Search results for: mobile Ad Hoc networks

Authors: Karolina Mazur, Stanislaw Kuciel

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Polylactides (PLA) and polyhydroxyalkanoates (PHA) are the two groups of biodegradable and biocompatible thermoplastic polymers most commonly utilised in medicine and rehabilitation. The aim of this work is to determine the changes in the strength properties and the microstructures taking place in biodegradable polymer composites during their long-term storage in a highly cooled environment (i.e. a freezer at -24ºC) and to initially assess the durability of such biocomposites when used as single-use elements of rehabilitation or medical equipment. It is difficult to find any information relating to the feasibility of long-term storage of technical products made of PLA or PHA, but nonetheless, when using these materials to make products such as casings of hair dryers, laptops or mobile phones, it is safe to assume that without storing in optimal conditions their degradation time might last even several years. SEM images and the assessment of the strength properties (tensile, bending and impact testing) were carried out and the density and water sorption of two polymers, PLA and PHA (NaturePlast PLE 001 and PHE 001), filled with cellulose fibres (corncob grain – Rehofix MK100, Rettenmaier&Sohne) up to 10 and 20% mass were determined. The biocomposites had been stored at a temperature of -24ºC for 4 years. In order to find out the changes in the strength properties and the microstructure taking place after such a long time of storage, the results of the assessment have been compared with the results of the same research carried out 4 years before. Results shows a significant change in the manner of fractures – from ductile with developed surface for the PHA composite with corncob grain when the tensile testing was performed directly after the injection into a more brittle state after 4 years of storage, which is confirmed by the strength tests, where a decrease of deformation is observed at point of fracture. The research showed that there is a way of storing medical devices made out of PLA or PHA for a reasonably long time, as long as the required temperature of storage is met. The decrease of mechanical properties found during tensile testing and bending for PLA was less than 10% of the tensile strength, while the modulus of elasticity and deformation at fracturing slightly rose, which may implicate the beginning of degradation processes. The strength properties of PHA are even higher after 4 years of storage, although in that case the decrease of deformation at fracturing is significant, reaching even 40%, which suggests its degradation rate is higher than that of PLA. The addition of natural particles in both cases only slightly increases the biodegradation.

Keywords: biocomposites, PLA, PHA, storage

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Authors: Omar Shafqat, Jaime Arias, Cristian Bogdan, Björn Palm

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Heating constitutes a major part of the overall energy consumption in Sweden. In 2013 heating and hot water accounted for about 55% of the total energy use in the housing sector. Historically, the end users have not been able to make a significant impact on their consumption on account of traditional control systems that do not facilitate interaction and control of the heating systems. However, in recent years internet connected home energy management systems have become increasingly available which allow users to visualize the indoor temperatures as well as control the heating system. However, the adoption of these systems is still in its nascent stages. This paper presents the outcome of a study carried out in a community of single-family houses in Stockholm. Heating in the area is provided through district heating, and the neighbourhood is connected through a local micro thermal grid, which is owned and operated by the local community. Heating in the houses is accomplished through a hydronic system equipped with radiators. The system installed offers the households to control the indoor temperature through a mobile application as well as through a physical thermostat. It was also possible to program the system to, for instance, lower the temperatures during night time and when the users were away. The users could also monitor the indoor temperatures through the application. It was additionally possible to create different zones in the house with their own individual programming. The historical heating data (in the form of billing data) was available for several previous years and has been used to perform quantitative analysis for the study after necessary normalization for weather variations. The experiment involved 30 households out of a community of 178 houses. The area was selected due to uniform construction profile in the area. It was observed that despite similar design and construction period there was a large variation in the heating energy consumption in the area which can for a large part be attributed to user behaviour. The paper also presents qualitative analysis done through survey questions as well as a focus group carried out with the participants. Overall, considerable energy savings were accomplished during the trial, however, there was a considerable variation between the participating households. The paper additionally presents recommendations to improve the impact of home energy management systems for heating in terms of improving user engagement and hence the energy impact.

Keywords: energy efficiency in buildings, energy behavior, heating control system, home energy management system

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Authors: Jonathan K. Hill

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The transportation networks throughout Africa remain the only transportation infrastructure system in the world that is attacked by terrorists at a high frequency, so the international community can learn from each attack. The targeting of transportation should be recognized as a direct attack against a civilian population, so the international community should work to better understand the types of attacks utilized, the types of improvised explosive device designs adapted to transportation targets, and the ways the various modes of transportation have been attacked throughout the continent. Some countries have seen grenade attacks that have resulted in only injuries, while some countries have experienced large vehicle bombings that have resulted in hundreds of injuries and numerous deaths. With insurgencies, explosive devices have been small, complex, and generally target an enemy of the insurgency. With terrorist bombings, the explosive devices have been large, brazen, and targeted at civilian populations. And, these civilian populations are easily targeted within the transportation system. The presentation provided by Assess Africa LLC is titled ‘Evolution of Bombings Against Transportation Infrastructure’ and covers improvised explosive device characteristics, how improvised explosive devices have been adapted to transportation targets in Africa, analyses recent incidents, and provides some advice for effective protective measures. A main component of the improvised explosive device characteristics portion of the presentation focuses on the link between explosive device components, the intelligence network, and the bomb-builder’s network. By understanding the components, how the use of various components can be linked to a terrorist group’s capabilities, and how the bomb-builder acquires materials, the analysis of improvised explosive device attacks takes on a new direction – one that focuses on defeating the network instead of merely reviewing incidents of the past.

