Search results for: minimum spanning algorithm

Authors: Gulfam Haider, sana danish

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Convolutional Neural Networks (CNNs) have emerged as powerful tools for image classification, and the choice of optimizers profoundly affects their performance. The study of optimizers and their adaptations remains a topic of significant importance in machine learning research. While numerous studies have explored and advocated for various optimizers, the efficacy of these optimization techniques is still subject to scrutiny. This work aims to address the challenges surrounding the effectiveness of optimizers by conducting a comprehensive analysis and evaluation. The primary focus of this investigation lies in examining the performance of different optimizers when employed in conjunction with the popular activation function, Rectified Linear Unit (ReLU). By incorporating ReLU, known for its favorable properties in prior research, the aim is to bolster the effectiveness of the optimizers under scrutiny. Specifically, we evaluate the adjustment of these optimizers with both the original Softmax activation function and the modified ReLU activation function, carefully assessing their impact on overall performance. To achieve this, a series of experiments are conducted using a well-established benchmark dataset for image classification tasks, namely the Canadian Institute for Advanced Research dataset (CIFAR-10). The selected optimizers for investigation encompass a range of prominent algorithms, including Adam, Root Mean Squared Propagation (RMSprop), Adaptive Learning Rate Method (Adadelta), Adaptive Gradient Algorithm (Adagrad), and Stochastic Gradient Descent (SGD). The performance analysis encompasses a comprehensive evaluation of the classification accuracy, convergence speed, and robustness of the CNN models trained with each optimizer. Through rigorous experimentation and meticulous assessment, we discern the strengths and weaknesses of the different optimization techniques, providing valuable insights into their suitability for image classification tasks. By conducting this in-depth study, we contribute to the existing body of knowledge surrounding optimizers in CNNs, shedding light on their performance characteristics for image classification. The findings gleaned from this research serve to guide researchers and practitioners in making informed decisions when selecting optimizers and activation functions, thus advancing the state-of-the-art in the field of image classification with convolutional neural networks.

Keywords: deep neural network, optimizers, RMsprop, ReLU, stochastic gradient descent

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Authors: Nicholas P. Fenlon, Aisling Lavelle, David Mclean, Margaret O'riordan

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Background: The case for continuing professional development has been well made, and was formalized in Ireland in recent years through the enactment of the Medical Practitioner’s Act, which requires registered medical practitioners to complete a minimum of 50 hours CPD each year. The ICGP, who have been providing CPD opportunities to its members for many years, have responded to this need by developing a series of evidence-based, high-quality, multimedia modules across a range of clinical and non-clinical areas. (More traditional education opportunities are still being provided by the college also). Overview of Programme: The first module was released in September 2011, since when the eLearning program has grown steadily, and there are currently almost 20 modules available, with a further 5 in production. Each module contains three to six 10-minute video lessons, which use a combination of graphics, images, text, voice-over and clinical clips. These are supported by supplementary videos of expert pieces-to-camera, Q&As with content experts, clinical scenarios, external links and relevant documentation and other resources. Successful completion of MCQs will result in a Certificate of Completion, which can be printed or stored in Professional Competence portfolio. The Medical Practitioner’s Act requires doctors to gather CPD credits across 8 domains of practice, and various eLearning modules have been developed to address each. For instance, modules with a strong clinical content would include Management of Hypertension, Management of COPD, and Management of Asthma. Other modules focus on health promotion such as Promoting Smoking Cessation, Promoting Physical Activity, and Addressing Childhood Obesity. Modules where communication skills are keys include modules on Suicide Prevention and Management of Depression. Other modules, currently in development include non-clinical topics around risk management, including Confidentiality, Consent etc. Each module is developed by a core group, which includes where possible, a GP with a special interest in the area, and a content expert(s). The college works closely with a medical education consultant and a production company in developing and producing the modules. Modules can be accessed (with password) through the ICGP website and are available free to all ICGP members. Summary of Evaluation: There are over 1700 registered users to date (over 55% of College membership). The program was evaluated using an online survey in 2013 (N = 144/950 – 12%) and results were very positive overall but provided material for the further improvement of the program also. Future Plans: While knowledge can be imparted well through eLearning, skills and attitudes are more difficult to influence through an online environment. The college is now developing a series of linked workshops, which will lead to ICGP Professional Competence Awards. The first pilot workshop is scheduled for February 2015 and is Cardiology-themed. Participants will be required to complete the following 4 modules in advance of attending – Management of Hypertension, Management of Heart Failure, Promoting Smoking Cessation, and Promoting Physical Activity. The workshop will be case-based and interactive, addressing ECG Interpretation in General Practice. Conclusions: The ICGP have responded to members needs for high-quality evidence-based education delivered in a way that suits GPs.

Keywords: CPD opportunities, evidence-based, high quality, multimedia modules across a range of clinical and non-clinical areas, medical practitioner’s act

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Authors: Damtew Samson Zerihun

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This paper primarily describes the concept of a hierarchical safety control (HSC) in discrete manufacturing to up-hold productivity with human intervention and machine failures using a systematic approach, through increasing the system availability and using additional knowledge on machines so as to improve the human machine collaboration (HMC). It also highlights the implemented PLC safety algorithm, in applying this generic concept to a concrete pro-duction line using a lab demonstrator called FATIE (Factory Automation Test and Integration Environment). Furthermore, the paper describes a model and provide a systematic representation of human-machine collabora-tion in discrete manufacturing and to this end, the Hierarchical Safety Control concept is proposed. This offers a ge-neric description of human-machine collaboration based on Finite State Machines (FSM) that can be applied to vari-ous discrete manufacturing lines instead of using ad-hoc solutions for each line. With its reusability, flexibility, and extendibility, the Hierarchical Safety Control scheme allows upholding productivity while maintaining safety with reduced engineering effort compared to existing solutions. The approach to the solution begins with a successful partitioning of different zones around the Integrated Manufacturing System (IMS), which are defined by operator tasks and the risk assessment, used to describe the location of the human operator and thus to identify the related po-tential hazards and trigger the corresponding safety functions to mitigate it. This includes selective reduced speed zones and stop zones, and in addition with the hierarchical safety control scheme and advanced safety functions such as safe standstill and safe reduced speed are used to achieve the main goals in improving the safe Human Ma-chine Collaboration and increasing the productivity. In a sample scenarios, It is shown that an increase of productivity in the order of 2.5% is already possible with a hi-erarchical safety control, which consequently under a given assumptions, a total sum of 213 € could be saved for each intervention, compared to a protective stop reaction. Thereby the loss is reduced by 22.8%, if occasional haz-ard can be refined in a hierarchical way. Furthermore, production downtime due to temporary unavailability of safety devices can be avoided with safety failover that can save millions per year. Moreover, the paper highlights the proof of the development, implementation and application of the concept on the lab demonstrator (FATIE), where it is realized on the new safety PLCs, Drive Units, HMI as well as Safety devices in addition to the main components of the IMS.

Keywords: discrete automation, hierarchical safety controller, human machine collaboration, programmable logical controller

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Authors: Sakshi Gupta, Anupam Agrawal, Ekta Singla

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An exoskeleton design for rehabilitation purpose encounters many challenges, including ergonomically acceptable wearing technology, architectural design human-motion compatibility, actuation type, human-robot interaction, etc. In this paper, a passive architecture for upper limb exoskeleton is proposed for assisting in rehabilitation tasks. Kinematic modelling is detailed for task-based kinematic synthesis of the wearable exoskeleton for self-feeding tasks. The exoskeleton architecture possesses expansion and torsional springs which are able to store and redistribute energy over the human arm joints. The elastic characteristics of the springs have been optimized to minimize the mechanical work of the human arm joints. The concept of hybrid combination of a 4-bar parallelogram linkage and a serial linkage were chosen, where the 4-bar parallelogram linkage with expansion spring acts as a rigid structure which is used to provide the rotational degree-of-freedom (DOF) required for lowering and raising of the arm. The single linkage with torsional spring allows for the rotational DOF required for elbow movement. The focus of the paper is kinematic modelling, analysis and task-based synthesis framework for the proposed architecture, keeping in considerations the essential tasks of self-feeding and self-exercising during rehabilitation of partially healthy person. Rehabilitation of primary functional movements (activities of daily life, i.e., ADL) is routine activities that people tend to every day such as cleaning, dressing, feeding. We are focusing on the feeding process to make people independent in respect of the feeding tasks. The tasks are focused to post-surgery patients under rehabilitation with less than 40% weakness. The challenges addressed in work are ensuring to emulate the natural movement of the human arm. Human motion data is extracted through motion-sensors for targeted tasks of feeding and specific exercises. Task-based synthesis procedure framework will be discussed for the proposed architecture. The results include the simulation of the architectural concept for tracking the human-arm movements while displaying the kinematic and static study parameters for standard human weight. D-H parameters are used for kinematic modelling of the hybrid-mechanism, and the model is used while performing task-based optimal synthesis utilizing evolutionary algorithm.

