bluuebunny/crossref_metadata_2025 huggingface dataset:
Online Exercise Programming Among Older Adults: A Scoping Review
Authors: Dagenais Matthieu , Parker Olivia , Galway Sarah , Gammage Kimberley
Published: September 2022

Online exercise programming may promote physical activity while at home, but little is known about its use among older adults. Using the Arksey and O’Malley framework, we describe the nature and extent of the research pertaining to the use of online exercise programming among adults 65 years of age and older. We ran two separate searches (January 2005–September 2020 and October 2020–October 2021), yielding 17 articles that met our inclusion criteria. A total of 1,767 participants (69% female) ranging from 65 to 94 years of age were included. Most studies delivered the online programs asynchronously. The majority of studies assessed the feasibility of online programs, with 14 studies investigating health-related outcomes such as physical, psychological, and social health. Future research should explore perceptions and experiences of online exercise programming among older adults and the mechanisms by which it impacts physical, psychological, social, and behavioral outcomes.
Uncontrolled Manifold Analysis of the Effects of Different Fatigue Locations on Kinematic Coordination During a Repetitive Upper-Limb Task
Authors: Slopecki Matthew , Hasanbarani Fariba , Yang Chen , Bailey Christopher A. , Côté Julie N.
Published: September 2022

Fatigue at individual joints is known to affect interjoint coordination during repetitive multijoint tasks. However, how these coordination adjustments affect overall task stability is unknown. Twelve participants completed a repetitive pointing task at rest and after fatigue of the shoulder, elbow, and trunk. Upper-limb and trunk kinematics were collected. Uncontrolled manifold framework was applied to a kinematic model to link elemental variables to endpoint fingertip position. Mixed and one-way analysis of variances determined effects (phase and fatigue location) on variance components and synergy index, respectively. The shoulder fatigue condition had the greatest impact in causing increases in variance components and a decreased synergy index in the late phase of movement, suggesting more destabilization of the interjoint task caused by shoulder fatigue.
Title IX and the Expectation of Equity
Authors: Oglesby Carole
Published: September 2022

This essay gives a personal viewpoint of the environment for girls’ and women’s sport in the years surrounding the origination of Title IX and the organizational impacts unleashed by this legislation. Particular attention is given to the, somewhat surprising, degree to which men in power positions in sport benefitted from Title IX’s uneven pattern of enactment. A cautionary note suggests that advocates in future work take careful steps so that language and protocols of potential legislation assure benefits to the intended beneficiaries rather than others. A concluding assessment asserts that one of the strongest positives of Title IX is a shift in stance of female athletes from being “grateful for banquet leftovers” to an expectation of equity in treatment and benefits.
The onset of double diffusive convection in a rotating bi-disperse porous medium
Authors: Capone Florinda , De Luca Roberta , Massa Giuliana
Published: September 2022

It is well known that when a horizontal layer of fluid is heated from below, a thermal boundary layer of less dense and hot fluid rises up. When this boundary layer becomes unstable, convective motion in the fluid above sets in. Forecasting when instabilities take place is essential. When a salt dissolved in a fluid saturating a porous medium heated from below is considered, simultaneous mass diffusion and thermal diffusion occur. Unlike the diffusion of heat, the diffusion of salt can take place only through the fluid phase, so an additional physical effect has to be considered: the Soret effect, that is the mass flux created by a temperature gradient. In the present paper the onset of convection in a rotating layer of bi-disperse porous medium saturated by a binary fluid mixture, taking into account theSoret effect, is analysed. Linear stability analysis is performed in order to determine the instability thresholds for the onset of convection via a steady state (stationary convection) and via an oscillatory state (oscillatory convection). Nonlinear stability analysis is performed to obtain the global stability threshold with respect to the$$L^2$$L2-norm.
Evolutionary dynamics of social inequality and coincidence of Gini and Kolkata indices under unrestricted competition
Authors: Banerjee Suchismita , Biswas Soumyajyoti , Chakrabarti Bikas K. , Challagundla Sai Krishna , Ghosh Asim , Guntaka Suhaas Reddy , Koganti Hanesh , Kondapalli Anvesh Reddy , Maiti Raju , Mitra Manipushpak , Ram Dachepalli R. S.
Published: September 2022

Social inequalities are ubiquitous, and here we show that the values of the Gini ([Formula: see text]) and Kolkata ([Formula: see text]) indices, two generic inequality indices, approach each other (starting from [Formula: see text] and [Formula: see text] for equality) as the competitions grow in various social institutions like markets, universities and elections. It is further shown that these two indices become equal and stabilize at a value (at [Formula: see text]) under unrestricted competitions. We propose to view this coincidence of inequality indices as a generalized version of the (more than a) century old 80-20 law of Pareto. Furthermore, the coincidence of the inequality indices noted here is very similar to the ones seen before for self-organized critical (SOC) systems. The observations here, therefore, stand as a quantitative support toward viewing interacting socio-economic systems in the framework of SOC, an idea conjectured for years.
ROLE OF SUSTAINABLE INFRASTRUCTURE DEVELOPMENT TO POVERTY ALLEVIATION IN ASIA: DOES INCLUSIVE GROWTH MATTER IN A COLLECTIVE ECONOMIC ENVIRONMENT
Authors: CHEN BENCHANG , KUANG GUILIN
Published: September 2022

