Search results for: fuzzy genetic network programming

Authors: Sophia Barquete, Adriana H. Trevisan, Janaina Mascarenhas

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The circular economy (CE) aims at the cycling of resources through restorative and regenerative strategies. To achieve circularity, coordination of several actors who have different responsibilities is necessary. The interaction among multiple actors allows the connection between the CE and business ecosystem research fields. Although fundamental, the relationships between actors within an ecosystem to foster circularity are not deeply explored in the literature. The objective of this study was to identify the possibilities of cooperation, competition, or even coopetition among the members of business ecosystems that adopt circular strategies. In particular, the motivations that make these actors interact to achieve a circular economy were investigated. A systematic literature review was adopted to select business ecosystem cases that adopt circular strategies. As a result, several motivations were identified for actors to engage in relationships within ecosystems, such as sharing knowledge and infrastructure, developing products with a circular design, promoting reverse logistics, among others. The results suggest that partnerships between actors are, in fact, important for the implementation of circular strategies. In order to achieve a complete and circular solution, actors must be able to clearly understand their roles and relationships within the network so that they can establish new partnerships or reframe those already established.

Keywords: business ecosystem, circular economy, cooperation, coopetition, competition

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Authors: Jana Heimel, Franziska Drescher, Lauren Ugur, Graciela Kuchle

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Sharing platforms are a widely investigated field. However, there is a research gap with a lack of focus on ‘real’ (non-profit-orientated) sharing platforms. The research project addresses this gap by conducting an empirical study on a private peer-to-peer (P2P) network to investigate cooperative behavior from a socio-psychological perspective. In recent years the conversion from possession to accessing is increasingly influencing different sectors, particularly the traveling industry. The number of people participating in hospitality exchange platforms like Airbnb, Couchsurfing, and Warmshowers (WS) is rapidly growing. WS is an increasingly popular online community that is linking cycling tourists and locals. It builds on the idea of the “sharing economy” as a not-for-profit hospitality network for bicycle tourists. Hosts not only provide a sleeping berth and warm shower free of charge but also offer additional services to their guests, such as cooking and washing clothes for them. According to previous studies, they are motivated by the idea of promoting cultural experience and forming new friendships. Trust and reciprocity are supposed to play major roles in the success of such platforms. The objective of this research project is to analyze the reciprocity behavior within the WS community. Reciprocity is the act of giving and taking among each other. Individuals feel obligated to return a favor and often expect to increase their own chances of receiving future benefits for themselves. Consequently, the drivers that incite giving and taking, as well as the motivation for hosts and guests, are examined. Thus, the project investigates a particular tourism offer that contributes to sustainable tourism by analyzing P2P resp. cyclist-to-cyclist, C2C) tourism. C2C tourism is characterized by special hospitality and generosity. To find out what motivations drive the hosts and which determinants drive the sharing cycling economy, an empirical study has been conducted globally through an online survey. The data was gathered through the WS community and comprised responses from more than 10,000 cyclists around the globe. Next to general information mostly comprising quantitative data on bicycle tourism (year/tour distance, duration and budget), qualitative information on traveling with WS as well as hosting was collected. The most important motivations for a traveler is to explore the local culture, to save money, and to make friends. The main reasons to host a guest are to promote the use of bicycles and to make friends, but also to give back and pay forward. WS members prefer to stay with/host cyclists. The results indicate that C2C tourists share homogenous characteristics and a similar philosophy, which is crucial for building mutual trust. Members of WS are generally extremely trustful. The study promotes an ecological form of tourism by combining sustainability, regionality, health, experience and the local communities' cultures. The empirical evidence found and analyzed, despite evident limitations, enabled us to shed light, especially on the issue of motivations and social capital, and on the functioning of ‘sharing’ platforms. Final research results are intended to promote C2C tourism around the globe to further replace conventional by sustainable tourism.

Keywords: bicycle tourism, homogeneity, reciprocity, sharing economy, trust

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Authors: Colette Faucher

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In modern asymmetric conflicts, the Armed Forces generally have to intervene in countries where the internal peace is in danger. They must make the local population an ally in order to be able to deploy the necessary military actions with its support. For this purpose, psychological operations (PSYOPs) are used to shape people’s behaviors and emotions by the modification of their attitudes in acting on their perceptions. PSYOPs aim at elaborating and spreading a message that must be read, listened to and/or looked at, then understood by the info-targets in order to get from them the desired behavior. A message can generate in the info-targets, reasoned thoughts, spontaneous emotions or reflex behaviors, this effect partly depending on the means of conveyance used to spread this message. In this paper, we focus on psychological operations that generate emotions. We present a method based on the Intergroup Emotion Theory, that determines, from the characteristics of the conveyed message and of the people from the population directly reached by the means of conveyance (direct info-targets), the emotion likely to be triggered in them and we simulate the propagation of the effects of such a message on indirect info-targets that are connected to them through the social networks that structure the population.

Keywords: military psychological operations, social identity, social network, emotion propagation

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Authors: Juan Sebastián Hernández

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The Nevado del Ruíz volcano, located between the limits of the Departments of Caldas and Tolima in Colombia, presented an unstable behaviour in the course of the year 2020, this volcanic activity led to secondary effects on the crust, which is why the prediction of deformations becomes the task of geoscientists. In the course of this article, the use of tropospheric variables such as evapotranspiration, UV aerosol index, carbon monoxide, nitrogen dioxide, methane, surface temperature, among others, is used to train a set of neural networks that can predict the behaviour of the resulting phase of an unrolled interferogram with the DInSAR technique, whose main objective is to identify and characterise the behaviour of the crust based on the environmental conditions. For this purpose, variables were collected, a generalised linear model was created, and a set of neural networks was created. After the training of the network, validation was carried out with the test data, giving an MSE of 0.17598 and an associated r-squared of approximately 0.88454. The resulting model provided a dataset with good thematic accuracy, reflecting the behaviour of the volcano in 2020, given a set of environmental characteristics.

