Search results for: fuzzy credibility constrained programming

Authors: Francisco Fernandez de Castro

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The Conditional Cash Transfer (CCT) model gives monetary transfers to beneficiary families on the condition that they take specific education and health actions. According to the economic rationale of CCTs the poor need incentives to invest in their human capital because they are trapped by a lack of information and misleading beliefs. If left to their own decision, the poor will not be able to choose what is in their best interests. The basic assumption of the CCT model is that the poor need incentives to take care of their own education and health-nutrition. Due to the incentives (income cash transfers and conditionalities), beneficiary families are supposed to attend doctor visits and health talks. Children would stay in the school. These incentivized behaviors would produce outcomes such as better health and higher level of education, which in turn will reduce poverty. Based on a grounded theory approach to conduct a two-year period of qualitative data collection in northern Mexico, this study shows that this explanation is incomplete. In addition to the information failure and inadequate beliefs, there are structural barriers in everyday life of households that make health-nutrition and education investments difficult. In-depth interviews and observation work showed that the program takes for granted local conditions in which beneficiary families should fulfill their co-responsibilities. Data challenged the program’s assumptions and unveiled local obstacles not contemplated in the program’s design. These findings have policy and research implications for the CCT agenda. They bring elements for late programming due to the gap between the CCT strategy as envisioned by policy designers, and the program that beneficiary families experience on the ground. As for research consequences, these findings suggest new avenues for scholarly work regarding the causal mechanisms and social processes explaining CCT outcomes.

Keywords: conditional cash transfers, incentives, poverty, structural barriers

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Authors: Javad Sadidi, Ehsan Babaei, Hani Rezayan

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The advent of web 2.0 gives a possibility to scale down the costs of data collection and mapping, specifically if the process is done by volunteers. Every volunteer can be thought of as a free and ubiquitous sensor to collect spatial, descriptive as well as multimedia data for tourist services. The lack of large-scale information, such as real-time climate and weather conditions, population density, and other related data, can be considered one of the important challenges in developing countries for tourists to make the best decision in terms of time and place of travel. The current research aims to design and implement a spatiotemporal web map service using volunteer-submitted data. The service acts as a tourist-guide service in which tourists can search interested places based on their requested time for travel. To design the service, three tiers of architecture, including data, logical processing, and presentation tiers, have been utilized. For implementing the service, open-source software programs, client and server-side programming languages (such as OpenLayers2, AJAX, and PHP), Geoserver as a map server, and Web Feature Service (WFS) standards have been used. The result is two distinct browser-based services, one for sending spatial, descriptive, and multimedia volunteer data and another one for tourists and local officials. Local official confirms the veracity of the volunteer-submitted information. In the tourist interface, a spatiotemporal search engine has been designed to enable tourists to find a tourist place based on province, city, and location at a specific time of interest. Implementing the tourist-guide service by this methodology causes the following: the current tourists participate in a free data collection and sharing process for future tourists, a real-time data sharing and accessing for all, avoiding a blind selection of travel destination and significantly, decreases the cost of providing such services.

Keywords: VGI, tourism, spatiotemporal, browser-based, web mapping

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Authors: Cassandra A. Skinner, Linda R. Chamberlain

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The nonprofit and public sectors are increasingly turning to Geographic Information Systems for data visualizations which can better inform programmatic and policy decisions. Additionally, the private and nonprofit sectors are turning to systems mapping to better understand the ecosystems within which they operate. This study explores the potential which combining these data visualization methods—a method which is called geographic systems mapping—to create an exhaustive and comprehensive understanding of a social problem’s ecosystem may have in social innovation efforts. Researchers with Grand Valley State University collaborated with Talent 2025 of West Michigan to conduct a mixed-methods research study to paint a comprehensive picture of the workforce development ecosystem in West Michigan. Using semi-structured interviewing, observation, secondary research, and quantitative analysis, data were compiled on workforce development organizations’ locations, programming, metrics for success, partnerships, funding sources, and service language. To best visualize and disseminate the data, a geographic system map was created which identifies programmatic, operational, and geographic gaps in workforce development services of West Michigan. By combining geographic and systems mapping methods, the geographic system map provides insight into the cross-sector relationships, collaboration, and competition which exists among and between workforce development organizations. These insights identify opportunities for and constraints around cross-sectoral social innovation in the West Michigan workforce development ecosystem. This paper will discuss the process utilized to prepare the geographic systems map, explain the results and outcomes, and demonstrate how geographic systems mapping illuminated the needs of the community and opportunities for social innovation. As complicated social problems like unemployment often require cross-sectoral and multi-stakeholder solutions, there is potential for geographic systems mapping to be a tool which informs, enables, and inspires these solutions.

Keywords: cross-sector collaboration, data visualization, geographic systems mapping, social innovation, workforce development

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Authors: Sigal Portnoy, Yael Kaufman-Cohen, Yafa Levanon

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Introduction: Rehabilitation following wrist fractures often includes exercising flexion-extension movements with a dynamic splint. However, during daily activities, we combine most of our wrist movements with radial and ulnar deviations. Also, the multi-plane wrist motion, named the ‘dart throw motion’ (DTM), was found to be a more stable motion in healthy individuals, in term of the motion of the proximal carpal bones, compared with sagittal wrist motion. The aim of this study was therefore to explore the pathomechanics of the wrist in a common multi-plane movement pattern (DTM) and design a novel splint for rehabilitation following distal radius fractures. Methods: First, a multi-axis electro-goniometer was used to quantify the plane angle of motion of the dominant and non-dominant wrists during various activities, e.g. drinking from a glass of water and answering a phone in 43 healthy individuals. The following protocols were then implemented with a population following distal radius fracture. Two dynamic scans were performed, one of the sagittal wrist motion and DTM, in a 3T magnetic resonance imaging (MRI) device, bilaterally. The scaphoid and lunate carpal bones, as well as the surface of the distal radius, were manually-segmented in SolidWorks and the angles of motion of the scaphoid and lunate bones were calculated. Subsequently, a patient-specific splint was designed using 3D scans of the hand. The brace design comprises of a proximal attachment to the arm and a distal envelope of the palm. An axle with two wheels is attached to the proximal part. Two wires attach the proximal part with the medial-palmar and lateral-ventral aspects of the distal part: when the wrist extends, the first wire is released and the second wire is strained towards the radius. The opposite occurs when the wrist flexes. The splint was attached to the wrist using Velcro and constrained the wrist movement to the desired calculated multi-plane of motion. Results: No significant differences were found between the multi-plane angles of the dominant and non-dominant wrists. The most common daily activities occurred at a plane angle of approximately 20° to 45° from the sagittal plane and the MRI studies show individual angles of the plane of motion. The printed splint fitted the wrist of the subjects and constricted movement to the desired multi-plane of motion. Hooks were inserted on each part to allow the addition of springs or rubber bands for resistance training towards muscle strengthening in the rehabilitation setting. Conclusions: It has been hypothesized that activation of the wrist in a multi-plane movement pattern following distal radius fractures will accelerate the recovery of the patient. Our results show that this motion can be determined from either the dominant or non-dominant wrists. The design of the patient-specific dynamic splint is the first step towards assessing whether splinting to induce combined movement is beneficial to the rehabilitation process, compared to conventional treatment. The evaluation of the clinical benefits of this method, compared to conventional rehabilitation methods following wrist fracture, are a part of a PhD work, currently conducted by an occupational therapist.

