Search results for: hierarchical Bayesian framework

Authors: Shah Muhammad Butt, Ahmed Masaud Ansair

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Present financial crisis in Higher Educational Institutes (HEIs) is causing lots of problems such as considerable budget cuts, which makes it difficult to meet the ever growing IT based research and learning needs. Institutions are rapidly planning and promoting cloud based approaches for their academic and research needs. A cost-effective hybrid cloud framework for HEIs will provide educational services for campus or intercampus communication. Hybrid cloud framework comprises private and public cloud approaches. This paper will propose the framework based on the Open Source Cloud (OpenNebula for Virtualization, Eucalyptus for Infrastructure and Aneka for programming development environment) combined with CSPs services which are delivered to the end-user via the internet from public clouds such as Google, Microsoft, Zoho, and Salesforce.

Keywords: educational services, hybrid campus cloud, open source, higher educational institutes

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Authors: Gaurav Malik, Satyendra Mourya, Jyoti Jaiswal, Ramesh Chandra

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The attractive and multifunctional properties of ZnO make it a promising material for the fabrication of highly sensitive and selective efficient gas sensors at room temperature. This presented article focuses on the development of highly selective and sensitive H₂ gas sensor based on the Pd-Pt decorated ZnO framework and its sensing mechanisms. The gas sensing performance of sputter made Pd-Pt/ZnO electrode on anodized porous silicon (PSi) substrate toward H₂ gas is studied under low detection limit (2–500 ppm) of H₂ in the air. The chemiresistive sensor demonstrated sublimate selectivity, good sensing response, and fast response/recovery time with excellent stability towards H₂ at low temperature operation under ambient environment. The elaborate selective measurement of Pd-Pt/ZnO/PSi structure was performed towards different oxidizing and reducing gases. This structure exhibited advance and reversible response to H₂ gas, which revealed that the acquired architecture with ZnO framework is a promising candidate for H₂ gas sensor.

Keywords: sputtering, porous silicon, ZnO framework, XPS spectra, gas sensor

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Authors: Ezekiel Mwangi, Stephen Kimani, Agnes Mindila

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Usability is one of the key factors that determine the success or failure of any WebGIS (technology normally applied on the internet to analyze and present spatial data on the Internet). However, not all the usability attributes have the same impact on usability. It is, therefore, necessary to prioritize WebGIS usability elements and determine the ones that are more crucial to the success of the WebGIS. This research aims to identify the main elements of WebGIS usability, investigate the order of importance and priority of the usability elements of WebGIS, and propose a WebGIS development framework that incorporates the prioritization of the usability elements. This will be achieved through a literature review. The outcome of this research will help usability specialists and WebGIS developers in determining specific usability elements that should be accorded more emphasis during the design and development of WebGIS.

Keywords: framework, prioritization, usability, WebGIS

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Authors: N. J. Shanono, J. G. Ndiritu

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The poor performance of water resource systems (WRS) has been reportedly linked to not only climate variability and the water demand dynamics but also human behaviour-driven unlawful activities. Some of these unlawful activities that have been adversely affecting water sector include unauthorized water abstractions, water wastage behaviour, refusal of water re‐use measures, excessive operational losses, discharging untreated or improperly treated wastewater, over‐application of chemicals by agricultural users and fraudulent WRS operation. Despite advances in WRS planning, operation, and analysis incorporating such undesirable human activities to quantitatively assess their impact on WRS performance remain elusive. This study was then inspired by the need to develop a methodological framework for WRS performance assessment that integrates the impact of human behaviour with WRS performance assessment analysis. We, therefore, proposed a conceptual framework for assessing the impact of human behaviour on WRS performance using the concept of socio-hydrology. The framework identifies and couples four major sources of WRS-related values (water values, water systems, water managers, and water users) using three missing links between human and water in the management of WRS (interactions, outcomes, and feedbacks). The framework is to serve as a database for choosing relevant social and hydrological variables and to understand the intrinsic relations between the selected variables to study a specific human-water problem in the context of WRS management.

Keywords: conceptual framework, human behaviour; socio-hydrology; water resource systems

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Authors: Kurmet Kivipõld, Maaja Vadi

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Current paper develops the conceptual framework for organizational leadership capability. Organizational leadership here is understood as collective multi-level phenomenon which has been embedded into organizational processes as a capability at the level of the entire organization. The paper analyses and systematises the theo¬retical approaches to multi-level leadership in existing literature. This analysis marks the foundation of collective leadership at the organizational level, which forms the basis for the development of the conceptual framework of organi¬zational leadership capability. The developed conceptual framework of organiza¬tional leadership capability is formed from the synthesis of the three groups of base theories – traditional leadership theories, the resource-based view from strategic management and complexity theory from system theories. These conceptual sources present the main characteristics that determine the nature of organizational leadership capability and are the basis for its mea¬surement.