Keywords: passive mechanism, task-based synthesis, emulating human-motion, exoskeleton

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Authors: Viviana Manzulli, Luigina Serrecchia, Adelia Donatiello, Valeria Rondinone, Sabine Zange, Alina Tscherne, Antonio Parisi, Antonio Fasanella

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Anthrax is a zoonotic disease that affects a wide range of animal species (primarily ruminant herbivores), and can be transmitted to humans through consumption or handling of contaminated animal products. The etiological agent B.anthracis is able to survive in unfavorable environmental conditions by forming endospore which remain viable in the soil for many decades. Furthermore, B.anthracis is considered as one of the most feared agents to be potentially misused as a biological weapon and the importance of the disease and its treatment in humans has been underscored before the bioterrorism events in the United States in 2001. Due to the often fatal outcome of human cases, antimicrobial susceptibility testing plays especially in the management of anthrax infections an important role. In Italy, animal anthrax is endemic (predominantly found in the southern regions and on islands) and is characterized by sporadic outbreaks occurring mainly during summer. Between 2012 and 2017 single human cases of cutaneous anthrax occurred. In this study, 90 diverse strains of B.anthracis, isolated in Italy from 2001 to 2017, were screened to their susceptibility to sixteen clinically relevant antimicrobial agents by using the broth microdilution method. B.anthracis strains selected for this study belong to the strain collection stored at the Anthrax Reference Institute of Italy located inside the Istituto Zooprofilattico Sperimentale of Puglia and Basilicata. The strains were isolated at different time points and places from various matrices (human, animal and environmental). All strains are a representative of over fifty distinct MLVA 31 genotypes. The following antibiotics were used for testing: gentamicin, ceftriaxone, streptomycin, penicillin G, clindamycin, chloramphenicol, vancomycin, linezolid, cefotaxime, tetracycline, erythromycin, rifampin, amoxicillin, ciprofloxacin, doxycycline and trimethoprim. A standard concentration of each antibiotic was prepared in a specific diluent, which were then twofold serial diluted. Therefore, each wells contained: bacterial suspension of 1–5x104 CFU/mL in Mueller-Hinton Broth (MHB), the antibiotic to be tested at known concentration and resazurin, an indicator of cell growth. After incubation overnight at 37°C, the wells were screened for color changes caused by the resazurin: a change from purple to pink/colorless indicated cell growth. The lowest concentration of antibiotic that prevented growth represented the minimal inhibitory concentration (MIC). This study suggests that B.anthracis remains susceptible in vitro to many antibiotics, in addition to doxycycline (MICs ≤ 0,03 µg/ml), ciprofloxacin (MICs ≤ 0,03 µg/ml) and penicillin G (MICs ≤ 0,06 µg/ml), recommend by CDC for the treatment of human cases and for prophylactic use after exposure to the spores. In fact, the good activity of gentamicin (MICs ≤ 0,25 µg/ml), streptomycin (MICs ≤ 1 µg/ml), clindamycin (MICs ≤ 0,125 µg/ml), chloramphenicol(MICs ≤ 4 µg/ml), vancomycin (MICs ≤ 2 µg/ml), linezolid (MICs ≤ 2 µg/ml), tetracycline (MICs ≤ 0,125 µg/ml), erythromycin (MICs ≤ 0,25 µg/ml), rifampin (MICs ≤ 0,25 µg/ml), amoxicillin (MICs ≤ 0,06 µg/ml), towards all tested B.anthracis strains demonstrates an appropriate alternative choice for prophylaxis and/or treatment. All tested B.anthracis strains showed intermediate susceptibility to the cephalosporins (MICs ≥ 16 µg/ml) and resistance to trimethoprim (MICs ≥ 128 µg/ml).

Keywords: Bacillus anthracis, antibiotic susceptibility, treatment, minimum inhibitory concentration

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Authors: Kriti Jain, Sucha Singh Gill

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Subsidies have been a major part of agricultural policies around the world, and more extensively since the green revolution in developing countries, for the sake of attaining higher agricultural productivity and achieving food security. But entrenched subsidies lead to distorted incentives and promote inefficiencies in the agricultural sector, threatening the viability of these very subsidies and sustainability of the agricultural production systems, posing a threat to the livelihood of farmers and laborers dependent on it. This paper analyzes the economic and ecological sustainability implications of prolonged input and output subsidies in agriculture by studying the case of Indian Punjab, an agriculturally developed state responsible for ensuring food security in the country when it was facing a major food crisis. The paper focuses specifically on the environmentally unsustainable cropping pattern changes as a result of Minimum Support Price (MSP) and assured procurement and on the resource use efficiency and cost implications of power subsidy for irrigation in Punjab. The study is based on an analysis of both secondary and primary data sources. Using secondary data, a time series analysis was done to capture the changes in Punjab’s cropping pattern, water table depth, fertilizer consumption, and electrification of agriculture. This has been done to examine the role of price and output support adopted to encourage the adoption of green revolution technology in changing the cropping structure of the state, resulting in increased input use intensities (especially groundwater and fertilizers), which harms the ecological balance and decreases factor productivity. Evaluation of electrification of Punjab agriculture helped evaluate the trend in electricity productivity of agriculture and how free power imposed further pressure on the extant agricultural ecosystem. Using data collected from a primary survey of 320 farmers in Punjab, the extent of wasteful application of groundwater irrigation, water productivity of output, electricity usage, and cost of irrigation driven electricity subsidy to the exchequer were estimated for the dominant cropping pattern amongst farmers. The main findings of the study revealed how because of a subsidy has driven agricultural framework, Punjab has lost area under agro climatically suitable and staple crops and moved towards a paddy-wheat cropping system, that is gnawing away the state’s natural resources like water table has been declining at a significant rate of 25 cms per year since 1975-76, and excessive and imbalanced fertilizer usage has led to declining soil fertility in the state. With electricity-driven tubewells as the major source of irrigation within a regime of free electricity and water-intensive crop cultivation, there is both wasteful application of irrigation water and electricity in the cultivation of paddy crops, burning an unproductive hole in the exchequer’s pocket. There is limited access to both agricultural extension services and water-conserving technology, along with policy imbalance, keeping farmers in an intensive and unsustainable production system. Punjab agriculture is witnessing diminishing returns to factor, which under the business-as-usual scenario, will soon enter the phase of negative returns to factor.

Keywords: cropping pattern, electrification, subsidy, sustainability

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Authors: Changdong Oh

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With the arrival of post-industrial society, social scientists have been giving attention to issues of which factors shape cleavage of political parties. Especially, there is a heated controversy over whether and how social and cultural values influence the identities of parties and voting behavior. Drawing from Moral Foundations Theory (MFT), which approached similar issues by considering the effect of five moral foundations on political decision-making of people, this study investigates the role of moral rhetoric in the evolution of Korean political parties. Researcher collected official announcements released by the major two parties (Democratic Party of Korea, Saenuri Party) from 2007 to 2016, and analyzed the data by using Word2Vec algorithm and Moral Foundations Dictionary. Five moral decision modules of MFT, composed of care, fairness (individualistic morality), loyalty, authority and sanctity (group-based, Durkheimian morality), can be represented in vector spaces consisted of party announcements data. By comparing the party vector and the five morality vectors, researcher can see how the political parties have actively used each of the five moral foundations to express themselves and the opposition. Results report that the conservative party tends to actively draw on collective morality such as loyalty, authority, purity to differentiate itself. Notably, such moral differentiation strategy is prevalent when they criticize an opposition party. In contrast, the liberal party tends to concern with individualistic morality such as fairness. This result indicates that moral cleavage does exist between parties in South Korea. Furthermore, individualistic moral gaps of the two political parties are eased over time, which seems to be due to the discussion of economic democratization of conservative party that emerged after 2012, but the community-related moral gaps widened. These results imply that past political cleavages related to economic interests are diminishing and replaced by cultural and social values associated with communitarian morality. However, since the conservative party’s differentiation strategy is largely related to negative campaigns, it is doubtful whether such moral differentiation among political parties can contribute to the long-term party identification of the voters, thus further research is needed to determine it is sustainable. Despite the limitations, this study makes it possible to track and identify the moral changes of party system through automated text analysis. More generally, this study could contribute to the analysis of various texts associated with the moral foundation and finding a distributed representation of moral, ethical values.

