Search results for: casual-leisure information behaviors

Authors: Valeria Abreu Minero, Edward Barker, Hannah Dickson, Francois Husson, Sandra Flynn, Jennifer Shaw

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Purpose – The purpose of this paper is to identify offender typologies based on aspects of the offenders’ psychopathology and their associations with crime scene behaviours using data derived from the National Confidential Enquiry into Suicide and Safety in Mental Health concerning homicides in England and Wales committed by offenders in contact with mental health services in the year preceding the offence (n=759). Design/methodology/approach – The authors used multiple correspondence analysis to investigate the interrelationships between the variables and hierarchical agglomerative clustering to identify offender typologies. Variables describing: the offender’s mental health history; the offenders’ mental state at the time of offence; characteristics useful for police investigations; and patterns of crime scene behaviours were included. Findings – Results showed differences in the offender’s histories in relation to their crime scene behaviours. Further, analyses revealed three homicide typologies: externalising, psychosis and depression. Analyses revealed three homicide typologies: externalising, psychotic and depressive. Practical implications – These typologies may assist the police during homicide investigations by: furthering their understanding of the crime or likely suspect; offering insights into crime patterns; provide advice as to what an offender’s offence behaviour might signify about his/her mental health background; findings suggest information concerning offender psychopathology may be useful for offender profiling purposes in cases of homicide offenders with schizophrenia, depression and comorbid diagnosis of personality disorder and alcohol/drug dependence. Originality/value – Empirical studies with an emphasis on offender profiling have almost exclusively focussed on the inference of offender demographic characteristics. This study provides a first step in the exploration of offender psychopathology and its integration to the multivariate analysis of offence information for the purposes of investigative profiling of homicide by identifying the dominant patterns of mental illness within homicidal behaviour.

Keywords: offender profiling, mental illness, psychopathology, multivariate analysis, homicide, crime scene analysis, crime scene behviours, investigative advice

Procedia PDF Downloads 129

Authors: Ksenia Volkova, Artur Petrosyan, Ignatii Dubyshkin, Alexei Ossadtchi

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Brain-computer interfaces are a growing research field producing many implementations that find use in different fields and are used for research and practical purposes. Despite the popularity of the implementations using non-invasive neuroimaging methods, radical improvement of the state channel bandwidth and, thus, decoding accuracy is only possible by using invasive techniques. Electrocorticography (ECoG) is a minimally invasive neuroimaging method that provides highly informative brain activity signals, effective analysis of which requires the use of machine learning methods that are able to learn representations of complex patterns. Deep learning is a family of machine learning algorithms that allow learning representations of data with multiple levels of abstraction. This study explores the potential of deep learning approaches for ECoG processing, decoding movement intentions and the perception of proprioceptive information. To obtain synchronous recording of kinematic movement characteristics and corresponding electrical brain activity, a series of experiments were carried out, during which subjects performed finger movements at their own pace. Finger movements were recorded with a three-axis accelerometer, while ECoG was synchronously registered from the electrode strips that were implanted over the contralateral sensorimotor cortex. Then, multichannel ECoG signals were used to track finger movement trajectory characterized by accelerometer signal. This process was carried out both causally and non-causally, using different position of the ECoG data segment with respect to the accelerometer data stream. The recorded data was split into training and testing sets, containing continuous non-overlapping fragments of the multichannel ECoG. A deep convolutional neural network was implemented and trained, using 1-second segments of ECoG data from the training dataset as input. To assess the decoding accuracy, correlation coefficient r between the output of the model and the accelerometer readings was computed. After optimization of hyperparameters and training, the deep learning model allowed reasonably accurate causal decoding of finger movement with correlation coefficient r = 0.8. In contrast, the classical Wiener-filter like approach was able to achieve only 0.56 in the causal decoding mode. In the noncausal case, the traditional approach reached the accuracy of r = 0.69, which may be due to the presence of additional proprioceptive information. This result demonstrates that the deep neural network was able to effectively find a representation of the complex top-down information related to the actual movement rather than proprioception. The sensitivity analysis shows physiologically plausible pictures of the extent to which individual features (channel, wavelet subband) are utilized during the decoding procedure. In conclusion, the results of this study have demonstrated that a combination of a minimally invasive neuroimaging technique such as ECoG and advanced machine learning approaches allows decoding motion with high accuracy. Such setup provides means for control of devices with a large number of degrees of freedom as well as exploratory studies of the complex neural processes underlying movement execution.

Keywords: brain-computer interface, deep learning, ECoG, movement decoding, sensorimotor cortex

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Authors: Min Kyeong Cha, Ju Yeon Mun, Seong Baeg Kim

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Recently, ‘play of learning’ became important and is emphasized as a useful learning tool. Therefore, interest in edutainment contents is growing. Storytelling is considered first as a method that improves the transmission of information and learner's interest when planning edutainment contents. In this study, we designed edutainment contents in the form of an adventure game that applies the storytelling method. This content provides questions and items constituted dynamically and reorganized learning contents through analysis of test results. It allows learners to solve various questions through effective iterative learning. As a result, the learners can reach mastery learning.