Keywords: distal radius fracture, rehabilitation, dynamic magnetic resonance imaging, dart throw motion

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Authors: A. Afrashi

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Introduction: Synaesthesia is a term denoting the perception or description of the perception of one sense modality in terms of another. In literature, synaesthesia refers to a technique adopted by writers to present ideas, characters or places in such a manner that they appeal to more than one sense like hearing, seeing, smell etc. at a given time. In everyday language too we find many examples of synaesthesia. We commonly hear phrases like ‘loud colors’, ‘frozen silence’ and ‘warm colors’, ‘bitter cold’ etc. Empirical cognitive studies have proved that synaesthetic representations both in literature and everyday languages are constrained ie. they do not map randomly among sensory domains. From the beginning of the 20th century Synaesthesia has been a research domain both in literature and structural linguistics. However the exploration of cognitive mechanisms motivating synaesthesia, have made it an important topic in 21st century cognitive linguistics and literary studies. Synaesthetic metaphors are linguistic representations of those mental mechanisms, the study of which reveals invaluable facts about perception, cognition and conceptualization. According to the main tenets of cognitive approach to language and literature, unified and similar cognitive mechanisms are active both in everyday language and literature, and synaesthesia is one of those cognitive mechanisms. Main objective of the present research is to answer the following questions: What types of sense transfers are accessible in Persian synaesthetic metaphors. How are these types of sense transfers cognitively explained. What are the results of cross-linguistic comparative study of synaestetic metaphors based on the existing observations? Methodology: The present research employs a cognitive - corpus based method, and the theoretical framework adopted to analyze linguistic synaesthesia is the contemporary theory of metaphor, where conceptual metaphor is the result of systemic mappings across cognitive domains. Persian Language Data- base (PLDB) in the Institute for Humanities and Cultural Studies which consists mainly of Persian modern prose, is searched for synaesthetic metaphors. Then for each metaphorical structure, the source and target domains are determined. Then sense transfers are identified and the types of synaesthetic metaphors recognized. Findings: Persian synaesthetic metaphors conform to the hierarchical distribution principle, according to which transfers tend to go from touch to taste to smell to sound and to sight, not vice versa. In other words mapping from more accessible or basic concepts onto less accessible or less basic ones seems more natural. Furthermore the most frequent target domain in Persian synaesthetic metaphors is sound. Certain characteristics of Persian synaesthetic metaphors are comparable with existing related researches carried on English, French, Hungarian and Chinese synaesthetic metaphors. Conclusion: Cognitive corpus based approaches to linguistic synaesthesia, are applicable to stylistics and literary criticism and this recent research domain is an efficient approach to study cross linguistic variations to find out which of the five senses is dominant cross linguistically and cross culturally as the target domain in metaphorical mappings , and so forth receiving dominance in conceptualizations.

Keywords: cognitive semantics, conceptual metaphor, synaesthesia, corpus based approach

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Authors: Elżbieta Turska

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Introduction: One of the most difficult times in a person’s life is adolescence. The experiences in this period may shape the future life of this person to a large extent. This is the reason why many young people experience sadness, dejection, hopelessness, sense of worthlessness, as well as losing interest in various activities and social relationships, all of which are often classified as mood disorders. As many as 15-40% adolescents experience depressed moods and for most of them they resolve and are not carried into adulthood. However, (5-6%) of those affected by mood disorders develop the depressive syndrome and as many as (1-3%) develop full-blown clinical depression. Materials: A large questionnaire was given to 2508 students, aged 13–16 years old, and one of its parts was the Burns checklist, i.e. the standard test for identifying depressed mood. The questionnaire asked about many aspects of the student’s life, it included a total of 53 questions, most of which had subquestions. It is important to note that the data suffered from many problems, the most important of which were missing data and collinearity. Aim: In order to identify the correlates of mood disorders we built predictive models which were then trained and validated. Our aim was not to be able to predict which students suffer from mood disorders but rather to explore the factors influencing mood disorders. Methods: The problems with data described above practically excluded using all classical statistical methods. For this reason, we attempted to use the following Artificial Intelligence (AI) methods: classification trees with surrogate variables, random forests and xgboost. All analyses were carried out with the use of the mlr package for the R programming language. Resuts: The predictive model built by classification trees algorithm outperformed the other algorithms by a large margin. As a result, we were able to rank the variables (questions and subquestions from the questionnaire) from the most to least influential as far as protection against mood disorder is concerned. Thirteen out of twenty most important variables reflect the relationships with parents. This seems to be a really significant result both from the cognitive point of view and also from the practical point of view, i.e. as far as interventions to correct mood disorders are concerned.

Keywords: mood disorders, adolescents, family, artificial intelligence

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Authors: Gul Rahim Rahimi

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The IoT-based soil moisture monitoring system for indoor plants is designed to address the challenges of maintaining optimal moisture levels in soil for plant growth and health. The system utilizes sensor technology to collect real-time data on soil moisture levels, which is then processed and analyzed using machine learning algorithms. This allows for accurate and timely monitoring of soil moisture levels, ensuring plants receive the appropriate amount of water to thrive. The main objectives of the system are twofold: to keep plants fresh and healthy by preventing water deficiency and to provide users with comprehensive insights into the water content of the soil on a daily and hourly basis. By monitoring soil moisture levels, users can identify patterns and trends in water consumption, allowing for more informed decision-making regarding watering schedules and plant care. The scope of the system extends to the agriculture industry, where it can be utilized to minimize the efforts required by farmers to monitor soil moisture levels manually. By automating the process of soil moisture monitoring, farmers can optimize water usage, improve crop yields, and reduce the risk of plant diseases associated with over or under-watering. Key technologies employed in the system include the Capacitive Soil Moisture Sensor V1.2 for accurate soil moisture measurement, the Node MCU ESP8266-12E Board for data transmission and communication, and the Arduino framework for programming and development. Additionally, machine learning algorithms are utilized to analyze the collected data and provide actionable insights. Cloud storage is utilized to store and manage the data collected from multiple sensors, allowing for easy access and retrieval of information. Overall, the IoT-based soil moisture monitoring system offers a scalable and efficient solution for indoor plant care, with potential applications in agriculture and beyond. By harnessing the power of IoT and machine learning, the system empowers users to make informed decisions about plant watering, leading to healthier and more vibrant indoor environments.

Keywords: IoT-based, soil moisture monitoring, indoor plants, water management

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Authors: U. Gunal, H. I. Turgut, H. Gurler, S. Kaya

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There are many ever-increasing and newly emerging problems with rapid population growth in the world. With the increase in people's quality of life in our daily life, acoustic comfort has become an important feature in the textile industry. In order to meet all these expectations in people's comfort areas and survive in challenging competitive conditions in the market without compromising the customer product quality expectations of textile manufacturers, it has become a necessity to bring functionality to the products. It is inevitable to research and develop materials and processes that will bring these functionalities to textile products. The noise we encounter almost everywhere in our daily life, in the street, at home and work, is one of the problems which textile industry is working on. It brings with it many health problems, both mentally and physically. Therefore, noise control studies become more of an issue. Besides, materials used in noise control are not sufficient to reduce the effect of the noise level. The fabrics used in acoustic studies in the textile industry do not show sufficient performance according to their weight and high cost. Thus, acoustic textile products can not be used in daily life. In the thesis study, the attributions used in the noise control and building acoustics studies in the literature were analyzed, and the product with the highest damping value that a textile material will have was designed, manufactured, and tested. Optimum values were obtained by using different material samples that may affect the performance of the acoustic material. Acoustic measurement methods should be applied to verify the acoustic performances shown by the parameters and the designed three-dimensional structure at different values. In the measurements made in the study, the device designed for determining the acoustic performance of the material for both the impedance tube according to the relevant standards and the different noise types in the study was used. In addition, sound records of noise types encountered in daily life are taken and applied to the acoustic absorbent fabric with the aid of the device, and the feasibility of the results and the commercial ability of the product are examined. MATLAB numerical computing programming language and libraries were used in the frequency and sound power analyses made in the study.