Keywords: Africa, bombings, critical infrastructure protection, transportation security

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Authors: Halonen

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Covid-19 pandemic and the succeeding crises have profoundly shaken the accustomed ways of interaction and thereby challenged the customary engagement patterns among entrepreneurs Consequently, this has led to the experience of lack of collegial interaction for some. Networks and relationships are a crucial factor to strengthening resilience, being especially significant in non-ordinary times. This study aims to shed light on entrepreneurs’ resilience development in and through entrepreneurs’ communal and training space. The context for research is a communal training space in a municipality in Finland of which goal is to help entrepreneurs to experience of peer support and community as part of the "tribe" is strengthened, the entrepreneurs' well-being at work, resilience, ability to change, innovativeness and general life management is strengthened. This communal space is regarded as an example of a physical community of practice (CoP) of entrepreneurs. The research aims to highlight the importance of rediscovering the “new normal” communality as itself but as a key building block of resilience. The initial research questions of the study are: RQ1: What is the role of entrepreneurs’ CoP and communal space in nurturing resilience development among them? RQ2: What positive entrepreneurial outcomes can be achieved through established CoP. The data will be gathered starting from the launch of the communality space in September 2023 onwards. It includes participatory observations of training gatherings, interviews with entrepreneurs and utilizes action research as the method. The author has an active role in participating and facilitating the development. The full paper will be finalized by the fall 2024. The idea of the new normal communality in a CoP among entrepreneurs is to be rediscovered due to its positive impact on entrepreneur’s resilience and business success. The other implications of study can extend to wider entrepreneurial ecosystem and other key stakeholders. Especially emphasizing the potential of communality in CoP for fostering entrepreneurs’ resilience and well-being ensuing business growth, community-driven entrepreneurship development and vitality of the case municipality.

Keywords: resilience, resilience development, communal space, community of practice (CoP)

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Authors: Maria Sofia Palagonia, Doriano Brogioli, Fabio La Mantia

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In the last decade, lithium has become an important raw material in various sectors, in particular for rechargeable batteries. Its production is expected to grow more and more in the future, especially for mobile energy storage and electromobility. Until now it is mostly produced by the evaporation of water from salt lakes, which led to a huge water consumption, a large amount of waste produced and a strong environmental impact. A new, clean and faster electrochemical technique to recover lithium has been recently proposed: electrochemical ion pumping. It consists in capturing lithium ions from a feed solution by intercalation in a lithium-selective material, followed by releasing them into a recovery solution; both steps are driven by the passage of a current. In this work, a new configuration of the electrochemical cell is presented, used to study and optimize the process of the intercalation of lithium ions through the hydrodynamic condition. Lithium Manganese Oxide (LiMn₂O₄) was used as a cathode to intercalate lithium ions selectively during the reduction, while Nickel Hexacyano Ferrate (NiHCF), used as an anode, releases positive ion. The effect of hydrodynamics on the process has been studied by conducting the experiments at various fluxes of the electrolyte through the electrodes, in terms of charge circulated through the cell, captured lithium per unit mass of material and overvoltage. The result shows that flowing the electrolyte inside the cell improves the lithium capture, in particular at low lithium concentration. Indeed, in Atacama feed solution, at 40 mM of lithium, the amount of lithium captured does not increase considerably with the flux of the electrolyte. Instead, when the concentration of the lithium ions is 5 mM, the amount of captured lithium in a single capture cycle increases by increasing the flux, thus leading to the conclusion that the slowest step in the process is the transport of the lithium ion in the liquid phase. Furthermore, an influence of the concentration of other cations in solution on the process performance was observed. In particular, the capturing of the lithium using a different concentration of NaCl together with 5 mM of LiCl was performed, and the results show that the presence of NaCl limits the amount of the captured lithium. Further studies can be performed in order to understand why the full capacity of the material is not reached at the highest flow rate. This is probably due to the porous structure of the material since the liquid phase is likely not affected by the convection flow inside the pores. This work proves that electrochemical ion pumping, with a suitable hydrodynamic design, enables the recovery of lithium from feed solutions at the lower concentration than the sources that are currently exploited, down to 1 mM.

Keywords: desalination battery, electrochemical ion pumping, hydrodynamic, lithium

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Authors: Faiza Belbachir, Thibault Schienhinski

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The massive development of online social networks allows users to post and share their opinions on various topics. With this huge volume of opinion, it is interesting to extract and interpret these information for different domains, e.g., product and service benchmarking, politic, system of recommendation. This is why opinion detection is one of the most important research tasks. It consists on differentiating between opinion data and factual data. The difficulty of this task is to determine an approach which returns opinionated document. Generally, there are two approaches used for opinion detection i.e. Lexical based approaches and Machine Learning based approaches. In Lexical based approaches, a dictionary of sentimental words is used, words are associated with weights. The opinion score of document is derived by the occurrence of words from this dictionary. In Machine learning approaches, usually a classifier is trained using a set of annotated document containing sentiment, and features such as n-grams of words, part-of-speech tags, and logical forms. Majority of these works are based on documents text to determine opinion score but dont take into account if these texts are really correct. Thus, it is interesting to exploit other information to improve opinion detection. In our work, we will develop a new way to consider the opinion score. We introduce the notion of trust score. We determine opinionated documents but also if these opinions are really trustable information in relation with topics. For that we use lexical SentiWordNet to calculate opinion and trust scores, we compute different features about users like (numbers of their comments, numbers of their useful comments, Average useful review). After that, we combine opinion score and trust score to obtain a final score. We applied our method to detect trust opinions in TRIPADVISOR collection. Our experimental results report that the combination between opinion score and trust score improves opinion detection.

Keywords: Tripadvisor, opinion detection, SentiWordNet, trust score

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Authors: Yoganathan Karthik, Guhanathan Poravi

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Colorizing near-infrared (NIR) images poses unique challenges due to the absence of color information and the nuances in light absorption. In this paper, we present an approach to NIR image colorization utilizing a synthetic dataset generated from visible light images. Our method addresses two major challenges encountered in NIR image colorization: accurately colorizing objects with color variations and avoiding over/under saturation in dimly lit scenes. To tackle these challenges, we propose a Generative Adversarial Network (GAN)-based framework that learns to map NIR images to their corresponding colorized versions. The synthetic dataset ensures diverse color representations, enabling the model to effectively handle objects with varying hues and shades. Furthermore, the GAN architecture facilitates the generation of realistic colorizations while preserving the integrity of dimly lit scenes, thus mitigating issues related to over/under saturation. Experimental results on benchmark NIR image datasets demonstrate the efficacy of our approach in producing high-quality colorizations with improved color accuracy and naturalness. Quantitative evaluations and comparative studies validate the superiority of our method over existing techniques, showcasing its robustness and generalization capability across diverse NIR image scenarios. Our research not only contributes to advancing NIR image colorization but also underscores the importance of synthetic datasets and GANs in addressing domain-specific challenges in image processing tasks. The proposed framework holds promise for various applications in remote sensing, medical imaging, and surveillance where accurate color representation of NIR imagery is crucial for analysis and interpretation.