Keywords: storytelling, edutainment, mastery learning, computer operating principle

Procedia PDF Downloads 317

Authors: Jan Van Uden

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The paper reviews classical agency-theory and its contributions to the analysis of complex business negotiations and gives an approach for the modification of the basic agency-model in order to examine the negotiation specific dimensions of agency-problems. By illustrating fundamental potentials for the modification of agency-theory in context of business negotiations the paper highlights recent empirical research that investigates agent-based negotiations and inter-team constellations. A general theoretical analysis of complex negotiation would be based on a two-level approach. First, the modification of the basic agency-model in order to illustrate the organizational context of business negotiations (i.e., multi-agent issues, common-agencies, multi-period models and the concept of bounded rationality). Second, the application of the modified agency-model on complex business negotiations to identify agency-problems and relating areas of risk in the negotiation process. The paper is placed on the first level of analysis – the modification. The method builds on the one hand on insights from behavior decision research (BRD) and on the other hand on findings from agency-theory as normative directives to the modification of the basic model. Through neoclassical assumptions concerning the fundamental aspects of agency-relationships in business negotiations (i.e., asymmetric information, self-interest, risk preferences and conflict of interests), agency-theory helps to draw solutions on stated worst-case-scenarios taken from the daily negotiation routine. As agency-theory is the only universal approach able to identify trade-offs between certain aspects of economic cooperation, insights obtained provide a deeper understanding of the forces that shape business negotiation complexity. The need for a modification of the basic model is illustrated by highlighting selected issues of business negotiations from agency-theory perspective: Negotiation Teams require a multi-agent approach under the condition that often decision-makers as superior-agents are part of the team. The diversity of competences and decision-making authority is a phenomenon that overrides the assumptions of classical agency-theory and varies greatly in context of certain forms of business negotiations. Further, the basic model is bound to dyadic relationships preceded by the delegation of decision-making authority and builds on a contractual created (vertical) hierarchy. As a result, horizontal dynamics within the negotiation team playing an important role for negotiation success are therefore not considered in the investigation of agency-problems. Also, the trade-off between short-term relationships within the negotiation sphere and the long-term relationships of the corporate sphere calls for a multi-period perspective taking into account the sphere-specific governance-mechanisms already established (i.e., reward and monitoring systems). Within the analysis, the implementation of bounded rationality is closely related to findings from BRD to assess the impact of negotiation behavior on underlying principal-agent-relationships. As empirical findings show, the disclosure and reservation of information to the agent affect his negotiation behavior as well as final negotiation outcomes. Last, in context of business negotiations, asymmetric information is often intended by decision-makers acting as superior-agents or principals which calls for a bilateral risk-approach to agency-relations.

Keywords: business negotiations, agency-theory, negotiation analysis, interteam negotiations

Procedia PDF Downloads 139

Authors: Chinasa U. Imo, Queen Chikwendu, Jonathan Ajuma, Mario Banuelos

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Background: Sociocultural norms primarily influence the health-seeking behavior of populations in rural communities. In the Nkporo community, Abia State, Nigeria, their sociocultural perception of diseases runs counter to biomedical definitions, wherein they rely heavily on traditional medicine and practices. In a state where birth asphyxia and sepsis account for the significant causes of death for neonates, malaria leads to the causes of other mortalities, followed by common preventable diseases such as diarrhea, pneumonia, acute respiratory tract infection, malnutrition, and HIV/AIDS. Most local mothers attribute their health conditions and that of their children to witchcraft attacks, the hand of God, and ancestral underlining. This influences how they see antenatal and postnatal care, choice of place of accessing care and birth delivery, response to children's illnesses, immunization, and nutrition. Method: To implement a community health improvement program, we adopted an asset-based community development model to address health's normative and social determinants. The first step was to use a qualitative approach to conduct a community health needs baseline assessment, involving focus group discussions with twenty-five (25) youths aged 18-25, semi-structured interviews with ten (10) officers-in-charge of primary health centers, eight (8) ward health committee members, and nine (9) community leaders. Secondly, we designed an intervention program. Going forward, we will proceed with implementing and evaluating this program. Result: The priority needs identified by the communities were malaria, lack of clean drinking water, and the need for behavioral change information. The study also highlighted the significant influence of youths on their peers, family, and community as caregivers and information interpreters. Based on the findings, the NGO SieDi-Hub collaborated with the Abia State Ministry of Health, the State Primary Healthcare Agency, and Empower Next Generations to design a one-year "Community Health Youth Champions Pilot Program." Twenty (20) youths in the community were trained and equipped to champion a participatory approach to bridging the gap between access and delivery of primary healthcare, to adjust sociocultural norms to improve health equity for people in Nkporo community – with limited education, lack of access to health information, and quality healthcare facilities using an innovative community-led improvement approach. Conclusion: Youths play a vital role in achieving health equity, being a vulnerable population with significant influence. To ensure effective primary healthcare, strategies must include cultural humility. The asset-based community development model offers valuable tools, and this article will share ongoing lessons from the intervention's behavioral change strategies with young people.