Keywords: leadership, organizational capability, organizational leadership, resource-based view, system theory

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Authors: Diana F. Lopes, Jorge Simoes, José Martins, Eduardo Castro

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Health professionals are the keystone of any health system, by delivering health services to the population. Given the time and cost involved in training new health professionals, the planning process of the health workforce is particularly important as it ensures a proper balance between the supply and demand of these professionals and it plays a central role on the Health 2020 policy. In the past 40 years, the planning of the health workforce in Portugal has been conducted in a reactive way lacking a prospective vision based on an integrated, comprehensive and valid analysis. This situation may compromise not only the productivity and the overall socio-economic development but the quality of the healthcare services delivered to patients. This is even more critical given the expected shortage of the health workforce in the future. Furthermore, Portugal is facing an aging context of some professional classes (physicians and nurses). In 2015, 54% of physicians in Portugal were over 50 years old, and 30% of all members were over 60 years old. This phenomenon associated to an increasing emigration of young health professionals and a change in the citizens’ illness profiles and expectations must be considered when planning resources in healthcare. The perspective of sudden retirement of large groups of professionals in a short time is also a major problem to address. Another challenge to embrace is the health workforce imbalances, in which Portugal has one of the lowest nurse to physician ratio, 1.5, below the European Region and the OECD averages (2.2 and 2.8, respectively). Within the scope of the HEALTH 2040 project – which aims to estimate the ‘Future needs of human health resources in Portugal till 2040’ – the present study intends to get a comprehensive dynamic approach of the problem, by (i) estimating the needs of physicians and nurses in Portugal, by specialties and by quinquenium till 2040; (ii) identifying the training needs of physicians and nurses, in medium and long term, till 2040, and (iii) estimating the number of students that must be admitted into medicine and nursing training systems, each year, considering the different categories of specialties. The development of such approach is significantly more critical in the context of limited budget resources and changing health care needs. In this context, this study presents the drivers of the healthcare needs’ evolution (such as the demographic and technological evolution, the future expectations of the users of the health systems) and it proposes a Bayesian methodology, combining the best available data with experts opinion, to model such evolution. Preliminary results considering different plausible scenarios are presented. The proposed methodology will be integrated in a user-friendly decision support system so it can be used by politicians, with the potential to measure the impact of health policies, both at the regional and the national level.

Keywords: bayesian estimation, health economics, health workforce planning, human health resources planning

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Authors: Nikita Valluri, Vatcharaporn Esichaikul

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With the popularity of data-driven decision making on the rise, this study focuses on providing an alternative outlook towards the process of decision-making. Combining quantitative and qualitative methods rooted in the social sciences, an integrated framework is presented with a focus on delivering a much more robust and efficient approach towards the concept of data-driven decision-making with respect to not only Big data but also 'Thick data', a new form of qualitative data. In support of this, an example from the retail sector has been illustrated where the framework is put into action to yield insights and leverage business intelligence. An interpretive approach to analyze findings from both kinds of quantitative and qualitative data has been used to glean insights. Using traditional Point-of-sale data as well as an understanding of customer psychographics and preferences, techniques of data mining along with qualitative methods (such as grounded theory, ethnomethodology, etc.) are applied. This study’s final goal is to establish the framework as a basis for providing a holistic solution encompassing both the Big and Thick aspects of any business need. The proposed framework is a modified enhancement in lieu of traditional data-driven decision-making approach, which is mainly dependent on quantitative data for decision-making.

Keywords: big data, customer behavior, customer experience, data mining, qualitative methods, quantitative methods, thick data

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Authors: Zahra Asgar Pour

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Two types of Ti-Al-containing zeolitic beads with an average diameter of 450 to 750 µm and hierarchical porosity were synthesized using a hard template method and tested as heterogeneous catalysts in the epoxidation of cycloalkenes (i.e. cyclohexene and cis-cyclooctene) with aqueous hydrogen peroxide (H₂O₂) or tert-butyl hydroperoxide(TBHP) as the oxidant agent. The first type of zeolitic beads was prepared by hydrothermal treatment of a primarygel (containing the Si, Ti, and Al precursors) in the presence of porous anion-exchange resin beads as the hard shaping template. After calcination, these beads (Ti-Al-Beta-HDT-B) consisted of both crystalline zeolite Beta and an amorphous silicate phase. The second type of zeolitic beads (Ti-Beta-PS-deAl-14.4-B) was obtained by post-synthesis dealumination of Al-containing zeolite Beta beads using 14.4 M HNO₃, followed by Ti grafting (3 wt% per gram of zeolite). The prepared materials were characterised by means of XRD, N2-physisorption, UV-vis, XRF, SEM, and TEM and tested as heterogeneous epoxidation catalysts. This post-synthetically prepared catalyst demonstrated higher activity (cyclohexene conversion of 22.7 % and epoxide selectivity of 33.5 %) after 5 h at60 °C, which emanates from the crystalline structure and higher degrees of hydrophobicity. In addition, the post-synthetically prepared beads were prone to partial Ti leaching in the presence of H₂O₂, whereas they showed to be resistant against Ti leaching using tert-butyl hydroperoxide as the oxidant agent.

