Distributed Applications Research Articles

Diet composition and prey choice in prehistoric human individuals from Northwest Patagonia: An application of species distribution and isotope mixing models

AbstractObjectivesAncient hunter‐gatherer diets were heterogeneous, varying substantially across time and space, and frequently showing considerable intrapopulation variation. The diet composition of these human groups depended primarily on resource availability, but also on the active selection of certain prey due to different bio‐cultural factors. In this context, we explore resource availability, diet composition, and prey choice in the human populations of the Middle‐Late Holocene from Northwest Patagonia.Material and MethodsWe employ species distribution models using current and zooarchaeological data to estimate species availability throughout Northwest Patagonia, and we use Bayesian stable isotope mixing models on a large number of samples to analyze human diet composition at the individual level during the Middle‐Late Holocene. Finally, we calculate a prey selectivity index to address the different dietary choices of human individuals in the region.ResultsOur results show large differences in species available for consumption throughout the region, as well as a high dietary variation between human individuals, which is mainly related to their spatial location. Some species, such as guanaco, were widely distributed and consumed in the region. Notably, species of small mammals were actively selected in several areas, indicating greater importance in human diets than previously appreciated.DiscussionSpecies availability does not appear as the only factor driving human diets in the region, since prey choice seems to have been a recurring phenomenon among these populations. The novel approach used in this study overcomes several limitations of previous studies employing isotopic analysis in prehistoric human diets, allowing new insights into the bioarchaeology of the region.

Exponentiated Half Logistic-Generalized G Power Series Class of Distributions: Properties and Applications

A new generalized class of distributions called the Exponentiated Half Logistic-Generalized G Power Series (EHL-GGPS) distribution is proposed. We present some special cases in the proposed distribution. Several mathematical properties of the EHL-GGPS distribution were also derived including order statistics, moments and maximum likelihood estimates. A simulation study for selected parameter values is presented to examine the consistency of the maximum likelihood estimates. Finally, some real data applications of the EHL-GGPS distribution are presented to illustrate the usefulness of the proposed class of distributions.

Cerium oxide nanosheets-based tertiary composites (CeO2/ZnO/ZnWO4) for supercapattery application and evaluation of faradic & non-faradic capacitive distribution by using Donn's model

Batteries and supercapacitors are trending energy storage devices nowadays but the low energy density of supercapacitors and modest power density of batteries limit their applications for practical usage. Recently a new class of energy storage devices called supercapattery has emerged as an ultimate energy storage device which shows a hybrid storage mechanism of both battery and supercapacitor. In the present work, CeO2-based binary and tertiary composites have been prepared by the facile hydrothermal method, characterized by different techniques, and applied as an electrode in supercapacitors and supercapattery. The X-ray diffraction patterns and EDX confirm the successful synthesis of tertiary composites with the average crystallite size of 26 nm while the SEM images show the randomly orientated nanosheets of CeO2 loaded with ZnO and ZnWO4 over it. The XPS spectrum depicts the presence of variable oxidation states of cerium (Ce+2 and Ce+4) in the tertiary composites. The electrochemical performance shows the highest specific capacity (496.9 Cg−1) for tertiary composite. The supercapattery device is fabricated by using mixed (AC/Ternary composite) as a working electrode and AC as a cathode which is separated by filter paper. The evaluation of capacitive and battery mechanisms shows the dominant feature of diffusive and surface-controlled processes at lower and high scan rates respectively due to varying diffusion time of electrolyte species. The fabricated device shows the energy density and power density of 56.92 Whkg−1 and 2000 Wkg−1 at a current density of 0.5 mAg−1 respectively with 88 % cyclic stability and 82 % coulombic efficiency.

Lithium-sulfur battery diagnostics through distribution of relaxation times analysis

• Distribution of relaxation times (DRT) is a powerful tool for deconvolution relaxation processes occurring in the overlapping frequency region in the EIS. • DRT peaks are correlated to the specific electrode process through analysis of relaxation processes in symmetric cells and evolution of the DRT profile of a fresh cell at different state-of-charge. • Impedance features of a pristine cell are dominated by the Li metal negative electrode and by the positive electrode after the initial charge-discharge. • Flooded electrolyte improves electrode kinetics and ion diffusion while lean electrolyte conditions cause high polarization throughout the discharge. Electrochemical impedance spectroscopy (EIS) is widely used in battery analysis as it is simple to implement and non-destructive. However, the data provided is a global representation of all electrochemical processes within the cell and much useful information is ambiguous or inaccessible when using traditional analysis techniques. This is a major challenge when EIS is used to analyse systems with complex cell chemistries, like lithium-sulfur (Li-S), one of the strongest candidates to supersede conventional Li-ion batteries. Here we demonstrate the application of distribution of relaxation times (DRT) analysis for quantitative deconvolution of EIS spectra from Li-S batteries, revealing the contributions of (eight) distinct electrode processes to the total cell polarisation. The DRT profile is shown to be strongly dependent on cell state-of-charge, offering a route to automated and on-board analysis of Li-S cells.