Keywords: asset-based community development, community health, primary health systems strengthening, youth empowerment

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Authors: Payam Mousavi, Andrew L. Stewart, Huiwen You, Aryeh F. G. Fayerman

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We present a decision-support tool to assist an operator in the detection and tracking of a suspect vehicle traveling to an unknown target destination. Multiple data sources, such as traffic cameras, traffic information, weather, etc., are integrated and processed in real-time to infer a suspect’s intended destination chosen from a list of pre-determined high-value targets. Previously, we presented our work in the detection and tracking of vehicles using traffic and airborne cameras. Here, we focus on the fusion and processing of that information to predict a suspect’s behavior. The network of cameras is represented by a directional graph, where the edges correspond to direct road connections between the nodes and the edge weights are proportional to the average time it takes to travel from one node to another. For our experiments, we construct our graph based on the greater Los Angeles subset of the Caltrans’s “Performance Measurement System” (PeMS) dataset. We propose a Bayesian approach where a posterior probability for each target is continuously updated based on detections of the suspect in the live video feeds. Additionally, we introduce the concept of ‘soft interventions’, inspired by the field of Causal Inference. Soft interventions are herein defined as interventions that do not immediately interfere with the suspect’s movements; rather, a soft intervention may induce the suspect into making a new decision, ultimately making their intent more transparent. For example, a soft intervention could be temporarily closing a road a few blocks from the suspect’s current location, which may require the suspect to change their current course. The objective of these interventions is to gain the maximum amount of information about the suspect’s intent in the shortest possible time. Our system currently operates in a human-on-the-loop mode where at each step, a set of recommendations are presented to the operator to aid in decision-making. In principle, the system could operate autonomously, only prompting the operator for critical decisions, allowing the system to significantly scale up to larger areas and multiple suspects. Once the intended target is identified with sufficient confidence, the vehicle is reported to the authorities to take further action. Other recommendations include a selection of road closures, i.e., soft interventions, or to continue monitoring. We evaluate the performance of the proposed system using simulated scenarios where the suspect, starting at random locations, takes a noisy shortest path to their intended target. In all scenarios, the suspect’s intended target is unknown to our system. The decision thresholds are selected to maximize the chances of determining the suspect’s intended target in the minimum amount of time and with the smallest number of interventions. We conclude by discussing the limitations of our current approach to motivate a machine learning approach, based on reinforcement learning in order to relax some of the current limiting assumptions.

Keywords: autonomous surveillance, Bayesian reasoning, decision support, interventions, patterns of life, predictive analytics, predictive insights

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Authors: Mohammad Reza Babaei, Sanaz Kamrani

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The main problems That arise In adopting most Enterprise resources planning (ERP) strategies come from organizational, complex information systems like the ERP integrate the data of all business areas within the organization. The implementation of ERP is a difficult process as it involves different types of end users. Based on literature, we proposed a conceptual framework and examined it to find the effect of some of the individual, organizational, and technological factors on the usage of ERP and its impact on the end user. The results of the analysis suggest that computer self-efficacy, organizational support, training, and compatibility have a positive influence on ERP usage which in turn has significant influence on panoptic empowerment and individual performance.

Keywords: factor, influencing, enterprise, system

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Authors: Bonguen Onouck Rolande Carole, Ndongo Barthelemy

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The climate change effects on the Lake Chad, coupled with population growth, have pushed large masses of people of various origins towards the lower part of the lower Logonewatershed in search of the benefits of environmental services, causing pressure on the environment and its resources. Economic services are therefore threatened, and the decrease in resources contributes to the deterioration of the social wellbeing resulting to conflicts among/between local communities, immigrants, displaced people, and foreigners. This paper is an information contribution on ethnical conflicts drivers in the area and the provided local management mechanisms such can help mitigate present or future conflicts in similar areas. It also prints out the necessity to alleviate water access deficit and encourage good practices for the population wellbeing. In order to meet the objective, in 2018, through the interface of the World Bank-Cameroon project-PULCI, data were collected on the field directly by discussing with the population and visiting infrastructures, indirectly by a questionnaire survey. Two administrative divisions were chosen (Logoneet Chari, Mayo-Danay) in which targeted localities were Zina, Mazera, Lahai, Andirni near the Waza Park and Yagoua, Tekele, Pouss, respectively. Due to some sociocultural and religious reasons, some information were acquired through the traditional chiefs. A desk study analysis based on resources access and availability conflicts history, and management mechanism was done. As results, roots drivers of ethnical conflicts are struggles over natural resources access, and the possibility of conflicts increases as the scarcity and vulnerabilities persist, creating more sociocultural gaps and tensions. The mitigation mechanisms though fruitful, are limited. There is poor documentation on the topic, the resources management policies of this basin are unsuitable and ineffective for some. Therefore, the restoration of environmental and ecosystems, the mitigation of climate change effects, and food insecurity are the challenges that must be met to alleviate conflicts in these localities.

Keywords: ethnic, communities, conflicts, mitigation mechanisms, natural resources, logone basin

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Authors: Victor Bloch, Eerikki Kaila, Reetta Palva

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Field robots are an important tool for enhancing efficiency and decreasing the climatic impact of food production. There exists a number of commercial field robots; however, since this technology is still new, the robot advantages and limitations, as well as methods for optimal using of robots, are still unclear. In this study, the performance of a commercial field robot for seeding and weeding was assessed. A research 2-ha sugar beet field with 0.5m row width was used for testing, which included robotic sowing of sugar beet and weeding five times during the first two months of the growing. About three and five percent of the field were used as untreated and chemically weeded control areas, respectively. The plant detection was based on the exact plant location without image processing. The robot was equipped with six seeding and weeding tools, including passive between-rows harrow hoes and active hoes cutting inside rows between the plants, and it moved with a maximal speed of 0.9 km/h. The robot's performance was assessed by image processing. The field images were collected by an action camera with a height of 2 m and a resolution 27M pixels installed on the robot and by a drone with a 16M pixel camera flying at 4 m height. To detect plants and weeds, the YOLO model was trained with transfer learning from two available datasets. A preliminary analysis of the entire field showed that in the areas treated by the robot, the weed average density varied across the field from 6.8 to 9.1 weeds/m² (compared with 0.8 in the chemically treated area and 24.3 in the untreated area), the weed average density inside rows was 2.0-2.9 weeds / m (compared with 0 on the chemically treated area), and the emergence rate was 90-95%. The information about the robot's performance has high importance for the application of robotics for field tasks. With the help of the developed method, the performance can be assessed several times during the growth according to the robotic weeding frequency. When it’s used by farmers, they can know the field condition and efficiency of the robotic treatment all over the field. Farmers and researchers could develop optimal strategies for using the robot, such as seeding and weeding timing, robot settings, and plant and field parameters and geometry. The robot producers can have quantitative information from an actual working environment and improve the robots accordingly.