Keywords: moral foundations theory, moral politics, party system, Word2Vec

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Authors: Joanna Grzybowska, Magdalena Igras, Mariusz Ziółko

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Association rule learning is an approach for discovering interesting relationships in large databases. The analysis of relations, invisible at first glance, is a source of new knowledge which can be subsequently used for prediction. We used this data mining technique (which is an automatic and objective method) to learn about interesting affects associations in a corpus of emergency phone calls. We also made an attempt to match revealed rules with their possible situational context. The corpus was collected and subjectively annotated by two researchers. Each of 3306 recordings contains information on emotion: (1) type (sadness, weariness, anxiety, surprise, stress, anger, frustration, calm, relief, compassion, contentment, amusement, joy) (2) valence (negative, neutral, or positive) (3) intensity (low, typical, alternating, high). Also, additional information, that is a clue to speaker’s emotional state, was annotated: speech rate (slow, normal, fast), characteristic vocabulary (filled pauses, repeated words) and conversation style (normal, chaotic). Exponentially many rules can be extracted from a set of items (an item is a previously annotated single information). To generate the rules in the form of an implication X → Y (where X and Y are frequent k-itemsets) the Apriori algorithm was used - it avoids performing needless computations. Then, two basic measures (Support and Confidence) and several additional symmetric and asymmetric objective measures (e.g. Laplace, Conviction, Interest Factor, Cosine, correlation coefficient) were calculated for each rule. Each applied interestingness measure revealed different rules - we selected some top rules for each measure. Owing to the specificity of the corpus (emergency situations), most of the strong rules contain only negative emotions. There are though strong rules including neutral or even positive emotions. Three examples of the strongest rules are: {sadness} → {anxiety}; {sadness, weariness, stress, frustration} → {anger}; {compassion} → {sadness}. Association rule learning revealed the strongest configurations of affects (as well as configurations of affects with affect-related information) in our emergency phone calls corpus. The acquired knowledge can be used for prediction to fulfill the emotional profile of a new caller. Furthermore, a rule-related possible context analysis may be a clue to the situation a caller is in.

Keywords: data mining, emergency phone calls, emotional profiles, rules

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Authors: Taylan Kabbani, Ekrem Duman

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The design of adaptive systems that take advantage of financial markets while reducing the risk can bring more stagnant wealth into the global market. However, most efforts made to generate successful deals in trading financial assets rely on Supervised Learning (SL), which suffered from various limitations. Deep Reinforcement Learning (DRL) offers to solve these drawbacks of SL approaches by combining the financial assets price "prediction" step and the "allocation" step of the portfolio in one unified process to produce fully autonomous systems capable of interacting with its environment to make optimal decisions through trial and error. In this paper, a continuous action space approach is adopted to give the trading agent the ability to gradually adjust the portfolio's positions with each time step (dynamically re-allocate investments), resulting in better agent-environment interaction and faster convergence of the learning process. In addition, the approach supports the managing of a portfolio with several assets instead of a single one. This work represents a novel DRL model to generate profitable trades in the stock market, effectively overcoming the limitations of supervised learning approaches. We formulate the trading problem, or what is referred to as The Agent Environment as Partially observed Markov Decision Process (POMDP) model, considering the constraints imposed by the stock market, such as liquidity and transaction costs. More specifically, we design an environment that simulates the real-world trading process by augmenting the state representation with ten different technical indicators and sentiment analysis of news articles for each stock. We then solve the formulated POMDP problem using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, which can learn policies in high-dimensional and continuous action spaces like those typically found in the stock market environment. From the point of view of stock market forecasting and the intelligent decision-making mechanism, this paper demonstrates the superiority of deep reinforcement learning in financial markets over other types of machine learning such as supervised learning and proves its credibility and advantages of strategic decision-making.

Keywords: the stock market, deep reinforcement learning, MDP, twin delayed deep deterministic policy gradient, sentiment analysis, technical indicators, autonomous agent

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Authors: Rajan Goyal, S. Lamba, S. Annapoorni

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The indirect exchange coupled system consists of two ferromagnetic layers separated by non-magnetic spacer layer. The type of exchange coupling may be either ferro or anti-ferro depending on the thickness of the spacer layer. In the present work, the strength of exchange coupling in FePt/Al₂O₃/Fe₃Pt has been investigated by varying the thickness of the spacer layer Al₂O₃. The FePt/Al₂O₃/Fe₃Pt trilayer structure is fabricated on Si <100> single crystal substrate using sputtering technique. The thickness of FePt and Fe₃Pt is fixed at 60 nm and 2 nm respectively. The thickness of spacer layer Al₂O₃ was varied from 0 to 16 nm. The normalized hysteresis loops recorded at room temperature both in the in-plane and out of plane configuration reveals that the orientation of easy axis lies along the plane of the film. It is observed that the hysteresis loop for ts=0 nm does not exhibit any knee around H=0 indicating that the hard FePt layer and soft Fe₃Pt layer are strongly exchange coupled. However, the insertion of Al₂O₃ spacer layer of thickness ts = 0.7 nm results in appearance of a minor knee around H=0 suggesting the weakening of exchange coupling between FePt and Fe₃Pt. The disappearance of knee in hysteresis loop with further increase in thickness of the spacer layer up to 8 nm predicts the co-existence of ferromagnetic (FM) and antiferromagnetic (AFM) exchange interaction between FePt and Fe₃Pt. In addition to this, the out of plane hysteresis loop also shows an asymmetry around H=0. The exchange field Hex = (Hc↑-HC↓)/2, where Hc↑ and Hc↓ are the coercivity estimated from lower and upper branch of hysteresis loop, increases from ~ 150 Oe to ~ 700 Oe respectively. This behavior may be attributed to the uncompensated moments in the hard FePt layer and soft Fe₃Pt layer at the interface. A better insight into the variation in indirect exchange coupling has been investigated using recoil curves. It is observed that the almost closed recoil curves are obtained for ts= 0 nm up to a reverse field of ~ 5 kOe. On the other hand, the appearance of appreciable open recoil curves at lower reverse field ~ 4 kOe for ts = 0.7 nm indicates that uncoupled soft phase undergoes irreversible magnetization reversal at lower reverse field suggesting the weakening of exchange coupling. The openness of recoil curves decreases with increase in thickness of the spacer layer up to 8 nm. This behavior may be attributed to the competition between FM and AFM exchange interactions. The FM exchange coupling between FePt and Fe₃Pt due to porous nature of Al₂O₃ decreases much slower than the weak AFM coupling due to interaction between Fe ions of FePt and Fe₃Pt via O ions of Al₂O₃. The hysteresis loop has been simulated using Monte Carlo based on Metropolis algorithm to investigate the variation in strength of exchange coupling in FePt/Al₂O₃/Fe₃Pt trilayer system.

Keywords: indirect exchange coupling, MH loop, Monte Carlo simulation, recoil curve

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Authors: Nazia Roushan, Maria Kipti

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The sensitive issue of a large-scale entry of Rohingya refugees to Bangladesh has arisen again since August of 2017. Incited by ethnic and religious conflict, the Rohingyas—an ethnic group concentrated in the north-west state of Rakhine in Myanmar—have been fleeing to what is now Bangladesh from as early as the late 1700s in four main exoduses. This long-standing persecution has recently escalated, and accommodating the recent wave of exodus has been especially challenging due to the sheer volume of a million refugees concentrated in refugee camps in two small administrative units (upazilas) in the south-east of the country: the host area. This drastic change in the host area’s social fabric is putting a lot of strain on the country’s economic, demographic and environmental stability, and security. Although Bangladesh’s long-term experience with disaster management has enabled it to respond rapidly to the crisis, the government is failing to cope with this enormous problem and has taken insufficient steps towards improving the living conditions to inhibit the inflow of more refugees. On top of that, the absence of a comprehensive national refugee policy, and the density of the structures of the camps are constricting the upgrading of the shelters to international standards. As of December 2016, the combined number of internally displaced persons (IDPs) due to conflict and violence (stock), and new displacements due to disasters (flow) in Bangladesh had exceeded 1 million. These numbers have increased dramatically in the last few months. Moreover, by 2050, Bangladesh will have as much as 25 million climate refugees just from its coastal districts. To enhance the resilience of the vulnerable, it is crucial to methodically factorize further interventions between Disaster Risk Reduction for Resilience (DRR) and the concept of Building Back Better (BBB) in the rehabilitation-reconstruction period. Considering these points, this paper provides a palette of options for design guidelines related to the living spaces and infrastructures for refugees. This will encourage the development of national standards for refugee camps, and the national and local level rehabilitation-reconstruction practices. Unhygienic living conditions, vulnerability, and the general lack of control over life are pervasive throughout the camps. This paper, therefore, proposes site-specific strategic and physical planning and design for shelters for refugees in Bangladesh that will lead to sustainable living environments through the following: a) site survey of existing two registered and one makeshift unregistered refugee camps to document and study their physical conditions, b) questionnaires and semi-structured focus group discussions carried out among the refugees and stakeholders to understand what the lived experiences and needs are; and c) combining the findings with international minimum standards for shelter and settlement from International Federation of Red Cross and Red Crescent (IFRC), Médecins Sans Frontières (MSF), United Nations High Commissioner for Refugees (UNHCR). These proposals include temporary shelter solutions that balance between lived spaces and regimented, repetitive plans using readily available and cheap materials, erosion control and slope stabilization strategies, and most importantly, coping mechanisms for the refugees to be self-reliant and resilient.

