Search results for: global innovation network

Authors: Marzena Hajduk-Stelmachowicz

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This paper presents the reasons why the implementation of the organizational eco-innovation (based on requirements of the International Standard ISO 14001) can be an expression of a strategic organization approach. An elaboration about different issues associated with the Environmental Management Systems are given.

Keywords: envionmental management system, ISO 14001, organizational ecoinnovativeness, ecoinnovation

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Authors: Tian Lan, Yixiang Wang, Wenxin Tai, Yilan Lyu, Zufeng Wu

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The monaural speech enhancement task in the time-frequencydomain has a myriad of approaches, with the stacked con-volutional neural network (CNN) demonstrating superiorability in feature extraction and selection. However, usingstacked single convolutions method limits feature represen-tation capability and generalization ability. In order to solvethe aforementioned problem, we propose an attention-basedadaptive convolutional network that integrates the multi-scale convolutional operations into a operation-specific blockvia input dependent attention to adapt to complex auditoryscenes. In addition, we introduce a two-stage progressivelearning method to enlarge the receptive field without a dra-matic increase in computation burden. We conduct a series ofexperiments based on the TIMIT corpus, and the experimen-tal results prove that our proposed model is better than thestate-of-art models on all metrics.

Keywords: speech enhancement, adaptive convolu-tion, progressive learning, time-frequency domain

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Authors: Aynur Charkasova

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The scientific and economic contributions of international students cannot be underestimated. International education continues to be a competitive global industry, and many countries are seeking to recruit the best and the brightest to reinforce scientific innovations, boost intercultural learning, and bring more funding to universities and colleges. Substantial changes in international educational policies and migration regulations have been made in the hopes of recruiting global talent. This paper explores and compares recruitment strategies, employment opportunities, and a legal path to permanent residency policies related to international students in the United States of America and Canada. This study will utilize the legal information available from the government websites of both countries and peer-reviewed scholarly articles and will highlight which approach promises a better path in recruiting and retention of international students. The findings from the study will be discussed and recommendations will be provided.

Keywords: International students, current immigration policies, STEM, employability, visa reforms for international students, Canadian recruitment policy

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Authors: Alexander N. Pisarchik, Parth Chholak

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Intrinsic brain noise was estimated via magneto-encephalograms (MEG) recorded during perception of flickering visual stimuli with frequencies of 6.67 and 8.57 Hz. First, we measured the mean phase difference between the flicker signal and steady-state event-related field (SSERF) in the occipital area where the brain response at the flicker frequencies and their harmonics appeared in the power spectrum. Then, we calculated the probability distribution of the phase fluctuations in the regions of frequency locking and computed its kurtosis. Since kurtosis is a measure of the distribution’s sharpness, we suppose that inverse kurtosis is related to intrinsic brain noise. In our experiments, the kurtosis value varied among subjects from K = 3 to K = 5 for 6.67 Hz and from 2.6 to 4 for 8.57 Hz. The majority of subjects demonstrated leptokurtic kurtosis (K < 3), i.e., the distribution tails approached zero more slowly than Gaussian. In addition, we found a strong correlation between kurtosis and brain complexity measured as the correlation dimension, so that the MEGs of subjects with higher kurtosis exhibited lower complexity. The obtained results are discussed in the framework of nonlinear dynamics and complex network theories. Specifically, in a network of coupled oscillators, phase synchronization is mainly determined by two antagonistic factors, noise, and the coupling strength. While noise worsens phase synchronization, the coupling improves it. If we assume that each neuron and each synapse contribute to brain noise, the larger neuronal network should have stronger noise, and therefore phase synchronization should be worse, that results in smaller kurtosis. The described method for brain noise estimation can be useful for diagnostics of some brain pathologies associated with abnormal brain noise.

Keywords: brain, flickering, magnetoencephalography, MEG, visual perception, perception time

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Authors: Yili Wang, Jarmo Kinos, Tuire Palonen, Tarja-Riitta Hurme

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This longitudinal study aims to examine how five- and six-year-old children’s peer relationships are formed and fostered during one preschool year in a southwestern Finnish preschool. All 16 kindergarteners participated in the study (at dyad level N=240; i.e., 16 x 15 relationships among the children). The children were divided into four daily groups, based on the table order during the daily routines, and four intervention groups, based on the teachers’ pedagogical plan. During the intervention, one iPad was given to each group in order to stimulate interaction among peers and, thus, enable the children to form new peer relationships. In the data gathering, sociometric nomination techniques were used to investigate the nature (i.e., stability and mutuality) of the peer relationships. The data was collected five times during the year to see what kind of peer relationship changes occurred at the dyad level and the group level, i.e., in establishing and losing friendship ties among the children. Social network analyses were used to analyze the data. The results indicate that the children’s preference for gender segregation was strong compared to age preference and intervention. In all, the number of reciprocal friendship ties and the mutual absence of friendship ties increased towards the end of the year, whereas the number of unilateral friendship ties decreased. This indicates that children’s nominations narrow down; thus, the group structure becomes more crystalized. Instead of extending their friendship networks, children seek stable and mutual relationships with their peers in their middle childhood years. The intervention only had a slightly negative influence on children’s peer relationships.