Keywords: agricultural robot, field robot, plant detection, robot performance

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Authors: Mona Awad Wanis Gad

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The method of factory making plans has changed loads, in particular, whilst it's miles approximately making plans the factory building itself. Factory making plans have the venture of designing merchandise, plants, tactics, organization, regions, and the construction of a factory. Ordinary restructuring is turning into greater essential for you to preserve the competitiveness of a manufacturing unit. Regulations in new regions, shorter lifestyle cycles of product and manufacturing era, in addition to a VUCA global (Volatility, Uncertainty, Complexity and Ambiguity) cause extra common restructuring measures inside a factory. A digital factory model is the planning foundation for rebuilding measures and turns into a critical device. Furthermore, digital building fashions are increasingly being utilized in factories to help facility management and manufacturing processes. First, exclusive styles of digital manufacturing unit fashions are investigated, and their residences and usabilities to be used instances are analyzed. Within the scope of research are point cloud fashions, building statistics fashions, photogrammetry fashions, and those enriched with sensor information are tested. It investigated which digital fashions permit a simple integration of sensor facts and in which the variations are. In the end, viable application areas of virtual manufacturing unit models are determined by a survey, and the respective digital manufacturing facility fashions are assigned to the application areas. Ultimately, an application case from upkeep is selected and implemented with the assistance of the best virtual factory version. It is shown how a completely digitalized preservation process can be supported by a digital manufacturing facility version by offering facts. Among different functions, the virtual manufacturing facility version is used for indoor navigation, facts provision, and display of sensor statistics. In summary, the paper suggests a structuring of virtual factory fashions that concentrates on the geometric representation of a manufacturing facility building and its technical facilities. A practical application case is proven and implemented. For that reason, the systematic selection of virtual manufacturing facility models with the corresponding utility cases is evaluated.

Keywords: augmented reality, digital factory model, factory planning, restructuring digital factory model, photogrammetry, factory planning, restructuring building information modeling, digital factory model, factory planning, maintenance

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Authors: Yasaman Ashrafi, Stephen Kajewski, Annastiina Silvennoinen, Madhav Nepal

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Mega projects either in construction, urban development or energy sectors are one of the key drivers that build the foundation of wealth and modern civilizations in regions and nations. Such projects require economic justification and substantial capital investment, often derived from individual and corporate investors as well as governments. Cost overruns and time delays in these mega projects demands a new approach to more accurately predict project costs and establish realistic financial plans. The significance of this paper is that the cost efficiency of megaprojects will improve and decrease cost overruns. This research will assist Project Managers (PMs) to make timely and appropriate decisions about both cost and outcomes of ongoing projects. This research, therefore, examines the oil and gas industry where most mega projects apply the classic methods of Cost Performance Index (CPI) and Schedule Performance Index (SPI) and rely on project data to forecast cost and time. Because these projects are always overrun in cost and time even at the early phase of the project, the probabilistic methods of Monte Carlo Simulation (MCS) and Bayesian Adaptive Forecasting method were used to predict project cost at completion of projects. The current theoretical and mathematical models which forecast the total expected cost and project completion date, during the execution phase of an ongoing project will be evaluated. Earned Value Management (EVM) method is unable to predict cost at completion of a project accurately due to the lack of enough detailed project information especially in the early phase of the project. During the project execution phase, the Bayesian adaptive forecasting method incorporates predictions into the actual performance data from earned value management and revises pre-project cost estimates, making full use of the available information. The outcome of this research is to improve the accuracy of both cost prediction and final duration. This research will provide a warning method to identify when current project performance deviates from planned performance and crates an unacceptable gap between preliminary planning and actual performance. This warning method will support project managers to take corrective actions on time.

Keywords: cost forecasting, earned value management, project control, project management, risk analysis, simulation

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Authors: Amarjit Singh

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In this paper, a method for extraction of text subtitles in large video is proposed. The video data needs to be annotated for many multimedia applications. Text is incorporated in digital video for the motive of providing useful information about that video. So need arises to detect text present in video to understanding and video indexing. This is achieved in two steps. First step is text localization and the second step is text verification. The method of text detection can be extended to text recognition which finds applications in automatic video indexing; video annotation and content based video retrieval. The method has been tested on various types of videos.