Keywords: crustal deformation, Tropomi, neural networks (ANN), volcanic activity, DInSAR

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Authors: Dina Magnaye

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In the Philippines, the performance of the agricultural sector is gauged through crop productivity and returns from farm production rather than the biodiversity in the agricultural ecosystem. Agricultural development hinges on the overall goal of increasing productivity through intensive agriculture, monoculture system, utilization of high yielding varieties in plants, and genetic upgrading in animals. This merits an analysis of the role of agro-biodiversity in terms of increasing productivity, food security and economic returns from community-based pro-biodiversity enterprises. These enterprises conserve biodiversity while equitably sharing production income in the utilization of biological resources. The study aims to determine how community-based pro-biodiversity enterprises become instrumental in local climate change adaptation and agro-biodiversity conservation as input to local green economic development planning. It also involves an assessment of the role of agrobiodiversity in terms of increasing productivity, food security and economic returns from community-based pro-biodiversity enterprises. The perceptions of the local community members both in urban and upland rural areas on community-based pro-biodiversity enterprises were evaluated. These served as a basis in developing a planning modality that can be mainstreamed in the management of local green economic enterprises to benefit the environment, provide local income opportunities, conserve species diversity, and sustain environment-friendly farming systems and practices. The interviews conducted with organic farmer-owners, entrepreneur-organic farmers, and organic farm workers revealed that pro-biodiversity enterprise such as organic farming involved the cyclic use of natural resources within the carrying capacity of a farm; recognition of the value of tradition and culture especially in the upland rural area; enhancement of socio-economic capacity; conservation of ecosystems in harmony with nature; and climate change mitigation. The suggested planning modality for community-based pro-biodiversity enterprises for a green economy encompasses four (4) phases to include community resource or capital asset profiling; stakeholder vision development; strategy formulation for sustained enterprises; and monitoring and evaluation.

Keywords: agro-biodiversity, agro-biodiversity conservation, local green economy, organic farming, pro-biodiversity enterprise

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Authors: Demelash Kassaye

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The Ethiopia case study reveals a unique model of community policing that has developed from a particular political context in which there is a history of violent political transition, a political structure characterized by ethnic federalism and a political ideology that straddles liberal capitalism and democracy on the one hand, and state-led development and centralized control on the other. The police see community policing as a way to reduce crime. Communities speak about community policing as an opportunity to take on policing responsibilities themselves. Both of these objectives are brought together in an overarching rhetoric of community policing as a way of ‘mobilizing for development’ – whereby the community cooperate with the police to reduce crime, which otherwise inhibits development progress. Community policing in Amhara has primarily involved the placement of Community Police Officers at the kebele level across the State. In addition, a number of structures have also been established in the community, including Advisory Councils, Conflict Resolving Committees, family police and the use of shoe shiner’s and other trade associations as police informants. In addition to these newly created structures, community policing also draws upon pre-existing customary actors, such as militia and elders. Conflict Resolving Committees, Community Police Officers and elders were reported as the most common first ports of call when community members experience a crime. The analysis highlights that the model of community policing in Amhara increased communities’ access to policing services, although this is not always attended by increased access to justice. Community members also indicate that public perceptions of the police have improved since the introduction of community policing, in part due to individual Community Police Officers who have, with limited resources, innovated some impressive strategies to improve safety in their neighborhoods. However, more broadly, community policing has provided the state with more effective surveillance of the population – a potentially oppressive function in the current political context. Ultimately, community policing in Amhara is anything but straightforward. It has been a process of attempting to demonstrate the benefits of newfound (and controversial) ‘democracy’ following years of dictatorship, drawing on generations of customary dispute resolution, providing both improved access to security for communities and an enhanced surveillance capacity for the state. For external actors looking to engage in community policing, this case study reveals the importance of close analysis in assessing potential merits, risks and entry points of programming. Factors found to be central in shaping the nature of community policing in the Amhara case include the structure of the political system, state-society relations, cultures dispute resolution and political ideology.

Keywords: community policing, community, militias, ethiopia

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Authors: Haiyan Wu, Ying Liu, Shaoyun Shi

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Authorship attribution is to extract features to identify authors of anonymous documents. Many previous works on authorship attribution focus on statistical style features (e.g., sentence/word length), content features (e.g., frequent words, n-grams). Modeling these features by regression or some transparent machine learning methods gives a portrait of the authors' writing style. But these methods do not capture the syntactic (e.g., dependency relationship) or semantic (e.g., topics) information. In recent years, some researchers model syntactic trees or latent semantic information by neural networks. However, few works take them together. Besides, predictions by neural networks are difficult to explain, which is vital in authorship attribution tasks. In this paper, we not only utilize the statistical style and content features but also take advantage of both syntactic and semantic features. Different from an end-to-end neural model, feature selection and prediction are two steps in our method. An attentive n-gram network is utilized to select useful features, and logistic regression is applied to give prediction and understandable representation of writing style. Experiments show that our extracted features can improve the state-of-the-art methods on three benchmark datasets.