Keywords: intervention study, peer relationship, preschool education, social network analysis, sociometric ratings

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Authors: Mihai Rebenciuc, Simona Mihaela Bibic

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Branch of modern mathematics, graphs represent instruments for optimization and solving practical applications in various fields such as economic networks, engineering, network optimization, the geometry of social action, generally, complex systems including contemporary urban problems (path or transport efficiencies, biourbanism, & c.). In this paper is studied the interconnection of some urban network, which can lead to a simulation problem of a digraph through another digraph. The simulation is made univoc or more general multivoc. The concepts of fragment and atom are very useful in the study of connectivity in the digraph that is simulation - including an alternative evaluation of k- connectivity. Rough set approach in (bi)digraph which is proposed in premier in this paper contribute to improved significantly the evaluation of k-connectivity. This rough set approach is based on generalized rough sets - basic facts are presented in this paper.

Keywords: (bi)digraphs, rough set theory, systems of interacting agents, complex systems

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Authors: Ahmed ElAzab, Amira M. Idrees, Mahmoud A. Mahmoud, Hesham Hefny

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Social networking sites such as Twitter and Facebook attracts over 500 million users across the world, for those users, their social life, even their practical life, has become interrelated. Their interaction with social networking has affected their life forever. Accordingly, social networking sites have become among the main channels that are responsible for vast dissemination of different kinds of information during real time events. This popularity in Social networking has led to different problems including the possibility of exposing incorrect information to their users through fake accounts which results to the spread of malicious content during life events. This situation can result to a huge damage in the real world to the society in general including citizens, business entities, and others. In this paper, we present a classification method for detecting fake accounts on Twitter. The study determines the minimized set of the main factors that influence the detection of the fake accounts on Twitter, then the determined factors have been applied using different classification techniques, a comparison of the results for these techniques has been performed and the most accurate algorithm is selected according to the accuracy of the results. The study has been compared with different recent research in the same area, this comparison has proved the accuracy of the proposed study. We claim that this study can be continuously applied on Twitter social network to automatically detect the fake accounts, moreover, the study can be applied on different Social network sites such as Facebook with minor changes according to the nature of the social network which are discussed in this paper.

Keywords: fake accounts detection, classification algorithms, twitter accounts analysis, features based techniques

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Authors: Glykeria A. Visvini, George Ν. Mathioudakis, Amaia Soto Beobide, George A. Voyiatzis

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Polymer nanocomposites are materials in which a polymer matrix is reinforced with nanoscale inclusions, such as nanoparticles, nanoplates, or nanofibers. These nanoscale inclusions can significantly enhance the mechanical, thermal, electrical, and other properties of the polymer matrix, making them attractive for a wide range of industrial applications. These properties can be tailored by adjusting the type and the concentration of the nanoinclusions, which provides a high degree of flexibility in their design and development. An important property that polymeric membranes can exhibit is water vapor permeability (WVP). This can be accomplished by various methods, including the incorporation of micro/nano-fillers into the polymer matrix. In this way, a micro/nano-pore network can be formed, allowing water vapor to permeate through the membrane. At the same time, the membrane can be stretched uni- or bi-axially, creating aligned or cross-linked micropores in the composite, respectively, which can also increase the WVP. Nowadays, in industry, stretched films reinforced with CaCO3 develop micro-porosity sufficient to give them breathability characteristics. Carbon-based nanomaterials, such as graphene oxide (GO), are tentatively expected to be able to effectively improve the WVP of corresponding composite polymer films. The presence in the GO structure of various functional oxidizing groups enhances its ability to attract and channel water molecules, exploiting the unique large surface area of graphene that allows the rapid transport of water molecules. Polypropylene (PP) is widely used in various industrial applications due to its desirable properties, including good chemical resistance, excellent thermal stability, low cost, and easy processability. The specific properties of PP are highly influenced by its crystalline behavior, which is determined by its processing conditions. The development of the β-crystalline phase in PP, in combination with stretching, is anticipating improving the microporosity of the polymer matrix, thereby enhancing its WVP. The aim of present study is to create breathable PP composite membranes using carbon-based nanomaterials, such as graphene oxide (GO), reduced graphene oxide (rGO), and graphene nanoplatelets (GNPs). Unlike traditional methods that rely on the drawing process to enhance the WVP of PP, this study intents to develop a low-cost approach using melt mixing with β-nucleating agents and carbon fillers to create highly breathable PP composite membranes. The study aims to investigate how the concentration of these additives affects the water vapor transport properties of the resulting PP films/membranes. The presence of β-nucleating agents and carbon fillers is expected to enhance β-phase growth in PP, while an alternation between β- and α-phase is expected to lead to improved microporosity and WVP. Our ambition is to develop highly breathable PP composite films with superior performance and at a lower cost compared to the benchmark. Acknowledgment: This research has been co‐financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call «Special Actions "AQUACULTURE"-"INDUSTRIAL MATERIALS"-"OPEN INNOVATION IN CULTURE"» (project code: Τ6YBP-00337)

