Search results for: backpressure scheduling and routing

Authors: Aschalew C. Workneh, K. S. Hari Prasad, C. S. P. Ojha

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The crop water stress index (CWSI) is a cost-effective, non-destructive, and simple technique for tracking the start of crop water stress. This study investigated the feasibility of CWSI derived from canopy temperature to detect the water status of wheat crops. Artificial intelligence (AI) techniques have become increasingly popular in recent years for determining CWSI. In this study, the performance of two AI techniques, adaptive neuro-fuzzy inference system (ANFIS) and self-organizing maps (SOM), are compared while determining the CWSI of paddy crops. Field experiments were conducted for varying irrigation water applications during two seasons in 2022 and 2023 at the irrigation field laboratory at the Civil Engineering Department, Indian Institute of Technology Roorkee, India. The ANFIS and SOM-simulated CWSI values were compared with the experimentally calculated CWSI (EP-CWSI). Multiple regression analysis was used to determine the upper and lower CWSI baselines. The upper CWSI baseline was found to be a function of crop height and wind speed, while the lower CWSI baseline was a function of crop height, air vapor pressure deficit, and wind speed. The performance of ANFIS and SOM were compared based on mean absolute error (MAE), mean bias error (MBE), root mean squared error (RMSE), index of agreement (d), Nash-Sutcliffe efficiency (NSE), and coefficient of correlation (R²). Both models successfully estimated the CWSI of the paddy crop with higher correlation coefficients and lower statistical errors. However, the ANFIS (R²=0.81, NSE=0.73, d=0.94, RMSE=0.04, MAE= 0.00-1.76 and MBE=-2.13-1.32) outperformed the SOM model (R²=0.77, NSE=0.68, d=0.90, RMSE=0.05, MAE= 0.00-2.13 and MBE=-2.29-1.45). Overall, the results suggest that ANFIS is a reliable tool for accurately determining CWSI in wheat crops compared to SOM.

Keywords: adaptive neuro-fuzzy inference system, canopy temperature, crop water stress index, self-organizing map, wheat

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Authors: Sima Aznavi, Poria Fajri, Hanif Livani

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Smart homes are small energy systems which may be equipped with renewable energy sources, storage devices, and loads. Energy management strategy plays a main role in the efficient operation of smart homes. Effective energy scheduling of the renewable energy sources and storage devices guarantees efficient energy management in households while reducing the energy imports from the grid. Nevertheless, despite such strategies, independently day ahead energy schedules for multiple households can cause undesired effects such as high power exchange with the grid at certain times of the day. Therefore, the interactions between multiple smart home day ahead energy projections is a challenging issue in a smart grid system and if not managed appropriately, the imported energy from the power network can impose additional burden on the distribution grid. In this paper, a central energy management strategy for a network consisting of multiple households each equipped with renewable energy sources, storage devices, and Plug-in Electric Vehicles (PEV) is proposed. The decision-making strategy alongside the smart home energy management system, minimizes the energy purchase cost of the end users, while at the same time reducing the stress on the utility grid. In this approach, the smart home energy management system determines different operating scenarios based on the forecasted household daily load and the components connected to the household with the objective of minimizing the end user overall cost. Then, selected projections for each household that are within the same cost range are sent to the central decision-making system. The central controller then organizes the schedules to reduce the overall peak to average ratio of the total imported energy from the grid. To validate this approach simulations are carried out for a network of five smart homes with different load requirements and the results confirm that by applying the proposed central energy management strategy, the overall power demand from the grid can be significantly flattened. This is an effective approach to alleviate the stress on the network by distributing its energy to a network of multiple households over a 24- hour period.

Keywords: energy management, renewable energy sources, smart grid, smart home

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Authors: Eronini Iheanyi Umez-Eronini

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Most studies and existing implementations of compressed air energy storage (CAES) coupled with a wind farm to overcome intermittency and variability of wind power are based on bulk or centralized CAES plants. A dynamic model of a hybrid wind farm with wind turbines and distributed CAES, consisting of air storage tanks and compressor and expander trains at each wind turbine station, is developed and simulated in MATLAB. An ad hoc supervisory controller, in which the wind turbines are simply operated under classical power optimizing region control while scheduling power production by the expanders and air storage by the compressors, including modulation of the compressor power levels within a control range, is used to regulate overall farm power production to track minute-scale (3-minutes sampling period) TSO absolute power reference signal, over an eight-hour period. Simulation results for real wind data input with a simple wake field model applied to a hybrid plant composed of ten 5-MW wind turbines in a row and ten compatibly sized and configured Diabatic CAES stations show the plant controller is able to track the power demand signal within an error band size on the order of the electrical power rating of a single expander. This performance suggests that much improved results should be anticipated when the global D-CAES control is combined with power regulation for the individual wind turbines using available approaches for wind farm active power control. For standalone power plant fuel electrical efficiency estimate of up to 60%, the round trip electrical storage efficiency computed for the distributed CAES wherein heat generated by running compressors is utilized in the preheat stage of running high pressure expanders while fuel is introduced and combusted before the low pressure expanders, was comparable to reported round trip storage electrical efficiencies for bulk Adiabatic CAES.

Keywords: hybrid wind farm, distributed CAES, diabatic CAES, active power control, dynamic modeling and simulation

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Authors: Madia Safdar, G. Amjad Hussain, Mashhood Ahmad

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One of the major challenges of today’s era is peak demand which causes stress on the transmission lines and also raises the cost of energy generation and ultimately higher electricity bills to the end users, and it was used to be managed by the supply side management. However, nowadays this has been withdrawn because of existence of potential in the demand side management (DSM) having its economic and- environmental advantages. DSM in domestic load can play a vital role in reducing the peak load demand on the network provides a significant cost saving. In this paper the potential of demand response (DR) in reducing the peak load demands and electricity bills to the electric users is elaborated. For this purpose the domestic appliances are modeled in MATLAB Simulink and controlled by a module called energy management controller. The devices are categorized into controllable and uncontrollable loads and are operated according to real-time tariff pricing pattern instead of fixed time pricing or variable pricing. Energy management controller decides the switching instants of the controllable appliances based on the results from optimization algorithms. In GAMS software, the MILP (mixed integer linear programming) algorithm is used for optimization. In different cases, different constraints are used for optimization, considering the comforts, needs and priorities of the end users. Results are compared and the savings in electricity bills are discussed in this paper considering real time pricing and fixed tariff pricing, which exhibits the existence of potential to reduce electricity bills and peak loads in demand side management. It is seen that using real time pricing tariff instead of fixed tariff pricing helps to save in the electricity bills. Moreover the simulation results of the proposed energy management system show that the gained power savings lie in high range. It is anticipated that the result of this research will prove to be highly effective to the utility companies as well as in the improvement of domestic DR.

Keywords: controllable and uncontrollable domestic loads, demand response, demand side management, optimization, MILP (mixed integer linear programming)

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Authors: Joyce K. Anastasi, Bernadette Capili

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Community Engagement Strategies to Assist with the Development of an RCT Among People Living with HIV Our research team focuses on developing and testing protocols to manage chronic symptoms. For many years, our team designed and implemented symptom management studies for people living with HIV (PLWH). We identify symptoms that are not curative and are not adequately controlled by conventional therapies. As an exemplar, we describe how we successfully engaged PLWH in developing and refining our research feasibility protocol for distal sensory peripheral neuropathy (DSP) associated with HIV. With input from PLWH with DSP, our research received National Institutes of Health (NIH) research funding support. Significance: DSP is one of the most common neurologic complications in HIV. It is estimated that DSP affects 21% to 50% of PLWH. The pathogenesis of DSP in HIV is complex and unclear. Proposed mechanisms include cytokine dysregulation, viral protein-produced neurotoxicity, and mitochondrial dysfunction associated with antiretroviral medications. There are no FDA-approved treatments for DSP in HIV. Purpose: Aims: 1) to explore the impact of DSP on the lives of PLWH, 2) to identify patients’ perspectives on successful treatments for DSP, 3) to identify interventions considered feasible and sensitive to the needs of PLWH with DSP, and 4) to obtain participant input for protocol/study design. Description of Process: We conducted a needs assessment with PLWH with DSP. From our needs assessment, we learned from the patients’ perspective detailed descriptions of their symptoms; physical functioning with DSP; self-care remedies tried, and desired interventions. We also asked about protocol scheduling, instrument clarity, study compensation, study-related burdens, and willingness to participate in a randomized controlled trial (RCT) with a placebo and a waitlist group. Implications: We incorporated many of the suggestions learned from the need assessment. We developed and completed a feasibility study that provided us with invaluable information that informed subsequent NIH-funded studies. In addition to our extensive clinical and research experience working with PLWH, learning from the patient perspective helped in developing our protocol and promoting a successful plan for recruitment and retention of study participants.