Keywords: authorship attribution, attention mechanism, syntactic feature, feature extraction

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Authors: Josiane N. Muller, Allan K. R. Galardo, Tatiane A. Barbosa, Evan P. Ferro, Wellington M. Dos Santos, Ana Paula S. A. Correa, Edinaldo C. Rego, Jose B. P. Lima

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The susceptibility status of an insect population to any larvicide depends on several factors such includes genetic constitution, environmental conditions and others. The mosquito Aedes aegypti is the primary vector of three important viral diseases, Zika, Dengue, and Chikungunya. The frequent outbreaks of those diseases in different parts of Brazil demonstrate the importance of testing the susceptibility of vectors in different environments. Since the control of this mosquito leads to the control of disease, alternatives for vector control that value the different Brazilian environmental conditions are needed for effective actions. The aim of this study was to evaluate a new commercial formulation of Bacillus thuringiensis israelenses (DengueTech: Brazilian innovative technology) in the Brazilian Legal Amazon considering the climate conditions. Semi-field tests were conducted in the Institute of Scientific and Technological Research of the State of Amapa in two different environments, one in a shaded area and the other exposed to sunlight. The mosquito larvae were exposed to larvicide concentration and a control; each group was tested in three containers of 40 liters each. To assess persistence 50 third instar larvae of Aedes aegypti laboratory lineages (Rockefeller) and 50 larvae of Aedes aegypti collected in the municipality of Macapa, Brazil’s Amapa state, were added weekly and after 24 hours the mortality was assessed. In total 16 tests were performed, where 12 were done with replacement of water (1/5 of the volume, three times per week). The effectiveness of the product was determined through mortality of ≥ 80%, as recommend by the World Health Organization. The results demonstrated that high-water temperatures (26-35 °C) on the containers influenced the residual time of the product, where the maximum effect achieved was 21 days in the shaded area; and no effectiveness of 60 days was found in any of the tests, as expected according to the larvicide company. The test with and without water replacement did not present significant differences in the mortality rate. Considering the different environments and climate, these results stimulate the need to test larvicide and its effectiveness in specific environmental settings in order to identify the parameters required for better results. Thus, we see the importance of semi-field researches considering the local climate conditions for a successful control of Aedes aegypti.

Keywords: Aedes aegypti, bioassay, larvicida, vector control

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Authors: Graciela Soares Marcelli, Elison Eduardo Jardim Bierhals, Luciane Teresa Salvi, Claudineia Brazil, Rafael Haag

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The sun is a source of renewable energy, and its use as both a source of heat and light is one of the most promising energy alternatives for the future. To measure the thermal or photovoltaic systems a solar irradiation database is necessary. Brazil still has a reduced number of meteorological stations that provide frequency tests, as an alternative to the radio data platform, with reanalysis systems, quite significant. ERA-Interim is a global fire reanalysis by the European Center for Medium-Range Weather Forecasts (ECMWF). The data assimilation system used for the production of ERA-Interim is based on a 2006 version of the IFS (Cy31r2). The system includes a 4-dimensional variable analysis (4D-Var) with a 12-hour analysis window. The spatial resolution of the dataset is approximately 80 km at 60 vertical levels from the surface to 0.1 hPa. This work aims to make a comparative analysis between the ERA-Interim data and the data observed in the Solarimmetric Atlas of the State of Rio Grande do Sul, to verify its applicability in the absence of an observed data network. The analysis of the results obtained for a study region as an alternative to the energy potential of a given region.

Keywords: energy potential, reanalyses, renewable energy, solar radiation

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Authors: Abdulfetah Shobole, Arif Karakas, Ugur Savas Selamogullari, Mustafa Baysal

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The concern of reducing emissions of Co2 from the fossil fuel generating units and using renewable energy sources increased in our world. Due this fact the integration ratio of wind farms to grid reached 20-30% in some part of our world. With increased integration of large MW scaled wind farms to the electric grid, the stability of the electrical system is a great concern. Thus, operators of power systems usually deman the wind turbine generators to obey the same rules as other traditional kinds of generation, such as thermal and hydro, i.e. not affect the grid stability. FACTS devices such as SVC or STATCOM are mostly installed close to the connection point of the wind farm to the grid in order to increase the stability especially during faulty conditions. In this paper wind farm with DFIG turbine type and STATCOM are dynamically modeled and simulated under three phase short circuit fault condition. The dynamic modeling is done by DigSILENT PowerFactory for the wind farm, STATCOM and the network. The simulation results show improvement of system stability near to the connection point of the STATCOM.

Keywords: DFIG wind turbine, statcom, dynamic modeling, digsilent

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Authors: Giselda Cristina Ferreira, Carlos Alberto Kamienski, Ana Lígia Scott

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The COVID-19 pandemic has enhanced the anti-vaccination movement in Brazil, supported by unscientific theories and false news and the possibility of wide communication through social networks such as Twitter, Facebook, and YouTube. The World Health Organization (WHO) classified the large volume of information on the subject against COVID-19 as an Infodemic. In this paper, we present a protocol to identify polarizing users (called polarizers) and study the profiles of Brazilian polarizers on Twitter (renamed to X some weeks ago). We analyzed polarizing interactions on Twitter (in Portuguese) to identify the main polarizers and how the conflicts they caused influenced the COVID-19 vaccination rate throughout the pandemic. This protocol uses data from this social network, graph theory, Java, and R-studio scripts to model and analyze the data. The information about the vaccination rate was obtained in a public database for the government called OpenDataSus. The results present the profiles of Twitter’s Polarizer (political position, gender, professional activity, immunization opinions). We observed that social and political events influenced the participation of these different profiles in conflicts and the vaccination rate.