Keywords: epoxidation, structured catalysts, hierarchical porosity, bead-shape catalysts

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Authors: Gagandeep Kaur, Sonal Chawla

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In a Cloud Computing environment, resource provisioning, resource allocation and resource scheduling is the most complex issues these days. Cloud User expects the best resource utilization and Cloud Provider expects revenue maximization by considering budget and time constraints. In this research paper, Resource Management Framework has been proposed to allocate the resources to Cloud Users and Cloud Providers in Cloud environment. The main aim of the proposed work is to provide the resources and services to Cloud Providers and Cloud Users in an efficient and effective manner. The proposed framework has been simulated and tested using the CloudSim simulator tool.

Keywords: cloud computing, resource allocation, auction, provisioning

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Authors: Malini Nandi Majumdar, Avijan Dutta

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The West Police (WBP) is restructured under provisions of the Police Act 1861 during the period of British domination. It is one of the two police forces of the Indian state of west Bengal and is headed by an officer designated as Director General of Police (DG) who directly reports to the State Government. It covers a jurisdiction with eighteen revenue districts of the state and a District Superintendent of Police (SP) controls each district. The purpose of this empirical study is to explore the causes and factors of occupational stress in West Bengal Police officers so that the incumbents can perform their assigned tasks more diligently and the society could be free from evils and devils at a large. Using a self-developed close ended questionnaire that covers 20 critical job-related stressors, the study captures 310 respondents across the organizational hierarchy ranging from Sub Inspectors to the Superintendant of police and covers 5 districts and one commision rate under the jurisdiction of West Bengal Police. The present research has successfully indicated four major occupational stressors such as Organizational Stressors, Hierarchical Stressors, Situational Stressors and Environmental Stressors with 64% of the variance. Further we have employed CFA to determine the goodness of fit indices in terms of i) Absolute Fit Measures like CMIN, FMIN, RMSEA, ECVI ii) Incremental Fit Measures like TLI, NFI, AGFI, CFI(Byne, 2010) demonstrate that value of the measure has passed the requirement criteria and thus fit the model. The major stressors of West Bengal Police have been explored and the ways to deal with these inevitable stressors have been suggested.

Keywords: organizational stressors, hierarchical stressors, situational stressors, environmental stressors

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Authors: Martins Y. Otache, John J. Musa, Abayomi I. Kuti, Mustapha Mohammed

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The performance of an artificial neural network (ANN) is contingent on a host of factors, for instance, the network optimisation scheme. In view of this, the study examined the general implications of the ANN training optimisation algorithm on its forecast performance. To this end, the Bayesian regularisation (Br), Levenberg-Marquardt (LM), and the adaptive learning gradient descent: GDM (with momentum) algorithms were employed under different ANN structural configurations: (1) single-hidden layer, and (2) double-hidden layer feedforward back propagation network. Results obtained revealed generally that the gradient descent with momentum (GDM) optimisation algorithm, with its adaptive learning capability, used a relatively shorter time in both training and validation phases as compared to the Levenberg- Marquardt (LM) and Bayesian Regularisation (Br) algorithms though learning may not be consummated; i.e., in all instances considering also the prediction of extreme flow conditions for 1-day and 5-day ahead, respectively especially using the ANN model. In specific statistical terms on the average, model performance efficiency using the coefficient of efficiency (CE) statistic were Br: 98%, 94%; LM: 98 %, 95 %, and GDM: 96 %, 96% respectively for training and validation phases. However, on the basis of relative error distribution statistics (MAE, MAPE, and MSRE), GDM performed better than the others overall. Based on the findings, it is imperative to state that the adoption of ANN for real-time forecasting should employ training algorithms that do not have computational overhead like the case of LM that requires the computation of the Hessian matrix, protracted time, and sensitivity to initial conditions; to this end, Br and other forms of the gradient descent with momentum should be adopted considering overall time expenditure and quality of the forecast as well as mitigation of network overfitting. On the whole, it is recommended that evaluation should consider implications of (i) data quality and quantity and (ii) transfer functions on the overall network forecast performance.

Keywords: streamflow, neural network, optimisation, algorithm

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Authors: Murat Yildiz, Burcu Yildiz

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Price Cap Regulation is a form of economic regulation designed in the 1980s in the United Kingdom. Price cap regulation sets a cap on the price that the utility provider can charge. The cap is set according to several economic factors, such as the price cap index, expected efficiency savings and inflation. It has been used by several countries as a regulatory regime in several sectors. Container port privatization is still in early stages in some countries. Lack of a general framework can be an impediment to privatization. This paper aims a general framework to comprising decisions to be made for variables which are able to accommodate the variety of container terminals. Several approaches that may be needed as well as a passage between approaches.