Keywords: clinical trial development, peripheral neuropathy, traditional medicine, HIV, AIDS

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Authors: Wei Wang, Yannick Liu, Lu Zhen, H. Wang

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Maritime transport plays an important role in global economic development but also inevitably faces increasing pressures from all sides, such as ship operating cost reduction and environmental protection. An ideal innovation to address these pressures is electric ships. The electric ship is in the early stage. Considering the special characteristics of electric ships, i.e., travel range limit, to guarantee the efficient operation of electric ships, the service network needs to be re-designed carefully. This research designs a cost-efficient and environmentally friendly service network for electric ships, including the location of charging stations, charging plan, route planning, ship scheduling, and ship deployment. The problem is formulated as a mixed-integer linear programming model with the objective of minimizing total cost comprised of charging cost, the construction cost of charging stations, and fixed cost of ships. A case study using data of the shipping network along the Yangtze River is conducted to evaluate the performance of the model. Two operating scenarios are used: an electric ship scenario where all the transportation tasks are fulfilled by electric ships and a conventional ship scenario where all the transportation tasks are fulfilled by fuel oil ships. Results unveil that the total cost of using electric ships is only 42.8% of using conventional ships. Using electric ships can reduce 80% SOx, 93.47% NOx, 89.47% PM, and 42.62% CO2, but will consume 2.78% more time to fulfill all the transportation tasks. Extensive sensitivity analyses are also conducted for key operating factors, including battery capacity, charging speed, volume capacity, and a service time limit of transportation task. Implications from the results are as follows: 1) it is necessary to equip the ship with a large capacity battery when the number of charging stations is low; 2) battery capacity will influence the number of ships deployed on each route; 3) increasing battery capacity will make the electric ship more cost-effective; 4) charging speed does not affect charging amount and location of charging station, but will influence the schedule of ships on each route; 5) there exists an optimal volume capacity, at which all costs and total delivery time are lowest; 6) service time limit will influence ship schedule and ship cost.

Keywords: cost reduction, electric ship, environmental protection, sustainable shipping

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Authors: Ahmad Mohammad Sharqi, Mohamed Hechmi El Ouni, Saleh Alsulamy

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Due to the emerging technologies revolution worldwide, the need to exploit and employ innovative technologies for other functions and purposes in different aspects has become a remarkable matter. Saudi Arabia is considered one of the most powerful economic countries in the world, where the construction sector participates effectively in its economy. Thus, the construction sector in KSA should convoy the rapid digital revolution and transformation and implement smart devices on sites. A Smart Construction Site (SCS) includes smart devices, artificial intelligence, the internet of things, augmented reality, building information modeling, geographical information systems, and cloud information. This paper aims to study the level of implementation of SCS in KSA, analyze the obstacles and challenges of adopting SCS and find out critical success factors for its implementation. A survey of close-ended questions (scale and multi-choices) has been conducted on professionals in the construction sector of Saudi Arabia. A total number of twenty-nine questions has been prepared for respondents. Twenty-four scale questions were established, and those questions were categorized into several themes: quality, scheduling, cost, occupational safety and health, technologies and applications, and general perception. Consequently, the 5-point Likert scale tool (very low to very high) was adopted for this survey. In addition, five close-ended questions with multi-choice types have also been prepared; these questions have been derived from a previous study implemented in the United Kingdom (UK) and the Dominic Republic (DR), these questions have been rearranged and organized to fit the structured survey in order to place the Kingdom of Saudi Arabia in comparison with the United Kingdom (UK) as well as the Dominican Republic (DR). A total number of one hundred respondents have participated in this survey from all regions of the Kingdom of Saudi Arabia: southern, central, western, eastern, and northern regions. The drivers, obstacles, and success factors for implementing smart devices and technologies in KSA’s construction sector have been investigated and analyzed. Besides, it has been concluded that KSA is on the right path toward adopting smart construction sites with attractive results comparable to and even better than the UK in some factors.

Keywords: artificial intelligence, construction projects management, internet of things, smart construction sites, smart devices

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Authors: Nyashadzashe Chiraga, Anthony Walker, Glen Bright

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The manufacturing industry is currently experiencing a paradigm shift into the Fourth Industrial Revolution in which customers are increasingly at the epicentre of production. The high degree of production customization and personalization requires a flexible manufacturing system that will rapidly respond to the dynamic and volatile changes driven by the market. They are a gap in technology that allows for the optimal flow of information and optimal manufacturing operations on the shop floor regardless of the rapid changes in the fixture and part demands. Information is the reduction of uncertainty; it gives meaning and context on the state of each cell. The amount of information needed to describe cellular manufacturing systems is investigated by two measures: the structural entropy and the operational entropy. Structural entropy is the expected amount of information needed to describe scheduled states of a manufacturing system. While operational entropy is the amount of information that describes the scheduled states of a manufacturing system, which occur during the actual manufacturing operation. Using Anylogic simulator a typical manufacturing job shop was set-up with a cellular manufacturing configuration. The cellular make-up of the configuration included; a Material handling cell, 3D Printer cell, Assembly cell, manufacturing cell and Quality control cell. The factory shop provides manufactured parts to a number of clients, and there are substantial variations in the part configurations, new part designs are continually being introduced to the system. Based on the normal expected production schedule, the schedule adherence was calculated from the structural entropy and operation entropy of varying the amounts of information communicated in simulated runs. The structural entropy denotes a system that is in control; the necessary real-time information is readily available to the decision maker at any point in time. For contractive analysis, different out of control scenarios were run, in which changes in the manufacturing environment were not effectively communicated resulting in deviations in the original predetermined schedule. The operational entropy was calculated from the actual operations. From the results obtained in the empirical study, it was seen that increasing, the efficiency of a factory communication system increases the degree of adherence of a job to the expected schedule. The performance of downstream production flow fed from the parallel upstream flow of information on the factory state was increased.

Keywords: information entropy, communication in manufacturing, mass customisation, scheduling

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Authors: Merertu Wakuma Rundassa

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Global competition and rapidly changing customer requirements are forcing major changes in the production styles and configuration of manufacturing organizations. Increasingly, traditional centralized and sequential manufacturing planning, scheduling, and control mechanisms are being found insufficiently flexible to respond to changing production styles and highly dynamic variations in product requirements. The traditional approaches limit the expandability and reconfiguration capabilities of the manufacturing systems. Thus many business houses face increasing pressure to lower production cost, improve production quality and increase responsiveness to customers. In a textile and apparel manufacturing, globalization has led to increase in competition and quality awareness and these industries have changed tremendously in the last few years. So, to sustain competitive advantage, companies must re-examine and fine-tune their business processes to deliver high quality goods at very low costs and it has become very important for the textile and apparel industries to integrate themselves with information technology to survive. IT can create competitive advantages for companies to improve coordination and communication among trading partners, increase the availability of information for intermediaries and customers and provide added value at various stages along the entire chain. Ethiopia is in the process of realizing its potential as the future sourcing location for the global textile and garments industry. With a population of over 90 million people and the fastest growing non-oil economy in Africa, Ethiopia today represents limitless opportunities for international investors. For the textile and garments industry Ethiopia promises a low cost production location with natural resources such as cotton to enable the setup of vertically integrated textile and garment operation. However; due to lack of integration of their business activities textile and apparel industry of Ethiopia faced a problem in that it can‘t be competent in the global market. On the other hand the textile and apparel industries of other countries have changed tremendously in the last few years and globalization has led to increase in competition and quality awareness. So the aim of this paper is to study the trend of Ethiopian Textile and Apparel Industry on the application of different IT system to integrate them in the global market.

