Search results for: multidimensional hierarchical graph neuron

Authors: Sabah Mohammed, Jinan Fiaidhi, Darien Sawyer

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Data integration of health through connected services can save lives in the event of a medical emergency or provide efficient and effective interventions for the benefit of the patients through the integration of bedside and bench side clinical research. Such integration will support all wind of change in healthcare by being predictive, pre-emptive, personalized, problem-oriented and participatory. Prototyping a healthcare system that enables data integration has been a big challenge for healthcare for a long time. However, an innovative solution started to emerge by focusing on problem lists where everything can connect the problem list forming a growing graph. This notion was introduced by Dr. Lawrence Weed in early 70’s, but the enabling technologies weren’t mature enough to provide a successful implementation prototype. In this article, we are describing our efforts in prototyping Dr. Lawrence Weed's problem-oriented medical record (POMR) and his patient case schema (SOAP) to shape a prototype for connected health. For this, we are using the TypeGraphQL API and our enterprise-based QL4POMR to describe a Web-Based gateway for healthcare services connectivity. Our prototype has reported success in connecting to the HL7 FHIR medical record and the OpenTarget biomedical repositories.

Keywords: connected health, problem-oriented healthcare record, SOAP, QL4POMR, typegraphQL

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Authors: Teresita Castillo, Concepción Campo, Carlos Carrillo

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Attention, comprehension and solution of poverty can be worked considering a socioeconomic approach; but it also can be attended from a multidimensional perspective that allows considering other dimensions including psychological variables manifested in behaviors, thoughts and feelings concerning this phenomenon. Considering the importance of research regarding psychology and poverty, this paper presents results about psychosocial impacts of poverty on young people related to mental health issues and its relation to fatalism. These results are part of a bigger transcultural study done in collaboration with the Federal University of Ceará, in Brazil. Participants were 101 young men and women, between 12 and 29 years old, living in two emarginated suburbs in Mérida, Mexico, located in the southeastern zone of the country. Participants responded the Self Report Questionnaire (SRQ- 20), with 20 items dichotomous presence/absence that assess anxious and depressive issues and the Fatalism Scale, with 30 items Likert five-point spread over five factors. Results show that one third of participants mentioned to get easily frightened, feeling nervous, tense or worried as well as unhappy, difficulty on making decisions, and troubles in thinking clearly. About 20% mentioned to have headaches, to sleep badly, to cry more than usual and to feel tired all the time. Regarding Fatalism, results show there is a greater internal allocation and lower external attribution in young participants, but they have some symptoms regarding poor mental health. Discussion is in terms of possible explanations about the results and emphasizes the importance of holistic approaches for a better understanding of the psychosocial impacts of poverty on young people and strengthening the resilience to increase positive mental health in emarginated contexts, where Community Psychology could have an important duty in community health promotion.

Keywords: fatalism, mental health, poverty, youth

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Authors: Bayar Shahab

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The fast development of technology that has advanced neuroscience and human interaction with computers has enabled solutions to various problems, and issues of this new era have been found and are being found like no other time in history. Brain-computer interface so-called BCI has opened the door to several new research areas and have been able to provide solutions to critical and important issues such as supporting a paralyzed patient to interact with the outside world, controlling a robot arm, playing games in VR with the brain, driving a wheelchair or even a car and neurotechnology enabled the rehabilitation of the lost memory, etc. This review work presents state-of-the-art methods and improvements of canonical correlation analyses (CCA), which is an SSVEP-based BCI method. These are the methods used to extract EEG signal features or, to be said in a different way, the features of interest that we are looking for in the EEG analyses. Each of the methods from oldest to newest has been discussed while comparing their advantages and disadvantages. This would create a great context and help researchers to understand the most state-of-the-art methods available in this field with their pros and cons, along with their mathematical representations and usage. This work makes a vital contribution to the existing field of study. It differs from other similar recently published works by providing the following: (1) stating most of the prominent methods used in this field in a hierarchical way (2) explaining pros and cons of each method and their performance (3) presenting the gaps that exist at the end of each method that can open the understanding and doors to new research and/or improvements.

Keywords: BCI, CCA, SSVEP, EEG

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Authors: Roman Graf, Sergiu Gordea, Heather M. Ryan

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This paper presents an approach for easy creation and classification of institutional risk profiles supporting endangerment analysis of file formats. The main contribution of this work is the employment of data mining techniques to support set up of the most important risk factors. Subsequently, risk profiles employ risk factors classifier and associated configurations to support digital preservation experts with a semi-automatic estimation of endangerment group for file format risk profiles. Our goal is to make use of an expert knowledge base, accuired through a digital preservation survey in order to detect preservation risks for a particular institution. Another contribution is support for visualisation of risk factors for a requried dimension for analysis. Using the naive Bayes method, the decision support system recommends to an expert the matching risk profile group for the previously selected institutional risk profile. The proposed methods improve the visibility of risk factor values and the quality of a digital preservation process. The presented approach is designed to facilitate decision making for the preservation of digital content in libraries and archives using domain expert knowledge and values of file format risk profiles. To facilitate decision-making, the aggregated information about the risk factors is presented as a multidimensional vector. The goal is to visualise particular dimensions of this vector for analysis by an expert and to define its profile group. The sample risk profile calculation and the visualisation of some risk factor dimensions is presented in the evaluation section.