Keywords: Price Cap Regulation, ports privatization, container terminal price regime, earning sharing

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Authors: Edwin Njoroge Kimani, Alan M. Munyao

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Despite Kenya’s status as a regional economic powerhouse, it struggles with economic shocks that expose the consumers. This, however, seems not to affect major cooperates such as those in the telecommunication and energy sectors. Through their operations, they have not only been able to fluctuate prices at will but also they have been accused of curtailing their rivals from penetrating the market. This study, through literature review of the legal and institutional framework, reports and publications interrogates the law and uncovers the following; i) failings of the legal framework to define market dominance and abuse of such positions, ii) the participation of the state, iii) the inertia of the government to prosecute corporations that abuse their market dominance, iv) the role of the state as a market player and as a regulator through the Competition Authority of Kenya. This study concludes that the market distortion is as a result of weak legal and institutional framework as well as conflict of interest by the government. Not much has been researched in the field of competition law the greater East Africa. This research is intended to form part of the growing research in the field and inform legal reform.

Keywords: competition law, economic power, dominance, Kenya

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Authors: Niroshan K. Walgama Wellalage, Tieling Zhang, Richard Dwight

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State-based Markov deterioration models (SMDM) sometimes fail to find accurate transition probability matrix (TPM) values, and hence lead to invalid future condition prediction or incorrect average deterioration rates mainly due to drawbacks of existing nonlinear optimization-based algorithms and/or subjective function types used for regression analysis. Furthermore, a set of separate functions for each condition state with age cannot be directly derived by using Markov model for a given bridge element group, which however is of interest to industrial partners. This paper presents a new approach for generating Homogeneous SMDM model output, namely, the Modified Weibull approach, which consists of a set of appropriate functions to describe the percentage condition prediction of bridge elements in each state. These functions are combined with Bayesian approach and Metropolis Hasting Algorithm (MHA) based Markov Chain Monte Carlo (MCMC) simulation technique for quantifying the uncertainty in model parameter estimates. In this study, factors contributing to rail bridge deterioration were identified. The inspection data for 1,000 Australian railway bridges over 15 years were reviewed and filtered accordingly based on the real operational experience. Network level deterioration model for a typical bridge element group was developed using the proposed Modified Weibull approach. The condition state predictions obtained from this method were validated using statistical hypothesis tests with a test data set. Results show that the proposed model is able to not only predict the conditions in network-level accurately but also capture the model uncertainties with given confidence interval.

Keywords: bridge deterioration modelling, modified weibull approach, MCMC, metropolis-hasting algorithm, bayesian approach, Markov deterioration models

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Authors: Ashraf Osman

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Effective deployable bridges that are characterized by an increased capacity to weight ratio are recently needed for post-disaster rapid mobility and military operations. In deployable bridging, replacing metals as the fabricating material with advanced composite laminates as lighter alternatives with higher strength is highly advantageous. This article presents a hierarchical optimization strategy of a composite bridge treadway considering maximum strength design and bridge weight minimization. Shape optimization of a generic deployable bridge beam cross-section is performed to achieve better stress distribution over the bridge treadway hull. The developed cross-section weight is minimized up to reserving the margins of safety of the deployable bridging code provisions. Hence, the strength of composite bridge plates is maximized through varying the plies orientation. Different loading cases are considered of a tracked vehicle patch load. The orthotropic plate properties of a composite sandwich core are used to simulate the bridge deck structural behavior. Whereas, the failure analysis is conducted using Tsai-Wu failure criterion. The naturally inspired particle swarm optimization technique is used in this study. The proposed technique efficiently reduced the weight to capacity ratio of the developed bridge beam.

Keywords: CFRP deployable bridges, disaster relief, military bridging, optimization of composites, particle swarm optimization

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Authors: Belgherbi Aicha, Hadjidj Ismahen, Bessaid Abdelhafid

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Interpretation of medical images benefits from anatomical and physiological priors to optimize computer-aided diagnosis applications. Segmentation of liver, spleen and kidneys is regarded as a major primary step in the computer-aided diagnosis of abdominal organ diseases. In this paper, a semi-automated method for medical image data is presented for the abdominal organ segmentation data using mathematical morphology. Our proposed method is based on hierarchical segmentation and watershed algorithm. In our approach, a powerful technique has been designed to suppress over-segmentation based on mosaic image and on the computation of the watershed transform. Our algorithm is currency in two parts. In the first, we seek to improve the quality of the gradient-mosaic image. In this step, we propose a method for improving the gradient-mosaic image by applying the anisotropic diffusion filter followed by the morphological filters. Thereafter, we proceed to the hierarchical segmentation of the liver, spleen and kidney. To validate the segmentation technique proposed, we have tested it on several images. Our segmentation approach is evaluated by comparing our results with the manual segmentation performed by an expert. The experimental results are described in the last part of this work.