Keywords: Twitter, polarization, vaccine, Brazil

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Authors: R. Hurtado-Ortiz, D. Clermont, M. Schüngel, C. Bizet, D. Smith, E. Stackebrandt

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Microbiological resources and their derivatives are the essential raw material for the advancement of human health, agro-food, food security, biotechnology, research and development in all life sciences. Microbial resources, and their genetic and metabolic products, are utilised in many areas such as production of healthy and functional food, identification of new antimicrobials against emerging and resistant pathogens, fighting agricultural disease, identifying novel energy sources on the basis of microbial biomass and screening for new active molecules for the bio-industries. The complexity of public collections, distribution and use of living biological material (not only living but also DNA, services, training, consultation, etc.) and service offer, demands the coordination and sharing of policies, processes and procedures. The Microbial Resource Research Infrastructure (MIRRI) is an initiative within the European Strategy Forum Infrastructures (ESFRI), bring together 16 partners including 13 European public microbial culture collections and biological resource centres (BRCs), supported by several European and non-European associated partners. The objective of MIRRI is to support innovation in microbiology by provision of a one-stop shop for well-characterized microbial resources and high quality services on a not-for-profit basis for biotechnology in support of microbiological research. In addition, MIRRI contributes to the structuring of microbial resources capacity both at the national and European levels. This will facilitate access to microorganisms for biotechnology for the enhancement of the bio-economy in Europe. MIRRI will overcome the fragmentation of access to current resources and services, develop harmonised strategies for delivery of associated information, ensure bio-security and other regulatory conditions to bring access and promote the uptake of these resources into European research. Data mining of the landscape of current information is needed to discover potential and drive innovation, to ensure the uptake of high quality microbial resources into research. MIRRI is in its Preparatory Phase focusing on governance and structure including technical, legal governance and financial issues. MIRRI will help the Biological Resources Centres to work more closely with policy makers, stakeholders, funders and researchers, to deliver resources and services needed for innovation.

Keywords: culture collections, microbiology, infrastructure, microbial resources, biotechnology

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Authors: Jayanta Kumar Ghosh, Swastik Sunil Goriwale, Himangshu Sarkar

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Groundwater is one of the most valuable natural resources that society consumes for its domestic, industrial, and agricultural water supply. Its bulk and indiscriminate consumption affects the groundwater resource. Often, it has been found that the groundwater recharge rate is much lower than its demand. Thus, to maintain water and food security, it is necessary to monitor and management of groundwater storage. However, it is challenging to estimate groundwater storage (GWS) by making use of existing hydrological models. To overcome the difficulties, machine learning (ML) models are being introduced for the evaluation of groundwater level (GWL). Thus, the objective of this research work is to develop an ML-based model for the prediction of GWL. This objective has been realized through the development of an artificial neural network (ANN) model based on hydro-gravimetry. The model has been developed using training samples from field observations spread over 8 months. The developed model has been tested for the prediction of GWL in an observation well. The root means square error (RMSE) for the test samples has been found to be 0.390 meters. Thus, it can be concluded that the hydro-gravimetric-based ANN model can be used for the prediction of GWL. However, to improve the accuracy, more hydro-gravimetric parameter/s may be considered and tested in future.

Keywords: machine learning, hydro-gravimetry, ground water level, predictive model

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Authors: Angelis P. Barlampas

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Objective: The confusion of terms is a common practice in many situations of the everyday life. But, in some circumstances, such as in medicine, the precise meaning of a word curries a critical role for the health of the patient. Fahr disease and Fahr syndrome are often falsely used interchangeably, but they are two different conditions with different physical histories of different etiology and different medical management. A case of the seldom Fahr disease is presented, and a comparison with the more common Fahr syndrome follows. Materials and method: A 72 years old patient came to the emergency department, complaining of some kind of non specific medal disturbances, like anxiety, difficulty of concentrating, and tremor. The problems had a long course, but he had the impression of getting worse lately, so he decided to check them. Past history and laboratory tests were unremarkable. Then, a computed tomography examination was ordered. Results: The CT exam showed bilateral, hyperattenuating areas of heavy, dense calcium type deposits in basal ganglia, striatum, pallidum, thalami, the dentate nucleus, and the cerebral white matter of frontal, parietal and iniac lobes, as well as small areas of the pons. Taking into account the absence of any known preexisting illness and the fact that the emergency laboratory tests were without findings, a hypothesis of the rare Fahr disease was supposed. The suspicion was confirmed with further, more specific tests, which showed the lack of any other conditions which could probably share the same radiological image. Differentiating between Fahr disease and Fahr syndrome. Fahr disease: Primarily autosomal dominant Symmetrical and bilateral intracranial calcifications The patient is healthy until the middle age Absence of biochemical abnormalities. Family history consistent with autosomal dominant Fahr syndrome :Earlier between 30 to 40 years old. Symmetrical and bilateral intracranial calcifications Endocrinopathies: Idiopathic hypoparathyroidism, secondary hypoparathyroidism, hyperparathyroidism, pseudohypoparathyroidism ,pseudopseudohypoparathyroidism, e.t.c The disease appears at any age There are abnormal laboratory or imaging findings. Conclusion: Fahr disease and Fahr syndrome are not the same illness, although this is not well known to the inexperienced doctors. As clinical radiologists, we have to inform our colleagues that a radiological image, along with the patient's history, probably implies a rare condition and not something more usual and prompt the investigation to the right route. In our case, a genetic test could be done earlier and reveal the problem, and thus avoiding unnecessary and specific tests which cost in time and are uncomfortable to the patient.