Keywords: carbon based nanomaterials, nanocomposites, nucleating agent, polypropylene, water vapor permeability

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Authors: Jonathan Adeyemi

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Context: This study focuses on El Anatsui and his global acclaim in the art world despite his origins from the global artworld’s margins. It addresses the disparities in the treatment between Western and non-Western artists and questions whether Anatsui’s consecration is a result of exoticism or the growing consensus on decolonization. Research Aim: The aim of this study is to investigate how El Anatsui achieved global acclaim from the margins of the art world and determine if his consecration represents a mark of decolonization or the typical Western desire for exoticism. Methodology: The study utilizes a case study approach, literature analysis, and in-depth interviews. The artist, the organizers of the Venice Biennale, the relevant curators at Tate Modern London, and the October Gallery in London, and other galleries in Nigeria, which represent the artist were interviewed for data collection. Findings: The study seeks to determine the authenticity of the growing consensus on decolonization, inclusion, and diversity in the global artistic field. Preliminary findings show that domestic socio-economic and political factors debilitated the mechanisms for local validation in Nigeria, weakening the domestic foundation for international engagement. However, alternative systems of exhibition, especially in London and the USA contributed critically to providing the initial international visibility, which formed the foundation for his global acclaim. Out of the 21 winners of the Golden Lion for Lifetime Achievement since its inception at the 47th Venice Biennale in 1997, American artists have dominated with 10 recipients, 8 recipients from Europe, 2 recipients from Africa (2007 and 2015) and 1 from Asia. This aligns with Bourdieu’s concept of cultural and economic capital, which prevented Africa countries from participation until recently. Moreover, while the average age of recipients is 76 years, Anatsui received the award at the age of 71, while Malick Sidibé (Mali) was awarded at 72. Thus, the Venice Biennale award for El Anatsui incline more towards a commitment to decolonisation than exoticism. Theoretical Importance: This study contributes to the field by examining the dynamics of the art world's monopoly of legitimation and the role of national, ethnicity and cultural differences in the promotion of artists. It aims to challenge the Westernized hierarchy of valorization and consecration in the art world. The research supports Bourdieu’s artistic field theory, which emphasises the importance of cultural, economic and social capital in determining agents’ position and access to the field resources (symbolic capital). Bourdieu also established that dominated agents can change their position in the field’s hierarchy either by establishing or navigating alternative systems. Data Collection and Analysis Procedures: The opacity of art world’s operations places the required information within the purview of the insiders (agents). Thus, the study collects data through in-depth interviews with relevant and purposively selected individuals and organizations. The data was/will be analyzed using qualitative methods, such as thematic analysis and content analysis. The interpretive analytical approach adopted facilitated the construction of meanings that may not be apparent in the data or responses. Questions Addressed: The study addresses how El Anatsui achieved global acclaim despite being from the margins, whether his consecration represents decolonization or exoticism, and the extent to which the global artistic field embraces decolonization, inclusion, and diversity. Conclusion: The study will contribute to knowledge by providing insights into the extent of commitment to decolonization, inclusion, and diversity in the global artistic field. It also shed light on the mechanisms behind El Anatsui's rise to global acclaim and challenge Western-dominated artistic hierarchies.

Keywords: decolonisation, exorticism, artistic field, culture game

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Authors: Emilia Segun-Ajao

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The strategy of relocation aimed at strengthening supply chain resilience in the United States is examined, taking into account recent global disturbances and vulnerabilities in offshore manufacturing. It explains the procurement challenges faced by enterprises and offers solutions to mitigate risks and improve resilience. Through the analysis of innovative approaches, including technological advances, policy considerations, and strategic frameworks, this study provides insights to decision-makers about the complexity of supply chain management. Reshoring has gained attention as a strategy to improve supply chain resilience in the face of global disruptions. This analysis focuses on the importance of relocating as a multifaceted approach to strengthening supply chains, advocating economic benefits, technological advances, and policy frameworks to create a more robust supply landscape in the United States.

Keywords: collaborative partnerships, supply chain resilience, procurement challenges, technology adoption

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Authors: Aleksandra Szunke

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Monetary policy is an important pillar of the economy, directly affecting on the condition of banking sector. Depending on the strategy may both support functioning of banking institutions, as well as limit their excessively risky activities. The literature studies indicate a large number of publications, which include characteristics of initiatives, implemented by central banks during the global financial crisis and the potential effects of the use of non-standard monetary policy instruments. However, the empirical evidence about their effects and real consequences for the financial markets are still not final. Even before the escalation of instability, Bernanke, Reinhart, and Sack (2004) analyzed the effectiveness of various unconventional monetary tools in lowering long-term interest rates in the United States and Japan. The obtained results largely confirmed the effectiveness of the zero-interest-rate policy and Quantitative Easing (QE) in achieving the goal of reducing long-term interest rates. Japan, considered as the precursor of QE policy, also conducted research about the consequences of non-standard instruments, implemented to restore financial stability of the country. Although the literature about the effectiveness of Quantitative Easing in Japan is extensive, it does not uniquely specify whether it brought permanent effects. The main aim of the study is to identify the implications of non-standard monetary policy, implemented by selected central banks (the Federal Reserve System, Bank of England and European Central Bank), paying particular attention to the consequences into three areas: the size of money supply, financial markets, and the real economy.