Efficient Generative Adversarial Networks for Imbalanced Traffic Collision Datasets

Rapid breakthroughs in information technologies have driven substantial developments in artificial intelligence applications, particularly the widespread use of deep learning techniques in domains such as speech, image and text recognition. However, real world data distribution applications suffer from significant problems including data imbalance which can easily lead to machine learning biased towards the side with more data, resulting in inaccurate classification or prediction results. Therefore, effectively addressing data imbalance is a pressing research topic. Generative Adversarial Networks (GAN) addresses data imbalance, but is prone to vanishing gradients. Recent work has thus focused on improving the GAN architecture to resolve this problem. The present research extends these efforts, applying C4.5, Random Forest, Support Vector Machine, K-Nearest Neighbor and Naïve Bayes classification algorithms to a single imbalanced traffic collision dataset to identify methods for improving prediction results. Experimental results show that classification performance significantly improves after data augmentation using Synthetic Minority Oversampling Technique, GAN, Conditional GAN, and Gaussian Discriminant Analysis GAN as compared with the non-augmented dataset. In addition, the Gaussian Discriminant Analysis GAN with Naïve Bayes classifier produces a dataset that optimizes classification performance for traffic accident prediction at highway intersections.

Living in the Moment: A Mathematically Verified Approach for Molecular Weight Distribution Analysis and Application to Data Storage

Living in the Moment: A Mathematically Verified Approach for Molecular Weight Distribution Analysis and Application to Data Storage

A Physiologically-Based Pharmacokinetic Model of the Brain Considering Regional Lipid Variance.

Modeling and simulation of the central nervous system provides a tool for understanding and predicting the distribution of small molecules throughout the brain tissue and cerebral spinal fluid (CSF), and these efforts often rely on empirical data to make predictions of distributions to move toward a better mechanistic understanding. A physiologically based pharmacokinetic model presented here incorporates multiple means of drug distribution to assemble a model for understanding potential factors that may determine the distribution of drugs across various regions of the brain, including both intra- and extracellular regions. Two classes of parameters are presented. The first concerns regional gross anatomic variability of the brain; the second concerns estimation of unbound fractions of drugs using know membrane phospholipid heterogeneity derived from regional lipid content. The model was then tested by comparing its outcomes to data from published human pharmacokinetic studies of acetaminophen, morphine, and phenytoin. The alignment of model predictions in the plasma, CSF, and tissue concentrations with the published data from studies of those three drugs suggests that the model can be a template for identifying drug localization in the brain. Clearly, knowledge of differentiated drug distribution in the brain is a requisite step in postulating pharmacodynamic and certain disease mechanisms. SIGNIFICANCE STATEMENT: This study concerns the application of heterogenous lipid distribution in brain tissue to predict regional variations in drug distribution in the brain via a mathematical model, thus expanding upon the current understanding of mechanisms of drug distribution in the central nervous system.

A Fast Accurate Attention-Enhanced ResNet Model for Fiber-Optic Distributed Acoustic Sensor (DAS) Signal Recognition in Complicated Urban Environments

The fiber-optic distributed acoustic sensor (DAS), which utilizes existing communication cables as its sensing media, plays an important role in urban infrastructure monitoring and natural disaster prediction. In the face of a wide, dynamic environment in urban areas, a fast, accurate DAS signal recognition method is proposed with an end-to-end attention-enhanced ResNet model. In preprocessing, an objective evaluation method is used to compare the distinguishability of different input features with the Euclidean distance between the posterior probabilities classified correctly and incorrectly; then, an end-to-end ResNet is optimized with the chosen time-frequency feature as input, and a convolutional block attention module (CBAM) is added, which can quickly focus on key information from different channels and specific signal structures and improves the system recognition performance further. The results show that the proposed ResNet+CBAM model has the best performance in recognition accuracy, convergence rate, generalization capability, and computational efficiency compared with 1-D CNN, 2-D CNN, ResNet, and 2-D CNN+CBAM. An average accuracy of above 99.014% can be achieved in field testing; while dealing with multi-scenario scenes and inconsistent laying or burying environments, it can still be kept above 91.08%. The time cost is only 3.3 ms for each signal sample, which is quite applicable in online long-distance distributed monitoring applications.