Keywords: linked open data, information integration, digital libraries, data mining

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Authors: Parisa Göker, Hilal Kahveci, Özlem Candan Hergül

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Humans are generating culture while being in touch with nature. Along with skills, local knowledge based on experience, and many other subjects developed within this process, 'culture' offers humans a chance to survive. For this reason, culture forms the equipment for humans, which facilitates their survival in all ecosystems. Together with technology, quick consumption of natural sources and overuse culture of humans have brought up the eco-village concept. Ecovillages are ecologically, economically, socio-culturally, and spiritually sustainable settlement models. It is known that the eco-village approach is applying a proper methodology on behalf of integrative and versatile solution generation. Today, the eco-village approach, introducing a radical criticism to the understanding of civilization and consumption culture and deeming urban solutions inadequate as a spatial reflection to civilization and consumption culture, while making a difference about integrative solution offering with multidimensional features, along with the goal of creating self-sufficient communities, is creating solutions on the subject of both reducing the ecological footprint of humans and to provide social order and also to solve the injustice seen in terms of income and life standards. In this study, environmental issues, sustainable development, and environmental sustainability topics are examined within the context of eco-tourism and eco-village. Alongside this, the natural and cultural landscape values of Kurşunlu village which are located in Bilecik province’s Gölpazarı county, and a contextual frame is created for the facilitation of sustainability in the event of dynamizing the Kurşunlu village in terms of tourism-oriented activities.

Keywords: eco village, sustainability, rural landscape, cultural landscape

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Authors: Seemal Mazhar Khan, Tahira Mubashar

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The present research aimed to investigate the relationship in perceived organizational justice, organizational trust and employee engagement in bank employees. It was hypothesized: there is likely to be a relationship in perceived organizational justices, organizational trust and employee engagement; perceived organizational justice and organizational trust are likely to predict employee engagement; there is likely to be effect of bank type and designation on perceived organizational justice, organizational trust and employee engagement. The sample consisted of 150 bank employees (50 from government, 50 from private and 50 from privatized banks) selected from different banks in Lahore, Pakistan. Correlational research design was used to conduct this study. Perceived Organizational Justices Questionnaire, Organizational Trust Questionnaire and Employee Engagement Scale were used for assessment. Pearson product moment correlation, hierarchical regression and multivariate analysis of covariance were applied. Results showed a positive significant relationship in perceived organizational justice and organizational engagement and there were also a positive significant relation between organizational trust and job and organizational engagement. Results showed that organizational trust predicts organizational engagement after controlling the effect of age, marital status and socio-economic status and there is a significant interaction effect of bank type and designation level on organizational trust in bank employees. The findings of the research can serve as a platform for the awareness of important antecedents of employee engagement and organizations can inculcate trust for better and improved engagement of its employees, thereby, enhancing the productivity of their employees.

Keywords: bank employees, organizational engagement, perceived organizational justice, trust

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Authors: Bashiru Abdullahi, Isah Bala Yabo, Ibrahim Yakubu Seini

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In this paper, the steady/transient MHD heat transfer within radiative porous channel due to convective boundary conditions is considered. The solution of the steady-state and that of the transient version were conveyed by Perturbation and Finite difference methods respectively. The heat transfer mechanism of the present work ascertains the influence of Biot number〖(B〗_i1), magnetizing parameter (M), radiation parameter(R), temperature difference, suction/injection(S) Grashof number (Gr) and time (t) on velocity (u), temperature(θ), skin friction(τ), and Nusselt number (Nu). The results established were discussed with the help of a line graph. It was found that the velocity, temperature, and skin friction decay with increasing suction/injection and magnetizing parameters while the Nusselt number upsurges with suction/injection at y = 0 and falls at y =1. The steady-state solution was in perfect agreement with the transient version for a significant value of time t. It is interesting to report that the Biot number has a cogent influence consequently, as its values upsurge the result of the present work slant the extended literature.

Keywords: heat transfer, thermal radiation, porous channel, MHD, transient, convective boundary condition

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Authors: Almas Rajguru, Archana Kamath, Rachana Singh

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The study aims to determine the depositional facies, high-resolution sequence stratigraphy, and diagenetic processes of Early Oligocene carbonates in N & N-W of Heera, Mumbai Offshore. Foraminiferal assemblage and microfacies from cores of Well A, B, C, D and E are indicative of facies association related to four depositional environments, i.e., restricted inner lagoons-tidal flats, shallow open lagoons, high energy carbonate bars-shoal complex and deeper mid-ramps of a westerly dipping homoclinal carbonate ramp. Two high-frequency (4th Order) depositional sequences bounded by sequence boundary, DS1 and DS2, displaying hierarchical stacking patterns, are identified and correlated across wells. Vadose zone diagenesis effect during short diastem/ subaerial exposure has rendered good porosity due to dissolution in HST carbonates and occasionally affected underlying TST sediments (Well D, C and E). On mapping and correlating the sequences, the presence of thin carbonate bars that can be potential reservoirs are envisaged along NW-SE direction, towards north and south of Wells E, D and C. A more pronounced development of these bars in the same orientation can be anticipated towards the west of the study area.