Keywords: consequences of modern monetary policy, quantitative easing policy, banking sector instability, global financial crisis

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Authors: Akumeyam Elvis Akum, Njanjo Thecla Anyongo Mukete, Fonkeng George Epah

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Globally, the goal of higher education is to enhance graduate employability skills. Paradoxically, Cameroon’s graduate employability rate is far below the graduation rate. This worrisome situation caused the researcher to hypothesize that the teaching and learning experiences account for this increasing disparity. The study sought to investigate the effect on graduate employability of the teaching of organizational, problem-solving, innovation, and risk management skills on graduate employability. The study adopted a descriptive survey design with a quantitative approach. Data was collected by quantitative techniques from a random sample of 385 graduates using closed-ended structured questionnaire. Generally, findings revealed that entrepreneurship education does not sufficiently enhance graduate employability in the University of Buea. Specifically, the teaching of organizational skills does not significantly enhance their employability, as an average of 55% of graduates indicated that the course did not sufficiently help them develop skills for planning, management of limited resources, collaboration, and the setting of priorities. Also, 60% of the respondents indicated that the teaching of problem-solving skills does not significantly enhance graduate employability at the University of Buea. Contrarily, 57% of the respondents agreed that through their experiences in entrepreneurship education, their innovation skills were improved. The study recommended that a practical approach to teaching should be adopted, with attention to societal needs. A framework to ensure the teaching of entrepreneurship to students at the undergraduate level is recommended, such that those who do not continue with university studies after their Bachelor’s degree would have acquired the needed skills for employability.

Keywords: employability, entrepreneurship education, graduate, innovative skills, organizational skills, problem-solving skills, risk management skills

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Authors: Bokkasam Sasidhar, Ibrahim Aljasser

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The problem of finding optimal schedules for each equipment in a production process is considered, which consists of a single stage of manufacturing and which can handle different types of products, where changeover for handling one type of product to the other type incurs certain costs. The machine capacity is determined by the upper limit for the quantity that can be processed for each of the products in a set up. The changeover costs increase with the number of set ups and hence to minimize the costs associated with the product changeover, the planning should be such that similar types of products should be processed successively so that the total number of changeovers and in turn the associated set up costs are minimized. The problem of cost minimization is equivalent to the problem of minimizing the number of set ups or equivalently maximizing the capacity utilization in between every set up or maximizing the total capacity utilization. Further, the production is usually planned against customers’ orders, and generally different customers’ orders are assigned one of the two priorities – “normal” or “priority” order. The problem of production planning in such a situation can be formulated into a Multiple Arc Network (MAN) model and can be solved sequentially using the algorithm for maximizing flow along a MAN and the algorithm for maximizing flow along a MAN with priority arcs. The model aims to provide optimal production schedule with an objective of maximizing capacity utilization, so that the customer-wise delivery schedules are fulfilled, keeping in view the customer priorities. Algorithms have been presented for solving the MAN formulation of the production planning with customer priorities. The application of the model is demonstrated through numerical examples.

Keywords: scheduling, maximal flow problem, multiple arc network model, optimization

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Authors: Xiaocong Liu, Huazhen Wang, Ting He, Xiaozheng Li, Weihan Zhang, Jian Chen

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The utilization of electronic medical record (EMR) data to establish the disease diagnosis model has become an important research content of biomedical informatics. Deep learning can automatically extract features from the massive data, which brings about breakthroughs in the study of EMR data. The challenge is that deep learning lacks semantic knowledge, which leads to impracticability in medical science. This research proposes a method of incorporating lexical-semantic knowledge from abundant entities into a convolutional neural network (CNN) framework for pediatric disease diagnosis. Firstly, medical terms are vectorized into Lexical Semantic Vectors (LSV), which are concatenated with the embedded word vectors of word2vec to enrich the feature representation. Secondly, the semantic distribution of medical terms serves as Semantic Decision Guide (SDG) for the optimization of deep learning models. The study evaluate the performance of LSV-SDG-CNN model on four kinds of Chinese EMR datasets. Additionally, CNN, LSV-CNN, and SDG-CNN are designed as baseline models for comparison. The experimental results show that LSV-SDG-CNN model outperforms baseline models on four kinds of Chinese EMR datasets. The best configuration of the model yielded an F1 score of 86.20%. The results clearly demonstrate that CNN has been effectively guided and optimized by lexical-semantic knowledge, and LSV-SDG-CNN model improves the disease classification accuracy with a clear margin.