Discrete fractional distributed Halanay inequality and applications in discrete fractional order neural network systems

In this paper, the discrete fractional-order Halanay inequality with distributed delays is introduced. Then, based on the generalized discrete fractional Halanay inequality and Lyapunov functional method, several novel Mittag-Leffler stability and synchronization conditions of discrete fractional-order neural network systems with distributed delays are derived. Finally, an example is given to illustrated one of the results.

Privacy-aware genetic algorithm based data security framework for distributed cloud storage

An increased use of data driven applications and integrated systems have caused an accelerating expansion in data volumes and increase in the number of digital records, over the past few decades. Exponentially growing data volumes being processed by large-scale distributed data-intensive applications have placed an increasing pressure on the underlying storage services for timely and efficient storage and retrieval of the data. The use of cloud storage is among the best strategies to efficiently store growing volumes of data. However, outsourcing data to public cloud storage leads to the challenge of data confidentiality preservation. Data Confidentiality is among the top challenges associated with cloud storage which have contributed substantially as an inhibitor for cloud computing adoption all over the world and is considered a serious concern, especially in case of big data, where securing data in a timely and accurate manner is an arduous task. Our study aims to contribute in anti-cybercrime by protecting the confidentiality of sensitive growing data. We enunciate an optimized confidentially preserving framework on distributed cloud storage, that works for growing data with time-efficiency and minimum memory usage. Our framework uses a merger of Genetic Algorithm (GA), parallel data distribution, and privacy-aware selective encryption techniques. The experiments and comparative analysis depict that our proposed framework outperforms others under consideration, in terms of execution time, memory usage and network throughput respectively.

UWB/INS-based robust anchor-free relative positioning scheme for UGVs

With the development of new distributed cluster applications such as distributed radar and UGV cooperation, the concept of relative positioning springs up—different from the classic positioning application, relative positioning focuses on the real-time determination of relative position relationships in multi-node networks without pre-deployment anchor, prior map and GNSS. Under such application conditions, relative localization also requires high real-time performance, and high accuracy in short baseline scenes, and can solve the positioning failure caused by occlusion and other factors. A high-precision relative positioning scheme based on UWB-INS fusion is proposed in this paper: a robust relative positioning algorithm based on iterative extended Kalman filter is optimized by modeling the system error state, and the adaptive residual elimination algorithm is introduced to ensure the robustness of positioning service. A series of experiments show that the scheme proposed in this paper can provide centimeter-level real-time positioning service for multi-node dynamic networks without anchors and maps, and is expected to become a potential effective relative positioning technology scheme.

Detecting Earnings Management Practices in European Insurance Sector: Purpose and Application of Accrual Modelling and Distribution of Earnings

Purpose: The purpose of the present paper was to gauge the extent of the impact on earnings management derived from the adoption of International Financial Reporting Standards (IFRS) 17 as well as detecting whether the impact will be similar in different European “Insurance” and “Reinsurance” industries.
 Design/Method/Approach: To provide empirical evidence that earnings management is more frequent in some countries and less frequent in others by means of a statistical analysis, a sample of European listed “Insurance” and “Reinsurance” companies in the period of 2018-2020 was observed. The Jones Model was applied.
 Findings: This study concerns the implementation of a new principle, written to discipline the accountancy of insurances contracts, and its consequences must be carefully analyzed and monitored by regulators, as well as correctly adopted by managers, as the determined revenues could have an impact on the pre-existing earning management practices.
 Theoretical Implications: The paper of the present research also concerns the predictions about the behavior of managers that can be foreseen considering the agency theory; therefore, knowing ex-ante in which industries earnings management has a high impact, provides the option to foresee the hypothetical moves of the managers in the implementation of IFRS 17.
 Practical Implications: The analysis demonstrated that the “Insurance” industry is impacted by earnings management practices to a greater extent than the “Reinsurance” industry. These results should be analyzed simultaneously with the results from by the “Big Four” analysis concerning the impact of the introduction of IFRS 17.
 Originality/Value: Specifically, companies belonging to two sectors were selected: “Insurance” and “Reinsurance”. The statistical analysis brought to light that earnings management practices are “commonly adopted” in the “Insurance” and “Reinsurance” industry, which is consequently highly impacted by the introduction of IFRS 17.
 Research Limitations/Future Research: The total number of observations is high, all companies analyzed belong to only two industries. The analysis could be extended to a greater number of industries and companies in order to provide a more complete overview of the presence and the persistence of earnings management policies in different typologies of European financial listed companies.
 Paper type: Empirical