The impact of Asia’s infrastructure development and inclusive income growth on poverty alleviation from 2000 to 2020 is well-documented in this research by applying the generalized method of moments (GMM). With the use of economic and social indices, it has been established that infrastructure improvement significantly impacts reducing poverty levels. The findings demonstrate the important impact of energy poverty alleviation on income-inclusive growth. It is easier to survive poverty in rural areas with access to water infrastructure and health insurance. Infrastructure and poverty seem to have a bidirectional causal relationship in the long term. To put it another way, boosting infrastructure investment might be a key to promoting poverty reduction. At the same time, greater performance in the public sector may result in a more effective allocation of resources toward building large infrastructure. Thus, it is postulated that with increasing knowledge, infrastructural development in most developing nations, including South Asian countries, is insufficient; fresh and sustained infrastructure investments are crucial for poverty reduction. This research sheds light on the critical role that energy out-of-poverty plays in inclusive development and infrastructure development for all people, regardless of their income level.
FORECASTING SINGAPORE’s ECONOMY USING STATISTICAL LEARNING AND FACTOR MODELS
Authors: FOO BENEDICT , KOH DENG YAO , TAN JUAN PANG , WANG WENJIE
Published: September 2022

We evaluate the performance of the penalized vector autoregression (VAR), diffusion index (DI), and regression tree-based ensemble learning models to forecast Singapore’s macroeconomy using high-dimensional data. Our dataset consists of 220 monthly time series that capture the economy of Singapore and 20 monthly times series that capture the global economic environment. We find that the penalized VAR model and the ensemble learning model give an outstanding performance in both short and long horizons. On the other hand, the performance of the DI model depends crucially on the methods to select the number of factors. In particular, a conventional selection method may overestimate the true number of factors and thus deteriorate the forecasting performance of the DI model. Additionally, the VAR and DI models may utilize different information in forecasting.
A 5.5-GHz Low-Power Divide-By-8/9 Dual Modulus Prescaler Using Pulse Extension Logic
Authors: Kumar Ravi , Bohara Pooja , Thakur Krishna , Vishvakarma Santosh Kumar
Published: September 2022

This paper presents a new optimized high-speed divide-by-8/9 dual modulus prescaler. Simulation results show 54% reduction in power consumption, 40% of speed improvement and almost 48% area reduction as compared to the conventional architecture. Power consumption in the proposed prescaler is reduced by eliminating one True-Single-Phase Clocked (TSPC) D Flip-Flop (DFF) from the standard divide-by-2/3 prescaler, replacing it with Pulse Extension Logic (PEL) circuit. Redundant stages from asynchronous divide-by-2 units were also removed to save more power and reduce more delay. The simulation results show that the prescaler is capable of running at 5.5[Formula: see text]GHz of maximum frequency with 1.9[Formula: see text]mW power consumption. The divider is implemented in 0.18[Formula: see text][Formula: see text]m CMOS technology with 1.8[Formula: see text]V power supply.
Construction and Application of Piano to Intelligent Teaching System Based on Multi-Source Data Fusion
Authors: Jing Zhen
Published: September 2022

With the rapid development of modern information technology represented by the Internet, cloud computing and big data, education and teaching have gradually realized the deep integration of the Internet, which has changed people’s way of life, study and work to a certain extent. Intelligence [Formula: see text] education provides a new information-based teaching method for the development of education. From the perspective of improving the accuracy of data fusion results, this paper proposes a new multi-source data fusion method based on a set pair analysis connection degree for the situation that multiple sensors with unknown prior knowledge detect the same target feature parameters multiple times. By using the advantages of the set pair analysis feature function, the degree of opposition, the identity and difference between the measurement data are mined to adjust the degree of connection between the data. According to the existing signal-to-noise ratio weighting method in the fusion process, the weight of the measurement data is reasonably allocated to realize the weighted fusion of multi-source data, and the effectiveness and reliability of the algorithm are verified through simulation experiments. Through the summary of the questions, it can be seen that the learners are very satisfied with the mobile piano learning mode based on the intelligent teaching system, and they believe that this learning mode is conducive to the learner’s mastery of the basic knowledge of piano, and effectively improves the learners’ learning ability. It also improves the individual’s self-directed learning ability and academic performance. Through the research, it is concluded that the students’ piano mobile learning mode based on the intelligent teaching system is more conducive to the learner’s mastery of knowledge, and improves the learning interest and academic performance.
A Deep Learning-Based Text Detection and Recognition Approach for Natural Scenes
Authors: Li Xuexiang
Published: September 2022

