bluuebunny/crossref_metadata_2025 huggingface dataset:
QoS Improvement Using In-Network Caching Based on Clustering and Popularity Heuristics in CCN
Authors: Kumar Sumit , Tiwari Rajeev , Hong Wei-Chiang
Published: November 2021

Content-Centric Networking (CCN) has emerged as a potential Internet architecture that supports name-based content retrieval mechanism in contrast to the current host location-oriented IP architecture. The in-network caching capability of CCN ensures higher content availability, lesser network delay, and leads to server load reduction. It was observed that caching the contents on each intermediate node does not use the network resources efficiently. Hence, efficient content caching decisions are crucial to improve the Quality-of-Service (QoS) for the end-user devices and improved network performance. Towards this, a novel content caching scheme is proposed in this paper. The proposed scheme first clusters the network nodes based on the hop count and bandwidth parameters to reduce content redundancy and caching operations. Then, the scheme takes content placement decisions using the cluster information, content popularity, and the hop count parameters, where the caching probability improves as the content traversed toward the requester. Hence, using the proposed heuristics, the popular contents are placed near the edges of the network to achieve a high cache hit ratio. Once the cache becomes full, the scheme implements Least-Frequently-Used (LFU) replacement scheme to substitute the least accessed content in the network routers. Extensive simulations are conducted and the performance of the proposed scheme is investigated under different network parameters that demonstrate the superiority of the proposed strategy w.r.t the peer competing strategies.
A Study of Biofeedback Gait Training in Cerebral Stroke Patients in the Early Recovery Phase with Stance Phase as Target Parameter
Authors: Skvortsov Dmitry V. , Kaurkin Sergey N. , Ivanova Galina E.
Published: November 2021

Walking function disorders are typical for patients after cerebral stroke. Biofeedback technology (BFB) is currently considered effective and promising for training walking function, including in patients after cerebral stroke. Most studies recognize that BFB training is a promising tool for improving walking function; however, the data on the use of highly selective walking parameters for BFB training are very limited. The aim of our study was to investigate the feasibility of using BFB training targeting one of the basic parameters of gait symmetry—stance phase duration—in cerebral stroke patients in the early recovery period. The study included 20 hemiparetic patients in the early recovery period after the first hemispheric ischemic stroke. The control group included 20 healthy subjects. The BFB training and biomechanical analysis of walking (before and after all BFB sessions) were done using an inertial system. The mean number of BFB sessions was nine (from 8 to 11) during the three weeks in clinic. There was not a single negative response to BFB training among the study patients, either during the sessions or later. The spatiotemporal parameters of walking showed the whole syndrome complex of slow walking and typical asymmetry of temporal walking parameters, and did not change significantly as a result of the study therapy. The changes were more significant for the functioning of hip and knee joints. The contralateral hip amplitude returned to the normal range. For the knee joint, the amplitude of the first flexion increased and the value of the amplitude of hyperextension decreased in the middle of the stance phase. Concerning muscle function, the observed significant decrease in the function of m. Gastrocnemius and the hamstring muscles on the paretic side remained without change at the end of the treatment course. We obtained positive dynamics of the biomechanical parameters of walking in patients after the BFB training course. The feasibility and efficacy of their use for targeted correction need further research.
Atrial Fibrillation Classification with Smart Wearables Using Short-Term Heart Rate Variability and Deep Convolutional Neural Networks
Authors: Ramesh Jayroop , Solatidehkordi Zahra , Aburukba Raafat , Sagahyroon Assim
Published: November 2021

Atrial fibrillation (AF) is a type of cardiac arrhythmia affecting millions of people every year. This disease increases the likelihood of strokes, heart failure, and even death. While dedicated medical-grade electrocardiogram (ECG) devices can enable gold-standard analysis, these devices are expensive and require clinical settings. Recent advances in the capabilities of general-purpose smartphones and wearable technology equipped with photoplethysmography (PPG) sensors increase diagnostic accessibility for most populations. This work aims to develop a single model that can generalize AF classification across the modalities of ECG and PPG with a unified knowledge representation. This is enabled by approximating the transformation of signals obtained from low-cost wearable PPG sensors in terms of Pulse Rate Variability (PRV) to temporal Heart Rate Variability (HRV) features extracted from medical-grade ECG. This paper proposes a one-dimensional deep convolutional neural network that uses HRV-derived features for classifying 30-s heart rhythms as normal sinus rhythm or atrial fibrillation from both ECG and PPG-based sensors. The model is trained with three MIT-BIH ECG databases and is assessed on a dataset of unseen PPG signals acquired from wrist-worn wearable devices through transfer learning. The model achieved the aggregate binary classification performance measures of accuracy: 95.50%, sensitivity: 94.50%, and specificity: 96.00% across a five-fold cross-validation strategy on the ECG datasets. It also achieved 95.10% accuracy, 94.60% sensitivity, 95.20% specificity on an unseen PPG dataset. The results show considerable promise towards seamless adaptation of gold-standard ECG trained models for non-ambulatory AF detection with consumer wearable devices through HRV-based knowledge transfer.
Monte Carlo Dropout for Uncertainty Estimation and Motor Imagery Classification
Authors: Milanés-Hermosilla Daily , Trujillo Codorniú Rafael , López-Baracaldo René , Sagaró-Zamora Roberto , Delisle-Rodriguez Denis , Villarejo-Mayor John Jairo , Núñez-Álvarez José Ricardo
Published: November 2021