Keywords: fahr disease, fahr syndrome, CT, brain calcifications

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Authors: Yassamina Berkane, Leila Kloul, Yoann Demoli

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Quality of life, environmental impact, congestion of mobility means, and infrastructures remain significant challenges for urban mobility. Solutions like car sharing, spatial redesign, eCommerce, and autonomous vehicles will likely increase the unit veh-km and the density of cars in urban traffic, thus reducing congestion. However, the impact of such solutions is not clear for researchers. Congestion arises from growing populations that must travel greater distances to arrive at similar locations (e.g., workplaces, schools) during the same time frame (e.g., rush hours). This paper first reviews the research and application cases of urban congestion methods through recent years. Rethinking the question of time, it then investigates people’s willingness and flexibility to adapt their arrival and departure times from workplaces. We use neural networks and methods of supervised learning to apply a new methodology for predicting peoples' intentions from their responses in a questionnaire. We created and distributed a questionnaire to more than 50 companies in the Paris suburb. Obtained results illustrate that our methodology can predict peoples' intentions to reschedule their activities (work, study, commerce, etc.).

Keywords: urban mobility, decongestion, machine learning, neural network

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Authors: Hung Nguyen

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With the rise of global supply chain network, the choice of supply chain orientation is critical. The alignment between cultural similarity and supply chain information exchange could help identify appropriate supply chain orientations, which would differentiate the stronger competitors and performers from the weaker ones. Through developing a taxonomy, this study examined whether the choices of action programs and manufacturing performance differ depending on the levels of attainment cultural similarity and information exchange. This study employed statistical tests on a large-scale dataset consisting of 680 manufacturing plants from various cultures and industries. Firms need to align cultural practices with the level of information exchange in order to achieve good overall business performance. There appeared to be consistent three major orientations: the Proactive, the Initiative and the Reactive. Firms are experiencing higher payoffs from various improvements are the ones successful alignment in both information exchange and cultural similarity The findings provide step-by-step decision making for supply chain information exchange and offer guidance especially for global supply chain managers. In including both cultural similarity and information exchange, this paper adds greater comprehensiveness and richness to the supply chain literature.

Keywords: culture, information exchange, supply chain orientation, similarity

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Authors: P. S. Jagadeesh Kumar, Yang Yung, Mingmin Pan, Xianpei Li, Wenli Hu

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Glaucoma is impelled by optic nerve mutilation habitually represented as cupping and visual field injury frequently with an arcuate pattern of mid-peripheral loss, subordinate to retinal ganglion cell damage and death. Glaucoma is the second foremost cause of blindness and the chief cause of permanent blindness worldwide. Consequently, all-embracing study into the analysis and empathy of glaucoma is happening to escort deep learning based neural network intrusions to deliberate this substantial optic neuropathy. This paper advances an ophthalmic hashing based supervision of glaucoma and corneal disorders preeminent on deep graphical model. Ophthalmic hashing is a newly proposed method extending the efficacy of visual hash-coding to predict glaucoma corneal disorder matching, which is the faster than the existing methods. Deep graphical model is proficient of learning interior explications of corneal disorders in satisfactory time to solve hard combinatoric incongruities using deep Boltzmann machines.

Keywords: corneal disorders, deep Boltzmann machines, deep graphical model, glaucoma, neural networks, ophthalmic hashing

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Authors: Smriti Agarwal, Ashish Payal, B. V. R. Reddy

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IEEE 802.15.4 is a Low Rate Wireless Personal Area Networks (LR-WPAN) standard combined with ZigBee, which is going to enable new applications in Wireless Sensor Networks (WSNs) and Internet of Things (IoT) domain. In recent years, it has become a popular standard for WSNs. Wireless communication among sensor motes, enabled by IEEE 802.15.4 standard, is extensively replacing the existing wired technology in a wide range of monitoring and control applications. Researchers have proposed a routing framework and mechanism that interacts with the IEEE 802.15.4 standard using software platform. In this paper, we have designed and implemented MAC based routing (MBR) based on IEEE 802.15.4 standard using a hardware platform “SENSEnuts”. The experimental results include data through light and temperature sensors obtained from communication between PAN coordinator and source node through coordinator, MAC address of some modules used in the experimental setup, topology of the network created for simulation and the remaining battery power of the source node. Our experimental effort on a WSN Testbed has helped us in bridging the gap between theoretical and practical aspect of implementing IEEE 802.15.4 for WSNs applications.

Keywords: IEEE 802.15.4, routing, WSN, ZigBee

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Authors: A. Yawootti, P. Intra, P. Sardyoung, P. Phoosomma, R. Puttipattanasak, S. Leeragreephol, N. Tippayawong

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The aim of this work is to design, develop and test the low-cost implementation of a particulate air pollution sensor system for continuous monitoring of outdoors and indoors particulate air pollution at a lower cost than existing instruments. In this study, measuring electrostatic charge of particles technique via high efficiency particulate-free air filter was carried out. The developed detector consists of a PM10 impactor, a particle charger, a Faraday cup electrometer, a flow meter and controller, a vacuum pump, a DC high voltage power supply and a data processing and control unit. It was reported that the developed detector was capable of measuring mass concentration of particulate ranging from 0 to 500 µg/m3 corresponding to number concentration of particulate ranging from 106 to 1012 particles/m3 with measurement time less than 1 sec. The measurement data of the sensor connects to the internet through a GSM connection to a public cellular network. In this development, the apparatus was applied the energy by a 12 V, 7 A internal battery for continuous measurement of about 20 hours. Finally, the developed apparatus was found to be close agreement with the import standard instrument, portable and benefit for air pollution and particulate matter measurements.