Keywords: information technology, business integrity, textile and apparel industries, Ethiopia

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Authors: Qingqing Feng, S. Thomas Ng, Frank J. Xu, Jiduo Xing

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Faced with unprecedented challenges including aging assets, lack of maintenance budget, overtaxed and inefficient usage, and outcry for better service quality from the society, today’s infrastructure systems has become the main focus of many metropolises to pursue sustainable urban development and improve resilience. Digital twin, being one of the most innovative enabling technologies nowadays, may open up new ways for tackling various infrastructure asset management (IAM) problems. Digital twin application for IAM, as its name indicated, represents an evolving digital model of intended infrastructure that possesses functions including real-time monitoring; what-if events simulation; and scheduling, maintenance, and management optimization based on technologies like IoT, big data and AI. Up to now, there are already vast quantities of global initiatives of digital twin applications like 'Virtual Singapore' and 'Digital Built Britain'. With digital twin technology permeating the IAM field progressively, it is necessary to consider the maturity of the application and how those institutional or industrial digital twin application processes will evolve in future. In order to deal with the gap of lacking such kind of benchmark, a draft maturity model is developed for digital twin application in the IAM field. Firstly, an overview of current smart cities maturity models is given, based on which the draft Maturity Model of Digital Twin Application for Infrastructure Asset Management (MM-DTIAM) is developed for multi-stakeholders to evaluate and derive informed decision. The process of development follows a systematic approach with four major procedures, namely scoping, designing, populating and testing. Through in-depth literature review, interview and focus group meeting, the key domain areas are populated, defined and iteratively tuned. Finally, the case study of several digital twin projects is conducted for self-verification. The findings of the research reveal that: (i) the developed maturity model outlines five maturing levels leading to an optimised digital twin application from the aspects of strategic intent, data, technology, governance, and stakeholders’ engagement; (ii) based on the case study, levels 1 to 3 are already partially implemented in some initiatives while level 4 is on the way; and (iii) more practices are still needed to refine the draft to be mutually exclusive and collectively exhaustive in key domain areas.

Keywords: digital twin, infrastructure asset management, maturity model, smart city

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Authors: Natalia Rudeli, Elisabeth Viles, Adrian Santilli

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Delays in the construction industry are a global phenomenon. Many construction projects experience extensive delays exceeding the initially estimated completion time. The main purpose of this study is to identify construction projects typical behaviors in order to develop a prognosis and management tool. Being able to know a construction projects schedule tendency will enable evidence-based decision-making to allow resolutions to be made before delays occur. This study presents an innovative approach that uses Cluster Analysis Method to support predictions during Earned Value Analyses. A clustering analysis was used to predict future scheduling, Earned Value Management (EVM), and Earned Schedule (ES) principal Indexes behaviors in construction projects. The analysis was made using a database with 90 different construction projects. It was validated with additional data extracted from literature and with another 15 contrasting projects. For all projects, planned and executed schedules were collected and the EVM and ES principal indexes were calculated. A complete linkage classification method was used. In this way, the cluster analysis made considers that the distance (or similarity) between two clusters must be measured by its most disparate elements, i.e. that the distance is given by the maximum span among its components. Finally, through the use of EVM and ES Indexes and Tukey and Fisher Pairwise Comparisons, the statistical dissimilarity was verified and four clusters were obtained. It can be said that construction projects show an average delay of 35% of its planned completion time. Furthermore, four typical behaviors were found and for each of the obtained clusters, the interim milestones and the necessary rhythms of construction were identified. In general, detected typical behaviors are: (1) Projects that perform a 5% of work advance in the first two tenths and maintain a constant rhythm until completion (greater than 10% for each remaining tenth), being able to finish on the initially estimated time. (2) Projects that start with an adequate construction rate but suffer minor delays culminating with a total delay of almost 27% of the planned time. (3) Projects which start with a performance below the planned rate and end up with an average delay of 64%, and (4) projects that begin with a poor performance, suffer great delays and end up with an average delay of a 120% of the planned completion time. The obtained clusters compose a tool to identify the behavior of new construction projects by comparing their current work performance to the validated database, thus allowing the correction of initial estimations towards more accurate completion schedules.

Keywords: cluster analysis, construction management, earned value, schedule

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Authors: Catherine Begin, Jeanne Berthod, Manon Truchon

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In Quebec, there are more than 8,000 community organizations throughout the province, representing more than 72,000 jobs. Working in a community setting involves several particularities (e.g., contact with the suffering of users, feelings of powerlessness, institutional pressure, unstable funding, etc.), which can put workers at risk of fatigue, burnout, and psychological distress. A 2007 study shows that 52% of community workers surveyed have a high psychological distress index. The Ricochet project, founded in 2019, is an initiative aimed at providing various care and services to community workers in the Quebec City region, with a global health approach. Within this program, mindful self-compassion training (MSC) is offered at a low cost. MSC is one of the effective strategies proposed in the literature to help prevent and reduce burnout. Self-compassion is the recognition that suffering, failure, and inadequacies are inherent in the human experience and that everyone, including oneself, deserves compassion. MSC training targets several behavioral, cognitive, and emotional learnings (e.g., motivating oneself with caring, better managing difficult emotions, promoting resilience, etc.). A mixed-method evaluation was conducted with the participants in order to explore the effects of the training on community workers in the Quebec City region. The participants were community workers (management or caregiver). 15 participants completed satisfaction and perceived impact surveys, and 30 participated in structured interviews. Quantitative results showed that participants were generally completely satisfied or satisfied with the training (94%) and perceived that the training allowed them to develop new strategies for dealing with stress (87%). Participants perceived effects on their mood (93%), their contact with others (80%), and their stress level (67%). Some of the barriers raised were scheduling constraints, length of training, and guilt about taking time for oneself. The qualitative results show that individuals experienced long-term benefits, as they were able to apply the tools they received during the training in their daily lives. Some barriers were noted, such as difficulty in getting away from work or problems with the employer, which prevented enrollment. Overall, the results of this evaluation support the use of MSC (mindful self-compassion) training among community workers. Future research could support this evaluation by using a rigorous design and developing innovative ways to overcome the barriers raised.

Keywords: mindful self-compassion, community workers, work stres, burnout, wellbeing at work

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Authors: Eric Granholm, Christophe Delay, Jason Holden, Peter Link

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Negative symptoms are an important unmet treatment needed for schizophrenia. We conducted an open trial of a novel blended intervention called mobile-assisted cognitive behavior therapy for negative symptoms (mCBTn). mCBTn is a weekly group therapy intervention combining in-person and smartphone-based CBT (CBT2go app) to improve experiential negative symptoms in people with schizophrenia. Both the therapy group and CBT2go app included recovery goal setting, thought challenging, scheduling of pleasurable activities and social interactions, and pleasure savoring interventions to modify defeatist attitudes, a target mechanism associated with negative symptoms, and improve experiential negative symptoms. We tested whether participants with schizophrenia or schizoaffective disorder (N=31) who met prospective criteria for persistent negative symptoms showed improvement in experiential negative symptoms. Retention was excellent (87% at 18 weeks) and severity of defeatist attitudes and motivation and pleasure negative symptoms declined significantly in mCBTn with large effect sizes. We also tested whether pupillary responses, a measure of cognitive effort, predicted improvement in negative symptoms mCBTn. Pupillary responses were recorded at baseline using a Tobii pupillometer during the digit span task with 3-, 6- and 9-digit spans. Mixed models showed that greater dilation during the task at baseline significantly predicted a greater reduction in experiential negative symptoms. Pupillary responses may provide a much-needed prognostic biomarker of which patients are most likely to benefit from CBT. Greater pupil dilation during a cognitive task predicted greater improvement in experiential negative symptoms. Pupil dilation has been linked to motivation and engagement of executive control, so these factors may contribute to benefits in interventions that train cognitive skills to manage negative thoughts and emotions. The findings suggest mCBTn is a feasible and effective treatment for experiential negative symptoms and justify a larger randomized controlled clinical trial. The findings also provide support for the defeatist attitude model of experiential negative symptoms and suggest that mobile-assisted interventions like mCBTn can strengthen and shorten intensive psychosocial interventions for schizophrenia.