Keywords: architecture, Bangladesh, refugee camp, resilience, Rohingya

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Authors: Maria Sciammarella, Uttam M. Shrestha, Youngho Seo, Grant T. Gullberg, Elias H. Botvinick

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Background: Myocardial perfusion imaging (MPI) is typically performed with static imaging protocols and visually assessed for perfusion defects based on the relative intensity distribution. Dynamic cardiac SPECT, on the other hand, is a new imaging technique that is based on time varying information of radiotracer distribution, which permits quantification of myocardial blood flow (MBF). In this abstract, we report a progress and current status of dynamic cardiac SPECT using conventional gamma camera (Infinia Hawkeye 4, GE Healthcare) for estimation of myocardial blood flow and coronary flow reserve. Methods: A group of patients who had high risk of coronary artery disease was enrolled to evaluate our methodology. A low-dose/high-dose rest/pharmacologic-induced-stress protocol was implemented. A standard rest and a standard stress radionuclide dose of ⁹⁹ᵐTc-tetrofosmin (140 keV) was administered. The dynamic SPECT data for each patient were reconstructed using the standard 4-dimensional maximum likelihood expectation maximization (ML-EM) algorithm. Acquired data were used to estimate the myocardial blood flow (MBF). The correspondence between flow values in the main coronary vasculature with myocardial segments defined by the standardized myocardial segmentation and nomenclature were derived. The coronary flow reserve, CFR, was defined as the ratio of stress to rest MBF values. CFR values estimated with SPECT were also validated with dynamic PET. Results: The range of territorial MBF in LAD, RCA, and LCX was 0.44 ml/min/g to 3.81 ml/min/g. The MBF between estimated with PET and SPECT in the group of independent cohort of 7 patients showed statistically significant correlation, r = 0.71 (p < 0.001). But the corresponding CFR correlation was moderate r = 0.39 yet statistically significant (p = 0.037). The mean stress MBF value was significantly lower for angiographically abnormal than that for the normal (Normal Mean MBF = 2.49 ± 0.61, Abnormal Mean MBF = 1.43 ± 0. 0.62, P < .001). Conclusions: The visually assessed image findings in clinical SPECT are subjective, and may not reflect direct physiologic measures of coronary lesion. The MBF and CFR measured with dynamic SPECT are fully objective and available only with the data generated from the dynamic SPECT method. A quantitative approach such as measuring CFR using dynamic SPECT imaging is a better mode of diagnosing CAD than visual assessment of stress and rest images from static SPECT images Coronary Flow Reserve.

Keywords: dynamic SPECT, clinical SPECT/CT, selective coronary angiograph, ⁹⁹ᵐTc-Tetrofosmin

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Authors: Golnush Masghati-Amoli, Paul Chin

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Over the past recent years, with the rapid increases in data availability and computing power, Machine Learning (ML) techniques have been called on in a range of different industries for their strong predictive capability. However, the use of Machine Learning in commercial banking has been limited due to a special challenge imposed by numerous regulations that require lenders to be able to explain their analytic models, not only to regulators but often to consumers. In other words, although Machine Leaning techniques enable better prediction with a higher level of accuracy, in comparison with other industries, they are adopted less frequently in commercial banking especially for scoring purposes. This is due to the fact that Machine Learning techniques are often considered as a black box and fail to provide information on why a certain risk score is given to a customer. In order to bridge this gap between the explain-ability and performance of Machine Learning techniques, a Hybrid Model is developed at Dun and Bradstreet that is focused on blending Machine Learning algorithms with traditional approaches such as scorecards. The Hybrid Model maximizes efficiency of traditional scorecards by merging its practical benefits, such as explain-ability and the ability to input domain knowledge, with the deep insights of Machine Learning techniques which can uncover patterns scorecard approaches cannot. First, through development of Machine Learning models, engineered features and latent variables and feature interactions that demonstrate high information value in the prediction of customer risk are identified. Then, these features are employed to introduce observed non-linear relationships between the explanatory and dependent variables into traditional scorecards. Moreover, instead of directly computing the Weight of Evidence (WoE) from good and bad data points, the Hybrid Model tries to match the score distribution generated by a Machine Learning algorithm, which ends up providing an estimate of the WoE for each bin. This capability helps to build powerful scorecards with sparse cases that cannot be achieved with traditional approaches. The proposed Hybrid Model is tested on different portfolios where a significant gap is observed between the performance of traditional scorecards and Machine Learning models. The result of analysis shows that Hybrid Model can improve the performance of traditional scorecards by introducing non-linear relationships between explanatory and target variables from Machine Learning models into traditional scorecards. Also, it is observed that in some scenarios the Hybrid Model can be almost as predictive as the Machine Learning techniques while being as transparent as traditional scorecards. Therefore, it is concluded that, with the use of Hybrid Model, Machine Learning algorithms can be used in the commercial banking industry without being concerned with difficulties in explaining the models for regulatory purposes.

Keywords: machine learning algorithms, scorecard, commercial banking, consumer risk, feature engineering

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Authors: Anders Schou Simonsen, Kim Sorensen, Thomas Condra

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Wet scrubbers are used for flue gas desulfurization by injecting water directly into the flue gas stream from a set of sprayers. The water droplets will flow freely inside the scrubber, and flow down along the scrubber walls as a thin wall film while reacting with the gas phase to remove SO₂. This complex multiphase phenomenon can be divided into three main contributions: the continuous gas phase, the liquid droplet phase, and the liquid wall film phase. This study proposes a complete model, where all three main contributions are taken into account and resolved using OpenFOAM for the continuous gas phase, and MATLAB for the liquid droplet and wall film phases. The 3D continuous gas phase is composed of five species: CO₂, H₂O, O₂, SO₂, and N₂, which are resolved along with momentum, energy, and turbulence. Source terms are present for four species, energy and momentum, which are affecting the steady-state solution. The liquid droplet phase experiences breakup, collisions, dynamics, internal chemistry, evaporation and condensation, species mass transfer, energy transfer and wall film interactions. Numerous sub-models have been implemented and coupled to realise the above-mentioned phenomena. The liquid wall film experiences impingement, acceleration, atomization, separation, internal chemistry, evaporation and condensation, species mass transfer, and energy transfer, which have all been resolved using numerous sub-models as well. The continuous gas phase has been coupled with the liquid phases using source terms by an approach, where the two software packages are couples using a link-structure. The complete CFD model has been verified using 16 experimental tests from an existing scrubber installation, where a gradient-based pattern search optimization algorithm has been used to tune numerous model parameters to match the experimental results. The CFD model needed to be fast for evaluation in order to apply this optimization routine, where approximately 1000 simulations were needed. The results show that the complex multiphase phenomena governing wet scrubbers can be resolved in a single model. The optimization routine was able to tune the model to accurately predict the performance of an existing installation. Furthermore, the study shows that a coupling between OpenFOAM and MATLAB is realizable, where the data and source term exchange increases the computational requirements by approximately 5%. This allows for exploiting the benefits of both software programs.