In this paper, we design a natural scene text detection and recognition model based on deep learning by model construction and in-depth study of wild scene text detection and recognition. This paper proposes a scene text recognition method based on connection time classification and attention mechanism for the situation where natural scene text is challenging to recognize due to the high complexity of text and background. The method converts the text recognition problem in natural scenes into a sequence recognition problem, avoiding the drawback of overall recognition performance degradation due to the difficulty of character segmentation. At the same time, the attention mechanism introduced can reduce the network complexity and improve the recognition accuracy. The performance of the improved PSE-based text detection algorithm in this paper is tested on the curved text datasets SCUT-ctw1500 and ICDAR2017 in natural scenes for comparison. The results show that the proposed algorithm achieves 88.5%, 77%, and 81.3% in the three indexes of accuracy, recall, and F1 value, respectively, without adding the pre-training module. The algorithm can detect text in any direction well without adding the pre-training module; the improved text recognition algorithm based on CRNN in this paper is tested on the natural scene dataset ICDAR2017, and the results show that the accuracy rate reaches 94.5% under the condition of no constraint, which is a good performance.
A Resource Efficient CNN Accelerator for Sensor Signal Processing Based on FPGA
Authors: Wu Ruidong , Liu Bing , Fu Ping , Chen Haolin
Published: September 2022

With the use of Convolutional Neural Network (CNN) in the application of sensor signal processing system, it usually faces the urgent requirements of system integration, high throughput, hardware resource and energy efficiency. This paper introduces a resource efficient accelerator with general two-dimensional multiply-add array operator to focus on the characteristic of sensor signal processing, which can be applied to standard CNN, depth-wise CNN, Fully Connected (FC) layer for varied networks. Meanwhile, resource estimation model is also constructed to provide the exploration of parallel parameters for computing efficiency. Finally, a board-level verification is implemented to demonstrate the efficiency of proposed accelerator with common scene of LeNet and complex scene of MobileNetV1. Experimental results show that the Inferences Per Second (IPS) of 332225 and 1498 is realized with 100[Formula: see text]MHz frequency. The corresponding efficiency is 88.84% and 61.09%, which outperforms other related works about CNN accelerator design in terms of signal processing. This paper is also applicable and scalable to other fields about effective acceleration research.
A Novel Hybrid Hexagonal Star Topology for On-Chip Interconnection Networks
Authors: Lakshmi Kiranmai V. , Srinivasarao B. K. N.
Published: September 2022

Network-on-Chip (NoC) is an emerging and efficient on-chip interconnection network. NoC is expected to be the communication backbone of next-generation Multi-processor System-on-Chip (MPSoC) architectures. Topology is a crucial design aspect of NoC, as it affects the performance of the interconnection network. This paper proposes a novel, scalable, hybrid Hexagonal Star (HS) topology for on-chip interconnection networks. Properties of the proposed topology have been explored and compared with those of Mesh, Torus and Honeycomb Mesh topologies. The performance of the Hexagonal Star topology has been evaluated and compared with that of Mesh topology for different scenarios. The comparative studies of topological properties have indicated that the proposed topology can be a potential choice for on-chip interconnection networks. The simulation results have shown that the proposed topology outperforms Mesh topology in terms of latency for low traffic loads. For different traffic patterns and traffic loads, HS topology has registered a reduction of packet latency ranging from 15% to 50% and from 9% to 23% for 18 nodes and 32 nodes, respectively, compared to Mesh topology.
Research on Personalized Recommendation Method of Educational Resources Based on Learner Behavior Analysis
Authors: Han Jiaguo
Published: September 2022

The explosion of online learning resources makes the research on personalized recommendation of educational resources increasingly prominent. Based on the theory of learner behavior analysis, this paper analyzes the learning behavior logs of the online learning platform, and constructs a personalized recommendation method for educational resources. It analyzes the learner behavior from the three dimensions of basic attributes, behavior characteristics and result characteristics, and solves the problem of resource analysis. In the simulation process, the method uses the vector space model to complete the modeling of learner behavior, and realizes the division of learner groups based on learner behavior, and further evaluates and optimizes the division of learner groups. In order to further verify the clustering effect in combination with the actual data, the classification results are used for example analysis: the experimental results show that the sample points in the educational resource space are divided into 23 categories according to the distance relationship, and [Formula: see text] is the best. The silhouette coefficient value is 0.54436, and the Calinski–Harabasz score value is 1464.9, which effectively improves the clustering effect of the personalized recommendation method.
Axion–neutrino couplings and the effective neutrino mass
Authors: Mosquera M. E. , Civitarese O.
Published: September 2022

In this work, we study the interaction between a complex neutral scalar boson, the axion and neutrinos, as a possible channel for the generation of the neutrino mass. We calculate one-loop corrections to the neutrino propagator and determine the value of the neutrino mass induced by the interactions with axions. We have obtained a set of relationships between the axion mass, the electron–neutrino mass and the strength of the coupling using current experimental limits on the nonobservation of the neutrinoless double beta decay.
Dynamic Response and Riding Comfort Analysis of a Passenger-Train-Bridge System in Urban Rail Transit
Authors: Wang Shaoqin , Guo Minghao , Wang Xiaotong , Qiao Hong , Xia He , Ye Qing
Published: September 2022