Graph-Based Heuristic Solution for Placing Distributed Video Processing Applications on Moving Vehicle Clusters

Vehicular fog computing (VFC) is envisioned as an extension of cloud and mobile edge computing to utilize the rich sensing and processing resources available in vehicles. We focus on slow-moving cars that spend a significant time in urban traffic congestion as a potential pool of onboard sensors, video cameras, and processing capacity. For leveraging the dynamic network and processing resources, we utilize a stochastic mobility model to select nodes with similar mobility patterns. We then design two distributed applications that are scaled in real-time and placed as multiple instances on selected vehicular fog nodes. We handle the unstable vehicular environment by a), Using real vehicle density data to build a realistic mobility model that helps in selecting nodes for service deployment b), Using community-detection algorithms for selecting a robust vehicular cluster using the predicted mobility behavior of vehicles. The stability of the chosen cluster is validated using a graph centrality measure, and c), Graph-based placement heuristics is developed to find the optimal placement of service graphs based on a multi-objective constrained optimization problem with the objective of efficient resource utilization. The heuristic solves an important problem of processing data generated from distributed devices by balancing the trade-off between increasing the number of service instances to have enough redundancy of processing instances to increase resilience in the service in case of node or link failure, versus reducing their number to minimize resource usage. We compare our heuristic to a mixed integer program (MIP) solution and a first-fit heuristic. Our approach performs better than these comparable schemes in terms of resource utilization and/or has a lesser service latency when compared to an edge computing-based service placement scheme.

Zero-inflated count distributions for capture-mark-reencounter data.

The estimation of demographic parameters is a key component of evolutionary demography and conservation biology. Capture–mark–recapture methods have served as a fundamental tool for estimating demographic parameters. The accurate estimation of demographic parameters in capture–mark–recapture studies depends on accurate modeling of the observation process. Classic capture–mark–recapture models typically model the observation process as a Bernoulli or categorical trial with detection probability conditional on a marked individual's availability for detection (e.g., alive, or alive and present in a study area). Alternatives to this approach are underused, but may have great utility in capture–recapture studies. In this paper, we explore a simple concept: in the same way that counts contain more information about abundance than simple detection/non‐detection data, the number of encounters of individuals during observation occasions contains more information about the observation process than detection/non‐detection data for individuals during the same occasion. Rather than using Bernoulli or categorical distributions to estimate detection probability, we demonstrate the application of zero‐inflated Poisson and gamma‐Poisson distributions. The use of count distributions allows for inference on availability for encounter, as well as a wide variety of parameterizations for heterogeneity in the observation process. We demonstrate that this approach can accurately recover demographic and observation parameters in the presence of individual heterogeneity in detection probability and discuss some potential future extensions of this method.

Design of a Brushless Doubly-Fed Machine for Aviation Electric Propulsion

This article discusses a high-speed brushless doubly-fed machine (BDFM) for aviation turboelectric distributed propulsion application. After a brief overview of the machine fundamentals and propulsion architecture, the electromagnetic properties of the newly designed BDFM are analyzed. An innovative rotor mechanical design is introduced, which allows for a significant increase of speed compared to legacy rotor designs. Experiments were conducted to verify the 12 000-r/min high-speed operation. The findings in this article will give prominence to the feasibility of applying BDFMs for aviation applications.

Half Cauchy-Modified Exponential Distribution: Properties and Applications

A new distribution having three parameters using half Cauchy family of distribution named half Cauchy modified exponential distribution is deliberated and studied in this work. Its mathematical and statistical properties are examined. Model parameters of novel distribution are evaluated using least-square, maximum likelihood and Cramer-Von-Mises estimations approaches. R programming software is applied to carry out all of the calculations. To evaluate the new distribution's application and goodness-of-fit test, an actual data set is studied for illustration. The suggested new distribution is performed better as compared to some existing distributions.