Keywords: sequence stratigraphy, depositional facies, diagenesis petrography, early Oligocene, Mumbai offshore

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Authors: Lalita Subedi, Jae Kyoung Chae, Yong Un Park, Cho Kyo Hee, Lee Jae Hyuk, Kang Min Cheol, Sun Yeou Kim

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Neuroinflammation may mediate the relationship between low levels of estrogens and neurodegenerative disease. Estrogens are neuroprotective and anti-inflammatory in neurodegenerative disease models. Due to the long term side effects of estrogens, researches have been focused on finding an effective phytoestrogens for biological activities. Daidzein present in soybeans and its active metabolite equol (7-hydroxy-3-(4'-hydroxyphenyl)-chroman) bears strong antioxidant and anticancer showed more potent anti-inflammatory and neuroprotective role in neuroinflammatory model confirmed its in vitro activity with molecular mechanism through NF-κB pathway. Three major CNS cells Microglia (BV-2), Astrocyte (C6), Neuron (N2a) were used to find the effect of equol in inducible nitric oxide synthase (iNOS), cyclooxygenase (COX-2), MAPKs signaling proteins, apoptosis related proteins by western blot analysis. Nitric oxide (NO) and prostaglandin E2 (PGE2) was measured by the Gries method and ELISA, respectively. Cytokines like tumor necrosis factor-α (TNF-α) and IL-6 were also measured in the conditioned medium of LPS activated cells with or without equol. Equol inhibited the NO production, PGE-2 production and expression of COX-2 and iNOS in LPS-stimulated microglial cells at a dose dependent without any cellular toxicity. At the same time Equol also showed promising effect in modulation of MAPK’s and nuclear factor kappa B (NF-κB) expression with significant inhibition of the production of proinflammatory cytokine like interleukin -6 (IL-6), and tumor necrosis factor -α (TNF-α). Additionally, it inhibited the LPS activated microglia-induced neuronal cell death by downregulating the apoptotic phenomenon in neuronal cells. Furthermore, equol increases the production of neurotrophins like NGF and increase the neurite outgrowth as well. In conclusion the natural daidzein metabolite equol are more active than daidzein, which showed a promising effectiveness as an anti-neuroinflammatory and neuroprotective agent via downregulating the LPS stimulated microglial activation and neuronal apoptosis. This work was supported by Brain Korea 21 Plus project and High Value-added Food Technology Development Program 114006-4, Ministry of Agriculture, Food and Rural Affairs.

Keywords: apoptosis, equol, neuroinflammation, phytoestrogen

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Authors: Lei Zhang

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The carbon cycle greatly influences global change, and the land cover changes contribute to the status and rate of the carbon budget in ecosystems. This paper proposes a land cover classification system for mapping land cover, the national ecological environment assessment, and estimating carbon storage in ecosystems. The classification system consists of basic land cover classes at levels Ⅰ and Ⅱ and auxiliary features at level III. The basic 38 classes characterizing land cover features are derived from 19 criteria referring to composition, structure, pattern, phenology, etc. The basic classes reflect the status of carbon storage in ecosystems. The auxiliary classes at level III complement the attributes of higher levels by 9 criteria. The 5 environmental criteria of temperature, moisture, landform, aspect and slope mainly reflect the potential and intensity of carbon storage in ecosystems. The disturbance of vegetation succession caused by land use type influences the vegetation carbon budget. The other 3 vegetation cover criteria, growth period, and species characteristics further refine the vegetation types. The hierarchical structure of the land cover map (the classes of levels Ⅰ and Ⅱ) is independent of the products of level III, which is helpful for land cover product management and applications. The classification system has been adopted in the Chinese national land cover database for the carbon budget in ecosystems at a 30 m scale.

Keywords: classification system, land cover, ecosystem, carbon storage, object based

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Authors: F. Hamidi, N. Amiri, H. Mishmast Nehi

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The Bilevel Programming (BP) model has been presented for a decision making process that consists of two decision makers in a hierarchical structure. In fact, BP is a model for a static two person game (the leader player in the upper level and the follower player in the lower level) wherein each player tries to optimize his/her personal objective function under dependent constraints; this game is sequential and non-cooperative. The decision making variables are divided between the two players and one’s choice affects the other’s benefit and choices. In other words, BP consists of two nested optimization problems with two objective functions (upper and lower) where the constraint region of the upper level problem is implicitly determined by the lower level problem. In real cases, the coefficients of an optimization problem may not be precise, i.e. they may be interval. In this paper we develop an algorithm for solving interval bilevel linear fractional programming problems. That is to say, bilevel problems in which both objective functions are linear fractional, the coefficients are interval and the common constraint region is a polyhedron. From the original problem, the best and the worst bilevel linear fractional problems have been derived and then, using the extended Charnes and Cooper transformation, each fractional problem can be reduced to a linear problem. Then we can find the best and the worst optimal values of the leader objective function by two algorithms.