Keywords: anisotropic diffusion filter, CT images, morphological filter, mosaic image, simultaneous organ segmentation, the watershed algorithm

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Authors: Femi Elegbeleye, Omobayo Esan, Muienge Mbodila, Patrick Bowe

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This paper focused on cost effective storage architecture using fog and cloud data storage gateway and presented the design of the framework for the data privacy model and data analytics framework on a real-time analysis when using machine learning method. The paper began with the system analysis, system architecture and its component design, as well as the overall system operations. The several results obtained from this study on data privacy model shows that when two or more data privacy model is combined we tend to have a more stronger privacy to our data, and when fog storage gateway have several advantages over using the traditional cloud storage, from our result shows fog has reduced latency/delay, low bandwidth consumption, and energy usage when been compare with cloud storage, therefore, fog storage will help to lessen excessive cost. This paper dwelt more on the system descriptions, the researchers focused on the research design and framework design for the data privacy model, data storage, and real-time analytics. This paper also shows the major system components and their framework specification. And lastly, the overall research system architecture was shown, its structure, and its interrelationships.

Keywords: IoT, fog, cloud, data analysis, data privacy

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Authors: Wenwen Lei, David R. Mckenzie

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Recent findings of anomalously high gas flow rates in carbon nanotubes show smooth hydrophobic walls can increase specular reflection of molecules and reduce the tangential momentum accommodation coefficient (TMAC). Here we report the first measurements of water vapor flows in microtubes over a wide humidity range and show that for hydrophobic silica there is a range of humidity over which an adsorbed water layer reduces TMAC and accelerates flow. Our results show that this association between hydrophobicity and accelerated moisture flow occurs in readily available materials. We develop a hierarchical theory that unifies Maxwell’s ideas on TMAC with Langmuir’s ideas on adsorption. We fit the TMAC data as a function of humidity with the hierarchical theory based on two stages of Langmuir adsorption and derive total adsorption isotherms for water on hydrophobic silica that agree with direct observations. We propose structures for each stage of the water adsorption, the first reducing TMAC by a passivation of adsorptive patches and a smoothing of the surface, the second resembling bulk water with large TMAC. We find that leak testing of moisture barriers with an ideal gas such as helium may not be accurate enough for critical applications and that direct measurements of the water leak rate should be made.

Keywords: water vapor flows, silica microtubes, TMAC, enhanced flow rates

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Authors: Mingbao Chen

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After Rio+20, the blue economy rises all over the world, and it has become the focus field of national development. At present, the blue economy has become a new growth point in the field of global economy and the direction of the development of ‘green’ in the ocean. However, in fact, the key factors affecting the development of the blue economy have not been explored in depth, and the development policies and performance of the blue economy have not been scientifically evaluated. This cannot provide useful guidance for the development of the blue economy. Therefore, it is urgent to establish a quantitative evaluation framework to measure the performance of the blue economic development. Based on the full understanding of the connotation and elements of the blue economy, and studying the literature, this article has built an universality and operability evaluation index system, including ecological environment, social justice, sustainable growth, policy measures, and so on. And this article also established a sound evaluation framework of blue economic development performance. At the same time, this article takes China as a sample to test the framework of the adaptability, and to assess the performance of China's blue economic.

Keywords: Blue economy, development performance, evaluation framework, assess method

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Authors: Tofigh Hamidavi, Sepehr Abrishami, Pasquale Ponterosso, David Begg

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Structural design and analysis is an important and time-consuming process, particularly at the conceptual design stage. Decisions made at this stage can have an enormous effect on the entire project, as it becomes ever costlier and more difficult to alter the choices made early on in the construction process. Hence, optimisation of the early stages of structural design can provide important efficiencies in terms of cost and time. This paper suggests a structural design optimisation (SDO) framework in which Genetic Algorithms (GAs) may be used to semi-automate the production and optimisation of early structural design alternatives. This framework has the potential to leverage conceptual structural design innovation in Architecture, Engineering and Construction (AEC) projects. Moreover, this framework improves the collaboration between the architectural stage and the structural stage. It will be shown that this SDO framework can make this achievable by generating the structural model based on the extracted data from the architectural model. At the moment, the proposed SDO framework is in the process of validation, involving the distribution of an online questionnaire among structural engineers in the UK.

Keywords: building information, modelling, BIM, genetic algorithm, GA, architecture-engineering-construction, AEC, optimisation, structure, design, population, generation, selection, mutation, crossover, offspring

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Authors: Shimaa Nagro, Russell Campion

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Mobile devices are becoming ever more widely available, with growing functionality, and they are increasingly used as enabling technology to give students access to educational material anytime and anywhere. However, the design of educational material's user interfaces for mobile devices is beset by many unresolved research problems such as those arising from constraints associated with mobile devices or from issues linked to effective learning. The proposed research aims to produce: (i) a method framework for the design and evaluation of educational material’s interfaces to be delivered on mobile devices, in multimedia form based on Human Computer Interaction strategies; and (ii) a software tool implemented as a fast-track alternative to use the method framework in full. The investigation will combine qualitative and quantitative methods, including interviews and questionnaires for data collection and three case studies for validating the method framework. The method framework is a framework to enable an educational designer to effectively and efficiently create educational multimedia interfaces to be used on mobile devices by following a particular methodology that contains practical and usable tools and techniques. It is a method framework that accepts any educational material in its final lesson plan and deals with this plan as a static element, it will not suggest any changes in any information given in the lesson plan but it will help the instructor to design his final lesson plan in a multimedia format to be presented in mobile devices.