Keywords: computer vision, near-infrared images, automatic image colorization, generative adversarial networks, synthetic data

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Authors: Ortega Ivan, De La Plaza Edurne

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Background: Spain and other European countries have implemented Uncontrolled Donation after Cardiac Death (uDCD) programs. After 15 years of experience in Spain, many things have changed. Recent evidence and technical breakthroughs achieved in resuscitation are relevant for uDCD programs and raise some ethical concerns related to these protocols. Aim: To rethink current uDCD programs in the light of recent evidence on available therapeutic procedures applicable to victims of out-of-hospital cardiac arrest (OHCA). To address the following question: What is the current standard of treatment owed to victims of OHCA before including them in an uDCD protocol? Materials and Methods: Review of the scientific and ethical literature related to both uDCD programs and innovative resuscitation techniques. Results: 1) The standard of treatment received and the chances of survival of victims of OHCA depend on whether they are classified as Non-Heart Beating Patients (NHBP) or Non-Heart-Beating-Donors (NHBD). 2) Recent studies suggest that NHBPs are likely to survive, with good quality of life, if one or more of the following interventions are performed while ongoing CPR -guided by suspected or known cause of OHCA- is maintained: a) direct access to a Cath Lab-H24 or/and to extra-corporeal life support (ECLS); b) transfer in induced hypothermia from the Emergency Medical Service (EMS) to the ICU; c) thrombolysis treatment; d) mobile extra-corporeal membrane oxygenation (mini ECMO) instituted as a bridge to ICU ECLS devices. 3) Victims of OHCA who cannot benefit from any of these therapies should be considered as NHBDs. Conclusion: Current uDCD protocols do not take into account recent improvements in resuscitation and need to be adapted. Operational criteria to distinguish NHBDs from NHBP should seek a balance between the technical imperative (to do whatever is possible), considerations about expected survival with quality of life, and distributive justice (costs/benefits). Uncontrolled DCD protocols can be performed in a way that does not hamper the legitimate interests of patients, potential organ donors, their families, the organ recipients, and the health professionals involved in these processes. Families of NHBDs’ should receive information which conforms to the ethical principles of respect of autonomy and transparency.

Keywords: uncontrolled donation after cardiac death resuscitation, refractory cardiac arrest, out of hospital cardiac, arrest ethics

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Authors: Tesfaye Fenta Boka, Niu Zhendong

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Recommender systems play a crucial role in connecting individuals with the items they require, as is particularly evident in the rapid growth of online grocery shopping platforms. These systems predominantly rely on user-centered recommendations, where items are suggested based on individual preferences, garnering considerable attention and adoption. However, our focus lies on the item-centered recommendation task within the grocery shopping context. In the reverse next item period recommendation (RNPR) task, we are presented with a specific item and challenged to identify potential users who are likely to consume it in the upcoming period. Despite the ever-expanding inventory of products on online grocery platforms, the cold start item problem persists, posing a substantial hurdle in delivering personalized and accurate recommendations for new or niche grocery items. To address this challenge, we propose a Graph Neural Network (GNN)-based approach. By capitalizing on the inherent relationships among grocery items and leveraging users' historical interactions, our model aims to provide reliable and context-aware recommendations for cold-start items. This integration of GNN technology holds the promise of enhancing recommendation accuracy and catering to users' individual preferences. This research contributes to the advancement of personalized recommendations in the online grocery shopping domain. By harnessing the potential of GNNs and exploring item-centered recommendation strategies, we aim to improve the overall shopping experience and satisfaction of users on these platforms.

Keywords: recommender systems, cold start item recommendations, online grocery shopping platforms, graph neural networks

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Authors: Soheila Sadeghi

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In construction projects, predicting project performance metrics accurately is essential for effective management and successful delivery. However, conventional methods often depend on fixed baseline plans, disregarding the evolving nature of project progress and external influences. To address this issue, we introduce a distinct approach based on machine learning to forecast key performance indicators, such as cost variance and earned value, for each Work Breakdown Structure (WBS) category within an urban road reconstruction project. Our proposed model leverages time series forecasting techniques, namely Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) networks, to predict future performance by analyzing historical data and project progress. Additionally, the model incorporates external factors, including weather patterns and resource availability, as features to improve forecast accuracy. By harnessing the predictive capabilities of machine learning, our performance forecasting model enables project managers to proactively identify potential deviations from the baseline plan and take timely corrective measures. To validate the effectiveness of the proposed approach, we conduct a case study on an urban road reconstruction project, comparing the model's predictions with actual project performance data. The outcomes of this research contribute to the advancement of project management practices in the construction industry by providing a data-driven solution for enhancing project performance monitoring and control.

Keywords: project performance forecasting, machine learning, time series forecasting, cost variance, schedule variance, earned value management

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Authors: Shativel S., Chandana B. R., Kavya B. C., Obli B. Vikram, Suganthi J., Nagendra Rao G.

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Satellite communication plays a pivotal role in modern global communication networks, serving as a vital link between terrestrial infrastructure and remote regions. The demand for reliable satellite reception systems, especially in UHF (Ultra High Frequency) and VHF (Very High Frequency) bands, has grown significantly over the years. This research paper presents the design and optimization of a high-gain, dual-band crossed Yagi-Uda antenna in CST Studio Suite, specifically tailored for satellite reception. The proposed antenna system incorporates a circularly polarized (Right-Hand Circular Polarization - RHCP) design to reduce Faraday loss. Our aim was to use fewer elements and achieve gain, so the antenna is constructed using 6x2 elements arranged in cross dipole and supported with a boom. We have achieved 10.67dBi at 146MHz and 9.28dBi at 437.5MHz.The process includes parameter optimization and fine-tuning of the Yagi-Uda array’s elements, such as the length and spacing of directors and reflectors, to achieve high gain and desirable radiation patterns. Furthermore, the optimization process considers the requirements for UHF and VHF frequency bands, ensuring broad frequency coverage for satellite reception. The results of this research are anticipated to significantly contribute to the advancement of satellite reception systems, enhancing their capabilities to reliably connect remote and underserved areas to the global communication network. Through innovative antenna design and simulation techniques, this study seeks to provide a foundation for the development of next-generation satellite communication infrastructure.