To exactly study the riding comfort of passengers in urban rail transit trains, a coupling vibration model, including passengers, train, and bridge is proposed in this paper. According to the Lagrange Equation, the differential equations of motion of each part are derived, which are coupled with each other through displacement and dynamic interaction forces. A corresponding calculation program is compiled by the Fortran language. A representative steel-concrete composite girder bridge on Beijing Metro Line 5 is selected as the research background, the dynamic responses of passengers and train are simulated in detail, and the dynamic response differences between the passenger and carriage are compared. The passengers’ riding comfort is evaluated according to ISO-2631 Standard based on the simulated data. The result shows that passengers in the middle carriages of the train feel better than those in the head and tail ones. While in the same carriage, passengers on the middle seats feel better than those on both ends of it. If only vibration is considered, passengers will feel better in a fully loaded carriage than those with few passengers. The riding comfort of passengers will be gradually reduced with the increase of the train speed.
Static and Dynamic Bifurcations Analysis of a Fluid-Conveying Pipe with Axially Moving Supports Surrounded by an External Fluid
Authors: Fasihi Ali , Shahgholi Majid , Kudra Grzegorz , Awrejcewicz Jan
Published: September 2022

In this study, nonlinear vibration and stability of a pipe conveying fluid, with axially moving supports, immersed in an external fluid is studied. The equation of motion is derived with the aid of the Newtonian method via employing the Euler–Bernoulli beam theory and plug flow assumption. Considering the stretching effect, the nonlinear equation of motion is obtained, and it is discretized via the Galerkin method. Afterward, the stability of the system is investigated using two different approaches, and their results are compared. Numerical simulations show the system has complex dynamic behavior. To unveil the dynamics of the system, various analysis techniques such as bifurcation diagram of Poincaré map, phase portrait, power spectrum, and time trajectories are used. Limit cycle, period-n, quasi-periodic, and chaotic regime are observed. It is found that pipe can flutter around the trivial solution or post-buckling equilibrium points. Moreover, the influences of some system parameters on the chaotic behavior of the pipe are discussed.
How Far is the Difference Between Mechanical Behavior of Ideal and Non-Ideal FG-GPLRC Beams?
Authors: Eiadtrong Suppakit , Wattanasakulpong Nuttawit
Published: September 2022

This study explored the disparities in bending, buckling, and vibration results of ideal and non-ideal functionally graded graphene nanoplatelet reinforced composite (FG-GPLRC) beams. The smooth and continuous profiles of material distributions of ideal FG-GPLRC beams were modified for making the controlling tracks to produce two different forms of non-ideal FG-GPLRC beams which had in-and out-stepwise distributions of material constituents across the beam’s thickness. The Halpin–Tsai model and the rule of mixture were used to predict the effective material properties of the nanocomposite beams. The closed-form solution possessing less time of computation was provided for predicting the mechanical behavior of the beams, and it was validated for accuracy by comparing with the results of the Ritz method. The study’s results suggest that non-ideal beams with an out-stepwise distribution of material constituents have a better dispersion of reinforcing nanomaterials than in-stepwise distribution. Therefore, the results of the beams with an out-stepwise distribution are closer to those of ideal beams than with in-stepwise distribution.
Deformation Resistance of Prestressed Geosynthetics-Reinforced Soil Wall with a Buffer Layer Subjected to Impact of Rockfall
Authors: Ma Shu-Wen , Lu Liang , Wang Zong-Jian , Shi Tong-Hui , Xiao Liang , Arai Katsuhiko
Published: September 2022

Collapse hazards occur frequently in mountainous areas. Geosynthetics-reinforced soil (GRS) wall, combing the damping characteristics of soil and the tensile properties of reinforcement materials, can effectively resist rockfall impact through its flexible deformation. However, excessive deformation is easy to occur near the impact point of reinforced soil retaining wall subjected to impact loading, resulting in instantaneous instability and long repair time as well as high repair cost of the structure. Therefore, the prestressed GRS wall with buffer layer was proposed in this paper. Four retaining walls with different types and the impactor consist of pendulum device and solid steel ball was designed to study the impact resistance and dynamic stability of the retaining wall. The results show that the cumulative deformation of the wall panel experiences acceleration phase, deceleration phase and stable stage subjected to the impact loading. The application of buffer layer and prestress member can effectively improve the impact resistance and dynamic stability of retaining wall, and further control the deformation of retaining wall under the impact loading, in comparison with ordinary reinforced retaining wall. The peak impact stress of the retaining wall and the responsive strain of the reinforcement increase gradually as the distance between the measure point and the impact point decreases. The research results can provide reference for the design and the application of prestressed GRS wall under the impact of rockfall.
On weakly radical ideals of non-commutative rings
Authors: Groenewald Nico
Published: September 2022