Keywords: particulate, air pollution, wireless communication, sensor

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Authors: K. Thiel, P. Patrikainen, C. Nagy, D. Fitzpatrick, E.-M. Aro, P. Kallio

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Photosynthetic cyanobacteria have been recognized as potential future biotechnological hosts for the direct conversion of CO₂ into chemicals of interest using sunlight as the solar energy source. However, in order to develop commercially viable systems, the flux of electrons from the photosynthetic light reactions towards specified target chemicals must be significantly improved. The objective of the study was to investigate whether the autotrophic production efficiency of specified end-metabolites can be improved in engineered cyanobacterial cells by rescuing excited electrons that are normally lost to molecular oxygen due to the cyanobacterial flavodiiron protein Flv1/3. Natively Flv1/3 dissipates excess electrons in the photosynthetic electron transfer chain by directing them to molecular oxygen in Mehler-like reaction to protect photosystem I. To evaluate the effect of flavodiiron inactivation on autotrophic production efficiency in the cyanobacterial host Synechocystis sp. PCC 6803 (Synechocystis), sucrose was selected as the quantitative reporter and a representative of a potential end-product of interest. The concept is based on the native property of Synechocystis to produce sucrose as an intracellular osmoprotectant when exposed to high external ion concentrations, in combination with the introduction of a heterologous sucrose permease (CscB from Escherichia coli), which transports the sucrose out from the cell. In addition, cell growth, photosynthetic gas fluxes using membrane inlet mass spectrometry and endogenous storage compounds were analysed to illustrate the consequent effects of flv deletion on pathway flux distributions. The results indicate that a significant proportion of the electrons can be lost to molecular oxygen via Flv1/3 even when the cells are grown under high CO₂ and that the inactivation of flavodiiron activity can enhance the photosynthetic electron flux towards optionally available sinks. The flux distribution is dependent on the light conditions and the genetic context of the Δflv mutants, and favors the production of either sucrose or one of the two storage compounds, glycogen or polyhydroxybutyrate. As a conclusion, elimination of the native Flv1/3 reaction and concomitant introduction of an engineered product pathway as an alternative sink for excited electrons could enhance the photosynthetic electron flux towards the target endproduct without compromising the fitness of the host.

Keywords: cyanobacterial engineering, flavodiiron proteins, redirecting electron flux, sucrose

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Authors: Azam Abkhiz, Abolghasem Nasir

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To achieve sustainable competitive advantage in the market, it is necessary to provide and improve customer satisfaction and Loyalty. To reach this objective, companies need to identify and analyze their customers. Thus, it is critical to measure the level of customer satisfaction and Loyalty very carefully. This study attempts to build a conceptual model to provide clear insights of customer loyalty. Using Bayesian networks (BNs), a model is proposed to evaluate customer loyalty and its consequences, such as repurchase and positive word-of-mouth. BN is a probabilistic approach that predicts the behavior of a system based on observed stochastic events. The most relevant determinants of customer loyalty are identified by the literature review. Perceived value, service quality, trust, corporate image, satisfaction, and switching costs are the most important variables that explain customer loyalty. The data are collected by use of a questionnaire-based survey from 1430 customers of a home appliances manufacturer in Iran. Four scenarios and sensitivity analyses are performed to run and analyze the impact of different determinants on customer loyalty. The proposed model allows businesses to not only set their targets but proactively manage their customer behaviors as well.

Keywords: customer satisfaction, customer loyalty, Bayesian networks, home appliances industry

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Authors: M. Inzunza

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In contemporary society, victims of crime has been given more recognition, which have contributed to advancing the knowledge on the effects of crime. There exists a complexity of who gets the status of victim and that the typology of good versus bad can interfere with the contact situation of the victim with the police. The aim of this study is to identify the most central areas affecting the contact situation between crime victims and the police to develop a conceptual model to be useful empirically. By considering previously documented problem areas and different theoretical domains, a conceptual model has been developed. Preliminary findings suggest that an area that should be given attention is to get a better understanding of the victim, not only in terms of demographics but also in terms of risk behavior and social network. This area has been considered to influence the status of the crime victim. Another domain of value is the type of crime and the context of the incident in more detail. The police officer approach style in the contact situation is also a pertinent area that is influenced by how the police based victim services are organized and how individual police officers are suited for the mission. Suitability includes constructs from empathy models adapted to the police context and especially focusing on sub-constructs such as perspective taking. Discussion will focus on how these findings can be operationalized in practice and how they are used in ongoing empirical studies.

Keywords: empathy, perspective taking, police contact, victim of crime

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Authors: Dong-Cheol Jeong, Jookyung Lee, Yu Hyeon Ro, Changsik Song

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The design and synthesis of photo-active polymeric systems are important in regard to solar energy harvesting and utilization. In this study, we synthesized photo-active polymer, thin films, and polymer gel via iterative self-assembly using reversible metal-terpyridine (M-tpy) interactions. The photocurrent generated in the polymeric thin films with Zn(II) was much higher than those of other films. Apparent diffusion rate constant (kapp) was measured for the electron hopping process via potential-step chronoamperometry. As a result, the kapp for the polymeric thin films with Zn(II) was almost two times larger than those with other metal ions. We found that the anodic photocurrents increased with the inclusion of the multi-walled carbon nanotube (MWNT) layer. Inclusion of MWNTs can provide efficient electron transfer pathways. In addition, polymer gel based on interactions between terpyridine and metal ions was shown the photocatalytic activity. Interestingly, in the Mg-terpyridine gel, the reaction rate of benzylamine to imine photo-oxidative coupling was faster than Fe-terpyridine gel because the Mg-terpyridine gel has two steps electron transfer pathway but Fe-terpyridine gel has three steps electron transfer pathway.