Keywords: mobile learning, M-Learn, HCI, educational multimedia, interface design

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Authors: Ruthanne Tobin

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The shift to a personalized learning (PL) curriculum in Canada represents an innovative approach to teaching and learning that is also evident in various initiatives across the 32-nation OECD. The premise behind PL is that empowering individual learners to have more input into how they access and construct knowledge, and express their understanding of it, will result in more meaningful school experiences and academic success. In this paper presentation, the author reports on a document analysis of the new curriculum in the province of British Columbia. Three theoretical frameworks are used to analyze the new curriculum. Framework 1 focuses on five dominant aspects (FDA) of PL at the classroom level. Framework 2 focuses on conceptualizing and enacting personalized learning (CEPL) within three spheres of influence. Framework 3 focuses on the integration of three types of knowledge (content, technological, and pedagogical). Analysis is ongoing, but preliminary findings suggest that the new curriculum addresses framework 1 quite well, which identifies five areas of personalized learning: 1) assessment for learning; 2) effective teaching and learning; 3) curriculum entitlement (choice); 4) school organization; and 5) “beyond the classroom walls” (learning in the community). Framework 2 appears to be less well developed in the new curriculum. This framework speaks to the dynamics of PL within three spheres of interaction: 1) nested agency, comprised of overarching constraints [and enablers] from policy makers, school administrators and community; 2) relational agency, which refers to a capacity for professionals to develop a network of expertise to serve shared goals; and 3) students’ personalized learning experience, which integrates differentiation with self-regulation strategies. Framework 3 appears to be well executed in the new PL curriculum, as it employs the theoretical model of technological, pedagogical content knowledge (TPACK) in which there are three interdependent bodies of knowledge. Notable within this framework is the emphasis on the pairing of technologies with excellent pedagogies to significantly assist students and teachers. This work will be of high relevance to educators interested in innovative school reform.

Keywords: curriculum reform, K-12 school change, innovations in education, personalized learning

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Authors: Mukisa Simon Peter Turker, Etomaru Irene

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This study examined factors determining the success of University-Industry Collaboration (UIC) in the science academic units (SAUs) at Makerere University. This was prompted by concerns about weak linkages between industry and the academic units at Makerere University. The study examined institutional, relational, output, and framework factors determining the success of UIC in the science academic units at Makerere University. The study adopted a predictive cross-sectional survey design. Data was collected using a questionnaire survey from 172 academic staff from the six SAUs at Makerere University. Stratified, proportionate, and simple random sampling techniques were used to select the samples. The study used descriptive statistics and linear multiple regression analysis to analyze data. The study findings reveal a coefficient of determination (R-square) of 0.403 at a significance level of 0.000, suggesting that UIC success was 40.3% at a standardized error of estimate of 0.60188. The strength of association between Institutional factors, Relational factors, Output factors, and Framework factors, taking into consideration all interactions among the study variables, was at 64% (R= 0.635). Institutional, Relational, Output and Framework factors accounted for 34% of the variance in the level of UIC success (adjusted R2 = 0.338). The remaining variance of 66% is explained by factors other than Institutional, Relational, Output, and Framework factors. The standardized coefficient statistics revealed that Relational factors (β = 0.454, t = 5.247, p = 0.000) and Framework factors (β = 0.311, t = 3.770, p = 0.000) are the only statistically significant determinants of the success of UIC in the SAU in Makerere University. Output factors (β = 0.082, t =1.096, p = 0.275) and Institutional factors β = 0.023, t = 0.292, p = 0.771) turned out to be statistically insignificant determinants of the success of UIC in the science academic units at Makerere University. The study concludes that Relational Factors and Framework Factors positively and significantly determine the success of UIC, but output factors and institutional factors are not statistically significant determinants of UIC in the SAUs at Makerere University. The study recommends strategies to consolidate Relational and Framework Factors to enhance UIC at Makerere University and further research on the effects of Institutional and Output factors on the success of UIC in universities.

Keywords: university-industry collaboration, output factors, relational factors, framework factors, institutional factors

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Authors: Sujoy Anirudha Das, Albert Thomas

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Building sector is a major consumer of energy globally, and it has corresponding effects to the environment with respect to the carbon emissions. Given the fact that India is expected to add 40-billion square meter of new buildings till 2050, we need frameworks that help in reducing the overall energy consumption in the building sector. Even though several simulation-based frameworks that help in analyzing the building energy consumption are developed globally, in the Indian context, to the best of our knowledge, there is a lack of a comprehensive, yet user-friendly framework to simulate and optimize the effects of various energy influencing factors, specifically for Indian buildings. Therefore, this study is aimed at developing a simulation-based optimization framework to model the energy interactions in different types of Indian buildings by considering the dynamic nature of various energy influencing factors. This comprehensive framework can be used by various building stakeholders to test the energy effects of different factors such as, but not limited to, the various building materials, the orientation, the weather fluctuations, occupancy changes and the type of the building (e.g., office, residential). The results from the case study involving several building types would help us in gaining insights to build new energy-efficient buildings as well as retrofit the existing structures in a more convenient way to consume less energy, exclusively for an Indian scenario.