Keywords: video, subtitles, extraction, annotation, frames

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Authors: Ignatio Madanhire, Charles Mbohwa

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Efficiency in manufacturing is critical in raising the value of exports so as to gainfully trade on the regional and international markets. There seems to be increasing popularity of continuous improvement strategies availed to manufacturing entities, but this research study established that there has not been a similar popularity accorded to the Six Sigma methodology. Thus this work was conducted to investigate the applicability, effectiveness, usefulness, application and suitability of the Six Sigma methodology as a competitiveness option for discrete manufacturing entity. Development of Six-sigma center in the country with continuous improvement information would go a long way in benefiting the entire industry

Keywords: discrete manufacturing, six-sigma, continuous improvement, efficiency, competitiveness

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Authors: Kyle Saltmarsh

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Acoustic testing possesses many benefits due to its non-destructive nature and practicality. There hence exists many scenarios in which using acoustic testing for condition testing shows powerful feasibility. A wealth of information is contained within the acoustic and vibration characteristics of structures, allowing the development meaningful features for the classification of their respective condition. In this paper, methods, results, and discussions are presented on the use of non-destructive acoustic testing coupled with acoustic feature extraction and machine learning techniques for the condition testing of manufactured circular steel plates subjected to varied levels of damage.

Keywords: plates, deformation, acoustic features, machine learning

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Authors: Zuzana Crhová, Karel Kolman, Drahomíra Pavelková

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Knowledge is considered as an important asset which can help organizations to create competitive advantage. The necessity of taking care of these assets is more important in these days – in days of turbulent changes in business environment. Knowledge could facilitate adaption to constant changes. The aim of this paper is to describe how the knowledge sharing can be supported in the manufacturing companies. The methods of case studies and grounded theory were used to present information gained by carrying out semi-structured interviews. Results show that knowledge sharing is supported in very similar ways in respondent companies.

Keywords: case study, human resource management, knowledge, knowledge sharing

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Authors: Dolhadi Bin Zainudin

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The study attempts to predict the relationship between influencing factors in the adoption of ISO 9000:2008 and to identify which how these factors play the main role in achieving ISO 9000 standard. A survey using structured questionnaire was employed. A total of 255 respondents from 255 small and medium enterprises participated in this study. With regards to influencing factors, a discriminant analysis was conducted and the results showed that three out of nine critical success factors is statistically significant between ISO 9000:2008 and non-ISO 9000 certified companies which are communication for quality, information and analysis and organizational culture.

Keywords: ISO 9000, quality management, factors, small and medium enterprise, Malaysia, influencing factors

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Authors: Abdul Basit Kiani, Maryam Kiani

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Online developers now have the tools necessary to create online apps that are not only reliable but also highly interactive, thanks to the introduction of JavaScript frameworks and APIs. The objective is to give a broad overview of the recent advances in the area. The fusion of machine learning (ML) and artificial intelligence (AI) has expanded the possibilities for web development. Modern websites now include chatbots, clever recommendation systems, and customization algorithms built in. In the rapidly evolving landscape of modern websites, it has become increasingly apparent that user engagement and personalization are key factors for success. To meet these demands, websites now incorporate a range of innovative technologies. One such technology is chatbots, which provide users with instant assistance and support, enhancing their overall browsing experience. These intelligent bots are capable of understanding natural language and can answer frequently asked questions, offer product recommendations, and even help with troubleshooting. Moreover, clever recommendation systems have emerged as a powerful tool on modern websites. By analyzing user behavior, preferences, and historical data, these systems can intelligently suggest relevant products, articles, or services tailored to each user's unique interests. This not only saves users valuable time but also increases the chances of conversions and customer satisfaction. Additionally, customization algorithms have revolutionized the way websites interact with users. By leveraging user preferences, browsing history, and demographic information, these algorithms can dynamically adjust the website's layout, content, and functionalities to suit individual user needs. This level of personalization enhances user engagement, boosts conversion rates, and ultimately leads to a more satisfying online experience. In summary, the integration of chatbots, clever recommendation systems, and customization algorithms into modern websites is transforming the way users interact with online platforms. These advanced technologies not only streamline user experiences but also contribute to increased customer satisfaction, improved conversions, and overall website success.

Keywords: Javascript, machine learning, artificial intelligence, web development

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Authors: Fulya Zaralı, Harun Resit Yazgan

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Logistics centers represent areas that all national and international logistics and activities related to logistics can be implemented by the various businesses. Logistics centers have a key importance in joining the transport stream and the transport system operations. Therefore, it is important where these centers are positioned to be effective and efficient and to show the expected performance of the centers. In this study, the location selection problem to position the logistics center is discussed. Alternative centers are evaluated according certain criteria. The most appropriate center is identified using the axiomatic design method.

Keywords: axiomatic design, logistic center, facility location, information systems

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Authors: Alvaro Valera Sosa

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Background: In recent years, childhood overweight and obesity have become an increasing and challenging phenomenon in Berlin and Germany in general. The highest shares of childhood overweight in Berlin are district localities within the inner city ring with lowest socio-economic levels and the highest number of migration background populations. Most factors explaining overweight and obesity are linked to individual dispositions and nutrition balances. Among various strategies, to target drinking behaviors of children and adolescents has been proven to be effective. On the one hand, encouraging the intake of water – which does not contain energy and thus may support a healthy weight status – on the other hand, reducing the consumption of sugar-containing beverages – which are linked to weight gain and obesity. Anyhow, these preventive approaches have mostly developed into individual or educational interventions widely neglecting environmental modifications. Therefore, little is known on how urban physical environment patterns and features can act as influence factors for childhood overweight. Aiming the development of a physical environment intervention tackling children overweight, this study evaluated urban situations surrounding public playgrounds in Berlin where the issue is evident. It verified the presence and state of physical environmental conditions that can be conducive for children to engage physical activity and water intake. Methods: The study included public playgrounds for children from 0-12 y/o within district localities with the highest prevalence of childhood overweight, highest population density, and highest mixed uses. A systematic observation was realized to describe physical environment patterns and features that may affect children health behavior leading to overweight. Neighborhood walkability for all age groups was assessed using the Walkability for Health framework (TU-Berlin). Playground physical environment conditions were evaluated using Active Living Research assessment sheets. Finally, the food environment in the playground’s pedestrian catchment areas was reviewed focusing on: proximity to suppliers offering sugar-containing beverages, and physical access for 5 y/o children and up to drinking water following the Drinking Fountains and Public Health guidelines of the Pacific Institute. Findings: Out of 114 locations, only 7 had a child population over 3.000. Three with the lowest socio-economic index and highest percentage of migration background were selected. All three urban situations presented similar walkability: large trafficked avenues without buffer bordering at least one side of the playground, and important block to block disconnections for active travel. All three playgrounds rated equipment conditions from good to very good. None had water fountains at the reach of a 5 y/o. and all presented convenience stores and/or fast food outlets selling sugar-containing beverages nearby the perimeter. Conclusion: The three playground situations selected are representative of Berlin locations where most factors that influence children overweight are found. The results delivered urban and architectural design directives for an environmental intervention, used to study children health-related behavior. A post-intervention evaluation could prove associations between designed spaces and children overweight rate reduction creating a precedent in public health interventions and providing novel strategies for the health sector.