Keywords: convolutional neural network, electronic medical record, feature representation, lexical semantics, semantic decision

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Authors: Harold Mauricio Díaz-Vargas, Cristian Alfonso Jimenez-Castaño, David Augusto Cárdenas-Peña, Guillermo Alberto Ortiz-Gómez, Alvaro Angel Orozco-Gutierrez

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Precise identification of the nerves is a crucial task performed by anesthesiologists for an effective Peripheral Nerve Blocking (PNB). Now, anesthesiologists use ultrasound imaging equipment to guide the PNB and detect nervous structures. However, visual identification of the nerves from ultrasound images is difficult, even for trained specialists, due to artifacts and low contrast. The recent advances in deep learning make neural networks a potential tool for accurate nerve segmentation systems, so addressing the above issues from raw data. The most widely spread U-Net network yields pixel-by-pixel segmentation by encoding the input image and decoding the attained feature vector into a semantic image. This work proposes a conditioning approach and encoder pre-training to enhance the nerve segmentation of traditional U-Nets. Conditioning is achieved by the one-hot encoding of the kind of target nerve a the network input, while the pre-training considers five well-known deep networks for image classification. The proposed approach is tested in a collection of 619 US images, where the best C-UNet architecture yields an 81% Dice coefficient, outperforming the 74% of the best traditional U-Net. Results prove that pre-trained models with the conditional approach outperform their equivalent baseline by supporting learning new features and enriching the discriminant capability of the tested networks.

Keywords: nerve segmentation, U-Net, deep learning, ultrasound imaging, peripheral nerve blocking

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Authors: Gagandeep Singh, Navdeep Singh

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Due to limited battery of sensor nodes, so energy efficiency found to be main constraint in WSN. Therefore the main focus of the present work is to find the ways to minimize the energy consumption problem and will results; enhancement in the network stability period and life time. Many researchers have proposed different kind of the protocols to enhance the network lifetime further. This paper has evaluated the issues which have been neglected in the field of the WSNs. WSNs are composed of multiple unattended ultra-small, limited-power sensor nodes. Sensor nodes are deployed randomly in the area of interest. Sensor nodes have limited processing, wireless communication and power resource capabilities Sensor nodes send sensed data to sink or Base Station (BS). I-LEACH gives adaptive clustering mechanism which very efficiently deals with energy conservations. This paper ends up with the shortcomings of various adaptive clustering based WSNs protocols.

Keywords: WSN, I-Leach, MATLAB, sensor

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Authors: Carmen E. Zapata, Juan Bautista, Olga Montoya, Claudia Moreno, Marisol Suarez, Alejandra Betancur, Duvan Nanclares, Natalia A. Cano

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This study aims to evaluate the diversity of microorganisms in filters PM2.5 and PM10; and determine the genotoxic and cytotoxic activity of the complex mixture present in PM2.5 filters used in the Aburrá Valley Air Quality Monitoring Network (Colombia). The research results indicate that particulate matter PM2.5 of different monitoring stations are bacteria; however, this study of detection of bacteria and their phylogenetic relationship is not complete evidence to connect the microorganisms with pathogenic or degrading activities of compounds present in the air. Additionally, it was demonstrated the damage induced by the particulate material in the cell membrane, lysosomal and endosomal membrane and in the mitochondrial metabolism; this damage was independent of the PM2.5 concentrations in almost all the cases.

Keywords: cytotoxic, genotoxic, microbiological analysis, PM10, PM2.5

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Authors: Biao Hu, Mark E. J. Cutler, Alexandra C. Morel

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Global climate change introduces both opportunities and challenges for crop productivity, with differences in temperature stress across latitudes and crop types, one of the most important meteorological factors impacting crop productivity. The development and productivity of crops are particularly impacted when temperatures occur outwith their preferred ranges, which has implications for global agri-food sector. This study investigated the spatiotemporal dynamics of heat stress and cold stress on UK arable lands for rapeseed cropping between 1961 and 2020, using a 1 km spatial resolution temperature dataset. Stress indices, including heat stress index (fHS) defined as the ratio of “Tmax - Tcrit_h” to “Tlimit_h - Tcrit_h” where Tmax, Tcrit_h and Tlimit_h represent the daily maximum temperature (°C), critical high temperature threshold (°C) and limiting high temperature threshold (°C) of rapeseed crop respectively; cold degree days (CDD) as the difference between daily Tmin (minimum temperature) and Tcrit_l (critical low temperature threshold); and a normalized rapeseed production loss index (fRPL) as the product of fHS and attainable rapeseed yield in the same land pixel were established. The values of fHS and CDD, percentages of days experiencing each stress and fRPL were investigated. Results found increasing fHS and the areas impacted by heat stress during flowering (from April to May) and reproductive (from April to July) stages over time, with the mean fHS being negatively correlated with latitude. This pattern of increased heat stress agrees with previous research on rapeseed cropping, which have been noted at global scale in response to changes in climate. The decreasing number of CDD and frequency of cold stress suggest cold stress decreased during flowering, vegetative (from September to March next year) and reproductive stages, and the magnitude of cold stress in the south of the UK was smaller to that compared to northern regions over the studied periods. The decreasing CDD matches observed declining cold stress of global rapeseed and of other crops such as rice in the northern hemisphere. Notably, compared with previous studies which mainly tracked the trends of heat stress and cold stress individually, this study conducted a comparative analysis of the rate of their changes and found heat stress of rapeseed crops in the UK was increasing at a faster rate than cold stress, which was seen to decrease during flowering. The increasing values of fRPL, with statistically significant differences (p < 0.05) between regions of the UK, suggested an increasing loss in rapeseed due to heat stress in the studied period. The largest increasing trend in heat stress was observed in South-eastern England, where a decreasing cold stress was taking place. While the present study observed a relatively slowly increasing heat stress, there is a worrying trend of increasing heat stress for rapeseed cropping into the future, as the cases of other main rapeseed cropping systems in the northern hemisphere including China, European counties, the US, and Canada. This study demonstrates the negative impact of global warming on rapeseed cropping, highlighting the adaptation and mitigations strategies for sustainable rapeseed cultivation across the globe.