Keywords: acoustic, egg crate, fabric, textile

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Authors: Alessandra Chiara Maraschin, Estelle Trengove

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In South Africa, only 23% of engineering students attain their degrees in the minimum time of 4 years. This begs the question: Why is the 4-year throughput rate so low? Improving the throughput rate is crucial in assisting students to the shortest possible path to completion. The Electrical Engineering programme has a fixed curriculum and students must pass all courses in order to graduate. In South Africa, as is the case in several other countries, many students rely on external funding such as bursaries from companies in industry. If students fail a course, they often lose their bursaries, and most might not be able to fund their 'repeating year' fees. It is thus important to improve the throughput rate, since for many students, graduating from university is a way out of poverty for an entire family. In Electrical Engineering, it has been found that the Software Development I course (an introduction to C++ programming) is a significant hurdle course for students and has been found to have a low pass rate. It has been well-documented that students struggle with this type of course as it introduces a number of new threshold concepts that can be challenging to grasp in a short time frame. In an attempt to mitigate this situation, a part-time night-school for Software Development I was introduced in 2015 as an intervention measure. The course includes all the course material from the Software Development I module and allows students who failed the course in first semester a second chance by repeating the course through taking the night-school course. The purpose of this study is to determine whether the introduction of this intervention course could be considered a success. The success of the intervention is assessed in two ways. The study will first look at whether the night-school course contributed to improving the pass rate of the Software Development I course. Secondly, the study will examine whether the intervention contributed to improving the overall throughput from the 2nd year to the 3rd year of study at a South African University. Second year academic results for a sample of 1216 students have been collected from 2010-2017. Preliminary results show that the lowest pass rate for Software Development I was found to be in 2017 with a pass rate of 34.9%. Since the intervention course's inception, the pass rate for Software Development I has increased each year from 2015-2017 by 13.75%, 25.53% and 25.81% respectively. To conclude, the preliminary results show that the intervention course is a success in improving the pass rate of Software Development I.

Keywords: academic performance, electrical engineering, engineering education, intervention course, low pass rate, software development course, throughput

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Authors: Zahid Ullah, Atlas Khan

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This abstract focuses on the advancements in mathematical modeling and optimization techniques that play a crucial role in enhancing the efficiency, reliability, and performance of these systems. In this era of rapidly evolving technology, mathematical modeling and optimization offer powerful tools to tackle the complex challenges faced by control, signal processing, and energy systems. This abstract presents the latest research and developments in mathematical methodologies, encompassing areas such as control theory, system identification, signal processing algorithms, and energy optimization. The abstract highlights the interdisciplinary nature of mathematical modeling and optimization, showcasing their applications in a wide range of domains, including power systems, communication networks, industrial automation, and renewable energy. It explores key mathematical techniques, such as linear and nonlinear programming, convex optimization, stochastic modeling, and numerical algorithms, that enable the design, analysis, and optimization of complex control and signal processing systems. Furthermore, the abstract emphasizes the importance of addressing real-world challenges in control, signal processing, and energy systems through innovative mathematical approaches. It discusses the integration of mathematical models with data-driven approaches, machine learning, and artificial intelligence to enhance system performance, adaptability, and decision-making capabilities. The abstract also underscores the significance of bridging the gap between theoretical advancements and practical applications. It recognizes the need for practical implementation of mathematical models and optimization algorithms in real-world systems, considering factors such as scalability, computational efficiency, and robustness. In summary, this abstract showcases the advancements in mathematical modeling and optimization techniques for control, signal processing, and energy systems. It highlights the interdisciplinary nature of these techniques, their applications across various domains, and their potential to address real-world challenges. The abstract emphasizes the importance of practical implementation and integration with emerging technologies to drive innovation and improve the performance of control, signal processing, and energy.

Keywords: mathematical modeling, optimization, control systems, signal processing, energy systems, interdisciplinary applications, system identification, numerical algorithms

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Authors: Samart Homthong, Wutthipong Moungnoi

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Critical success factors (CSFs) and the criteria to measure project success have received much attention over the decades and are among the most widely researched topics in the context of project management. However, although there have been extensive studies on the subject by different researchers, to date, there has been little agreement on the CSFs. The aim of this study is to identify the CSFs that influence the performance of construction projects, and determine their relative importance for different objectives across five stages in the project life cycle. A considerable literature review was conducted that resulted in the identification of 179 individual factors. These factors were then grouped into nine major categories. A questionnaire survey was used to collect data from three groups of respondents: client representatives, consultants, and contractors. Out of 164 questionnaires distributed, 93 were returned, yielding a response rate of 56.7%. Using the mean score, relative importance index, and weighted average method, the top 10 critical factors for each category were identified. The agreement of survey respondents on those categorised factors were analysed using Spearman’s rank correlation. A one-way analysis of variance was then performed to determine whether the mean scores among the various groups of respondents were statistically significant. The findings indicate the most CSFs in each category in procurement phase are: proper procurement programming of materials (time), stability in the price of materials (cost), and determining quality in the construction (quality). They are then followed by safety equipment acquisition and maintenance (health and safety), budgeting allowed in a contractual arrangement for implementing environmental management activities (environment), completeness of drawing documents (productivity), accurate measurement and pricing of bill of quantities (risk management), adequate communication among the project team (human resource), and adequate cost control measures (client satisfaction). An understanding of CSFs would help all interested parties in the construction industry to improve project performance. Furthermore, the results of this study would help construction professionals and practitioners take proactive measures for effective project management.

Keywords: critical success factors, procurement phase, project life cycle, project performance

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Authors: Andy Alubaidy

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In this work, a robotic additive manufacturing system (RAMS) that is capable of three-dimensional (3D) printing in six degrees of freedom (DOF) with very high accuracy and virtually on any surface has been designed and built. One of the major shortcomings in existing 3D printer technology is the limitation to three DOF, which results in prolonged fabrication time. Depending on the techniques used, it usually takes at least two hours to print small objects and several hours for larger objects. Another drawback is the size of the printed objects, which is constrained by the physical dimensions of most low-cost 3D printers, which are typically small. In such cases, large objects are produced by dividing them into smaller components that fit the printer’s workable area. They are then glued, bonded or otherwise attached to create the required object. Another shortcoming is material constraints and the need to fabricate a single part using different materials. With the flexibility of a six-DOF robot, the RAMS has been designed to overcome these problems. A feeding mechanism using an adaptive Proportional-Integral-Derivative (PID) controller is utilized along with a national instrument compactRIO (NI cRIO), an ABB robot, and off-the-shelf sensors. The RAMS have the ability to 3D print virtually anywhere in six degrees of freedom with very high accuracy. It is equipped with an ABB IRB 120 robot to achieve this level of accuracy. In order to convert computer-aided design (CAD) files to digital format that is acceptable to the robot, Hypertherm Robotic Software Inc.’s state-of-the-art slicing software called “ADDMAN” is used. ADDMAN is capable of converting any CAD file into RAPID code (the programing language for ABB robots). The robot uses the generated code to perform the 3D printing. To control the entire process, National Instrument (NI) compactRIO (cRio 9074), is connected and communicated with the robot and a feeding mechanism that is designed and fabricated. The feeding mechanism consists of two major parts, cold-end and hot-end. The cold-end consists of what is conventionally known as an extruder. Typically, a stepper-motor is used to control the push on the material, however, for optimum control, a DC motor is used instead. The hot-end consists of a melt-zone, nozzle, and heat-brake. The melt zone ensures a thorough melting effect and consistent output from the nozzle. Nozzles are made of brass for thermo-conductivity while the melt-zone is comprised of a heating block and a ceramic heating cartridge to transfer heat to the block. The heat-brake ensures that there is no heat creep-up effect as this would swell the material and prevent consistent extrusion. A control system embedded in the cRio is developed using NI Labview which utilizes adaptive PID to govern the heating cartridge in conjunction with a thermistor. The thermistor sends temperature feedback to the cRio, which will issue heat increase or decrease based on the system output. Since different materials have different melting points, our system will allow us to adjust the temperature and vary the material.