Keywords: children overweight, evaluation research, public playgrounds, urban design, urban health

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Authors: Mohammad Shahjahan, Md. Monoarul Haque

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In the research work information and data were collected from both types of sources, direct and indirect. Numerological, qualitative and participatory analysis methods have been followed. There were two principal sources of collecting information and data. Firstly, the data provided by the service recipients (300 nos. of women and children victims) in the Victim Support Centre and service providing policemen, executives and staffs (60 nos.). Secondly, data collected from Specialists, Criminologists and Sociologists involved in victim support services through Consultative Interview, KII, Case Study and FGD etc. The initial data collection has been completed with the help of questionnaires as per strategic variations and with the help of guidelines. It is to be noted that the main objective of this research was to determine whether services provided to the victims for their facilities, treatment/medication and rehabilitation by different government/non-government organizations was veritable at all. At the same time socio-economic background and demographic characteristics of the victims have also been revealed through this research. The results of the study show that although the number of victims has increased gradually due to socio-economic, political and cultural realities in Bangladesh, the number of victim support centers has not increased as expected. Awareness among the victims about the effectiveness of the 8 centers working in this regard is also not up to the mark. Two thirds of the victims coming to get service were not cognizant regarding the victim support services at all before getting the service. Most of those who have finally been able to come under the services of the Victim Support Center through various means, have received sheltering (15.5%), medical services (13.32%), counseling services (13.10%) and legal aid (12.66%). The opportunity to stay in security custody and psycho-physical services were also notable. Usually, women and children from relatively poor and marginalized families of the society come to victim support center for getting services. Among the women, young unmarried women are the biggest victims of crime. Again, women and children employed as domestic workers are more affected. A number of serious negative impacts fall on the lives of the victims. Being deprived of employment opportunities (26.62%), suffering from psycho-somatic disorder (20.27%), carrying sexually transmitted diseases (13.92%) are among them. It seems apparent to urgently enact distinct legislation, increase the number of Victim Support Centers, expand the area and purview of services and take initiative to increase public awareness and to create mass movement.

Keywords: crime, victim, support, Bangladesh

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Authors: Carla Idely Palencia-Aguilar

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Artificial wetlands play an important role at Guasca Municipality in Colombia, not only because they are used for the agroindustry, but also because more than 45 species were found, some of which are endemic and migratory birds. Remote sensing was used to determine the changes in the area occupied by water of artificial wetlands by means of Aster and Modis images for different time periods. Evapotranspiration was also determined by three methods: Surface Energy Balance System-Su (SEBS) algorithm, Surface Energy Balance- Bastiaanssen (SEBAL) algorithm, and Potential Evapotranspiration- FAO. Empirical equations were also developed to determine the relationship between Normalized Difference Vegetation Index (NDVI) versus net radiation, ambient temperature and rain with an obtained R2 of 0.83. Groundwater level fluctuations on a daily basis were studied as well. Data from a piezometer placed next to the wetland were fitted with rain changes (with two weather stations located at the proximities of the wetlands) by means of multiple regression and time series analysis, the R2 from the calculated and measured values resulted was higher than 0.98. Information from nearby weather stations provided information for ordinary kriging as well as the results for the Digital Elevation Model (DEM) developed by using PCI software. Standard models (exponential, spherical, circular, gaussian, linear) to describe spatial variation were tested. Ordinary Cokriging between height and rain variables were also tested, to determine if the accuracy of the interpolation would increase. The results showed no significant differences giving the fact that the mean result of the spherical function for the rain samples after ordinary kriging was 58.06 and a standard deviation of 18.06. The cokriging using for the variable rain, a spherical function; for height variable, the power function and for the cross variable (rain and height), the spherical function had a mean of 57.58 and a standard deviation of 18.36. Threatens of eutrophication were also studied, given the unconsciousness of neighbours and government deficiency. Water quality was determined over the years; different parameters were studied to determine the chemical characteristics of water. In addition, 600 pesticides were studied by gas and liquid chromatography. Results showed that coliforms, nitrogen, phosphorous and prochloraz were the most significant contaminants.