For a commutative ring [Formula: see text] denote the Jacobson radical of the ring by [Formula: see text]. Khashan et al. introduced and studied J-ideals and weakly J-ideals for commutative rings with identity. In this paper, let [Formula: see text] be a non-commutative ring. We introduce weakly [Formula: see text]-ideals for a special radical [Formula: see text] and show that if [Formula: see text] is the Jacobson radical many of the results proved by Khashan et al. are also satisfied for non-commutative rings. The results for weakly J-ideals for non-commutative rings follow as special cases of a more general situation.
Double and Lagrangian extensions for quasi-Frobenius Lie superalgebras
Authors: Bouarroudj Sofiane , Maeda Yoshiaki
Published: September 2022

A Lie superalgebra is called quasi-Frobenius if it admits a closed anti-symmetric non-degenerate bilinear form. We study the notion of double extensions of quasi-Frobenius Lie superalgebra when the form is either orthosymplectic or periplectic. We show that every quasi-Frobenius Lie superalgebra that satisfies certain conditions can be obtained as a double extension of a smaller quasi-Frobenius Lie superalgebra. We classify all 4-dimensional quasi-Frobenius Lie superalgebras, and show that such Lie superalgebras must be solvable. We study the notion of [Formula: see text]-extensions (or Lagrangian extensions) of Lie superalgebras, and show that they are classified by a certain cohomology space we introduce. Several examples are provided to illustrate our construction.
Sustainable Innovation and Creative Behavior: the Mediating Effect of Technology Application from Early Adopters
Authors: Chou Sheng-Fang , Lin Jun-You
Published: September 2022

Innovation adoption and technology application models have revolutionized the hospitality and food sectors. Innovation is very important to the sustainable development of enterprises. However, current knowledge on whether and how sustainable innovation and innovation adoption can be understood from the perspective of technology application behavior has not been examined empirically. Based on decision-making levels derived from the attitude-implementation-confirmation (A-I-C) framework and following rule conditions on front- and back-of-house processes, the aim of this study is to provide practical recommendations for the establishment of a pattern of green innovation action that influences the implementation of innovation among early adopters in the restaurant industry. Using 499 questionnaire responses and diverse statistical methods, we verify that the effect of innovation adoption is significant and positive on sustainable innovation, and the effects of innovation adoption and sustainability are both significant and positive on technology application and behavior, respectively. Moreover, the effects of technology application are significant and positive on behavior and mediate the link between innovation adoption and behavior. Finally, technology application is modestly moderated by green resource motivation techniques (GrRMT) to influence behavior. The empirical results proved that innovation adoption, sustainable innovation, technology application and GrRMT improve the mechanism of creative behavior in the food/ hospitality sector.
A Quantified Ship Condition Inspection Model Based on SWARA and SAW
Authors: Cicek Kadir , Kurtel Gulden
Published: September 2022

To provide sustainable, safe, and resilient maritime transport, it is an essential aspect to monitor the operational conditions of ships at regular intervals. Therefore, public and private maritime authorities spend considerable effort monitoring the ships’ conditions through adapting and implementing various inspection regimes. This paper proposes a new hybrid approach to quantify the ship condition inspections to determine whether the ship complies with the international maritime regulations or not. In the proposed approach, Step-wise weight assessment ratio analysis (SWARA) and simple additive weighting (SAW) methodologies are integrated aiming to achieve a global score that reflects the condition of the inspected ship. In the proposed approach, SWARA methodology is used to determine the weights of inspection criteria. SAW methodology is used to calculate the global score considering the criteria weights. Also, in the study, a ship condition assessment (SCA) form is developed which can be used for ship inspections. The applicability of the proposed approach and developed form is demonstrated on the inspection of the selected general cargo ships.
A non-parametric Bayesian joint model for latent individual molecular profiles and survival in oncology
Authors: Rincourt Sarah-Laure , Michiels Stefan , Drubay Damien
Published: September 2022

The development of prognostic molecular signatures considering the inter-patient heterogeneity is a key challenge for the precision medicine. We propose a joint model of this heterogeneity and the patient survival, assuming that tumor expression results from a mixture of a subset of independent signatures. We deconvolute the omics data using a non-parametric independent component analysis with a double sparseness structure for the source and the weight matrices, corresponding to the gene-component and individual-component associations, respectively. In a simulation study, our approach identified the correct number of components and reconstructed with high accuracy the weight ([Formula: see text]0.85) and the source ([Formula: see text]0.75) matrices sparseness. The selection rate of components with high-to-moderate prognostic impacts was close to 95%, while the weak impacts were selected with a frequency close to the observed false positive rate ([Formula: see text]25%). When applied to the expression of 1063 genes from 614 breast cancer patients, our model identified 15 components, including six associated to patient survival, and related to three known prognostic pathways in early breast cancer (i.e. immune system, proliferation, and stromal invasion). The proposed algorithm provides a new insight into the individual molecular heterogeneity that is associated with patient prognosis to better understand the complex tumor mechanisms.
Pricing Problem of Geometric Average Asian Barrier Option Based on Uncertain Fractional Differential Equation
Authors: Yu Pengfei , Zhu Yuanguo
Published: September 2022