Keywords: best and worst optimal solutions, bilevel programming, fractional, interval coefficients

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Authors: Zeinab. Shahrokhifar, Abolfazl Meshkini, Seyed Ali. Alavi

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Considering the different dimensions of deprivation, housing supply is noted as a basic requirement in Iran after 1979 (coming to work of the new government). The government had built the constitution and obliged to meet this need in the form of five-year development programs in Iran’s provinces. This study focused on the adaptive analysis of housing policies in these five development programs in Kermanshah province located in western Iran. Our research is divided into two different analytical sections. In the first section, we collected the documentary information using approved plans and field studies. In the second section, a questionnaire was prepared and designed for the elite community (30) to support the documentary analysis. The results showed that various projects adopted in the form of strategic plans and implemented the policies included both quantitative and qualitative housing in Kermanshah province after 1979. The quality of housing, from the first to the fifth development plans has improved the situation in the housing indicators. The quantity of housing units for households has also been implemented through various policies that has desired results. The sequences of housing policies and plans do not overlap in the five development programs. According to the radar graph, the development programs overlapped in some policies, which shows the continuation of the previous policies, but this overlap is not perfect.

Keywords: law enforcement policy, housing policy, development programs, housing indicators, the city of Kermanshah

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Authors: Alexis Thouki

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In our post-Christian societies, ecclesiastical heritage acquired a new extrovert profile aiming to reach out an increasingly diverse audience. In this context, the various motivations, interests, personalities and cultural exchanges, found in the ‘post-modern pilgrimage’, bequeath a hybrid and multidimensional character to religious tourism education. In consequence, churches have acquired the challenging role of enriching visitors cultural and spiritual capital. Despite this promising diversification to relate, reveal and provoke constructive discourses, due to the various ‘conflicting interests’, practitioners attempt to tame the rich in symbolism and meanings religious environment through ‘neutral interpretations’. This paper aims to present the results of an ongoing developing strategy related to the presentation of contentious meanings in English churches. The paper will explore some of the underlying issues related to the capacity of ‘neutrality’ to spark, downplay or eliminate contentious conversations relating to the cultural, religious, and social dimension of Christian cultural heritage thematology. In an effort to understand this issue, the paper examines the concept of neutrality and what it stands for, executing a discourse analysis in the semantic context in which the theological lexicon is interwoven with the cultural and social meanings of sacred sites. Following that, the paper examines whether the preferable interpretive strategies meet the post-modern interpretative framework which is marked by polysemy and critical active engagement. The ultimate aim of the paper is to investigate the hypothesis that the preferable neutral strategies, managing the ‘conflicting’ demands of worshippers and visitors, result in the uneven treatment of both, the religious and historical spirit of the place.

Keywords: contentious dialogue, interpretation, meaning making, religious tourism

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Authors: Yasser Almazrua

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Terrorism is a multidimensional phenomenon that has increased recently. Many countries started combating terrorism through security forces; however, there has been relatively little attention given to rehabilitation programs for people involved in such terrorism acts. In Saudi Arabia, after facing so many terrorist attacks, they started understanding and countering terrorism differently by establishing Mohammad bin Naif Counselling and Care Center in 2006. The center now is considered one of the top experience centers in the world for terrorist rehabilitation and ideology correction. The center offers different programs such as training, educational, social, art and psychological programs. One of the approaches that have been used by psychological experts at the center is Narrative Therapy. It is a therapeutic approach that focuses on the ability of the client to identify their personal life story. The client during therapy works as a storyteller where he or she gets insight, meaning and better understanding of their own lives. Because each client at the center had a story, it can be better fit method for rehabilitation towards healing and personal development. The case describes a 34-years-old man who was involved in some terrorism activities locally by technically and financially supporting a terrorist group related to Al-Qaida. The beneficiary joined Mohammad bin Naif Counseling and Care Center after serving his sentence. Informed of consent has been given to the beneficiary before starting the therapeutic program. Both qualitative and quantitative data on the beneficiary are collected by self-reporting during the initial session, and by using a psychological measurement. The result found that the beneficiary was not insightful about himself, and he had a high level of repression which relatedly moved him to be targeted for recruitment in the terrorist group. With rehabilitation and by using the therapeutic approach, the beneficiary improved on the level of insight, specifically about himself and also about the experience. This case illustrates the importance of considering the effect of Narrative Therapy in terrorist rehabilitation programs.

Keywords: narrative therapy, rehabilitation, Saudi Arabia, terrorism

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Authors: Min Kyung An

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In Wireless Sensor Networks which consist of tiny wireless sensor nodes with limited battery power, one of the most fundamental applications is data aggregation which collects nearby environmental conditions and aggregates the data to a designated destination, called a sink node. Important issues concerning the data aggregation are time efficiency and energy consumption due to its limited energy, and therefore, the related problem, named Minimum Latency Aggregation Scheduling (MLAS), has been the focus of many researchers. Its objective is to compute the minimum latency schedule, that is, to compute a schedule with the minimum number of timeslots, such that the sink node can receive the aggregated data from all the other nodes without any collision or interference. For the problem, the two interference models, the graph model and the more realistic physical interference model known as Signal-to-Interference-Noise-Ratio (SINR), have been adopted with different power models, uniform-power and non-uniform power (with power control or without power control), and different antenna models, omni-directional antenna and directional antenna models. In this survey article, as the problem has proven to be NP-hard, we present and compare several state-of-the-art approximation algorithms in various models on the basis of latency as its performance measure.