Motor Imagery (MI)-based Brain–Computer Interfaces (BCIs) have been widely used as an alternative communication channel to patients with severe motor disabilities, achieving high classification accuracy through machine learning techniques. Recently, deep learning techniques have spotlighted the state-of-the-art of MI-based BCIs. These techniques still lack strategies to quantify predictive uncertainty and may produce overconfident predictions. In this work, methods to enhance the performance of existing MI-based BCIs are proposed in order to obtain a more reliable system for real application scenarios. First, the Monte Carlo dropout (MCD) method is proposed on MI deep neural models to improve classification and provide uncertainty estimation. This approach was implemented using Shallow Convolutional Neural Network (SCNN-MCD) and with an ensemble model (E-SCNN-MCD). As another contribution, to discriminate MI task predictions of high uncertainty, a threshold approach is introduced and tested for both SCNN-MCD and E-SCNN-MCD approaches. The BCI Competition IV Databases 2a and 2b were used to evaluate the proposed methods for both subject-specific and non-subject-specific strategies, obtaining encouraging results for MI recognition.
Optimization and Security of Hazardous Waste Incineration Plants with the Use of a Heuristic Algorithm
Authors: Wajda Agata , Jaworski Tomasz
Published: November 2021

The amount of generated waste, which increases every year, is a serious problem of the modern world. In particular, attention should be paid to hazardous waste and methods of its disposal. One of the most used in this context is thermal treatment in dedicated incinerators equipped with a rotary kiln. Conducting the process requires, inter alia, supplying the furnace with a batch of batch material with appropriate parameters. Improper operation in this regard may cause negative environmental effects and operational problems. The key here is to select different types of hazardous waste and compose batch portions. The paper presents an application that optimizes the work of waste incineration plant operators. At the same time, this tool can be described as ensuring security at this stage of the process. The application implements an ant colony algorithm that selects the optimal solution to the problem, which has been formulated here as the types and masses of the batch mixture components with given parameters. The application has been tested in the laboratory and real conditions with satisfactory results.
Pedestrian and Vehicle Detection in Autonomous Vehicle Perception Systems—A Review
Authors: Galvao Luiz G. , Abbod Maysam , Kalganova Tatiana , Palade Vasile , Huda Md Nazmul
Published: November 2021

Autonomous Vehicles (AVs) have the potential to solve many traffic problems, such as accidents, congestion and pollution. However, there are still challenges to overcome, for instance, AVs need to accurately perceive their environment to safely navigate in busy urban scenarios. The aim of this paper is to review recent articles on computer vision techniques that can be used to build an AV perception system. AV perception systems need to accurately detect non-static objects and predict their behaviour, as well as to detect static objects and recognise the information they are providing. This paper, in particular, focuses on the computer vision techniques used to detect pedestrians and vehicles. There have been many papers and reviews on pedestrians and vehicles detection so far. However, most of the past papers only reviewed pedestrian or vehicle detection separately. This review aims to present an overview of the AV systems in general, and then review and investigate several detection computer vision techniques for pedestrians and vehicles. The review concludes that both traditional and Deep Learning (DL) techniques have been used for pedestrian and vehicle detection; however, DL techniques have shown the best results. Although good detection results have been achieved for pedestrians and vehicles, the current algorithms still struggle to detect small, occluded, and truncated objects. In addition, there is limited research on how to improve detection performance in difficult light and weather conditions. Most of the algorithms have been tested on well-recognised datasets such as Caltech and KITTI; however, these datasets have their own limitations. Therefore, this paper recommends that future works should be implemented on more new challenging datasets, such as PIE and BDD100K.
Automated Processing and Phenotype Extraction of Ovine Medical Images Using a Combined Generative Adversarial Network and Computer Vision Pipeline
Authors: Robson James Francis , Denholm Scott John , Coffey Mike
Published: November 2021