Keywords: robotic, additive manufacturing, PID controller, cRIO, 3D printing

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Authors: Rosa Figueroa, Christopher Flores

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Text categorization is the problem of assigning a new document to a set of predetermined categories, on the basis of a training set of free-text data that contains documents whose category membership is known. To train a classification model, it is necessary to extract characteristics in the form of tokens that facilitate the learning and classification process. In text categorization, the feature extraction process involves the use of word sequences also known as N-grams. In general, it is expected that documents belonging to the same category share similar features. The Smith-Waterman (SW) algorithm is a dynamic programming algorithm that performs a local sequence alignment in order to determine similar regions between two strings or protein sequences. This work explores the use of SW algorithm as an alternative to feature extraction in text categorization. The dataset used for this purpose, contains 2,610 annotated documents with the classes Obese/Non-Obese. This dataset was represented in a matrix form using the Bag of Word approach. The score selected to represent the occurrence of the tokens in each document was the term frequency-inverse document frequency (TF-IDF). In order to extract features for classification, four experiments were conducted: the first experiment used SW to extract features, the second one used unigrams (single word), the third one used bigrams (two word sequence) and the last experiment used a combination of unigrams and bigrams to extract features for classification. To test the effectiveness of the extracted feature set for the four experiments, a Support Vector Machine (SVM) classifier was tuned using 20% of the dataset. The remaining 80% of the dataset together with 5-Fold Cross Validation were used to evaluate and compare the performance of the four experiments of feature extraction. Results from the tuning process suggest that SW performs better than the N-gram based feature extraction. These results were confirmed by using the remaining 80% of the dataset, where SW performed the best (accuracy = 97.10%, weighted average F-measure = 97.07%). The second best was obtained by the combination of unigrams-bigrams (accuracy = 96.04, weighted average F-measure = 95.97) closely followed by the bigrams (accuracy = 94.56%, weighted average F-measure = 94.46%) and finally unigrams (accuracy = 92.96%, weighted average F-measure = 92.90%).

Keywords: comorbidities, machine learning, obesity, Smith-Waterman algorithm

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Authors: Bai-Chen Xie, Xian-Peng Chen

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China has become the world's largest energy producer and consumer, and its development of renewable energy is of great significance to global energy governance and the fight against climate change. The rapid growth of solar power in China could help achieve its ambitious carbon peak and carbon neutrality targets early. However, the non-technical costs of solar power in China are much higher than at international levels, meaning that inefficiencies are rooted in poor management and improper policy design and that efficiency distortions have become a serious challenge to the sustainable development of the renewable energy industry. Unlike fossil energy generation technologies, the output of solar power is closely related to the volatile solar resource, and the spatial unevenness of solar resource distribution leads to potential efficiency spatial distribution differences. It is necessary to develop an efficiency evaluation method that considers the volatility of solar resources and explores the mechanism of the influence of natural geography and social environment on the spatially varying characteristics of efficiency distribution to uncover the root causes of managing inefficiencies. The study sets solar resources as stochastic inputs, introduces a chance-constrained data envelopment analysis model combined with the directional distance function, and measures the solar resource utilization efficiency of 222 solar power plants in representative photovoltaic bases in northwestern China. By the meta-frontier analysis, we measured the characteristics of different power plant clusters and compared the differences among groups, discussed the mechanism of environmental factors influencing inefficiencies, and performed statistical tests through the system generalized method of moments. Rational localization of power plants is a systematic project that requires careful consideration of the full utilization of solar resources, low transmission costs, and power consumption guarantee. Suitable temperature, precipitation, and wind speed can improve the working performance of photovoltaic modules, reasonable terrain inclination can reduce land cost, and the proximity to cities strongly guarantees the consumption of electricity. The density of electricity demand and high-tech industries is more important than resource abundance because they trigger the clustering of power plants to result in a good demonstration and competitive effect. To ensure renewable energy consumption, increased support for rural grids and encouraging direct trading between generators and neighboring users will provide solutions. The study will provide proposals for improving the full life-cycle operational activities of solar power plants in China to reduce high non-technical costs and improve competitiveness against fossil energy sources.

Keywords: solar power plants, environmental factors, data envelopment analysis, efficiency evaluation

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Authors: L. Matthewman, J. Nowlan

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Background: Holistic or Integrative Psychology emphasizes the interdependence of physiological, spiritual and psychological dynamics. Studying “wholeness and well-being” from a systems perspective combines innovative psychological science interventions with Eastern orientated healing wisdoms and therapies. The literature surrounding holistic/integrative psychology focuses on multi-stage interventions in attempts to enhance the mind-body experiences of well-being for participants. This study proposes a new single stage model as an intervention for UG/PG students, time-constrained workplace employees and managers/leaders for improved well-being and life enhancement. The main research objective was to investigate participants’ experiences of holistic and mindfulness interventions for impact on emotional well-being. The main research question asked was if single stage holistic interventions could impact on psychological well-being. This is of consequence because many people report that a reason for not taking part in mind-body or wellness programmes is that they believe that they do not have sufficient time to engage in such pursuits. Experimental Approach: The study employed a mixed methods pre-test/post-test research design. Data was analyzed using descriptive statistics and interpretative phenomenological analysis. Purposive sampling methods were employed. An adapted mindfulness measurement questionnaire (MAAS) was administered to 20 volunteer final year UG student participants prior to the single stage intervention and following the intervention. A further post-test longitudinal follow-up took place one week later. Intervention: The single stage model intervention consisted of a half hour session of mindfulness, yoga stretches and head and neck massage in the following sequence: Mindful awareness of the breath, yoga stretches 1, mindfulness of the body, head and neck massage, mindfulness of sounds, yoga stretches 2 and finished with pure awareness mindfulness. Results: The findings on the pre-test indicated key themes concerning: “being largely unaware of feelings”, “overwhelmed with final year exams”, “juggling other priorities” , “not feeling in control”, “stress” and “negative emotional display episodes”. Themes indicated on the post-test included: ‘more aware of self’, ‘in more control’, ‘immediately more alive’ and ‘just happier’ compared to the pre-test. Themes from post-test 2 indicated similar findings to post-test 1 in terms of themes. but on a lesser scale when scored for intensity. Interestingly, the majority of participants reported that they would now seek other similar interventions in the future and would be likely to engage with a multi-stage intervention type on a longer-term basis. Overall, participants reported increased psychological well-being after the single stage intervention. Conclusion: A single stage one-off intervention model can be effective to help towards the wellbeing of final year UG students. There is little indication to suggest that this would not be generalizable to others in different areas of life and business. However this study must be taken with caution due to low participant numbers. Implications: Single stage one-off interventions can be used to enhance peoples’ lives who might not otherwise sign up for a longer multi-stage intervention. In addition, single stage interventions can be utilized to help participants progress onto longer multiple stage interventions. Finally, further research into one stage well-being interventions is encouraged.