Keywords: rapeseed, UK, heat stress, cold stress, global climate change, spatiotemporal analysis, production loss index

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Authors: John Bankier

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Development of academic English occurs not only in communities of practice but also within wider social networks, referred to by Zappa-Hollman and Duff as individual networks of practice. Such networks may exist whether students are developing academic English in English-dominant contexts or in contexts in which English is not a majority language. As yet, little research has examined how newcomers to universities interact with a variety of social ties in such networks to receive academic and emotional support as they develop the academic English necessary to succeed in local and global academia. The one-year ethnographic study described in this presentation followed five Japanese university students enrolled on an academic English program in their home country. We graphically represent participants’ individual networks of practice related to academic English and display the role of interaction in these networks to socialization. Specific examples of academic practices will be linked to specific instances of social interaction. Interaction supportive of the development of academic practices often occurred during unplanned interactions outside the classroom and among small groups of close friends who were connected to each other in more than one way, such as those taking multiple classes together. These interactions occurred in study spaces, in hallways between class periods, at lunchtimes, and online. However, constraints such as differing accommodation arrangements, class scheduling and the hierarchical levelling of English classes by test scores discouraged some participants both from forming strong ties related to English and from interacting with existing ties. The presentation will briefly describe ways in which teachers in all contexts can maximise interaction outside the classroom.

Keywords: academic, english, practice, network

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Authors: Vishnu Pratap Singh Kirar

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Artificial Neural Network (ANN) can be trained using backpropagation (BP). It is the most widely used algorithm for supervised learning with multi-layered feed-forward networks. Efficient learning by the BP algorithm is required for many practical applications. The BP algorithm calculates the weight changes of artificial neural networks, and a common approach is to use a two-term algorithm consisting of a learning rate (LR) and a momentum factor (MF). The major drawbacks of the two-term BP learning algorithm are the problems of local minima and slow convergence speeds, which limit the scope for real-time applications. Recently the addition of an extra term, called a proportional factor (PF), to the two-term BP algorithm was proposed. The third increases the speed of the BP algorithm. However, the PF term also reduces the convergence of the BP algorithm, and criteria for evaluating convergence are required to facilitate the application of the three terms BP algorithm. Although these two seem to be closely related, as described later, we summarize various improvements to overcome the drawbacks. Here we compare the different methods of convergence of the new three-term BP algorithm.

Keywords: neural network, backpropagation, local minima, fast convergence rate

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Authors: Arslan Murtaza

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RFID (Radio Frequency Identification) and WSN (Wireless sensor network) are two significant wireless technologies that have extensive diversity of applications and provide limitless forthcoming potentials. RFID is used to identify existence and location of objects whereas WSN is used to intellect and monitor the environment. Incorporating RFID with WSN not only provides identity and location of an object but also provides information regarding the condition of the object carrying the sensors enabled RFID tag. It can be widely used in stock management, asset tracking, asset counting, security, military, environmental monitoring and forecasting, healthcare, intelligent home, intelligent transport vehicles, warehouse management, and precision agriculture. This assessment presents a brief introduction of RFID, WSN, and integration of WSN and RFID, and then applications related to both RFID and WSN. This assessment also deliberates status of the projects on RFID technology carried out in different computing group projects to be taken on WSN and RFID technology.

Keywords: wireless sensor network, RFID, embedded sensor, Wi-Fi, Bluetooth, integration, time saving, cost efficient

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Authors: A. Baloglu, A. Çınar

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Different tools and technologies were implemented for Crisis Response and Management (CRM) which is generally using available network infrastructure for information exchange. Depending on type of disaster or crisis, network infrastructure could be affected and it could not be able to provide reliable connectivity. Thus any tool or technology that depends on the connectivity could not be able to fulfill its functionalities. As a solution, a new message exchange framework has been developed. Framework provides offline/online information exchange platform for CRM Information Systems (CRMIS) and it uses XML compression and packet prioritization algorithms and is based on open source web technologies. By introducing offline capabilities to the web technologies, framework will be able to perform message exchange on unreliable networks. The experiments done on the simulation environment provide promising results on low bandwidth networks (56kbps and 28.8 kbps) with up to 50% packet loss and the solution is to successfully transfer all the information on these low quality networks where the traditional 2 and 3 tier applications failed.

