaluating the computational efficiency and accuracy of our proposed method in comparison with a method based on global Scale-Invariant Feature Transform (SIFT) features, named SIFT-only. Our method can retrieve the nearest dictionary image for 94–100% of test frames in under 55[Formula: see text]ms per image, whereas the SIFT-only method can only find the image match for 56–100% of test frames in 6000–40000[Formula: see text]ms per image depending on size of the dictionary set and richness of SIFT features in the images. Our method, with a speed of around 20 Hz for the retrieval stage, is a promising tool for real-time image-based scope localization in robotic cystoscopy when prior cystoscopy images are available.
Generating User-Centred Explanations via Illocutionary Question Answering: From Philosophy to Interfaces
Authors: Sovrano Francesco , Vitali Fabio
Published: April 2022

We propose a new method for generating explanations with Artificial Intelligence (AI) and a tool to test its expressive power within a user interface. In order to bridge the gap between philosophy and human-computer interfaces, we show a new approach for the generation of interactive explanations based on a sophisticated pipeline of AI algorithms for structuring natural language documents into knowledge graphs, answering questions effectively and satisfactorily. With this work, we aim to prove that the philosophical theory of explanations presented by Achinstein can be actually adapted for being implemented into a concrete software application, as an interactive and illocutionary process of answering questions. Specifically, our contribution is an approach to frame
illocution
in a computer-friendly way, to achieve user-centrality with statistical question answering. Indeed, we frame the
illocution
of an explanatory process as that mechanism responsible for anticipating the needs of the explainee in the form of unposed, implicit, archetypal questions, hence improving the user-centrality of the underlying explanatory process. Therefore, we hypothesise that if an explanatory process is an illocutionary act of providing content-giving answers to questions, and illocution is as we defined it, the more explicit and implicit questions can be answered by an explanatory tool, the more usable (as per ISO 9241-210) its explanations. We tested our hypothesis with a user-study involving more than 60 participants, on two XAI-based systems, one for credit approval (finance) and one for heart disease prediction (healthcare). The results showed that increasing the
illocutionary power
of an explanatory tool can produce statistically
significant
improvements (hence with a
P
value lower than .05) on effectiveness. This, combined with a visible alignment between the increments in effectiveness and satisfaction, suggests that our understanding of
illocution
can be correct, giving evidence in favour of our theory.
Rescuing ReRAM-based Neural Computing Systems from Device Variation
Authors: Huang Chenglong , Xu Nuo , Zeng Junwei , Wang Wenqing , Hu Yihong , Fang Liang , Ma Desheng , Chen Yanting
Published: April 2022

Resistive random-access memory (ReRAM)-based crossbar array (RCA) is a promising platform to accelerate vector-matrix multiplication in deep neural networks (DNNs). There are, however, some practical issues, especially device variation, that hinder the versatile development of ReRAM in neural computing systems. The device variations include device-to-device variation (DDV) and cycle-to-cycle variation (CCV) that deviate the devise resistance in the RCA from their target state. Such resistance deviation seriously degrades the inference accuracy of DNNs. To address this issue, we propose a software-hardware compensation solution that includes compensation training based on scale factors (CTSF) and variation-aware compensation training based on scale factors (VACTSF) to protect the ReRAM-based DNN accelerator against device variation. The scale factors in CTSF can be flexibly set for reducing accuracy loss due to device variation when the weights programmed into RCA are determined. For effectively handling CCV, the scale factors are introduced into the training process for obtaining variation-tolerant weights by leveraging the inherent self-healing ability of DNNs. Simulation results based on our method confirm that the accuracy losses due to device variation on LeNet-5, ResNet, and VGG16 with different datasets are less than 5% under a large device variation by CTSF. More robust weights for conquering CCV are also obtained by VACTSF. The simulation results present that our method is competitive in comparison to other variation-tolerant methods.
Helicopter Money: What Is It and What Does It Do?
Authors: Reis Ricardo , Tenreyro Silvana
Published: April 2022

We review the different meanings of helicopter money, both in the literature and in the public debate around it, and we clarify the conditions under which helicopter money can have an impact on real activity. To do so, we set out a simple model that encapsulates a number of potential channels of policy transmission. The model provides a taxonomy of possibilities for helicopter money to affect the economy, as well as a benchmark set of conditions under which helicopter money is neutral. We use the model to analyze and discuss the impact that helicopter drops might have in response to a number of economic shocks, including a financial crisis, a fiscal crisis, and a pandemic.