Keywords: data aggregation, convergecast, gathering, approximation, interference, omni-directional, directional

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Authors: Meilee Shen

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Buddhist nuns in Taiwan have shouldered various responsibilities in educational, cultural, economic, and social aspects that transforms and transcends Chinese Buddhism to a higher level in Taiwan and overseas. In the recent years, Nuns in Taiwan have formed various associations to reach their goals and satisfy their needs. This research will focus on the following: 1. How to distinguish a Buddhist organization from temple? 2. Whether the forming of female Buddhist organizations reveals religious purpose or gender conflict in Buddhism? 3. How can nuns in Taiwan be unified together to establish their identification? This paper will mainly study on the Chinese Buddhist Bhikkhuni Association (CBBA) because they have gained allies together to work for religious causes and social needs since 1996. However, with a mission to promote female practitioners’ role in Buddhist circle, CBBA did not contribute much to the gender issue in Buddhism. The research found that CBBA did not achieve their goal to unite nuns in Taiwan because they failed to support nuns' education and did not recruit young and highly educated ones as CBBA's faculties. In conclusion, the research suggests i) to connect with other Buddhist organizations in order to achieve the dream of unity, ii) to fill the generation gap by overturn hierarchical system in Buddhist community and create a new environment for new generation to grow, iii) to shift financial contribution from social charity to nuns’ education to promote female role in Buddhism in the future.

Keywords: Bhikkhuni in Taiwan, Bhikkhuni population and education, Buddhism in Taiwan, Chinese Buddhist Bhikkhuni Association

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Authors: Lovorka Galetic, Zeljko Vukelic

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The advanced concepts of strategic and innovation management in the sphere of company dynamic and innovation capabilities, and achieving their mutual alignment and a synergy effect, are important elements in business today. This paper analyses the theory and empirically investigates the influence of a company’s dynamic capabilities on its innovation capabilities. A new multidimensional model of dynamic capabilities is presented, consisting of five factors appropriate to real time requirements, while innovation capabilities are considered pursuant to the official OECD and Eurostat standards. After examination of dynamic and innovation capabilities indicated their theoretical links, the empirical study testing the model and examining the influence of a company’s dynamic capabilities on its innovation capabilities showed significant results. In the study, a research model was posed to relate company dynamic and innovation capabilities. One side of the model features the variables that are the determinants of dynamic capabilities defined through their factors, while the other side features the determinants of innovation capabilities pursuant to the official standards. With regard to the research model, five hypotheses were set. The study was performed in late 2014 on a representative sample of large and very large Croatian enterprises with a minimum of 250 employees. The research instrument was a questionnaire administered to company top management. For both variables, the position of the company was tested in comparison to industry competitors, on a fivepoint scale. In order to test the hypotheses, correlation tests were performed to determine whether there is a correlation between each individual factor of company dynamic capabilities with the existence of its innovation capabilities, in line with the research model. The results indicate a strong correlation between a company’s possession of dynamic capabilities in terms of their factors, due to the new multi-dimensional model presented in this paper, with its possession of innovation capabilities. Based on the results, all five hypotheses were accepted. Ultimately, it was concluded that there is a strong association between the dynamic and innovation capabilities of a company. 

Keywords: dynamic capabilities, innovation capabilities, competitive advantage, business results

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Authors: Strahinja Kovačević, Lidija Jevrić, Miloš Kuzmanović, Sanja Podunavac-Kuzmanović

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In this paper, various derivatives of benzimidazole have been evaluated against Gram-negative bacteria Escherichia coli. For all investigated compounds the minimum inhibitory concentration (MIC) was determined. Quantitative structure-activity relationships (QSAR) attempts to find consistent relationships between the variations in the values of molecular properties and the biological activity for a series of compounds so that these rules can be used to evaluate new chemical entities. The correlation between MIC and some absorption, distribution, metabolism and excretion (ADME) parameters was investigated, and the mathematical models for predicting the antibacterial activity of this class of compounds were developed. The quality of the multiple linear regression (MLR) models was validated by the leave-one-out (LOO) technique, as well as by the calculation of the statistical parameters for the developed models and the results are discussed on the basis of the statistical data. The results of this study indicate that ADME parameters have a significant effect on the antibacterial activity of this class of compounds. Principal component analysis (PCA) and agglomerative hierarchical clustering algorithms (HCA) confirmed that the investigated molecules can be classified into groups on the basis of the ADME parameters: Madin-Darby Canine Kidney cell permeability (MDCK), Plasma protein binding (PPB%), human intestinal absorption (HIA%) and human colon carcinoma cell permeability (Caco-2).