Applications in Some Area-Biased Distribution

The preference of the proper distribution for modelling data is often a substantial concern to researchers and practitioners. For this reason, new statistical distributions or the generalizations of well-known distributions have been proposed for exible modeling. Weighted distributions are one of the generalization methods for these distributions. In this article, area biased beta, Rayleigh and log-normal distributions are introduced. Some main statistical properties, like probability density functions, cumulative distribution functions, moments and the estimation of the parameters of these distributions are obtained. Real data examples are used for illustration of these distributions.

Additively Manufactured Nested and Non-Nested Cellular Solids for Effective Stress Distribution and Thermal Insulation Applications: An Experimental and Finite Element Analysis Study

In this study, the design, additive manufacturing and experimental as well as simulation investigation of mechanical and thermal properties of cellular solids are addressed. For this, two cellular solids having nested and non-nested structures are designed and additively manufactured via laser powder bed fusion. The primary objective is to design cellular solids which absorb a significant amount of energy upon impact loading without transmitting a high amount of stress into the cellular solids. Therefore, compression testing of the two cellular solids is performed. The nested and non-nested cellular solids show similar energy absorption properties; however, the nested cellular solid transmits a lower amount of stress in the cellular structure compared to the non-nested cellular solid. The experimentally measured strain (by DIC) in the interior region of the nested cellular solid is lower despite a higher value of externally imposed compressive strain. The second objective of this study is to determine the thermal insulation properties of cellular solids. For measuring the thermal insulation properties, the samples are placed on a hot plate; and the surface temperature distribution is measured by an infrared camera. The thermal insulating performance of both cellular types is sufficient for temperatures exceeding 100 °C. However, the thermal insulating performance of a non-nested cellular solid is slightly better than that of the nested cellular solid. Additional thermal simulations predict a relatively higher temperature distribution on the cellular solid surfaces compared to experimental results. The simulated residual stress shows a similar distribution for both types, but the magnitude of residual stress is different for the cellular solids upon cooling from different temperatures of the hot plate.

Mining Stress Distribution and Gas Drainage Application of Coal Seam Group under Fault Influence

The existence of fault structures often seriously endangers the safe production of coal mines. In order to ensure the safe and efficient production of close coal seam group mines under the influence of faults, it is of wide engineering significance to study the gas occurrence and extraction technology between coal seam groups under the influence of faults. In this study, the mining and gas drainage of 13817 working face in Yueliangtian Mine were taken as the research background. Combined with theoretical research, laboratory analysis, and numerical simulation analysis, the pressure relief range of coal seam and the evolution characteristics of surrounding rock stress were studied, the coal and gas comining system under the influence of faults was determined, the gas drainage technology was optimized, and the effect was investigated and analyzed. The results show that the gas pressure reduction zone is formed in the range of 20 m and 6 m of the lower wall of F1 fault, and the pressure relief effect is good in the range of 40 m above the roof of the working face to 20 m below the bottom plate during the advancing process. The optimized gas drainage technology has a good effect on gas control in coal seam group mining under the influence of faults. The absolute gas emission of the working face is reduced to 28~36 m3/min, the gas concentration in the upper corner of the mining face is about 0.6%, and the gas concentration of the return airflow is reduced to less than 0.5%. The gas extraction rate of No. 8 coal seam is 52.9%, and the gas content in the fault zone is reduced by 60%, realizing the safe and efficient comining of coal and gas. The research results have certain reference and application value for mine gas control with similar mining conditions.

AWESOME: an auction and witness enhanced SLA model for decentralized cloud marketplaces

In recent decades, the world has witnessed cloud computing as an essential technology that changes the traditional application Development and Operation (DevOps) lifecycle. However, current cloud software DevOps and Service Level Agreement (SLA) management often face challenges of 1) selecting the best fitting service providers, customizing services and planning capacities for large-scale distributed applications; 2) guaranteeing high-quality and trustworthy SLAs among multiple service providers; 3) enhancing the interoperability of cloud services across different providers; and 4) designing effective incentive models among stakeholders. This paper proposes a novel framework called Auction and Witness Enhanced trustworthy SLA for Open, decentralized service MarkEtplaces (AWESOME) to build a trustworthy cloud marketplace and address the above challenges. The proposed framework contains four subsystems: a customizable graphical user interface, an auction-based service selection model, a witness committee management mechanism, and a smart contract factory orchestration. We developed a prototype AWESOME decentralized application (DApp) based on the Ethereum blockchain. Extensive experiments are designed to evaluate the latency and cost of our model. The experimental results demonstrate that our model is economical and feasible.