Keywords: desulfurization, discrete phase, scrubber, wall film

Procedia PDF Downloads 231

Authors: Priyanka Chaudhary, M. Rizwan

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This paper presents a grid synchronization technique based on adaptive notch filter for SPV (Solar Photovoltaic) system along with MPPT (Maximum Power Point Tracking) techniques. An efficient grid synchronization technique offers proficient detection of various components of grid signal like phase and frequency. It also acts as a barrier for harmonics and other disturbances in grid signal. A reference phase signal synchronized with the grid voltage is provided by the grid synchronization technique to standardize the system with grid codes and power quality standards. Hence, grid synchronization unit plays important role for grid connected SPV systems. As the output of the PV array is fluctuating in nature with the meteorological parameters like irradiance, temperature, wind etc. In order to maintain a constant DC voltage at VSC (Voltage Source Converter) input, MPPT control is required to track the maximum power point from PV array. In this work, a variable step size P & O (Perturb and Observe) MPPT technique with DC/DC boost converter has been used at first stage of the system. This algorithm divides the dPpv/dVpv curve of PV panel into three separate zones i.e. zone 0, zone 1 and zone 2. A fine value of tracking step size is used in zone 0 while zone 1 and zone 2 requires a large value of step size in order to obtain a high tracking speed. Further, adaptive notch filter based control technique is proposed for VSC in PV generation system. Adaptive notch filter (ANF) approach is used to synchronize the interfaced PV system with grid to maintain the amplitude, phase and frequency parameters as well as power quality improvement. This technique offers the compensation of harmonics current and reactive power with both linear and nonlinear loads. To maintain constant DC link voltage a PI controller is also implemented and presented in this paper. The complete system has been designed, developed and simulated using SimPower System and Simulink toolbox of MATLAB. The performance analysis of three phase grid connected solar photovoltaic system has been carried out on the basis of various parameters like PV output power, PV voltage, PV current, DC link voltage, PCC (Point of Common Coupling) voltage, grid voltage, grid current, voltage source converter current, power supplied by the voltage source converter etc. The results obtained from the proposed system are found satisfactory.

Keywords: solar photovoltaic systems, MPPT, voltage source converter, grid synchronization technique

Procedia PDF Downloads 569

Authors: Mohamed El Morsy, Gabriela Achtenová

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Condition monitoring is used to increase machinery availability and machinery performance, whilst reducing consequential damage, increasing machine life, reducing spare parts inventories, and reducing breakdown maintenance. An efficient condition monitoring system provides early warning of faults by predicting them at an early stage. When a localized fault occurs in gears, the vibration signals always exhibit non-stationary behavior. The periodic impulsive feature of the vibration signal appears in the time domain and the corresponding gear mesh frequency (GMF) emerges in the frequency domain. However, one limitation of frequency-domain analysis is its inability to handle non-stationary waveform signals, which are very common when machinery faults occur. Particularly at the early stage of gear failure, the GMF contains very little energy and is often overwhelmed by noise and higher-level macro-structural vibrations. An effective signal processing method would be necessary to remove such corrupting noise and interference. In this paper, a new hybrid method based on optimal Morlet wavelet filter and autocorrelation enhancement is presented. First, to eliminate the frequency associated with interferential vibrations, the vibration signal is filtered with a band-pass filter determined by a Morlet wavelet whose parameters are selected or optimized based on maximum Kurtosis. Then, to further reduce the residual in-band noise and highlight the periodic impulsive feature, an autocorrelation enhancement algorithm is applied to the filtered signal. The test stand is equipped with three dynamometers; the input dynamometer serves as the internal combustion engine, the output dynamometers induce a load on the output joint shaft flanges. The pitting defect is manufactured on the tooth side of a gear of the fifth speed on the secondary shaft. The gearbox used for experimental measurements is of the type most commonly used in modern small to mid-sized passenger cars with transversely mounted powertrain and front wheel drive: a five-speed gearbox with final drive gear and front wheel differential. The results obtained from practical experiments prove that the proposed method is very effective for gear fault diagnosis.

Keywords: wavelet analysis, pitted gear, autocorrelation, gear fault diagnosis

Procedia PDF Downloads 364

Authors: Berker Bayazit, Gulgun Kayakutlu

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The World Energy Outlook shows that energy markets will substantially change within a few forthcoming decades. First, determined action plans according to COP21 and aim of CO₂ emission reduction have already impact on policies of countries. Secondly, swiftly changed technological developments in the field of renewable energy will be influential upon medium and long-term energy generation and consumption behaviors of countries. Furthermore, share of electricity on global energy consumption is to be expected as high as 40 percent in 2040. Electrical vehicles, heat pumps, new electronical devices and digital improvements will be outstanding technologies and innovations will be the testimony of the market modifications. In order to meet highly increasing electricity demand caused by technologies, countries have to make new investments in the field of electricity production, transmission and distribution. Specifically, electricity generation mix becomes vital for both prevention of CO₂ emission and reduction of power prices. Majority of the research and development investments are made in the field of electricity generation. Hence, the prime source diversity and source planning of electricity generation are crucial for improving the wealth of citizen life. Approaches considering the CO₂ emission and total cost of generation, are necessary but not sufficient to evaluate and construct the product mix. On the other hand, employment and positive contribution to macroeconomic values are important factors that have to be taken into consideration. This study aims to constitute new investments in renewable energies (solar, wind, geothermal, biogas and hydropower) between 2018-2023 under 4 different goals. Therefore, a multi-objective programming model is proposed to optimize the goals of minimizing the CO₂ emission, investment amount and electricity sales price while maximizing the total employment and positive contribution to current deficit. In order to avoid the user preference among the goals, Dinkelbach’s algorithm and Guzel’s approach have been combined. The achievements are discussed with comparison to the current policies. Our study shows that new policies like huge capacity allotment might be discussible although obligation for local production is positive. The improvements in grid infrastructure and re-design support for the biogas and geothermal can be recommended.

Keywords: energy generation policies, multi-objective linear programming, portfolio planning, renewable energy

Procedia PDF Downloads 218

Authors: Mernoosh Abouzari, Shahrokh Sahraei

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Electronic government is a way for governments to use new technology that provides people with the necessary facilities for proper access to government information and services, improving the quality of services and providing broad opportunities to participate in democratic processes and institutions. That leads to providing the possibility of easy use of information technology in order to distribute government services to the customer without holidays, which increases people's satisfaction and participation in political and economic activities. The expansion of e-government services and its movement towards intelligentization has the ability to re-establish the relationship between the government and citizens and the elements and components of the government. Electronic government is the result of the use of information and communication technology (ICT), which by implementing it at the government level, in terms of the efficiency and effectiveness of government systems and the way of providing services, tremendous commercial changes are created, which brings people's satisfaction at the wide level will follow. The main level of electronic government services has become objectified today with the presence of artificial intelligence systems, which recent advances in artificial intelligence represent a revolution in the use of machines to support predictive decision-making and Classification of data. With the use of deep learning tools, artificial intelligence can mean a significant improvement in the delivery of services to citizens and uplift the work of public service professionals while also inspiring a new generation of technocrats to enter government. This smart revolution may put aside some functions of the government, change its components, and concepts such as governance, policymaking or democracy will change in front of artificial intelligence technology, and the top-down position in governance may face serious changes, and If governments delay in using artificial intelligence, the balance of power will change and private companies will monopolize everything with their pioneering in this field, and the world order will also depend on rich multinational companies and in fact, Algorithmic systems will become the ruling systems of the world. It can be said that currently, the revolution in information technology and biotechnology has been started by engineers, large economic companies, and scientists who are rarely aware of the political complexities of their decisions and certainly do not represent anyone. Therefore, it seems that if liberalism, nationalism, or any other religion wants to organize the world of 2050, it should not only rationalize the concept of artificial intelligence and complex data algorithm but also mix them in a new and meaningful narrative. Therefore, the changes caused by artificial intelligence in the political and economic order will lead to a major change in the way all countries deal with the phenomenon of digital globalization. In this paper, while debating the role and performance of e-government, we will discuss the efficiency and application of artificial intelligence in e-government, and we will consider the developments resulting from it in the new world and the concepts of governance.

Keywords: electronic government, artificial intelligence, information and communication technology., system

Procedia PDF Downloads 55

Authors: Hyeon-Seung Kim, Sang-Mi Park, Sun-Ju Han, Leen-Seok Kang

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As BIM (Building Information Modeling) application continually expands, the visual simulation techniques used for facility design and construction process information are becoming increasingly advanced and diverse. For building structures, BIM application is design - oriented to utilize 3D objects for conflict management, whereas for civil engineering structures, the usability of nD object - oriented construction stage simulation is important in construction management. Simulations of 5D and 6D objects, for which cost and resources are linked along with process simulation in 4D objects, are commonly used, but they do not provide a decision - making function for process management problems that occur on site because they mostly focus on the visual representation of current status for process information. In this study, an nD CAD system is constructed that facilitates an optimized schedule simulation that minimizes process conflict, a construction duration reduction simulation according to execution progress status, optimized process plan simulation according to project cost change by year, and optimized resource simulation for field resource mobilization capability. Through this system, the usability of conventional simple simulation objects is expanded to the usability of active simulation objects with which decision - making is possible. Furthermore, to close the gap between field process situations and planned 4D process objects, a technique is developed to facilitate a comparative simulation through the coordinated synchronization of an actual video object acquired by an on - site web camera and VR concept 4D object. This synchronization and simulation technique can also be applied to smartphone video objects captured in the field in order to increase the usability of the 4D object. Because yearly project costs change frequently for civil engineering construction, an annual process plan should be recomposed appropriately according to project cost decreases/increases compared with the plan. In the 5D CAD system provided in this study, an active 5D object utilization concept is introduced to perform a simulation in an optimized process planning state by finding a process optimized for the changed project cost without changing the construction duration through a technique such as genetic algorithm. Furthermore, in resource management, an active 6D object utilization function is introduced that can analyze and simulate an optimized process plan within a possible scope of moving resources by considering those resources that can be moved under a given field condition, instead of using a simple resource change simulation by schedule. The introduction of an active BIM function is expected to increase the field utilization of conventional nD objects.