Keywords: holistic/integrative psychology, mindfulness, well-being, yoga

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Authors: Óscar Muñoz Garrigós, José Manuel Bernabeu Aubán

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Time-sensitive services are the base of the cloud services industry. Keeping low service saturation is essential for controlling response time. All auto-scalable services make use of reactive auto-scaling. However, reactive auto-scaling has few in-depth studies. This presentation shows a model for reactive auto-scaling methodologies with a MAPE-k architecture. Queuing theory can compute different properties of static services but lacks some parameters related to the transition between models. Our model uses queuing theory parameters to relate the transition between models. It associates MAPE-k related times, the sampling frequency, the cooldown period, the number of requests that an instance can handle per unit of time, the number of incoming requests at a time instant, and a function that describes the acceleration in the service's ability to handle more requests. This model is later used as a solution to horizontally auto-scale time-sensitive services composed of microservices, reevaluating the model’s parameters periodically to allocate resources. The solution requires limiting the acceleration of the growth in the number of incoming requests to keep a constrained response time. Business benefits determine such limits. The solution can add a dynamic number of instances and remains valid under different system sizes. The study includes performance recommendations to improve results according to the incoming load shape and business benefits. The exposed methodology is tested in a simulation. The simulator contains a load generator and a service composed of two microservices, where the frontend microservice depends on a backend microservice with a 1:1 request relation ratio. A common request takes 2.3 seconds to be computed by the service and is discarded if it takes more than 7 seconds. Both microservices contain a load balancer that assigns requests to the less loaded instance and preemptively discards requests if they are not finished in time to prevent resource saturation. When load decreases, instances with lower load are kept in the backlog where no more requests are assigned. If the load grows and an instance in the backlog is required, it returns to the running state, but if it finishes the computation of all requests and is no longer required, it is permanently deallocated. A few load patterns are required to represent the worst-case scenario for reactive systems: the following scenarios test response times, resource consumption and business costs. The first scenario is a burst-load scenario. All methodologies will discard requests if the rapidness of the burst is high enough. This scenario focuses on the number of discarded requests and the variance of the response time. The second scenario contains sudden load drops followed by bursts to observe how the methodology behaves when releasing resources that are lately required. The third scenario contains diverse growth accelerations in the number of incoming requests to observe how approaches that add a different number of instances can handle the load with less business cost. The exposed methodology is compared against a multiple threshold CPU methodology allocating/deallocating 10 or 20 instances, outperforming the competitor in all studied metrics.

Keywords: reactive auto-scaling, auto-scaling, microservices, cloud computing

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Authors: Akiode Ayobami, Akiode Akinsewa, Odeku Mojisola, Salako Busola, Odutolu Omobola, Nuhu Khadija

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Purpose: Evidence exists that family planning use can contribute to reduction in infant and maternal mortality in any country. Despite these benefits, contraceptive use in Nigeria still remains very low, only 10% among married women. Understanding factors that predict contraceptive use is very important in order to improve the situation. In this paper, we analysed data from the 2013 Nigerian Demographic and Health Survey (NDHS) to better understand predictors of contraceptive use in Nigeria. The use of logistics regression and other traditional models in this type of situation is not appropriate as they do not account for social structure influence brought about by the hierarchical nature of the data on response variable. We therefore used multilevel modelling to explore the determinants of contraceptive use in order to account for the significant variation in modern contraceptive use by socio-demographic, and other proximate variables across the different Nigerian states. Method: This data has a two-level hierarchical structure. We considered the data of 26, 403 married women of reproductive age at level 1 and nested them within the 36 states and the Federal Capital Territory, Abuja at level 2. We modelled use of modern contraceptive against demographic variables, being told about FP at health facility, heard of FP on TV, Magazine or radio, husband desire for more children nested within the state. Results: Our results showed that the independent variables in the model were significant predictors of modern contraceptive use. The estimated variance component for the null model, random intercept, and random slope models were significant (p=0.00), indicating that the variation in contraceptive use across the Nigerian states is significant, and needs to be accounted for in order to accurately determine the predictors of contraceptive use, hence the data is best fitted by the multilevel model. Only being told about family planning at the health facility and religion have a significant random effect, implying that their predictability of contraceptive use varies across the states. Conclusion and Recommendation: Results showed that providing FP information at the health facility and religion needs to be considered when programming to improve contraceptive use at the state levels.

Keywords: multilevel modelling, family planning, predictors, Nigeria

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Authors: Rosina U. Ndukwe, Diane Crone, Nick Cavill

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Active transport (i.e. walking to school, cycle to work schemes etc.) is an effective approach with multiple social and environmental benefits for transforming urban environments into active urban environments. Although walking and cycling often remain on the margins of urban planning and infrastructure, there are new approaches emerging, along with policy intervention relevant for the creation of sustainable urban active environments conductive to active travel, increasing physical activity levels of involved communities and supporting social inclusion through more active participation. SPAcE - Supporting Policy and Action for Active Environments is a 3 year Erasmus+ project that aims to integrate active transport programmes into public policy across the EU. SPAcE focuses on cities/towns with recorded low physical activity levels to support the development of active environments in 5 sites: Latvia [Tukums], Italy [Palermo], Romania [Brasov], Spain [Castilla-La Mancha] and Greece [Trikala]. The first part of the project involved a review of good practice including case studies from across the EU and project partner countries. This has resulted in the first output from the project, an evidence of good practice summary with case study examples. In the second part of the project, working groups across the 5 sites have carried out co-production to develop Urban Active Environments (UActivE) Action Plans aimed at influencing policy and practice for increasing physical activity primarily through the use of cycling and walking. Action plans are based on international evidence and guidance for healthy urban planning. Remaining project partners include Universities (Gloucestershire, Oxford, Zurich, Thessaly) and Fit for Life programme (National physical activity promotion program, Finland) who provide support and advice incorporating current evidence, healthy urban planning and mentoring. Cooperation and co-production with public health professionals, local government officers, education authorities and transport agencies has been a key approach of the project. The third stage of the project has involved training partners in the WHO HEAT tool to support the implementation of the Action Plans. Project results show how multi-agency, transnational collaboration can produce real-life Action Plans in five EU countries, based on published evidence, real-life experience, consultation and collaborative working with other organisations across the EU. Learning from the processes adopted within this project will demonstrate how public health, local government and transport agencies across the EU, can work together to create healthy environments that have the aim of facilitating active behaviour, even in times of constrained public budgets. The SPAcE project has captured both the challenges and solutions for increasing population physical activity levels, health and wellness in urban spaces and translating evidence into policy and practice ensuring innovation at policy level. Funding acknowledgment: SPAcE (www.activeenvironments.eu) is co-funded by the Sport action of the ERASMUS+ programme.