Keywords: Yagi-Uda antenna, RHCP, gain, UHF antenna, VHF antenna, CST, radiation pattern.

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Authors: Abeer A. Aljohani

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COVID-19 virus spread has been one of the extreme pandemics across the globe. It is also referred to as coronavirus, which is a contagious disease that continuously mutates into numerous variants. Currently, the B.1.1.529 variant labeled as omicron is detected in South Africa. The huge spread of COVID-19 disease has affected several lives and has surged exceptional pressure on the healthcare systems worldwide. Also, everyday life and the global economy have been at stake. This research aims to predict COVID-19 disease in its initial stage to reduce the death count. Machine learning (ML) is nowadays used in almost every area. Numerous COVID-19 cases have produced a huge burden on the hospitals as well as health workers. To reduce this burden, this paper predicts COVID-19 disease is based on the symptoms and medical history of the patient. This research presents a unique architecture for COVID-19 detection using ML techniques integrated with feature dimensionality reduction. This paper uses a standard UCI dataset for predicting COVID-19 disease. This dataset comprises symptoms of 5434 patients. This paper also compares several supervised ML techniques to the presented architecture. The architecture has also utilized 10-fold cross validation process for generalization and the principal component analysis (PCA) technique for feature reduction. Standard parameters are used to evaluate the proposed architecture including F1-Score, precision, accuracy, recall, receiver operating characteristic (ROC), and area under curve (AUC). The results depict that decision tree, random forest, and neural networks outperform all other state-of-the-art ML techniques. This achieved result can help effectively in identifying COVID-19 infection cases.

Keywords: supervised machine learning, COVID-19 prediction, healthcare analytics, random forest, neural network

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Authors: Vipul M. Patel, Hemantkumar B. Mehta

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Technological innovations in electronic world demand novel, compact, simple in design, less costly and effective heat transfer devices. Closed Loop Pulsating Heat Pipe (CLPHP) is a passive phase change heat transfer device and has potential to transfer heat quickly and efficiently from source to sink. Thermal performance of a CLPHP is governed by various parameters such as number of U-turns, orientations, input heat, working fluids and filling ratio. The present paper is an attempt to predict the thermal performance of a CLPHP using Artificial Neural Network (ANN). Filling ratio and heat input are considered as input parameters while thermal resistance is set as target parameter. Types of neural networks considered in the present paper are radial basis, generalized regression, linear layer, cascade forward back propagation, feed forward back propagation; feed forward distributed time delay, layer recurrent and Elman back propagation. Linear, logistic sigmoid, tangent sigmoid and Radial Basis Gaussian Function are used as transfer functions. Prediction accuracy is measured based on the experimental data reported by the researchers in open literature as a function of Mean Absolute Relative Deviation (MARD). The prediction of a generalized regression ANN model with spread constant of 4.8 is found in agreement with the experimental data for MARD in the range of ±1.81%.

Keywords: ANN models, CLPHP, filling ratio, generalized regression, spread constant

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Authors: Patricia Girrbach

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Currently, our society is facing a new and incremental upheaval technological revolution named digitalization. In order to face the relating challenges organizations have to be prepared. They need appropriate circumstances in order to cope with current issues concerning digital transformation processes. Nowadays digitalization emerged as top issues for companies and business leaders. In this context, it is a pressure on companies to have a positive, productive digital culture. And indeed, Organizations realize that they need to address this important issue. In this context 87 percent of organizations quote culture and engagement as one of their top challenges in terms of any change process, but especially in terms of the digital turn. Executives can give their company a competitive advantage and attract top talent by having a strong workplace culture that supports digitalization. Many current studies attest that fact. Digital-oriented companies can hire more easily, they have the lowest voluntary turnover rates, deliver better customer service, and are more profitable over the long run. Based on this background it is important to provide companies starting points and practical measurements how to reach this goal. The major findings are that firms need to make sense out of digitalization. In this context, they should focus on internal but also on external stakeholders. Furthermore, they should create certain working conditions and they should support the qualification of employees, e.g. by Virtual Reality. These measurements can create positive experiences in terms of digitalization in order to ensure the support of stuff in terms of the digital turn. Based on several current studies and literature research this paper provides concrete measurements for companies in order to enable the digital turn. Therefore, the aim of this paper is providing possible practical starting points which support both the education of employees by digitalization as well as the digital turn itself within the organization.

Keywords: digitalization, industry 4.0, education 4.0, virtual reality

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Authors: Nurulhuda A. Manaf, Nor Najihah Zainal Abidin, Nur Amalina Jamaludin

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Coordinating service interactions are a vital part of developing distributed applications that are built up as networks of autonomous participants, e.g., software components, web services, online resources, involve a collaboration between a diverse number of participant services on different providers. The complexity in coordinating service interactions reflects how important the techniques and approaches require for designing and coordinating the interaction between participant services to ensure the overall goal of a collaboration between participant services is achieved. The objective of this research is to develop capability of steering a complex service interaction towards a desired outcome. Therefore, an efficient technique for modelling, generating, and verifying the coordination of service interactions is developed. The developed model describes service interactions using service choreographies approach and focusing on a declarative approach, advocating an Object Management Group (OMG) standard, Semantics of Business Vocabulary and Rules (SBVR). This model, namely, SBVR model for service choreographies focuses on a declarative deontic rule expressing both obligation and prohibition, which can be more useful in working with coordinating service interactions. The generated SBVR model is then be formulated and be transformed into Alloy model using Alloy Analyzer for verifying the generated SBVR model. The transformation of SBVR into Alloy allows to automatically generate the corresponding coordination of service interactions (service choreography), hence producing an immediate instance of execution that satisfies the constraints of the specification and verifies whether a specific request can be realised in the given choreography in the generated choreography.