The speed and accuracy of phenotype detection from medical images are some of the most important qualities needed for any informed and timely response such as early detection of cancer or detection of desirable phenotypes for animal breeding. To improve both these qualities, the world is leveraging artificial intelligence and machine learning against this challenge. Most recently, deep learning has successfully been applied to the medical field to improve detection accuracies and speed for conditions including cancer and COVID-19. In this study, we applied deep neural networks, in the form of a generative adversarial network (GAN), to perform image-to-image processing steps needed for ovine phenotype analysis from CT scans of sheep. Key phenotypes such as gigot geometry and tissue distribution were determined using a computer vision (CV) pipeline. The results of the image processing using a trained GAN are strikingly similar (a similarity index of 98%) when used on unseen test images. The combined GAN-CV pipeline was able to process and determine the phenotypes at a speed of 0.11 s per medical image compared to approximately 30 min for manual processing. We hope this pipeline represents the first step towards automated phenotype extraction for ovine genetic breeding programmes.
Three-Dimensional Outdoor Analysis of Single Synthetic Building Structures by an Unmanned Flying Agent Using Monocular Vision
Authors: Bielecki Andrzej , Śmigielski Piotr
Published: November 2021

An algorithm designed for analysis and understanding a 3D urban-type environment by an autonomous flying agent, equipped only with a monocular vision, is presented. The algorithm is hierarchical and is based on the structural representation of the analyzed scene. Firstly, the robot observes the scene from a high altitude to build a 2D representation of a single object and a graph representation of the 2D scene. The 3D representation of each object arises as a consequence of the robot’s actions, as a result of which it projects the object’s solid on different planes. The robot assigns the obtained representations to the corresponding vertex of the created graph. The algorithm was tested by using the embodied robot operating on the real scene. The tests showed that the robot equipped with the algorithm was able not only to localize the predefined object, but also to perform safe, collision-free maneuvers close to the structures in the scene.
Water Quality Prediction Method Based on Multi-Source Transfer Learning for Water Environmental IoT System
Authors: Zhou Jian , Wang Jian , Chen Yang , Li Xin , Xie Yong
Published: November 2021

Water environmental Internet of Things (IoT) system, which is composed of multiple monitoring points equipped with various water quality IoT devices, provides the possibility for accurate water quality prediction. In the same water area, water flows and exchanges between multiple monitoring points, resulting in an adjacency effect in the water quality information. However, traditional water quality prediction methods only use the water quality information of one monitoring point, ignoring the information of nearby monitoring points. In this paper, we propose a water quality prediction method based on multi-source transfer learning for a water environmental IoT system, in order to effectively use the water quality information of nearby monitoring points to improve the prediction accuracy. First, a water quality prediction framework based on multi-source transfer learning is constructed. Specifically, the common features in water quality samples of multiple nearby monitoring points and target monitoring points are extracted and then aligned. According to the aligned features of water quality samples, the water quality prediction models based on an echo state network at multiple nearby monitoring points are established with distributed computing, and then the prediction results of distributed water quality prediction models are integrated. Second, the prediction parameters of multi-source transfer learning are optimized. Specifically, the back propagates population deviation based on multiple iterations, reducing the feature alignment bias and the model alignment bias to improve the prediction accuracy. Finally, the proposed method is applied in the actual water quality dataset of Hong Kong. The experimental results demonstrate that the proposed method can make full use of the water quality information of multiple nearby monitoring points to train several water quality prediction models and reduce the prediction bias.
Comparison of Mathematical Methods for Compensating a Current Signal under Current Transformers Saturation Conditions
Authors: Odinaev Ismoil , Gulakhmadov Aminjon , Murzin Pavel , Tavlintsev Alexander , Semenenko Sergey , Kokorin Evgenii , Safaraliev Murodbek , Chen Xi
Published: November 2021

Current measurements from electromagnetic current transformers are essential for the construction of secondary circuit systems, including for protection systems. Magnetic core of these transformers are at risk of saturation, as a result of which maloperation of protection algorithms can possibly occur. The paper considers methods for recovering a current signal in the saturation mode of current transformers. The advantages and disadvantages of methods for detecting the occurrence of current transformers core saturation are described. A comparative analysis of mathematical methods for recovering a current signal is given, their approbation was carried out, and the most promising of them was revealed. The stability and sensitivity of recovery methods were tested by adding white noise to the measured signal and taking into account the initial flux density (remanent magnetization) in the current transformers core. Their comparison is given on the basis of angular, magnitude, and total errors at a given simulation interval.
A Fast Intrusion Detection Method for High-Speed Railway Clearance Based on Low-Cost Embedded GPUs
Authors: Wang Yao , Yu Peizhi
Published: November 2021

The efficiency and the effectiveness of railway intrusion detection are crucial to the safety of railway transportation. Most current methods of railway intrusion detection or obstacle detection are inappropriate for large-scale applications due to their high cost or limited coverage. In this study, we present a fast and low-cost solution to intrusion detection of high-speed railways. As the solution to heavy computational burdens in the current convolutional-neural-network-based detection methods, the proposed method is mainly a novel neural network based on the SSD framework, which includes a feature extractor using an improved MobileNet and a lightweight and efficient feature fusion module. In addition, aiming to improve the detection accuracy of small objects, the feature map weights are introduced through convolution operation to fuse features at different scales. TensorRT is employed to optimize and deploy the proposed network in the low-cost embedded GPU platform, NVIDIA Jetson TX2, to enhance the efficiency. The experimental results show that the proposed methods achieved 89% mAP on the railway intrusion detection dataset, and the average processing time for a single frame was 38.6 ms on the Jetson TX2 module, which satisfies the need of real-time processing.
The Impact of Gender on Entrepreneurial Intention in a Peripheral Region of Europe: A Multigroup Analysis
Authors: Gomes Sofia , Morais Lopes João , Oliveira José , Oliveira Márcio , Santos Tânia , Sousa Marlene
Published: November 2021