Keywords: action plans, active transport, SPAcE, UActivE urban active environments, walking and cycling

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Authors: Tesfaye Mengistu

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Power cables play a crucial role in the transmission and distribution of electrical energy. As the electricity generation, transmission, distribution, and storage systems become smarter, there is a growing emphasis on incorporating intelligent approaches to ensure the reliability of power cables. Various types of electrical cables are employed for transmitting and distributing electrical energy, with cross-linked polyethylene (XLPE) cables being widely utilized due to their exceptional electrical and mechanical properties. However, insulation defects can occur in XLPE cables due to subpar manufacturing techniques during production and cable joint installation. To address this issue, experts have proposed different methods for monitoring XLPE cables. Some suggest the use of interdigital capacitive (IDC) technology for online monitoring, while others propose employing continuous wave (CW) terahertz (THz) imaging systems to detect internal defects in XLPE plates used for power cable insulation. In this study, we have developed models that employ a custom dataset collected locally to classify the physical safety status of individual power cables. Our models aim to replace physical inspections with computer vision and image processing techniques to classify defective power cables from non-defective ones. The implementation of our project utilized the Python programming language along with the TensorFlow package and a convolutional neural network (CNN). The CNN-based algorithm was specifically chosen for power cable defect classification. The results of our project demonstrate the effectiveness of CNNs in accurately classifying power cable defects. We recommend the utilization of similar or additional datasets to further enhance and refine our models. Additionally, we believe that our models could be used to develop methodologies for detecting power cable defects from live video feeds. We firmly believe that our work makes a significant contribution to the field of power cable inspection and maintenance. Our models offer a more efficient and cost-effective approach to detecting power cable defects, thereby improving the reliability and safety of power grids.

Keywords: artificial intelligence, computer vision, defect detection, convolutional neural net

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Authors: Mercy Marimirofa, Farai Machinga, Alfred Zvoushe, Tsitsidzaishe Musvosvi

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The Government of Zimbabwe embarked on integrating family planning with Sexually Transmitted Infection (STI) and Human Immunodeficiency Virus (HIV) services in May 2020 with support from the World Health Organization (WHO). There was high HIV prevalence, incidence rates and STI infections among women attending FP clinics. Spilhaus is a specialized center of excellence clinic which offers a range of sexual reproductive health services. HIV services were limited to testing only, and clients were referred to other facilities for further management. Integration of services requires that all the services be available at one point so that clients will access them during their visit to the facility. Objectives: The study was conducted to assess the impact the one-stop-shop model has made in accessing integrated Family Planning services and sexual reproductive health services compared to the supermarket approach. It also assessed the relationship family planning services have with other sexual reproductive health services. Methods: A secondary data analysis was conducted at Spilhaus clinic in Harare using family planning registers and HIV services registers comparing years 2019 and 2021. A 2 sample t-test was used to determine the difference in clients accessing the services under the two models. A Spearman’s rank correlation was used to determine if accessing family planning services has a relationship with other sexual reproductive health services. Results: In 2019, 7,548 clients visited the Spilhaus clinic compared to 8,265 during the period January to December 2021. The median age for all clients accessing services was 32 years. An increase of 69% in the number of services accessed was recorded from 2019 to 2021. More services were accessed in 2021. There was no difference in the number of clients accessing family planning services cervical cancer, and HIV services. A difference was found in the number of clients who were offered STI screening services. There was also a relationship between accessing family planning services and STI screening services (ρ = 0.729, p-value=0.006). Conclusion: Programming towards SRH services was a great achievement, the use of an integrated approach proved to be cost-effective as it minimised the required resources for separate programs. Clients accessed important health needs at once. The integration of these services provided an opportunity to offer comprehensive information which addressed an individual’s sexual reproductive health needs.

Keywords: intergration, one stop shop, family planning, reproductive health

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Authors: Mahbuba Rahman, Sabri Boughorbel, Scott Presnell, Charlie Quinn, Darawan Rinchai, Damien Chaussabel, Nico Marr

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Collections of large-scale datasets made available in public repositories can be used to identify and fill gaps in biomedical knowledge. But first, these data need to be made readily accessible to researchers for analysis and interpretation. Here a collection of transcriptome datasets was made available to investigate the functional programming of human hematopoietic cells in early life. Thirty two datasets were retrieved from the NCBI Gene Expression Omnibus (GEO) and loaded in a custom, interactive web application called the Gene Expression browser (GXB), designed for visualization and query of integrated large-scale data. Multiple sample groupings and gene rank lists were created based on the study design and variables in each dataset. Web links to customized graphical views can be generated by users and subsequently be used to graphically present data in manuscripts for publication. The GXB tool also enables browsing of a single gene across datasets, which can provide information on the role of a given molecule across biological systems. The dataset collection is available online. As a proof-of-principle, one of the datasets (GSE25087) was re-analyzed to identify genes that are differentially expressed by regulatory T cells in early life. Re-analysis of this dataset and a cross-study comparison using multiple other datasets in the above mentioned collection revealed that PMCH, a gene encoding a precursor of melanin-concentrating hormone (MCH), a cyclic neuropeptide, is highly expressed in a variety of other hematopoietic cell types, including neonatal erythroid cells as well as plasmacytoid dendritic cells upon viral infection. Our findings suggest an as yet unrecognized role of MCH in immune regulation, thereby highlighting the unique potential of the curated dataset collection and systems biology approach to generate new hypotheses which can be tested in future mechanistic studies.

Keywords: early-life, GEO datasets, PMCH, interactive query, systems biology

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Authors: R. Edward Minchin Jr.

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The U.S. Federal Highway Administration (FHWA) Every Day Counts Program (EDC) has resulted in state DOTs putting evermore emphasis on speeding up the delivery of highway and bridge construction projects for use by the driving public. This has resulted in an increase in the use of alternative construction delivery systems such as design-build (D-B), construction manager at-risk (CMR) or construction manager/general contractor (CM/GC), and adding alternative technical concepts (ATCs) to traditional design-bid-build (DBB) contracts. ATCs have exhibited great potential for delivering substantial benefits like cost savings, increased constructability, and quicker project delivery. Previous research has found that knowledge of project constructability was lacking in state Department of Transportation (DOT) planning, programming, and environmental staffs. Many agencies have therefore relied on a set of ‘acceptable’ design solutions over the years of working with their local resource agencies. The result is that the permitting process for several government agencies has become increasingly restrictive with the result that the DOTs and their industry partners lose the ability to innovate after a permit is approved. The intent of this paper is to report on the research team’s progress in this ongoing effort furnish the United States government with a uniform set of guidelines for the application of constructability reviews during all phases of project development and delivery. The research uses surveys and interviews to determine which states have implemented formal programs to ensure that the constructor is furnished with a set of contract documents that affords said constructor with the best possible opportunity to successfully construct the project with the highest quality standards, within the contract duration and without exceeding the construction budget. Once these states are identified, workshops are held all over the nation, resulting in the team learning the best current practices and giving the team the ability to recommend new practices that will improve the process. The plan is for the FHWA to encourage or require state DOTs to use these practices on all federally funded highway and bridge construction projects. The project deliverable is a Guidebook for FHWA to use in disseminating the recommended practices to the states.