Keywords: service choreography, service coordination, behavioural modelling, complex interactions, declarative specification, verification, model transformation, semantics of business vocabulary and rules, SBVR

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Authors: Anjan Hazra, Nirjhar Dasgupta, Chandan Sengupta, Sauren Das

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Tea (Camellia sinensis) has received substantial attention from the scientific world time to time, not only for its commercial importance, but also for its demand to the health-conscious people across the world for its extensive use as potential sources of antioxidant supplement. These health-benefit traits primarily rely on some regulatory networks of different metabolic pathways. Development of microsatellite markers from the conserved genomic regions is being worthwhile for studying the genetic diversity of closely related species or self-pollinated species. Although several SSR markers have been reported, in tea the trait-specific Simple Sequence Repeats (SSRs) are yet to be identified, which can be used for marker assisted breeding technique. MicroRNAs are endogenous, noncoding, short RNAs directly involved in regulating gene expressions at the post-transcriptional level. It has been found that diversity in miRNA gene interferes the formation of its characteristic hair pin structure and the subsequent function. In the present study, the precursors of small regulatory RNAs (microRNAs) has been fished out from tea Expressed Sequence Tag (EST) database. Furthermore, the simple sequence repeat motifs within the putative miRNA precursor genes are also identified in order to experimentally validate their existence and function. It is already known that genic-SSR markers are very adept and breeder-friendly source for genetic diversity analysis. So, the potential outcome of this in-silico study would provide some novel clues in understanding the miRNA-triggered polymorphic genic expression controlling specific metabolic pathways, accountable for tea quality.

Keywords: micro RNA, simple sequence repeats, tea quality, trait specific marker

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Authors: Pin Yu Chen, Puay Chuan Lee, Yu Jen Loo, Ju Xia Zhang, Deborah Teo, Jack Wei Chieh Tan, Biauw Chi Ong

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Background: Synchronous communication, such as telephone calls, remains the standard communication method between nurses and other healthcare professionals in Singapore public hospitals despite advances in asynchronous technological platforms, such as instant messaging. Although miscommunication is one of the most common causes of lapses in patient care, there is a scarcity of research characterizing baseline inter-professional healthcare communications in a hospital setting due to logistic difficulties. Objective: This study aims to characterize the frequency and patterns of communication behaviours among healthcare professionals. Methods: The one-week observational study was conducted on Monday through Sunday at the nursing station of a cardiovascular medicine and cardiothoracic surgery inpatient ward at the National Heart Centre Singapore. Subjects were shadowed by two physicians for sixteen hours or consecutive morning and afternoon nursing shifts. Communications were logged and characterized by type, duration, caller, and recipient. Results: A total of 1,023 communication events involving the attempted use of the common telephones at the nursing station were logged over a period of one week, corresponding to a frequency of one event every 5.45 minutes (SD 6.98, range 0-56 minutes). Nurses initiated the highest proportion of outbound calls (38.7%) via the nursing station common phone. A total of 179 face-to-face communications (17.5%), 362 inbound calls (35.39%), 481 outbound calls (47.02%), and 1 emergency alert (0.10%) were captured. Average response time for task-oriented communications was 159 minutes (SD 387.6, range 86-231). Approximately 1 in 3 communications captured aimed to clarify patient-related information. The total duration of time spent on synchronous communication events over one week, calculated from total inbound and outbound calls, was estimated to be a total of 7 hours. Conclusion: The results of our study showed that there is a significant amount of time spent on inter-professional healthcare communications via synchronous channels. Integration of patient-related information and use of asynchronous communication channels may help to reduce the redundancy of communications and clarifications. Future studies should explore the use of asynchronous mobile platforms to address the inefficiencies observed in healthcare communications.

Keywords: healthcare communication, healthcare management, nursing, qualitative observational study

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Authors: K. Sathishkumar, V. Thiagarasu

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Due to recent advances in DNA microarray technology, it is now feasible to obtain gene expression profiles of tissue samples at relatively low costs. Many scientists around the world use the advantage of this gene profiling to characterize complex biological circumstances and diseases. Microarray techniques that are used in genome-wide gene expression and genome mutation analysis help scientists and physicians in understanding of the pathophysiological mechanisms, in diagnoses and prognoses, and choosing treatment plans. DNA microarray technology has now made it possible to simultaneously monitor the expression levels of thousands of genes during important biological processes and across collections of related samples. Elucidating the patterns hidden in gene expression data offers a tremendous opportunity for an enhanced understanding of functional genomics. However, the large number of genes and the complexity of biological networks greatly increase the challenges of comprehending and interpreting the resulting mass of data, which often consists of millions of measurements. A first step toward addressing this challenge is the use of clustering techniques, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. This work presents an analysis of several clustering algorithms proposed to deals with the gene expression data effectively. The existing clustering algorithms like Support Vector Machine (SVM), K-means algorithm and evolutionary algorithm etc. are analyzed thoroughly to identify the advantages and limitations. The performance evaluation of the existing algorithms is carried out to determine the best approach. In order to improve the classification performance of the best approach in terms of Accuracy, Convergence Behavior and processing time, a hybrid clustering based optimization approach has been proposed.

Keywords: microarray technology, gene expression data, clustering, gene Selection

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Authors: Debatosh Das, Moxian Chen, Jianhua Zhang, Caroline Gutjahr

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Arbuscular mycorrhiza (AM) is a mutualistic symbiosis between plant roots and Glomeromycotina fungi, which is activated under low but inhibited by high phosphate. The effect of phosphate on AM development has been observed for many years, but mechanisms regulating it under contrasting phosphate levels remain unknown. Based on previous observations that promoters of several AM functional genes contain PHR binding motifs, we hypothesized that PHR2, a master regulator of phosphate starvation response in rice, was recruited to regulate AM symbiosis development. We observed a drastic reduction in root colonization and significant AM transcriptome modulation in phr2. PHR2 targets genes required for root colonization and AM signaling. The role of PHR2 in improving root colonization, mycorrhizal phosphate uptake, and growth response was confirmed in field soil. In conclusion, rice PHR2, which is considered a master regulator of phosphate starvation responses, acts as a positive regulator of AM symbiosis between Glomeromycotina fungi and rice roots. PHR2 directly targets the transcription of plant strigolactone and AM genes involved in the establishment of this symbiosis. Our work facilitates an understanding of ways to enhance AMF propagule populations introduced in field soils (as a biofertilizer) in order to restore the natural plant-AMF networks disrupted by modern agricultural practices. We show that PHR2 is required for AM-mediated improvement of rice yield in low phosphate paddy field soil. Thus, our work contributes knowledge for rational application of AM in sustainable agriculture. Our data provide important insights into the regulation of AM by the plant phosphate status, which has a broad significance in agriculture and terrestrial ecosystems.