Keywords: crisis response and management, XML messaging, web services, XML compression, mining

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Authors: Sadaf Sahar, Usman Qamar, Sadaf Ayaz

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In the software development lifecycle, the quality prediction techniques hold a prime importance in order to minimize future design errors and expensive maintenance. There are many techniques proposed by various researchers, but with the increasing complexity of the software lifecycle model, it is crucial to develop a flexible system which can cater for the factors which in result have an impact on the quality of the end product. These factors include properties of the software development process and the product along with its operation conditions. In this paper, a neural network (perceptron) based software quality prediction technique is proposed. Using this technique, the stakeholders can predict the quality of the resulting software during the early phases of the lifecycle saving time and resources on future elimination of design errors and costly maintenance. This technique can be brought into practical use using successful training.

Keywords: software quality, fuzzy logic, perception, prediction

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Authors: Noradin Ghadimi, Nima Amjady, Oveis Abedinia, Roza Poursoleiman

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This paper proposed an Improved Honey Bee Mating Optimization (IHBMO) for a planning paradigm for network upgrade. The proposed technique is a new meta-heuristic algorithm which inspired by mating of the honey bee. The paradigm is able to select amongst several choices equi-cost one assuring the optimum in terms of voltage profile, considering various scenarios of DG penetration and load demand. The distributed generation (DG) has created a challenge and an opportunity for developing various novel technologies in power generation. DG prepares a multitude of services to utilities and consumers, containing standby generation, peaks chopping sufficiency, base load generation. The proposed algorithm is applied over the 30 lines, 28 buses power system. The achieved results demonstrate the good efficiency of the DG using the proposed technique in different scenarios.

Keywords: distributed generation, IHBMO, renewable energy units, network upgrade

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Authors: Mayada Al Meghari

Abstract:

Recently, grid middlewares have provided large integrated use of network resources as the shared data and the CPU to become a virtual supercomputer. In this work, we present the design and implementation of the middleware for Data Warehouse Signature, DWS Framework. The aim of using the middleware in our DWS framework is to achieve the high performance by the parallel computing. This middleware is developed on Alchemi.Net framework to increase the security among the network nodes through the authentication and group-key distribution model. This model achieves the key security and prevents any intermediate attacks in the middleware. This paper presents the flow process structures of the middleware design. In addition, the paper ensures the implementation of security for DWS middleware enhancement with the authentication and group-key distribution model. Finally, from the analysis of other middleware approaches, the developed middleware of DWS framework is the optimal solution of a complete covering of security issues.

Keywords: middleware, parallel computing, data warehouse, security, group-key, high performance

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Authors: Beata Zima, Octavio A. Márquez Reyes, Masoud Mohammadgholiha, Jochen Moll, Luca de Marchi

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This document presents the approach of using compressed sensing in signal encoding and information transferring within a guided wave sensor network, comprised of specially designed frequency steerable acoustic transducers (FSATs). Wave propagation in a damaged plate was simulated using commercial FEM-based software COMSOL. Guided waves were excited by means of FSATs, characterized by the special shape of its electrodes, and modeled using PIC255 piezoelectric material. The special shape of the FSAT, allows for focusing wave energy in a certain direction, accordingly to the frequency components of its actuation signal, which makes available a larger monitored area. The process begins when a FSAT detects and records reflection from damage in the structure, this signal is then encoded and prepared for transmission, using a combined approach, based on Compressed Sensing Matching Pursuit and Quadrature Amplitude Modulation (QAM). After codification of the signal is in binary chars the information is transmitted between the nodes in the network. The message reaches the last node, where it is finally decoded and processed, to be used for damage detection and localization purposes. The main aim of the investigation is to determine the location of detected damage using reconstructed signals. The study demonstrates that the special steerable capabilities of FSATs, not only facilitate the detection of damage but also permit transmitting the damage information to a chosen area in a specific direction of the investigated structure.

Keywords: data compression, ultrasonic communication, guided waves, FEM analysis

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Authors: Shubham Gogri, Lucia Florescu

Abstract:

Medical diagnosis and radiotherapy treatment planning using Computed Tomography (CT) rely on the quantitative accuracy and quality of the CT images. At the same time, requirements for CT imaging include reducing the radiation dose exposure to patients and minimizing scanning time. A solution to this is the sparse-view CT technique, based on a reduced number of projection views. This, however, introduces a new problem— the incomplete projection data results in lower quality of the reconstructed images. To tackle this issue, deep learning methods have been applied to enhance the quality of the sparse-view CT images. A first approach involved employing Mir-Net, a dedicated deep neural network designed for image enhancement. This showed promise, utilizing an intricate architecture comprising encoder and decoder networks, along with the incorporation of the Charbonnier Loss. However, this approach was computationally demanding. Subsequently, a specialized Generative Adversarial Network (GAN) architecture, rooted in the Pix2Pix framework, was implemented. This GAN framework involves a U-Net-based Generator and a Discriminator based on Convolutional Neural Networks. To bolster the GAN's performance, both Charbonnier and Wasserstein loss functions were introduced, collectively focusing on capturing minute details while ensuring training stability. The integration of the perceptual loss, calculated based on feature vectors extracted from the VGG16 network pretrained on the ImageNet dataset, further enhanced the network's ability to synthesize relevant images. A series of comprehensive experiments with clinical CT data were conducted, exploring various GAN loss functions, including Wasserstein, Charbonnier, and perceptual loss. The outcomes demonstrated significant image quality improvements, confirmed through pertinent metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) between the corrected images and the ground truth. Furthermore, learning curves and qualitative comparisons added evidence of the enhanced image quality and the network's increased stability, while preserving pixel value intensity. The experiments underscored the potential of deep learning frameworks in enhancing the visual interpretation of CT scans, achieving outcomes with SSIM values close to one and PSNR values reaching up to 76.