Keywords: DEM, evapotranspiration, geostatistics, NDVI

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Authors: Bilal Saoud

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In this paper, we propose a new method to find the community structure in networks. Our method is based on bee colony and the maximization of modularity to find the community structure. We use a bee colony algorithm to find the first community structure that has a good value of modularity. To improve the community structure, that was found, we merge communities until we get a community structure that has a high value of modularity. We provide a general framework for implementing our approach. We tested our method on computer-generated and real-world networks with a comparison to very known community detection methods. The obtained results show the effectiveness of our proposition.

Keywords: bee colony, networks, modularity, normalized mutual information

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Authors: Rituraj Deka, Nomi Baruah

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The development of information technology during the past few years resulted in designing of more and more complex software. The outsourcing of software development makes a higher requirement for the management of software development project. Various software enterprises follow various paths in their pursuit of excellence, applying various principles, methods and techniques along the way. The new research is proving that CMMI and Agile methodologies can benefit from using both methods within organizations with the potential to dramatically improve business performance. The paper describes a mapping between CMMI key process areas (KPAs) and Feature-Driven Development (FDD) communication perspective, so as to increase the understanding of how improvements can be made in the software development process.

Keywords: Agile, CMMI, FDD, KPAs

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Authors: Saroj Bijarnia, Preety Singh

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We propose a simple and effective biometrics system based on face verification across aging using a new variant of texture feature, Pyramid Binary Pattern. This employs Local Binary Pattern along with its hierarchical information. Dimension reduction of generated texture feature vector is done using Principal Component Analysis. Support Vector Machine is used for classification. Our proposed method achieves an accuracy of 92:24% and can be used in an automated age-invariant face verification system.

Keywords: biometrics, age invariant, verification, support vector machine

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Authors: Yu-Min Chang, Kuo-Sheng Tsai, Hung-Te Tsai, Chia-Hsin Li

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This paper presents the early-warning lights classification management system for industrial parks promoted by the Taiwan Environmental Protection Administration (EPA) since 2011, including the definition of each early-warning light, objectives, action program and accomplishments. All of the 151 industrial parks in Taiwan were classified into four early-warning lights, including red, orange, yellow and green, for carrying out respective pollution management according to the monitoring data of soil and groundwater quality, regulatory compliance, and regulatory listing of control site or remediation site. The Taiwan EPA set up a priority list for high potential polluted industrial parks and investigated their soil and groundwater qualities based on the results of the light classification and pollution potential assessment. In 2011-2013, there were 44 industrial parks selected and carried out different investigation, such as the early warning groundwater well networks establishment and pollution investigation/verification for the red and orange-light industrial parks and the environmental background survey for the yellow-light industrial parks. Among them, 22 industrial parks were newly or continuously confirmed that the concentrations of pollutants exceeded those in soil or groundwater pollution control standards. Thus, the further investigation, groundwater use restriction, listing of pollution control site or remediation site, and pollutant isolation measures were implemented by the local environmental protection and industry competent authorities; the early warning lights of those industrial parks were proposed to adjust up to orange or red-light. Up to the present, the preliminary positive effect of the soil and groundwater quality management system for industrial parks has been noticed in several aspects, such as environmental background information collection, early warning of pollution risk, pollution investigation and control, information integration and application, and inter-agency collaboration. Finally, the work and goal of self-initiated quality management of industrial parks will be carried out on the basis of the inter-agency collaboration by the classified lights system of early warning and management as well as the regular announcement of the status of each industrial park.

Keywords: industrial park, soil and groundwater quality management, early-warning lights classification, SOP for reporting and treatment of monitored abnormal events

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Authors: M. Kolotylo, H. Zheng, R. Parente, R. Dahiya

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Large number of organizations, frequently motivated by budget and cost cuts, outsource their Information Technology (IT) positions every year. Although the objective of reduction in financial obligations is often not accomplished, many buyer companies still manage to benefit from outsourcing projects. Knowledge Transfer (KT), being one of the major processes that take place during IT outsourcing partnership, may exert a strong impact on the performance of the parties involved, particularly that of the buyer. Research, however, lacks strong conceptual basis for the possible benefits that KT from supplier may bring to the buyer; and for the mechanisms that may be adopted by the buyer to maximize such benefit. This paper aims to fill this gap by proposing a conceptual framework of organizational learning and development of dynamic capabilities enabled by KT from the supplier to the buyer. The study examines buyer-supplier relationships in the context of IT outsourcing transactions, and theorizes how KT from the supplier to the buyer helps the performance of the buyer. It warrants that more research is carried out in order to explicate and provide evidence regarding the role that KT plays in strategic improvements for the buyer. The paper proposes to take up a two-fold approach to the research: conceptual development that utilizes logical argumentation and interpretive historical research, as well as a qualitative case study which aims to capture and understand the complex processes involved. Thus, the study provides a comprehensive visualization of the dynamics of the conditions under which participation in IT outsourcing partnership might be of benefit to the buyer company. The framework demonstrates the mechanisms involved in buyer’s achievement of strategic alignment through organizational learning enabled by KT from the supplier. It highlights that organizational learning involves a balance between exploitation of assets and exploration of new possibilities, and further notes that the dynamic capabilities mediate the effect of organizational learning on firm performance. The paper explicates in what ways managers can leverage outsourcing projects to execute strategy, which would enable their organization achieve better performance. The study concludes that organizational learning enables the firm to develop IT capabilities of strategic planning, IT integration, and IT relationships in the outsourcing context, and that IT capabilities developed through the organizational learning would help the firm in achieving strategic alignment.