Keywords: terpyridine, photocatalyst, self-assebly, metal-ligand

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Authors: Daniel Osorio, Janneth Gonzalez, Andres Pinzon

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In this work, proteins and metabolic pathways associated with the neuroprotective response mediated by the synthetic neurosteroid tibolone under a palmitate-induced inflammatory model were identified by flux balance analysis (FBA). Three different metabolic scenarios (‘healthy’, ‘inflamed’ and ‘medicated’) were modeled over a gene expression data-driven constructed tissue-specific metabolic reconstruction of mature astrocytes. Astrocyte reconstruction was built, validated and constrained using three open source software packages (‘minval’, ‘g2f’ and ‘exp2flux’) released through the Comprehensive R Archive Network repositories during the development of this work. From our analysis, we predict that tibolone executes their neuroprotective effects through a reduction of neurotoxicity mediated by L-glutamate in astrocytes, inducing the activation several metabolic pathways with neuroprotective actions associated such as taurine metabolism, gluconeogenesis, calcium and the Peroxisome Proliferator Activated Receptor signaling pathways. Also, we found a tibolone associated increase in growth rate probably in concordance with previously reported side effects of steroid compounds in other human cell types.

Keywords: astrocytes, flux balance analysis, genome scale metabolic reconstruction, inflammation, neuroprotection, tibolone

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Authors: Ashish Dhamaniya, Vineet Jain, Rajesh Chouhan

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Traffic management is one of the gigantic issue in most of the urban roads in al-most all metropolitan cities in India. Speed is one of the critical traffic parameters for effective Intelligent Transportation System (ITS) implementation as it decides the arrival rate of vehicles on an intersection which are majorly the point of con-gestions. The study aimed to leverage Machine Learning (ML) models to produce precise predictions of speed on urban roadway links. The research objective was to assess how categorized traffic volume and road width, serving as variables, in-fluence speed prediction. Four tree-based regression models namely: Decision Tree (DT), Random Forest (RF), Extra Tree (ET), and Extreme Gradient Boost (XGB)are employed for this purpose. The models' performances were validated using test data, and the results demonstrate that Random Forest surpasses other machine learning techniques and a conventional utility theory-based model in speed prediction. The study is useful for managing the urban roadway network performance under mixed traffic conditions and effective implementation of ITS.

Keywords: stream speed, urban roads, machine learning, traffic flow

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Authors: Liang Wang, Beste F. Yuksel, David Guy Brizan

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A computational model of affect that can distinguish between spontaneous and posed smiles with no errors on a large, popular data set using deep learning techniques is presented in this paper. A Long Short-Term Memory (LSTM) classifier, a type of Recurrent Neural Network, is utilized and compared to human classification. Results showed that while human classification (mean of 0.7133) was above chance, the LSTM model was more accurate than human classification and other comparable state-of-the-art systems. Additionally, a high accuracy rate was maintained with small amounts of training videos (70 instances). The derivation of important features to further understand the success of our computational model were analyzed, and it was inferred that thousands of pairs of points within the eyes and mouth are important throughout all time segments in a smile. This suggests that distinguishing between a posed and spontaneous smile is a complex task, one which may account for the difficulty and lower accuracy of human classification compared to machine learning models.

Keywords: affective computing, affect detection, computer vision, deep learning, human-computer interaction, machine learning, posed smile detection, spontaneous smile detection

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Authors: Nathenal Thomas Lambamo

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Based on the fact that more than 85% of the labor force and 90% of the export earnings are taken by agriculture in Ethiopia and it can be said that it is the backbone of the overall socio-economic activities in the country. Among the cereal crops that the agriculture sector provides for the country, wheat is the third-ranking one preceding teff and maize. In the present day, wheat is in higher demand related to the expansion of industries that use them as the main ingredient for their products. The local supply of wheat for these companies covers only 35 to 40% and the rest 60 to 65% percent is imported on behalf of potential customers that exhaust the country’s foreign currency reserves. The above facts show that the need for this crop in the country is too high and in reverse, the productivity of the crop is very less because of these reasons. Wheat disease is the most devastating disease that contributes a lot to this unbalance in the demand and supply status of the crop. It reduces both the yield and quality of the crop by 27% on average and up to 37% when it is severe. This study aims to detect the most frequent and degrading wheat diseases, Septoria and Leaf rust, using the most efficiently used subset of machine learning technology, deep learning. As a state of the art, a deep learning class classification technique called Convolutional Neural Network (CNN) has been used to detect diseases and has an accuracy of 99.01% is achieved.