Keywords: biofertilizer, phosphate, mycorrhiza, rice, sustainable, symbiosis

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Authors: Germans Natuhwera, Peter Ellis, Acuda Wilson, Anne Merriman, Martha Rabwoni

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Objective: The aim of the study was to diagnose the socio-economic burden and impact of a diagnosis of cervical cancer (CC) in rural women in the context of low-resourced country Uganda, using a phenomenological enquiry. Methods: This was a multi-site phenomenological inquiry, conducted at three hospice settings; Mobile Hospice Mbarara in southwestern, Little Hospice Hoima in Western, and Hospice Africa Uganda Kampala in central Uganda. A purposive sample of women with a histologically confirmed diagnosis of CC was recruited. Data was collected using open-ended audio-recorded interviews conducted in the native languages of participants. Interviews were transcribed verbatim in English, and Braun and Clarke’s (2019) framework of thematic analysis was used. Results: 13 women with a mean age of 49.2 and age range 29-71 participated in the study. All participants were of low socioeconomic status. The majority (84.6%) had advanced disease at diagnosis. A fuller reading of transcripts produced four major themes clustered under; (1) socioeconomic characteristics of women, (2) impact of CC on women’s relationships, (3) disrupted and impaired activities of daily living (ADLs), and (4) economic disruptions. Conclusions: A diagnosis of CC introduces significant socio-economic disruptions in a woman’s and her family’s life. CC causes disability, impairs the woman and her family’s productivity hence exacerbating levels of poverty in the home. High and expensive out-of-pocket expenditure on treatment, investigations, and transport costs further compound the socio-economic burden. Decentralizing cancer care services to regional centers, scaling up screening services, subsidizing costs of cancer care services, or making cervical cancer care treatment free of charge, strengthening monitoring mechanisms in public facilities to curb the vice of healthcare workers soliciting bribes from patients, increased mass awareness campaigns about cancer, training more healthcare professionals in cancer investigation and management, and palliative care, and introducing an introductory course on gynecologic cancers into all health training institutions are recommended.

Keywords: activities of daily living, cervical cancer, out-of-pocket, expenditure, phenomenology, socioeconomic

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Authors: Roman Kulikov, Svetlana Kolesnikova

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Last decade Cloud Computing technologies have been rapidly becoming ubiquitous. Each year more and more organizations, corporations, internet services and social networks trust their business sensitive information to Public Cloud. The data storage in Public Cloud is protected by security mechanisms such as firewalls, cryptography algorithms, backups, etc.. In this way, however, only outsider attacks can be prevented, whereas virtualization tools can be easily compromised by insider. The protection of Public Cloud’s critical elements from internal intruder remains extremely challenging. A hypervisor, also called a virtual machine manager, is a program that allows multiple operating systems (OS) to share a single hardware processor in Cloud Computing. One of the hypervisor's functions is to enforce access control policies. Furthermore, it prevents guest OS from disrupting each other and from accessing each other's memory or disk space. Hypervisor is the one of the most critical and vulnerable elements in Cloud Computing infrastructure. Nevertheless, it has been poorly protected from being compromised by insider. By exploiting certain vulnerabilities, privilege escalation can be easily achieved in insider attacks on hypervisor. In this way, an internal intruder, who has compromised one process, is able to gain control of the entire virtual machine. Thereafter, the consequences of insider attacks in Public Cloud might be more catastrophic and significant to virtual tools and sensitive data than of outsider attacks. So far, almost no preventive security countermeasures have been developed. There has been little attention paid for developing models to assist risks mitigation strategies. In this paper formal model of insider attacks on hypervisor is designed. Our analysis identifies critical hypervisor`s vulnerabilities that can be easily compromised by internal intruder. Consequently, possible conditions for successful attacks implementation are uncovered. Hence, development of preventive security countermeasures can be improved on the basis of the proposed model.

Keywords: insider attack, public cloud, cloud computing, hypervisor

Procedia PDF Downloads 361

Authors: Kasra Majbouri Yazdi, Adel Majbouri Yazdi, Saeid Khodayi, Jingyu Hou, Wanlei Zhou, Saeed Saedy

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Fake news and false information are big challenges of all types of media, especially social media. There is a lot of false information, fake likes, views and duplicated accounts as big social networks such as Facebook and Twitter admitted. Most information appearing on social media is doubtful and in some cases misleading. They need to be detected as soon as possible to avoid a negative impact on society. The dimensions of the fake news datasets are growing rapidly, so to obtain a better result of detecting false information with less computation time and complexity, the dimensions need to be reduced. One of the best techniques of reducing data size is using feature selection method. The aim of this technique is to choose a feature subset from the original set to improve the classification performance. In this paper, a feature selection method is proposed with the integration of K-means clustering and Support Vector Machine (SVM) approaches which work in four steps. First, the similarities between all features are calculated. Then, features are divided into several clusters. Next, the final feature set is selected from all clusters, and finally, fake news is classified based on the final feature subset using the SVM method. The proposed method was evaluated by comparing its performance with other state-of-the-art methods on several specific benchmark datasets and the outcome showed a better classification of false information for our work. The detection performance was improved in two aspects. On the one hand, the detection runtime process decreased, and on the other hand, the classification accuracy increased because of the elimination of redundant features and the reduction of datasets dimensions.

Keywords: clustering, fake news detection, feature selection, machine learning, social media, support vector machine

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Authors: Elucir Gir, Laelson Rochelle Milanês Sousa, Pedro Henrique Tertuliano Leoni, Carla Aparecida Arena Ventura, Ana Cristina de Oliveira e Silva, Renata Karina Reis

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Context and significance: Resilience is a protective agent for the physical and mental well-being of nursing professionals. Team members are constantly subjected to high levels of work stress that can negatively impact care performance and users of health services. Stress levels have been exacerbated with the COVID-19 pandemic. Objective: The aim of this study was to analyze the resilience of nursing professionals in Brazil during the COVID-19 pandemic. Method: Cross-sectional study with a quantitative approach carried out with professionals from nursing teams from all regions of Brazil. Data collection took place in the first year of the pandemic between October and December 2020. Data were obtained through an online questionnaire posted on social networks. The information collected included the sociodemographic characterization of the nursing professionals and the Brief Resilient Coping Scale was applied. Student's t-test for independent samples and analysis of variance (ANOVA) were used to compare resilience scores with sociodemographic variables. Results: 8,792 nursing professionals participated in the study, 5,767 (65.6%) were nurses, 7,437 (84.6%) were female and 2,643 (30.1%) were from the Northeast region of Brazil, 5,124 (58.8% ) had low levels of resilience. The results showed a statistically significant difference between the resilience score and the variables: professional category (p<0.001); sex (p = 0.003); age range (p<0.001); region of Brazil (p<0.001); marital status (p=0.029) and providing assistance in a field hospital (p<0.001). Conclusion: Participants in this study had, in general, low levels of resilience. There is an urgent need for actions aimed at promoting the psychological health of nursing professionals inserted in pandemic contexts. Descriptors: Psychological Resilience; Nursing professionals; COVID-19; SARSCoV-2.