Keywords: cognitive-behavioral therapy, mobile interventions, negative symptoms, pupillometry schizophrenia

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Authors: Leigh Ann Wilson, Melanie Borrego

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The purpose of institutional learning outcomes (ILOs) is to assess what students across the university know and what they do not. The issue is gathering this information in a systematic and usable way. This presentation will explain how one institution has engineered this process for both student success and maximum faculty curriculum and course design input. At Brandman University, there are three levels of learning outcomes: course, program, and institutional. Institutional Learning Outcomes (ILOs) are mapped to specific courses. Faculty course developers write the signature assignments (SAs) in alignment with the Institutional Learning Outcomes for each course. These SAs use a specific rubric that is applied consistently by every section and every instructor. Each year, the 12-member General Education Team (GET), as a part of their work, conducts the calibration and assessment of the university-wide SAs and the related rubrics for one or two of the five ILOs. GET members, who are senior faculty and administrators who represent each of the university's schools, lead the calibration meetings. Specifically, calibration is a process designed to ensure the accuracy and reliability of evaluating signature assignments by working with peer faculty to interpret rubrics and compare scoring. These calibration meetings include the full time and adjunct faculty members who teach the course to ensure consensus on the application of the rubric. Each calibration session is chaired by a GET representative as well as the course custodian/contact where the ILO signature assignment resides. The overall calibration process GET follows includes multiple steps, such as: contacting and inviting relevant faculty members to participate; organizing and hosting calibration sessions; and reviewing and discussing at least 10 samples of student work from class sections during the previous academic year, for each applicable signature assignment. Conversely, the commitment for calibration teams consist of attending two virtual meetings lasting up to three hours in duration. The first meeting focuses on interpreting the rubric, and the second meeting involves comparing scores for sample work and sharing feedback about the rubric and assignment. Next, participants are expected to follow all directions provided and participate actively, and respond to scheduling requests and other emails within 72 hours. The virtual meetings are recorded for future institutional use. Adjunct faculty are paid a small stipend after participating in both calibration meetings. Full time faculty can use this work on their annual faculty report for "internal service" credit.

Keywords: assessment, assurance of learning, course design, institutional learning outcomes, rubrics, signature assignments

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Authors: Anwesha Pathak

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With the increasing number of satellites and space debris, space traffic management (STM) becomes crucial. AI can aid in STM by predicting and preventing potential collisions, optimizing satellite trajectories, and managing orbital slots. Governance frameworks need to address the integration of AI algorithms in STM to ensure safe and sustainable satellite activities. AI and governance play significant roles in the context of satellite activities in space. Artificial intelligence (AI) technologies, such as machine learning and computer vision, can be utilized to process vast amounts of data received from satellites. AI algorithms can analyse satellite imagery, detect patterns, and extract valuable information for applications like weather forecasting, urban planning, agriculture, disaster management, and environmental monitoring. AI can assist in automating and optimizing satellite operations. Autonomous decision-making systems can be developed using AI to handle routine tasks like orbit control, collision avoidance, and antenna pointing. These systems can improve efficiency, reduce human error, and enable real-time responsiveness in satellite operations. AI technologies can be leveraged to enhance the security of satellite systems. AI algorithms can analyze satellite telemetry data to detect anomalies, identify potential cyber threats, and mitigate vulnerabilities. Governance frameworks should encompass regulations and standards for securing satellite systems against cyberattacks and ensuring data privacy. AI can optimize resource allocation and utilization in satellite constellations. By analyzing user demands, traffic patterns, and satellite performance data, AI algorithms can dynamically adjust the deployment and routing of satellites to maximize coverage and minimize latency. Governance frameworks need to address fair and efficient resource allocation among satellite operators to avoid monopolistic practices. Satellite activities involve multiple countries and organizations. Governance frameworks should encourage international cooperation, information sharing, and standardization to address common challenges, ensure interoperability, and prevent conflicts. AI can facilitate cross-border collaborations by providing data analytics and decision support tools for shared satellite missions and data sharing initiatives. AI and governance are critical aspects of satellite activities in space. They enable efficient and secure operations, ensure responsible and ethical use of AI technologies, and promote international cooperation for the benefit of all stakeholders involved in the satellite industry.

Keywords: satellite, space debris, traffic, threats, cyber security.

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Authors: Aziz Ikhlef

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Most of multi-slices CT scanners are built with detectors composed of scintillator - photodiodes arrays. The photodiodes arrays are mainly based on front-illuminated technology for detectors under 64 slices and on back-illuminated photodiode for systems of 64 slices or more. The designs based on back-illuminated photodiodes were being investigated for CT machines to overcome the challenge of the higher number of runs and connection required in front-illuminated diodes. In backlit diodes, the electronic noise has already been improved because of the reduction of the load capacitance due to the routing reduction. This translated by a better image quality in low signal application, improving low dose imaging in large patient population. With the fast development of multi-detector-rows CT (MDCT) scanners and the increasing number of examinations, the clinical community has raised significant concerns on radiation dose received by the patient in both medical and regulatory community. In order to reduce individual exposure and in response to the recommendations of the International Commission on Radiological Protection (ICRP) which suggests that all exposures should be kept as low as reasonably achievable (ALARA), every manufacturer is trying to implement strategies and solutions to optimize dose efficiency and image quality based on x-ray emission and scanning parameters. The added demands on the CT detector performance also comes from the increased utilization of spectral CT or dual-energy CT in which projection data of two different tube potentials are collected. One of the approaches utilizes a technology called fast-kVp switching in which the tube voltage is switched between 80kVp and 140kVp in fraction of a millisecond. To reduce the cross-contamination of signals, the scintillator based detector temporal response has to be extremely fast to minimize the residual signal from previous samples. In addition, this paper will present an overview of detector technologies and image chain improvement which have been investigated in the last few years to improve the signal-noise ratio and the dose efficiency CT scanners in regular examinations and in energy discrimination techniques. Several parameters of the image chain in general and in the detector technology contribute in the optimization of the final image quality. We will go through the properties of the post-patient collimation to improve the scatter-to-primary ratio, the scintillator material properties such as light output, afterglow, primary speed, crosstalk to improve the spectral imaging, the photodiode design characteristics and the data acquisition system (DAS) to optimize for crosstalk, noise and temporal/spatial resolution.

Keywords: computed tomography, X-ray detector, medical imaging, image quality, artifacts

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Authors: Aziz Ikhlef

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Most of multi-slices Computed Tomography (CT) scanners are built with detectors composed of scintillator - photodiodes arrays. The photodiodes arrays are mainly based on front-illuminated technology for detectors under 64 slices and on back-illuminated photodiode for systems of 64 slices or more. The designs based on back-illuminated photodiodes were being investigated for CT machines to overcome the challenge of the higher number of runs and connection required in front-illuminated diodes. In backlit diodes, the electronic noise has already been improved because of the reduction of the load capacitance due to the routing reduction. This is translated by a better image quality in low signal application, improving low dose imaging in large patient population. With the fast development of multi-detector-rows CT (MDCT) scanners and the increasing number of examinations, the clinical community has raised significant concerns on radiation dose received by the patient in both medical and regulatory community. In order to reduce individual exposure and in response to the recommendations of the International Commission on Radiological Protection (ICRP) which suggests that all exposures should be kept as low as reasonably achievable (ALARA), every manufacturer is trying to implement strategies and solutions to optimize dose efficiency and image quality based on x-ray emission and scanning parameters. The added demands on the CT detector performance also comes from the increased utilization of spectral CT or dual-energy CT in which projection data of two different tube potentials are collected. One of the approaches utilizes a technology called fast-kVp switching in which the tube voltage is switched between 80 kVp and 140 kVp in fraction of a millisecond. To reduce the cross-contamination of signals, the scintillator based detector temporal response has to be extremely fast to minimize the residual signal from previous samples. In addition, this paper will present an overview of detector technologies and image chain improvement which have been investigated in the last few years to improve the signal-noise ratio and the dose efficiency CT scanners in regular examinations and in energy discrimination techniques. Several parameters of the image chain in general and in the detector technology contribute in the optimization of the final image quality. We will go through the properties of the post-patient collimation to improve the scatter-to-primary ratio, the scintillator material properties such as light output, afterglow, primary speed, crosstalk to improve the spectral imaging, the photodiode design characteristics and the data acquisition system (DAS) to optimize for crosstalk, noise and temporal/spatial resolution.