The last two decades were characterized by an increase in attention on entrepreneurship. An emerging trend in the literature is associated with the different contributions that genders may have regarding entrepreneurship and entrepreneurial intentions. This paper assesses gender impact on entrepreneurial intention in a peripheral region of Europe. The methodology used is quantitative and based on a sample of 1114 observations. The obtained results suggest that women’s perceived behavioral control does not have a less direct positive impact on entrepreneurial intention when compared to men’s. Women’s personal attitude has a less direct positive impact on entrepreneurial intention when compared to men’s, as social norms, on the one hand, have a less direct positive impact on women’s personal attitude when compared to men’s, and, on the other hand, have a less direct positive impact on women’s perceived behavioral control when compared to men’s. Finally, it was also verified that social norms have a less positive indirect impact through personal attitude and perceived behavioral control on women’s entrepreneurial intention when compared to men’s. These findings contribute to enlighten the literature by strengthening the theoretical framework on women’s entrepreneurial intention in peripheral regions. Regarding practical contributions, suggestions are addressed to public decision makers, universities, and civil society, to adopt practices to increase entrepreneurship in women. This research is original because it is the first to perform this study in Portugal, a peripheral region of Europe.
Objective and Subjective Determinants of Neighborhood Satisfaction in the Context of Retrofitting Suburbs
Authors: Mantey Dorota
Published: November 2021

A strong preference for suburban living has led to extensively developed suburbs that need retrofitting by improving their compactness. However, an attempt to make suburban areas more sustainable only by shaping their spatial form, without considering individual demands and preferences, is usually ineffective. The aim of this research is to better understand the factors that are important for suburban neighborhood satisfaction and to determine the relationship between neighborhood satisfaction and both the objective spatial attributes reflecting different levels of spatial chaos and satisfaction with particular neighborhood characteristics. The factor analysis and a linear multiple regression model have revealed that there are four significant subjective factors explaining neighborhood satisfaction, namely: assessed suburban assets, assessed accessibility, assessed walkability, and assessed mental and social attitude towards the neighborhood. Among these, the assessed accessibility is the most important predictor of the neighborhood satisfaction and synthetic indicator of spatial chaos the least significant one. Although the research proved that subjective measures are more important determinants of neighborhood satisfaction, fighting urban sprawl should be based on the interference in both subjective evaluations and objective spatial attributes, since two of the four subjective factors are likely to be strongly influenced by improving accessibility in the process of retrofitting suburbs.
Thermal Behavior and Energy Efficiency of Modified Concretes in the Tropical Climate: A Systemic Review
Authors: Lee Yeong Huei , Amran Mugahed , Lee Yee Yong , Kueh Ahmad Beng Hong , Kiew Siaw Fui , Fediuk Roman , Vatin Nikolai , Vasilev Yuriy
Published: November 2021

Concrete remains the most utilised construction material for building envelopes, which regulate the indoor temperature to achieve human thermal comfort. Often, the energy consumption for building performance appraisal is related to the thermal behaviour of building materials as heating, ventilation, and air conditioning systems all variously contribute to human comfort. Following the development of concrete technology, many types of concrete have been invented to serve several purposes in the construction industry. To clearly understand the concrete type tailored for the specifics of a construction project, the local climate, concrete mechanical properties, and concrete thermal behaviours should be primarily identified to achieve energy efficiency, which also suits the sustainability of global materials. This paper, therefore, reviews the modified concrete thermal behaviours in the tropical climate for more systematic city planning in order to achieve better energy efficiency. Urban heat islands in the tropics and contributing factors, as well as heat transfer mechanisms, are first highlighted. The requirements of concrete thermal behaviour for building envelopes are then discussed through specific heat capacity, thermal conductivity, thermal diffusivity, time lag, and decrement factor in the context of applications and energy consumption in the tropical regions. With a case study, it is found that concrete thermal behaviours directly affect the energy consumption attributed mainly to the use of cooling systems in the tropics. The study can be a reference to mitigating the urban heat island phenomenon in the planning of urban development.
Participatory Selection of Amaranthus Genotypes in the KwaMbonambi Area, KwaZulu-Natal, South Africa
Authors: Mncwango Ngcebo C. , Van Jaarsveld Corlina M. , Ntuli Nontuthuko R. , Mavengahama Sydney
Published: November 2021