Keywords: 4D, 5D, 6D, active BIM

Procedia PDF Downloads 253

Authors: Gilberto Goracci, Fabio Curti

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This work presents a robust, light, and inexpensive algorithm to perform autonomous orbit determination using onboard magnetometer data in real-time. Magnetometers are low-cost and reliable sensors typically available on a spacecraft for attitude determination purposes, thus representing an interesting choice to perform real-time orbit determination without the need to add additional sensors to the spacecraft itself. Magnetic field measurements can be exploited by Extended/Unscented Kalman Filters (EKF/UKF) for orbit determination purposes to make up for GPS outages, yielding errors of a few kilometers and tens of meters per second in the position and velocity of a spacecraft, respectively. While this level of accuracy shows that Kalman filtering represents a solid baseline for autonomous orbit determination, it is not enough to provide a reliable state estimation in the absence of GPS signals. This work combines the solidity and reliability of the EKF with the versatility of a Recurrent Neural Network (RNN) architecture to further increase the precision of the state estimation. Deep learning models, in fact, can grasp nonlinear relations between the inputs, in this case, the magnetometer data and the EKF state estimations, and the targets, namely the true position, and velocity of the spacecraft. The model has been pre-trained on Sun-Synchronous orbits (SSO) up to 2126 kilometers of altitude with different initial conditions and levels of noise to cover a wide range of possible real-case scenarios. The orbits have been propagated considering J2-level dynamics, and the geomagnetic field has been modeled using the International Geomagnetic Reference Field (IGRF) coefficients up to the 13th order. The training of the module can be completed offline using the expected orbit of the spacecraft to heavily reduce the onboard computational burden. Once the spacecraft is launched, the model can use the GPS signal, if available, to fine-tune the parameters on the actual orbit onboard in real-time and work autonomously during GPS outages. In this way, the provided module shows versatility, as it can be applied to any mission operating in SSO, but at the same time, the training is completed and eventually fine-tuned, on the specific orbit, increasing performances and reliability. The results provided by this study show an increase of one order of magnitude in the precision of state estimate with respect to the use of the EKF alone. Tests on simulated and real data will be shown.

Keywords: artificial intelligence, extended Kalman filter, orbit determination, magnetic field

Procedia PDF Downloads 69

Authors: Luis Guilherme Resende Santos, Ido Nevat, Leslie Norford

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The Urban Heat Island (UHI) is characterized by increased air temperatures in urban areas compared to undeveloped rural surrounding environments. With urbanization and densification, the intensity of UHI increases, bringing negative impacts on livability, health and economy. In order to reduce those effects, it is required to take into consideration design factors when planning future developments. Given design constraints such as population size and availability of area for development, non-trivial decisions regarding the buildings’ dimensions and their spatial distribution are required. We develop a framework for optimization of urban design in order to jointly minimize UHI intensity and buildings’ energy consumption. First, the design constraints are defined according to spatial and population limits in order to establish realistic boundaries that would be applicable in real life decisions. Second, the tools Urban Weather Generator (UWG) and EnergyPlus are used to generate outputs of UHI intensity and total buildings’ energy consumption, respectively. Those outputs are changed based on a set of variable inputs related to urban morphology aspects, such as building height, urban canyon width and population density. Lastly, an optimization problem is cast where the utility function quantifies the performance of each design candidate (e.g. minimizing a linear combination of UHI and energy consumption), and a set of constraints to be met is set. Solving this optimization problem is difficult, since there is no simple analytic form which represents the UWG and EnergyPlus models. We therefore cannot use any direct optimization techniques, but instead, develop an indirect “black box” optimization algorithm. To this end we develop a solution that is based on stochastic optimization method, known as the Cross Entropy method (CEM). The CEM translates the deterministic optimization problem into an associated stochastic optimization problem which is simple to solve analytically. We illustrate our model on a typical residential area in Singapore. Due to fast growth in population and built area and land availability generated by land reclamation, urban planning decisions are of the most importance for the country. Furthermore, the hot and humid climate in the country raises the concern for the impact of UHI. The problem presented is highly relevant to early urban design stages and the objective of such framework is to guide decision makers and assist them to include and evaluate urban microclimate and energy aspects in the process of urban planning.

Keywords: building energy consumption, stochastic optimization, urban design, urban heat island, urban weather generator

Procedia PDF Downloads 109

Authors: Anamika Paul, Sudipto Sarkar

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The flow behaviour of non-Newtonian fluid is quite complicated, although both the pseudoplastic (n < 1, n being the power index) and dilatant (n > 1) fluids under this category are used immensely in chemical and process industries. A limited research work is carried out for flow over a bluff body in non-Newtonian flow environment. In the present numerical simulation we control the vortices of a square cylinder by placing an upstream vertical splitter plate for pseudoplastic (n=0.8), Newtonian (n=1) and dilatant (n=1.2) fluids. The position of the upstream plate is also varied to calculate the critical distance between the plate and cylinder, below which the cylinder vortex shedding suppresses. Here the Reynolds number is considered as Re = 150 (Re = U∞a/ν, where U∞ is the free-stream velocity of the flow, a is the side of the cylinder and ν is the maximum value of kinematic viscosity of the fluid), which comes under laminar periodic vortex shedding regime. The vertical plate is having a dimension of 0.5a × 0.05a and it is placed at the cylinder centre-line. Gambit 2.2.30 is used to construct the flow domain and to impose the boundary conditions. In detail, we imposed velocity inlet (u = U∞), pressure outlet (Neumann condition), symmetry (free-slip boundary condition) at upper and lower domain. Wall boundary condition (u = v = 0) is considered both on the cylinder and the splitter plate surfaces. The unsteady 2-D Navier Stokes equations in fully conservative form are then discretized in second-order spatial and first-order temporal form. These discretized equations are then solved by Ansys Fluent 14.5 implementing SIMPLE algorithm written in finite volume method. Here, fine meshing is used surrounding the plate and cylinder. Away from the cylinder, the grids are slowly stretched out in all directions. To get an account of mesh quality, a total of 297 × 208 grid points are used for G/a = 3 (G being the gap between the plate and cylinder) in the streamwise and flow-normal directions respectively after a grid independent study. The computed mean flow quantities obtained from Newtonian flow are agreed well with the available literatures. The results are depicted with the help of instantaneous and time-averaged flow fields. Qualitative and quantitative noteworthy differences are obtained in the flow field with the changes in rheology of fluid. Also, aerodynamic forces and vortex shedding frequencies differ with the gap-ratio and power index of the fluid. We can conclude from the present simulation that fluent is capable to capture the vortex dynamics of unsteady laminar flow regime even in the non-Newtonian flow environment.