Keywords: computed tomography, X-ray detector, medical imaging, image quality, artifacts

Procedia PDF Downloads 195

Authors: A. Basu, A. A. Purohit, M. M. Vaidya, M. D. Kudale

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Mumbai, being traditionally the epicenter of India's trade and commerce, the existing major ports such as Mumbai and Jawaharlal Nehru Ports (JN) situated in Thane estuary are also developing its waterfront facilities. Various developments over the passage of decades in this region have changed the tidal flux entering/leaving the estuary. The intake at Pir-Pau is facing the problem of shortage of water in view of advancement of shoreline, while jetty near Ulwe faces the problem of ship scheduling due to existence of shallower depths between JN Port and Ulwe Bunder. In order to solve these problems, it is inevitable to have information about tide levels over a long duration by field measurements. However, field measurement is a tedious and costly affair; application of artificial intelligence was used to predict water levels by training the network for the measured tide data for one lunar tidal cycle. The application of two layered feed forward Artificial Neural Network (ANN) with back-propagation training algorithms such as Gradient Descent (GD) and Levenberg-Marquardt (LM) was used to predict the yearly tide levels at waterfront structures namely at Ulwe Bunder and Pir-Pau. The tide data collected at Apollo Bunder, Ulwe, and Vashi for a period of lunar tidal cycle (2013) was used to train, validate and test the neural networks. These trained networks having high co-relation coefficients (R= 0.998) were used to predict the tide at Ulwe, and Vashi for its verification with the measured tide for the year 2000 & 2013. The results indicate that the predicted tide levels by ANN give reasonably accurate estimation of tide. Hence, the trained network is used to predict the yearly tide data (2015) for Ulwe. Subsequently, the yearly tide data (2015) at Pir-Pau was predicted by using the neural network which was trained with the help of measured tide data (2000) of Apollo and Pir-Pau. The analysis of measured data and study reveals that: The measured tidal data at Pir-Pau, Vashi and Ulwe indicate that there is maximum amplification of tide by about 10-20 cm with a phase lag of 10-20 minutes with reference to the tide at Apollo Bunder (Mumbai). LM training algorithm is faster than GD and with increase in number of neurons in hidden layer and the performance of the network increases. The predicted tide levels by ANN at Pir-Pau and Ulwe provides valuable information about the occurrence of high and low water levels to plan the operation of pumping at Pir-Pau and improve ship schedule at Ulwe.

Keywords: artificial neural network, back-propagation, tide data, training algorithm

Procedia PDF Downloads 485

Authors: Doaa Alhaboby

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Behavior change is a complex process that often requires ongoing support and guidance. Telecoaching programs have emerged as effective tools in facilitating behavior change by providing personalized support remotely. This abstract explores the lessons learned from a randomized controlled trial (RCT) evaluation of a telecoaching program focused on behavior change for Diabetics and discusses strategies for implementing these lessons to overcome the challenge of making physical activity a habit. The telecoaching program involved participants engaging in regular coaching sessions delivered via phone calls. These sessions aimed to address various aspects of behavior change, including goal setting, self-monitoring, problem-solving, and social support. Over the course of the program, participants received personalized guidance tailored to their unique needs and preferences. One of the key lessons learned from the RCT was the importance of engagement, readiness to change and the use of technology. Participants who set specific, measurable, attainable, relevant, and time-bound (SMART) goals were more likely to make sustained progress toward behavior change. Additionally, regular self-monitoring of behavior and progress was found to be instrumental in promoting accountability and motivation. Moving forward, implementing the lessons learned from the RCT can help individuals overcome the hardest part of behavior change: making physical activity a habit. One strategy is to prioritize consistency and establish a regular routine for physical activity. This may involve scheduling workouts at the same time each day or week and treating them as non-negotiable appointments. Additionally, integrating physical activity into daily life routines and taking into consideration the main challenges that can stop the process of integrating physical activity routines into the daily schedule can help make it more habitual. Furthermore, leveraging technology and digital tools can enhance adherence to physical activity goals. Mobile apps, wearable activity trackers, and online fitness communities can provide ongoing support, motivation, and accountability. These tools can also facilitate self-monitoring of behavior and progress, allowing individuals to track their activity levels and adjust their goals as needed. In conclusion, telecoaching programs offer valuable insights into behavior change and provide strategies for overcoming challenges, such as making physical activity a habit. By applying the lessons learned from these programs and incorporating them into daily life, individuals can cultivate sustainable habits that support their long-term health and well-being.

Keywords: lifestyle, behavior change, physical activity, chronic conditions

Procedia PDF Downloads 61

Authors: Sherly Shelton, Zhaohui Lin

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In recent decades, the inter-annual variability of summer precipitation over the India and Sri Lanka has intensified significantly with an increased frequency of both abnormally dry and wet summers. Therefore prediction of the inter-annual variability of summer precipitation is crucial and urgent for water management and local agriculture scheduling. However, none of the hypotheses put forward so far could understand the relationship to monsoon variability and related factors that affect to the South West Monsoon (SWM) variability in Sri Lanka. This study focused to identify the spatial and temporal variability of SWM rainfall events from June to September (JJAS) over Sri Lanka and associated trend. The monthly rainfall records covering 1980-2013 over the Sri Lanka are used for 19 stations to investigate long-term trends in SWM rainfall over Sri Lanka. The linear trends of atmospheric variables are calculated to understand the drivers behind the changers described based on the observed precipitation, sea surface temperature and atmospheric reanalysis products data for 34 years (1980–2013). Empirical orthogonal function (EOF) analysis was applied to understand the spatial and temporal behaviour of seasonal SWM rainfall variability and also investigate whether the trend pattern is the dominant mode that explains SWM rainfall variability. The spatial and stations based precipitation over the country showed statistically insignificant decreasing trends except few stations. The first two EOFs of seasonal (JJAS) mean of rainfall explained 52% and 23 % of the total variance and first PC showed positive loadings of the SWM rainfall for the whole landmass while strongest positive lording can be seen in western/ southwestern part of the Sri Lanka. There is a negative correlation (r ≤ -0.3) between SMRI and SST in the Arabian Sea and Central Indian Ocean which indicate that lower temperature in the Arabian Sea and Central Indian Ocean are associated with greater rainfall over the country. This study also shows that consistently warming throughout the Indian Ocean. The result shows that the perceptible water over the county is decreasing with the time which the influence to the reduction of precipitation over the area by weakening drawn draft. In addition, evaporation is getting weaker over the Arabian Sea, Bay of Bengal and Sri Lankan landmass which leads to reduction of moisture availability required for the SWM rainfall over Sri Lanka. At the same time, weakening of the SST gradients between Arabian Sea and Bay of Bengal can deteriorate the monsoon circulation, untimely which diminish SWM over Sri Lanka. The decreasing trends of moisture, moisture transport, zonal wind, moisture divergence with weakening evaporation over Arabian Sea, during the past decade having an aggravating influence on decreasing trends of monsoon rainfall over the Sri Lanka.