Participatory variety selection (PVS) is a process that tests promising genotypes in farmers’ fields through a close farmer–researcher collaboration approach, which enhances the acceptance of new varieties by farmers. However, limited studies have been conducted to select Amaranthus genotypes that have potential for future breeding programmes in South Africa. Therefore, this study was aimed at selecting Amaranthus genotype(s) that is/are preferred by farmers in northern KwaZulu-Natal, using the PVS approach. Seedlings of fifteen Amaranthus genotypes were each planted in separate 10 × 10 m plots. Fourteen local farmers managed these genotypes and also determined the preferred traits to be used to evaluate them. These traits were: mild taste; profuse stem branching; big and numerous leaves; soft texture; and longer shelf life. Plants at four months after transplant were then evaluated and ranked according to farmers’ preferred traits using score sheets designed on a four-point Likert scale or five-point hedonic scale. However, genotype ACAT seed fair had the best scoring for appealing taste and aroma, and profuse branching. The TOT 8789 genotype had the largest and softest leaves. Again, A. thunbergii had the most numerous leaves of them all. These genotypes are thus recommended for multi-environment testing, seed multiplication, genetic improvement, and promotion for cultivation in South Africa.
Nitrogen Fertilizer Driven Bacterial Community Structure in a Semi-Arid Region of Northeast China
Authors: Wang Meng , Wang Ling , Li Qian , Liu Hang , Lin Yuan , Wang Lichun
Published: November 2021

The soil nitrogen (N) cycle is an essential role of the biogeochemical cycle. Bacteria play an irreplaceable part in the soil N cycle, but the impact of different N gradients on bacterial communities remains unclear. The purpose of this research was to explore the bacterial abundance, community composition, and diversity under different N application rates in a water-limited area. We investigated the bacterial abundance, diversity, community composition, and structure under five different N gradients (0, 90, 150, 210, and 270 kg ha−1) using real-time quantitative PCR and high-throughput sequencing, and then explored bacterial functional groups with FAPROTAX. N application significantly affected bacterial abundance and community composition. Bacterial diversity was enhanced at low N application rates and reduced at higher N application rates. Principal coordinate analysis showed that bacterial community structure was separated into two groups between low N application rates and high N application rates; these differences in bacterial community structure may be driven by available nitrogen (AN). The results of FAPROTAX revealed that N application promoted the functions of Aerobic_nitrite_oxidation, Nitrate_reduction, and Aerobic_ammonia_oxidation, but inhibited the Nitrogen_fixation function of the bacterial community. The high N network caused the reduction of network structure stability. Our results revealed that N fertilizer driven bacterial community structure and soil nutrients were the main influential factors in the variation of bacterial community structure. We suggest that the optimal N application rate in this study may be approximately 150 kg ha−1, based on the variations of soil properties and bacterial community structure in semi-arid areas.
Ethical Banking and Poverty Alleviation Banking: The Two Sides of the Same Solidary Coin
Authors: Valls Martínez María del Carmen , Martín-Cervantes Pedro Antonio , Peña Rodríguez Sandra
Published: November 2021

(1) Background: The growing number of banking entities linked to the field of banking since the 1980s requires a preliminary classification of this sector in order to identify the main stylized facts of this wide conglomerate of institutions oriented to financial sustainability as well as the establishment of an effective differentiation that can objectively distinguish the different types of institutions operating in this subfield of finance. The objective of this research is to obtain a frame of reference by determining the main defining characteristics of these entities and their differentiating elements, by verifying, on an analytical basis, the ways in which they provide a social service in the pursuit of financial inclusion. (2) Methods: A double methodological perspective is used jointly: Factor Analysis and Cluster Analysis. (3) Results: It was possible to delimit two significant groups: Ethical Banks per se and Poverty Alleviation Banks, defining their main differences and analogies. (4) The taxonomy conducted revealed that Ethical Banks per se are primarily established in developed countries, while Poverty Alleviation Banks focus their actions on developing nations. Based on this classification, we establish a series of practical policies that support the future deployment of sustainable banking.
Workspace Integration and Sustainability: Linking the Symbolic and Social Affordances of the Workspace to Employee Wellbeing
Authors: Vilnai-Yavetz Iris , Rafaeli Anat
Published: November 2021

Our goal in this paper is to connect workspace design to employee wellbeing and social sustainability. Toward this connection, we introduce and empirically test a new concept of “workspace integration”. This concept refers to the continuum of integration of an employee’s workspace with the organizational, physical space. We further define three workspace affordances that predict the social sustainability of workspace arrangements by influencing employee wellbeing. The three affordances are perceptions of symbolism of the organization and of symbolism of the self, and opportunities for social interactions. We evaluate our theory using data collected from an online survey of British employees (n = 392) working in offices at home and/or in the organization in various industries. As predicted, workspace integration positively influences wellbeing directly as well as indirectly via mediation of symbolism of the organization and opportunities for social interactions. The third affordance—symbolism of the self—positively influences wellbeing, but is not affected by workspace integration. Our findings confirm the impact of workspace affordances on employee wellbeing, and thus their utility for the analysis of social sustainability. The findings also contribute to understanding of the differences between workspace symbolism of the organization and of the self, their impact on wellbeing, and thus their implications for social sustainability.
Sustainable Approaches for Accelerated Learning
Authors: Glassey Richard , Bälter Olle
Published: November 2021