Keywords: CFD, critical gap-ratio, splitter plate, wake-wake interactions, dilatant, pseudoplastic

Procedia PDF Downloads 93

Authors: Ke Zhang, Yongli Zhu

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Due to rapid load growths in today’s highly electrified societies and the requirement for green energy sources, large-scale wind farm power transmission system is constantly developing. This system is a typical multi-terminal power supply system, whose structure of the network topology of transmission lines is complex. What’s more, it locates in the complex terrain of mountains and grasslands, thus increasing the possibility of transmission line faults and finding the fault location with difficulty after the faults and resulting in an extremely serious phenomenon of abandoning the wind. In order to solve these problems, a fault location method for multi-terminal transmission line based on wind farm characteristics and improved single-ended traveling wave positioning method is proposed. Through studying the zero sequence current characteristics by using the characteristics of the grounding transformer(GT) in the existing large-scale wind farms, it is obtained that the criterion for judging the fault interval of the multi-terminal transmission line. When a ground short-circuit fault occurs, there is only zero sequence current on the path between GT and the fault point. Therefore, the interval where the fault point exists is obtained by determining the path of the zero sequence current. After determining the fault interval, The location of the short-circuit fault point is calculated by the traveling wave method. However, this article uses an improved traveling wave method. It makes the positioning accuracy more accurate by combining the single-ended traveling wave method with double-ended electrical data. What’s more, a method of calculating the traveling wave velocity is deduced according to the above improvements (it is the actual wave velocity in theory). The improvement of the traveling wave velocity calculation method further improves the positioning accuracy. Compared with the traditional positioning method, the average positioning error of this method is reduced by 30%.This method overcomes the shortcomings of the traditional method in poor fault location of wind farm transmission lines. In addition, it is more accurate than the traditional fixed wave velocity method in the calculation of the traveling wave velocity. It can calculate the wave velocity in real time according to the scene and solve the traveling wave velocity can’t be updated with the environment and real-time update. The method is verified in PSCAD/EMTDC.

Keywords: grounding transformer, multi-terminal transmission line, short circuit fault location, traveling wave velocity, wind farm

Procedia PDF Downloads 227

Authors: Mikhail O. Eremin

Abstract:

Seismic activity around the world is clearly a threat to people's lives, as well as infrastructure and capital construction. It is the instability of the latter to powerful earthquakes that most often causes human casualties. Therefore, during construction it is necessary to take into account the risks of large-scale natural disasters. The task of assessing the risks of natural disasters is one of the most urgent at the present time. The final goal of any study of earthquakes is forecasting. This is especially important for seismically active regions of the planet where earthquakes occur frequently. Gorni Altai is one of such regions. In work, we developed the physical-mathematical model of stress-strain state evolution of loaded geomedium with the purpose of numerical simulation of seismic process accompanying the formation of Chuya-Kuray fault zone Gorni Altay, Russia. We build a structural model on the base of seismotectonic and paleoseismogeological investigations, as well as SRTM-data. Base of mathematical model is the system of equations of solid mechanics which includes the fundamental conservation laws and constitutive equations for elastic (Hooke's law) and inelastic deformation (modified model of Drucker-Prager-Nikolaevskii). An initial stress state of the model correspond to gravitational. Then we simulate an activation of a buried dextral strike-slip paleo-fault located in the basement of the model. We obtain the stages of formation and the structure of Chuya-Kuray fault zone. It is shown that results of numerical simulation are in good agreement with field observations in statistical sense. Simulated seismic process is strongly bound to the faults - lineaments with high degree of inelastic strain localization. Fault zone represents en-echelon system of dextral strike-slips according to the Riedel model. The system of surface lineaments is represented with R-, R'-shear bands, X- and Y-shears, T-fractures. Simulated seismic process obeys the laws of Gutenberg-Richter and Omori. Thus, the model describes a self-similar character of deformation and fracture of rocks and geomedia. We also modified the algorithm of determination of separate slip events in the model due to the features of strain rates dependence vs time.

Keywords: Drucker-Prager model, fault zone, numerical simulation, Riedel bands, seismic process, strike-slip fault

Procedia PDF Downloads 114

Authors: Kunihisa Kakumoto

Abstract:

“Purpose”: Global environmental issues are now being raised in a global dimension. By predicting sprawl phenomena beyond the limits of nature with algorithms, we can expect to protect our social life within the limits of nature. It turns out that the sustainable state of the planet now consists in maintaining a balance between the capabilities of nature and the possibilities of our social life. The amount of water on earth is finite. Sustainability is therefore highly dependent on water capacity. A certain amount of water is stored in the forest by planting and green space, and the amount of water can be considered in relation to the green space. CO2 is also absorbed by green plants. "Possible measurements and methods": The concept of the solar community has been introduced in technical papers on the occasion of many international conferences. The solar community concept is based on data collected from one solar model house. This algorithmic study simulates the amount of water stored by lush green vegetation. In addition, we calculated and compared the amount of CO2 emissions from the Taiyo Community and the amount of CO2 reduction from greening. Based on the trial calculation results of these solar communities, we are simulating the sustainable state of the earth as an algorithm trial calculation result. We believe that we should also consider the composition of this solar community group using digital technology as control technology. "Conclusion": We consider the solar community as a prototype for finding sustainable conditions for the planet. The role of water is very important as the supply capacity of water is limited. However, the circulation of social life is not constructed according to the mechanism of nature. This simulation trial calculation is explained using the total water supply volume as an example. According to this process, algorithmic calculations consider the total capacity of the water supply and the population and habitable numbers of the area. Green vegetated land is very important to keep enough water. Green vegetation is also very important to maintain CO2 balance. A simulation trial calculation is possible from the relationship between the CO2 emissions of the solar community and the amount of CO2 reduction due to greening. In order to find this total balance and sustainable conditions, the algorithmic simulation calculation takes into account lush vegetation and total water supply. Research to find sustainable conditions is done by simulating an algorithmic model of the solar community as a prototype. In this one prototype example, it's balanced. The activities of our social life must take place within the permissive limits of natural mechanisms. Of course, we aim for a more ideal balance by utilizing auxiliary digital control technology such as AI.

Keywords: solar community, sustainability, prototype, algorithmic simulation

Procedia PDF Downloads 37

Authors: Guoyu Wang, Meilian Zhang, Chunhui LI, Bing Ren

Abstract:

In recent decades, a variety of floating structures have played a crucial role in ocean and marine engineering, such as ships, offshore platforms, floating breakwaters, fish farms, floating airports, etc. It is common for floating structures to suffer from loadings under waves, and the responses of the structures mounted in marine environments have a significant relation to the wave impacts. The interaction between surface waves and floating structures is one of the important issues in ship or marine structure design to increase performance and efficiency. With the progress of computational fluid dynamics, a number of numerical models based on the NS equations in the time domain have been developed to explore the above problem, such as the finite difference method or the finite volume method. Those traditional numerical simulation techniques for moving bodies are grid-based, which may encounter some difficulties when treating a large free surface deformation and a moving boundary. In these models, the moving structures in a Lagrangian formulation need to be appropriately described in grids, and the special treatment of the moving boundary is inevitable. Nevertheless, in the mesh-based models, the movement of the grid near the structure or the communication between the moving Lagrangian structure and Eulerian meshes will increase the algorithm complexity. Fortunately, these challenges can be avoided by the meshless particle methods. In the present study, a moving particle semi-implicit model is explored for the numerical simulation of fluid–structure interaction with surface flows, especially for coupling of fluid and moving rigid body. The equivalent momentum transfer method is proposed and derived for the coupling of fluid and rigid moving body. The structure is discretized into a group of solid particles, which are assumed as fluid particles involved in solving the NS equation altogether with the surrounding fluid particles. The momentum conservation is ensured by the transfer from those fluid particles to the corresponding solid particles. Then, the position of the solid particles is updated to keep the initial shape of the structure. Using the proposed method, the motions of a free-floating body in regular waves are numerically studied. The wave surface evaluation and the dynamic response of the floating body are presented. There is good agreement when the numerical results, such as the sway, heave, and roll of the floating body, are compared with the experimental and other numerical data. It is demonstrated that the presented MPS model is effective for the numerical simulation of fluid-structure interaction.

Keywords: floating body, fluid structure interaction, MPS, particle method, waves

Procedia PDF Downloads 46

Authors: Meron Zenaselase Rata, Euridice Leyequien Abarca

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The production of major crops in Northern Ethiopia, especially the Tigray Region, is at subsistence level due to drought, erratic rainfall, and poor soil fertility. Since cactus pear is a drought-resistant plant, it is considered as a lifesaver fruit and a strategy for poverty reduction in a drought-affected area of the region. Despite its contribution to household income and food security in the area, the cactus pear sub-sector is experiencing many constraints with limited attention given to its post-harvest loss management. Therefore, this research was carried out to identify opportunities for reducing post-harvest losses and recommend possible strategies to reduce post-harvest losses, thereby improving production and smallholder’s income. Both probability and non-probability sampling techniques were employed to collect the data. Ganta Afeshum district was selected from Eastern Tigray, and two peasant associations (Buket and Golea) were also selected from the district purposively for being potential in cactus pear production. Simple random sampling techniques were employed to survey 30 households from each of the two peasant associations, and a semi-structured questionnaire was used as a tool for data collection. Moreover, in this research 2 collectors, 2 wholesalers, 1 processor, 3 retailers, 2 consumers were interviewed; and two focus group discussion was also done with 14 key farmers using semi-structured checklist; and key informant interview with governmental and non-governmental organizations were interviewed to gather more information about the cactus pear production, post-harvest losses, the strategies used to reduce the post-harvest losses and suggestions to improve the post-harvest management. To enter and analyze the quantitative data, SPSS version 20 was used, whereas MS-word were used to transcribe the qualitative data. The data were presented using frequency and descriptive tables and graphs. The data analysis was also done using a chain map, correlations, stakeholder matrix, and gross margin. Mean comparisons like ANOVA and t-test between variables were used. The analysis result shows that the present cactus pear value chain involves main actors and supporters. However, there is inadequate information flow and informal market linkages among actors in the cactus pear value chain. The farmer's gross margin is higher when they sell to the processor than sell to collectors. The significant postharvest loss in the cactus pear value chain is at the producer level, followed by wholesalers and retailers. The maximum and minimum volume of post-harvest losses at the producer level is 4212 and 240 kgs per season. The post-harvest loss was caused by limited farmers skill on-farm management and harvesting, low market price, limited market information, absence of producer organization, poor post-harvest handling, absence of cold storage, absence of collection centers, poor infrastructure, inadequate credit access, using traditional transportation system, absence of quality control, illegal traders, inadequate research and extension services and using inappropriate packaging material. Therefore, some of the recommendations were providing adequate practical training, forming producer organizations, and constructing collection centers.