Keywords: Arabian Sea, moisture flux convergence, South West Monsoon, Sri Lanka, sea surface temperature

Procedia PDF Downloads 133

Authors: Reham Khalaf-Nazzal, Imad Dweikat, Paula Gimenez, Iker Oyenarte, Alfonso Martinez-Cruz, Domonik Muller

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Metal cation transport mediator (CNNM) gene family comprises 4 isoforms that are expressed in various human tissues. Structurally, CNNMs are complex proteins that contain an extracellular N-terminal domain preceding a DUF21 transmembrane domain, a ‘Bateman module’ and a C-terminal cNMP-binding domain. Mutations in CNNM2 cause familial dominant hypomagnesaemia. Growing evidence highlights the role of CNNM2 in neurodevelopment. Mutations in CNNM2 have been implicated in epilepsy, intellectual disability, schizophrenia, and others. In the present study, we aim to elucidate the function of CNNM2 in the developing brain. Thus, we present the genetic origin of symptoms in two family cohorts. In the first family, three siblings of a consanguineous Palestinian family in which parents are first cousins, and consanguinity ran over several generations, presented a varying degree of intellectual disability, cone-rod dystrophy, and autism spectrum disorder. Exome sequencing and segregation analysis revealed the presence of homozygous pathogenic mutation in the CNNM2 gene, the parents were heterozygous for that gene mutation. Magnesium blood levels were normal in the three children and their parents in several measurements. They had no symptoms of hypomagnesemia. The CNNM2 mutation in this family was found to locate in the CBS1 domain of the CNNM2 protein. The crystal structure of the mutated CNNM2 protein was not significantly different from the wild-type protein, and the binding of AMP or MgATP was not dramatically affected. This suggests that the CBS1 domain could be involved in pure neurodevelopmental functions independent of its magnesium-handling role, and this mutation could have affected a protein partner binding or other functions in this protein. In the second family, another autosomal dominant CNNM2 mutation was found to run in a large family with multiple individuals over three generations. All affected family members had hypomagnesemia and hypermagnesuria. Oral supplementation of magnesium did not increase the levels of magnesium in serum significantly. Some affected members of this family have defects in fine motor skills such as dyslexia and dyslalia. The detected mutation is located in the N-terminal part, which contains a signal peptide thought to be involved in the sorting and routing of the protein. In this project, we describe heterogenous clinical phenotypes related to CNNM2 mutations and protein functions. In the first family, and up to the authors’ knowledge, we report for the first time the involvement of CNNM2 in retinal photoreceptor development and function. In addition, we report the presence of a neurophenotype independent of magnesium status related to the CNNM2 protein mutation. Taking into account the different modes of inheritance and the different positions of the mutations within CNNM2 and its different structural and functional domains, it is likely that CNNM2 might be involved in a wide spectrum of neuropsychiatric comorbidities with considerable varying phenotypes.

Keywords: magnesium transport, autosomal recessive, autism, neurodevelopment, CBS domain

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Authors: Julio Jesus Salazar, Manuel Andres Jesus De Lama

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There are very few models that serve to analyze the use of water in the socio-economic process. On the supply side, the joint use of groundwater has been considered in addition to the simple limits on the availability of surface water. In addition, we have worked on waterlogging and the effects on water quality (mainly salinity). In this paper, a 'complex' water economy is examined; one in which demands grow differentially not only within but also between sectors, and one in which there are limited opportunities to increase consumptive use. In particular, high-value growth, the growth of the production of irrigated crops of high value within the basins of the case study, together with the rapidly growing urban areas, provides a rich context to examine the general problem of water management at the basin level. At the same time, the long-term aridity of nature has made the eco-environment in the basins located on the coast of Peru very vulnerable, and the exploitation and immediate use of water resources have further deteriorated the situation. The presented methodology is the optimization with embedded simulation. The wide basin simulation of flow and water balances and crop growth are embedded with the optimization of water allocation, reservoir operation, and irrigation scheduling. The modeling framework is developed from a network of river basins that includes multiple nodes of origin (reservoirs, aquifers, water courses, etc.) and multiple demand sites along the river, including places of consumptive use for agricultural, municipal and industrial, and uses of running water on the coast of Peru. The economic benefits associated with water use are evaluated for different demand management instruments, including water rights, based on the production and benefit functions of water use in the urban agricultural and industrial sectors. This work represents a new effort to analyze the use of water at the regional level and to evaluate the modernization of the integrated management of water resources and socio-economic territorial development in Peru. It will also allow the establishment of policies to improve the process of implementation of the integrated management and development of water resources. The input-output analysis is essential to present a theory about the production process, which is based on a particular type of production function. Also, this work presents the Computable General Equilibrium (CGE) version of the economic model for water resource policy analysis, which was specifically designed for analyzing large-scale water management. As to the platform for CGE simulation, GEMPACK, a flexible system for solving CGE models, is used for formulating and solving CGE model through the percentage-change approach. GEMPACK automates the process of translating the model specification into a model solution program.

Keywords: water economy, simulation, modeling, integration

Procedia PDF Downloads 156

Authors: Mohammad Lemar Zalmai, Osman Hurol Turkakin, Cemil Akcay, Ekrem Manisali

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In public construction projects, determining the contract period in the award phase is one of the most important factors. The contract period establishes the baseline for creating the cash flow curve and progress payment planning in the post-award phase. If overestimated, project duration causes losses for both the owner and the contractor. Therefore, it is essential to base construction project duration on reliable forecasting. In Turkey, schedules are usually built using the bar chart (Gantt) schedule, especially for governmental construction agencies. The usage of these schedules is limited for bidding purposes. Although the bar-chart schedule is useful in some cases, it lacks logical connections between activities; it would be harder to obtain the activities that have more effects than others on the project's total duration, especially in large complex projects. In this study, a construction schedule is prepared with Critical Path Method (CPM) that addresses the above-mentioned discrepancies. CPM is a simple and effective method that displays project time and critical paths, showing results of forward and backward calculations with considering the logic relationships between activities; it is a powerful tool for planning and managing all kinds of construction projects and is a very convenient method for the construction industry. CPM provides a much more useful and precise approach than traditional bar-chart diagrams that form the basis of construction planning and control. CPM has two main application utilities in the construction field; the first one is obtaining project duration, which is called an as-planned schedule that includes as-planned activity durations with relationships between subsequent activities. Another utility is during the project execution; each activity is tracked, and their durations are recorded in order to obtain as-built schedule, which is named as a black box of the project. The latter is more useful for delay analysis, and conflict resolutions. These features of CPM have been popular around the world. However, it has not been yet extensively used in Turkey. In this study, a real construction project is investigated as a case study; CPM-based scheduling is used for establishing both of as-built and as-planned schedules. Problems that emerged during the construction phase are identified and categorized. Subsequently, solutions are suggested. Two scenarios were considered. In the first scenario, project progress was monitored based as CPM was used to track and manage progress; this was carried out based on real-time data. In the second scenario, project progress was supposedly tracked based on the assumption that the Gantt chart was used. The S-curves of the two scenarios are plotted and interpreted. Comparing the results, possible faults of the latter scenario are highlighted, and solutions are suggested. The importance of CPM implementation has been emphasized and it has been proposed to make it mandatory for preparation of construction schedule based on CPM for public construction projects contracts.

Keywords: as-built, case-study, critical path method, Turkish government sector projects

Procedia PDF Downloads 122

Authors: Gaurav Kumar Sinha

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In an era defined by the proliferation of data and the utilization of cloud computing environments, the efficient transfer and processing of big data workloads across multi-cloud platforms have emerged as critical challenges. This research paper embarks on a comprehensive exploration of the complexities associated with managing and optimizing big data in a multi-cloud ecosystem.The foundation of this study is rooted in the recognition that modern enterprises increasingly rely on multiple cloud providers to meet diverse business needs, enhance redundancy, and reduce vendor lock-in. As a consequence, managing data across these heterogeneous cloud environments has become intricate, necessitating innovative approaches to ensure data integrity, security, and performance.The primary objective of this research is to investigate strategies and techniques for enhancing the efficiency of data transfer and processing in multi-cloud scenarios. It recognizes that big data workloads are characterized by their sheer volume, variety, velocity, and complexity, making traditional data management solutions insufficient for harnessing the full potential of multi-cloud architectures.The study commences by elucidating the challenges posed by multi-cloud environments in the context of big data. These challenges encompass data fragmentation, latency, security concerns, and cost optimization. To address these challenges, the research explores a range of methodologies and solutions. One of the key areas of focus is data transfer optimization. The paper delves into techniques for minimizing data movement latency, optimizing bandwidth utilization, and ensuring secure data transmission between different cloud providers. It evaluates the applicability of dedicated data transfer protocols, intelligent data routing algorithms, and edge computing approaches in reducing transfer times.Furthermore, the study examines strategies for efficient data processing across multi-cloud environments. It acknowledges that big data processing requires distributed and parallel computing capabilities that span across cloud boundaries. The research investigates containerization and orchestration technologies, serverless computing models, and interoperability standards that facilitate seamless data processing workflows.Security and data governance are paramount concerns in multi-cloud environments. The paper explores methods for ensuring data security, access control, and compliance with regulatory frameworks. It considers encryption techniques, identity and access management, and auditing mechanisms as essential components of a robust multi-cloud data security strategy.The research also evaluates cost optimization strategies, recognizing that the dynamic nature of multi-cloud pricing models can impact the overall cost of data transfer and processing. It examines approaches for workload placement, resource allocation, and predictive cost modeling to minimize operational expenses while maximizing performance.Moreover, this study provides insights into real-world case studies and best practices adopted by organizations that have successfully navigated the challenges of multi-cloud big data management. It presents a comparative analysis of various multi-cloud management platforms and tools available in the market.