Sustainable education does not yet have a widely accepted definition in the literature. In this work, we start from the Sustainable Development Goal of Quality Education for All (SDG4) and interpret sustainable education as increasing the quality of learning whilst conserving the resources required to produce and deliver it. From this interpretation, we argue that one path towards realising sustainable education is through the identification of teaching practices that satisfy these conditions of increased quality whilst conserving resources. We present an overview of four case studies, where the conditions for sustainable education are demonstrated through the effective use of people, processes and technologies. Each case represents an intervention that was made to improve the quality of education within an intensive three-month project, which trained immigrants to be employable in the IT industry as junior software developers. Whilst the interventions are independent and unique, they are connected by the themes of quality improvement and resource conservation. In isolation, each specific case produced improvements for both teachers and students; however, it is by combining such approaches that we can start to realise the path towards sustainable education that will help lead to a better quality of education for all. The findings of this work suggest that quality education does not come at the cost of increased resource demands; rather, approaches exist that can be considered to satisfy the conditions for sustainable education.
Assessment of Sustainable Development Goals (SDG) Implementation in Bulgaria and Future Developments
Authors: Ionescu George H. , Jianu Elena , Patrichi Ioana C. , Ghiocel Florin , Țenea Lili , Iancu Delia
Published: November 2021

The European Union has taken on the role of global leader in implementing the Sustainable Development Goals set out in the 2030 Agenda for Sustainable Development, making substantial efforts to support this ambitious approach at the level of each Member State. The objective of this research is the assessment of Sustainable Development Goals (SDG) implementation in Bulgaria as well as the existing development potential, given the existing knowledge gap related to this important topic. The data available on Eurostat were processed using dynamic indices and time series analysis based on ARIMA methodology in order to identify the evolution trend of the main indicators associated with the SDGs, as well as the dynamics of progress. The results obtained estimate, for 2030, a degree of fulfillment of the assumed targets of 36.28%, but also reveal the existence of high potential for accelerating the transition process to a low-carbon economy and a more sustainable and inclusive society.
The Impact of Social Responsibility Performance on the Value Relevance of Financial Data in the Banking Sector: Evidence from Poland
Authors: Bolibok Piotr
Published: November 2021

Corporate social responsibility (CSR) is inevitably becoming an increasingly important part of almost every business. This is particularly true for the banking industry, which suffered substantial losses in reputation and public trust in the aftermath of the global financial crisis. Not surprisingly therefore, banks around the world have visibly intensified their CSR efforts. One of the key dimensions of CSR regards the reliability and transparency of a firm’s communication with the market, which suggests that information disclosed by responsible companies may be more value relevant. The related evidence, especially in the banking sector, is however modest and mixed. The paper aims, therefore, at empirical investigation of the impact of social responsibility performance on the value relevance of financial data in the Polish banking sector. The research employs multivariate regression analysis based on the Ohlson model and the Chow test for structural breaks. The examined sample covers 154 bank-year observations of 17 banks listed on the Warsaw Stock Exchange from 2009–2020. The results suggest that financial disclosures of banks included in CSR indices are generally more value relevant. Additionally, more responsible banks exhibit higher (lower) responsiveness of market values to net earnings (book values of equity) compared to their less socially responsible counterparts.
Wireless Sensor Networks in Agriculture: Insights from Bibliometric Analysis
Authors: Abdollahi Alireza , Rejeb Karim , Rejeb Abderahman , Mostafa Mohamed M. , Zailani Suhaiza
Published: November 2021

This study investigates how wireless sensor network (WSN) applications in agriculture are discussed in the current academic literature. On the basis of bibliometric techniques, 2444 publications were extracted from the Scopus database and analyzed to identify the temporal distribution of WSN research, the most productive journals, the most cited authors, the most influential studies, and the most relevant keywords. The computer program VOSviewer was used to generate the keyword co-occurrence network and partition the pertinent literature. Findings show the remarkable growth of WSN research in recent years. The most relevant journals, cited countries, and influential studies were also identified. The main results from the keyword co-occurrence clustering and the detailed analysis illustrate that WSN is a key enabler for precision agriculture. WSN research also focuses on the role of other technologies such as the Internet of Things, cloud computing, artificial intelligence, and unmanned aerial vehicles in supporting several agriculture activities, including smart irrigation and soil management. This study illuminates researchers’ and practitioners’ views of what has been researched and identifies possible opportunities for future studies. To the authors’ best knowledge, this bibliometric study represents the first attempt to map global WSN research using a comprehensive sample of documents published over nearly three decades.
Consolidating Unorganised Retail Businesses through Digital Platforms: Implications for Achieving the UN Sustainable Development Goals
Authors: Mukherjee Malobi , Wood Jacob
Published: November 2021