Keywords: benzimidazoles, QSAR, ADME, in silico

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Authors: Ahmed Al-Adaileh

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Renewable energy sources, domestic storage systems, controllable loads and machine learning technologies will be key components of future smart homes management systems. An energy management scheme that uses a Deep Learning (DL) approach to support the smart home management systems, which consist of a standalone photovoltaic system, storage unit, heating ventilation air-conditioning system and a set of conventional and smart appliances, is presented. The objective of the proposed scheme is to apply DL-based machine learning to predict various running parameters within a smart home's environment to achieve maximum comfort levels for occupants, reduced electricity bills, and less dependency on the public grid. The problem is using Reinforcement learning, where decisions are taken based on applying the Continuous-time Markov Decision Process. The main contribution of this research is the proposed framework that applies DL to enhance the system's supervised dataset to offer unlimited chances to effectively support smart home systems. A case study involving a set of conventional and smart appliances with dedicated processing units in an inhabited building can demonstrate the validity of the proposed framework. A visualization graph can show "before" and "after" results.

Keywords: smart homes systems, machine learning, deep learning, Markov Decision Process

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Authors: Smita Singh

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The paper develops a critical framework to the hegemonic discourses resorted to by the dominant powers in the global security architecture. Within this framework, security is viewed as a discourse through which identities and threats are represented and produced to legitimize the security concerns of few at the cost of others. International security have long been driven and dominated by power relations. Since the end of the Cold War, the global transformations have triggered contestations to the idea of security at both theoretical and practical level. These widening and deepening of the concept of security have challenged the existing power hierarchies at the theoretical level but not altered the substance and actors defining it. When discourses are introduced into security studies, several critical questions erupt: how has power shaped security policies of the globe through language? How does one understand the meanings and impact of those discourses? Who decides the agenda, rules, players and outliers of the security? Language as a symbolic system and form of power is fluid and not fixed. Over the years the dominant Western powers, led by the United States of America have employed various discursive practices such as humanitarian intervention, responsibility to protect, non proliferation, human rights, war on terror and so on to reorient the constitution of identities and interests and hence the policies that need to be adopted for its actualization. These power relations are illustrated in this paper through the narratives used in the nonproliferation regime. The hierarchical security dynamics is a manifestation of the global power relations driven by many factors including discourses.

Keywords: hegemonic discourse, global security, non-proliferation regime, power politics

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Authors: Sharifah Mousli, Sona Taheri, Jiayuan He

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Autism spectrum disorder (ASD) is a complex developmental condition involving persistent difficulties with social communication, restricted interests, and repetitive behavior. The challenges associated with ASD can interfere with an affected individual’s ability to function in social, academic, and employment settings. Although there is no effective medication known to treat ASD, to our best knowledge, early intervention can significantly improve an affected individual’s overall development. Hence, an accurate diagnosis of ASD at an early phase is essential. The use of machine learning approaches improves and speeds up the diagnosis of ASD. In this paper, we focus on the application of unsupervised clustering methods in ASD as a large volume of ASD data generated through hospitals, therapy centers, and mobile applications has no pre-existing labels. We conduct a comparative analysis using seven clustering approaches such as K-means, agglomerative hierarchical, model-based, fuzzy-C-means, affinity propagation, self organizing maps, linear vector quantisation – as well as the recently developed optimization-based clustering (COMSEP-Clust) approach. We evaluate the performances of the clustering methods extensively on real-world ASD datasets encompassing different age groups: toddlers, children, adolescents, and adults. Our experimental results suggest that the COMSEP-Clust approach outperforms the other seven methods in recognizing ASD with well-separated clusters.

Keywords: autism spectrum disorder, clustering, optimization, unsupervised machine learning

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Authors: Abhimanyu Pati, Krishna Kumar Veluri

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The paper intends to bring out a real life experience encountered during actual implementation of a large scale Tier-1 Enterprise Resource Planning (ERP) system in a multi-location, discrete manufacturing organization in India, involved in manufacturing of auto components and aggregates. The business complexities, prior to the implementation of ERP, include multi-product with hierarchical product structures, geographically distributed multiple plant locations with disparate business practices, lack of inter-plant broadband connectivity, existence of disparate legacy applications for different business functions, and non-standardized codifications of products, machines, employees, and accounts apart from others. On the other hand, the manufacturing environment consisted of processes like Assemble-to-Order (ATO), Make-to-Stock (MTS), and Engineer-to-Order (ETO) with a mix of discrete and process operations. The paper has highlighted various business plan areas and concerns, prior to the implementation, with specific focus on strategic issues and objectives. Subsequently, it has dealt with the complete process of ERP implementation, starting from strategic planning, project planning, resource mobilization, and finally, the program execution. The step-by-step process provides a very good learning opportunity about the implementation methodology. At the end, various organizational challenges and lessons emerged, which will act as guidelines and checklist for organizations to successfully align and implement ERP and achieve their business objectives.