Keywords: multi-cloud environments, big data workloads, data transfer optimization, data processing strategies

Procedia PDF Downloads 69

Authors: M. Louise Humbert, Karen E. Chad, Natalie E. Houser, Marta E. Erlandson

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Physical literacy is the motivation, confidence, physical competence, knowledge, and understanding to value and takes responsibility for engagement in physical activities for life. In recent years, physical literacy has emerged as a determinant of health, promoting a positive lifelong physical activity trajectory. Physical literacy’s holistic approach and emphasis on the intrinsic valuation of movement provide an encouraging avenue for intervention among children to develop competent and confident movers. Although there is research on physical literacy interventions, no evidence exists on the outcomes of multi-sectoral interventions involving a combination of home, school, and community contexts. Since children interact with and in a wide range of contexts (home, school, community) daily, interventions designed to address a combination of these contexts are critical to the development of physical literacy. Working with school administrators and teachers, sports and recreation leaders, and community members, our team of university and community researchers conducted and evaluated one of the first multi-contextual and multi-sectoral physical literacy interventions in Canada. Schools played a critical role in this multi-sector intervention, and in this project, teachers and administrators focused their actions on developing physical literacy in students 10 to 14 years of age through the instruction of physical literacy-focused physical education lessons. Little is known about the experiences of educators when they work alongside an array of community representatives to develop physical literacy in school-aged children. Given the uniqueness of this intervention, we sought to answer the question, ‘What were the experiences of school-based educators involved in a multi-sectoral partnership focused on creating a physical literacy enriched community intervention?’ A thematic analysis approach was used to analyze data collected from interviews with educators and administrators, informal conversations, documents, and observations at workshops and meetings. Results indicated that schools and educators played the largest role in this multi-sector intervention. Educators initially reported a limited understanding of physical literacy and expressed a need for resources linked to the physical education curriculum. Some anxiety was expressed by the teachers as their students were measured, and educators noted they wanted to increase their understanding and become more involved in the assessment of physical literacy. Teachers reported that the intervention’s focus on physical literacy positively impacted the scheduling and their instruction of physical education. Administrators shared their desire for school and division-level actions targeting physical literacy development like the current focus on numeracy and literacy, treaty education, and safe schools. As this was one of the first multi-contextual and multi-sectoral physical literacy interventions, it was important to document creation and delivery experiences to encourage future growth in the area and develop suggested best practices.

Keywords: physical literacy, multi sector intervention, physical education, teachers

Procedia PDF Downloads 103

Authors: Mikko Salminen, Niklas Ravaja

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Previous studies have shown that emotional intelligence (EI) has an important role in leadership and social interaction. On the other hand, physiological synchronization between two interacting participants has been related to, for example, intensity of the interaction, and interestingly also to empathy. It is suggested that the amount of covariation in physiological signals between the two interacting persons would also be related to how the discussion is perceived subjectively. To study the interrelations between physiological synchronization, emotional intelligence, and subjective perception of the interaction, performance review discussions between real manager – subordinate dyads were studied using psychophysiological measurements and self-reports. The participants consisted of 40 managers, of which 24 were female, and 78 of their subordinates, of which 45 were female. The participants worked in various fields, for example banking, education, and engineering. The managers had a normal performance review discussion with two subordinates, except two managers who, due to scheduling issues, had discussion with only one subordinate. The managers were on average 44.5 years old, and the subordinates on average 45.5 years old. Written consent, in accordance with the Declaration of Helsinki, was obtained from all the participants. After the discussion, the participants filled a questionnaire assessing their emotions during the discussion. This included a self-assessment manikin (SAM) scale for the emotional valence during the discussion, with a 9-point graphical scale representing a manikin whose facial expressions ranged from smiling and happy to frowning and unhappy. In addition, the managers filled EI360, a 37-item self-report trait emotional intelligence questionnaire. The psychophysiological activity of the participants was recorded using two Varioport-B portable recording devices. Cardiac activity (ECG, electrocardiogram) was measured with two electrodes placed on the torso. Inter-beat interval (IBI, time between two successive heart beats) was calculated from the ECG signals. The facial muscle activation (EMG, electromyography) was recorded on three sites of the left side of the face: zygomaticus major (cheek muscle), orbicularis oculi (periocular muscle), and corrugator supercilii (frowning muscle). The facial-EMG signals were rectified and smoothed, and cross-coherences were calculated between members of each dyad, for all the three EMG signals, for the baseline and discussion periods. The values were natural-log transformed to normalize the distributions. Higher cross-coherence during the discussion between the manager’s and the subordinate’s zygomatic muscles was related to more positive valence self-reported emotions, F(1; 66,137) = 7,051; p=0,01. Thus, synchronized cheek muscle activation, either due to synchronous smiling or talking, was related to more positive perception of the discussion. In addition, higher IBI synchronization between the manager and the subordinate during the discussion was related to the manager’s higher self-reported emotional intelligence, F(1; 27,981)=4,58; p=0,041. That is, the EI was related to synchronous cardiac activity and possibly to similar physiological arousal levels. The results imply that the psychophysiological synchronization could be a potentially useful index in the study of social interaction and a valuable tool in the coaching of leadership skills in organizational contexts.

Keywords: emotional intelligence, leadership, psychophysiology, social interaction, synchronization

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Authors: Michael Picard

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This proposal offers to shed light on the changing legal geography of the global waste economy. Global waste recycling has become a multi-billion-dollar industry. NASDAQ predicts the emergence of a worldwide 1,296G$ waste management market between 2017 and 2022. Underlining this evolution, a new generation of preferential waste-trade agreements has emerged in the Pacific. In the last decade, Japan has concluded a series of bilateral treaties with Asian countries, and most recently with China. An agreement between Tokyo and Beijing was formalized on 7 May 2008, which forged an economic partnership on waste transfer and mining. The agreement set up International Recycling Zones, where certified recycling plants in China process industrial waste imported from Japan. Under the joint venture, Chinese companies salvage the embedded value from Japanese industrial discards, reprocess them and send them back to Japanese manufacturers, such as Mitsubishi and Panasonic. This circular economy is designed to convert surplus garbage into surplus value. Ever since the opening of Sino-Japanese eco-parks, millions of tons of plastic and e-waste have been exported from Japan to China every year. Yet, quite unexpectedly, China has recently closed its waste market to imports, jeopardizing Japan’s billion-dollar exports to China. China notified the WTO that, by the end of 2017, it would no longer accept imports of plastics and certain metals. Given China’s share of Japanese waste exports, a complete closure of China’s market would require Japan to find new uses for its recyclable industrial trash generated domestically every year. It remains to be seen how China will effectively implement its ban on waste imports, considering the economic interests at stake. At this stage, what remains to be clarified is whether China's ban on waste imports will negatively affect the recycling trade between Japan and China. What is clear, though, is the rapid transformation in the legal geography of waste mining in East-Asia. For decades, East-Asian waste trade had been tied up in an ‘ecologically unequal exchange’ between the Japanese core and the Chinese periphery. This global unequal waste distribution could be measured by the Environmental Stringency Index, which revealed that waste regulation was 39% weaker in the Global South than in Japan. This explains why Japan could legally export its hazardous plastic and electronic discards to China. The asymmetric flow of hazardous waste between Japan and China carried the colonial heritage of international law. The legal geography of waste distribution was closely associated to the imperial construction of an ecological trade imbalance between the Japanese source and the Chinese sink. Thus, China’s recent decision to ban hazardous waste imports is a sign of a broader ecological shift. As a global economic superpower, China announced to the world it would no longer be the planet’s junkyard. The policy change will have profound consequences on the global circulation of waste, re-routing global waste towards countries south of China, such as Vietnam and Malaysia. By the time the Berlin Conference takes place in May 2018, the presentation will be able to assess more accurately the effect of the Chinese ban on the transboundary movement of waste in Asia.

