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2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Parameshwaran Ramalingam ◽  
Abolfazl Mehbodniya ◽  
Julian L. Webber ◽  
Mohammad Shabaz ◽  
Lakshminarayanan Gopalakrishnan

Telemetric information is great in size, requiring extra room and transmission time. There is a significant obstruction of storing or sending telemetric information. Lossless data compression (LDC) algorithms have evolved to process telemetric data effectively and efficiently with a high compression ratio and a short processing time. Telemetric information can be packed to control the extra room and association data transmission. In spite of the fact that different examinations on the pressure of telemetric information have been conducted, the idea of telemetric information makes pressure incredibly troublesome. The purpose of this study is to offer a subsampled and balanced recurrent neural lossless data compression (SB-RNLDC) approach for increasing the compression rate while decreasing the compression time. This is accomplished through the development of two models: one for subsampled averaged telemetry data preprocessing and another for BRN-LDC. Subsampling and averaging are conducted at the preprocessing stage using an adjustable sampling factor. A balanced compression interval (BCI) is used to encode the data depending on the probability measurement during the LDC stage. The aim of this research work is to compare differential compression techniques directly. The final output demonstrates that the balancing-based LDC can reduce compression time and finally improve dependability. The final experimental results show that the model proposed can enhance the computing capabilities in data compression compared to the existing methodologies.


2021 ◽  
Vol 13 (24) ◽  
pp. 13814
Author(s):  
Olena Liakh

Accountability assessment is a highly relevant challenge for companies nowadays. The COVID-19 pandemic prompted a digital acceleration in business environments, which in turn brought more focus on sustainability practices that could help organizations better demonstrate their accountability, thus making them more resilient to the ever-changing socio-economic context. Therefore, this paper aims to evaluate how to further improve corporate accountability (on a strategic and operational level), taking advantage of the digitalization changes that companies are being forced to go through and applying them to the sustainability evaluation process, including the reporting as its final output. The first research outcome is a combined framework, based on data governance and sustainability literature models, seeking to optimize the manageability of sustainability data. The second outcome is a matrix, based on a content analysis of 20 sustainability reports, representing eight possible types of behavior that companies adopt when integrating digitalization practices into their sustainability evaluation process. The aim is to explore how the communication of digital activities could refine the diligence of the sustainability assessment process, with disclosure representing its last step. Finally, the ‘leading’ case was broken down into the general strategic components that could potentially be included in a balanced data-sustainability reporting strategy.


2021 ◽  
Vol 11 (24) ◽  
pp. 11684
Author(s):  
Mona Khalifa A. Aljero ◽  
Nazife Dimililer

Detecting harmful content or hate speech on social media is a significant challenge due to the high throughput and large volume of content production on these platforms. Identifying hate speech in a timely manner is crucial in preventing its dissemination. We propose a novel stacked ensemble approach for detecting hate speech in English tweets. The proposed architecture employs an ensemble of three classifiers, namely support vector machine (SVM), logistic regression (LR), and XGBoost classifier (XGB), trained using word2vec and universal encoding features. The meta classifier, LR, combines the outputs of the three base classifiers and the features employed by the base classifiers to produce the final output. It is shown that the proposed architecture improves the performance of the widely used single classifiers as well as the standard stacking and classifier ensemble using majority voting. We also present results on the use of various combinations of machine learning classifiers as base classifiers. The experimental results from the proposed architecture indicated an improvement in the performance on all four datasets compared with the standard stacking, base classifiers, and majority voting. Furthermore, on three of these datasets, the proposed architecture outperformed all state-of-the-art systems.


Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 8177
Author(s):  
Marco Casazza ◽  
Francesco Gonella ◽  
Gengyuan Liu ◽  
Antonio Proto ◽  
Renato Passaro

Energy is the main driver of human Social-Ecological System (SES) dynamics. Collective energy properties of human SES can be described applying the principles of statistical mechanics: (i) energy consumption repartition; (ii) efficiency; (iii) performance, as efficient power, in relation to the least-action principle. International Energy Agency data are analyzed through the lens of such principles. Declining physical efficiency and growth of power losses emerge from our analysis. Losses mainly depend on intermediate system outputs and non-energy final output. Energy performance at Country level also depends on efficient power consumption. Better and worse performing Countries are identified accordingly. Five policy-relevant areas are identified in relation to the physical principles introduced in this paper: Improve efficiency; Decouple economic growth from environmental degradation; Focus on high value added and labor-intensive sectors; Rationalize inefficient fossil fuel subsidies that encourage wasteful consumption; Upgrade the technological capabilities. Coherently with our findings, policies should support the following actions: (1) redefine sectoral energy distribution shares; (2) Improve Country-level performance, if needed; (3) Reduce intermediate outputs and non-energy final output; (4) Reduce resources supply to improve eco-efficiency together with system performance.


2021 ◽  
Vol 4 ◽  
pp. 1-7
Author(s):  
Adolfo Pérez ◽  
Felisa Quesada ◽  
Alicia González ◽  
Alfonso Boluda ◽  
Ana Maldonado ◽  
...  

Abstract. Several reasons have prompted the National Geographic Institute of Spain (IGN-Spain) to implement an automatic process to generate the National Topographic Map 1:25,000 (MTN) instead trough the traditional manual way, pointing out the growing lack of human resources, in addition to the search for a quick response to the increasing demand of updated geoinformation by the society.This new automated process provides an annual production of all the map sheets composing the MTN25 (4.019 files), what is an unprecedented time record, so that the users can quickly both download them from the Download Centre Website and visualize the maps through the visualization web services WMS and WMTS. This methodology is also applied to the creation of sheets for printed publication, whose final output requires a simplified manual editing process.