Keywords: building energy consumption, building energy simulations, energy efficient buildings, optimization framework

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Authors: Amin Salighehdar, Ziwen Ye, Mingzhe Liu, Ionut Florescu, Alan F. Blumberg

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Storm surge is an abnormal water level caused by a storm. Accurate prediction of a storm surge is a challenging problem. Researchers developed various ensemble modeling techniques to combine several individual forecasts to produce an overall presumably better forecast. There exist some simple ensemble modeling techniques in literature. For instance, Model Output Statistics (MOS), and running mean-bias removal are widely used techniques in storm surge prediction domain. However, these methods have some drawbacks. For instance, MOS is based on multiple linear regression and it needs a long period of training data. To overcome the shortcomings of these simple methods, researchers propose some advanced methods. For instance, ENSURF (Ensemble SURge Forecast) is a multi-model application for sea level forecast. This application creates a better forecast of sea level using a combination of several instances of the Bayesian Model Averaging (BMA). An ensemble dressing method is based on identifying best member forecast and using it for prediction. Our contribution in this paper can be summarized as follows. First, we investigate whether the ensemble models perform better than any single forecast. Therefore, we need to identify the single best forecast. We present a methodology based on a simple Bayesian selection method to select the best single forecast. Second, we present several new and simple ways to construct ensemble models. We use correlation and standard deviation as weights in combining different forecast models. Third, we use these ensembles and compare with several existing models in literature to forecast storm surge level. We then investigate whether developing a complex ensemble model is indeed needed. To achieve this goal, we use a simple average (one of the simplest and widely used ensemble model) as benchmark. Predicting the peak level of Surge during a storm as well as the precise time at which this peak level takes place is crucial, thus we develop a statistical platform to compare the performance of various ensemble methods. This statistical analysis is based on root mean square error of the ensemble forecast during the testing period and on the magnitude and timing of the forecasted peak surge compared to the actual time and peak. In this work, we analyze four hurricanes: hurricanes Irene and Lee in 2011, hurricane Sandy in 2012, and hurricane Joaquin in 2015. Since hurricane Irene developed at the end of August 2011 and hurricane Lee started just after Irene at the beginning of September 2011, in this study we consider them as a single contiguous hurricane event. The data set used for this study is generated by the New York Harbor Observing and Prediction System (NYHOPS). We find that even the simplest possible way of creating an ensemble produces results superior to any single forecast. We also show that the ensemble models we propose generally have better performance compared to the simple average ensemble technique.

Keywords: Bayesian learning, ensemble model, statistical analysis, storm surge prediction

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Authors: Lazhar Belkhiri, Ammar Tiri, Lotfi Mouni, Fatma Elhadj Lakouas

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This study evaluates the surface water quality in the Kebir Rhumel Basin by analyzing hydrochemical parameters. To assess spatial and temporal variations in water quality, we applied the Water Quality Index (WQI), Mann-Kendall (MK) trend analysis, and hierarchical cluster analysis (HCA). Monthly measurements of eleven hydrochemical parameters were collected across eight stations from January 2016 to December 2020. Calcium and sulfate emerged as the dominant cation and anion, respectively. WQI analysis indicated a high incidence of poor water quality at stations Ain Smara (AS), Beni Haroune (BH), Grarem (GR), and Sidi Khalifa (SK), where 89.5%, 90.6%, 78.2%, and 62.7% of samples, respectively, fell into this category. The MK trend analysis revealed a significant upward trend in WQI at Oued Boumerzoug (ON) and SK stations, signaling temporal deterioration in these areas. HCA grouped the dataset into three clusters, covering approximately 22%, 30%, and 48% of the months, respectively. Within these clusters, specific stations exhibited elevated WQI values: GR and ON in the first cluster, OB and SK in the second, and AS, BH, El Milia (EM), and Hammam Grouz (HG) in the third. Furthermore, approximately 38%, 41%, and 38% of samples in clusters one, two, and three, respectively, were classified as having poor water quality. These findings provide essential insights for policymakers in formulating strategies to restore and manage surface water quality in the region.

Keywords: surface water quality, water quality index (WQI), Mann-Kendall Trend Analysis, hierarchical cluster analysis (HCA), spatial-temporal distribution, Kebir Rhumel Basin

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Authors: Ryan Messina, Mehedi Hasan

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This research is to examine the impacts of using data to generate performance requirements for automation in visual inspections using machine vision. These situations are intended for design and how projects can smooth the transfer of tacit knowledge to using an algorithm. We have proposed a framework when specifying machine vision systems. This framework utilizes varying levels of automation as contingency planning to reduce data processing complexity. Using data assists in extracting tacit knowledge from those who can perform the manual tasks to assist design the system; this means that real data from the system is always referenced and minimizes errors between participating parties. We propose using three indicators to know if the project has a high risk of failing to meet requirements related to accuracy and reliability. All systems tested achieved a better integration into operations after applying the framework.