Keywords: alternative construction delivery, alternative technical concepts, constructability, construction design plans

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Authors: Nan Hu, Chao Wang, Xi Li, Xuehai Zhou

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The field-programmable gate array (FPGA) has been widely adopted in the high-performance computing domain. In recent years, the embedded system-on-a-chip (SoC) contains coarse granularity multi-core CPU (central processing unit) and mobile GPU (graphics processing unit) that can be used as general-purpose accelerators. The motivation is that algorithms of various parallel characteristics can be efficiently mapped to the heterogeneous architecture coupled with these three processors. The CPU and GPU offload partial computationally intensive tasks from the FPGA to reduce the resource consumption and lower the overall cost of the system. However, in present common scenarios, the applications always utilize only one type of accelerator because the development approach supporting the collaboration of the heterogeneous processors faces challenges. Therefore, a systematic approach takes advantage of write-once-run-anywhere portability, high execution performance of the modules mapped to various architectures and facilitates the exploration of design space. In this paper, A servant-execution-flow model is proposed for the abstraction of the cooperation of the heterogeneous processors, which supports task partition, communication and synchronization. At its first run, the intermediate language represented by the data flow diagram can generate the executable code of the target processor or can be converted into high-level programming languages. The instantiation parameters efficiently control the relationship between the modules and computational units, including two hierarchical processing units mapping and adjustment of data-level parallelism. An embedded system of a three-dimensional waveform oscilloscope is selected as a case study. The performance of algorithms such as contrast stretching, etc., are analyzed with implementations on various combinations of these processors. The experimental results show that the heterogeneous computing system with less than 35% resources achieves similar performance to the pure FPGA and approximate energy efficiency.

Keywords: FPGA-CPU-GPU collaboration, design space exploration, heterogeneous computing, intermediate language, parameterized instantiation

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Authors: Zachary Huffman, Joana Rocha

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Wall-pressure fluctuations induced by the turbulent boundary layer (TBL) developed over aircraft are a significant source of aircraft cabin noise. Since the power spectral density (PSD) of these pressure fluctuations is directly correlated with the amount of sound radiated into the cabin, the development of accurate empirical models that predict the PSD has been an important ongoing research topic. The sound emitted can be represented from the pressure fluctuations term in the Reynoldsaveraged Navier-Stokes equations (RANS). Therefore, early TBL empirical models (including those from Lowson, Robertson, Chase, and Howe) were primarily derived by simplifying and solving the RANS for pressure fluctuation and adding appropriate scales. Most subsequent models (including Goody, Efimtsov, Laganelli, Smol’yakov, and Rackl and Weston models) were derived by making modifications to these early models or by physical principles. Overall, these models have had varying levels of accuracy, but, in general, they are most accurate under the specific Reynolds and Mach numbers they were developed for, while being less accurate under other flow conditions. Despite this, recent research into the possibility of using alternative methods for deriving the models has been rather limited. More recent studies have demonstrated that an artificial neural network model was more accurate than traditional models and could be applied more generally, but the accuracy of other machine learning techniques has not been explored. In the current study, an original model is derived using a stepwise regression algorithm in the statistical programming language R, and TBL wall-pressure fluctuations PSD data gathered at the Carleton University wind tunnel. The theoretical advantage of a stepwise regression approach is that it will automatically filter out redundant or uncorrelated input variables (through the process of feature selection), and it is computationally faster than machine learning. The main disadvantage is the potential risk of overfitting. The accuracy of the developed model is assessed by comparing it to independently sourced datasets.

Keywords: aircraft noise, machine learning, power spectral density models, regression models, turbulent boundary layer wall-pressure fluctuations

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Authors: Sandra Cecilia Bautista-Rodríguez, Vladimir Melgarejo

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Climate change, loss of ecosystems, increasing poverty, increasing marginalization of rural communities and declining food security are global issues that require urgent attention. In this regard, a great deal of research has focused on how agroecosystems respond to these challenges as they provide ecosystem services (ES) that lead to higher levels of resilience, adaptation, productivity and self-sufficiency. Hence, the valuing of ecosystem services plays an important role in the decision-making process for the design and management of agroecosystems. This paper aims to define the link between ecosystem service valuation methods and ES value dimensions in agroecosystems from ecological economics and agroecology. The method used to identify valuation methodologies was a literature review in the fields of Agroecology and Ecological Economics, based on a strategy of information search and classification. The conceptual framework of the work is based on the multidimensionality of value, considering the social, ecological, political, technological and economic dimensions. Likewise, the valuation process requires consideration of the ecosystem function associated with ES, such as regulation, habitat, production and information functions. In this way, valuation methods for ES in agroecosystems can integrate more than one value dimension and at least one ecosystem function. The results allow correlating the ecosystem functions with the ecosystem services valued, and the specific tools or models used, the dimensions and valuation methods. The main methodologies identified are multi-criteria valuation (1), deliberative - consultative valuation (2), valuation based on system dynamics modeling (3), valuation through energy or biophysical balances (4), valuation through fuzzy logic modeling (5), valuation based on agent-based modeling (6). Amongst the main conclusions, it is highlighted that the system dynamics modeling approach has a high potential for development in valuation processes, due to its ability to integrate other methods, especially multi-criteria valuation and energy and biophysical balances, to describe through causal cycles the interrelationships between ecosystem services, the dimensions of value in agroecosystems, thus showing the relationships between the value of ecosystem services and the welfare of communities. As for methodological challenges, it is relevant to achieve the integration of tools and models provided by different methods, to incorporate the characteristics of a complex system such as the agroecosystem, which allows reducing the limitations in the processes of valuation of ES.

Keywords: ecological economics, agroecosystems, ecosystem services, valuation of ecosystem services

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Authors: Martin Kleynhans, Margot Soler, Gavin Quibell

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Malawi is one of the world’s poorest nations and consequently, the design of flood risk management infrastructure comes with a different set of challenges. There is a lack of good quality hydromet data, both in spatial terms and in the quality thereof and the challenge in the design of flood risk management infrastructure is compounded by the fact that maintenance is almost completely non-existent and that solutions have to be simple to be effective. Solutions should not require any further resources to remain functional after completion, and they should be resilient. They also have to be cost effective. The Lower Shire Valley of Malawi suffers from frequent flood events. Various flood risk management interventions have been designed across the valley during the course of the Shire River Basin Management Project – Phase I, and due to the data poor environment, indigenous knowledge was relied upon to a great extent for hydrological and hydraulic model calibration and verification. However, indigenous knowledge comes with the caveat that it is ‘fuzzy’ and that it can be manipulated for political reasons. The experience in the Lower Shire valley suggests that indigenous knowledge is unlikely to invent a problem where none exists, but that flood depths and extents may be exaggerated to secure prioritization of the intervention. Indigenous knowledge relies on the memory of a community and cannot foresee events that exceed past experience, that could occur differently to those that have occurred in the past, or where flood management interventions change the flow regime. This complicates communication of planned interventions to local inhabitants. Indigenous knowledge is, for the most part, intuitive, but flooding can sometimes be counter intuitive, and the rural poor may have a lower trust of technology. Due to a near complete lack of maintenance of infrastructure, infrastructure has to be designed with no moving parts and no requirement for energy inputs. This precludes pumps, valves, flap gates and sophisticated warning systems. Designs of dykes during this project included ‘flood warning spillways’, that double up as pedestrian and animal crossing points, which provide warning of impending dangerous water levels behind dykes to residents before water levels that could cause a possible dyke failure are reached. Locally available materials and erosion protection using vegetation were used wherever possible to keep costs down.