Keywords: psychological resilience, nursing professionals, COVID-19, SARS-CoV-2

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Authors: Vivek Rangarajan, Kim G. Clarke

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Bacillus lipopeptides are biosurfactants with wide ranging applications in the medical, food, agricultural, environmental and cosmetic industries. They are produced as a mix of three families, surfactin, iturin and fengycin, each comprising a large number of homologues of varying functionalities. Consequently, the method and degree of purification of the lipopeptide cocktail becomes particularly important if the functionality of the lipopeptide end-product is to be maximized for the specific application. However, downstream processing of Bacillus lipopeptides is particularly challenging due to the subtle variations observed in the different lipopeptide homologues and isoforms. To date, the most frequently used lipopeptide purification operations have been acid precipitation, solvent extraction, membrane ultrafiltration, adsorption and size exclusion. RP-HPLC (reverse phase high pressure liquid chromatography) also has potential for fractionation of the lipopeptide homologues. In the studies presented here, membrane ultrafiltration and RP-HPLC were evaluated for lipopeptide purification to different degrees of purities for maximum functionality. Batch membrane ultrafiltration using 50 kDa polyether sulphone (PES) membranes resulted in lipopeptide recovery of about 68% for surfactin and 82 % for fengycin. The recovery was further improved to 95% by using size-conditioned lipopeptide micelles. The conditioning of lipopeptides with Ca2+ ions resulted in uniformly sized micelles with average size of 96.4 nm and a polydispersity index of 0.18. The size conditioning also facilitated removal of impurities (molecular weight ranging between 2335-3500 Da) through operation of the system under dia-filtration mode, in a way similar to salt removal from protein by dialysis. The resultant purified lipopeptide was devoid of macromolecular impurities and could ideally suit applications in the cosmetic and food industries. Enhanced purification using RP-HPLC was carried out in an analytical C18 column, with the aim to fractionate lipopeptides into their constituent homologues. The column was eluted with mobile phase comprising acetonitrile and water over an acetonitrile gradient, 35% - 80%, over 70 minutes. The gradient elution program resulted in as many as 41 fractions of individual lipopeptide homologues. The efficacy test of these fractions against fungal phytopathogens showed that first 21 fractions, identified to be homologues of iturins and fengycins, displayed maximum antifungal activities, suitable for biocontrol in the agricultural industry. Thus, in the current study, the downstream processing of lipopeptides leading to tailor-made products for selective applications was demonstrated using two major downstream unit operations.

Keywords: bacillus lipopeptides, membrane ultrafiltration, purification, RP-HPLC

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Authors: Irene Yi

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Machine learning, natural language processing, and neural network models of language are becoming more and more prevalent in the fields of technology and linguistics today. Training data for machines are at best, large corpora of human literature and at worst, a reflection of the ugliness in society. Machines have been trained on millions of human books, only to find that in the course of human history, derogatory and sexist adjectives are used significantly more frequently when describing females in history and literature than when describing males. This is extremely problematic, both as training data, and as the outcome of natural language processing. As machines start to handle more responsibilities, it is crucial to ensure that they do not take with them historical sexist and misogynistic notions. This paper gathers data and algorithms from neural network models of language having to deal with syntax, semantics, sociolinguistics, and text classification. Results are significant in showing the existing intentional and unintentional misogynistic notions used to train machines, as well as in developing better technologies that take into account the semantics and syntax of text to be more mindful and reflect gender equality. Further, this paper deals with the idea of non-binary gender pronouns and how machines can process these pronouns correctly, given its semantic and syntactic context. This paper also delves into the implications of gendered grammar and its effect, cross-linguistically, on natural language processing. Languages such as French or Spanish not only have rigid gendered grammar rules, but also historically patriarchal societies. The progression of society comes hand in hand with not only its language, but how machines process those natural languages. These ideas are all extremely vital to the development of natural language models in technology, and they must be taken into account immediately.

Keywords: gendered grammar, misogynistic language, natural language processing, neural networks

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Authors: Izabel Cristina Zattar, Gilberto Passos Lima, Guilherme Schünemann de Oliveira

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In Brazil, there are several institutions committed to the development of new businesses based on product innovation. Among them are business incubators, universities and science institutes. Business incubators can be defined as nurseries for new companies, which may be in the technology segment, discussed in this article. Business incubators provide services related to infrastructure, such as physical space and meeting rooms. Besides these services, incubators also offer assistance in the form of information and communication, access to finance, relationship networks and business monitoring and mentoring processes. Business incubators support not all technology companies. One of the business incubators tasks is to assess the nature and feasibility of new business proposals. To assist this goal, this paper proposes a methodology for evaluating new business using the Analytic Hierarchy Process (AHP). This paper presents the concepts used in the assessing methodology application for new business, concepts that have been tested with positive results in practice. This study counts on three main steps: first, a hierarchy was built, based on new business manuals used by the business incubators. These books and manuals relate business selection requirements, such as the innovation status and other technological aspects. Then, a questionnaire was generated, in order to guide incubator experts in the parity comparisons at all hierarchy levels. The weights of each requirement are calculated from information obtained from the questionnaire responses. Finally, the proposed method was applied to evaluate five new business proposals, which were applying to be part of a company incubator. The main result is the classification of these new businesses, which helped the incubator experts to decide what companies were more eligible to work with. This classification may also be helpful to the decision-making process of business incubators in future selection processes.