Keywords: ERP, ATO, MTS, ETO, discrete manufacturing, strategic planning

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Authors: Elham Zabetian Targhi, Niusha Fardnava, Saba Saghafi

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Urban resilience to Corona Virus has become a major concern for cities these days. Our country also has not been safe from the destructive effects of this virus in social, economic, physical, governance, and management dimensions; and according to official statistics, hundreds of thousands of people in Iran have been infected with this virus and tens of thousands have died so far. Therefore, to measure urban resilience to this pandemic, some criteria and sub-criteria were developed based on the authors’ documentary and field studies, and their significance or weights were determined using analytical-comparative research method using a questionnaire of paired or L-Saati comparisons from the viewpoint of experts in urban sciences and urban development using AHP hierarchical analysis in EXPERT CHOICE software. Then, designing a questionnaire with a five-point Likert scale, the satisfaction of Tehran residents with the extracted criteria and sub-criteria was measured and the correlation between the important criteria in each dimension was assessed using correlation tests in SPSS16 software. According to the obtained results of AHP analysis and the scores of each sub-criterion, the weight of all criteria was normal. In the next stage, according to the pairwise correlation tests between the important criteria in each dimension from the viewpoint of urban science experts and Tehran residents, it was concluded that the reliability of the correlation between the criteria is 99%. In all the cases, the P-value or the same significance level was less than 0.05, which indicated the significance of the pairwise relations between the variables.

Keywords: Urban Resilience, Pandemics, Corona Virus (Covid-19), Criteria.

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Authors: Wenjun Zhang, Yunqing Du, Steven W. Cranford, Ming L. Wang

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The beginning of 21st century has witnessed new advancements in the design and use of new materials for biosensing applications, from nano to macro, protein to tissue. Traditional analytical methods lack a complete toolset to describe the complexities introduced by living systems, pathological relations, discrete hierarchical materials, cross-phase interactions, and structure-property dependencies. Materiomics – via systematic molecular dynamics (MD) simulation – can provide structure-process-property relations by using a materials science approach linking mechanisms across scales and enables oriented biosensor design. With this approach, DNA biosensors can be utilized to detect disease biomarkers present in individuals’ breath such as acetone for diabetes. Our wireless sensor array based on single-stranded DNA (ssDNA)-decorated single-walled carbon nanotubes (SWNT) has successfully detected trace amount of various chemicals in vapor differentiated by pattern recognition. Here, we present how MD simulation can revolutionize the way of design and screening of DNA aptamers for targeting biomarkers related to oral diseases and oral health monitoring. It demonstrates great potential to be utilized to build a library of DNDA sequences for reliable detection of several biomarkers of one specific disease, and as well provides a new methodology of creating, designing, and applying of biosensors.

Keywords: biosensor, DNA, biomarker, molecular dynamics simulation

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Authors: Konrad Rejdak, Agnieszka Korchut, Sebastian Szklener, Urszula Skrobas, Justyna Gerlowska, Katarzyna Grabowska-Aleksandrowicz, Dorota Szczesniak-Stanczyk

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Introduction: Neurodegenerative diseases are frequently accompanied by loss and unwanted change in functional independence, social relationships, and economic circumstances. Currently, the achievements of social robots to date is being projected to improve multidimensional quality of life among people with cognitive impairment and others. Objectives: Identification of particular human needs in context of the changes occurring in course of neurodegenerative diseases. Methods: Based on the 110 surveys performed in Medical University of Lublin from medical staff, patients, and caregivers we made prioritization of the users' needs as: high, medium, and low. The issues included in the surveys concerned four aspects: user acceptance, functional requirements, design of the robotic assistant and preferred types of human-robot interaction. Results: We received completed questionnaires: 50 from medical staff, 30 from caregivers and 30 from potential users. Above 90% of the respondents from each of the three groups, accepted robotic assistant as a potential caregiver. High priority functional capability of assistive technology was to handle emergencies in a private home like recognizing life-threatening situations and reminding about medication intake. With reference to design of the robotic assistant, the majority of the respondent would like to have an anthropomorphic appearance with positive emotionally expressive face. The most important type of human-robot interaction was voice-operated system and by touchscreen. Conclusion: The results from our study might contribute to a better understanding of the system and users’ requirements for the development of a service robot intended to support patients with dementia.

Keywords: social robot, dementia, requirements, patients needs

Procedia PDF Downloads 245

Authors: Abdoulaye Diop, Nahid Emad, Thierry Winter, Mohamed Hilia

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Data assets protection is a crucial issue in the cybersecurity field. Companies use logical access control tools to vault their information assets and protect them against external threats, but they lack solutions to counter insider threats. Nowadays, insider threats are the most significant concern of security analysts. They are mainly individuals with legitimate access to companies information systems, which use their rights with malicious intents. In several fields, behavior anomaly detection is the method used by cyber specialists to counter the threats of user malicious activities effectively. In this paper, we present the step toward the construction of a user and entity behavior analysis framework by proposing a behavior anomaly detection model. This model combines machine learning classification techniques and graph-based methods, relying on linear algebra and parallel computing techniques. We show the utility of an ensemble learning approach in this context. We present some detection methods tests results on an representative access control dataset. The use of some explored classifiers gives results up to 99% of accuracy.