2021 ◽  
Vol 20 (11) ◽  
pp. 2134-2150
Author(s):  
Dmitrii V. BALANDIN ◽  
Yurii A. KUZNETSOV

Subject. The article addresses the economic, scientific, technological, and production activities of processing plants within the agri-industrial complex. Objectives. The study aim to analyze and describe the specifics of storing perishable agricultural products, and the impact on both the further processing and final results of a processing enterprise. Methods. We employ methods of analysis and synthesis, grouping and comparison, abstraction, generalization, and analogy. We provide a mathematical model that describes the sugar beet processing, taking into account the level of sugar content, based on methods of mathematical modeling and optimization theory. Results. Our analysis of the influence of storage time of perishable agricultural products on their further processing showed that this factor can have a serious impact on the results of processing enterprises. We investigated the issue, using the case of such an important technical culture as sugar beet. We highlight an important factor, which can significantly improve the results of economic activity of enterprises, i.e. the formation of a schedule for the order of processing of sugar beet with different levels of sugar content. Conclusions. To maximize the final output, the sugar beet processing should start with batches of maximum sugar content. We provide assessments, characterizing the level of final output, should this rule be violated. The findings may serve as a basis for developing more general mathematical models, which describe the sugar beet processing and consider other factors, including the dynamics of the process of beet pile fields replenishment with sugar beet of various sugar content.


2021 ◽  
Vol 15 ◽  
pp. 123-129
Author(s):  
Federico Pérez ◽  
Isidro Calvo ◽  
Fabian López ◽  
Ismael Etxeberria‐Agiriano

Traditional approaches for developing automation systems consider system itself hardly can be changed. Current challenges in automation applications include the need of autoreconfiguration in response to process changes or event triggering. In order to face these requirements, new automation methodologies are necessary. Component-based technologies, initially defined for achieving efficient, structured and reusable designs can also be used to achieve adaptation. In this work, an IEC 61499-based framework that uses the concept to deal with reconfiguration issues is presented. The final output of the framework is a distributed system IEC61499 compliant. A new concept, the communication channel, is introduced providing a new abstraction layer to deal with communication between components. The joint use of automation components and communication channels allows defining complex automation systems in an easy way.


2021 ◽  
Author(s):  
◽  
Eliot Blenkarne

<p>Architectural visualisation is often viewed with a degree of hesitancy by the architectural profession, for a perceived lack of criticality in the methods and outputs – particularly with the rise of hyper-real still imagery production. However, photography too suffers from a certain disconnect from an authentic experience of space, which we experience through our moving within it, our sensory gamut stimulated by the atmosphere memorable architecture possesses. This atmosphere is a holistic assemblage of design decisions made by the building designer, connected to mass, light, materiality, sound, among others. The field of gaming has been able to deploy many of these characteristics in virtual space for decades in some manner, and the tools used have been refined to the point where they are technically, and fiscally accessible to architecture.  This thesis proposes that real-time virtual engines, as used by game designers, can extend the field of architectural representation and design, by better conveying a sense of architectural atmosphere and providing increased immersion in virtual space compared to traditional techniques. It first seeks to define what architectural atmosphere may be recognised as, and how it may be caused to manifest, and then applies these findings to virtual space as a means to test the relationship between the real and unreal. Further to this, it applies this methodology to an iterative design process of both an architectural and virtual nature, with a final output that demonstrates the result of both concurrently.</p>


2021 ◽  
Author(s):  
◽  
Eliot Blenkarne

<p>Architectural visualisation is often viewed with a degree of hesitancy by the architectural profession, for a perceived lack of criticality in the methods and outputs – particularly with the rise of hyper-real still imagery production. However, photography too suffers from a certain disconnect from an authentic experience of space, which we experience through our moving within it, our sensory gamut stimulated by the atmosphere memorable architecture possesses. This atmosphere is a holistic assemblage of design decisions made by the building designer, connected to mass, light, materiality, sound, among others. The field of gaming has been able to deploy many of these characteristics in virtual space for decades in some manner, and the tools used have been refined to the point where they are technically, and fiscally accessible to architecture.  This thesis proposes that real-time virtual engines, as used by game designers, can extend the field of architectural representation and design, by better conveying a sense of architectural atmosphere and providing increased immersion in virtual space compared to traditional techniques. It first seeks to define what architectural atmosphere may be recognised as, and how it may be caused to manifest, and then applies these findings to virtual space as a means to test the relationship between the real and unreal. Further to this, it applies this methodology to an iterative design process of both an architectural and virtual nature, with a final output that demonstrates the result of both concurrently.</p>


2021 ◽  
pp. 136216882110528
Author(s):  
María Martínez-Adrián ◽  
Francisco Gallardo-del-Puerto

Task modality (oral vs. writing) has been found to affect the production, nature and resolution of the language-related episodes (LREs) produced by adult learners in collaborative interaction, a finding also attested in very recent and still limited research with young learners, a population that deserves greater attention in the literature. Besides, previous research has not yet considered the incorporation of LREs in the final output of both oral and written tasks. Nor has it controlled for the differential levels of accuracy that the oral vs. the written modality demand, or the opportunity for revising the output equally in both modalities. Besides, little is known about learners’ motivation towards tasks of different modality. This article fills these gaps by examining the effect of task-modality on the production of LREs by 10- to 12-year-old schoolchildren performing an oral+writing task and an oral+editing task, as well as its effect on their task motivation. Task modality effects were evinced in terms of nature and incorporation of LREs, the written mode leading to greater focus on form and incorporation of accurately resolved LREs. The possibility of editing the oral output resulted in enhanced target-likeness of resolved LREs. As for task motivation, learners perceived both tasks as equally motivating.


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