Keywords: generative adversarial networks, sparse view computed tomography, CT image correction, Mir-Net

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Authors: Brenden M. Shutt, Lubjana Beshaj, Paul L. Goethals, Ambrose Kam

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Communication security is of particular interest to military data networks. A relatively novel approach to network security is blockchain, a cryptographically secured distribution ledger with a decentralized consensus mechanism for data transaction processing. Recent advances in blockchain technology have proposed new techniques for both data validation and trust management, as well as different frameworks for managing dataflow. The purpose of this work is to test the feasibility of different blockchain architectures as applied to military command and control networks. Various architectures are tested through discrete-event simulation and the feasibility is determined based upon a blockchain design’s ability to maintain long-term stable performance at industry standards of throughput, network latency, and security. This work proposes a consortium blockchain architecture with a computationally inexpensive consensus mechanism, one that leverages a Proof-of-Identity (PoI) concept and a reputation management mechanism.

Keywords: blockchain, consensus mechanism, discrete-event simulation, fog computing

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Authors: Olubisi Friday Oluduro

Abstract:

The Paris Agreement 2015, the result of negotiations under the United Nations Framework Convention on Climate Change (UNFCCC), after Kyoto Protocol expiration, sets a long-term goal of limiting the increase in the global average temperature to well below 2 degrees Celsius above pre-industrial levels, and of pursuing efforts to limiting this temperature increase to 1.5 degrees Celsius. An advancement on the erstwhile Kyoto Protocol which sets commitments to only a limited number of Parties to reduce their greenhouse gas (GHGs) emissions, it includes the goal to increase the ability to adapt to the adverse impacts of climate change and to make finance flows consistent with a pathway towards low GHGs emissions. For it achieve these goals, the Agreement requires all Parties to undertake efforts towards reaching global peaking of GHG emissions as soon as possible and towards achieving a balance between anthropogenic emissions by sources and removals by sinks in the second half of the twenty-first century. In addition to climate change mitigation, the Agreement aims at enhancing adaptive capacity, strengthening resilience and reducing the vulnerability to climate change in different parts of the world. It acknowledges the importance of addressing loss and damage associated with the adverse of climate change. The Agreement also contains comprehensive provisions on support to be provided to developing countries, which includes finance, technology transfer and capacity building. To ensure that such supports and actions are transparent, the Agreement contains a number reporting provisions, requiring parties to choose the efforts and measures that mostly suit them (Nationally Determined Contributions), providing for a mechanism of assessing progress and increasing global ambition over time by a regular global stocktake. Despite the somewhat global look of the Agreement, it has been fraught with manifold limitations threatening its very existential capability to produce any meaningful result. Considering these obvious limitations some of which were the very cause of the failure of its predecessor—the Kyoto Protocol—such as the non-participation of the United States, non-payment of funds into the various coffers for appropriate strategic purposes, among others. These have left the developing countries largely threatened eve the more, being more vulnerable than the developed countries, which are really responsible for the climate change scourge. The paper seeks to examine the mitigation and adaptation measures under the Paris Agreement 2015, appraise the present situation since the Agreement was concluded and ascertain whether the developing countries have been better or worse off since the Agreement was concluded, and examine why and how, while projecting a way forward in the present circumstance. It would conclude with recommendations towards ameliorating the situation.

Keywords: mitigation, adaptation, climate change, Paris agreement 2015, framework

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Authors: Jiwan Kumar, Pooja, Sandeep Negi, Anjum Rouf, Amit Kumar, Naveen Lakra

Abstract:

In modern times where, health issues are increasing day by day, breast cancer is also one of them, which is very crucial and really important to find in the early stages. Doctors can use this model in order to tell their patients whether a cancer is not harmful (benign) or harmful (malignant). We have used the knowledge of machine learning in order to produce the model. we have used algorithms like Logistic Regression, Random forest, support Vector Classifier, Bayesian Network and Radial Basis Function. We tried to use the data of crucial parts and show them the results in pictures in order to make it easier for doctors. By doing this, we're making ML better at finding breast cancer, which can lead to saving more lives and better health care.

Keywords: Bayesian network, radial basis function, ensemble learning, understandable, data making better, random forest, logistic regression, breast cancer

Procedia PDF Downloads 29