Keywords: Analytic Hierarchy Process (AHP), Brazilian companies incubators, technology companies, incubator

Procedia PDF Downloads 374

Authors: Ghulamhossein Nezami

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Civilizations based in Iran, especially in the south, have always realized the all-around importance of the Persian Sea and for various reasons, have paid full attention to it. The first of these was the pre-Aryan government, Ilam in the coastal province of Sharihum and the city of Lian (now the port of Bushehr) in terms of trade, defense and religion. With the establishment of the Achaemenids on the entire plateau of Iran to the center of Persia, they created several communication routes from Parseh to the shores of the Persian Gulf, which ended in the present Bushehr province. This coastal area was extended by a road in the coastal plain to the more southern parts of the ports of Ausinze - according to Ptolemy the port of Siraf before the Sassanids - and Epstane and Hormozia in the present-day Strait of Hormuz. Meanwhile, the ancient city of Temukknana, whose new historical documents testify to its extraordinary importance in the Achaemenid period, especially Darius I of the Achaemenids, from a strategic position with the coastal areas, the coasts and on the other hand with the gamers, the political center. - Achaemenid administration, had. New archeological evidence, research, and excavations show that both the famous Achaemenid kings and courtiers paid special attention to Tamukknana. The discovery of a tomb and three Achaemenid palaces from before the reign of Cyrus to Xerxes in this region showed the importance of the strategic, security-defense and commercial position of this region, extraordinary for the Achaemenids. Therefore, the city of Temukkana in the Dashtestan region of present-day Bushehr province became an important Achaemenid center on the Persian Gulf coast and became the political-economic center of gravity of the Achaemenids and the regulator of communication networks on the Persian Gulf coast. This event showed that the Achaemenids attached importance to their economic goals and oversight of their vast territory by the Persian Gulf. Methods: Book resources and field study.

Keywords: Achaemenids, Bushehr, Persian Gulf, Tamukkana

Procedia PDF Downloads 193

Authors: Ayse Ozbil, Gorsev Argin, Demet Yesiltepe

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Data for this cross-sectional study were drawn from questionnaires conducted in 10 elementary schools (1000 students, ages 12-14) located in Istanbul, Turkey. School environments (1600 meter buffers around the school) were evaluated through GIS-based land-use data (parcel level land use density) and street-level topography. Street networks within the same buffers were evaluated by using angular segment analysis (Integration and Choice) implemented in Depthmap as well as two segment-based connectivity measures, namely Metric and Directional Reach implemented in GIS. Segment Angular Integration measures how accessible each space from all the others within the radius using the least angle measure of distance. Segment Angular Choice which measures how many times a space is selected on journeys between all pairs of origins and destinations. Metric Reach captures the density of streets and street connections accessible from each individual road segment. Directional Reach measures the extent to which the entire street network is accessible with few direction changes. In addition, socio-economic characteristics (annual income, car ownership, education-level) of parents, obtained from parental questionnaires, were also included in the analysis. It is shown that surrounding street network configuration is strongly associated with both walk-mode shares and average walking distances to/from schools when controlling for parental socio-demographic attributes as well as land-use compositions and topographic features in school environments. More specifically, findings suggest that the scale at which urban form has an impact on pedestrian travel is considerably larger than a few blocks around the school.

Keywords: Istanbul, street network layout, urban form, walking to/from school

Procedia PDF Downloads 408

Authors: Jessica Achebe, Susan Tighe

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The reduction of material consumption and greenhouse gas emission when maintain and rehabilitating road networks can achieve added benefits including improved life cycle performance of pavements, reduced climate change impacts and human health effect due to less air pollution, improved productivity due to an optimal allocation of resources and reduced road user cost. This is the essence of incorporating environmental sustainability into pavement management. The functionality of performance measurement approach has made it one of the most valuable tool to Pavement Management Systems (PMSs) to account for different criteria in the decision-making process. However measuring the environmental performance of road network is still a far-fetched practice in road network management, more so an ostensive agency-wide environmental sustainability or sustainable maintenance specifications is missing. To address this challenge, this present research focuses on the environmental sustainability performance of network-level pavement management. The ultimate goal is to develop a framework to incorporate environmental sustainability in pavement management systems for network-level maintenance programming. In order to achieve this goal, this paper present the first step, the intention is to review the previous studies that employed environmental performance measures, as well as the suitability of environmental performance indicators for the evaluation of the sustainability of network-level pavement maintenance strategies. Through an industry practice survey, this paper provides a brief forward regarding the pavement manager motivations and barriers to making more sustainable decisions, and data needed to support the network-level environmental sustainability. The trends in network-level sustainable pavement management are also presented, existing gaps are highlighted, and ideas are proposed for network-level sustainable maintenance and rehabilitation programming.

Keywords: pavement management, environment sustainability, network-level evaluation, performance measures

Procedia PDF Downloads 306

Authors: Ahmed Ismail, Mustafa Baysal

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Although the energy source in the world is mainly based on fossil fuels today, there is a need for alternative energy generation systems, which are more economic and environmentally friendly, due to continuously increasing demand of electric energy and lacking power resources and networks. Distributed Energy Resources (DERs) such as fuel cells, wind and solar power have recently become widespread as alternative generation. In order to solve several problems that might be encountered when integrating DERs to power system, the microgrid concept has been proposed. A microgrid can operate both grid connected and island mode to benefit both utility and customers. For most distributed energy resources (DER) which are connected in parallel in LV-grid like micro-turbines, wind plants, fuel cells and PV cells electrical power is generated as a direct current (DC) and converted to an alternative currents (AC) by inverters. So the inverters are assumed to be primary components in a microgrid. There are many control techniques of parallel inverters to manage active and reactive sharing of the loads. Some of them are based on droop method. In literature, the studies are usually focused on improving the transient performance of inverters. In this study, the performance of two different controllers based on droop control method is compared for the inverters operated in parallel without any communication feedback. For this aim, a microgrid in which inverters are controlled by conventional droop controller and modified droop controller is designed. Modified controller is obtained by adding PID into conventional droop control. Active and reactive power sharing performance, voltage and frequency responses of those control methods are measured in several operational cases. Study cases have been simulated by MATLAB-SIMULINK.

Keywords: active and reactive power sharing, distributed generation, droop control, microgrid

Procedia PDF Downloads 592