Keywords: septoria, leaf rust, deep learning, CNN

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Authors: Kalangi Rodrigo

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Sri Lanka has been known as an island which has a diverse variety of prehistoric occupation among ecological zones. Defining the paleoecology of the past societies has been an archaeological thought developed in the 1960s. It is mainly concerned with the reconstruction from available geological and biological evidence of past biota, populations, communities, landscapes, environments, and ecosystems. Sri Lanka has dealt with this subject, and considerable research has been already undertaken. The fossil and material record of Sri Lanka’s Wet Zone tropical forests continues from c. 38,000–34,000 ybp. This early and persistent human fossil, technical, and cultural florescence, as well as a collection of well-preserved tropical-forest rock shelters with associated 'on-site' palaeoenvironmental records, makes Sri Lanka a central and unusual case study to determine the extent and strength of early human tropical forest encounters. Excavations carried out in prehistoric caves in the low country wet zone has shown that in the last 50,000 years, the temperature in the lowland rainforests has not exceeded 5°C. When taking Batadombalena alone, the entire seven layers have yielded an uninterrupted occupation of Acavus sp and Canerium zeylanicum, a plant that grows in the middle of the rainforest. Acavus, which is highly sensitive to rainforest ecosystems, has been well documented in many of the lowland caves, confirming that the wetland rainforest environment has remained intact at least for the last 50,000 years. If the dry and arid conditions in the upper hills regions affected the wet zone, the Tufted Gray Lunger (semnopithecus priam), must also meet with the prehistoric caves in the wet zone thrown over dry climate. However, the bones in the low country wet zone do not find any of the fragments belonging to Turfed Gray Lunger, and prehistoric human consumption is bestowed with purple-faced leaf monkey (Trachypithecus vetulus) and Toque Macaque (Macaca Sinica). The skeletal remains of Lyriocephalus scutatus, a full-time resident in rain forests, have also been recorded among lowland caves. But, in zoological terms, these remains may be the remains of the Barking deer (Muntiacus muntjak), which is currently found in the wet zone. For further investigations, the mtDNA test of genetic diversity (Bottleneck effect) and pollen study from lowland caves should determine whether the wet zone climate has persisted over the last 50,000 years, or whether the dry weather affected in the mountainous region has invaded the wet zone.

Keywords: paleoecology, prehistory, zooarchaeology, reconstruction, palaeo-climate

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Authors: Elisa Coraggio, Dawei Han, Weiru Liu, Theo Tryfonas

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Water is one of the most important resources for human society. The world is currently undergoing a wave of urban growth, and pollution problems are of a great impact. Monitoring water quality is a key task for the future of the environment and human species. In recent times, researchers, using Smart Cities technologies are trying to mitigate the problems generated by the population growth in urban areas. The availability of huge amounts of data collected by a pervasive urban IoT can increase the transparency of decision making. Several services have already been implemented in Smart Cities, but more and more services will be involved in the future. Water quality monitoring can successfully be implemented in the urban IoT. The combination of water quality sensors, cloud computing, smart city infrastructure, and IoT technology can lead to a bright future for environmental monitoring. In the past decades, lots of effort has been put on monitoring and predicting water quality using traditional approaches based on manual collection and laboratory-based analysis, which are slow and laborious. The present study proposes a methodology for implementing a water quality prediction model using artificial intelligence techniques and comparing the results obtained with different algorithms. Furthermore, a 3D numerical model will be created using the software D-Water Quality, and simulation results will be used as a training dataset for the artificial intelligence algorithm. This study derives the methodology and demonstrates its implementation based on information and data collected at the floating harbour in the city of Bristol (UK). The city of Bristol is blessed with the Bristol-Is-Open infrastructure that includes Wi-Fi network and virtual machines. It was also named the UK ’s smartest city in 2017.In recent times, researchers, using Smart Cities technologies are trying to mitigate the problems generated by the population growth in urban areas. The availability of huge amounts of data collected by a pervasive urban IoT can increase the transparency of decision making. Several services have already been implemented in Smart Cities, but more and more services will be involved in the future. Water quality monitoring can successfully be implemented in the urban IoT. The combination of water quality sensors, cloud computing, smart city infrastructure, and IoT technology can lead to a bright future for the environment monitoring. In the past decades, lots of effort has been put on monitoring and predicting water quality using traditional approaches based on manual collection and laboratory-based analysis, which are slow and laborious. The present study proposes a new methodology for implementing a water quality prediction model using artificial intelligence techniques and comparing the results obtained with different algorithms. Furthermore, a 3D numerical model will be created using the software D-Water Quality, and simulation results will be used as a training dataset for the Artificial Intelligence algorithm. This study derives the methodology and demonstrate its implementation based on information and data collected at the floating harbour in the city of Bristol (UK). The city of Bristol is blessed with the Bristol-Is-Open infrastructure that includes Wi-Fi network and virtual machines. It was also named the UK ’s smartest city in 2017.

Keywords: artificial intelligence, hydroinformatics, numerical modelling, smart cities, water quality

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Authors: A. N. Dagang, E. I. Ismail, Z. Zakaria

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This paper presents the analysis on the performance of monopole antenna with fluorescent tubes. In this research, the simulation and experimental approach is conducted. The fluorescent tube with different length and size is designed using Computer Simulation Technology (CST) software and the characteristics of antenna parameter are simulated throughout the software. CST was used to simulate antenna parameters such as return loss, resonant frequency, gain and directivity. Vector Network Analyzer (VNA) was used to measure the return loss of plasma antenna in order to validate the simulation results. In the simulation and experiment, the supply frequency is set starting from 1 GHz to 10 GHz. The results show that the return loss of plasma antenna changes when size of fluorescent tubes is varied, correspond to the different plasma properties. It shows that different values of plasma properties such as plasma frequency and collision frequency gives difference result of return loss, gain and directivity. For the gain, the values range from 2.14 dB to 2.36 dB. The return loss of plasma antenna offers higher value range from -22.187 dB to -32.903 dB. The higher the values of plasma frequency and collision frequency, the higher return loss can be obtained. The values obtained are comparative to the conventional type of metal antenna.

Keywords: plasma antenna, fluorescent tube, CST, plasma parameters

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