In this paper, we investigate pricing problems of geometric average Asian barrier options based on a Caputo type of uncertain fractional differential equation. Unlike traditional stock models based on stochastic/ uncertain processes, we assume that underlying assets are subject to uncertain fractional processes. First, it provides a Caputo type of uncertain fractional mean-reverting model based on fractional differential equation. Then the pricing formulas are acquired for four geometric average Asian barrier options, namely, the up-and-in call option, the down-and-in put option, the up-and-out put option and the down-and-out call option. Meanwhile, numerical simulations are devised to validate the results of the model.
On the sum of a Fibonacci number and a prime
Authors: Wang Rui-Jing , Chen Yong-Gao
Published: September 2022

In this paper, we prove that the set of integers which can be represented as the sum of a Fibonacci number and a prime in at least one and at most [Formula: see text] ways has the lower asymptotic density at least [Formula: see text].
k-Diophantine m-tuples in finite fields
Authors: Hammonds Trajan , Kim Seoyoung , Miller Steven J. , Nigam Arjun , Onghai Kyle , Saikia Dishant , Sharma Lalit M.
Published: September 2022

In this paper, we define a [Formula: see text]-Diophantine [Formula: see text]-tuple to be a set of [Formula: see text] positive integers such that the product of any [Formula: see text] distinct positive integers is one less than a perfect square. We study these sets in finite fields [Formula: see text] for odd prime [Formula: see text] and guarantee the existence of a [Formula: see text]-Diophantine [Formula: see text]-tuple provided [Formula: see text] is larger than some explicit lower bound. We also give a formula for the number of 3-Diophantine triples in [Formula: see text] as well as an asymptotic formula for the number of [Formula: see text]-Diophantine [Formula: see text]-tuples.
Discrimination and quantification of scar tissue by Mueller matrix imaging with machine learning
Authors: Liu Xi , Sun Yanan , Gu Weixi , Sun Jianguo , Wang Yi , Li Li
Published: September 2022

Scarring is one of the biggest areas of unmet need in the long-term success of glaucoma filtration surgery. Quantitative evaluation of the scar tissue and the post-operative structure with micron scale resolution facilitates development of anti-fibrosis techniques. However, the distinguishment of conjunctiva, sclera and the scar tissue in the surgical area still relies on pathologists’ experience. Since polarized light imaging is sensitive to anisotropic properties of the media, it is ideal for discrimination of scar in the subconjunctival and episcleral area by characterizing small differences between proportion, organization and the orientation of the fibers. In this paper, we defined the conjunctiva, sclera, and the scar tissue as three target tissues after glaucoma filtration surgery and obtained their polarization characteristics from the tissue sections by a Mueller matrix microscope. Discrimination score based on parameters derived from Mueller matrix and machine learning was calculated and tested as a diagnostic index. As a result, the discrimination score of three target tissues showed significant difference between each other ([Formula: see text]). The visualization of the discrimination results showed significant contrast between target tissues. This study proved that Mueller matrix imaging is effective in ocular scar discrimination and paves the way for its application on other forms of ocular fibrosis as a substitute or supplementary for clinical practice.
Mobile Localization Techniques for Wireless Sensor Networks: Survey and Recommendations
Authors: Oliveira Leonardo L. de , Eisenkraemer Gabriel H. , Carara Everton A. , Martins João B. , Monteiro Jose
Published: September 2022

This article provides a comprehensive survey of pioneer and state-of-the-art localization algorithms based on the mobility of the network. The basic concepts of the localization task in a wireless sensor network are revisited and the most common techniques suitable for random mobility are reviewed. This survey compiles and discusses the most relevant algorithms regarding localization in mobile networks, focusing on scenarios where nodes have no control over their mobility and hardware restrictions are imposed, including recent advances in learning-based solutions. It focuses on presenting techniques that do not rely on human intervention or a specialized field configuration. This unpredictability brings challenges that are not present in a static network nor in a mobile network built upon robotic entities. The basis for theoretical concepts of localization algorithms is gathered and organized in a comprehensive way, so researchers may quickly get started in this field. Significant aspects addressed throughout the article, such as mobility pattern, range scheme, and computational complexity are organized and discussed as well as performance data for quantitative analysis alongside related ponderations. This survey concludes by pointing out current and future trends.
Abstractive Summarization of Text Document in Malayalam Language: Enhancing Attention Model Using POS Tagging Feature
Authors: K. Nambiar Sindhya , Peter S. David , Mary Idicula Sumam
Published: September 2022