Keywords: dynamic capabilities, it outsourcing, knowledge transfer, organizational learning, strategic alignment

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Authors: Meital Amzalag, Sharon Hardof-Jaffe

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In the last few decades, children and youth have been immersed in digital technologies. Indeed, recent studies explored the implication of technology use in their leisure and learning activities. Educators face an essential need to utilize technology and implement them into the curriculum. To do that, educators need to understand how young people use digital technology. This study aims to explore K12 students' digital lives from their point of view, to reveal their digital activities, age and gender differences with respect to digital activities, and to present the students' attitudes towards technologies in learning. The study approach is quantitative and includes354 students ages 6-16 from three schools in Israel. The online questionnaire was based on self-reports and consists of four parts: Digital activities: leisure time activities (such as social networks, gaming types), search activities (information types and platforms), and digital application use (e.g., calendar, notes); Digital skills (requisite digital platform skills such as evaluation and creativity); Social and emotional aspects of digital use (conducting digital activities alone and with friends, feelings, and emotions during digital use such as happiness, bullying); Digital attitudes towards digital integration in learning. An academic ethics board approved the study. The main findings reveal the most popular K12digital activities: Navigating social network sites, watching TV, playing mobile games, seeking information on the internet, and playing computer games. In addition, the findings reveal age differences in digital activities, such as significant differences in the use of social network sites. Moreover, the finding raises gender differences as girls use more social network sites and boys use more digital games, which are characterized by high complexity and challenges. Additionally, we found positive attitudes towards technology integration in school. Students perceive technology as enhancing creativity, promoting active learning, encouraging self-learning, and helping students with learning difficulties. The presentation will provide an up-to-date, accurate picture of the use of various digital technologies by k12 students. In addition, it will discuss the learning potentials of such use and how to implement digital technologies in the curriculum. Acknowledgments: This study is a part of a broader study about K-12 digital life in Israel and is supported by Mofet-the Israel Institute for Teachers'Development.

Keywords: technology and learning, K-12, digital life, gender differences

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Authors: Aymen Bouzid, Jihen Ben Smida

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In this paper, we propose to prove the importance of the application of blind source separation methods on remote sensing data in order to index multispectral images. The proposed method starts with Gabor Filtering and the application of a Blind Source Separation to get a more effective representation of the information contained on the observation images. After that, a feature vector is extracted from each image in order to index them. Experimental results show the superior performance of this approach.

Keywords: blind source separation, content based image retrieval, feature extraction multispectral, satellite images

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Authors: Babak Forouraghi

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A genetic circuit is a collection of interacting genes and proteins that enable individual cells to implement and perform vital biological functions such as cell division, growth, death, and signaling. In cell engineering, synthetic gene circuits are engineered networks of genes specifically designed to implement functionalities that are not evolved by nature. These engineered networks enable scientists to tackle complex problems such as engineering cells to produce therapeutics within the patient's body, altering T cells to target cancer-related antigens for treatment, improving antibody production using engineered cells, tissue engineering, and production of genetically modified plants and livestock. Construction of computational models to realize genetic circuits is an especially challenging task since it requires the discovery of the flow of genetic information in complex biological systems. Building synthetic biological models is also a time-consuming process with relatively low prediction accuracy for highly complex genetic circuits. The primary goal of this study was to investigate the utility of a pre-trained bidirectional encoder transformer that can accurately predict gene expressions in genetic circuit designs. The main reason behind using transformers is their innate ability (attention mechanism) to take account of the semantic context present in long DNA chains that are heavily dependent on the spatial representation of their constituent genes. Previous approaches to gene circuit design, such as CNN and RNN architectures, are unable to capture semantic dependencies in long contexts, as required in most real-world applications of synthetic biology. For instance, RNN models (LSTM, GRU), although able to learn long-term dependencies, greatly suffer from vanishing gradient and low-efficiency problem when they sequentially process past states and compresses contextual information into a bottleneck with long input sequences. In other words, these architectures are not equipped with the necessary attention mechanisms to follow a long chain of genes with thousands of tokens. To address the above-mentioned limitations, a transformer model was built in this work as a variation to the existing DNA Bidirectional Encoder Representations from Transformers (DNABERT) model. It is shown that the proposed transformer is capable of capturing contextual information from long input sequences with an attention mechanism. In previous works on genetic circuit design, the traditional approaches to classification and regression, such as Random Forrest, Support Vector Machine, and Artificial Neural Networks, were able to achieve reasonably high R2 accuracy levels of 0.95 to 0.97. However, the transformer model utilized in this work, with its attention-based mechanism, was able to achieve a perfect accuracy level of 100%. Further, it is demonstrated that the efficiency of the transformer-based gene expression classifier is not dependent on the presence of large amounts of training examples, which may be difficult to compile in many real-world gene circuit designs.

Keywords: machine learning, classification and regression, gene circuit design, bidirectional transformers

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Authors: Abhishek Bhardwaj, Manish Kumar Bajpai

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An image with its content and schema of colors presents an effective mode of information sharing and processing. By changing its color schema different visions and prospect are discovered by the users. This phenomenon of color transfer is being used by Social media and other channel of entertainment. Reinhard et al’s algorithm was the first one to solve this problem of color transfer. In this paper, we make this algorithm efficient by introducing domain parallelism among different processors. We also comment on the factors that affect the speedup of this problem. In the end by analyzing the experimental data we claim to propose a novel and efficient parallel Reinhard’s algorithm.

Keywords: Reinhard et al’s algorithm, color transferring, parallelism, speedup

Procedia PDF Downloads 614