Keywords: automation, contingency planning, continuous engineering, control theory, machine vision, system requirements, system thinking

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Authors: Serag El Hegazi

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The development of an assessment methodological framework for local and institutional sustainability is a key factor for future development plans and visions. This paper develops an approach to local and institutional sustainability assessment (ALISA). The ALISA methodology is a methodological framework that assists in the clarification, formulation, preparation, selection, and ranking of key indicators to facilitate the assessment of the level of sustainability at the local and institutional levels in North African and Middle Eastern cities. According to the literature review, this paper formulates a methodological framework, ALISA, which is a combination of the UNCSD (2001) Theme Indicators Framework and the issue-based Framework illustrated by McLaren (1996). The methodological framework has been implemented to formulate, select, and prioritise key indicators that most directly reflect the issues of a case study at the local community and institutional level. Yet, in the meantime, there is a lack of clear indicators and frameworks that can be developed to apply successfully at the local and institutional levels in the MENA Region, particularly in the cities of Benghazi and Amman. This is an essential issue for sustainability development estimation. Therefore, a conceptual framework was developed to be tested as a methodology to collect and classify data. The Approach to Local and Institutional Sustainability Assessment (ALISA) is a methodological framework that was developed to apply to certain cities in the MENA region. The main goal is to develop the ALISA framework to formulate, choose, and prioritize sustainability key indicators, which then can assist in guiding an assessment progress to improve decisions and policymakers towards the development of sustainable cities at the local and institutional level in the city of Benghazi. The conceptual, methodological framework, which supports this research with joint documentary and analysed data in two case studies, including focus-group discussions, semi-structured interviews, and questionnaires, reflects the approach required to develop a combined framework that assists the development of sustainability indicators. To achieve this progress and reach the aim of this paper, which is developing a practical approach for sustainability indicators framework that could be used as a tool to develop local and institutional sustainability indicators, appropriate stages must be applied to propose a set of local and institutional sustainability indicators as follows: Step one: issues clarifications, Step two: objectives formation/analysing of issues and boundaries, Step three: indicators preparation, First list of proposed indictors, Step four: indicator selection, Step five: indicator rating/ranking.

Keywords: sustainability indicators, approach to local and institutional level, ALISA, policymakers

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Authors: L. Yu, W. K. Li, S. K. Ong, A. Y. C. Nee

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In this paper, a scalable augmented reality framework for handheld devices is presented. The presented framework is enabled by using a server-client data communication structure, in which the search for tracking targets among a database of images is performed on the server-side while pixel-wise 3D tracking is performed on the client-side, which, in this case, is a handheld mobile device. Image search on the server-side adopts a residual-enhanced image descriptors representation that gives the framework a scalability property. The tracking algorithm on the client-side is based on a gravity-aligned feature descriptor which takes the advantage of a sensor-equipped mobile device and an optimized intensity-based image alignment approach that ensures the accuracy of 3D tracking. Automatic content streaming is achieved by using a key-frame selection algorithm, client working phase monitoring and standardized rules for content communication between the server and client. The recognition accuracy test performed on a standard dataset shows that the method adopted in the presented framework outperforms the Bag-of-Words (BoW) method that has been used in some of the previous systems. Experimental test conducted on a set of video sequences indicated the real-time performance of the tracking system with a frame rate at 15-30 frames per second. The presented framework is exposed to be functional in practical situations with a demonstration application on a campus walk-around.

Keywords: augmented reality framework, server-client model, vision-based tracking, image search

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Authors: Patricia Heredia, Isabel Saz, Marta Fernández

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This article proposes that the decision regarding exporting and the choice of export channel are nested and non-independent decisions. We assume that firms make two sequential decisions before arriving at their final choice: the decision to access foreign markets and the decision about the type of channel. This hierarchical perspective of the choices involved in the process is appealing for two reasons. First, it supports the idea that people have a limited analytical capacity. Managers often break down a complex decision into a hierarchical process because this makes it more manageable. Secondly, it recognizes that important differences exist between entry modes. In light of the above, the objective of this study is to test different entry mode choice processes: independent decisions and nested and non-independent decisions. To do this, the methodology estimates and compares the following two models: (i) a simultaneous single-stage model with three entry mode choices (using a multinomial logit model); ii) a two-stage model with the export decision preceding the channel decision using a sequential logit model. The study uses resource-based factors in determining these decision processes concerning internationalization and the study carries out empirical analysis using a DOC Rioja sample of 177 firms.Using the Akaike and Schwarz Information Criteria, the empirical evidence supports the existence of a nested structure, where the decision about exporting precedes the export mode decision. The implications and contributions of the findings are discussed.

Keywords: sequential logit model, two-stage choice process, export mode, wine industry

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