Keywords: cybersecurity, data protection, access control, insider threat, user behavior analysis, ensemble learning, high performance computing

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Authors: Raymond Xu, Cindy Jingru Wang

Abstract:

Social media has become a globally connected and influencing platform. Social media data, such as tweets, can help predict the spread of pandemics and provide individuals and healthcare providers early warnings. Public psychological reactions and opinions can be efficiently monitored by AI models on the progression of dominant topics on Twitter. However, statistics show that as the coronavirus spreads, so does an infodemic of misinformation due to pandemic-related factors such as unemployment and lockdowns. Social media algorithms are often biased toward outrage by promoting content that people have an emotional reaction to and are likely to engage with. This can influence users’ attitudes and cause confusion. Therefore, social media is a double-edged sword. Combating fake news and biased content has become one of the essential tasks. This research analyzes the variety of methods used for fake news detection covering random forest, logistic regression, support vector machines, decision tree, naive Bayes, BoW, TF-IDF, LDA, CNN, RNN, LSTM, DeepFake, and hierarchical attention network. The performance of each method is analyzed. Based on these models’ achievements and limitations, a multi-dimensional AI framework is proposed to achieve higher accuracy in infodemic detection, especially pandemic-related news. The model is trained on contextual content, images, and news metadata.

Keywords: artificial intelligence, fake news detection, infodemic detection, image recognition, sentiment analysis

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Authors: Melissa Dickinson

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In the context of intense global competitiveness and urban transformations, today’s cities are faced with enormous challenges. There is increasing pressure among cities and regions to respond promptly and efficiently to fierce market progressions, to offer a competitive advantage, higher flexibility, and to be pro-active in creating future markets. Consequently, competition among cities and regions within the dynamics of a worldwide spatial economic system is growing fiercer, amplifying the importance of intangible capital in shaping the competitive and dynamic economic performance of organisations and firms. Accordingly, this study addresses how intangible capital influences urban economic development within an urban environment. Despite substantial research on the economic, and strategic determinants of urban economic development this multidimensional phenomenon remains to be one of the greatest challenges for economic geographers. The research provides a unique contribution, exploring intangible capital through the lenses of entrepreneurial capital and social-network capital. Drawing on business surveys and in-depth interviews with key stakeholders in the case of the three UK Core Cities Birmingham, Bristol and Cardiff. This paper critically considers how entrepreneurial capital and social-network capital is a crucial source of competitiveness and urban economic development. This paper deals with questions concerning the complexity of operationalizing ‘network capital’ in different urban settings and the challenges that reside in characterising its effects. The paper will highlight the role of institutions in facilitating urban economic development. Particular emphasis will be placed on exploring the roles formal and informal institutions have in delivering, supporting and nurturing entrepreneurial capital and social-network capital, to facilitate urban economic development. Discussions will then consider how institutions moderate and contribute to the economic development of urban areas, to provide implications in terms of future policy formulation in the context of large and medium sized cities.

Keywords: urban economic development, network capital, entrepreneurialism, institutions

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Authors: Yang Zhou, Heli Sun, Jianbin Huang, Jizhong Zhao, Shaojie Qiao

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Traffic prediction is a fundamental problem in urban environment, facilitating the smart management of various businesses, such as taxi dispatching, bike relocation, and stampede alert. Most earlier methods rely on identifying the intrinsic spatial-temporal correlation to forecast. However, the complex nature of this problem entails a more sophisticated solution that can simultaneously capture the mutual influence of both adjacent and far-flung areas, with the information of time-dimension also incorporated seamlessly. To tackle this difficulty, we propose a new multi-phase architecture, DSTDS (Dual Spatial-Temporal Data Scheme for traffic prediction), that aims to reveal the underlying relationship that determines future traffic trend. First, a graph-based neural network with an attention mechanism is devised to obtain the static features of the road network. Then, a multi-granularity recurrent neural network is built in conjunction with the knowledge from a grid-based model. Subsequently, the preceding output is fed into a spatial-temporal super-resolution module. With this 3-phase structure, we carry out extensive experiments on several real-world datasets to demonstrate the effectiveness of our approach, which surpasses several state-of-the-art methods.

Keywords: traffic prediction, spatial-temporal, recurrent neural network, dual data scheme

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Authors: Rikson Gultom

Abstract:

Hate Speech and Abusive language on social media is difficult to detect, usually, it is detected after it becomes viral in cyberspace, of course, it is too late for prevention. An early detection system that has a fairly good accuracy is needed so that it can reduce conflicts that occur in society caused by postings on social media that attack individuals, groups, and governments in Indonesia. The purpose of this study is to find an early detection model on Twitter social media using machine learning that has high accuracy from several machine learning methods studied. In this study, the support vector machine (SVM), Naïve Bayes (NB), and Random Forest Decision Tree (RFDT) methods were compared with the Support Vector machine with genetic algorithm (SVM-GA), Nave Bayes with genetic algorithm (NB-GA), and Random Forest Decision Tree with Genetic Algorithm (RFDT-GA). The study produced a comparison table for the accuracy of the hate speech and abusive language detection model, and presented it in the form of a graph of the accuracy of the six algorithms developed based on the Indonesian-language Twitter dataset, and concluded the best model with the highest accuracy.

Keywords: abusive language, hate speech, machine learning, optimization, social media

Procedia PDF Downloads 107