This study explores whether digitalisation of unorganised retail (UR) businesses in emerging markets have a positive socio-economic impact on the lives of the subsistence consumer–merchants who perform the dual role of being, not only consumers providing for themselves and their families, but also the managers of micro-UR businesses. We develop a framework for sustainable business model innovation at the base of the pyramid by undertaking content analysis of 65 newspaper articles (interviews with senior executives and founders of digital app-based platforms, expert analysis) as well as social media and YouTube testimonials of UR business users of these platforms in Vietnam, Indonesia, Malaysia and the Philippines. Using the Gioian coding structure of open and second order comparative coding, we develop a framework for sustainable business model innovation from the base of the pyramid perspective. The framework captures how digitalisation enables the UR businesses to circumvent resource shortages, consolidate procurement and inventory management and diversify product and service offerings, which leads to significant economic and social transformation for both the micro enterprises and their owners. Our framework contributes to the emergent literature on sustainable business model innovation and inclusive innovation in resource-constrained contexts. We conclude that sustainable business model innovations enable socio-economic transformations similar to those experienced by UR businesses in emerging Asian markets. Such developments can create local level pathways for local tropical countries seeking to achieve UN social development goals.
Assessment of Climate Change Impacts on the Hydroclimatic Response in Burundi Based on CMIP6 ESMs
Authors: Kim Jeong-Bae , Habimana Jean de Dieu , Kim Seon-Ho , Bae Deg-Hyo
Published: November 2021

Burundi is susceptible to future water-related disasters, but examining the influence of climate change on regional hydroclimatic features is challenging due to a lack of local data and adaptation planning. This study investigated the influence of climate change on hydroclimate-focused changes in the climatology of heavy precipitation (and streamflow) means and extremes based on the multi-model ensemble mean of earth system models in the sixth phase of the Coupled Model Intercomparison Project (CMIP). For runoff analysis, hydrologic responses to future climate conditions were simulated using the Soil and Water Assessment Tool (SWAT) model over the Ruvubu River basin, Burundi. Temperature increases by 5.6 °C, with strong robustness, under future climate conditions. The mean annual precipitation (and runoff) undergoes large seasonal variations, with weak robustness. Precipitation (and streamflow) changes between the wet and dry seasons differ in signal and magnitude. However, alterations in both the amount and frequency of precipitation reveal the intensification of the water cycle due to anthropogenic climate change. Thus, the highest variability in the maximum daily streamflow is shown in months of long wet seasons, especially in the far future (2085). Without considering the regional climate characteristics and shared socioeconomic pathway (SSP) scenarios, this behavior is expected to be enhanced in 2085 (compared with 2045) and increase the severity of extreme precipitation and flood risk. Climate change will cause alterations in the magnitude and seasonal distributions of extreme precipitation (and streamflow). These findings could be important for flood planning and mitigation measures to cope with climate change in Burundi.
MATHEMATICAL MODEL OF THE INTERACTION OF LOW-ENERGY INERT GAS IONS WITH POLYPROPYLENE IN RADIO-FREQUENCY PLASMA OF LOW PRESSURE
Authors: Timoshina Yulia A. , Voznesensky Emil F. , Zheltukhin Victor S.
Published: November 2021

Results of the molecular dynamic simulation of the interaction of low-energy ions (from 10 to 100 eV) with the surface of polypropylene fibrous materials in low pressure radio-frequency (RF) argon plasma is presented. A full-atomic model using the LAMMPS classical molecular dynamics code was made. As a result of numerical calculations, it was found that argon ion bombardment initiates the breaking both of an intermolecular and intramolecular bond of polypropylene with sputtered particles being the hydrocarbon radicals and single atoms. The depth of implantation of the ion is determined, the change in the kinetic energy of the argon atom and the temperature of the simulated cell is obtained.
UNIFICATION OF THE CODING SYSTEM TEXTILE PRODUCTS
Authors: Lysova Marina A. , Dryagina Lyudmila V. , Gruzintseva Natalia A. , Gusev Boris N.
Published: November 2021

One of the ways to achieve the required level of quality of industrial products and, consequently, to increase the competitiveness of Russian manufacturers is to implement the task of optimising and unifying the nomenclature of products produced by enterprises. However, at present, due to the differences in the classification and coding systems of consumer products at the corresponding stages of its life cycle, there are problems with the unification of the nomenclature of industrial products, including textiles, since textile and light industry enterprises, trade organisations and customs authorities each use their own classification of these types of products. The paper analyses the functional capabilities of the currently existing coding systems for textile products, and also proposes and implements a method for matrix coding of textile products on the range of geosynthetic materials produced. In addition, the possibility of combining matrix coding of products with information about its manufacturer and quality in the framework of a two-dimensional barcode is shown.
DEVELOPMENT OF VENTILATED CLOTHING DESIGN PRINCIPLES FOR PROTECTION AGAINST BIOLOGICAL HAZARDS
Authors: Rodicheva Margarita V. , Abramov Anton V. , Gneusheva Elena M.
Published: November 2021