Keywords: design of dykes in low-income countries, flood warning spillways, indigenous knowledge, Malawi

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Authors: Yan-Shen Yang, Bai-Chen Xie

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Background and significance of the study: Both green power trading schemes and interregional power transmission are effective ways to increase green power absorption and achieve renewable power development goals. China accelerates the construction of interregional power transmission lines and the green power market. A critical issue focusing on the close interaction between these two approaches arises, which can heavily affect the green power quota allocation and renewable power development. Existing studies have not discussed this issue adequately, so it is urgent to figure out their relationship to achieve a suitable power market design and a more reasonable power grid construction.Basic methodologies: We develop an equilibrium model of the power market in China to analyze the coupling effect of these two approaches as well as their influence on power generation and interregional transmission in China. Our model considers both the Tradable green certificate (TGC) and green power market, which consists of producers, consumers in the market, and an independent system operator (ISO) minimizing the total system cost. Our equilibrium model includes the decision optimization process of each participant. To reformulate the models presented as a single-level one, we replace the producer, consumer, ISO, and market equilibrium problems with their Karush-Kuhn-Tucker (KKT) conditions, which is further reformulated as a mixed-integer linear programming (MILP) and solved in Gurobi solver. Major findings: The result shows that: (1) the green power market can significantly promote renewable power absorption while the TGC market provides a more flexible way for green power trading. (2) The phenomena of inefficient occupation and no available transmission lines appear simultaneously. The existing interregional transmission lines cannot fully meet the demand for wind and solar PV power trading in some areas while the situation is vice versa in other areas. (3) Synchronous implementation of green power and TGC trading mechanism can benefit the development of green power as well as interregional power transmission. (4) The green power transaction exacerbates the unfair distribution of carbon emissions. The Carbon Gini Coefficient is up to 0.323 under the green power market which shows a high Carbon inequality. The eastern coastal region will benefit the most due to its huge demand for external power.

Keywords: green power market, tradable green certificate, interregional power transmission, power market equilibrium model

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Authors: Guenther Schuh, Michael Riesener, Frederic Diels

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Nowadays, manufacturing companies are faced with the challenge of meeting heterogeneous customer requirements in short product life cycles with a variety of product functions. So far, some of the functional requirements remain unknown until late stages of the product development. A way to handle these uncertainties is the highly iterative product development (HIP) approach. By structuring the development project as a highly iterative process, this method provides customer oriented and marketable products. There are first approaches for combined, hybrid models comprising deterministic-normative methods like the Stage-Gate process and empirical-adaptive development methods like SCRUM on a project management level. However, almost unconsidered is the question, which development scopes can preferably be realized with either empirical-adaptive or deterministic-normative approaches. In this context, a development scope constitutes a self-contained section of the overall development objective. Therefore, this paper focuses on a methodology that deals with the uncertainty of requirements within the early development stages and the corresponding selection of the most appropriate development approach. For this purpose, internal influencing factors like a company’s technology ability, the prototype manufacturability and the potential solution space as well as external factors like the market accuracy, relevance and volatility will be analyzed and combined into an Agile-Indicator. The Agile-Indicator is derived in three steps. First of all, it is necessary to rate each internal and external factor in terms of the importance for the overall development task. Secondly, each requirement has to be evaluated for every single internal and external factor appropriate to their suitability for empirical-adaptive development. Finally, the total sums of internal and external side are composed in the Agile-Indicator. Thus, the Agile-Indicator constitutes a company-specific and application-related criterion, on which the allocation of empirical-adaptive and deterministic-normative development scopes can be made. In a last step, this indicator will be used for a specific clustering of development scopes by application of the fuzzy c-means (FCM) clustering algorithm. The FCM-method determines sub-clusters within functional clusters based on the empirical-adaptive environmental impact of the Agile-Indicator. By means of the methodology presented in this paper, it is possible to classify requirements, which are uncertainly carried out by the market, into empirical-adaptive or deterministic-normative development scopes.

Keywords: agile, highly iterative development, agile-indicator, product development

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Authors: S. Ozeri, D. Shmilovitz

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Micro‐Turbine Generators (MTG) are small size power plants that consist of a high speed, gas turbine driving an electrical generator. MTGs may be fueled by either natural gas or kerosene and may also use sustainable and recycled green fuels such as biomass, landfill or digester gas. The typical ratings of MTGs start from 20 kW up to 200 kW. The primary use of MTGs is for backup for sensitive load sites such as hospitals, and they are also considered a feasible power source for Distributed Generation (DG) providing on-site generation in proximity to remote loads. The MTGs have the compressor, the turbine, and the electrical generator mounted on a single shaft. For this reason, the electrical energy is generated at high frequency and is incompatible with the power grid. Therefore, MTGs must contain, in addition, a power conditioning unit to generate an AC voltage at the grid frequency. Presently, this power conditioning unit consists of a rectifier followed by a DC/AC inverter, both rated at the full MTG’s power. The losses of the power conditioning unit account to some 3-5%. Moreover, the full-power processing stage is a bulky and costly piece of equipment that also lowers the overall system reliability. In this study, we propose a new type of power conditioning stage in which only a small fraction of the power is processed. A low power converter is used only to program the rotor current (i.e. the excitation current which is substantially lower). Thus, the MTG's output voltage is shaped to the desired amplitude and frequency by proper programming of the excitation current. The control is realized by causing the rotor current to track the electrical frequency (which is related to the shaft frequency) with a difference that is exactly equal to the line frequency. Since the phasor of the rotation speed and the phasor of the rotor magnetic field are multiplied, the spectrum of the MTG generator voltage contains the sum and the difference components. The desired difference component is at the line frequency (50/60 Hz), whereas the unwanted sum component is at about twice the electrical frequency of the stator. The unwanted high frequency component can be filtered out by a low-pass filter leaving only the low-frequency output. This approach allows elimination of the large power conditioning unit incorporated in conventional MTGs. Instead, a much smaller and cheaper fractional power stage can be used. The proposed technology is also applicable to other high rotation generator sets such as aircraft power units.

Keywords: gas turbine, inverter, power multiplier, distributed generation

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Authors: Karan Sharma, Ajay Kumar

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Epileptic seizure is a type of disease due to which electrical charge in the brain flows abruptly resulting in abnormal activity by the subject. One percent of total world population gets epileptic seizure attacks.Due to abrupt flow of charge, EEG (Electroencephalogram) waveforms change. On the display appear a lot of spikes and sharp waves in the EEG signals. Detection of epileptic seizure by using conventional methods is time-consuming. Many methods have been evolved that detect it automatically. The initial part of this paper provides the review of techniques used to detect epileptic seizure automatically. The automatic detection is based on the feature extraction and classification patterns. For better accuracy decomposition of the signal is required before feature extraction. A number of parameters are calculated by the researchers using different techniques e.g. approximate entropy, sample entropy, Fuzzy approximate entropy, intrinsic mode function, cross-correlation etc. to discriminate between a normal signal & an epileptic seizure signal.The main objective of this review paper is to present the variations in the EEG signals at both stages (i) Interictal (recording between the epileptic seizure attacks). (ii) Ictal (recording during the epileptic seizure), using most appropriate methods of analysis to provide better healthcare diagnosis. This research paper then investigates the effects of a noninvasive healing therapy on the subjects by studying the EEG signals using latest signal processing techniques. The study has been conducted with Reiki as a healing technique, beneficial for restoring balance in cases of body mind alterations associated with an epileptic seizure. Reiki is practiced around the world and is recommended for different health services as a treatment approach. Reiki is an energy medicine, specifically a biofield therapy developed in Japan in the early 20th century. It is a system involving the laying on of hands, to stimulate the body’s natural energetic system. Earlier studies have shown an apparent connection between Reiki and the autonomous nervous system. The Reiki sessions are applied by an experienced therapist. EEG signals are measured at baseline, during session and post intervention to bring about effective epileptic seizure control or its elimination altogether.

Keywords: EEG signal, Reiki, time consuming, epileptic seizure

Procedia PDF Downloads 381