Over the past few years, researchers are showing huge interest in sentiment analysis and summarization of documents. The primary reason being that huge volumes of information are available in textual format, and this data has proven helpful for real-world applications and challenges. The sentiment analysis of a document will help the user comprehend the content’s emotional intent. Abstractive summarization algorithms generate a condensed version of the text, which can then be used to determine the emotion represented in the text using sentiment analysis. Recent research in abstractive summarization concentrates on neural network-based models, rather than conjunctions-based approaches, which might improve the overall efficiency. Neural network models like attention mechanism are tried out to handle complex works with promising results. The proposed work aims to present a novel framework that incorporates the part of speech tagging feature to the word embedding layer, which is then used as the input to the attention mechanism. With POS feature being part of the input layer, this framework is capable of dealing with words containing contextual and morphological information. The relevance of POS tagging here is due to its strong reliance on the language’s syntactic, contextual, and morphological information. The three main elements in the work are pre-processing, POS tagging feature in the embedding phase, and the incorporation of it into the attention mechanism. The word embedding provides the semantic concept about the word, while the POS tags give an idea about how significant the words are in the context of the content, which corresponds to the syntactic information. The proposed work was carried out in Malayalam, one of the prominent Indian languages. A widely used and accepted dataset from the English language was translated to Malayalam for conducting the experiments. The proposed framework gives a ROUGE score of 28, which outperformed the baseline models.
DRL based Joint Affective Services Computing and Resource Allocation in ISTN
Authors: Xu Kexin , Zhang Haijun , Long Keping , Wang Jianquan , Sun Lei
Published: September 2022

Affective services will become a research hotspot in
artificial intelligence (AI)
in the next decade. In this paper, a novel service paradigm combined with wireless communication in
integrated satellite-terrestrial network (ISTN)
is proposed. On this basis, an
affective services computing offloading and transmission network (ASCTN)
with a three-tier computation architecture is proposed, which is able to assist users to obtain affective computing services and regulate emotions. The optimization problem is investigated in the ASCTN, which is a discrete, non-linear, and non-convex problem with the limitation of computation ability of satellite and transmit power. Specifically, with the objective to minimize the cost utility related to latency and energy consumption, a joint affective services tasks computing offloading strategy, sub-channel, and power allocation algorithm based on
dueling deep Q-network (Dueling-DQN)
is proposed, which is in possession of better stability. The simulation results reveal the effectiveness of the optimization algorithm in terms of the cost utility in the ASCTN system.
Precise Quantitative Analysis of Binarized Neural Networks: A BDD-based Approach
Authors: Zhang Yedi , Zhao Zhe , Chen Guangke , Song Fu , Chen Taolue
Published: September 2022

As a new programming paradigm, neural-network-based machine learning has expanded its application to many real-world problems. Due to the black-box nature of neural networks, verifying and explaining their behavior are becoming increasingly important, especially when they are deployed in safety-critical applications. Existing verification work mostly focuses on qualitative verification, which asks whether there exists an input (in a specified region) for a neural network such that a property (e.g., local robustness) is violated. However, in many practical applications, such an (adversarial) input almost surely exists, which makes a qualitative answer less meaningful. In this work, we study a more interesting yet more challenging problem, i.e.,quantitativeverification of neural networks, which asks how often a property is satisfied or violated. We target binarized neural networks (BNNs), the 1-bit quantization of general neural networks. BNNs have attracted increasing attention in deep learning recently, as they can drastically reduce memory storage and execution time with bit-wise operations, which is crucial in recourse-constrained scenarios, e.g., embedded devices for Internet of Things. Toward quantitative verification of BNNs, we propose a novel algorithmic approach for encoding BNNs as Binary Decision Diagrams (BDDs), a widely studied model in formal verification and knowledge representation. By exploiting the internal structure of the BNNs, our encoding translates the input-output relation of blocks in BNNs to cardinality constraints, which are then encoded by BDDs. Based on the new BDD encoding, we develop a quantitative verification framework for BNNs where precise and comprehensive analysis of BNNs can be performed. To improve the scalability of BDD encoding, we also investigate parallelization strategies at various levels. We demonstrate applications of our framework by providing quantitative robustness verification and interpretability for BNNs. An extensive experimental evaluation confirms the effectiveness and efficiency of our approach.
Research on Super-Resolution Relationship Extraction and Reconstruction Methods for Images Based on Multimodal Graph Convolutional Networks
Authors: Xiao Jie
Published: September 2022

This study constructs a multimodal graph convolutional network model, conducts an in-depth study on image super-resolution relationship extraction and reconstruction methods, and constructs a model of image super-resolution relationship extraction and reconstruction methods based on multimodal graph convolutional networks. In this study, we study the domain adaptation model algorithm based on chart convolutional networks, which constructs a global relevance graph based on all samples using pre-extracted features and performs distribution approximation of sample features in two domains using a diagram convolutional neural network with maximum mean difference loss; with this approach, the model effectively preserves the structural information among the samples. In this study, several comparison experiments are designed based on the COCO and VG datasets; the image space information-based and knowledge graph-based target detection and recognition models substantially improve recognition performance over the baseline model. The super-pixel-based target detection and recognition model can also effectively reduce the number of floating-point operations and the complexity of the model. In this study, we propose a multiscale GAN-based image super-resolution reconstruction algorithm. Aiming at the problems of detail loss or blurring in the reconstruction of detail-rich images by SRGAN, it integrates the idea of the Laplace pyramid to complete the task of multiscale reconstruction of images through staged reconstruction. It incorporates the concept of a discriminative network with patch GAN to effectively improve the recovery effect of graph details and improve the reconstruction quality of images. Using Set5, Set14, BSD100, and Urban100 datasets as test sets, experimental analysis is conducted from objective and subjective evaluation metrics to effectively validate the performance of the improved algorithm proposed in this study.
Asymptotic Optimality and Rates of Convergence of Quantized Stationary Policies in Continuous‐Time Markov Decision Processes
Authors: Wu Xiao , Tang Yanqiu
Published: September 2022