Keywords: Asia, ecological unequal exchange, global waste trade, legal geography

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Authors: Elena Collino, Dario A. Ronzio, Goffredo Decimi, Maurizio Riva

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The rapid increase of renewable energy in Italy is led by wind and solar installations. The 2017 Italian energy strategy foresees a further development of these sustainable technologies, especially solar. This fact has resulted in new opportunities, challenges, and different problems to deal with. The growth of renewables allows to meet the European requirements regarding energy and environmental policy, but these types of sources are difficult to manage because they are intermittent and non-programmable. Operationally, these characteristics can lead to instability on the voltage profile and increasing uncertainty on energy reserve scheduling. The increasing renewable production must be considered with more and more attention especially by the Transmission System Operator (TSO). The TSO, in fact, every day provides orders on energy dispatch, once the market outcome has been determined, on extended areas, defined mainly on the basis of power transmission limitations. In Italy, six market zone are defined: Northern-Italy, Central-Northern Italy, Central-Southern Italy, Southern Italy, Sardinia, and Sicily. An accurate hourly renewable power forecasting for the day-ahead on these extended areas brings an improvement both in terms of dispatching and reserve management. In this study, an operational forecasting tool of the hourly solar output for the six Italian market zones is presented, and the performance is analysed. The implementation is carried out by means of a numerical weather prediction model, coupled with a statistical post-processing in order to derive the power forecast on the basis of the meteorological projection. The weather forecast is obtained from the limited area model RAMS on the Italian territory, initialized with IFS-ECMWF boundary conditions. The post-processing calculates the solar power production with the Analog Ensemble technique (AN). This statistical approach forecasts the production using a probability distribution of the measured production registered in the past when the weather scenario looked very similar to the forecasted one. The similarity is evaluated for the components of the solar radiation: global (GHI), diffuse (DIF) and direct normal (DNI) irradiation, together with the corresponding azimuth and zenith solar angles. These are, in fact, the main factors that affect the solar production. Considering that the AN performance is strictly related to the length and quality of the historical data a training period of more than one year has been used. The training set is made by historical Numerical Weather Prediction (NWP) forecasts at 12 UTC for the GHI, DIF and DNI variables over the Italian territory together with corresponding hourly measured production for each of the six zones. The AN technique makes it possible to estimate the aggregate solar production in the area, without information about the technologic characteristics of the all solar parks present in each area. Besides, this information is often only partially available. Every day, the hourly solar power forecast for the six Italian market zones is made publicly available through a website.

Keywords: analog ensemble, electricity market, PV forecast, solar energy

Procedia PDF Downloads 159

Authors: Yogesh Kumar Singh, T. S. Murugesh Prabhu, Upasana Dutta, Girishchandra Yendargaye, Rahul Yadav, Rohini Gopinath Kale, Binay Kumar, Manoj Khare

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The Indian subcontinent is severely affected by floods that cause intense irreversible devastation to crops and livelihoods. With increased incidences of floods and their related catastrophes, an Early Warning System for Flood Prediction and an efficient Flood Management System for the river basins of India is a must. Accurately modeled hydrological conditions and a web-based early warning system may significantly reduce economic losses incurred due to floods and enable end users to issue advisories with better lead time. This study describes the design and development of an EWS-FP using advanced computational tools/methods, viz. High-Performance Computing (HPC), Remote Sensing, GIS technologies, and open-source tools for the Mahanadi River Basin of India. The flood prediction is based on a robust 2D hydrodynamic model, which solves shallow water equations using the finite volume method. Considering the complexity of the hydrological modeling and the size of the basins in India, it is always a tug of war between better forecast lead time and optimal resolution at which the simulations are to be run. High-performance computing technology provides a good computational means to overcome this issue for the construction of national-level or basin-level flash flood warning systems having a high resolution at local-level warning analysis with a better lead time. High-performance computers with capacities at the order of teraflops and petaflops prove useful while running simulations on such big areas at optimum resolutions. In this study, a free and open-source, HPC-based 2-D hydrodynamic model, with the capability to simulate rainfall run-off, river routing, and tidal forcing, is used. The model was tested for a part of the Mahanadi River Basin (Mahanadi Delta) with actual and predicted discharge, rainfall, and tide data. The simulation time was reduced from 8 hrs to 3 hrs by increasing CPU nodes from 45 to 135, which shows good scalability and performance enhancement. The simulated flood inundation spread and stage were compared with SAR data and CWC Observed Gauge data, respectively. The system shows good accuracy and better lead time suitable for flood forecasting in near-real-time. To disseminate warning to the end user, a network-enabled solution is developed using open-source software. The system has query-based flood damage assessment modules with outputs in the form of spatial maps and statistical databases. System effectively facilitates the management of post-disaster activities caused due to floods, like displaying spatial maps of the area affected, inundated roads, etc., and maintains a steady flow of information at all levels with different access rights depending upon the criticality of the information. It is designed to facilitate users in managing information related to flooding during critical flood seasons and analyzing the extent of the damage.

Keywords: flood, modeling, HPC, FOSS

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Authors: Sandra Cardoso, Gaspar Pais, Judite Neves, Sandra Cavaca, Fernando Araújo

Abstract:

In 2021, Portugal has 63 emergency department (ED) visits per 100 people annually, the highest numbers in Europe. While EDs provide a critical service, high use is indicative of inappropriate and inefficient healthcare. In Portugal, all ED have the Manchester Triage System (MTS), a clinical risk management tool to enable that patients are seen in order of clinical priority. In 2023, more than 40% of the ED visits were of non-urgent conditions (blue and green), that could be better managed in primary health care (PHC), meaning wrong use of resources and lack of health literacy. From 2017, the country has a phone line, SNS24 (Contact Centre of the National Health Service), for triage, counseling, and referral service, 24 hours/7 days a week. The pilot project ‘Call before, save lives’ was implemented in the municipalities of Póvoa de Varzim and Vila do Conde (around 150.000 residents), in May 2023, by the executive board of the Portuguese Health Service, with the support of the Shared Services of the Ministry of Health, and local authorities. This geographical area has short travel times, 99% of the population a family doctor and the region is organized in a health local unit (HLU), integrating PHC and the local hospital. The purposes of this project included to increase awareness to contact SNS 24, before going to an ED, and non-urgent conditions oriented to a family doctor, reducing ED visits. The implementation of the project involved two phases, beginning with: i) development of campaigns using local influencers (fishmonger, model, fireman) through local institutions and media; ii) provision of telephone installed on site to contact SNS24; iii) establishment of open consultation in PHC; iv) promotion of the use of SNS24; v) creation of acute consultations at the hospital for complex chronic patients; and vi) direct referral for home hospitalization by PHC. The results of this project showed an excellent level of access to SNS24, an increase in the number of users referred to ED, with great satisfaction of users and professionals. The second phase, initiated in January 2024, for access to the ED, the need for prior referral was established as an admission rule, except for certain situations, as trauma patients. If the patient refuses, their registration in the ED and subsequent screening in accordance with the MTS must be ensured. When the patient is non-urgent, shall not be observed in the ED, provided that, according to his clinical condition, is guaranteed to be referred to PHC or to consultation/day hospital, through effective scheduling of an appointment for the same or the following day. In terms of results, 8 weeks after beginning of phase 2, we assist of a decrease in self-reported patients to ED from 59% to 15%, and a reduction of around 7% of ED visits. The key for this success was an effective public campaign that increases the knowledge of the right use of the health system, and capable of changing behaviors.

Keywords: contact centre of the national health service, emergency department visits, public campaign, health literacy, SNS24

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