A set of ventilated clothing to protect against tick-borne infections has been proposed. In addition to the protective function, such a kit allows you to normalising the thermal state of a person at elevated temperatures or physical activity by increasing the intensity of heat and moisture removal from the human body. A numerical model of heat transfer in ventilated air layers was developed. With its help, studies of heat and mass transfer were carried out under conditions of maximum activity of blood-sucking insects in the ambient temperature range of 15–25 °C. The balance of the human body was assessed for the level of physical activity typical for representatives of social groups with the highest risk of contracting tick-borne infections. It has been established that an air gap with a thickness of hag = 20 mm in ventilated clothing will provide a standard thermal state of a person and effective protection against infection with tick-borne infections.
A MODEL OF THE WOVEN FABRIC ELONGATION AND BREAKING WITH INFLUENCE OF WARP AND WEFT THREADS UNEVENNESS AND INTERACTION
Authors: Sevostyanov Petr A. , Monakhov Vladimir I. , Samoilova Tatiana A. , Vakhromeeva Ekaterina N. , Zenzinova Julia B.
Published: November 2021

The article presents the results of development of the woven fabric rectangular sample stretching and tearing computer simulation model. The model allows to set the sample size, elastic modulus and tensile breaking strength of the threads, random variations of these indicators for the warp and weft threads along their length. The modelling algorithm provides for the breakage of the warp threads, the redistribution of stress and strain between the sections of the threads and allows to get a detailed picture of the fabric sample deformation dynamics before it breaks. Examples of modelling results, the influence of the interaction degree between two systems of threads on the features of the strain distribution over the sections of the warp threads are given. The similarity of the simulation results according to the developed algorithm and the finite element method is shown, and the advantages of the proposed algorithm are noted.
На пути к общественному развитию. Информационные технологии и ресурсы в высшем образовании
Authors: Хлебников Игорь , Донецкая Наталья
Published: November 2021

The evolution of social relations at the present stage requires teachers
to work in the field of higher education to develop skills in using information
resources and digital technologies. The intensity of communication in the information sphere determines the effectiveness of not only the learning process
at the university, but also to a large extent, the secondary socialization of young
people. The article deals with the problem of using information resources and
digital technologies by teachers of Kazakh universities (on the example of the
Karaganda region). The article is based on the data of a sociological survey
(questionnaire survey) conducted at the universities of the Karaganda region of
Kazakhstan. The purpose of the study was to analyze the effectiveness of the
use of information technologies and resources in the process of studying at
universities and to develop practical recommendations to improve the effectiveness of training. According to the results of the study, it has been found out
that the development of the information and communication sphere in universities is influenced by "objective" and "subjective" factors. The first ones are connected with shortcomings in the technical support of the educational process,
the second ones– with some teachers’ lack of awareness of the modern Internet
resources in their professional field. The identified problems can be overcome
with the help of measures aimed at increasing teachers' awareness of available
information resources, increasing the speed of the Internet connection and general technical equipment of universities
ICT security in tax administration - Rapid7 Nexpose vulnerability analysis
Authors: Muliński Tomasz
Published: November 2021

The article focuses on the subject of IT security in tax administration. This study presents the research on the security level of endpoints, servers, printing devices, network switches and other ICT devices using the Rapid Nexpose vulnerability scanner. We discuss the specifics of security research in public administration resulting from the laws in force in these institutions.
E-SERVICES IN PUBLIC ADMINISTRATION ON THE BASIS OF THE POLISH SOCIAL INSURANCE INSTITUTION (ZUS)
Authors: Owczarczyk Anna , Lazurek Joanna
Published: November 2021

The term e-services refers to such services that are delivered over the Internet and their rendition is frequently automated and remote. Apart from the Internet, an e-service may also be provided by means of mobile devices or television. The wider and wider range of e-services is offered by institutions from the public administration sector, striving to anticipate their client's expectations (especially those of the younger generation) and using the ongoing digitisation process. This study aims at presenting the role and opinions of e-service in one of the social insurance institutions, i.e. Social Insurance Institution (ZUS) where numerous electronic tools have been implemented facilitating work and enabling clients to make contact, get information, send and receive documents. All these efforts are made so as to increase accessibility for prospective and current clients as well as to make the institution's operations more efficient. To achieve this, the following research methods were used: a critical analysis of the literature, an analysis of statistical data, an analysis of survey results (conducted by ZUS), and a survey questionnaire (own research).