Keywords: cactus pear, post-harvest losses, profit margin, value-chain

Procedia PDF Downloads 86

Authors: Viktorya Sargsyan, Arax Hovhannesyan, Karine Abelyan

Abstract:

Introduction: During the last decade, scientific studies have proven the importance of Early Childhood Development (ECD) interventions. These interventions are shown to create strong foundations for children’s intellectual, emotional and physical well-being, as well as the impact they have on learning and economic outcomes for children as they mature into adulthood. Many children in rural Armenia fail to reach their full development potential due to lack of early brain stimulation (playing, singing, reading, etc.) from their parents, and lack of community tools and services to follow-up children’s neurocognitive development. This is exacerbated by high rates of stunting and anemia among children under 3(CU3). This research study tested the effectiveness of an integrated ECD and Maternal, Newborn and Childhood Health (MNCH) model, called “Go Baby, Go!” (GBG), against the traditional (MNCH) strategy which focuses solely on preventive health and nutrition interventions. The hypothesis of this quasi-experimental study was: Children exposed to GBG will have better neurocognitive and nutrition outcomes compared to those receiving only the MNCH intervention. The secondary objective was to assess the effect of GBG on parental child care and nutrition practices. Methodology: The 14 month long study, targeted all 1,300 children aged 0 to 23 months, living in 43 study communities the in Gavar and Vardenis regions (Gegharkunik province, Armenia). Twenty-three intervention communities, 680 children, received GBG, and 20 control communities, 630 children, received MCHN interventions only. Baseline and evaluation data on child development, nutrition status and parental child care and nutrition practices were collected (caregiver interview, direct child assessment). In the intervention sites, in addition to MNCH (maternity schools, supportive supervision for Health Care Providers (HCP), the trained GBG facilitators conducted six interactive group sessions for mothers (key messages, information, group discussions, role playing, video-watching, toys/books preparation, according to GBG curriculum), and two sessions (condensed GBG) for adult family members (husbands, grandmothers). The trained HCPs received quality supervision for ECD counseling and screening. Findings: The GBG model proved to be effective in improving ECD outcomes. Children in the intervention sites had 83% higher odd of total ECD composite score (cognitive, language, motor) compared to children in the control sites (aOR 1.83; 95 percent CI: 1.08-3.09; p=0.025). Caregivers also demonstrated better child care and nutrition practices (minimum dietary diversity in intervention site is 55 percent higher compared to control (aOR=1.55, 95 percent CI 1.10-2.19, p =0.013); support for learning and disciplining practices (aOR=2.22, 95 percent CI 1.19-4.16, p=0.012)). However, there was no evidence of stunting reduction in either study arm. he effect of the integrated model was more prominent in Vardenis, a community which is characterised by high food insecurity and limited knowledge of positive parenting skills. Conclusion: The GBG model is effective and could be applied in target areas with the greatest economic disadvantages and parenting challenges to improve ECD, care practices and developmental outcomes. Longitudinal studies are needed to view the long-term effects of GBG on learning and school readiness.

Keywords: early childhood development, integrated interventions, parental practices, quasi-experimental study

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Authors: Sneha Shankar, Orlando Buendia, Will Evans

Abstract:

Alpha-1 antitrypsin deficiency (AATD) is a rare, genetic, and multisystemic condition. Underdiagnosis is common, leading to chronic pulmonary and hepatic complications, increased resource utilization, and additional costs to the healthcare system. Currently, there is limited evidence of the direct medical costs of AATD diagnosis in the UK. This study explores the economic impact of AATD patients during the 3 years before diagnosis and to identify the major cost drivers using primary and secondary care electronic health record (EHR) data. The 3 years before diagnosis time period was chosen based on the ability of our tool to identify patients earlier. The AATD algorithm was created using published disease criteria and applied to 148 known AATD patients’ EHR found in a primary care database of 936,148 patients (413,674 Biobank and 501,188 in a single primary care locality). Among 148 patients, 9 patients were flagged earlier by the tool and, on average, could save 3 (1-6) years per patient. We analysed 101 of the 148 AATD patients’ primary care journey and 20 patients’ Hospital Episode Statistics (HES) data, all of whom had at least 3 years of clinical history in their records before diagnosis. The codes related to laboratory tests, clinical visits, referrals, hospitalization days, day case, and inpatient admissions attributable to AATD were examined in this 3-year period before diagnosis. The average cost per patient was calculated, and the direct medical costs were modelled based on the mean prevalence of 100 AATD patients in a 500,000 population. A deterministic sensitivity analysis (DSA) of 20% was performed to determine the major cost drivers. Cost data was obtained from the NHS National tariff 2020/21, National Schedule of NHS Costs 2018/19, PSSRU 2018/19, and private care tariff. The total direct medical cost of one hundred AATD patients three years before diagnosis in primary and secondary care in the UK was £3,556,489, with an average direct cost per patient of £35,565. A vast majority of this total direct cost (95%) was associated with inpatient admissions (£3,378,229). The DSA determined that the costs associated with tier-2 laboratory tests and inpatient admissions were the greatest contributors to direct costs in primary and secondary care, respectively. This retrospective study shows the role of EHRs in calculating direct medical costs and the potential benefit of new technologies for the early identification of patients with AATD to reduce the economic burden in primary and secondary care in the UK.

Keywords: alpha-1 antitrypsin deficiency, costs, digital health, early diagnosis

Procedia PDF Downloads 141

Authors: Nareshkumar Harale, B. B. Meshram

Abstract:

The continued exponential growth of successful cyber intrusions against today’s businesses has made it abundantly clear that traditional perimeter security measures are no longer adequate and effective. We evolved the network trust architecture from trust-untrust to Zero-Trust, With Zero Trust, essential security capabilities are deployed in a way that provides policy enforcement and protection for all users, devices, applications, data resources, and the communications traffic between them, regardless of their location. Information exchange over the Internet, in spite of inclusion of advanced security controls, is always under innovative, inventive and prone to cyberattacks. TCP/IP protocol stack, the adapted standard for communication over network, suffers from inherent design vulnerabilities such as communication and session management protocols, routing protocols and security protocols are the major cause of major attacks. With the explosion of cyber security threats, such as viruses, worms, rootkits, malwares, Denial of Service attacks, accomplishing efficient and effective intrusion detection and prevention is become crucial and challenging too. In this paper, we propose a design and analysis model for next generation network intrusion detection and protection system as part of layered security strategy. The proposed system design provides intrusion detection for wide range of attacks with layered architecture and framework. The proposed network intrusion classification framework deals with cyberattacks on standard TCP/IP protocol, routing protocols and security protocols. It thereby forms the basis for detection of attack classes and applies signature based matching for known cyberattacks and data mining based machine learning approaches for unknown cyberattacks. Our proposed implemented software can effectively detect attacks even when malicious connections are hidden within normal events. The unsupervised learning algorithm applied to network audit data trails results in unknown intrusion detection. Association rule mining algorithms generate new rules from collected audit trail data resulting in increased intrusion prevention though integrated firewall systems. Intrusion response mechanisms can be initiated in real-time thereby minimizing the impact of network intrusions. Finally, we have shown that our approach can be validated and how the analysis results can be used for detecting and protection from the new network anomalies.

Keywords: network intrusion detection, network intrusion prevention, association rule mining, system analysis and design

Procedia PDF Downloads 201