This paper is concerned with the asymptotic optimality of quantized stationary policies for continuous‐time Markov decision processes (CTMDPs) in Polish spaces with state‐dependent discount factors, where the transition rates and reward rates are allowed to be unbounded. Using the dynamic programming approach, we first establish the discounted optimal equation and the existence of its solutions. Then, we obtain the existence of optimal deterministic stationary policies under suitable conditions by more concise proofs. Furthermore, we discretize and incentivize the action space and construct a sequence of quantizer policies, which is the approximation of the optimal stationary policies of the CTMDPs, and get the approximation result and the rates of convergence on the expected discounted rewards of the quantized stationary policies. Also, we give an iteration algorithm on the approximate optimal policies. Finally, we give an example to illustrate the asymptotic optimality.
Zerumbone‐Loaded Nanostructured Lipid Carrier Gel Enhances Wound Healing in Diabetic Rats
Authors: Albaayit Shaymaa Fadhel Abbas , Abdullah Rasedee , Noor Mohd Hezmee Mohd
Published: September 2022

This study investigated the healing effects of topical application of zerumbone, a well‐known anti‐inflammatory compounds loaded on nanostructured lipid carrier gel (Carbopol 940) (ZER‐NLCG) on excisional wounds in streptozotocin‐induced diabetic rats. Diabetic rats with inflicted superficial skin wound were topically treated with ZER‐NLCG, empty NLCG, and silver sulfadiazine cream (SSDC) once daily for 21 days. Wound tissue samples were analyzed for proinflammatory cytokines, namely, interleukin‐6 (IL‐6), interleukin‐1 β (IL‐1β), and tumor necrosis factor‐α (TNF‐α), hydroxyproline contents, catalase, superoxide dismutase activities, and lipid peroxidation level, and were subjected to histopathological analysis, respectively. Among the treated groups, ZER‐NLCG was the most effective at decreasing proinflammatory cytokine level and inflammatory cell infiltration while increasing antioxidant enzyme activities, hydroxyproline content, and granulation of wound tissues of diabetic rats. ZER‐NLCG is a potent formulation for the enhancement of wound healing in diabetic rats through its anti‐inflammatory, antioxidant, and tissue repair activities.
Anthocyanin Encapsulated Nanoparticles as a Pulmonary Delivery System
Authors: Amararathna Madumani , Hoskin David W. , Rupasinghe H. P. Vasantha
Published: September 2022

Anthocyanins are known for their therapeutic efficacy for many human diseases, including cancer. After ingestion, anthocyanins degrade due to oxidation and enzymatic breakdown, resulting in reduced therapeutic efficacy. Direct delivery to target tissues and entrapment of anthocyanins increases their stability, bioavailability, and therapeutic efficacy. The objective of the present study was to develop a direct delivery system of anthocyanins into pulmonary tissues via encapsulated nanocarriers. A cyanidin-3-O-glucoside (C3G)-rich anthocyanin extract was prepared from well-ripened haskap (Lonicera caerulea L.) berries (HB) and encapsulated in three different polymeric nanocarrier systems: polyethylene glycol-poly(lactide-co-glycolide), maltodextrin, and carboxymethyl chitosan (CMC). The anthocyanin encapsulation efficiency was significantly higher in CMC (10%) than in the other two polymers. The cytotoxicity and cytoprotective effect of HB anthocyanin-encapsulated CMC (HB-CMC, 4 μg of C3G equivalent anthocyanin in 2 mg/mL nanoparticle) and anthocyanin-free CMC (E-CMC, 2 mg/mL) were tested for cytotoxicity using human normal lung epithelial BEAS-2B cells. The CMC nanoparticles were not cytotoxic for BEAS-2B cells. The HB-CMC nanoparticles reduced carcinogen-induced oxidative stress in BEAS-2B cells and restored the expression of superoxide dismutase and glutathione peroxidase enzymes. The HB-CMC nanoparticles also reduced carcinogen-induced DNA single-strand breaks and alkaline-labile sites but not the double-strand breaks. The E-CMC, HB-CMC (28 μg C3G equivalent/mouse/day for six days), or the same dose of free HB anthocyanin was administered to A/JCr mice through a nose-only passive inhalation device. C3G and its metabolites, cyanidin, peonidin-3-O-glucoside, and cyanidin-3-O-glucuronide, were detected by UPLC/ESI/Q-TOF-MS in the lungs of mice after one hour of exposure. Therefore, the CMC could be a promising noncytotoxic candidate to encapsulate HB anthocyanin. Direct delivery of anthocyanin to lung tissues enhances tissue retention, slows phase 2 metabolism, and improves therapeutic efficacy.